Special Issue on Advancement in Engineering and Computer Science 2019

Location-based service has been widely used in modern life. It brings a lot of convenience to our lives. Improving the accuracy of location prediction can provide better location- based service. We propose a location prediction method based on the variable-order Markov model with time feature and user’s spatio-temporal rule. First, the user’s trajectory data needs to be abstracted, and then the useful stay points in the user’s trajectory are extracted. The location prediction is performed by scoring each candidate area, and the score is composed of scores in time and space dimensions. Finally, for the possible zero frequency problem, it is solved by mining the spatio-temporal rule of the user. Experiments using the actual data set GeoLife show that the proposed method improves the prediction accuracy.

We examine design principles from more than 20 years of experience in the implementation and protection of mission critical flight systems used by the Mission Design and Navigation Section at NASA’s Jet Propulsion Laboratory. Spacecraft navigation has rigorous requirements for completeness and accuracy, often under critical and uncompromising time pressure. Fault tolerant and robust design in the ground data system is crucial for the numerous space missions we support, from the Cassini orbital tour of Saturn to the Mars rover Curiosity. This begins with the examination of principles learned from fault tolerant design to protect against random failures, and continues to the consideration of computer security engineering as a derivative effort to protect against the promotion of malicious failures. Examples for best practice of reliable system design from aviation and computer industries are considered and security fault tolerance principles are derived from such efforts. Computer security design approaches are examined, both as abstract postulates (starting from cornerstone principles with the concepts of Confidentiality, Integrity, and Availability) and from implementation. Strategic design principles including defense in depth, defense in breadth, least privilege, and vulnerability removal are target points for the design. Additionally, we consider trust in the system over time from its sterile implementation, viewed against the backdrop of Time Based Security. The system design is assessed from external access data flows, through internal host security mechanisms, and finally to user access controls. Throughout this process we evaluate a complementary intersection – a balance between protecting the system and its ease of use by engineers.  Finally, future improvements to secure system architecture are considered.

The distribution of bit patterns is an important measure to check the randomness of a sequence. The authors of this paper observed this crucial property in a binary sequence which generated by using a primitive polynomial, trace function, and Legendre symbol defined over the sub extension field. The authors create a new dimension in the sequence generation research area by considering the sub extension field, whereas all our previous works are focused in the prime field. In terms of the distribution of bit patterns property, this research work has notable outcomes more specifically the binary sequence (defined over the sub extension field) holds much better (close to uniform) bit distribution than the previous binary sequence (defined over the prime field). Furthermore, the authors theoretically proved the distribution of bit property in this paper.

Time scheduling as seen in timetabling processes with few and/or competing resources has exposed complex interoperable time scheduling. Attempts to resolving these time scheduling processes has been undertaken, using several classical methods, with difficulty due to inherent complexities, constraints and conflicting issues. The use of ontology-based approaches to resolve time complexity is recently adopted due to its ease in interoperability and reuse of data. The probability weighted ontology provides the various types of complexity as a requirement for the complexity reduction. To determine the optimality of the resulting timetable required the evaluation of its criteria using the analytical hierarchy process. The need for a formal representation to explicate the intelligent behaviour of the ontology-based framework of the temporal scheduler arises. Hence, this work is aimed at providing that formal representation of the logical part of the ontology. The basic rules handling the constraints in the timetabling process are outlined with the corresponding formal representations of the interval-based logic using first-order logic. The semantic model of the temporal scheduler is further described following Guarino’s formal ontology model. The unified modeling language (UML) design of a system framework prototype that adopts the formal model is also given. Through the formal ontology-based framework, all constraints that will give optimality are explicated and incorporated into the allocation reasoned, which results in an optimal formal ontology-based model. This will ensure reliability, ease of use and the likelihood of re-usability of the resulting timetable.

The purpose of this paper is to improve the thermographic method for detection the places of heat energy losses on the urban underground thermal pipelines using the computer system. This goal was achieved by building a heat map of the surveyed heating main with data layering directly on a computer map of the area, which can be accessed from a mobile device by authorization via the Internet (profile on the social network Google, Facebook, Twitter, etc.). An analytical model was proposed for converting the temperature values measured over the underground heating main by a non-contact digital pyrometer to an image of the RGB color model using the parameters of the HSV color model. The implementation of the integrated Google technologies complex (Google Firebase, Firebase Authentication, and Google Maps) in the technological process of urban housing and public services was realized. The integrated information and control system (IICS) was developed. The proposed improvements in the thermographic method provided independently from each other work of different service teams due to the exchange of data between structural units through the cloud data storage. The results of the practical research allowed detecting the place of the underground breakthrough of the heat pipe quickly, to position the repair equipment over the accident section precisely using the GPS module on the mobile device. Thus, the size of the dug road section was minimized, material resources were saved. The experimental studies carried out confirmed the correctness of the theoretical assumptions.

A new microstrip patch antenna for ultra-wideband applications is presented in this paper. The design and performance of the antenna component are discussed. The propounded antenna is mounted on a compact FR-4 substrate having dimensions 20 x 30 x 1.6 mm3 with relative permittivity ?r=4.3. The rectangular patch antenna is slotted with two types; rectangular and semi-circle slots so as to ensure a broad bandwidth. The results reveal that the antenna covers the frequency range of 3.1–7.5 GHz with a reflection coefficient reduced to -55 dB and a maximum gain of 5.9 dB. The details of the simulated and measured results for reflection coefficient are presented, showing a good agreement between them. To analyze the effect of the slots, the surface current distribution is investigated. The performances of good impedance matching are achieved within the operating band. Simulations are performed using CST Microwave Studio. The propounded antenna can be deployed for UWB applications and other radio communication services systems such as high-resolution radar, military communication, communications and sensors, position location and tracking

The study proposed a self-driven guidance walker through user-oriented integration design. The common two-wheel drive performs left and right turn. By reducing the weight of the walker body, the durability of the battery can be increased. Secondly, we use a foldable structure so that the walker can be conveniently stored in a small space. A small number of sensors are required to reduce the complexity of signal processing. The front sensors of the walker are infra-red and ultra-sonic detectors, which determine and evade obstacles. The walker handle has three to five force sensors. The combination of the pressure data from these sensors can be used to determine the intent of the user, like turning or stopping.

The article of given research presents the mathematical and computer modeling of flame attenuation in narrow channels for different fire extinguishing and explosion suppression compositions. Such kind of flame distribution and attenuation could be considered for different media and channels but in this case it was considered for narrow channels. The effect on the combustion reaction is possible with gas mixture components concentration reducing, cooling the combustion zone and slowing down of chain reactions with the help of a phlegmatizing or inhibiting substances, of which the most universal and perspective are powder materials. Therefore, a necessary condition for solving the problems of developing effective flame arresters is to find common indicators and properties of substances that can become criteria for their phlegmatizing ability. The computer model in MatLab was created for methane allowing the choice of diameter and length of channel and velocity of explosion suppression composition, showing the dependence of temperature on length of channel.

Birefringence is a known property of anisotropic media where incidence of a single propagating wave on a biaxial boundary gives rise to what scientists refer to as an ordinary wave and extraordinary wave. This article explores the anisotropic electromagnetic theory that gives rise to the phenomenon of birefringence focusing on both ordinary and extraordinary plane wave propagation. The dispersion equation in an unbounded anisotropic medium leads to a fourth order polynomial solution for the wave number which is shown to be the root cause of birefringence. Finally, a derivation of the unique properties for a partially filled biaxial rectangular waveguide that serves to suppress wave propagation in the vertical direction will negate the effect of birefringence in the case of the first propagating mode.

In Banach spaces an iteration algorithm for two finite families of total asymptotically quasi-nonexpansive maps is introduced. Weak and strong convergence theorems of this algorithm to approximation common fixed points are proved by using suitable conditions. As well as, numerical example by using Mat-lab is given.

This paper is an extension of work originally presented in 2018 Global Smart Industry Conference (GloSIC). Researches are devoted to the development of Smart technology for complex objects control and prediction on the basis of a distributed Honeywell DCS control system of the TengizChevroil enterprise using the example of a technological process of medium pressure gas cleaning. The article describes how on the basis of the multi-algorithm approach there was developed a modified algorithm based on modern artificial intelligence methods in order to select informative features (principal component method, Random Forest algorithm, particle swarm algorithm) and artificial immune systems (clonal selection) solving the image recognition problem and predicting the state of a complex control object. There was conducted a comparative analysis of the simulation results using the example of real production data (daily data of sensors from an average pressure absorber).

The task of fuzzy mathematical modeling of the composition of a polymer composition is singled out and formulated, as one of the types of chemical-technological systems for creating the theoretical foundations for managing the physicochemical properties of the material produced from this composition. Fuzzy data on the technical functions and concentration of individual ingredients that included in the composition in the form of fuzzy numbers are used as the initial formalized information at modeling the polymer composition. As a result, a parametric space is formed that describes the multicomponent system under study. The index of compliance of each parameter of the polymer composition with the requirements for the physicochemical properties and quality of the polymer material is calculated. This allows you to go to the relative dimensionless real values. Weighted voting procedure is used as an aggregate function. The obtained numerical value reflects the integral measure of the compliance of the chemical-technological system with the requirements for the polymer composition.
An example of the choice of ingredients of the polymer composition by calculating using fuzzy numbers is presented. In addition to the formulation of the problem, the study develops the mathematical software for the intellectual system supporting the research and production activities of the chemical technologist to substantiate the decision to choose a polymer composition.

The use of technology in the learning process has become the most significant point in helping teachers reaching the objectives of the learning. This study focuses on the use of multimedia by novice teachers in comprehending the internship program’s guidance. The Islamic Critical Reflection model is a new and innovative model in the field of education which development is a result of integration between Islamic value and science. This study aims to answer problems related to the internship supervision which occurs among novice teachers. The multimedia application in the form of Flash Macromedia is developed using Islamic Critical Reflection model for internship program of novice teachers at the Tarbiyah and Teacher Training Faculty UIN Sunan Ampel Surabaya. The research design of this study is Research and Development. It includes the process of collecting information, designing a product, validating the design, improving the design, piloting the product, re-revising the product, trying out the product, revising further and creating products for large numbers. The results of this study indicate that the products produced by Macromedia Flash application integrated with Islamic Critical Reflection Model for Tarbiyah and Teacher Training Faculty of UIN Sunan Ampel Surabaya’s internship program are feasible to use. The results are from the experts’ analysis and users’ trials. Experts involved in the validation of this product are Islamic Education specialists, application experts, educational technology experts, and curriculum expert.

The article proposes a Digital Predistortion (DPD) methodology that substantially meliorates the linearity of limited range Mobile Front Haul links for the extant Long-Term Evolution (LTE) and future (5G) networks. Specifically, the DPD is employed to Radio over Fiber links that contrive of Vertical Cavity Surface Emitting Lasers (VCSELs) working at 850 nm. Both, Memory and Generalized Memory Polynomial models are implied to Single Mode (SM) and Multi-Mode (MM) VCSELs respectively. The effectiveness of the proposed DPD methodology is analyzed in terms of Normalized Mean Square Error, Normalized Magnitude, Normalized phase and Adjacent Channel Power Ratio. The demonstration has been carried out with a complete (Long Term Evolution) LTE frame of 10 ms having 5 MHz bandwidth with 64-QAM modulation configuration. Additionally, the effectuality of the proposed DPD technique is evaluated for varying levels of input power and link lengths. The experimental outcomes signify the novel capability of the implied DPD methodology for different link lengths to achieve higher system linearization.

This article presents a bridge crane nonlinear dynamic model in 2-dimensional space, and then given a novel adaptive fuzzy-sliding mode controller based on combining sliding mode control with fuzzy logic and Lyapunov function. Firstly, the article proposes an intermediate variable to link signal between two slide surfaces, related to trolley movement and payload swing. Then the fuzzy controller, compensative controller and parameter adaptive update law for the bridge crane are defined. The asymptotic stability of the proposed bridge crane control system is proven based on Lyapunov stability theory. Simulation results show that the adaptive fuzzy-sliding mode controller ensures the trolley follows the input reference with the short settling time, eliminating steady error, and anti- payload swing, anti-disturbance.

Vision-based defect detection techniques are widely used for quality control purposes. In this work, an efficient deflectometry based detection system is developed for semi-specular/painted surface defect detection. This system consists of a robotic arm that carries a screen/camera setup and can detect defects on large surfaces with different topologies, such as a car bumper, by traversing its profile. A hybrid pipeline is designed that utilizes multi-threading for optimal resource utilization and process speed. Specific filters are also designed to remove spurious defects introduced by acute curvature changes and part edges. The system was successful in consistently detecting various defects on small test samples as well as on large bumper parts with varying topology and color and can accommodate inherent ambient lighting and vibration issues.

This study is the extension of the previous study about “Traffic Service Quantitative Analysis Method under Developing Country” in 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI). In the previous study, it is introduced how to make quantitative calculation for traffic congestion by traffic parameters and its characteristics curve such as traffic volume (q) to inverse of vehicle average speed (=1/v). In order to identify the traffic congestion condition, it is focused on vehicle speed ratio which is average speed (vave) to its free speed (vf). And the threshold level is 2/3 (=vave/vf). This 2/3 value comes from Viscous fluids model between parallel flat plates by using similarity of the viscous fluids flow and the traffic flow in India which is introduced at the CODATU XVII and UMI Conference 2017. This threshold value definition needs more traffic theory back up because its similarity between viscous fluids flow and traffic flow comes from the basis of traffic flow measurement results. In this extension study, it focuses on the occupancy of one of typical traffic parameter for traffic congestion. When it is compared between the traffic occupancy measurement data and Speed Ratio, the Speed Ratio 2/3 is the level of the occupancy 30%, which is used as traffic congestion condition. According to daily based traffic volume and average vehicle speed, the congestion condition is occurred by not only those traffic parameter, it is also considered about time zone traffic condition and its actual traffic condition. One of measurement point is always congested in the early evening even if its traffic volume is smaller than the morning traffic congestion. Therefore it is important to analyze traffic condition by time zone. As the result, it clarifies the relationship between Speed Ratio and occupancy. As the result, there are two types of traffic congestion in Ahmedabad city traffic.

Photoluminescence (PL) of inhomogeneous porous silicon (PS) of p-type is investigated in this contribution. We measured the PL signal at equidistant positions separated by 0.05 mm in area localized between original crystalline Si (c-Si) wafer surface and electrochemically prepared PS layer. Two PL peaks localized at energies 1.8 and 1.9 eV were identified and their parameters were determined. The changes of PL maxima energy (“blue shift”) in dependence on position were observed.

Pursuit Evasion Game (PEG) is an abstract model of various significant problems that appear in both civil and military applications. Bio- Inspired strategies are found to be very useful in studying the PEG. While optimal response to the pursuit strategies are available using geometric control theory, it is shown in this paper that application of linear feedback control laws can further improve the time and tracking response of these strategies in capturing the evader by the pursuer. Empirical results based on computer simulation are used to illustrate the findings. Further, considering the case of sudden turn of the evader, moving at a lower speed, it is shown that both in theory and simulation that the evader can delay the capture by pursuer and in some cases even escape from being captured. These findings are in line with what is found in nature.

This work presents the development of an original idea for a walking robot with a minimum number of motors, simple construction and a control system based on the brain bioelectrical activities. Described are geometric and kinematic dependencies related to the robot movement, as well as brain-inspired IoT control method. Various aspects are discussed for improving the robot’s qualities, concerning the shape of the robot’s feet and base in order to overcome various obstacles and maintain the static mechanical equilibrium. Improvements in the mechanical design are provided to improve reliability and enhance the scope of robot’s applications. A new IoT framework for creating Human-robot interaction applications based on Node-RED “wiring” of Emotiv Brain Computer Interface (BCI) and Arduino based robot is designed, developed and tested. An educational application how to train the joint attention of children by a mind control method based on neurofeedback from beta oscillation in the right temporoparietal region is illustrated in a Node-RED flow. The neurofeedback is exposed on the walking robot.

This article investigates different versions of a new multi-band micro-strip antenna design. NASA inspired the antenna design from “Hand of God” galaxy discovery. A multi-band operation between 0.15 and 8 [GHz] is obtained and captures up to 10 frequency bands. The antenna has a small area of 4 x 5 cm2 and is printed on 1.6 mm epoxy-FR4 substrate. Adding slots and changing the feed position, led to different designs and increase in multi-band operation. Moreover, varactors are mounted on antenna surface, which resulted in frequencies as low as 50 MHz and as high as 8 GHz when. S11 simulations results were successfully compared with measured ones. Applications include different mobile generations, TV and different IoT systems.

The paper deals with hardware emulation of bio-inspired devices and nonlinear dynamic processes of complex nature by means of mixed-mode analog-digital emulators. The discretized state model of the emulated system serves for real-time calculation of dependent quantities. In contrast to input-output emulation known in control systems, the proposed approach emulates the ports of an electrical multiport network. The paper discusses the stability of the emulation process and the possibility of partitioning the system into two parts, one being emulated digitally and the other via an analog circuitry. The procedure is illustrated on the example of emulating the Fitzhugh-Nagumo model of neuron and the model of amoeba adaptation. The paper is an extension of our paper presented at the NGCAS 2018 conference in Valletta, Malta. The extended version deals newly with the choice of the integration method and provides a deeper stability analysis and more examples of emulation of biological models.

Recently, the trend of mobile communication network in Korea has been dramatically increasing the number of operating equipment for managing the explosion of wireless data traffic in 4G LTE network. This phenomenon has caused an increase in the operating cost to the mobile telecommunication service provider. Especially, the study about the reduction of the operating cost through the reduction of the power consumption in the power consumption area and the efficient operation method are under study. Through these studies, we consider the method to maintain the best service quality of mobile communication subscriber while minimizing the power consumption of LTE equipment, find out PSi & PSo algorithm that can realize this and verify it in commercial network. Applying this algorithm to a commercial network will save both power consumption and service experience of mobile subscribers to the best level, and it will be more effective in the 5G era that has already begun.

In this paper, a frequency-tunable antenna is proposed based on a previous antenna which has a ladder shape and a size of 120 x 50 mm2. It is fed using a coaxial cable and it is operational at 700 MHz which is in the UHF band with a bandwidth of 30 MHz. Varactors, having a very low resistance value and a wide range of capacitance values, are implemented on the surface of the antenna which achieves tunability in the DTV applications band used for mobile handheld devices. By using such varactors, the electromagnetic characteristics of this antenna could be changed by changing its electrical length. Multiple cases are studied; either adding a varactor on each step of the ladder antenna or adding only one varactor in a certain well justified position. The operating frequency is swept in different ranges where all of the obtained frequencies could be used for digital TV broadcasting on mobile devices.

This paper analyzes the process of reactive power compensation using thyristors switched capacitors TSC topology based on multiterminal switch during the starting of induction motor. The paper explains the control principle of this TSC topology which depends on the multiterminal switch. To actively track the required reactive power for the dynamic load and to perfectly transit from it from one level to other, proper control system is established. MATLAB/SIMULINK is used to develop a simulation model to prove the dynamic performance of the proposed TSC during the compensation of reactive power required for induction motor during starting.

This paper analyzes the process of reactive power compensation using thyristors switched capacitors TSC topology based on multiterminal switch during the starting of induction motor. The paper explains the control principle of this TSC topology which depends on the multiterminal switch. To actively track the required reactive power for the dynamic load and to perfectly transit from it from one level to other, proper control system is established. MATLAB/SIMULINK is used to develop a simulation model to prove the dynamic performance of the proposed TSC during the compensation of reactive power required for induction motor during starting.

Knowledge Based Systems play a very important role, within Healthcare, with a primary goal of supporting a high quality service at an optimal cost. A widely accepted knowledge acquisition technique is through the use of Business Rules in a natural language like format. As clinical terminology is centric to HealthCare, a large percentage of these rules use industry standard codes to describe clinical concerns. Usually there is a large number of codes to denote the various clinical processes andprocedures within Healthcare. These can translate to a combinatorially explosive number of Business Rules within the HealthCare IT system. It is common practice to leverage a Rule Engine to execute these rules and produce decisions within the system. A Rule Engine accepts a collection of rules called a ruleset. Within Healthcare Insurance, these rules and rulesets embody regulatory, policy and contractual concerns. To effectively manage this huge body of rules and rulesets, it is typical for Knowledge Based Systems to reuse rules across rulesets. Further, the knowledge required to author these rules and constructing comprehensive rulesets is specialized and requires deep expertise within the domain. Further, this also requires an expertise with authoring unambiguous business rules and operating the Rule Management Systems or Knowledge Based Systems. There is a need for the continuous Governance of these rule based knowledge assets for the organization. Typically knowledge experts in this field have a decade or more of experience. An emergent challenge or trend witnessed within the industry is that the experienced knowledge workers are retiring and their positions are being replaced by people not as experienced. This paper proposes techniques to build a layer of intelligent capabilities that can transform the methods for the automation, creation and maintenance of knowledge artifacts, that can aid and support the inexperienced knowledge workers with the effective management and administration of these assets within a Healthcare system.

The IEEE 802.11n wireless local-area network (WLAN) has been extensively deployed around the world due to the flexibility, lower cost, and the high-speed data transmission capability at 2.4 GHz ISM band. However, in the WLAN deployment, one key challenge is to optimize the channel assignment of access-points (APs) under the small number of partially overlapping channels (POCs) to reduce radio interference, particularly for the channel bonding. In POCs, the frequency spectrums of adjacent channels are partially overlapped with one another, which will result to low throughput for concurrently communicating links using them. The accurate throughput estimation of a link is critical in the optimal WLAN deployment. Previously, we studied the throughput drop estimation model using the receiving signal strength (RSS) from the interfered link for two concurrently communicating links under POCs. In this paper, based on measurement results, we have extended this model for three concurrently communicating links. The accuracy of this model extension is verified by comparing the estimated results with the measured ones.

From the collection of supervised machine learning technique, an ensemble procedure is used in Random Forest. In the arena of Data mining, there is an excellent claim for machine learning techniques. Random Forest has tremendous latent of becoming a widespread technique for forthcoming classifiers as its performance has been found analogous with ensemble techniques bagging and boosting. In the present work we have proposed an algorithm, Aggrandized Random Forest to detect fraud from credit card transactions/ATM transactions with high accuracy considering both balanced and imbalanced dataset, comparatively to the defined classification algorithm Random Forest in Data mining.

An experimental transmission between two XBee modules using Radio over Fiber technique is demonstrated. Data issued from an XBee module coded on a wireless microwave carrier at 2.44GHz is transmitted through an optical link of 25.24 km. The optical transmission is based on an external modulation scheme over a dispersive channel. Frequency response of the external modulation scheme is determined by simulation and experimentally. In particular, the chromatic dispersion of the optical fiber associated to its length allow the generation of a pass-band filter whose bandwidth is used to code the RF signal. The recovered RF signal exhibits a SNR of 27dB. Obtained results allow proposing this RoF scheme as a good contender for data transmission.

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This study introduces the implementation of fuzzy set theory to solve machine layout design issues, in order to handle vague information, using a genetic algorithm with tournament selection as the selection operator. The material handling inputs, including frequency and volume of materials that move between machines, were the parameters regarded as fuzzy numbers. The experimental results came from 2 case studies in a manufacturing system. In the first case, examining the difference in shapes of the triangular membership functions of input data, the total distances were reduced from 38.45 m to 29.72 m, a 22.71% reduction in distance. In the second case, examining the uncertainty of fuzzy data by an expert, the total distances were reduced from 103.45 m to 82.45 m, a 20.03% reduction in distance. It was found that given the uncertainty in input data, a shorter total material handling distance might not give a lower cost. The selection operator of tournament selection can compete effectively to converge to near the optimum solution. This can, therefore, be an alternative technique in managing manufacture.

In this paper, an optimal discrete-time sliding mode control is proposed for single-input single-output nonlinear systems with input constraints. The sliding surface is designed on the basis of particle swarm optimization algorithm in order to optimize the system response characteristics while ensuring the follow-up of reference model in presence of constraints. Moreover, the controller is developed such that the elimination of chattering phenomenon, the finite-time convergence and the stability of the closed loop system are guaranteed. Performed on an inverted pendulum system, simulation results demonstrate the e_ectiveness of the proposed approach over the discrete-time sliding mode controller using the saturation function and the discrete-time second order sliding mode controller in terms of fast response.

This article is addressed to show the results of hybrid dynamical modeling in the form of PWA (piecewise-affine) and equivalent MLD (mixed-logical dynamical) model for multi-restricted areas avoidance of an autonomous system. It is a problem of determining the optimal moving trajectory from plant’s initial position to some desired position while avoiding some restricted areas (obstacles) between them. In order to calculate the optimal input value capable of generating the optimal trajectory, the model predictive control (MPC) approach was utilized by minimizing an objective function of state/output prediction subject to the formulated hybrid dynamical model. To illustrate the formulated model and its responses, some computational simulations were performed in a three-dimensional state using two/three box-shape restricted areas. From the simulation results, the optimal trajectory was achieved, and the plant avoided the restricted area.

Collision risk index has been studied as the required quantitative values for decision-making of collision avoidance between ships. Recently, inference methods of the collision risk were proposed on the basis of the fuzzy theory because of being possible to collect data in real time. Existing fuzzy inference system was composed of only simulation results using virtual navigation situation. In this study, we obtained the fuzzy inference rule based on ship near-collision data via the adaptive neuro fuzzy inference system. Proposed fuzzy inference rule expressed various collision risk index in order that a ship could avoid collision with an encounter ship at appropriate distance and time. It would support for navigators to make an appropriate decision for collision avoidance with encounter ships.

The definition of equipment and components of physical plants is a necessary step towards the development of simulation, control, and supervisory applications for the petrochemical industry. Often it happens that the same plant/equipment is (re)modeled on each application, causing a waste-oftime on repetitive (re)design, besides introducing potential inconsistencies between the models. Moreover, even though each software platform normally o_ers a di_erent view of the same plant, it is desirable to have some kind of interoperability between them. This paper presents a solution for such issues named M4PIA, which consists in a Model-Driven Engineering (MDE) tool support developed mainly for petrochemical industry automation. M4PIA allows representing industrial plants by means of di_erent and interchangeable object-oriented models, providing means to perform automatic code generation from a plant specification for di_erent software platforms. Currently, our work involves using M4PIA in high-level automation manouvers and plant simulations. Evaluations studies performed with M4PIA shows that it covers most features expected from a MDE tool suite. Besides, the use of M4PIA is expected to result in less development time and costs, while it increases e_ciency, maintainability, and reliability of the developed applications.

The authors propose a well balanced multi-value sequence (including a binary sequence). All the sequence coe_cients (except the zero) appear almost the same in number, thus, the proposed sequence is so called the well balanced sequence. This paper experimentally describes some prominent features regarding a sequence, for instance, its period, autocorrelation, and cross-correlation. The value of the autocorrelation and cross-correlation can be explicitly given by the authors formulated theorems. In addition, to ensure the usability of the proposed multi-value sequence, the authors introduce its flexibility by making it a binary sequence. Furthermore, this paper also introduces a comparison in terms of the linear complexity and distribution of bit patterns properties with their previous works. According to the comparison results, the proposed sequence holds better properties compared to our previous sequence.

Dysphoria is a state faced when one experienced disappointment. If it is not handled properly, dysphoria may trigger acute stress, anxiety and depression. Typically, the individual who experienced dysphoria are in-denial because dysphoria is always being associated with negative connotations such as incompetency to handle pressure, weak personality and lack of will power. To date, there is no accurate instrument to measure dysphoria except using questionnaire by psychologists, such as: Depression, Anxiety and Stress Scale (DASS) and Nepean Dysphoria Scale (NDS-24). Participants may suppress or exaggerate their answers resulting in misdiagnosis. In this work, a theoretical Dysphoria Model of Affect (DMoA) is developed for dysphoria detection. Based on the hypothesis that dysphoria is related to negative emotion, the input from brain signal is captured using electroencephalogram (EEG) device to detect negative emotions. The results from analyzing the EEG signals were compared with DASS and NDS questionnaires for correlation analysis. It is observed that the proposed DMoA approach can identify negative emotions ranging from 55% to 77% accuracy. In addition, the NDS questionnaire seems to provide better distinction for dysphoria as compared to DASS and is similar to the result yielded by DMoA in detecting dysphoria. Thus, DMoA approach can be used as an alternative for early dysphoria detection to assist early intervention in identifying the patients’ mental states. Subsequently, DMoA approach can be implemented as another possible solution for early detection of dysphoria thus providing an enhancement to the present NDS instruments providing psychologists and psychiatrists with a quantitative tool for better analysis of the patients’ state.

This work deals with the issue of understanding a user’s behaviour as this is expressed via a gamified application. The notion of ontologies and the association of concepts in relevance to decisions that have to be made is used. The current work introduces a new process-based approach, based on collected large log files and associations of underlying decisions based on them. Both of them deal with work in extracting information for intelligent use, the main difference being that the first discovers but stops on a concept relation basis, while the other based on processes, as knowledge transactions, further to the associations on a 1:1 level maybe applied on a multi associative model. The objective of the current work is to introduce the methodology into gamified environments (such as but not limited to) games, for semi-automated understanding of user behaviour and furthermore, prediction and in instances, guidance via optimal paths of decision making activities, that are useful in gamified applications in various areas like the education. Both the initial ontology based, and the extension work on it, are based on mining association rules, in one instance treated as knowledge nodes (concepts) and in the second as underlying knowledge processes, based on big log files. This may be applicable to online games, that generate big log files of user selections, that are available for study and examination for extracting user behavioural patterns. As a result, maximum length of sequential patterns and items in them, are discovered in an algorithmic based methodological approach, providing in this way a set of guidelines for designing gamified applications.

Braille system is purposely made for visually impaired people, to support their literal communication in order to share their knowledge. Louis Braille introduced the braille system consists of series dots which are embossed to read by touching. Early days Braille papers are made manually, but at current days braille documents are made using machines. Due to lack of perceiving on braille symbols and characters, it was highly needed fact to develop Braille system to different languages. In the Sri Lankan context, we found that the mostly inconvenience are happening inside of Sri Lankan education system. Such as in Special Education centers, Colleges, and universities. Written Braille scripts are evolution by a limited number of people who are specialized in the Sinhala braille system. Also, the process of marking braille documents are not effective and efficient. The focus of this research is to address the issue of literal communication gaps between society and the blind people in Sri Lanka. Average quality single-sided composed Braille dot characters are identified with maximum accuracy by using several novel methodologies. Obtained results denote the proposed methodologies are with the highest accuracy and system performance are more efficient as promised. The research presents executable software prototype, includes proposed methods which align with optical braille recognition in order to transpose the recognize braille characters. Introduce of new binaries cell transcription method of Braille character from a Braille document and decoding them into Sinhala characters. The proposed cost-effective system can display decoded braille characters by normalizing to Sinhala text which is in a human-understandable form.

This paper describes an architectural approach to the development of dynamic service-oriented systems for e-Health using the service orchestration mechanism and semantic technologies. The main idea is the dynamic synthesis of the complex functionality required by user or by software agent. This idea should help to build and easily extend the applied loose-coupled systems without strict dependencies on concrete web services and their invocation details. A sample scenario of such dynamic orchestration is covered and analyzed, possible ways of further improvement of this approach are given.

In this study, we develop the author’s approach, based on the principles of modeling of the International Flows Equilibrium Exchange Rate (IFEER). As a result, a new model is built, depending on the main macroeconomic factors in the medium term. The model presents a wide system of basic factors, but many of the intermediate factors in the modeling were eliminated. The results are discussed in the context of the previously known results of economic theory.

With the advancement in the field of Internet-of-Things (IoT), event monitoring applications have rapidly evolved from a simple event data acquisition towards predictive event analytics involving multi-sensory data aggregation in a distributed environment. Existing event monitoring schemes are mainly relying on ine_cient centralized processing mechanism, which may lead to the common single-point of failure for the entire system. In addition, there is no proper method for verifying the event data generated by the monitoring system. In this paper, we present a distributed event monitoring scheme using a Hierarchical Graph Neuron (HGN) distributed pattern recognition algorithm. HGN is a single-cycle learning graph-based recognition scheme that is modelled for in-network deployment. In this work, event data retrieved from multi-sensory IoT devices within a distributed event monitoring network is converted into pattern. To address the event data verification problem, we integrate our proposed scheme with blockchain technology. Combining this IoT event monitoring capabilities with blockchain-based data storage and verification could leads towards a scalable event detection and monitoring model for large-scale network. The results obtained from our simulation shows that the proposed scheme o_ers high event detection accuracy and capable of minimizing the event storage requirements on blockchain network.

Popularity and a_ordability of smart phones and other data hungry devices add exponentially to the tra_c demand of existing cellular networks. Cell densification and small cell deployment over existing macrocell has been identified as an e_ective solution to high tra_c demand predicted for future wireless networks like 5G. Small cells are deployed over existing macrocells in a Heterogeneous network to o_oad tra_c, ensure balanced load and good quality of service in the network. To achieve the purpose of HetNet, small cells are deployed in locations of high user concentration. Operators are required to be able to identify these locations for optimum small cell deployment. Presently, this decision is based on long term tra_c data which create latency in small cell deployment especially in areas of unexpected hotspots. The work presented in this paper is a new strategy that can practically help operators to promptly identify locations for small cell deployment based on user cluster. This strategy involves a monitoring system that can be easily incorporated in a mobile cellular network base transceiver station (BTS) to monitor the coverage area and periodically identify positions for prompt and optimum small-cell deployment in HetNets. The monitoring system is a two element array system that uses RSS data to resolve DoA, range, user cluster and identify positions for small cell deployment. At any point in time, the network operator can visualize cluster locations relative to the base station and is able to identify hotspots. When small cells like micro-, pico-, or femto-cells are promptly deployed in areas of higher user concentration, high data rates and good quality of service in the network are maintained at all times. The network coverage area is divided into a series of azimuthal and range sectors, and users are located into the sectors where they belong. The strategy was simulated in MATLAB environment for WiFi networks with thirty users and results indicate 100% accurate DoA estimation and MAE in range estimation of 6m for a known environment. Optimum positions for small cell deployment based on user clusters were correctly determined. Simulation was validated with experiment and result indicate close relationship between experiment and simulation with MAE di_erence in range estimation of 1m.

Water distribution networks are infrastructural systems designed for providing potable water to consumers. In these last decades, the importance of assessing and identifying emerging leakages has become a primary issue, because of the high level of water loss characterizing such systems worldwide. In this paper, a new approach aimed at the prompt localization of leakages occurring in water distribution systems is introduced. The methodology relies on the analysis of real-time pressure measurements and on Complex Networks Theory. Starting from a collection of nodes representing the locations of pressure sensors, links of a virtual, complex network are created on the basis of the values assumed by correlation coefficients between pressure measurements: if such values are above a given threshold, relevant nodes are considered to be connected to each other. In this way, information about the structure and topology of the complex network is easily derived. In particular, the degree centrality of the nodes is a key parameter allowing to identify the position of a leakage. The paper first analyzes a well-known literature example, and then proves the high reliability of the methodology for a real water distribution system.

The Malaysian government has recently announced to increase the share of renewable energy generation from 2% to 20% of the total electricity consumption by year 2030. However, integrating a significant level of renewable energy sources on the distribution network without proper coordination will cause adverse effects that leads to unnecessary operation costs to the stakeholders. Various solutions including energy storage system are proven to be an effective way to overcome the issues caused by the integration of renewable energy sources. However, energy storage system using batteries have limitations such as limited life cycle and environmental hazards. As such, alternative source such as fuel cell which is well known for its zero pollutant can be considered. In this paper, a distributed static synchronous compensator integrated with a hydrogen system (DSTATCOM-Fuel cell) is proposed. The hydrogen system consists of fuel cell and electrolyzer to perform power exchange within the grid for regulating the voltage magnitude at 1 p.u. Various case studies have been conducted to validate the efficacy of this proposed combination and the results have shown that the DSTATCOM-Fuel cell can effectively mitigates the voltage rise up to 12.3% while maintaining high penetration of the photovoltaic systems.

Improving the hand motor skills in post-stroke patients through rehabilitation based on movement intention derived signals from the brain in conjunction with robot-assistive technologies are explored. The experimental work is conducted using Electroencephalogram based Brain-Computer Interface (EEG-BCI) system and the AMADEO hand rehabilitation robotic device. Two protocols using visual-cues and then using a 2-Dimensional (2D) interactive game is presented on a computer screen to healthy subjects as well as post-stroke patients performing the hand movements. The movement intention signals during hand movement are detected through the Support Vector Machine (SVM) classifier. The intent signals produced at six distinct electrodes are investigated to determine electrodes contributing most to the SVM classifier’s performance. Overall, the game protocol shows better classification results for both healthy and stroke patients compared to the visual-cues protocol. FC3 is found to be the most consistent electrode site for the detection of the motor intention of the hand for both protocols. In the experimental work, average classification accuracy for the visual-cues protocol of 67.56% for healthy subjects and 56.24% for stroke patients were obtained. For the game protocol, the classifier accuracy produced for healthy participants was 79.7% and for the post-stroke patients was 66.64%. The results confirm that the intention signal is more pronounced during more engaging activities, such as playing games, for both healthy and stroke subjects. Therefore, the effectiveness of rehabilitation therapy for post-stroke patients could be significantly enhanced using interactive and engaging exercise protocols.

This study reviewed articles on information technology governance (ITG) in the Malaysian context and categorized the issues found in ITG implementation according to organizational-related and human-related issues. It further analysed the impact and role of information technology (IT) human capability in ITG implementation in developing countries like Malaysia, which is still in the infancy stage of ITG. This study found that most organizations in Malaysia are not only facing organizational-related issues but also human-related issues as well. However, there are insufficient studies on how implementation issues between organization and humans are interrelated. The roles of IT human capability in ITG implementation has been heavily underrated as most studies focused on ITG mechanisms and the impact of ITG on firm performance. Issues highlighted in past studies were summarized and used to explain the roles of IT human capability in ITG implementation. The findings revealed that the number of issues reported in the human-related category was slightly more than organizational-related issues. This study explains the role of IT human capability in ITG and reduces the ambiguity of IT human capability in ITG for future research activities.

The current industrial landscape is becoming increasingly aware of the need to optimize energy use and management for all domains, including telecommunications. Among others, RF Energy Harvesting is a promising technique for 5G systems as an alternative to traditional energy supply sources. This paper presents 5G landscape and ecosystem. It positions RF-Energy Harvesting in 5G context and reviews its techniques. It also considers constraints and research trends for different aspects needed to make RF energy harvesting ready for deployment along with 5G enabling technologies.

eSign is an online electronic signature service which is recently gaining more prominence in India. eSign is based on two online services from UIDAI, viz. a viz., Aadhaar based authentication and retrieval of resident’s eKYC information after taking his/her consent. With increased adoption of Aadhaar based services, privacy of user data has become more and more important. Present method of taking boolean consent from resident through non-UIDAI entity may not be acceptable for two main reasons, first is that the consent does not include in itself a proof from resident that the consent is indeed taken from him/her and second is that the resident may wish to have better privacy and fine grained access control rules to access his/her eKYC data. Bakshi et.el have introduced a mechanism to improve amortized performance of eSign using a digital access token. In this work, the digital access token is enhanced to include Privacy Enhancing and Fine- Grained Access Control (PEaFGAC) Statements for facilitating Privacy Aware eSign. These tokens can be used by other entities to access eKYC data of the resident with better access controls enforced by the resident. This paper briefly describes the present model of eSign, the earlier proposed model of eSign followed by the proposed model of Privacy Aware eSign. The proposed model of Privacy Aware eSign is also analyzed using BAN logic assuming Dolev-Yao security environment.

Recently, augmented reality (AR) technology has been applied to the inter- action technology between human and virtual objects. In AR interaction technology, it is necessary for users to be able to manipulate virtual objects intuitively. Thus, we focus on manipulating virtual objects directly with the user’s bare hands in this study. On the other hand, in AR technology, since the 3-dimensional (3D) model is superimposed on the image of the real space afterwards, it is always displayed on the front side than the user’s hand (occlusion problem). Thus, it becomes an unnatural scene in some cases. In this study, this system detects hand area of the user by extracting depth information, color information and using canny edge detection in the user’s hand. Thus, this system performs hidden surface removal along the area of the user’s hand by considering the object-context relations between the user’s hand and the virtual object. In the evaluation experiment, it is confirmed that the hidden surface removal in this study make it possible to distinguish between finger boundaries and to clarify and process finger contours. This work is an extension of the paper entitled “Hidden Surface Processing for Interaction of Hand and Virtual Objects Using Leap Motion Controller” published in 2018 International Conference on Informationand Communication Technology Robotics (ICT-ROBOT).

Peer-to-peer (P2P) energy trading is a mechanism that allows people to share locally the energy they have generated from distributed renewable resources (DER), to generate profit form the unused resources and to reduce the cost for electricity for the household and in the community itself. This calls for the design of new energy markets, accompanied with the development of comprehensive exchange strategies, which reflect both the consumer?s preferences and the heterogeneity of the renewable sources. The paper presents a study of the behavior patterns of individual prosumers using bidding strategies based on the State-of-Charge of the battery and two different pricing algorithm, one with fixed prices and one based on the battery price of each standalone system as well as the effect such patterns have on a local energy market with solar panels, fuels cells and batteries. The evaluation is achieved by the use of a prototype based on the Open Energy Systems (OES), a community in Okinawa, Japan, made of 19 interconnected houses with residential storage, photovoltaic cells and AC grid connection. In order to simulate heterogeneity, a fuel cell, modeled after Ene-Farm, is added to the original configuration. Each house has a power flow management system, which uses a priority-based algorithm to maintain demand-response efficiency, capable of scheduling the operating hours of the available fuels cells. The energy market is based on Zaraba, a continuous double auction algorithm used by the Japanese Stock Exchange, in which prosumer can bid for a desired amount and price of electricity for a 30-minutes time slot in the future The preliminary numerical evaluation is based on the results from several simulations using different versions of the bidding agent.

Although social media is used by an ever-increasing number of businesses, its benefits have not yet been fully exploited. Social media analytics tools can prove helpful in this regard, since they comprise a unique information source for business intelligence. As the social media environment rapidly evolves, various social media analytics tools are emerging. This research examines the usefulness of social media analytics tools for assessing corporate websites and Facebook pages. Two social media analytics tools, Website Grader and LikeAlyzer, are selected to evaluate the wine industry in Greece and Germany. A comparative study of the two countries is subsequently presented. The paper suggests that researchers and practitioners can easily use these analytics tools to acquire valuable quantitative data for a fast, effective and efficient assessment of corporate websites and Facebook pages and thus improve their social media usage. The results show that the two countries present similarities, but there is great potential for improvement in their use of social media.

Recently, communication demands often change because of the various network services in companies and individuals. Software Defined Networking (SDN) has emerged as a viable control paradigm that allows flexible communication, using OpenFlow as its default standard and enabler. However, when changes happen frequently in SDN networks due to unforeseen reasons -such as a network failure or topology changes- it takes a long time to perform all the operations. For instance, to change a routing path, first new paths must be calculated, then the controller must transmit the commands to the network elements, which has to process those commands. This process can cause a delay, or even disruption, in the communication service. Therefore, this paper proposes a network control method to reduce the time to change a path using OpenFlow.

The increase of elderly population creates the need to promote healthy aging, with autonomy and independence, for preserving the functional capacity and quality of life as much as possible. To achieve this goal the recommendations include walking a certain amount of steps daily, given that the exact amount of steps changes with age, lesions, and chronic health conditions that can affect health and well-being.
Understanding how much exercise is adequate for an individual requires spe- cialized knowledge and training. It is important to avoid risks that include: (1) be too aggressive in exercising and increase injuries, create new ones, and later refrain in exercising more; or (2) be too conservative and in the long run have an activity level under each one’s potential and below what is recommended. In this article is proposed and discussed a framework that aims to support peo- ple having an adequate level of activity for its particular condition and to do it without disrupting daily routines. The framework is supported by a software system built for monitoring well-being and physical activity in the context of Smart Cities. The system is composed by: (1) smartphone applications that interact with the end-user for showing possible exercises and walk routes, and that collects some relevant data; (2) a back-office application that collects and presents data obtained from the smartphone applications. The back-office application is designed for health professionals to follow users’ progression over time, to recommend new exercises or to correct less optimal situations. The first tests with a system implementing this framework show that the solution is robust and able to be used in a large scale.

Neurological signals are generally very weak in amplitude and strongly noisy. As a result, one of the major challenges in neuroscience is to be able to eliminate noise and thus exploit the maximum amount of information contained in neurological signals (EEG…). In this paper, we aimed at studying the N400 wave of the Event-Related Potentials (ERPs) that may reflect the effects of vowelling and semantic priming in Arabic language. To improve the quality of the recorded ERP signals, we considered a nonlinear filtering method based on 10th order Daubechies discrete wavelet transform combined to principal component analysis (PCA). Among all tested wavelets, the Daubechies one showed high values of the used signal processing metrics. Thus, it allowed a significant enhancement of the signal to noise ratio while using only 10 ERP trials. In addition, we confirm its effectiveness while comparing the filtered outputs to those obtained using the averaging technique implemented in the conventional EEGLab toolbox. In a second step, the Mexican Hat function was used to achieve continuous wavelet analysis of the filtered signals. This time-scale analysis method permitted to get an alternative representation of the ERPs and to detect the N400 wave with significantly greater accuracy.

FM broadcast stations generate reverse intermodulation signals that cause communication problems between ATC controllers and pilots in the Air Traffic Control systems (ATC) in Thailand. In this paper proposes a balanced RF power amplifier using the improvement
3-dB quadrature hybrid couplers to reduce the reverse intermodulation of FM broadcasting systems. The mathematics analysis of the balanced RF power amplifier for two closely located FM stations shows that can reduce the reverse intermodulation products. A reverse signal from nearby FM stations travels into two amplifiers of a balanced amplifier that create intermodulation signals. The quadrature hybrid coupler in a balanced RF amplifier to provide intermodulation signals of two amplifiers have a 180? out-of-phase which cause reducing intermodulation signals. In this paper designs 3-dB quadrature hybrid couplers for a proposed RF balanced amplifier. The low-cost PCBs are used to make prototype couplers. The proposed couplers are provided experimental results of return losses that are lower than -15 dB. The directed port has a coupling coefficient -3.3 dB and the coupled port has a coupling coefficient -3.4 dB. The prototype couplers are applied to a balanced amplifier that obtains a maximum output power 210 watts with 17.20 dB gain. A proposed balanced RF amplifier is measured the reverse intermodulation products of with a situation of two closely located FM broadcast stations to compare with a single-stage class-C amplifier. The experimentation demonstrates of the transmitter using a balanced RF power amplifier that can reduce an intermodulation product 18.47 dBc.

The entire electrical system is changing at an abrupt speed since its beginning. With the rapid advance of renewable energies (which are constantly subject to fluctuations), the electricity generation is distributed to countless locations. The traditional model of the unidirectional flow of electricity is leaning towards multidirectional flows. This situation needs significant changes in the transport infrastructure of the electrical power system and mode of operation. Technological advancements are making a smarter and more organized management system of electricity supply and demand. This development needs to incorporate modern communication, monitoring, and control capabilities, along with the intelligent transport, generation, and storage value chain. Digital substations integrate thousands of sensors by providing greater comfort, self-reliance, availability, and safety while at the same time reducing costs, risks, and environmental damage. Conventional power system lacks digital communication, remote monitoring & control, and consumer participation. Fiber-optic cabling in digital substations will attain not only real-time data transmission but also remote maintenance. The comparison between conventional and digital substations has also been presented in this work along with latest trends in smart protection. Electronic protection and control devices based on a microprocessor, communicating with cloud servers, will enhance the flexibility to control and monitor the digital substations. A significant amount of data generated by components in the electrical substations will allow greater sophistication of the monitoring, diagnostic, protection and optimization capabilities of the facilities. The goal for the review of said advancements is to have a smart electrical system with greater efficiency and better user experience.

This paper proposes Halbach array PM linear generators for direct conversion of ocean or sea wave energy. This generator is proposed to be directly coupled to a reciprocating wave energy system which may be a valued alternative for pneumatic and hydraulic systems. In this research, air-cored permanent magnet PM linear generator has been optimally designed according to optimal cost and performance indices. The design depends on Halbach array PM arrangement. The design this linear generator is appropriate for direct extraction of energy from sea waves specially at small wave amplitude. The finite element method and the space harmonic analysis are used to develop the electromagnetic design of this topology. In this article, the concept of the extraction of maximum permissible power from sea waves is established according to the direct wave energy converters hydrodynamics model. The dynamic analysis of the Halbach array linear generator is investigated during the state of maximum power extraction.

Recommender Systems (RSs) are termed as web-based applications that make use of filtering methods and several machine learning algorithms to suggest relevant user objects. It can be said that some techniques are usually adopted or trained to develop these systems that generate lists of suitable recommendations. Conventionally, RS uses a single rating approach to preference user recommendation over an item. Recently, multi-criteria technique has been identified as a new approach of recommending user items based on several attributes or features of user items. This new technique of item recommendation has been adopted to solve several recommendation problems compared to the single rating approach. Furthermore, the predictive performance of the multi-criteria technique when tested proves to be further efficient as compared to the traditional single ratings approach. This paper gives a comparative study between two models that are based on the features and architecture of fuzzy sets system and adaptive genetic algorithm. Genetic Algorithms (GAs) are robust and stochastic search techniques centered on natural selection and evaluation that are often applied when encountering optimization problems. Fuzzy logic (FL) on the other hand, is known for its wide application in diverse fields in science. This study aims to evaluate, analyze, and compare the predictive performance of both methods and present their results. The study has been accomplished using Yahoo! Movies dataset, and the results of the performance of each model have been presented in this paper. The results proved that both techniques have significantly enhanced the system’s accuracy.

To reduce its high last mile logistics cost, a company may explore different options such as a horizontal collaboration with another company that has a similar logistics requirement. Traditionally, the collaboration can be conducted through an outsource contract mechanism where the company may need to guarantee a certain number of logistics demands or usage for a fixed period of time. This may incur a fixed transportation and logistics cost for the company. For a company who has more fluctuated logistics demands, it would be difficult to guarantee a certain number of logistics demands or usage for an outsourcing mechanism. The company may end up paying more than it should. Alternatively, the company may want to explore horizontal collaboration with a more flexible contract mechanism such as the “4th party milk run” (4PMR). The 4PMR model leverages on the last mile excess capacity of one company to fulfill the last mile logistics demands for another company based on a pay-per-use arrangement. Using the 4PMR model, the fixed transportation and logistics cost would be translated into a marginal cost. This paper describes the 4PMR model, including the optimization model and its computation experiment on two last mile logistics scenarios. The first scenario is a hypothetical scenario based on our field study in Jakarta, Indonesia with a small number of deliveries, while the second scenario is an actual scenario with a large number of deliveries based on existing routes of a Logistics Service Provider (LSP) in Surabaya, Indonesia. The experiment results show that 4PMR is able to provide a significant reduction in last mile logistics cost. To complement the experiment results, industry perspectives for implementing the 4PMR model is also reviewed.

While the Kalman filter, including its many variants, has been the staple of the tracking community, it also has been shown to have drawbacks, particularly when tracking through a maneuver. The most common issue is a lag in the position of the target track compared to the true target position as the target performs its maneuver. Another more problematic issue can occur where the filter covariance collapses, requiring the filter to be reinitialized. Techniques exist to compensate for maneuvers, but generating their response relies on detection of error between the estimated trajectory and the measured target position. In this effort, a maneuver detection routine is developed that can be used in conjunction with more standard maneuver compensation approaches. This routine is able to validate the existence of a maneuver more quickly than use of the inherent detection relied upon in the other methods. Maneuver detection is performed by an evidence accrual system that uses a fuzzy Kalman filter to incorporate new information and provide a level of evidence that maneuver is occurring. The input data uses behavior characteristics of the Kalman gain vector from the tracking algorithm.

Nowadays, the realization and follow-up of a team activity takes place in different execution environments (e.g. mobile phone, PC) through the use of computer applications. This implies context changes that generate interruptions in the activity. To improve the continuity of an activity with the reduction of interruptions during the development of this activity, would help to finish a task in a shorter time. An example of interruption occurs when a user initiates an activity on a device and decides to continue working on it on a different device, thus facing additional steps that must be performed to obtain the updated information on the desired device. The interruptions can be found in several domains as in the previous example that refers to the work developed through multi-devices or collaborative systems (CS). In this article we present a proposal for a model that incorporates the continuity attribute in the CS development process, to support its users to carry out Collaborative Activities (CA) through different devices with the least possible number of interruptions, so that that way the user does not require additional steps that extend the time of the task and negatively affect the process of transferring his CA to another device.

High gain-bandwidth product and visible/UV contrast photodetectors are vital in Visible Light Communication (VLC) and Ultraviolet (UV) reflectance imaging applications respectively. We adopt material and structural optimization to perceive such photodetectors with back-illuminated Optical Field Effect Transistor (OPFET) wherein any potential difference in absorption coefficient of the semiconductor material between the visible and the UV range (higher in the UV region) can be explored at its full potential. The results have been analyzed using the photoconductive and the photovoltaic effects, the series resistance effects, scaling rules- induced effects, and channel length-variation effects. We consider the three most prominent and functional materials in the visible range (Si, GaAs, and InP) for material-based optimization. Structural optimization is performed employing a range of medium gate lengths. The gate electrodes utilized are Indium-Tin-Oxide (ITO) for Si and GaAs with high Schottky barrier heights of ~0.71 eV and ~0.98 eV respectively whereas the Schottky contact on InP is gold (Au) with a high barrier height of ~0.8 eV. The operating visible and UV wavelengths are 600 nm and 350 nm respectively. The results suggest that GaAs OPFET has wide bandwidth potential in the gigahertz range apart from its high sensitivity and visible/UV contrast features. The InP-based OPFET exhibits high sensitivity and sub-gigahertz frequency response; and can compete or surpass the GaAs OPFET in terms of the visible/UV contrast ratio. The Si OPFET shows bandwidth in the megahertz range along with high sensitivity but exhibits low contrast ratio. The structural parameters have a significant effect on the detector response. The results are in-line with the experiments. This paper reflects the performance of the investigated detectors towards the said applications through optimization and the associated analysis represents the dependence of the obtained response on the device material and structural parameters, thus, opening the door for further research.

The integrating model becomes one of key trends in engineering design. The more the product has functional specifications, the more the industrial consumer apply in many fields. In this paper, a fusion design of architecture platform for autonomous system is demonstrated. It is greater due to combining the specifications of various types in commercial market such as driving structure, multi-functional model or autonomous level. Therefore, plentiful advantages of systems are converged into one platform. From customer’s requirements, the system parameters and specifications are offered to consult during the process of design. To compare with previous works, the enhancement of tractor-trailer, lifting-carrier and auto-feeder using differential driving mechanism is merged successfully. The detailed design of prototype involves the development of hardware components and infrastructure. Later, the modeling of autonomous system is simulated on computer to meet the employed conditions. The force analyzing scheme helps to predict the working ability to complete their mission. To verify the proposed design, an experimental version of this system is tested in different cases. In mode of lifting carrier, the vehicle and cargo that become a rigid body, follow the reference trajectory in practical map. In another circumstance, vehicle plays a role as leader which take along with cargo, as follower. From these results, it can be seen that the proposed design is feasible, effective and capable in real world. In future works, a fleet of autonomous vehicles mixing multi-modes in the same map should be considered.

The problem of recognizing traffic sign boards in a correct fashion is one of the major challenges since there is an alarming rate of increase in the number of road accidents happening because of incorrect interpretation of traffic sign boards in bad weather conditions. In this paper, a comparative analysis of various noise removal techniques based on calculating different parameters which decide the quality of input roadway symbol like Mean Squared Error (MSE), Peak Signal to Noise Ratio (PSNR) as well as Structural Similarity for measuring Image Quality (SSIM) is being performed and the best technique will be chosen among them which gives minimum Mean Squared Error (MSE) value and maximum Peak Signal to Noise Ratio (PSNR) and Structural Similarity for measuring the Image Quality (SSIM) values. This technique will be quite useful for de-noising a given image which is present in both the testing and the training image databases.

Spatial and temporal isolation is a crucial issue in embedded systems executing multiple tasks with several levels of criticality. This is considerably significant in the context of multi-processor (or multi-core) embedded systems running multiple mixed-criticality applications in parallel. This work deals with the issue of isolation of different application classes on Network on Chip (NoC) architectures and proposes a lightweight hardware mechanism able to support mixed-criticality requirements and specifically designed to be introduced into existing network interfaces. This mechanism supports the execution of different and contemporary applications with several criticality levels by supervising the messages exchange among network nodes, with the introduction of limited hardware and software overhead on the monitored network. The proposed solution is described and evaluated by means of logical simulations and an implementation on reconfigurable logic, using a reference NoC architecture with mesh topology. Scalability of the proposed approach is also discussed and evaluated by means of network simulations. Results show an area occupation less than 1% in a 3×3 mesh NoC, and a good scalability of the proposed mechanism in an 8×8 mesh network, indicating it as a valid lightweight solution able to enforce isolation in NoCs.

In this paper, a multiple-objective Metaheuristics study is discussed. Initially, three mono-objective metaheuristics will be explored in order to design and optimize Radio-Frequency integrated inductors. These metaheuristics are: An evolutionary algorithm called The Differential Evolution (DE), An algorithm supported on Newton’s laws of gravity and motion called the Gravitational Search Algorithm (GSA) and, finally, A swarm intelligence algorithm called the Particle Swarm Optimization (PSO). The performances of these three mono-objective metaheuristics are evaluated and compared over three benchmark functions and one application to optimize the layout of a RF silicon-based planar spiral inductor, the double ?-model is adopted. Secondly, three references multi-objective metaheuristics using Pareto front are used respectively the multi-objective PSO (MOPSO), the Pareto envelope-based selection algorithm-II (PESAII) and the multi-objective evolutionary algorithm based on decomposition (MOEA/D). The performances of these multi-objective optimization algorithms are evaluated and compared over two bi-objective benchmark functions and the same application used in the first section. Two conflicting performances were optimized, namely the quality factor ‘Q’ (to be maximized) and the device area ‘dout’ (to be minimized) for the RF inductor. It was concluded that the multiple-objective PSO are significantly more efficient and robust for difficult problems than the other metaheuristics.

The importance of programming skills has increased with advances in information and communication technology (ICT). However, the di ? culty of learning programming is a major problem for novices. Therefore, we propose a logic error detection algorithm based on structure patterns, which are an index of similarity based on abstract syntax trees, and error degree, which is a measure of appropriateness for feedback. We de?ne structure patterns and error degree and present the proposed algorithm. In addition, we develop a Logic Error Detector (LED) Application Programming Interface (API) based on the proposed algorithm. An implementation of the proposed algorithm is used in experiments using actual data from an e-learning system. The results show that the proposed algorithm can accurately detect logic errors in many programs solving problems in the Introduction to Programming set.

All forms of skin cancer are becoming widespread. These forms of cancer, and melanoma in particular, are insidious and aggressive and if not treated promptly can be lethal to humans. Effective treatment of skin lesions depends strongly on the timeliness of the diagnosis: for this reason, artificial vision systems are required to play a crucial role in supporting the diagnosis of skin lesions. This work offers insights into the state of the art in the field of melanoma image classification. We include a numerical section where a preliminary analysis of some classification techniques is performed, using color and texture features on a data set constituted by plain photographies, to which no pre-processing technique has been applied. This is motivated by the necessity to open new horizons in creating self-diagnosis systems for accessible skin lesions, due also to a huge innovation of cameras, smartphones technology and wearable devices.

Current work demonstrates how open-source optimization library ”Extremum” (OSOL Extremum) can be used to build feedback controller of a satellite. Proposed software was developed to as an attempt to eliminate current problems that are present in scientific area: black-box effect (i.e. there is no opportunity to explore source code, modify it, or simply verify), no code reuse (i.e. implemented procedures are accessible only within software that includes it), limitated application of modern optimization algorithms (i.e. number of optimization algorithms increases but most of them were verified only on synthetic tests). All of them lead to so-called reproducibility crisis.

In this paper, we present a new Somewhat Homomorphic Encryption (SHE) scheme using computation over complex numbers. We then use Bootstrapping technique to make the scheme Fully Homomorphic (FH) and supports unbounded number of circuit depth. In addition to its homomorphic properties and security level, a main characteristic of the proposed new scheme is its simplicity as it is merely based on addition and multiplication operations over complex numbers. The new scheme is implemented under Python using SAGEMath library and evaluated. Then a crypt-analysis based on Approximate GCD problem is done. A comparison with the BGV, a well known Fully Homomorphic Encryption (FHE) scheme, shows that this new scheme is an e ion scheme.

In this paper, we present a new Somewhat Homomorphic Encryption (SHE) scheme using computation over complex numbers. We then use Bootstrapping technique to make the scheme Fully Homomorphic (FH) and supports unbounded number of circuit depth. In addition to its homomorphic properties and security level, a main characteristic of the proposed new scheme is its simplicity as it is merely based on addition and multiplication operations over complex numbers. The new scheme is implemented under Python using SAGEMath library and evaluated. Then a crypt-analysis based on Approximate GCD problem is done. A comparison with the BGV, a well known Fully Homomorphic Encryption (FHE) scheme, shows that this new scheme is an e ion scheme.Great efforts have been shown in the literature to improve the efficiency of photovoltaic (PV) systems. In this workpaper, authors present the development of a computational system based on free software that allows to estimate with an accuracy superior to 99.9 percent the performance of the PV array connected to a current inverter block, from the physical models behind. For this purpose, a solar radiation measures was made in the laboratory, and also voltage, current and temperature tests over the whole system, adding a storage interface and serial data transmission port to drive and process the data from a computer. Once the system was tested and calibrated in laboratory, the I-V experimental curves of the PV system were obtained in real time, in order to define the most important parameters: short-circuit current, open-circuit voltage, maximum current and voltage, in a natural environment. Those parameters were the input data to the computational tool based on Scilab, which allow estimate the performance of PV system described, solving the non-linear equations of the physical models used. The performance results were compared with a similar computational tool developed in Matlab, as well as with a commercial solar simulator used by the manufacturer of PV module. A highly accurate computational system based in Scilab was obtained allows to evaluate the efficiency and the filling factor of an array for di evels in real time.

We present findings on classifying the class of executable code using convolutional, re- current neural networks by creating images from only the .text section of executables and dividing them into standard-size windows, using minimal preprocessing. We achieve up to 98.24% testing accuracy on classifying 9 types of malware, and 99.50% testing accuracy on classifying malicious vs. benign code. Then, we find that a recurrent network may not entirely be necessary, opening the door for future neural network architectures.

3D-printing technologies have greatly influenced the field of fabrication of medical devices. In particular, Fused Deposition Modeling 3D printing has emerged as one the most popular and most promising technologies for fabricating upper-limb prostheses. Over the last years, a variety of types and designs of 3D-printed hand prostheses have been created and are commercially available. However, there are no standards or established procedures for testing these devices. Available information regarding their long-term performance and functionality is very limited. This paper presents a case study of mechanical testing methods applied to a specific design of an upper-limb prosthesis. The device and its subassemblies were subjected to flexion test in hyperextension and abduction conditions, fatigue/wear test, and tensile test. The experimental results are presented and examined. Testing procedures, adaptations and recommendations are described and discussed to demonstrate ways of generating reliable data that serve for comparison among different hand prostheses designs.

Perishable inventory management contributes simultaneously to society and the economy, by reducing food wastage and capitalizing on the freshness of goods. For this reason, countless mathematical models have been developed for their effectiveness and cost-efficient management. Yet, the majority of these models can only optimize systems for a limited time frame, allowing for small gains in operations management, but failing to change the recurring patterns in inventory levels. System dynamics (SD) modelling shifts emphasizes these patterns and the recurring decisions that make them. Moreover, the framework has generated insight for other supply chain cases that could not have been derived from a short-term perspective. Thus, the current study now seeks to apply the SD framework in modelling perishable inventory systems, in designing policies that benefit the environment and the economy by reducing waste production and increasing the viability of goods reaching the customer. In particular, it evaluates the impact of opposing issuance policies (i.e. First-In-First-Out (FIFO) and Last-In-First-Out (LIFO)) on perishables to demonstrate the potential of SD in improving perishable inventory management. The simulated results share the sentiments of optimization models, that FIFO will ultimately generate less wastes and incur less material costs. Yet, the simulations also reveal implementing FIFO will result in larger fluctuations in inventory levels, which imply greater inconsistency in age-based quality. These suggest that LIFO would be preferable for quality-sensitive products, while FIFO would be preferable for cases sensitive to waste production. The current study demonstrates the efficiency of system dynamics in generating insight beyond that which can be derived from the existing mathematical models. Future studies may likewise extend this approach in the evaluation of policies on the use of technology in perishable inventory systems, which are the prevalent in present literature.

Physical computing is important for ICT (information and communication technology) Education and other informatics education such as software education since physical computing can provide learning-by-doing education for students. It is also a strong tool to increase students’ programming ability using various type of physical computing tools like a robot. In physical computing, it is necessary to have evaluation metrics for learners in order to test students’ progresses and environmental safety, etc. However, in the literature, there is little work for evaluation metrics for learners. In this paper, for the proposed evaluation metrics for learners developed in the previous research, the usefulness and validity are discussed and justified with statistical analysis. The final 23 evaluation metric will be very useful in actual physical computing education.

Recently, there is an enormous research on the smart campus concept due to the revolution of the IoT technologies. The motivation of this paper is to: reinforce the safety on campus, reduce the cost, and take one step forward toward a University smart campus. In this paper, we are not only proposing a framework that would act as an instantaneous responder, but we also provide a glimpse of the evolving research on smart campus. In addition, we explore the challenges, and highlight the future work regarding this on-the-spot responder system.

This paper considers active disturbance rejection control (ADRC) as a new control approach for pH neutralization process. The pH process is challenging to control due to its sensitivity to disturbance, nonlinear dynamics, and time-varying characteristics. Special attention is paid to the impact of disturbances typically exists in the chemical process. First, the pH neutralization process nonlinear model is presented, then the active disturbance rejection control is described briefly and a pH neutralization process closed-loop control system is designed on its bases, and investigated with simulation. More robust and better disturbance rejection performances are achieved compared to the control technique of feedback linearization (FL).

The most important biological factors, mainly determining the economic efficiency of milk production, are the productivity potential and the level of viability of cows. The aim of the work is to predict the bio-economic effects in a heterogeneous population of dairy cows taking into account the decrease in the length of productive life with increase in milk productivity. In the considered model situation, with an increase in the productivity of cows, the total profit per cow per year increases, while the profitability of production may decline. The developed algorithms are supposed to be used for solving the problems of optimizing the management of the dairy cow herd and planning technological development in animal husbandry.

Clustering is a branch of data mining which involves grouping similar data in a collection known as cluster. Clustering can be used in many fields, one of the important applications is the intelligent text clustering. Text clustering in traditional algorithms was collecting documents based on keyword matching, this means that the documents were clustered without having any descriptive notions. Hence, non-similar documents were collected in the same cluster. The key solution for this problem is to cluster documents based on semantic similarity, where the documents are clustered based on the meaning and not keywords. In this research, fifty papers which use semantic similarity in different fields have been reviewed, thirteen of them that are using semantic similarity based on document clustering in five recent years have been selected for a deep study. A comprehensive literature review for all the selected papers is stated. A comparison regarding their algorithms, used tools, and evaluation methods is given. Finally, an intensive discussion comparing the works is presented.

An optimization method using a semi-analytical approach is detailed in this paper to design a wideband multilayer meander-line polarizer. It consists of the transmission line equivalent circuit study to find the best values of the shunt components that allow the maximum transmission and a 90 phase difference between two orthogonal components over the entire bandwidth, followed by a simulated annealing algorithm to optimize the polarizer’s dimensions by converging the values of the shunt components found earlier, to some rigorous empirical formulas of the equivalent admittances.
To validate the proposed method, a wideband (6-18 GHz) four layer’s meander-line polarizer is optimized and manufactured. The polarizer prototype is stacked to a horn antenna operating at the same frequency band and tested. The experimental results verify that the linear waves of the horn antenna alone were converted to circular ones when the multilayer meander-line polarizer is stacked to it.

Due to the recent development of information and communication technology, such as sensor technology, IoT technology, and smart technology, interest in maker education is increasing. Maker education offers students experience-based education that allows them to make their own results, giving them various interests and motivations. Recently, various research results on maker education have been under way. However, there is little research work on the teaching aids selection criteria for maker education. The purpose of this study is to justify the teaching aids selection standards that are developed in the previous research works. In the previous research work, 14 selection standards were developed in a total of eight areas. In this work, through rigorous statistical analysis, justification of the 14 standards are verified. The findings are expected to help a lot in future policy proposals and related research on maker education.

This research focussed on assessing the impact of using upgraded biogas from chicken waste on the performance of a gasoline generator. In the study, the characteristics of raw biogas were determined and a biogas upgrading device was developed before assessing the generator performance on upgraded biogas. A Geotech GA 2000 plus gas analyser was used for biogas characterization and a slightly modified Elemax SH 2900 gasoline generator (5 HP) was used. The findings revealed that biogas from chicken waste comprised of 57% CH4 and 41% CO2 with small amounts of H2S, O2 and NH4. During biogas upgrading, the highest solvent to biogas flow rate used was 0.80. NaOH excelled with 93.0% CO2 reduction followed by KOH at 82.5% and lastly water at 63.0%. Using upgraded biogas improved the generator’s brake thermal efficiency from 14.2% for raw biogas to 17.6% for upgraded biogas. A locally fabricated scrubbing device significantly upgraded the biogas quality.

Data mining is recognized as an effective technique for extracting and retrieving valuable information or decision from the vast available data. Because of the nature of the functionality of medical centers and hospitals, their data centers contain a collection of valuable information about their patients. By properly processing these data, different applications can be developed to utilize them. These applications could participate in predicting and diagnosing particular diseases. Two prime diseases realized to impact the overall health of society are heart diseases and diabetes. The presented work intends to develop and test a software application that helps doctors and practitioners predict the emergence of noncommunicable diseases (NCDs) such as diabetes and heart diseases. The application applies the predictive data mining model to the medical records which are collected from the Bahrain Defense Force Hospital (BDFH). The BDFH doctors evaluated the application and executed it on actual patients. The results obtained are accurately matching the expectation of doctors in BDFH. All kinds of risks are categorized appropriately according to the defined categories. As a conclusion, this application can help doctors in making proper decisions toward patient health risks. In addition, data mining is more supportive for the health sector and is essential for exploring the knowledge to be used in the health care sector.

This paper presents a solution for question answering system for Vietnamese language by integrating diacritics restoration and question classification via deep learning approach. It could be said that this will be the first research integrating two phases into Vietnamese question answering system. Question classification has a critical role in the question answering system. However if the question has too many missing diacritics, this will make the classification extremely more difficult. In this paper, both automatic insertion of diacritics and question classification tasks are built to rely on deep learning approach. For diacritics restoration task, we apply the Encoder-Decoder LSTM model. The result of the first step will be the input of question classification. We use pre-train word embeddings in the Bidirectional LSTM model for Vietnamese question classification. The deep learning approach for both tasks is powerful and highly accurate model. By integrating diacritics restoration and question classification into Vietnamese question answering system – ICTbot of Binh Duong Department of Information and Communications Support System – it has produced remarkably positive results; thus proves the practicability of this proposed system.

Manufacturing System (MS) sizing is a crucial task to complete in order to obtain the desired MS performance and efficiency. It involves selecting the required number of resources from each used type in a given planning horizon. In fact, different approaches coupling simulation/optimization tools have been developed to solve this issue and evaluate the MS performance. One of these approaches is the Simulation Expert System Approach (SESA). Unfortunately, the application domain of this approach is limited in sizing only the production resources (machines and labor) but neglects the material handling system (MHS) components. Moreover, omitting the transferring problem is not viable in the real world due to its importance in each shop floor. Thus, the aim of this paper is to describe the evolution of SESA, then, to check if the simulation optimization tools used in SESA are still relevant. This paper also investigates the importance of incorporating MHS in this approach and finally proposes some improvement opportunities for SESA including the tackling of the MHS fleet sizing problem. In fact, the wide literature review performed in this research indicates that SESA is still a pertinent approach but it must be improved. Therefore, it is expected that SESA improvement opportunities proposed in this work will greatly assist industrialists in enhancing the overall MS performance, providing a significant productivity increase and a minimization of the total production costs.

Research is dedicated to fuzzy reconstructions in the diachronic associative linguistics, and encompasses several simulation practices. The principles of an associative memory model and fuzzy computing with words are adopted in the working methodology. The evolved mathematical model reanimates the older patterns of Scottish associative map by means of fuzzy logic. The compiled fuzzy associative fields are connected by epidigmatic relations represented by fuzzy associative word structures in a fuzzy associative map constituting the grounds of the proposed thesaurus. The simulation of the historical associative thesaurus enables extended associative scrutiny: tracing associative antipodes, reconstructing an associative experiment for any map associate, finding words’ similarities and distances, as well as associative differentiating and stratifying the Older Scottish lexis. The special interest represents the fuzzy associative differential, an electronic tool for estimating the lexis through its stable syntagmatic associates. All the simulation processes are fuzzy initiated and quantified. The proposed practice in its final configuration has no precedents, representing the novelty of theoretical interpretation and empirical application. The proposed terminology is mainly introduced by the researchers.

In this work of technological development and research is presented the work done in the ITSAO to contribute to the use of clean energy such as photovoltaics and the implementation of activities to reduce electricity consumption by the technological community, the monitoring of electricity consumption is made on a monthly basis and an annual report is prepared in which the consumption monitoring and the reduction percentage thereof are shown by tables and graphs. The students of electronic Engineering carry out activities of optoelectronics and sustainable development where they intervene with the design and installation of photovoltaic systems that are part of their academic formation. It has a photovoltaic system that produces 2.16 kW-h / d and another will be installed to have photovoltaic energy in the tables of the ITSAO planters; these actions are part of the operational control related to the energy of the Integral Management System (SGI).

Thermo-acoustic technology is very potential to be applied to convert heat into another energy source. The complete thermoacoustic engine construction requires a resonator connected to a core where there is a heat exchanger. The thermoacoustic core has a function as an acoustic power generator while the resonator functions as a storage of acoustic power which will shift the amplitude or phase of oscillating pressure or volume flow rate. Experimental testing was carried out to obtain the performance of a quarter wave length of standing wave thermoacoustic engine (SWTE). The resonator is made of a 2 inch Sch 40 stainless steel pipe with a length of 390 mm. The results of this experiment showed that the measured pressure amplitude was 4800 kPa with a working frequency of 138 HZ and produced an acoustic power of 22.85 watts. The parameters of this experimental test are used for the simulation program. The acoustic power generated from the simulation is 20.13 watts, 18.67 watts and 17.82 watts respectively for the length of the resonator 390 mm, 780 mm and 1170 mm.

Predicting trainees’ final academic results in the early stage of programming class is a significant mission in the field of learning analytics. Performing exercises in programming class is hard and it takes a lot of time for trainees. For this reason, careful support with trainees are offered in many classes through classroom assistants (CAs). Even with CAs’ assistances, managing a programming class is difficult. Because each trainee’s coding activity is different from another’s, even when each of them is solving the same exercise. Classroom assistants with little teaching experience have difficulty for understanding the unique features of trainee’s coding activity. We have employed data mining to analyze trainees’ coding activities to distinguish those various features. The objective of this research is to present such behavioral features of trainees to CAs to enrich their assistance for the trainees. In order to investigate the timing of guidance, we conjectured the academic results from the chronicle record of coding activities.

Hadoop has certain issues that could be taken care to execute the job efficiently. These limitations are due to the locality of the data in the cluster, allocation of the jobs, scheduling of the tasks and resource allocations in Hadoop. Execution in the mapreduce remains a challenge in terms of efficiency. So, an improved Hadoop architecture that takes care of the computation time has been discussed. The improved architecture addresses the communication issues with the task trackers, inefficient clean up task, heartbeat function. Comparing with native Hadoop, the improved Hadoop reduces the total time taken for running the reducer tasks. The performance of the improved system using optimization serves better in the computation time.

Health Information Technology is playing a key role in healthcare. Specifically, the use of electronic health records has been found to bring about most significant improvements in healthcare quality, mainly as relates to patient management, healthcare delivery and research support. Health record systems adoption has been promoted in many countries to support efficient, high quality integrated healthcare. The objective of this work is the implementation of an Electronic Health Record system based on a relational database. The system architecture is modular and based on the concentration of specific pathology related parameters in one module, therefore the system can be easily applied to different pathologies. Several examples of its application are described. It is intended to extend the system integrating genomic data.

In the past decades, various ideas have proposed for reconductoring of overhead lines to enhance power transmission capacity considering growing energy demand. Conventional method of reconductoring is use to take couple of weeks but availability of shutdown for a prolong period is a major constraint in line uprating. Emergency restoration structure (ERS) is effective to deliver such challenging task within hardly 4-5 hours line shutdown only. This quick restoration and easiness is mobility features are making ERS versatile for restoration even after any natural disasters. In this article, a case study of 132 kV restorations is described as a conceptual proof of concepts. Successful execution of this project resolved a 400 kV transmission line hindrance between Atur and Indira Puram since March 2017 and was likely to remain idle due to a 132 kV single circuit transmission line crossing its route.

In the past decades, various ideas have proposed for reconductoring of overhead lines to enhance power transmission capacity considering growing energy demand. Conventional method of reconductoring is use to take couple of weeks but availability of shutdown for a prolong period is a major constraint in line uprating. Emergency restoration structure (ERS) is effective to deliver such challenging task within hardly 4-5 hours line shutdown only. This quick restoration and easiness is mobility features are making ERS versatile for restoration even after any natural disasters. In this article, a case study of 132 kV restorations is described as a conceptual proof of concepts. Successful execution of this project resolved a 400 kV transmission line hindrance between Atur and Indira Puram since March 2017 and was likely to remain idle due to a 132 kV single circuit transmission line crossing its route.

With the development of big data, artificial intelligence and deep learning, various social information networks are becoming exponentially intelligent. Of all the various social networks, YouTube is so popular that it is called the YouTube era. Not only video viewers, but also actual video producers, influencer youtubers, are increasing, allowing individuals as well as operators to use and market contents on various topics. As the number of users and contents increases, the choice of information increases, but it is more difficult for individual and business owners to select information that meets their needs. Therefore, this study aims to analyze the channel of the subject that the user needs from various angles and provide the ranking of the channel to individuals and businesses. We crawl the channels and measure the average awareness and influencer youtuber of channels and channel videos by analyzing the qualitative data of quantitative data and comments which are statistical information. As a final case study, we recommend the English learning channel to individual users based on numerical data statistics and emotional analysis results to show the maximum of flipped learning effect regardless of time and space. Plus, we prove the maximum of the effectiveness of marketing by influencer channel which is influential to operators in the medium and long term.

Digitization forces industry players to adapt to transforming market situations and buyer behavior. Technological advances, buyer power, and sharpened competitive intensity imply that businesses are confronted with the menace of commoditization. For companies to perform successfully in the market, outdated business models ought to be rethought and new business models should be created. Unique selling propositions and differentiation through research, innovation, and holistic stakeholder involvement help industry players to master the change. A tool was built to support businesses facing the consequences of digital transformation: the Business Model Builder. This research paper explores the steps of creating a software version of the analog Business Model Builder. The digital twin enables firms to simulate the iterative adaptation of business models to permanently changing market circumstances and customer demands on an ongoing basis. The user of the tool can edit single variables, understand interrelations, and see the effect on the outcome of the business case, e.g., earnings before interest and taxes or economic value added. Accordingly, the simulation offers the opportunity to have a dynamic view of the business model where any variances of input parameters are reflected in the business case. Thus, profitability, feasibility, and functionality of a business model can be validated, tested, and reviewed in the digital simulation tool.

Source parameters of tsunami waves are an essential part of any modern tsunami warning system. Recalculation of a measured time series (wave profile obtained by a seabed-based pressure sensor) in terms of initial sea surface displacement at tsunami source is among the most (or) one of the promising approaches to be applied in a warning center. The “orthogonal decomposition”, that was proposed and studied earlier by the authors, is numerically studied here. Realistic shape of sea surface displacement and digital bathymetry of the southern part of Japan are used. To study functionality of the proposed approach, wave profiles are obtained by the sensors of DONET – Dense Oceanfloor Network system for Earthquakes and Tsunamis – pressure gauge network of Japan. We stress on the quality of tsunami source parameters reconstruction as well as on time required. As observed, just a part of the first wave period is enough for robust determination of such source parameters as amplitude and total volume of water. Results of numerical tests are summarized in tables and then discussed.

This paper presents the setup of eye tracking calibration methodology and the preliminary test results of the training model from the eye tracking data. Eye tracking requires good accuracy from the calibration process of the human eyes feature extraction from facial region. Viola-Jones algorithm is applied for this purpose by using Haar Basic feature filters based on Adaboost algorithm which extract the facial region from an image. From the extracted region, the eyes feature is selected to find the center coordinate of the iris and be mapped with the calibration point coordinates to create the training model of the eye calibration process. Thus, this paper shows the performance and efficiency of three training functions in Neural Network algorithm to get the best training model with fewer error for more efficient eye tracking calibration process.

Agriculture, being the vital sector, contributes to India’s GDP by 18%. It is necessary to enhance the production in this sector with minimal need of resources to get the good yield of crops. It in turn boosts up the necessity of automation in the field of agriculture. This paper discusses about the experimental study which includes the invasive/non-invasive method to sense the approximate supply of macro nutrients such as Nitrogen, Phosphorous & Potassium to the plants. The nutrient content of leaf is sensed by the usage of optical sensors. The results have been obtained based on the sensor data carried out for the sample test leaf. Thus, the level of the estimated nutrient contents is indicated by three different ranges such as Low (L), Medium (M) and High (H). A mathematical model is developed for nutrient measurement especially for nitrogen and validated with SPAD meters for the measurement of nitrogen content. The other macro nutrients were also estimated, and the entire system is tested at different test conditions with the support of UASD in the field of University of Agricultural Science.

News companies have a need to automate and make the process of writing about popular and new events more effective. Current technologies involve robotic programs that fill in values in templates and website listeners that notify editors when changes are made so that the editor can read up on the source change on the actual website. Editors can provide news faster and better if directly provided with abstracts of the external sources and categorical meta-data that supports what the text is about. To make categorical meta-data a reality an auto-categorization model was created and optimized for Swedish articles written by local news journalists. The problem was that it was not scale-able enough to use out of the box. Instead of having this local model that could make good predictions of the text documents, the model is to be deployed in the cloud and an API interface is created. The API can be accessed from the tools where the articles is being written and therefore these services can automatically assign categories to the articles once the journalist is done writing it. To allow scale-ability to several thousands of simultaneously categorized articles and at the same time improving the workflow of deploying new models easier the API is uploaded to Sagemaker where several models are trained and once an improved model is found that model will be used in production in such a way that the system organically adapts to new written articles. An evaluation of Sagemaker API was done and it was concluded that the complexity of this solution was polynomial.