Volume 7, Issue 5

We have been studying the application of the musical saw, an unknown and advanced tool, to a cooperative humanoid robot for industrial use. A sound feedback system using mallet strike technique and the sound generated by the technique was constructed in the previous reports. The system enables tuning to a target frequency and control to enhance the purity of the pronunciation. However, the bow manipulation often used by skilled players has not yet been examined. In addition to striking, we concentrated on bowing motion, which is used in musical saw manipulation evaluation. Furthermore, it is necessary to generate self-excited vibration by stick-slip based on manipulation. Therefore, it is necessary to control the pressure and speed between the bow and the musical saw to confirm the occurrence of self-excited vibration and the vibration itself. In this study, in addition to the generation of sound by the self-excited vibration, the pure sound nature of the vibration will be discussed. This is different from the approach to suppressing self-excited vibration that has been implemented in previous studies. Furthermore, we will show how an industrial cooperative humanoid robot can be used to manipulate a musical saw with a bow skillfully.

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The article is devoted to the methods of monitoring and control of vibration processes occurring in the structure and units of complex and unique electromechanical equipment. The monitoring object is considered as a dynamic multidimensional information object, for the study of which analytical and numerical methods of modeling and simulation of multidimensional chaotic systems are used in the context of the scientific direction of physics of open systems. The structure of research of signals of vibration activity of equipment, description of mathematical models and algorithms based on them are presented. Demonstrative results of experiments carried out to analyze and evaluate the possibilities of controlling the behavior of a complex system using methods of influencing signals of various nature are presented. Using the methodology of Visual Thinking will improve the quality and efficiency of monitoring the vibrational activity of a complex technical object. Such a technique will make it possible to reasonably interpret the decision made to control the vibration process. The calculated parameters and the constructed visual images of the processed signals are proposed for use in the Input Layer of the Recurrent Neural Network of the Deep Learning algorithm.

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Electrifying or connecting remote and isolated communities to the national grid is very difficult and expensive. This challenge is due to the geographic locations of these isolated communities and terrain that needs navigation when installing transmission lines to transmit power to the communities. This article presents a case study of the design of a hybrid energy system for electrification of a remote region in South Africa. Upper blink water is about 20km away from the national grid. An effort has been made to design hybrid energy system which consists of solar photovoltaic (PV), diesel generator and battery storage. Homer software used to evaluate the technical characteristics and efficiency of the proposed hybrid energy system. Reticmaster software used to calculate the voltage drops of distribution lines to the residential houses of the communities. The results show that it is feasible to supply the isolated community with the proposed hybrid energy system. The average electricity cost is R1.1093c/kWh and the amount of diesel fuel per year is 16685L/year.

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In this paper, an improved model has been proposed for investigating the impact of cyber-attacks on power systems regarding frequency disturbances and voltage disruption while changing the load called ICAPS. The proposed ICAPS model is formulated by five different controllers, such as LFC, AGC, AGC-PID, AVR, and AVR-PID, implemented in two sets of the system model. Specifically, a stable limit of the speed regulation of LFC, integral controller gain of AGC, and amplifier gain of AVR are determined from their characteristic equations derived from the Routh-Hurwitz array. In contrast, the Proportional-Integral-Derivative (PID) controller gains for AGC-PID and AVR-PID are determined using the tuning process. The key aspect of this paper is to obtain the impact of cyber-attacks on the power system in terms of frequency disturbances and voltage disruptions while changing the load. According to our knowledge, no one has considered these issues at a time. In order to evaluate the proposed ICAPS model and how it works, a series of experiments was conducted using the MATLAB Simulink tool. The simulation results are presented in this paper in terms of frequency deviation and voltage disruption (i.e., positive and negative biased cyber-attack) and system oscillations. Finally, the simulation results successfully identified the most severe attack in this model to prevent the power system from unstable conditions.

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The impact of previous energy crisis gives an insight into what happens when the oil price crashes. Μany countries were affected due the slow reaction in boosting the economic growth in key sectors such as oil-importing activities and the economic restructuring progress to face the challenges. But the present energy crisis resulting from the COVID-19 pandemic period and closure of many borders, has encouraged power distributors and generators to have recourse to renewable energy for grid integration. Countries such as USA, Germany, Italy, Spain and India are moving towards increasing the share of renewable energy on the grid. The increasing use of solar power systems over the past few years is being favoured for the decarbonization process. The percentage growth in integrating solar PV energy is forecasted to reach 23% in the future. This widespread application of renewable power energy sources (RES) such as wind and solar power comprise of many challenges namely power quality and stability. With this consequent increase in RES, synchronous generators are being displaced and replaced by power electronics grid interface, which reduces the overall rotating masses, hence the system inertia. Stability study in the presence of renewable energy is therefore an important aspect to be considered to meet the required power quality of the grid. This paper brings forward the use of equal area criterion (EAC) method for assessing the stability of the power system network with presence of renewable energy such as solar energy system in the generation portfolio. EAC provides an effective visual and analytical approach for transient stability analysis. The investigations have been performed within the steady and transient conditions in form of a 3-phase fault. The modelling and analysis were carried out using MATLAB/Simulink. Calculation of the critical clearing time (CCT) for stability assessment is the main contribution to knowledge. The increase in the CCT confirms that the Solar PV penetration to the grid will improve the transient characteristics of the national grid network.

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To examine the effects of wearing a flexible knee orthosis (FKO) on the lateral thrust in patients with knee osteoarthritis (OA) by changing the wearing pressure. This study was a cross-sectional study. Thirteen patients (mean age: 82.8 ± 7.5years with Kellgren Lawrence Stages I and II knee OA were included and prescribed FKO. Patient with osteoarthritis of the knee was attending outpatient rehabilitation. Using a wireless 3-axis accelerometer, we analyzed the lateral thrust in the proximal lower leg during walking (10 m) with the knee orthosis under different wearing pressures (without orthosis, using “standard force” of application, and using “tight force”). The peak values in the outward direction of the gait cycle over the three experimental conditions were selected for analysis. The mean values for patients with K-L stages I and II were calculated and compared. We found that “tight force,” i.e., tightening the hook-and-loop fastener of the knee orthosis to its maximum, resulted in significantly lower lateral thrust compared to the absence of an orthosis. The effect was more pronounced in K-L Stage II patients. Our findings confirm that increasing the wearing pressure of the knee orthosis reduces lateral thrust in patients with knee OA. In Stage 1, the lateral thrust could be suppressed by the “standard force,” but the lateral thrust in “Stage 2” required “tight force” to be suppressed. Knee orthosis for knee osteoarthritis were found to have the potential to inhibit lateral thrust.

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This investigation demonstrates the analysis of various layout arrangements for oscillator (OSC) realized by CMOS technologies. Moreover, the analysis reveals that the serpentine style of OSC stages attains the minimum output variation on silicon. This investigation is firstly verified by post-layout simulations, comparing the variation with different kinds of layout arrangement for OSC designs, including serpentine layout style, straight layout style, and staggered layout style, etc. The proposed design is then realized using 0.18 μm process to justify the performance, where a straight line layout style and a serpentine layout style of OSC are physically fabricated on the same die. Besides, the on-silicon measurement is conducted to give the comparison for these two different styles of OSC designs. The proposed serpentine layout style attains the lowest layout variation when the variations are not homogeneous in different directions on the same silicon plane.

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In agile manufacturing, due to the desire to meet customer’s requirements, processing times are stochastic because operations could be done by robots or humans. This can cause several problems in scheduling the jobs, and it is necessary to select the dispatch rule with the least change in costs and times, to respond quickly to different processing times. In this work, some experimental tests are carried out through a simulation algorithm and the runs are made in a robot with 6 jobs where the delivery dates and the probability distribution of the processing times are known. The dispatch rules are compared, varying the ratio of Holding-Penalty cost in different proportions to analyze how the total cost is affected, which is the novelty of this work. It was found that the most robust rule is the Shortest Processing Time (SPT) no matter if HC>PC or PC<=HC; with less variance in cost and the least average completion time compare with the others. With the mean lowest cost and that simultaneously minimizes early, and late production average time is the Earliest Due Date (EDD), when processing times are stochastic. As the dispatch rules present different degrees of sensitivity according to the cost relationship, it is convenient to explore which is the Holding-Penalty Cost relationship, that provides greater robustness and not just selects the least expensive rule.

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This paper looks for the merging of solar PV power plant with thermal power generation and interlinked through tie-lines approaching into an interlinked PV-Thermal model for the design and analysis of load frequency controllers. Moreover, the different models of controllers are also proposed and tested to regulate the frequency of PV-Thermal interlinked system for a certain disturbance in one of the regions of an interlinked system. The choice of auxiliary controllers is assessed utilizing assorted tuning strategies and the comparative examination of all models are shown via graphical response and via calculating numerical values to bring back the frequency and tie-line power deviations back to the standard level and to analyze the viable control activity. In addition, an HVDC link is also used between linked PV-Thermal areas to stabilize the frequency and tie-power quickly as well as to stabilize the system operation for alteration in changing loading conditions of the interlinked system.

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In different areas of industry (mining, food processing, pharmaceutical, manufacturing…), the problem of grain, aggregate and clusters flow arises during the handling activities especially loading and unloading. Thus, the study and control of the parameters that govern the flow of the granular medium and its interaction with its environment are key parameters to achieve the desired operational excellence and performance of these activities. The adhesion of granular materials on various surfaces of equipments (trucks surfaces, hoppers, silos…) is one of the major problems facing mining companies. In this paper we presented the Calibration of the Discrete Element Method (DEM) parameters for modeling phosphate ore built on the identification of the repose angle. This will help us to specify the correct inputs parameters that will be introduced for the modeling of adhesion phenomena. First we introduced the model contact which allow us to well define contact between phosphate particles-particles (or clusters-clusters) and phosphate particles (clusters) with tipper surface taking into consideration the cohesive and plastic nature of the contact. Secondly, we presented a calibration method based on the determination of the repose angle of the phosphate ore. This method allowed us, using fractional factorial designs and Box Behnken designs, to determine the optimal parameters for a more accurate simulation. Thirdly, we studied the impact of particle velocities on the tipper surface during charging and discharging of phosphate ore. This study allowed us to predict the areas most affected by the abrasion-erosion phenomenon caused by the impact of particles on the tipper surface. This calibration method allowed us to identify the optimal values for the key parameters that will be used later in the modelling of the phosphate clogging phenomenon on the surfaces of transport truck tippers in the mines.

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Given the strict Quality of Experience (QoE) and Quality of Service (QoS) criteria for video transmission, such as delivery ratio, transmission delay, and mean opinion Score (MOS), video streaming is one of the hardest challenges in Vehicular Ad-Hoc Networks (VANETs). Additionally, VANET attributes, including environmental impediments, fluctuating vehicle density, and highly dynamic topology, have an impact on video streaming. Creating efficient visual communications in these networks will give drivers access to business and entertainment applications, greater support, safer navigation, and better traffic management. This work particularly investigates the mobility characteristics of autos and the causes of link failure in order to precisely anticipate the dependability of connections between vehicles and build a reliable routing service protocol to satisfy various QoS application demands. Then, a link-time duration model is suggested. Link dependability is assessed and taken into consideration when creating a new self-adaptative routing protocol for video transmission. Due to the quick changes in topology, finding and maintaining the optimum end-to-end route is quite challenging. However, the heuristic Q-Learning algorithm could continually alter the routing path via interactions with the surrounding. This study suggests an algorithm for reliable self-adaptive routing (RSAR). Through changing the heuristic function and integrating the reliability parameter, RSAR works well with VANET. The Network Simulator NS-2 is used to illustrate how well RSAR performs. According to the findings, RSAR is particularly beneficial for many VANET applications since it efficiently addresses the issues brought on by changes in topology.

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The current paper focuses on the design of cascade control systems employed in photovoltaic (PV) powered microgrids (MG), which have been well received in remote agricultural farms in Ecuador. The study case is composed of a three-phase inverter powered by a DC-DC converter fed by a PV array and its control system. To simulate the effect of MG peak voltage regulation, a comparison of three cascade schemes was done, using MATLAB/SIMULINK®. These schemes are based on digital control, model-based predictive control (MBPC), and a combination of both. It was found that the best stabilization of voltage although with a fast response and a low overvoltage in the MG are reached with a combined scheme, compound by the classical controller combined with the MBPC. The results were validated using the RT-LAB software and the OPAL-RT real-time (RT) simulator, in the tested microgrid.

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In recent years, people-counting problems have increased in popularity, especially in crowded indoor spaces, e.g., public transport. In peak hours, trains move significant numbers of passengers, producing delays and inconveniences for their users. Therefore, analysing how people use public transport is essential to solving this problem. The current analysis estimates how many people are inside a train station by using the number of people entering and leaving the ticket gates or estimating the train occupancy based on conventional CCTV cameras. However, this information is insufficient for knowing the train occupancy. The required data includes vehicle usage: how many people enter or leave a vehicle or which door is the most used. This paper presents a solution to the stated problem based on a multi-object tracker with a sequential visual appearance predictor and a line-based counting strategy to analyse each passenger’s trajectory using an overhead fisheye camera. The camera selection inside the train was made after profoundly studying the railway environment. The method proposes a module to compute the total train occupancy. The solution is robust against occlusions thanks to the selected tracker and the fisheye camera field of view. This work shows a proof of concept dataset containing pseudo-real case scenarios of people’s affluence in train doors recorded by fisheye cameras. Its purpose is to prove the system’s functionality in these scenarios. The proposed approach achieved an overall accuracy of counting people getting on and off of 90.78% in the pseudo-real dataset, proving that this approach is valid.

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Renewable energy is an essential source of green growth for countries facing a shortage of fossil fuels. They offer a sustainable, inexhaustible, carbon-free solution to the future energy dependency of nations. Morocco, which has no traditional energy resources, depends entirely on the international primary energy market to meet its growing demand. For this reason, Morocco launched the National Energy Strategy in 2009 to reach 42% renewable production by 2020. This strategy has been renewed to 52% by 2050. Thanks to this policy, the country has been able to address most of its energy challenges. This study analyzes the energy mix of Morocco from 2010 to 2050. The methodology adopted is to simulate Morocco’s electricity mix for this period. We assumed we were at the beginning of deploying the country’s energy policy to assess the adopted strategic decisions. The analysis shows that the different technological solutions for electricity production chosen at the beginning of Morocco’s energy transition could be better. Indeed, the decision to develop concentrated solar power as the leading renewable source and coal as a backup option, for example, appears to be contested. However, according to the third scenario of our study, renewables have the potential to become the main source of energy for the Moroccan power grid.

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We present in this contribution the distribution of a global multi-input-multi-output system in a sensor and actuator network. Based on controllability and observability, the global system is decentralized and the system properties are preserved as a result. This results in multiple decentralized local single-input-single-output systems with the same system order as the global system. As these local systems are implemented on decentralized CPUs in the network, the computational effort of the nodes has to be minimized. This is achieved by approximating the input and output behavior and reducing the system order of the decentralized local systems. For this purpose, the two most common techniques, Balanced Truncation and Krylov subspace methods, are presented. Kalman filters are used for state reconstruction. To approximate the input/output behavior of the global system, information from all decentralized reduced local systems is necessary, thus a fully interconnected network is used for communication. By decentralized fusion algorithms in the network nodes, the Kalman filter algorithm is separated and distributed in the network.

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Electroencephalography (EEG) is one of the most common and benign methods for analyzing and identifying abnormalities in the human brain. EEG is an incessant measure of the activities of the human brain. In contrast, when the measurement of EEG is bounded by time and the EEG is synchronized to an exterior stimulus, is known as Event-Related Potential (ERP). ERP has the capability to perceive and explore the human brain’s responses to specific sensitive, cognitive, or motor events in real time with high temporal resolution. Among the various techniques, the oddball paradigm is very famous in EEG studies. In an oddball paradigm experiment, brain responses to frequent and infrequent stimuli are measured. However, the success of ERP research is very much dependent on the analysis of clean data sets and unfortunately, EEG is a combination of both neural and non-neural activities which introduce significant sources of noise that are not related to the brain’s response to the external stimulus. These unrelated non-EEG components are acknowledged as artifacts and due to these, the quality of the EEG may damage by decreasing SNR (signal-to-noise ratio). In addition, these artifacts may mislead the actual information in the study. Addressing this problem, the purpose of this research is to introduce a machine learning algorithm (ML) that can screen EEG/ERP data to remove data epochs that are disrupted by artifacts and thus produce a clean data set. Overall, three unsupervised ML algorithms are applied to identify noisy epochs and it is found that the DBScan method performs best with 93.43% accuracy. Finally, the success of this study will allow the ERP study to have a cleaner ERP data set in normal laboratory conditions with less complexity in the ERP studies.

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The emergence of blockchain technology has facilitated the digital transformation of many businesses and thereby increased the competitiveness of China in the global market. As a cutting-edge technology, Blockchain has significantly influenced the practices across all business sectors. Focusing on the retail and insurance sectors, this paper analyzes the blockchain technology applications in two companies – Suning and PingAn. Success factors in blockchain implementation were discussed by using TOE framework. Similarities and differences in the implementation and development processes are compared. Features from the two corresponding industries – retail and insurance are also summarized. The major results revealed that retail and insurance industries are two of the most important application sectors of blockchain technology, and Suning and PingAn are both pioneers in blockchain technology development and implementation in their specific sectors. However, there are still distances between Chinese blockchain providers and world leading providers of blockchain, even though China has the largest number of blockchain patents in the world. The research results provide meaningful insights and practical implications to the blockchain application fields.

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Owing to technological development, the internet has become the world’s largest platform where an unaccountable amount of e-news information is freely available to use. Most of the time, e-newspaper readers have to examine the massive collection of e-news articles to locate necessary information relevant to them. Massive semi-structured and unstructured texts usually mislead the readers when they search and understand data for some knowledge. Furthermore, manually reading a collection of e-news articles for some knowledge is tedious and unproductive. The literature related to Knowledge Discovery from text documents has had a substantial improvement in this regard and Association Rule Extraction using text documents, in particular, has become a more frequent and imperative research approach to finding out the most significant information, patterns, and features in the text documents while diminishing the time for reading all the documents. This study provides a comprehensive review of Association Rule extraction using textual data covering the essential topics; Pre-processing, steps in Association Rule Mining, and rule mining algorithms. Out of the various existing association rule mining algorithms, the two most important algorithms, Apriori and FP Growth, are chosen for the experiment using e-news articles. Based on the experimental results, this study discusses the performance, significant bottlenecks, recent breakthroughs of rule mining algorithms, and finally the perspective directions to facilitate future research.

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The use of a battery to power an electrical or electronic system is accompanied by battery management, i.e. a set of measures intended to preserve it for preventative maintenance, thus the cost reduction. This management is generally based on two key parameters, the (remaining useful life) RUL and the (State-of-health) SOH, which relate respectively to the charge output and the aging of the Lithium-ion battery. The issue will be resolved and advances in production, battery utilization, and optimization will be made possible by accurate SOH determination and dependable RUL prediction. The CNN-BGRU-DNN hybrid strategy, which we suggest in this study, integrates Convolutional Neural Networks (CNN), Bidirectional Gated Recurrent Units (BGRU), and Deep Neural Networks (DNN) to increase the precision of SOH and RUL estimates for Lithium-ion batteries. To that purpose, the performance of the prediction findings is assessed using the MAE, RMSE, AE, and RE as well as the NASA datasets of lithium-ion batteries for experimental validation. The verification tests’ findings show that, in comparison to existing approaches in the literature, the suggested method may greatly reduce prediction error and achieve high estimation accuracy of the battery’s state of health.

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