Special Issue on Innovative Research in Applied Science, Engineering and Technology 2020

In this work, we completed a validation of the Varian Clinac IX equipped with the High Definition Multi-Leaf Collimator (HD 120 MLC) instead of the removable jaws, using GATE Monte Carlo Platform version 8.2. We validated the multileaf collimator (MLC) geometry by simulating two dosimetric functions (Percentage Depth Dose (PDD) and Dose Profile (DP)), for 6MV photon beam energy and different field sizes (3×3, 4×4, 6×6, 8×8, 10×10, 12×12, 15×15, and 20×20 cm²). We then compared the results with measurements realized with two detectors, namely the cylindrical ionization chamber and the micro-diode PTW silicon. By applying the Relative Dose Difference method (RDD), we noted a less than 2% and 1% agreement for the field sizes (10×10, 12×12, 15×15, 20×20 cm²) and (3×3, 4×4, 6×6, 8×8 cm²) respectively. Moreover, to evaluate the relevance of Monte Carlo method in a heterogeneous media, particularly in small field sizes (1×1, 2×2, 3×3 cm²), we have simulated three clinical studies based on the Physical Test Objects (PTOs) that are the equivalent slabs of lung and bone included in a water phantom. We noticed that the simulated PDDs exhibit two significant irregularities in the interface between water and lung. To eliminate these phenomena, we have used the “setMaxStepSizeInRegion” parameter implemented in GATE. We also noticed an important difference of 5% corresponding to the small field sizes, between homogeneous and heterogeneous simulated PDDs. We used the RDD method in this case as well. Moreover, we observed a difference between 1-4% between the simulated PDDs and the calculated ones by ECLIPSE Treatment Planning System (TPS). These results indicate that GATE (8.2) is useful in dosimetry with heterogeneous situations as well such as bone and lung.

Oscillator phase noise (PN) has a strong impact on the spectral purity of the RF signal in wireless systems and is, therefore, a main challenge when designing a local oscillator. In this paper, we propose a new approach for designing a low PN oscillator based on the Time-Invariant Linear Model of phase noise. It leads on voltage-controlled oscillator (VCO) simulated good performances: a low phase noise (PN) near -123.2 dBc/Hz@1MHz offset from the carrier, an output power of 3.26 dBm, and an output signal frequency ranging from 27.98 GHz to 29.67 GHz. Low power-consumption (51mW) and small size (0.237 mm2) benefit from MMIC UMS foundry (United Monolithic Semiconductors) and 0.15 µm-pHEMT GaAs technology.

Vectorization is extracting data from strings through Natural Language Processing by using different approaches; one of the best approaches used in vectorization is word2vec. To make the vectorized data secure, we must apply a security method, which will be CryptDB. The paper is analyzing the survey, which is created to interview security engineers through the SPSS tool. By analyzing the responses from software security engineers, it is seen that both word2vec and CryptDB works significantly. Word2vec is an effective vectorization approach, while CryptDB is an effective, secure database. In future work, we will be developing a secure vectorization using both approaches.

Soil moisture is a fundamental factor for smart farming that is used to control the water management system. In this paper, the unmanned aerial vehicle (UAV) small cell (UAV-SC) can provide Internet of things (IoT) as the hotspot mobility network, due to the minimum limitation energy of connected IoT. The development of ground sensor (GS) communicates to the UAV-SC called GS-UAV-SC model for soil moisture monitoring is proposed to smart farming. UAV-SC aims to fulfill the data collection task with a limitation of GSs power. In the experiment, the two case scenarios: Napier grass farm and Ruzi grass farm are implemented. The result of soil moisture status is demonstrated as an example of data in real time on a mobile application monitoring system. The proposed system is useful for users/farmers to know the soil moisture data quickly for smart farming applications.

Knowledge Management processes present a vital role in improving AI systems and algorithms. Many studies and reviews were carried out to examine the relationship between KM processes and AI systems. However, studies were focusing on specific methods and the impact on some AI algorithms, neglecting the role of other KM processes and how it may affect the AI system to achieve the objective, which reduces the adoption in some organizations. The current study shows the relation between KM processes and AI systems from a higher perspective, giving different options to apply other KM processes for the same AI algorithm to reduce any implementation challenges and enhance the adoption level. The review looks into 16 studies collected from a different database from 2014 to 2019. The main finding of the research was the massive impact of some KM processes like knowledge acquisition and knowledge creation on the different types of AI systems and algorithms to give an additional option for organizations during the implementation. Additionally, the research finds that most of the studies agree on the positive relationship between knowledge management processes and the role-plays to enhance AI systems and algorithms. Finally, the study shows a decrease in the number of researches done for this topic in the selected databases, which can be enhanced by other researchers by examining other databases to increase results accuracy.

Tinnitus is a phenomenon for which the patient hears sound in the absence of any external sound source. To this day, there is no cure for this phantom sound perception. But it can be masked temporarily to help relief the patient’s pain. In order to allow this, a better understanding of the phenomenon is needed. A validated acoustic model of the outer ear developed by the authors is used in this study. This model allows to study the effect of the presence of an anomaly (a cavity, a swelling or a foreign fluid) in the human auditory canal. These anomalies are modeled as a modification of the section of the ear canal or as an alternation of the medium of sound propagation in the ear canal. A parametric study involving the position, width and height of the anomaly as well as the sound velocity in the ear canal is conducted. The results obtained make it possible to conclude on the effect of each parameter on the frequency response of an auditory canal with anomaly.

During service, oil and gas pipelines may be exposed to cyclical loads during service, which may lead to a structural failure. Failure is due in most cases to cracks in the structure. Mechanics of the propagation of surface cracks pose a serious challenge and, therefore, models are required to help analyze it. In this study, a nonlinear model is proposed to estimate the accumulation of fatigue damage in the case of defected pipes subjected to a water hammer. The studied pipes are in the presence of a semi-elliptical longitudinal surface crack. This numerical model allows the load sequence to be considered when the structure is under variable amplitude loading. The validated model is used in a parametric analysis, the purpose of which is to determine the influence of the fluid transported and the defect parameters on the evolution of the accumulated damage. The results allow the conclusion of the parameters that have the most impact on the harmfulness of the crack defect as well as the most dangerous cracks in the case of a pipe subjected to water hammer.

This paper is a case study of the integration of an energy management system (EMS) in a commercial building. A detailed analysis of this EMS’s impact on energy consumption is presented. High energy demand is very common in commercial and industrial facilities. With this in mind, the present work aims to improve the energy consumption rate within a commercial facility by proposing an energy management system based on a central fuzzy logic controller. Thus, the central controller adapts the energy management to a pre-established schedule taking at consideration primarily the Heating, Ventilation and Air Conditioning (HVAC) of the targeted building.

In their verbal interactions, humans are often afforded with language barriers and communication problems and disabilities. This problem is even more serious in the fields of education and health care for children with special needs. The use of robotic agents, notably humanoids integrated within human groups, is a very important option to face these limitations. Many scientific research projects attempt to provide solutions to these communication problems by integrating intelligent robotic agents with natural language communication abilities. These agents will thus be able to help children suffering from verbal communication disorders, more particularly in the fields of education and medicine. In addition, the introduction of robotic agents into the child’s environment creates stimulating effects for more verbal interaction. Such stimulation may improve their ability to interact with pairs. In this paper, we propose a new approach for the human-robot multilingual verbal interaction based on hybridization of recent and performant approach on translation machine system consisting of neural network model reinforced by a large distributed domain-ontology knowledge database. We have constructed this ontology by crawling a large number of educational web sites providing multi-lingual parallel texts and speeches. Furthermore, we present the design of augmented LSTM neural Network models and their implementation to permit, in learning context, communication between robots and children using multiple natural languages. The model of a general ontology for multilingual verbal communication is produced to describe a set of linguistic and semantic entities, their properties and relationships. This model is used as an ontological knowledge base representing the verbal communication of robots with children.

The aim of this work is to study the effect of infill brick panels on the response of multi-storey buildings under seismic loading. An eight storey building is investigated. The building is analysed under gravity and seismic loads. Infill panel is replaced by two struts according to FEMA306. Time history and pushover analyses are performed to assess seismic strength of the building. Simulations are performed by SAP2000. Numerical results show that behavior of bare and infill frames under lateral loading are too distinct. There is a change in the manner in which the infill frame carries the lateral loads.

Recently, the number of connected machines around the worldwide has become very large, generating a huge amount of data either to be stored or to be communicated. Data protection is a concern for all institutions, it is difficult to manage the masses of data that are susceptible to multiple threats. In this work, we present a novel method of Intrusion Detection System (IDS) based on the detection of anomalies in computer networks. The aim is to use artificial intelligence techniques in the form of Machine Learning (ML) for intrusion detection. For this purpose, we have proposed a Support Vector Machine (SVM) classification model with two kernels, one Polynomial and the other Gaussian. This model is trained and tested with the recent UNSWNB-15 dataset. Regarding the results obtained, we have evaluated our model with six metrics capable of offering all potential threats. As a result, we have achieved a percentage of 94% for the detection rate (DR).

Wireless Body Area Networks support the operation within multiple frequency bands. Thus, they can be integrated in several applications, one of which is on-body medical monitoring applications, as concerned in this paper. Therefore, the purpose of this study is to present the impact of transmission power and both of Differential Binary Phase Shift Keying and Differential Quadrature Phase Shift Keying modulation schemes, on the performance of a WBAN model based on the IEEE 802.15.6 2.4 GHz narrow-band, dedicated to on-body medical applications. This involves identifying the modulation scheme(s) and transmission power level(s) to be adopted for these applications, that can be classified into three types depending on their data rate (low, medium and high data rate medical applications), in order to meet Packet Loss Rate and latency requirements. The numerical study has confirmed that the adoption of DBPSK modulation and low transmission powers provides good performance for low data rate monitoring applications. At medium data rates, a relatively increased transmit power was required. However, at high data rates, DQPSK modulation with a 0 dBm transmission power seemed to be the right choice to be made in terms of the mentioned performance indicators.

The technology of Big Data got the capability to process large amounts of data, manage them effectively and make retrieval whenever it is required. Decision making in any organisation is a challenging task since decisions need to be made based on the accessibility of data and its status, this becomes more challenging especially in large organisations that generate massive amount of information and data every single day.
Implementation of latest advanced technologies like Big Data is imperative for any organization to make decisions which reduces time and pave the fourth industrial revolution. Even though this is vital in today’s business world, there are still few organizations that are hesitant to adopt it.
This paper illustrates the relation between Big Data and effective decision making by implementing a quantitative analysis through questionnaire. The conducted analysis is implemented using SPSS through correlational perspective, where percentage analysis, Chi-Square, correlation, and regression analyses are performed to obtain results.
It is clearly depicted that large organizations transition to adopt Big Data to aid their decision making, where medium and smaller organizations were slowly transitioning the adoption. In spite of this, most of employees, irrespective of the type of organization agree that Big Data is indeed a powerful advanced technology as they were satisfied by the organizational direction they have taken.

Analysis and classification of lung diseases using X-ray images is a primary step in the procedure of pneumonia diagnosis, especially in a critical period as pandemic of COVID-19 that is type of pneumonia. Therefore, an automatic method with high accuracy of classification is needed to perform classification of lung diseases due to the increasing number of cases. Convolutional Neural Networks (CNN) based classification has gained a big popularity over the last few years because of its speed and level of accuracy on the image’s classification tasks. Through this article, we propose an implementation a CNN-based classification models using transfer learning technique to perform pneumonia detection and compare the results in order to detect the best model for the task according to certain parameters. As this has become a fast expanding field, there are several models but we will focus on the best outperforming algorithms according to their architecture, length and type of layers and evaluation parameters for the classification tasks. Firstly, we review the existing conventional methods and deep learning architectures used for segmentation in general. Next, we perform a deep performance and analysis based on accuracy and loss function of implemented models. A critical analysis of the results is made to highlight all important issues to improve.

The water heating with parabolic trough solar collectors (PTC) is a very widespread and at the same time quite promising solar technology. However, PTC presents several problems in terms of the profitability of water heating. For this reason, our study of water heating with PTC collectors consists of two main parts. In the first part, we investigate the effect of direct normal irradiation in the absorber tube using the TRNSYS software of the Errachidia city. In the second part, the study is entirely focused on the heat balance of the absorber tube in order to estimate the fluid outlet temperature. Besides, a mathematical model is developed to simulate and control the fluid outlet temperature circulating through the absorber tube of the collector. The water outlet temperature prediction was carried out by a thermal performance study of the PTC in weather conditions of Errachidia city (Morocco) using TRNSYS software and Matlab Code in the year’s typical days.

In this article, we concentrate on the use of a metaheuristic technique based on an Evolutionary Algorithm (EA) for determining the optimal geometrical parameters of spiral inductors for RF circuits. For this purpose, we have opted for an optimization procedure through an enhanced Differential Evolution (DE) algorithm. The proposed tool allows the design of optimized integrated inductors not only with a maximum quality factor(Q), but also with a maximum self-resonant frequency (SRF), and a minimum surface area, in addition to being adapted to any model of any technology. This paper presents also a comparison between performances of the optimized inductors (inductor square shape and inductor circular shape), in terms of the quality factor, SRF, and circuit size. For the purpose of mitigating the impact of parasitic effects, design basics have been taken into consideration. Then, in order to investigate the efficacy of evaluated results, an (EM) simulator has been employed.

The Music Composition Library of the great composers constitutes an intellectual heritage. This article introduces an algorithm of artificial Oriental composing music based on the descriptors determined on a large learning base to automatically write Oriental music as the logic identical to any composer. Musical words are called a grammatical alphabet. Each word derived is created with the descriptors through its very own alphabet by crossing a number of filters removing all improper combinations and maintaining the features correctly responding with each filtering process while honoring the grammar of oriental music. A musical word is a combination of a rhythmic word and a symbolic word.

Contextual continuous word representation showed promising performances in different natural language processing tasks. It stems from the fact that these word representations consider the context in which a word appears. But until recently, very little attention was paid to the contextual representations in Arabic question classification task. In the present study, we employed a contextual representation called Embeddings from Language Models (ELMo) to extract semantic and syntactic relations between words. Then, we build different deep neural models according to three types: Simple models, CNN and RNN mergers models, and Ensemble models. These models are trained on Arabic questions corpus to optimize the cross entropy loss given questions representations and their expected labels. The dataset consists of 3173 questions labeled according the Arabic taxonomy and an updated version of the Li & Roth taxonomy. We performed various comparisons with models based on the widely known context- free word2vec word representation. These evaluations confirm that ELMo representation achieves top performances. The best model scores up to 94.17%, 94.07%, 94.17% in accuracy, macro F1 score, and weighted F1 score, respectively.

This paper deals with a multi closed-loop adaptive neuro-fuzzy inference system (ANFIS) design for the under-actuated quadrotor systems. First, the training data set for the fuzzy inference system is obtained using a proportional integral derivative controller. Then, an initial ANFIS controller is designed, where the integral control action is preserved in the multi-closed-cloop ANFIS for each quadrotor system state. Thereafter, scaling gains are added to the controller inputs/outputs, and a multidimensional PSO algorithm is used to tune all the control parameters. Besides, using a simulation example, the aerial vehicle performances are investigated in the presence of an unknown payload mass parameter. Specifically, the position tracking performances of the proposed multi closed-loop PSO-based ANFIS plus integral control strategy is compared with the classical PID, conventional ANFIS, and non-optimized ANFIS plus integral controllers. Thus, using the conducted simulation results, it results that the multi closed-loop PSO-based ANFIS plus integral can achieve perfect translational trajectory-tracking and ensure better attitude stabilization despite unknown quadrotor payload mass parameter. Therefore, the proposed new multi closed-loop PSO-based control strategy may be considered as an efficient controller when considering an arbitrary trajectory-tracking problem for the quadrotor system.

A rarefied gas flow is modeled inside two cases of triangular lid-driven microcavity using single (SRT) and multi-relaxation time (MRT) lattice Boltzmann approaches. In the first one, the right angle is in the top-left corner and the upper wall moves with positive horizontal velocity. However, in the second case, the right angle is in the bottom-left corner and the bottom wall moves with negative horizontal velocity. Unlike the classical form of square cavities, widely treated in the literature, the triangular form has a diagonal wall that affects the flow motion. At the moving wall, diffuse scattering boundary condition (DSBC) is employed while at the stationary sides, a combination of bounce-back and specular reflection boundary conditions (BSBC) is used. The computations are primarily performed in the slip and early transition regimes. The rarefaction effect, given by the Knudsen number (Kn) value, on the profiles of velocity components, is examined for both approaches. This study proves that for the higher values of Kn, the SRT-LBM approach cannot provide accurate results, particularly, near the inclined wall. However, the MRT-LBM approach confirms its validity even in the transition regime. A comparison with Direct Simulation Monte Carlo (DSMC) results for horizontal velocity contours shows the efficiency of the MRT-LBM approach than the SRT-LBM one which breaks down for rarefied flows.

Increased recycling of PVC has become a requirement in industrial and scientific research level. Several studies will be realized. To confirm and check the recycled materials performance, it will be important to go through numerical modeling, which consists not only in validating the results of the experiments, but also in predicting what happens when the material is loaded. PVC material is more used in different fields, that ultimately means, after service life, an increase in waste requiring a high recycling rate. This article presents an approach validating the aging model as well as a numerical analysis predicting the mechanical properties of PVC after aging. The analysis samples (rigid and flexible) PVC which are subject to two types of accelerated aging, allows to obtain an aging model. Numerical modeling of PVC after aging is carried out using the finite element method and has been able to confirm the results obtained experimentally. Predicting the mechanical properties of rigid PVC after aging loaded with coconut and cow horn fibers after a first recycling is made by the finite element method, Mori-Tanaka and Double inclusion models. The obtained results have showed an improvement in the mechanical characteristics of the PVC studied using this natural bio-loading with these two fibers which respect the environment and have a lower cost and more lightness.

The objective of this article is to highlight the relationship between the integration of sustainable development practices in Moroccan Small and Medium Enterprises (Smes) and the challenge sought by managers in order to ensure competitiveness and sustainability. Indeed, present-day Smes are operating in a world where economic, social, societal or environmental constraints are constantly evolving. The Sme has to face, on a daily basis, new challenges such as economic stability and sustainability alongside the major structures because the challenge of any company is to be able to differentiate itself by adding its own trace promoting a more stable positioning in a fairly hostile environment. The investigation is carried out on a sample 30 Smes in the city of Kénitra due to the importance that this city holds in terms of both geographical dimension and economic inputs on the regional scale, 13.6% of the jobs generated by the SME according to the report of the high Planning Commission (HCP 2017). The results obtained reveal that the major concern of the managers of the Moroccan Sme is a purely commercial concern and that the integration of the various practices related to sustainable development in their managerial vision is only a means and does not constitute an objective to ensure the expected survival and sustainability.

Feature extraction is an important process in image classification for achieving an efficient accuracy for the classification learning models. One of these methods is using the convolution neural networks. The use of the trained classic deep convolution neural networks as features extraction gives a considerable results in the remote sensing images classification models. So, this paper proposes three classification approaches using the support vector machine where based on the use of the ImageNet pre-trained weights classic deep convolution neural networks as features extraction from the remote sensing images. There are three convolution models that used in this paper; the Densenet 169, the VGG 16, and the ResNet 50 models. A comparative study is done by extract features using the outputs of the mentioned ImageNet pre-trained weights convolution models after transfer learning, and then use these extracted features as input features for the support vector machine classifier. The used datasets in this paper are the UC Merced land use dataset and the SIRI-WHU dataset. The comparison is based on calculating the overall accuracy to assess the classification model performance.

In recent years, the transport of large packages with super weight from 100 tons to several thousand tons is no longer a difficult problem due to the continuous development of technology. Experienced transport companies, specializing in transporting heavy goods in Vietnam, have invested in very modern equipment and machinery such as self-propelled trailers of Cometto (Italy) in order to transport safely mentioned parcels of great economic value arrive at the requested location. This trailer can be self-propelled, does not need to use a tractor, and only needs to use a remote-control handheld device. Moreover, the trailer gear shaft can rotate 360 degrees. In particular, the hydraulic system supports trailers operating with high accuracy and absolute safety including functions such as 360-degree rotation, lifting, transmission, braking, etc. In order to improve the performance of trailers when actually used in large projects, an important detail in the trailer’s hydraulic system has been inserted a throttle valve with to increase the safety of the hydraulic pump and the entire system as well as the safety of the goods that trailers are transporting. The trailer system has transported the rig with a capacity of up to 3,200 tons in Vietnam, the shipment of 15,000 tons in the world and beyond in the future.

Novel ionic polymers were synthesized by crosslinking of poly (vinylalcohol) (PVA) with sulfosuccinic acid (SSA) and silicotungstic acid (SiWA) with or without silica. The polymer electrolyte membrane fuel cell (PEMFC) was developed using solution casting method. Infrared (IR) spectra revealed that the Keggin structure was insered in the PVA films. The thermal decomposition of the PVA/SSA/SiWA/SiO2 membranes showed good thermal stability up to 200°C. Water uptake ranged between 31% and 88%. The maximum conductivity has been found to be 6,72.10-3 S.cm-1 at room temperature for PVA/SSA/SiWA containing 10% of silica weight. The ion exchange capacity of this membrane was 1,75 mmol.g-1. The results showed that these membranes presented very promising performances for use in Proton Exchange Membrane Fuel Cells.

Ethereum smart contract system has seen a steady adoption as it continues to support tens of thousands of contracts. This feature has evolved to give a practical shape to the ideas leading up to fractional ownership transfer, using advanced smart contracts such as ERC-981. However, alongside its numerous benefits, various risks arise with the actual implementation of the ERC-981. This paper documents high-level processes and risk factors involved in the transfer-system, building a theoretical risk model based on Electre Tri-framework belonging to MCDA classification/sorting models. This model deals with detecting problems that are pre-defined on a central reference. The approach is illustrated through several stages: following comparison between the methods of risk analysis towards a risk assessment model, proposing recommendations and solutions. The framework was able to detect 18 major risks and bugs assigned to 6 categories.

This article is part of the field of Multi-Criteria Decision Aid (MCDA), where several criteria must be considered in decision making. All criteria are generally as varied as possible and express different dimensions, and aspects of the decision problem posed. For more than four decades, several MCDA methods have emerged and have been applied perfectly to solve a large number of multi-criteria decision problems. Several studies have tried to compare these methods directly with one another. Since each method has its disadvantages and advantages, a direct comparison between the two methods is normally far from common sense and becomes subjective. In this article, we propose a rational and objective approach that will be used to compare the methods between them. This approach consists of using the famous correlation measure to evaluate the quality of the results obtained by different MCDA approaches. To prove the effectiveness of the proposed approach, experimental examples, as well as a study of real cases, will be studied. Indeed, a set of indicators, known as The Europe 2020 indicators, are defined by the European Commission (EC) to control the smart, sustainable and inclusive growth performance of the European Union countries (EU). In this proposed real study, a subset of indicators is used to compare the performance of environmental preservation and protection of the EU states. For this, the two-renowned methods MCDA ELECTRE II and TOPSIS are used to classify from the best to the worst CE countries with regard to environmental preservation.
The results of the experiment that the proposed ranking quality measure is significant. For the case study shows that the ELECTRE II method results in a better ranking than that obtained by the TOPSIS method.

Mobile wallet is an application that allows users to make online payments using their mobile phones. In the context of an outbreak of COVID-19 epidemic in Vietnam and the world, mobile wallets are considered having many opportunities to change people’s cash spending habits. The current study assesses the factors impact on the intention to use the mobile wallet, focusing on understanding the relationship of promotion with common factors in technology adoption research such as perceived risk, perceived usefulness, habits, social influences. The research results also show the direct and indirect effects of promotion on intent to use. From this result, 7 of 9 research hypotheses were accepted. Promotion, often overlooked by researchers, has been proven to be a critical factor when researching technology adoption because it significantly improves the level of intentional interpretation of the research model. This study will be the premise for future researches when studying adoption technology, researchers could integrate promotion to the model to achieve a significant improvement in the level of interpretation as showed in this study.

In this report, a probe mark discoloration with donut/circle shape was found. The corrosion formed on Aluminum bond pads. The SEM/EDS, TEM/EELS & AES surface techniques were used to identify this corrosion product. All these material analysis results pointed out the role of fluorine to this discoloration. The HAST testing, all the chip samples obtained from wafer center/middle area, were simulated to have been exposed in air for 1 year. HAST test result showed no fluorine source came from the center/middle zones of impact wafers. Another full wafer-level FOSB storage test showed the source of corrosive fluorine came from wafer itself. EDS mapping results, from extreme wafer edge zone, indicated the fluorine element distribution. The mechanism of this novel discoloration was proposed. However, more evidences were necessary for understanding the formation of donut discoloration corrosion.

In this study, an adaptive nonlinear sensorless controller for doubly fed induction generator (DFIG) driven by a wind turbine is proposed. The aim is to maximize the extracted power by tracking the wind turbine optimal torque-speed characteristic without the need for rotor speed measurement. This controller ensures a satisfactory tracking of both stator ?ux and rotor speed. It considers the detailed model of DFIG in an arbitrary (d–q) rotating frame without any simplifying assumption. An observer provides the rotor speed estimation incorporated in the control loop. To guarantee the system stability under parametric uncertainties like the aerodynamic torque and the rotor winding resistance, which harms the efficiency and the robustness of the controller, update laws are established to estimate the uncertain parameters. Lyapunov’s theory is used to prove the system stability. The proposed adaptive sensorless controller validity is demonstrated by simulation in Matlab/Simulink environment. The robustness of the controller is confirmed by the comparison between the same controller with and without adaptation.

This paper presents an efficient maximum power point tracking controller for photovoltaic modules. The MPPT technique consists of a combination between backstepping controller and artificial neural network (ANN).The (ANN) has been employed to generate the optimum voltage, which corresponds to the maximum power voltage delivered by photovoltaic modules, while the backstepping controller is developed to track the generated voltage, by computing the duty cycle of the Single Ended Primary Inductor Converter (SEPIC). The control of the boost converter is based on Lyapunov stability analysis, and an integral action is added to increase system robustness. In order to prove the accuracy of the developed control a comparison between the proposed method and sliding mode was carried out. In addition the stability was evaluated under sudden variation of environmental conditions. The simulation was carried out in MATLAB software, the results shows that the proposed controller tracks the reference voltage within 25 ms, in addition the systems reacts to sudden environments change with no oscillations which demonstrate good robustness and stability against sliding mode control.

Ant Colony Optimization (ACO) algorithm, a well-known robust technique to solve easily both a simple and multiple objective optimization problems. This article presents an application of the ACO in order to achieve the optimal sizing of analog circuit. The proposed technique is employed to optimizing the sizing of a positive second-generation current conveyor (CCII+). Results show better objective functions than previously achieved by other optimization procedure. PSPICE simulations are used to confirm the validity of the reached optimal results and the accuracy of the proposed procedure.