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This paper proposes a design for a modified distribution static compensator (DSTATCOM) with a proposed current controller for microgrid (MG) applications. The modified DSTATCOM is incorporated in a three-phase four-wire network system to deal accurately at grid connected mode with balanced, unbalanced and excursion load for both nonlinear and inductive loads. Moreover, it handles the stability issues of the MG at fault caused islanding incidents. The design criteria of the modified DSTATCOM parameters is presented and discussed. The hysteresis current controller is introduced to drive the voltage source converter (VSC) and its reference voltage signal is generated based on the instantaneous symmetrical components theory (ISCT). Matlab/Simulink software is used to verify the performance of the modified DSTATCOM with the proposed controller under different load types and loading conditions for grid connected mode and islanded mode of MG operation. Based on the simulation results, the power factor is improved and the non-linear harmonics currents are compensated therefore the harmonic distortion of the supply currents and voltage are reduced, moreover the stability operation at fault cause islanding is maintained. The results show the superiority of the proposed modification of DSTATCOM in dealing with unbalanced, distorted and bad power factor system in comparing with conventional DSTATCOM.

This paper proposes a new active power flow controller for DC-DC multi active bridge (MAB) converters with minimum current point tracking (MCPT) to minimize conductive losses. Unlike recent efforts in this area, the proposed controller does not require look-up table for implementation nor complex non-linear converter modelling and is not circuit parameter-dependent. The proposed control scheme is also generalized for MAB converters with any number of ports and is derived in per unit form to enable standard implementation for buck/boost/unity-gain operating modes. The proposed MCPT algorithm is based on an innovative adaptive perturb and observe (P&O) tracking of the minimum current point (MCP) on the MAB converter current characteristic plane. This MCP is tracked while active power regulation is simultaneously achieved based on a new phase shift decoupler to enable direct regulation of individual port active power. Detailed design procedure of the proposed controller is presented and extensive simulation is carried out to validate the effectiveness of the proposed MCPT closed loop controller. Experimental implementation is also provided to substantiate the simulation results.

We propose a robust Nonlinear Model Predictive Control (NMPC) scheme that provides an improved trade-off between optimality and complexity when compared to other available strategies. Two controllers are employed in the proposed framework: A multi-stage primary controller that optimizes a given objective in the presence of uncertainties with tightened constraints and a multi-stage ancillary controller that tracks the predicted tree of state and input trajectories of the primary controller. The primary controller optimizes the original objective by considering different realizations of the most significant uncertainties in the predictions. The ancillary controller provides robustness against other uncertainties by tracking the predicted tree of state and input trajectories of the primary controller. We establish sufficient conditions for closed-loop stability. The advantages of the scheme are demonstrated for a continuous stirred tank reactor (CSTR) example.

The modular multilevel converter (MMC) is a promising candidate for high-/medium-voltage high-power applications such as medium voltage ac drives due to its modularity, reliability, and scalability. The main challenge of a conventional MMC is low-frequency operation. Capacitor voltage ripple is inversely proportional to the operating frequency. Hence, operating at low frequency necessitates employing large submodule capacitances to limit voltage ripple or the utilization of energy equalization modules (EEMs). The first option negatively affects converter lifetime, and the second option requires a large number of semiconductor devices and isolating transformers. In this article, a modified MMC topology with self-energy equalization is proposed to ensure converter operation with balanced capacitor voltages with low ripple during zero-/low-frequency conditions. Operating with balanced capacitor voltages with low ripple is achieved by maintaining the same energy level in the involved upper and lower arms. A low device count alternative to the EEMs approach is proposed in this work. Detailed illustration of the operational concept and design of the associated passive components is presented. The performance of the proposed medium voltage ac drive is elucidated through simulation and experimental results.

The electro-chemical proton exchange membrane fuel cell (PEMFC) is an inventing electrical generator from chemical reaction process as a green energy source. An accurate PEMFC model with its precise parameters should be used to carefully fitting of polarization curve to best study and design of its characteristics and performance. This paper introduces an accurate PEMFC model based on recent metaheuristics algorithms to evaluate precisely the unknown parameters of PEMFC. Algorithms of; Whale Optimization Algorithm (WOA) , Weighted Differential Evolution Algorithm (WDE) , Differential evolution algorithm with strategy adaptation (SADE) , Moth- Flame Optimization Algorithm (MFO) , adaptive differential evolution with optional external archive (JADE), Improved mine blast algorithm (IMBA), Gray Wolf Optimizer (GWO), Dragonfly algorithm (DA), Differential EVOLUTION ALGORITHM (DE) , Cumulative Population Distribution Information in Differential Evolution (CPIJDE) , Differential evolution based on covariance matrix learning (COBIDE) , Covariance Matrix Adaptation Evolution Strategy (CMA-ES) , Bernstain-search differential evolution algorithm (BSD), Backtracking Search Optimization Algorithm (BSA) , Bezier Search Differential Evolution Algorithm (BESD ) , DIFFERENTIAL SEARCH ALGORITHM (DSA) and Bijective DSA (B-DSA) , Biogeography-based optimization (BBO); have been applied to estimate model of PEMFC. The verification of the suggested optimizing algorithms is applied on three practical PEMFC stacks of BCS 500-W PEM, 500 W SR-12PEM and 250 W stacks, for different operating conditions. The accuracies of the PEMFC extracted parameters are measured in sum of square errors (SSE) between the results obtained by the optimizing parameters and the test results of the fuel cell stacks in the objective function. Also, the applied methods have been validated as compared results with different research works that were listed in literatures. Moreover, the polarization curves of the applied methods are clear and coinciding with manufacturing polarization curves for all the case study results. So, the suggested PEMFC optimizing model has superiority on the comparative models with respect to the system accuracy and convergence process.

Diabetic Retinopathy (DR) has been considered one of the leading causes of blindness or vision loss in the long run. Retinal microvasculature formed due to diabetes mellitus may lead to this sickness case. Therefore, early identification of abnormalities in retinal images will help not only in curing but also in preventing or delaying vision loss. The main objective of this research is to detect and classify the DR stages as non-proliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR) via multifractal geometry, as well as automating the classification process by enforcing multistage artificial neural network. Two hundred and seventy eyes have been used for normal, diabetic without DR, non-proliferative diabetic retinopathy (Mild to Moderate), and PDR. The macula superficial capillary plexus of the subjects’ images had been imaged using the swept-source optical coherence tomography angiography (SS-OCTA). The proposed classification technique based on a multistage artificial neural network has achieved accuracy with 98.1%. It provides a detecting method for the early diabetic retinopathy stages before the precipitate treatment.

In this paper, a comparative study assessing net metering and feed-in tariffs is proposed for grid-connected photovoltaic (PV) systems in the Kuwaiti market. This study measures the impact of the two mechanisms as well as a mechanism combining both approaches in deploying solar energy systems in residential areas. The advantages and disadvantages of each approach are inspected to determine the optimal approach for Kuwaiti market conditions. A feasibility study on installing a roof-mounted PV system in a typical Kuwaiti residential building is utilized to validate the optimum solar electricity mechanism. The proposed study intends to reduce the peak load demand in Kuwait’s electric grid as a solution to the shortage of electric production capacity by encouraging investors and house owners to invest in solar energy. PV*Sol premium 2018 (R10) and RETScreen software packages are utilized to perform the assessment and study analysis.

Medium‐/High Voltage DC grids are interesting for the integration of renewable energy sources. DC‐DC conversion systems are highly needed for the development of DC grids. Recently, Modular Multilevel Converter (MMC) is the most promising technology for medium‐/high‐voltage applications, but employing the conventional MMC with DC output voltage leads to diversion in the Submodule (SM) capacitor voltages, that is energy drift. This paper proposes a modified modular multilevel DC‐DC converter with self‐energy equalization, which ensures successful DC‐DC conversion with balanced capacitors voltages. In the modified topology, clamping Insulated Gate Bipolar Transistors (IGBTs) are employed in each arm to enable parallel‐connection of the capacitors in the same arm. During the operation (equalization period), the parallel‐connected upper capacitors are connected to the parallel‐connected lower capacitors in each leg through a small limiting inductor to transfer energy between the arms to ensure balanced capacitor voltages. The proposed configuration, along with the operational concepts, mathematical analysis, and design, are presented. Finally, simulation and experimental results are presented for validation.

Security is one of the primary concerns when designing wireless networks. Along detecting user identity, it is also important to detect the devices at the hardware level. The trivial identity create-and-discard process at higher layers of the protocol stack alone is not sufficient to effectively counter security threats, such as masquerading and Sybil attacks. To counter these attacks, various radio frequency fingerprinting-based solutions are proposed for the identification of the devices. However, these approaches use expansive devices for signal capturing and rely on high sampling rates and large feature sets for analysis. In this paper, we propose a radio frequency fingerprinting-based device identification technique. The proposed technique is tested on 4G-LTE network for combined intra and inter-manufacturer device detection. It uses low-cost software defined radio to capture smartphone emissions at a lower sampling rate, using our proposed preamble threshold-based detection algorithm. The results show that our proposed technique provides classification accuracy of 95.6% at different SNR levels.

Recognition of power quality (PQ) troubles is a critical task in the electrical power system. All previous works solve this problem using two‐step methodology: Feature extraction and classification steps with each step using its own techniques, and this consumes a computation time. The purpose of this study is to utilise a novel artificial intelligence (AI) technique for recognition of PQ events. The proposed AI technique is the long short‐term memory (LSTM) network, which detects and classifies the PQ events in one step. This technique extracts amplitude, disturbance duration and total harmonic distortion from the captured waveform, and the LSTM uses its own rules to classify the PQ events. Many simple PQ events such as interruption, sag, flicker, swell and surge or complex PQ events such as sag plus harmonics and swell plus harmonics are generated using MATLAB programming environment to evaluate the performance of LSTM. Also, real‐time measurements are collected from an industrial substation and are used to ensure the effectiveness of the proposed LSTM technique. A comparison with other techniques is conducted and the results verify the good performance of LSTM in classifying the PQ problems.

Because of the world's acute energy crisis, the need for renewable energy sources is increasing today. In recent years, Standalone photovoltaic systems have been widely used in remote regions, following the growth of the photovoltaic cell industry. The key features of the systems of photovoltaic (PV) used to ingather solar energy while reducing the gas emissions of the greenhouse, maintenance costs are low, reduced site-related restrictions as a mechanical noise reduction due to no moving parts. Nevertheless, Photovoltaic systems are suffering from comparatively poor conversion efficiency. Hence, a PV system needs maximum power point tracking (MPPT) of the solar array. Several factors affect the maximum resulted power as nonlinear behavior of PV systems, temperature and the level of solar radiation that complicate monitoring of the maximum power point (MPP). This paper represents an evolutionary optimization algorithm using improved particle swarm optimization (PSO) technique for MPPT. The proposed technique has achieved high power in the conditions of partial shading rather than the conventional (PSO). The results of the simulation showed that the strategy could be reliable in monitoring the global MPPT in PV systems.

A robust adaptive controller for nonlinear plants with parametric uncertainties, additive disturbances, and state estimation errors based on the tube-enhanced multi-stage (TEMS) nonlinear model predictive (NMPC) framework is proposed. In TEMS NMPC, primary multi-stage NMPC is used to achieve robustness against the uncertainties which have a large effect on the evolution of the state of the plant, and ancillary multi-stage NMPC is used to track the predictions of the primary controller to counteract the effect of the small uncertainties. We propose updating, at each time step, the uncertainty set considered by the scenario trees of the primary and ancillary controllers with a tighter non-falsified uncertainty set, which results from solving a guaranteed parameter estimation (GPE) optimization problem. This produces significant performance improvements over the non-adaptive implementation as will be shown on the Williams-Otto continuous stirred tank reactor (CSTR) case study.

Due to the continuous increase of fuel prices and pollutions, the use of renewable energy especially wind has increased. In developing countries including Egypt, squirrel cage induction generator wind turbine (SCIG- WT) represents a considerable proportion of the total capacity of installed wind farm due to its qualities such as low cost and easy availability. However, its operation has a substantial effect on system stability. In contrast, doubly fed induction generator wind turbine (DFIG-WT) is broadly penetrated the electrical grid as it keeps the system stable. In this work, the ability of WT generators to continue operating rather than tripping at the time of faults is analyzed for proper stability investigation. The detailed control and stability of a grid-connected large scale SCIG and DFIG of Zafarana, Suez Gulf area, Egypt are discussed whereas the parameters of fault ride through (FRT) curve of Egypt grid code is utilized. Moreover, a precise analytical stability argument using a proposed integrated nonlinear dynamical model is presented. Conditions for global asymptotic stability of the SCIG in the sense of Lyapunov function (LF) are given and tested by time domain simulation. The eigenvalues of the matrices of LF and its derivative are determined by which the stability boundaries are determined depending on the positivity of these matrices. The dynamic behavior of the whole system is simulated in MATLAB/Simulink interface programming while the practical data are collected from an experimental model consisting of DFIG-WT to demonstrate the efficacy of the FRT control system.

Change Detection (CD) is a hot remote sensing topic where the change zones are highlighted by analyzing bi-temporal or multi-temporal images. Recently, Deep learning (DL) paved the road to implement various reliable change detection approaches that overcome traditional CD methods limitation. However, high performance DL based approaches have explosion number of parameters that demanded extensive computation and memory usage in addition to large volumes of training data. To address this issue, we proposed a teacher-student setting for remote sensing imagery change detection. To distill the knowledge from the over-parameterized Siamese teacher network, we proposed tiny student network that was trained using the obtained categorical distribution of probability from the teacher paired Softmax output at high temperature. Practical Swarm Optimization (PSO) was applied in order to optimally configure student architecture. Finally, ample experiments were conducted on LEVIR-CD dataset. Also, we introduced EGSAR-CD dataset, which contains of a large set of bi-temporal SAR images with 460 image pairs ( $256 \times 256$ ). Experiment results indicate that we can reach up to $5.4\times $ reduction rate in number of parameters with loss of accuracy between 5% and 6% on the LEVIR-CD and EGSAR-CD datasets utilizing self-knowledge distillation.

Diabetic Retinopathy (DR) is a complication of diabetes that affects the eyes. It is caused by blood vessel damage of the light-sensitive tissue at the back of the retina. Neovascularization are emerged and the small blood vessels are blocked. The prevention or delaying vision loss can be obtained by DR early detection. The retinal microvascular network as a biological system has its own multifractal features as generalized dimensions, lacunarity and singularity spectrum. In this study, a novel approach for DR early detection based on the multifractal geometry has been proposed in some details. Analyzing the macular optical coherence tomography angiography (OCTA) images for diagnosing early non-proliferative diabetic retinopathy (NPDR). Using a supervised machine learning method as a Support Vector Machine (SVM) algorithm to automate the diagnosis process and improving the resultant accuracy. The classification technique had achieved 98.5 % accuracy. This approach also can classify easily other diabetic retinopathy stages or other retinal diseases, which affect the vessels or neovascularization distribution.

This paper presents a new automatic detection and classification approach of power quality (PQ) problems using Kalman filter. Kalman filter is used as an estimator to calculate the fundamental frequency and harmonic components amplitudes of the voltage or current signals. Then the instantaneous total harmonic distortion (iTHD) and the energy are calculated. For each half cycle of the processed signal, five decision quantities are calculated based on iTHD and energy and these quantities are the three consecutive maximum values of iTHD, standard deviation and energy difference between distorted signal and its fundamental frequency component. Decision rules based on these decision quantities are applied to identify and classify the PQ events in this captured signal. The proposed approach is tested on single and combined PQ events that are generated using the MATLAB with the help of mathematical models that are conformity with standard IEEE-1159. The performance is assessed using more than 100 dataset of every PQ event and the results show that the accuracy is 100 and 98.8 for noiseless and high-level of noise, respectively. In addition, the proposed approach performance is validated through comparisons with other classification. Several practical PQ events are generated by lab experiments to validate the proposed approach. The simulation and experimental results show that the proposed approach is efficient and robust and can be implemented to design PQ monitoring device.

Expert finding systems try to alleviate the information overload problem and recommend experts who can satisfy users’ needs. They support researchers to find research collaborators automatically. The main challenge of current expert finding systems is that they retrieve experts based on the content of their documents but ignore the human interaction perspective. The human interaction perspective comprises the factors that influence collaborator selection decisions in real life. This study aimed to develop a collaborator selection model for expert finding systems in research universities. This model includes human capital, social capital, and cultural capital factors that influence collaborator selection. The researchers integrated the Scientific and Technical Human Capital (STHC) model and Social Capital Theory to determine these factors. The authors conducted a survey comprising 349 researchers from Malaysian research universities to validate the research hypotheses. A partial least squares structural equation model (PLS-SEM) was employed to analyze all the survey data. The empirical results revealed that the significant factors that influence collaborator selection in the research universities context were cognitive accessibility, reliability, relevance, commitment, physical accessibility, cultural experiences, complementary skills, and research experience. Surprisingly, the results revealed that network ties, relational accessibility, and reputation were insignificant factors for collaborator selection. This study proposed a research model for collaborator selection in the research universities context and provided several recommendations for the policymakers and practitioners. The model will help to provide sufficient criteria to select academic research collaborator in universities and can be used by expert finding systems designers, researchers, collaborators, and universities.

This article presents an isolated high-frequency (HF) multiport dc–dc converter for photovoltaics (PV) interfacing systems. In the proposed multiport converter, an HF transformer with multiple input windings and single output winding is utilized. Each input port is based on a proposed two-quadrant inverter topology, which connects a PV module to the HF transformer. The output port is tied to a dc bus using a rectifier. The proposed two-quadrant inverter topology prevents current flow from the ac side to the dc side. This action prevents circulating currents between different ports. Each two-quadrant inverter is controlled to harvest maximum power from its associated PV module. The analysis and operation scenarios of the proposed multiport converter are discussed. PSCAD/EMTDC software package is used to simulate the proposed PV interfacing system to investigate the operation of the proposed multiport converter topology and to examine the dynamic system behavior. In addition, an experimental prototype is developed to validate the operation scenarios of the proposed multiport dc–dc converter and to examine the performance of the presented PV interfacing system.

Several organizations are utilizing cloud technologies and resources to run a range of applications. These services help businesses save on hardware management, scalability and maintainability concerns of underlying infrastructure. Key cloud service providers (CSPs) like Amazon, Microsoft and Google offer Infrastructure as a Service (IaaS) to meet the growing demand of such enterprises. This increased utilization of cloud platforms has made it an attractive target to the attackers, thereby, making the security of cloud services a top priority for CSPs. In this respect, malware has been recognized as one of the most dangerous and destructive threats to cloud infrastructure (IaaS). In this paper, we study the effectiveness of Recurrent Neural Networks (RNNs) based deep learning techniques for detecting malware in cloud Virtual Machines (VMs). We focus on two major RNN architectures: Long Short Term Memory RNNs (LSTMs) and Bidirectional RNNs (BIDIs). These models learn the behavior of malware over time based on run-time fine-grained processes system features such as CPU, memory, and disk utilization. We evaluate our approach on a dataset of 40,680 malicious and benign samples. The process level features were collected using real malware running in an open online cloud environment with no restrictions, which is important to emulate practical cloud provider settings and also capture the true behaviour of stealth and sophisticated malware. Both our LSTM and BIDI models achieve high detection rates over 99% for different evaluation metrics. In addition, an analysis study is conducted to understand the significance of input data representations. Our results suggest that in particular cases, input ordering does have some affect on the performance of the trained RNN models.

Model predictive control (MPC), manly based on a direct use of an explicit and identifiable model, has been widely used in controller design in different applications both by academia and industry. The reason for such popularity is due to its strong ability for providing high performance electric drive systems, as highly recognized as the most reliable control approach compared with field-oriented control (FOC) and direct torque control (DTC). In general, the MPC has numerous features and advantages, such as direct switching states to the converter without any modulation, online optimization with multivariable control, low current total harmonic distortion, low switching loss, etc. The aim of this paper is to provide a comprehensive review for major development and achievements of the recent progress on the MPC for electrical machines and drives. This review begins with the innovative topologies and operating principles of fundamental MPC, and ends to summary on different advanced MPC algorithms. Typical MPC techniques have been fully adopted to enhance the drive performance of the electrical drives, mainly including finite-set model predictive control (FS-MPC) based on tuning weighting factors, without weighting factors, maximum torque per ampere, low number of switching vectors, and multi voltage vectors in one sample period. Finally, great attention has been paid to the discussion of the new trends and future research topics.

Photovoltaic (PV) energy systems rapidly penetrate global renewable energy market recently due to their direct energy conversion, environmental friendly and modularity features. Partial shading and irradiance mismatch create one of the major challenges that face commercial PV converters. Software based maximum power point tracking (MPPT) algorithms show limited performance as only the global peak power of the attached PV string can be captured. On the contrary, modified PV string converters can achieve true MPP acquisition, hence maximizing the system power yield and enhance the overall performance. In this article, a partial-shading-tolerant PV string system is proposed that feature (i) a multi-input multi-output (MISO) PV string converter and (ii) true MPP seeking event-driven based MPPT technique as well. The presented converter feature a single inductor buck configuration with multi-input ports attached to N number of PV panels offering reduced passive components and extended modularity feature in addition to single digital controller implementation. The presented MPPT technique exhibits decoupled tracking of the true power of each connected PV panel independently irrespective of the other attached panels even under extreme inhomogeneous irradiance distribution. Rigorous simulation results accompanying matched experimental validation assure the proposed system applicability. Dynamic modelling, in addition to various operating conditions assessments, verify the claimed effectiveness of the proposed system.

This paper introduces a multifunction interactive PV system that has the ability to feed a nonlinear local load in either grid-tied topology (GTT) or off-grid topology (OGT) and transits between the two topologies are seamless. The interactive PV system utilizes a sliding Fourier transform-based phase-locked loop (SFT-PLL) as an adaptive and robust control technique for extracting the fundamental load current. Further, the SFT-PLL is also used to synchronize the inverter output voltage with the grid during the GTT. The proposed synchronization technique has a superior immunity to the weak grid problems. The SFT-PLL based control technique has a very good ability to mitigate the nonlinear load harmonics and to deliver active current to the grid with a unity power factor. Moreover, the proposed control technique is able to keep a balanced grid current when the connected load is unbalanced whether the PV source is available or not. The DC-link voltage is controlled by the controller of the voltage source inverter (VSI) during GTT. During the OGT, the AC load voltage is regulated by the VSI controller while the DC-link voltage is regulated by the boost converter controller to satisfy the power requirements of the connected load. The simulation and experimental results show the effectiveness of the proposed control technique in feeding a balanced current with THD adheres to the IEEE-519 standard at different operating conditions. The performance of the proposed method is compared with recently published adaptive filtering techniques to show its effectiveness especially when the load current has a DC-offset.

In this work, a novel three-phase unidirectional off-board Electric Vehicles (EVs) fast charger is proposed. Each phase of the proposed converter consists of a two-stage converter. The first stage is an AC–DC front-end converter with power factor correction (PFC) control. The front-end AC–DC converter is based on an isolated boost converter, which has a boosting capability and provides galvanic isolation. In this stage, parallel-in parallel-out isolated boost converter modules are employed in each phase. This enables sharing the current among modules to avoid implementing one module with a high current rating to meet the fast-charging requirements. The multimodule option provides fault-ride through capability for the proposed charger. The DC outputs of involved phases, that is, three isolated DC voltages, are fed to three cascaded DC–DC unidirectional buck converters. The charging terminals are used to charge the EV battery through a filter inductor with a charging current controller's aid. A detailed illustration of the suggested closed-loop controllers is presented to ensure a successful operation for the proposed architecture. A simulation case study for a 25-kW charger is presented. Finally, a 1-kW prototype is implemented for experimental validation. This research was supported by NPRP grant NPRP (10-0130-170286) from the Qatar National Research Fund (a member of Qatar Foundation). Open Access funding was provided by the Qatar National Library. The statements made herein are solely the responsibility of the authors.

Error injection has become critically important for testing the reliability of newly designed hardware systems. Evaluating how a design under test (DUT) reacts to different error-injection methodologies is essential for verification engineers to design dependable universal verification methodology (UVM) scoreboards for error-detection purposes. The first main contribution of this paper is to decide on the feasibility and compatibility of some error-injection techniques when used with networks-on-chip (NoC) platforms for simulation and hardware emulation environments. We target a UVM-based error-injection and detection environment with reusable components. Proposed techniques, introducing both positive and negative test scenarios, are applied to two examples of NoC components: 1) a base router and 2) Daniel router. Base router is a simple case study to prove proposed schemes, whereas Daniel router is a complex reconfigurable open-source case study. Daniel router provides the ability to change router architecture with some parameters and applied algorithms. The second main contribution of this paper is to integrate a full UVM environment with various verification approaches. Target approaches include error injection and detection using reusable and generic UVM environment and components for NoC. Network response is inspected according to error type and methodology. Finally, the proposed UVM environment is used to test and verify an ${N}\,\,{\times }\,\,{N}\,\,2$ -D network composed of base routers or Daniel routers.

Power system utilities need to replace the conventional measuring and monitoring devices to the phasor measurement units (PMUs)-based wide area monitoring system (WAMS) to be able to move from conventional to smart grids. The challenge of this action is the cost of WAMS. Many researchers have tried to deal with this challenge by optimal allocation of PMUs to find the minimum number that fulfils the full observability of the system. The WAMS is not only PMUs but also it comprises communication infrastructures (CI) such as communication links, switches and phasor data concentrators. This paper proposes an optimization algorithm to solve the problem based on two strategies. In the first strategy, the objective function is to find the minimum number of PMUs, i.e. conventional objective function. While in the second strategy, the objective function is the minimum cost of the WAMS, i.e. cost of PMUs and their CI. The objective function in the two strategies is solved under normal operating condition and under contingencies such as failure of PMU and outage of network lines. The proposed artificial electric field algorithm (AEFA) is applied using two strategies on IEEE standard networks: 14-bus, 30-bus, 57-bus and 118-bus. The results show that the number of PMUs using two strategies is not the same and this means that the second strategy should be chosen to solve this problem. The results are also compared with other optimization algorithms, and they depict that the AEFA has the efficiency and superiority to find minimum cost of WAMS and lesser distance path of establishing communication links with higher observability.

The demand-side management (DSM) has a great significance as it covers economical activities through decreasing demand of electricity during high consumption hours. Meanwhile, the multi-agent system (MAS) is a computational intelligence technique that has been applied to reduce the overall consumers’ cost as well as total loads. This paper proposes a hybrid technique based on DSM and MAS to manage the energy in residential, commercial, and industrial microgrids. First, the DSM mechanism based on load shifting technique for managing and coordinating load demand is implemented. Then, according to the resulting scheduled load curve, the MAS is utilized in order to optimally coordinate different renewable sources to cover the demand and allowable energy to exchange between the main grid and microgrids. The problem is formulated as an optimization problem and solved by a powerful Antlion optimizer algorithm with an objective of maximization of load factor and minimization of overall consumers’ cost. The validation of the proposed algorithm is achieved via comparative studies with other published works. The results show the prosperity of the proposed technique and its capability of saving energy cost and reducing peak demands.

Assisting table tennis coaching using modern technologies is one of the most trending researches in the sports field. In this paper, we present a methodology to identify and recognize the wrong strokes executed by players to improve the training experience by the usage of an IR depth camera. The proposed system focuses mainly on the errors in table tennis player’s strokes and evaluating them efficiently and based on the analysis and classification of the data obtained from an IR depth camera using multiple algorithms. This paper is a continuation of our previous work [10], focusing more on identifying common wrong strokes in table tennis by utilizing IR depth camera classification algorithms. The classification of the mistakes that took place while playing can be classified based on each player dependently or independently for all players.

In a previous work, multi-stage NMPC and tube-based NMPC schemes were combined into a single framework called tube-enhanced multi-stage NMPC with the goal of achieving an improved trade-off between simplicity and performance. In tube-enhanced multistage NMPC, the large uncertainties are handled using a multi-stage primary controller and the small uncertainties are handled using a multi-stage ancillary controller that tracks the predictions of the primary controller. In this work, we propose the replacement of the multistage ancillary controller by a single scenario NMPC that tracks the predicted trajectories of one of the scenarios of the multi-stage primary controller. The scenario that will be tracked by the ancillary controller as well as the ancillary controller model are time varying and are adapted to the current plant dynamics. The benefits of the new formulation are demonstrated on the benchmark Williams-Otto Continuous Stirred Tank Reactor (CSTR) example.

This paper proposes a new approach for rapid detection of islanding events in a microgrid (MG). The proposed approach is a two-step procedure in which the first step is to extract some valuable features from the voltage and current signals. Such signals are analyzed for finding the second harmonic by the discrete Fourier transform (DFT). Then, the symmetrical components of this second harmonic are calculated for voltage and current, resulting in six features; positive, negative and zero sequence components. In the second step, a novel deep learning classifier based on long short-term memory (LSTM) network to identify the islanding decision is applied. The LSTM is a new artificial intelligence technique which is a distinctive pattern of recurrent neural networks. To evaluate the performance of the proposed approach, simulated and practical voltage and current signals are used. The simulated signals are generated by simulating a MG consisting of inverter based wind DGs using Matlab Simulink, while the practical data are collected from an experimental model consisting of wind and PV DGs. Different intentional and unintentional islanding events are conducted and processed using the proposed approach. The results show that in comparison with other artificial intelligence algorithms such as decision tree (DT), support vector machine (SVM) and artificial neural network (ANN), the proposed approach is efficient and reliable in detecting the islanding with high accuracy, high dependability and small detection time.

Table tennis is a complex sport with a distinctive style of play. Due to the rising interest in this sport the past years, attempts have been targeted towards enhancing the training experience and quality through various techniques. Technology has been used to support training sessions for table tennis players before, with a focus on players’ performance measures rather than technique. In this paper, we propose a methodology based on IR depth camera for detecting and classifying the efficiency of strokes performed by players in order to enhance the training experience. Our system is to based on analyzing depth data collected from IR depth camera and recognized using fastDTW algorithm. The results show an average accuracy of 88% - 100%. This is the first paper to address the usage of IR depth camera on the table tennis player to detect and classify the strokes played.

Phasor measurement unit (PMU) is among the most important measurement devices in modern power systems. It measures the voltage and current phasors which have both magnitude and phase angle using a common timing reference. These measurements are utilized in real time applications of electrical power systems. The main drawback of PMUs is its high cost so if PMUs are used to collect or send online real data from a system to a data centre or a decision maker, the used number of PMUs should be minimum with full observability of system measurements. This paper proposes a flower pollination algorithm (FPA) to find the optimal number and locations of PMUs in power systems. The optimization objectives of the work are the minimization of PMUs number, the achievement of complete observability of power system states, and the maximization of measurement redundancy. Existence of zero-injection buses in the system decreases the number of PMUs that is required to fulfil the complete observability of system states. Furthermore, additional constraints for remaining system full observable following failure of single PMU and single line outage are also included to increase the system reliability. The performance and efficiency of the proposed FPA is tested on IEEE standard networks such as 14-bus, 30-bus, 57-bus and 118-bus and new England 39-bus network. The obtained simulation results approve the ability of the proposed optimization method to find the optimal allocation of PMUs in different cases with fulfilling complete observability and reliability of electric power systems. The superiority of FPA is also verified by comparing its results of other optimization algorithms.

In the past few years, online impulse purchase has garnered attention from researchers in various fields, especially noted in the relatively new field of social commerce (S-commerce). This interactive envronment is a full of impulse buying stimulators. However, no previous studies have been conducted to evaluate the status of the research about online impulse buying behavior (IBB) in S-commerce. Hence, the authors reviewed studies published between 2005 and 2019, to gain an insight into IBB. The authors used an input-moderator-mediator-output model for identifying and classifying factors that influence consumer's online IBB in S-commerce. The authors adopted a review protocol that involved two stages (i.e., automatic and manual), and identified 68 studies that addressed online IBB, of which 24 studies focused on IBB in S-commerce. The systematic review results indicated that survey-based studies (83%) and experiment methods (17%) were prevalent in online IBB in S-commerce. The authors also noted that a majority of existing studies adopted stimulus-organism-response. In this study, the factors that influence online IBB in S-commerce were classified, and a causal-chain framework for online impulse buying was developed. Finally, the authors made recommendations for future research in this field.

There is a wide range of available biological databases developed by bioinformatics experts, employing different methods to extract biological data. In this paper, we investigate and evaluate the performance of some of these methods in terms of their ability to efficiently access bioinformatics databases using web-based interfaces. These methods retrieve bioinformatics information using structured and semi-structured data tools, which are able to retrieve data from remote database servers. This study distinguishes each of these approaches and contrasts these tools. We used Sequence Retrieval System (SRS) and Entrez search tools for structured data, while Perl and BioPerl search programs were used for semi-structured data to retrieve complex queries including a combination of text and numeric information. The study concludes that the use of semi-structured data tools for accessing bioinformatics databases is a viable alternative to the structured tools, though each method is shown to have certain inherent advantages and disadvantages., 16 pages

Conventional modular multilevel converters (MMCs) are promising converters for high-voltage high-power applications. Unlike DC-AC conversion, DC-DC conversion of MMCs suffers from unbalanced capacitor voltages due to the energy drift problem. In this study, a new modular DC-DC converter is proposed, which consists of front-end half-bridge MMC with half-bridge submodules followed by full-bridge MMC with full-bridge submodules. The proposed modular multilevel DC-DC converter can be used effectively for connecting two different DC voltage levels in high-/medium-voltage DC grids. The proposed architecture provides successful DC-DC conversion with balanced capacitor voltages throughout the arm interchange concept. It also provides a high conversion ratio, bidirectional power flow, low losses, low current stresses, reduced number of submodules, and DC fault blocking capability. Detailed illustration, operational concept, and design of the proposed architecture with the arm interchange concept are presented. Simulation results for a high-voltage high-power DC-DC converter with the suggested approach are presented to demonstrate the system performance during different normal as well as abnormal operating conditions. Finally, a scaled-down prototype has been implemented for validation. The extracted simulation and experimental results elucidate the presented claims.

The interconnection between diverse Distribution Generations (DGs) that utilize various technologies and complex structure of networks are the most characteristic of modern Distribution Networks (DN). The wide adoption of DGs considerably affects the power flow dynamics in the DN and consequently the fault characteristics. The excessive level of fault currents can pose risks of heat (high temperature) and pressure in accordance to Arc Flash (AF) incident energy in microgrids. This research studies the relationship between AF severity and the solving of coordination problem of Overcurrent Relays (OCRs) in DN, and introduces a novel equation that considers the AF qualities in solving the coordination problem for OCRs. In this study, a novel optimization problem, the AF severity with the optimal coordination of OCRs in DN is presented and the Water Cycle Optimization Method (WCOM) is employed to find the best combination of the OCR’s settings in the DN while considering the AF induced energy. The proposed optimization approach and the novel equation are evaluated with an IEC microgrid and compared with the conventional protection method and Particle Swarm Optimization (PSO) used in optimizing the coordination of OCR in the DN. The optimal settings of the OCR scheme are achieved and examined on the modified IEC microgrid benchmark system. In order to verify the result, an industrial simulation package (ETAP) and OCR (GE Multiin, model-750/760) was used in this work.

Non-communicable diseases (NCDs) are the cause for over 70% of global deaths. Various levels of healthcare delivery from home-care to tertiary care exist for patients where patients with NCDs are treated. Demand for services provided by tertiary level institutions has increased tremendously along with the growth and prevalence of chronic diseases. Few of the other reasons include co-morbidities, greater complexities of diseases, greater public expectations, higher life expectancy, an aging baby-boomer population, identification of diseases at later stages of life and deferral of care among many other complex scenarios. Globally, rising demand for healthcare services presently sets challenges of under-capacity and under-staffed healthcare infrastructure. With the advent of technology in healthcare and by providing tools in the hands of patients, a shift in healthcare delivery is evidenced towards early detection of diseases and prevention as a means of patient-care and for tackling non-communicable diseases. Evidence based delivery models tend to focus on patient experience in the course of treatment. This has consequences on the physical spaces where care is delivered, as the focus shifts from the space to the patient. This paper explores how greater demand to address prevalence of non-communicable diseases and the advent of technology can create opportunities for development of healing spaces. For patient-centric care, this would entail from inclusion of technologically driven healthcare environment within a home-care setting to improving the functional efficiencies within existing and proposed tertiary level hospitals for patient-centered care. The notion of bringing hospital (healthcare) to the patient is becoming a necessity to create a future where patients would depend less on the model of in-efficiently functioning tertiary level hospitals and a greater effort will be required towards home-settings, applying the adage 'prevention is better than cure.'

In this paper, a new modulated finite control set-model predictive control (FCS-MPC) methodology is proposed for a quasi-Z-source inverter (qZSI). The application of the qZSI in this paper is to drive the permanent magnet synchronous machine (PMSM). The proposed methodology calculates the optimal duration time (ODT) for the candidate vector from the switching patterns of the inverter after it is selected from the FCS-MPC algorithm. The control objective of the FCS-MPC are the three-phase currents of PMSM, when the motor speed is below or equal to the base speed. While at a speed beyond the based speed, the inductor current and capacitor voltage of the qZS network are added as control objectives. For each candidate optimal vector, the optimal time, which is a part of the sampling interval, is determined based on minimizing the ripples of the control objectives using a quadratic cost function. Then, the optimal vector is applied only to the inverter switches during the calculated ODT at the start of the sampling interval, while the zero vector is applied during the remaining part of the sampling interval. To reduce the calculation burden, the zero-state is excluded from the possible states of the inverter, and the sub-cost function definition is used for the inductor current regulation. The proposed modulated FCS-MPC is compared with the unmodulated FCS-MPC at the same parameters to handle a fair comparison. The simulation results based on the MATLAB/Simulink© software shows the superiority of the proposed algorithm compared to the unmodulated FCS-MPC in terms of a lower ripple in the inductor current and capacitor voltage, and a lower THD for the PMSM currents.

The output power against voltage curve of the photovoltaic system changes its characteristics under partial shading conditions because of using bypass diodes. These bypass diodes are connected across the PV modules inside the string to avoid hotspot formation in the shaded PV modules. Therefore, the output curve has multiple power peaks with only one Global Max Power Point. The classical Maximum Power Point Tracking algorithms may fail to track that Global Max Power. Several soft computing algorithms have been proposed to improve tracking efficiency with different optimization principles. In this paper, an Improved Cuckoo Search Algorithm has been proposed to increase the tracking speed with minimum output power oscillation. The proposed algorithm avoids spreading the initial particles among the whole curve to predict shading pattern, but it reduces the exploration area after each iteration to compensate for the algorithm’s randomness. The proposed algorithm was compared with other methods by simulation using MATLAB/Simulink program and with practical experiments under the same operating conditions. The comparison showed that the proposed algorithm overcomes the other methods’ drawbacks and concurrently minimizes the convergence time, power oscillation, and system power losses.

Highlights • Double Generative Adversarial Networks for Image Enhancement and Super Resolution. • Improving the accuracy of LPR systems using image super resolution., License Plate Recognition (LPR) is an important implemented application of Artificial Intelligence (AI) and deep learning in the past decades. However, due to the low image quality caused by the fast movement of vehicles and low-quality analogue cameras, many plate numbers cannot be recognised accurately by LPR models. To solve this issue, we propose a new deep learning architecture called D_GAN_ESR (Double Generative Adversarial Networks for Image Enhancement and Super Resolution) used for effective image denoising and super-resolution for license plate images. In this paper, we show the limitation of the existing networks for image enhancement and image super-resolution. Furthermore, a feature-based evaluation metric called Peak Signal to Noise Ratio Features (PSNR-F) is used to evaluate and compare performance between different methods. It is shown that the use of PSNR-F has a better performance indicator than the classical PSNR-pixel-to-pixel (PSNR-pixel) evaluation metric. The results show that using D_GAN_ESR to enhance the license plate images increases the LPR accuracy from 30% to 78% when blur images are used and increases the accuracy from 59% to 74.5% when low-quality images are used., Graphical abstract, Image Super Resolution; Image Enhancement; LPR

Requirements engineering is a discipline of software engineering that is concerned with the identification and handling of user and system requirements. Aspect-Oriented Requirements Engineering (AORE) extends the existing requirements engineering approaches to cope with the issue of tangling and scattering resulted from crosscutting concerns. Crosscutting concerns are considered as potential aspects and can lead to the phenomena tyranny of the dominant decomposition. Requirements-level aspects are responsible for producing scattered and tangled descriptions of requirements in the requirements document. Validation of requirements artefacts is an essential task in software development. This task ensures that requirements are correct and valid in terms of completeness and consistency, hence, reducing the development cost, maintenance and establish an approximately correct estimate of effort and completion time of the project. In this paper, we present a validation framework to validate the aspectual requirements and the crosscutting relationship of concerns that are resulted from the requirements engineering phase. The proposed framework comprises a high-level and low-level validation to implement on software requirements specification (SRS). The high-level validation validates the concerns with stakeholders, whereas the low-level validation validates the aspectual requirement by requirements engineers and analysts using a checklist. The approach has been evaluated using an experimental study on two AORE approaches. The approaches are viewpoint-based called AORE with ArCaDe and lexical analysis based on Theme/Doc approach. The results obtained from the study demonstrate that the proposed framework is an effective validation model for AORE artefacts., 20 pages