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One crucial objective of this article is to present simplified and accurate approaches to study the current distribution and ground surface potential around the area surrounding an earthed and isolated system in case of a line‐to‐earth fault. The present study is done in the case of uniform and two‐layer soils. Suggested models for calculating the distribution of the earth's surface current density, step, and touch voltages in grounded and isolated systems are presented, discussed, and adapted. The contact and arc resistances of line to ground faulty conductors are considered. Rods and/or grounding grids usually do grounded systems. The impact of both step and touch potentials and current density are investigated in this article. The methods of the calculations are based on the electrical concepts, the charge simulation method, and the imaging procedure for the grounding system. The suggested method can be considered a novel and simple technique for calculating the current distributions of the ground surface during line to ground fault. The results agree with those obtained by others, with the advantage of the proposed algorithm for its ease of application and simplicity. 3‐D dimensions' contours of the current density and the electric potential on the earth surface around the faulty point are presented in case of homogeneous and two layers' soil. Finally, this article's novelty is to devise a method for calculating the step and touch potentials and current density around the point of contact of the conductor carrying the electric voltages in the event of faults occurring in the electrical network, whether the network is grounded or isolated. Such faults of electrical networks may cause human hazards. [ABSTRACT FROM AUTHOR]

This paper discusses the impact of the feature input vector on the performance of dissolved gas analysis-based intelligent power transformer fault diagnosis methods. For this purpose, 22 feature vectors from traditional diagnostic methods were used as feature input vectors for four tree-based ensemble algorithms, namely random forest, tree ensemble, gradient boosted tree, and extreme gradient tree. To build the proposed diagnostics models, 407 samples were used for training and testing. For validation and comparison with the existing methods of literature, 89 samples were used. Based on the results obtained on the training and testing datasets, the best performance was achieved with feature vector 16, which consists of the gas ratios of Rogers' four ratios method and the three ratios technique. The test accuracies based on these vectors are 98.37, 96.75, 95.93, and 97.56% for the namely random forest, tree ensemble, gradient boosted tree, and extreme gradient tree algorithms, respectively. Furthermore, the performance of the methods based on best input feature was evaluated and compared with other methods of literature such as Duval triangle, modified Rogers' four ratios method, combined technique, three ratios technique, Gouda triangle, IEC 60599, NBR 7274, the clustering method, and key gases with gas ratio methods. These methods suffer from unreliability, and this is the motivation behind the current work to develop a new technique that enhances the diagnostic accuracy of transformer faults to avoid unwanted faults and outages from the network. On validating dataset, diagnostic accuracies of 92.13, 91.01, 89.89, and 91.01% were achieved by the namely random forest, tree ensemble, gradient boosted tree, and extreme gradient tree models, respectively. These diagnostic accuracies are higher than 83.15% for the clustering method, 82.02% for the combined technique, 80.90% for the modified IEC 60599, and 79.78% for key gases with gas ratios, which are the best existing methods. Even if the performance of dissolved gas analysis-based intelligent methods depends strongly on the shape of the feature vector used, this study provides scholars with a tool for choosing the feature vector to use when implementing these methods. [ABSTRACT FROM AUTHOR]

The article introduces a new model of a Reference Signal Tracking (RST) controller for the fractional order quantities. The controller is applied to an indirect field‐oriented control (IFOC) system used for the speed control of an asynchronous motor powered by a photovoltaic (PV) generator with a maximum power point tracking (MPPT) algorithm. This model utilizes two fractional order controllers: the fractional order proportional‐integral (FOPI) regulator and the fractional‐order integral‐proportional (FOIP) regulator. These controllers are used with the generalized predictive control (GPC) technique. The first step in the approach is to derive the equivalent digital RST controller's model from the FOPI and FOIP controller's transfer functions. The GPC technique converts the continuous‐time FOPI (and FOIP) controller into a discrete‐time version. This conversion ensures a fast response and effective disturbance rejection. Simulation tests are conducted to analyze the rotor speed and stator current ripples to evaluate the performance of the proposed method. The results demonstrate the effectiveness of the introduced scheme in achieving improved control performance in terms of response speed and disturbance rejection. The article presents a modified RST controller model based on the fractional order approach applied to an IFOC system for motor speed control driven by a photovoltaic generator. Using FOPI, FOIP controllers, and GPC contributes to enhanced control performance, as evidenced by the simulation results. [ABSTRACT FROM AUTHOR]

Series arc fault is the main cause of electrical fire in low-voltage distribution system. A fast and accurate detection system can reduce the risk of fire effectively. In this paper, series arc experiment is carried out for different kinds of electrical load. The time-domain current is analyzed by Morlet wavelet. Then, the multiscale wavelet coefficients are expressed as the coefficient matrix. In order to meet the data dimension requirements of neural networks, a color domain transformation method is used to transform the feature matrix into an image. A regularization method based on gamma transform is proposed for small sample data sets. The results showed that the proposed regularization method improved the validation set accuracy of ResNet50 from 66.67% to 96.53%. The overfitting problem of neural network was solved. In addition, this method fused fault features of 64 different scales, and provided a valuable manually labeled arc fault dataset. Compared with the threshold detection method, this method was more objective. The use of image features increased intuitiveness and generality. Compared with other typical lightweight networks, this method had the best detection performance. [ABSTRACT FROM AUTHOR]

This paper outlines the development of a high-performance maximum power point tracking (MPPT)-based solar irradiance estimator for photovoltaic (PV) systems. The suggested estimator is constructed around a simple current–voltage-based algebraic equation that hinges on the operation of the PV system at its maximum power point (MPP). In the realm of MPP operation, the overall system is driven by a nonlinear MPPT controller. To achieve this function, we integrated a hybrid incremental conductance integral backstepping (H-INC-IBS) controller to effectively regulate the PV system. This controller was specially chosen for its powerful potency in maximizing the dynamics of the PV system, leading to heightened robustness against changing environmental conditions. The simulation results are provided to showcase the suitability of the proposed estimator. Furthermore, the estimator was verified under experimental conditions, highlighting its soundness and practicality. Through evaluations and comparisons with the conventional irradiance estimator, this paper aimed to emphasize the superiority of the proposed solar irradiance estimator in providing more accurate estimations of solar irradiance for PV systems operating under MPPT supervision. [ABSTRACT FROM AUTHOR]

A photovoltaic emulator (PVE) is a specialized device designed to mimic the static and dynamic properties of a solar panel. It serves as a crucial tool for testing and validating PV systems. Despite the unpredictable variations in real environmental conditions, the PVE offers a controlled environment, facilitating the smooth implementation and testing of PV subsystems. The PVE consists of two essential components: the reference PV model and the PVE power electronics controller. For simplicity, both these elements must be inherently straightforward. Numerous advanced methods have been proposed in the literature to efficiently integrate the PV model into the PVE system. However, these approaches often involve complex iterative computations of intricate equations related to solar panels, making them less practical. To address these limitations, this paper introduces a novel PVE that aims to simplify the implementation and integration of the PV model. The proposed system employs a simple and non-iterative approach to provide the reference to the PVE, using a straightforward explicit model of the solar panel. In comparison to existing works, the proposed PVE stands out for its high flexibility and simplicity as it does not require the complex iterative computations of implicit equations related to the solar panel model. The overall PVE system is implemented with a basic proportional-integral (PI) controller and a DC-DC buck power electronic converter. It is then validated using a 200 W solar panel through a series of experiments, including the EN 50530 Test. The results of these experiments demonstrate that the proposed PVE efficiently reproduces the static and dynamic characteristics of a solar panel. [ABSTRACT FROM AUTHOR]

Facial mask detection technology has become increasingly important even beyond the context of the COVID-19 pandemic. Along with the advancement in facial recognition technology, face mask detection has become a crucial feature for various applications. This paper introduces an Internet of Things (IoT) architecture based on a developed deep learning algorithm named You Only Look Once (YOLO) to keep society healthy, and secured, and collect data for future research. The proposed paradigm is built on the basis of economic consideration and is easy to implement. Yet, the used YOLOv4-tiny is one of the fastest object detection models to exist. A mask detection camera (MaskCam) that leverages the computing power of NVIDIA's Jetson Nano edge nanodevices was built side by side with a smart camera application to detect a mask on the face of an individual. MaskCam distinguishes between mask wearers, those who are not wearing masks, and those who are not wearing masks properly according to MQTT protocol. Furthermore, a self-developed web browsing application comes with the MaskCam system to collect and visualize statistics for qualitative and quantitative analysis. The practical results demonstrate the superiority and effectiveness of the proposed smart mask detection system. On the one hand, YOLOv4-full obtained the best results even at smaller resolutions, although the frame rate is too small for real-time use. On the other hand, it is twice as fast as the other detection models, regardless of the quality of detection. Consequently, inferences may be run more frequently over the entire video sequence, resulting in more accurate output. [ABSTRACT FROM AUTHOR]

Assessing water quality is crucial for improving global water resource management, particularly in arid regions. This study aims to assess and monitor the status of groundwater quality based on hydrochemical parameters and by using artificial intelligence (AI) approaches. The irrigation water quality index (IWQI) is predicted by using support vector machine (SVM) and k-nearest neighbors (KNN) classifiers in Matlab's classification learner toolbox. The classifiers are fed with the following hydrochemical input parameters: sodium adsorption ratio (SAR), electrical conductivity (EC), bicarbonate level (HCO3), chloride concentration (Cl), and sodium concentration (Na). The proposed methods were used to assess the quality of groundwater extracted from the desertic region of Adrar in Algeria. The collected groundwater samples showed that 9.64% of samples were of very good quality, 12.05% were of good quality, 21.08% were satisfactory, and 57.23% were considered unsuitable for irrigation. The IWQI prediction accuracies of the classifiers with the standardized, normalized, and raw data were 100%, 100%, and 90%, respectively. The cubic SVM with the normalized data develops the highest prediction accuracy for training and testing samples (94.2% and 100%, respectively). The findings of this work showed that the multiple regression model and machine learning could effectively assess water quality in desert zones for sustainable water management. [ABSTRACT FROM AUTHOR]

This article proposes an enhanced quasi-reflection jellyfish optimization (QRJFO) algorithm for solving the optimal power flow (OPF) problem. The multi-dimension objective functions are the fuel costs, transmission losses and pollutant emissions. Despite the simple structure of the jellyfish optimization algorithm, it requires significant exploitation and exploration control characteristics to support its capability. In the proposed QRJFO, a cluster is chosen randomly for every jellyfish from the population to reflect the social group that shares information in it. It varies from one to the next. The exploration phase is supported by introducing quasi-opposition-based learning. The performance of the proposed QRJFO algorithm is evaluated on the IEEE 57-bus, practical West Delta Region system and large-scale IEEE 118 bus. The simulation results demonstrate the quality of the solution and resilience of QRJFO. It is very significant for operating power systems from economic, technical and environmental perspectives. [ABSTRACT FROM AUTHOR]

Classification of electroencephalogram (EEG) signals for humans can be achieved via artificial intelligence (AI) techniques. Especially, the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic regions. From this perspective, an automated AI technique with a digital processing method can be used to improve these signals. This paper proposes two classifiers: long short-term memory (LSTM) and support vector machine (SVM) for the classification of seizure and non-seizure EEG signals. These classifiers are applied to a public dataset, namely the University of Bonn, which consists of 2 classes –seizure and nonseizure. In addition, a fast Walsh-Hadamard Transform (FWHT) technique is implemented to analyze the EEG signals within the recurrence space of the brain. Thus, Hadamard coefficients of the EEG signals are obtained via the FWHT. Moreover, the FWHT is contributed to generate an efficient derivation of seizure EEG recordings from non-seizure EEG recordings. Also, a k-fold cross-validation technique is applied to validate the performance of the proposed classifiers. The LSTM classifier provides the best performance, with a testing accuracy of 99.00%. The training and testing loss rates for the LSTM are 0.0029 and 0.0602, respectively, while the weighted average precision, recall, and F1-score for the LSTM are 99.00%. The results of the SVM classifier in terms of accuracy, sensitivity, and specificity reached 91%, 93.52%, and 91.3%, respectively. The computational time consumed for the training of the LSTM and SVM is 2000 and 2500 s, respectively. The results show that the LSTM classifier provides better performance than SVM in the classification of EEG signals. Eventually, the proposed classifiers provide high classification accuracy compared to previously published classifiers. [ABSTRACT FROM AUTHOR]

According to the stability process of smart grids, which starts by gathering information of consumers, and then evaluating this information based on specifications of a power supply, and finally, information of a price is sent to the consumers as a report about the utilization. From this perspective, this process is too much time consuming, thus it should predict a smart grid stability via artificial intelligence (e.g., neural networks). Recent advances in the accuracy of neural network have effective solutions to solving the smart grid stability prediction issues, but it remains necessary to develop high performance neural networks that give higher accuracy. In this paper, an artificial neural network (ANN) is proposed to predict a smart grid stability for Decentral Smart Grid Control (DSGC) systems. This neural network is applied to a dataset aggregated from simulations of grid stability, executed on a four-node network with star topology, and engaged in two classes of grid stability–stable and unstable. Keras framework is used to train the proposed neural network, and a hyperparameter tuning method is utilized to achieve high accuracy. Receiver operating characteristic (ROC) curves and confusion matrices are experimentally utilized to evaluate the performance of the proposed neural network. The neural network provides high performance, with a testing loss rate of 0.0619, and a testing accuracy of 97.36%. The weighted average recall, precision, and F1-score for the proposed neural network are 98.02%, 98.03%, and 98.02%, respectively, while the area under the ROC curves (AUCs) is 100%. This neural network with the utilized dataset indeed provides an accurate and quick approach of predicting grid stability to analyze DSGC systems. [ABSTRACT FROM AUTHOR]

With the rapid development of emerging 5G and beyond (B5G), Unmanned Aerial Vehicles (UAVs) are increasingly important to improve the performance of dense cellular networks. As a conventional metric, coverage probability has been widely studied in communication systems due to the increasing density of users and complexity of the heterogeneous environment. In recent years, stochastic geometry has attracted more attention as a mathematical tool for modeling mobile network systems. In this paper, an analytical approach to the coverage probability analysis of UAV-assisted cellular networks with imperfect beam alignment has been proposed. An assumption was considered that all users are distributed according to Poisson Cluster Process (PCP) around base stations, in particular, Thomas Cluster Process (TCP). Using thismodel, the impact of beam alignment errors on the coverage probabilitywas investigated. Initially, the ProbabilityDensity Function (PDF) of directional antenna gain between the user and its serving base station was obtained. Then, association probability with each tier was achieved. A tractable expression was derived for coverage probability in both Line-of-Sight (LoS) andNon-Line-of-Sight (NLoS) condition links. Numerical results demonstrated that at low UAVs altitude, beam alignment errors significantly degrade coverage performance. Moreover, for a small cluster size, alignment errors do not necessarily affect the coverage performance. [ABSTRACT FROM AUTHOR]

This paper discusses the efficient implementation of a new hybrid approach to forecasting short-term PV power production for four different PV plants in Algeria. The developed model incorporates a time-varying filter-empirical mode decomposition (TVFEMD) and an extreme learning machine (ELM) as an essence regression. The TVF-EMD technique is used to deal with the fluctuation of PV power data by splitting it into a series of more stable and constant subseries. The specified set of features (intrinsic mode functions (IMFs)) is utilized for training and improving our forecasting extreme learning machine model. The adjusted ELM model is used to evaluate prediction e1ciency. The suggested TVF-EMD-ELM model is assessed and verified in various Algerian locations with varying climate conditions. In all examined regions, the TVF-EMD-ELM model generates less than 4% error in terms of normalized root mean square error (nRMSE). [ABSTRACT FROM AUTHOR]

Nowadays, the operation and control of power systems are a big challenge. An essential part of the power system (PS) control is load frequency control (LFC). Different secondary controllers are implemented for the frequency control problem. Hence, cascaded two-degree freedom and a tilt-integral-derivative controller having a filter (2DOFTIDF) are intended in this paper and implemented for load frequency control. In order to determine the efficiency of the 2DOFTIDF controller, a well-known non-reheat thermal system with/without a governor dead band is considered. A new whale optimization algorithm (WOA) is used to enhance the suggested controller parameters. The predominance of the presented method is exhibited by comparing the consequences with different heuristic techniques tuned to controllers published recently. Further, the simulation results for two test cases indicate that system enactments are enhanced by introducing the suggested controller and are also best suited in the presence of system nonlinearity. Finally, random load fluctuation along with noise and changing the system parameters are also used to determine the reliability of the suggested controller. Compared to the WOA-tuned TIDF controller, the settling time of ΔF1, ΔF2, and ΔPTie is improved by 45.45%, 56.77%, and 20.26%, respectively, with the WOA-tuned 2DOFTIDF controller and by 40%, 48.27%, and 20%, respectively, with the DE-tuned TIDF controller. Experimental validation using the hardware-in-the-loop real-time simulation based on OPAL-RT has been carried out to confirm the viability of the proposed approach. [ABSTRACT FROM AUTHOR]

In this manuscript, a compact in size yet geometrically simple Ultra-Wideband (UWB) antenna is demonstrated. The flexible-by-nature substrate ROGERS 5880, having a thickness of 0.254 mm, is utilized to design the proposed work. The antenna configuration is an excerpt of a traditional rectangular monopole antenna resonating at 5 GHz. Initially, a pair of triangular slots are employed to extend the impedance bandwidth of the antenna. In addition, a semi-circular-shaped, short-ended stub is connected at the upper edges of the patch to further increase the operational bandwidth. After optimization, the proposed antenna offers UWB ranging from 2.73–9.68 GHz, covering almost the entire spectrum allocated globally for UWB applications. Further, the antenna offers a compact size of 15 × 20 mm2 that can easily be integrated into small, flexible electronics. The flexibility analysis is done by bending the antenna on both the x and y axes. The antenna offers performance stability in terms of return loss, radiation pattern, and gain for both conformal and non-conformal conditions. Furthermore, the strong comparison between simulated and measured results for both rigid and bent cases of the antenna, along with the performance comparison with the state-of-the-art, makes it a potential candidate for present and future compact-sized flexible devices. [ABSTRACT FROM AUTHOR]

Several security tools have been described in recent times to assist security teams; however, the effectiveness and success remain limited to specific devices. Phishing is a type of cyberattack that uses fraudulent emails and websites to obtain personal information from unsuspecting users, such as passwords and credit card numbers. Hackers can gain access to your information through a variety of methods, and the most common of which are king, phishing, spear phishing, social engineering, and dictionary attacks. Each of these techniques is unique, but they all have the same goal: to obtain your personal information. Nevertheless, there is the potential to exploit this problem in terms of security. In this paper, we used the Bash Bunny (BB), a new tool designed to assist military, law enforcement, and penetration tester teams with their work to conduct exfiltration without privilege escalation through T1200, T1052, and T1052.001 techniques in air-gapped networks with effectiveness/success 99.706%. [ABSTRACT FROM AUTHOR]

The power transformer is a vital element in power system assets. When failure occurs in transformers, it leads to more revenue loss for the electrical utilities. Power transformer failures may occur due to high stresses on the insulating systems (oil and paper). These stresses categorize as electrical, thermal, and mechanical stresses. The main contribution of the current work is to increase the contribution of essential test parameters, which contribute to determining the insulating transformer state. In this work, principal component analysis (PCA) is not used as a prediction or classification tool. Still, it preserves only the necessary information and marginalizes or reduces the role of parameters with fewer contributions. PCA is a statistical method to compress an extensive data set of variables to a smaller number of orthogonal factors unrelated to interpreting the correlation matrix. One hundred forty-seven data samples were collected from the Saudi Electricity Company and used in this study. The data include the dissolved gas analysis (DGA) test, the breakdown voltage (BDV), moisture content (MC), Acidity (AC), interfacial tension (IFT), oil color (OC), dissipation factor (DF) or tan □, furan content (2-FAL). Based on PCA, some test variables can be selected as indicators for the insulating paper state from more test variables groups, reducing the cost of all tests. [ABSTRACT FROM AUTHOR]

Technology for electric vehicles (EVs) is a developing subject that offers numerous advantages, such as reduced operating costs. Since the goal of EVs has always been to have long-lasting batteries, any new hardware might drastically diminish battery life. Errors are common among human beings. Because of that, accidents and fatalities may occur due to drivers' different behaviors such as sports style and moderation. To advance driver safety, security, and comfort, Advanced Driver Assistance Systems (ADAS) must be personalized. Modern cars have ADAS that relieves the driver of some of the tasks they perform while driving. As a part of this research, a driver identification system based on a deep driver classification model (deep neural network as DNN) with feature reduction techniques (random forest as RF and principal component analysis as PCA) is implemented to help automate and aid in crucial jobs such as the brake system in an efficient manner. Using task models, we simulate a low-cost driver assisted scheme in real time, where various scenarios are explored and the schedulability of tasks is established before implementing them in EV. The new driver assistance scheme has several advantages over the existing options. It lowers the risk of an accident and ensures driver safety. The proposed model (RF-DNN) achieved 97.05% of accuracy and the PCA-DNN model achieved 95.55% of accuracy, whereas the artificial neural network as ANN with PCA and RF achieved nearly 92% of accuracy. [ABSTRACT FROM AUTHOR]

This study describes the design and implementation of a small printed ultra-wideband (UWB) antenna for smart electronic systems with on-demand adjustable notching properties. A contiguous sub-band between 3–4.1 GHz, 4.45–6.5 GHz, or for both bands concurrently, can be mitigated by the antenna. Numerous technologies and applications, including WiMAX, Wi-Fi, ISMA, WLAN, and sub-6 GHz, primarily utilize these band segments remitted by the UWB. The upper notch band is implemented by inserting an open-ended stub with the partial ground plane; the lower notch band functionality is obtained by etching a U-shaped slot from the radiating structure. The basic UWB mode is then changed to a UWB mode, with a single or dual notch band, using two diodes to achieve reconfigurability. The antenna has a physically compact size of 17 × 23 mm2 and a quasi-omnidirectional maximum gain of 4.9 dBi, along with a high efficiency of more than 80%, according to both simulation and measurement data. A significant bandwidth in the UWB region is also demonstrated by the proposed design, with a fractional bandwidth of 180% in relation to the 5.2 GHz center frequency. Regarding compactness, consistent gain, and programmable notch features, the proposed antenna outperforms the antennas described in the literature. In addition to these benefits, the antenna's compact size makes it simple to incorporate into small electronic devices and enables producers to build many antennas without complications. [ABSTRACT FROM AUTHOR]

The article presents a miniaturized monopole antenna dedicated to modern flexible electronic systems. The antenna combines three fundamental properties in a single structure. Firstly, it is characterized by a compact size compared to the state-of-the-art literature with an overall size of 18×18×0.254 mm3, secondly, the proposed antenna integrates the reconfigurability function of frequency, produced by means of a Positive-Intrinsic- Negative (PIN) diode introduced into the radiating element. Thus, the antenna is able to switch between different frequencies and different modes, making it suitable to meet the ever-changing demands of communication systems. third, the antenna is equipped by the property of flexibility. In fact, a conformability test is performed and has demonstrated the stability of the antenna performance under normal and bending conditions. Finally, in order to demonstrate the potential of the proposed antenna, a comparison between the simulated and measured results is made and turned out to be a strong agreement, making the antenna an excellent candidate for future miniaturized rigid and conformal devices. [ABSTRACT FROM AUTHOR]

Recently, many researchers have tried to develop a robust, fast, and accurate algorithm. This algorithm is for eye-tracking and detecting pupil position in many applications such as head-mounted eye tracking, gaze-based human-computer interaction, medical applications (such as deaf and diabetes patients), and attention analysis. Many real-world conditions challenge the eye appearance, such as illumination, reflections, and occasions. On the other hand, individual differences in eye physiology and other sources of noise, such as contact lenses or make-up. The present work introduces a robust pupil detection algorithm with and higher accuracy than the previous attempts for real-time analytics applications. The proposed circular hough transform with morphing canny edge detection for Pupillometery (CHMCEP) algorithm can detect even the blurred or noisy images by using different filtering methods in the pre-processing or start phase to remove the blur and noise and finally the second filtering process before the circular Hough transform for the center fitting to make sure better accuracy. The performance of the proposed CHMCEP algorithm was tested against recent pupil detectionmethods. Simulations and results show that the proposed CHMCEP algorithm achieved detection rates of 87.11, 78.54, 58, and 78 according to 'Swirski, ExCuSe, Else, and labeled pupils in the wild (LPW) data sets, respectively. These results show that the proposed approach performs better than the other pupil detection methods by a large margin by providing exact and robust pupil positions on challenging ordinary eye pictures. [ABSTRACT FROM AUTHOR]

This study aims to develop an accurate dynamic cutting force model in the milling process. In the proposed model, the estimated cutting force tackles the effect of the self-excited vibration that causes machining instability during the cutting process. In particular, the square root of the residual cutting force between the prediction and the actual cutting force is considered as an objective function for optimizing the cutting force coefficients using the equilibrium optimizer (EO) approach instead of the trial-and-error approach. The results confirm that the proposed model can provide higher prediction accuracy when the EO is applied. In addition, the proposed EO has a minimum integral square error (ISE) of around 1.12, while the genetic algorithm (GA) has an ISE of around 1.14 and the trial-and-error method has an ISE of around 2.4. Moreover, the proposed method can help to investigate the cutting stability and to suspend the chatter phenomenon by selecting an optimal set of cutting parameters. [ABSTRACT FROM AUTHOR]

Any engineering system involves transitions that reduce the performance of the system and lower its comfort. In the field of automotive engineering, the combination of multiple motors and multiple power sources is a trend that is being used to enhance hybrid electric vehicle (HEV) propulsion and autonomy. However, HEV riding comfort is significantly reduced because of high peaks that occur during the transition from a single power source to a multisource powering mode or from a single motor to a multiple motor traction mode. In this study, a novel model-based soft transition algorithm (STA) is used for the suppression of large transient ripples that occur during HEV drivetrain commutations and power source switches. In contrast to classical abrupt switching, the STA detects transitions, measures their rates, generates corresponding transition periods, and uses adequate transition functions to join the actual and the targeted operating points of a given HEV system variable. As a case study, the STA was applied to minimize the transition ripples that occur in a fuel cell-supercapacitor HEV. The transitions that occurred within the HEV were handled using two proposed transition functions which were: a linear-based transition function and a stair-based transition function. The simulation results show that, in addition to its ability to improve driving comfort by minimizing transient torque ripples and DC bus voltage fluctuations, the STA helps to increase the lifetime of the motor and power sources by reducing the currents drawn during the transitions. It is worth noting that the considered HEV runs on four-wheel drive when the load torque applied on it exceeds a specified torque threshold; otherwise, it operates in rear-wheel drive. [ABSTRACT FROM AUTHOR]

Water quality and scarcity are key topics considered by the Sustainable Development Goals (SDGs), institutions, policymakers and stakeholders to guarantee human safety, but also vital to protect natural ecosystems. However, conventional approaches to deciding the suitability of water for drinking purposes are often costly because multiple characteristics are required, notably in low-income countries. As a result, building right and trustworthy models is mandatory to correctly manage available groundwater resources. In this research, we propose to check multiple classification techniques such as Decision Trees (DT), K-Nearest Neighbors (KNN), Discriminants Analysis (DA), Support Vector Machine (SVM), and Ensemble Trees (ET) to design the best strategy allowing the forecast a Water Quality Index (WQI). To achieve this goal, an extended dataset characterized by water samples collected in a total of twelve municipalities of the Wilaya of Naâma in Algeria was considered. Among them, 151 samples were examined as training samples, and 18 were used to test and confirm the prediction model. Later, data samples were classified based on the WQI into four states: excellent water quality, good water quality, poor water quality, and very poor or unsafe water. The main results revealed that the SVM classifier obtained the highest forecast accuracy, with 95.4% of prediction accuracy when the data are standardized and 88.9% for the accuracy of the test samples. The results confirmed that the use of machine learning models are powerful tools for forecasting drinking water as larger scales to promote the design of efficient and sustainable water quality control and support decision-plans. [ABSTRACT FROM AUTHOR]

Insulator monitoring using leakage current characteristics is essential for predicting an insulator's health. To evaluate the risk of flashover on the porcelain insulator using leakage current, experimental investigation of leakage current indices was carried out. In the first stage of the experiment, the effect of contamination, insoluble deposit density, wetting rate, and uneven distribution pollution were determined on the porcelain insulator under test. Then, based on the laboratory test results, leakage current information in time and frequency characteristics was extracted and employed as assessment indicators for the insulator's health. Six indicators, namely, peak current indicator, phase shift indicator, slope indicator, crest factor indicator, total harmonic distortion indicator, and odd harmonics indicator, are introduced in this work. The obtained results indicated that the proposed indicators had a significant role in evaluating the insulator's health. To evaluate the insulator's health levels based on the extracted indicator values, this work presents the naïve Bayes technique for the classification and prediction of the insulator's health. Finally, the confusion matrix for the experimental and prediction results for each indicator was established to determine the appropriateness of each indicator in determining the insulator's health status. [ABSTRACT FROM AUTHOR]

Detecting transformer faults is critical to avoid the undesirable loss of transformers from service and ensure utility service continuity. Transformer faults diagnosis can be determined based on dissolved gas analysis (DGA). The DGA traditional techniques, such as Duval triangle, Key gas, Rogers' ratio, Dornenburg, and IEC code 60599, suffer from poor transformer faults diagnosis. Therefore, recent research has been developed to diagnose transformer fault and the diagnostic accuracy using combined traditional methods of DGA with artificial intelligence and optimization methods. This paper used a novel meta-heuristic technique, based on Gravitational Search and Dipper Throated Optimization Algorithms (GSDTO), to enhance the transformer faults' diagnostic accuracy, which was considered a novelty in this work to reduce the misinterpretation of the transformer faults. The robustness of the constructed GSDTO-based model was addressed by the statistical study using Wilcoxon's rank-sum and ANOVA tests. The results revealed that the constructed model enhanced the diagnostic accuracy up to 98.26% for all test cases. [ABSTRACT FROM AUTHOR]

This paper highlights a robust optimization and power management algorithm that supervises the energy transfer flow to meet the photovoltaic (PV) electric vehicle demand, even when the traction system is in motion. The power stage of the studied system consists of a triple-junction PV generator as the main energy source, a lithium-ion battery as an auxiliary energy source, and an electric vehicle. The input–output signal adaptation is made by using a stage of energy conversion. A bidirectional DC-DC buck-boost connects the battery to the DC-link. Two unidirectional boost converters interface between the PV generator and the DC link. One is controlled with a maximum power point tracking (MPPT) algorithm to reach the maximum power points. The other is used to control the voltage across the DC-link. The converters are connected to the electric vehicle via a three-phase inverter via the same DC-link. By considering the nonlinear behavior of these elements, dynamic models are developed. A robust nonlinear MPPT algorithm has been developed owing to the nonlinear dynamics of the PV generator, metrological condition variations, and load changes. The high performance of the MPPT algorithm is effectively highlighted over a comparative study with two classical P & O and the fuzzy logic MPPT algorithms. A nonlinear control based on the Lyapunov function has been developed to simultaneously regulate the DC-link voltage and control battery charging and discharging operations. An energy management rule-based strategy is presented to effectively supervise the power flow. The conceived system, energy management, and control algorithms are implemented and verified in the Matlab/Simulink environment. Obtained results are presented and discussed under different operating conditions. [ABSTRACT FROM AUTHOR]

Many authors have recently examined the relationship between symmetry and generalized convexity. Generalized convexity and symmetry have become a new area of study in the field of inequalities as a result of this close relationship. In this article, we introduce the idea of preinvex fuzzy-interval-valued functions (preinvex F∙I-V∙F) on coordinates in a rectangle drawn on a plane and show that these functions have Hermite–Hadamard-type inclusions. We also develop Hermite–Hadamard-type inclusions for the combination of two coordinated preinvex functions with interval values. The weighted Hermite–Hadamard-type inclusions for products of coordinated convex interval-valued functions discussed in a recent publication by Khan et al. in 2022 served as the inspiration for our conclusions. Our proven results expand and generalize several previous findings made in the body of literature. Additionally, we offer appropriate examples to corroborate our theoretical main findings. [ABSTRACT FROM AUTHOR]

This paper proposes a novel Fuzzy-MPDTC control applied to a fuel cell battery electric vehicle whose traction is ensured using a permanent magnet synchronous motor (PMSM). On the traction side, model predictive direct torque control (MPDTC) is used to control PMSM torque, and guarantee minimum torque and current ripples while ensuring satisfactory speed tracking. On the sources side, an energy management strategy (EMS) based on fuzzy logic is proposed, it aims to distribute power over energy sources rationally and satisfy the load power demand. To assess these techniques, a driving cycle under different operating modes, namely cruising, acceleration, idling and regenerative braking is proposed. Real-time simulation is developed using the RT LAB platform and the obtained results match those obtained in numerical simulation using MATLAB/Simulink. The results show a good performance of the whole system, where the proposed MPDTC minimized the torque and flux ripples with 54.54% and 77%, respectively, compared to the conventional DTC and reduced the THD of the PMSM current with 53.37%. Furthermore, the proposed EMS based on fuzzy logic shows good performance and keeps the battery SOC within safe limits under the proposed speed profile and international NYCC driving cycle. These aforementioned results confirm the robustness and effectiveness of the proposed control techniques. [ABSTRACT FROM AUTHOR]

The main objective of this work is to propose an approach for improving the performance of miniaturized microstrip patch antennas (MPAs) that are loaded with a thin film consisting of a high relative permittivity material. The method uses a thin film to decrease the antenna's resonance frequency while keeping the antenna's patch dimensions. For the enhancement of the antenna's performance with a thin film, the dimensions of the patch of the designed antenna are optimized utilizing genetic algorithms (GAs). The resonance frequency of the microstrip patch antenna was changed from 5.8 GHz to 4.0 GHz, and the area of the proposed antenna was minimized by around 60%, especially in comparison to a conventional antenna alone without thin film. Most of the performances of the proposed antenna such as the return loss, bandwidth, and voltage standing wave ratio (VSWR) were improved. [ABSTRACT FROM AUTHOR]