Distribution systems face significant challenges in maintaining power quality issues and maximizing renewable energy hosting capacity due to the increased level of photovoltaic(PV)systems integration associated with v...Distribution systems face significant challenges in maintaining power quality issues and maximizing renewable energy hosting capacity due to the increased level of photovoltaic(PV)systems integration associated with varying loading and climate conditions.This paper provides a comprehensive overview on the exit strategies to enhance distribution system operation,with a focus on harmonic mitigation,voltage regulation,power factor correction,and optimization techniques.The impact of passive and active filters,custom power devices such as dynamic voltage restorers(DVRs)and static synchronous compensators(STATCOMs),and grid modernization technologies on power quality is examined.Additionally,this paper specifically explores machine learning and AI-driven solutions for power quality enhancement,discussing their potential to optimize system performance and facilitate renewable energy integration.Modern optimization algorithms are also discussed as effective procedures to find the settings for power system components for optimal operation,including the allocation of distributed energy resources and the tuning of control parameters.Added to that,this paper explores the methods to maximize renewable energy hosting capacity while ensuring reliable and efficient system operation.By synthesizing existing research,this review aims to provide insights into the challenges and opportunities in distribution system operation and optimization,highlighting future research directions that enhance power quality and facilitate renewable energy integration.展开更多
The diagnosis of brain tumors is an extended process that significantly depends on the expertise and skills of radiologists.The rise in patient numbers has substantially elevated the data processing volume,making conv...The diagnosis of brain tumors is an extended process that significantly depends on the expertise and skills of radiologists.The rise in patient numbers has substantially elevated the data processing volume,making conventional methods both costly and inefficient.Recently,Artificial Intelligence(AI)has gained prominence for developing automated systems that can accurately diagnose or segment brain tumors in a shorter time frame.Many researchers have examined various algorithms that provide both speed and accuracy in detecting and classifying brain tumors.This paper proposes a newmodel based on AI,called the Brain Tumor Detection(BTD)model,based on brain tumor Magnetic Resonance Images(MRIs).The proposed BTC comprises three main modules:(i)Image Processing Module(IPM),(ii)Patient Detection Module(PDM),and(iii)Explainable AI(XAI).In the first module(i.e.,IPM),the used dataset is preprocessed through two stages:feature extraction and feature selection.At first,the MRI is preprocessed,then the images are converted into a set of features using several feature extraction methods:gray level co-occurrencematrix,histogramof oriented gradient,local binary pattern,and Tamura feature.Next,the most effective features are selected fromthese features separately using ImprovedGrayWolfOptimization(IGWO).IGWOis a hybrid methodology that consists of the Filter Selection Step(FSS)using information gain ratio as an initial selection stage and Binary Gray Wolf Optimization(BGWO)to make the proposed method better at detecting tumors by further optimizing and improving the chosen features.Then,these features are fed to PDM using several classifiers,and the final decision is based on weighted majority voting.Finally,through Local Interpretable Model-agnostic Explanations(LIME)XAI,the interpretability and transparency in decision-making processes are provided.The experiments are performed on a publicly available Brain MRI dataset that consists of 98 normal cases and 154 abnormal cases.During the experiments,the dataset was divided into 70%(177 cases)for training and 30%(75 cases)for testing.The numerical findings demonstrate that the BTD model outperforms its competitors in terms of accuracy,precision,recall,and F-measure.It introduces 98.8%accuracy,97%precision,97.5%recall,and 97.2%F-measure.The results demonstrate the potential of the proposed model to revolutionize brain tumor diagnosis,contribute to better treatment strategies,and improve patient outcomes.展开更多
A review of the literature published on topics interrelated to electrochemical treatment within wastewater by using sacrificial anodes was presented. Electrocoagulation (EC) is a technique used for water and has a gre...A review of the literature published on topics interrelated to electrochemical treatment within wastewater by using sacrificial anodes was presented. Electrocoagulation (EC) is a technique used for water and has a great ability on various wastewater treatments, industrial processed water, and medical treatment. It has potential in removing various pollutants such as chemical oxygen demand turbidity, ammonia, color, and suspended solid. One of the most necessities industries is Textile industries which release large volumes of wastewater that contains different dyes. Azo dyes contain strong N = N bond which is not easily broken by conventional methods. The discharge of this type of wastewater to natural watercourse can pose serious environmental impacts to aquatic life. Electrocoagulation (EC) method depends on several factors as electrode material, current density, operation time and PH. The review describes, discusses and compares the types of that electrode influencing the EC process in various wastewater and leachate. Both operating costs and electrical energy consumption values were found to vary greatly depending on the type of electrodes material and solution being treated.展开更多
This study investigates the influence of CaO(0.5,1(wt.%))alloying on the microstructural evolution,texture development and deformation behavior of AZ61 magnesium alloy.The uniaxial tension tests at room(RT)and cryogen...This study investigates the influence of CaO(0.5,1(wt.%))alloying on the microstructural evolution,texture development and deformation behavior of AZ61 magnesium alloy.The uniaxial tension tests at room(RT)and cryogenic(CT,-150℃)temperature were performed to investigate the twinability and dislocation behavior and its consequent effect on flow stress,ductility and strain hardening rate.The results showed that the AZ61-1CaO exhibited superior strength/ductility synergy at RT with a yield strength(YS)of 223 MPa and a ductility of 23% as compared to AZ61(178 MPa,18.5%)and AZ61-0.5CaO(198 MPa,21%).Similar trend was witnessed for all the samples during CT deformation,where increase in the YS and decrease in ductility were observed.The Mtex tools based in-grain misorientation axis(IGMA)analysis of RT deformed samples revealed the higher activities of prismatic slip in AZ61-CaO,which led to superior ductility.Moreover,subsequent EBSD analysis of CT deformed samples showed the increased fraction of fine{10-12}tension twins and nucleation of multiple{10-12}twin variants caused by higher local stress concentration at the grain boundaries,which imposed the strengthening by twin-twin interaction.Lastly,the detailed investigations on strengthening contributors showed that the dislocation strengthening has the highest contribution towards strength in all samples.展开更多
This study explores the influence of Al addition on the microstructure,texture and mechanical deformation behavior of Mg-x Al-1Zn-1Ca(x=1,2 wt.%)alloy(referred as AZX211 and AZX311,respectively).Tensile tests were per...This study explores the influence of Al addition on the microstructure,texture and mechanical deformation behavior of Mg-x Al-1Zn-1Ca(x=1,2 wt.%)alloy(referred as AZX211 and AZX311,respectively).Tensile tests were performed at room(24℃,RT)and cryogenic temperature(-150℃,CT)to probe the dislocation and twinning evolution and its consequent effect on the strength,ductility and hardening characteristics.The results revealed that AZX311 exhibited an outstanding combination of superior strength and excellent ductility at both temperatures.This unique balance of high tensile strength and consistent ductility outperforms previously documented magnesium alloys,positioning AZX311 as an ideal material for applications that demand both robust mechanical properties and reliable ductility,particularly under low-temperature conditions.The exceptional strength at cryogenic temperatures in this alloy is attributed to the synergistic effect of dislocation strengthening and boundary strengthening,where the increased barriers to dislocation movement lead to significant hardening.The presence of nano-stacking faults and greater activation of pyramidal slip,along with their interactions,result in a substantial increase in tensile strength while maintaining ductility at cryogenic temperature making it a suitable fit for cryogenic applications.展开更多
In this article, a modified version of the Differential Transform Method (DTM) is employed to examine soliton pulse propagation in a weakly non-local parabolic law medium and wave propagation in optical fibers. This s...In this article, a modified version of the Differential Transform Method (DTM) is employed to examine soliton pulse propagation in a weakly non-local parabolic law medium and wave propagation in optical fibers. This semi-analytic method has the advantage of overcoming the obstacle of the hardest nonlinear terms and is used to explain the origin of the bright and dark soliton solutions through the Schrödinger equation in its non-local form and the Radhakrishnan-Kundu-Laksmannan (RKL) equation. Numerical examples demonstrate the effectiveness of this method.展开更多
The confidentiality of pseudonymous authentication and secure data transmission is essential for the protection of information and mitigating risks posed by compromised vehicles.The Internet of Vehicles has meaningful...The confidentiality of pseudonymous authentication and secure data transmission is essential for the protection of information and mitigating risks posed by compromised vehicles.The Internet of Vehicles has meaningful applications,enabling connected and autonomous vehicles to interact with infrastructure,sensors,computing nodes,humans,and fellow vehicles.Vehicular hoc networks play an essential role in enhancing driving efficiency and safety by reducing traffic congestion while adhering to cryptographic security standards.This paper introduces a privacy-preserving Vehicle-to-Infrastructure authentication,utilizing encryption and the Moore curve.The proposed approach enables a vehicle to deduce the planned itinerary of Roadside Units(RSUs)before embarking on a journey.Crucially,the Certification Authority remains unaware of the specific route design,ensuring privacy.The method involves transforming all Roadside Units(RSUs)in a region into a vector,allowing for instant authentication of a vehicle’s route using RSU information.Real-world performance evaluations affirm the effectiveness of the proposed model.展开更多
Wire arc additive manufacturing(WAAM)presents a promising approach for fabricating medium-to-large austenitic stainless steel components,which are essential in industries like aerospace,pressure vessels,and heat excha...Wire arc additive manufacturing(WAAM)presents a promising approach for fabricating medium-to-large austenitic stainless steel components,which are essential in industries like aerospace,pressure vessels,and heat exchangers.This research examines the mi-crostructural characteristics and tensile behaviour of SS308L manufactured via the gas metal arc welding-based WAAM(WAAM 308L)process.Tensile tests were conducted at room temperature(RT,25℃),300℃,and 600℃in as-built conditions.The microstructure con-sists primarily of austenite grains with retainedδ-ferrite phases distributed within the austenitic matrix.The ferrite fraction,in terms of fer-rite number(FN),ranged between 2.30 and 4.80 along the build direction from top to bottom.The ferrite fraction in the middle region is 3.60 FN.Tensile strength was higher in the horizontal oriented samples(WAAM 308L-H),while ductility was higher in the vertical ones.Tensile results show a gradual reduction in strength with increasing test temperature,in which significant dynamic strain aging(DSA)is observed at 600℃.The variation in serration behavior between the vertical and horizontal specimens may be attributed to microstructural differences arising from the build orientation.The yield strength(YS),ultimate tensile strength(UTS),and elongation(EL)of WAAM 308L at 600℃were(240±10)MPa,(442±16)MPa,and(54±2.00)%,respectively,in the horizontal orientation(WAAM 308L-H),and(248±9)MPa,(412±19)MPa,and(75±2.80)%,respectively,in the vertical orientation(WAAM 308L-V).Fracture surfaces revealed a transition from ductile dimple fracture at RT and 300℃to a mixed ductile-brittle failure with intergranular facets at 600℃.The research explores the applicability and constraints of WAAM-produced 308L stainless steel in high-temperature conditions,offering crucial in-sights for its use in thermally resistant structural and industrial components.展开更多
Detecting COVID-19 cases as early as possible became a critical issue that must be addressed to avoid the pandemic’s additional spread and early provide the appropriate treatment to the affected patients.This study a...Detecting COVID-19 cases as early as possible became a critical issue that must be addressed to avoid the pandemic’s additional spread and early provide the appropriate treatment to the affected patients.This study aimed to develop a COVID-19 diagnosis and prediction(AIMDP)model that could identify patients with COVID-19 and distinguish it from other viral pneumonia signs detected in chest computed tomography(CT)scans.The proposed system uses convolutional neural networks(CNNs)as a deep learning technology to process hundreds of CT chest scan images and speeds up COVID-19 case prediction to facilitate its containment.We employed the whale optimization algorithm(WOA)to select the most relevant patient signs.A set of experiments validated AIMDP performance.It demonstrated the superiority of AIMDP in terms of the area under the curve-receiver operating characteristic(AUC-ROC)curve,positive predictive value(PPV),negative predictive rate(NPR)and negative predictive value(NPV).AIMDP was applied to a dataset of hundreds of real data and CT images,and it was found to achieve 96%AUC for diagnosing COVID-19 and 98%for overall accuracy.The results showed the promising performance of AIMDP for diagnosing COVID-19 when compared to other recent diagnosing and predicting models.展开更多
Deformation twinning is profusely activated in the Mg alloys due to lower critical resolved shear stress(CRSS) compared to the non-basal slip systems(prismatic and pyramidal ) and plays a significant role in texture r...Deformation twinning is profusely activated in the Mg alloys due to lower critical resolved shear stress(CRSS) compared to the non-basal slip systems(prismatic and pyramidal ) and plays a significant role in texture reorientation, grain refinement and enhancement of mechanical performance. Twinning is a sequential process comprising twin nucleation, twin propagation and twin growth, hence several intrinsic and extrinsic parameters that facilitate or suppress the process have been critically reviewed. The dependence of twinning on the grain size, deformation temperature, favorable grain orientation and shear strain have been thoroughly discussed in the context of published literature and an attempt has been made to provide a benchmark conclusive finding based on the majority of works. Furthermore, the subsequent effect of twinning on the mechanical performance of Mg alloys, including ductility, formability and tension-compression asymmetry has been discussed in detail. Lastly, the stability of twins, including stress and thermal stability, is summarized and critical issues related to pertinent bottlenecks have been addressed.展开更多
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 betwee...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 non-seizure.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.展开更多
In the Internet of Things(IoT)scenario,many devices will communi-cate in the presence of the cellular network;the chances of availability of spec-trum will be very scary given the presence of large numbers of mobile u...In the Internet of Things(IoT)scenario,many devices will communi-cate in the presence of the cellular network;the chances of availability of spec-trum will be very scary given the presence of large numbers of mobile users and large amounts of applications.Spectrum prediction is very encouraging for high traffic next-generation wireless networks,where devices/machines which are part of the Cognitive Radio Network(CRN)can predict the spectrum state prior to transmission to save their limited energy by avoiding unnecessarily sen-sing radio spectrum.Long short-term memory(LSTM)is employed to simulta-neously predict the Radio Spectrum State(RSS)for two-time slots,thereby allowing the secondary node to use the prediction result to transmit its information to achieve lower waiting time hence,enhanced performance capacity.A frame-work of spectral transmission based on the LSTM prediction is formulated,named as positive prediction and sensing-based spectrum access.The proposed scheme provides an average maximum waiting time gain of 2.88 ms.The proposed scheme provides 0.096 bps more capacity than a conventional energy detector.展开更多
Metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve performance.Metamaterial antennas can overcome the bandwidth constraint associated with tiny antennas.Machine learning is receiv...Metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve performance.Metamaterial antennas can overcome the bandwidth constraint associated with tiny antennas.Machine learning is receiving a lot of interest in optimizing solutions in a variety of areas.Machine learning methods are already a significant component of ongoing research and are anticipated to play a critical role in today’s technology.The accuracy of the forecast is mostly determined by the model used.The purpose of this article is to provide an optimal ensemble model for predicting the bandwidth and gain of the Metamaterial Antenna.Support Vector Machines(SVM),Random Forest,K-Neighbors Regressor,and Decision Tree Regressor were utilized as the basic models.The Adaptive Dynamic Polar Rose Guided Whale Optimization method,named AD-PRS-Guided WOA,was used to pick the optimal features from the datasets.The suggested model is compared to models based on five variables and to the average ensemble model.The findings indicate that the presented model using Random Forest results in a Root Mean Squared Error(RMSE)of(0.0102)for bandwidth and RMSE of(0.0891)for gain.This is superior to other models and can accurately predict antenna bandwidth and gain.展开更多
This paper is concerned with a vital topic in image processing:color image forgery detection. The development of computing capabilitieshas led to a breakthrough in hacking and forgery attacks on signal, image,and data...This paper is concerned with a vital topic in image processing:color image forgery detection. The development of computing capabilitieshas led to a breakthrough in hacking and forgery attacks on signal, image,and data communicated over networks. Hence, there is an urgent need fordeveloping efficient image forgery detection algorithms. Two main types offorgery are considered in this paper: splicing and copy-move. Splicing isperformed by inserting a part of an image into another image. On the otherhand, copy-move forgery is performed by copying a part of the image intoanother position in the same image. The proposed approach for splicingdetection is based on the assumption that illumination between the originaland tampered images is different. To detect the difference between the originaland tampered images, the homomorphic transform separates the illuminationcomponent from the reflectance component. The illumination histogramderivative is used for detecting the difference in illumination, and henceforgery detection is accomplished. Prior to performing the forgery detectionprocess, some pre-processing techniques, including histogram equalization,histogram matching, high-pass filtering, homomorphic enhancement, andsingle image super-resolution, are introduced to reinforce the details andchanges between the original and embedded sections. The proposed approachfor copy-move forgery detection is performed with the Speeded Up RobustFeatures (SURF) algorithm, which extracts feature points and feature vectors. Searching for the copied partition is accomplished through matchingwith Euclidian distance and hierarchical clustering. In addition, some preprocessing methods are used with the SURF algorithm, such as histogramequalization and single-mage super-resolution. Simulation results proved thefeasibility and the robustness of the pre-processing step in homomorphicdetection and SURF detection algorithms for splicing and copy-move forgerydetection, respectively.展开更多
Multimodal medical image fusion is a powerful tool for diagnosing diseases in medical field. The main objective is to capture the relevant information from input images into a single output image, which plays an impor...Multimodal medical image fusion is a powerful tool for diagnosing diseases in medical field. The main objective is to capture the relevant information from input images into a single output image, which plays an important role in clinical applications. In this paper, an image fusion technique for the fusion of multimodal medical images is proposed based on Non-Subsampled Contourlet Transform. The proposed technique uses the Non-Subsampled Contourlet Transform (NSCT) to decompose the images into lowpass and highpass subbands. The lowpass and highpass subbands are fused by using mean based and variance based fusion rules. The reconstructed image is obtained by taking Inverse Non-Subsampled Contourlet Transform (INSCT) on fused subbands. The experimental results on six pairs of medical images are compared in terms of entropy, mean, standard deviation, Q<sup>AB/F</sup> as performance parameters. It reveals that the proposed image fusion technique outperforms the existing image fusion techniques in terms of quantitative and qualitative outcomes of the images. The percentage improvement in entropy is 0% - 40%, mean is 3% - 42%, standard deviation is 1% - 42%, Q<sup>AB/F</sup>is 0.4% - 48% in proposed method comparing to conventional methods for six pairs of medical images.展开更多
Brain neoplasms are recognized with a biopsy,which is not commonly done before decisive brain surgery.By using Convolutional Neural Networks(CNNs)and textural features,the process of diagnosing brain tumors by radiolo...Brain neoplasms are recognized with a biopsy,which is not commonly done before decisive brain surgery.By using Convolutional Neural Networks(CNNs)and textural features,the process of diagnosing brain tumors by radiologists would be a noninvasive procedure.This paper proposes a features fusion model that can distinguish between no tumor and brain tumor types via a novel deep learning structure.The proposed model extracts Gray Level Co-occurrence Matrix(GLCM)textural features from MRI brain tumor images.Moreover,a deep neural network(DNN)model has been proposed to select the most salient features from the GLCM.Moreover,it manipulates the extraction of the additional high levels of salient features from a proposed CNN model.Finally,a fusion process has been utilized between these two types of features to form the input layer of additional proposed DNN model which is responsible for the recognition process.Two common datasets have been applied and tested,Br35H and FigShare datasets.The first dataset contains binary labels,while the second one splits the brain tumor into four classes;glioma,meningioma,pituitary,and no cancer.Moreover,several performance metrics have been evaluated from both datasets,including,accuracy,sensitivity,specificity,F-score,and training time.Experimental results show that the proposed methodology has achieved superior performance compared with the current state of art studies.The proposed system has achieved about 98.22%accuracy value in the case of the Br35H dataset however,an accuracy of 98.01%has been achieved in the case of the FigShare dataset.展开更多
Skin segmentation participates significantly in various biomedical applications,such as skin cancer identification and skin lesion detection.This paper presents a novel framework for segmenting the skin.The framework ...Skin segmentation participates significantly in various biomedical applications,such as skin cancer identification and skin lesion detection.This paper presents a novel framework for segmenting the skin.The framework contains two main stages:The first stage is for removing different types of noises from the dermoscopic images,such as hair,speckle,and impulse noise,and the second stage is for segmentation of the dermoscopic images using an attention residual U-shaped Network(U-Net).The framework uses variational Autoencoders(VAEs)for removing the hair noises,the Generative Adversarial Denoising Network(DGAN-Net),the Denoising U-shaped U-Net(D-U-NET),and Batch Renormalization U-Net(Br-U-NET)for remov-ing the speckle noise,and the Laplacian Vector Median Filter(MLVMF)for removing the impulse noise.In the second main stage,the residual attention u-net was used for segmentation.The framework achieves(35.11,31.26,27.01,and 26.16),(36.34,33.23,31.32,and 28.65),and(36.33,32.21,28.54,and 27.11)for removing hair,speckle,and impulse noise,respectively,based on Peak Signal Noise Ratio(PSNR)at the level of(0.1,0.25,0.5,and 0.75)of noise.The framework also achieves an accuracy of nearly 94.26 in the dice score in the process of segmentation before removing noise and 95.22 after removing different types of noise.The experiments have shown the efficiency of the used model in removing noise according to the structural similarity index measure(SSIM)and PSNR and in the segmentation process as well.展开更多
The present work tried to estimate the runoff discharge and groundwater recharge volumes for the catchments of Ras Gharib area using the Soil Conservation Service curve number (SCS-curve number) and the water balance ...The present work tried to estimate the runoff discharge and groundwater recharge volumes for the catchments of Ras Gharib area using the Soil Conservation Service curve number (SCS-curve number) and the water balance methods. The two methods were selected among other methods used by hydrologists due to simplicity and popularity for application in arid and semi-arid areas like Egypt. The watershed delineation and streamlines for Ras Gharib region have been accomplished using ArcMap 10 GIS and the 1-arc second DEM which demonstrated three basins in the study area. The rainfall data points nearby the study area, extracted from the TRMM data, have been used as input for the Log-Pearson III distribution in order to calculate the design storm for different return periods (100, 50, 25, and 10 years). The results of applying the SCS model estimated the runoff depths as 19.86, 8.00, 2.32, and 0.06 mm for the different return periods, respectively. The total surface runoff volumes reached the study area are 34.78, 14.02, 4.07, and 0.11 Mm3, respectively for the selected return periods, whereas the total groundwater recharge volumes for the selected storm return periods are 58.16, 31.34, 18.14, 3.18 Mm3, respectively.展开更多
Direct torque control (DTC) of Switched reluctance motor is known straightforward control structure with similar execution to that of field situated control strategies. In any case, the part of ideal determination of ...Direct torque control (DTC) of Switched reluctance motor is known straightforward control structure with similar execution to that of field situated control strategies. In any case, the part of ideal determination of the voltage space vector is one of the weakest focuses in a routine DTC drive because of adjustable switching frequency and high torque ripple. In this paper, ideal choice of voltage space vectors is accomplished utilizing ANFIS (Adaptive Neuro Fuzzy Inference System) with space vector Modulation. SVM-DTC gives consistent switching frequency and the proposed ANFIS controller’s structure manages the torque and stator flux error signals through the fuzzy deduction to get a yield that takes the type of space voltage vector. Simulation results accept the proposed evolutionary system with quick torque and flux reaction with minimized torque ripple and flux ripple.展开更多
This work presents an implementation of an innovative single phase multilevel inverter using capacitors with reduced switches. The proposed Capacitor pattern H-bridge Multilevel Inverter (CPHMLI) topology consists of ...This work presents an implementation of an innovative single phase multilevel inverter using capacitors with reduced switches. The proposed Capacitor pattern H-bridge Multilevel Inverter (CPHMLI) topology consists of a proper number of Capacitor connected with switches and power sources. The advanced switching control supplied by Pulse Width Modulation (PDPWM) to attain mixed staircase switching state. The charging and discharging mode are achieved by calculating the voltage error at the load. Furthermore, to accomplish the higher voltage levels at the output with less number of semiconductors switches and simple commutation designed using CPHMLI topology. To prove the performance and effectiveness of the proposed approach, a set of experiments performed under various load conditions using MATLAB tool.展开更多
文摘Distribution systems face significant challenges in maintaining power quality issues and maximizing renewable energy hosting capacity due to the increased level of photovoltaic(PV)systems integration associated with varying loading and climate conditions.This paper provides a comprehensive overview on the exit strategies to enhance distribution system operation,with a focus on harmonic mitigation,voltage regulation,power factor correction,and optimization techniques.The impact of passive and active filters,custom power devices such as dynamic voltage restorers(DVRs)and static synchronous compensators(STATCOMs),and grid modernization technologies on power quality is examined.Additionally,this paper specifically explores machine learning and AI-driven solutions for power quality enhancement,discussing their potential to optimize system performance and facilitate renewable energy integration.Modern optimization algorithms are also discussed as effective procedures to find the settings for power system components for optimal operation,including the allocation of distributed energy resources and the tuning of control parameters.Added to that,this paper explores the methods to maximize renewable energy hosting capacity while ensuring reliable and efficient system operation.By synthesizing existing research,this review aims to provide insights into the challenges and opportunities in distribution system operation and optimization,highlighting future research directions that enhance power quality and facilitate renewable energy integration.
文摘The diagnosis of brain tumors is an extended process that significantly depends on the expertise and skills of radiologists.The rise in patient numbers has substantially elevated the data processing volume,making conventional methods both costly and inefficient.Recently,Artificial Intelligence(AI)has gained prominence for developing automated systems that can accurately diagnose or segment brain tumors in a shorter time frame.Many researchers have examined various algorithms that provide both speed and accuracy in detecting and classifying brain tumors.This paper proposes a newmodel based on AI,called the Brain Tumor Detection(BTD)model,based on brain tumor Magnetic Resonance Images(MRIs).The proposed BTC comprises three main modules:(i)Image Processing Module(IPM),(ii)Patient Detection Module(PDM),and(iii)Explainable AI(XAI).In the first module(i.e.,IPM),the used dataset is preprocessed through two stages:feature extraction and feature selection.At first,the MRI is preprocessed,then the images are converted into a set of features using several feature extraction methods:gray level co-occurrencematrix,histogramof oriented gradient,local binary pattern,and Tamura feature.Next,the most effective features are selected fromthese features separately using ImprovedGrayWolfOptimization(IGWO).IGWOis a hybrid methodology that consists of the Filter Selection Step(FSS)using information gain ratio as an initial selection stage and Binary Gray Wolf Optimization(BGWO)to make the proposed method better at detecting tumors by further optimizing and improving the chosen features.Then,these features are fed to PDM using several classifiers,and the final decision is based on weighted majority voting.Finally,through Local Interpretable Model-agnostic Explanations(LIME)XAI,the interpretability and transparency in decision-making processes are provided.The experiments are performed on a publicly available Brain MRI dataset that consists of 98 normal cases and 154 abnormal cases.During the experiments,the dataset was divided into 70%(177 cases)for training and 30%(75 cases)for testing.The numerical findings demonstrate that the BTD model outperforms its competitors in terms of accuracy,precision,recall,and F-measure.It introduces 98.8%accuracy,97%precision,97.5%recall,and 97.2%F-measure.The results demonstrate the potential of the proposed model to revolutionize brain tumor diagnosis,contribute to better treatment strategies,and improve patient outcomes.
文摘A review of the literature published on topics interrelated to electrochemical treatment within wastewater by using sacrificial anodes was presented. Electrocoagulation (EC) is a technique used for water and has a great ability on various wastewater treatments, industrial processed water, and medical treatment. It has potential in removing various pollutants such as chemical oxygen demand turbidity, ammonia, color, and suspended solid. One of the most necessities industries is Textile industries which release large volumes of wastewater that contains different dyes. Azo dyes contain strong N = N bond which is not easily broken by conventional methods. The discharge of this type of wastewater to natural watercourse can pose serious environmental impacts to aquatic life. Electrocoagulation (EC) method depends on several factors as electrode material, current density, operation time and PH. The review describes, discusses and compares the types of that electrode influencing the EC process in various wastewater and leachate. Both operating costs and electrical energy consumption values were found to vary greatly depending on the type of electrodes material and solution being treated.
基金supported by the National Research Foundation of Korea(NRF)grants funded by the Korean government(MSIT)(No.2020R1C1C1004434 and No.RS-202400398068)Incheon National University Research Grant in 2022(2022-0120)。
文摘This study investigates the influence of CaO(0.5,1(wt.%))alloying on the microstructural evolution,texture development and deformation behavior of AZ61 magnesium alloy.The uniaxial tension tests at room(RT)and cryogenic(CT,-150℃)temperature were performed to investigate the twinability and dislocation behavior and its consequent effect on flow stress,ductility and strain hardening rate.The results showed that the AZ61-1CaO exhibited superior strength/ductility synergy at RT with a yield strength(YS)of 223 MPa and a ductility of 23% as compared to AZ61(178 MPa,18.5%)and AZ61-0.5CaO(198 MPa,21%).Similar trend was witnessed for all the samples during CT deformation,where increase in the YS and decrease in ductility were observed.The Mtex tools based in-grain misorientation axis(IGMA)analysis of RT deformed samples revealed the higher activities of prismatic slip in AZ61-CaO,which led to superior ductility.Moreover,subsequent EBSD analysis of CT deformed samples showed the increased fraction of fine{10-12}tension twins and nucleation of multiple{10-12}twin variants caused by higher local stress concentration at the grain boundaries,which imposed the strengthening by twin-twin interaction.Lastly,the detailed investigations on strengthening contributors showed that the dislocation strengthening has the highest contribution towards strength in all samples.
基金National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(No.2020R1C1C1004434)funding grant of Korea Institute of Industrial Technology(KITECH)(No.KITECH UR-24-0008)。
文摘This study explores the influence of Al addition on the microstructure,texture and mechanical deformation behavior of Mg-x Al-1Zn-1Ca(x=1,2 wt.%)alloy(referred as AZX211 and AZX311,respectively).Tensile tests were performed at room(24℃,RT)and cryogenic temperature(-150℃,CT)to probe the dislocation and twinning evolution and its consequent effect on the strength,ductility and hardening characteristics.The results revealed that AZX311 exhibited an outstanding combination of superior strength and excellent ductility at both temperatures.This unique balance of high tensile strength and consistent ductility outperforms previously documented magnesium alloys,positioning AZX311 as an ideal material for applications that demand both robust mechanical properties and reliable ductility,particularly under low-temperature conditions.The exceptional strength at cryogenic temperatures in this alloy is attributed to the synergistic effect of dislocation strengthening and boundary strengthening,where the increased barriers to dislocation movement lead to significant hardening.The presence of nano-stacking faults and greater activation of pyramidal slip,along with their interactions,result in a substantial increase in tensile strength while maintaining ductility at cryogenic temperature making it a suitable fit for cryogenic applications.
文摘In this article, a modified version of the Differential Transform Method (DTM) is employed to examine soliton pulse propagation in a weakly non-local parabolic law medium and wave propagation in optical fibers. This semi-analytic method has the advantage of overcoming the obstacle of the hardest nonlinear terms and is used to explain the origin of the bright and dark soliton solutions through the Schrödinger equation in its non-local form and the Radhakrishnan-Kundu-Laksmannan (RKL) equation. Numerical examples demonstrate the effectiveness of this method.
文摘The confidentiality of pseudonymous authentication and secure data transmission is essential for the protection of information and mitigating risks posed by compromised vehicles.The Internet of Vehicles has meaningful applications,enabling connected and autonomous vehicles to interact with infrastructure,sensors,computing nodes,humans,and fellow vehicles.Vehicular hoc networks play an essential role in enhancing driving efficiency and safety by reducing traffic congestion while adhering to cryptographic security standards.This paper introduces a privacy-preserving Vehicle-to-Infrastructure authentication,utilizing encryption and the Moore curve.The proposed approach enables a vehicle to deduce the planned itinerary of Roadside Units(RSUs)before embarking on a journey.Crucially,the Certification Authority remains unaware of the specific route design,ensuring privacy.The method involves transforming all Roadside Units(RSUs)in a region into a vector,allowing for instant authentication of a vehicle’s route using RSU information.Real-world performance evaluations affirm the effectiveness of the proposed model.
基金supported by the Korea Institute of Energy Technology Evaluation and Planning(KETEP)and the Ministry of Trade,Industry&Energy(MOTIE)of the Republic of Korea program(No.RS-2025-02603127,Innovation Research Center for Zero-carbon Fuel Gas Turbine Design,Manufacture,and Safety).
文摘Wire arc additive manufacturing(WAAM)presents a promising approach for fabricating medium-to-large austenitic stainless steel components,which are essential in industries like aerospace,pressure vessels,and heat exchangers.This research examines the mi-crostructural characteristics and tensile behaviour of SS308L manufactured via the gas metal arc welding-based WAAM(WAAM 308L)process.Tensile tests were conducted at room temperature(RT,25℃),300℃,and 600℃in as-built conditions.The microstructure con-sists primarily of austenite grains with retainedδ-ferrite phases distributed within the austenitic matrix.The ferrite fraction,in terms of fer-rite number(FN),ranged between 2.30 and 4.80 along the build direction from top to bottom.The ferrite fraction in the middle region is 3.60 FN.Tensile strength was higher in the horizontal oriented samples(WAAM 308L-H),while ductility was higher in the vertical ones.Tensile results show a gradual reduction in strength with increasing test temperature,in which significant dynamic strain aging(DSA)is observed at 600℃.The variation in serration behavior between the vertical and horizontal specimens may be attributed to microstructural differences arising from the build orientation.The yield strength(YS),ultimate tensile strength(UTS),and elongation(EL)of WAAM 308L at 600℃were(240±10)MPa,(442±16)MPa,and(54±2.00)%,respectively,in the horizontal orientation(WAAM 308L-H),and(248±9)MPa,(412±19)MPa,and(75±2.80)%,respectively,in the vertical orientation(WAAM 308L-V).Fracture surfaces revealed a transition from ductile dimple fracture at RT and 300℃to a mixed ductile-brittle failure with intergranular facets at 600℃.The research explores the applicability and constraints of WAAM-produced 308L stainless steel in high-temperature conditions,offering crucial in-sights for its use in thermally resistant structural and industrial components.
文摘Detecting COVID-19 cases as early as possible became a critical issue that must be addressed to avoid the pandemic’s additional spread and early provide the appropriate treatment to the affected patients.This study aimed to develop a COVID-19 diagnosis and prediction(AIMDP)model that could identify patients with COVID-19 and distinguish it from other viral pneumonia signs detected in chest computed tomography(CT)scans.The proposed system uses convolutional neural networks(CNNs)as a deep learning technology to process hundreds of CT chest scan images and speeds up COVID-19 case prediction to facilitate its containment.We employed the whale optimization algorithm(WOA)to select the most relevant patient signs.A set of experiments validated AIMDP performance.It demonstrated the superiority of AIMDP in terms of the area under the curve-receiver operating characteristic(AUC-ROC)curve,positive predictive value(PPV),negative predictive rate(NPR)and negative predictive value(NPV).AIMDP was applied to a dataset of hundreds of real data and CT images,and it was found to achieve 96%AUC for diagnosing COVID-19 and 98%for overall accuracy.The results showed the promising performance of AIMDP for diagnosing COVID-19 when compared to other recent diagnosing and predicting models.
基金supported by the National Research Foundation of Korea (NRF)grant funded by the Korean government (MSIT) (No.2020R1C1C1004434)。
文摘Deformation twinning is profusely activated in the Mg alloys due to lower critical resolved shear stress(CRSS) compared to the non-basal slip systems(prismatic and pyramidal ) and plays a significant role in texture reorientation, grain refinement and enhancement of mechanical performance. Twinning is a sequential process comprising twin nucleation, twin propagation and twin growth, hence several intrinsic and extrinsic parameters that facilitate or suppress the process have been critically reviewed. The dependence of twinning on the grain size, deformation temperature, favorable grain orientation and shear strain have been thoroughly discussed in the context of published literature and an attempt has been made to provide a benchmark conclusive finding based on the majority of works. Furthermore, the subsequent effect of twinning on the mechanical performance of Mg alloys, including ductility, formability and tension-compression asymmetry has been discussed in detail. Lastly, the stability of twins, including stress and thermal stability, is summarized and critical issues related to pertinent bottlenecks have been addressed.
基金The authors would like to thank the support of the Taif University Researchers Supporting Project TURSP 2020/34,Taif University,Taif Saudi Arabia for supporting this work.
文摘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 non-seizure.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.
文摘In the Internet of Things(IoT)scenario,many devices will communi-cate in the presence of the cellular network;the chances of availability of spec-trum will be very scary given the presence of large numbers of mobile users and large amounts of applications.Spectrum prediction is very encouraging for high traffic next-generation wireless networks,where devices/machines which are part of the Cognitive Radio Network(CRN)can predict the spectrum state prior to transmission to save their limited energy by avoiding unnecessarily sen-sing radio spectrum.Long short-term memory(LSTM)is employed to simulta-neously predict the Radio Spectrum State(RSS)for two-time slots,thereby allowing the secondary node to use the prediction result to transmit its information to achieve lower waiting time hence,enhanced performance capacity.A frame-work of spectral transmission based on the LSTM prediction is formulated,named as positive prediction and sensing-based spectrum access.The proposed scheme provides an average maximum waiting time gain of 2.88 ms.The proposed scheme provides 0.096 bps more capacity than a conventional energy detector.
文摘Metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve performance.Metamaterial antennas can overcome the bandwidth constraint associated with tiny antennas.Machine learning is receiving a lot of interest in optimizing solutions in a variety of areas.Machine learning methods are already a significant component of ongoing research and are anticipated to play a critical role in today’s technology.The accuracy of the forecast is mostly determined by the model used.The purpose of this article is to provide an optimal ensemble model for predicting the bandwidth and gain of the Metamaterial Antenna.Support Vector Machines(SVM),Random Forest,K-Neighbors Regressor,and Decision Tree Regressor were utilized as the basic models.The Adaptive Dynamic Polar Rose Guided Whale Optimization method,named AD-PRS-Guided WOA,was used to pick the optimal features from the datasets.The suggested model is compared to models based on five variables and to the average ensemble model.The findings indicate that the presented model using Random Forest results in a Root Mean Squared Error(RMSE)of(0.0102)for bandwidth and RMSE of(0.0891)for gain.This is superior to other models and can accurately predict antenna bandwidth and gain.
文摘This paper is concerned with a vital topic in image processing:color image forgery detection. The development of computing capabilitieshas led to a breakthrough in hacking and forgery attacks on signal, image,and data communicated over networks. Hence, there is an urgent need fordeveloping efficient image forgery detection algorithms. Two main types offorgery are considered in this paper: splicing and copy-move. Splicing isperformed by inserting a part of an image into another image. On the otherhand, copy-move forgery is performed by copying a part of the image intoanother position in the same image. The proposed approach for splicingdetection is based on the assumption that illumination between the originaland tampered images is different. To detect the difference between the originaland tampered images, the homomorphic transform separates the illuminationcomponent from the reflectance component. The illumination histogramderivative is used for detecting the difference in illumination, and henceforgery detection is accomplished. Prior to performing the forgery detectionprocess, some pre-processing techniques, including histogram equalization,histogram matching, high-pass filtering, homomorphic enhancement, andsingle image super-resolution, are introduced to reinforce the details andchanges between the original and embedded sections. The proposed approachfor copy-move forgery detection is performed with the Speeded Up RobustFeatures (SURF) algorithm, which extracts feature points and feature vectors. Searching for the copied partition is accomplished through matchingwith Euclidian distance and hierarchical clustering. In addition, some preprocessing methods are used with the SURF algorithm, such as histogramequalization and single-mage super-resolution. Simulation results proved thefeasibility and the robustness of the pre-processing step in homomorphicdetection and SURF detection algorithms for splicing and copy-move forgerydetection, respectively.
文摘Multimodal medical image fusion is a powerful tool for diagnosing diseases in medical field. The main objective is to capture the relevant information from input images into a single output image, which plays an important role in clinical applications. In this paper, an image fusion technique for the fusion of multimodal medical images is proposed based on Non-Subsampled Contourlet Transform. The proposed technique uses the Non-Subsampled Contourlet Transform (NSCT) to decompose the images into lowpass and highpass subbands. The lowpass and highpass subbands are fused by using mean based and variance based fusion rules. The reconstructed image is obtained by taking Inverse Non-Subsampled Contourlet Transform (INSCT) on fused subbands. The experimental results on six pairs of medical images are compared in terms of entropy, mean, standard deviation, Q<sup>AB/F</sup> as performance parameters. It reveals that the proposed image fusion technique outperforms the existing image fusion techniques in terms of quantitative and qualitative outcomes of the images. The percentage improvement in entropy is 0% - 40%, mean is 3% - 42%, standard deviation is 1% - 42%, Q<sup>AB/F</sup>is 0.4% - 48% in proposed method comparing to conventional methods for six pairs of medical images.
基金This research was funded by Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through the project number RI-44-0190.
文摘Brain neoplasms are recognized with a biopsy,which is not commonly done before decisive brain surgery.By using Convolutional Neural Networks(CNNs)and textural features,the process of diagnosing brain tumors by radiologists would be a noninvasive procedure.This paper proposes a features fusion model that can distinguish between no tumor and brain tumor types via a novel deep learning structure.The proposed model extracts Gray Level Co-occurrence Matrix(GLCM)textural features from MRI brain tumor images.Moreover,a deep neural network(DNN)model has been proposed to select the most salient features from the GLCM.Moreover,it manipulates the extraction of the additional high levels of salient features from a proposed CNN model.Finally,a fusion process has been utilized between these two types of features to form the input layer of additional proposed DNN model which is responsible for the recognition process.Two common datasets have been applied and tested,Br35H and FigShare datasets.The first dataset contains binary labels,while the second one splits the brain tumor into four classes;glioma,meningioma,pituitary,and no cancer.Moreover,several performance metrics have been evaluated from both datasets,including,accuracy,sensitivity,specificity,F-score,and training time.Experimental results show that the proposed methodology has achieved superior performance compared with the current state of art studies.The proposed system has achieved about 98.22%accuracy value in the case of the Br35H dataset however,an accuracy of 98.01%has been achieved in the case of the FigShare dataset.
文摘Skin segmentation participates significantly in various biomedical applications,such as skin cancer identification and skin lesion detection.This paper presents a novel framework for segmenting the skin.The framework contains two main stages:The first stage is for removing different types of noises from the dermoscopic images,such as hair,speckle,and impulse noise,and the second stage is for segmentation of the dermoscopic images using an attention residual U-shaped Network(U-Net).The framework uses variational Autoencoders(VAEs)for removing the hair noises,the Generative Adversarial Denoising Network(DGAN-Net),the Denoising U-shaped U-Net(D-U-NET),and Batch Renormalization U-Net(Br-U-NET)for remov-ing the speckle noise,and the Laplacian Vector Median Filter(MLVMF)for removing the impulse noise.In the second main stage,the residual attention u-net was used for segmentation.The framework achieves(35.11,31.26,27.01,and 26.16),(36.34,33.23,31.32,and 28.65),and(36.33,32.21,28.54,and 27.11)for removing hair,speckle,and impulse noise,respectively,based on Peak Signal Noise Ratio(PSNR)at the level of(0.1,0.25,0.5,and 0.75)of noise.The framework also achieves an accuracy of nearly 94.26 in the dice score in the process of segmentation before removing noise and 95.22 after removing different types of noise.The experiments have shown the efficiency of the used model in removing noise according to the structural similarity index measure(SSIM)and PSNR and in the segmentation process as well.
文摘The present work tried to estimate the runoff discharge and groundwater recharge volumes for the catchments of Ras Gharib area using the Soil Conservation Service curve number (SCS-curve number) and the water balance methods. The two methods were selected among other methods used by hydrologists due to simplicity and popularity for application in arid and semi-arid areas like Egypt. The watershed delineation and streamlines for Ras Gharib region have been accomplished using ArcMap 10 GIS and the 1-arc second DEM which demonstrated three basins in the study area. The rainfall data points nearby the study area, extracted from the TRMM data, have been used as input for the Log-Pearson III distribution in order to calculate the design storm for different return periods (100, 50, 25, and 10 years). The results of applying the SCS model estimated the runoff depths as 19.86, 8.00, 2.32, and 0.06 mm for the different return periods, respectively. The total surface runoff volumes reached the study area are 34.78, 14.02, 4.07, and 0.11 Mm3, respectively for the selected return periods, whereas the total groundwater recharge volumes for the selected storm return periods are 58.16, 31.34, 18.14, 3.18 Mm3, respectively.
文摘Direct torque control (DTC) of Switched reluctance motor is known straightforward control structure with similar execution to that of field situated control strategies. In any case, the part of ideal determination of the voltage space vector is one of the weakest focuses in a routine DTC drive because of adjustable switching frequency and high torque ripple. In this paper, ideal choice of voltage space vectors is accomplished utilizing ANFIS (Adaptive Neuro Fuzzy Inference System) with space vector Modulation. SVM-DTC gives consistent switching frequency and the proposed ANFIS controller’s structure manages the torque and stator flux error signals through the fuzzy deduction to get a yield that takes the type of space voltage vector. Simulation results accept the proposed evolutionary system with quick torque and flux reaction with minimized torque ripple and flux ripple.
文摘This work presents an implementation of an innovative single phase multilevel inverter using capacitors with reduced switches. The proposed Capacitor pattern H-bridge Multilevel Inverter (CPHMLI) topology consists of a proper number of Capacitor connected with switches and power sources. The advanced switching control supplied by Pulse Width Modulation (PDPWM) to attain mixed staircase switching state. The charging and discharging mode are achieved by calculating the voltage error at the load. Furthermore, to accomplish the higher voltage levels at the output with less number of semiconductors switches and simple commutation designed using CPHMLI topology. To prove the performance and effectiveness of the proposed approach, a set of experiments performed under various load conditions using MATLAB tool.
