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A Comprehensive Review on Charging Topologies and Power Electronic Converter Solutions for Electric Vehicles

Journal of Modern Power Systems and Clean Energy 2024年第3期12卷 675-694页

作者： Abdelfatah Ali Hossam H.H.Mousa Mostafa F.Shaaban Maher A.Azzouz Ahmed S.A.Awad the department of Electrical engineering American University of Sharjah the department of Electrical engineering South Valley University the department of Electrical and computer engineering University of Windsor Electrical engineering department Qatar University the department of Electrical and computer engineering Sultan Qaboos University

Electric vehicles(EVs) are becoming more popular worldwide due to environmental concerns, fuel security, and price volatility. The performance of EVs relies on the energy stored in their batteries, which can be charged using either AC(slow) or DC(fast) chargers. Additionally, EVs can also be used as mobile power storage devices using vehicle-to-grid(V2G) technology. Power electronic converters(PECs) have a constructive role in EV applications, both in charging EVs and in V2G. Hence, this paper comprehensively investigates the state of the art of EV charging topologies and PEC solutions for EV applications. It examines PECs from the point of view of their classifications, configurations, control approaches, and future research prospects and their impacts on power quality. These can be classified into various topologies: DC-DC converters, AC-DC converters, DC-AC converters, and AC-AC converters. To address the limitations of traditional DC-DC converters such as switching losses, size, and high-electromagnetic interference(EMI), resonant converters and multiport converters are being used in high-voltage EV applications. Additionally, power-train converters have been modified for high-efficiency and reliability in EV applications. This paper offers an overview of charging topologies, PECs, challenges with solutions, and future trends in the field of the EV charging station applications.

关键词： Topology DC-DC power converters Vehicle-to-grid Electric vehicle charging Batteries Transportation Electromagnetic interference

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Revisiting non-linear Thomson scattering with metamaterials

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National Science Review 2024年第1期11卷 71-72页

作者： Natalia M.Litchinitser department of Electrical and computer engineering Duke University

Non-linear optics is a branch of optics that studies the intriguing and sometimes unexpected ways in which light and matter interact at high intensities, when the polarization density does not respond linearly to the electric field of the light. The pursuit of the perfect non-linear optical material has been ongoing ever since the pioneering experiment on second harmonic generation carried out by Franken in 1961 [1]. Indeed,

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Advances in Manta Ray Foraging Optimization:A Comprehensive Survey

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Journal of Bionic engineering 2024年第2期21卷 953-990页

作者： Farhad Soleimanian Gharehchopogh Shafi Ghafouri Mohammad Namazi Bahman Arasteh department of computer engineering Urmia BranchIslamic Azad UniversityUrmiaIran department of computer engineering Tabriz BranchIslamic Azad UniversityTabrizIran department of computer engineering Maybod BranchIslamic Azad UniversityMaybodIran department of Software engineering Faculty of Engineering and Natural ScienceIstinye UniversityIstanbulTurkey

This paper comprehensively analyzes the Manta Ray Foraging Optimization(MRFO)algorithm and its integration into diverse academic fields.Introduced in 2020,the MRFO stands as a novel metaheuristic algorithm,drawing inspiration from manta rays’unique foraging behaviors—specifically cyclone,chain,and somersault foraging.These biologically inspired strategies allow for effective solutions to intricate physical challenges.With its potent exploitation and exploration capabilities,MRFO has emerged as a promising solution for complex optimization problems.Its utility and benefits have found traction in numerous academic sectors.Since its inception in 2020,a plethora of MRFO-based research has been featured in esteemed international journals such as IEEE,Wiley,Elsevier,Springer,MDPI,Hindawi,and Taylor&Francis,as well as at international conference proceedings.This paper consolidates the available literature on MRFO applications,covering various adaptations like hybridized,improved,and other MRFO variants,alongside optimization challenges.Research trends indicate that 12%,31%,8%,and 49%of MRFO studies are distributed across these four categories respectively.

关键词： Manta ray foraging optimization Metaheuristic algorithms Hybridization Improved Optimization

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Electron beam lithography on nonplanar and irregular surfaces

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Microsystems & Nanoengineering 2024年第2期10卷 1-23页

作者： Chenxu Zhu Huseyin Ekinci Aixi Pan Bo Cui Xiaoli Zhu department of Electrical and computer engineering and Waterloo Institute for Nanotechnology(WIN) University of WaterlooWaterlooONCanada

E-beam lithography is a powerful tool for generating nanostructures and fabricating nanodevices with fine features approaching a few nanometers in size.However,alternative approaches to conventional spin coating and development processes are required to optimize the lithography procedure on irregular surfaces.In this review,we summarize the state of the art in nanofabrication on irregular substrates using e-beam lithography.To overcome these challenges,unconventional methods have been developed.For instance,polymeric and nonpolymeric materials can be sprayed or evaporated to form uniform layers of electron-sensitive materials on irregular substrates.Moreover,chemical bonds can be applied to help form polymer brushes or self-assembled monolayers on these surfaces.In addition,thermal oxides can serve as resists,as the etching rate in solution changes after e-beam exposure.Furthermore,e-beam lithography tools can be combined with cryostages,evaporation systems,and metal deposition chambers for sample development and lift-off while maintaining low temperatures.Metallic nanopyramids can be fabricated on an AFM tip by utilizing ice as a positive resistor.Additionally,Ti/Au caps can be patterned around a carbon nanotube.Moreover,3D nanostructures can be formed on irregular surfaces by exposing layers of anisole on organic ice surfaces with a focused e-beam.These advances in e-beam lithography on irregular substrates,including uniform film coating,instrumentation improvement,and new pattern transferring method development,substantially extend its capabilities in the fabrication and application of nanoscale structures.

关键词： lithography beam coating

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A blockchain based privacy-preserving federated learning scheme for Internet of Vehicles

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Digital Communications and Networks 2024年第1期10卷 126-134页

作者： Naiyu Wang Wenti Yang Xiaodong Wang Longfei Wu Zhitao Guan Xiaojiang Du Mohsen Guizani School of Control and computer engineering North China Electric Power UniversityChina department of Mathematics&computer Science Fayettevlle State UniversityFayetteilleNCUSA department of Electrical and computer engineering Stevens Institute of TechnologyHobokenNJ07030USA department of computer Science and engineering Qatar UniversityDohaQatar

The application of artificial intelligence technology in Internet of Vehicles(lov)has attracted great research interests with the goal of enabling smart transportation and traffic management.Meanwhile,concerns have been raised over the security and privacy of the tons of traffic and vehicle data.In this regard,Federated Learning(FL)with privacy protection features is considered a highly promising solution.However,in the FL process,the server side may take advantage of its dominant role in model aggregation to steal sensitive information of users,while the client side may also upload malicious data to compromise the training of the global model.Most existing privacy-preserving FL schemes in IoV fail to deal with threats from both of these two sides at the same time.In this paper,we propose a Blockchain based Privacy-preserving Federated Learning scheme named BPFL,which uses blockchain as the underlying distributed framework of FL.We improve the Multi-Krum technology and combine it with the homomorphic encryption to achieve ciphertext-level model aggregation and model filtering,which can enable the verifiability of the local models while achieving privacy-preservation.Additionally,we develop a reputation-based incentive mechanism to encourage users in IoV to actively participate in the federated learning and to practice honesty.The security analysis and performance evaluations are conducted to show that the proposed scheme can meet the security requirements and improve the performance of the FL model.

关键词： Federated learning Blockchain Privacy-preservation Homomorphic encryption Internetof vehicles

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Relational Turkish Text Classification Using Distant Supervised Entities and Relations

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computers, Materials & Continua 2024年第5期79卷 2209-2228页

作者： Halil Ibrahim Okur Kadir Tohma Ahmet Sertbas department of computer engineering Faculty of Engineering and Natural SciencesIskenderun Technical UniversityHatay31200Turkey department of computer engineering Faculty of EngineeringIstanbul University-CerrahpasaIstanbul34310Turkey

Text classification,by automatically categorizing texts,is one of the foundational elements of natural language processing applications.This study investigates how text classification performance can be improved through the integration of entity-relation information obtained from the Wikidata(Wikipedia database)database and BERTbased pre-trained Named Entity Recognition(NER)models.Focusing on a significant challenge in the field of natural language processing(NLP),the research evaluates the potential of using entity and relational information to extract deeper meaning from texts.The adopted methodology encompasses a comprehensive approach that includes text preprocessing,entity detection,and the integration of relational information.Experiments conducted on text datasets in both Turkish and English assess the performance of various classification algorithms,such as Support Vector Machine,Logistic Regression,Deep Neural Network,and Convolutional Neural Network.The results indicate that the integration of entity-relation information can significantly enhance algorithmperformance in text classification tasks and offer new perspectives for information extraction and semantic analysis in NLP applications.Contributions of this work include the utilization of distant supervised entity-relation information in Turkish text classification,the development of a Turkish relational text classification approach,and the creation of a relational database.By demonstrating potential performance improvements through the integration of distant supervised entity-relation information into Turkish text classification,this research aims to support the effectiveness of text-based artificial intelligence(AI)tools.Additionally,it makes significant contributions to the development ofmultilingual text classification systems by adding deeper meaning to text content,thereby providing a valuable addition to current NLP studies and setting an important reference point for future research.

关键词： Text classification relation extraction NER distant supervision deep learning machine learning

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WebFLex:A Framework for Web Browsers-Based Peer-to-Peer Federated Learning Systems Using WebRTC

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computers, Materials & Continua 2024年第3期78卷 4177-4204页

作者： Mai Alzamel Hamza Ali Rizvi Najwa Altwaijry Isra Al-Turaiki department of computer Science College of Computer and Information SciencesKing Saud UniversityRiyadhKingdom of Saudi Arabia department of computer Science and engineering Punjab Engineering CollegeChandigarhIndia

Scalability and information personal privacy are vital for training and deploying large-scale deep learning models.Federated learning trains models on exclusive information by aggregating weights from various devices and taking advantage of the device-agnostic environment of web browsers.Nevertheless,relying on a main central server for internet browser-based federated systems can prohibit scalability and interfere with the training process as a result of growing client numbers.Additionally,information relating to the training dataset can possibly be extracted from the distributed weights,potentially reducing the privacy of the local data used for training.In this research paper,we aim to investigate the challenges of scalability and data privacy to increase the efficiency of distributed training models.As a result,we propose a web-federated learning exchange(WebFLex)framework,which intends to improve the decentralization of the federated learning process.WebFLex is additionally developed to secure distributed and scalable federated learning systems that operate in web browsers across heterogeneous devices.Furthermore,WebFLex utilizes peer-to-peer interactions and secure weight exchanges utilizing browser-to-browser web real-time communication(WebRTC),efficiently preventing the need for a main central server.WebFLex has actually been measured in various setups using the MNIST dataset.Experimental results show WebFLex’s ability to improve the scalability of federated learning systems,allowing a smooth increase in the number of participating devices without central data aggregation.In addition,WebFLex can maintain a durable federated learning procedure even when faced with device disconnections and network variability.Additionally,it improves data privacy by utilizing artificial noise,which accomplishes an appropriate balance between accuracy and privacy preservation.

关键词： Federated learning web browser privacy deep learning

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Optimal monitoring and attack detection of networks modeled by Bayesian attack graphs

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Cybersecurity 2024年第1期7卷 1-15页

作者： Armita Kazeminajafabadi Mahdi Imani department of Electrical and computer engineering Northeastern UniversityBostonMAUSA

Early attack detection is essential to ensure the security of complex networks,especially those in critical infrastructures.This is particularly crucial in networks with multi-stage attacks,where multiple nodes are connected to external sources,through which attacks could enter and quickly spread to other network elements.Bayesian attack graphs(BAGs)are powerful models for security risk assessment and mitigation in complex networks,which provide the probabilistic model of attackers’behavior and attack progression in the network.Most attack detection techniques developed for BAGs rely on the assumption that network compromises will be detected through routine monitoring,which is unrealistic given the ever-growing complexity of threats.This paper derives the optimal minimum mean square error(MMSE)attack detection and monitoring policy for the most general form of BAGs.By exploiting the structure of BAGs and their partial and imperfect monitoring capacity,the proposed detection policy achieves the MMSE optimality possible only for linear-Gaussian state space models using Kalman filtering.An adaptive resource monitoring policy is also introduced for monitoring nodes if the expected predictive error exceeds a user-defined value.Exact and efficient matrix-form computations of the proposed policies are provided,and their high performance is demonstrated in terms of the accuracy of attack detection and the most efficient use of available resources using synthetic Bayesian attack graphs with different topologies.

关键词： Multi-stage attacks Bayesian attack graph Attack detection Optimal monitoring

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Transfer Learning-Based Class Imbalance-Aware Shoulder Implant Classification from X-Ray Images

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Journal of Bionic engineering 2024年第2期21卷 892-912页

作者： Marut Jindal Birmohan Singh department of computer Science and engineering Sant Longowal Institute of Engineering and TechnologySangrur148106PunjabIndia

Total shoulder arthroplasty is a standard restorative procedure practiced by orthopedists to diagnose shoulder arthritis in which a prosthesis replaces the whole joint or a part of the joint.It is often challenging for doctors to identify the exact model and manufacturer of the prosthesis when it is unknown.This paper proposes a transfer learning-based class imbalance-aware prosthesis detection method to detect the implant’s manufacturer automatically from shoulder X-ray images.The framework of the method proposes a novel training approach and a new set of batch-normalization,dropout,and fully convolutional layers in the head network.It employs cyclical learning rates and weighting-based loss calculation mechanism.These modifications aid in faster convergence,avoid local-minima stagnation,and remove the training bias caused by imbalanced dataset.The proposed method is evaluated using seven well-known pre-trained models of VGGNet,ResNet,and DenseNet families.Experimentation is performed on a shoulder implant benchmark dataset consisting of 597 shoulder X-ray images.The proposed method improves the classification performance of all pre-trained models by 10–12%.The DenseNet-201-based variant has achieved the highest classification accuracy of 89.5%,which is 10%higher than existing methods.Further,to validate and generalize the proposed method,the existing baseline dataset is supplemented to six classes,including samples of two more implant manufacturers.Experimental results have shown average accuracy of 86.7%for the extended dataset and show the preeminence of the proposed method.

关键词： Biomedical engineering Artificial intelligence Total shoulder arthroplasty Prosthesis identification Biomedical image classification

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Robust Malicious Executable Detection Using Host-Based Machine Learning Classifier

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computers, Materials & Continua 2024年第4期79卷 1419-1439页

作者： Khaled Soliman Mohamed Sobh Ayman M.Bahaa-Eldin department of computer and Systems engineering Ain Shams UniversityCairo11517Egypt department of computer engineering Technology ElSewedy University of TechnologyCairo44629Egypt

The continuous development of cyberattacks is threatening digital transformation endeavors worldwide and leadsto wide losses for various organizations. These dangers have proven that signature-based approaches are insufficientto prevent emerging and polymorphic attacks. Therefore, this paper is proposing a Robust Malicious ExecutableDetection (RMED) using Host-based Machine Learning Classifier to discover malicious Portable Executable (PE)files in hosts using Windows operating systems through collecting PE headers and applying machine learningmechanisms to detect unknown infected files. The authors have collected a novel reliable dataset containing 116,031benign files and 179,071 malware samples from diverse sources to ensure the efficiency of RMED approach.The most effective PE headers that can highly differentiate between benign and malware files were selected totrain the model on 15 PE features to speed up the classification process and achieve real-time detection formalicious executables. The evaluation results showed that RMED succeeded in shrinking the classification timeto 91 milliseconds for each file while reaching an accuracy of 98.42% with a false positive rate equal to 1.58. Inconclusion, this paper contributes to the field of cybersecurity by presenting a comprehensive framework thatleverages Artificial Intelligence (AI) methods to proactively detect and prevent cyber-attacks.

关键词： Portable executable malware intrusion detection cybersecurity zero-day threats Host IntrusionDetection System(HIDS) machine learning Anomaly-based Intrusion Detection System(AIDS) deep learning

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