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A Mathematical and Dynamical Model for Analyzing H-Shaped PM Vernier Motor for Electric Motorcycle Mid-Drive Applications

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IEEE Transactions on Energy Conversion 2025年第1期40卷 529-543页

作者： Ghods, Mehrage Faiz, Jawad Bazrafshan, Mohammad Amin Gorginpour, Hamed Toulabi, Mohammad Sedigh University of Tehran School of Electrical and Computer Engineering College of Engineering Tehran*** Iran Persian Gulf University Department of Intelligent Systems Engineering and Data Science Bushehr*** Iran Oakland University Department of Electrical and Computer Engineering RochesterMI48309 United States

The permanent magnet (PM) Vernier machines enhance torque density and decrease cogging torque compared to conventional permanent magnet synchronous motor. This paper presents a novel fractional-slot H-shaped PM Vernier machines, which incorporates unique design for the PM housing and rotor core construction. These designs are specifically optimized for the small-scale electric vehicles application. The choice of the PM arrangement is based on the combination of V-shaped and spoke-array PM topologies. The aim is to achieve a higher torque density than V-shaped PM machines, while reducing cogging torque compared to spoke-array PM machines. By using this methodology, leakage fluxes in the terminal region of the PMs reduce, resulting in an augmentation of both flux-linkage and power factor. A prototype 680 W motor was fabricated with 12 slots and 8 poles to verify the accuracy and effectiveness of the proposed model. Mathematical model and dynamical analysis are proposed for the analytical prediction of machine performance. Due to the unique shape feature of the machine, the conformal mapping approach is applied to design the rotor core. To validate the model, the predicted performance of the motor was compared with those obtained by finite element analysis and experiments. © 1986-2012 IEEE.

关键词： Permanent magnets

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A graph-based method for identity resolution to assist police force investigative process

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Journal of Cyber Security Technology 2025年第2期9卷 127-150页

作者： Amirhosseini, Mohammad Hossein Kazemian, Hassan Phillips, Michael Department of Computer Science and Digital Technologies University of East London London United Kingdom Intelligent Systems Research Centre School of Computing and Digital Media London Metropolitan University London United Kingdom

The ability to prove an individual identity has become crucial in social, economic, and legal aspects of life. Identity resolution is the process of semantic reconciliation that determines whether a single identity is the same when being described differently. This paper introduces a novel graph-based methodology for identity resolution, designed to reconcile identities by analysing the similarity of attribute values associated with different identities within a policing dataset. The proposed methodology employs graph analysis techniques, including centrality measurement and community detection, to enhance the identity resolution process. This paper also presents a new identity model for identity resolution. SPIRIT policing dataset was used for testing the proposed methodology. This dataset is an anonymised dataset used in the SPIRIT project funded by EU Horizon. It contains 892 identity records and among these, two ’known’ identities utilize different names but actually represent the same individual. The presented method successfully recognised these two identities. Additionally, another experimental evaluation was conducted on a refined and extended version of the dataset and the false identities were successfully detected. This method can assist police forces in identifying criminals and fraudsters using fake identities and has applications across finance, marketing, and customer service. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Statistical tests

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intelligent Optimization Under Multiple Factories: Hybrid Flow Shop Scheduling Problem with Blocking Constraints Using an Advanced Iterated Greedy Algorithm

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Complex System Modeling and Simulation 2023年第4期3卷 282-306页

作者： Yong Wang Yuting Wang Yuyan Han Junqing Li Kaizhou Gao Yusuke Nojima School of Computer Science Liaocheng UniversityLiaocheng 252000China School of Computer Science Shandong Normal UniversityJinan 252000China Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China Department of Computer Science and Intelligent Systems Osaka Prefecture UniversityOsaka 599-8531Japan

The distributed hybrid flow shop scheduling problem(DHFSP),which integrates distributed manufacturing models with parallel machines,has gained significant ***,in actual scheduling,some adjacent machines do not have buffers between them,resulting in *** paper focuses on addressing the DHFSP with blocking constraints(DBHFSP)based on the actual production *** solve DBHFSP,we construct a mixed integer linear programming(MILP)model for DBHFSP and validate its correctness using the Gurobi ***,an advanced iterated greedy(AIG)algorithm is designed to minimize the makespan,in which we modify the Nawaz,Enscore,and Ham(NEH)heuristic to solve blocking *** balance the global and local search capabilities of AIG,two effective inter-factory neighborhood search strategies and a swap-based local search strategy are ***,each factory is mutually independent,and the movement within one factory does not affect the *** view of this,we specifically designed a memory-based decoding method for insertion operations to reduce the computation time of the ***,two shaking strategies are incorporated into the algorithm to mitigate premature *** advanced algorithms are used to conduct comparative experiments with AIG on 80 test instances,and experimental results illustrate that the makespan and the relative percentage increase(RPI)obtained by AIG are 1.0%and 86.1%,respectively,better than the comparative algorithms.

关键词： blocking distributed hybrid flow shop neighborhood search iterated greedy algorithm

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Deep Learning-Based ECG Classification for Arterial Fibrillation Detection

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computers, Materials & Continua 2024年第6期79卷 4805-4824页

作者： Muhammad Sohail Irshad Tehreem Masood Arfan Jaffar Muhammad Rashid Sheeraz Akram Abeer Aljohani Faculty of Computer Science&Information Technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54000Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of Computer Science Applied CollegeTaibah UniversityMedina42353Saudi Arabia

The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant *** and timely diagnosis increases the patient’s chances of ***,issues like overfitting and inconsistent accuracy across datasets remain *** a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib *** aim was to create a robust detection mechanism that consistently performs *** such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for *** findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated *** demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib *** insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG *** comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib ***,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.

关键词： Convolution neural network atrial fibrillation area under curve ECG false positive rate deep learning classification

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Understanding the Definitions of Microcredentials in Higher Education: Systematic Literature Review

Understanding the Definitions of Microcredentials in Higher ...

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2024 International Conference on TVET Excellence and Development, ICTeD 2024

作者： Wihendro Warnars, Harco Leslie Hendric Spits Prabowo, Harjanto Sfenrianto Bina Nusantara University BINUS Graduate Program- Computer Science Information Systems Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program- Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Business School Undergraduate Program Management Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program-Master of Information Systems Management Information Systems Management Department Jakarta Indonesia

ISBN: (纸本)9798331516413

The growing focus on microcredentials emphasizes the urgent need for precise and widely accepted definitions, as existing uncertainties hinder their effective implementation. This research aims to investigate the comprehension of microcredentials definitions in the context of higher education by conducting a systematic literature review. The goal is to identify current definitions of microcredentials to facilitate standardization initiatives. Following The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) methodology to systematically search in Scopus database within 2019-2024 resulting 940 articles, by removing the duplicates, 280 articles were initially reviewed, with 29 selected for detailed thematic analysis based on specific inclusion and exclusion criteria. By adapting the six phases of thematic analysis, selected articles were identified analyzed, organized, described, and themes found the data set were reported. Key findings reveal that, while a universally accepted definition is lacking, common themes emerge: microcredentials are competency-based, modular, portable, stackable, and often include quality assurance. These attributes highlight microcredentials as valuable for upskilling and reskilling in a flexible manner. The study concludes that establishing standard definitions can increase the recognition and utility of microcredentials across educational and professional sectors and recommends further research to strengthen core elements for consistency. © 2024 IEEE.

关键词： Competency-based Higher Education Microcredentials Modular Reskilling Upskilling

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Towards Generalization of Tactile Image Generation: Reference-Free Evaluation in a Leakage-Free Setting

arXiv

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arXiv 2025年

作者： Gungor, Cagri Eppinger, Derek Kovashka, Adriana Intelligent Systems Program Department of Computer Science University of Pittsburgh United States

Tactile sensing, which relies on direct physical contact, is critical for human perception and underpins applications in computer vision, robotics, and multimodal learning. Because tactile data is often scarce and costly to acquire, generating synthetic tactile images provides a scalable solution to augment real-world measurements. However, ensuring robust generalization in synthesizing tactile images—capturing subtle, material-specific contact features—remains challenging. We demonstrate that overlapping training and test samples in commonly used datasets inflate performance metrics, obscuring the true generalizability of tactile models. To address this, we propose a leakage-free evaluation protocol coupled with novel, reference-free metrics—TMMD, I-TMMD, CI-TMMD, and D-TMMD—tailored for tactile generation. Moreover, we propose a vision-to-touch generation method that leverages text as an intermediate modality by incorporating concise, material-specific descriptions during training to better capture essential tactile features. Experiments on two popular visuo-tactile datasets, Touch and Go and HCT, show that our approach achieves superior performance and enhanced generalization in a leakage-free setting. © 2025, CC BY.

关键词： Information leakage

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Mobile Application to Help Abandoned Pets 3

Mobile Application to Help Abandoned Pets

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3rd IEEE International Conference on Blockchain and Distributed systems Security, ICBDS 2024

作者： Rahajeng, Salicca Dewi Nurfitriyani, Siti Julianingsih Akbar, Zakin Izzani Leonardo, Calvin Warnars, Harco Leslie Hendric Spits Warnars, Laurens Spits Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350354348

As social beings, humans like to interact with each other, including other creatures of God, such as animals, and keep them as pets. Many pets, such as dogs, cats, birds, fish, rabbits, and so on, include unusual pets such as reptiles, pigs, insects, etc. As a result of the lack of human awareness of pets and the nature of keeping animals, which is only for fun, it is not soulful to keep animals. So when people are bored, they abandon their pets, which causes new problems in human life today. In this case, we created a mobile application that can be shared with pet lovers where they can share information about abandoned pets and the possibility of owning pets from these abandoned pets. Mobile applications were designed using use case diagrams to show business processes, class diagrams to show database models, and user interfaces to present a similar interface to users. This mobile application will become a platform that can gather all people who care about pets and make pets a part of humans. © 2024 IEEE.

关键词： Mobile applications

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Classification and Comprehension of Software Requirements Using Ensemble Learning

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computers, Materials & Continua 2024年第8期80卷 2839-2855页

作者： Jalil Abbas Arshad Ahmad Syed Muqsit Shaheed Rubia Fatima Sajid Shah Mohammad Elaffendi Gauhar Ali School of Computer Science and Technology Anhui UniversityHefei230039China School of Computing Sciences Pak Austria FachhochschuleInstitute of Applied Sciences and TechnologyHaripur22620Pakistan Department of Computer Science and IT University of LahoreLahore55150Pakistan Department of Computer Science Emerson UniversityPunjabMultan60000Pakistan EIAS(Emerging Intelligent Autonomous Systems)Data Science Lab Prince Sultan UniversityRiyad12435Saudi Arabia

The software development process mostly depends on accurately identifying both essential and optional ***,user needs are typically expressed in free-form language,requiring significant time and human resources to translate these into clear functional and non-functional *** address this challenge,various machine learning(ML)methods have been explored to automate the understanding of these requirements,aiming to reduce time and human ***,existing techniques often struggle with complex instructions and large-scale *** our study,we introduce an innovative approach known as the Functional and Non-functional Requirements Classifier(FNRC).By combining the traditional random forest algorithm with the Accuracy Sliding Window(ASW)technique,we develop optimal sub-ensembles that surpass the initial classifier’s accuracy while using fewer *** results demonstrate that our FNRC methodology performs robustly across different datasets,achieving a balanced Precision of 75%on the PROMISE dataset and an impressive Recall of 85%on the CCHIT *** datasets consistently maintain an F-measure around 64%,highlighting FNRC’s ability to effectively balance precision and recall in diverse *** findings contribute to more accurate and efficient software development processes,increasing the probability of achieving successful project outcomes.

关键词： Ensemble learning machine learning non-functional requirements requirement engineering accuracy sliding window

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The optimization of deep learning classification through the comparison between different algorithm by using LABR 28th

The optimization of deep learning classification through the...

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28th International Conference on Knowledge Based and intelligent information and Engineering systems, KES 2024

作者： Kadhem, Rasha Mallat, Souheyl Zrigui, Mounir Department of Computer Science Faculty of Sciences of Monastir University of Monastir Tunisia Research Laboratory in Algebra Numbers theory and Intelligent Systems Monastir Tunisia Department of Computer Science University of ALQadisiyah Iraq

The deep learning model depends on the specific requirements of our recommender system and the characteristics of the Arabic textual data we are working with it Consider experimenting with multiple models to determine which one performs best for our particular use case, in our case we used Large Arabic Book Review (LABR) is a large sentiment analysis dataset for the Arabic language. It consists of over 63,000 book reviews and Other Datasets such as Amazon Products reviews that we can translate to Arabic and then use for the task, each dataset are tested by three models of deep learning (CNN, RNN,LSTM)and classified by three method (rating, polarity, balance polarity)classification for three type of feature extraction (Bow, TF-IDF, word embedding) after do train 70% and test 30% we get based on the results and the task at hand, if CNN (Convolutional Neural Network) has proven in both training and testing phases to be the most effective among LSTM (Long Short-Term Memory) and RNN (Recurrent Neural Network). © 2024 The Authors.

关键词： Convolutional neural networks

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Internet of Things Challenges and the Emerging Technology of TinyML 19

Internet of Things Challenges and the Emerging Technology of...

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19th Annual International Conference on Distributed Computing in Smart systems and the Internet of Things, DCOSS-IoT 2023

作者： Tsoukas, Vasileios Gkogkidis, Anargyros Kakarountas, Athanasios University of Thessaly Intelligent Systems Laboratory Department of Computer Science and Biomedical Informatics Lamia Greece

ISBN: (纸本)9798350346497

The merger of Internet of Things (IoT) and Machine Learning undoubtedly offers several benefits, but due to the nature of IoT devices and their underlying design, a number of difficulties and challenges exist. The most fundamental of these has to do with the security dangers posed by the transmission of voluminous amounts of data, as well as several networking obstacles, such as delay and bandwidth constraints. Moving all processing close to the network's edge and preventing the transmission of data to third parties, such as the cloud or other devices, is a potential solution. Embedded Machine Learning, and TinyML in particular, is a new technology that allows the implementation of Machine Learning on hardware with limited resources, such as Microcontrollers and FPGAs. In this paper, the fundamental issues of IoT devices are examined, the TinyML technology's advantages are outlined, and a summary of the most recent TinyML apps and systems is offered. © 2023 IEEE.

关键词： Microcontrollers

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