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16th Annual Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2025

作者： Sarshar, Mohammad Reza Kondelaji, Mohammad Amin Jalali Mirsalim, Mojtaba Montazeri, Seyed Hamid Amirkabir University of Technology Tehran Polytechnic Department of Electrical Engineering Tehran Iran Department of Electrical Engineering and Computer Science Cambridge United States

ISBN: (纸本)9798331533946

This article studies a new stator-permanent magnet (PM) motor with flux-switching (FS) and flux-reversal (FR) effects synergies. The proposed structure benefits from the splitting stator pole PMs and consequent-pole FRPM, which reduces the PM utilization compared to the conventional ones. Also, the proposed structure is optimized by a random search multiobjective algorithm to attain the structure with the highest output torque and minimum torque ripple. Additionally, the proposed motor is compared to a conventional FSPM to investigate the advantages of the main motor. Using finite element analysis (FEA) studies, the flux density distribution results show that the FR effect lowers the saturation risk compared to FSPM. Also, the back-EMF generation of the proposed motor has improved by 135% compared to its benchmark. Correspondingly, fullload results show that the proposed topology has enhanced the average torque by 75% and deteriorated the ripple by about 55% compared to FSPM. Therefore, the proposed structure offers a profitable for high torque density applications. © 2025 IEEE.

关键词： Permanent magnets

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DemoCraft: Using In-Context Learning to Improve Code Generation in Large Language Models 3

DemoCraft: Using In-Context Learning to Improve Code Generat...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, electrical and Electronics, IITCEE 2025

作者： Joshua, Kapu Nirmal Sreejith, Mihit Indian Institute of Technology Department of Electrical Engineering Kanpur India Indian Institute of Technology Department of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798331515911

Producing executable code from natural-language directives via Large Language Models (LLMs) involves obstacles like semantic uncertainty and the requirement for task-focused context interpretation. To resolve these difficulties, we introduce DemoCraft, a framework that refines code generation through in-context learning and demonstration selection, in tandem with latent concept learning. Latent concept learning appends extra concept tokens, which act as trainable embeddings encapsulating task-oriented insights. We evaluate our approach on two promi- nent datasets: MBPP and Humaneval. Our experimental findings reveal that this method attains roughly a 2x boost in the pass@k metric compared to baseline models. Moreover, we propose two new evaluation measurements: correctness@k and similarity@k. Empirical analyses indicate that our system achieves nearly a 3x advancement in these measures as well. © 2025 IEEE.

关键词： Semantics

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Semi‑supervised contour‑driven broad learning system for autonomous segmentation of concealed prohibited baggage items

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Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 1-18页

作者： Divya Velayudhan Abdelfatah Ahmed Taimur Hassan Muhammad Owais Neha Gour Mohammed Bennamoun Ernesto Damiani Naoufel Werghi Department of Electrical Engineering and Computer Sciences Center for Cyber‑Physical SystemsKhalifa University of Science and TechnologyAbu Dhabi 127788United Arab Emirates Department of Electrical Computer and Biomedical EngineeringAbu Dhabi UniversityAbu Dhabi 59911United Arab Emirates Department of Computer Science and Software Engineering the University of Western AustraliaPerthWA 6009Australia

With the exponential rise in global air traffic,ensuring swift passenger processing while countering potential security threats has become a paramount concern for aviation *** X-ray baggage monitoring is now standard,manual screening has several limitations,including the propensity for errors,and raises concerns about passenger *** address these drawbacks,researchers have leveraged recent advances in deep learning to design threatsegmentation ***,these models require extensive training data and labour-intensive dense pixelwise annotations and are finetuned separately for each dataset to account for inter-dataset ***,this study proposes a semi-supervised contour-driven broad learning system(BLS)for X-ray baggage security threat instance segmentation referred to as *** research methodology involved enhancing representation learning and achieving faster training capability to tackle severe occlusion and class imbalance using a single training routine with limited baggage *** proposed framework was trained with minimal supervision using resource-efficient image-level labels to localize illegal items in multi-vendor baggage *** specifically,the framework generated candidate region segments from the input X-ray scans based on local intensity transition cues,effectively identifying concealed prohibited items without entire baggage *** multi-convolutional BLS exploits the rich complementary features extracted from these region segments to predict object categories,including threat and benign *** contours corresponding to the region segments predicted as threats were then utilized to yield the segmentation *** proposed C-BLX system was thoroughly evaluated on three highly imbalanced public datasets and surpassed other competitive approaches in baggage-threat segmentation,yielding 90.04%,78.92%,and 59.44%in terms of mIoU on GDXray,SIXray,and Compass-XP,***,the lim

关键词： Baggage X-ray imagery Broad learning systems Threat detection Threat segmentation

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Build to Win: electrical Hardware Opportunities for STEM Education 14

Build to Win: Electrical Hardware Opportunities for STEM Edu...

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14th IEEE Integrated STEM Education Conference, ISEC 2024

作者： Monagle, Daniel Ponce, Eric A. Leeb, Steven B. Saathoff, Erik K. Brooks, Dylan E. Krause, Thomas C. Green, Daisy H. Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science United States University of Hawai'i Department of Electrical and Computer Engineering Manoa United States

ISBN: (纸本)9798350352801

engineering outreach and introductory courses are essential for motivating and training the next generation of capable engineers. Accessibility and portability of the infrastructure for a STEM course is critical for spreading STEM education and motivation efforts. The ubiquity of student personal computers and laptops provides a convenient platform for introducing computer programming, but the relatively cumbersome equipment deemed necessary for a hands-on electronics learning environment contributes to the lack of electronic circuit hardware exposure for many K-12 students and university undergraduates. This paper presents the relatively low-cost, versatile, electronics platform we have developed at MIT upon which our new, hands-on introductory STEM electrical engineering course has been built and taught in recent years. This paper focuses its discussion on the power supply and measurement systems we have produced that allow students and instructors to engage in hands-on electric circuit exercises and projects without the requirement for expensive laboratory infrastructure. Example demonstrations of introductory laboratory exercises using our platform are shown. © 2024 IEEE.

关键词： Students

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Dynamic Evolutionary Game-based Modeling,Analysis and Performance Enhancement of Blockchain Channels

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IEEE/CAA Journal of Automatica Sinica 2023年第1期10卷 188-202页

作者： PeiYun Zhang MengChu Zhou ChenXi Li Abdullah Abusorrah IEEE the School of Computer Science Nanjing University of Information Science&TechnologyNanjing 210044China the Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA the School of Computer and Information Anhui Normal UniversityWuhu 241003China the Department of Electrical and Computer Engineering Faculty of Engineeringand Center of Research Excellence in Renewable Energy and Power SystemsKing Abdulaziz UniversityJeddah 21481Saudi Arabia

The recent development of channel technology has promised to reduce the transaction verification time in blockchain *** transactions are transmitted through the channels created by nodes,the nodes need to cooperate with each *** one party refuses to do so,the channel is unstable.A stable channel is thus *** nodes may show uncooperative behavior,they may have a negative impact on the stability of such *** order to address this issue,this work proposes a dynamic evolutionary game model based on node *** model considers various defense strategies'cost and attack success ratio under *** can dynamically adjust their strategies according to the behavior of attackers to achieve their effective *** equilibrium stability of the proposed model can be *** proposed model can be applied to general channel *** is compared with two state-of-the-art blockchain channels:Lightning network and Spirit *** experimental results show that the proposed model can be used to improve a channel's stability and keep it in a good cooperative stable *** its use enables a blockchain to enjoy higher transaction success ratio and lower transaction transmission delay than the use of its two peers.

关键词： Blockchain channel network evolutionary game malicious behavior secure computing stability analysis

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Automatic emotion recognition using deep neural network

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Multimedia Tools and Applications 2025年 1-30页

作者： Sujatha, R. Chatterjee, Jyotir Moy Pathy, Baibhav Hu, Yu-Chen School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of CSE Graphic Era University Dehradun India School of Electrical Engineering Vellore Institute of Technology Vellore India Department of Computer Science Tunghai University Taichung City Taiwan

Emotions are a vital semantic part of human correspondence. Emotions are significant for human correspondence as well as basic for human–computer cooperation. Viable correspondence between people is possibly achieved when both the importance and the emotion of the correspondence are perceived by all groups included. Understanding the significance of language has generally been concentrated on in natural language processing (NLP) as a semantic examination. In NLP, the text can be handled appropriately for classification. Emotion detection from facial emotion is the subfield of social signal processing applied in a wide assortment of regions, explicitly for human and PC collaboration. Many researchers have proposed various approaches, generally utilizing machine learning concepts. Automatic emotion recognition (AER) is significant for working with consistent intuitiveness between a person and a smart device toward fully acknowledging an intelligent society. Many researchers examined cross-lingual and multilingual speech emotion as a stage toward language-free emotion acknowledgment in natural speech. In the present work, we are proposing a deep learning-based AER system using four openly accessible datasets, namely Basic Arabic Vocal Emotions Dataset (BAVED), Acted Emotional Speech Dynamic Database (AESDD), Urdu written in Latin/Roman Script (URDU), and Toronto Emotional Speech Set (TESS), by utilizing the Jupyter notebook and a Python library for music and audio synthesis named Librosa. The experimental results exhibited that the proposed approach achieves better than the existing approaches, i.e., the accuracy of the proposed system with the URDU dataset is 96.24%, the TESS dataset is 99.10%, the AESDD dataset is 65.97%, and the BAVED dataset is 73.12%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semantics

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Unveiling Marketing Potential: Harnessing Advanced Analytics and Machine Learning for Gold Membership Strategy Optimization in a Superstore

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SN computer Science 2024年第4期5卷 374页

作者： Singh Mahala, Vikas Ranveer Garg, Neeraj Saxena, D. Kumar, Rajesh Department of Electrical Engineering Swami Keshvanand Institute of Technology Management & amp Department of Computer Science and Engineering Swami Keshvanand Institute of Technology Management & amp Department of Electrical Engineering Malaviya National Institute of Technology Rajasthan Jaipur 302017 India

This research paper presents a comprehensive case study conducted in a superstore, introducing a novel gold membership offer and employing sophisticated analytics and machine learning methodologies to identify potential customers. The primary objective of this study is to explore available data to discern the factors influencing customers’ responses to a new supermarket offering. Subsequently, a predictive model is developed to accurately gauge the likelihood of a favorable customer response. In pursuit of enhancing marketing strategies and bolstering sales, this study employs a suite of machine learning techniques, including decision trees, support vector machines, random forests, and XGBoost. Furthermore, the study incorporates metaheuristic optimization algorithms such as grey wolf optimization, slime mold algorithm, multi-verse optimizer, and particle swarm optimization to fine-tune hyperparameters of the machine learning models. These optimization algorithms serve as effective search mechanisms, facilitating the identification of optimal solutions and significantly improving classification performance in the context of the complex superstore problem. The research findings highlight the substantial impact of the metaheuristic strategy, specifically grey wolf optimization, on the performance of all machine learning models. Notably, the random forest model achieved the highest accuracy of 95% with the application of grey wolf optimization. Moreover, the decision tree model demonstrated remarkable improvement in accuracy following hyperparameter tuning with grey wolf optimization. Collectively, these results underscore the critical role of metaheuristic optimization in enhancing the performance of machine learning models for marketing strategies in the superstore industry. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Decision tree GWO Hyperparameter MVO Optimization PSO Random Forest SMA Supermarket SVM XGBoost

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KGFakeNet: A Knowledge Graph-Enhanced Model for Fake News Detection 31

KGFakeNet: A Knowledge Graph-Enhanced Model for Fake News De...

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2025 Workshop on Generative AI and Knowledge Graphs, GenAIK 2025, co-located with the 31st International Conference on Computational Linguistics, COLING 2025

作者： Kumar, Anuj Kumar, Pardeep Yadav, Abhishek Ahlawat, Satyadev Prasad, Yamuna Department of Computer Science & Engineering Indian Institute of Technology Jammu India Department of Electrical Engineering Indian Institute of Technology Jammu India

ISBN: (纸本)9798891762121

The proliferation of fake news on social media has intensified the spread of misinformation, promoting societal biases, hate, and violence. While recent advancements in Generative AI (GenAI), particularly large language models (LLMs), have shown promise, these models often need more structured representation for accurate verification, as they rely on pre-trained data patterns without access to real-time or validated information. This study presents a framework that utilizes Open Information Extractor 6 (OpenIE6) to extract triplet relationships (subject-predicate-object) from statements and justifications to compute the cosine similarity between the Knowledge Graphs (KGs) of the statements and their supporting justification to precisely measure the relevance and alignment between them. This similarity feature is integrated with an attention mechanism over GenAI-generated embeddings to enhance the model’s ability to capture semantic features accurately. In addition, a Multi-Layer Perceptron (MLP) classifier is employed to integrate all features, resulting in a 4% improvement in accuracy and a 5% increase in F1-score over state-of-the-art LLM-based approaches. ©2025 International Committee on Computational Linguistics (ICCL)

关键词： Knowledge graph

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Sliding mode control of three-phase AC/DC converters using exponential rate reaching law

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Journal of Systems engineering and Electronics 2022年第1期33卷 210-221页

作者： JEERANANTASIN Narin NUNGAM Suksun Department of Electrical and Computer Engineering Faculty of EngineeringKing Mongkut’s University of Technology North BangkokBangkok 10800Thailand

Sliding mode control(SMC) becomes a common tool in designing robust nonlinear control systems, due to its inherent characteristics such as insensitivity to system uncertainties and fast dynamic *** modes are involved in the SMC operation, namely reaching mode and sliding *** the reaching mode, the system state is forced to reach the sliding surface in a finite *** major drawback of the SMC approach is the occurrence of chattering in the sliding mode, which is undesirable in most ***, the trade-off between chattering reduction and fast reaching time must be considered in the conventional SMC *** paper proposes SMC design with a novel reaching law called the exponential rate reaching law(ERRL) to reduce chattering, and the control structure of the converter is designed based on the multiinput SMC that is applied to a three-phase AC/DC power *** simulation and experimental results show the effectiveness of the proposed technique.

关键词： exponential rate reaching law(ERRL) multi-input sliding mode control(SMC) chattering reduction three-phase AC-to-DC converter

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Fusion of color and hallucinated depth features for enhanced multimodal deep learning-based damage segmentation

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Earthquake engineering and engineering Vibration 2023年第1期22卷 55-68页

作者： Tarutal Ghosh Mondal Mohammad Reza Jahanshahi Civil Architectural and Environmental EngineeringMissouri University of Science and TechnologyRollaMOUSA Lyles School of Civil Engineering Purdue UniversityWest LafayetteINUSA School of Electrical and Computer Engineering Purdue UniversityWest LafayetteINUSA

Recent advances in computer vision and deep learning have shown that the fusion of depth information can significantly enhance the performance of RGB-based damage detection and segmentation ***,alongside the advantages,depth-sensing also presents many practical *** instance,the depth sensors impose an additional payload burden on the robotic inspection platforms limiting the operation time and increasing the inspection ***,some lidar-based depth sensors have poor outdoor performance due to sunlight contamination during the *** this context,this study investigates the feasibility of abolishing depth-sensing at test time without compromising the segmentation *** autonomous damage segmentation framework is developed,based on recent advancements in vision-based multi-modal sensing such as modality hallucination(MH)and monocular depth estimation(MDE),which require depth data only during the model *** the time of deployment,depth data becomes expendable as it can be simulated from the corresponding RGB *** makes it possible to reap the benefits of depth fusion without any depth perception per *** study explored two different depth encoding techniques and three different fusion strategies in addition to a baseline RGB-based *** proposed approach is validated on computer-generated RGB-D data of reinforced concrete buildings subjected to seismic *** was observed that the surrogate techniques can increase the segmentation IoU by up to 20.1%with a negligible increase in the computation ***,this study is believed to make a positive contribution to enhancing the resilience of critical civil infrastructure.

关键词： multimodal data fusion depth sensing vision-based inspection UAV-assisted inspection damage segmentation post-disaster reconnaissance modality hallucination monocular depth estimation

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