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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Shiekh, Solihah Sharief Pillay, Pragasen Department of Electrical and Computer Engineering Concordia University MontrealQC Canada

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464543

Rising recognition of the financial risks posed by sudden failures in electric machines, particularly those operating under critical conditions, underscores the need for advanced fault diagnosis. Among the various mechanical faults, eccentricity faults, especially inclined or axially non-uniform static eccentricity, remain less explored in the literature despite their practical relevance. This paper develops a mathematical model based on the modified winding function method (MWFM) for a wound rotor induction motor to address this gap. Furthermore, it explores the application of power hardware-in-the-loop (PHIL) based emulation techniques to accelerate the testing of these eccentricity faults. Experimental data validate the mathematical model and underscore the efficacy of the PHIL-based machine emulator in emulating different degrees of eccentricity fault conditions. Additionally, the study contrasts the impacts of axially uniform and nonuniform static eccentricity, providing valuable insights into the detection of these faults. © 2024 IEEE.

关键词： Induction motors

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Blockchain-Based Knowledge Repository for Training Artificial Intelligence Models: Bridging AIML with Decentralized Data

Blockchain-Based Knowledge Repository for Training Artificia...

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2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Das, Amit Kumar Tonoy, Md. Thouhedul Alam Hossain, Meherab Chandigarh University Department of Computer Engineering Mohali India United International University Department of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9798350364866

This paper presents a Blockchain-Based Knowledge Repository (BBKR) tailored for the secure storage and management of training data crucial for Artificial Intelligence and Machine Learning (AIML) models. Leveraging Ethereum blockchain technology and smart contracts, the BBKR ensures data integrity, enhances security measures, and promotes decentralization principles. The proposed mechanism includes a detailed explanation of data validation processes, decentralized access control, and scalable transaction mechanisms. Our empirical evaluations validate the efficacy of BBKR in upholding data integrity, enhancing scalability, and fortifying security provisions. The results show significant improvements in data management performance compared to traditional centralized repositories. This research underscores the transformative potential of BBKR in revolutionizing AIML data management paradigms and offers a robust foundation for further advancements in the field. Future work will explore real-world applications and potential enhancements using advanced cryptographic techniques. © 2024 IEEE.

关键词： Artificial Intelligence and Machine Learning (AIML) Blockchain blockhain-Based Knowledge Repository (BBKR) Data integrity Decentralization Ethereum Security Smart Contracts Training data

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The Distributed Intelligent Collaboration to UAV-Assisted VEC: Joint Position Optimization and Task Scheduling

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IEEE Internet of Things Journal 2025年第12期12卷 21473-21487页

作者： Yi, Meng Lee, Vincent C.S. Yang, Peng Li, Peisong Zhang, Yifan Wei, Wei Gao, Honghao Southeast University School of Computer Science and Engineering Nanjing211189 China Monash University Faculty of IT Department of DS and AI ClaytonVIC3800 Australia Southeast University School of Computer Science and Engineering the Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing211189 China Xidian University Hangzhou Institute of Technology Hangzhou311200 China Southeast University-Monash University Joint Graduate School Suzhou215123 China Xi'an University of Technology School of Computer Science and Engineering Xi'an710048 China Shanghai University School of Computer Engineering and Science Shanghai200444 China

Deploying Unmanned Aerial Vehicles (UAVs) as aerial base stations enhances the coverage and performance of communication networks in Vehicular Edge Computing (VEC) scenarios. However, due to the limited communication range and energy capacity of UAVs, they cannot continuously cover entire areas or sustain long flights. Therefore, achieving full communication coverage of a target area with a minimal number of UAVs and efficient task offloading remains a significant challenge. To address this problem, the UAV-assisted Two-stage Intelligent Collaboration (UTIC) method is proposed in this paper to tackle the joint position optimization and task scheduling issue. Firstly, a UAV-assisted Two-stage Task Scheduling (UTTS) system model is designed to optimize the allocation process. Secondly, an Enhanced Particle Swarm Optimization (E-PSO) algorithm is designed to determine the optimal positions of UAVs, ensuring complete coverage of all mobile vehicles (MVs) with the minimum number of UAVs. Thirdly, Deep Deterministic Policy Gradient (DDPG) method is employed to find the optimal scheduling decisions for MVs, considering energy consumption, delay, and task priorities. Simulation results demonstrate that the proposed UTIC method can achieve nearly 20% reduction in UAV deployment and outperform three other classical reinforcement learning (RL) algorithms in terms of reducing system cost. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Incident Analysis of Decentralized Finance 6

Incident Analysis of Decentralized Finance

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6th IEEE International Conference on Blockchain and Cryptocurrency, ICBC 2024

作者： Lin, Cheng-Chieh Tsai, Chia-Cheng Liao, Shih-Wei National Cheng Kung University Department of Computer Science and Information Engineering Taiwan National Taiwan University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350316742

Decentralized Finance (DeFi) has emerged as a transformative force in the financial landscape, bringing about challenges in ensuring blockchain security. This paper systematically examines prominent DeFi incidents from June 2022 to May 2023. Our findings underscore the significance of continuous vigilance in DeFi operations. © 2024 IEEE.

关键词： Decentralized finance

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Analysis of Eye Disease classification by Fundus Images using Different Machine/Deep/Transfer Learning Techniques 4

Analysis of Eye Disease classification by Fundus Images usin...

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4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Sharma, Himanshu Wasim, Javed Sharma, Pankaj Mangalayatan University Computer Science & Engineering Uttar Pradesh India Mangalayatan University Department of Computer Engineering and Applications Uttar Pradesh India Eshan College of Engineering Computer Science & Engineering Mathura India

ISBN: (纸本)9798350307757

The fundus image is a crucial diagnostic tool for diseases like diabetic retinopathy (DR) because it catches a retina, a back of an eye. This issue is most prevalent among diabetics and a leading contributor to blindness, particularly in young adults and those engaged in the workforce. Detecting DR in its early stages is crucial in reducing the likelihood of blindness in patients. An ophthalmologist performed DR screening and looked for lesions on the fundus imaging. Intelligent systems have the ability to make early identification of DR more efficient, especially with the recent advancements in machine learning in healthcare. This study compares and contrasts many methods for detecting DR in fundus images. This study lays out methods for improving upon pre-trained architectures such that they can correctly classify fundus images, allowing for early detection and prediction of diabetes. A concatenate model is presented in this comparative study as a method for classifying fundus images using ML and deep/transfer learning. Various model architectures are used, including SVM, Gradient Boosting, Efficient Net, Efficient-Net B4 and B7, MLP, CNN, ResNet, VGG-19, and DNN, among others. The models' accuracy, F1-score, recall, and precision are measured in experimental findings that compare them. Our strategies outperform a single model with results ranging from 84% to 96% accuracy, respectively. They found that our ML- DL-TL methods work well for automated DR classification employing fundus image data in this experiment. © 2024 IEEE.

关键词： Intelligent systems

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Resist Denial-of-service Attack and Enhance Network Security with Protocol-independent Packet Processor-Based Two-Layer Firewall 4

Resist Denial-of-service Attack and Enhance Network Security...

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4th IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2024

作者： Chou, Pin-Lin Wei, Hsin-Wen Lee, Wei-Tsong Tamkang University Department of Electrical and Computer Engineering New Taipei Taiwan

ISBN: (纸本)9798350360721

We introduced a Protocol-independent Packet Processor (P4)-based two-layer firewall with a detection system for identifying denial-of-service (DDoS) attacks. P4-based switch enables real-time packet inspection in the Data Plane by reducing latency associated with packet filtering. The system enhances network security through dynamic traffic-blocking rules trained by machine learning modeling. Using Software-Defined Networking (SDN), the system seamlessly operates with or without current SDN protocols and dynamically adapts to changing threats. The simulation results showed that the proposed approach enhanced network security in the continuously evolving internet environment. © 2024 IEEE.

关键词： Software defined networking

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Advancing Agricultural Sustainability Through an AI Powered Classification Framework of Plant Pests and Diseases 13th

Advancing Agricultural Sustainability Through an AI Powered...

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13th Mining Humanistic Data Workshop, MHDW 2024, 9th Workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2024 and 1st Workshop on AI in Applications for Achieving the Green Deal Targets, AI4GD 2024 held as parallel events of the IFIP WG 12.5 International Workshops on Artificial Intelligence Applications and Innovations, AIAI 2024

作者： Hasan, Hadi Al Kakoun, Razan Massaad, Gaby Awad, Mariette Department of Electrical and Computer Engineering American University of Beirut Beirut Lebanon

ISBN: (纸本)9783031632266

Plants pests and diseases can cause significant economic losses for farmers, and accurate identification and classification of these issues is crucial. Deep learning techniques, have demonstrated promising results in correctly identifying and classifying plant pests and diseases. However, the accuracy of these models heavily relies on the quality and size of the training dataset. Gathering a large and diverse dataset of plant pests and diseases can be challenging due to the variation in the appearance of diseased plants and the high cost of data collection. To tackle this issue, a web portal has been developed to gather datasets of plant pests and diseases, making it simpler for researchers to contribute data and enhance the accuracy of these models. In addition, Generative Adversarial Networks (GANs) have been used to expand the current datasets by creating synthetic images that closely resemble real-world data. This has resulted in improved accuracy and robustness of CNN and YOLO models in identifying and classifying plant pests and diseases. © IFIP International Federation for Information Processing 2024.

关键词： Deep learning

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Resilient control design for EV charging unit with multiple modules consisting of single active bridge (SAB) converters 63

Resilient control design for EV charging unit with multiple ...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Manasis, Apostolos S. Papageorgiou, Panos C. Konstantopoulos, George C. University of Patras Department of Electrical and Computer Engineering Rion26504 Greece

ISBN: (纸本)9798350316339

Fast charging stations for electric vehicles (EVs) often consist of charging units with multiple modules connected in parallel to achieve high power ratings and can suffer from cyber-attacks in the modern smart grid architecture or measurement/sensor/communication failure. In this manuscript, an EV charging unit consisting of multiple single active bridge (SAB) converters connected in parallel is examined and a new nonlinear resilient controller is suggested to realize accurate power sharing among the converters with inherent current limitation, while enhancing the entire charging unit resilience under irregular scenarios. By taking into account the dynamic model of the parallel SAB converters and using invariant set and nonlinear ultimate boundedness theory, it is proven that the current injected to the EV battery by each converter is limited below a maximum value at all times. Furthermore, based on the novel dynamic structure of the controller, it is shown that when an unexpected event (e.g. setpoint attack, sensor fault) occurs at one or more converters, the inherent current-limiting property protects the corresponding devices and the remaining non-compromised units automatically share the remaining power to the battery, thus enhancing the EV charger resilience. An EV charging unit with 4 modules is simulated to verify the effectiveness and increased resilience of the proposed control approach under setpoint attacks and sensor/measurement faults. © 2024 IEEE.

关键词： Charging stations

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A comparative study of recommender systems in different domains 4th

A comparative study of recommender systems in different doma...

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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Rohit Sharma, Aditi Kumar, Aditya Singh, Prabal Singh, Ashutosh Kumar Department of Computer Science and Engineering Institute of Engineering and Rural Technology Prayagraj India

ISBN: (纸本)9781032911571

Recommender systems are techniques designed to enhance user experience in various domains. They suggest relevant items to users based on their behavior and preferences (Linyuan et al. Feb 2012). These systems are being extensively utilized in domains such as e-commerce, social media, and video streaming platforms. A recommender system aims to provide personalized recommendations to users based on their past behavior, preferences, and other similar data. The recommender system receives input data from users. Several recommender systems are available nowadays, making it challenging to select one. It would be beneficial to conduct a comparative analysis of various recommendation systems. The research paper attempts to conclude the performance of various recommender systems by using a variety of evaluation metrics. Evaluation metrics provide insights into the effectiveness of the recommendation algorithms. This guides the selection of the most appropriate model for a given application (Oona et al. 2024). The comparative study aims to examine and analyze different recommender systems across various domains. We evaluate the performance of these systems in improving user experiences and increasing user interaction. By studying the performance of recommender systems in different domains, we gain insights into the strengths and limitations of each approach. © 2025 the Author(s).

关键词： collaborative content-based evaluation metric hybrid recommendation recommender

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Exploring Alzheimer's disease:a comprehensive brain connectome-based survey

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Psychoradiology 2024年第1期4卷 43-57页

作者： Lu Zhang Junqi Qu Haotian Ma Tong Chen Tianming Liu Dajiang Zhu Department of Computer Science and Engineering The University of Texas at ArlingtonArlingtonTX 76019USA Department of Computer Science The University of GeorgiaAthensGA 30602USA

Dementia is an escalating global health challenge,with Alzheimer's disease(AD)at its *** evidence highlights the accumulation of AD-related pathological proteins in specific brain regions and their subsequent dissemination throughout the broader area along the brain network,leading to disruptions in both individual brain regions and their *** a comprehensive understanding of the neurodegeneration-brain network link is lacking,it is undeniable that brain networks play a pivotal role in the development and progression of *** thoroughly elucidate the intricate network of elements and connections constituting the human brain,the concept of the brain connectome was *** based on the connectome holds immense potential for revealing the mechanisms underlying disease development,and it has become a prominent topic that has attracted the attention of numerous *** this review,we aim to systematically summarize studies on brain networks within the context of AD,critically analyze the strengths and weaknesses of existing methodologies,and offer novel perspectives and insights,intending to serve as inspiration for future research.

关键词： Alzheimer’s disease brain-connectome graph theory deep graph neural networks

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