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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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SkinSage - Lesion Diagnosis Using Deep Learning Techniques 14

SkinSage - Lesion Diagnosis Using Deep Learning Techniques

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14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Geetika Bhardwaj, Paarth Nimish Bhargava, Vaibhav Singh, Shivendra Amity School of Engineering and Technology Department of Computer Science and Engineering Noida201301 India

ISBN: (纸本)9798350344837

The alarming rise in instances of skin cancer in recent years, one of the most prevalent malignancies worldwide, emphasises how important early and accurate identification is. The SkinSage project, which combines the precision of deep learning models and technologies. Earlier, dermatologists relied on eye exams to spot skin lesions. But with datasets like HAM10000 and digital photography, it became easy to record a variety of skin conditions. SkinSage uses cutting-edge neural architectures like CNN, ResNet, DenseNet, and InceptionV3 to recognise patterns and nuances that are much above the capabilities of the human eye. In addition to being a computational marvel, the SkinSage project also exemplifies accessibility by making its analytical capabilities available via a smartphone application in future. High-quality skin lesion diagnostics will be accessible to everyone thanks to this platform, regardless of location or degree of training. With the confluence of these developments, SkinSage becomes more than just a tool for diagnosing skin cancer;it signals a paradigm shift that has the potential to democratise early detection and save countless lives in the future. © 2024 IEEE.

关键词： Deep learning

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A Multimodal Multiobjective Evolutionary Algorithm for Filter Feature Selection in Multilabel Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4428-4442页

作者： Hancer, Emrah Xue, Bing Zhang, Mengjie Victoria University of Wellington Center for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand Mehmet Akif Ersoy University Department of Software Engineering Burdur15039 Turkey

Multilabel learning is an emergent topic that addresses the challenge of associating multiple labels with a single instance simultaneously. Multilabel datasets often exhibit high dimensionality with noisy, irrelevant, and redundant features. In recent years, multilabel feature selection (MLFS) has gained prominence as a crucial and emerging machine learning task due to its ability to handle such data effectively. However, existing approaches for MLFS often prioritize top-ranked features based on intrinsic data criteria, disregarding relationships within the feature subset. Additionally, compared with conventional feature selection, multiobjective evolutionary algorithms (MOEAs) have not been widely explored in the context of MLFS. This study aims to address these gaps by proposing a multimodal multiobjective evolutionary algorithm (MMOEA) called MMDE_SICD which incorporates a preelimination scheme, an improved initialization scheme, an exploration scheme inspired by genetic operations and a statistically inspired crowding distance scheme. The results show that the proposed MMDE_SICD algorithm can outperform a variety of MOEAs and MMOEAs as well as conventional MLFS algorithms. Notably, this study is the first of its kind to consider MLFS as a multimodal multiobjective problem. © 2020 IEEE.

关键词： Multiobjective optimization

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Machine Learning Approaches to Securing IoT Networks: A Comprehensive Review of Recent Advances 2

Machine Learning Approaches to Securing IoT Networks: A Comp...

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2nd IEEE International Conference on Advancements and Key Challenges in Green Energy and Computing, AKGEC 2024

作者： Saini, Harnit Prasad, Sanjeev Kumar Galgotias University School of Computer Science and Engineering Greater Noida India Ajay Kumar Garg Engineering College Computer Science and Engineering Department Ghaziabad India

ISBN: (纸本)9798350390179

The IoT is a network of physical objects embedded with sensors and software, connecting and exchanging data over the internet. With over 7 billion connected devices, experts predict growth to 10 billion by 2020 and 22 billion by 2025. Machine learning ML technologies are advancing quickly, with new algorithms and approaches emerging regularly. In this paper, a review is carried out that helps provide insights into their effectiveness in the current landscape. Different ML techniques are categorized and evaluated based on how they address diverse challenges. Reviewing current ML techniques helps in understanding their strengths and limitations in combating novel attack vectors. Later on, a comparative analysis of these techniques is conducted based on performance metrics. A comprehensive review identifies gaps in current research and practice, highlighting areas where ML techniques might need further development or improvement. © 2024 IEEE.

关键词： Adversarial machine learning

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Scaling Up the Transformers: A Survey of Training and Inference Optimization Techniques

Scaling Up the Transformers: A Survey of Training and Infere...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Jahangir, Hadya Goel, Shobhit Kumar Khurana, Shiraz Sharda School of Engineering and Technology Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350378092

Transformer models have shown great success in Natural Language Processing (NLP) tasks, but they come with unique challenges such as the vanishing gradient, unbalanced gradient, and computational costs. To overcome these challenges, various optimization techniques have been developed including regularization, parallelism, and hybrid architectures. In this survey paper, we review the recent advances in optimizing transformers for NLP tasks and highlight their applications on weakly labelled data and low-resource settings. We discuss how to improve the performance of Transformers on Convolutional Neural Network (CNN)-Transformer models, which leverage both CNN and Transformer architectures. Furthermore, we explore techniques specifically designed for weak supervision and low-resource scenarios such as Segment-wise training, Cluster-based attention mechanism, and Transfer learning from a teacher model to a student model. We also highlight some recent works on Sound Event Detection using CNN-Transformer, which leverages the strength of CNNs for feature extraction and Transformers for long range dependencies. Overall, this paper provides an overview of the challenges faced during optimization of transformers in NLP tasks and offers insights into the state-of-the-art techniques to overcome these challenges. © 2024 IEEE.

关键词： Convolutional neural networks

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Long-Short Term Memory decision model for device argumentation in ambient assisted living

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Journal of Ambient Intelligence and Humanized Computing 2024年第12期15卷 4073-4083页

作者： Kumar, G. S. Madhan ShivaPrakash, S.P. Krinkin, Kirill Shichkina, Yulia A. Department of Information Science and Engineering JSS Science and Technology University Karnataka Mysore570006 India School of Computer Science and Engineering Constructor University Bremen Germany Department of Technologies of Artificial Intelligence in Physiology and Medicine Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia

The global elderly population is on the rise, posing significant challenges in both developed and developing nations. These challenges include issues related to mobility, falls, and the performance of daily tasks. However, the demands of today’s fast-paced lifestyles make it difficult to provide constant care for the elderly, driving them towards a preference for independent living. To address this need for independent living, the concept of Ambient assisted living (AAL) has been developed. AAL is an intelligent system that employs sensors and devices to monitor and assist elderly individuals in their daily activities. Nevertheless, conflicts can arise when multiple sensors report conflicting activities concurrently, leading to device argumentation. To tackle this issue, this paper introduces a novel Long Short-Term Memory (LSTM)-based learning technique for aiding decision-making during device argumentation concerning activity occurrences. The LSTM model classifies user activities and sensor status values, employing an innovative approach to identify device argumentation within these classified activities. This LSTM decision-making (LDM) model effectively resolves device argumentation and accurately identifies the target activity. An experiment using a combined dataset from the ARAS and CASE datasets demonstrates the superiority of the proposed model, achieving an accuracy of 90.30%, precision of 85.66%, recall of 95.23%, and an F1-Score of 90.22%. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Intelligent systems

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A Hybrid Approach to Dynamic Thread Allocation Driven by Workload and Task Prioritization

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SN computer science 2025年第3期6卷 1-19页

作者： Abirami, K. Harini, N. Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham Coimbatore India

In today’s rapidly changing world, cloud service providers face numerous challenges in managing resources and meeting customer demands. To address these challenges, cloud service providers should prioritize the tasks and allocate threads efficiently. By allocating threads efficiently, service providers can optimize resource utilization and reduce response times and job completion times which in turn improves customer satisfaction. We propose a hybrid model that integrates the threshold-driven thread Balancer (TDTB) for dynamic thread allocation with a Customer Retention Model (CRM) to prioritize tasks based on customer behaviour. Our model initiates the establishment of a thread pool configured with a dynamic threshold based on workload (on-demand requests). Concurrently, a queue to handle incoming customer requests is set up alongside a CRM priority queue designed for task prioritization. Our model calculates Job duration and completion times for n priority classes. Crown consumers experience shorter durations and completion times compared to other priority classes. Tasks are completed in descending order of priority. The results of thorough experimentation clearly show that crown consumer tasks are highly satisfied. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Customer segmentation Response time Service differentiation Thread allocation Throughput Workload

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Caterpillar: A Pure-MLP Architecture with Shifted-Pillars-Concatenation 24

Caterpillar: A Pure-MLP Architecture with Shifted-Pillars-Co...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Sun, Jin Shi, Xiaoshuang Wang, Zhiyuan Xu, Kaidi Shen, Heng Tao Zhu, Xiaofeng School of Computer Science and Engineering University of Electronic Science and Technology of China SiChuan Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Tsinghua University SiChuan Chengdu China Department of Computer Science Drexel University Philadelphia United States School of Computer Science and Engineering University of Electronic Science and Technology of China Tongji University SiChuan Chengdu China

ISBN: (纸本)9798400706868

Modeling in computer Vision has evolved to MLPs. Vision MLPs naturally lack local modeling capability, to which the simplest treatment is combined with convolutional layers. Convolution, famous for its sliding window scheme, also suffers from this scheme of redundancy and lower parallel computation. In this paper, we seek to dispense with the windowing scheme and introduce a more elaborate and parallelizable method to exploit locality. To this end, we propose a new MLP module, namely Shifted-Pillars-Concatenation (SPC), that consists of two steps of processes: (1) Pillars-Shift, which generates four neighboring maps by shifting the input image along four directions, and (2) Pillars-Concatenation, which applies linear transformations and concatenation on the maps to aggregate local features. SPC module offers superior local modeling power and performance gains, making it a promising alternative to the convolutional layer. Then, we build a pure-MLP architecture called Caterpillar by replacing the convolutional layer with the SPC module in a hybrid model of sMLPNet. Extensive experiments show Caterpillar's excellent performance on both small-scale and ImageNet-1k classification benchmarks, with remarkable scalability and transfer capability possessed as well. The code is available at https://***/sunjin19126/Caterpillar. © 2024 ACM.

关键词： Convolution

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Extending Scratch Framework to Improve the Analytical Skills of Undergraduate Students 24

Extending Scratch Framework to Improve the Analytical Skills...

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10th International Conference on computer Technology Applications, ICCTA 2024

作者： Akbar, Ubaid Ul Akbar, Saeed Ali, Mumtaz Ullah, Rahmat Khan, Rizwan Lopes, Ivandro Ortet Department of Computer Science City University of Science and Information Technology Peshawar Pakistan School of Computer Science and Technology Zhejiang Normal University Jinhua321004 China School of Computer Science and Electronic Engineering University of Essex United Kingdom School of Cyber Science & Engineering Huazhong University of Science & Technology Wuhan China

ISBN: (纸本)9798400716386

Scratch is an innovative and the most popular block-based Visual Programming Language designed for beginners to learn programming effectively. However, it lacks the capacity to allow users to learn and solve larger real-world problems such as the Travelling Salesman Problem (TSP). Hence, there is a need for an accessible and effective tool to assist beginners and novice programmers in learning and tackling the TSP. This paper introduces ScratchTSP, an extension of the Scratch programming Framework, specifically designed for the TSP. It offers an opportunity for beginners and novice programmers to grasp the TSP and the various algorithms used to solve it. For evaluation, we conduct a survey among undergraduate students to assess the usability and usefulness of the proposed ScratchTSP extension. Survey findings indicate that ScratchTSP offers a user-friendly and effective tool for beginners or novice programmers, aiding them in learning the TSP while developing their critical thinking and problem-solving skills. © 2024 ACM.

关键词： Students

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Advancing Underwater Communication with NBO-MFO Hybrid Routing in UWSNs 10

Advancing Underwater Communication with NBO-MFO Hybrid Routi...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Kalaiselvan, S.A. Kumar, M.Lenin Amanullah, M. Mani Chigurupati, T.R. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Vignan Institute of Technology and Science Department of Electronics and Communications Engineering Telangana India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Tkr College of Engineering and Technology Department of Computer Science and Engineering Telangana India

ISBN: (纸本)9798350353068

In this research provides a solution for optimizing routing path selection in Underwater Wireless Sensor Networks (UWSNs). The algorithm combines Namib Beetle Optimization (NBO) and Moth-Flame Optimization (MFO), integrating them into the ILEACH methodology for efficient routing path selection. The algorithm operates in an iterative manner, commencing with NBO's feature selection process, evaluating fitness through average classification error and feature selection ratio. Simultaneously, MFO mimics moth behavior, updating positions via a logarithmic spiral function based on flame locations. The mutual learning phase facilitates information exchange between NBO and MFO, enhancing their collective intelligence and refining the routing path selection process. ILEACH's contribution lies in its fitness aggregation and selection methods. It amalgamates various criteria, including energy levels, distances, and node conditions, into a comprehensive fitness metric. Subsequently, ILEACH selectively identifies optimal routing paths aligned with energy-efficient criteria and node statuses, ensuring efficient data transmission and energy conservation within UWSNs. This integrated approach fosters a comprehensive strategy for routing path selection in UWSNs, ensuring sustainability and efficiency in data transmission within challenging underwater environments. © 2024 IEEE.

关键词： Iterative methods

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