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Modeling and Simulating more efficient and reliable fuel cell vehicles through enhancing the integration of the 6kW PEM fuel cell stack with DC/DC boost converter and BLDC motor 33

Modeling and Simulating more efficient and reliable fuel cel...

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33rd Southern African Universities Power engineering Conference, SAUPEC 2025

作者： Mtumka, Olona Krishnamurthy, Senthil Cape Peninsula University of Technology Bellville Center for Intelligent Systems and Emerging Technologies Department of Electrical Electronic and Computer Engineering Cape Town7530 South Africa

ISBN: (纸本)9798331535162

This study focuses on enhancing the integration of the PEM fuel cell, DC/DC boost converter, and BLDC motor, which help to develop more efficient and dependable fuel cell vehicles. The development of renewable energy sources for power generation has received much attention due to the depletion of fossil fuel supplies worldwide and economic difficulties. Fuel cells are one of the most efficient and cleanest power generation methods. Polymer electrolyte membrane (PEM) fuel cell is the preferred type of fuel cell for vehicles because of its high energy density, fast start-up, and lack of pollution. This paper aims to model and simulate a PEM fuel cell stack to power a Brushless DC (BLDC) motor for a fuel cell vehicle to reduce emissions in the transportation sectors. A boost converter is implemented to boost the output voltage of the PEM fuel cell stack to 100 V. The paper utilizes the trapezoidal commutation of the BLDC motor to drive the inverter. The trapezoidal commutation uses the six-switching technique to control the inverter's phase currents. The variation of fuel flow rate and drive cycle are included to observe the effect on the operation of the fuel cell stack. The paper presents the modeling and simulation of the PEM Fuel cell stack to improve power conversion, energy management, and vehicle control, helping to make fuel cell vehicles more viable for widespread adoption in the future. © 2025 IEEE.

关键词： Brushless DC motors

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NFV-Enabled Service Recovery in Space–Air–Ground Integrated Networks: A Matching Game-Based Approach

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IEEE Transactions on Network Science and engineering 2025年第3期12卷 1732-1744页

作者： Jia, Ziye Cao, Yilu He, Lijun Li, Guangxia Zhou, Fuhui Wu, Qihui Han, Zhu Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing211106 China Xidian University State Key Laboratory of ISN Xian710071 China China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

To achieve ubiquitous connectivity of the sixth generation communication, the space-air-ground integrated network (SAGIN) is a popular topic. However, the dynamic nodes in SAGIN such as satellites and unmanned aerial vehicles, may be fragile and out of operation, which can potentially cause service failure. Therefore, the research on service recovery in SAGIN under situations of resource failure is critical. In order to facilitate the flexible resource utilization of SAGIN, the network function virtualization technology (NFV) is proposed to be employed. Firstly, the task management is transformed into the deployment of service function chains (SFCs). Then, we design an NFV-based SFC recovery model in SAGIN in the face of resource failure, so that tasks can quickly select alternative resources to complete deployments. Moreover, the problem of SFC recovery is formulated to minimize the total time consumption for all completed SFCs. Since it is an NP-hard integer linear programming problem, we propose the efficient recovery algorithm based on the matching game. Finally, via various simulations, the effectiveness of the proposed algorithm and its advantages are verified, where the total time consumption is optimized by about 25%, compared with other benchmark methods. © 2013 IEEE.

关键词： Network function virtualization

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A comparison study between Modbus and IEC 61850 MMS Protocols 33

A comparison study between Modbus and IEC 61850 MMS Protocol...

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33rd Southern African Universities Power engineering Conference, SAUPEC 2025

作者： Johnson, Rayner Krishnamurthy, Senthil Mataifa, Haltor Esmail, Mohammed Cape Peninsula University of Technology Center for Substation Automation and Energy Management Systems Department of Electrical Electronic and Computer Engineering Cape Town7530 South Africa

ISBN: (纸本)9798331535162

The growing adoption of renewable energy primary plants has been seen globally. South Africa has seen a significant development in utility-scale photovoltaic (PV) farms in recent years due to the alleviated regulations stipulated in the latest Electricity Regulation Amendment Act. Connecting the PV plants to the power grid has also undergone a technical evolution by incorporating modern embedded computers known as Intelligent Electronic Devices (IEDs). The International Electrotechnical Committee (IEC) 61850 standard defines a suite of protocols used in modern power systems. This study compares the IEC 61850 Manufacturing Messaging Specification (MMS) with the well-known MODBUS protocol. The comparison is achieved by simulating a solar farm's Power Plant Controller (PPC). © 2025 IEEE.

关键词： Solar power plants

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Multimodal Pain Assessment Βased on Physiological Biosignals: The Impact of Demographic Factors on Perception and Sensitivity 11

Multimodal Pain Assessment Βased on Physiological Biosignal...

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11th International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2025

作者： Pavlidou, Elisavet Tsiknakis, Manolis Department of Electrical and Computer Engineering Hellenic Mediterranean University Estavromenos Heraklion71410 Greece Computational BioMedicine Laboratory Institute of Computer Science Foundation for Research & Technology-Hellas Vassilika Vouton Heraklion70013 Greece

ISBN: (纸本)9789897587436

Pain is a multidimensional and highly personalized sensation that affects individuals’ physical and emotional state. Visual analog scales, numeric rate indicators, and various questionnaires, all relying on patient-reported outcome measurements, are considered the "gold" standard methods for assessing the severity of pain. Nevertheless, self-report tools require cognitive, linguistic, and social abilities, which may manifest variations in certain populations such as neonates, individuals with intellectual disabilities, and those affected by dementia. The purpose of this study is to automate the process through multimodal physiological-data-driven machine-learning models in order to gain deeper insights into pain sensation. We developed a pipeline using electrocardiogram (ECG), galvanic skin response (GSR), and electromyogram (EMG), along with demographic information from the BioVid dataset. The Pan & Tompkins algorithm was applied for ECG signal processing, while statistical analysis was used for feature extraction across all signals. Our study achieved 82.83% accuracy in the SVM classification task of baseline (BL) vs the highest level of pain (PA4) for females aged 20-35. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Sensory perception

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BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration 31

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration

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31st IEEE International Symposium on High Performance computer Architecture, HPCA 2025

作者： Chen, Yuzong Abouelhamayed, Ahmed F. Dai, Xilai Wang, Yang Andronic, Marta Constantinides, George A. Abdelfattah, Mohamed S. Cornell University Computer Systems Lab United States Systems and Networking Research Group Microsoft Research United States Imperial College London Department of Electrical and Electronic Engineering United Kingdom

ISBN: (纸本)9798331506476

Large language models (LLMs) have demonstrated remarkable performance across various machine learning tasks. Yet the substantial memory footprint of LLMs significantly hinders their deployment. In this paper, we improve the accessibility of LLMs through BitMoD1, an algorithm-hardware co-design solution that enables efficient LLM acceleration at low weight precision. On the algorithm side, BitMoD introduces fine-grained data type adaptation that uses a different numerical data type to quantize a group of (e.g., 128) weights. Through the careful design of these new data types, BitMoD is able to quantize LLM weights to very low precision (e.g., 4 bits and 3 bits) while maintaining high accuracy. On the hardware side, BitMoD employs a bitserial processing element to easily support multiple numerical precisions and data types;our hardware design includes two key innovations: First, it employs a unified representation to process different weight data types, thus reducing the hardware cost. Second, it adopts a bit-serial dequantization unit to rescale the per-group partial sum with minimal hardware overhead. Our evaluation on six representative LLMs demonstrates that BitMoD significantly outperforms state-of-the-art LLM quantization and acceleration methods. For discriminative tasks, BitMoD can quantize LLM weights to 4 -bit with © 2025 IEEE.

关键词： Integrated circuit design

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Velocity control of a Stephenson III six-bar linkage-based gait rehabilitation robot using deep reinforcement learning

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Neural Computing and Applications 2025年第7期37卷 5671-5682页

作者： Kapsalyamov, Akim Brown, Nicholas A. T. Goecke, Roland Jamwal, Prashant K. Hussain, Shahid School of Information Technology and Systems University of Canberra CanberraACT2617 Australia School of Clinical Sciences Queensland University of Technology BrisbaneQLD Australia School of Systems and Computing UNSW CanberraACT Australia Department of Electrical and Computer Engineering Nazarbayev University Nur-Sultan City Kazakhstan

Lower limb rehabilitation robots can help to improve the locomotor capabilities of patients experiencing gait impairments and help medical workers by reducing strain on them. However, since commercially available exoskeletons are expensive and there is a lack of number of physiotherapists many patients are still not able to get proper rehabilitation training. The closed-loop linkage mechanisms have recently drawn much attention in the realization of gait rehabilitation robots. Such mechanisms are affordable and capable of providing suitable trajectories for gait training therapy. In this work, we have proposed a fully operational one degree-of-freedom mechanism which can generate complex naturalistic lower limb trajectories. Although in theory, it is assumed that the constant speed applied at the input crank is sufficient to control the system, in reality, the external forces exerted by human legs and the inertia of the links can greatly alter the rotational velocity at the crank, which may negatively affect the training process. Therefore, we have explored the performance of a deep reinforcement learning-based control algorithm designed to regulate the speed of the input crank to reach satisfactory performance needed for gait rehabilitation training. Experimental evaluations with healthy human subjects were conducted to demonstrate that the mechanism is capable of directing lower limbs on naturalistic gait trajectories with a required walking speed. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Exoskeleton (Robotics)

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Leveraging SAM for Automatic Prostate Segmentation on Micro-Ultrasound Images

Leveraging SAM for Automatic Prostate Segmentation on Micro-...

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Medical Imaging 2025: Ultrasonic Imaging and Tomography

作者： Radcliffe, Olivia Lawford-Wickham, Imogen A. Wilson, Paul F.R. Abolmaesumi, Purang Mousavi, Parvin Medical Informatics Laboratory School of Computing Queen's University Kingston Canada Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (纸本)9781510686021

PURPOSE: Deep learning is promising for enabling accurate and automatic prostate segmentation. Existing deep learning segmentation model approaches often rely on large training datasets for good generalization. We aim to adapt pre-trained foundation models to enable accurate, data-efficient, and robust prostate segmentation. METHODS: We adapt the promptable foundation model SAM1 for prostate segmentation on rotational micro-ultrasound scans2 of the prostate. We design novel prompting strategies to provide the model with enhanced 3D context awareness to improve segmentation. RESULTS: Our model, which we call SliceTrack-SAM, outperforms prior state-of-the-art in micro-ultrasound prostate segmentation. We achieve a Dice coefficient of 94.0% and Hausdorff distance 1.76 mm on the MicroSegNet dataset. Qualitative analysis and ablation studies further validate the success of our approach. CONCLUSION: Transfer learning from pre-trained foundation models can alleviate the challenge of data scarcity and improve the generalization of medical imaging deep learning systems. Prompts provide a flexible and effective way to leverage auxiliary information, such as 3D context, when using these models. © 2025 SPIE.

关键词： Image segmentation

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Autonomous 3D Reconstruction with Small Unmanned Aerial Vehicle (UAV) using Structure from Motion: Practical Aspects

Autonomous 3D Reconstruction with Small Unmanned Aerial Vehi...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Rayon, Nathan Stevens, Raymond Sevil, Hakki Erhan Department of Computer Science University of West Florida FL32514 United States Department of Electrical and Computer Engineering University of West Florida FL32514 United States Department of Intelligent Systems & Robotics University of West Florida FL32514 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This paper explores the practical considerations and challenges involved in achieving autonomous 3D reconstruction utilizing small Unmanned Aerial Vehicles (UAVs) through the framework of Structure from Motion (SFM). The utilization of small UAVs equipped with lightweight cameras presents a promising resource for cost-effective and rapid 3D mapping in various domains such as disaster response, infrastructure inspection, and search and rescue. In this study, the critical practical aspects of this process, including height level for taking images, UAV selection, number of images, image acquisition strategies, and computational time. The effectiveness and limitations of autonomous 3D reconstruction with small UAVs through practical insights are summarized, with discussions on potential advancements and areas for future research. This paper serves as a guide for researchers interested in utilizing small UAVs for autonomous 3D reconstruction tasks applications. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Optimal coordination of Production, Logistics, and Maintenance 4.0: A framework for Multi-Site Stamping Plants 6

Optimal coordination of Production, Logistics, and Maintenan...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Zerrad, Raoua Diaz, Kamar Mechkouri, Meriem Hayani Sefiani, Naoufal Department of Mechanical Engineering Research Laboratory in Engineering Innovation and Management of Industrial Systems Faculty of Sciences and Techniques Abdelmalek Essaadi University Tangier Morocco Department of Industrial Engineering and Maintenance Computer Engineering Production and Maintenance Laboratory University of Lorraine Metz France

This paper presents a comprehensive framework for optimal coordination of production, logistics, and maintenance in multi-site stamping plants within the context of Industry 4.0. By integrating key technologies such as the Internet of Things, cloud computing and big data analytics, we developed a system that would enable real-time data sharing, predictive analytics, and intelligent decision-making. This framework aims to enhance operational efficiency, reduce downtime, and improve service rate. Additionally, our approach aims to optimize spare parts inventory management across different sites, minimizing inventory costs and lead times by ensuring timely availability and efficient sourcing of spare parts. © 2024 The Authors. Published by Elsevier B.V.

关键词： Inventory control

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Binary Stochastic Flip Optimization for Training Binary Neural Networks

Binary Stochastic Flip Optimization for Training Binary Neur...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Shibuya, Tatsukichi Inoue, Nakamasa Kawakami, Rei Sato, Ikuro Department of Computer Science Institute of Science Tokyo Tokyo Japan Department of Systems and Control Engineering Institute of Science Tokyo Tokyo Japan Denso IT Laboratory Tokyo Japan

ISBN: (纸本)9798350368741

For deploying deep neural networks on edge devices with limited resources, binary neural networks (BNNs) have attracted significant attention, due to their computational and memory efficiency. However, once a neural network is binarized, finetuning it on edge devices becomes challenging because most conventional training algorithms for BNNs are designed for use on centralized servers and require storing real-valued parameters during training. To address this limitation, this paper introduces binary stochastic flip optimization (BinSFO), a novel training algorithm for BNNs. BinSFO employs a parameter update rule based on Boolean operations, eliminating the need to store real-valued parameters and thereby reducing memory requirements and computational overhead. In experiments, we demonstrated the effectiveness and memory efficiency of BinSFO in finetuning scenarios on six image classification datasets. BinSFO performed comparably to conventional training algorithms with a 70.7% smaller memory requirement. Code is released at https://***/TatsukichiShibuya/ICASSP2025_BinSFO. © 2025 IEEE.

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