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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： A. Munthuli M. Thepsoparn P. Pooprasert P. Phienphanich P. Pongcharoen D. Sakonlaya P. Sittiwattanawong S. Weschawalit P. Chakkavittumrong B. Thongthawee T. Pathomjaruwat C. Tantibundhit Department of Electrical and Computer Engineering Faculty of Engineering Medical Invention and Innovation Laboratory (MeDii) Thammasat School of Engineering Thammasat University Thailand MeDii and Bangkok Patana School Thailand Faculty of Medicine Thammasat University Thailand Faculty of Nursing Thammasat University Thailand

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

This study addresses extravasation, a critical issue requiring prompt detection for effective management to avoid severe complications. This work harnesses the capability of zero-shot capabilities from pre-trained vision transformer models–specifically, GroundingDINO and Segment Anything Model (SAM) for segment human skin regions, and Contrastive Language-Image Pretraining (CLIP) for extracting rich features from these regions using its frozen image encoder. Our methodology applies linear probe techniques to feature vectors obtained from CLIP using few-shot instances. The results demonstrate accurate classification of extravasation severities with training using only 64 instances per class, achieving average F1 macro scores of 74.08% for GroundingDINO-CLIP. This marks an improvement result to the previous study which utilized dual U-Nets for skin and lesion segmentation alongside DenseNet-121 models for classification with training using 975 instances. Notably, this approach increases previous F1 macro scores by 3.27% in mild extravasation cases. This research advances fine-grained extravasation classification, specifically in early detection, achieved through few-shot models in a unique context.

关键词： Training Image segmentation Feature extraction Transformers Skin Vectors Lesions Probes engineering in medicine and biology Context modeling

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Intelligent Energy Management Energy Storage systems Using Machine Learning

Intelligent Energy Management Energy Storage Systems Using M...

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Intelligent systems for Cybersecurity (ISCS), International Conference on

作者： Chinnaraj Govindasamy T.R Nisha Dayana Siddhika Dilip Adkar D. Shyam Prakash A. Shilpa Gupta Sanjeev Kumar Trivedi Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India Department of Computer Science Vels Institute of Science Technology and Advance Studies Chennai School of Computer Science Engineering and Applications D Y Patil International University Akurdi Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Department of Electrical and Electronics Engineering Aurora's Technological Research Institute Uppal Hyderabad Department of Electronics Faculty of Engineering and Technology Khwaja Moinuddin chishti Language University Lucknow

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

A nevertheless-emerging generation called cloud computing permits customers to pay for services on a usage-based foundation. Internet-primarily based IT offerings are supplied through cloud computing, at the same time as virtualization enables the availability of PC sources. The muse of cloud computing is the information center, which is made up of networked computers, cables, electricity components, and different components that host and shop corporate records. With cloud facts centers, high overall performance has continually been the most critical concern, yet it compromises strength utilization. The key hassle is to lessen power consumption at the same time as preserving provider nice and performance a good way to stability device performance and strength intake. A detailed grasp of strength use styles within the cloud environment is needed for our suggested technique. We look at power consumption tendencies and reveal how, through the use of the right optimization standards based on our strength intake models, we can keep more strength in cloud records facilities. All through the prediction section, tablet optimization, which has a 97 percent accuracy rate, permits this era to provide extra correct future price forecasts.

关键词： Performance evaluation Cloud computing Power demand Electricity Stability criteria Machine learning Load management

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HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud Computing System 14

HealthEdge: A Machine Learning-Based Smart Healthcare Framew...

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14th International Conference on Ambient systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Hennebelle, Alain Materwala, Huned Ismail, Leila Melbourne Australia Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates Lab School of Computing and Information Systems The University of Melbourne Australia

Diabetes Mellitus has no permanent cure to date and is one of the leading causes of death globally. The alarming increase in diabetes calls for the need to take precautionary measures to avoid/predict the occurrence of diabetes. This paper proposes HealthEdge, a machine learning-based smart healthcare framework for type 2 diabetes prediction in an integrated IoT-edge-cloud computing system. Numerical experiments and comparative analysis were carried out between the two most used machine learning algorithms in the literature, Random Forest (RF) and Logistic Regression (LR), using two real-life diabetes datasets. The results show that RF predicts diabetes with 6% more accuracy on average compared to LR. © 2023 Elsevier B.V.. All rights reserved.

关键词： Edge computing

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Channel Estimation for Stacked Intelligent Metasurfaces in Rician Fading Channels

arXiv

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arXiv 2025年

作者： Papazafeiropoulos, Anastasios Kourtessis, Pandelis Kaklamani, Dimitra I. Venieris, Iakovos S. Communications and Intelligent Systems Research Group University of Hertfordshire HatfieldAL10 9AB United Kingdom Microwave and Fiber Optics Laboratory Greece Intelligent Communications and Broadband Networks Laboratory School of Electrical and Computer Engineering National Technical University of Athens Zografou Athens15780 Greece

The recent combination of the rising architectures, known as stacked intelligent metasurface (SIM) and holographic multiple-input multiple-output (HMIMO), drives toward breakthroughs for next-generation wireless communication systems. Given the fact that the number of elements per surface of the SIM is much larger than the base station (BS) antennas, the acquisition of the channel state information (CSI) in SIM-aided multi-user systems is challenging, especially when a line-of-sight (LoS) component is present. Thus, in this letter, we address the channel procedure under conditions of Rician fading by proposing a protocol in terms of a minimum mean square error (MMSE) estimator for wave-based design in a single phase. Moreover, we derive the normalized mean square error (NMSE) of the suggested estimator, and provide the optimal phase shifts minimising the NMSE. Numerical results illustrate the performance of the new channel estimation protocol. © 2025, CC BY.

关键词： Mean square error

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Mode-Symmetry-Assisted Optical Pulling by Bound States in the Continuum

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Physical Review Letters 2024年第25期132卷 253802-253802页

作者： Hang Li Yongyin Cao Rui Feng Bojian Shi Yuzhi Shi Yang Chen Dongliang Gao Tongtong Zhu Donghua Tang Fangkui Sun Cheng-Wei Qiu Weiqiang Ding Institute of Advanced Photonics School of Physics Institute of Precision Optical Engineering School of Physics Science and Engineering Chinese Academy of Sciences Key Laboratory of Mechanical Behavior and Design of Materials Department of Precision Machinery and Precision Instrumentation School of Physical Science and Technology Collaborative Innovation Center of Suzhou Nano Science and Technology School of Optoelectronic Engineering and Instrumentation Science Department of Physics School of Science Department of Electrical and Computer Engineering Collaborative Innovation Center of Extreme Optics

Aside from optical pushing and trapping that have been implemented successfully, the transportation of objects backward to the source by the optical pulling forces (OPFs) has attracted tremendous attention, which was usually achieved by increasing the forward momentum of light. However, the limited momentum transfer between light and object greatly constrains the amplitudes of OPFs. Here, we present a mechanism to generate strong interactions between object and background through the bound states in the continuums, which can generate large OPFs without increasing the forward momentum of light. The underlying physics is the extraction of momentum from the designed background lattice units assisted by mode symmetry. This work paves the way for extraordinary optical manipulations and shows great potential for exploring the momenta of light in media.

关键词： Mechanical effects of light on material media Nanophotonics Photonic crystals

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Accelerating Stable Matching between Workers and Spatial-Temporal Tasks for Dynamic MCS: A Stagewise Service Trading Approach

arXiv

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arXiv 2025年

作者： Qi, Houyi Liwang, Minghui Wang, Xianbin Fu, Liqun Hong, Yiguang Li, Li Cheng, Zhipeng School of Informatics Xiamen University Fujian China Department of Control Science and Engineering Canada Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China Department of Electrical and Computer Engineering Western University ON Canada School of Future Science and Engineering Soochow University Jiangsu China

Designing proper incentives in mobile crowdsensing (MCS) networks represents a critical mechanism in engaging distributed mobile users (workers) to contribute heterogeneous data for diverse applications (tasks). We develop a novel stagewise trading framework to reach efficient and stable matching between tasks and workers, upon considering the diversity of tasks and the dynamism of MCS networks. This framework integrates futures and spot trading stages, where in the former, we propose futures trading-driven stable matching and pre-path-planning (FT-SMP3) for long-term task-worker assignment and pre-planning of workers’ paths based on historical statistics and risk analysis. While in the latter, we investigate spot trading-driven DQN path planning and onsite worker recruitment (ST-DP2WR) mechanism to enhance workers’ and tasks’ practical utilities by facilitating temporary worker recruitment. We prove that our proposed mechanisms support crucial properties such as stability, individual rationality, competitive equilibrium, and weak Pareto optimality theoretically. Also, comprehensive evaluations confirm the satisfaction of these properties in practical network settings, demonstrating our commendable performance in terms of service quality, running time, and decision-making overheads. Copyright © 2025, The Authors. All rights reserved.

关键词： Risk analysis

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A Stable Lithium-ion Battery SOH Estimation Framework for Suppressing Measurement Noise with Unknown Distribution

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IEEE Transactions on Transportation Electrification 2025年

作者： Ma, Wentao Xue, Jingsong Li, Yang Guo, Peng Liu, Xinghua Wei, Zhongbao Wang, Yiwen Chen, Badong Xi’an University of Technology School of Electrical Engineering 710048 China Chalmers University of Technology Department of Electrical Engineering Gothenburg41296 Sweden Beijing Institute of Technology National Engineering Laboratory for Electric Vehicles School of Mechanical Engineering Beijing100081 China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Institute of artificial intelligence and robotics Xi’an Jiaotong University Xi’an710048 China

Existing methods for estimating the state of health (SOH) of lithium-ion battery (LIB) typically rely on the assumption that the distribution of noise (or outliers) in the measurement data is known. However, this assumption rarely holds true for LIB operating under real-word conditions. This article proposes a stable framework for accurate SOH estimation that accommodates noises with unknown distribution in both measurement data and label values. The framework combines generalized correntropy loss (GCL) with Savitzky-Golay (SG) filter and extreme learning machine (ELM) to obtain measurement data filter named SG-GCL and SOH estimator named generalized ELM (GELM), respectively. The SG-GCL filtering of the measurement data keeps the root mean square error (RMSE) within 0.0365%, and Pearson correlation between extracted feature and SOH improves by 0.4963, which in turn leads to the reduction of the RMSE metrics of the ELM for the estimation of the SOH by 43.69%. From the filtering results, feature extraction and estimation results proved its necessity and effectiveness. GELM effectively suppresses the influence of label value noise on the model in the training process, which reduces the SOH estimation RMSE index by more than 0.66%. The results from experiments with different distributional noise conditions show that the proposed SOH estimation framework has excellent and stable performance. © 2015 IEEE.

关键词： Mean square error

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Multi-energy systems: Modelling approaches and energy storage potential 12

Multi-energy systems: Modelling approaches and energy storag...

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12th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion, MEDPOWER 2020

作者： Vomva, Styliani A. Christoforidis, Georgios C. Papagiannis, Grigoris K. Power Systems Laboratory School of Electrical and Computer Engineering Aristotle University of Thessaloniki Greece Department of Electrical and Computer Engineering University of W. Macedonia Kozani Greece

ISBN: (纸本)9781839535635

The decarbonization of energy systems implies the further exploitation of renewable energy resources. To facilitate their integration into the existing systems, while also improving their efficiency, multi-energy systems could play a vital role, accompanied also by the integration of storage components. In such an approach, electricity, thermal energy and gas infrastructures collaborate to meet the overall energy demand. The modelling and optimization of such systems are complex, as multiple heterogeneous variables and parameters should be considered when designing the system and time-scheduling its operation. This paper presents an overview of modelling approaches in this framework. Moreover, storage potential that arises through the collaboration of multiple energy forms is introduced. To enhance the usage of such approaches by policymakers, dedicated tools must be developed, incorporating networks limitations and components constraints. © 2020 Institution of engineering and Technology. All rights reserved.

关键词： Renewable energy resources

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Integrated Real-Time Control and Scheduling for Safety Critical Cyber-Physical systems

Integrated Real-Time Control and Scheduling for Safety Criti...

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IEEE Symposium on Real-Time and Embedded Technology and Applications

作者： Marion Sudvarg Andrew Clark Chris Gill Department of Computer Science and Engineering James McKelvey School of Engineering Washington University in St. Louis St. Louis Missouri United States Department of Electrical and Systems Engineering James McKelvey School of Engineering Washington University in St. Louis St. Louis Missouri United States

ISBN: (数字)9798331543402

ISBN: (纸本)9798331543419

Cyber-physical systems (CPS) must interact with varying environments at fine-grained time-scales, assuring control safety and stability while optimizing application-specific performance objectives. To address those requirements, co-design of real-time control and scheduling has received considerable attention over multiple decades, to allow rigorous assurance of system properties while enabling diverse forms of adaptation to changing operating conditions. In this paper, we present a new formalization of the periodicity requirements for control inputs to (1) guarantee reachability of safe (and avoidance of unsafe) portions of the system state space, (2) adaptively manage dynamic periodicity constraints that may change as the state space is traversed, and (3) express minimum periods to enable safe hand-offs between high-performance controllers and more conservative backup controllers. Our evaluations of this approach confirm that it is able to maintain system safety and stability while optimizing system performance.

关键词： System performance Cyber-physical systems Aerospace electronics Dynamic scheduling Control systems Real-time systems Safety

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KKL Observer Synthesis for Nonlinear systems via Physics-Informed Learning

arXiv

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arXiv 2025年

作者： Niazi, M.B. Umar Cao, John Barreau, Matthieu Johansson, Karl Henrik Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology StockholmSE-100 44 Sweden Laboratory for Information and Decision Systems Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering Stanford University StanfordCA94305 United States

This paper proposes a novel learning approach for designing Kazantzis-Kravaris/Luenberger (KKL) observers for autonomous nonlinear systems. The design of a KKL observer involves finding an injective map that transforms the system state into a higher-dimensional observer state, whose dynamics is linear and stable. The observer’s state is then mapped back to the original system coordinates via the inverse map to obtain the state estimate. However, finding this transformation and its inverse is quite challenging. We propose to sequentially approximate these maps by neural networks that are trained using physics-informed learning. We generate synthetic data for training by numerically solving the system and observer dynamics. Theoretical guarantees for the robustness of state estimation against approximation error and system uncertainties are provided. Additionally, a systematic method for optimizing observer performance through parameter selection is presented. The effectiveness of the proposed approach is demonstrated through numerical simulations on benchmark examples and its application to sensor fault detection and isolation in a network of Kuramoto oscillators using learned KKL observers. © 2025, CC BY.

关键词： State estimation

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