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IEEE International Conference on Human-Machine systems

作者： Fan He Karim Elhammady Sebastian Fischmeister Catherine M. Burns Department of System Design Engineering University of Waterloo Waterloo Ontario Canada Department of Electrical and Computer Engineering University of Waterloo Waterloo Ontario Canada

ISBN: (数字)9798350315790

ISBN: (纸本)9798350315806

This research investigates the vulnerability and resilience of human cognitive processes in the context of rapidly advancing driving technologies susceptible to multi-modal cyber-attacks. By simulating scenarios where Advanced Driver Assistance systems (ADAS) are attacked to distract drivers, we aim to understand the decision-making process and cognitive limits affecting safe driving and to devise a systematic approach to assess cognitive impairments. Our simulations of three generic attack models revealed that attacks requiring complex cognitive processing pose greater risks than mere distractions. To assess human driver vulnerabilities in this rapidly evolving technological landscape, we utilized Blood Alcohol Concentration (BAC) levels as a benchmark.

关键词： Measurement systematics Cognitive processes Human-machine systems Decision making Security Cyberattack

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Cryptographic Protocols for Securing Internet of Things (IoT) 15

Cryptographic Protocols for Securing Internet of Things (IoT...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Manimekala, B. Aravind, T. Sounderrajan, T. Rajakumareswaran, V. Kondreddi, Krishnaveni Mohanambal, K. Christ University Department of Computer Science Yeshwanthpur Bengaluru India Vel TecRangarajanDr.Sagunthala R&d Institute of Science and Technology Department of Cse School of Computing India Erode Sengunthar Engineering College Department of Ai and Data Science Tamil Nadu Thuduppathi India Erode Sengunthar Engineering College Department of Computer Science and Design Tamil Nadu Thuduppathi India Chaitanya Bharathi Institute of Technology Electrical and Electronics Engineering Ranga Reddy Telangana Gandipet500075 India Bannariamman Institute of Technology Department of Computer Science and Engineering Sathyamangalam India

ISBN: (纸本)9798350370249

Cryptographic protocols are used to relax the ever-developing quantity of linked gadgets that make up the net of things (IoT). Those cryptographic protocols have been designed to make certain that IoT tool traffic stays cozy and personal, even while nevertheless allowing tool-to-device and cloud-to-tool communications. Examples of these protocols consist of TLS/SSL, PGP/GPG, IPsec, SSL VPN, and AES encryption. Every one of these protocols enables authentication, message integrity, and confidentiality via encryption and key trade. Moreover, a lot of these protocols are carried out in the form of diverse hardware and software answers, such as smart playing cards and gateways, to make certain that IoT traffic is secured. With the appropriate implementation of those cryptographic protocols, establishments can ensure that their IoT facts are blanketed and securely transmitted. © 2024 IEEE.

关键词： Authentication Communications Cryptographic EverDeveloping Gadgets Protocols

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Prescribing Decision Conservativeness in Two-Stage Power Markets: A Distributionally Robust End-to-End Approach 59

Prescribing Decision Conservativeness in Two-Stage Power Mar...

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59th Annual Conference on Information sciences and systems, CISS 2025

作者： Liang, Zhirui Li, Qi Liu, Anqi Dvorkin, Yury Johns Hopkins University Department of Applied Mathematics and Statistics BaltimoreMD United States Johns Hopkins University Department of Computer Science BaltimoreMD United States Johns Hopkins University Department of Electrical and Computer Engineering BaltimoreMD United States Johns Hopkins University Department of Civil and System Engineering BaltimoreMD United States

ISBN: (纸本)9798331513269

This paper presents an end-to-end framework for calibrating wind power forecast models to minimize operational costs in two-stage power markets, where the first stage involves a distributionally robust optimal power flow (DR-OPF) model. Unlike traditional methods that adjust forecast parameters and uncertainty quantification (UQ) separately, this framework jointly optimizes both the forecast model parameters and the decision conservativeness, which determines the size of the ambiguity set in the DR-OPF model. The framework aligns UQ with actual uncertainty realizations by directly optimizing downstream operational costs, a process referred to as cost-oriented calibration. The calibration is achieved using a gradient descent approach. To enable efficient differentiation, the DR-OPF problem is reformulated into a convex form, and the Envelope Theorem is leveraged to simplify gradient derivation in the two-stage setting. Additionally, the framework supports distributed implementation, effectively reducing computational overhead and enhancing efficiency. By proactively calibrating forecast parameters and prescribing optimal decision conservativeness, the framework significantly enhances cost efficiency and reliability in power system operations. Numerical experiments on an IEEE 5-bus system demonstrate the effectiveness and efficiency of the proposed approach. © 2025 IEEE.

关键词： Wind forecasting

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Leveraging Adaptive Algorithms for Real-Time Data Analysis 15

Leveraging Adaptive Algorithms for Real-Time Data Analysis

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sahoo, Girija Shankar Hannah Jessie Rani, R. Goyal, Manish Kumar Mohammed, Vazeer Ali Jana, D Little Femilin Wasatkar, Namrata N. Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Electrical and Electronics Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Lewis University Illinois Department of Computer & Information System United States Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350370249

This paper proposes the use of adaptive algorithms for real-time data analysis. Adaptive algorithms are a set of practical methods used for acting information-driven optimization and studying. They have a wide variety of applications and may be implemented to a selection of facts analysis obligations. This paper offers an overview of existing adaptive algorithms and descriptions of several new programs in the area of actual-time facts analysis. In particular, the paper discusses the notion of adaptive thresholding, which allows for practical real-time evaluation of large datasets. The paper also provides a novel technique for making use of these algorithms in an online learning environment. Results from simulations illustrate the ability of the proposed method in real-international packages. Sooner or later, the paper presents numerous key lessons discovered and affords recommendations for future research and packages in this area. © 2024 IEEE.

关键词： Adaptive algorithms

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Intelligent Caching Strategies for 5G Edge Networks using Machine Learning 4

Intelligent Caching Strategies for 5G Edge Networks using Ma...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Guduri, Naga Venkata Ramakrishna Sethu, S. Shalinirajan, R. Reena, R. Karthikeyan, M. Uthayakumar, G.S. University of Technology and Applied Sciences-Al Musannah Electrical and Electronics Section Engineering Department Oman Department of Computer Science and Engineering Tamilnadu Chennai India Panimalar Engineering College Department of Computer Science and Business System Tamilnadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Tamilnadu Chennai India St. Joseph's Institute of Technology Department of Electronics and Communication Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350300888

To reduce latency and improve the user experience, content caching solutions at the network periphery will become increasingly important as 5G networks and data-intensive applications. In this proposed work, the deployment of a caching architecture for 5G periphery networks using machine learning is done. Utilizing information on user behavior, network conditions, and content prominence enables more accurate forecasts and cache management decisions. This trains the prediction models using a wide range of machine-learning techniques to accommodate the ever-changing needs of both networks and content. To evaluate the efficiency of the proposed architecture by measuring its ability to increase cache hit rates and decrease latency. These techniques significantly outperform the standard caching methods. With the intelligent caching architecture of 5G periphery networks, throughput is increased, latency is reduced, and congestion is alleviated. The findings of this study contribute to the growing corpus of literature on AI-driven optimization in 5G networks and shed light on how such solutions could be enhanced for future networks. Neural Networks achieved 96% accuracy than SVM which is 93%. © 2023 IEEE.

关键词： 5G mobile communication systems

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A Comparative Study of Vibration Damping Based on Wave Model—Decoupling Reflected Waves from Traveling Waves

A Comparative Study of Vibration Damping Based on Wave Model...

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International Conference on electrical Machines and systems (ICEMS)

作者： Kosuke Shikata Krzysztof Szabat Seiichiro Katsura Department of System Design Engineering Keio University Yokohama Japan Faculty of Electrical Engineering Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (数字)9784886864406

ISBN: (纸本)9798350379105

Vibration or ripple reduction is a common topic in motion control, electrical systems, and motor drives. Although wave model-based vibration suppression is novel, the control structures must be systematized. Thus, this study addresses the comparative analysis of control structures derived from the wave model. The primary contribution is to discuss the similarities and differences between an integral-proportional controller and a proportional controller with a disturbance observer through analyses of their transfer function and experimental verification.

关键词： Vibrations Damping Motor drives Analytical models Torque Transfer functions Disturbance observers IP networks Motion control Electromagnetics

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Data-Driven Norm-Optimal Control of Discrete Repetitive Processes 63

Data-Driven Norm-Optimal Control of Discrete Repetitive Proc...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Moore, Kevin L. Chu, Bing Rapisarda, Paolo Colorado School of Mines Division of Engineering Design and Society and the Department of Electrical Engineering 1500 Illinois St. GoldenCO80401 United States University of Southampton School of Electronics and Computer Science SO17 1BJ United Kingdom

ISBN: (纸本)9798350316339

We present a data-driven, model-free approach to norm-optimal control of discrete repetitive processes, exploiting Willems' fundamental lemma. The algorithm is described in detail, and a rigorous analysis of its convergence properties is performed. A numerical example is provided to demonstrate the effectiveness of the proposed design methodology. © 2024 IEEE.

关键词： Adversarial machine learning

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Improved Bat Algorithm with Deep Learning-Based Biomedical ECG Signal Classification Model

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computers, Materials & Continua 2023年第2期74卷 3151-3166页

作者： Marwa Obayya Nadhem NEMRI Lubna A.Alharbi Mohamed K.Nour Mrim M.Alnfiai Mohammed Abdullah Al-Hagery Nermin M.Salem Mesfer Al Duhayyim Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Science and Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyTabuk UniversitySaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Electrical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia

With new developments experienced in Internet of Things(IoT),wearable,and sensing technology,the value of healthcare services has *** evolution has brought significant changes from conventional medicine-based healthcare to real-time observation-based *** Electrocardiogram(ECG)signals are generally utilized in examination and diagnosis of Cardiovascular Diseases(CVDs)since it is quick and non-invasive in *** to increasing number of patients in recent years,the classifier efficiency gets reduced due to high variances observed in ECG signal patterns obtained from *** such scenario computer-assisted automated diagnostic tools are important for classification of ECG *** current study devises an Improved Bat Algorithm with Deep Learning Based Biomedical ECGSignal Classification(IBADL-BECGC)*** accomplish this,the proposed IBADL-BECGC model initially pre-processes the input ***,IBADL-BECGC model applies NasNet model to derive the features from test ECG *** addition,Improved Bat Algorithm(IBA)is employed to optimally fine-tune the hyperparameters related to NasNet ***,Extreme Learning Machine(ELM)classification algorithm is executed to perform ECG classification *** presented IBADL-BECGC model was experimentally validated utilizing benchmark *** comparison study outcomes established the improved performance of IBADL-BECGC model over other existing methodologies since the former achieved a maximum accuracy of 97.49%.

关键词： Data science ECG signals improved bat algorithm deep learning biomedical data data classification machine learning

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BatteryCDE: A Transferable Future Capacity Estimation Method for Battery Degradation with Irregular Sampling and Missing Data

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IEEE Transactions on Transportation Electrification 2025年

作者： Wang, Tianjing Ren, Chao Xiong, Houbo Song, Qun Dong, ZhaoYang City University of Hong Kong Department of Electrical Engineering Hong Kong KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm Sweden Zhejiang University School of Electrical Engineering China Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore

To overcome critical limitations in existing methods for battery capacity estimation, including long-term estimation, handling of irregular sampling and missing data, and transferability across different battery types, this study proposes a novel estimation method, termed batteryCDE, by integrating neural controlled differential equations (CDEs) with attention mechanisms. It first constructs a continuous data path using cubic spline interpolation, followed by neural CDEs that generate feature-wise and cycle-wise attention features to capture essential battery characteristics. These attention-weighted features are processed by another neural CDE to model differential relationships, leading to precise capacity estimations. This study also provides a theoretical analysis of the advantages of batteryCDE over conventional discrete models in terms of long-term estimation, handling of irregular sampling and missing data, and transferability. Extensive experiments evaluate batteryCDE’s performance, utilizing three scenarios: varying estimation horizons, missing data, and cross-battery transfer. Results show that batteryCDE outperforms traditional models like LSTM, Transformer and neural CDE networks. Even with 50% missing data, an estimation horizon of 100 cycles, and application to different batteries, batteryCDE maintains an estimation error below 0.05. Compared to other methods, batteryCDE reduces estimation errors by 6.59% for long-term predictions, 15.09% for handling missing data, and 17.01% for cross-battery transferability. © 2025 IEEE. All rights reserved.

关键词： Battery degradation capacity irregular and missing data neural controlled differential equations transferability

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Synergistic effects of heating and traction during fibrous tissue elongation

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Journal of Biomechanical science and engineering 2023年第2期18卷

作者： Yagi, Ippei Koike, Kazuki Uchida, Satoshi Department of Electrical Engineering and Computer Science Faculty of System Design Tokyo Metropolitan University 1-1 Minami-Osawa Hachioji-shi Tokyo192-0397 Japan

Fibrosis involves the abnormal accumulation of components of the extracellular matrix, such as collagen, and can lead to organ failure, which accounts for approximately one-third of all deaths worldwide. Accordingly, there is an urgent need to develop low-invasive treatment strategies for fibrosis as there is currently a lack of anti-fibrotic pharmacological agents available. In the present study, we tested the hypothesis that applying traction to fibrous tissues while using heat to denature proteins can be used to adjust the shape and flexibility of tissues. Bovine Achilles tendon samples as a model of fibrous tissue contracted by approximately 25% compared to the original length in response to heating, in line with previous findings. In contrast, the application of traction during heating and cooling increased tissue length by 25% and decreased Young’s modulus by 16%. Tissue staining and detection of unfolded collagen using collagen-hybridizing peptide demonstrated thermal degradation of collagen fibers and conversion to gelatinous material throughout samples, confirming that traction, and heating damages collagen. These results indicate that this technique can be used to alter the shape and reduce the rigidity of organs affected by fibrosis. © 2023 The Japan Society of Mechanical Engineers. This is an open access article under the terms of the Creative Commons Attribution 4.0 International license (https://***/licenses/by/4.0/).

关键词： Collagen

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