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2022 IEEE Power and Energy Society General Meeting, PESGM 2022

作者： MacHlev, R. Malka, A. Perl, M. Levron, Y. Belikov, J. Technion-Israel Institute of Technology The Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering Haifa3200003 Israel Tallinn University of Technology Department of Software Science Akadeemia tee 15a Tallinn12618 Estonia

ISBN: (数字)9781665408233

ISBN: (纸本)9781665408233

Non-Intrusive Load Monitoring (NILM) techniques estimate the consumption of individual appliances in a household or facility, based on readings of a centralized meter. Lately, deep learning techniques have demonstrated outstanding performance for NILM predictions. Nevertheless, a possible problem is that users and consumers may find it hard to trust the results of such algorithms if they do not fully understand the reasons for their outputs. In this light, this work presents a method that explains and justifies the outputs of NILM convolutional neural network (CNN) classifiers, using Explainable Artificial Intelligence (XAI) techniques. The method operates as follows: a CNN model for NILM is used to estimate which appliances are activated in the system based on the total power consumption. Then, a XAI technique uses this model and its outputs to explain and justify the prediction of this model. Thereby, the NILM CNN classifier outputs are both accurate and are more interpretable, allowing users to make informed and trustworthy decisions. These ideas are demonstrated on the REDD dataset using a convolutional neural network classifier and two state-of-the-art XAI techniques. © 2022 IEEE.

关键词： Convolutional neural networks

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Implementing an NFT by Developing a Smart Contract in OpenSea Marketplace 6th

Implementing an NFT by Developing a Smart Contract in OpenSe...

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6th International Conference on Advanced Informatics for Computing Research, ICAICR 2023

作者： Shanmugam, Udhayakumar Krisha, Kotte Mohan Nancy John Mari Vienni, Silviya Nandhini, D. Uma Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidhyapeetham Chennai India Software Analyst Virtusa Corporation Chennai India School of Information and Communications Technology Saskatchewan Polytechnic College SaskatoonSK Canada Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9783031840586

Blockchain-based security applications have tremendously increased in the recent past with a decentralized network being the best way to implement a secure environment. Blockchain is one such implementation methodology that is considered the biggest disruptive technology. Smart contract-based transactions and assets have a high value and work because of a consensus mechanism without centralized trusted authority. Developing a smart contract for non-fungible tokens has various scopes and our proposed work uses various tools and libraries to achieve it. Brownie is used to implement the smart contract and Infura is used for the Ethereum node. Opensea marketplace is used for rendering the transaction. An Interplanetary distributed file system is used to store the NFTs. The work proposes the coordination of multiple tools to create a blockchain that is reliable and efficient. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Smart contract

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Design and Development of a Smart Antenna System for Electromagnetic Compatibility

Design and Development of a Smart Antenna System for Electro...

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Artificial Intelligence and Machine Learning applications Theme: Healthcare and Internet of Things (AIMLA), International Conference on

作者： Manish Kumar Goyal H Malathi Binod Kumar Choudhary Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Biotechnology and Genetics School of Sciences JAIN (Deemed to be University) Bangalore Karnataka India Department of Electrical & Electronics Engineering ARKA JAIN University Jamshedpur Jharkhand India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349238

This technical abstract introduces the design and development of a smart Antenna gadget for Electromagnetic Compatibility (EMC). The clever antenna device pursuits to lessen unwanted interference in wireless verbal exchange systems, and also to locate time Electromagnetic interference (EMI) alerts and optimize running conditions. This antenna gadget is built on both software and hardware systems. Its design relies on the improvement of antenna arrays, which make use of adaptive sign-processing techniques with the intention of reaping the desired EMC necessities. The antenna array, with its signal-processing architecture, gives the possibility of at the same time optimizing radiation samples and electrical parameters by way of electronically scanning the antenna in more than one direction and taking into consideration disturbances in the environment. The layout additionally incorporates an optimized weight matrix that is used to optimize the antenna’s signal-processing structure. Subsequently, the advanced system is simulated and evaluated for its overall performance, and the results are compared with theoretical predictions.

关键词： Wireless communication Performance evaluation Layout Electromagnetic interference Adaptive arrays Medical services Machine learning

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Meta Pattern Concern Score: A Novel Evaluation Measure with Human Values for Multi-classifiers

Meta Pattern Concern Score: A Novel Evaluation Measure with ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Yanyun Wang Dehui Du Yuanhao Liu Department of Computer Science The University of Hong Kong Hong Kong China Software Engineering Institute East China Normal University Shanghai China

While advanced classifiers have been increasingly used in real-world safety-critical applications, how to properly evaluate the black-box models given specific human values remains a concern in the community. Such human values include punishing error cases of different severity in varying degrees and making compromises in general performance to reduce specific dangerous cases. In this paper, we propose a novel evaluation measure named Meta Pattern Concern Score based on the abstract representation of probabilistic prediction and the adjustable threshold for the concession in prediction confidence, to introduce the human values into multi-classifiers. Technically, we learn from the advantages and disadvantages of two kinds of common metrics, namely the confusion matrix-based evaluation measures and the loss values, so that our measure is effective as them even under general tasks, and the cross entropy loss becomes a special case of our measure in the limit. Besides, our measure can also be used to refine the model training by dynamically adjusting the learning rate. The experiments on four kinds of models and six datasets confirm the effectiveness and efficiency of our measure. And a case study shows it can not only find the ideal model reducing 0.53% of dangerous cases by only sacrificing 0.04% of training accuracy, but also refine the learning rate to train a new model averagely outperforming the original one with a 1.62% lower value of itself and 0.36% fewer number of dangerous cases.

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Satellite Federated Fine-Tuning for Foundation Models in Space Computing Power Networks

arXiv

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arXiv 2025年

作者： Zhu, Yan Zhu, Jingyang Wang, Ting Shi, Yuanming Jiang, Chunxiao Letaief, Khaled Ben MoE Engineering Research-Center of Software/Hardware Co-design Technology and Application The Shanghai Key Lab. of Trustworthy Computing East China Normal University Shanghai200062 China School of Information Science and Technology ShanghaiTech University Shanghai201210 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Advancements in artificial intelligence (AI) and low-earth orbit (LEO) satellites have promoted the application of large remote sensing foundation models for various downstream tasks. However, direct downloading of these models for fine-tuning on the ground is impeded by privacy concerns and limited bandwidth. Satellite federated learning (FL) offers a solution by enabling model fine-tuning directly on-board satellites and aggregating model updates without data downloading. Nevertheless, for large foundation models, the computational capacity of satellites is insufficient to support effective on-board fine-tuning in traditional satellite FL frameworks. To address these challenges, we propose a satellite-ground collaborative federated fine-tuning framework. The key of the framework lies in how to reasonably decompose and allocate model components to alleviate insufficient on-board computation capabilities. During fine-tuning, satellites exchange intermediate results with ground stations or other satellites for forward propagation and back propagation, which brings communication challenges due to the special communication topology of space transmission networks, such as intermittent satellite-ground communication, short duration of satellite-ground communication windows, and unstable inter-orbit inter-satellite links (ISLs). To reduce transmission delays, we further introduce tailored communication strategies that integrate both communication and computing resources. Specifically, we propose a parallel intra-orbit communication strategy, a topology-aware satellite-ground communication strategy, and a latency-minimalization inter-orbit communication strategy to reduce space communication costs. Simulation results demonstrate significant reductions in training time with improvements of approximately 33%. Copyright © 2025, The Authors. All rights reserved.

关键词： Satellite communication systems

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Intelligent Energy Management for Electric Vehicles: Fuzzy Logic and Neural Network Integration

Intelligent Energy Management for Electric Vehicles: Fuzzy L...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： T Thirumalaikumari D Lita Princy S Uma R Sadaieswaran P Malathi N Legapriyadharshini Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamil Nadu India Department of Master of Computer Applications Meenakshi College of Engineering Chennai Tamil Nadu India Department of Computer Science Engineering Dhanalakshmi Srinivasan College of Engineering and Technology Mahabalipuram Tamil Nadu India Department of Computer Applications Tagore College of arts and science Chromepet Chennai India Department of Data Analytics Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamil Nadu India Department of Computer Application Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamil Nadu India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

This study suggests a novel methodology for intelligent energy management in electric vehicles (EVs) through the integration of neural networks and fuzzy logic. Achieving enhanced energy efficiency for electric vehicles is a critical concern that is tackled in this research through the integration of neural networks' decision-making and pattern recognition functionalities with fuzzy logic's adaptability and learning capabilities. The proposed method seeks to optimize energy consumption, enhance overall performance, and extend driving range. The integrated system is subjected to rigorous testing using a meticulously planned experimental setup and simulation environment, which enables it to surpass the performance of more conventional approaches to energy management. The results emphasize the potential of the novel methodology to improve environmentally sustainable transportation options and propel the development of electric vehicle technology. This study not only offers valuable insights into efficient energy management but also establishes the groundwork for subsequent developments in the domain.

关键词： Fuzzy logic Roads Neural networks Transportation Propulsion Electric vehicles Energy efficiency Pollution measurement Energy management systems Testing

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Fast Second-order Method for Neural Networks under Small Treewidth Setting

Fast Second-order Method for Neural Networks under Small Tre...

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IEEE International Conference on Big Data

作者： Xiaoyu Li Jiangxuan Long Zhao Song Tianyi Zhou Department of Computer Science Stevens Institute of Technology Hoboken NJ School of Software Engineering South China University of Technology Guangzhou China The Simons Institute for the Theory of Computing University of California Berkeley Berkeley CA Department of Computer Science University of Southern California Los Angeles CA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Training neural networks is a fundamental problem in theoretical machine learning. Second-order methods are rarely used in practice due to their high computational cost, even they converge much faster than first-order methods. The state-of-the-art result for the second-order method to train an over-parameterized neural network can run in O(log(1/ϵ)) iterations and each iteration has O(mnd + n 3 ) running time, where n represent the number of data points, d is the dimension of the feature space of each data and m is the width of the neural network [Brand, Peng, Song and Weinstein ITCS 2021]. In this work, we further improve the convergence rate and the running time for each iteration to O(loglog(1/ϵ)) and O(mdτ 2 ) respectively, where m = Ω(n 4 ) and τ is the treewidth of the data matrix and usually very small. Our algorithm has a quadratic convergence rate, so it can be regarded as a truly second-order algorithm. To the best of our knowledge, our algorithm is the first to achieve a quadratic convergence rate for neural network training, with a per-iteration running time of O(mdτ 2 ).

关键词： Training Knowledge engineering Costs Neural networks Machine learning Big Data Computational efficiency Matrix decomposition Optimization Convergence

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A 32-Channel Time-to-Digital Converter with 20-ps Resolution for ToF applications 4

A 32-Channel Time-to-Digital Converter with 20-ps Resolution...

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4th IEEE International Conference on Circuits and Systems, ICCS 2022

作者： Ke, Qin Yu, Hang Li, Yan Ma, Siguang Xi, Daoming Chen, Rui Shenzhen University Shenzhen Laboratory of Ic Design for Iot College of Computer Science and Software Engineering Shenzhen China Physical Electronics Group of Jihua Laboratory Foshan China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Huazhong University of Science and Technology Department of Biomedical Engineering Wuhan China

ISBN: (数字)9781665460361

ISBN: (纸本)9781665460361

Single-photon avalanche diodes (SPAD) enable sensitive detection of single photons under ultra-weak light conditions. Combined with direct time-of-flight (ToF) measurements, SPAD sensors are widely used in 3D imaging systems. Time-to-Digital Converter (TDC) can accurately measure time intervals in picoseconds, which is widely used for direct TOF measurements. This paper proposes a 32-channel TDC of two-level architecture containing a delay locked loop (DLL) and a vernier delay line (VDL) loop to increase timing accuracy. Designed in a 180 nm CMOS process, the TDC occupies an area of 4.1 mm × 3.6 mm and achieves 20 ps resolution at 2 Msps measurement rate. © 2022 IEEE.

关键词： Particle beams

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Model for Recognition of Sign Language and Hand Gesture by Hard Hearing People

Model for Recognition of Sign Language and Hand Gesture by H...

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Sustainable Communication Networks and application (ICSCNA), International Conference on

作者： Manjula R Jagadeesan S Venkadeshan Ramalingam Vetriselvi T Johnpeter T Department of Computing Technologies College of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamilnadu India Information Technology Department University of Technology and Applied Sciences Sultanate of Oman Department of IOT School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamilnadu India Department of Computer Science and Engineering K.Ramakrishnan College of Engineering Trichy India

Hard hearing and deaf people are communicating with other people using the sign language. This is the only way of communication of such people to convey Since this is the only means by which these individuals can communicate, it is imperative that others be able to understand their language. Sign language recognition by computer begins with the acquisition of sign gestures and ends with recognition and conversion to text or voice. Static and dynamic are the two ways of sign gestures used. This study aims at focusing on the dynamic gesture recognition and the steps for recognizing sign language are discussed. The collected data are preprocessed and feature are extracted for classification. Finally the results are all investigated and developed for single user.

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Full Robotics Automation: Approaches and Implementing Concerns

Full Robotics Automation: Approaches and Implementing Concer...

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2024 International Conference on Communication, Computing and Energy Efficient Technologies, I3CEET 2024

作者： Choriev, A. Kumar, B. Arun Baghel, Abhishek Singh Grover, Varsha Alabdeli, Haider Sharma, Gunjan Research University Tashkent Uzbekistan Western Caspian University Baku Azerbaijan Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore641021 India Ies University Ies Institute of Technology and Management Department of Computer Science & Engineering M.P. Bhopal India Chandigarh Engineering College Chandigarh Group of Colleges Department of Computer Application Jhanjeri Punjab Mohali140307 India The Islamic University College Of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Gla University Institute of Business Management Mathura India

ISBN: (纸本)9798331541583

By using computer robotics that connect to systems via an operator interface to automate corporate activities, robotic process automation aims to reduce costs and boost productivity. However, if you make the wrong decision, a few important tasks, such as determining which procedures are suitable for her RPA robotics, might have a negative impact on your business. Thus, this study provides a way to investigate the advancement of RPA in corporate environments. To identify the essential RPA concepts that need to be investigated and considered in RPA case studies, this study offers a convincing analysis of the relevant literature. Apart from evaluating and applying the model based on his previous RPA research on case studies, a model that combines the fundamental ideas of RPA with research in design science is provided. The results of the research show that most of the core RPA ideas listed in the published review are not addressed by the selected RPA CS. RPA is a very young field, hence there isn't a lot of literature outlining its key concepts. This study attempts to bridge this gap by recognizing and including the main RPA topics and providing a model that integrates the key ideas. Researchers may use the model as a basis for their research and contribute RPA case examples. © 2024 IEEE.

关键词： Office automation

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