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IEEE General Meeting Power& Energy Society

作者： Ahmad M. Saber Amr Youssef Davor Svetinovic Hatem Zeineldin Deepa Kundur Ehab El-Saadany EECS Department Advanced Power and Energy Center Khalifa University Abu Dhabi UAE Department of Electrical and Computer Engineering University of Toronto Toronto Canada Concordia Institute for Information Systems Engineering Montreal Canada

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

Recently, there has been a growing interest in utilizing machine learning for accurate classification of power quality events (PQEs). However, most of these studies are performed assuming an ideal situation, while in reality, we can have measurement noise, DC offset, and variations in the voltage signal’s amplitude and frequency. Building on the prior PQE classification works using deep learning, this paper proposes a deep-learning framework that leverages attention-enabled Transformers as a tool to accurately classify PQEs under the aforementioned considerations. The proposed framework can operate directly on the voltage signals with no need for a separate feature extraction or calculation phase. Our results show that the proposed framework outperforms recently proposed learning-based techniques. It can accurately classify PQEs under the aforementioned conditions with an accuracy varying between 99.81%–91.43% depending on the signal-to-noise ratio, DC offsets, and variations in the signal amplitude and frequency.

关键词： Voltage measurement Noise Power quality Transformers Harmonic analysis Frequency measurement Numerical models Noise measurement Transient analysis Signal to noise ratio

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Large Scale Model Enabled Semantic Communications Based on Robust Knowledge Distillation

Large Scale Model Enabled Semantic Communications Based on R...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Ding, Kuiyuan Liu, Fangfang Yang, Yang Chen, Mingzhe Guo, Caili Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering The Institute for Data Science and Computing Coral GablesFL33146 United States Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China

ISBN: (纸本)9798350351255

Large scale artificial intelligence (AI) models possess excellent capabilities in semantic representation and understanding, making them particularly well-suited for semantic encoding and decoding. However, the substantial scale of these AI models imposes unacceptable computational resources and communication delays. To address this issue, we propose a semantic communication scheme based on robust knowledge distillation (RKD-SC) for large scale model enabled semantic communications. In the considered system, a transmitter extracts the features of the source image for robust transmission and accurate image classification at the receiver. To effectively utilize the superior capability of large scale model while make the cost affordable, we first transfer knowledge from a large scale model to a smaller scale model to serve as the semantic encoder. Then, to enhance the robustness of the system against channel noise, we propose a channel-aware autoencoder (CAA) based on the Transformer architecture. Experimental results show that the encoder of proposed RKD-SC system can achieve over 93.3% of the performance of a large scale model while compressing 96.67% number of parameters. Code: https://***/echojayne/RKD-SC. © 2024 IEEE.

关键词： Channel coding

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Guest Editorial: Special issue on computational methods and artificial intelligence applications in low-carbon energy systems

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IET Renewable Power Generation 2024年第3期18卷 303-305页

作者： Wang, Yishen Zhou, Fei Guerrero, Josep M. Baker, Kyri Chen, Yize Wang, Hao Xu, Bolun Xu, Qianwen Zhu, Hong Agwan, Utkarsha State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd. Beijing China Department of Energy Technology Aalborg University Aalborg Denmark Department of Civil Environmental and Architectural Engineering University of Colorado-Boulder BoulderCO United States Guangzhou China Department of Data Science and AI Monash University ClaytonVIC Australia Department of Earth and Environmental Engineering Columbia University New YorkNY United States Electric Power and Energy Systems Division KTH Royal Institute of Technology Stockholm Sweden State Grid Jiangsu Electric Power Co. Ltd. Nanjing China Department of Electrical Engineering and Computer Sciences University of California - Berkeley BerkeleyCA United States

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Revealing Non-Hermitian Band Structure of Photonic Floquet Media

Revealing Non-Hermitian Band Structure of Photonic Floquet M...

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Frontiers in Optics, FiO 2022

作者： Park, Jagang Cho, Hyukjoon Lee, Seojoo Lee, Kyungmin Lee, Kanghee Park, Hee Chul Ryu, Jung-Wan Park, Namkyoo Jeon, Sanggeun Min, Bumki Department of Mechanical Engineering Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of Department of Physics Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of Center for Theoretical Physics of Complex Systems Institute of Basic Science Daejeon34126 Korea Republic of Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of School of Electrical Engineering Korea University Seoul02841 Korea Republic of

We experimentally reveal the unique non-Hermitian band structures of a photonic Floquet medium emulated in the microwave regime. We observe the presence of the momentum gaps and the subharmonic parametric oscillations...

ISBN: (纸本)9781557528209

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A Digital Twin-Based Intelligent Network Architecture for Underwater Acoustic Sensor Networks

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IEEE Transactions on Mobile Computing 2025年

作者： Song, Shanshan Huangfu, Bingwen Guo, Jiani Liu, Jun Cui, Junhong Shen, Xuemin Beihang University School of Electronic and Information Engineering Beijing100191 China Jilin University College of Computer Science and Technology Changchun130012 China UESTC Shenzhen Institute for Advanced Study Shenzhen China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Underwater acoustic sensor networks (UASNs) drive toward strong environmental adaptability, intelligence, and multifunctionality. However, due to unique UASN characteristics, such as long propagation delay, dynamic channel quality, and high attenuation, existing studies present untimeliness, inefficiency, and inflexibility in real practice. Digital twin (DT) technology is promising for UASNs to break the above bottlenecks by providing high-fidelity status prediction and exploring optimal schemes. In this article, we propose a Digital Twin-based Network Architecture (DTNA), enhancing UASNs' environmental adaptability, intelligence, and multifunctionality. By extracting real UASN information from local (node) and global (network) levels, we first design a layered architecture to improve the DT replica fidelity and UASN control flexibility. In local DT, we develop a resource allocation paradigm (RAPD), which rapidly perceives performance variations and iteratively optimizes allocation schemes to improve real-time environmental adaptability of resource allocation algorithms. In global DT, we aggregate decentralized local DT data and propose a collaborative Multi-agent reinforcement learning framework (CMFD) and a task-oriented network slicing (TNSD). CMFD patches scarce real data and provides extensive DT data to accelerate AI model training. TNSD unifies heterogeneous tasks' demand extraction and efficiently provides comprehensive network status, improving the flexibility of multi-task scheduling algorithms. Finally, practical and simulation experiments verify the high fidelity of DT. Compared with the original UASN architecture, experiment results demonstrate that DTNA can: (i) improve the timeliness and robustness of resource allocation;(ii) greatly reduce the training time of AI algorithms;(iii) more rapidly obtain network status for multi-task scheduling at a low cost. © 2002-2012 IEEE.

关键词： Resource allocation

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ECG-LLM: Leveraging Large Language Models for Low-Quality ECG Signal Restoration

ECG-LLM: Leveraging Large Language Models for Low-Quality EC...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Longfei Liu Guosheng Cui Cheng Wan Dan Wu Ye Li Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China Department of Electrical and Computer Engineering Georgia Institute of Technology Atlanta USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Electrocardiography (ECG) signals are often plagued by various types of noise, which substantially undermines the precision of subsequent analysis. This paper presents ECG-LLM, an innovative model designed to address the challenges posed by low-quality ECG signals. Leveraging the capabilities of Large Language Models (LLMs), ECG-LLM forecasts and imputes missing values in 12-lead ECG data. ECG-LLM adapts the auto-regressive characteristics of LLMs, utilizes textual markers for timestamp information, and employs a strategy of freezing Transformer layers while training new embedding and projection layers. We used ECG datasets from multiple sources for our experiments. Experimental results demonstrate that ECG-LLM significantly outperforms state-of-the-art time series forecasting models, achieving a Mean Squared Error (MSE) of 0.644 and a Mean Absolute Error (MAE) of 0.456 for a forecast length of 64. Additionally, after using ECG-LLM to restore the electrocardiogram, the disease recognition accuracy of the baseline model was improved. Our findings highlight the feasibility and effectiveness of applying LLMs to ECG signal processing, offering a new perspective for medical signal analysis and providing a potential new approach for signal preprocessing in healthcare. Code is available at https://***/dragonlfy/ECG-LLM.

关键词： Training Adaptation models Large language models Biological system modeling Medical services Electrocardiography Predictive models Transformers Forecasting Medical diagnostic imaging

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Integrated self-referencing single shot digital holographic microscope and optical tweezer

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Light(advanced Manufacturing) 2022年第3期3卷 205-218页

作者： Subhash Utadiya Sachin Joshi Nimit Patel Chetna Patel Mugdha Joglekar Vani Cahhniwal Timothy O’Connor Bahram Javidi Arun Anand Optics Laboratory Applied Physics DepartmentFaculty of Technology and EngineeringThe Maharaja Sayajirao University of BarodaVadodara 390001India Department of Electrical and Computer Engineering University of ConnecticutStorrsCT06269-4157USA Institute of Interdisciplinary Studies The Maharaja Sayajirao University of BarodaVadodaraIndia

Digital holographic microscopy is a single-shot technique for quantitative phase imaging of samples,yielding thickness profiles of phase *** provides sample features based on their morphology,leading to their classification and ***,observing samples,especially cells,in fluids using holographic microscopes is difficult without immobilizing the *** tweezers can be used for sample immobilization in *** present manuscript provides an overview of our ongoing work on the development of a compact,low-cost microscopy system for digital holographic imaging of optically trapped *** of digital holographic microscopy system with tweezers is realized by using the optical pickup unit extracted from DVD burners to trap microsamples,which are then holographically imaged using a highly compact self-referencing interferometer along with a low-cost,in-house developed quadrant photodiode,providing morphological and spectral information of trapped *** developed integrated module was tested using polystyrene microspheres as well as human *** investigated system offers a multitude of sample features,including physical and mechanical parameters and corner frequency information of the *** features were used for sample *** proposed technique has vast potential in opening up new avenues for low-cost,digital holographic imaging and analysis of immobilized samples in fluids and their classification.

关键词： Digital holographic microscopy Optical tweezers Red blood cells Quantitative phase imaging

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Communication-aided Terahertz Sensing: A Novel Indoor People Counting System Via Deep Learning

Communication-aided Terahertz Sensing: A Novel Indoor People...

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Virtual Conference on Communications (VCC), IEEE

作者： Eslam Hasan Elmahedi Mahalal Muhammad Ismail Zi-Yang Wu Mostafa M. Fouda Zubair Md Fadlullah Department of Computer Science Tennessee Technological University Cookeville TN USA Department of Computer Science University of New Haven West Haven CT USA College of Information Science and Engineering Northeastern University Shenyang China Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA Department of Computer Science Western University London ON Canada

ISBN: (数字)9798331530099

ISBN: (纸本)9798331530105

Indoor people counting systems are used in security monitoring, energy management, room resources adjustment, market research, and smart homes. However, the existing radio-frequency-based indoor people counting systems use a different frequency than the utilized radio frequency communication signal, which adds more costs for system deployment and wastes the radio frequency resources. This paper introduces a novel communication-aided terahertz (THz) sensing approach for robust indoor people counting that utilizes the THz communication downlink signal to sense the number of indoor people. Leveraging the wireless THz communication downlink signal, we propose a device-free, cost-effective, and non-intrusive indoor people counting system. Our method employs a 1D convolutional neural network (CNN) to process historical THz downlink channel gain data and accurately estimate the number of indoor occupants. The numerical results demonstrate the effectiveness of the proposed approach, achieving a remarkable 99.5% accuracy in people counting indoors up to eight people. The proposed model's ability to maintain high accuracy in indoor people counting across different numbers of users demonstrates its effectiveness and robustness in capturing the occupancy signature from the wireless THz downlink communication signal in indoor environments. Also, the accuracy of the proposed CNN time series classifier outperforms the random forest times series classifier with the catch22 feature extractor by more than 10% without needing any feature extraction methods. To the best of the authors' knowledge, this study represents the first investigation into indoor people counting in the THz frequency band utilizing the THz downlink communication signal for sensing the number of indoor occupants.

关键词： Wireless communication Wireless sensor networks Accuracy Time series analysis Downlink Sensors Numerical models Convolutional neural networks Random forests Terahertz communications

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Technologies for Modulation of Visible Light and their Applications

arXiv

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arXiv 2024年

作者： Park, Sanghyo Notaros, Milica Mohanty, Aseema Kim, Donggyu Notaros, Jelena Mouradian, Sara Department of Physics Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical & Computer Engineering Tufts University MedfordMA02155 United States Department of Electrical & Computer Engineering University of Washington SeattleWA98195 United States

Control over the amplitude, phase, and spatial distribution of visible-spectrum light underlies many technologies, but commercial solutions remain bulky, require high control power, and are often too slow. Active integrated photonics for visible light promises a solution, especially with recent materials and fabrication advances. In this review, we discuss three growing application spaces which rely on control of visible light: control and measurement of atomic quantum technologies, augmented-reality displays, and measurement and control of biological systems. We then review the commercial dynamic surfaces and bulk systems which currently provide visible-light modulation and the current state-of-the-art integrated solutions. Throughout the review we focus on speed, control power, size, optical bandwidth, and technological maturity when comparing technologies. Copyright © 2024, The Authors. All rights reserved.

关键词： Light modulation

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Analysis of the Requirement and Artificial Intelligence-based Resource Management System in Cloud 9

Analysis of the Requirement and Artificial Intelligence-base...

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9th International Conference on advanced Computing and Communication Systems, ICACCS 2023

作者： Nagasundaram, S. Bobinath, B.N. Shedthi, Ashwini Rajalakshmi, K. Humnekar, Trupti Dandekar Millerjothi Vels Institute of Science Technology and Advanced Studies School of Computing Sciences TamilNadu Chennai India NPR College of Engineering Department of Computer Science and Engineering TamilNadu Dindigul India New Horizon College Marathahalli Research Scholar at Amrita University Department of Commerce and Management Bangalore India University CMS Business School Department of Marketing Bangalore India Sri Eshwar College of Engineering Department of Mechanical Coimbatore India

ISBN: (纸本)9798350397376

Artificial Intelligence (AI) approaches, such as the big data, mobile computing, and Internet of Things (IoT) have become more common in the computer science area in recent years. It is critical to properly manage resources in order to maintain service quality, service level agreements, and the system's overall availability. AI may be used to address the needs of cloud resource management, and this study examines and evaluates several approaches. Fog computing systems, intelligent cloud computing systems and edge-cloud systems are all types of AI-based cloud resource management systems that have been reviewed in this contribution. In this research, an intelligent resource management method that controls mobile resources by monitoring device states and projecting their future stability is proposed. We look at the possibility of using our suggested resource management system to a variety of cloud-based platforms. © 2023 IEEE.

关键词： Fog computing

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