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A New Method for AC Voltage Withstand Test of High-Voltage Power Cable Based on Gas-Insulated Line and Distribution Partial Discharge Measurement

A New Method for AC Voltage Withstand Test of High-Voltage P...

引用

IEEE international conference on Smart Grid and Smart Cities (ICSGSC)

作者： Wei Ding Lvxuan Huang Junrui Pan Xu Wang Zhenchang Wei Hao Zhang Guoming Wang Anhui Power Transmission and Transformation Co. Ltd. Hefei China Research and Development Center Hangzhou Guozhou Power Technology Co. Ltd. Hangzhou China

ISBN: (数字)9798350377408

ISBN: (纸本)9798350377415

the AC withstand voltage test is an effective method to identify the main insulation performance and insulation defects of high-voltage cable. In the paper, gas-insulated line is designed and fabricated to connect the series resonance system with the cable under test. It is filled with SF 6 gas with a pressure of 0.35MPa and is suitable for test in limited spaces such as indoor substation. the distributed partial discharge (PD) detection system is developed, which consists of high frequency current transformer, synchronous sensor, detection unit, and communication unit, elastic cloud server, and analysis software. By applying distributed networking, PD detection of terminal and joint along the whole cable length during the AC voltage withstand test can be accomplished simultaneously with high efficiency. In addition, the equivalent time and the equivalent frequency map is proposed for separating different type of detected signals, by which sensitivity and accuracy of PD detection can be improved.

关键词： Partial discharge measurement Cloud computing Voltage measurement Power cables Sulfur hexafluoride Power cable insulation High-voltage techniques Safety Servers Gas insulated transmission lines

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distributed Event-Triggering Secondary Frequency Control of Islanded Microgrids Under DoS Attacks 8th

Distributed Event-Triggering Secondary Frequency Control of...

引用

8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

作者： Zhou, Lihua Zhang, Changda Du, Dajun Fei, Minrui Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronic Engineering and Automation Shanghai University Shanghai China School of Electrical Engineering Hebei University of Science and Technology Hebei China

ISBN: (纸本)9789819602247

High reliability and efficiency of microgrids are being threatened by cyber attacks. this paper investigates a Secondary Frequency Control Scheme (SFCS) for an islanded microgrid with Event-Triggering Mechanism (ETM) under Denial of Service (DoS) attacks. Firstly, the microgrid dynamic model is reviewed and control objectives are formulated. Secondly, a cooperative frequency control scheme combined with distributed event-triggering mechanism is presented to achieve the desired frequency control under DoS attacks. thirdly, the stability and Zeno Behaviour of the closed-loop system are analysed by Lyapunov theory. Finally, simulation results of the microgrid are explored to verify the effectiveness of the proposed frequency control scheme. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cyber attacks

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Efficient Federated Learning with Adaptive Channel Pruning for Edge Devices 19

Efficient Federated Learning with Adaptive Channel Pruning f...

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2022 IEEE SmartWorld, 19th IEEE international conference on Ubiquitous Intelligence and computing, 2022 IEEE international conference on Autonomous and Trusted Vehicles conference, 22nd IEEE international conference on Scalable computing and Communications, 2022 IEEE international conference on Digital Twin, 8th IEEE international conference on Privacy computing and 2022 IEEE international conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Jia, Yongzhe Zhang, Xuyun Liu, Bowen Dou, Wanchun Nanjing University State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing China Shenzhen China Macquarie University Department of Computing SydneyNSW Australia Shiga University Faculty of Data Science Hikone Japan

ISBN: (纸本)9798350346558

Federated learning (FL) is an emerging machine learning paradigm that allows distributed participants to train a global model collaboratively. In edge networks, FL involves numerous edge devices updating the local models with the assistance of a central server while keeping the local data on the devices. However, edge devices are commonly resource-limited while neural network models used in FL require significant resource consumption to reach a satisfactory accuracy. Moreover, different devices are various in terms of system capabilities, and employing a uniform model on all devices leads to the derogation of model accuracy. Several pioneer work integrate model pruning techniques into the FL process and focus on reducing the resource consumption on edge devices, whereas neglecting the impact of model generality on accuracy. In our work, we propose FedACP, an efficient FL approach with adaptive channel pruning for edge devices to achieve better resource-accuracy trade-offs. Specifically, we design a two-phase channel pruning approach to adaptively prune local models, in which both the resource constraints and model generalities are taken into consideration. In addition, we also design an aggregation algorithm for aggregating heterogeneous local models produced by the two-phase channel pruning approach. To evaluate the performance of the FedACP, we implement it with a real FL framework FedML and compare it with several state-of-the-art methods. the experimental results show that FedACP achieves better model accuracy while simultaneously reducing 22.41% to 27.34% model parameters. © 2022 IEEE.

关键词： Economic and social effects

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A Column Sparse Controller Design for distributed Networked Control Systems 8th

A Column Sparse Controller Design for Distributed Networked...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

作者： Guan, Yanpeng Yin, Hongjie Sun, Chao Yao, Qingsheng Shanxi University Taiyuan030013 China Kunming Shipborne Equipment Research and Test Center Kunming650216 China

ISBN: (纸本)9789819602933

this paper presents a distributed structured controller design method in the case of finite number of subsystems with sensor failures. the constraint on the number of sensor failures is represented by a cardinality constraint on the controller gain matrix. A mixed integer programming method is employed to deal with this type of column cardinality constraint. By using the Lyapunov functional method, we present a sufficient condition that guarantees the H∞ asymptotic stability of the closed-loop system. And a column sparse controller design method is presented in the form of linear matrix inequalities. Finally, a numerical example is provided to illustrate the effectiveness of the developed approach. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lyapunov functions

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Cervical Cancer Identification using Deep Learning Approaches 8

Cervical Cancer Identification using Deep Learning Approache...

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8th IEEE international conference on computing, Engineering and Design, ICCED 2022

作者： Kong, Shien Nie Dwi Handayani, Dini Oktarina Mun, Hou Kit Pei Chong, Pei Mantoro, Teddy Taylor's University School of Computer Science Engineering Faculty of Innovation Technology Selangor Subang Jaya Malaysia International Islamic University Malaysia Kuliyyah of Information Communication and Technology Computer Science Department Kuala Lumpur Malaysia Universiti Tunku Abdul Rahman Bandar Faculty of Engineering and Science Department of Mechatronic and Biomedical Engineering Selangor Subang Jaya Malaysia Taylor's University School of Biosciences Faculty of Health and Medical Sciences Selangor Subang Jaya Malaysia Sampoerna University Faculty of Engineering and Technology Department of Computer Science Jakarta Indonesia

ISBN: (纸本)9781665453899

the presence of cervical cancer is not apparent as its incubation period is long. A pap smear screening is the only diagnostic method;examining the pap smear slides uses a microscope. However, problems happen where humans make mistakes during the diagnostic process, causing inaccurate results and delaying the individual who needs to receive comprehensive treatments. With hopes to improve the current situation, assisting cervical cancer diagnosis with artificial intelligence techniques is suggested. this paper will use ResNet101v2, an upgraded residual network from ResNet, to develop a cervical cancer detection model to predict the severity of cervical cells. the Herlev dataset distributed 70% into training and 30% into validation;remaining 98 unique images will be used during the testing stage. Transfer learning techniques were introduced to develop the model. Using the testing images, the model reached 71.4% accuracy contributing better accuracy compared to other research studies in predicting the cervical cells using 7 classification classes. the model shows potential for clinical cytotechnologists' during the pap smear diagnosis. © 2022 IEEE.

关键词： Diseases

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A Community-Driven Approach to Democratize Access to Satellite Ground Stations 21

A Community-Driven Approach to Democratize Access to Satelli...

引用

27th ACM Annual international conference on Mobile computing and networking (MobiCom)

作者： Singh, Vaibhav Prabhakara, Akarsh Zhang, Diana Yagan, Osman Kumar, Swarun Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781450383424

Should you decide to launch a nano-satellite today in Low-Earth Orbit (LEO), the cost of renting ground station communication infrastructure is likely to significantly exceed your launch costs. While space launch costs have lowered significantly with innovative launch vehicles, private players, and smaller payloads, access to ground infrastructure remains a luxury. this is especially true for smaller LEO satellites that are only visible at any location for a few tens of minutes a day and whose signals are extremely weak, necessitating bulky and expensive ground station infrastructure. In this paper, we present a community-driven distributed reception paradigm for LEO satellite signals where signals received on many tiny handheld receivers (not necessarily deployed on rooftops but also indoors) are coherently combined to recover the desired signal. this is made possible by employing new synchronization and receiver orientation techniques that study satellite trajectories and leverage the presence of other ambient signals. We compare our results with a large commercial receiver deployed on a rooftop and show a 8 dB SNR increase both indoors and outdoors using 8 receivers, costing $38 per RF frontend.

关键词： LEO Satellites Ground stations Synchronization Coherent Combination low-cost distributed Systems

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Energy-Efficient and Reliable Federated Learning in Heterogeneous Mobile-Edge computing 19

Energy-Efficient and Reliable Federated Learning in Heteroge...

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作者： Xu, Yan Li, Liying Cong, Peijin Zhou, Junlong Nanjing University of Science and Technology School of Computer Science and Engineering China Institute of Computing Technology Chinese Academy of Sciences State Key Laboratory of Computer Architecture China

ISBN: (纸本)9798350346558

As a decentralized machine learning paradigm, Federated Learning (FL) is an emerging technique to protect user data privacy in Mobile Edge computing (MEC). FL adopts the idea of distributed privacy computing, enabling the terminal devices to train machine learning models required by servers locally and upload model parameters to servers for aggregation. However, in the process of coupling FL with MEC systems, device heterogeneity, malicious use of poisoned data by users and limited system resources will seriously affect FL training accuracy, system reliability and energy efficiency. In this paper, a highly reliable and energy-efficient FL scheme is designed to solve these issues. Specifically, we first propose a calculation method for estimating the reliability of heterogeneous devices. then, we design a highly reliable device selection scheme based on devices' reliability, computing power, and participation times. Finally, we utilize the slack time in FL training to dynamically adjust the voltage and frequency of devices for reducing the energy consumption of training. Experimental results show that our proposed scheme improves the accuracy by up to 36.45% on average and saves energy by up to 48.63% when compared to the baseline and benchmarking methods. © 2022 IEEE.

关键词： Reliability

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A Research Proposal for Combatting Misinformation through Social Media Design 8

A Research Proposal for Combatting Misinformation Through So...

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8th IEEE international conference on Behavioural and Social computing, BESC 2021

作者： Gurgun, Selin Faculty of Science and Technology Department Bournemouth University Poole United Kingdom

ISBN: (纸本)9781665400237

Use of Social Network Sites (SNS) for information seeking has amplified the dissemination of misinformation. One of the promising strategies to mitigate the problem is social corrections, defined as users correcting other users' posts on SNS. However, such social corrections are rare. Two factors are identified to explain why people do not challenge misinformation. First, users may hold negative views about challenging other users. Second, features for correcting misinformation or questioning it are not apparent in the design of SNS. this research aims to analyze users' perceptions, whether these perceptions discourage them from engaging and then to explore, devise and test SNS features that empower users to challenge misinformation. the main contributions of the present study are in two aspects. First, it will be the first study to investigate reasons why people avoid challenging misinformation. Second, this research could be a useful aid for empowering users to express their concerns on cyberspace when they encounter misinformation without feeling constrained. © 2021 IEEE

关键词： Social networking (online)

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SVM-based Sarcasm Detection System: NLP Using Heuristic Approach

SVM-based Sarcasm Detection System: NLP Using Heuristic Appr...

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international conference on computing Communication Control and Automation (ICCUBEA)

作者： Rajesh Bharati Wasudeo Rahane P.D. Patil Shubham Tapkeer Vijay Waghmare Jaydeep Patil Simran Desai Department of Computer Engineering DYPIT Pune India

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

To create a system that will detect and interpret sarcastic messages in blog and tweet using natural language processing (NLP) technology and support vector machines (SVM). the data used for training and evaluation are available on global blog profiles, making it useful for capturing the nuances of informal conversations. Using NLP, the system understands the context and speech patterns that indicate malicious language, allowing for more accurate detection. Using SVM as a classification technique helps build a robust and reliable model of sarcasm by enabling the system to discern between tweets that are sarcastic and those that aren't. With sarcasm increasingly used in online communication, developing this technique can help improve thinking and understanding of tone and meaning on social media. In the age of thinking, different analytics have emerged that analyze people's behavior and thoughts on social media (such as Facebook, Twitter, blogs and other social networking sites). the main purpose of sentiment analysis is to identify the text's polarity—positive, negative, or neutral.

关键词： Support vector machines Training Sentiment analysis Accuracy Social networking (online) Blogs Process control Nearest neighbor methods Vectors Reliability

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Sentiment Analysis on Indonesian-Sundanese Code-Mixed Data

Sentiment Analysis on Indonesian-Sundanese Code-Mixed Data

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international conference for Convergence of Technology (I2CT)

作者： Hajarot Najiha Ade Romadhony School of Computing Telkom University Bandung Indonesia

In this work, we conduct sentiment analysis on Indonesian-Sundanese code-mixed tweets. Sundanese is one of Indonesia’s regional languages with over 42.000.000 speakers. We use a pre-trained language model, IndoBERT, to tackle the sentiment analysis task. Our evaluation result shows that the best accuracy is 81%. We analyze the errors and find that most mislabeled tweets are because the words on the wrongly predicted tweet contain many words from other labels. It is also possible that it happens since the sentence in the tweet is ambiguous, the words used in the tweet are unavailable in the training data set, or the use of abbreviated words in the tweet.

关键词： Sentiment analysis Analytical models Codes Social networking (online) Training data Data models Labeling

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