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检索条件"任意字段=IEEE International Conference on e-Science and Grid Computing"

共 17242 条记录，以下是271-280 订阅

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Recognition of Meteorological Conditions in Snowy Days for Transmission Based on Deep Learning 5

Recognition of Meteorological Conditions in Snowy Days for T...

引用

5th international conference on Intelligent computing and Human-Computer Interaction, ICHCI 2024

作者： Yang, Xifei Shi, Ying Li, Zhe Liang, Yun Wang, Jinyu College of Automation Wuhan University of Technology Wuhan430070 China Electric Power Science Research Institute State Grid Henan Electric Power Company Zhengzhou450052 China

ISBN: (纸本)9798350368284

The stable operation of transmission lines under severe weather conditions is critical to the reliability of power systems. Accurately identifying the state of transmission lines during snowy and other extreme weather conditions can enhance the efficiency of grid maintenance and dispatch. This thesis proposes a computer vision-based method for recognizing the snow weather state of transmission lines. By constructing a dataset of images taken during clear and snowy days, this study compares and analyzes the performance of several deep learning models, including ResNet, RepLKNet, and TinyViT, in snow weather recognition. experimental results demonstrate that the RepLKNet model exhibits the best accuracy and robustness, effectively distinguishing between clear and snowy weather images with an accuracy of over 99%. Compared to traditional methods, the proposed deep learning-based approach significantly improves the accuracy and efficiency of snow weather state recognition for transmission lines. The findings provide technical support for intelligent maintenance of power systems under extreme weather conditions. © 2024 ieee.

关键词： Power transmission lines

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LexiFuse+: A Unified One-Class Solution for Imbalanced Short-Text Classification 19

LexiFuse+: A Unified One-Class Solution for Imbalanced Short...

引用

19th ieee international conference on e-science, e-science 2023

作者： Bose, Saugata Su, Guoxin School of Computing and Information Technology University of Wollongong Wollongong Australia

ISBN: (纸本)9798350322231

Introducing LexiFuse+: a semi-supervised model merging lexicon features, BeRT transfer learning, and one-class classifiers to detect anomalous content in short texts. It tackles challenges of informal text and imbalanced datasets, excelling in hate speech detection. By leveraging one-class classifiers in a fully deep fine-tuned network trained with unlabeled data, LexiFuse+ surpasses base models. © 2023 ieee.

关键词： Feature extraction

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Smart grid Attack Region Partitioning Strategy Based on Node Importance 9

Smart Grid Attack Region Partitioning Strategy Based on Node...

引用

9th ieee international conference on Data science in Cyberspace, DSC 2024

作者： Chen, Zilong Xu, Li Li, Mohan Sun, Yanbin Guangzhou University School of Cyberspace Security Guangzhou China Guangzhou University Cyberspace Institute of Advanced Technology Guangzhou China Guangdong Key Laboratory of Industrial Control System Security Guangzhou China

ISBN: (纸本)9798350391367

With the high degree of integration between physical power grids and information network systems in smart grids, false data injection attacks pose a significant threat to power systems. Traditional reactive power source partitioning methods typically rely on static electrical distances or preset rules, failing to fully consider the dynamic changes in the grid's real-time operational status, resulting in suboptimal partitioning outcomes in dynamic environments. This paper proposes a power grid attack region partitioning strategy based on node importance, using node importance and electrical control distance as partitioning criteria. This approach allows for dynamic adjustments according to the operational status of the power system, ensuring strong coupling within regions and weak coupling between different regions. Finally, we conducted extensive experimental validation in a power system simulation environment. The results demonstrate that this method can effectively reduce attack costs and improve attack success rates, exhibiting good stability and robustness. © 2024 ieee.

关键词： Power grids

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Distribution System Voltage Management with SVR and Multi PV Systems 2

Distribution System Voltage Management with SVR and Multi PV...

引用

ieee 2nd international conference on Power science and Technology (ICPST)

作者： Chang, Yiming Xu, Yi Ma, Xiaosong Fu, Dihui Chen, Zhuozhou State Grid Liaoning Elect Power Co Ltd Shenyang Power Supply Co Shenyang Peoples R China Hangzhou Zhiyuan Technol Co Ltd Hangzhou Peoples R China

ISBN: (纸本)9798350349047;9798350349030

In traditional Var control methods, inverters have to absorb reactive power for daylong to eliminate voltage rise caused by PV power. While, with the existence of the step voltage regulator (SVR) in a distribution network, the potential overvoltage problem can be eliminated by a few SVR tap operations. Consequently, the responsibility of Var control method can be changed only to reduce voltage fluctuations. In this paper, an innovative local Var control method is developed for voltage fluctuation alleviation in real time. Compared with traditional local Var control methods (e. g. Q-P curve), the Var support burden of inverters can be significantly reduced in the proposed method. In addition, due to less reactive power injection, less line loss (save around 20%) can be expected and smaller inverter capacity is required. On the basis of this local method, a global scheme is established for coordination among PV inverters. Specifically, the Var support burden will be redistributed among inverters by the global method with a certain time interval (communication time delay). Numerical results have proved the effectiveness of the proposed model, and future reactive power support design for distributed PV systems can benefit from this research.

关键词： photovoltaic (PV) reactive power SVR distribution system.

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enhancing Spam email Detection with a Soft Voting ensemble of Optimized Machine Learning

Enhancing Spam Email Detection with a Soft Voting Ensemble o...

引用

2024 ieee international conference on computing, Applications and Systems, COMPAS 2024

作者： Tazwar, Asif Daiyan, Md Mahir Jiabul Hoque, Md Saifuddin, Mohammed Khaliluzzaman, Md. International Islamic University Chittagong Dept. of Computer and Communication Engineering Chittagong Bangladesh Fse International Islamic University Chittagong Dept. of Computer and Communication Engineering Chittagong Bangladesh Fse International Islamic University Chittagong Dept. of Computer Science and Engineering Chittagong Bangladesh

ISBN: (纸本)9798331529765

Spam email detection is crucial for cybersecurity, as it protects user privacy and reduces security risks. The persistent presence of spammers necessitates continuous improvements in spam filtering measures. To address this challenge, this study employs grid Search Optimizer to fine-tune the parameters of four distinct classifiers: Support Vector Machine (SVM), Random Forest, Naive Bayes, and XGBoost. Soft Voting serves as the final classifier, enhancing overall accuracy by integrating results from optimized classifiers to detect and classify spam emails across the Spam Mails Dataset and enron1 Dataset. The experimental study compared the proposed model with relevant works and found that hyperparameter tuning and Soft Voting significantly enhanced its performance compared to existing approaches. The proposed ensemble model outperformed individual classifiers, achieving accuracies of 99.32% and 99.12%, respectively. Also, the ensemble model demonstrated an AUC of 1.00 on both datasets, indicating its effectiveness in distinguishing between spam and ham emails. This approach surpasses prior studies in accuracy, generalization, and robustness by innovatively combining grid Search and Soft Voting. The constructed model exhibits high effectiveness and efficiency in the detection of spam emails. © 2024 ieee.

关键词： Decision trees

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Smart grid Solutions

Smart Grid Solutions

引用

2024 international conference on Sustainable computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Sharma, Preeti Gupta, Vaishnavi Gupta, Shivnarayan Kumari, Anjali Jindal, Vanshika Raj Kumar Goel Institute of Technology Uttar Pradesh India

ISBN: (纸本)9798350355338

The energy Utility Project is a multifaceted initiative focused on creating a sustainable and modern energy infrastructure. It includes aspects of electricity, gas, and renewable energy, with goals such as improving efficiency, promoting sustainability, and ensuring reliability. The project involves feasibility studies, regulatory compliance, and advanced technology integration. Key features include renewable energy incorporation, customer engagement, cybersecurity measures, and comprehensive financial planning. environmental impact assessment and emergency response planning are integral, along with stakeholder engagement for community support. The project emphasizes transparent implementation, effective project management, and continuous evaluation for successful outcomes. Overall, it aims to serve as a model for advanced, resilient, and eco-friendly energy infrastructure, contributing to broader goals of energy security and community well-being.. © 2024 ieee.

关键词： Data science eDA IoT Machine Learning Renewable energy Solar energy

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Intelligent error Detection in electric Power Texts Based on ALBeRT-GCNN-CRF Model 5

Intelligent Error Detection in Electric Power Texts Based on...

引用

5th international conference on Intelligent computing and Human-Computer Interaction, ICHCI 2024

作者： Sun, Feng Li, Tianyi Shang, erzhen Zhang, Yuanbo Electric Power Research Institute of State Grid Liaoning Electric Power Co. Ltd Shenyang China State Grid Liaoning Electric Power Co. Ltd Shenyang China

ISBN: (纸本)9798350368284

Detecting grammatical errors in electric power texts is a crucial task in natural language processing, aimed at identifying typos, grammatical issues, and sentence structure problems. Due to the flexibility of grammar and the absence of features such as tense and voice, local information is essential. Traditional machine learning methods often struggle with accuracy, and existing deep learning methods fail to fully leverage local information. We propose a novel model, ALBert-GCNN-CRF, which combines the ALBert pre-Trained language model with a convolutional neural network (GCNN) for local information extraction and a residual gating mechanism. This model treats grammatical error detection as a sequence labeling task, using a Conditional Random Field (CRF) to learn relationships between labels. experimental results show that the ALBert-GCNN-CRF model significantly improves the F1 score for electric power texts, with average increases of 16.9%, 11.2%, and 4.4% compared to other models, demonstrating its effectiveness in enhancing grammatical error detection. © 2024 ieee.

关键词： Convolutional neural networks

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Semantic Segmentation and Localization of Doublesplit Diversion Line Based on Improved Yolov8 3

Semantic Segmentation and Localization of Doublesplit Divers...

引用

3rd international conference on Artificial Intelligence, Internet of Things and Cloud computing Technology, AIoTC 2024

作者： Mo, Fan Xiao, Lijun Lin, Jianghui Zhuhai Power Supply Bureau of Guangdong Power Grid Co. Zhuhai China Changsha University of Science & Technology Changsha China

ISBN: (纸本)9798331530280

In order to make the diversion line accurately penetrate into the grounding wire clamp hole position through the actuation structure without human intervention, the computer must accurately acquire the position of the diversion line relative to the end of the operation. Images captured in real substation scenes have a large number of interference backgrounds similar to diversion lines, and the foreground and background areas of the images are not clearly differentiated, which greatly increases the difficulty of semantic segmentation and localization of double-split diversion lines. Aiming at this problem, this paper designs a semantic segmentation and localization method for double-split diversion lines in complex scenarios of substations. © 2024 ieee.

关键词： Semantic Segmentation

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Using Accessibility Awareness Interventions to Improve computing education 46

Using Accessibility Awareness Interventions to Improve Compu...

引用

ACM/ieee 44th international conference on Software engineering: Software engineering education and Training (ICSe-SeeT)

作者： Liu, Yang Mangano, Domenic Neupane, Krishna Prasad Malachowsky, Samuel Krutz, Daniel e. Rochester Inst Technol B Thomas Golisano Coll Comp & Informat Sci Dept Software Engn Rochester NY 14623 USA

ISBN: (纸本)9798400704987

Research demonstrates that we continue to be deficient in creating inclusive and equitable software, with a lack of empathy among software developers attributed to this issue, even if unintentional. Although increasing empathy has been shown to be part of the solution, unfortunately there is a lack of data that can be analyzed that shows how empathy-building and accessibility awareness interventions can be utilized in computing education, along with a shortage of free, public, and easily adoptable educational material regarding empathy and the creation of accessible software. Through large in-person studies that involve real-world participants, we are able to contribute to the knowledge regarding the potential benefits that can be used to improve computing education by implementing empathy-building strategies in the curriculum. Two forms of accessibility awareness exercises are utilized to display the effectiveness of empathy-based learning and teach the importance of accessibility in computing, including experiential and expression-based interventions. In these interventions, we expect to find an increase in awareness and empathy in participants, as well as an increase in participant interest in the topic of software inclusivity and accessibility by placing the participant in a situation they may encounter in the real world. We additionally present a web-hosted, easily adoptable experiential and expression-based educational lab to support the inclusion of inclusive development and/or empathy-focused topics into a variety of curricula formats. Complete materials are available on the project website: https://***

关键词： experiential education computing education empathy-Building Interventions

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Stealthy False Data Injection Cyberattack Targeting under Load Tap Changing Transformers in Smart Power grid Causing Abnormal Voltage Profile 3

Stealthy False Data Injection Cyberattack Targeting under Lo...

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3rd international conference on Power, Control and computing Technologies, ICPC2T 2024

作者： Naderi, ehsan Asrari, Arash College of Engineering and Computer Science Arkansas State University Department of Electrical Engineering JonesboroAR United States Purdue University Northwest Department of Electrical and Computer Engineering HammondIN United States

ISBN: (纸本)9798350349207

In the process of protecting power systems against different types of cyberattacks, the primary step is to precisely model such frameworks from attacker's perspective. This paper investigates a false data injection (FDI) attack framework, which can target under-load tap changing (ULTC) transformers, resulting in manipulated voltage profile in radial smart distribution networks. The developed FDI model compromises the voltage profile of a distribution feeder through misleading the volt/var optimization, that optimally manages system-wide voltage profile and flow of reactive power. The presented attack model is formulated as a bi-objective optimization problem. The objective functions from the attacker's point of view are 1) minimizing the level of false data to be injected into the smart meters associated with load data and 2) maximizing the voltage deviation of the distribution grid. Negative impacts of such a cyberattack model have been validated and discussed in this work on an ieee distribution test system, necessitating proper remedial actions, which will be elaborated in the next step of this research. © 2024 ieee.

关键词： Reactive power

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