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Stackelberg Game Model for Optimal Dispatching of electricity Consumption for Community Microgrid Considering Demand Response Mechanism 8

Stackelberg Game Model for Optimal Dispatching of Electricit...

引用

8th international conference on Power and Renewable energy, ICPRe 2023

作者： Tan, Di Fang, Xiaohan Fan, Yuan Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and Automation Hefei230601 China

ISBN: (纸本)9798350328813

Community microgrid is a kind of new power system that acts as an intermediary between community consumers and the grid, playing an important role in realising distributed autonomy and improving the power supply level. To achieve optimal power dispatch in community microgrid, this paper establishes a Stackelberg game model with the community microgrid operator (CMO) as the leader and the consumers aggregator as the follower, and combines the internal pricing method with the demand response mechanism. The paper uses a combination of genetic algorithm and solver to solve the Stackelberg game model to maximise the interests of all parties. Simulation results show that the model can secure the interests of each game subject, balance power supply and demand, and promote peak shaving and valley filling of electrical loads. © 2023 ieee.

关键词： Community microgrid Demand response mechanism Genetic algorithm Internal prices Stackelberg game model

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Construction of Power Forecasting and environmental Protection Data Platform Based on Smart grid Big Data 1

Construction of Power Forecasting and Environmental Protecti...

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1st international conference on Applied Artificial Intelligence and computing, ICAAIC 2022

作者： Li, Guang-Ye Zhang, Jia-Xin Wen, Xin Xu, Lang-Ming Yuan, Ying State Grid Liaoning Electric Power Supply Co. Ltd Shenyang110004 China Liaoyang Branch of State Grid Liaoning Electric Power Supply Co. Ltd Liaoyang111000 China School of Business Administration Northeastern University Shenyang110169 China

ISBN: (纸本)9781665497107

In today's era, the smart grid is the carrier of the new energy technology revolution and a very critical development stage for grid intelligence. In the process of smart grid operation, maintenance and maintenance, many heterogeneous and polymorphic data can be formed, that is to say big data. This paper analyzes the power big data prediction technology for smart grid applications, and proposes practical application strategies In this paper, an in-depth analysis of the relationship between cloud computing and big data key technologies and smart grid is carried out, and an overview of the key technologies of electric power big data is carried out. © 2022 ieee.

关键词： Smart power grids

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Predicting Cloud Applications Failures from Infrastructure Level Data 53

Predicting Cloud Applications Failures from Infrastructure L...

引用

53rd Annual ieee/IFIP international conference on Dependable Systems and Networks (DSN)

作者： Domingos, Jomar Cerveira, Frederico Barbosa, Raul Madeira, Henrique Univ Coimbra CISUC DEI Coimbra Portugal

ISBN: (纸本)9798350325430

Cloud computing assumes a crucial role in the internet applications and services landscape, serving as the default infrastructure for deploying and delivering web applications. To harness the cloud's technical and financial benefits, one must address the challenges of the increased consequences of software faults. Online failure prediction has emerged as a solution allowing for actions to be taken before the failures. However, conventionally, each target application requires a specific online failure predictor to be trained from application-specific data, which is impractical. In this paper we explore and assess the possibility to achieve online failure prediction for cloud applications from infrastructure-level data, i.e., data from the hypervisor. For this purpose, we present a method to accomplish this type of failure prediction, through fault injection on the hypervisor and machine learning techniques to create failure prediction models. From our experiments we find that it is possible to predict application failures from low-level infrastructural data, i.e., the infrastructure data presented enough application failure predictive power (with an accuracy of 96%, precision of 95% and a recall of 64%), showing the great potential that infrastructural data has to contribute to this task. Additionally, our study also opens interesting future research directions, such as multilevel failure prediction or generic failure prediction models.

关键词： failure prediction machine learning feature engineering time series forecast dependability cloud computing

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Mathematical evaluation of Medical Image computing by Application of Mathematical Computer-Aided Interventions to Soft Tissues 5

Mathematical Evaluation of Medical Image Computing by Applic...

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5th ieee international conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Chinoy, Danesh Dinyar Mavi, Renu Shrivastav, A.K. Jothi, Naresh Kumar Utsab, Tuhin Rajput, Akshay D y Patil Deemed to Be University School of Physiotherapy Dept of Kinesio Therapeutics and Diagnosis Department of Mathematics Department of Chemistry Navi Mumbai India Swami Vivekananda Subharti University Department of Chemistry Uttar Pradesh Meerut India Mmec Maharshi Markandashwar Department of Mathematics Haryana Ambala India R&d Institute of Science and Technology Department of Mathematics India St. Xavier's University Kolkata India Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350383522

Mini-invasive digestive surgery was seemingly one of the main disciplines where soft tissues were handled utilizing test systems, anatomical structure tracking, and specialized assistance robots. Yet, other therapeutic areas, such as urology, are concerned. Unquestionably, laparoscopic surgery, creative growth killing methods (e.g., HIFU, radiofrequency, or cryoablation), prior disease distinguishing proof, and the usage of interventional and diagnostic imaging modalities have of late acquainted new troubles with urologists and researchers keen on CAMI. This has driven in a significant flood in innovative work of computer-aided urology frameworks throughout recent years. In this study, we give a conversation of the main pressing issues related with computer-aided diagnostic and therapy of soft tissues, as well as an overview of the many kinds of help accessible to urologists: robotization, picture combination, and careful route. There is conversation of both exploration projects and functional modern frameworks. © 2024 ieee.

关键词： Computer Aided Medical Interventions Medical Image computing

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Testing and Reliability of computing-In Memories: Solutions and Challenges 6

Testing and Reliability of Computing-In Memories: Solutions ...

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6th ieee international Test conference in Asia (ITC-Asia)

作者： Li, Jin-Fu Natl Cent Univ Dept Elect Engn Adv Reliable Syst ARES Lab Taoyuan 320 Taiwan

ISBN: (数字)9781665455237

ISBN: (纸本)9781665455237

Various computing-in-memory designs have been proposed as a possible computing architecture for the datacentric computing applications. existing memories such as random access memories, flash memories, and emerging memories can be modified as computing-in-memories (CIMs) to support computing operations. In CIMs, computing operations typically are more complex and have smaller sensing margin than the read operation. Furthermore, storage devices of emerging memories are more unreliable than transistors. Those make the testing and reliability of CIMs become worse. In this paper, we first review existing testing and reliability-enhancement methods for CIMs. Then, testing and reliability challenges of CIMs are discussed.

关键词： computing in memory RAM RRAM STTMRAM test reliability

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A Generic Real-Time Simulation Modeling Method of Modular DC Chopper for VSC-Based Offshore Wind Farm Connection System 7

A Generic Real-Time Simulation Modeling Method of Modular DC...

引用

7th international conference on Power and energy engineering, ICPee 2023

作者： Kou, Longze Gao, Yuqin Gu, Huaiguang Gao, Yang Li, Jin Li, Fangyuan State Grid Smart Grid Research Institute Co. Ltd State Key Laboratory of Advanced Power Transmission Technology Beijing China State Grid Smart Grid Research Institute Co. Ltd Beijing Key Laboratory of DC Grid Technology & Simulation Beijing China

ISBN: (纸本)9798350381146

The DC chopper is a crucial equipment in VSC-based offshore wind farm connection system to dissipate the surplus power during fault scenarios and enhance the system fault ride through capability. The modular DC chopper technology has been used in real engineering project due to its good transient performance. It has numerous SMs stacked in arm bridge with different topologies and thus poses challenges to the electromagnetic modeling, simulation analysis and control system testing of the equipment. This paper proposes a generic electromagnetic modeling method of modular DC chopper, which is especially suitable for the real time simulation. It has both high simulation speed and accuracy and can be used to simulate modular DC chopper with different SM topology. The modeling method is validated and then implemented in real time simulation platform HYPeRSIM. Hardware in loop testing of VBC of DC chopper is carried out and the study cases demonstrates that the real time model of modular DC chopper works properly under transient operation conditions. © 2023 ieee.

关键词： Offshore wind farms

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Reproducible escience: The Data Containerization Challenge 19

Reproducible eScience: The Data Containerization Challenge

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

作者： Malik, Tanu School of Computing DePaul University ChicagoIL United States

ISBN: (纸本)9798350322231

Computational reproducibility is the cornerstone of the scientific method. We are witnessing a surge of reproducible practices emerging in scientific disciplines. Use of containers is a common theme across several of these practices. Containers isolate applications and make it simple to share code, data, and experiment settings. This paper presents current escience infrastructures that use containers for reproducible research. We then present our view of the important research issues in data containerization for reproducible escience. Containerization requires considering packaging, sharing, security, and language choices to ensure reproducibility and preservation. Despite its benefits, containerization can be inefficient and impact experiment reproducibility when users stop using it or transition the experiment to different environments. We describe reproducible containers that in addition to containerization include services to ensure and maintain long-term reproducibility and lay forward a vision based on reproducible containers. © 2023 ieee.

关键词： Containers

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Metaverse Services: The Way of Services Towards the Future

Metaverse Services: The Way of Services Towards the Future

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2023 ieee international conference on Web Services, ICWS 2023

作者： Xu, Xiaofei Sheng, Quan Z. Benatallah, Boualem Chen, Zhong Gazda, Robert Saddik, Abdulmotableb ei Singh, Munindar P. Faculty of Computing Harbin Institute of Technology China Macquarie University School of Computing NSW2109 Australia Dublin City University School of Computing Ireland Peking University School of Computer Science China Wireless Networking Lab Interdigital United States University of Ottawa School of Electrical Engineering and Computer Science Canada North Carolina State University Department of Computer Science United States

ISBN: (纸本)9798350304855

With the emergence of new generation of digital technologies, e.g., artificial intelligence, blockchain, cloud computing, big data, edge computing, 5G/6G, VR/AR/MR, and the Internet of Things, an exciting era of metaverse is coming. Interacted and linked with the physical world, metaverse offers a platform of a new social ecosystem, dealing with digital twins and empowering virtual-reality symbiosis. In metaverse, social activities and business processes are performed based on the sequences of workflow or service processes. Bridging both the virtual space and the real world, such metaverse services are more complicated and present many new challenges and research topics. In this paper, the concept and characteristics of metaverse services are presented, the key technologies and typical use cases are reviewed, and the future challenges and opportunities of metaverse services are also discussed. © 2023 ieee.

关键词： Internet of things

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Traffic Anomaly Detection Model of electric Power Industrial Control Based on DBN-LSTM 23

Traffic Anomaly Detection Model of Electric Power Industrial...

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23rd ieee international conference on High Performance computing and Communications, 7th ieee international conference on Data science and Systems, 19th ieee international conference on Smart City and 7th ieee international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Zhang, Shijun Lai, Ji Yao, Qigui Platform Operation and Safety Center Information and Communication Branch of State Grid Jibei Electric Power Co. Ltd Beijing China Global Energy Interconnection Research Institute Co. Ltd State Grid Key Laboratory of Information & Network Security Nanjing China

ISBN: (纸本)9781665494571

With the construction of smart city and smart grid, energy Internet is booming, and the deployment of smart power terminals has increased, with a wide range of deployment locations, and the level of intelligence of the terminals has increased. However, most of the intelligent terminals are deployed in the non-controlled industrial control network environment, which brings new challenges to the network security. Therefore, the traffic anomaly detection of electric power industrial control becomes the first step to ensure the safety of the peripheral network. Currently, the traffic anomaly detection system of electric power industrial control based on machine learning has the problems of low computing efficiency and weak representation ability. To solve the problem of low efficiency in traditional deep learning, the deep belief network (DBN) is used to extract the non-linear features of data, which can improve the time efficiency to a certain extent. To solve the problem that the representation ability of traditional machine learning is weak, long short term memory network (LSTM) is used to improve the representation ability of traditional shallow machine learning. The experimental results suggest that the method is better than the traditional machine learning method in accuracy, and the recall rate reaches up to 97.3%, AUC runs up to 0.927. © 2021 ieee.

关键词： Smart cities Industrial control Network security Feature extraction Power systems Smart grids Safety

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Few-shot Biomedical NeR via Multi-task Learning and More Fine-grained grid-tagging Strategy

Few-shot Biomedical NER via Multi-task Learning and More Fin...

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2024 ieee international conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Mu, Wenxuan Zhao, Di Meng, Jiana Liu, Shuang Lin, Hongfei Dalian Minzu University School of Computer Science and Engineering Dalian China Postdoctoral Workstation of Dalian Yongia Electronic Technology Co. Ltd China Dalian University of Technology School of Computer Science and Technology Dalian China

ISBN: (纸本)9798350386226

Biomedical Named entity Recognition (NeR) serves as a crucial task in biomedical information extraction, aiming to identify and classify entities from unstructured biomedical texts. However, obtaining high-quality annotated biomedical texts is scarce due to their high privacy and the specialized expertise required. Therefore, more researchers are focusing on few-shot NeR in the biomedical field. Recent methods mainly fall into three categories: 1) transferring knowledge from high-resource data and fine-tuning with low-resource data, 2) modifying data using various methods to generate new samples, and 3) decompose the NeR task into two subtasks with multi-task learning. However, these methods either suffer from domain shift, generate low-quality synthetic data, or encounter error propagation issues. To address these limitations, we have investigated an few-shot NeR method, which firstly employs specific prompt templates to guide Large Language Models in generating high-quality new samples. Then, we propose a novel more fine-grained grid-tagging strategy and single-end sensitive multi-task learning framework. This method sets multiple losses to enable the model to learn from multiple task objectives: what constitutes a fully correct, partially correct, and incorrect entity. Additionally, through the new grid-tagging strategy, the model can decode entities based on more tagging clues. To better align with real-world scenarios, we trained in a few-shot scenario and evaluated on the full test set. extensive experimental results on the NCBI, BC5CDR, BioNLP11ePI, and BioNLP13Ge datasets confirm that our method outperforms previous state-of-the-art methods in most scenarios. © 2024 ieee.

关键词： Multi-task learning

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