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Distributed Photovoltaic Island Monitoring Method Based on Gated Loop Unit

Distributed Photovoltaic Island Monitoring Method Based on G...

引用

ieee Information Technology and Mechatronics Engineering conference (ITOEC)

作者： Jianyong Ao Yanping Li Zhukun Li Yangjiang Power Supply Bureau of Guangdong Power Grid Co. Ltd. Yangjiang China NARI Technology Co. Ltd. Nanjing China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

The rapid deployment of distributed photovoltaic (PV) systems has introduced new challenges in islanding detection, a critical task for grid stability and safety. This paper presents a novel Distributed Photovoltaic Island Monitoring Method Based on Gated Loop Unit (GLU). The GLU leverages advanced signal processing and dynamic response mechanisms to enhance detection sensitivity and accuracy. The proposed distributed architecture enables real-time data acquisition and collaborative decision-making across PV nodes, ensuring reliable monitoring under diverse grid conditions. Simulations and case studies validate the method’s effectiveness, demonstrating superior performance in terms of detection accuracy, low latency, and robustness when compared to conventional techniques. The proposed approach provides a scalable and efficient solution for improving islanding detection in distributed PV systems, contributing to the development of resilient and intelligent power grids.

关键词： Photovoltaic systems Accuracy Islanding Logic gates Power system stability Signal processing Stability analysis Time factors Springs Monitoring

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Skill Learning Using a Single Demonstration for Hierarchical Robotic Navigation

Skill Learning Using a Single Demonstration for Hierarchical...

引用

ieee international conference on Autonomous Robot systems and Competitions (ICARSC)

作者： João Bernardo Alves Nuno Lau Filipe Silva IEETA DETI University of Aveiro Aveiro Portugal Intelligent System Associate Laboratory LASI Portugal

ISBN: (数字)9798331538606

ISBN: (纸本)9798331538613

Training robots to perform complex navigation tasks traditionally requires extensive datasets of expert demonstrations, which are expensive and time-consuming to collect. This paper introduces a hierarchical reinforcement learning approach that distils meaningful skills using a single expert trajectory. By combining reverse curriculum learning and goal-conditioned reinforcement learning, our method segments complex tasks into manageable sub-tasks and learns these sub-tasks using sparse rewards. A learned skill selection mechanism then dynamically chains the skills based on their estimated values. We evaluate our approach in a high-dimensional autonomous driving scenario, demonstrating skill acquisition and task completion rates that surpass a state-of-the-art baseline that also uses a single demonstration. Our results show that flexible skill extraction and chaining using a reduced amount of data can effectively tackle the challenges of sparse rewards and planning in a robotic navigation environment.

关键词： Training Navigation Reinforcement learning Trajectory Planning data mining Autonomous vehicles Autonomous robots

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Research on Urban Air Quality Prediction Method Based on the Fusion of Wide-area Spatiotemporal Distribution data

Research on Urban Air Quality Prediction Method Based on the...

引用

ieee international conference on Industrial Technology (ICIT)

作者： Zixing Tao Weihua Cao Chao Gan School of Future Technology China University of Geosciences (Wuhan) Wuhan China School of Future Technology School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences (Wuhan) Wuhan China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

Air quality data exhibit nonlinearity, sensitivity to environmental factors, and long-term dependencies. Numerous factors influence air quality, making accurate predictions based on a single-dimensional dataset impractical. This study proposes a method for urban air quality prediction that integrates wide-area spatiotemporal data, designed to address the unique characteristics of air quality datasets. First, relevant wide-area spatiotemporal data are selected, and their correlations with air quality are systematically analyzed. Second, a Long Short-Term Memory (LSTM) network-based Transformer model is utilized to capture the long-term dependencies in the air quality sequences. Finally, the model successfully generates hourly multi-step predictions for six air pollutants. The experimental results show that the proposed method outperforms the method that relies on air quality data alone for multi-step air quality prediction.

关键词： Deep learning Sensitivity Atmospheric modeling Time series analysis Predictive models Air quality Transformers Environmental factors Spatiotemporal phenomena Long short term memory

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Implementing and Managing Green IT Strategies to Enhance Corporate Sustainability

Implementing and Managing Green IT Strategies to Enhance Cor...

引用

ieee Students' conference on Electrical, Electronics and Computer Science (SCEECS)

作者： K. Sudharson N. Vijayakumar L. Anitha V. Subhashini G. Madhumita A. SureshKumar Department of AIML R.M.D. Engineering College Chennai India Department of Commerce B.S.Abdur Rahman Crescent Institute of Science & Technology Chennai India Department of MBA JNN Institute of Engineering Chennai India Department of Accounting and Finance Dwaraka Doss Goverdhan DossVaishnav College Chennai India School of Management Studies Vels Institute of Science Technology and Advanced Studies (VISTAS Chennai India Department of MBA VelTech HighTech Dr. Rangarajan Dr. Sankunthala Engineering College

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

Importance of Green IT practices is that it improves corporate sustainability through conserving the natural environment and minimizing the use of resource. This paper focuses on next generation IT management for power-optimized hardware, virtualization, cloud computing, and intelligent system through SITOF. Some of the key technologies are: ARM-based low power servers, vSphere-server visualization, Docker- Containerization, Solar/wind hybrid power. It shows that the main benefits of the IT of smart buildings are a 35% saving in energy and 40% reduction in carbons, as well as a quarter decrease in operation costs when compared to traditional IT practices. Moreover, data analytic improve performance measurement and management and hence contributing to an improvement in sustainability issues. These results also show that SITOF is feasible to assist firms in attaining corporate sustainability objectives while embracing the best practice of technology management.

关键词： Measurement Technology management Smart buildings Green products Hybrid power systems Servers Sustainable development Virtualization intelligent systems Next generation networking

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Proceedings of the Second ieee international Workshop on intelligent data acquisition and advanced computing systems: Technology and Applications. IDAACS'2003 (Cat. No.03EX742)

Proceedings of the Second IEEE International Workshop on Int...

引用

ieee international Workshop on intelligent data acquisition and advanced computing systems: Technology and Applications

The following topics are dealt with: data acquisition systems; intelligent measurement systems; artificial intelligence; cyber security; distributed computing; virtual instrumentation; data mining; artificial neural n... 详细信息

关键词： data acquisition Artificial intelligence Distributed computing

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Pipeline Anomaly Detection Based on KPCA and Cosine Distance Prototypical Network

Pipeline Anomaly Detection Based on KPCA and Cosine Distance...

引用

ieee international conference on Industrial Technology (ICIT)

作者： Kang Yi Xin Liu Wenkai Hu Yupeng Li School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

Accurately and promptly detecting the pipeline anomaly is crucial to the safe operation of pipeline systems, while a difficulty lies in that many existing methods require massive data for training models. However, pipelines are running under normal state for the most of the time, and labeled pipeline anomaly data is usually scarce. Among the commonly used sensors, vibration sensors are widely utilized in pipeline detection because of their advantages such as easy installation and high sensitivity. However, the vibration signal shows non-stationary characteristics when anomalies occur, and are contaminated by noises, making it difficult to represent the actual state with features extracted from either the time or frequency domain. Accordingly, this paper proposes a pipeline anomaly detection method based on the KPCA (kernel principal component analysis) and cosine distance prototypical network. First, features are extracted from original signals; then, the feature dimension is reduced by KPCA; last, the cosine distance is introduced to the prototypical network for anomaly detection. The effectiveness of the proposed method is demonstrated by case studies involving experimental data.

关键词： Vibrations Training Time-frequency analysis Sensitivity Pipelines Sensor phenomena and characterization Feature extraction Time-domain analysis Anomaly detection Principal component analysis

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intelligent Monitoring Based on Integrated Temporal Dictionary Learning with Isometric Mapping for Geological Drilling Process

Intelligent Monitoring Based on Integrated Temporal Dictiona...

引用

ieee international conference on Industrial Technology (ICIT)

作者： Tianyu Ma Sheng Du Haipeng Fan Cheng Huang School of Future Technology China University of Geosciences Wuhan China School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

Geological drilling process, owing to Complex geological environment and harsh downhole conditions, generates data including characteristics such as pressure, rotational speed, and depth, which are frequently high-dimensional and noisy. These characteristics make real-time monitoring more complex. Existing methods in the geological drilling process, such as rule-based systems and threshold techniques, struggle to handle the complexity and high dimensionality of drilling data, leading to high false alarm rates and low detection accuracy. This paper develops an integrated temporal dictionary learning with isometric mapping method for monitoring geological drilling process. Specifically, Isometric Mapping is employed to perform dimensionality reduction on high-dimensional data, thereby retaining the structural features in the lower-dimensional space. Subsequently, Lasso regularization is applied for sparse coding to extract essential features from the reduced data. To address the fluctuations arising from the iterative dictionary learning process, a temporal smoothing term is incorporated to ensure the stability of the dictionary across different time steps. After that, the reconstruction errors were adopted to achieve comprehensive statistical indicators. Then the overall monitoring was realized for the plant-wide process. The effectiveness and robustness of the proposed method are demonstrated through case studies on the Tennessee-Eastman process and the actual geothermal drilling process.

关键词： Drilling Smoothing methods Accuracy Geology Velocity control Feature extraction Robustness Kernel Monitoring Principal component analysis

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Research on the Application of Cloud computing-based Dual-Machine Hot Standby System in Railway Signal Safety Management

Research on the Application of Cloud Computing-based Dual-Ma...

引用

Integrated Circuits and Communication systems (ICICACS), ieee international conference on

作者： Zhe Ji Ding Wang Bing Liu Yiming Liu China Academy of Railway Sciences (Shenzhen) Research and Design Institute Co. Ltd Shenzhen China China Academy of Railway Sciences Co. Ltd Beijing China

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

This paper investigates the application of cloud computing-based dual-computer hot standby computer security platform in railway signalling security management, aiming to enhance the reliability and security of railway signalling system. With the increasing importance of railway transport to modern society, the safety of railway signal system has become a key factor for public safety and social stability. This paper first analyses the security challenges faced by railway signalling systems and explores the potential of dual-machine hot-standby systems in improving system availability and security. Then, this paper details the design and implementation of a cloud-based dual-machine hot standby architecture, including system architecture, signal monitoring module, data encryption module, security audit module, and event response module. Through experimental validation, this paper compares the performance of the cloud-based dual-machine hot-standby architecture with that of the traditional single-machine system in terms of failure recovery time, resource utilisation and system response time, and the results show that the dual-machine hot-standby architecture has significant advantages in these key performance indicators. Finally, the paper discusses the limitations of the research and proposes directions for future research, including cost-benefit analysis, resource optimisation, technology adaptation research and intelligent development, with a view to providing a more efficient and intelligent solution for railway signal safety management.

关键词： Cloud computing Safety management systems architecture Computer architecture Rail transportation Stability analysis Security Reliability Resource management Time factors

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Smart Transducers, Robotics and Artificial Intelligence in Industry 4.0

Smart Transducers, Robotics and Artificial Intelligence in I...

引用

ieee international conference on Industrial Technology (ICIT)

作者： Helbert Da Rocha Gang Chen En Li José A. Salvado Antonio Espirito-Santo Dept. of Electromechanical Engineering University of Beira Interior IT – Institute of Telecommunications Covilhã Portugal College of Mechanical Engineering Jiaxing University Jiaxing China Engineering Laboratory of Industrial Intelligent Vision Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

This paper evaluates the possibility of implementing a production line that incorporates the strategies of Industry 4.0 into its operation system, which includes the use of the Internet of Things (IoT), artificial intelligence (AI), big data, cloud computing, and robotics to develop a smart factory. These factories have a high level of connectivity and integration of systems, allowing an effective sharing of information in real-time. Topics include the function of smart transducers in real-time management and the improvement of process interoperability through the application of the ieee 1451 standard. This is achieved through the integration of robotics with AI to enable the adaptive and precise control of production variables, thus improving the efficiency and quality of products. The paper also discusses the issues and scenarios of using these technologies where standardization plays a major role.

关键词： Transducers Service robots Robot sensing systems Real-time systems Fourth Industrial Revolution Internet of Things Artificial intelligence Interoperability Standards Smart manufacturing

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advanced Distribution System State Estimation Techniques: A Review

Advanced Distribution System State Estimation Techniques: A ...

引用

Smart and Sustainable Developments in Electrical Engineering (SSDEE), ieee international conference on

作者： Sruthi Ankam Vedik Basetti Sachidananda Sen Dept. of E.E.E. SR University Warangal India

ISBN: (数字)9798331542108

ISBN: (纸本)9798331542115

This study provides an outline of Distribution System State Estimation (DSSE), a technique introduced by Fred Schweppes in 1970. Since then, SE has been crucial in managing and controlling electric transmission operations. It has also played a role in enhancing Supervisory Control and data acquisition systems (SCADA) as well as Energy Management systems (EMS). DSSE has certain difficulties like high R/X ratios, unbalanced phases, inadequate current and voltage measurements, scalability problems, etc. To improve the DSSE accuracy phasor measurement units are used. study on state estimation techniques based on Branch-Current and Node-Voltages are being conducted. The methods applied for dynamic state estimation play a significant role in power systems for monitoring, control, and stability assessment. These methods are used in state estimation in power systems to enhance the system's reliability, stability, monitoring, and control in modern distribution systems.

关键词： Accuracy Power measurement Scalability Power system dynamics Distribution networks Power system stability Phasor measurement units Real-time systems Reliability State estimation

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