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Development of A GIS Equipment Partial Discharge intelligent Detection System Based on Ultra-High Frequency Detection Technology 4

Development of A GIS Equipment Partial Discharge Intelligent...

引用

4th international conference on Energy Engineering and Power systems, EEPS 2024

作者： Qin, Yurui Shen, Dongyan Tong, Shengkai Gu, Tianyu North China Electric Power University Hebei Baoding071000 China

ISBN: (纸本)9798350366914

This article introduces an intelligent detection system for Gas Insulated Switchgear (GIS) based on ultra-high frequency detection technology, which is of great significance for ensuring the stability and safety of power systems. GIS is widely used as a key power equipment in modern power systems. The partial discharge detection results of GIS equipment are important basis for judging its health status. Based on this, the project team has developed an intelligent positioning detection system that integrates ultra-high frequency sensors, digital storage oscilloscopes, test hosts, and advanced software algorithms. This system uses digital signal processing technology and artificial intelligence algorithms to achieve real-time acquisition, intelligent analysis, and high-precision positioning of partial discharge signals. By improving the variational mode decomposition algorithm (IVMD) and the target detection algorithm based on YOLOv5, the system can effectively separate noise from multi-source partial discharge signals and obtain accurate fault diagnosis information. In practical tests, the system has demonstrated excellent anti-interference ability and detection accuracy, providing strong technical support for maintenance and fault prevention of GIS equipment. © 2024 ieee.

关键词： Cathode ray oscilloscopes

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Towards data Empowered intelligent Transportation systems: Use Cases and Future Opportunities 9

Towards Data Empowered Intelligent Transportation Systems: U...

引用

9th ieee international conference for Convergence in Technology, I2CT 2024

作者： Alahmadi, Abdulrahman Taibah University Department of Computer Science Medina42353 Saudi Arabia

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394474

intelligent Transportation systems (ITS) is an evolving technology that has many applications to improve traffic safety and management. The utilization of advanced sensors and wireless communications is instrumental in implementing the applications of ITS. Vehicles can regularly transmit their position information to other vehicles on the road and also to the infrastructure units, thus forming a vehicular network. data shared by vehicles is thus used to improve safety-related decisions, manage the flow of traffic, and reduce carbon emissions. It is thus critical to collect the traffic data efficiently and transmit data to the servers and other vehicles reliably, use intelligent learning models to analyze the data, and quickly compute the data analysis-related tasks. In this paper, an overview of the use of big data in ITS is provided. A brief overview of the ITS and its applications is presented. A review of the recent work in ITS is also explained. A case study of data computation using k-means clustering is added to highlight the use of big data for improving the performance of ITS. In the end, the future opportunities and open challenges related to ITS are presented. © 2024 ieee.

关键词： Big data

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Research on Key Technologies of intelligent Management and Control Platform for Aviation Industrial Park 11

Research on Key Technologies of Intelligent Management and C...

引用

11th international conference on Information systems and computing Technology, ISCTech 2023

作者： Cai, Xianqing Su, Kaige Li, Zhao Song, Yongjie Co. Ltd. Beijing China

ISBN: (纸本)9798350342406

Aviation industrial parks are characterized by a variety of spatial types, diverse personnel roles, large scale, multiple and difficult management dimensions. However, they have problems of extensive management, severe information islands, individualistic management, and weak experience. Therefore, the contradiction between the demand for high-quality development of aviation industrial parks and the lack of advanced park management urgently needs to be resolved. In this respect, research on the key technologies of the intelligent management and control platform for the aviation industrial park was carried out by discussing the application scenarios, platform architecture, and key technologies. It proposed to build a data interoperability, business collaboration, resource integration intelligent management and control platform centered on production, with business as the mainline, and park management and services as the grasp, so as to provide a safe, environmentally friendly, green, low-carbon, intelligent, and efficient service-oriented industrial park for park-related activities. © 2023 ieee.

关键词： intelligent systems

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A Novel Concept of Traffic data Collection and Utilization: Autonomous Vehicles as a Sensor 26

A Novel Concept of Traffic Data Collection and Utilization: ...

引用

ieee 26th international conference on intelligent Transportation systems (ITSC)

作者： Zhang, Yunfei Ilic, Mario Bogenberger, Klaus Tech Univ Munich Dept Mobil Syst Engn Traff Engn & Control D-80333 Munich Germany

ISBN: (纸本)9798350399462

Urban traffic state estimation aims at providing accurate and reliable information about traffic flow characteristics, which can be used for urban traffic management. Traditional estimation approaches mainly use loop detectors and/or floating car data, which are labour- and cost-intensive. With the ongoing technological development in autonomous driving, more and more research focuses on the use of onboard sensor data. In this paper, a novel concept for the collection and utilization of traffic data is presented: Autonomous Vehicles as a Sensor. The proposed concept capitalizes on the advanced sensor technologies deployed in autonomous vehicles, particularly those employed in Mobility-on-Demand services, to collect link-level traffic states. These microscopic traffic states are further utilized by e.g., Mobility-on-Demand operators to estimate network-level traffic states. A first proof of concept was examined through a case study in a microscopic traffic simulation with a grid network and generic demand. The results demonstrate that both, moving and parked autonomous vehicles, can effectively contribute to the estimation of the macroscopic fundamental diagram. When the results from both are combined, the resulting estimation yields the most accurate fit compared to the ground truth. These findings underline the potential of the 'Autonomous Vehicles as a Sensor' concept for accurate and reliable traffic state estimation.

关键词： State estimation

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Optimization of Electrical Equipment in intelligent Transmission Engineering Under Fuzzy Neural Network Algorithm 3

Optimization of Electrical Equipment in Intelligent Transmis...

引用

3rd ieee international conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Li, Xiping Wang, Jitao School Of Intelligent And Electrical Electronic Engineering Harbin Cambridge College Heilongjiang Harbin China School Of Electrical And Electronic Engineering Harbin Institute Of Technology Heilongjiang Harbin China

ISBN: (纸本)9798350318609

Traditional optimization analysis of electrical equipment in transmission engineering is often based on experience and rules, lacking intelligent methods. This leads to limitations in the adaptability and effectiveness of traditional methods when facing complex and variable power system conditions. By introducing the fuzzy neural network (FNN) algorithm, this paper fully utilizes its adaptability to nonlinear and complex correlations, thereby improving the intelligence level of optimization analysis of electrical equipment in transmission engineering. It collects actual power system operation data information and preprocesses the data to ensure the quality of the data. This article constructs a FNN model to effectively handle fuzzy and uncertain information, and applies it to the complex environment of power systems. The experimental results show that the average RMSE of the FNN for electrical equipment optimization prediction is 0.07, and the optimized voltage is very stable. The application of FNN algorithm can effectively improve the optimization effect of electrical equipment. © 2024 ieee.

关键词： Fuzzy neural networks

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Text Semantic Analysis and intelligent Interaction in Parallel Dispatching systems for Technical Station 4

Text Semantic Analysis and Intelligent Interaction in Parall...

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4th ieee international conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Jiao, Yuantao Wang, Jian Li, Runmei Xiong, Gang Chen, Shichao Beijing Jiaotong University School of Automation and Intelligence Beijing China Institute of Automation Chinese Academy of Sciences Beijing Engineering Research Center of Intelligent Systems and Technology Beijing China Cloud Computing Center Chinese Academy of Sciences Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Dongguan China Chinese Academy of Sciences State Key Laboratory for Multimodal Artificial Intelligence Systems Beijing China

ISBN: (纸本)9798350349252

Technical station dispatching system plays an important role in cargo operation, but due to the large number of dispatching systems and complex operation, dispatchers rely on manual experience to complete the task, and many scenarios cannot be applied in the real system. Therefore, this paper utilizes the data-driven form to establish a parallel dispatching system for technical stations, semantically analyzes text data rich in dispatcher experience, completes the Computational Experiments on the textual semantic data in the artificial dispatching system, and synchronizes the Parallel Execution of data interaction with the real dispatching system. © 2024 ieee.

关键词： parallel intelligence railway dispatching system technical station

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MedSync: A Multi-layered Medical Resource Management System with Cloud-edge Synergy

MedSync: A Multi-layered Medical Resource Management System ...

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ieee international conference on Web Services (ieee ICWS)

作者： Wang, Can Gao, Zibo Wang, Lindsay Zhang, Yuejia Sun, Hao Tong, Yue Lyu, Ting Chinese Peoples Liberat Army Gen Hosp Beijing Peoples R China Peking Univ Beijing Peoples R China Beijing Huaxing Changtai IoT Technol Res Inst Co Beijing Peoples R China

ISBN: (纸本)9798350368567;9798350368550

The healthcare landscape is increasingly challenged by the need for efficient management of medical resources amidst advancing medical technology and aging populations. Traditional medical resource management systems often suffer from limitations in data collection and system stability, hindering decision-making and impacting service quality. However, the advent of IoT (Internet of Things) and cloud computing technologies has revolutionized medical resource management by enabling seamless data integration and storage across multiple layers. This paper introduces, MedSync, a multi-layered medical resource management system with Cloud-Edge Synergy by simultaneously leveraging IoT and cloud computing technologies. By efficiently coordinating data service and storage across four layers, a.k.a., the smart terminal layer, the hospital center layer, the SPD (Supply Processing and Distribution) edge layer, and the SPD cloud layer, the system provides hospitals with a comprehensive, intelligent solution for resource management. Through collaborative operation, hospitals can better understand patients' needs, adjust medical plans, and enhance service effectiveness and satisfaction. Moreover, the system enhances management and decision support, thus improving operational efficiency and service quality. Through detailed system design, implementation, and efficacy evaluation, this paper offers novel insights and approaches for medical resource management, aiming to elevate service standards and patient experiences.

关键词： Hospital data processing

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An intelligent SCADA System for Power Distribution Network Cable Fault Detection with Real-Time Monitoring and Autonomous Maintenance 4

An Intelligent SCADA System for Power Distribution Network C...

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4th international conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2025

作者： Nahidul Islam, S.M. Ahmad, Shameem Prodhan, Md. Mahbub-E-Elahi Khan Chowdhury, Md. Shoaib Bin Mizan, Sakib Ahmed, Abir Jahan, Effat Hazari, Md. Rifat Hossain, Chowdhury Akram American International University-Bangladesh Faculty of Engineering Department of Electrical and Electronic Engineering Dhaka1229 Bangladesh American International University-Bangladesh Faculty of Engineering Department of Computer Engineering Dhaka1229 Bangladesh

ISBN: (纸本)9798331530938

Supervisory Control and data acquisition (SCADA) systems are significant for boosting industrial operations, but established systems often struggle with fault management and real-time monitoring. To address these issues, this paper proposes an advanced SCADA system that integrates IoT technologies, multi-agent systems, and predictive maintenance for seamless data collection, analysis, and decision-making. Machine learning enhances fault detection and cybersecurity, while a modular hardware design allows scalability and flexibility to various industrial environments. The system is highly sensitive to detecting faults, including open circuit faults, line-to-ground faults, line-to-line faults, double line to ground faults and three-phase faults. It improves operational safety and efficiency by accurately locating faults and notifying professionals in real time. Extensive simulations and hardware tests were conducted to validate its performance in different fault scenarios. The results show that the proposed SCADA system has successfully detected different types of faults, offering a unified approach for fault management, maintenance, and real-time monitoring. © 2025 ieee.

关键词： SCADA systems

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Boosting Software Quality: A Machine Learning Technique for Predicting Defects 6

Boosting Software Quality: A Machine Learning Technique for ...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Akilan Daniel, E. Alwin Infant, P. Jainish, G.R. Panimalar Engineering College Chennai City Campus Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Loyola ICAM College of Engineering & Technology Chennai India

ISBN: (纸本)9798331522667

In order to increase automation, strengthen decision- making, and improve efficiency while reducing costs and maintaining precision, the development of intelligent machinery is becoming more and more dependent on the integration of data, embedded software, and modern technologies. Reliability of embedded software becomes essential for guaranteeing high system performance and an improved user experience as technology and hardware merge to build smart devices. This work explores methods to quantify software reliability, which measures the likelihood of residual failures in a system. The objective is to build a statistical relationship between quality parameters and product or process metrics through the analysis of software metrics produced from development data. These indicators aid in evaluating the software's dependability and performance, enabling developers to spot and fix possible problems early on. Using pattern recognition algorithms to pinpoint software's fault-prone areas is a crucial strategy for enhancing software reliability prediction. These algorithms enable developers to focus attention on software areas that have a higher likelihood of failure by identifying patterns in the product's code or structure that suggest increased risk. In summary, these methods focus on enhancing the reliability of intelligent machines by equipping developers with advanced tools to reduce defects, improve dependability, and ensure that smart systems perform optimally. This approach aligns with modern trends in using machine learning and data-driven models to tackle complex technological challenges. © 2025 ieee.

关键词： data reliability

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Research on Mechanical Automation Control based on Bidirectional Long Short-Term Memory 1

Research on Mechanical Automation Control based on Bidirecti...

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1st international conference on intelligent systems and Computational Networks, ICISCN 2025

作者： Song, Xiaolu Zaozhuang Vocational College of Science & Technology College of Mechanical and Electrical Engineering China

ISBN: (纸本)9798331529246

The rapid advancement of industrial automation has led to increased reliance on intelligent control systems for optimizing mechanical processes. Traditional automation methods often struggle with dynamic environmental conditions, system uncertainties, and real-time decision-making challenges. Many existing approaches, such as PID controllers and conventional neural networks, fail to effectively handle sequential dependencies in time-series data, resulting in suboptimal system performance. To address these limitations, this research presents an advanced mechanical automation control framework based on a Bidirectional Long Short-Term Memory (BiLSTM) algorithm. The BiLSTM model enhances automation efficiency by capturing long-range dependencies in sequential data, improving predictive accuracy and system adaptability. By leveraging bidirectional processing, the model effectively learns past and future context, leading to superior performance in control precision and response time. Experimental evaluations demonstrate that the proposed framework achieves an accuracy of 98.2%, significantly outperforming conventional automation control techniques in terms of robustness, adaptability, and computational efficiency. This study contributes to the development of intelligent, data-driven mechanical automation systems that align with Industry 4.0 objectives and enhance operational reliability. © 2025 ieee.

关键词： Three term control systems

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