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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...

引用

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 ...

引用

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...

引用

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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An Approach to Systematic data acquisition and data-Driven Simulation for the Safety Testing of Automated Driving Functions 26

An Approach to Systematic Data Acquisition and Data-Driven S...

引用

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

作者： Eisemann, L. Fehling-Kaschek, M. Gommel, H. Hermann, D. Klemp, M. Lauer, M. Lickert, B. Luettner, F. Moss, R. Neis, N. Pohle, M. Romanski, S. Stadler, D. Stolz, A. Ziehn, J. Zhou, J. Stuttgart Media Univ D-70569 Stuttgart Germany Fraunhofer Gesell eV D-80007 Munich Germany GOTECH GmbH D-71287 Weissach Germany Tech Univ Munich D-85748 Garching B Munchen Germany Karlsruhe Inst Technol D-76131 Karlsruhe Germany UnderstandAI GmbH D-76227 Karlsruhe Germany Porsche Engn Grp GmbH D-71287 Weissach Germany

ISBN: (纸本)9798350399462

With growing complexity and criticality of automated driving functions in road traffic and their operational design domains (ODD), there is increasing demand for covering significant proportions of development, validation, and verification in virtual environments and through simulation models. If, however, simulations are meant not only to augment real-world experiments, but to replace them, quantitative approaches are required that measure to what degree and under which preconditions simulation models adequately represent reality, and thus, using their results accordingly. Especially in R&D areas related to the safety impact of the "open world", there is a significant shortage of real-world data to parameterize and/or validate simulations-especially with respect to the behavior of human traffic participants, whom automated driving functions will meet in mixed traffic. We present an approach to systematically acquire data in public traffic by heterogeneous means, transform it into a unified representation, and use it to automatically parameterize traffic behavior models for use in data-driven virtual validation of automated driving functions.

关键词： Safety testing

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Design of Network Information Security System Based on Artificial Intelligence Algorithms 2

Design of Network Information Security System Based on Artif...

引用

2nd international conference on data Science and Information System, ICDSIS 2024

作者： Liu, Yuexi China West Normal Univ Sch Comp Sci Sichuan Nanchong China

ISBN: (纸本)9798350381665

The purpose of this study was to explore the use of artificial intelligence methods to establish an effective and intelligent network information security system, thereby enhancing the network's protection and information protection capabilities. Firstly, artificial intelligence technology was applied in the field of network information security, and research was conducted from the perspectives of intrusion detection, malicious program analysis, cryptography applications, and user behavior analysis. Then, artificial intelligence algorithms were utilized to study network information security from four aspects: data acquisition, model training, intelligent analysis, and emergency response. The experimental results showed that the average user evaluation score of the system designed in this article reached 94.1, which was higher than the traditional 68.2. Finally, further research directions were proposed, and it was pointed out that the innovation and application of artificial intelligence algorithms are important ways to build a more efficient and intelligent network information security system. © 2024 ieee.

关键词： systems analysis

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Improving Image Security through data Concealment Methods Added with Arnold Transformation 6

Improving Image Security through Data Concealment Methods Ad...

引用

6th ieee international conference on Recent Advances in intelligent Computational systems, RAICS 2024

作者： Hari, S. Pandiaraj, K. Kalasalingam Academy of Research and Education Krishnakoil Department of Electronics and Communication Virudhunagar India

ISBN: (纸本)9798350381689

In spite of advancements in digital communications, the challenge of securing reliable data protection and safeguarding the exchange of confidential information persists. The least significant bit (LSB) method is favoured in steganography for its straightforwardness and user-friendliness. Nonetheless, its simplicity also makes it vulnerable to decoding, potentially exposing secret messages to unauthorised interception. This study introduces a hybrid approach that incorporates a revised version of the Collatz Conjecture with the Diffie-Hellman Key Exchange and the Arnold transform to create distinctive random sequences. The simulation outcomes, derived from certain datasets, resulted in average values of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) of 80.24 and 0.9999, respectively. These results suggest that the suggested approach is highly efficient in creating stego-images that are almost imperceptible, using random numbers to select specific pixel locations for embedding the concealed message. © 2024 ieee.

关键词： Image Image Encryption Image Steganography PSNR SSIM

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Pomelo: Alternative Mechanism of Threads Communication for Accelerating Convolution on SIMT Based Processor 9

Pomelo: Alternative Mechanism of Threads Communication for A...

引用

9th international conference on intelligent computing and Signal Processing, ICSP 2024

作者： Feng, Zhenfu Yang, Lele Zhang, Yaying Xi'an University of Posts and Telecommunications School of Engineering Xi'an China

ISBN: (纸本)9798350376548

Single Instruction Multiple Thread (SIMT) based processor and parallel model are effective ways to solve computation problems exist in big data era. Commonly, work load is organized into mass parallel computing threads according the specific application. However, interaction and communication between parallel individual threads is inevitable during the whole computing process. In this work, 1) a SIMT based processor named Pomelo is developed, 2) a fundamental thread communication method based on shard memory is implemented, 3) communication avoiding mechanism for reducing the time cost of thread communication is studied, 4) and convolution based test set is adapted on Pomelo to verify the effectiveness of the proposed mechanism. Experimental result illustrates that Pomelo processor equipped with proposed mechanism achieves 2X performance augmentation on test sets based on convolution. © 2024 ieee.

关键词： Parallel processing systems

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Big data Governance Technology for Open-Pit Mine Safety

Big Data Governance Technology for Open-Pit Mine Safety

引用

2024 international Annual conference on Complex systems and intelligent Science, CSIS-IAC 2024

作者： Xin, Haotian Guoneng Baorixile Energy Company China Energy China

ISBN: (纸本)9798331504755

To address the complexities and high risks associated with safety management in open-pit mining, this paper proposes a comprehensive governance framework utilizing big data technology. The study thoroughly examines critical aspects such as data acquisition, transmission, storage, processing, and analysis to establish an efficient and reliable safety data governance system. Empirical analysis demonstrates that big data governance technology significantly enhances real-time monitoring, accident prediction, and emergency response in mining operations. Furthermore, the paper investigates the primary challenges in current technological applications and offers corresponding solutions and future development recommendations, providing new perspectives and methodologies for improving safety management in open-pit mines. © 2024 ieee.

关键词： data governance technology Internet of Things (IoT) Multi-source big data Open-pit mining

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Edge AI-Enabled Smart Parking systems

Edge AI-Enabled Smart Parking Systems

引用

2024 international conference on advanced Technologies for Communications, ATC 2024

作者： Pham-Quoc, Cuong Loc, Nguyen Thanh Vung, Doan Minh 268 Ly Thuong Kiet District 10 Ho Chi Minh City Viet Nam Linh Trung Thu Duc Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350353983

In recent years, edge computing has emerged as a promising paradigm for data computation, allowing for pre-processing of data prior to transmission to servers. This approach finds significant application in the development of intelligent systems for various domains such as urban environments, workplaces, and agriculture. In the context of smart cities, the adoption of smart parking systems has been on the rise, owing to their manifold advantages. Unlike conventional systems that necessitate considerable time investment from drivers in locating parking spaces, the smart parking approach with the support of edge AI platforms and cloud-based services help users to locate their booked slots quickly by utilizing video and sensor data. This paper introduce our proposed our smart parking system based on edge AI computing paradigm where AI models and heavy workload are processed by edge devices before sending data to cloud services. Our proposed architecture employs edge computing to handle the bulk of video processing tasks, thereby mitigating challenges related to network bandwidth. A prototype of our system developed with two Jetson Xaiver boards and two Yolo-based AI models have been deployed and validated inside our institution's campus Our experiments demonstrate that in terms of processing throughput, we obtains up to 30 frames per second (FPS) when detecting license plates and up to 24 FPS when recognizing empty parking slots. Furthermore, the AI models after training with certified datasets, achieve an accuracy and precision of up to 98.0% and 99.6%, respectively. © 2024 ieee.

关键词： Cloud platforms

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Review of Digital Twins in intelligent Driving 8

Review of Digital Twins in Intelligent Driving

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8th CAA international conference on Vehicular Control and Intelligence, CVCI 2024

作者： Zhou, Yangyang Hu, Dong Huang, Chao Huang, Hailong The Hong Kong Polytechnic University Department of Industrial and Systems Engineering Hong Kong

ISBN: (纸本)9798331504892

This paper aims to explore the technology of Digital Twin (DT) in intelligent vehicles, especially its integration with intelligent driving. DT is able to create virtual models that are synchronized with the physical counterparts in real time. It can provide services such as digital manufacturing, predictive maintenance and battery management for intelligent vehicles. In the field of intelligent driving, DT can greatly improve vehicle operation management and decision-making processes. The ap-plication of DT in intelligent driving includes the integration of technologies such as advanced driver assistance systems (ADAS) and driver digital twins in electric vehicles. Furthermore, this study examines the DT system framework within intelligent driving. It illustrates how DT builds a powerful data integration and system simulation platform that improves the reliability and safety of intelligent vehicles. © 2024 ieee.

关键词： advanced driver assistance systems

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