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检索条件"任意字段=IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems"

共 34966 条记录，以下是131-140 订阅

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Tensor Decomposition-Based Feature Transfer Learning for BCI Applications 13

Tensor Decomposition-Based Feature Transfer Learning for BCI...

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13th international conference on intelligent control and Information Processing, ICICIP 2025

作者： Bonab, Zahra Sohrabi Shamsollahi, Mohammad Bagher Department of Electrical Engineering Sharif University of Technology Tehran Iran

ISBN: (纸本)9798331516147

Event-Related Potential (ERP) detection and enhancement remain significant challenges in EEG signal processing due to the complex and multidimensional nature of ERP signals. This paper addresses these challenges by proposing a novel framework that combines low-rank tensor representation with heuristic feature transfer learning for efficient ERP processing. By leveraging tensor decomposition, we capture intricate multidimensional patterns in ERP data, facilitating the extraction of informative features. These features greatly reduce processing time, making the method particularly suitable for online Brain-Computer Interface (BCI) applications. We also present a feature transfer learning mechanism utilizing tensor decomposition, which facilitates the efficient adaptation of features trained on multiple subjects to new subjects, requiring only a small amount of calibration data. This approach leverages shared classification structures across subjects, allowing for rapid deployment and minimizing the need for extensive retraining. Our results demonstrate that the proposed method achieves reasonable ERP detection accuracy while drastically reducing computational overhead, presenting a robust solution for real-time BCI systems. © 2025 ieee.

关键词： Event Related Potentials (ERPs) Feature Extraction Feature Transfer Learning Tensor Decomposition

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Boosting Fault-Tolerant Scheduling with Smart Resource Management for Industrial cyber-Physical systems 6

Boosting Fault-Tolerant Scheduling with Smart Resource Manag...

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

作者： Prasad, K.V.S. Varman, Ravi Prasad, K.D.V. Praveena, D. Chinniah, P. Sathyakala, S. Department of Basic Sciences and Humanities GMR Institute of Technology Andhra Pradesh Vizianagaram India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Tamil Nadu Coimbatore India Department of Research Symbiosis Institute of Business Management Telangana Hyderabad India Department of Information Technology R.M.D. Engineering College Tamil Nadu Chennai India Department of Electronics and Communication Engineering ST. Joseph College of Engineering Tamilnadu Kanchipuram India Department of Management Studies St. Sona College of Technology Tamil Nadu Salem India

ISBN: (纸本)9798331522667

Industrial cyber-Physical systems (ICPS) are integral to modern industrial automation, where precise scheduling, efficient resource allocation, and robust fault tolerance are paramount. As the complexity of ICPS grows, so do the challenges of maintaining system resilience and network performance under dynamic and potentially unpredictable conditions. Traditional scheduling and resource management techniques in ICPS often fall short of meeting the stringent requirements of real-time responsiveness and fault tolerance. Existing methods struggle to optimize performance while simultaneously managing system faults, which can lead to significant operational downtime. This research is driven by the need to create a more robust approach that not only enhances scheduling efficiency but also integrates fault detection and network performance optimization in a resource-constrained environment. This study aims to boost fault-tolerant scheduling by integrating Reinforcement Q-Learning Using LSTM Attention mechanisms for optimized resource allocation and scheduling. Additionally, it incorporates Slimmable Pruned Neural Networks for efficient fault detection and a Modified Crayfish Optimization technique to enhance network performance. The proposed model achieved a scheduling accuracy exceeding 99.5%, fault detection precision above 99.2%, and stability of the network throughput more than 99.7 % compared to the traditional approaches. These metrics highlight the model's high performance and capability to adapt dynamically to ICPS requirements. By fusing advanced reinforcement learning, neural pruning for fault detection, and optimized network strategies, this research presents a transformative approach for ICPS. The presented method demonstrate a significant leap in scheduling reliability, fault resilience, and overall network efficiency, marking a critical advancement in fault-tolerant industrial system design. © 2025 ieee.

关键词： Reinforcement learning

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Application of PMS Graphic intelligent Recognition and Analysis Based on Contact Diagram Automatic Generation technology

Application of PMS Graphic Intelligent Recognition and Analy...

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6th international conference on cyber Security Intelligence and Analytics, CSIA 2024

作者： Ma, Wei Li, Qiang Yao, Yuan Chen, Xinkai Power Supply Service Command State Grid Xinjiang Electric Power Co. Ltd. Changji Power Supply Company Xinjiang Changji China

ISBN: (纸本)9783031882869

With the continuous expansion and increasing complexity of the power grid, the traditional method of manually drawing electrical connection diagrams is no longer able to meet the needs of real-time updates and maintenance. This article studies the application of PMS graphic intelligent recognition and analysis based on contact graph automatic generation technology, aiming to improve the efficiency of power grid construction and management. The study adopts the Generalized Chromosomes Genetic Algorithm (GCGA) algorithm, combined with the advantages of graph computing and quantum genetic algorithm, to optimize search efficiency and solution quality through adaptive crossover and mutation rates, as well as grouping competition and optimal selection mechanisms. The research includes steps such as topology data analysis, intelligent layout, and graphic rendering to ensure that the automatically generated contact diagram is both accurate and easy to understand. The experimental results show that the application of automatic generation technology for contact diagrams can achieve a maximum topology integrity of 99.8%, and the time required for data organization, layout optimization, and graphic rendering is significantly faster than traditional manual drawing methods. The system can quickly respond and accurately recover in the face of power grid changes and abnormal situations. These results demonstrate the effectiveness and practicality of automatic generation technology in improving the automation level of power grid management, reducing human errors, and supporting real-time monitoring and rapid fault response of the power grid. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Lean production

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Chatbot and its Impact on the Retail Industry 3

Chatbot and its Impact on the Retail Industry

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3rd international conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Tatikonda, Reshmi Kempanna, M. Thatikonda, Ramya Bhuvanesh, A. Thota, Ragupathi Keerthanadevi, R. Department of Computer Information Systems University of the Central Missouri Warrensburg United States Department of AI and ML Bangalore Institute of Technology Bengaluru India Department of Information Technology University of the Cumberlands Williamsburg United States Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli India Department of Information Technology Jawaharlal Technological University Hyderbad India Department of Computer Science and Engineering PSN College of Engineering and Technology Tirunelveli India

ISBN: (纸本)9798331527549

This study explores the transformative potential of chatbots in the retail industry, highlighting their role in enhancing customer engagement, optimizing business processes, and revolutionizing the nature of retail interactions. Through a comprehensive review of existing literature and case studies, the paper delves into the cognitive processes underlying human-chatbot interactions and examines their impact on customer experience, satisfaction, and loyalty. Chatbots serve a wide array of operational functions, including inventory management, automated order processing, data-driven analytics, and employee training. These functionalities streamline workflows, improve efficiency, and maximize resource utilization. The paper further explores the innovative applications of chatbots, including AI-driven personalization, conversational commerce, and seamless integration with emerging technologies. By analyzing both the current applications and challenges of chatbot technology in retail, the study uncovers the exciting opportunities they present for shaping the future of retail by fostering more personalized, efficient, and interactive customer experiences. © 2025 ieee.

关键词： Inventory control

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Harnessing AI for cybersecurity: Performance Evaluation of Machine Learning Techniques in Intrusion Detection systems 1

Harnessing AI for Cybersecurity: Performance Evaluation of M...

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1st international conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Kumar, Naween Sharma, Supragya Gupta, Sahil Jain, Sajal Singh, Sujal School of Computer Science Engineering & Technology Bennett University Greater Noida India

ISBN: (纸本)9798331518578

Due to the current development of networked systems and the emergence of global cyber threats, efficient and fast NIDS are required. The suggested article aims to analyse new and improved AI and Machine Learning (ML) frameworks to design and implement efficient Network Intrusion Detection systems (NIDSs) that accurately detect and differentiate network intrusions. The article assesses several effective algorithms for intrinsic and practical use: Logistic Regression, Random Forest, Feedforward Neural Networks (FNN), K Nearest Neighbours (KNN), and Long Short-Term Memory (LSTM), in terms of actual positive intrusions without creating high positive false intrusions. A comparative analysis is then made to evaluate the models based on precision, recall rate, accuracy, and the model processing rate. This suggested article presents a comparison of the two techniques regarding their effectiveness and applicability of the techniques for implementation in the next generation of NIDS. Although observations showed good performance and improvement regarding class imbalance, they remain key issues affecting such systems' reliability, including high computational demand and model interpretability. The reported results such as accuracy 99.897%, precision 99.894%, recall 99.898%, and F1 Score 99.892% in case of Logistic Regression and accuracy 99.882%, precision 99.876%, recall 99.882%, and F1 Score 99.892% in case of Random Forest from the work contribute to the advancement of fully intelligent NIDS that will improve the system stability to defend and adapting for new emerging threats, hence improving the advance of technologies in the complex and sophisticated era of cyberstalking. © 2025 ieee.

关键词： Network intrusion

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Pseudoinverse Learning Algorithm As a Unified Framework For Normalization In Deep Learning 13

Pseudoinverse Learning Algorithm As a Unified Framework For ...

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13th international conference on intelligent control and Information Processing, ICICIP 2025

作者： Guo, Ping Deng, Xiyan Wang, Yuping International Academic Center of Complex Systems Beijing Normal University Zhuhai China School of Computer Science and Technology Xidian University Xi’an China

ISBN: (纸本)9798331516147

In recent years, several normalization methods have been proposed in order to train neural networks, including batch normalization, layer normalization, weight normalization, and group normalization. However, it does not exist a unified framework which can unify these normalization techniques for easy using to most researchers. In this paper, we proved that most of the normalization methods can be realized in a unified framework, namely PseudoInverse Learning (PIL) algorithm. When training neural network with PIL algorithm, no other normalization methods are needed further since normalization is natural embedded in PIL algorithm. And like normalization techniques, PIL algorithm not only can speed up training deep neural networks, but also can act as a regularizer to improve generalization when utilizing low rank constraint PIL for autoencoders. Hence, our proposed unified framework can help researchers to tackle the difficult of choosing proper normalization methods, and this can greatly facilitate their research process. © 2025 ieee.

关键词： Adversarial machine learning

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Cloud Based Automated control System Workshops and Rooms for controlling Parameters 1

Cloud Based Automated Control System Workshops and Rooms for...

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1st international conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Kumar, Dinesh Kuntal, Ravinder Singh Deep, Pragya Chamoli, Anup Shekhar Singh, Pinki Mandal, Rishabh SRM Institute of Science & Technology Department of Computer Science and Engineering UP Ghaziabad India Nitte Meenakshi Institute of Technology Department of Mathematics Karnataka Bangalore India IIMT College of Engineering Department of AI & DS Greater Noida India Uttaranchal School of Computing Sciences Uttaranchal University Dehradun248001 India Accurate College of Engineering & Management Department of Computer Science and Engineering Greator Noida India Tula's Institute Department of Computer Science and Engineering UK Dehradun India

ISBN: (纸本)9798331518578

Across all industries, cloud-based automated control systems have completely changed how operational and environmental parameters are tracked and controlled. With cloud-integrated systems for real-time data collection, processing, and control, this concept presents intelligent, remote-controlled workshops and control rooms. To guarantee accurate control of crucial variables like temperature, humidity, pressure, and equipment efficiency, these systems make use of IoT devices, AI techniques, and big data analytics. Through sophisticated data perspectives, the cloud-based strategy enables predictive maintenance, improves flexibility, and lowers operating expenses. Stakeholders may make prompt, data-driven choices from anywhere with remote access to control rooms. These seminars provide practical experience with cloud-controlled settings and function as training and development centres. The system's flexibility to meet a range of industrial demands is further enhanced by its compatibility with external tools and equipment. The design, deployment, and possible uses of cloud-based control systems are covered in this paper, with an emphasis on how they might promote operational excellence, energy efficiency, and smart manufacturing. © 2025 ieee.

关键词： Smart manufacturing

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Research on A Miniaturized Rubidium Atomic Frequency Standard 5.3125mhz Frequency Synthesizer based on Direct Digital Synthesis (DDS) technology 1

Research on A Miniaturized Rubidium Atomic Frequency Standar...

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

作者： Huang, Zheng Wang, Erlan Wuhan Donghu University School of Electronics and Information Engineering Hubei Wuhan China

ISBN: (纸本)9798331529246

Rubidium atomic frequency standard has small size, low power consumption, higher performance, navigation, position measurement, and is widely used in geodesy and communication fields. This paper introduces the design and implementation of 5.3125mhz frequency synthesizer based on direct digital synthesis (DDS) technology. The system does not use drainage circuit, and DIRECTLY inputs 10mhz reference signal into the DDS device. This method is simple in structure, has the advantages of pure spectrum and low phase noise, improves the overall performance and reduces the manufacturing cost. A phase-locked circuit driven by a PLL (digital phase-locked loop) for a caproct oscillator is proposed, which fixed the 10mhz signal at 12.6568287mhz by digital PLL and produced an incident frequency of 6834.6875mhz by frequency multiplication. This method not only avoids the interference of mixed noise, eliminates the complexity of traditional frequency synthesizer design, but also makes the circuit more compact, easier to test and control, optimizes the filter, and further reduces the phase noise. The experimental results show that the design not only effectively transmits the frequency stability of the caudate oscillator, but also significantly improves the frequency standard performance of the atom. © 2025 ieee.

关键词： Frequency synthesizers

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Enhancing Student Engagement and Performance in Online Classrooms through a Smart Management Application 3

Enhancing Student Engagement and Performance in Online Class...

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3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Gowthambalagi, M. Vishnu, S.M. NaveenKumar, M. Raja, S. K.S. Rangasamy College of Technology Department of Artificial Intelligence and Data Science Tiruchengode India

ISBN: (纸本)9798331509675

The fast pace of technological growth has revolutionized the education industry, and there is a need for embracing intelligent solutions to improve classroom management and student engagement. The Smart Classroom Management System (SCMS) takes advantage of innovative technologies like facial recognition, screen sharing, and real-time analytics to automate administrative processes, track student attentiveness, and promote interactive learning spaces. With process automation for features such as tracking attendance and evaluating student interaction, SCMS eliminates the strain on teachers but presents meaningful analytics for enhancing instructional techniques. Further, the software integrates AI-enabled components for one-to-one testing and adaptive questioning capabilities to promote tailored learning outcomes. SCMS allows hybrid learning and remote schooling by leveraging cloud capabilities, assuring accessibility, flexibility, and safety. This review research investigates current literature regarding smart classroom technologies, identifies major implementation challenges, and discusses future developments in AI-based education. The results focus on the use of SCMS to develop a dynamic and student-focused learning environment, ultimately enhancing educational outcomes and preparing students for the changing digital world. © 2025 ieee.

关键词： Convolutional neural networks

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intelligent control algorithms for industrial automation systems

Intelligent control algorithms for industrial automation sys...

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

作者： Sarathkumar D Anil. A. R J Sivadasan T. Jayakumar Murugesan Manivel Anandhakumar C Department of Electrical and Electronics Engineering Kongu Engineering College Tamil Nadu India Department of Computer Science &Engineering Sree Buddha College of Engineering Pattoor Alappuzha Kerala India Department of Electronics and Communication Engineering PSR Engineering College Sivakasi Tamil Nadu India Department of Electrical and Electronics Engineering Nandha Engineering College Tamil Nadu India Department of Electrical and Electronics Engineering Karpagam Institute of Technology Coimbatore Tamilnadu India Department of Electrical and Electronic Engineering Sri Ramakrishna Institute of Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

Industrial automation has become a cornerstone of modern manufacturing, enhancing efficiency, reliability, and scalability. The integration of intelligent control algorithms, such as fuzzy logic, neural networks, genetic algorithms, and model predictive control, has revolutionized traditional automation systems, enabling more adaptive, efficient, and robust operations. This paper explores the application and impact of these intelligent control techniques in industrial settings. It highlights how machine learning algorithms can predict equipment failures, optimize processes, and enhance decision-making in real-time. Case studies are presented to illustrate the benefits and challenges of deploying these algorithms across various automated systems, such as robotic assembly lines, process control in chemical plants, and smart warehousing. Furthermore, the paper discusses the integration of these algorithms with industrial IoT (IIoT) platforms, facilitating seamless communication and data exchange between machines and control systems. The research also addresses the challenges of implementing intelligent control, such as computational complexity, security concerns, and the need for real-time processing. Finally, this study evaluates future trends, emphasizing the potential of combining artificial intelligence and deep learning techniques with industrial automation for a more adaptive and autonomous manufacturing ecosystem.

关键词： Fuzzy logic automation Machine learning algorithms Neural networks Prediction algorithms Real-time systems Optimization intelligent control Industrial Internet of Things Predictive control

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