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共 15478 条记录，以下是91-100 订阅

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Smart Sensors and IoT in Mechanical engineering: Enhancing Monitoring and control of Industrial Processes 1

Smart Sensors and IoT in Mechanical Engineering: Enhancing M...

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1st international conference on Advances in Computer science, electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Annavarapu, Babu J Hareesha, N.G. Kacheru, Goutham Mohammad, Ashif Chin, Jacky Ghule, Gauri KLEF Deemed to be University Department of Management Andhra Pradesh Guntur India Dayananda Sagar College of Engineering Department of Aeronautical Engineering Bengaluru India Wilmington University Department of IST United States University of Dhaka Department of Institute of Energy Bangladesh Mercu Buana University Department of Industrial Engineering West Jakarta Indonesia Department of E and TC VIIT Pune India

ISBN: (纸本)9798331518578

Unmanned aerial vehicles (UAVs) are currently regarded as among the most effective remote sensing methods for collecting data across wide regions. Along with measuring output and aiding in decision-making, they are now employed in the industrial domain as sensing equipment for proactively resolving or avoiding numerous problems. UAVs offer an economical, functional, and reliable technical system for event monitoring and data collecting. Cloud computation has proven to be an essential instrument for addressing processing needs, and the Internet of Things (IoT) transmits industrial data from platforms that monitor and manage surroundings to data processing platforms. The IoTs port connects many items to the internet in fog computation. It supports many communication standards and can function as a joint portal for various systems. Much work has gone into creating UAVs and platforms with several UAVs. This work presents a smart IoT monitoring and control platform depending on a UAV. It utilizes fog computation as an interface between IoT tiers and cloud computation activities. Its ability to seamlessly connect UAVs to industrial control platforms via IoT port platforms and process and analyze UAV shots in real-time and in a structured manner in the cloud is what makes it distinctive. The outcomes show unequivocally that the suggested structure is feasible in offering a dependable and effective UAV distant control platform to enhance product grade and minimize waste. Hence this study investigated the communication delay in several IoT ports between the various IoT levels. © 2025 IEEE.

关键词： Fog computing

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Model Predictive control for Contour control in Biaxial System 3rd

Model Predictive Control for Contour Control in Biaxial Syst...

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3rd international conference on Machine Learning, Cloud computing and Intelligent Mining, MLCCIM 2024

作者： Li, Haoyu Fan, Hao Gu, Chaochen Wu, Kaijie Department of Automation Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9789819616930

To enhance the contour accuracy of biaxial systems, this paper proposes a compensation method that models independent single-axis systems and then couples multiple servo axes through model predictive control algorithm. Building upon this method, contour and tracking error are introduced as optimization variables in the cost function. The experimental results demonstrate that the method works effectively on contour control and enhances system performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Model predictive control

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Linepack Flexibility in Integrated Electricity-Gas Systems: A Comparative Study of Gas Flow Models and Solution Methods 5

Linepack Flexibility in Integrated Electricity-Gas Systems: ...

引用

5th international conference on Advances in electrical, Electronics and computing Technology, EECT 2025

作者： Wang, Haoyu Li, Zhengshuo School of Electrical Engineering Shandong University Jinan China

ISBN: (纸本)9798331541545

In integrated electricity-gas systems (IEGS), natural gas networks provide flexibility to power systems through linepack-a short-term storage mechanism governed by partial differential equations (PDEs). This study examines how modeling methods (dynamic/DY, steady-state/ST) and solution methods quantify linepack flexibility in mitigating wind power variability. Case studies demonstrate that DY model, which preserve transient flow physics through PDE discretization and pressure-flow coupling, reduce operating costs by 37.5% compared to ST models ignoring dynamics. Comparative analysis of solution methods reveals that relaxation-based approaches enhance computational efficiency by simplifying PDE constraints;however, insufficiently tight relaxation bounds introduce substantial physical feasibility deviations, thereby severely overestimating linepack flexibility. © 2025 IEEE.

关键词： Flow of gases

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Iterative Learning control for Nonlinear Switched Continuous-Time Systems with Time-Varying Delay 3rd

Iterative Learning Control for Nonlinear Switched Continuou...

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3rd international conference on Machine Learning, Cloud computing and Intelligent Mining, MLCCIM 2024

作者： Shao, Zhen Xue, Songyi Hohai University Nanjing211100 China

ISBN: (纸本)9789819616930

The present study focuses on the iterative learning control to switched nonlinear systems incorporating time-varying delay. Initially, the system is reformulated as a nonlinear switched continuous-discrete two-dimensional (2D) model. Subsequently, employing the technique of multiple 2D Lyapunov functions, rigorous criteria for exponential stability of the transformed system are derived. These stability conditions subsequently serve as the foundation for the design of the control strategy. To substantiate the practicality and efficacy of the proposed methodology, a numerical example is provided. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Continuous time systems

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Neural Contextual Adaptation (NCA): For Unsupervised Learning in Dynamic Environments 3

Neural Contextual Adaptation (NCA): For Unsupervised Learnin...

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3rd international conference on Intelligent and Innovative Technologies in computing, electrical and Electronics, IITCEE 2025

作者： Praveen Kumar, G.D. Sanjan, S. Suraj, P. Rohan, U.A. Srinivas, R. Thanush, V. Visvesvaraya Technological University P.E.S College of Engineering Department of Computer Science and Engineering Mandya India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

In recent years, artificial intelligence (AI) has made significant strides in various domains, primarily driven by supervised learning techniques requiring vast amounts of labeled data. However, real-world applications, particularly those involving rapidly changing environments, demand models capable of autonomous adaptation without the need for extensive labeled datasets. In this paper, we present a novel framework for unsupervised learning that allows AI models to dynamically adapt in real-time to changing environments. Our approach leverages a combination of self-organizing maps and contrastive predictive coding to enhance model flexibility and adaptability across diverse settings. We introduce an architecture that incorporates temporal data processing and on-the-fly adaptation mechanisms, enabling the model to learn and generalize from evolving data distributions. Through extensive practical assessments and experimentation, we demonstrate our model's ability to outperform existing unsupervised approaches in real-time object detection and decision-making scenarios. The results, which show a significant reduction in model retraining time and increased adaptability to novel environments, suggest that our framework can be applied to various fields including robotics, autonomous vehicles, and dynamic control systems. © 2025 IEEE.

关键词： Self-supervised learning

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IoT-Based Transfer Learning and Genetic Algorithm Approaches for Smart Environmental Pollution Detection and Management 5

IoT-Based Transfer Learning and Genetic Algorithm Approaches...

引用

5th IEEE international conference on Advances in electrical, computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Alauddin, Shafqat Sree Vidhya, S. Pragadeeswaran, S. Sowjanya, P. Bhokare, Rajashree Khan, Maaz Allah Shibli National College Department of Chemistry Uttar Pradesh Azamgarh India Erode Sengunthar Engineering College Department of Computer Science and Engineering Tamilnadu Thudupathi India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Cse Chennai India Department of Ece Telangana Dhulapally India Dr. D. Y. Patil Institute of Technology Department of Electrical Engineering Pimpri Maharashtra Pune India Department of Engineering & Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9798350356236

The identification of environmental pollution remains to be a significant issue, as conventional technologies lack scalability and flexibility to dynamic, real-time situations. This study presents an IoT-enabled hybrid model that combines transfer learning and genetic algorithms to overcome these restrictions. IoT networks enable real-time, extensive data acquisition, whereas transfer learning uses pre-trained models for effective feature extraction and domain adaption. Genetic algorithms enhance hyperparameters and sensor configurations, guaranteeing energy economy and optimal model performance. The suggested CNN-LSTM architecture utilising multi-scale attention attains a training accuracy of 95.6% and an F1 score of 0.96, exceeding the performance of conventional Random Forest and CNN models in accuracy, recall, and precision. The comparative study confirms the model's strength and flexibility, illustrating its capacity for scaled, practical applications. This framework creates a dependable and effective system for intelligent environmental pollution control, providing substantial improvements in detection accuracy and operational efficacy compared to current methods. © 2025 IEEE.

关键词： Environmental management

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Minimizing Average Job Completion Time for Multi-resource Queuing computing Systems Using Drift-Plus-Penalty 3rd

Minimizing Average Job Completion Time for Multi-resource Q...

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3rd international conference on Machine Learning, Cloud computing and Intelligent Mining, MLCCIM 2024

作者： Chen, Chuanxu Li, Ruiting Guan, Quansheng Shi, Zhan Zeng, Ying Zheng, Jianping School of Electronic and Information Engineering South China University of Technology Guangzhou China Electric Power Dispatch and Control Center Guangdong Power Grid Co. Ltd. Guangzhou China

ISBN: (纸本)9789819616978

The Lyapunov drift-based scheduling algorithms control the queue length to stabilize the system and maximize the throughput for multi-resource multi-queue cloud/edge systems. However, these algorithms often ignore the heterogeneity in job length, thus leading to a long delay for job processing. To this end, this paper takes the job completion time (JCT) as a penalty in the drift-plus-penalty expression to minimize the average job competition time, and proposes a Lyapunov drift-plus-time (LDPT) based scheduling algorithm for multi-resource queuing computing systems. The optimality of LDPT with respect to average JCT and queue length is analyzed. By using the Amazon EC2 to simulate, the results show that LDPT can improve average JCT and average queuing time fairness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Scheduling algorithms

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Research on Abnormal Behavior Identification Method for Autonomous Driving Formation Vehicles in Urban Road Environment

Research on Abnormal Behavior Identification Method for Auto...

引用

international conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Li, Kailong Zhang, Feng Li, Min Wang, Li Beijing Key Laboratory of Urban Intelligent Traffic Control Technology North China University of Technology Beijing10041 China School of Electrical and Automation Engineering East China Jiaotong University Nanchang330100 China Beijing Jidao Technology Co. Ltd. Beijing100044 China

ISBN: (纸本)9789819639687

With the development of science and technology and social progress, the landing of automatic driving technology seems to be close at hand, but the reality is that high-level automatic driving vehicles want to drive safely on urban roads, but also need to have the ability to accurately identify abnormal behaviors and accurately assess the risks in their operation. This paper focuses on the scientific issues in the process of abnormal behavior identification and risk assessment of autonomous driving formation vehicles in urban road environments. We constructed a framework for identifying abnormal behaviors of autonomous driving multi-vehicle formations in urban road environments, simulated urban road traffic scenarios using the CARLA-SUMO joint simulation platform, and completed several groups of independent simulation tests, and created a 17-dimensional multi-vehicle formation dataset by using five typical machine learning methods, and completed the identification and classification of six types of abnormal behaviors: sharp acceleration, sharp deceleration, overspeed, sharp steering, discrete, and falling out of line, abnormal behavior identification and classification. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： automatic vehicle identification

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automatic Modulation Recognition Using Parallel Feature Extraction Architecture 18th

Automatic Modulation Recognition Using Parallel Feature Extr...

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18th international conference on Wireless Artificial Intelligent computing Systems and Applications, WASA 2024

作者： Tang, Haolin Zhao, Yanxiao Kuzlu, Murat Luo, Changqing Catak, Ferhat Ozgur Wang, Wei Department of Electrical and Computer Engineering Virginia Commonwealth University RichmondVA United States Department of Engineering Technology Old Dominion University NorfolkVA United States Department of Computer Science Virginia Commonwealth University RichmondVA United States Department of Electrical Engineering and Computer Science University of Stavanger Rogaland Norway Department of Computer Science San Diego State University San DiegoCA United States

ISBN: (纸本)9783031714665

automatic Modulation Recognition (AMR) plays a critical role in wireless communication and can be applied in various applications such as spectrum monitoring and signal surveillance. Recently, different AMR approaches have been proposed based on Deep Learning (DL), which are inspired by the powerful abilities of feature extraction in neural networks, i.e., Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). Among these solutions, hybrid methods extract spatial features and temporal features of signals by combining CNN and RNN into a single architecture to provide better performance. However, it is inefficient to extract two types of features using a single architecture since the temporal feature could be degraded after CNN layers, and the spatial feature may be compromised after RNN layers. To overcome this challenge, we propose a parallel neural network architecture for AMR, which extracts spatial features using CNN layers and temporal features using LSTM (Long Short-Term Memory) layers, respectively, in two parallel routes. Afterward, the extracted features will be combined to predict the modulation scheme of a signal. Extensive simulations are conducted on signals with 11 different modulation methods at a wide range of Signal-to-Noise Ratio (SNR) levels to evaluate our proposed parallel feature extraction architecture. In addition, we compare our solution against three other DL-based methods, and the results show that our method outperforms in terms of recognition accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Utilizing Machine Vision for Steerable Robotic control 5th

Utilizing Machine Vision for Steerable Robotic Control

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5th international conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Agarwal, Trapty Kavitha, R. Verma, Sunny Sasi, Archana Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Electronic & Communication Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

This paper describes the design and implementation of a steerable robot control device making use of machine imaginative and prescient. The machine consists of a robot car ready with a 3-d digital camera, a show and a manipulate panel. The camera captures 3-d intensity pix of the surroundings and the manage panel lets in the consumer to output instructions that manipulate the motion of the auto. The vision module of the system uses a Convolutional Neural community (CNN) to discover objects inside the environment and small variations in elevation and assign distinct that means to them. This facts is then utilized by the control module to formulate the commands in step with the person’s commands, which then get sent to the car’s pressure device. The system additionally uses an optimization set of rules to help become aware of most beneficial paths thru the environment and avoid limitations. Eventually, the gadget can be used to monitor the automobiles positioning within the surroundings permitting the user to benefit information which include the place, orientation and pace of the automobile. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Digital cameras

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