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检索条件"任意字段=International Conference on Electrical Engineering Computing Science and Automatic Control"

共 15478 条记录，以下是211-220 订阅

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Design and Implementation of Signal Processing for 2D Scanning Radar with Multi-Reflection Detection Using 60 GHz NRD Guide Pulse Radar

Design and Implementation of Signal Processing for 2D Scanni...

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2025 international conference on Intelligent control, computing and Communications, IC3 2025

作者： Thethi, H. Pal Reddy, N. V. Uma Sengar, R.S. Dashyam, Preethi Mounika, T. Abedi, Tamam Ali Abd Ulabbas Sharma, Geetika Lovely Professional University Phagwara India New Horizon College of Engineering Department of Artificial Intelligence and Machine Learning Bangalore India Gl Bajaj Group of Institutions Department of Electrical & Electronics Engineering Mathura India Griet Department of Information Technology Bachupally Telangana Hyderabad India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India

ISBN: (纸本)9798331527495

The design and implementation of a signal processing system for a two dimensional (2D) electric scanning radar using multi-reflection detection to identify short range object using 60 GHz NRD (Non Radiative Dielectric) guide pulse radar is presented in this research paper. Designed to increase detection accuracy and precision by processing multiple reflections of the transmitted signals in order to recognize objects at short distances, the radar system is the one. The signal processing algorithms are adapted for MPTS handling, and the system takes advantage of the high frequency properties of the 60 GHz NRD guide. Hardware design, simulation results, and performance evaluation regarding the detection accuracy, range resolution, and processing latency, are also discussed. However, applications like autonomous driving, indoor navigation, and industrial automation require short range, high precision radar system, and this approach offers a big advantage. © 2025 IEEE.

关键词： Radar signal processing

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Comparative Analysis of Different control Strategies for Enhancing the Performance of automatic Voltage Regulator

Comparative Analysis of Different Control Strategies for Enh...

引用

international conference on electrical, Computer and Communication engineering (ECCE)

作者： Md Rezaul Karim Shoaib Rufsun Rakibul Hasan Hasan Monir Sunjida Sultana Department of Electrical Electronic and Communication Engineering Military Institute of Science and Technology Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

In modern power systems, optimized automatic Voltage Regulators (AVRs) in synchronous machines has a crucial role to maintain voltage stability, to increase efficiency and to assure reliability. In optimizing the dynamic load changes and system parameters, previous studies have largely limited themselves to conventional methods. In this study, classical Proportional Integral Derivative (PID), fuzzy PID control and Genetic Algorithm (GA) techniques are integrated into an AVR model to analyze their output responses in the system. A detailed comparison of these methods basing on trial and error is illustrated where the findings show that the GA outperforms traditional methods by achieving improved control matrix and optimal performance.

关键词： PI control Regulators Power system dynamics Power system stability Stability analysis PD control Synchronous machines Optimization Tuning Genetic algorithms

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QoS-Aware TAS Scheduling Algorithm with Latest-Data-Only Transmission 11

QoS-Aware TAS Scheduling Algorithm with Latest-Data-Only Tra...

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11th international conference on Mechatronics and Robotics engineering, ICMRE 2025

作者： Choi, Hyeok Jun Lim, Seong Hyeon Do, Young Soo Oh, Sung Bhin Lim, Se Jeong Jeon, Jae Wook Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9798331509293

Recent advances in autonomous driving and Advanced Driver Assistance System (ADAS) technologies are expanding in-vehicle entertainment capabilities, and vehicles are evolving beyond simple transport to become multi-functional entertainment systems. These changes have pushed the limits of existing vehicle networks, making the introduction of automotive Ethernet essential for high-bandwidth data transmission. Time-Sensitive Networking (TSN) technology was introduced to address Ethernet real-time issues. Time-Aware Shaper (TAS) supports deterministic data transmission by using a time-triggered scheduler to control queue gates to ensure that traffic types are transmitted in designated time slots. However, when TAS is applied, short cycles of scheduled traffic or time synchronization errors cause scheduled traffic, such as control data, to arrive and stack up in non-scheduled traffic time slots. This may cause the accumulated scheduled traffic to be transmitted at once at the beginning of the next scheduled traffic time slot, resulting in malfunction. To solve these problems, this paper proposes a QoS-aware TAS scheduling algorithm with Latest-Data-Only transmission. This prevents malfunctions caused by temporary transmission of scheduled traffic and improves network QoS. The proposed algorithm is expected to be applicable not only to vehicles, but also to various fields such as industrial automation and robots where real-time control is important. © 2025 IEEE.

关键词： Advanced driver assistance systems

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A Survey on Application of Computational Technique for Two Area Interconnected automatic Generation control in Power System Using PSO-PID

A Survey on Application of Computational Technique for Two A...

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IEEE Students' conference on electrical, Electronics and Computer science (SCEECS)

作者： Arpit Yadav Lal Bahadur Prasad Rajan Kumar Department of Electrical Engineering Central Institute of Technology Kokrajhar Kokrajhar Assam India Department of Electrical Engineering Madan Mohan Malaviya University of Technology Gorakhpur U.P. India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

In this paper the various computational approaches for automatic generation control (AGC) of power systems is covers, with the goal of improving the automatic voltage regulator (AVR) and load frequency control (LFC) that are utilized with proportional integrative and derivatives (PID) controllers. PID tuning has long been a main area of current study, and PID controllers, sometimes known as proportional integrative and derivatives, are utilized in industry. The techniques being reviewed are conventional methods designed for PID based PSO optimization strategies that fit tuning requirements. Power engineering research and development now places a strong emphasis on optimization due to the growing requirement for energy. This paper deals on the electric power system applications of two area interconnected PSO based PID computational techniques to inspires power sector to uncover fresh information about the ideal rate of energy provision because of its creative application.

关键词： Surveys Industries Power engineering Regulators Reviews automatic generation control Power systems Optimization Tuning Research and development

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A UAV Target State Estimation Method by Fusing Game Reasoning to Eliminate Negative Particles 3rd

A UAV Target State Estimation Method by Fusing Game Reasonin...

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

作者： Han, Wei Yang, Guang Luo, Ling Dong, Jingwei Xu, Peng Sun, Xuefei Command and Control Engineering College Army Engineering University of PLA Nanjing210007 China

ISBN: (纸本)9789819624676

Aiming at Unmanned Aerial Vehicle (UAV) target state estimation, a dynamic space model between the electro-optical platform and the target is established, and a target state estimation method by fusing game reasoning is proposed. The payoff matrix is defined according to the game concept. Then, the proposed density distribution function of particle swarm is established, and the particle filter method is improved by fusing the game reasoning. The state of the moving target can be accurately estimated by the retained effective particles. Through simulation experiments, the validity of state estimation results of moving target under different moving trajectories is analyzed and verified. The research in this paper can meet the requirements of the current UAV velocity measurement of maneuvering targets in terms of real-time and reliability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Unmanned aerial vehicles (UAV)

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Dynamic Knowledge Graph Construction for Real-Time Natural Language Understanding in Autonomous Systems

Dynamic Knowledge Graph Construction for Real-Time Natural L...

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2025 international conference on Intelligent control, computing and Communications, IC3 2025

作者： Subhashini, P. Veeresh, G. Lakhanpal, Sorabh Sharma, Nidhi Saeed, Hazim Y. Soujanya, Rajireddy Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India New Horizon College of Engineering Bangalore India Lovely Professional University Phagwara India Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq Griet Department of Cse Bachupally Telangana Hyderabad India

ISBN: (纸本)9798331527495

This work presents Real- Time Semantic Mapping (RTSM), a new idea for improving the language comprehension in autonomous systems using the knowledge graph. RTSM solely relies on ConceptNet as its dataset and incorporate Spacy to parse and synchronize commends and signals from natural language appropriately. However, through the continuous updating of the knowledge graph, RTSM allows an autonomous system to provide accurate responding to real-time contextual changes with relatively low computational cost. Constant learning and semantic enhancement in this model make RTSM light and easily scalable, putting it at a higher ground as a natural language processing technique for use in autonomous systems. Due to a simple framework that makes it easy to incorporate all the knowledge, the RTSM is ideal in environments that are challenged for time and resources, but need to provide real time interaction capabilities. © 2025 IEEE.

关键词： Natural language processing systems

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LQR Based control Strategies for DFIG-Based Wind Energy System 16th

LQR Based Control Strategies for DFIG-Based Wind Energy Sys...

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16th APCA international conference on automatic control and Soft computing, controlO 2024

作者： Boukili, Yassine Aguiar, A. Pedro Faculty of Engineering SYSTEC-ARISE Research Center for Systems and Technologies University of Porto Porto4200-465 Portugal

ISBN: (纸本)9783031817236

In the realm of distributed energy resources, Doubly-Fed Induction Generator (DFIG)-based Wind Energy Systems are pivotal technologies. However, integrating them into the grid poses challenges like ensuring Low Voltage Ride Through capability and enhancing energy quality. Advancing DFIG control techniques is crucial to address these challenges. This study provides a customized Linear Quadratic Regulator (LQR) control mechanism along with gain scheduled strategy, applied on a state equation model for the DFIG’s Rotor-Side Converter. Stability results are presented. The results demonstrate the effectiveness of the proposed controller in the presence of noise compared to LQR with fixed gain. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Equations of state

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Data Entry Errors Detection for Industrial Quality control Variables Using Data-Driven Models 16th

Data Entry Errors Detection for Industrial Quality Control ...

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16th APCA international conference on automatic control and Soft computing, controlO 2024

作者： Salles, Rodrigo Leiria, Joana Mendes, Jérôme Institute of Systems and Robotics ARISE University of Coimbra Coimbra Portugal CEMMPRE ARISE Department of Mechanical Engineering University of Coimbra Coimbra Portugal

ISBN: (纸本)9783031817236

In modern industrial environments, quality control is imperative for ensuring product reliability and operational efficiency. Despite advancements in automatic data collection and data-driven models for quality processes, certain critical variables still require manual entry due to the branch of activity from which they originate, leading to potential errors from human involvement. This paper focuses on developing and applying two methodologies, Soft Sensors (SS) and Principal Component Analysis (PCA), to detect data entry errors in quality control variables. The developed methodologies were tested across three industrial datasets and the following error types were studied: blank spaces, duplicates, measurement errors, order errors, and extra number errors. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Model predictive control

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Adaptive Particle Swarm Optimization-Based Tuning of Fractional Order PID for Moisture control in an Industrial Rotary Dryer System 11th

Adaptive Particle Swarm Optimization-Based Tuning of Fractio...

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11th EAI international conference on Advancement of science and Technology, ICAST 2023

作者： Bosera, Adisu Safo Sori, Raji Ababe Bayesa, Ashebir Berhanu Department of Electrical Power and Control Engineering Adama Science and Technology University Adama Ethiopia

ISBN: (纸本)9783031641503

In this paper, a dynamic model of an industrial rotary dryer was formulated, and the model of the system was linearized using the Jacobian method. Adaptive particle swarm and gray wolf optimization-based tuning of parameters of fractional order proportional, integral, and derivative (FOPID) and conventional Ziegler-Nichols proportional, integral, and derivative (ZN-PID) was assessed with simulation for a solid moisture control rotary dryer system. The main feature of fractional order PID is that it may have a greater degree of freedom compared to conventional PID. Here, particle swarm and gray wolf optimization algorithms will be used for tuning the parameters of the FOPID controller against the kinds of disturbances that exist in this plant. The performance of the controller is compared to that of typical PID and gray wolf optimizer (GWO) tuning of fractional order PID controllers (FOPID). The performance of the developed controller is evaluated by assessing various time domain metrics. Using the application of the PSO-FOPID control, a satisfactory result was obtained with an integral time square error (ITSE) of 0.0032 compared to 0.0039 and 0.0366 in the cases of GWO-FOPID and ZN-FOPID, returning the expected and desired output moisture. In order to determine the robustness of the proposed control system, random linear density of solids and drying air, inlet temperature of solids, inlet moisture of solids, and linear velocity of solids are subjected to the controller. Therefore, from the simulation result, the performance of the PSO-FOPID controller will show good time domain specifications, such as an overshoot of 16.14%, a rise time of 0.1378 s and a settling time of 5.39 s. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Moisture control

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Optimizing the Allocation of Dynamic Workloads in Cloud Infrastructure through the Use of Machine Learning for Cost-Effective Cloud Resource Management

Optimizing the Allocation of Dynamic Workloads in Cloud Infr...

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2025 international conference on Intelligent control, computing and Communications, IC3 2025

作者： Sivamuni, Kalaimagal Pugalendhi, G.S. Pandi, V. Samuthira Shobana, D. Lakshmi Priya, J. Archana, V. Panimalar Engineering College Department of Ai & Ds Varadharaj apuram Poonamallee Tamilnadu Chennai India Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Kuniamuthur Tamil Nadu Coimbatore India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India Rajalakshmi Engineering College Department of Mechatronics Engineering Chennai India Saveetha Engineering College Department of Ece Chennai India Jeppiaar Institute of Technology Department of Aids Tamil Nadu Chennai India

ISBN: (纸本)9798331527495

The effective administration of dynamic workloads has emerged as a major obstacle for enterprises moving their operations to cloud computing. Intelligent allocation algorithms that guarantee optimal performance while minimizing operational expenses are necessary due to the fluctuation in demand for cloud resources. In order to achieve efficient and economical management of cloud resources, this study explores the use of machine learning (ML) methods to improve the distribution of dynamic workloads inside cloud infrastructure. In order to examine past workload data and forecast future resource needs, we present a thorough system that employs multiple ML algorithms, including supervised and unsupervised learning approaches. Our framework's goal is to automatically adjust to changing workload patterns and avoid under- or overprovisioning of resources by using data-driven insights to improve resource allocation in real-time. The platform relies on predictive analytics to foretell changes in workload, automatic resource scaling according to demand forecasts, and reinforcement learning to enhance allocation tactics in real-time. Extensive simulations and case studies are conducted across various cloud settings to test the efficiency of the proposed framework. In comparison to more conventional allocation strategies, the results show markedly higher rates of resource usage and lower costs. The flexibility of the framework to handle different types of workloads also highlights its wide range of possible uses in cloud computing. This research adds to the existing literature on cloud resource management and provides a solid method for improving the overall efficiency of cloud infrastructure and maximizing the distribution of workloads. Results highlight machine learning's revolutionary effect in propelling affordable cloud-based solutions for dynamic workload control. © 2025 IEEE.

关键词： Resource allocation

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