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共 12917 条记录，以下是21-30 订阅

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2024 IEEE 3rd international conference on control, Instrumentation, Energy and Communication, CIEC 2024 - Proceedings

2024 IEEE 3rd International Conference on Control, Instrumen...

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3rd IEEE international conference on control, Instrumentation, Energy and Communication, CIEC 2024

ISBN: (纸本)9798350313703

The proceedings contain 75 papers. The topics discussed include: design of a weather adoptive and traffic adoptive wireless LED street light control scheme;impedance measurement technique for vehicle battery health assessment using on-board CAN signals;a proficient cascaded filter design for image denoising by arithmetic optimization algorithm;classroom automation & protection;determining maximum rooftop solar penetration to mitigate voltage swelling in small-scale distribution systems;MicroNET: microarray image orientation predictor and automated data extraction;different cascaded modified forms of fractional order compensation for unstable non-minimum phase systems and plants with transport delay;realization of space vector pulse width modulation scheme for a quasi Z-source inverter in FPGA;and a comparative analysis on application of pi and LQR based control for speed tracking of a DC motor.

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Design of an Augmented Reality System with Internet of Things Features Applied to Mechatronic Stations 3

Design of an Augmented Reality System with Internet of Thing...

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3rd international conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Solís-Solís, Daniel Carrasquilla-Batista, Arys Instituto Tecnológico de Costa Rica Cartago Costa Rica

ISBN: (纸本)9798331532420

Internet of Things (IoT) allows the interconnection of billions of devices, sensors, actuators and machines to the Internet. The potential and capabilities of IoT can be improved by the integration of Augmented Reality (AR). AR is a blending between the digital and the physical (real) space. The aim of this project is to update three FESTO didactic mechatronic stations: buffer, robot and sorting. Each system is upgraded with IoT, AR and a Supervisory control and Data Acquisition (SCADA) system. This integration also gives the possibility for data storing, remote access, local and remote control and visualization, human-machine interaction and a didactic process optimization. The potential use of this project on other areas of applications gives further opportunities for research in Industry 4.0. © 2024 IEEE.

关键词： Remote control

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Optimizing Distribution Generation and EV Charging Station Placement with Particle Swarm optimization under Variable Load Condition 3

Optimizing Distribution Generation and EV Charging Station P...

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3rd international conference on Power Electronics and IoT applications in Renewable Energy and its control, PARC 2024

作者： Singh, Kamna Mistry, Khyati D. Patel, Hiren G. S.V.National Institute of Engineering Department of Electrical Engineering Surat India

ISBN: (纸本)9798350309409

Effectively managing a distribution system entails a crucial undertaking to minimize power losses and conserve energy. The utilization of Distributed Generation (DG) emerges as a highly efficient strategy for achieving this goal. However, the widespread implementation of Electric Vehicle Charging Stations (EVCSs) on a larger scale presents a notable challenge for distribution system operators in terms of optimal planning. The increasing integration of Electric Vehicles (EVs) into the distribution network exacerbates the mismatch between power generation and demand, leading to escalated power losses and compromised voltage levels. Addressing the adverse effects of the expanding EV load on distribution systems requires strategically integrating EVCS at suitable locations. This work delves into an optimization approach that concurrently determines the sizing and placement of both EVCS and DG, considering diverse load models. The primary objective is to minimize active power loss in the electrical distribution system. The proposed method is tested on a 33-bus system, employing the Particle Swarm optimization (PSO) algorithm for system objective optimization. The results illustrate that this optimization technique significantly improves system performance and efficiency by optimizing the planning and operation of DG and Electric Vehicles while considering various load models. © 2024 IEEE.

关键词： Electric power distribution

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Enhanced Color Image Quantization Utilizing Hybrid Glowworm Swarm optimization and K-Means Clustering: A Case Study with Chandrayaan-3 Images 3

Enhanced Color Image Quantization Utilizing Hybrid Glowworm ...

引用

3rd international conference on Power, control and Computing Technologies, ICPC2T 2024

作者： Desur, Arjun Govind Jatoth, Ravi Kumar National Institute of Technology Warangal Department of Electronics and Communication Engineering Warangal India

ISBN: (纸本)9798350349207

The necessity for color image quantization arises from limitations in image display, output, storage, and transmission devices. This paper proposes a novel metaheuristic approach using Glowworm Swarm optimization (GSO) hybridized with K-means clustering to achieve optimal color quantization solutions. We start by analyzing the quantization problem and its formulation, followed by introducing GSO and its application to the problem. To evaluate GSO's effectiveness, we conduct a comparative study with conventional heuristics and the K-means clustering algorithm. Experiments on diverse images, including the Kodak dataset and Chandrayaan-3 mission photos, reveal that a well-implemented GSO with an appropriate initialization strategy serves as a highly effective metaheuristic color quantizer. Results underscore GSO's superiority over other algorithms, highlighting its potential to enhance color quantization in various applications. © 2024 IEEE.

关键词： K-means clustering

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Optimal Integration of Metaheuristic Techniques with Correction-Based Approaches for Profit Maximization in Combinatorial Production Planning: A Comparative Analysis 3

Optimal Integration of Metaheuristic Techniques with Correct...

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3rd IEEE international conference on control, Instrumentation, Energy and Communication, CIEC 2024

作者： Mukherjee, Arijit Mukherjee, Surojit Future Institute of Technology Deptartment of Electrical Engineering Kolkata India Great Lakes Institute of Management Post-Graduate Program in Management Chennai India

ISBN: (纸本)9798350313703

To enhance production planning efficiency, it's crucial to have well-structured models that align with metaheuristic and other optimization techniques. This research conducts a comparative analysis of three unique metaheuristic methods: sanitized-teaching-learning-based optimization (sTLBO), Particle Swarm optimization (PSO), and Differential Evolution (DE) with a termination criterion of equal number of functional evaluations. The primary focus lies on profit-maximizing production planning within a single-level framework, encompassing intricate domain constraints, nonlinear costs, budget limitations and raw material constraints. The paper introduces three correction-based solution approaches. Upon evaluating 300 distinct instances of the problem, the study reveals that s-TLBO integrated with correction-based approach 1 and 3 consistently outperforms the other methods when it comes to devising optimal production plans for a Saudi Arabian petrochemical industry. This industry scenario involves 24 products, 54 processes, and three raw materials. © 2024 IEEE.

关键词： Combinatorial optimization

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The Fusion of Deep Reinforcement Learning and Edge Computing for Real-time Monitoring and control optimization in IoT Environments 3

The Fusion of Deep Reinforcement Learning and Edge Computing...

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3rd international conference on Energy and Power Engineering, control Engineering (EPECE)

作者： Xu, Jingyu Wan, Weixiang Pan, Linying Sun, Wenjian Liu, Yuxiang No Arizona Univ 1900 S Knoles Dr Flagstaff AZ 86011 USA Univ Elect Sci & Technol China Chengdu Peoples R China Trine Univ Phoenix AZ USA Yantai Univ Tokyo Japan Northwestern Univ Atlanta GA USA

ISBN: (纸本)9798350366105;9798350366099

In response to the demand for real-time performance and control quality in industrial Internet of Things (IoT) environments, this paper proposes an optimization control system based on deep reinforcement learning and edge computing. The system leverages cloud-edge collaboration, deploys lightweight policy networks at the edge, predicts system states, and outputs controls at a high frequency, enabling monitoring and optimization of industrial objectives. Additionally, a dynamic resource allocation mechanism is designed to ensure rational scheduling of edge computing resources, achieving global optimization. Results demonstrate that this approach reduces cloud-edge communication latency, accelerates response to abnormal situations, reduces system failure rates, extends average equipment operating time, and saves costs for manual maintenance and replacement. This ensures real-time and stable control.

关键词： Deep reinforcement learning Edge computing Industrial Internet of Things Lightweight policy network Dynamic resource allocation

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Fuzzy Fractional Order PID controller Design for AVR System 3

Fuzzy Fractional Order PID Controller Design for AVR System

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3rd IEEE international conference on control, Instrumentation, Energy and Communication, CIEC 2024

作者： Chakraborty, Susmit Mondal, Arindam Das, Chandan Brainware University Department of Computer Science and Engineering Kolkata India Dr. B. C. Roy Engineering College Department of Electrical Engineering Durgapur India Dr. B. C. Roy Engineering College Department of Computer Science and Engineering Durgapur India

ISBN: (纸本)9798350313703

An automated voltage regulator (AVR) is a feedback control system that examines the output voltage of generator, compares it to a predetermined value, and outputs an error signal that is used to modify excitation of the generator. In this manuscript, Fuzzy logic-based fractional order PID controller (Fuzzy-FOPID) is designed for the AVR problem. The parameters of the controller are tuned with a newly introduced metaheuristic optimization algorithm, known as Firebug Swarm optimization (FSO). When compared to other types of controllers described in the literature, the effectiveness of the FSO-tuned Fuzzy-FOPID controller performs the most effective control. The optimized gains of the proposed controller are evaluated on two different types of objective functions Integral Absolute Error (IAE) and Integral of time multiplied absolute error (ITAE). The output of the MATLAB simulation demonstrates the superiority of the suggested controller for the AVR system in terms of the per unit terminal voltage (TV) of the alternator. © 2024 IEEE.

关键词： optimization

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Efficient Utilization of Metaheuristic Techniques for Profit Maximization in Combinatorial Production Planning: A Comparative Study 3

Efficient Utilization of Metaheuristic Techniques for Profit...

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3rd IEEE international conference on control, Instrumentation, Energy and Communication, CIEC 2024

ISBN: (纸本)9798350313703

Efficient production planning requires well-structured models compatible with metaheuristic and other optimization techniques. This study compares three established metaheuristic methods - sanitized-teaching-learning-based optimization (s-TLBO), Particle Swarm optimization (PSO), and Differential Evolution (DE) with a termination criterion of equal number of functional evaluations - for profit-maximizing production planning. The focus is on a single-level problem involving complex constraints, non-linear costs, budget and raw material limitations. The paper presents two solution strategies: a penalty-based and a correction-based approach. Analyzing 300 unique instances of the problem reveals that s-TLBO implemented with correction-based approach consistently outperforms the other methods in devising optimal production plans for two Saudi Arabian petrochemical industries with 24 products, 54 processes, and three raw materials for plant 1 and two raw materials for plant 2. © 2024 IEEE.

关键词： Combinatorial optimization

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Solar Photovoltaic MPPT under irradiance change using a corrected grey wolf optimization algorithm 3

Solar Photovoltaic MPPT under irradiance change using a corr...

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3rd international conference on Communication, control, and Intelligent Systems, CCIS 2024

作者： Tompala, Jyothi Bali, Sravana Kumar Gitam Deemed to Be University Department of Eee Visakhapatnam530045 India

ISBN: (纸本)9798331528201

This paper proposes a new maximum power point tracking technique in PV systems by using the corrected Grey Wolf optimization (CGWO) algorithm as one of the effective techniques, which could overcome negative issues caused by changing environmental conditions, such as partial shading and dynamic irradiance change. The CGWO-based MPPT algorithm had better tracking accuracy and convergence speed to the GMPP than the conventional techniques, such as the perturb and observe method and incremental conductance method. Extensive simulations prove the effectiveness of the CGWO algorithm with a few parameters;hence, it is guaranteed to be user-friendly and provide robust performance in various operational scenarios. Results confirm that the CGWO-based strategy can considerably improve energy output from PV systems and contribute further to the domain with a reliable and efficient MPPT solution. This opens an avenue for further research into how CGWO applications in real-world PV systems interact and integrate with smart energy grids. © 2024 IEEE.

关键词： Solar irradiance

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Deep Learning Based QoE-Driven Packet Scheduling and Multipath Routing Using Fractional Coati optimization 3

Deep Learning Based QoE-Driven Packet Scheduling and Multipa...

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3rd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Kumari, Priya Jain, Nitin Babu Banarasi Das University Department Of Electronics And Communication Uttar Pradesh Lucknow India

ISBN: (纸本)9798350318609

Nowadays, multipath routing is performed in the Internet of Things (IoT) to provide reliable data transmission under different traffic conditions. In this research, multipath routing in IoT is performed by Fractional Coati optimization Algorithm (FCOA). Here, the Quality of Experience (QoE)-driven data delivery is performed by considering IoT users and applications initially. The Transmission control Protocol/Internet Protocol (TCP/IP) networks are considered to promote the QoE of IoT users and for prioritizing applications. The data from various IoT applications is transmitted in the form of packets and Deep Long Short-Term Memory (DLSTM) is used to perform QoE-driven packet scheduling from the packets. The multipath routing is effectuated by the FCOA by considering fitness parameters, namely energy and delay. Furthermore, the multipath routing performance of FCOA is validated by comparing the performance with existing methods. Here, the FCOA attained superior performance with residual energy and delay of 0.433J, and 0.744mS. © 2024 IEEE.

关键词： Internet of things

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