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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141-150 订阅

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A Spline-Based Path Planning for Lane Keeping in Autonomous Vehicles 11

A Spline-Based Path Planning for Lane Keeping in Autonomous ...

引用

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

作者： Hong, Hyeong Keun Lee, Jin Sun Kim, Eun Ho Choi, Yeong Kwang Seo, Young Hun Jeon, Jae Wook Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9798331509293

This paper presents a spline-based path planning approach for lane keeping in autonomous vehicles. The method utilizes cubic spline interpolation to generate smooth and continuous paths from target points derived from lane centerline information. By applying spline curves, the vehicle can follow the lane with high precision and stability. The approach is implemented using ROS 2, providing a robust and efficient platform for integration into autonomous driving systems. Simulation results demonstrate the effectiveness of the proposed method in lane keeping tasks, highlighting its potential for improving path planning accuracy in autonomous vehicles. © 2025 IEEE.

关键词： Splines

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Simulation of a 100 MW Grid-Connected Solar Power Plant and MPPT control Using the PSO-P&O Technique 11th

Simulation of a 100 MW Grid-Connected Solar Power Plant and ...

引用

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

作者： Bruk, Betelhem Dereje Shewarega, Fekadu Belay, Birhanu Hailu Yenealem, Mezigebu Getinet Negash, Dereje Shiferaw Faculty of Electrical and Computer Engineering Bahir Dar Institute of Technology Bahir Dar University Bahir Dar Ethiopia University Duisburg-Essen Essen Germany Faculty of Computing Bahir Dar Institute of Technology Bahir Dar University Bahir Dar Ethiopia School of Electrical and computer Engineering Addis Ababa Institute of Technology Addis Ababa Ethiopia

ISBN: (纸本)9783031641503

Among the most prominent sources of alternative power in recent years is solar energy. Thus, photovoltaic (PV) systems need to be made more efficient and reliable. A PV panel’s conversion capacity is between 23% and 24% when it converts solar energy to electricity. However, the average conversion efficiency for most PV panels was around 15–20%. Identifying the maximum power point (MPP) on PV panels is one way to increase conversion efficiency. MPPT system control is presented and discussed in this paper for a 100 MW system in Metehara, Ethiopia. The aim of this paper is to present a novel hybrid approach for maximum power point tracking (MPPT). A particle swarm optimization (PSO) algorithm and a P&O MPPT technique are combined in this method. In order to achieve the MPP with improved speed and accuracy, the algorithm goes through a series of stages. MATLAB/Simulink was used to model the proposed algorithm under different operating conditions. Through comprehensive simulations across diverse input conditions, the proposed approach’s MPP tracking performance is assessed. When compared with systems lacking MPPT techniques, the hybrid approach demonstrates an average efficiency of 99.76%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： MATLAB

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Highway Toll Station Entrance control Under Accident Bottleneck Based on Particle Swarm Optimization Algorithm 3rd

Highway Toll Station Entrance Control Under Accident Bottlen...

引用

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

作者： Shou, Yanfang Lu, Kai Xu, Jianmin Li, Hongjie Guangzhou Institute of Modern Industrial Technology South China University of Technology Guangzhou511458 China School of Civil Engineering and Transportation South China University of Technology Guangzhou510640 China Engineering Technology Department Poly Changda Engineering Co. Ltd. Guangzhou China

ISBN: (纸本)9789819616930

To seek the best traffic management and control method to alleviate traffic congestion under sudden traffic accidents on highways, the paper proposed a method for toll station entrance control aimed at accident bottlenecks. Firstly, an modified METANET model was used to predict traffic density and flow of highway network. Taking the storage space of ramp and toll plaza, the traffic capacity of road section as constraints, and aiming to minimize the travel time on the main line and the waiting time at toll station, an optimal control rate model for toll station entrance was established. The particle swarm optimization algorithm (PSOA) was used to solve the optimal control rate at the toll station entrance, which was substituted into the established toll station channel control model to obtain the control scheme for each toll station channel. Finally, the VISSIM simulation software was applied for secondary development, and case simulations were conducted to verify the proposed method can reduce vehicle delays and congestion duration time. After accident cleaning, the vehicles could quickly dissipate, which reached a ideal effect. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Travel time

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sEMG Signal Processing and Recognition Based on CNN 3rd

sEMG Signal Processing and Recognition Based on CNN

引用

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

作者： Tian, Zimo Xu, Guangjie Cai, Senwu He, Xuansen Guangzhou College of Commerce Guangzhou511363 China College of Information Science and Engineering Hunan University Changsha410082 China

ISBN: (纸本)9789819624676

Extraction and processing of surface electromyography (sEMG) signals play an important role in medical rehabilitation, artificial limb control and other applications. This paper proposes a new method for sEMG signal processing based on convolutional neural networks (CNN). Firstly, the time-domain feature extraction method is used to extract the features of sEMG signals. Then, a CNN model is constructed for identifying and processing sEMG signals. The parameters of the model are calibrated based on the sEMG dataset to find the most suitable model parameters for the objective of this paper, achieving optimization of the model. The constructed CNN network is applied to NinanPro dataset for simulation and experiment, and the results show that the optimized CNN model proposed in this paper has a high accuracy rate for sEMG signal recognition. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Optimal Cooperative Intersection control of Heterogeneous Vehicle Platoons with MiTraS 16th

Optimal Cooperative Intersection Control of Heterogeneous Ve...

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

作者： Bejarbaneh, Elham Yazdani Du, Haiping Naghdy, Fazel School of Electrical Computer and Telecommunications Engineering University of Wollongong WollongongNSW2500 Australia

ISBN: (纸本)9783031817236

Platooning control of connected autonomous vehicles (CAVs) is emerging as a promising strategy to enhance fuel efficiency, traffic flow, and overall safety at road intersections. However, coordinating multiple self-driving vehicles while ensuring traffic safety and efficiency is challenging due to inherent nonlinearities in vehicle dynamics. Towards addressing this challenge, an optimal distributed leader-tracking control for heterogeneous vehicle platoons with nonlinear dynamics at an unsignalized intersection is proposed. The distributed control system is designed based on reinforcement learning (RL) theory that learns the optimal control actions from the vehicle system data without requiring any prior knowledge of vehicle dynamics. The performance of the developed control strategy is validated through a novel virtual testing platform, called Mixed Traffic Simulator (MiTraS), combining MATLAB/Simulink and SUMO microscopic traffic simulator. The design and structure of MiTraS is explained and various cooperative driving maneuvers are simulated using this platform to demonstrate its potential and effectiveness in validity the theoretical findings of the work. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Reinforcement learning

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Design and Simulation of SCADA System to Improve Reliability of a Distribution Feeder: A Case Study on Bahir Dar, Feeder R6-G2 11th

Design and Simulation of SCADA System to Improve Reliability...

引用

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

作者： Fenitaw, Melaku Asrat Beza, Teketay Mulu Faculty of Electrical and Computer Engineering Bahir Dar Institute of Technology – Bahir Dar University Bahir Dar Ethiopia

ISBN: (纸本)9783031641503

This paper presents a design and simulation of a supervisory control and data acquisition (SCADA) system to improve the reliability of a distribution feeder in Bahir Dar, Ethiopia. The SCADA system was modeled using MATLAB Simulink and Java programming language, and it includes sensors, circuit breakers, and a human-machine interface (HMI) for remote monitoring and control of the distribution feeder. The results of the simulation show that the proposed SCADA system has significantly improved the reliability of the distribution feeder, with a 60% reduction in the system average interruption duration index (SAIDI), a 61% reduction in the customer average interruption duration index (CAIDI), and a 1.11% improvement in the average service availability index (ASAI). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Java programming language

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Pioneering Privacy-First Approaches in IoT Healthcare with Secure Wearable Monitoring Systems

Pioneering Privacy-First Approaches in IoT Healthcare with S...

引用

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

作者： Najm, Rusul Awasthy, Neeta Kumari, E.N.Vijaya Lakhannal, Sorabh Sharma, Tarunika Singh, Anurag Prashanthi, Bh. College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq Gl Bajaj Group of Institutions Department of Computer Science and Engineering U.P. Mathura India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Lovely Professional University Phagwara India New Horizon College of Engineering Department of Applied Sciences Bangalore India Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India Griet Department of Cse Telangana Hyderabad India

ISBN: (纸本)9798331527495

In the world of IoT healthcare right now, protecting the privacy of patients' medical data collected by wearable tracking devices is an urgent and important issue. It is very important that this condition be met. The Secure Health Data Aggregation and Homomorphic Encryption (SHAHE) method is being thought about as a possible alternative while this investigation is going on. We thought about the need for processing, data collection, and access control as we worked on the SHAHE approach. We can fully reach this goal by using homomorphic cryptography and safe aggregation methods. The main goal of the program is to protect people's privacy by making data value analysis better and reducing unauthorized access to important health information. This is the reason why the program was made. © 2025 IEEE.

关键词： Authentication

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A Double Layer control Method for Sustainable Agricultural Production Management in a Competitive Market 16th

A Double Layer Control Method for Sustainable Agricultural ...

引用

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

作者： Hadj Abdelkader, Oussama Bouzebiba, Hadjer Faculty of Engineering SYSTEC - Research Center for Systems and Technologies ARISE Associated Laboratory University of Porto Porto4200-465 Portugal

ISBN: (纸本)9783031817236

This article proposes a decision support system in the competitive agricultural production context that also promotes environmental sustainability. It is formulated as a hierarchic double-layer control problem that conciliates contradicting objectives of the short-term profit-seeking producers and the long-term environmental and economic sustainability. While a multi-production cycle optimal control is considered for each production unit, a model predictive control scheme with an embedded dynamics learning procedure is adopted at the coordination level. The producers compete in a free market where the price of the produced commodity is driven by the difference between its total demand and total offer. The resulting analysis highlights the advantage of the proposed sustainability measures over the conventional ones and the effect of free market on the global process. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cultivation

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LSTM for Modelling and Predictive control of Multivariable Processes 44th

LSTM for Modelling and Predictive Control of Multivariabl...

引用

44th SGAI international conference on Innovative Techniques and Applications of Artificial Intelligence, SGAI 2024

作者： Zarzycki, Krzysztof Lawryńczuk, Maciej Institute of Control and Computation Engineering Warsaw University of Technology ul. Nowowiejska 15/19 Warsaw00-665 Poland

ISBN: (纸本)9783031779145

This paper presents the use of Long Short-Term Memory (LSTM) networks as models for prediction in the Model Predictive control (MPC) algorithm. LSTMs are recurrent neural networks often used to model dynamical processes. MPC is an advanced control technique using a process model to calculate online predictions. MPC sets the control policy for the process by solving an optimisation problem to minimise the prediction error and not violate the constraints. This paper compares two LSTM model architectures for MPC in a process with Multiple Inputs and Multiple Outputs (MIMO): a single LSTM model with multiple inputs and multiple outputs (LSTM MIMO) and several parallel LSTM models, each with Multiple Inputs and Single Output (LSTM MISO). The quality of modelling using these two architectures is analysed. Next, the selected MIMO and MISO LSTM models are implemented in the MPC algorithm. Finally, the control quality and execution time are investigated. It is concluded that both MIMO and MISO approaches offer distinct advantages;however, for the benchmark neutralisation reactor, MIMO models provide a more efficient solution for MPC implementation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Long short-term memory

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Enhancing Tomato Leaf Disease Classification with Augmentation Techniques in Deep Learning 14th

Enhancing Tomato Leaf Disease Classification with Augmentati...

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14th international conference on Frontier computing, FC 2024

作者： Debnath, Anik Gil, Joon-Min Byun, Yung-Cheol Department of Computer Engineering Jeju National University Jeju63243 Korea Republic of Department of Computer Engineering Major of Electronic Engineering Inistitute of Information Science and Technology Jeju National University Jeju63243 Korea Republic of

ISBN: (纸本)9789819623945

Tomato leaf diseases pose a significant threat to crop yield and agricultural productivity. Addressing the challenge of automatic detection and classification of diseases found in tomato leaves through the utilization of Convolutional Neural Networks (CNNs), a subset of deep neural networks. Initially, the dataset is augmented to address the issue of limited labeled data, employing traditional data augmentation methods to balance and enlarge the dataset. Exploring the effectiveness of various fine-tuned pre-trained models, including VGG-16, VGG-19, InceptionV3, EfficientNetB7, and ResNet-50 for classifying tomato leaf diseases. Ten common diseases affecting tomato leaves are classified, with the dataset expanded fourfold through data augmentation. Experiments demonstrate that the ResNet-50 model achieves the highest accuracy of 96.15% in disease classification. Through a detailed analysis, evaluating the performance of each technique and providing insights into their effectiveness for enhancing disease classification in tomato plants. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Fruits

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