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检索条件"任意字段=13th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2024"

共 716 条记录，以下是221-230 订阅

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Dynamic Obstacle Avoidance Method for Unmanned Surface Vessel Based on Deep Reinforcement learning and Dynamic Window Approach 13

Dynamic Obstacle Avoidance Method for Unmanned Surface Vesse...

引用

13th ieee data driven control and learning systems conference, ddcls 2024

作者： Mengmeng, Lou Xiaofei, Yang Jun, Guan Jiabao, Hu Qi, Wang Xu, Wang School of Automation Jiangsu University of Science and Technology Zhenjiang212003 China

ISBN: (纸本)9798350361674

the obstacle avoidance technology of Unmanned Surface Vessels (USV) is the key to ensuring their safe navigation. this article proposes a collision avoidance technology for USV based on Deep Deterministic Policy Gradient (DDPG) and Dynamic Window Approach (DWA), aiming to solve the obstacle avoidance problem of USV. Based on the idea of DWA, the action space of DDPG was designed. A new reward mechanism is proposed according to the trajectory evaluation function standards in the DWA algorithm, which solves the sparse reward problem of traditional DDPG and improves the convergence and obstacle avoidance effects of the algorithm. To demonstrate the effectiveness of the algorithm, a multi-ship collision avoidance experiment was designed. the results indicate that the proposed algorithm can achieve effective collision avoidance. © 2024 ieee.

关键词： Deep reinforcement learning

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Adaptive Parameter Identification Based Sliding Mode control for Servo Mechanism Using Nonlinear Disturbance Observer 13

Adaptive Parameter Identification Based Sliding Mode Control...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Du, Hailan Tao, Liang Tao, Meiling Xu, Binzi Deng, Xiongfeng College of Electrical Engineering Anhui Polytechnic University Wuhu241000 China

ISBN: (纸本)9798350361674

In this paper, a friction compensation method is proposed based on adaptive parameter identification and nonlinear disturbance observer for servo mechanism system to achieve desired tracking performance. Firstly, the traditional nonlinear friction model is transformed into a continuously differentiable linear model. An adaptive parameter identifier is then designed using a state observer to facilitate finite-time estimation of friction parameters, thereby enhancing the overall performance of the control system. Subsequently, a nonlinear disturbance observer is utilized to enable real-time estimation and compensation of unknown disturbances in the system, as well as errors in parameter identification. Additionally, a sliding mode controller is developed to achieve robust tracking control. Finally, stability analysis is performed using Lyapunov stability theory, and the feasibility of the proposed method is validated through simulation. © 2024 ieee.

关键词： Sliding mode control

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Slow Feature thermography for Non-Destructive Evaluation of Defects in Composite Materials 13

Slow Feature Thermography for Non-Destructive Evaluation of ...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Liu, Kaixin Zhong, Xin Chen, Ping Dai, Yun Liu, Yi North University of China The State Key Lab for Electronic Testing Technology China North University of China Department of Information and Communication Engineering Taiyuan030051 China College of Mechanical Engineering Nantong Vocational University Nantong226007 China Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou310023 China

ISBN: (纸本)9798350361674

In recent years, various methods for processing and analyzing thermographic data have been applied in non-destructive testing to improve the visibility of defects. Representatively, traditional linear dimensionality reduction methods (e.g. principal component analysis) have achieved good results in thermographic data analysis. In this work, a slow feature thermography (SFT) method for non-destructive evaluation of defects in composite materials. thermographic data can be regarded as a continuous time series generated by an infrared imager at different moments. Slow feature analysis algorithm is subsequently used to extract slow features of the thermal image sequence. Slow features refer to features that are unchanged or slowly changing in time-series data, such as the background of a thermal image. By extracting the slow features of data, the inhomogeneous background, noise and defect features contained in thermal images can be effectively distinguished. Test results on carbon fiber reinforced polymer specimens demonstrate the feasibility and effectiveness of the SFT method. © 2024 ieee.

关键词： Carbon fiber reinforced plastics

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Application of CPU Parallel Computing in MATLAB Environment for Analysis of Anti-Saturation and Gain Effects in PID controller 13

Application of CPU Parallel Computing in MATLAB Environment ...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Huang, Ruirui Wu, Zhenhui Hou, Yandong Cheng, Qianshuai Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Technology and Media University of Henan Kaifeng Kaifeng475004 China School of Artificial Intelligence Henan University Zhengzhou450000 China

ISBN: (纸本)9798350361674

With the continuous increase in data size and model complexity, the computational workload has grown rapidly, posing a significant challenge to the capabilities of computer data processing and simulation calculations. therefore, parallel programming based on multicore and cluster architectures has become one of the mainstream technologies to enhance program execution efficiency and numerical computation efficiency. the theoretical foundations of parallel program computing have been applied in various aspects of engineering applications and theoretical simulations. In this paper, a new parallel PID anti-integral saturation controller is designed for a second-order closed-loop control system of unmanned aerial vehicles (UAVs). It compares and analyzes the runtime, execution efficiency, and speedup ratio of parallel programs and general serial programs under the same scenario. the experimental results demonstrate that parallel computing significantly improves the simulation program's efficiency for PID controller anti-saturation control systems under identical scenarios, exhibiting a high speedup ratio on the existing computing platform. Additionally, this study consolidates common issues encountered in MATLAB parallel program design, offering valuable insights into overcoming challenges in this domain. © 2024 ieee.

关键词： MATLAB

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A Supervised Stacked Dual-Guided Autoencoder with Deep learning Networks for Industrial Soft Sensing 13

A Supervised Stacked Dual-Guided Autoencoder with Deep Learn...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Hu, Tingting Yang, Zeyu Ye, Lingjian Zhou, Zhe Yao, Le Shen, Bingbing School of Engineering Huzhou University Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems Huzhou313000 China School of Mathematics Hangzhou Normal University Hangzhou311121 China

ISBN: (纸本)9798350361674

Stacked autoencoder (SAE) has been applied to the prediction of manufacturing process data quality owing to the excellent feature selection capabilities. Nevertheless, a challenge arises in the accumulation of losses from the lowest to the highest level when minimizing reconstruction losses at each layer. Additionally, many SAE models struggle to extract sophisticated features linked to quality crucial for output predictions. this article delves into a supervised stacked dual-guided autoencoder (SDAE) proposed to describe complex data patterns and learn quality-related features better. It reconstructs the same original input data for each dual-guided autoencoder (DAE) and measures the variables at the input layer related to quality. A new pretraining objective function aims to supervise the network's pre-training process, ensureing that the extracted features include more quality prediction information. Lastly, the efficiency of the introduced SDAE is validated on the CO2 absorption column process. © 2024 ieee.

关键词： Deep learning

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Dynamic Optimization for SP of control Loops Based on Both Rapid Intelligent Optimization and UniSim/EPKS Environments 13

Dynamic Optimization for SP of Control Loops Based on Both R...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Zhang, Hehui Wang, Zhu Wang, Shao Xian Department of Automation Beijing102249 China

ISBN: (纸本)9798350361674

Advanced Process control is frequently applied in industrial chemical processes, where Dynamic Matrix control is widely employed for its capability to handle complex optimization control problems with multiple variables and constraints. However, during prolonged operation of industrial processes, factors such as changes in operating conditions and external environmental variations may lead to a mismatch between the predictive model and the actual plant in DMC. To address the Model-plant Mismatch, this paper proposes a dynamic trajectory rapid intelligent optimization method based on online reconstruction of the predictive model. Constructing the system model based on the known linear dynamic model of the control loop and PID parameters, the new dynamic matrix is obtained using the closed-loop step response of the system model. then, updating the prediction model online and simultaneously updating the optimization-constrained conditions in real time ensures the long-term effectiveness of the advanced control scheme. To rapidly solve the optimization objective, this paper utilizes an improved GA-PSO algorithm to compute the Set Point trajectory of the control loop. Finally, this study conducted simulations by integrating Honeywell's UniSim Design simulation platform with the EPKS DCS control system, verifying the effectiveness of dynamic matrix online updating and demonstrating the long-term effectiveness of the advanced control scheme. © 2024 ieee.

关键词： Constrained optimization

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Adaptive Containment control for Uncertain Networked Agent systems with Second-Order Dynamics 13

Adaptive Containment Control for Uncertain Networked Agent S...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： You, Long Lin, Boxian Yue, Jiangfeng Shi, Mengji Qin, Kaiyu Ren, Zili School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu611731 China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu611731 China National Laboratory on Adaptive Optics Chengdu611731 China

ISBN: (纸本)9798350361674

this article proposes a neural network-based adaptive distributed containment controller for second-order integral multi-agent systems with nonlinear dynamics and external disturbances. Firstly, the proposed controller effectively suppresses the effects of nonlinear dynamics and external disturbances while achieving containment control. Secondly, for the adaptive update law of neural networks, unlike existing studies, we simultaneously consider the influence of position error and velocity error on the convergence speed of the weight matrix. Further, two positive definite matrices are constructed to prove the stability of the system based on Lyapunov theory, and the upper bound for the containment errors is provided. Finally, the effectiveness of the proposed method is verified through numerical simulation experiments. © 2024 ieee.

关键词： Adaptive control systems

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Bifurcation Analysis and Synchronization control of e-HR Neuron Model with Time Delay 13

Bifurcation Analysis and Synchronization Control of e-HR Neu...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Jiang, Jingna Sun, Dongsheng Cheng, Zunshui Shang, Yun Xin, Youming School of Mathematics and Physics Qingdao University of Science and Technology Qingdao266061 China

ISBN: (纸本)9798350361674

Derived from the Hindmarsh-Rose (HR) neuron model, the extended Hindmarsh-Rose (e-HR) neuron model with time delay is constructed considering the effect of intracellular ion transfer and the inherent slow current delay on the change of neuron membrane potential. Firstly, the stability of the model is analyzed by using the Routh-Hurwitz criterion, and the conditions for the existence of Hopf bifurcation are derived by using the Hopf bifurcation theorem. Secondly, through numerical simulation, it is found that synchronization occurs in the limited range of delay value without control. therefore, this paper introduces a nonlinear feedback control scheme, designed based on a Lyapunov-Krasovskii functional with a double integral term. the derived sufficient conditions for the synchronization of the master-slave neuron model with self-feedback delay are presented. Finally, the results of theoretical analysis are verified by numerical simulation and the effectiveness and viability of the controller are proved. © 2024 ieee.

关键词： Synchronization

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Detection of Melanoma Based on Improved YOLOv8 13

Detection of Melanoma Based on Improved YOLOv8

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Ji, Honghai Zheng, Xuechen Xiong, Shuangshuang Fan, Lingling Wang, Li School of Electronics & Control Engineering North China University of Technology Beijing100144 China School of Automation Beijing Information Science and Technology University Beijing100096 China

ISBN: (纸本)9798350361674

the incidence of skin cancer is increasing globally, with melanoma being the most formidable form of skin cancer. Melanoma originates from melanocytes, which exhibit a high degree of visual similarity. Moreover, dermoscopy image data often suffer from hair occlusion and the lesion area appears relatively small in the images, making it challenging for traditional methods to achieve accurate recognition and detection results. In this regard, we propose a YOLOv8 framework based on multi-scale sequence fusion(MSSF) for melanoma detection. this approach enhances the Neck structure of YOLOv8 and improves the network's ability to extract multi-scale information by introducing the Multi-scale Sequence Fusion module. In addition, we implemented a morphological black hat based hair processing method to reduce hair occlusion. In the ISIC datasets, compared with the unimproved algorithm, the mean average precision(Map) at 50 IoU is increased by 5.7%, Map between 50 and 95 IoU is increased by 7.5%, and the number of parameters is decreased by 12.5%. © 2024 ieee.

关键词： Lung cancer

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Intelligent Fault Diagnosis of Bearings Using a Flexible Residual Neural Network Tuned by Seagull Optimization Algorithm 13

Intelligent Fault Diagnosis of Bearings Using a Flexible Res...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Li, Xianfeng Chen, Chuang Shi, Jiantao College of Electrical Engineering and Control Science Nanjing Tech University Nanjing211816 China

ISBN: (纸本)9798350361674

As an important part of most machinery, the normal operation of bearings greatly influences the lifespan of machine. Aimed at improving the accuracy of fault diagnosis for rolling bearings, a seagull optimization algorithm (SOA)-tuned flexible residual neural network (FResNet) is proposed. Concretely, a CNN based flexible residual module is designed to form the FResNet, which allows changing the numbers of convolution layers and convolution kernels as an optimizer iterates, to enhance the fault feature extraction ability while reducing network degradation. the SOA is used to configure the optimal model structure and parameters of FResN et. thus, the integration of SOA and FResN et forms an efficacious model for bearing fault diagnosis, named SOA-FResNet. Finally, the performance of the proposed SOA-FResNet model is verified using the bearing fault dataset from Case Western Reserve University, and the higher rate of accuracy of the proposed model compared to other diagnostic models is demonstrated under different noise environments. © 2024 ieee.

关键词： Roller bearings

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