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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131-140 订阅

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Defect Detection of Composite Materials Using Channel Attention and Convolutional Autoencoder 13

Defect Detection of Composite Materials Using Channel Attent...

引用

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

作者： Jiang, Jiahao Jiang, Zhili Wang, Fumin Yao, Yuan Liu, Yi Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou310023 China National Tsing Hua University Department of Chemical Engineering Hsinchu30013 Taiwan

ISBN: (纸本)9798350361674

As an effective non-destructive technology, infrared thermography is often used to detect inner defects in composites. However, non-homogeneous background in the thermal images reduces image quality inevitably. A squeeze and excitation convolutional autoencoder thermography (SE-CAT) technique is introduced for detecting underlying defects. Specifically, the channel attention mechanism assigns weights to different convolutional channels to highlight certain prominent features. As a result, the defect information can be extracted in a targeted manner using the convolutional autoencoder module. Finally, an experiment of carbon fiber reinforced composite materials shows good defect detection performance of the proposed SE-CAT method. © 2024 ieee.

关键词： Carbon fiber reinforced plastics

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Scalable Reliable control for Cyber- Physical Microgrids Using Chordal Graph 13

Scalable Reliable Control for Cyber- Physical Microgrids Usi...

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

作者： Zhao, Yunlong Song, Yang Wang, Aimin Chen, Yonghui Mehran, Kamyar School of Mechatronic Engineering and Automation Shanghai University Shanghai201900 China School of Electronic Engineering and Computer Science Queen Mary University LondonE1 4NS United Kingdom

ISBN: (纸本)9798350361674

We consider a microgrid system with sparse structure, where the security is susceptible to the impact of potential faults. To enhance the reliability and scalability of the microgrid system, this paper investigates the structured controller design problem for the microgrid system under fault conditions using chordal decomposition. By establishing a microgrid fault model with a sparse structure, the controller design problem for large-scale microgrid systems is decomposed into a set of smaller-scale problems using chordal decomposition, and then solved clique-by-clique in a sequential approach. the results demonstrate that this approach reduces the sensitivity of the microgrid system to faults and improves solution efficiency and scalability. © 2024 ieee.

关键词： Microgrids

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Model Analysis and Optimization Design Strategy for a Class of Heliostat Field systems 13

Model Analysis and Optimization Design Strategy for a Class ...

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

作者： Fa, Yuchao Zhang, Chenxuan Song, Yunjin Zhang, Ruikun College of Information Science and Technology Qingdao University of Science and Technology Qingdao266061 China School of Mathematics and Physics Qingdao University of Science and Technology Qingdao266061 China College of Chemistry and Molecular Engineering Qingdao University of Science and Technology QingDao266045 China

ISBN: (纸本)9798350361674

It is a critical issue to accurately calculate and optimize the various performances of a heliostat field. this paper presents a model for calculating the cosine efficiency using vector computation. Subsequently, another model based on ray tracing is established to calculate both the shading and blocking efficiency and receiver interception efficiencies. Lastly, this paper has adopted a graph-theory algorithm to expedite the calculation process of the ray tracing model and has presented an optimization design strategy for a specific heliostat field, which has a radius limit of 350m and is located at a latitude of 39.4°. this have increased its average annual power generation by 31.66% and per square meter power lightly. © 2024 ieee.

关键词： Graph algorithms

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A Cascade Active Disturbance Rejection control Method Based on SPSA Online Optimization for SCR Denitration System 13

A Cascade Active Disturbance Rejection Control Method Based ...

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

作者： Ai, Wei Wang, Yang Zhang, Tong Li, Xiangyang College of Automation Science and Technology South China University of Technology Guangzhou510640 China

ISBN: (纸本)9798350361674

Due to the characteristics of large inertia, significant lag, and model uncertainty in the Selective Catalytic Reduction (SCR) denitrification system, traditional control schemes encounter challenges in achieving precise control. To tackle this issue, this paper proposes an IMC cascade self-adaptive disturbance rejection controller based on SPSA online parameter optimization and applies it to the SCR denitrification control system. this approach effectively addresses the challenges of precise setpoint tracking, disturbance suppression due to model variations, and difficulties in controlling significant lag. the introduction of the SPSA algorithm allows the controller to update optimal parameters in real-time as the operating conditions change over time. Experimental results from setpoint tracking and disturbance rejection demonstrate that the proposed approach outperforms traditional PID control and linear self-adaptive disturbance rejection control. © 2024 ieee.

关键词： Disturbance rejection

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Detection of Anatomical Landmarks during Laparoscopic Cholecystectomy Surgery Based on Improved Yolov7 Algorithm 13

Detection of Anatomical Landmarks during Laparoscopic Cholec...

引用

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

作者： Yang, Zongjin Yang, Guang Chen, Xingyu Chen, Zhenhao Qin, Na Huang, Deqing Institute of Systems Science and Technology Southwest Jiaotong University Chengdu610031 China

ISBN: (纸本)9798350361674

Many adverse events during laparoscopic cholecystectomy surgery occur due to errors in visual perception and judgment by surgeons, leading to misunderstandings of anatomical structures, particularly in surgeries performed by young and inexperienced doctors. this paper proposes a YOLOv7_CA model for detecting anatomical landmarks during laparoscopic cholecystectomy surgery, based on an attention mechanism. the network efficiently detects the gallbladder, Calot's triangle, and common bile duct, which are prone to intraoperative mis-manipulations. We introduce data augmentation and transfer learning methods to address the issue of small datasets. the mAP of YOLOv7_CA is 69.34%, with improvements of 3.22%, 5.59%, and 6.10% in mAP, Precision, and Recall, respectively, compared to YOLOv7. Experimental results demonstrate the network's excellent performance in detecting critical intraoperative anatomical landmarks, thereby achieving real-time assistance for surgeons. It also provides new ideas for the application of deep learning in the surgical field of vision. © 2024 ieee.

关键词： Laparoscopy

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ESO-Based data driven Set-Point learning control for Nonlinear batch Processes with PD-Type Feedback control Structure Subject to Nonrepetitive Uncertainties* 13

ESO-Based Data Driven Set-Point Learning Control for Nonline...

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

作者： Ahmad, Naseem Hao, Shoulin Liu, Tao Gong, Yihui Zhang, Jiyan Wang, Haixia Zhu, Yong Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian116024 China Institute of Advanced Control Technology Dalian University of Technology Dalian116024 China School of Biomedical Engineering Dalian University of Technology Dalian116024 China

ISBN: (纸本)9798350361674

In this paper, an extended state observer (ESO) based data-driven set-point learning control (DDSPLC) scheme is proposed for nonlinear batch processes configured with an inherent PD-type control loop, so as to actively suppress the adverse effect of nonrepetitive external disturbances and nonidentical initial resetting conditions, by only utilizing the available process input/output data. the controlled process is firstly reformulated as an iterative dynamic linearization data model (IDLDM) with an extra generalized disturbance, where the unknown pseudo partial derivative and the generalized disturbance are, respectively, estimated by a projection-like algorithm and iterative ESO along the batch direction. By virtue of the estimated PPD and generalized disturbance, a set-point learning updating law with adaptive learning gain is then developed to optimize the tracking performance from batch-to-batch, by tuning the set-point command of the closed-loop PD-type control. Robust convergence analysis for the established closed-loop learning system is analyzed rigorously. the effectiveness and superiority of the proposed scheme are verified by an illustrative example. © 2024 ieee.

关键词： State estimation

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First Order Nonlinear Filtering Adaptive Dynamic Surface Fixed Time Fault-Tolerant control Method for Nonlinear Pure Feedback systems 13

First Order Nonlinear Filtering Adaptive Dynamic Surface Fix...

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

作者： Ge, Leijiao Li, Xingyun School of Electrical and Information Engineering Tianjin University Tianjin300072 China School of Mechanical Engineering Tianjin University of Science and Technology Tianjin300222 China

ISBN: (纸本)9798350361674

For a class of uncertain functions and nonlinear pure feedback systems with actuator faults, an adaptive dynamic surface fixed time fault-tolerant control method based on a first-order nonlinear filter is proposed. Using Lagrange's mean value theorem, the non-affine function in the system is treated and the control input signal is decoupled effectively. the dynamic surface is designed to reduce the input parameters of radial basis function neural network. the fuzzy logic control technique is used to approach the unknown function. the fixed-time control theory is used to ensure that the output signal can track the expected output signal in a fixed time. through theoretical analysis, it is proved that the tracking error of the system converges in a small neighborhood of the origin, and the convergence time is independent of the initial state of the system. Finally, through a simulation example, the proposed method improves the error accuracy by about 25% compared with the comparison method. © 2024 ieee.

关键词： Radial basis function networks

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Bipartite Consensus Problems of Heterogeneous Multi-Agent systems with Cooperative and Antagonistic Interactions 13

Bipartite Consensus Problems of Heterogeneous Multi-Agent Sy...

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

作者： Zhang, Yi Li, Jinchao Du, Mingjun Pang, Shaopeng Ji, Peng Shandong Province Jinan250353 China

ISBN: (纸本)9798350361674

this article explores the distributed control issue within a heterogeneous signed multi-agent system, comprising both first-order and second-order integrator agents. It devises consensus protocols for an undirected communication topology within structurally balanced networks. through the application of graph theory and Lyapunov methods, it establishes the necessary conditions for achieving consensus in networks characterized by both cooperative and competitive interactions. the theoretical results are corroborated by simulation experiments. © 2024 ieee.

关键词： bipartite consensus heterogeneous signed multi-agent system Structural balance

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Fault Detection Method for Closed-Loop control Loop of Water Jet Propulsion Device Based on Convolutional Autoencoder and Support Vector data Description 13

Fault Detection Method for Closed-Loop Control Loop of Water...

引用

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

作者： Li, Zhaoyang Yang, Cheng Li, Gangqiang Wu, Meixi China Institute of Marine Technology & Economy Beijing100081 China Science and Technology on Water Jet Propulsion Laboratory Shanghai200011 China

ISBN: (纸本)9798350361674

the waterjet propulsion device, as an efficient power propulsion method, has been widely used in various types of ships. However, due to its complex structure, harsh working environment, and frequent failures caused by long-term high-load operation, it seriously affects the ship's mission performance. this paper proposes a data-driven fault detection method that introduces convolutional autoencoder (CAE) networks to implement adaptive feature extraction on the raw signal samples of closed-loop control systems, through convolutional encoding and decoding operations, while reducing the data dimension, the nonlinear inherent information in the raw signal samples is obtained, reducing the interference of redundant information. Using Particle swarm optimization and support vector data description (PSO-SVDD) and the Kernel Density Estimation (KDE) to obtain Statistics and control Limits for Feature Space and Residual Space. then Using composite statistics combining feature statistics and residual statistics to characterize health status of closed-loop control systems. the effectiveness of the proposed method is verified by physical experiments based on NI Compact RIO. the experimental results indicate that the proposed method has strong engineering value and can improve the reliability of waterjet propulsion devices. © 2024 ieee.

关键词： Closed loop control systems

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Quantitative Trading Model Building and Stock Investment Decision Making from a data-driven Perspective 13

Quantitative Trading Model Building and Stock Investment Dec...

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

作者： Wang, Guoliang Guo, Guangxing Liaoning Petrochemical University Liaoning Fushun113001 China

ISBN: (纸本)9798350361674

this paper explores the problem of quantitative trading based on stock data from a data-driven perspective and develops a new model that relies deeply on buy, sell and hold behaviour to quantify and optimise investment returns. Considering the structural characteristics of the given model, traditional optimisation methods cannot meet the requirements. therefore, we derive the optimal trading factors for the past time period based on historical stock price data and modify them so that they can be used to guide actual trading in the future. Although there are some differences between the modified trading factor and the ideal optimal trading factor, the results of the study show that the modified trading factor has utility in safeguarding investment returns, while the ideal optimal trading factor at future moments is not available. Meanwhile, we propose a data-driven investment strategy to further improve investment returns. Finally, the practical feasibility and generality of the proposed model and methodology are verified by detailed trading of actual stock price data. © 2024 ieee.

关键词： Computer Simulations data driven data Visualization Investment Decisions Quantitative Trading

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