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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171-180 订阅

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Automatic Extraction Technology of Ionospheric Vertical data with Handwritten Font 13

Automatic Extraction Technology of Ionospheric Vertical Data...

引用

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

作者： Su, Guichang Zhang, Ruikun Liu, Xiangpeng School of Mathematics and Physics Qingdao University of Science and Technology Qingdao266061 China

ISBN: (纸本)9798350361674

To solve the problems such as large size, dense text area and various handwriting styles, an automatic data extraction technology based on DB and CRNN algorithms is proposed, which mainly includes four modules: image preprocessing, form text detection, text recognition and layout processing. Firstly, contour extraction and tilt correction are used to preprocess the scanned images of handwriting vertical data. then, Hough transform is used for table detection and DB algorithm is used for text detection. the image segmentation function is added before DB detection algorithm to improve detection accuracy. Finally, CRNN algorithm is used for text recognition, and coordinate fusion algorithm is used to save the recognition results into Excel standardized format to realize automatic data extraction and saving. the experimental results show that the text detection recall rate of the proposed algorithm is 96.77%, and the F-value of the comprehensive evaluation index of text recognition is 95.2%. Compared with other algorithms, its effectiveness is verified. therefore, the automatic data extraction technology proposed in this paper has high practicability and can meet the actual needs of engineering applications. © 2024 ieee.

关键词： Hough transforms

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Compound Disturbance Rejection control for Nanopositioning Using a Phase-Locking Loop Observer 9

Compound Disturbance Rejection Control for Nanopositioning U...

引用

9th ieee data driven control and learning systems conference (ddcls)

作者： Wei, Wei Xia, Pengfei Liu, Zaiwen Zuo, Min Beijing Technol & Business Univ Sch Comp & Informat Engn Beijing 100048 Peoples R China

ISBN: (纸本)9781728159225

In nano-positioning, accuracy and speed are important issues to guarantee the system performance. Integral resonant control (IRC) is able to improve the bandwidth, and phase-locking loop observer (PLLO) based active disturbance rejection control (ADRC) is capable of achieving better closed-loop accuracy. By combining the advantages of PLLO based ADRC and IRC, a compound control technique is proposed. the compound control can deal with hysteresis and vibration, which are main factors affecting the accuracy and speed of a nano-positioning stage driven by a piezoelectric actuator. An identified model of a nano-positioning stage is utilized, and simulations have been performed. Presented numerical results confirm the proposed compound control technology.

关键词： Active Disturbance Rejection control Phase-locking Loop Observer Integral Resonant control Piezoelectric Actuator

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Predefined-Time Adaptive control for Multiagent systems with Asymmetric Input Amplitude and Rate Saturations 13

Predefined-Time Adaptive Control for Multiagent Systems with...

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

作者： Zeng, Binbin Yao, Minghai Zhang, Linchuang Pan, Yingnan College of Information Science and Technology Bohai University Jinzhou121013 China College of Control Science and Engineering Bohai University Jinzhou121013 China

ISBN: (纸本)9798350361674

In this paper, the adaptive predefined-time control issue is studied for multiagent systems subjected to asymmetric input amplitude and rate saturations. Firstly, a quadratic-fraction controller is designed to directly address the singularity issue caused by the differentiation of virtual control signals in predefined-time control methods. Following that, a novel auxiliary design signal system and a unified smooth representation function are developed to compensate for the effects of asymmetric input amplitude and rate saturations, which ensures predefined-time convergence of system signals. Additionally, by applying a predefined-time filter method, the proposed approach solves the 'explosion of complexity' issue caused by iterative derivation of the virtual controller, where the settling time of the system is dependent on a tuning parameter. Based on the Lyapunov stability method, it is inferred that the synchronization error and the filter error can converge to a small region of origin within a predefined time interval, and the stability can be ensured in the closed-loop systems. Ultimately, the simulation results confirm the efficacy of the proposed approach. © 2024 ieee.

关键词： Amplitude and Rate Saturations Multiagent systems Predefined-Time control Quadratic-Fraction controller

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Tumor Cell Small Object Detection Algorithm Based on Improved YOLOv5 13

Tumor Cell Small Object Detection Algorithm Based on Improve...

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

作者： Cong, Weiqi Sun, Junqing Sun, Hao School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China College of Artificial Intelligence Nankai University Tianjin300350 China

ISBN: (纸本)9798350361674

Deep learning methods are widely used in the task of detecting tumor cells. However, due to the small tumor cells and the presence of impurity interference, it results in the inability to meet the accuracy requirements for assisted diagnosis. To address this problem, this paper proposes an improved YOLOv5 algorithm, the specific process is as follows: use Mosaic image enhancement technology to improve the accuracy and robustness of neural network training, use the self-attention mechanism bottleneck transformers module to replace the original C3 module, and improve the performance of the detection model through effective feature extraction and feature fusion techniques. then, the Squeeze-and-Excitation (SE) module is added to enhance the extraction of object features between channels, which can adaptively correct the channel features and help the detection of small objects. the experimental results show that the mAP_0.5 of the improved YOLOv5 algorithm reaches 75.87%, which is a significant improvement over the original algorithm for small object detection. © 2024 ieee.

关键词： attention mechanism Deep learning small object detection tumor cells YOLOv5

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the Pareto Optimal control of Bilinear Stochastic systems 13

The Pareto Optimal Control of Bilinear Stochastic Systems

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

作者： Wang, Yanshuang Jiang, Xiushan Zhao, Dongya Zhang, Weihai Qingdao266580 China College of Information and Electrical Engineering Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9798350361674

In complex industrial big-data systems, bilinear stochastic systems have the ability to provide accurate industrial process modeling, precise control, optimization and decision support. Bilinear stochastic systems can provide effective solutions for the intelligence and optimization of industrial processes, thus achieving precise control and stable operation. In this paper, a bilinear stochastic system based on multi-player in finite horizon is investigated. the Pareto cooperative game problem can be solved by applying a weighted sum approach for the objective function of each player. Firstly, we obtain the Pareto-optimal control strategy for the system under the consideration of the indefinite objective function. Secondly, because of the difficulty of solving the Hamilton-Jacobi equation (HJE), the obtained control strategy is represented according to the method of tensor power series expansion. thirdly, we obtain all Pareto solutions and the Pareto frontier. Finally, a simulation example is provided to illustrate the accuracy of the theory. © 2024 ieee.

关键词： Stochastic systems

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Application-Oriented State-of-Charge Estimation of Lithium-ion Batteries Based on Appropriate Modeling and EKF 13

Application-Oriented State-of-Charge Estimation of Lithium-i...

引用

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

作者： Luo, Chang Li, Yan Yang, Tong You, Jin Xu, Huiqin School of Control Science and Engineering Shandong University Jinan250061 China

ISBN: (纸本)9798350361674

the state of charge (SOC) estimation is essential for battery management systems (BMS), necessitating proper modeling and filtering approaches. this study zeroes in on enhancing the accuracy of SOC estimation of battery thanks to an appropriate modeling technique. Considering comprehensive criteria for model structure identification (AIC, BIC, CIC), a second-order RC equivalent circuit model has been chosen. Whose estimation of parameters is transformed into a nonlinear optimization challenge. the cuckoo search algorithm, acclaimed for multi-objective optimization, is employed with a well-defined fitness function, facilitating the determination of optimized parameters. Subsequently, in view of real-world applications, the extended Kalman filter (EKF) algorithm is applied to estimate the SOC with the above optimized second order model. Experimental data analysis affirms that, with appropriate dimensions and iterations, this algorithm efficiently and accurately solves for modeling and SOC estimation of lithium-ion batteries, as well as bolstering its advantages in BMS applications. © 2024 ieee.

关键词： State of charge

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Fixed- Time Adaptive Neural Pitch Angle control for Variable-Speed Wind Turbines 13

Fixed- Time Adaptive Neural Pitch Angle Control for Variable...

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

作者： Xie, Shuzong Yang, Qinmin Li, Qingyi Wang, Shaodong Yang, Jun College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China Zhejiang Provincial Energy Group Company Ltd. Jiaxing314000 China

ISBN: (纸本)9798350361674

this paper mainly focuses on the challenging problem of fixed-time adaptive neural pitch angle control for variable-speed wind turbines. Firstly, a switching continuous fixed-time sliding mode variable is adopted to avoid the singularity problem. then, an adaptive sigmoid function neural network is introduced to approximate the unknown nonlinear function of wind tur-bines. On this basis, a fixed-time adaptive neural pitch angle controller is systematically developed, such that the tracking error can converge to a small neighborhood of the origin in fixed-time, thereby improving the power generation efficiency of wind turbines. Finally, the fixed-time stability of the whole closed-loop system is analyzed with rigorous theoretical proof, and the effectiveness of the proposed scheme is verified through a 1.5-MW three-blade wind turbine in Matlab/Simulink. © 2024 ieee.

关键词： Windmill

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Performance Evaluation and Order Selection for Pattern Modeling Methods through Granger Causality Analysis 13

Performance Evaluation and Order Selection for Pattern Model...

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

作者： Zhu, Shaotong Zheng, Niannian Luan, Xiaoli Liu, Fei Jiangnan University Wuxi214028 China Wuxi214122 China

ISBN: (纸本)9798350361674

In the realm of processes modeling and control, the monitoring of processes is crucial for ensuring safety, and a key task in multivariate statistical process monitoring is to extract the operating patterns of the process. Dynamic strategies, exemplified by dynamic-inner principal components analysis (DIPCA), dynamic-inner partial least squares (DIPLS), and dynamic-inner canonical correlation analysis (DICCA), are employed to tackle temporal correlation and the ever-changing nature of processes using AR structures. Dynamic controlled principal components analysis (DCPCA) additionally considers dynamic controlled characteristics, showcasing remarkable accuracy in both the monitoring of processes and dynamic prediction. Notwithstanding the precision demonstrated by DCPCA, the task of ascertaining optimal control input orders remains intricate, often necessitating reliance on empirical methodologies or trial-and-error approaches. this research introduces an innovative methodology, drawing inspiration from Granger causality analysis, to establish causal relationships between control inputs and patterns. Causal influence (CI) was employed to assess the impact of introducing control inputs and the effects of control inputs at different orders, aiding the model in selecting the optimal control input order. theoretically supported performance evaluation and order selection, providing assurance for practical applications. © 2024 ieee.

关键词： Statistical process control

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Event Triggered control of Ship Automatic Berthing Based on Network Attack 13

Event Triggered Control of Ship Automatic Berthing Based on ...

引用

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

作者： Liu, Xin Zhang, Qiang Wang, QiWen Xue, GuoQing Xu, Bo School of Navigation of Shipping Shandong Jiaotong University Weihai264209 China Shandong Port Group Co. Ltd Qing Dao266000 China Qingdao Pilot Station 266000 China

ISBN: (纸本)9798350361674

this article proposes an adaptive neural network event triggered control scheme based on virtual parameter learning for ship automatic berthing control under false data injection (FDI) attack environment, which is simultaneously affected by external interference, uncertain dynamics, and input saturation. In the controller design process, neural network technology and adaptive virtual parameter learning methods are combined to perform online reconstruction compensation for actuator signal mutations, uncertain dynamics, and external disturbances caused by FDI attacks. In addition, event triggering mechanism is adopted to reduce communication load and channel occupancy rate. Finally, the stability of the control law was analyzed using Lyapunov theory, and the effectiveness of the scheme was verified through comparative simulation experiments. the simulation results showed that the control algorithm proposed in this paper has superior control performance and steady-state performance in FDI attack environments. In practical engineering applications, this scheme can provide theoretical support for the automatic berthing of intelligent ships. © 2024 ieee.

关键词： Sliding mode control

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Pruning-Based Knowledge Transfer Method for Power System Scheduling 13

Pruning-Based Knowledge Transfer Method for Power System Sch...

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

作者： Yan, Zhen Tang, Hao Fang, Daohong Tan, Qi Wang, Song Wang, Tongwen School of Electrical Engineering and Automation Hefei University of Technology China State Grid Anhui Electric Power Co. Ltd China

ISBN: (纸本)9798350361674

In order to enhance the learning optimization efficiency of deep reinforcement learning algorithms in the scenario of unit shutdown and maintenance, this study delves into knowledge transfer issues arising from the reduction of target task state and action vector dimensions compared to the source task. A dimensionality reduction transfer method for deep reinforcement learning based on neural network pruning is proposed. Firstly, a method for calculating the information richness of neural network nodes is introduced, utilizing L1 norm and information entropy. Subsequently, guided by the richness of information, the significance of each neural network node in the source task to the target task is analyzed, enabling effective network pruning. Furthermore, a mapping relationship between the neural networks of the target task and the source task is established, facilitating the transfer of deep reinforcement learning network parameters. Finally, through numerical simulations, the effectiveness of the proposed method is validated by comparing the transfer effects of different transfer methods, pruning techniques, and pruning rates. © 2024 ieee.

关键词： Deep reinforcement learning

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