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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291-300 订阅

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Anti-Windup Active Power control for Wind Turbines Considering Loads Reduction 13

Anti-Windup Active Power Control for Wind Turbines Consideri...

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

作者： Wang, Guozhong Luo, Hao Jiao, Xuguo Yang, Zhibo Zhao, Xinxin School of Information and Control Engineering Qingdao University of Technology Qingdao266520 China College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China

ISBN: (纸本)9798350361674

In recent years, the proportion of wind power in electricity contribution is increasing, and wind power generation technology has developed rapidly. How to enhance the stability of wind turbine systems and extend their service life are still challenging problems due to uncertain and intermittent characteristics of natural wind. In order to ensure that variable speed wind turbines track the active power instructions released by the power system, an improved anti-windup rotor speed control strategy is designed to switch different control strategies according to the wind turbine rotor speed value, with the aim of optimizing the mechanical loads of pitch systems. Firstly, in the low wind speed region, the maximum wind energy capture is achieved by optimal torque control. When the rotor speed increases to a critic value, torque control determined by the turbine's rated output power and rotor speed is adopted. In the high wind speed region, the pitch controller is used to adjust the wind energy capture coefficient to maintain the rotor speed at its rated value. Moreover, in order to solve the problem of integral saturation in traditional rotor speed control methods, this paper proposes a bounded constraints assisted Proportional-Integral (PI) pitch angle control scheme to optimize the operation of pitch system. this control method can effectively eliminates integral saturation, mitigate fatigue loads of wind turbines, reduces overshoot of rotor speed and ensures safe and stable operation of wind turbines. Finally, the proposed method is simulated on OpenFAST platform to verify its advancement. © 2024 ieee.

关键词： Wind turbines

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Capacity Configuration Model for Power Dispatch and Clean Energy Peak Shaving with Large-Scale Liquid Flow Batteries 13

Capacity Configuration Model for Power Dispatch and Clean En...

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

作者： Li, Dianyang Cui, Dai Xiao, Nan Li, Jiaze Jiang, Huijie Yang, Feiran State Grid Liaoning Electric Power Co. Ltd Power Dispatching Control Center Shenyang China College of Information Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9798350361674

the rapid development of renewable energy has brought us closer to achieving carbon peak and carbon neutrality goals, but it has also exacerbated the challenge of load fluctuation in power grids. To enhance grid stability and security, it is essential to implement effective peak shaving measures. Relying solely on various types of power generation to complement each other has limitations. In this paper, we propose a model and method for configuring the capacity requirements of large-scale liquid flow batteries involved in grid peak shaving, within the context of increasing demand for renewable energy integration. Our model utilizes an optimization approach with the objective of minimizing the operational costs of the peak shaving system. the model not only considers the technical constraint index of the system, but also innovatively introduces the reliability constraint of the probability of insufficient peak regulation and the reliability constraint of the mean value of insufficient peak regulation, and forms the optimal configuration model of peak regulation capacity of large-capacity flow battery assisted power system. the model takes the 24-hour load time series and wind power time series as inputs, employs the YALMIP toolbox, and utilizes the CPLEX solver to optimize the capacity and power requirements for large-scale liquid flow batteries assisting in renewable energy dispatch. A simulation analysis is conducted using a case study with a 24-hour load time series and wind power time series as inputs to evaluate the proposed model. © 2024 ieee.

关键词： Clean energy

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A Bottom-Up Human Pose Estimation Correction Method Based on Object Detection 13

A Bottom-Up Human Pose Estimation Correction Method Based on...

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

作者： Zhang, Xiaoping Meng, Xiangpeng Chu, Kai-Fung Wang, Li School of Electrical and Control Engineering North China University of Technology Beijing100144 China University of Cambridge Department of Engineering Cambridge United Kingdom

ISBN: (纸本)9798350361674

As computer vision technology is widely applied in various domains, it inevitably faces complex and crowded scenes. However, human images in these scenes are more susceptible to occlusion, which degrades the performance of human pose estimation. Since human pose estimation methods typically represent the human pose feature as the skeleton of the human keypoints, the keypoint set estimation significantly influences the overall accuracy. To improve the human pose estimation accuracy in crowded scenes, a bottom-up human pose estimation correction method based on object detection is proposed in this paper. the keypoint set estimated by the bottom-up human pose estimation is corrected by introducing an object detection algorithm to reduce estimation errors and fill in missing keypoints, so as to alleviate the decline of detection effect caused by superimposed multilevel errors in occlusion scenes. the method is verified by our self-built human pose dataset in the bus compartment scene. the results show that the OpenPose human pose estimation algorithm with the method in this paper can obtain 4.41 mAP improvement, especially for the keypoints with strong local information. © 2024 ieee.

关键词： Object recognition

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A Short-term Traffic Flow Forecasting Method Based on Markov Chain and Grey Verhulst Model 6

A Short-term Traffic Flow Forecasting Method Based on Markov...

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2017 ieee 6th data driven control and learning systems conference (ddcls’17)

作者： Darong Huang Zhenping Deng Ling Zhao Bo Mi Institute of Information Science and Engineering Chongqing Jiaotong University

ISBN: (纸本)9781509054626

the traffic system is a nonlinear and time-varying complex system with the human body being involved. At the same time, the highly uncertain and nonlinear of short-term traffic flow has been presented due to the complicated factors such as road construction, traffic accident, complex weather and trip distribution. et. So the short-term traffic flow prediction is more difficult than the long-term forecast. First of all, aiming at the uncertain and time-varying characteristics of the short-term traffic flow, the main single-model prediction methods and the combination forecast models are summarized and analyzed. Finally, a combined forecasting method based on Markov chain theory and grey Verhuls model with less data demand and parameter is proposed. the experimental results show that the combined model can obtain a high prediction accuracy.

关键词： Traffic System Traffic Flow Forecasting Markov Chain Grey Verhuls Model

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data-driven Active Power Dispatch for Wind Farms 13

Data-Driven Active Power Dispatch for Wind Farms

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

作者： Jiao, Xuguo Zhang, Daoyuan Wang, Xin Ma, Zhaoxing Zhang, Zhenyong Liu, Wenfeng School of Information and Control Engineering Qingdao University of Technology Qingdao266520 China College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang550025 China School of Civil Engineering Qingdao University of Technology Qingdao266520 China

ISBN: (纸本)9798350361674

Active power dispatch of wind farms plays an important role in power grid scheduling. In this paper, a data-driven active power dispatch strategy for wind farms is proposed, which uses the key point of minimizing the standard deviation of wind turbines' output power series to reduce their fluctuations. First, to build the optimization problem, the historical active power output data of each wind turbine is collected from the Supervisory control And data Acquisition (SCADA) system. then, the objective function is developed based on the idea of minimizing the standard deviation of the output power time series. Meanwhile, considering the constraints of wind turbines' output power and active power balance of the grid, the feasible domain of the cost function is constructed. Further, to solve the proposed nonlinear optimization problem with equality and inequality constraints, the trust region interior point method (TRIPM) is employed to explore optimal solution within the feasible domain. Finally, actual data collected from wind farms is used to test effectiveness of the designed approach and experimental results show that our method can achieve better output power smoothing performance than conventional Proportional Dispatch (PD) method. © 2024 ieee.

关键词： Cost functions

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Semi-Supervised Soft Sensor Modeling for Industrial Dynamic Information Mining Based on Two-Path Weight Comparison 13

Semi-Supervised Soft Sensor Modeling for Industrial Dynamic ...

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

作者： Jia, Ruining Jin, Xinyi Ni, Xinru Ruan, Mengjiao Yang, Zeyu Wang, Peiliang School of Engineering Huzhou University Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems Huzhou313000 China

ISBN: (纸本)9798350361674

Deep learning methods can extract reliable feature representations from massive process data to build accurate soft sensor models. However, the data in actual industrial production is often nonlinear, dynamic, and even limited in label samples due to untimely sampling and analysis. Aiming at the above problems, this paper proposes a semi-supervised soft sensor modeling method (SSACF-LSTM) for industrial dynamic information mining based on two-path weight comparison. An unsupervised long short-term memory network (LSTM) encoder-decoder is utilized to extract the hidden dynamic nonlinear information in unlabeled samples. this information is then spliced and masked with the initial input, which is subsequently fed into a deep neural network encoder-decoder for pretraining to learn a self-supervised task that mines correlation information between sequences as well as between samples that is useful for the model. In addition, the LSTM with attentional convolutional fusion serves as an auxiliary path that mines correlation information between variables directly from the initial input to complement the information extracted by self-supervision. Finally, the results of both are linearly weighted to obtain the final prediction. the method is validated in a debutanizer process. the results show that SSACF-LSTM can utilize both labeled and unlabeled data to extract quality-related features for soft sensor modeling, which is better than the semi-supervised LSTM network with historical feature fusion (HFF-ssLSTM), semi-supervised stacked auto-encoder (SSAE), and variational auto-encoder regression (SVAER). © 2024 ieee.

关键词： Self-supervised learning

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Lidar-Inertial and Downward Vision Based Mobile Robot SLAM in Digital Workshops 13

Lidar-Inertial and Downward Vision Based Mobile Robot SLAM i...

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

作者： Zhang, Yanchao Geng, Hao Liu, Bin Zhang, Yinlong Yuan, Xudong Zhang, Sichao School of Information Science and Engineering Shenyang University of Technology Shenyang110020 China Guangzhou Institute of Industrial Intelligence Guangzhou511458 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

ISBN: (纸本)9798350361674

In order to solve the problems of long-term cumulative error and the absence of absolute pose in the existing Lidar-Inertial Measurement Unit (IMU) fusion methods for Automated Guided Vehicle (AGV) Simultaneous Localization and Mapping (SLAM), we propose a new SLAM method for mobile robot, integrating Lidar, IMU, and a downward vision camera. Firstly, we tightly couple Lidar-IMU and downward vision camera using a multimodal fusion framework. Downward visual measurements are integrated as QR factors into the factor graph, providing absolute pose information to correct cumulative error in the Lidar-IMU system and enable the joint pose estimation of the AGV. Secondly, an enhanced Iterative Closest Point (ICP) point cloud matching algorithm has been developed to correct the incorrect pose. this approach achieves Scan-to-Map alignment by employing a coarse-to-fine matching strategy, thereby enhancing the accuracy of point cloud alignment. Finally, the extended Kalman filter is used to optimize the Lidar pose drift, as well as the Lidar mapping effectively by using the Lidar-IMU as the measurements and the downward visual measurements as the updates. the algorithm has been extensively evaluated on the developed AGV experimental platform. the experimental results show that the position error is less than 0.02 meters and the orientation error is less than 1°, which solves the problems of long-term error accumulation and the lack of absolute pose information for AGV navigation. © 2024 ieee.

关键词： Extended Kalman filters

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A KNN-SVR data Mending Method for Insufficient data of Magnetic Flux Leakage Detection 7

A KNN-SVR Data Mending Method for Insufficient Data of Magne...

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ieee 7th data driven control and learning systems conference (ddcls)

作者： Zhang, Xinbo Feng, Jian Yao, Zhiqiang Liu, Jinhai Zhang, Huaguang Northeastern Univ Sch Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China China Acad Safety Sci & Technol Beijing 100012 Peoples R China

ISBN: (纸本)9781538626184

In magnetic flux leakage (MFL) detection, transient fault appears unavoidably on individual sensor when we collect magnetic flux leakage signals, which makes MFL data insufficient. data mending for insufficient data concerns the accuracy of the defects inversion. A precise data mending method based on K Nearest Neighbor-Support Vector Regression (KNN-SVR) is introduced, which effectively reduces the training cost of SVR and greatly improves the accuracy of the algorithm. the method is tested by experiment data obtained. the results demonstrate that the proposed method can improve the accuracy rate of data mending of insufficient data with an acceptable time cost.

关键词： data mending K Nearest Neighbor Support Vector Regression magnetic flux leakage detection insufficient data

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Model Free control Based Nonlinear Integral-backstepping control for Blood Glucose Regulation 6

Model Free Control Based Nonlinear Integral-backstepping Con...

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2017 ieee 6th data driven control and learning systems conference (ddcls’17)

作者： Qi Wu Haoping Wang Yang Tian Sino-French International Joint Laboratory of Automatic Control and Signal Processing School of Automation Nanjing University of Science ＆ Technology

ISBN: (纸本)9781509054626

In this paper, a Model Free control based Nonlinear Integral Backstepping control（MFC-NIB） strategy is developed and applied to blood glucose regulation systems, which is a typical biological system with parameter variations, uncertainties and external disturbances. Firstly, an Intelligent Proportional controller（iP）, which is based on model-free theory and whose algebraic estimation technique is replaced by a Time-Delay Estimation（TDE） method is developed. Secondly, to improve the control convergence, the MFC-NIB is studied based on the proposed iP. Finally, to demonstrate the performance and effectiveness of the proposed method MFC-NIB, the simulations with comparisons with iP have been implemented on the referred glycemia regulation systems.

关键词： Glycemia regulation Model-free control Time-delay estimation Model-free control based nonlinear integral backstepping control

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Design of a Cough Detection System Based on Vibration and Audio Signals for Post- thoracic Surgery Patients 13

Design of a Cough Detection System Based on Vibration and Au...

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

作者： Qiu, Zhilin Liu, Quan Gao, Guanbin Peng, Jun Xing, Yashan Kunming University of Science and Technology Faculty of Mechanical and Electrical Engineering Kunming650500 China Kunming University of Science and Technology Yunnan International Joint Laboratory of Intelligent Control and Application of Advanced Equipment Kunming650500 China First People's Hosp. of Yunnan Province Affiliated to Kunming University of Science and Technology Department of Thoracic Surgery Kunming650100 China

ISBN: (纸本)9798350361674

Cough is a vital rehabilitation indicator which can exhibit many complications after thoracic surgical procedures. In this paper, a portable bimodal cough detector that can achieve consecutive detection is designed to recognize postoperative cough. the detector collects vibration and audio signals of cough and sends the raw data to the base station for data processing. the time domain and frequency domain features of vibration data are extracted. Meanwhile, 13 dimensions of Mel-frequency Cepstral Coefficients (MFCC) features and two dimensions of physical features are extracted from audio data. the threshold distinguish method and the bagged tree model are applied to classify the vibration and audio signals features respectively. the fusion discrimination method determines the result of the two signals and then gives the final judgment based on the two classifiers. the bagged tree model training results show that the audio classifier achieved an accuracy of 94.20% with an Area Under the Receiver Operating Characteristics Curve (AUC) of 0.984. We evaluate the performance of the fusion discrimination in actual use scenarios. the experiment results show that the detector achieved a sensitivity of 85.54% and a specificity of 98.76% in the silent environment. the detector in the interference environment can achieve a sensitivity of 83.44% and a specificity of 98.50% which exhibits a strong interference resisting ability. © 2024 ieee.

关键词： Patient rehabilitation

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