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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281-290 订阅

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A Comparative Study of the First Order Linear ADRC and PI controller in the Speed control System of Permanent Magnet Synchronous Motor 9

A Comparative Study of the First Order Linear ADRC and PI Co...

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9th ieee data driven control and learning systems conference, ddcls 2020

作者： Lei, Yang Xu, Jing Shuang, MenYi Hao, Qiang Yue, Sun China North Vehicle Research Institute Beijing100072 China

ISBN: (纸本)9781728159225

By analyzing the speed control performance of PMSM and establishing the mathematical model of PMSM, the velocity PI controller structure and parameters based on the model are designed in the frequency domain. By analyzing the frequency domain characteristics of the PI controller, a control parameter setting algorithm of ADRC controller is proposed. there are approximately the same frequency domain characteristics between the adjusted ADRC controller and PI controller. According to the characteristics of frequency domain, the fair comparison environment of two controllers is constructed. Finally, a speed control environment based on PMSM is built through simulation. through the comparison of stability, rapidity and overshoot, the stability margin of ADRC and PI controller is approximately the same. However, ADRC controller is superior to PI controller in rapidity and overshoot performance. © 2020 ieee.

关键词： Speed control

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Improved Adaptive DBSCAN for data Cleaning in Molten Iron Weighing Process 13

Improved Adaptive DBSCAN for Data Cleaning in Molten Iron We...

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

作者： Feng, Yu Li, Xiaoli Yu, Xiaowei Wang, Kang Beijing University of Technology Faculty of Information Technology Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124 China

ISBN: (纸本)9798350361674

In the intricate process of steel manufacturing, the precise measurement of molten iron is a pivotal procedure, which directly impacts the quality of steel production. Rail weighbridges are commonly deployed in the steel industry for molten iron measurement. therefore, the accuracy of rail weighbridges weighing data is extremely important. However, due to process intricacies and equipment nuances, weighbridge weight data often exhibits outliers. this problem can potentially hinder data-centric modeling and predictive tasks. To solve this problem, this paper introduces a data cleaning methodology based on an Improved Adaptive Density-Based Spatial Clustering of Applications with Noise (IA-DBSCAN). the proposed data cleaning method is rooted in an improved adaptive DBSCAN approach, which is particularly effective in identifying clusters of arbitrary shapes and can differentiate noise from actual clusters. the application of the IA-DBSCAN algorithm facilitates the identification and cleaning of concentrated outliers. the algorithm dynamically seeks eps and MinPts parameters to achieve optimal clustering outcomes. Constraints have been incorporated into the improved algorithm, enabling it to achieve data cleaning more rapidly and accurately. After data cleaning, based on the characteristics of the selected dataset in this paper, data imputation using the linear regression method can align with the original data characteristics. Experimental results indicate that the proposed method can effectively identify and clean outliers data in the dataset, supplement missing values based on the original data characteristics, and construct a complete and anomaly-free dataset, establish a robust foundation for modeling and prediction. © 2024 ieee.

关键词： Steelmaking

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Scheduling Optimization of Coal Washing Process Based on Two-Stage Fuzzy Heuristic Algorithm 13

Scheduling Optimization of Coal Washing Process Based on Two...

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

作者： Zhou, Hui Wang, Zhiyuan Han, Zhongyang Wang, Linqing Zhao, Jun Wang, Wei Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian116024 China School of Control Science and Engineering Dalian University of Technology Dalian116024 China

ISBN: (纸本)9798350361674

Coal washing and sorting plays a vital role for coal production. Reasonable scheduling of washing and sorting equipment, crushers and various coal silos in the coal washing and sorting process can effectively increase the total value of product coal and reduce the total energy consumption of equipment. In this paper, a multi-constrained nonlinear mixed integer programming model considering the start and stop of each equipment in the washing process is established to realize the specific regulation of the washing process. In order to overcome the problem such as local convergence when solving high-dimensional space problems, a two-stage fuzzy heuristic algorithm is proposed in this paper. In the first stage, a variety of population genetic algorithms based on fuzzy adaptive strategy control are designed for global search to establish an elite population bank to preserve high-quality individuals produced by various groups. In the second stage, an improved simulated annealing algorithm is used to perform a local search with a backtracking process on specific neighbors of individuals in the elite population bank, which can improve the stability of the algorithm and reduce the situation of falling into local convergence. then the real data of a Chinese coal mining company was used for experiments, where the proposed algorithm is compared with other commonly used methods. the experimental results demonstrate that the proposed algorithm can increase the total coal value of the washed product by at least 1.7% compared to other algorithms, and the average total number of starts and stops of the equipment is reduced by about 7.3%, which are effectively beneficial for the related enterprise. © 2024 ieee.

关键词： Coal mines

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Local-Global Dynamic Information Fusion Graph learning for Traffic Flow Prediction 13

Local-Global Dynamic Information Fusion Graph Learning for T...

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

作者： Xu, Yuan Qin, Fan Ke, Wei He, Yan-Lin Zhang, Ming-Qing Zhu, Qun-Xiong Zhang, Yang College of Information Science & Technology Beijing University of Chemical Technology Beijing100029 China Engineering Research Center of Intelligent Pse Ministry of Education of China Beijing100029 China Macao Polytechnic University Faculty of Applied Sciences 999078 China

ISBN: (纸本)9798350361674

In the field of intelligent transportation systems, accurately predicting traffic flow is a challenging endeavor for enabling efficient services in urban road networks. While current research has made significant advancements in capturing nonlinear features and spatiotemporal dependencies at a fixed geographic node level, it often overlooks the dynamic interactions of traffic flow across different regions. In this work, we present a novel local-global dynamic information fusion graph learning model (LGDF-GL) for traffic flow prediction. Different from other graph learning model, we first design a dynamic adjacency matrix by region-adaptive parameter learning, and fuse it with a static adjacency matrix based on node geographic information by dynamic information fusion module, and the fused result serves as the diagonal elements of the local adjacency matrix. Subsequently, we input it into the graph convolutional network (GCN) and gated recurrent unit (GRU) aggregation model to extract local feature information containing dynamic spatiotemporal features from traffic flow data. the local feature information is pruned and concatenated to obtain global feature representations. then we achieve global feature representation prediction through multiple fully connected layers. Finally, we conducted experiments on two real-world traffic flow datasets to evaluate the performance of our proposed method. the results clearly demonstrate that the LGDF-GL method achieves superior prediction ability compared to several baseline models. © 2024 ieee.

关键词： Information fusion

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CGA-MAE: Camera-Guided Alignment to Auxiliary Point Cloud Masked Autoencoders for 3D Object Detection 13

CGA-MAE: Camera-Guided Alignment to Auxiliary Point Cloud Ma...

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

作者： Peng, Haolong Chen, Yibo Chen, Yuxi Zhu, Ke Zhang, Jianming Zhejiang Polytechnic Institute Polytechnic Institute Zhejiang University Hangzhou China Zhejiang University Robotics Institute Yuyao China College of Control Science and Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9798350361674

Masked Autoencoders have demonstrated remarkable proficiency in acquiring robust visual representations and achieving cutting-edge outcomes across diverse independent modalities, However, only a handful of studies have delved into their efficacy within multi-modality environments. In this work, we focus on LiDAR point cloud and Camera image, two modalities commonly coexistent in real-world scenarios, aiming to explore their intricate interactions. To improve upon the cross-modal synergy in existing works, we project the feature in the bird's eye view(BEV) plane to receive more geometry information and propose CGA-MAE, a simple and effective cross-modal fusion self-supervised pretraining method based on Camera-Guided Alignment (CGA), which can bridge the BEV feature learning gap between LiDAR and Camera. We initially recognize the significance of masking strategies between these sources, leveraging a projection module, we harmonize the alignment of masks and visible tokens inherent in both modalities. then, we propose a new Modality Feature Fusion Model called MFFM to promote the interaction between the LiDAR feature and the Camera Feature. through extensive experiments performed on large NuScenes dataset, we discover it is nontrivial to interactively learn LiDAR-Camera features, where Our approach significantly advances the State-Of-the-Art (SOTA), achieving a mean Average Precision (mAP) of 67.8% and a nuScenes Detection Score (NDS) of 71.0%. © 2024 ieee.

关键词： Object detection

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Recognition of Scalp Psoriasis Dermoscopic Patterns Signs Based on Histogram Equalization and Convolutional Neural Networks 13

Recognition of Scalp Psoriasis Dermoscopic Patterns Signs Ba...

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

作者： Ji, Honghai Li, Jiaqi Jiang, Weiwei Wang, Li Liu, Shida Chen, Yang School of Electronics & Control Engineering North China University of Technology Beijing100144 China 72nd Group Army Hospital of Pla Department of Dermatology Zhejiang313000 China

ISBN: (纸本)9798350361674

Scalp psoriasis is a common chronic autoimmune inflammatory skin disease that affects a large number of people worldwide. Although scalp psoriasis is not physically a serious life-threatening condition for patients, the itchiness, inflammation, and social distress it causes have a significant negative impact on their quality of life. Currently, the urgent need for accurate diagnosis of scalp psoriasis has driven researchers to explore efficient diagnostic techniques utilizing deep learning techniques, among which dermoscopy is widely used as a simple and easy-to-use non-invasive testing device. In this paper, a convolutional neural network (HE-CNN) based on histogram equalization is proposed to solve the problem of automatic recognition of different dermoscopic patterns of scalp psoriasis. In this, histogram equalization is used to perform global brightness enhancement of dermoscopic images to ensure that important lesion features in the image are not obscured by hair shadows leading to neglect. CBAM is added to the CNN and the enhanced images are used to train a classification model for automated recognition of scalp psoriasis for clinical detection. We conducted experiments on a scalp psoriasis dataset provided by 72nd Group army hospital of PLA. In order to verify the effectiveness of the method, we use the unenhanced image as a control group and compare it with the image that has undergone enhancement, and the results after five-fold cross-validation show that the recognition effect of HE-CNN outperforms that of the control group in terms of accuracy, specificity, sensitivity and youden index. It is verified that HE-CNN has better effect in assisting doctors in the diagnosis of scalp psoriasis. © 2024 ieee.

关键词： Equalizers

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Design of a Full-Space Tracking and Measurement System Based on the Light Pen with Deep learning Methods 13

Design of a Full-Space Tracking and Measurement System Based...

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

作者： Cui, Yu Li, Xu Na, Jing Gao, Guanbin Xing, Yashan Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Intelligent Control and Application Kunming650500 China Luzhou Laojiao Co.Ltd Luzhou646000 China

ISBN: (纸本)9798350361674

Visual measurement, a critical component in industrial production, often faces accuracy challenges due to image distortion at the edges of the field of view, particularly in large spaces. this paper capitalizes on the robust feature-learning capabilities of deep-learning algorithms tailored for industrial robots in factory settings, and introduces a comprehensive tracking and measurement system, grounded in deep learning, specifically designed for light pen industrial robots. the approach begins with an analysis of the industrial robot's workspace, leading to the installation of a binocular vision sensor on a rotating platform. this setup forms the basis for a novel full-space tracking and measurement device for industrial robots, utilizing a light pen. the system integrates a target detection algorithm with a rotation platform control algorithm, ensuring the light pen remains centered in the camera's field of view in real-time. this innovation addresses the limitations of traditional light pens, particularly their restricted measurement range and reduced accuracy. Furthermore, the transformation matrix between the pixel and world coordinate systems is established by analyzing the relationship between the binocular vision sensor and the precision rotating platform. In the process of pinpointing the sphere center coordinates of the light pen probe, the system establishes a mapping relationship between the marker point and the probe's sphere center. It then employs the cross-ratio invariance principle and the least squares method for precise calculations. Finally, the system's accuracy is validated using the actual dimensions of standard gauge blocks and balls as references. Experimental results demonstrate that the geometric measurement error in the proposed tracking and measurement system on these standard objects is less than 0.1mm. these results offer an alternative for full-space tracking and measurement in complex industrial environments. © 2024 ieee.

关键词： Binocular vision

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A Hybrid MTS Anomaly Detection Method Based on Reconstruction and Adaptive Spatial-Temporal Graph Network 13

A Hybrid MTS Anomaly Detection Method Based on Reconstructio...

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

作者： Yuan, X. Ning, Ke Wei, K. He, Yan-Lin Zhu, Qun-Xiong Zhang, Ming-Qing Luo, Yi Zhang, Yang College of Information Science and Technology Beijing University of Chemical Technology Beijing100020 China Ministry of Education of China Engineering Research Center of Intelligent Pse Beijing100020 China Macao Polytechnic University Faculty of Applied Sciences 999078 China Research Institute of Mine Big Data Chinese Institute of Coal Science Beijing100013 China

ISBN: (纸本)9798350361674

Effective anomaly detection of multivariate time series data is crucial for modern industrial applications. However, existing works usually ignore spatial and temporal dependencies of series. the data with complicated spatial-temporal feature put forward higher requirements for accurate detection of anomalies. In this paper, we propose an anomaly detection method for multivariate time series based on spatial-temporal network (STN). Firstly, the time series are reconstructed by Generative Adversarial Networks (GAN). the reconstruction block is used to amplify the influence of the abnormal state and prompt the anomalies more detectable, as poorly reconstructed data of abnormal state further worsen the quality of the final predicted value. Secondly, an adaptive learning graph structure layer is proposed. the graph structure is learned by adaptive method without any prior knowledge and is used for reconstructed data prediction. thirdly, we use STN to extract spatial and temporal dependencies of series. Especially, attention mechanism is introduced into graph convolution network to enhance the prediction performance by focusing on more important neighbor node message. Finally, our model is tested on 3 public datasets to evaluate performance. the results demonstrate that our method performs better than baselines. © 2024 ieee.

关键词： Generative adversarial networks

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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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