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检索条件"任意字段=10th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2021"

共 359 条记录，以下是111-120 订阅

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Prediction of Social Ownership of Typical Household Appliances Based on Improved Grey Models 10

Prediction of Social Ownership of Typical Household Applianc...

引用

10th ieee data driven control and learning systems conference, ddcls 2021

作者： Sun, Xiaoan Luan, Xiaoli Liu, Fei Key Laboratory for Advanced Process Control of Light Industry of the Ministry of Education Jiangnan University Jiangsu Wuxi214122 China

ISBN: (纸本)9781665424233

With the advent of complicated system of electronic and electrical household appliances, the prediction of the social ownership based on a single time series does not conform to its nonlinear growth law. In the present study, the main factors affecting the social ownership of household electronics and electrical appliances are initially investigated based on the grey relational model. then the main influencing factors are considered to construct a multiple grey prediction model. Moreover, the model is optimized by various intelligent algorithms to improve prediction accuracy. Finally, the model accuracy is evaluated through the social ownership data of four typical household appliances in Jiangsu province. the optimized model is applied to predict the social ownership of household appliances in Jiangsu province in the period 2020 to 2022. the results of this article may provide a basis for promoting the resource utilization of e-wastes and assisting managers to improve electronic waste recycling. © 2021 ieee.

关键词： Domestic appliances

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Grasping Detection Based on YOLOv3 Algorithm 10

Grasping Detection Based on YOLOv3 Algorithm

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Li, Zhiyuan Liu, Shan Polytechnic Institute Zhejiang University Hangzhou310015 China College of Control Science and Engineering Zhejiang University Hangzhou310027 China

ISBN: (纸本)9781665424233

In order to achieve multi-object grasping detection, this paper draws on the YOLOv3 algorithm, which has good performance in the field of object detection, and designs a grasping detection model based on deep learning. this model can recognize and locate multi-object in real time, and can predict the grasping point and the grasping deflection angle at the same time, which can realize end-to-end object positioning and grasping detection. For the specific application scenario, the corresponding dataset is automatically produced through data enhancement, which greatly saves the cost and time of manual collection and labeling. In the actual scene, we identify and locate the object and estimate the grasping point and grasping deflection angle of the object at the same time. 99.5% of the objects can be detected on the self-made data set. this method can greatly improve the accuracy of grasping detection in a specific scene. © 2021 ieee.

关键词： Object detection

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the problem of identification parameter data saturation in repetitive control and its solution 10

The problem of identification parameter data saturation in r...

引用

10th ieee data driven control and learning systems conference, ddcls 2021

作者： Song, YongPan Zeng, ShuiPing Zhang, YuTao North China University of Technology Beijing100144 China

ISBN: (纸本)9781665424233

In the repetitive control of tracking periodic signals based on the principle of internal model, the control effect has a great relationship with the parameters of the controlled system. If the system is affected by noise and causes the internal parameters to change, failure to obtain the repeated control of the internal parameters in time will cause the system to lose stability. therefore, how to quickly identify the parameters of the controlled system is particularly important in the field of repetitive control. In the actual process, the traditional least square method is often used to identify the parameters of the controlled system. However, the convergence of the algorithm to parameter identification is very slow. Once the controlled system parameters are changed, the parameter information provided by the new data cannot be updated in time, and the convergence of the identification results is very slow. In order to overcome the data saturation phenomenon of the least squares algorithm, this paper uses three methods of forgetting factor algorithm, variable gain matrix algorithm, and introducing additional matrix R algorithm to improve the traditional least squares identification algorithm, and verified these three through MATLAB simulation. Effectiveness of the method. Compared with traditional methods, the improved three identification methods can speed up the convergence of parameter identification and improve the accuracy of parameter identification. © 2021 ieee.

关键词： Least squares approximations

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Cluster-based Generative Adversarial Network Imbalanced data Generation Method 10

Cluster-based Generative Adversarial Network Imbalanced Data...

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Fan, Min Yang, Qing Zhang, Bo Zhou, Meng Zhang, Ke Xia, Jialu School of Automation Chongqing University Chongqing400044 China China Electric Power Research Institute Beijing100192 China

ISBN: (纸本)9781665424233

data imbalance is widespread in data mining. However, the imbalance of data makes the minority recognition rate low but the cost of misclassification is high. In order to improve the minority recognition rate, this paper proposed a data generation method for balance data set. First, the minority data is clustered and divided into sub-categories, next it built a GAN model to generate each sub-category data separately to improve the quality of data generation and balance the number of majority and minority categories, and also alleviate the imbalance within the minority category. then, this paper built a cost-sensitive convolutional neural network model for classification. Finally, experiments on the UCI public data set show that the method has a good classification performance. © 2021 ieee.

关键词： Generative adversarial networks

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Unknown System Dynamics Estimator based control for Bilateral Teleoperation systems 10

Unknown System Dynamics Estimator based Control for Bilatera...

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Jing, Baorui Duan, Hao Na, Jing Wang, Shubo Kunming University of Science and Technology Faculty of Mechanical and Electrical Engineering Kunming650500 China School of Automation Qingdao University and Shandong Key Laboratory of Industrial Control Technology Qingdao266071 China

ISBN: (纸本)9781665424233

In this paper, we propose a novel control scheme for bilateral teleoperation system subject to the unknown system dynamics. To realize this purpose, the low-pass filter operation is introduced to construct a simple yet robustness unknown system dynamics estimator (USDE). With the help of the proposed USDE, not only the effect of unknown system dynamics can be eliminated but also the acceleration signal can be avoided. Moreover, one salient feature of the proposed USDE is that it only has one tuning parameter, which is more straightforward compared with traditional estimators. the convergence of the estimation error and the stability of the closed-loop control system are rigorously proved via the Lyapunov theorem. Finally, simulation is carried out to demonstrate the superior performance concerning the movement synchronization and transparency. © 2021 ieee.

关键词： Low pass filters

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Event-based Integral Reinforcement learning Algorithm for Non-zero-sum Games of Partially Unknown Nonlinear systems 10

Event-based Integral Reinforcement Learning Algorithm for No...

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Su, Hanguang Zhang, Huaguang Luo, Yanhong Sun, Qiuye School of Information Science and Engineering Northeastern University Liaoning Shenyang China

ISBN: (纸本)9781665424233

In this work, a novel event-based integral reinforcement learning (IRL) adaptive control method is developed to solve the multiplayer non-zero-sum (NZS) games of the nonlinear systems with unknown drift dynamics. By virtue of the IRL algorithm, the system drift dynamics is no more needed in the controller design. Moreover, different from the existing iteration computation methods, this method is online implemented, on which condition the event-triggered control framework can be combined with the IRL algorithm in solving the NZS game problems. In this method, a state-dependent triggering condition is proposed, thus the computation and communication loads are reduced in the control process. Moreover, the uniform ultimate boundedness (UUB) stability of the controlled system and the convergence of the critic weights have also been proved. Finally, a numerical example is provided to demonstrate the effectiveness of our method. © 2021 ieee.

关键词： Reinforcement learning

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Design and Implementation of A Novel Quadruped Robot 10

Design and Implementation of A Novel Quadruped Robot

引用

10th ieee data driven control and learning systems conference, ddcls 2021

作者： Lin, Wenqi Peng, Bo Jin, Long School of Information Science and Engineering Lanzhou University Lanzhou China Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China

ISBN: (纸本)9781665424233

Legged robots have unparalleled advantages over the traditional four-wheel and crawler ones. Particularly, they possess higher maneuverability in complex environments and can play a more significant role in military and emergency missions. To adapt the legged robot to the above situations, the primary task is to make the robot move freely like a human or animal, but this is a complicated and expensive project. the paper is dedicated to designing a novel and cheap quadruped robot and developing a complete and adequate control system. the overall design architecture is proposed, focusing on ease of manufacture and low cost of manufacture. Specifically, the control system runs on stm32, and the movement of the quadruped robot is controlled by a direct current motor that can be driven towards different directions by manipulating the terminal control device. Among the quadruped robots of similar performances, the quadruped robot BlackDog is cheaper and more stable in walking, and its structure is simple and easy to be implemented. © 2021 ieee.

关键词： control systems

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Short-Term Traffic Flow Prediction Based on Ensemble Machine learning Strategies 10

Short-Term Traffic Flow Prediction Based on Ensemble Machine...

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Zeng, Ximu Wang, Yixiong Deng, Xin Wang, Jin Key Laboratory of Data Engineering and Visual Computing College of Computer Science and Technology Chongqing University of Posts and Telecommunication Chongqing400065 China

ISBN: (纸本)9781665424233

In order to solve the problem of traffic congestion, many city governments have begun to develop intelligent transportation systems. As a research hotspot in the field of intelligent transportation, the short-term traffic flow prediction is of great significance to traffic diversion and route planning. In the recent big data era, the machine learning (ML) algorithms have been applied to mining deep information in the data. However, the performance of a single ML model is usually not good. the ensemble learning can improve accuracy in most cases. In this paper, we propose a new short-term traffic flow prediction model. this paper regards the traffic prediction problem as a regression prediction problem, rather than a time series forecasting problem. the proposed model combines the prediction results of the XGBoost, LightGBM and CatBoost models through the ensemble machine learning strategy. With the experiments on real traffic data in Xi'an city, this paper has obtained the comparison of predictive performance of the models. Compared with single models such as the ARIMA, LSTM, XGBoost, LightGBM and CatBoost models, the results show the proposed model is more accurate and more suitable for short-time traffic status prediction. Key Words: traffic prediction, ensemble learning, XGBoost, LightGBM, CatBoost © 2021 ieee.

关键词： Traffic congestion

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Consensus of Nonlinear Multiagent systems with Transmission Delays and Deception Attacks via Sampled-data control 10

Consensus of Nonlinear Multiagent Systems with Transmission ...

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Han, Sheng Zhong, Qishui Shi, Kaibo Yang, Jin School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu611731 China Institute of Electronic and Information Engineering of Uestc Dongguan Guangdong523808 China School of Information Science and Engineering Chengdu University Chengdu610106 China

ISBN: (纸本)9781665424233

the consensus issue for the nonlinear multiagent systems (MASs) with transmission delays subjected to deception attacks is addressed based on sampled-data control. Due to the universality of cyber attacks, this paper considers a kind of deception attacks which occur randomly at sampling instant and can modify the communication information. By employing the two-sided looped-functional method, an augmented Lyapunov-Krasovskii functional (LKF) is constructed to fully utilize the information on the sampling interval, which makes some free matrices be not necessarily positive definite. then, the sufficient criteria are presented to ensure the consensus of MASs with deception attacks. Furthermore, the controller gain matrix and the maximal sampling interval are obtained based on the decoupled approach. Finally, the numerical example is exploited to verify the effectiveness of our results. © 2021 ieee.

关键词： Multi agent systems

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Anomaly detection method based on multi-criteria evaluation for energy data of steel industry 10

Anomaly detection method based on multi-criteria evaluation ...

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Wu, Hao Jin, Feng Zhao, Jun Wang, Wei Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Dalian University of Technology Ministry of Education Dalian116024 China School of Control Science and Engineering Dalian University of Technology Dalian116024 China

ISBN: (纸本)9781665424233

the stability and integrity of the monitoring data in the energy system of the iron and steel industry is of great significance for ensuring the safety of the system. Aiming at the data with periodic characteristics in the steel energy system, an abnormal data detection method based on multi-criteria evaluation is proposed in this study. the data time series is divided according to its periodic characteristics, and each sub-period sequence are evaluated through the quasi-measures established in this paper. then the outlier detection of the periodic time series is realized through the AFCM (Adaptive fuzzy C-means) of these evaluation results. the simulation results based on the actual operating data of a steel enterprise show that this method can identify the local abnormal state in the original time series and improve the detection efficiency. © 2021 ieee.

关键词： Anomaly detection

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