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检索条件"任意字段=7th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2018"

共 474 条记录，以下是331-340 订阅

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Empirical Analysis of Arterial Fundamental Diagram and Optimization by Floating Car data

Empirical Analysis of Arterial Fundamental Diagram and Optim...

引用

ieee data driven control and learning systems conference

作者： Zhonghe He Ming Chen Haibo Zhang Li Wang Beijing Key Lab of Urban Intelligent Traffic Control Technology North China University of Technology Beijing 100144

ISBN: (纸本)9781538626191

the fundamental diagram (FD) reflects the operational status of road network traffic and it is a model describing the relationship between traffic density (traffic volume and occupancy rate) at steady state. In this paper, an empirical study is carried out on the model of Nanhuan Road in Changping District. the basic data of flow rate, occupancy rate and driving speed are obtained from the data of geomagnetic detector and floating car, and then the current road status are analyzed. According to the existing data, the traffic flow of the arterial is simulated. Based on the geographical features of the arterial and the traffic flow data, the green wave band control scheme is calculated and the data after the coordinated control is extracted. In the study of arterial coordination control, the influence of the coordination control parameters on FD shape and its influence on the capacity are analyzed.

关键词： Fundamental diagram Arterial coordination Floating car

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

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

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data driven control and learning systems (ddcls)

作者： Xinbo Zhang Jian Feng Zhiqiang Yao Jinhai Liu Huaguang Zhang School of Information Science and Engineering Northeastern University Shenyang P. R. China China Academy of Safety Science and Technology Beijing P. R. China

ISBN: (纸本)9781538626191

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： Magnetic flux leakage Training Support vector machines Classification algorithms Detectors Pipelines Magnetic fields

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Quantum Noise Protection via Weak Measurement for Quantum Mixed States

Quantum Noise Protection via Weak Measurement for Quantum Mi...

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data driven control and learning systems (ddcls)

作者： Sajede Harraz Shuang Cong Feng Shuang Department of Automation University of Science and Technology of China Hefei China Institute of Intelligent Machines Chinese Academy of Sciences Hefei China

ISBN: (纸本)9781538626191

Due to the interaction with the environment, a quantum state is often affected by the different types of noises which becomes to one of the biggest problems for practical quantum computation. We study the possibility of protecting the mixed state of a quantum system that goes through noise by weak measurements and control operations. the aim is to find the optimal measurement strength and control operations and make the input and output states as close as possible. We show that our scheme can effectively protect arbitrary mixed states against typical types of noise sources: amplitude damping, phase damping and amplitude-phase damping. the optimal measurement and control operators are deduced in different bases of the Bloch sphere to find the best control scheme for each type of noise. the effectiveness of our control scheme is demonstrated by simulation results.

关键词： Damping Noise measurement Feedback control Monte Carlo methods Noise reduction Rotation measurement control systems

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Velocity Decomposition Based Planning Algorithm for Grasping Moving Object

Velocity Decomposition Based Planning Algorithm for Grasping...

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data driven control and learning systems (ddcls)

作者： Xinyu Ye Shan Liu College of Control Science and Engineering Zhejiang University Hangzhou

ISBN: (纸本)9781538626191

An online planning method is proposed for an industrial manipulator to grasp a moving object whose motion is not long-term predictable. Due to the limited time when the moving object stays within the limited workspace of the fixed manipulator, the manipulator has to grasp the object before it leaves the workspace. the planning algorithm brings the end effector of the manipulator to the vicinity of the object quickly and makes it match the pose of the object at first, then grasps the object. In terms of the states of the object and the end effector, the velocity of the end effector is decomposed to three directions. the accelerations of each direction are planned to make sure that the end effector can achieve stably tracking of the moving object in a short time. According to these accelerations, the velocity of the end effector and the joints velocities are obtained through pseudo inverse of the Jacobian matrix of the manipulator. Several simulation examples show that the proposed method can finish the grasping tasks faster than conventional methods.

关键词： Acceleration End effectors Planning Grasping Robot sensing systems Visualization

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A Loading Balancing Equi-join Algorithm Based on Key Cost

A Loading Balancing Equi-join Algorithm Based on Key Cost

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data driven control and learning systems (ddcls)

作者： Xiang Jun Zheng Qian Li Chao College of Information Engineering Hubei University for Nationalities Enshi China

ISBN: (纸本)9781538626191

MapReduce parallel computing model is widely used in large-scale distributed data processing. However, this model does not support the join operation well. Especially in the face of skewed data, the original partitioning algorithm tends to cause unbalanced load between processing nodes. To solve this problem, a load balancing equi-join algorithm based on key cost is proposed. the algorithm generates more key partitions than the number of processing tasks according to the type of join attributes and calculates the cost information for each partition. By dynamically determining the partition function, the data in the partition is sent to the corresponding node, considering the network transmission cost and disk I/O cost of data and achieving load balancing. Experimental results show that the proposed algorithm has good effect in load balancing in homogeneous clusters.

关键词： Task analysis Load management Partitioning algorithms Computational modeling Load modeling Heuristic algorithms data models

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Sliding Mode control of the RTAC System

Sliding Mode Control of the RTAC System

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data driven control and learning systems (ddcls)

作者： Zhongtian Chen Xianqing Wu Xianhua Ou Xiongxiong He College of Information Engineering Zhejiang University of Technology Hangzhou China Faculty of Mechanical Engineering and Automation Zhejiang Sci-Tech University Hangzhou China

ISBN: (纸本)9781538626191

In this paper, a sliding mode control (SMC) scheme is presented for the rotational/translational actuator (RTAC) system, which is proposed without linearizing or approximating the dynamics. Different from the existing control methods, external disturbances are taken into consideration in this paper. In particular, after some model transformations, a novel dynamic equation with a cascade form of the underactuated RTAC system is obtained. then, based on the backstepping technique, a desired control variable is proposed to control the first subsystem and a corresponding deviation-based subsystem is constructed. On the basis of the introduced deviation-based subsystem, a sliding mode controller is proposed straightforwardly. Simulation results including a comparative study are included to examine the control performance of the presented scheme.

关键词： Mathematical model Sliding mode control Actuators Closed loop systems Backstepping Robustness Stability analysis

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Robust Stability of Lurie Nonlinear Discrete System with Time-Varying Delay via Scaled Small Gain theorem

Robust Stability of Lurie Nonlinear Discrete System with Tim...

引用

data driven control and learning systems (ddcls)

作者： Chaoqun Guo Hongqian Lu Yue Hu Xingping Liu Hongwei Chen School of Electrical Engineering and Automation Qilu University of Technology (Shandong Academy of Sciences) P. R. China Ji Nan Building Source Cement Products Co. LTD P. R. China

ISBN: (纸本)9781538626191

this paper is concerned with the robust stability of lurie nonlinear discrete time-varying delay system. the initial lurie nonlinear discrete system with time-varying delay is converted into two interconnected subsystems by using a model transformation. One of the subsystems has no uncertainty and delay and can be analyzed stability by Lyapunov-Krasovskii functional method. then using an input-output (IO) approach which is an application of the scaled small gain theorem to obtain the stability condition of the lurie nonlinear discrete system. A numerical example is provided to demonstrate the applicability of the presented method.

关键词： Delays Time-varying systems Robust stability Stability criteria Delay systems Uncertainty

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Recursive Autoregressive Dynamic Latent Variable Model for Fault Detection of Dynamic Process with Missing Values

Recursive Autoregressive Dynamic Latent Variable Model for F...

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ieee data driven control and learning systems conference

作者： Le Zhou Jiaxin Yu Jing Jie Zhihuan Song School of Automation and Electrical Engineering Zhejiang University of Science & Technology State Key Laboratory of Industrial Control Technology Zhejiang University

ISBN: (纸本)9781538626191

For the dynamic processes, both the auto-correlations and the cross-correlations need to be extracted. In the previous work, the autoregressive dynamic latent variable (ARDLV) model is able to achieve this goal since an AR process is used for high-order dynamic process modelling. However, the training data set usually contains the missing values, which leads to the normal ARDLV invalid. In this paper, a novel recursive ARDLV model is proposed for fault detection of the dynamic process with missing values. In the proposed model, the missing value and the model parameters are estimated alternatively in the probabilistic framework. Finally, a case study is illustrated to reveal the performance of proposed method, in which an incomplete data set is used for fault detection purpose.

关键词： Dynamic modelling Fault detection Missing values Autoregressive Dynamic Latent Variable (ARDLV)

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Identification of a Class of Multi-signal Based Neuro-fuzzy Wiener systems

Identification of a Class of Multi-signal Based Neuro-fuzzy ...

引用

data driven control and learning systems (ddcls)

作者： Yangyang Li Li Jia Feng Li Qi Xiong Sheng Gao Department of Automation Shanghai University Shanghai P.R. China Shanghai Key Laboratory of Power Station Automation Technology Shanghai University Shanghai P. R. China Shanghai Power Equipment Research Institute Company Limited Shanghai P.R. China

ISBN: (纸本)9781538626191

A novel multi-signal based identification approach is presented for the neuro-fuzzy Wiener model with process noise. A combined multi-signal composed of two random signals of different amplitude is adopted to solve the identification problem of the nonlinear block separated from the linear part. then the least square method is employed to identify the nonlinearity of the Wiener model. Next, the linear parameters of the Wiener model are obtained by using the recursive least square method based on auxiliary model. Finally, an example is used to verify the effectiveness of the proposed method.

关键词： Stochastic processes Automation Least mean squares methods Nonlinear dynamical systems Parameter estimation White noise Mechatronics

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Online Semi-supervised Quality Prediction Model for Batch Mixing Process

Online Semi-supervised Quality Prediction Model for Batch Mi...

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data driven control and learning systems (ddcls)

作者： Mingtao Zhang Bocheng Chen You Wu Weiwei Deng Xuelei Zhang Yi Liu Taizhou Vocational and Technical College Taizhou China Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou China Shanghai Entry-Exit Inspection and Quarantine Bureau Shanghai China

ISBN: (纸本)9781538626191

Current soft sensors for the Mooney viscosity prediction in rubber mixing processes only utilized the limited labeled data. By exploring the unlabeled data, a novel soft sensor, namely just-in-time semi-supervised extreme learning machine (JSELM), is presented to online predict the Mooney viscosity with multiple recipes. It integrates the just-in-time learning, extreme learning machine (ELM), and the graph Laplacian regularization into a unified online modeling framework. When a test sample is inquired online, the useful information in both of similar labeled and unlabeled data is absorbed into the JSELM model to enhance its prediction performance. Moreover, an efficient model selection strategy is formulated for online construction of the JSELM prediction model. the superiority of JSELM is validated via the industrial Mooney viscosity prediction.

关键词： Sensors Predictive models Viscosity data models Rubber Chemical sensors Semisupervised learning

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