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检索条件"任意字段=8th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2019"

共 336 条记录，以下是101-110 订阅

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Visual detection system of automotive parts attitude based on deep learning 8

Visual detection system of automotive parts attitude based o...

引用

8th ieee data driven control and learning systems conference, ddcls 2019

作者： Zhang, Jiaxu Hu, Shaolin Shi, Haoqiang Automation School Xian University of Technology Xian710048 China

ISBN: (纸本)9781728114545

In the automobile assembly process, pose detection technology is the key technology in the assembly system, which plays an important role in the assembly work and will directly affect the assembly results. In order to improve the detection intelligence and accuracy, the deep learning vision technology is applied to the attitude measurement of automotive parts assembly, and a method of extracting key points by deep learning is proposed. Different from the traditional methods, the deep convolutional network has strong robustness and versatility. therefore, this paper proposes a joint detection method for double convolutional networks. First, the deep convolutional network is used to predict the key points of the part. then, the recursive convolutional neural network is used to optimize the prediction result. Finally, the key point coordinates are substituted into the PnP algorithm to check whether the part pose conforms to the standard. the simulation results show that the method can accurately realize the target attitude detection. © 2019 ieee.

关键词： Convolution

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Torquer-based active vibration control for gyrowheel system with hooke joint 8

Torquer-based active vibration control for gyrowheel system ...

引用

8th ieee data driven control and learning systems conference, ddcls 2019

作者： Zhang, Yuanyuan Huo, Xin Liu, Haiyuan Zhao, Hui Control and Simulation Center Harbin Institute of Technology Harbin150080 China 14th Research Institute China Electronics Technology Group Corporation China

ISBN: (纸本)9781728114545

GyroWheel is considered to be an innovative device that is designed for the attitude control system of the micro satellite. Its actuating capabilities of the control moment gyro combined with the rate sensing capabilities of the tuned rotor gyro are realized by using a spinning flex-gimbal with Hooke Joint. For a 3-DOF GyroWheel rotor with high spinning velocity, the uneven mass distribution and undesirable factors related to the characteristics of Hooke Joint, are inevitable that bring about undesired vibration which hinders the improvement of the specification performance and control accuracy. To suppress unexpected vibration of GyroWheel rotor, two methods about active vibration control based on the torquer are proposed. Some theoretical analysis and simulation illustrations based on SimMechanics are developed in this paper, which provide useful guidance for further experimental studies. © 2019 ieee.

关键词： Vibration analysis

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A variable selection method for fault isolation through bayesian information criterion 8

A variable selection method for fault isolation through baye...

引用

8th ieee data driven control and learning systems conference, ddcls 2019

作者： Liu, Lang Yang, Weidong Zhang, Hong Fan, Huijin Tao, Bo School of Automation Hua Zhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781728114545

In industrial process, fault isolation technology can identify the major variables leading to faults. Contribution plots and reconstruction-based methods are common tools, but both of them have the smearing effect. To solve this problem, Bayesian theory based method have been developed. Unfortunately, they usually have a high misdiagnosis rate when handling multiple faults with small magnitudes. In this paper, a new fault isolation method based on Bayesian information criterion is proposed. Firstly, the fault isolation problem is transformed into a mixed integer nonlinear programming problem. then, to reduce the difficulty of calculation, the original problem is simplified into a series of nested mixed integer quadratic programming problems by using forward selection strategy. Finally, these problems can be solved by branch and bound algorithm. the effectiveness of the proposed method is verified by Monte Carlo simulation. © 2019 ieee.

关键词： Intelligent systems

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Eddy current scanning image denoising method based on principal component analysis and manifold learning 8

Eddy current scanning image denoising method based on princi...

引用

8th ieee data driven control and learning systems conference, ddcls 2019

作者： Bao, Jun Ye, Bo Deng, Weiquan Wu, Jiande Wang, Xiaodong Engineering Research Center Mineral Pipeline Transportation of Yunnan Province Kunming650500 China

ISBN: (纸本)9781728114545

Due to the complicated industrial environment and the poor surface conditions of detected materials, scanning images inevitably contain various noise in actual eddy current imaging detection, which seriously affects the detection result. Aiming at the above problem, we propose an eddy current scanning image denoising method based on principal component analysis (PCA) and locally linear embedding (LLE) in this paper. First, the method uses PCA to preliminarily remove noise from the scanning image. then, the method uses the reconstruction algorithm of LLE to reconstruct the PCA-processed image by its neighborhoods, which further denoise the eddy current scanning image and optimize their details and edges while retaining their local geometric constructions. the experimental results have shown that, compared with other methods, the proposed method not only removes noise more effectively but also retains the details of the scanning image. © 2019 ieee.

关键词： Principal component analysis

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Simulation of the array signals processing based on automatic gain control for two-wave mixing interferometer 8

Simulation of the array signals processing based on automati...

引用

8th ieee data driven control and learning systems conference, ddcls 2019

作者： Qian, Feiming Zhu, Haijiang Xing, Guangzhen Yang, Ping Beijing University of Chemical Technology Information Science and Technology Beijing100029 China National Institute of Metrology Division of Mechanics and Acoustics Beijing100029 China

ISBN: (纸本)9781728114545

Laser ultrasonic testing represents an attractive method for non-contact measurements with high sensitivity and broad detection bandwidth, which make it possible to measure ultrasound field in some offensive environments. Because of the noise caused by serious scattering, the conventional optical interferometer cannot work properly on rough surfaces. the environmental noise is also a big uncertainty budget for the measuring results. In this paper, an array signals processing method for a new type of two-wave mixing interferometer is introduced. In order to calibrate the displacement of the target, a mirror driven by the piezoelectric transducer introduces a known displacement at a low frequency on the reference beam. In addition, the wavelet neural network(WNN) is applied to denoise the input signals. Moreover, an automatic gain control (AGC) scheme is employed to the output of the photodiode array which can calibrate the output sensitivity. then according to the output sensitivity, the displacement of the target measured by the diode array can be demodulated. Both the out-plane and in-plane displacements can be measured simultaneously, which may result in a wide usage in non-destructive areas. © 2019 ieee.

关键词： Interferometers

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Parameter calibration of microscopic traffic simulation model based on floating car data 8

Parameter calibration of microscopic traffic simulation mode...

引用

8th ieee data driven control and learning systems conference, ddcls 2019

作者： Zhang, Lingyu Sun, Dehui Wang, Li Zhang, Haibo Beijing Key Lab of Urban Intelligent Traffic Control Technology North China University of Technology Beijing100144 China

ISBN: (纸本)9781728114545

Aiming at the problems of heavy workload of basic data collection, complicated manual parameters calibration and inaccurate calibration in the traditional microscopic traffic simulation model parameter calibration, an adaptive microscopic traffic simulation model parameter calibration method based on floating car data is proposed. First, the basic data of the simulation road network were obtained by using the floating car technology. Secondly, the parameters calibration process of the microscopic traffic simulation model was constructed, and the self- adaptive orthogonal genetic was used to achieve the model parameters calibration. Finally, using the actual data of the South Ring Road main line, District Changping, Beijing to simulate and verify. the results show that the proposed method in this paper can not only reduce the workload of manual calibration, but also the model parameter calibration is more accurate, which proves the feasibility and effectiveness of the method. © 2019 ieee.

关键词： Genetic algorithms

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Process monitoring using a sequence to sequence model 8

Process monitoring using a sequence to sequence model

引用

8th ieee data driven control and learning systems conference, ddcls 2019

作者： Wu, Haibin Chou, Cheng-Hung Yao, Yuan Wong, David Shan Hill Liu, Yi National Taiwan University Department of Electrical Engineering Taipei10617 Taiwan Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou310023 China

ISBN: (纸本)9781728114545

Industrial process monitoring systems aim to mine valuable patterns from a large volume of process data for detecting abnormalities in an efficient and effective manner. To deal with nonlinearity and sequentiality of process data, various nonlinear and dynamic models have been adopted in previous research, which usually take both manipulated and measured variables into consideration to maximize the utilization of the available information. However, from the viewpoint of process engineers, changes only in manipulated variables should not be considered as faults. Instead, these are routine control actions to reject disturbances. As long as the process can be regulated, it is unnecessary to raise alarms. In this work, a sequence to sequence neural network model with gated recurrent units (GRUs) is proposed for efficient process monitoring, while superfluous alarms are suppressed. the feasibility of the proposed method is illustrated by the case study on the benchmark Tennessee Eastman (TE) process. © 2019 ieee.

关键词： Recurrent neural networks

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Improved model free adaptive control based on compact form dynamic linearization 8

Improved model free adaptive control based on compact form d...

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8th ieee data driven control and learning systems conference, ddcls 2019

作者： Pang, Zhonghua Song, Wentai Luo, Wencheng Han, Cunwu Sun, Dehui Key Laboratory of Fieldbus Technology and Automation of Beijing North China University of Technology Beijing100144 China

ISBN: (纸本)9781728114545

With the development of modern industry and information science and technology, modern industrial processes become more and more complex, which brings many challenges for model-based controller design. In this case, data-driven control is a complementary approach to model-based control. this paper proposes an improved model free adaptive control method based on compact format dynamic linearization technique for a class of nonlinear systems. Its control law consists of a time-varying proportional term, a time-varying integral term, and a time-varying derivative term. As a result, compared with the original method where there is only a time-varying integral term, it can strongly improve the dynamical performance of control systems. the effectiveness of the proposed method is demonstrated through simulation results. © 2019 ieee.

关键词： Nonlinear systems

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An improved clustering algorithm based on cluster weight coefficient 8

An improved clustering algorithm based on cluster weight coe...

引用

8th ieee data driven control and learning systems conference, ddcls 2019

作者： Wang, Qiaoling Qiao, Fei Kong, Weichang Jiang, Youhao School of Electronics and Information Engineering Tongji University Shanghai201804 China

ISBN: (纸本)9781728114545

Clustering algorithms are typical unsupervised machine learning algorithms, which are widely used in many fields. As a popular clustering algorithm, K-means has a good performance, but it has difficulties in determining initial clustering centers and the number of clusters. Canopy algorithm is often used to support K-means as a coarse clustering process. the results obtained from Canopy are set as the initial clustering centers of K-means. However, the thresholds of Canopy are often randomly selected, which makes the clustering result inaccurate. In this paper, we improve the threshold selection of the traditional Canopy and the criterion function of the traditional K-means. As a result, a novel Cluster-Weight-based Canopy-Kmeans (CWC-Kmeans) is proposed. In order to evaluate the proposed algorithm, comparison experiments are conducted with UCI standard datasets and bearing nonstandard dataset. By typical clustering evaluation indices, CWC-Kmeans has better clustering results than the traditional Canopy and the traditional K-means. thus, the superiority of CWC-Kmeans is proved. © 2019 ieee.

关键词： learning algorithms

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Fault diagnosis of multi-source heterogeneous information fusion based on deep learning 8

Fault diagnosis of multi-source heterogeneous information fu...

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8th ieee data driven control and learning systems conference, ddcls 2019

作者： Zhou, Funa He, Yifan Han, Haotian School of Computer and Information Engineering Henan University Kaifeng China School of Software Henan University Kaifeng China

ISBN: (纸本)9781728114545

the complexity and diversity of equipments presented in the development of modern industrial technology brings the wide application of deep learning in fault diagnosis. However, the multi-source heterogeneous data collected from different sensors applied in industrial production cannot be used by traditional neural networks directly, which registers a difficulty to fault diagnosis based on deep learning. In addition, with only one of the data used, traditional deep learning fault diagnosis methods ignores the correlation between heterogeneous data, which may result in the loss of useful information and the accuracy of fault diagnosis. To solve the above problems, a fault diagnosis framework based on deep learning for multi-source heterogeneous data fusion is proposed. In the feature learning stage, stack self-encoder (SAE) and convolution neural network (CNN) are used to extract deep features from one-dimensional vibration data and two-dimensional image data respectively, and then feature fusion fault diagnosis is carried out. It solves the problem that traditional neural network can't make full use of multi-source heterogeneous data, and improves the accuracy of fault diagnosis. What's more, the bearing data of Case Western Reserve University are used to verify the validity of the diagnosis method proposed in this paper. © 2019 ieee.

关键词： Failure analysis

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