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检索条件"机构=Key Laboratory of Advanced Control and Optimization for Chemical Process of Ministry of Education"

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Decomposition based iterative estimation algorithm for autoregressive moving average models

Decomposition based iterative estimation algorithm for autor...

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Chinese control Conference (CCC)

作者： Huiyi Hu Ruifeng Ding Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Jiangnan University Wuxi China School of Internet of Things Engineering Jiangnan University Wuxi China

ISBN: (纸本)9781467325813

This paper discusses an iterative least squares algorithm for identifying the parameters of autoregressive moving average models using the matrix decomposition technique. The basic idea is to use the block matrix inversion lemma to avoid repeatedly computing the inverse of the involved data matrix at each iteration. The simulation results show that the proposed algorithm works well.

关键词： Parameter estimation Least squares Iterative method ARMA model autoregressive processes iterative methods Least squares method Parameter estimation decomposition Iterative

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Recursive relations of the criterion functions for the recursive least squares algorithms

Recursive relations of the criterion functions for the recur...

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The 24th Chinese control and Decision Conference (第24届中国控制与决策学术年会 2012 CCDC)

作者： Weili Xiong Junxia Ma Rui Ding Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Jiangnan Unive School of Internet of Things Engineering Jiangnan University Wuxi 214122 PR China

This paper derives the recursive formulas of the computation of the criterion functions for the well-known weighted recursive least squares algorithm and the finite-data-window recursive least squares algorithm for linear regressive models. The analysis indicates that the proposed recursive computation formulas can be extended to the least squares estimation algorithms for pseudo-linear regression models, e.g., the equation error systems.

关键词： Criterion function Recursive algorithm Parameter estimation Least squares Regressive model

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Model Reduction of Two-Dimensional Discrete-time Systems Based on Finite-Frequency Approach

Model Reduction of Two-Dimensional Discrete-time Systems Bas...

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第三十二届中国控制会议

作者： Da-Wei Ding Xiaoli Li School of Automation & Electrical Engineering University of Science and Technology Beijing Key Laboratory of Advanced Control of Iron and Steel Process(Ministry of Education) University of Science and Technology Beijing

ISBN: (纸本)9781479900305

This paper deals with the model reduction problem of two-dimensional(2-D) discrete-time systems described by the Roesser *** from existing fuU-frequency methods,we propose a finite-frequency model reduction method for 2-D Roesser *** the generalized Kalman-Yakubovich-Popov(GKYP) lemma for 2-D systems,sufficient conditions are developed for model reduction of 2-D Roessor systems over low-frequency,and middle-frequency,*** proposed finite-frequency model reduction method can get a better approximation accuracy than the existing full-frequency ones over the finite-frequency *** effectiveness of the proposed method is illustrated by a numerical example.

关键词： Model reduction two-dimensional Roesser systems finite-frequency ranges

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A high-order sliding mode stator flux observer for asynchronous motor based on feedback linearization

A high-order sliding mode stator flux observer for asynchron...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Yuedou Pan Tao Chen School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education) Beijing China

ISBN: (纸本)9781467376839

This paper proposes a high-order sliding mode stator flux estimation method for asynchronous motor based on feedback linearization. Selecting the rotor flux as the system's output, the coordinate change matrix and the nonlinear state feedback are derived based on the affine nonlinear model of asynchronous motors by using the nonlinear system's differential geometry theory. Then the asynchronous motor system is input-output linearized and the feedback linearization model of asynchronous motors is obtained. On this basis, a high-order sliding mode rotor flux observer for asynchronous motor is designed to estimate rotor flux, and this paper analysis the stability of the designed observer, then we can calculate stator flux by using the rotor flux. The designed observer is applied to Direct Torque control (DTC) of the asynchronous motor and achieves a good control performance. Simulation results verify the effectiveness and feasibility of the proposed method.

关键词： Stators Observers Rotors DC motors Nonlinear systems State feedback

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Weakly supervised object detection with interactive edge attentive collaboration

Weakly supervised object detection with interactive edge att...

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Chinese control and Decision Conference, CCDC

作者： Wenlong Gao Ying Chen Yong Peng Key Laboratory of Advanced Process Control for Light Industry Ministry of Education Jiangnan University Wuxi China School of Internet of Things Engineering Jiangnan University Wuxi China

ISBN: (纸本)9781665478977

Weakly supervised object detectors based on image-level annotation tend to overfit in the discriminative regions while ignoring the integrity of the object. In this paper, a novel weakly supervised object detection network with the interactive edge attentive collaboration module is proposed to alleviate the local optimal problem, in which edge attention is extracted as an object unity supervision for the detection, and a collaborative loss is introduced to enable VGG16 feature map with global attentive ability. The module can be detached from the network in the test period, which ensures the high efficiency of the detector without introducing any additional inference cost. Extensive experiments are carried out on the PASCAL VOC 2007 and VOC 2012 datasets, which reach 52.3% mAP, 67.7% CorLoc and 49.1% mAP, 68.0% CorLoc respectively, outperforming state-of-the-arts.

关键词： Costs Annotations Image edge detection Collaboration process control Object detection Detectors

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Yarn-dyed Fabric Defect Detection with YOLOV2 Based on Deep Convolution Neural Networks

Yarn-dyed Fabric Defect Detection with YOLOV2 Based on Deep ...

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Data Driven control and Learning Systems (DDCLS)

作者： Hong-wei Zhang Ling-jie Zhang Peng-fei Li De Gu Xi’an Polytechnic University Xi’an ShaanXi China Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Institute of Automation Jiangnan University Wuxi China

ISBN: (纸本)9781538626191

To reduce labor costs for manual extract image features of yarn-dyed fabric defects, a method based on YOLOV2 is proposed for yarn-dyed fabric defect automatic localization and classification. First, 276 yarn-dyed fabric defect images are collected, preprocessed and labelled. Then, YOLO9000, YOLO-VOC and Tiny YOLO are used to construct fabric defect detection models. Through comparative study, YOLO-VOC is selected to further model improvement by optimize super-parameters of deep convolutional neural network. Finally, the improved deep convolutional neural network is tested for yarn-dyed fabric defect detection on practical fabric images. The experimental results indicate the proposed method is effective and low labor cost for yarn-dyed fabric defect detection.

关键词： Fabrics Feature extraction Convolution Predictive models optimization Training

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Batch process Monitoring with Two-Dimensional LSTM-Variational Auto-Encoder

Batch Process Monitoring with Two-Dimensional LSTM-Variation...

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Chinese control Conference (CCC)

作者： Zhong Liu Xuyang Lou Jinlin Zhu Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Jiangnan University Wuxi China School of Food Science and Technology Jiangnan University Wuxi China

Batch process monitoring is crucial in production loss prevention and process safety promotion. However, the two-dimensional nonlinear dynamics in the batch process make the monitoring cumbersome. This paper proposes a novel dynamic deep model called two-dimensional LSTM variational auto-encoder (2DLSTM-VAE). Structurally, our model constructs with LSTM encoded under the conditional variational Bayes framework, which can cope with the nonlinear dynamics from two dimensions. Afterwards, a systematic fault detection framework is defined under the latent and residual domains to account for each running batch along with those run-to-run variations so as to reflect the process variability in the two-way monitoring aspects. Finally, a deep reconstruction-based contribution (DRBC) diagram is further developed to investigate root causes. For the case study, the validity of the 2DLSTM-VAE model is demonstrated in the penicillin fermentation process, and the results show that compared with other methods, the proposed deep model has better performance on fault detection and diagnosis.

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A Greedy Orthogonal Least Squares Algorithm for Nonlinear Systems with Time-Delays

A Greedy Orthogonal Least Squares Algorithm for Nonlinear Sy...

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Chinese control Conference (CCC)

作者： Xinyu Liu Yanjun Liu School of Internet of Things Engineering Jiangnan University Wuxi China Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Jiangnan University Wuxi China

This paper focuses on the identification of multiple variables Wiener systems with unknown time-delays and system orders. In order to jointly estimate the time-delays and parameters using small amounts of sampling data, an over-parameterized identification model with a sparse parameter vector is established by introducing a redundant rule, and a novel greedy orthogonal least squares identification method based on the Householder transformation is proposed. The active columns of the information matrix are selected one by one with the help of the Householder transformation-based greedy strategy, maintaining an upper-triangular structure for the sub-information matrix. The back-substitution method is adopted to solve the linear equation set instead of a matrix inversion, so that ill-conditioned solutions can be avoided. Finally, the Bayesian information criterion is used to choose the optimal sparse level. Numerical experiments show that this new scheme can achieve almost optimal generalization performance while requiring less computation than the traditional schemes.

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Burden Distribution control Based on Adaptive Genetic Algorithm in Bell-less Blast Furnace

Burden Distribution Control Based on Adaptive Genetic Algori...

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第三十三届中国控制会议

作者： Qiao Zhu Yi-Xin Yin Han-Qing Cheng Xian-Zhong Chen School of Automation & Electrical Engineering University of Science and Technology Beijing Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education) University of Science and Technology Beijing

ISBN: (纸本)9781479947249

The proper control of the ratio of ore to coke distribution(ROCD) can achieve energy saving of the blast furnace(BF).In order to achieve the desired ROCD,it is meaningful to search best charging system(especially the burden matrix).This paper deals with the problem of burden distribution control based on the adaptive genetic algorithm and multi-radar ***,the definition of the ratio of ore to coke(ROC) and the desired ROCD are ***,according to the desired ROCD,the concrete steps of searching the best burden matrices are presented based on the adaptive genetic ***,the operators of the adaptive genetic algorithm(especially the mutation operator) are properly adjusted,according to the actual production process of the bell-less ***,three computational experiments demonstrate the effectiveness of the method.

关键词： Bell-less blast furnace Adaptive genetic algorithm Ratio of ore to coke distribution Multi-loop burden matrices.

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A greedy iterative least squares algorithm for multivariable systems

A greedy iterative least squares algorithm for multivariable...

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Chinese Automation Congress (CAC)

作者： Ping Han Yanjun Liu School of Internet of Things Engineering Jiangnan University Wuxi China Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Jiangnan University Wuxi China

ISBN: (纸本)9781665426480

This paper considers the joint estimation of parameters and time-delays for multi-input output-error systems. By introducing the noise-free outputs and setting an input regression length, an over-parameterization linear sparse identification model can be obtained. A greedy iterative least squares algorithm based on the Householder transformation and auxiliary model is proposed. The proposed algorithm can estimate the multiple time-delays and parameters of the multivariable systems from a few number of sampled data, and has the advantage of avoiding computing the inverse of a high-dimensional matrix at each iteration compared to existing methods. Finally, a numerical simulation example demonstrates the effectiveness of the proposed algorithm.

关键词： Automation Computational modeling Estimation Numerical simulation Iterative algorithms Data models Numerical models

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