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第三十九届中国控制会议

作者： Dexin Zhang Yuan Zhu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

With the development of single-cell sequencing technology, it is a hot research topic to identify the cell types using single-cell sequencing data, and many single-cell clustering algorithms have been developed to study this issue. These methods capture partial information of single-cell sequencing data, and obtain the different performance on the same data set. Combining these different results into one can improve the accuracy and validity. Here, we proposed ECBN, Ensemble Clustering based on B ayesian Network. ECBN can ensemble several different results of state-of-the-art single cell clustering methods, such as Seurat, CIDR, SC3 and t-SNE+k-means, and generate a more optimal clustering result through Bayesian network. Experiments are carried on the 5 single cell data sets and compared with 4 individual single cell clustering methods and 3 integrative *** size of experiment data sets ranges from 822 to 3605 and the results show that our method can achieve good ***, ECBN can also use the graphical regularization to lighten the limitation which is generated by the different basis results.

关键词： Single-cell RNA-seq Ensemble clustering Bayesian network

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Feature Matching Algorithm Based on SURF and Lowes Algorithm

Feature Matching Algorithm Based on SURF and Lowes Algorithm

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第三十九届中国控制会议

作者： Xiaoqi Wang Weihua Cao Chaolong Yao Hang Yin School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Feature extraction and matching of images is a key step in 3D reconstruction, and its accuracy directly affects the accuracy of 3D reconstruction. In this paper, aiming at the mismatch caused by the high similarity between screws, proposes a feature matching algorithm based on median filtering, Lowes algorithm and scale-invariant feature transform(SURF), called M-L-SURF algorithm. First, the median filtering is performed on the screw image to remove noise, then the SURF algorithm is used for feature extraction and matching, and finally, the Lowe's algorithm is used to filter the matching results. The results of experiments show that the M-L-SURF algorithm can achieve a 97.4% correct rate of screw image matching. The matching results obtained in this paper can be better applied to the subsequent work of 3D reconstruction.

关键词： Median filtering SURF Feature matching Lowes algorithm

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Design of Ultrasonic Ranging system Based on Cross-correlation Method

Design of Ultrasonic Ranging System Based on Cross-correlati...

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第三十九届中国控制会议

作者： Han Lu Zhihua Li Pei Gao School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

In order to solve the problem that ultrasonic ranging is difficult to obtain the first wave, an ultrasonic ranging system based on cross-correlation method is designed. In this system, the signal generating unit is mainly responsible for transmitting high frequency sound waves, and the data acquisition unit is mainly responsible for collecting and storing the sound waves. Then,according to the time delay between the collected acoustic signals, the time difference between the signals received by the signal acquisition unit is obtained, so as to measure the distance.

关键词： Ultrasonic Cross-correlation Distance

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Positioning Method of Insulator Sheds Based on Depth Information

Positioning Method of Insulator Sheds Based on Depth Informa...

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第三十九届中国控制会议

作者： Xuan Gong Xin Chen Xu Jian Wenpeng He School of Automation China University of Geoscience Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

The identification and positioning of insulator shed feature is of vital significance for insulator cleaning. However,there exist few texture features the insulator and its surface is reflective, which makes it difficult to identify and locate the convex points on the surface of the insulator shed. In order to identify and locate the insulator shed, we propose a method to extract the convex points of the insulator skirt based on the binary image of the insulator and the position of the central axis. Binocular vision technology is applied to obtain the depth change curve of the insulator on its axis, with the help of the depth threshold method and principal component analysis（PCA） method. At the same time, the pixel coordinates on the central axis are used to traverse the depth on the central axis to extract the shed bumps, thereby achieving the spatial positioning of the insulator shed bumps. The experiments show that the bumps on the insulator’s central axis can be well marked and used in the insulator cleaning robot system.

关键词： Insulator cleaning Binocular vision Principal component analysis Shed bump Cleaning robot system

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Identification of drilling stick-slip vibration conditions based on Empirical Mode Decomposition threshold denoising and Support Vector Machine

Identification of drilling stick-slip vibration conditions b...

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第三十九届中国控制会议

作者： Ke Zhang Weihua Cao Chao Gan Wenkai Hu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

The characteristics of the normal and stick-slip vibration signals reflect the drilling conditions, which is significant in recognizing them. In this paper, a new method that combines the Empirical Mode Decomposition(EMD) threshold denoising and Support Vector Machine(SVM) is proposed to classify these characteristics. First, the EMD threshold denoising method is introduced to denoise the raw signals of the drill string vibration. Second, the features of these characteristics are selected by the Intrinsic Mode Function(IMF) energy entropy and marginal spectral energy. Last, the drilling conditions are classified and identified by the Support Vector Machine(SVM). The simulation results show that the identification accuracy of the proposed method is higher than the conventional methods.

关键词： Stick-slip Empirical Mode Decomposition Threshold denoising Intrinsic Mode Function energy entropy Marginal spectral energy Support Vector Machine

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Dissipativity analysis of time-delayed neural networks using a vector-related zero-value-term method and generalized reciprocally convex combination techniques

Dissipativity analysis of time-delayed neural networks using...

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第三十九届中国控制会议

作者： WANG Chen-Rui HE Yong School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

This paper studies the dissipative problem of neural networks with time-varying delay and external disturbance. A suitable augmented Lyapunov-Krasovskii functional(LKF) is constructed by taking full advantage of the delay information of the system and the conditions of the excitation function. Then by employing auxiliary function inequalities, the reciprocally convex combination and a vector zero-value method to deal with the derivative of the LKF, a less conservative delay-dependent dissipative criterion is obtained. Finally, a numerical example is given to show the effectiveness of this criterion.

关键词： Neural networks Time delay Dissipativeness Lyapunov-Krasovskii functional

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Detection of Weak Signal Amplitude of Winding Deformation Based on Duffing Oscillator

Detection of Weak Signal Amplitude of Winding Deformation Ba...

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Chinese control Conference (CCC)

作者： Sheng-Min Li Yuan-Yuan He Yue-Yue Che Xu-Xia Sun Academy of Automation & Information Engineering Xi’an University of Technology Xi’an Key Laboratory of Shaanxi Province for Complex System Control and Intelligent Information Processing Xi’an

In order to solve the problem that it is difficult to detect the weak sinusoidal response signal when the frequency response method is used to detect the transformer winding deformation fault online, this paper proposes a method to detect the amplitude of the response signal by using Duffing oscillator. Secondly, in order to solve the influence of the target signal phase shift on the amplitude detection in the actual detection process, a method of first detecting the signal phase using a vibrator array composed of Duffing vibrators and then detecting the amplitude is proposed. Finally, using this method to process the two sets of measured response signals of online detection, the detection results show that the maximum error between the detected signal amplitude and the standard signal is 5. 69%, which meets the requirements of the power industry standard, which proves that the method used in this article is It is feasible in winding deformation detection.

关键词： Phased arrays Simulation Windings Frequency response Power industry Oscillators Standards

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High Precision Waveform Reproduction of Shaking Table Based on Linear Active Disturbance Rejection control

High Precision Waveform Reproduction of Shaking Table Based ...

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第三十九届中国控制会议

作者： Wang Sheng Lei Qi School of Automation Central South University Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Seismic simulation shaking table plays a key role in many industrial structural dynamic ***-precision waveform repetition has a great impact on the experimental accuracy of structural dynamic *** equipment of seismic simulation shaker table has obvious uncertainties in the modeling process,so it is difficult to describe the seismic simulation shaker table with a more accurate mathematical ***,this paper adopt the Linear Active Disturbance Rejection control（LADRC） to achieve high-precision reproduction of the waveform of the seismic simulation *** method will all the model uncertainty and external disturbance as total disturbance,using the extended state observer（ESO） to estimate the total disturbance,and by using state feedback control law of generalized disturbance compensation,to get better performance of resistance to *** method does not rely on the model of the specimen and does not need to obtain prior knowledge of the *** this paper,the inertial and single-degree-of-freedom elastic specimens are simulated for vibration test,and the control effect of this strategy is fully verified by comparison with the traditional PID control method.

关键词： LADRC ESO seismic simulation shaking table

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Global Automatic Recognition and Localization of Multi Screws Based on Image Mosaic

Global Automatic Recognition and Localization of Multi Screw...

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第三十九届中国控制会议

作者： Hang Yin Yan Yuan Chaolong Yao Xiaoqi Wang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

In the tuning system based on vision sensor, the object recognition and localization is very important. In order to meet the practical tuning of the microwave cavity filter by the mechanical arm, a global recognition and localization method of multiple adjustable screws is proposed, which combines image mosaic and template matching. A high-definition global image of microwave cavity filter is composed of multiple local images according to feature point registration, and then all adjustable screws in the global image are recognized by template matching algorithm based on shape feature, which solves the problem of limited field of vision of a single industrial camera. It realizes the recognition, localization and numbering of all tuning objects in the whole tuning range, and transmits the coordinate information corresponding to the number to the mechanical arm. This method can be used to tune various types of microwave cavity filters, and it has certain versatility. The experimental results show that the recognition effect of the screws is favorable when the above method is applied to the actual tuning of 12-step microwave cavity filter with the hand-eye integrated four-axis mechanical arm. And it meets the requirements of tuning accuracy.

关键词： image mosaic microwave cavity filter image localization template matching

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A Generalized Probabilistic Monitoring Model with Both Randomand Sequential Data

arXiv

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arXiv 2022年

作者： Yu, Wanke Wu, Min Huang, Biao Lu, Chengda The School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China The Department of Chemical and Materials Engineering University of Alberta EdmontonABT6G 2G6 Canada

Many multivariate statistical analysis methods and their corresponding probabilistic counterparts have been adopted to develop process monitoring models in recent decades. However, the insightful connections between them have rarely been studied. In this study, a generalized probabilistic monitoring model (GPMM) is developed with both random and sequential data. Since GPMM can be reduced to various probabilistic linear models under specific restrictions, it is adopted to analyze the connections between different monitoring methods. Using expectation maximization (EM) algorithm, the parameters of GPMM are estimated for both random and sequential cases. Based on the obtained model parameters, statistics are designed for monitoring different aspects of the process system. Besides, the distributions of these statistics are rigorously derived and proved, so that the control limits can be calculated accordingly. After that, contribution analysis methods are presented for identifying faulty variables once the process anomalies are detected. Finally, the equivalence between monitoring models based on classical multivariate methods and their corresponding probabilistic graphic models is further investigated. The conclusions of this study are verified using a numerical example and the Tennessee Eastman (TE) process. Experimental results illustrate that the proposed monitoring statistics are subject to their corresponding distributions, and they are equivalent to statistics in classical deterministic models under specific restrictions. Copyright © 2022, The Authors. All rights reserved.

关键词： Process monitoring

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