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Design of Surface Magneto-Optical Kerr Effect Automatic Measurement System Based on LabVIEW

Design of Surface Magneto-Optical Kerr Effect Automatic Meas...

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第三十八届中国控制会议

作者： Laizhe Zhu Kaifeng Dong Fang Jin Junlei Song Wenqin Mo School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

It is of great significance for accurate and fast magnetic measurement of high magneto-crystalline anisotropy film materials. In this paper, an surface magneto-optical Kerr effect automatic measurement system based on LabVIEW is designed according to the practical requirements of hysteresis loop measurement of magnetic films. The system takes the SR830 lock-in amplifier as core, combined with virtual instrument technology and correlative detection technology, which achieves the effective measurement of the weak Kerr signal of Ni Fe soft magnetic film material under strong noise environment, and automatically draws its hysteresis loop. The measurement system has high resolution, high output signal-to-noise ratio, high automation, and has good application value.

关键词： SMOKE hysteresis loop virtual instrument LabVIEW lock-in amplifier

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Identification of Protein complex Based on Multi-label Learning Algorithm

Identification of Protein Complex Based on Multi-label Learn...

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第三十八届中国控制会议

作者： Zhu Yuan Liu Song School of Automation China University of Geoscienc Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Protein complexes are key molecular entities that play an integral role in human life activities. Systematic identification of protein complexes is an important application of data mining in the biological sciences. Existing multi-label learning algorithms can effectively label nodes belonging to different complexes in protein-protein interaction network to identify overlapping complexes. However, the protein complexes formed by the stochastic strategy may have unstable results and insufficient community quality. To solve these problems, this paper proposes a novel protein complex identification method based on multisource fused data and the multi-label learning algorithm. The descending order of the potential influence of the nodes is used as the node selection order to solve the problem of unstable partitioning of the composite results. The comprehensive similarity obtained by the link correlation and the similarity of the gene annotations is used as the node label update strategy to improve the quality of the composite. The experimental results show that the new proposed method is much more effective and feasible,and has higher precision and biological significance.

关键词： Protein-protein interaction network protein complex label propagation link coherence gene annotation

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Hidden-Markov-model-based finite-time fault detection for networked singularly perturbed systems

Hidden-Markov-model-based finite-time fault detection for ne...

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第三十八届中国控制会议

作者： Jiankang Li Xiongbo Wan School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

In this paper, the problem of finite-time fault detection(FD) is investigated for networked discrete-time singularly perturbed systems under the stochastic communication protocol(SCP). To alleviate the undesired data collisions caused by largescale data transmissions via the bandwidth-limited communication network, the SCP modeled by a Markov chain is *** to practical factors such as observation error, the Markov modes of the SCP are assumed to be available to the fault detection filter(FDF) according to a hidden Markov process. Our aim is to design a hidden-Markov-model-based FDF as the residual generator such that the resulting augmented system is stochastically H∞ finite-time bounded. A sufficient condition in terms of linear matrix inequalities(LMIs) is derived which ensures the existence of such an FDF. The parameters of the desired FDF are explicitly given when there exist feasible solutions to these LMIs. Finally, the effectiveness of the FD method is demonstrated by a numerical example.

关键词： Finite-time fault detection Singularly perturbed systems Hidden Markov model Stochastic communication protocol

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An improved synchronization criterion for chaotic Lur'e systems with sampled-data control

An improved synchronization criterion for chaotic Lur'e syst...

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第三十八届中国控制会议

作者： Lei-Chao Pang Xing-Chen Shang-Guan Chuan-Ke Zhang Yong He School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

This paper is concerned with the asymptotical synchronization of two identical chaotic Lur'e systems using sampleddata control. Firstly, by defining the synchronization error, the synchronization of original systems is converted into the stabilization design of the error system. Then, an augmented Lyapunov-Krasovskii functional with new terms is constructed and its derivative is estimated by using Wirtinger-based integral inequality and the reciprocally convex matrix inequality. As a result,a less conservative synchronization criterion is established to design the sampled-data scheme. Finally, the advantage of the proposed method is demonstrated by a numerical example.

关键词： Synchronization Chaotic Lur'e systems Sampled-data control Lyapunov-Krasovskii functional

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Target tracking algorithm based on convolutional neural network and particle filtering

Target tracking algorithm based on convolutional neural netw...

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第三十八届中国控制会议

作者： Lijun Zhang Peng Chen Hui Guo Shun Huang Wei Xia Cong Hu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Aiming at the phenomenon that the target particle tracking algorithm is affected by illumination changes and occlusion in the target tracking process, the goal is lost. A target tracking algorithm based on convolutional neural network and particle filtering is proposed. The algorithm uses the convolutional neural network to automatically learn the depth features of the target,and extracts the more abstract semantic information of the target. The semantic information makes the algorithm robust to the apparent changes of the target, which can alleviate the drift problem to some extent. The algorithm can effectively combine the target apparent model based on convolutional neural network with the particle filter framework. The experimental results show that compared with the other five algorithms in the particle filter framework, the algorithm can track the moving targets with partial occlusion and morphological changes more robustly.

关键词： Convolutional Neural Network Particle Filter Target Tracking

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Graph Optimization based Visual SLAM fusing keyPoints and Markers

Graph Optimization based Visual SLAM fusing KeyPoints and Ma...

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第三十九届中国控制会议

作者： Zhen Chen Yang Zhou Fengdi Zhang Min Xu Xiangdong Liu Zhen Li School of Automation and Key Laboratory for Intelligent Control & Decision on Complex Systems Beijing Institute of Technology Beijing Aerospace Automatic Control Institute National Key Laboratory of Science and Technology on Aerospace Intelligence Control

Simultaneous localization and mapping（SLAM） plays an important role in autonomous navigation for mobile *** of the visual SLAM approaches use keypoints for tracking, whose performance however suffers from the unstable landmarks during task due to uncertain light condition and frequently changeable viewpoint. The situation even becomes worse for visual SLAM in low texture environment especially in indoor buildings, where the supplementary artificial markers can be used to improve the robust detection under a wider range of circumstances. Inspired by this thought, this paper developed a visual SLAM system integrated with keypoints and artificial markers. A graph optimization problem is constructed to optimize the trajectory by taking both the reprojection error of keypoints and the influence of markers into consideration. The experimental results on SPM dataset demonstrate the superior accuracy of the proposed graph optimization algorithm compared with the start-of-the-art ORB-SLAM2.

关键词： Visual simultaneous localization and mapping（SLAM） graph optimization keypoints artificial markers

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Dynamic event-triggered communication based fault detection for networked singularly perturbed systems

Dynamic event-triggered communication based fault detection ...

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第三十八届中国控制会议

作者： Tizhuang Han Xiongbo Wan Jianqi An School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

This paper investigates the problem of fault detection(FD) for networked singularly perturbed systems under the dynamic event-triggered communication. To save the network resources, a dynamic event-triggered communication mechanism(DETCM) is proposed, which contains some existing triggering mechanisms as special cases. Our aim is to design a fault detection filter(FDF) under such a DETCM, which ensures that the resulting filtering error dynamics of FD is asymptotically stable and satisfies an H∞ performance requirement. By constructing a Lyapunov function dependent on both the singular perturbation parameter(SPP) and flexible variable of the DETCM, a sufficient condition is obtained in terms of linear matrix inequalities(LMIs), which ensures the existence of the desired FDF. When these LMIs have feasible solutions, the parameters of the FDF are explicitly given and the admissible upper and lower bounds of the SPP are evaluated. A numerical example is provided to demonstrate the effectiveness of the design method of the FDF.

关键词： Fault detection Dynamic event-triggered communication Singularly perturbed systems Networked systems

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Modeling and Optimization of the Activated Sludge Process

Modeling and Optimization of the Activated Sludge Process

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第三十八届中国控制会议

作者： Shun Huang Lijun Zhang Hui Guo Peng Chen Wei Xia Cong Hu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

In the process of urban sewage treating, reducing the energy consumption and improving the quality of the effluent are significantly meaningful. According to the activated sludge method, the key factors affecting the energy consumption and water quality of wastewater treatment are determined. In order to minimize the energy consumption of the activated sludge process and maximize the quality of the effluent, four different objective functions are modeled [i.e., the airflow rate, the carbonaceous biochemical oxygen demand(CBOD) of the effluent, the total phosphorus(TP) of the effluent, and the ammonia nitrogen of the effluent(NH4-N)]. These models are developed using a back propagation(BP) neural network based on industrial data, and dissolved oxygen(DO) is the controlled variable. A multi-objective model was evaluated by six evaluation indicators. Based on the analysis of the model and the mechanism of activated sludge process, the multi-objective particle swarm optimization(MOPSO)algorithm was used to optimize the energy consumption and water quality of the activated sludge process. The experimental results show that eventually reduce aeration energy consumption by 17%.

关键词： Activated sludge Wastewater treatment Energy consumption Back propagation neural network Multi-objective particle swarm optimization

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MatchMesh: Knowledge-based 3D Point Cloud Meshing Using Divide-and-conquer Deformation

MatchMesh: Knowledge-based 3D Point Cloud Meshing Using Divi...

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2020 IEEE International Conference on Networking, Sensing and control, ICNSC 2020

作者： Tang, Ying Sun, Shengtao Wu, Ben Department of Electrical and Computer Engineering Rowan University GlassboroNJ08028 United States State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Institute of Smart Education Qingdao Academy of Intelligent Industries Qingdao China

ISBN: (纸本)9781728168531

The reconstruction of surface mesh from point cloud is compute-intensive but also very important step in the remanufacturing and personalization industries. With more 3D scanners providing lower cost and higher resolution, further detailed point clouds can be gathered without so much effort as before. In manufacturing, there are databases which contain the origin 3D design models of the products. How to utilize the design model data for swift production of related products remains a problem for remanufacturing and customization. In order to develop a knowledge-based way of handling this problem, editing or deforming an existing mesh to match the target is an effective way of easing the workload. In this paper, we introduce a divide-and -conquer process which segments the depth scan data and then find the best match in the database as its source of deformation. The segmentation is performed on 3D point level using global features extracted by 3D CNN. After that we find best match to our knowledge with the same features to acquire a fast meshing of the target object by deforming the existing parts from the match. The deformation of parts are being done sequentially. For further performance improvement, we present a deformation training method employing transfer learning on segment editing process. © 2020 IEEE.

关键词： Image reconstruction

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Data Augment Using Deep Convolutional Generative Adversarial Networks for Transient Stability Assessment of Power systems

Data Augment Using Deep Convolutional Generative Adversarial...

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第三十九届中国控制会议

作者： Jiamin Li Hongying Yang Liping Yan Zonghan Li Daowei Liu Yuanqing Xia Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology China Electric Power Research Institute

Real–time and accurate transient stability assessment(TSA) is essential for planning, operation and control of power systems. As a data–driven technology, deep learning method plays an important role in TSA. Nevertheless, the fact that instability situations rarely occur would lead to a challenging class–imbalanced issue, which brings great difficulties to the deep learning methods. Besides, feature extraction from high dimensional input data and transient stability classification seem extremely difficult for conventional classification methods. To address these problems, this paper develops a class–imbalanced TSA method by combining nonlinear data synthesis method with the deep learning classification model. Firstly, deep convolutional generative adversarial network(DCGAN) is conducted to generate unstable instances based on the existing samples to balance the proportion of different classes. Furthermore, the convolutional neural network(CNN) is utilized to extract the nonlinear mapping relationship between the disturbance features and the stability category and realize TSA. Finally, the IEEE 10–machine, 39–bus New England system is utilized to verify the validity and effectiveness of the proposed method.

关键词： Class–imbalanced Data Generation DCGAN CNN TSA Power systems

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