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2019 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2019

作者： Liu, Yiwen Shang, Xiuqin Shen, Zhen Hu, Bin Wang, Zhihai Xiong, Gang State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing University of Technology Faculty of Information Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of Sciences Beijing China Beijing University of Technology Faculty of Information Beijing China

ISBN: (纸本)9781728128535

Recently, with the development of both 3D sensors and 3D virtual network that bring the needs of interaction with the real world, many 3D applications burst out. However, it is difficult to understanding these three-dimensional scenes with a fixed program. Then, a data-driven method is required to process these 3D data, which brings a strong demand of 3D Deep Learning in 3D data. Towards this goal, with an end-to-end deep learning, the experiment is based on PointNet++, a well proposed method for feature extraction. The experiment optimizes the network structure and parameters to improve the classification results. Finally, the network is applied to tooth model for classification and identification so that the dental model can be found from different perspectives. © 2019 IEEE.

关键词： 3D printers

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Design of a low-cost miniature robot to assist the COVID-19 nasopharyngeal swab sampling

arXiv

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arXiv 2020年

作者： Wang, Shuangyi Wang, Kehao Liu, Hongbin Hou, Zengguang The State Key Laboratory of Management and Control of Complex Systems Chinese Academy of Sciences Institute of Automation China Beijing Advanced Innovation Center for Biomedical Engineering Beihang University China The School of Biomedical Engineering and Imaging Sciences King's College London United Kingdom

Nasopharyngeal (NP) swab sampling is an effective approach for the diagnosis of coronavirus disease 2019 (COVID-19). Medical staffs carrying out the task of collecting NP specimens are in close contact with the suspected patient, thereby posing a high risk of cross-infection. We propose a low-cost miniature robot that can be easily assembled and remotely controlled. The system includes an active end-effector, a passive positioning arm, and a detachable swab gripper with integrated force sensing capability. The cost of the materials for building this robot is 55 USD and the total weight of the functional part is 0.23kg. The design of the force sensing swab gripper was justified using Finite Element (FE) modeling and the performances of the robot were validated with a simulation phantom and three pig noses. FE analysis indicated a 0.5mm magnitude displacement of the gripper's sensing beam, which meets the ideal detecting range of the optoelectronic sensor. Studies on both the phantom and the pig nose demonstrated the successful operation of the robot during the collection task. The average forces were found to be 0.35N and 0.85N, respectively. It is concluded that the proposed robot is promising and could be further developed to be used in vivo. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

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Adaboost-kNN for dynamic emotion recognition 8

Adaboost-kNN for dynamic emotion recognition

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8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and control Applications, ISCIIA and ITCA 2018

作者： Li, Min Su, Wanjuan Chen, Luefeng Wu, Min Hirota, Kaoru School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Tokyo Institute of Technology Yokohama226-8502 Japan

— K-nearest Neighbor based adaptive boosting (AdaBoost-KNN) is proposed for emotion understanding in human-robot interaction (HRI), where the real-time dynamic emotion is recognized according to facial expression. It can make robots capable of understanding human emotions and make HRI run smoothly. The Candide-3 model was adopted to extract the two-dimensional coordinate values of the key points of facial emotion, as the basis for emotion classification. The emotion classification is based on AdaBoost-KNN, which sets up seven basic KNN classifier. Then it iterates seven times to update the weight of samples, and combines the weak classifier with coefficient to establish the final classifier. Finally, the performance of the proposal is verified by using K-fold cross validation. Results show that the proposal achieves higher accuracy than that of a single KNN. © ISCIIA and ITCA 2018 - 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and control Application.

关键词： Adaptive boosting

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Suppression of Disturbances in Networked control systems with Time-Varying Delay Based on Equivalent-Input-Disturbance Approach

Suppression of Disturbances in Networked Control Systems wit...

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Annual Conference of the IEEE Industrial Electronics Society

作者： Meiliu Li Jinhua She Zhentao Liu Chuanke Zhang Min Wu Yasuhiro Ohyama Xinkai Chen Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan College of Systems Engineering and Sciences Shibaura Institute of Technology Saitama-shi Saitama Japan

A plant is controlled remotely on a network for a networked control system. Disturbances from the network and surroundings may deteriorate the control performance of such a system. To solve this problem, this paper presents a new method of suppressing an exogenous disturbance for those control systems. The control system has a state observer to estimate the state of the plant and an equivalent-input-disturbance (EID) estimator to produce an estimate of the disturbance on the control input channel in a real-time fashion. The system is divided into two subsystems for the analysis of system stability. A stability condition of the control system with a time-varying delay is presented in terms of a linear matrix inequality. Simulations demonstrate the validity of the method. And a comparative study shows the superior of our method to a conventional Smith-EID control method.

关键词： Stability analysis Observers Networked control systems Delay effects Delays Time-varying systems

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Load Curve Modeling Based on Behavior Analysis of Distributed Power Customers

Load Curve Modeling Based on Behavior Analysis of Distribute...

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2019 4th Asia Conference on Power and Electrical Engineering (ACPEE 2019)

作者： Xinyang HAN Yu ZHANG Yuqiao JIAO Yue ZHANG Yanjing QU Min DING State Grid Energy Research Institute School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System State Grid Shanghai Municipal Electric Power Company

As renewable power generation directly affects the customers' traditional electricity behavior and then offsets the power load,this paper proposes a load curve modeling method for renewable power customers based on the behavior ***,customers' active behavior is represented by the quantity of active customer *** on the analysis of customer behaviors,a modeling method for the quantity of active customer households is proposed based on Markov Chain Monte Carlo ***,with the inputs as the quantity of active customer households and time of photovoltaic power generation,an inference model based on fuzzy logic is proposed to get the quantity of customer household starting electrical *** combing the average usage time of electrical appliances,load characteristics are analyzed based on usage state of electrical appliance of distributed power ***,the simulation results verify the effectiveness of the proposed method.

关键词： Carlo Monte Markov Load Curve Modeling Based on Behavior Analysis of Distributed Power Customers

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Identification of Protein complex Based on a Novel Multi-Objective Method 37

Identification of Protein Complex Based on a Novel Multi-Obj...

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37th Chinese control Conference, CCC 2018

作者： Yuan, Zhu Xiaoyu, Peng Chong, Wu School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System China University of Geosciences Wuhan430074 China

ISBN: (纸本)9789881563941

Systematic identification of protein complexes is an important application of data mining in bioscience. The existing computation methods of detecting protein complexes are usually based on the topological properties of protein-protein interaction(PPI) network data. However, limited by the inherent single structure of the PPI network, the mining of protein complexes may not be fully resolved. In this paper, we propose an original multi-objective optimization strategy based protein complex detection method, which is used Adaptive Multi-Objective Black Hole algorithm, called AMOBH. In the selection of objective function strategy, we integrate the topological structure of PPI network data with semantic similarity based on GO (Gene Ontology) annotation data. Experiments demonstrate that the method we adopt provide more convinced results and higher computational efficiency compared with the existing prediction measures. © 2018 Technical Committee on control Theory, Chinese Association of automation.

关键词： Gene Ontology

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Spherical interpolated convolutional network with distance-feature density for 3D semantic segmentation of point clouds

arXiv

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arXiv 2020年

作者： Wang, Guangming Yang, Yehui Zhang, Huixin Liu, Zhe Wang, Hesheng Department of Automation Insititue of Medical Robotics Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China Department of Computer Science and Technology University of Cambridge United Kingdom

The semantic segmentation of point clouds is an important part of the environment perception for robots. However, it is difficult to directly adopt the traditional 3D convolution kernel to extract features from raw 3D point clouds because of the unstructured property of point clouds. In this paper, a spherical interpolated convolution operator is proposed to replace the traditional grid-shaped 3D convolution operator. This newly proposed feature extraction operator improves the accuracy of the network and reduces the parameters of the network. In addition, this paper analyzes the defect of point cloud interpolation methods based on the distance as the interpolation weight and proposes the self-learned distance-feature density by combining the distance and the feature correlation. The proposed method makes the feature extraction of spherical interpolated convolution network more rational and effective. The effectiveness of the proposed network is demonstrated on the 3D semantic segmentation task of point clouds. Experiments show that the proposed method achieves good performance on the ScanNet dataset and Paris-Lille-3D dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Fog Removal Method of Slope Monitoring Image Based On Vision Detection

Fog Removal Method of Slope Monitoring Image Based On Vision...

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Chinese control Conference

作者： Xianzhen Sang Yue Yang Xianhe Hou Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan

In the slope monitoring based on image detection, the main work is to process the acquired slope image. The landslide occurred mostly in the rain, fog and other complex weather conditions. If we can process effectively and fast fog images according to the fog horizon slope vision images. It would be helpful for subsequent image segmentation, object extraction, positioning, and improving the accuracy and efficiency of detection of slope deformation. Based on the visual technology in slope monitoring, we compared two kinds of defogging algorithm of slope images. One is a kind of image enhancement method of non-physical model, mainly including: equalization algorithm and homomorphic filtering algorithm, McCann Retinex algorithm and multi-scale Retinex algorithm and a global histogram. The other is the image restoration method based on physical model, including the dark channel prior bilateral filtering algorithm, and combined with the theory of dark channel prior to fog algorithm. The experimental results show that the histogram equalization method has the advantage of fast imaging quality in slope visual image processing, and is more suitable for slope monitoring.

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An optimization method for coupling matrix elements of microwave filter

An optimization method for coupling matrix elements of micro...

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Chinese control Conference

作者： Xiao-Long Zhuang Wei-Hua Cao Yan Yuan Can Liu Yu-Sheng Guo China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

This paper proposes a coupling matrix element optimization method for microwave filters. The traditional method is more complex and does not directly optimize the filter coupling matrix elements. The firefly algorithm optimization method used in this paper directly optimizes the elements of the N + 2 order coupling matrix. Compared with the traditional method, it has better speed and optimization effect. First, the elements of the specific coupling matrix are introduced into the optimization algorithm to be executed, then the matrix is iterated through the set objective function. Finally, when the optimized data is within the allowable range, the optimization of the elements of the coupling matrix is stopped and optimization is performed. The resulting coupling matrix outputs the response. To prove the effectiveness of the proposed method, three methods were used to optimize the coupling matrix elements of the fourth-order filter and compare the final optimization results.

关键词： Couplings Tuning Transmission line matrix methods Integrated circuit modeling Optimization methods Cats

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Hyperspectral Remote Sensing Image Classification Based on Convolutional Neural Network

Hyperspectral Remote Sensing Image Classification Based on C...

引用

Chinese control Conference

作者： Xiangyang Dai Wei Xue Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan

Remote sensing hyperspectral imaging can obtain rich spectral information of terrestrial objects, which allows the indistinguishable matter in the traditional wideband remote sensing to be distinguished in hyperspectral remote sensing. Hyperspectral image has the characteristics of "combining image with spectrum". Making full use of spectral information and spatial information in hyperspectral image is the premise of obtaining accurate classification results. At present, most of hyperspectral data feature extraction algorithms mainly utilize local spatial information in the same channel and spectral information in the same spatial location of different channels. However, these methods require a large amount of prior knowledge, it is difficult to fully grasp the hyperspectral data of all spatial and spectral information, and the model generalization ability is poor. With the development of deep learning, convolutional neural network shows superior performance in all kinds of visual tasks, especially in the two-dimensional image classification, and could get a high classification accuracy. In this paper, an image classification method based on three-dimensional convolution neural network is proposed based on the structural properties of hyperspectral data. In the proposed method, first the stereo image blocks of hyperspectral data are intercepted, then multi-layer convolution and pooling operation of extracted blocks by convolutional neural network are implemented to obtain the essential information of hyperspectral data, finally the classification of hyperspectral data is completed. The experimental results show the proposed method could provide better feature expression and classification accuracy for hyperspectral image.

关键词： Hyperspectral imaging Image classification Convolution Feature extraction Support vector machines

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