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arXiv 2017年

作者： Huan, Liu Haobin, Dong Liu, Zheng Ge, Jian Bai, Bingjie Zhang, Cheng School of Automation China University of Geosciences Hubei Wuhan430074 China School of Engineering University of British Columbia Okanagan Campus KelownaBCV1X 1V7 Canada Institute of Geophysics and Geomatics China University of Geosciences Hubei Wuhan430074 China Hubei Key Laboratory of Advance Control and Intelligent Automation for Complex Systems Hubei Wuhan430074

The parameters in a nuclear magnetic resonance (NMR) free induction decay (FID) signal contain information that is useful in magnetic field measurement, magnetic resonance sounding (MRS) and other related applications. A real time sampled FID signal is well modeled as a finite mixture of exponential sequences plus noise. We propose to use the Hilbert-Huang Transform (HHT) for noise reduction and characterization, where the generalized Hilbert-Huang represents a way to decompose a signal into so-called intrinsic mode function (IMF) along with a trend, and obtain instantaneous frequency data. Moreover, the HHT for an FID signal's feature analysis is applied for the first time. First, acquiring the actual untuned FID signal by a developed prototype of proton magnetometer, and then the empirical mode decomposition (EMD) is performed to decompose the noise and original FID. Finally, the HHT is applied to the obtained IMFs to extract the Hilbert energy spectrum, to indicate the energy distribution of the signal on the frequency axis. By theory analysis and the testing of an actual FID signal, the results show that, compared with general noise reduction methods such as auto correlation and singular value decomposition (SVD), combined with the proposed method can further suppress the interfered signals effectively, and can obtain different components of FID signal, which can use to identify the magnetic anomaly, the existence of groundwater etc. This is a very important property since it can be exploited to separate the FID signal from noise and to estimate exponential sequence parameters of FID signal. Copyright © 2017, The Authors. All rights reserved.

关键词： Singular value decomposition

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Model-Based and Data-Driven control of Event- and Self-Triggered Discrete-Time LTI systems

arXiv

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arXiv 2022年

作者： Wang, Xin Berberich, Julian Sun, Jian Wang, Gang Allgöwer, Frank Chen, Jie The Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing10081 China The Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China The University of Stuttgart Institute for Systems Theory and Automatic Control Stuttgart70550 Germany The Department of Control Science and Engineering Tongji University Shanghai201804 China The State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

The present paper considers the model-based and data-driven control of unknown linear time-invariant discretetime systems under event-triggering and self-triggering transmission schemes. To this end, we begin by presenting a dynamic event-triggering scheme (ETS) based on periodic sampling, and a discrete-time looped-functional approach, through which a model-based stability condition is derived. Combining the model-based condition with a recent data-based system representation, a data-driven stability criterion in the form of linear matrix inequalities (LMIs) is established, which also offers a way of co-designing the ETS matrix and the controller. To further alleviate the sampling burden of ETS due to its continuous/periodic detection, a self-triggering scheme (STS) is developed. Leveraging pre-collected input-state data, an algorithm for predicting the next transmission instant is given, while achieving system stability. Finally, numerical simulations showcase the efficacy of ETS and STS in reducing data transmissions as well as of the proposed co-design methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear matrix inequalities

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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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An Insulator Missing Defect Detection Method Based on Unmanned Aerial Vehicles

An Insulator Missing Defect Detection Method Based on Unmann...

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International Conference on control and automation (ICCA)

作者： Yulong Zhang Zhongxian Zhou Lingxia Mu Xianghong Xue Jing Xin Youmin Zhang School of Automation and Information Engineering Xi'an University of Technology Xi'an Shaanxi China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China Department of Mechanical Industrial and Aerospace Engineering Concordia University Montreal Quebec Canada

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

In this paper, an insulator missing defect detection method is proposed based on unmanned aerial vehicles to solve the problem of glass insulator burst fault detection in high-voltage transmission lines. Firstly, the proposed method utilizes the improved Mask R-CNN (region-based convolutional neural network) algorithm to segment insulator strings in aerial images. Then, the constructed encoder-decoder network is used to extract and reconstruct features of the insulators, resulting in residual images. Finally, the residual images preserve the location information of the fault and obtains the result of missing insulators. The experiment shows that the proposed algorithm has high segmentation accuracy for insulators and high recognition accuracy for insulator missing faults.

关键词： Image segmentation Accuracy Power transmission lines Fault detection High-voltage techniques Insulators Feature extraction

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Architectural distortion recognition based on a subclass technique and the sparse representation classifier

Architectural distortion recognition based on a subclass tec...

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International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Xiaoming Liu Leilei Zhai Ting Zhu Zhou Yang School of Automation Huazhong University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan China College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9781509037117

Architectural distortion is the third most common sign of breast cancer in mammograms. The accurate recognition is important for computer aided diagnosis of breast cancer. However, due to the subtle symptom and complex structures in the mammogram images, it is difficult to recognize whether a region of interest (ROI) is truly an architectural distortion. In this paper, we proposed a new method for architectural distortion recognition. In the proposed method, several texture features are extracted for each region of interest, including features from GLCM matrix, spiculated related features, entropy features, etc. Feature selection is obtained by a sub-classes clustering based multi-task learning method (SMTL), which can utilize the discriminative label information and reflect the multi-clustering characteristic of the data samples. Finally, the powerful sparse representation based classifier is used for the classification of AD or non-AD. The proposed method has been tested on DDSM dataset and compared with several other methods, the experimental results showed the effectiveness of the proposed method.

关键词： Feature extraction Distortion Entropy Training Mammography Delta-sigma modulation Breast cancer

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A threshold segmentation method for non-uniform illumination image based on brightness equalization

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IOP Conference Series: Materials Science and Engineering 2019年第5期569卷

作者： Hui Guo Peng Chen Shun Huang Cong Hu Lijun Zhang School of Automation China University of Geosciences Wuhan 430074 China Hubei key laboratory of Advanced Control and Intelligent Automation for Complex System Wuhan 430074 China

In the process of image acquisition, non-uniform illumination images are common due to poor lighting, surface reflection, or a combination of these two factors. In order to improve the quality of image segmentation, an image threshold segmentation method is proposed for non-uniform illumination images. First, the brightness of different regions in the image is compensated to make the brightness background of the whole image consistent. Second, the gradation histogram of the processed image is obtained and simulated into Gaussian distribution curve, then the inflection points of the curve are calculated. Finally, the two inflection points are set as thresholds to segment the image after brightness equalization. This method eliminates the influence of non-uniform illumination to a certain extent and gets a better segmentation effect. Experiments were carried out on images containing several different types of non-uniform illumination. The results demonstrate that the proposed method outperforms the compared enhancement algorithms in threshold segmentation.

关键词：

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Image retrieval based on convolutional neural network and linear discriminate analysis

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IOP Conference Series: Materials Science and Engineering 2019年第5期569卷

作者： Wei Xia Cong Hu Xianghui Cheng Peng Chen Xing Jin School of Automation China University of Geosciences Wuhan 430074 China Hubei key laboratory of Advanced Control and Intelligent Automation for Complex System Wuhan 430074 China

Image retrieval is a hot research topic in the field of computer vision image processing, and the user queries the image database for similar images and produces a list of recommendations. The paper firstly sets forth the research status of image retrieval, then the convolution neural network is briefly introduced. Due to the traditional image retrieval and recommendation system use manual extraction of image features is relatively cumbersome, and the retrieval accuracy is not high research status, the paper proposes an image retrieval method based on the improved convolutional neural network and linear discriminate analysis. Caltech256 and CIFAR-10 datasets were trained using the model in this paper, experimental, results show that the proposed method can effectively improve the performance of retrieval.

关键词：

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Open-/Closed-loop Active Learning for Data-driven Predictive control

arXiv

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arXiv 2024年

作者： Feng, Shilun Shi, Dawei Shi, Yang Zheng, Kaikai The State Key Laboratory of Intelligent Control and Decision of Complex Systems MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing Institute of Technology Beijing100081 China The Department of Mechanical Engineering Faculty of Engineering University of Victoria VictoriaBCV8N 3P6 Canada

An important question in data-driven control is how to obtain an informative dataset. In this work, we consider the problem of effective data acquisition of an unknown linear system with bounded disturbance for both open-loop and closed-loop stages. The learning objective is to minimize the volume of the set of admissible systems. First, a performance measure based on historical data and the input sequence is introduced to characterize the upper bound of the volume of the set of admissible systems. On the basis of this performance measure, an open-loop active learning strategy is proposed to minimize the volume by actively designing inputs during the open-loop stage. For the closed-loop stage, an closed-loop active learning strategy is designed to select and learn from informative closed-loop data. The efficiency of the proposed closed-loop active learning strategy is proved by showing that the unselected data cannot benefit the learning performance. Furthermore, an adaptive predictive controller is designed in accordance with the proposed data acquisition approach. The recursive feasibility and the stability of the controller are proved by analyzing the effect of the closed-loop active learning strategy. Finally, numerical examples and comparisons illustrate the effectiveness of the proposed data acquisition strategy. Copyright © 2024, The Authors. All rights reserved.

关键词： Active learning

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Local Path Planning Algorithm for UGV Based on Improved Covariance Matrix Adaptive Evolution Strategy

Local Path Planning Algorithm for UGV Based on Improved Cova...

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第33届中国控制与决策会议

作者： Jiangbo Zhao Jiaquan Zhang Junzheng Wang Xin Zhang Yanlong Wang Key Laboratory of Complex System Intelligent Control and Decision School of AutomationBeijing Institute of Technology Key Laboratory of Servo Motion System Drive and Control Ministry of Industry and Information TechnologySchool of AutomationBeijing Institute of Technology Technical center Xinxing Cathay International Group Co.Ltd. Techenology Center of Special Vehicle Manufacture and Evaluation Engineering of Beijing Beijing North Vehicle Group Co.Ltd.

Local path planning algorithm is one of the key technologies for unmanned ground vehicle(UGV).In order to reduce the computational complexity of the local path planning algorithm and ensure the real-time performance of the algorithm,a non-uniform column grid lines modeling method is introduced,and on this basis,a modeling method for planning paths is *** at the path planning problem in a multi-obstacle environment,the evaluation function of the path is constructed from four aspects,and the covariance matrix adaptive evolution strategy(CMA-ES)is used to solve the nonlinear optimization *** order to further reduce the amount of calculation,the CMA-ES algorithm is improved by the dynamic adjustment strategy of population *** results show that the path planning algorithm can effectively realize the local path planning in complex environment.

关键词： Local path planning CMA-ES Unmanned ground vehicle

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Path Planning and Evaluation for Obstacle Avoidance of Manipulator Based on Improved Artificial Potential Field and Danger Field

Path Planning and Evaluation for Obstacle Avoidance of Manip...

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第33届中国控制与决策会议

作者： Jiangbo Zhao Qiang Zhao Junzheng Wang Xin Zhang Yanlong Wang Key Laboratory of Complex System Intelligent Control and Decision School of AutomationBeijing Institute of Technology Key Laboratory of Servo Motion System Drive and Control Ministry of Industry and Information TechnologySchool of AutomationBeijing Institute of Technology Technical center Xinxing Cathay International Group Co.Ltd. Technology Center of Special Vehicle Manufacture and Evaluation Enginneering of Beijing Beijing North Vehicle Group Co.Ltd.

This paper takes 6-DOF manipulator as the research object,proposes the improved Artificial Potential Field(APF) method to plan the obstacle avoidance path of the manipulator,and combines the Danger Field(DF) method to evaluate the safety of the path planned using *** to the characteristics of the manipulator,the kinematics model of the manipulator is analyzed,and the ball envelope algorithm is applied to simplify the physical model of the *** with the traditional APF method,the improved APF searches in the joint space and introduces the joint attraction potential to improve the search speed and *** problem of local minimum is dealt with by the combination of adding virtual obstacles and increasing joint attraction *** improved APF not only has the advantages of good real-time performance and smooth path,but also solves the problem of the traditional APF falling into a local minimum,and combines the danger field method to judge the path rationality to ensure no collision with *** simulation verification,the proposed method realizes the obstacle avoidance path planning of the manipulator and the safety evaluation of the planned path.

关键词： Artificial potential field Danger field Obstacle avoidance Path planning Collision detection

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