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Chinese Automation Congress (CAC)

作者： Ya Wang Jiaqi Wang Shuai Yuan Naijin Liu Yuanming Lu Qian Xuesen Laboratory of Space Technology China Academy of Space Technology Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Electro-Mechanical Engineering Institute Beijing China Unit 63893 of PLA Luoyang China

In non-cooperative scenarios, wideband frequency hopping (FH) communication reconnaissance including FH signal detection, parameter estimation, and network sorting under single-channel reception is challenging. The FH pattern contains the most information about FH signals. So it is the core of FH parameter estimation. Based on the task analysis, this paper proposes a blind prediction framework combining deep learning (DL) and clustering methods to improve the accuracy and efficiency of FH pattern estimation for mixed signals in the wideband spectrum. The unique advantage of this framework is that it requires no prior signal information and anchors but exploits the inherent time-frequency (TF) properties of asynchronous FH signals. It has a strong generalization ability and can adapt to signals of any shape. Moreover, a new comprehensive evaluation metric, normalized root mean square error including missed detection (NRMSE-MD), suitable for sequence data prediction is proposed to evaluate the level of missed detection and false detection in signal monitoring. Experimental results demonstrate the superiority of the proposed framework and the effectiveness of the proposed evaluation metric.

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An Approach for Upper Limb Movement Intention Recognition Using EEG and sEMG Fusion based on the MCPSA-CIIM

An Approach for Upper Limb Movement Intention Recognition Us...

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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Weiming Zhang Xiaodong Zhang Cheng Xu Guchuan Zhou Teng Zhang Yu Wang Shaanxi Key Laboratory of Intelligent Robot School of Mechanical Engineering Xi’an Jiaotong University Xi’an Shaanxi China Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory Beijing China

The upper limb motion intention recognition method based on electroencephalography (EEG) and surface electromyography (sEMG) fusion has achieved significant results in fields such as prosthetic control. However, most existing fusion methods use manual means to select features, which cannot capture temporal and spatial features at different scales, and ignore the correlation features between the two types of signals. To address these issues, this article proposes a fusion recognition method for upper limb motion intention EEG and sEMG based on Multi-scale Convolution, Polarized Self-Attention, and Cross Intelligence Integration Module. This article extracts multidimensional temporal and spatial features of EEG and sEMG through multi-scale convolution, and introduce polarized self-attention mechanism to filter the extracted multi-scale features. Simultaneously using cross enhancement strategy to extract correlation features between EEG and sEMG. Finally, the features are input into the classification network for recognition. This method was validated on the Jeong database, and the results showed that compared with CNN-LSTM and EEGNet, the recognition accuracy of this method increased by 2.63% and 3.15%, respectively.

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Distributed square-root cubature Kalman filter for underwater cyber-physical systems with multiple fading measurements

Distributed square-root cubature Kalman filter for underwate...

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Cyber-Physical Social Intelligence (ICCSI), International Conference on

作者： Ye Chen Pengpeng Ye Yuan Liang Lin Meng Artificial Intelligence Research Institute of Industrial Technology Nanjing Institute of Technology Nanjing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory BeijingChina School of Automotive and Rail Transit Nanjing Institute of Technology Nanjing China

ISBN: (纸本)9781665426220

In this article, we focus on the problem of the distributed nonlinear state estimation for a class of underwater cyber-physical systems with fading measurements. In this article, we propose a modified square-root cubature Kalman filter to tackle with the nonlinear system model. And the diffusion strategy is introduced in this paper to improve the system's estimation performance. To ease the internode communication cost, we propose an event-triggered mechanism to avoid the unnecessary information transmission. The corresponding esti-mation algorithm is also derived in this article. The simulation results show that the proposed algorithm can estimate the target's state accurately with less communication resources.

关键词： Fading channels Costs Simulation Information processing Cyber-physical systems Filtering algorithms Social intelligence

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Robotic dynamic target recognition and tracking based on the monocular vision

Robotic dynamic target recognition and tracking based on the...

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26th Chinese control Conference, CCC 2007

作者： Lei, Liu Yongji, Wang Huazhong University of Science and Technology Intelligent and Complex System Laboratory Key Laboratory of Image Processing and Intelligent Control Ministry of Education

ISBN: (纸本)7900719229

Gather the information of the environment by the monocular vision. Using the H and S weight of the HSV color model, separate the target from the environment with a certain color, by a fast clustering algorithm for two-value image segmentation. Calculating the distance between the camera and target by the 3D reconstruction algorithm and sub-control strategy, and raise its veracity by laser information fusion. Furthermore, a vision servo system has been designed and utilized to achieve the robot's dynamic track. At last, some experiments were used to certification its availability.

关键词： Robot applications

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Generating infrared image from visible image using Generative Adversarial Networks

Generating infrared image from visible image using Generativ...

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IEEE International Conference on Unmanned systems (ICUS)

作者： Lei An Jiajia Zhao Huijun Di School of Computer Science & Technology Beijing Institute of Technology Beijing China Science and Technology on Complex System Control and Intelligent Agent Coorperation Laboratory Beijing Electro-Mechanical Engineering Institute Beijing China

ISBN: (纸本)9781728137933

Infrared images have properties that are unaffected by illumination compared to visible images, object can be clearly recognized at day or night. Therefore, it is a better choice to use infrared images when training deep learning algorithms in unmanned systems. However, how to obtain a large number of infrared images makes it possible to train deep learning algorithm, which is the focus of our attention. In contrast to difficulties in direct shooting, computer simulations can generate large infrared images from visible images. But models that are trained on simulated data usually do not translate well to real scenarios. To bridge the domain gap between simulated and real infrared images, we exploit recent advances in paired image-to-image translation. We extend Pixel2Pixel to generate infrared image from visible image and propose a novel way of multilayer semantic information fusion, which significantly improves the quality of generated images. Finally, the image generated by our proposed PixelPro network is almost consistent with the real image distribution, achieved the purpose of expanding the dataset.

关键词： Image translation Infrared image Generative adversarial networks Infrared imaging Visible Imagery images Infrared radiation Information fusion

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Regularized densely-connected pyramid network for salient instance segmentation

arXiv

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arXiv 2020年

作者： Wu, Yu-Huan Liu, Yun Zhang, Le Gao, Wang Cheng, Ming-Ming TKLNDST College of Computer Science Nankai University Tianjin300350 China ETH Zurich Switzerland School of Information and Communication Engineering University of Electronic Science and Technology of China China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

Much of the recent efforts on salient object detection (SOD) have been devoted to producing accurate saliency maps without being aware of their instance labels. To this end, we propose a new pipeline for end-to-end salient instance segmentation (SIS) that predicts a class-agnostic mask for each detected salient instance. To better use the rich feature hierarchies in deep networks and enhance the side predictions, we propose the regularized dense connections, which attentively promote informative features and suppress non-informative ones from all feature pyramids. A novel multi-level RoIAlign based decoder is introduced to adaptively aggregate multi-level features for better mask predictions. Such strategies can be well-encapsulated into the Mask R-CNN pipeline. Extensive experiments on popular benchmarks demonstrate that our design significantly outperforms existing state-of-the-art competitors by 6.3% (58.6% vs. 52.3%) in terms of the AP metric. The code is available at https://***/yuhuan-wu/RDPNet. © 2020, CC BY-NC-ND.

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Regional Geomagnetic Map Construction based on Support Vector Machine Residual Kriging

Regional Geomagnetic Map Construction based on Support Vecto...

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第三十九届中国控制会议

作者： Tong Liu Xingyu Li Mengyin Fu Zhaoxiang Liang Beijing Institute of Technology Key Laboratory of Complex System Intelligent Control and Decision Nanjing University of Science and Technology

Regional geomagnetic maps are widely used in geomagnetic navigation and magnetic anomaly detection. However,the complexity of geomagnetic spatial trend changes and the spatial sparseness of the geomagnetic data affect the accuracy of regional geomagnetic map construction. In order to improve the accuracy of regional geomagnetic maps, this paper proposes the Support Vector Machine Residual Kriging method（SVMRKriging）. First, Support Vector Machine（SVM） is used to fit the geomagnetic trend changes, then the residual component is interpolated by ordinary Kriging, and finally these two parts are added to construct a regional geomagnetic map. Experiments were performed using geomagnetic grid data and aeromagnetic data. The experiment results show that SVMRKriging method can improve the accuracy of regional geomagnetic maps with geomagnetic trend changes.

关键词： Regional Geomagnetic Map Kriging Support Vector Machine

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Aeromagnetic Compensation Based on Tikhonov Regularization with Limited L-curve Parameter-choice Algorithm

Aeromagnetic Compensation Based on Tikhonov Regularization w...

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第37届中国控制会议

作者： FU Mengyin LI Jie WU Tailin LIU Tong WANG Meiling WANG Kai KANG Jiapeng Beijing Institute of Technology Key Laboratory of Complex System Intelligent Control and Decision Nanjing University of Science and Technology

Magnetic anomaly detection has great application value to national economic development and national defense construction, while the presence of various interference magnetic fields on the aircraft will affect the detection of magnetic anomaly. Therefore, it is very important to compensate the aircraft interference magnetic field. In order to solve the problem that the accuracy of the parameters is poor by using ridge estimation in the process of solving the parameters of aircraft interference magnetic field model, we propose a method based on Tikhonov Regularization（TR）. Firstly, the optimal regularization parameter is obtained by the limited L-curve method in our work. Secondly, the aircraft interference magnetic field model parameters are obtained by using the TR method. Finally, we use the model parameters to calculate the aircraft interference magnetic field in the real flight, and complete the compensation. The effectiveness of this method is validated by simulation experiments. The results show that the method proposed in this paper has the advantages of high precision and convenient application, so it can be used to compensate the aircraft interference magnetic field effectively.

关键词： Aircraft Interference Magnetic Field Model Tikhonov Regularization L-curve Method Ridge Estimation

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The design of a displaying system for battlefield situation based on ArcGIS Engine

The design of a displaying system for battlefield situation ...

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Chinese Automation Congress (CAC)

作者： Jiaqi Wang Cheng Xu Hua Ji Science and Technology on Complex System Control and Ietelligent Agent Coopration Laboratory Beijing Beijing Electro-Mechanical Engineering Institute Beijing China

ISBN: (数字)9781665426473

ISBN: (纸本)9781665426480

To facilitate the precise analysis and judgment of battlefield situation for military stuffs inmodern warfare with abundant information, a modularized two-dimension battlefield situation displaysystem is designed based on ArcGIS Engine in this paper. The functions of each module are analyzed, andkey issues of the design are introduced. The real-time dynamic display of moving objects in the battlefield isachieved by reading data from the battlefield.

关键词： Computer languages Automation Real-time systems Software C# languages Engines

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Simulation and analysis of far-field single-photon response model in Geiger avalanche mode 7

Simulation and analysis of far-field single-photon response ...

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7th Global intelligent Industry Conference, GIIC 2024

作者： Ou, Hengchang Ma, Le Jiang, Peng Wang, Pengfei Lu, Jie Zhou, Xin Sun, Jianfeng National Key Laboratory of Laser Spatial Information Institute of Opto-Electronic Harbin Institute of Technology Heilongjiang Harbin150001 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China Beijing Institute of Remote Sensing Beijing100854 China 44th Research Institute China Electronics Technology Group Corporation Chongqing400060 China Research Center for Space Optical Engineering Harbin Institute of Technology Heilongjiang Harbin150001 China Zhengzhou Research Institute of Harbin Institute of Technology Zhengzhou Henan450046 China

ISBN: (纸本)9781510682986

Poisson distribution model is the basis of data analysis for GM-APD lidar, but it is only applicable to the mirror reflected target under ideal conditions, and cannot accurately describe the photon triggering process of actual GM-APD lidar detection. For the actual target with rough surface, the negative binomial distribution with M as the parameter can describe the photon distribution model more accurately. In order to solve this problem, this paper compares and analyzes Poisson distribution triggering model and the negative binomial distribution triggering model that conforms to the triggering situation of the echo signal of the target with rough surface, considering the differences in the triggering probability under different noise and signal levels. The results show that the trigger probability curve corresponding to the trigger model based on negative binomial distribution is lower in peak value, wider in bottom value and fatter overall than that of the trigger model based on Poisson distribution, and the difference between the two is more prominent under the conditions of low noise level and high signal level. This study has guiding significance for the signal extraction research based on different surface echoes. © 2024 SPIE.

关键词： Photons

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