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IEEE International Conference on control and Robotics Engineering (ICCRE)

作者： Baoshuang Ge Hai Zhang Wenxing Fu Jieling Chen School of Automation Science and Electrical Engineering Beihang University Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China China Ship Development and Design Center Wuhan China

ISBN: (数字)9781728167916

ISBN: (纸本)9781728167923

The low-cost inertial navigation system/global navigation satellite system (INS/GNSS) integration has been widely used to vehicle navigation. However, the positioning performance often declines due to the weak satellite signals. This paper presents a new approach to improve the accuracy of the vehicle navigation. In contrast to the current methods, a new velocity constraint model is developed to aid the estimation of the navigation errors. The altitude accuracy of GNSS is challenged in comparison with its position in the horizonal plane. Thus, an auxiliary low-cost barometer is used to obtain the height as well. The experimental results demonstrate the benefit of the proposed multi-constraints aiding algorithm.

关键词： Global navigation satellite system Global Positioning system Kalman filters Receivers Sea measurements

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Flight Parameter Prediction and Fault Propagation based on Machine Learning and Symbolic Directed Graph

Flight Parameter Prediction and Fault Propagation based on M...

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International Symposium on Autonomous systems (ISAS)

作者： Wenzhuo Li Kun Guo Yuehua Cheng Hengsong Hu Cheng Xu Ziquan Yu College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Institute of Mechanical and Electrical Engineering Beijing China

Fault influence and propagation analysis of flight vehicle systems is an important element of flight vehicle health management and also an important problem to be solved. A fault influence model based on data-driven is established, including a prediction model of flight parameters under fault, a dynamic influence path, and an influence degree model. Based on the historical experimental data, a long and short-term memory neural network (LSTM) model is proposed to predict the time-series data of each flight parameter of the flight vehicle under fault; based on the prediction results, a symbolic directed graph (SDG) is used to describe the fault of the flight vehicle system, and then introduce the concept of a compatible path with time-series characteristics to describe the dynamic propagation process of the fault. The case shows that the method proposed in this paper enables qualitative and quantitative analysis of the fault influence, and can reasonably describe the fault propagation path and influence characteristics.

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Distance-based separability criterion of ROI in classification of farmland hyper-spectral images

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International Journal of Agricultural and Biological Engineering 2017年第5期10卷 177-185页

作者： Tang Jinglei Miao Ronghui Zhang Zhiyong Xin Jing Wang Dong College of Information Engineering Northwest A&F UniversityYangling 712100ShaanxiChina Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of TechnologyXi’an 710048China College of Information Science and Engineering Shanxi Agricultural UniversityTaigu 030801ShanxiChina

The hyper-spectral image contains spectral and spatial information,which increases the ability and precision of objects *** the classification value of hyper-spectral imaging technology within various applications,users often find it difficult to effectively apply in practice because of the effect of light,temperature and wind in outdoor *** research presented a new classification model for outdoor farmland objects based on near-infrared(NIR)hyper-spectral *** involves two steps including region of interest(ROI)acquisition and establishment of classifiers.A distance-based method for quantitative analysis was proposed to optimize the reference pixels in ROI acquisition *** maximum likelihood(ML)and support vector machine(SVM)were used for farmland objects *** performance of the proposed method showed that the total classification accuracy based on the reference pixels was over 97.5%,of which the SVM-M model could reach 99.5%.The research provided an effective method for outdoor farmland image classification.

关键词： distance-based separability criterion near-infrared hyper-spectral image ROI farmland image classification

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Group Behaviour control Based on Aggregation and Dilation

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IFAC Proceedings Volumes 2008年第2期41卷 6072-6077页

作者： Bin Xin Jie Chen Li-Hua Dou Zhi-Hong Peng Department of Automatic Control School of Information Science and Technology Beijing Institute of Technology Beijing 100081 Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology Ministry of Education China Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology Ministry of Education China

The phenomenon of aggregation and dilation(A&D) widely exists in nature. The mechanism behind it is regarded as the effect of some kind of attraction and repulsion(A&R). A&R control becomes a popular and promising way of controlling the structure and distribution of a group composed of several and even numerous individuals. This paper presents the concepts of aggregation, dilation and group evolution criticality based on group variance. We investigate the relationship between different levels of A&D as a foundation for the introduction of A&D analysis. The applications of A&D analysis and A&R control in several researches, including population-based optimization and group behavior control about multi-agent, are given in the form of simulation experiments.

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Identity Re recognition technology Based on Improved Lightweight ConvNetx Model

Identity Re recognition Technology Based on Improved Lightwe...

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Chinese control Conference (CCC)

作者： Peng Siting Li Lei Hongbo Zhu Ren Jun Song Zhichao Jiabing Yang The Beijing Electro-Mechanical Engineering Institute Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Beihang University School of Electronics and Information Engineering Beijing China

In the engineering field, there is a requirement for re recognition, including the identity recognition of cooperative and non-cooperative targets, that is, different sensors shoot targets in the same scene, and the identity recognition of targets from two perspectives is required, also known as target accurate retrieval. This method is widely used in fields such as automatic driving and military strikes. In the actual application scenario, deploying the re-recognition algorithm on the embedded processor needs to ensure the real-time computing speed and accuracy, so the complexity of the deep learning algorithm must be reduced, and pruning and lightweight processing are required. In the engineering field, when the image acquired by the sensor is subject to natural interference such as cloud and fog weather or fire and smoke, the difficulty of re-recognition increases. This paper adopts the lightweight improvement of ConvNeXt network model, and fine-tuned it on the basis of its original feature extraction function, so that it can successfully migrate to infrared image classification and also complete the extraction of infrared image target detail distinguishing features, and at the same time introduce the alignment of similar targets in infrared cross-view recognition. In the original similarity calculation, the average value is directly obtained from the spatial dimension of the output feature layer, which is replaced by the weight of the position in each space for estimation. The feature information of the multi-view marine ship target is extracted from the multi-sensor marine image, and its similarity is calculated, so as to determine the identity, thus reducing the support conditions and reconnaissance costs, and improving the accurate detection and tracking ability under the diverse dynamic reconnaissance conditions.

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Fast Electromagnetic Scattering Modeling of complex Scenes Based on Multi-scale Coherent Computation 21st

Fast Electromagnetic Scattering Modeling of Complex Scenes B...

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21st Asia Simulation Conference, AsiaSim 2022

作者： Ningbo, Gong Guijie, Diao Hui, Chen Hong, Ni Xin, Du Zhe, Liu Beijing Electro-Mechanical Engineering Institute Beijing100074 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China South-East University Nanjing210096 China

ISBN: (纸本)9789811991943

Modeling of scattering characteristics between ground/offing targets and environment is one of the necessary conditions to realize the internal field reconstruction of complex electromagnetic scenes. According to the high-frequency approximation assumption of electromagnetic scattering localization, the coupling scattering electromagnetic modeling method of composite targets and ground/offing scenes is studied in the framework of the bouncing ray method. Based on the curvature adaptive bin separation algorithm and the principle of multi-scale coherent superposition, an integrated scattering modeling method for multi-scale targets and complex environments is developed. The scattering of typical ship target, background and the coupling scattering between ground/offing artificial targets and environment are calculated respectively. Theoretical analysis and simulation result show the correctness and effectiveness of the algorithm. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computational electromagnetics

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Adaptive SOM neural network algorithm 3

Adaptive SOM neural network algorithm

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3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing, AIAHPC 2023

作者： Wang, Xin Yan, Taishan School of Information Science and Engineering Hunan Institute of Science and Technology Hunan Yueyang China Hunan Institute of Science and Technology School of Information Science and Engineering China Key Laboratory of Hunan Province on Intelligent Control and Optimization of Complex Industrial Logistics System Hunan Yueyang China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

In order to improve the performance of traditional SOM model, an adaptive SOM neural network algorithm is proposed. By introducing the error square sum solution formula, the clustering error of the model sub-data set is calculated by using the method of cutting the data set, and the change trend of the clustering error is quickly obtained. Then the learning rate of the SOM network is adaptively adjusted according to this, and the convergence speed is accelerated on the basis of ensuring that the network is fully trained, thereby improving the performance of the SOM network. © 2023 SPIE.

关键词： Errors

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Low Frequency Wide Bandwidth Antenna Design Using the Concept of Lumped Elements Integration

Low Frequency Wide Bandwidth Antenna Design Using the Concep...

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IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Yinfeng Xia Yanyan Wang Yingsong Li Wei Xue Qiubo Ye College of Information and Communication Engineering Harbin Engineering University Harbin China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China School of Information Engineering Jimei University Xiamen China

ISBN: (数字)9781728198057

ISBN: (纸本)9781728198064

In this paper, we will propose a super wide bandwidth low frequency antenna with a compact size for HF, VHF and UHF applications. The developed low frequency antenna consists of coplanar guide feeding structure, sleeve-like structure with lumped elements, reactance loading in the feeding. The antenna is investigated and evaluated in the HFSS, and the simulation results shows that the proposed antenna has a very wide fractional bandwidth of 191.9% from 10 MHz to 481 MHz for supporting the HF, VHF, UHF and P-band detection system applications in various platform like aircraft, ship and vehicles.

关键词： Simulation Loading UHF antennas Broadband antennas Marine vehicles Loaded antennas Wideband

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Research on Rolling Bearing Fault Diagnosis Based on Classification Generative Adversarial Network

Research on Rolling Bearing Fault Diagnosis Based on Classif...

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UNIfied Conference of International Workshop on Defence Applications of Multi-agent systems, DAMAS 2023, International Conference on Maintenance Engineering, IncoME-V 2023, International conference on the Efficiency and Performance Engineering Network, TEPEN 2023

作者： Kang, Ma Hongjun, Wang School of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing100192 China Beijing International Science and Technology Cooperation Base for Intelligent Sensing and Control of High-End Equipment Beijing100192 China Beijing Key Laboratory of Measurement and Control of Electromechanical System Beijing100192 China

ISBN: (纸本)9783031494123

In practical engineering, it is usually difficult to label monitoring data, and the fault diagnosis accuracy is not high in strong noise environment. To solve the above problems, this paper uses Cwt-CatGAN method to process signals and diagnose faults. In this method, CWT is first used to convert one-dimensional original vibration signals into two-dimensional time–frequency graphs as the input of classification generation admixture network. Then, the CatGAN model is obtained through the adversarial training process, and fake samples similar to the extraction distribution of CWT are generated. Finally, the input samples are clustered into certain categories. In addition, the performance of the Cwt-CatGAN method is verified by using the classical rotating machine data set, the experiment is compared with the unsupervised fault diagnosis model CatAAE. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Failure analysis

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An Approximated Domination Relationship based on Binary Classifiers for Evolutionary Multiobjective Optimization

An Approximated Domination Relationship based on Binary Clas...

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Congress on Evolutionary Computation

作者： Hao Hao Aimin Zhou Hu Zhang Shanghai Key Laboratory of Multidimensional Information Processing School of Computer Science and Technology East China Normal University Shanghai China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Electro-mechanical Engineering Institute Beijing China

Preselection is an important strategy to improve evolutionary algorithms’ performance by filtering out unpromising solutions before fitness evaluations. This paper introduces a pre-selection strategy based on an approximated Pareto domination relationship for multiobjective evolutionary optimization. For each objective, a binary relation between each pair of solutions is constructed based on the current population, and a binary classifier is built based on the binary relation pairs. In this way, an approximated Pareto domination relationship can be defined. When new trial solutions are generated, the approximated Pareto domination is used to select promising solutions, which shall be evaluated by the real objective functions. The new preselection is integrated into two algorithms. The experimental results on two benchmark test suites suggest that the algorithms with preselection outperform their original ones.

关键词： Filtering Computational modeling Sociology Training data Evolutionary computation Approximation algorithms Linear programming

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