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International Conference on Signal Processing Proceedings (ICSP)

作者： Yuanqin Wang Ruoyu Zhang Jing Sun Institute of Ultra-precision Optical & Electronic Instrument Engineering Harbin Institute of Technology Harbin China Academy of Equipment Command슠and슠Technology Beijing China Beijing Aerospace Control Center Beijing China

The information on angular motion of projectile can be expressed by the Amplitude Modulation (AM) index of radar echo. In order to separate the information of slow-mode angular motion, a method based on Empirical Mode Decomposition (EMD) is proposed. Three issues are discussed in this article. First, the angular motion of projectile is approximated as a fast-mode circle superposing over that of the slow-mode according to the theory of exterior ballistics. Next, the characteristics of AM index that can represent the angular motion are discussed. Considering the effects of Discrete Fourier Transform (DFT) and the interference of noise, the index of AM is modeled as nonstationary time series in which the slow fluctuation corresponds to the slow-mode circular motion. Then an EMD-based separating method is suggested to obtain the index of the slow-mode motion. It includes two steps. The first step is to divide the index into a series of Intrinsic Mode Functions (IMFs) and the residual item. The second step is to reconstruct the index of the slow-mode motion with the components of the lower frequency IMFs and the residual by utilizing the prior knowledge about the difference in the periods. Compared with theoretical value and the result of stationary wavelet transform, as well as the in-flight experiment, the results show the validity and effectiveness.

关键词： Indexes Projectiles Discrete Fourier transforms Time series analysis Noise Wavelet transforms

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Nonlinear control algorithm based on adaptive wavelet neural network for anti-ship missile

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Yuhang Xuebao/Journal of Astronautics 2008年第3期29卷 952-956页

作者： Chen, Jie Pan, Chang-Peng Gu, Wen-Jin Department of Control Engineering Naval Aeronautical Engineering Institute Yantai 264001 China Department of Command Naval Aeronautical Engineering Institute Yantai 264001 China

A new nonlinear control algorithm using dynamic inversion, wavelet neural network and sliding mode control was presented. The dynamic inversion theory was used to approximately linearize the nonlinear system, the system error was compensated by capable of on-line learning wavelet neural network, A stable weights adaptive adjustment rule was derived by using Lyapunov stability theorem, and the system robustness was guaranteed by using sliding mode control and robustness control component. This control law was applied to one STT missile control system, and output-redefinition approach was adopted to resolve the non-minimum phase problem of original system. Simulation results slows that this control law can eliminate the influence from disturbance availably, raise the precision of missile overload control system response.

关键词： Sliding mode control

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Hybrid Independent Component Analysis and Support Vector Machine Approach for Audio Classification

Hybrid Independent Component Analysis and Support Vector Mac...

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IEEE International Conference on Networking, Sensing and control

作者： Xianzhong Zhou Xin He Yingchun Shi School of Management and Engineering Nanjing University Nanjing China Department of Automatic Control Nanjing University of Science and Technology of PLA Nanjing China Simulation Center Communication Command Institute of PLA Wuhan China

The recent explosive growth of multimedia information in many applications requires advanced techniques for multimedia information retrieval (MIR). Audio is one of important media in multimedia and contains abundant information. Recent development in MIR has revealed that audio information can provide effective contents to retrieve multimedia information. Consequently, it has been paid more and more attention to how to classify audio information. Many existing and new research issues can lead to improve the retrieval efficiency, effectiveness, and accessibility. This paper provides a hybrid classification method for audio indexing and retrieval. It firstly describes main audio characteristics and features and the basic theory of support vector machine (SVM) and independent component analysis (ICA). Finally, the hybrid method that integrates ICA and SVM is presented and applied into audio classification

关键词： Independent component analysis Support vector machines Support vector machine classification Information retrieval Neural networks Hidden Markov models Statistics Kernel Explosives Helium

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Collision Resolve for Datalink Random Access Based on Multi-Packet Reception

Collision Resolve for Datalink Random Access Based on Multi-...

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IEEE International Symposium on Computer Network and Multimedia Technology, CNMT

作者： Yunxia Jia Youyi Jiang Lening Wang Ce Zhang Communications Institute Air Force Engineering University Xi'an China Second Research Academy China Aerospace Science and Technology Corporation Beijing China Command & Control Center Equipment Academy Beijing China

In order to solve the inevitable data collision in competitive datalink random access of wireless sensors networking, this paper provides a blind property restoral maximum likelihood (ML) approach for demodulating multiple co-channel digital signals received synchronously at an antenna array. The finite alphabet property of digital signal combining with ML is analyzed, the array response and symbol sequence for each signal simultaneously are also estimated, which can obtain better approximation to the system performance of initial time-delay and throughput. Such works are confirmed by computer simulation and field-testing data verification.

关键词： Sensor arrays Wireless sensor networks Maximum likelihood estimation Signal resolution Signal restoration Receiving antennas Antenna arrays Signal analysis Performance analysis System performance

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Application of Intelligent Algorithms in UAV Geomagnetic Navigation Modeling

Application of Intelligent Algorithms in UAV Geomagnetic Nav...

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IEEE International Conference on Unmanned Systems (ICUS)

作者： Jingli Huang Dan Mei Feng Su Xiangyu Zhang Department of Aeronautical electrical control engineering and command Naval Aeronautical University Qingdao China Qingdao Branch Naval Aeronautical University Qingdao China Institute of Information Fusion Naval Aeronautical University Yantai China

ISBN: (纸本)9781728137933

Rectangular harmonic model is a common model in UAV geomagnetic navigation. In order to reduce the navigational error caused by the model factors and achieve high-precision navigation, the original Earth's main magnetic field model is replaced by rectangular harmonic model. In creating a rectangular harmonic model, inertia weight adaptive particle swarm algorithm is adopted to solve the problem that too many undetermined coefficients can easily fall into local optimum in the central region. The ability of generalization to inhibit the boundary effect of radial basis function neural network is used in the border region. Meanwhile, the design of the error box to a more reasonable distinction between center and boundary is designed. The simulation results show that the optimized combination of the two intelligent algorithm makes the accuracy of the model further higher.

关键词： Navigation Adaptation models Particle swarm optimization Harmonic analysis Neural networks Mathematical model Sociology

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Statistical distribution of HI 21cm intervening absorbers as potential cosmic acceleration probes

arXiv

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arXiv 2022年

作者： Lu, Chang-Zhi Zhang, Tingting Zhang, Tong-Jie Institute for Frontiers in Astronomy and Astrophysics Beijing Normal University Beijing102206 China Department of Astronomy Beijing Normal University Beijing100875 China College of Command and Control Engineering PLA Army Engineering University Nanjing210017 China Institute for Astronomical Science Dezhou University Dezhou253023 China

Damped Lyman-α Absorber (DLA), or HI 21cm Absorber (H21A), is an important probe to model-independently measure the acceleration of spectroscopic velocity (vS) via the Sandage-Loeb (SL) effect. Confined by the shortage of DLAs and Background Radio Sources (BRSs) with adequate information, the detectable amount of DLAs is ambiguous in the bulk of previous work. After differing the acceleration of scale factor (ä) from the first order time derivative of spectroscopic velocity (v¤S), we make a statistical investigation of the amount of potential DLAs in the most of this paper. Using Kernel Density Estimation (KDE) to depict general redshift distributions of BRSs, observed DLAs and a DLA detection rate with different limitations (1.4GHz flux, HI column density and spin temperature), we provide fitted multi-Gaussian expressions of the three components and their 1σ regions by bootstrap, with a proportional constant of H21As in detected DLAs, leading to the measurable number predictions of H21As for FAST, ASKAP and SKA1-Mid in HI absorption blind survey. In our most optimistic condition (F1.4GHz>10mJy, NHI > 2 × 1020cm−2 and TS>500K), the FAST, AKSAP and SKA1-Mid would probe about 80, 500 and 600 H21As respectively. Copyright © 2022, The Authors. All rights reserved.

关键词： Cosmology

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DeepBoundary: A Coverage Testing Method of Deep Learning Software based on Decision Boundary Representation

DeepBoundary: A Coverage Testing Method of Deep Learning Sof...

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IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)

作者： Yue Liu Lichao Feng Xingya Wang Shiyu Zhang College of Computer Science and Technology Nanjing Tech University Nanjing Jiangsu China Institute of Command and Control Engineering Army Engineering University of PLA Nanjing Jiangsu China State Key Laboratory for Novel Software Technology Nanjing University Nanjing Jiangsu China

With the increasing application of Deep Learning (DL) Software in safety-critical fields such as autonomous driving, we need adequate testing to ensure software quality. Observing the decision-making behavior of a Deep Neural Network (DNN) is an essential step in DL software testing. Taking Guiding Deep Learning System Testing Using Surprise Adequacy (SADL) as an example, it uses the independent neuron activation values in the DNN to represent the decision-making behavior. However, the behavior of the DNN needs to be jointly determined by the continuous outputs of all neurons. As a result, the coverage value of SADL constant volatility and lack of stability. To mitigate this problem, we propose a coverage testing method based on the decision boundary representation, DeepBoundary, for the decision-making behavior of DL software. Unlike SADL, DeepBoundary generates decision boundary data to represent the decision behavior of the DNN, which makes the testing results more stable. On this basis, we calculate the kernel density between the testing data and the decision boundary data. It measures the position of the testing data in the decision space and the distance from the decision boundary. Finally, as an adequacy indicator, we calculate the decision boundary density coverage (DBC) of the entire testing set. The experiment on the dataset MNIST and two DL software shows that DeepBoundary can generate actual decision boundary data. The average confidence error in the DNNs output layer is only 4.20E-05. Compared with SADL, DeepBoundary has a stronger correlation with the defect detection ratio, which can more accurately represent testing adequacy.

关键词： Deep learning Software testing System testing Correlation Decision making Neurons Software quality

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Corrections to “Fair Scheduling and Rate control for Service Function Chain in NFV-Enabled Data Center”

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IEEE Transactions on Network and Service Management 2022年第2期19卷 1937-1937页

作者： Jiachen Zu Guyu Hu Dongyang Peng Shengxu Xie Wenbin Gao Institute of Command and Control Engineering Army Engineering University Nanjing China

IN THE above article [1] , in (5) , the allocated bandwidth is corrected as

关键词： Service function chaining Bandwidth

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Overview of the Application of Knowledge Graph in Anomaly Detection and Fault Diagnosis

Overview of the Application of Knowledge Graph in Anomaly De...

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System Reliability and Safety engineering (SRSE), International Conference on

作者： Peizheng Huang Shulin Liu Kuan Zhang Tao Xu Xiaojian Yi Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing China School of Mechatronical Engineering Beijing Institute of Technology Beijing China Quality and Reliability Center Beijing China China Institute of Marine Technology and Economy Beijing China Beijing Aerospace Command Control Center Beijing China

Knowledge graph is a branch of artificial intelligence, which uses graph model to describe the relationship between knowledge and things. Using the technology of knowledge extraction, knowledge fusion and knowledge processing in knowledge graph, a large-scale knowledge base with semantic and open knowledge can be established quickly for a specific domain. Knowledge graph has gradually become an important means of knowledge management and application in various fields. This paper reviews the development history of knowledge graphs, introduces key technologies of knowledge graphs, and the application, rules and characteristics of knowledge graphs in anomaly detection and fault diagnosis. Furthermore, this paper summarizes the application of knowledge graph technology in anomaly detection and fault diagnosis in various industries, analyzes the applicability of knowledge graph in the field of anomaly detection and fault diagnosis, discusses the challenges faced in the application process, and proposes future development trends.

关键词： Fault diagnosis Knowledge engineering Industries Semantics Knowledge based systems Knowledge graphs Market research

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Likelihood-free cosmological constraints with artificial neural networks: An application on hubble parameters and SNe Ia

arXiv

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arXiv 2020年

作者： Wang, Yu-Chen Xie, Yuan-Bo Zhang, Tong-Jie Huang, Hui-Chao Zhang, Tingting Liu, Kun Department of Physics Beijing Normal University Beijing100875 China Department of Astronomy Beijing Normal University Beijing100875 China Institute for Astronomical Science Dezhou University Dezhou253023 China College of Command and Control Engineering PLA Army Engineering University Nanjing China

The errors of cosmological data generated from complex processes, such as the observational Hubble parameter data (OHD) and the Type Ia supernova (SN Ia) data, cannot be accurately modeled by simple analytical probability distributions, e.g. Gaussian distribution. To constrain cosmological parameters from these data, likelihood-free inference is usually used to bypass the direct calculation of the likelihood. In this paper, we propose a new procedure to perform likelihood-free cosmological inference using two artificial neural networks (ANN), the Masked Autoregressive Flow (MAF) and the denoising autoencoder (DAE). Our procedure is the first to use DAE to extract features from data, in order to simplify the structure of MAF needed to estimate the posterior. Tested on simulated Hubble parameter data with a simple Gaussian likelihood, the procedure shows the capability of extracting features from data and estimating posterior distributions without the need of tractable likelihood. We demonstrate that it can accurately approximate the real posterior, achieve performance comparable to the traditional MCMC method, and the MAF gets better training results for small number of simulation when the DAE is added. We also discuss the application of the proposed procedure to OHD and Pantheon SN Ia data, and use them to constrain cosmological parameters from the non-flat ΛCDM model. For SNe Ia, we use fitted light curve parameters to find constraints on H0,Ωm,ΩΛ similar to relevant work, using less empirical distributions. In addition, this work is also the first to use Gaussian process in the procedure of OHD simulation. Copyright © 2020, The Authors. All rights reserved.

关键词： Neural networks

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