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8th International Conference on Computer science and Application Engineering, CSAE 2024

作者： Hongxing, Hao Ronghuan, Yu Jing, Guo Huaquan, Hu Science and Technology on Complex Electronic System Simulation Laboratory Space Engineering University Huairou Beijing China

ISBN: (纸本)9798400718090

The Autonomous and controllable of the spacecraft component is of great importance for the development of our space industry. The key step of the spacecraft component is the production. Many researchers focus on the estimation of the autonomous and controllable of the spacecraft component production. This paper model the evaluation method including the construction of the evaluation index system and the estimation method. The proposed estimation method is based on the fuzzy model to synthesize the qualitative, quantitative indicators and the according weights. Compared to mapping the qualitative value to a fixed number, the proposed method avoids the effect of the subjectivity. The experimental results show that the proposed method tend to think highly of the quantitative values when the qualitative values are not very too much so as to avoid the effect of the subjectivity for the qualitative indicators in the evaluation. © 2024 Copyright held by the owner/author(s).

关键词： Aerospace industry

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Combining Local Electromagnetic Scattering and Global Structure Features for SAR Open Set Recognition

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12572-12587页

作者： Lin, Qinjie Sun, Hao Xu, Yanjie Wang, Junyu Ji, Kefeng Kuang, Gangyao National University of Defense Technology State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System College of Electronic Science and Technology Changsha410073 China

Automatic target recognition (ATR) is crucial for synthetic aperture radar (SAR) image interpretation. However, existing SAR ATR primarily rely on algorithms from the field of computer vision, many methods have not adequately considered the imaging sensitivity of SAR images. Moreover, SAR ATR typically follows the closed set assumption, but in dynamic and open real-world environments, SAR systems may encounter target classes that have not been seen during training. To address these issues, this paper proposes a SAR open set recognition (OSR) method that combines local electromagnetic scattering and global structure features. The global structural features focus on the overall relationship between target and background, while the local electromagnetic scattering features can effectively capture the scattering characteristics of target. Hence, we construct a global and local feature guidance (GLGE) network, fuses these complementary features as prior knowledge to guide deep features. Then, a class-anchor-based loss function is constructed by Anchor loss and Tuple loss, which cluster the features of known classes targets around predefined class anchors. Finally, the distance ratio is calculated to identify known classes targets and reject unknown classes targets. The experimental results on the MSTAR and SAR-ACD datasets show that our method outperforms other OSR methods in recall, precision, F1, and accuracy, demonstrating a superior performance in OSR. © 2008-2012 IEEE.

关键词： Automatic target recognition

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Coprime Factorization-Based Encryption and Attack Detection for Nonlinear Cyber-Physical systems Using Deep Learning Approach

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IEEE Transactions on Automation science and Engineering 2025年 22卷 14020-14029页

作者： Wu, Shimeng Luo, Hao Zhang, Jiusi Qiao, Xinyu Tian, Jilun Jiang, Yuchen Harbin Institute of Technology Department of Control and Simulation Center Harbin150001 China National Key Laboratory ofComplex System Control and Intelligent Agent Cooperation Harbin150001 China University of Electronic Science and Technology of China School of Automation Engineering Chengdu611731 China

This paper presents a data-driven framework for integrating encryption transmission and attack detection in cyber-physical systems (CPS) with nonlinear physical plants. The main focus of this research is to use deep neural networks to realize the coprime factorization (CF) of nonlinear systems. The definition of the CF guides the network training and designing process, and the model’s topology is designed in the state-space form, which improves the interpretability of the data-driven CF. Based on the CF-aided neural networks, an encrypted transmission module is designed that projects information related to system dynamics into a perpendicular data space, which complements existing encryption methods from a control theory perspective. Subsequently, an anomaly detector are designed using the same CF pairs. This detector not only provides high-accuracy detection of attacks but also distinguishes between attacks and faults, thereby reducing the false positive rate and enhancing the reliability of the attack detection. The proposed method has been validated in a real CPS using a mecanum-wheeled vehicle as the physical plant, demonstrating its effectiveness and applicability. © 2004-2012 IEEE.

关键词： Factorization

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A Service Mining Scheme Based on Semantic for Internet of Things

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Chinese Journal of electronics 2025年第2期23卷 236-242页

作者： Xinming Li Yan Sun The Science and Technology on Complex Electronic System Simulation Laboratory Academy of Equipment Beijing China The School of Computer Science Beijing University of Posts and Telecommunication Beijing China

As the development of Internet of things (IOT), massive sensors have been deployed as the public infrastructure. With the development of in-depth applications in IOT, service discovery and composition are challenges to the end users. To handle this challenge, we develop a service mining scheme based on semantic for IOT to provide users with interesting composite services. In this scheme, services can be combined and recommended to users actively according to the calculation of service similarity and an updatable semantic database. By the results of service similarity, useless compositions can be filtered out so that energy consumption on service flooding will be reduced. The update strategy of semantic database is also given out, by which the composite services can keep up with time and be more applicative. The benefits of the proposed method are that all operations such as calculating, filtering, and updating are simple enough to be performed in sensor networks.

关键词： Smart buildings Databases Filtering Semantics Public infrastructure Ontologies Sensor systems Internet of Things Floods Intelligent sensors

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A Grey Wolf Optimizer Variant with Weighted-leader Strategy 24

A Grey Wolf Optimizer Variant with Weighted-leader Strategy

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3rd International Conference on Signal Processing, Computer Networks and Communications, SPCNC 2024

作者： Liang, Yajun Yan, Su Ma, Qian Huang, Shunlun Liu, Xin China Aerodynamics Research & Development Center Sichuan Mianyang China Science and Technology on Complex Aviation Systems Simulation Laboratory Beijing China State Key Laboratory of Air Traffic Management System Beijing China Beijing Aviation Meteorological Institute Beijing China

ISBN: (纸本)9798400710834

To enhance the convergence capability of grey wolf optimizer (GWO), this research investigates an evolved GWO using weighted-leader strategy (WLS), namely WLSGWO. The key issue of WLS is realizing the adaptive adjustment of position weighting according to the control parameters, thus well overcoming the drawback of premature convergence in the basic GWO. The newly developed method’s performance is assessed through experiments conducted on a standardized test suite. Additionally, comparisons are made by utilizing GWO alongside other advanced optimization techniques. The results of non-parametric studies validate the outperformance of the WLSGWO, thus proving the efficiency of the proposed strategy. © 2024 Copyright held by the owner/author(s).

关键词： Swarm intelligence

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Moving Target Trajectory Reconstruction Based on the LM Algorithm in WasSAR system

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Chen, Jingwei An, Daoxiang Ge, Beibei Chen, Leping Zhou, Zhimin National University of Defense Technology College of Electronic Science and Technology Changsha410073 China Science and Technology on Complex Aviation System Simulation Laboratory Beijing100076 China

Wide-angle staring synthetic aperture radar ground moving target indication (WasSAR-GMTI) has garnered attention due to its capability to monitor a stationary area over an extended period and its ability to track moving targets. Recently, the research on moving target trajectory reconstruction has also raised interest. However, the low positioning accuracy and impractical assumption of the traditional method make it hardly a practical application. We propose a novel precise trajectory reconstruction of a moving target in this article. First, we segment the echo into subapertures, after clutter rejection and radial velocity estimation, quasi-maximum likelihood (QML) is applied to estimate the polynomial phase signal (PPS) parameters of the phase history. Then, the Levenberg-Marquardt (LM) algorithm is applied to decouple the motion parameters. Based on the motion parameters, the coarse trajectory can be reconstructed. After that, the envelope history of the moving target is extracted. Modeling the motion trajectory of the moving target in segments and estimating the trajectory of each segment using the corrected envelope history and coarse trajectory. Finally, after processing all segments, the precise trajectory of the moving target is obtained. The effectiveness and practicability of the proposed algorithm were demonstrated by the processing results of experimental wide-angle staring synthetic aperture radar (WasSAR) data. © 1980-2012 IEEE.

关键词： Polynomials

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NUDIF: A Non-Uniform Deployment Framework for Distributed Inference in Heterogeneous Edge Clusters

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Future Internet 2025年第4期17卷 168-168页

作者： Li, Peng Qing, Chen Liu, Hao National Key Laboratory of Complex Aviation System Simulation Chengdu610036 China Southwest China Institute of Electronic Technology Chengdu610036 China Beijing100876 China

Distributed inference in resource-constrained heterogeneous edge clusters is fundamentally limited by disparities in device capabilities and load imbalance issues. Existing methods predominantly focus on optimizing single-pipeline allocation schemes for partitioned sub-models. However, such approaches often lead to load imbalance and suboptimal resource utilization under concurrent batch processing scenarios. To address these challenges, we propose a non-uniform deployment inference framework (NUDIF), which achieves high-throughput distributed inference service by adapting to heterogeneous resources and balancing inter-stage processing capabilities. Formulated as a mixed-integer nonlinear programming (MINLP) problem, NUDIF is responsible for planning the number of instances for each sub-model and determining the specific devices for deploying these instances, while considering computational capacity, memory constraints, and communication latency. This optimization minimizes inter-stage processing discrepancies and maximizes resource utilization. Experimental evaluations demonstrate that NUDIF enhances system throughput by an average of 9.95% compared to traditional single-pipeline optimization methods under various scales of cluster device configurations. © 2025 by the authors.

关键词： Deep neural networks

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Analysis of the SEIR mean-field model in dynamic networks under intervention

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Infectious Disease Modelling 2025年第3期10卷 850-874页

作者： Li, Jiangmin Jin, Zhen Tang, Ming Complex System Research Center Shanxi University Shanxi Taiyuan 030006 China Key Laboratory of Complex Systems and Data Science of Ministry of Education Shanxi University Shanxi Taiyuan 030006 China Shanxi Key Laboratory for Mathematical Technology in Complex Systems Shanxi University Shanxi Taiyuan 030006 China School of Physics and Electronic Science East China Normal University Shanghai 200241 China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai 200241 China

For emerging respiratory infectious diseases like COVID-19, non-pharmaceutical interventions such as isolation are crucial for controlling the spread. From the perspective of network transmission, non-pharmaceutical interventions like isolation alter the degree distribution and other topological structures of the network, thereby controlling the spread of the infectious disease. In this paper, we establish a SEIR mean-field propagation dynamics model for the synchronous evolution of dynamic networks caused by propagation and tracing isolation. We employ the reducing-dimension method to convert the mean-field model in networks into an equivalent and simpler low-dimension model, and then calculate the exact expression of the final size. In addition, we get the differential equations of the degree distribution over time in dynamic networks under tracing isolation and the relationships between the first and second moment of the dynamic network. While the degree of a node remains constant regardless of its state in many previous studies, this paper takes into account that the degree of each node changes over time whatever its state under the disease spread and intervention measures. © 2025 The Authors

关键词： Degree distribution Dynamic network The final size Tracing isolation

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Random Coefficient-Moving Product Based Wireless Key Generation

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China Communications 2025年第2期22卷 199-212页

作者： Lu Xinjin Lei Jing Li Wei College of Electronic Science and Technology National University of Defense TechnologyChangsha 410073China State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System Luoyang 471003China China South Industries Group Corporation Zhi Yuan Institute310012China

The physical layer key generation technique provides an efficient method,which utilizes the natural dynamics of wireless ***,there are some extremely challenging security scenarios such as static or quasi-static environment,which lead to the low randomness of generated ***,the coefficients of the static channel may be dropped into the guard space and discarded by the quantization approach,which causes low key generation *** tackle these issues,we propose a random coefficient-moving product based wireless key generation scheme(RCMP-WKG),where new random resources with remarkable fluctuations can be obtained by applying random coefficient and by moving product on the legitimate ***,appropriate quantization approaches are used to increase the key generation ***,the security of our proposed scheme is evaluated by analyzing different attacks and the eavesdropper’s mean square error(MSE).The simulation results reveal that the proposed scheme can achieve better performances in key capacity,key inconsistency rate(KIR)and key generation rate(KGR)compared with the prior works in static ***,the proposed scheme can deteriorate the MSE performance of the eavesdropper and improve the key generation performance of legitimate nodes by controlling the length of the moving product.

关键词： moving product random coefficient static environment wireless key generation

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Enhancing Geolocation Accuracy of High-Altitude Airborne SAR Through Tropospheric Delay Compensation

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Xiang, Yaobing Sun, Yuli Lei, Lin Ji, Kefeng Kuang, Gangyao National University of Defense Technology State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System College of Electronic Science and Technology Changsha410073 China

Geolocation is a crucial step in the processing of synthetic aperture radar (SAR) images. High-altitude airborne SAR systems present unique geolocation challenges due to travelling long distances through the troposphere. However, the impact of tropospheric delay on geolocation is often overlooked in existing airborne SAR studies, which can lead to inaccuracies. To address this issue, we propose a new positioning method, the tropospheric delay-compensated range-Doppler (TDC-RD) model. The TDC-RD model leverages reference atmospheric models to estimate and compensate for the tropospheric delay in SAR images. This model effectively mitigates the impact of tropospheric delay in SAR geolocation. To further optimize the TDC-RD model solution, a digital elevation model (DEM)-assisted dual iteration method is proposed. This method iteratively adjusts the target's plane position and elevation in an alternating manner. The effectiveness of the TDC-RD model has been validated through both simulation experiments and actual flight experiments. The results show a significant improvement in geolocation accuracy compared to existing methods, with a maximum reduction of 11.39 m and 24.33% in the mean absolute error (MAE) of SAR geolocation. The TDC-RD model has a great advantage in long-range SAR geolocation. Our research enhances the accuracy and stability of high-altitude airborne SAR geolocation without requiring ground control points. © 2025 IEEE.

关键词： Troposphere

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