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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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Stochastic Block Models for complex Network Analysis: A Survey

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ACM Transactions on Knowledge Discovery from Data 2025年第3期19卷 1-35页

作者： Liu, Xueyan Song, Wenzhuo Musial, Katarzyna Li, Yang Zhao, Xuehua Yang, Bo College of Computer Science and Technology Jilin University Changchun China School of Information Science and Technology Northeast Normal University Changchun China Complex Adaptive Systems Lab Data Science Institute University of Technology Sydney Sydney Australia Aviation University of the Air Force Changchun China School of Digital Media Shenzhen Institute of Information Technology Shenzhen China Key Laboratory of Symbolic Computation and Knowledge Engineer Jilin University Ministry of Education Changchun China

complex networks enable to represent and characterize the interactions between entities in various complex systems which widely exist in the real world and usually generate vast amounts of data about all the elements, their behaviors and interactions over time. The studies concentrating on new network analysis approaches and methodologies are vital because of the diversity and ubiquity of complex networks. The stochastic block model (SBM), based on Bayesian theory, is a statistical network model. SBMs are essential tools for analyzing complex networks since SBMs have the advantages of interpretability, expressiveness, flexibility and generalization. Thus, designing diverse SBMs and their learning algorithms for various networks has become an intensively researched topic in network analysis and data mining. In this article, we review, in a comprehensive and in-depth manner, SBMs for different types of networks (i.e., model extensions), existing methods (including parameter estimation and model selection) for learning optimal SBMs for given networks and SBMs combined with deep learning. Finally, we provide an outlook on the future research directions of SBMs. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： complex Networks Structural Pattern Discovery Stochastic Block models Learning Methods of SBMs

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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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Constructing ECOC based on confusion matrix for multiclass learning problems

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science China(Information sciences) 2016年第1期59卷 135-148页

作者： Jindeng ZHOU Yun YANG Mingjie ZHANG Haibo XING Science and Technology on Complex Aviation Systems Simulation Laboratory

In the pattern recognition field, error-correcting output codes(ECOC) are a powerful tool to fuse any number of binary classifiers to model multiclass problems, and the research of encoding based on data is attracting more and more attention. In this paper, we are going to propose a new encoding method for constructing subclass Error-Correcting Output Codes, which was first introduced by Escalera et al. To achieve this goal, we first obtain the correlation between each pair of classes with the help of confusion matrix. Then,we select the most easily separated subclasses for classification by following Fisher's principle. At last, we were able to obtain binary partitions based on subclasses. After finishing this work, a new data-driven coding matrixSubclass ECOC will be achieved. Experimental results on University of California Irvine data sets and three kinds of high resolution range profile data sets with logistic linear classifier and support vector machine as the binary classifiers show that our approach can provide a better performance and the robustness of classification with a little longer but acceptable code length.

关键词： machine learning multiclass classification error correcting output codes subclass partition confusion matrix

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LPI radar signal detection based on radial integration of Choi-Williams time-frequency image

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Journal of systems Engineering and Electronics 2015年第5期26卷 973-981页

作者： Yongjian Liu Peng Xiao Hongchao Wu Weihua Xiao Science and Technology on Complex Aviation Systems Simulation Laboratory Aviation University of Air Force

This paper mainly revolves the time-frequency image of low probability of intercept(LPI) radar signals and carries out research work on image features selection and extraction and recognition. Since Choi-Williams distribution(CWD) uses the exponential kernel of bilinear generalized class of time-frequency distribution, it has an excellent time-frequency aggregation. And it is suitable for detecting LPI radar signals in a low signal-to-noise ratio(SNR) condition. A radial integration method based on the integral rotating factor is proposed to detect LPI radar signals when the signals’ time-frequency image is obtained. First, the digital image processing method is used to preprocess the LPI radar signals’ time-frequency images after CWD transformation; then, the radial integration method based on the integral rotating factor is used to detect LPI radar signals in the binary images. The analytic results of real data show that the method has a good performance on detecting LPI radar signals in a low SNR condition. Additionally,the method is simple and takes less logic resources and has the potential of real-time detection of LPI radar signals.

关键词： low probability of intercept(LPI) integral rotating factor signal detection image processing

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HRSTORY: Historical News Review Based Online Story Discovery 25

HRSTORY: Historical News Review Based Online Story Discovery

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Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1

作者： Renjie Zhou Haoran Ye Jian Wan Yong Liao Key Laboratory of Complex Systems Modeling and Simulation of the Ministry of Education Hangzhou Dianzi University Hangzhou Zhejiang China Zhejiang University of Science and Technology Hangzhou Zhejiang China University of Science and Technology of China Hefei Anhui China

ISBN: (纸本)9798400712456

Story discovery on news streams can help people quickly find story from vast amounts of news, improving the efficiency of information acquisition. Recent online story discovery methods encode text topics and then cluster articles into stories based on similarity. However, the results obtained by these methods are one-time, and clustered news cannot adaptively update in a continuous news stream. Additionally, the inadequate quality of article encoding and the presence of noise data deteriorate the performance of story discovery. To this end, we propose HRSTORY for online story discovery on news streams, which employs a historical news review method to enable news to continuously adapt to the latest environment in the stream data and make corrections and updates. Furthermore, HRSTORY captures better article embeddings through modeling multi-layer relational dependencies within the text. By using sentence-level noise masking, HRSTORY improves the relevance of news article representation to core topics and reduces the interference of noise data. Experiments on real news datasets show that HRSTORY outperforms the state-of-the-art algorithms in unsupervised online story discovery performance.

关键词： historical news

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Imp: Highly Capable Large Multimodal Models for Mobile Devices

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IEEE Transactions on Multimedia 2025年 27卷 2961-2974页

作者： Shao, Zhenwei Yu, Zhou Yu, Jun Ouyang, Xuecheng Zheng, Lihao Gai, Zhenbiao Wang, Mingyang Kuang, Zhenzhong Ding, Jiajun Hangzhou Dianzi University Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science Hangzhou310005 China Harbin Institute of Technology Shenzhen Department of Computer Science and Technology Shenzhen China Hangzhou Dianzi University HDU-ITMO Joint Institute Hangzhou310005 China

By harnessing the capabilities of large language models (LLMs), recent large multimodal models (LMMs) have shown remarkable versatility in open-world multimodal understanding. Nevertheless, they are usually parameter-heavy and computation-intensive, thus hindering their applicability in resource-constrained scenarios. To this end, several lightweight LMMs have been proposed successively to maximize the capabilities under constrained scale (e.g., 3B). Despite the encouraging results achieved by these methods, most of them only focus on one or two aspects of the design space, and the key design choices that influence model capability have not yet been thoroughly investigated. In this paper, we conduct a systematic study for lightweight LMMs from the aspects of model architecture, training strategy, and training data. Based on our findings, we obtain Imp - a family of highly capable LMMs at the 2B$\sim$4B scales. Notably, our Imp-3B model steadily outperforms all the existing lightweight LMMs of similar size, and even surpasses the state-of-the-art LMMs at the 13B scale. With low-bit quantization and resolution reduction techniques, our Imp model can be deployed on a Qualcomm Snapdragon 8Gen3 mobile chip with a high inference speed of about 13 tokens/s. © 2025 IEEE.

关键词： Visual languages

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Evolutionary neural architecture search for automatically designing CNN-GRU-Attention neural networks for turntable servo systems

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Expert systems with Applications 2025年 283卷

作者： Xie, Cheng Xue, Bing Yang, Mingyi Zhang, Mengjie Xu, Zhigang Wang, Junyi Chen, Songlin Liu, Yang Shenyang Institute of Automation Chinese Academy of Sciences Liaoning Shenyang110169 China Centre for Data Science and Artificial Intelligence & School of Engineering and Computer Science Victoria University of Wellington Wellington6140 New Zealand University of Chinese Academy of Sciences Beijing101408 China National Key Laboratory of Modeling and Simulation for Complex Systems Harbin Institute of Technology Heilongjiang Harbin150001 China

Turntable servo systems are important experimental devices utilized in the semi-physical simulation and testing of aircraft. Building a model for turntable servo systems, which can accurately predict their operating states or behaviors, is important for the development and debugging of the whole servo control system. However, servo systems usually have complex nonlinear characteristics and external disturbances, which brings challenges to the accurate modeling of them. Therefore, this paper develops a modeling method for turntable servo systems by using CNN-GRU-Attention hybrid neural network models. The CNN-GRU-Attention model is used for compensating the nonlinear terms of the known dynamics model, thus effectively improving the modeling accuracy. Considering the complex architectures of the above hybrid model, an evolutionary neural architecture search (ENAS) algorithm is proposed accordingly, which can automatically design the architecture of the CNN-GRU-Attention models. During the designing process, a variable-length encoding strategy is proposed to represent the possible architectures, and novel crossover and mutation operators are proposed accordingly for the evolution of individuals. In addition, the real-time constraints are considered in the design of the search space, so that all the searched models can meet the real-time requirements. The experimental results show the CNN-GRU-Attention models obtained by the proposed ENAS algorithm achieve superior prediction performance over the peer competitors in most tasks. The effectiveness of the proposed method is further verified by analyzing the convergence and the search results. © 2025

关键词： Synchros

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New self-calibration schemes for accelerometers in platform INS

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Journal of systems Engineering and Electronics 2015年第5期26卷 1032-1042页

作者： Hong Cai Anliang Li Yuan Cao College of Aerospace Science and Engineering National University of Defence Technology Science and Technology on Complex Land Systems Simulation Laboratory

To ensure success of precise navigation, it is necessary to carry out in-field calibration for the accelerometers in platform inertial navigation system(PINS) before a mission is *** continuous self-calibration methods are not fit for fast calibration of accelerometers because the platform misalignments have to be estimated precisely and the nonlinear coupling terms will affect accuracy. The multi-position methods with a "shape of motion" algorithm also have some existing disadvantages: High precision calibration results cannot be obtained when the accelerometer’s output data are used directly and it is difficult to optimize the calibration scheme. Focusing on this field, this paper proposes new fast self-calibration methods for the accelerometers of PINS. A data compression filter is employed to improve the accuracy of parameter estimation because it is impossible to obtain non-biased estimation for accelerometer parameters when using the "shape of motion" algorithm. Besides, continuous calibration schemes are designed and optimized by the genetic algorithm(GA) to improve the observability of parameters. simulations prove that the proposed methods can estimate the accelerometer parameter more precisely than traditional continuous methods and multi-position methods, and they are more practical to deal with urgent situations than multi-position methods.

关键词： self-calibration accelerometers data compression genetic algorithm

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Dynamic analysis of rigid-flexible coupling spacecraft based on Euler parameters

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Scientific Reports 2025年第1期15卷 1-12页

作者： Ji, Yi Zhang, Huan School of Astronautics Harbin Institute of Technology Harbin 150001 China Frontier Science Center for Interaction between Space Environment and Matter Harbin 150001 China National Key Laboratory of Modeling and Simulation for Complex Systems Harbin 150001 China MOE Key Laboratory of Dynamics and Control of Flight Vehicle Beijing Institute of Technology Beijing 100081 China

With the development of aerospace technology, rigid-flexible coupling spacecraft with strong nonlinearity dominates, bringing huge challenges for numerical tools. This paper aims to accurately and quickly analyze the dynamic behavior of rigid-flexible coupling spacecraft. In our work, rigid body and flexible body are both described by Euler parameters for avoiding singular angles. Then, for this type of strong nonlinear dynamic system, the BN-stable method that is unconditionally stable for nonlinear initial value problems proposed by the first author is introduced to solve the transient responses. To keep the accuracy of the numerical rotation matrix, the relation between the angular velocity and Euler parameters, and the constraint in the level of velocity, are introduced to reformulate the original BN-stable method. Through the dynamic analysis for rigid satellite bodies and flexible solar wings, we find that the proposed strategy can effectively simulate the dynamic responses of spacecraft, and compared to the currently popular strategy, our strategy enjoys considerable advantages in accuracy, stability, and dissipation. © The Author(s) 2025.

关键词： BN-stability Dynamic analysis Euler parameters Rigid-flexible coupling Rotation matrix

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