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3rd International Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Han, Wei Yang, Guang Luo, Ling Dong, Jingwei Xu, Peng Sun, Xuefei Command and Control Engineering College Army Engineering University of PLA Nanjing210007 China

ISBN: (纸本)9789819624676

Aiming at Unmanned Aerial Vehicle (UAV) target state estimation, a dynamic space model between the electro-optical platform and the target is established, and a target state estimation method by fusing game reasoning is proposed. The payoff matrix is defined according to the game concept. Then, the proposed density distribution function of particle swarm is established, and the particle filter method is improved by fusing the game reasoning. The state of the moving target can be accurately estimated by the retained effective particles. Through simulation experiments, the validity of state estimation results of moving target under different moving trajectories is analyzed and verified. The research in this paper can meet the requirements of the current UAV velocity measurement of maneuvering targets in terms of real-time and reliability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Unmanned aerial vehicles (UAV)

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Low-Cost Fine-Grained interpretation method of remote sensing images based on weakly supervised instance segmentation

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National Remote Sensing Bulletin 2025年第4期29卷 1002-1016页

作者： Chen, Man Huang, Yongjie Xu, Lei Pan, Zhisong College of Command and Control Engineering Army Engineering University of PLA Nanjing210007 China

Remote sensing image interpretation has essential application values in various fields, such as urban management, maritime monitoring, and resource planning. As an important and challenging task in remote sensing image interpretation, instance segmentation of remote sensing images can achieve target-level localization and pixel-level classification of objects of interest with fine granularity, making it a current research hotspot. However, most existing remote sensing image instance segmentation methods adopt the fully supervised paradigm and require expensive pixel-level labels. Moreover, remote sensing images often have issues such as mixed foreground and background and complex target contours, making segmentation challenging. To overcome these challenges, we propose a prior information-driven system suitable for weakly supervised instance segmentation tasks in remote sensing images and a multiprior driven weakly supervised instance segmentation network (MPD-WSIS-Net) to address these challenges. The prior information of weakly supervised instance segmentation can be divided into task prior and image prior. The task prior mainly comes from the bounding box detection task, which is closely related to instance segmentation. Specifically, the study obtains prior information from three components: box – mask projection consistency constraint, pixel discrimination difficulty representation function, and center position prior constraint. The image prior comes from summarizing or excavating information about the image itself. In this study, we focus on the relationships between neighboring pixels in the image and the gradient information of targets. By integrating these constraints and the pixel discrimination difficulty representation function, we establish a complete prior information driving system to enable MPD-WSIS-Net effectively to perform instance segmentation tasks under weakly supervised conditions in remote sensing images. MPD-WSIS-Net was compared with weakly

关键词： Photointerpretation

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Distributed Consensus control Research of Unmanned Aerial Vehicle (UAV) Swarms Based on Lennard-Jones Potential 3rd

Distributed Consensus Control Research of Unmanned Aerial Ve...

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3rd International Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Xu, Peng Liu, Jintao Sun, Xuefei Chen, Haotian Chen, Yinsheng Command and Control Engineering College Army Engineering University of PLA Houbiaoying Nanjing210007 China Naval Aviation University Yantai China College of Measurement and Control Technology and Telecommunications Engineering Harbin University of Science and Technology Heilongjiang Province Harbin China

ISBN: (纸本)9789819616930

The consensus control is a key issue in the research of unmanned aerial vehicle (UAV) swarm. Molecular dynamics is a method of stimulating complex group emergence behavior through simple interactions between individuals. This article draws inspiration from the principles of molecular dynamics to building a collective dynamics model for UAV swarm. Firstly, using Vicsek model to construct the self-organizing motion computational model. Then, using the Lennard-Jones two-body interaction potential in molecular dynamics as the interaction law of UAV swarm. By analyzing the Newtonian mechanical forces between each UAV in the swarm, consensus control of the UAV swarm can be achieved. Finally, simulation verification was conducted to analyze the effects of different power potential functions, different scales of swarm, and different white noise coefficient on the consensus control of UAV swarm. It is concluded that the 8–4 potential has better results for consensus control of UAV swarm and has good control effects on small, medium, and large scales UAV swarm. However, consensus cannot be fully guaranteed under strong noise conditions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lennard-Jones potential

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Offline prompt reinforcement learning method based on feature extraction

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PeerJ Computer Science 2025年 11卷 e2490-e2490页

作者： Yao, Tianlei Chen, Xiliang Yao, Yi Huang, Weiye Chen, Zhaoyang College of Command and Control Engineering Army Engineering University of PLA Nanjing China

Recent studies have shown that combining Transformer and conditional strategies to deal with offline reinforcement learning can bring better results. However, in a conventional reinforcement learning scenario, the agent can receive a single frame of observations one by one according to its natural chronological sequence, but in Transformer, a series of observations are received at each step. Individual features cannot be extracted efficiently to make more accurate decisions, and it is still difficult to generalize effectively for data outside the distribution. We focus on the characteristic of few-shot learning in pre-trained models, and combine prompt learning to enhance the ability of real-time policy adjustment. By sampling the specific information in the offline dataset as trajectory samples, the task information is encoded to help the pre-trained model quickly understand the task characteristics and the sequence generation paradigm to quickly adapt to the downstream tasks. In order to understand the dependencies in the sequence more accurately, we also divide the fixed-size state information blocks in the input trajectory, extract the features of the segmented sub-blocks respectively, and finally encode the whole sequence into the GPT model to generate decisions more accurately. Experiments show that the proposed method achieves better performance than the baseline method in related tasks, can be generalized to new environments and tasks better, and effectively improves the stability and accuracy of agent decision making. Copyright 2025 Yao et al. Distributed under Creative Commons CC-BY 4.0

关键词： Contrastive Learning

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Satellite Navigation Method Based on High-Speed Frequency Hopping Signal

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China Communications 2023年第7期20卷 321-337页

作者： En Yuan Peng Liu Weiwei Chen Rui Wang Bing Xu Wenyu Zhang Yanqin Tang Command&Control Engineering College Army Engineering University of PLANanjing 210007China

Global navigation satellite system has been widely used,but it is vulnerable to *** military satellite communications,frequency hopping(FH)signal is usually used for anti-jamming *** the FH signal can be used in satellite navigation,the anti-jamming ability of satellite navigation can be *** a recently proposed timefrequency matrix ranging method(TFMR)can use FH signals to realize pseudorange measurement,it cannot transmit navigation messages using the ranging signal which is crucial for satellite *** this article,we propose dual-tone binary frequency shift keyingbased TFMR(DBFSK-TFMR).DBFSK-TFMR designs an extended time-frequency matrix(ETFM)and its generation algorithm,which can use the frequency differences in different dual-tone signals in ETFM to modulate data and eliminate the negative impact of data modulation on pseudorange *** ETFM,DBFSK-TFMR not only realizes the navigation message transmission but also ensures the precision and unambiguous measurement range of pseudorange ***-TFMR can be used as an integrated solution for anti-jamming communication and navigation based on FH *** results show that DBFSK-TFMR has almost the same ranging performance as TFMR.

关键词： satellite navigation frequency hopping ranging navigation message transmission

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Use of sparse correlations for assessing financial markets

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Frontiers of Computer Science 2020年第6期14卷 189-191页

作者： Xin LI Guyu HU Yuhuan ZHOU Zhisong PAN College of Command and Control Engineering Army Engineering University of PLANanjing 210007China

1 Introduction In the financial market,the partial correlation is a technical tool for studying the relationships between stocks,pairs trading,and industry classification[1,2].Many of these studies[2-4]focus on traditional statistical methods,which solve the partial correlation of stocks with a large number of observation samples and a relatively small number of *** example,Jung and Chang[2]used 10 years of daily-level data to calculate the partial correlation matrix of 300 stocks.

关键词： trading financial market

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Modeling the Dynamics of Attentional Gamma Oscillations During the Encoding Process of Noise-Mixed Speech Signals

arXiv

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arXiv 2025年

作者： Feng, Duoyu Li, Jiajia Wu, Ying College of Information and Control Engineering Xi'an University of Architecture and Technology Shaanxi Xi’an710055 China Department of Neurosurgery General Hospital of Central Theater Command 627 Wuluo Road Wuhan430070 China State Key Laboratory for Strength and Vibration of Mechanical Structures National Demonstration Center for Experimental Mechanics Education School of Aerospace Engineering Xi’an Jiaotong University Xi’an710049 China

The brain's bottom-up loop for processing speech influx involves both the selective attention and the encoding of specific speech information. Previous human studies have found that such attention can be represented by the cortical gamma-rhythm oscillations. However, the underlying mechanisms remain unclear. To address this issue, this paper proposes a neural network model that incorporates speech signal input, the cochlea, the thalamus, and a balanced excitatory-inhibitory cortical neural network, with the aim of connecting real speech signals to brain cortical responses. Using this model, we explored neural oscillation patterns in response to mixed speech stimuli and background noise. The findings revealed that the peak of gamma oscillation decreased as the frequency of the pure-tone stimuli diminished. This suggests a strong correlation and coding role of gamma oscillation peaks in auditory attention. Similar results were confirmed by analyzing the rhythmic oscillations of EEG data in response to pure-tone signals. Further results indicated that dynamic gamma oscillations are involved in the encoding capacity of continuous speech input. The coding entropy of the dynamic series was found to be proportional to the complexity of the content. This suggests that gamma oscillations play multiple roles, not only in sustaining the bottom-up attentional state but also in potentially conveying specific information from external speech inputs. Finally, we found that enhancing the excitatory-inhibitory balance level properly could improve auditory attention. This finding provides a potential endogenous explanation for the dynamic switching process of brain attention in processing auditory *** Codes 92-10, 34C15 Copyright © 2025, The Authors. All rights reserved.

关键词： Encoding (symbols)

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Equivalent Version Sets Testing Method for Android Applications based on Code Analysis

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International Journal of Performability engineering 2019年第7期15卷 2008-2018页

作者： Huang, Song Yang, Sen Yao, Yongming Chen, Lele Command and Control Engineering College Army Engineering University of PLA Nanjing210007 China

The Android system is an open source mobile operating system that has been released in numerous versions. Android fragmentation is becoming more and more serious. This paper shows how different Android runtime environments affect test coverage results. To address this limitation, we run apps on all Android versions to collect coverage rate and also present an algorithm to generate equivalent test runtime-environment-set to exercise mobile apps. Our approach is to systematically test the targeted code of Android apps based on code analysis. It analyzes the decompiled code that identifies the code related to the Android SDK version and then generates the corresponding test cases. An empirical study of the practical usefulness of the technique is presented for six widely-used industrial apps. The test result shows that our equivalence test runtime-environment-set only requires less than half of all versions, which dramatically reduces the test resources. Moreover, the method coverage of these applications increased by an average of 49.3% on all versions and 46.8% on equivalent test runtime-environment-set, respectively. © 2019 Totem Publisher, Inc. All rights reserved.

关键词： Testing

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A Novel Cross-Project Software Defect Prediction Algorithm Based on Transfer Learning

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Tsinghua Science and Technology 2022年第1期27卷 41-57页

作者： Shiqi Tang Song Huang Changyou Zheng Erhu Liu Cheng Zong Yixian Ding Command&Control Engineering College Army Engineering University of PLANanjing 210000China Foreign Language College Liaoning Technical UniversityFuxin 123000China

Software Defect Prediction(SDP) technology is an effective tool for improving software system quality that has attracted much attention in recent ***,the prediction of cross-project data remains a challenge for the traditional SDP method due to the different distributions of the training and testing *** major difficulty is the class imbalance issue that must be addressed in Cross-Project Defect Prediction(CPDP).In this work,we propose a transfer-leaning algorithm(TSboostDF) that considers both knowledge transfer and class imbalance for *** experimental results demonstrate that the performance achieved by TSboostDF is better than those of existing CPDP methods.

关键词： Software Defect Prediction(SDP) transfer learning imbalance class cross-project

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Abnormal data detection method based on ant colony clustering 6

Abnormal data detection method based on ant colony clusterin...

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6th International Conference on Electronic Information Technology and Computer engineering, EITCE 2022

作者： Chen, Lianyong Song, Jinyu College of Command and Control Engineering Army Engineering University of PLA

ISBN: (纸本)9781450397148

With the rapid growth of data, low quality data emerges, and data quality problems are increasingly serious. Abnormal data detection becomes an important aspect of data quality validity measurement. We propose an improved ant colony clustering algorithm based on LF algorithm [8] by optimizing ant position, attribute weight and moving step speed and then apply it to the abnormal data detection. The improved algorithm is programmed by Python, and the experiment is carried out on UCI data set. The results show that the improved algorithm can obtain better clustering effect, and make the accuracy of abnormal data detection results higher. © 2022 Association for Computing Machinery.

关键词： Python

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