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International Conference on Data engineering

作者： Jiangtao Lu Song Huang College of Command and Control Engineering Army Engineering University of PLA Nanjing China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

There has been a growing number of software supply chain vulnerabilities disclosed annually, posing increasingly formidable challenges to vulnerability validation. Timely validation plays a critical role in mitigating the security risks across the entire software supply chain. Whenever a new vulnerability is disclosed, it may be difficult to generate a corresponding PoC for verification in a short time. Expediting the validation process can be achieved through reference to similar PoC reports, potentially reducing this time. Retrieving similar PoC reports manually is labor-intensive and time-consuming due to their distributed nature across different data sources. Moreover, PoC reports encompass diverse trigger methods, including code, instruction, and action. There is a limitation in the current modeling of different types of trigger methods in PoC reports, as it mainly focuses only on code-based trigger methods while disregarding other types of trigger methods. To tackle the issues, this Ph.D. research uses graph-based method to model all types of PoC and proposes a PoC report recommendation model utilizing graph neural network (PR-GNN) to provide related PoC reports when facing a new vulnerability. In this work, a collection of heterogeneous PoC reports from various sources is assembled and modeled as PoC heterogeneous graphs. PR-GNN accurately measures similarity by incorporating graph-level embedding comparison and fine-grained comparison of trigger method type nodes. To our knowledge, this research represents the first attempt to provide more accurate and efficient recommendations for PoC reports to assist security professionals in timely vulnerability verification and maintaining software supply chain security.

关键词： Accuracy Codes Soft sensors Supply chains Data collection Information retrieval Data engineering

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Cramer-Rao Bound for Direction-of-Arrival Estimation Based on Moving Array

Cramer-Rao Bound for Direction-of-Arrival Estimation Based o...

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IEEE Advanced Information Technology, Electronic and Automation control Conference (IAEAC)

作者： Shaozhuang Wu Qiao Su Lu Bai Sihang Yang Chen Yang Zhiqing Ma College of Communications Engineering Army Engineering University of PLA Nanjing China College of Command and Control Engineering Army Engineering University of PLA Nanjing China

This paper studies the Cramer-Rao bound (CRB) for direction-of-arrival (DOA) estimation based on moving array. Two cases that may appear in some applications are discussed, namely the case with more extended antenna number than the source number (overdetermined case) and the opposite case (underdetermined case). Firstly, the CRB of DOA estimation for the moving array under the overdetermined case is derived. Then, we deduce the CRB of DOA estimation for the moving array under the underdetermined case and the scope of application for this CRB is also given. Several numerical examples for the derived CRBs are provided by considering both the overdetermined and underdetermined cases.

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Learning predictive control of wheeled mobile robot with communication delays 3

Learning predictive control of wheeled mobile robot with com...

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2024 3rd International Conference on Electrical, control and Information Technology, ECITech 2024

作者： Wu, Ruizhuo Xu, Xin He, Ming Liu, Daxue Zhang, Xinglong College of Intelligence Science and Technology National University of Defense Technology Hunan Changsha410073 China Institute of Command and Control Engineering Army Engineering University Jiangsu Nanjing210007 China

This paper proposed a learning predictive control method for wheeled mobile robots with communication delays. In consideration of the property in the Networked control System (NCS), communication delay was divided into two types: delay from sensor to controller and delay from controller to actuator. For each type of delay, state prediction and state augmentation techniques were utilized respectively to diminish their passive impact on the control of WMR. Then receding horizon reinforcement learning approach was adopted to learn the optimal policy based on the delay compensation. Finally, simulations were performed applying the proposed method to the trajectory tracking of WMR and showed the validity of the proposed scheme. © 2024 Published under licence by IOP Publishing Ltd.

关键词： Mobile robots

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Empirical Analysis of Vulnerabilities Life Cycle in Golang Ecosystem 24

Empirical Analysis of Vulnerabilities Life Cycle in Golang E...

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44th ACM/IEEE International Conference on Software engineering, ICSE 2024

作者： Hu, Jinchang Zhang, Lyuye Liu, Chengwei Yang, Sen Huang, Song Liu, Yang College of Command and Control Engineering Army Engineering University of Pla Nanjing China Continental-NTU Corporate Lab Nanyang Technological University Singapore Singapore School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Academy of Military Science Nanjing China

ISBN: (纸本)9798400702174

Open-source software (OSS) greatly facilitates program development for developers. However, the high number of vulnerabilities in open-source software is a major concern, including in Golang, a relatively new programming language. In contrast to other commonly used OSS package managers, Golang presents a distinctive feature whereby commits are prevalently used as dependency versions prior to their integration into official releases. This attribute can prove advantageous to users, as patch commits can be implemented in a timely manner before the releases. However, Golang employs a decentralized mechanism for managing dependencies, whereby dependencies are upheld and distributed in separate repositories. This approach can result in delays in the dissemination of patches and unresolved vulnerabilities. To tackle the aforementioned concern, a comprehensive investi-gation was undertaken to examine the life cycle of vulnerability in Golang, commencing from its introduction and culminating with its rectification. To this end, a framework was established by gathering data from diverse sources and systematically amalgamating them with an algorithm to compute the lags in vulnerability patching. It turned out that 66.10% of modules in the Golang ecosystem were affected by vulnerabilities. Within the vulnerability life cycle, we found two kinds of lag impeding the propagation of vulnerability fixing. By analyzing reasons behind non-lagged and lagged vul-nerabilities, timely releasing and indexing patch versions could significantly enhance ecosystem security. © 2024 ACM.

关键词： Open source software

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An Energy Stable Bound-Preserving Finite Volume Scheme for the Allen-Cahn Equation Based on Operator Splitting Method

SSRN

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SSRN 2024年

作者： Peng, Gang Li, Yuan Command and Control Engineering College Army Engineering University of PLA Nanjing China Department of Basic Education Army Engineering University of PLA Nanjing China

In this paper, an energy stable bound-preserving finite volume scheme is constructed for the Allen-Cahn equation. The first-order operator splitting method is used to split the original equation into a nonlinear equation and a heat equation in each time interval. The nonlinear equation is solved by the backward Euler scheme, and the heat equation is discretized by the extremum-preserving scheme. The harmonic averaging points on cell facets are employed to define auxiliary unknowns, which enable our discrete scheme to be applicable to unstructured meshes. The energy stable and bound-preserving analysis of the finite volume scheme is also presented. Numerical experiments illustrate that this linear numerical scheme is practical and accurate in solving the Allen-Cahn equation. © 2024, The Authors. All rights reserved.

关键词： Nonlinear equations

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Simple Optical Frequency Comb Generation Based on the Optoelectronic Oscillator 20

Simple Optical Frequency Comb Generation Based on the Optoel...

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20th International Conference on Optical Communications and Networks, ICOCN 2022

作者： Zhang, X. Pu, T. Zheng, J. Li, J. He, Q. Wu, G. Zhao, J. Chen, S. Mou, W. College of Communications Engineering Army Engineering University of PLA Nanjing China College of Command and Control Engineering Army Engineering University of PLA Nanjing China College of Information and Communications National University of Defense Technology Wuhan China

ISBN: (数字)9781665458986

ISBN: (纸本)9781665458986

The simple optical frequency comb (OFC) with tunable free spectra range (FSR) based on the optoelectronic oscillator (OEO) is proposed and experimentally demonstrated. Thanks to the gain switching effect when the big microwave signal, which is generated by the OEO, is directly modulated to the laser, no optical modulator or microwave source are employed in the system. The FSR can be tuned from 4.33GHz to 7.1GHz by simply changing the bias current of the laser. There are at least 9 combs within the 10-dB power fluctuation. When FSR is 5.4GHz, the phase noise of basic microwave signal reaches to-122dBc/Hz@10kHz. © 2022 IEEE.

关键词： Optoelectronic devices

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Paying more attention to local contrast: improving infrared small target detection performance via prior knowledge

arXiv

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arXiv 2024年

作者： Wang, Peichao Wang, Jiabao Chen, Yao Zhang, Rui Li, Yang Miao, Zhuang Command and Control Engineering College Army Engineering University of PLA Jiangsu Nanjing210017 China

The data-driven method for infrared small target detection (IRSTD) has achieved promising results. However, due to the small scale of infrared small target datasets and the limited number of pixels occupied by the targets themselves, it is a challenging task for deep learning methods to directly learn from these samples. Utilizing human expert knowledge to assist deep learning methods in better learning is worthy of exploration. To effectively guide the model to focus on targets’ spatial features, this paper proposes the Local Contrast Attention Enhanced infrared small target detection Network (LCAE-Net), combining prior knowledge with data-driven deep learning methods. LCAE-Net is a U-shaped neural network model which consists of two developed modules: a Local Contrast Enhancement (LCE) module and a Channel Attention Enhancement (CAE) module. The LCE module takes advantages of prior knowledge, leveraging handcrafted convolution operator to acquire Local Contrast Attention (LCA), which could realize background suppression while enhance the potential target region, thus guiding the neural network to pay more attention to potential infrared small targets’ location information. To effectively utilize the response information throughout downsampling progresses, the CAE module is proposed to achieve the information fusion among feature maps’ different channels. Experimental results indicate that our LCAE-Net outperforms existing state-of-the-art methods on the three public datasets NUDT-SIRST, NUAA-SIRST, and IRSTD-1K, and its detection speed could reach up to 70 fps. Meanwhile, our model has a parameter count and Floating-Point Operations (FLOPs) of 1.945M and 4.862G respectively, which is suitable for deployment on edge devices. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Abnormal data detection method based on ant colony clustering 22

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

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Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer engineering

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

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.

关键词：

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Decoupling control of Geometrical Consistency for Formation UAVs Based on Node Importance Identification of Temporal Network

Decoupling Control of Geometrical Consistency for Formation ...

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Energy and Electrical engineering (EEE), International Conference on

作者： Yi Zhu Jiguang Li Kai Zhang Hui Tian Kan Zhou College of Field Engineering Army Engineering University of PLA Nanjing China School of Aircraft Xi'an Aeronautical Institute Xi'an China College of Command and Control Engineering Army Engineering University of PLA Nanjing China

ISBN: (数字)9798350341768

ISBN: (纸本)9798350341775

To address the issue of geometric consistency in the formation of unmanned aerial vehicles (UAVs) during flight, from the perspective of formation control, an identification method for the core nodes of the temporal network is introduced to analyze and evaluate the geometric position error of the UAV formation. A decoupling controller is designed to improve the geometric consistency of the UAV formation. During the design process, the UAVs' dynamics model is established and a Leader-Follower formation controller is designed based on LMI and virtual Leader. The node importance identification of the UAV formation is determined by constructing a time series network according to the similarity based super adjacency matrix (SSAM) of nodes, and the controller is improved in the light of the results of node importance recognition. In the simulation process, the tracking effect of the dual aircraft formation was first verified and analyzed. Then, taking the 6-aircraft formation as an example, the relationship between node importance and geometric consistency was analyzed, and the controller was improved. The simulation results showed that the improved controller, taking into account the factors affecting node importance, can improve the geometric consistency of the formation, providing meaningful reference value for the study of maintaining and consistency control of Formation organization and formation maintenance formations.

关键词： Simulation Time series analysis Process control Organizations Autonomous aerial vehicles Vectors Aircraft Vehicle dynamics Optimization Drones

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A Semisupervised Graph Convolutional Neural Network--based Earth Station Network planning Method

A Semisupervised Graph Convolutional Neural Network--based E...

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Ubiquitous Communication (Ucom), International Conference on

作者： Honghua Zhao Xuemiao Sun Guyu Hu Ke Ding Institute of Command and Control Engineering Army Engineering University NANJING CHINA

ISBN: (数字)9798350374216

ISBN: (纸本)9798350374223

Network planning is used for the rational allocation of satellite communication resources. In this paper, the low utilization and disorderly allocation problems of satellite communication network resources caused by the increasing demand for satellite communication are addressed. An algorithm based on a semisupervised graphical convolutional neural network for network planning is proposed to achieve a rational division of communication resources while improving the resource utilization rate. Based on the network management architectures of existing satellite communication systems, a system model is constructed based on the interoperability conditions of satellites and Earth stations, and the semisupervised graph convolution method is applied to Earth station network planning. Reasonable simulation parameters are set according to the characteristics of geosynchronous satellite networks, and simulation experiments are conducted on the Python platform. The simulation results show that the proposed method can rapidly group Earth stations according to the set optimization objectives and improve user experience and network resource utilization.

关键词： Earth Convolution Simulation Neural networks User experience planning Resource management Convolutional neural networks Satellite communications Optimization

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