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6th International Conference on Image and Graphics Processing, ICIGP 2023

作者： Xu, Liu Wang, Xiao Yuan, Xin Mao, Zhu Xu, Xin School of Computer Science and Technology Wuhan University of Science and Technology China School of Computer Science and Technology Wuhan University of Science and Technology ChinaHubei Prov. Key Lab. of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology China

ISBN: (纸本)9781450398572

Person re-identification (re-ID), which is to connect pedestrians with the same identity in different scenarios, has been more widely used in case detection of security. The re-ID datasets and technology have contributed greatly to advancing community research. There are currently no availab.e low-light datasets due to the difficulty of collecting and lab.ling images of people in low-light scenes. As a result, the vast majority of current re-ID methods do not have the recognition capability in low-light scenes. However, the recognition of persons in the low-light surveillance video has become an important issue to be solved for urban security, which plays an important role in improving the crime-solving rate of public safety departments and protecting people's lives and property. In this paper, we propose an Adversarial Recovery Network (ARN) that, unlike other approaches, enables a re-ID model with low-light recognition capability without the need for low-light data. Specifically, we first degrade the original images to generate multi-level low-light images;then obtain recovered images through the adversarial recovery module, and finally input the recovered images into the CNN network to extract pedestrian recognition knowledge. In addition, to validate the effectiveness of the proposed method, we also propose a low-light re-ID dataset (LOLR). Extensive experiments on LOLR have shown that our method outperforms other competitors by a wide margin. The dataset and code are availab.e online at https://***/xl02111/ARN. © 2023 ACM.

关键词： Recovery

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Disentangled latent factors for muti-cause treatment effect estimation 6

Disentangled latent factors for muti-cause treatment effect ...

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2024 6th International Conference on Artificial Intelligence Technologies and Applications, ICAITA 2024

作者： Zhao, Zhuoyue Wang, Haotian Xu, Liyang Wang, Qian Tang, Yuhua Department of Intelligent Data Science National University of Defense Technology Changsha410073 China Intelligent Game and Decision Lab. Academy of Military Science Beijing100071 China Institute for Quantum Information State Key Laboratory of High-Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China

Existing methods estimate treatment effects from observational data and assume that covariates are all confounders. However, observed covariates may not directly represent confounding variables that influence both treatment and outcome. They always include variables that only affect the treatment or the outcome. In addition, for multi-dimensional binary treatments, disentangled methods are mainly designed for binary variables and ignore the impact of multi-cause treatment variables on the inference of latent factors. To address these two issues, based on variable decomposition and proxy inference, we propose the Disentangled Latent Factors for Multi-cause Treatment Estimation (DEMTE) algorithm. It utilizes an identifiable autoencoder to infer and disentangle latent factors based on the joint distribution of variables in observational data. DEMTE evaluates the treatment effect on the disentangled factors. Synthetic experiments and semi-synthetic experiments demonstrate the effectiveness of the inference and disentanglement techniques and our method achieves more accurate treatment effect estimation. © 2024 Institute of Physics Publishing. All rights reserved.

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Small-Target Infrastructure Detection with Spatial Tracking and Multi-Level Feature Extraction of Railway Track Scenes

Small-Target Infrastructure Detection with Spatial Tracking ...

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2024 International Conference on intelligent Communication, Sensing and Electromagnetics, ICSE 2024

作者： Du, Heng Cang, Huaiming Bai, Yiwen Wang, Xinyi Beijing Elephant Technology Co. Ltd Beijing China School of Transportation Science and Engineering Beihang University Beijing102206 China State Key Lab of Intelligent Transportation System Beijing100191 China School of Mechanical Engineering Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechanical System Design and Intelligent Control Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin300384 China

ISBN: (纸本)9798350354935

In the field of railway transportation, with the continuous improvement of train running speed and the continuous increase of operating mileage, it becomes particularly important to ensure the safety and integrity of small-Target infrastructure along the railway train. Although many small-Target detection methods have been proposed, the research on small-Target infrastructure detection along rail trains is very rare. To solve this problem, this paper proposes a small-Target infrastructure detection network with spatial tracking and multi-level feature extraction based on Yolov8n. The experimental results show that ST-YOLO has a 3.6% mAP improvement on the OSDaR23 dataset compared to the baseline Yolov8n, and shows significant advantages over other state-of-The-Art methods. © 2024 IEEE.

关键词： Railroads

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Multi-domain blind source separation in-band full-duplex technique considering RF impairments 4

Multi-domain blind source separation in-band full-duplex tec...

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2023 4th International Conference on Electrical, Electronic Information and Communication Engineering, EEICE 2023

作者： Zhu, Hong Yu Wang, Chao Yuan, Nai Chang Huang, Jing Jian Wang, Qing Ping College of Electronic Science and Technology National University of Defense Technology Changsha410073 China State Key Lab. of Complex Electromagnetic Environment Effects on Electronics and Information System National University of Defense Technology Changsha410073 China

In-band full-duplex (IBFD) technology can potentially improve spectrum efficiency compared to half-duplex technology. The cancellation of the self-interference (SI) signal is the key to implementing the IBFD system to achieve accurate detection of the signal of interest. In this work, we fully account for the effect of IQ imbalance, phase noise (PN), additional Gaussian white noise (AWGN), and the nonlinear caused by the RF impairments of power amplifier, and adopt the method of canceling SI signal combined with the propagation, analog, and digital domain to cancel the SI signal, but the residual SI signal is still mixed with the signal of interest in the same time and frequency domain. In addition, a novel multi-domain blind source separation (MDBSS) self-interference cancellation method implemented by using the off-the-shelf components is proposed in this paper, which can realize the separation of residual self-interference signals from the signal of interest (SOI). Additionally, the multi-domain blind source separation (MDBSS) self-interference cancellation technique is used to achieve 75 dB of self-interference cancellation performance, which is proven through numerical simulation analysis, and the SOI is successfully separated in the receiver. Moreover, the multi-domain blind source separation (MDBSS) method in this paper can be applied to radar, jammer, and communication systems in the future. © Published under licence by IOP Publishing Ltd.

关键词： Blind source separation

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An Engineering Evaluation Methodology of Heterogeneous Network for Highway Infrastructure Monitoring Data Transmission Based on Graph Theory

An Engineering Evaluation Methodology of Heterogeneous Netwo...

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International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Li, Zhenhua Zhang, Yipeng Chen, Yi School of Electrical and Control Engineering North China University of Technology Beijing100144 China Research Institute of Highway Ministry of Transport Ministry of Transport Beijing100081 China State Key Lab of Intelligent Transportation System Ministry of Transport Beijing100081 China

ISBN: (纸本)9789819639564

A large-scale highway infrastructure monitoring system requires a complex and heterogeneous data transmission network to transmit different types and large amounts of sensor data. In order to meet the requirement of rapid modeling and performance evaluation of highway infrastructure monitoring data transmission networks in engineering construction projects, this paper proposes a graph modeling method for heterogeneous transmission networks of highway infrastructure monitoring data using relevant concepts and methods of graph theory. By combining network models with engineering implementation experience, an exploratory engineering evaluation method for evaluating network carrying capacity, end-to-end latency, and end-to-end reliability has been proposed. The above methods can be used to achieve rapid modeling of networks and rapid evaluation of key performance parameters. The evaluation conclusions can serve as the basis for optimizing and implementing the engineering design of highway infrastructure monitoring systems. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Network theory (graphs)

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Comparative Analysis of Monostatic and Bistatic RCS Scattering Characteristics for Corner Reflector

Comparative Analysis of Monostatic and Bistatic RCS Scatteri...

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2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022

作者： Qiu, Mengqi Ai, Xiaofeng Xu, Zhiming Zhao, Feng National University of Defense Technology State Key Lab. of Complex Electromagnetic Environment Effects on Electronics and Information System Changsha China

ISBN: (纸本)9781665452366

The radar corner reflector stands as a typical passive jamming decoy in electronic countermeasures, which presents distinctive Radar Cross Section (RCS) features in monostatic or bistatic radar owing to its particular geometric structure. In this paper, the monostatic and bistatic RCS characteristics of a single octahedral corner reflector and octahedral corner reflector array are analyzed. The impact of different bistatic base angles on the RCS area of the corner reflector is also studied by contrasting the main lobe width and statistical parameters of the RCS, which provides theoretical analysis and data support for the effective identification of targets and corner reflector decoys. © 2022 IEEE.

关键词： Radar cross section

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A Radar Emitter Fingerprint Feature Resistant to Receiver Distortion 5

A Radar Emitter Fingerprint Feature Resistant to Receiver Di...

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5th IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2022

作者： Liu, Zhao Liu, Zheng Ruan, Guangxin National University of Defense Technology State Key Lab. of Complex Electromagnetic Environment Effects on Electronics and Information System Changsha China

ISBN: (纸本)9781665479677

In radar specific emitter identification, the non-cooperative receiver itself has fingerprint features similar to the emitter, and the fingerprint features extracted at the receiver are coupled of the two, which limited cross-receiver radar specific emitter identification. To solve this problem, a new fingerprint feature is constructed in this paper, which takes the absolute value of the forward quotient of the neighboring points of the pulse signal spectrum as the emitter feature, weakens the receiver distortion interference by improving the frequency resolution and can be quickly extracted by the Chirp-Z transform. Simulation experiments show that the proposed features can effectively overcome the fingerprint feature shift caused by receiver distortion, have good intra-class aggregation and inter-class differentiability, and achieve higher recognition accuracy than traditional features under the same conditions, proving that the proposed features has important reference value for the practical application of cross-receiver radar specific emitter identification. © 2022 IEEE.

关键词： Radar receivers

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Radar HRRP Target Recognition Based on Composite Deep Networks

Radar HRRP Target Recognition Based on Composite Deep Networ...

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2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022

作者： Kong, Yameng Feng, Dejun Zhang, Jiang National University of Defense Technology State Key Lab. of Complex Electromagnetic Environment Effects on Electronics and Information System Changsha China

ISBN: (纸本)9781665452366

Radar target recognition is to extract the acquired target echo information to achieve the determination of target category and attribute. The feature extraction and classifier in radar target recognition determine the quality of the recognition. However, the shallow structure used by traditional feature extraction algorithms and classifiers cannot fully utilize the original measurement data of radar targets, and it is easy to ignore the hidden information in the data. To solve this problem, a target recognition method based on composite deep networks is proposed. In this paper, autoencoder and convolutional neural network are combined to make full use of the complex function representation capabilities of deep network structure. The effectiveness of the proposed method is demonstrated by experiments with radar high-resolution range profile (HRRP) data based on dynamic scene simulation. The experimental results show that the proposed method of radar target recognition can achieve better performance than traditional method. © 2022 IEEE.

关键词： Radar target recognition

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Variable stiffness design optimization of fiber-reinforced composite laminates with regular and irregular holes considering fiber continuity for additive manufacturing

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Chinese Journal of Aeronautics 2025年第3期38卷 334-354页

作者： Yi LIU Zunyi DUAN Chunping ZHOU Yuan SI Chenxi GUAN Yi XIONG Bin XU Jun YAN Jihong ZHU School of Mechanics Civil Engineering&ArchitectureNorthwestern Polytechnical UniversityXi’an 710072China Research&Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen 518063China Key Lab for Airborne Hi-performance Electro-magnetic Window RISACJinan 130012China School of System Design and Intelligent Manufacturing Southern University of Science and TechnologyShenzhen 518055China State Key Laboratory of Structural Analysis for Industrial Equipment Department of Engineering MechanicsInternational Research Center for Computational MechanicsDalian University of TechnologyDalian 116024China State IJR Center of Aerospace Design and Additive Manufacturing MIIT Lab of Metal Additive Manufacturing and Innovative DesignNorthwestern Polytechnical UniversityXi’an 710072China

Fiber-reinforced composites are an ideal material for the lightweight design of aerospace structures. Especially in recent years, with the rapid development of composite additive manufacturing technology, the design optimization of variable stiffness of fiber-reinforced composite laminates has attracted widespread attention from scholars and industry. In these aerospace composite structures, numerous cutout panels and shells serve as access points for maintaining electrical, fuel, and hydraulic systems. The traditional fiber-reinforced composite laminate subtractive drilling manufacturing inevitably faces the problems of interlayer delamination, fiber fracture, and burr of the laminate. Continuous fiber additive manufacturing technology offers the potential for integrated design optimization and manufacturing with high structural performance. Considering the integration of design and manufacturability in continuous fiber additive manufacturing, the paper proposes linear and nonlinear filtering strategies based on the Normal Distribution Fiber Optimization (NDFO) material interpolation scheme to overcome the challenge of discrete fiber optimization results, which are difficult to apply directly to continuous fiber additive manufacturing. With minimizing structural compliance as the objective function, the proposed approach provides a strategy to achieve continuity of discrete fiber paths in the variable stiffness design optimization of composite laminates with regular and irregular holes. In the variable stiffness design optimization model, the number of candidate fiber laying angles in the NDFO material interpolation scheme is considered as design variable. The sensitivity information of structural compliance with respect to the number of candidate fiber laying angles is obtained using the analytical sensitivity analysis method. Based on the proposed variable stiffness design optimization method for complex perforated composite laminates, the numerical examples cons

关键词： Variable stiffness composite laminates Discrete material interpolation scheme Normal distribution fiber optimization Discrete fiber continuous filtering strategy Additive manufacturing of composite laminates

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Polarimetric CSAR Image Quality Enhancement Using Joint Sub-aperture Processing 7

Polarimetric CSAR Image Quality Enhancement Using Joint Sub-...

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7th International Conference on Signal and Image Processing, ICSIP 2022

作者： Zheng, Yiying Cui, Xingchao Wu, Guoqing Chen, Siwei National University of Defense Technology The State Key Lab. of Complex Electromagnetic Environ. Effects on Electronics and Information System Changsha China

ISBN: (数字)9781665495639

ISBN: (纸本)9781665495639

Polarimetric circular synthetic aperture radar (CSAR) can obtain the 360° full-azimuth polarization information of target. Due to the anisotropy of most manmade targets, there are great differences in the polarization scattering characteristics at different observation angles. However, full-aperture processing is the coherent accumulation imaging of the full-azimuth polarimetric data directly, which may result in the failure of obtaining the target's polarization characteristics at each azimuth. Focus on this issue, a polarimetric CSAR imaging method using joint sub-aperture processing is proposed. The proposed method mainly contains three steps. Firstly, the polarimetric data is divided into multiple sub-aperture data since manmade targets usually exhibit scattering anisotropic properly. Then, the sub-aperture images are obtained using the back projection (BP) algorithm. Finally, the joint processing image is obtained via combining the maximum SPAN value among all sub-aperture images. The effectiveness of the proposed method is validated using the simulated data of civilian vehicle. Compared with the full-aperture images, the sub-aperture images achieve better image contrast and lower image entropy. In this vein, the image quality of the polarimetric CSAR from the proposed method is enhanced. © 2022 IEEE.

关键词： Synthetic aperture radar

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