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2024 International Conference on measurement, communication, and Virtual Reality, MCVR 2024

作者： Liu, Chengyang Wu, Haibin Li, Jiarong Liu, Xinyu Yan, Siqi Wang, Aili Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application College of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology Harbin150080 China School of Electronics and Information Engineering Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)9781510691773

To address the issues of domain discrepancies and limited labeled samples in hyperspectral image classification of cross scene, this paper proposes an ensemble learning framework that integrates eXtreme Gradient Boosting (XGBoost) with confidence domain adaptation strategies to improve classification performance. First, the Houston dataset from different scenes is input separately into a deep embedding model. The hyperspectral images are embedded into a low-dimensional space, ensuring that similar spectra are represented more closely in this space, thereby capturing the intrinsic structure and distribution of the hyperspectral image dataset. Subsequently, these samples are mapped into a discriminator model, where domain adaptation techniques are employed to reduce differences in feature distributions across different scenes. Additionally, confidence-based learning is used to identify and remove noisy samples in the Houston dataset, thereby enhancing the alignment of feature distributions among various scenes. Finally, through ensemble learning, a weighted voting strategy is employed to combine multiple base classifiers into a strong classification model. This approach better captures the complex features within the data, thereby reducing the bias of individual classifiers and enhancing the overall generalization ability and robustness of the model. Experimental results on the Houston dataset demonstrate that the proposed method outperforms popular domain adaptation learning models and traditional methods in terms of classification accuracy and robustness, showcasing its potential and advantages in practical applications. This result confirms the effectiveness of combining XGBoost and confidence domain adaptation strategies in handling hyperspectral image classification. © 2025 SPIE.

关键词： Photointerpretation

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Dunhuang murals image inpainting method based on fusion classification pre-training model

Dunhuang murals image inpainting method based on fusion clas...

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2024 International Conference on Culture-Oriented Science and technology, CoST 2024

作者： Ren, Hui Zhang, Chengya Sun, Ke Ye, Chenyu Communication University of China School of Information and Communication Engineering Beijing100024 China Ministry of Culture and Tourism Key Laboratory of Audiovisual Technology and Intelligent Control System Beijing100024 China Beijing Key Laboratory of Modern Performing Arts Technology Beijing100024 China University of Pennsylvania School of Engineering and Applied Science PA19104 United States

ISBN: (纸本)9798350380347

Murals are an important part of China's cultural heritage. Because of their age, Dunhuang murals have suffered from discoloration, fading and damage. With the development of computer image restoration technology, digital image processing provides strong support for mural restoration. Although there are many research results in the field of ancient mural images, there is no pre-trained model based on ancient mural images available for migration. There is a method for Dunhuang murals image restoration based on Generative Adversarial Network (GAN) with improved ResNet50 structure as an encoder in the generator. This research result proposes a generative adversarial network model with a parallel double convolutional extraction of deep features for the generator, combined with a ternary heterogeneous joint discriminator. On this basis, this paper proposes a binary classification task to distinguish ancient mural images from modern natural style images, so that the model pre-learns features such as color, texture, and structure of mural images, which are inputted into the restoration network as a priori information, in order to achieve the effect of reducing the probability of incomplete restoration of the model, and better generalization ability. The experimental results show that the method proposed in this paper has an overall improvement in the evaluation indexes such as Peak Signal to Noise Ratio(PSNR) and Structural Similarities(SSIM) when compared with other methods, and the restored images are more in line with human subjective vision, which proves the validity of the method. © 2024 IEEE.

关键词： Generative adversarial networks

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Mainlobe DRFM Jamming Suppression via Blind Source Separation Using Tensor Decomposition 8

Mainlobe DRFM Jamming Suppression via Blind Source Separatio...

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8th International Conference on Signal and Image Processing, ICSIP 2023

作者： Dong, Luxin Wang, Bing Pan, Bunian Kong, Yukai Yu, Xianxiang Cui, Guolong University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Science and Technology on Electronic Information Control Laboratory Chengdu610036 China

ISBN: (纸本)9798350397932

This paper deals with the suppression problem of mainlobe digital radio frequency memory (DRFM) jamming especially for intra-pulse slice forwarding jamming for phased array radar. A novel mainlobe DRFM jamming suppression method resorting to blind source separation based on tensor decomposition (TD-BSS) is proposed to separate the target echo signal and jamming signal. Specifically, the received signals are reconstructed into higher-order tensor form. Further, the separating matrix optimization problem relying on tensor Canonical Polyadic (CP) decomposition principle is formulated and then solved by an enhanced line search alternating least square (ELS-ALS) algorithm. Finally, the performance of the proposed method is demonstrated through numerical simulations, showing its superior to suppress mainlobe DRFM jamming over the exiting method in terms of interrupted sampling and cyclic repeater jamming (ISCRJ) and chopping and interval (C&I) jamming. © 2023 IEEE.

关键词： Blind source separation

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Collision-Free Formation control of Multi-agent System with Low Oscillation 7th

Collision-Free Formation Control of Multi-agent System with ...

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Li, Jingyi Han, Haoran Lv, Maolong Sun, Chenyang Cheng, Jian Glassgow School University of Electronic Science and Technology of China Chengdu611731 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China Air Traffic Control and Navigation College Air Force Engineering University Xian710051 China

ISBN: (纸本)9789819733279

In recent years, multi-agent systems are becoming more complex, scalable, adaptive, and integrating machine learning for enhanced capabilities. A popular approach for addressing this challenge involves the utilisation of artificial potential functions (APF). In this article, we address the problem of obstacle avoidance and achieving a predefined formation, particularly promoting smooth trajectory generation in multi-agent formation control. We employ the APF method to enhance the smoothness of the agents’ movement paths during these tasks. Collision damping terms are generated by differentiating the gradient of APF with respect to time. Incorporating generated damping terms in the APF control, we suppress irregular fluctuations during agents’ motion procedures. To ensure stability in the multi-agent system, we design an energy-based Lyapunov function and utilise it for proof. Simulation examples are presented to validate the theoretical results and demonstrate the advantages of the proposed control algorithm. Through these simulations, it becomes evident that the collision-free formation control algorithm enhances the smoothness of the agents’ trajectories. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Multi agent systems

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Semi-asynchronous personalized federated learning for short-term photovoltaic power forecasting

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Digital communications and Networks 2023年第5期9卷 1221-1229页

作者： Weishan Zhang Xiao Chen Ke He Leiming Chen Liang Xu Xiao Wang Su Yang College of Computer Science and Technology China University of Petroleum(East China)China Sichuan Energy Internet Research Institute Tsinghua UniversityChina School of Computer and Communication Engineering Beijing University of Science and TechnologyChina State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Computer Science Fudan UniversityChina

Accurate forecasting for photovoltaic power generation is one of the key enablers for the integration of solar photovoltaic systems into power *** deep-learning-based methods can perform well if there are sufficient training data and enough computational ***,there are challenges in building models through centralized shared data due to data privacy concerns and industry *** learning is a new distributed machine learning approach which enables training models across edge devices while data reside *** this paper,we propose an efficient semi-asynchronous federated learning framework for short-term solar power forecasting and evaluate the framework performance using a CNN-LSTM *** design a personalization technique and a semi-asynchronous aggregation strategy to improve the efficiency of the proposed federated forecasting *** evaluations using a real-world dataset demonstrate that the federated models can achieve significantly higher forecasting performance than fully local models while protecting data privacy,and the proposed semi-asynchronous aggregation and the personalization technique can make the forecasting framework more robust in real-world scenarios.

关键词： Photovoltaic power forecasting Federated learning Edge computing CNN-LSTM

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An Acoustic Dataset Construction Method of Chinese Traditional Musical Instrument Guqin

An Acoustic Dataset Construction Method of Chinese Tradition...

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2024 International Conference on Culture-Oriented Science and technology, CoST 2024

作者： Liu, Jingyu Yang, Chen Li, Zijin Jiang, Wei State Key Laboratory of Media Convergence and Communication Communication University of China 100024 China Ministry of Culture and Tourism Communication University of China Key Laboratory of Acoustic Visual Technology and Intelligent Control System 100024 China Communication University of China School of Information and Communication Engineering 100024 China Central Conservatory of Music Ai Music and Music Information Technology Department 100032 China

ISBN: (纸本)9798350380347

With the acceleration of modernization and the rapid development of popular music, the splendid culture of Guqin, a type of Chinese traditional musical instrument which has lasted for nearly 1,000 years, is facing a serious inheritance crisis. Therefore, how to make full use of modern technical means to digitally store and manage the valuable traditional music cultural resources is the current *** response to the aforementioned challenges, this study focuses on the Guqin, an emblematic Chinese traditional musical instrument. By employing advanced audio feature extraction techniques, this research systematically analyzes the diverse playing techniques of Guqin, thereby establishing a comprehensive acoustic dataset. This endeavor not only facilitates the digital preservation and management of the Guqin's musical heritage but also pioneers a standardized framework for the creation of acoustic datasets. This framework serves as a benchmark for the development of similar datasets, thereby promoting the conservation and standardization of resources associated with Chinese traditional musical instruments. This will provide a scientific basis for the inheritance, standardized management and construction of more acoustic datasets of ethnic musical instruments in the future. © 2024 IEEE.

关键词： Musical instruments

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Reconfigurable Feed Network For Multi-Polar Antennas

Reconfigurable Feed Network For Multi-Polar Antennas

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2023 International Applied Computational Electromagnetics Society Symposium, ACES-China 2023

作者： Shi, Fengyuan Chen, Juan Xu, Wenjing Yang, Jianfeng Tan, Jing School of Information and Communication Engineering Xi'an Jiaotong University Xi'an China School of Information and Control Engineering Xi'an University of Architecture and Technology Xi'an China Shanghai Aerospace Electronic Technology Institute Department of Antenna Shanghai China

ISBN: (纸本)9781733509657

A reconfigurable feed network for multi-polar antennas is proposed in this article. It uses PIN diodes to make reconfigurable structure and change the output. The network can produce four kinds of output signal with only one input port. It has four output ports with specific phase delay, which uses differential-feed to suppress cross polarization. The simulated results show that the operating frequency range of the feed network is about 2.5-3.5 GHz with good matching and isolation performance. The variable polarization and wide band make it be a good choice for late-model multi-polar antennas with both linear and circular polarization. © 2023 Applied Computational Electromagnetics Society (ACES).

关键词： Antenna feeders

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Exploiting the Direct Link in IRS Assisted NOMA Networks with Hardware Impairments

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Computer Modeling in engineering & Sciences 2023年第7期136卷 767-785页

作者： Ziwei Liu Xinwei Yue Shuo Chen Xuliang Liu Yafei Wang Wanwei Tang Key Laboratory of Modern Measurement&Control Technology Ministry of EducationSchool of Information and Communication EngineeringBeijing Information Science and Technology UniversityBeijing100101China The Party Committee Office Yellow River Conservancy Technical InstituteKaifeng475001China Tangshan University Tangshan063000China

Hardware impairments(HI)are always present in low-cost wireless *** paper investigates the outage behaviors of intelligent reflecting surface(IRS)assisted non-orthogonal multiple access(NOMA)networks by taking into account the impact of ***,we derive the approximate and asymptotic expressions of the outage probability for the IRS-NOMA-HI *** on the asymptotic results,the diversity orders under perfect self-interference cancellation and imperfect self-interference cancellation scenarios are obtained to evaluate the performance of the considered *** addition,the system throughput of IRS-NOMA-HI is discussed in delay-limited *** obtained results are provided to verify the accuracy of the theoretical analyses and reveal that:1)The outage performance and system throughput for IRS-NOMA-HI outperforms that of the IRS-assisted orthogonal multiple access-HI(IRS-OMA-HI)networks;2)The number of IRS elements,the pass loss factors,the Rician factors,and the value of HI are pivotal to enhancing the performance of IRS-NOMAHI networks;and 3)It is recommended that effective methods of reducing HI should be used to ensure system performance,in addition to self-interference cancellation techniques.

关键词： Hardware impairments imperfect SIC intelligent reflecting surface non-orthogonal multiple access outage probability

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Sparse Recovery Space Time Adaptive Processing Based on Log-Determinant 15

Sparse Recovery Space Time Adaptive Processing Based on Log-...

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15th International Conference on Signal Processing Systems, ICSPS 2023

作者： Chang, Yuqing Du, Xiaolin Jin, Biao Tian, Tuanwei Li, Jianbo School of Computer and Control Engineering Yantai University Yantai China Ocean College Jiangsu University of Science and Technology Zhenjiang China School of Physics and Electronics Henan University Kaifeng China School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781510675056

Airborne radars usually face non-uniform clutter environments, and it is difficult to obtain enough independent and identical distributed (i.i.d.) training samples, which degrades the clutter suppression performance of space time adaptive processing (STAP). To address the problem, a log-determinant sparse rocovery based STAP (LSR-STAP) method is proposed in this paper. Through the sparse recovery theory of low-rank matrices and the prior knowledge of clutter covariance matrices, the corresponding problem is modeled using the log-determinant (LogDet) approximation of the rank function, and the solution of the resulting nonconvex optimization problem is derived in the framework of the symmetric alternating direction method of multipliers (S-ADMM). The simulation results show the superiority of the proposed method over similar algorithms. © 2024 SPIE.

关键词： Space time adaptive processing

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Receiver-Agnostic Radio Frequency Fingerprint Identification via Feature Disentanglement 24

Receiver-Agnostic Radio Frequency Fingerprint Identification...

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24th IEEE International Workshop on Signal Processing Advances in Wireless communications, SPAWC 2023

作者： Liu, Hongli Zhu, Shaoxian Yang, Liu Li, Qiang Lin, Jingran University of Electronic Science and Technology of China School of Information and Communication Engineering China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing100089 China

ISBN: (纸本)9781665496261

The Radio Frequency Fingerprint Identification (RFFI) technique utilizes the subtle and unintentional modulation present in the transmitted RF waveform, caused by the non-ideal characteristics of the device, to uniquely identify an emitter. RFFI has recently gained significant attention, and deep learning (DL) approach has achieved superior performance. In this paper, we study the DL-based RFFI with an emphasis on the fingerprint contamination by receivers. Specifically, during reception, the receivers' fingerprints are inevitably introduced, and different receivers affect emitters' fingerprints differently, thereby degrading recognition performance when the RFFI recognition model is deployed at different receivers. To mitigate the receivers' effect, we propose a receiver-independent recognition model based on the idea of feature disentanglement. With the aid of some carefully designed model structure, loss functions and training strategy, the proposed model learns a feature space, in which the emitters' features are disentangled from the receivers', through multi-receiver's training data. When deploying the model on a new receiver, the emitter-dependent features are exploited to achieve receiver-independent identification. Experiments on two real-world datasets demonstrate that our method significantly outperforms the baseline method without considering fingerprint contamination. © 2023 IEEE.

关键词： Deep learning

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