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IEEE International Conference on Cyber technology in Automation, Control, and Intelligent Systems

作者： Binghao Yang Dongliang Wang Ziling Wen Wu Wei Wenji Li Zhun Fan Key Lab of Digital Signal and Image Processing of Guangdong Province Shantou University Guangdong China School of Department of Electronic and Information Engineering Shantou University Guangdong China Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Guangzhou Guangdong China School of Automation Science and Engineering South China University of Technology Guangzhou Guangdong China

ISBN: (数字)9798331506056

ISBN: (纸本)9798331506063

The application of unmanned vehicles in industrial cargo transportation is becoming increasingly widespread, particularly playing a significant role in transporting heavy-duty goods. However, when performing such tasks, whether in single-vehicle transportation or multi-vehicle collaborative transportation, the occurrence of wheel failures can potentially lead to serious safety accidents. Therefore, this paper proposes a fault-tolerant control scheme, establishing a kinematic model considering disturbances and failures, and adopts model predictive control for trajectory tracking. Additionally, an extended state observer is introduced to observe system states and counteract disturbances. To address potential failure issues, a controller reconstruction strategy is proposed. Finally, the effectiveness and superiority of the controller is validated through numerical simulation and comparative experiment.

关键词： Fault tolerance Trajectory tracking Fault tolerant systems Wheels Transportation Kinematics Predictive models Safety State estimation Predictive control

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Attention based Temporal convolutional network for Ф-OTDR event classification 19

Attention based Temporal convolutional network for Ф-OTDR e...

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19th International Conference on Optical Communications and Networks, ICOCN 2021

作者： Tian, Manling Dong, Hui Yu, Kuanglu Beijing Key Laboratory of Modern Information Science and Network Technology Beijing Jiaotong University Beijing100044 China Institute of Information Science Beijing Jiaotong University Beijing100044 China Signal Processing. RF & Optical Department Institute for Inforcomm Research A*Star Research Entities Singapore138632 Singapore

We designed a new attention based temporal convolutional network combined with bidirectional long short term memory model named ATCN-BiLSTM for Ф-OTDR event classification, achieving average classification accuracy o... 详细信息

ISBN: (纸本)9781665424462

关键词： Convolution

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Monitoring the Growth Status of Winter Wheat by Using the Machine Learning Algorithm and the Fusion of Spectral and Texture Features Derived from the Uav Remote Sensing

SSRN

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SSRN 2023年

作者： Su, YiMing Li, Hao Chen, Ping Zhang, Zhen Zhao, Yu Fahad, Shafiq Wang, Chao Yan, XiaoBin Liang, ZiHao Zhao, ShuangMei Qiao, XingXing Feng, MeiChen Song, XiaoYan Xiao, LuJie Yang, WuDe College of Agronomy Shanxi Agriculture University Taigu030801 China Shanxi Key Laboratory of Signal Capturing & Processing North University of China Shanxi Taiyuan030051 China Nanjing University of Information Science and Technology China Department of Botany Government College University Punjab Lahore54000 Pakistan

The rapid development of unmanned aerial vehicle (UAV) remote sensing could provide critical data support for estimating the real-time crop growth status. In this study, the vegetation indexes (VI) and texture features (T) in multispectral images were extracted, and the entropy method was used to construct a comprehensive growth index (CGI) which ultimately reflected growth of winter wheat. Afterwards, the predictive models of winter wheat growth were established and evaluated by using the machine learning modeling methods of BPNN, RF and SVM with the different combinations of spectral and texture features. The results revealed that the correlation of CGI was improved compared with other single growth indicators, and it also reached a significant correlation level (r>0.6) with the texture features based on the red edge band. For different input variables, the CGI estimation accuracy for most models based on the combination VI and T were better than that of VI or T alone with the mean R2=0.858, followed by VI (R2=0.825). It also indicated that fusing the spectral and texture features was helpful to improve the predictive performance of winter wheat growth. Among all the crop growth indicator, the CGI achieved the best performance as well by using the RF and VI+T variable (R2=0.888, RMSE=0.041, RPD=2.989), which confirmed the application potential of RF machine learning method in estimating the winter wheat growth. Lastly, it also proved the feasibility of constructing comprehensive indicators to monitor wheat growth by entropy method as the fact that the estimated results of CGI models were also better than most of the single growth indicators with the mean R2 =0.819 for all the CGI models. This study would provide theoretical and practical reference for monitoring the winter wheat growth by using the UAV-based multispectral technology. © 2023, The Authors. All rights reserved.

关键词： Antennas

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Few-Shot Medical Image Segmentation with High-Fidelity Prototypes

arXiv

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arXiv 2024年

作者： Tang, Song Yan, Shaxu Qi, Xiaozhi Gao, Jianxin Ye, Mao Zhang, Jianwei Zhu, Xiatian IMI Group School of Health Sciences and Engineering University of Shanghai for Science and Technology Shanghai China TAMS Group Department of Informatics Universität Hamburg Hamburg Germany School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Surrey Institute for People-Centred Artificial Intelligence Centre for Vision Speech and Signal Processing University of Surrey Guildford United Kingdom Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China

Few-shot Semantic Segmentation (FSS) aims to adapt a pretrained model to new classes with as few as a single labelled training sample per class. Despite the prototype based approaches have achieved substantial success, existing models are limited to the imaging scenarios with considerably distinct objects and not highly complex background, e.g., natural images. This makes such models suboptimal for medical imaging with both conditions invalid. To address this problem, we propose a novel Detail Self-refined Prototype Network (DSPNet) to constructing high-fidelity prototypes representing the object foreground and the background more comprehensively. Specifically, to construct global semantics while maintaining the captured detail semantics, we learn the foreground prototypes by modelling the multi-modal structures with clustering and then fusing each in a channel-wise manner. Considering that the background often has no apparent semantic relation in the spatial dimensions, we integrate channel-specific structural information under sparse channel-aware regulation. Extensive experiments on three challenging medical image benchmarks show the superiority of DSPNet over previous state-of-the-art methods. The code and data are available at https://***/tntek/DSPNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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A Probabilistic Jamming Strategy Model for Frequency Agility radar Anti-Jamming Problem

A Probabilistic Jamming Strategy Model for Frequency Agility...

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IEEE International Conference on radar

作者： Youlin Fan Bo Jiu Wenqiang Pu Kang Li Hailin Li Hongwei Liu National Laboratory of Radar Signal Processing Xidian University Xi'an China Shenzhen Research Institute of Big Data Shenzhen China Beijing Institute of Tracking and Telecommunication Technology Beijing China

ISBN: (纸本)9781665468893

With the development of electronic warfare, the jammer is much smarter than before and can adopt different strategies to jam the radar. To design efficient anti-jamming strategies, the radar anti-jamming problem is recently modeled as a Markov decision process (MDP) problem and reinforcement learning (RL) algorithms are used to solve it. However, common RL algorithms needs a large number of interaction samples, which is not realistic in practice. To address this problem, we propose a probabilistic jamming strategy model for the frequency agility radar (FA) that can learn the unknown jamming strategy through fewer interaction samples. Based on proposed model, a jamming strategy is first expressed by a series of unknown probability matrixes and weights. Then, the FA radar is used to induce jammer to emit jamming signals and collects samples of radar-jammer interaction in MDP. The model parameters are estimated by means of maximum likelihood estimation (MLE). Finally, RL algorithm can be used to train the anti-jamming strategies through offline interaction. Simulation results illustrate the effectiveness of the proposed model.

关键词： Maximum likelihood estimation Simulation radar Reinforcement learning Markov processes Probabilistic logic Iterative algorithms

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Research on OTFS Performance Based on Joint-Sparse Fast Time-Varying Channel Estimation 14th

Research on OTFS Performance Based on Joint-Sparse Fast Time...

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14th EAI International Conference on Communications and Networking in China, ChinaCom 2019

作者： Gao, Wenjing Li, Shanshan Zhao, Lei Guo, Wenbin Peng, Tao Wireless Signal Processing and Network Laboratory Beijing University of Posts and Telecommunications Beijing100876 China Science and Technology on Information Transmission and Dissemination in Communication Networks Laboratory Beijing China

ISBN: (纸本)9783030411138

Contraposing the problem of high pilot overhead and poor estimation performance for OFDM system in fast time-varying channels, a novel channel estimation method based on joint-sparse basis expansion model is proposed. In order to resist the inter-carrier interference (ICI) of OFDM system over fast time-varying channel, we introduce the OTFS (Orthogonal Time Frequency Space) technique and propose an implementation scheme of OTFS system based on time-frequency domain channel estimation. Simulation results demonstrate that the proposed OTFS system has higher reliability and better adaptability than the OFDM system in high dynamic scenarios. © 2020, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Frequency domain analysis

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A Dynamic Transformer Network for Vehicle Detection

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IEEE Transactions on Consumer Electronics 2025年

作者： Tian, Chunwei Liu, Kai Zhang, Bob Huang, Zhixiang Lin, Chia-Wen Zhang, David Harbin Institute of Technology School of Computer Science and Technology Harbin15001 China Macao Special Administrative Region of China PAMI Research Group University of Macau 999078 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Key Laboratory of Electromagnetic Environmental Sensing Hefei230601 China National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu Taiwan School of Data Science Shenzhen518172 China

Stable consumer electronic systems can assist traffic better. Good traffic consumer electronic systems require collaborative work between traffic algorithms and hardware. However, performance of popular traffic algorithms containing vehicle detection methods based on deep networks via learning data relation rather than learning differences in different lighting and occlusions is limited. In this paper, we present a dynamic Transformer network for vehicle detection (DTNet). DTNet utilizes a dynamic convolution to guide a deep network to dynamically generate weights to enhance adaptability of an obtained detector. Taking into relations of different information account, a mixed attention mechanism based channel attention and Transformer is exploited to strengthen relations of channels and pixels to extract more salient information for vehicle detection. To overcome the drawback of difference in an image account, a translation-variant convolution relies on spatial location information to refine obtained structural information for vehicle detection. Experimental results illustrate that our DTNet is competitive for vehicle detection. Code of the proposed DTNet can be obtained at https://***/hellloxiaotian/DTNet. © 2025 IEEE.

关键词： Dynamic convolution Hybrid Attention Mechanism Transformer Vehicle Detection

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Hyper-Parameter Auto-Tuning for Sparse Bayesian Learning

arXiv

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arXiv 2022年

作者： Gao, Dawei Guo, Qinghua Jin, Ming Liao, Guisheng Eldar, Yonina C. The Hangzhou Institute of Technology Xidian University Hangzhou311200 China The National Laboratory of Radar Signal Processing Xidian University Xi’an710071 China The School of Electrical Computer and Telecommunications Engineering University of Wollongong NSW2522 Australia The Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China The Faculty of Math and CS Weizmann Institute of Science Rehovot7610001 Israel

Choosing the values of hyper-parameters in sparse Bayesian learning (SBL) can significantly impact performance. However, the hyper-parameters are normally tuned manually, which is often a difficult task. Most recently, effective automatic hyper-parameter tuning was achieved by using an empirical auto-tuner. In this work, we address the issue of hyper-parameter auto-tuning using neural network (NN)-based learning. Inspired by the empirical auto-tuner, we design and learn a NN-based auto-tuner, and show that considerable improvement in convergence rate and recovery performance can be achieved. Copyright © 2022, The Authors. All rights reserved.

关键词： Tuners

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Self-decoupling 5G MIMO Antenna via Grounding for Mobile Phones

Self-decoupling 5G MIMO Antenna via Grounding for Mobile Pho...

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IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Wei Zhou Junwei Qi Yingsong Li College of Information and Communication Engineering Harbin Engineering University (HEU) China College of Computer Science and Technology HEU China Key Laboratory of Intelligent Computing Signal Processing Ministry of Education Anhui University Hefei China

This paper presents a self-decoupling high isolation terminal antenna for mobile phones which can applied to 5G multiple-input-multiple-output (MIMO) communication systems. The significant decoupling effect is achieved by introducing only one ground point in each asymmetric antenna pair with an interval of very close (2mm). Through the simulations and optimizations, it can be obtained that the bandwidth of the two antennas is different owing-to the asymmetric structures of the constructed MIMO antenna. The -10 dB resonant frequency ranges of the two antennas are 4.65 - 5.17 GHz and 4.45 - 5.58 GHz, respectively, but the coincidence bandwidth of the two antennas covers the 5G band of 4.8 - 5 GHz, and the coupling of the proposed MIMO antenna in the same bandwidth is lower than -18dB as well.

关键词： Couplings Wireless communication 5G mobile communication Grounding Resonant frequency Bandwidth Mobile antennas

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21-cm foreground removal using AI and the frequency-difference technique

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Physical Review D 2024年第6期109卷 063509-063509页

作者： Feng Shi Haoxiang Chang Le Zhang Huanyuan Shan Jiajun Zhang Suiping Zhou Ming Jiang Zitong Wang School of Aerospace Science and Technology Xidian University Xi’an 710126 People’s Republic of China Peng Cheng Laboratory No. 2 Xingke 1st Street Shenzhen 518000 People’s Republic of China School of Physics and Astronomy Sun Yat-sen University 2 Daxue Road Tangjia Zhuhai 519082 People’s Republic of China CSST Science Center for the Guangdong-Hong Kong-Macau Greater Bay Area Zhuhai 519082 People’s Republic of China Shanghai Astronomical Observatory (SHAO) Nandan Road 80 Shanghai 200030 China University of Chinese Academy of Sciences Beijing 100049 People’s Republic of China National Key Laboratory of Radar Signal Processing Xidian University Xi’an 710126 People’s Republic of China

The deep learning technique has been employed in removing foreground contaminants from 21-cm intensity mapping, but its effectiveness is limited by the large dynamic range of the foreground amplitude. In this study, we develop a novel foreground removal technique grounded in U-Net networks. The essence of this technique lies in introducing an innovative data preprocessing step specifically, utilizing the temperature difference between neighboring frequency bands as input. Combining with the frequency difference, we refer to our method as the UNet-fd (UNet frequency-difference), where the U-Net structure is the same as that in deep21. Based on our tests, we demonstrate that this frequency-difference preprocessing technique can substantially reduce the dynamic range of foreground amplitudes by approximately two orders of magnitude. This reduction proves to be highly advantageous for the U-Net foreground removal. We observe that the HI signal can be reliably recovered, as indicated by the cross-correlation power spectra showing unity agreement at the scale of k≲0.3 hMpc−1 in the absence of instrumental effects. Moreover, accounting for the systematic beam effects, our reconstruction displays consistent autocorrelation and cross-correlation power spectrum ratios at the 1σ level across scales k≲0.1 hMpc−1, with only a 10% reduction observed in the cross-correlation power spectrum at k≃0.2 hMpc−1. The effects of redshift-space distortion are also reconstructed successfully, as evidenced by the quadrupole power spectra matching with the target truth. In order to test how thermal noise affects the performance of our method, we simulated various white noise levels in the map. This shows the mean cross-correlation ratio R¯cross≳0.8 when the level of the thermal noise is smaller than or equal to that of the HI signal. In comparison, our method outperforms the traditional principal component analysis (PCA) method. The PCA-derived cross-correlation ratios are underestimated b

关键词： Large scale structure of the Universe

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