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2021 IEEE 4th International Conference on Big Data and Artificial Intelligence, BDAI 2021

作者： Cao, Wei Wu, Shi-Qian Wang, Rui-Ping Wu, Jia-Xin Deng, Gao-Xu Wuhan University of Science and Technology School of Information Science and Engineering Wuhan China Wuhan University of Science and Technology School of Machinery and Automation Wuhan China Wuhan University of Science and Technology Institute of Robotics and Intelligent Systems Wuhan China

ISBN: (纸本)9781665412704

This image enhancement on a single image for better visibility and higher SNR (signal-to-noise ratio) is an under-constrained problem, which becomes more challenging as this image is under the low-light condition. In order to solve the above problems, we firstly analyze the reason that classical Retinex enhancement result has low SNR due to ignoring the influence of noise in reflectance. To this end, combined with the actual illumination-reflectance prior, a new convex optimization function is proposed to estimate noise-suppressed/detail-preserved reflectance and spatial piece-wise smoothed illumination simultaneously. Especially, although the objective of the proposed method is a hybrid non-smooth problem, by subtly decomposition of the objective, we can solve it effectively. The final low-light enhancement result with pleasant visual performance and high SNR can be effectively obtained according to recompose the adjusting illumination and reflectance. Qualitative and quantitative experiments have illustrated that the proposed method has a higher SNR and more pleasing visual effect than state-of-the-art image enhanced techniques under low-light conditions. © 2021 IEEE.

关键词： Reflection

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A Development of Time-Varying Weight Model Predictive Control for Autonomous Vehicles

A Development of Time-Varying Weight Model Predictive Contro...

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Qazani, Mohammad Reza Chalak Asadi, Houshyar Shajari, Arian Najdovski, Zoran Lim, Chee Peng Nahavandi, Saeid Murray State University Murray Department of the Computer Science and Information Systems MurrayKY42071 United States Institute for Intelligent System Research and Innovation Deakin University GeelongVIC43216 Australia Swinburne University of Technology HawthornVIC Australia Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA United States

ISBN: (纸本)9798350337020

Autonomous vehicles, commonly known as self-driving cars, are rapidly gaining popularity due to their numerous advantages, such as reducing traffic, pollution, and emissions while increasing safety, convenience, and transportation connectivity. In order to accurately track the motion signal, these vehicles are now utilising advanced control techniques, such as model predictive control (MPC). However, the efficiency of MPCs heavily relies on properly tuning their weights. The primary function of the MPC is to recalculate the optimal values for the vehicle control commands, such as desired speed, steering angle, etc., while considering the dynamic model of the autonomous vehicle. The existing linear MPC models cannot reach higher efficiency because of using fixed weights without considering the error. This paper introduces a novel approach for developing an MPC model with a time-varying weights algorithm for autonomous vehicles. The study aims to minimise motion tracking errors such as lateral position and yaw angle errors. Relevant MPC weights are calculated online using fuzzy logic-based units considering the lateral position and yaw angle errors. The proposed linear time-varying MPC was designed and developed using MATLAB software, resulting in improved motion tracking performance with 31.62% and 20.89% reduction of the root means square error of lateral position and yaw angle. © 2023 IEEE.

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End-to-End Remote Sensing Image Scene Classification with Vision Transformers 23

End-to-End Remote Sensing Image Scene Classification with Vi...

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6th International Conference on Big Data Technologies, ICBDT 2023

作者： Yuan, Hao Liu, Kun Shi, Jiechuan Wang, Can Wang, Weiwei School of Information Science and Engineering University of Jinan Jinan250022 China China High-resolution Earth Observation System Shandong Center of Data and Application University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China QiLu Research Institute Jinan250200 China

ISBN: (纸本)9798400707667

In recent years, the development of deep learning technology has led to widespread attention on Vision Transformer (ViT) as an emerging image classification method. Remote sensing image classification is an important task in the field of remote sensing, with extensive application prospects. This paper aims to explore the remote sensing image classification method based on Vision Transformer, addressing the limitations of traditional convolutional neural networks in terms of global perception capability, context information retrieval, and positional encoding. The classification performance of the Vision Transformer model is evaluated and compared on remote sensing datasets. Vision Transformer is a deep neural network model based on self-attention mechanism that can capture the global context information in images and has achieved remarkable performance in various computer vision tasks. Furthermore, experimental results demonstrate that the remote sensing image classification method based on Vision Transformer exhibits outstanding accuracy and generalization ability. Compared to traditional convolutional neural networks, it can better capture the global features in remote sensing images and has better scalability when dealing with large-scale remote sensing image data. Experimental results on different remote sensing image datasets show that the model performs well compared to state-of-the-art methods. Specifically, Vision Transformer achieves average classification accuracies of 95.41%, 98.26%, 93.74% and 95.25% on the AID, UC-Merced, NWPU-RESISC45 and Optimal31 datasets, respectively. © 2023 ACM.

关键词： Neural network models

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Asymptotically mean-square synchronization of complex networks with multi-links and random coupling strength under hybrid adaptive control

Asymptotically mean-square synchronization of complex networ...

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第34届中国控制与决策会议

作者： Yanqin Sun Huaiyu Wu Zhihuan Chen Yang Chen Xiujuan Zheng School of Information Science and Engineering Wuhan University of Science and Technology School of Electromechanical and Automobile Engineering Huanggang Normal University Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology

In this paper,we investigate the asymptotically synchronization for multi-links complex networks(MLCNs) with random coupling *** order to suppress chattering phenomenon in synchronization,a novel hybrid adaptive control scheme named non-chattering adaptive control(NCAC) is *** with the existing results,the controller does not include sign function,which contains an improved appropriate update laws via differential ***,the case of MLCNs with unknown expected value of random coupling strength is *** to Lyapunov stability theory and Linear inequality technique,the sufficient condition for MLCNs to achieve asymptotically mean-square synchronization without chattering are *** results are finally to illustrate the effectiveness and advantage of the method proposed in this paper.

关键词： Asymptotically synchronization MLCNs Random coupling strength Adaptive control

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Hundredfold increase of stimulated Brillouin-scattering bandwidth in whispering-gallery mode resonators

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Photonics Research 2023年第6期11卷 917-924页

作者： GUOPING LIN JINGYI TIAN TANG SUN QINGHAI SONG YANNE K.CHEMBO Ministry of Industry and Information Technology Key Laboratory of Micro-Nano Optoelectronic Information System School of ScienceHarbin Institute of TechnologyShenzhen 518055China Guangdong Provincial Key Laboratory of Semiconductor Optoelectronic Materials and Intelligent Photonic Systems Shenzhen 518055China Department of Electrical and Computer Engineering&Institute for Research in Electronics and Applied Physics(IREAP) University of MarylandCollege ParkMaryland 20742USA

Backward stimulated Brillouin scattering(SBS)is widely exploited for various applications in optics and *** typically features a narrow gain bandwidth of a few tens of megahertz in fluoride *** we report a hundredfold increase of SBS bandwidth in whispering-gallery mode *** crystalline orientation results in a large variation of the acoustic phase velocity upon propagation along the periphery,from which a broad Brillouin gain is *** 2.5 GHz wide Brillouin gain profile is theoretically found and experimentally *** phenomena with Brillouin shift frequencies ranging from 11.73 to 14.47 GHz in ultrahigh Q Z-cut magnesium fluoride cavities pumped at the telecommunication wavelength are ***,the Brillouin-Kerr comb in this device is *** 400 comb lines spanning across a spectral window of 120 nm are *** finding paves a new way for tailoring and harnessing the Brillouin gain in crystals.

关键词： scattering pumped Brillouin

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Revealing the Trade-off in ISAC systems: The KL Divergence Perspective

arXiv

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arXiv 2024年

作者： Fei, Zesong Tang, Shuntian Wang, Xinyi Xia, Fanghao Liu, Fan Zhang, J. Andrew The School of Information and Electronics Beijing Institute of Technology Beijing100081 China The School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen518055 China The School of Electrical and Data Engineering University of Technology SydneyNSW2007 Australia

Integrated sensing and communication (ISAC) is regarded as a promising technique for 6G communication network. In this letter, we investigate the Pareto bound of the ISAC system in terms of a unified Kullback-Leibler (KL) divergence performance metric. We firstly present the relationship between KL divergence and explicit ISAC performance metric, i.e., demodulation error and probability of detection. Thereafter, we investigate the impact of constellation and beamforming design on the Pareto bound via deep learning and semi-definite relaxation (SDR) techniques. Simulation results show the tradeoff between sensing and communication performance in terms of bit error rate (BER) and probability of detection under different parameter set-ups. Copyright © 2024, The Authors. All rights reserved.

关键词： Beamforming

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Range-angle Imaging Based on the Prior of Target Scattering Characteristics in Distributed Arrays

Range-angle Imaging Based on the Prior of Target Scattering ...

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Signal, information and Data Processing (ICSIDP), IEEE International Conference on

作者： Yongjie Mei Guangbin Zhang Tianyao Huang Yan Wang School of Information and Electronics Beijng Institute of Technology Beijing China Radar System and Intelligent Signal Processing Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

We consider the problem of target range-angle imaging using distributed arrays. High resolution imaging requires a large array aperture. However, due to the limited number of array elements, the subarrays are usually sparsely located, which results in severe under-sampling and ambiguity problem, degrading the imaging performance. To this end, we propose a range-angle imaging method based on the prior of target scattering characteristic to resolve the ambiguity. The main idea is to use the information increment of priors to reduce the dependence on the measurements. Particularly, we first statistically extract the prior distribution of target scattering intensity by data analysis on the target images. Then we use these priors to design weighting parameters and achieve the ambiguity-free images by solving a weighted sparse recovery problem. Finally, numerical simulation results verify the feasibility of the proposed method.

关键词： Array signal processing Simulation Scattering Imaging Estimation Particle measurements Arrays Data mining Sparse matrices Signal resolution

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Track Slab Crack Detection Algorithm with Few Samples Based on Deep Learning

Track Slab Crack Detection Algorithm with Few Samples Based ...

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International Conference on intelligent Informatics and Biomedical sciences (ICIIBMS)

作者： Liu Zhongjie Li Wenju Gao Xiaosong Cui Liu School of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai China Shanghai Crrc Ro-Intelligent System. Co. Ltd. Shanghai China

In order to solve the problem of track slab crack detection under few samples’ conditions, this paper proposes a track slab crack detection algorithm with few samples based on deep learning. Firstly, after extracting multi-scale query and support features through the Swin Transformer backbone, the similarity between query pixels and support pixels is computed according to the scaled dot-product attention of the Transformer architecture, and the query masks are aggregated; in order to filter a large amount of irrelevant background information in the crack image, multi-scale prototypes are computed by using prototypical learning, highlighting the class information of the cracks, and introducing a lightweight attention module to generate class-representative attention feature maps using support features at different scales, which guides the network to focus its attention on the target region; finally, the final prediction of cracks is realized by the feature fusion module. The experimental results show that the track slab crack IoU reaches 66.94% and the FB-IoU reaches 83.02% under the few samples’ condition and exhibits strong generalization performance on the crack set of other scenarios as well.

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Safe and Reliable Distributed Fault-Tolerant Formation Control for Quadrotor Swarms

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IEEE Transactions on Aerospace and Electronic systems 2025年

作者： Hu, Yang Miao, Zhiqiang Wang, Yaonan Zhang, Hui Wang, Xiangke He, Wei Hunan University College of Electrical and Information Engineering Changsha410082 China Hunan University School of Robotics Changsha410082 China National University of Defense Technology College of Intelligence Science and Technology Changsha410073 China Beijing Information Science and Technology University School of Automation Institute of Artificial Intelligence China University of Science and Technology Beijing Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education Beijing100083 China

This paper addresses formation control of quadrotor swarms subject to external time-varying disturbances and actuator failures, while ensuring collision avoidance. Firstly, a complete dynamics model incorporating translational movement, rotation, motor, and actuator failures was developed for each quadrotor. Then, nominal controllers for the position and attitude subsystems are designed separately to achieve formation control under ideal conditions. A higher-order sliding mode (HOSM) observer is implemented to effectively estimate and compensate for external disturbances and actuator bias faults, thereby improving the stability of the system. Next, a potential energy function-based collision avoidance mechanism is implemented to ensure inter-quadrotor collision avoidance. Furthermore, a fault detection and fault-tolerant control(FTC) method is designed to estimate the lift coefficient of actuator partial failures online in real time by employing Newtons correction method, which ensures the safe and reliable flight of the system. The stability of the closed-loop system is analyzed using Lyapunov stability theory. Finally, numerical simulations and hardware-in-the-loop (HIL) experiment results are performed to validate the approach's effectiveness and performance in quadrotor swarm formation control. © 2025 IEEE. All rights reserved.

关键词： Potential energy functions

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Shift from Texture-bias to Shape-bias: Edge Deformation-based Augmentation for Robust Object Recognition

Shift from Texture-bias to Shape-bias: Edge Deformation-base...

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International Conference on Computer Vision (ICCV)

作者： Xilin He Qinliang Lin Cheng Luo Weicheng Xie Siyang Song Feng Liu Linlin Shen Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University Shenzhen Institue of Artificial Intelligence & Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing University of Leicester

Recent studies have shown the vulnerability of CNNs under perturbation noises, which is partially caused by the reason that the well-trained CNNs are too biased toward the object texture, i.e., they make predictions mainly based on texture cues. To reduce this texture-bias, current studies resort to learning augmented samples with heavily perturbed texture to make networks be more biased toward relatively stable shape cues. However, such methods usually fail to achieve real shape-biased networks due to the insufficient diversity of the shape cues. In this paper, we propose to augment the training dataset by generating semantically meaningful shapes and samples, via a shape deformation-based online augmentation, namely as SDbOA. The samples generated by our SDbOA have two main merits. First, the augmented samples with more diverse shape variations enable networks to learn the shape cues more elaborately, which encourages the network to be shape-biased. Second, semantic-meaningful shape-augmentation samples could be produced by jointly regularizing the generator with object texture and edge-guidance soft constraint, where the edges are represented more robustly with a self information guided map to better against the noises on them. Extensive experiments under various perturbation noises demonstrate the obvious superiority of our shape-bias-motivated model over the state of the arts in terms of robustness performance. Code is available at https://***/C0notSilly/-ICCV-23-Edge-Deformation-based-Online-Augmentation.

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