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

作者： Li, Haoqi Hu, Jiangping Zhou, Qingrui Lv, Maolong Ghosh, Bijoy K. School of Automation Engineering University of Electronic Science and Technology of China Chengdu611731 China University of Electronic Science and Technology of China Huzhou313000 China Qian Xuesen Laboratory of Space Technology China Academy of Space Technology Beijing100094 China Air Traffic Control and Navigation College Air Force Engineering University Xian710051 China Department of Mathematics and Statistics Texas Tech University LubbockTX79409-1042 United States

ISBN: (纸本)9789819733279

The safety of multiple unmanned aerial vehicle (multi-UAV) systems threatened by enemy missiles is crucial for their effective formation control. In this paper, a distributed safety control strategy is developed that enables multi-UAV systems to execute formation tasks while avoiding enemy missiles. The proposed method considers enemy missiles as dynamic constraints on the multi-UAV system and is adaptable to changes in the number of UAVs. By ensuring each UAV remains within a defined safe set, the system can avoid incoming missiles while maximizing operational freedom for each UAV. The control strategy utilizes the backstepping control technique, making the computational process relatively inexpensive. Simulation results validate the effectiveness and adaptability of the proposed method in continuously changing threat environments. Our approach offers a promising solution for achieving safe and efficient multi-UAV cooperative operations in hostile environments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Lyapunov functions

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Quantitatively mapping local quality of super-resolution microscopy by rolling Fourier ring correlation

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Light(science & Applications) 2023年第12期12卷 2826-2844页

作者： Weisong Zhao Xiaoshuai Huang Jianyu Yang Liying Qu Guohua Qiu Yue Zhao Xinwei Wang Deer Su Xumin Ding Heng Mao Yaming Jiu Ying Hu Jiubin Tan Shiqun Zhao Leiting Pan Liangyi Chen Haoyu Li Innovation Photonics and Imaging Center School of Instrumentation Science and EngineeringHarbin Institute of TechnologyHarbinChina Key Laboratory of Ultra-Precision Intelligent Instrumentation of Ministry of Industry and Information Technology Harbin Institute of TechnologyHarbinChina Biomedical Engineering Department International Cancer InstitutePeking University Cancer Hospital and InstituteHealth Science CenterPeking UniversityBeijingChina The Key Laboratory of Weak-Light Nonlinear Photonics of Education Ministry School of Physics and TEDA Institute of Applied PhysicsFrontiers Science Center for Cell ResponsesNankai UniversityTianjinChina State Key Laboratory of Membrane Biology Beijing Key Laboratory of Cardiometabolic Molecular MedicineInstitute of Molecular MedicineNational Biomedical Imaging CenterSchool of Future TechnologyPeking UniversityBeijingChina Department of Control Science and Engineering Harbin Institute of TechnologyHarbinChina School of Mathematical Sciences Peking UniversityBeijingChina Unit of Cell Biology and Imaging Study of Pathogen Host Interaction The Center for MicrobesDevelopment and HealthKey Laboratory of Molecular Virology and ImmunologyShanghai Institute of Immunity and InfectionChinese Academy of SciencesShanghaiChina School of Life Science and Technology Harbin Institute of TechnologyHarbinChina PKU-IDG/McGovern Institute for Brain Research BeijingChina Beijing Academy of Artificial Intelligence BeijingChina Frontiers Science Center for Matter Behave in Space Environment Harbin Institute of TechnologyHarbinChina Key Laboratory of Micro-Systems and Micro-Structures Manufacturing of Ministry of Education Harbin Institute of TechnologyHarbinChina

In fluorescence microscopy,computational algorithms have been developed to suppress noise,enhance contrast,and even enable super-resolution(SR).However,the local quality of the images may vary on multiple scales,and these differences can lead to *** mapping methods fail to finely estimate the local quality,challenging to associate the SR scale ***,we develop a rolling Fourier ring correlation(rFRC)method to evaluate the reconstruction uncertainties down to SR *** visually pinpoint regions with low reliability,a filtered rFRC is combined with a modified resolution-scaled error map(RSM),offering a comprehensive and concise map for further *** demonstrate their performances on various SR imaging modalities,and the resulting quantitative maps enable better SR images integrated from different ***,we expect that our framework can become a routinely used tool for biologists in assessing their image datasets in general and inspire further advances in the rapidly developing field of computational imaging.

关键词： resolution local enable

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Multi-target Tracking with Random Finite Set in Decentralized Large-scale Sensor Networks

Multi-target Tracking with Random Finite Set in Decentralize...

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2022 IEEE International Conference on Unmanned Systems, ICUS 2022

作者： Su, Lingfei Dong, Xiwang Hua, Yongzhao Yu, Jianglong Ren, Zhang School of Automation Science and Electrical Engineering Beihang University Science and Technology On Aircraft Control Laboratory Beijing China Institute of Artificial Intelligence Beihang University Beijing China

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

This paper presents the multi-target tracking (MTT) problem in decentralized large-scale sensor networks. A novel random finite set (RFS)-based framework is designed, including an improved Probability Hypothesis Density filter with Gaussian Mixture (GM-PHD) representation, and an improved Generalized Covariance Intersection (GCI) fusion algorithm. For the local GM-PHD filter, the intensity of new-born targets is initialized by pre-segmenting the measurement set to address the uncertainty of measurement origin and the computational burden of large-scale measurements. Then, a sensor node-dependent clutter model is established to deal with the cardinality overestimation problem brought by the decentralized structure. For the proposed fusion algorithm, i.e., fusing the posterior PHDs generated by the proposed GM-PHD filters in a distributed manner, it is based on the idea of compensating the severe missed detection problem of GCI in the case of large-scale fusing resources by arithmetic average (AA) fusion. In this way, GCI and AA can be fully utilized to guarantee the estimation accuracy for common targets and, respectively, the robustness for exclusive targets. A simulation of a decentralized large-scale network demonstrates the effectiveness of the proposed framework with respect to estimation accuracy and computational cost. © 2022 IEEE.

关键词： Sensor nodes

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Cooperative Target-Surrounding control of Unmanned Surface Vessels Based on MADDPG 49

Cooperative Target-Surrounding Control of Unmanned Surface V...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Li, Taoman Zhu, Fei Gan, Zihan Zhou, Zexing Wang, Xiaotao Lu, Renzhi China Ship Scientific Research Center Wuxi214082 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Wuhan430074 China Wuhan University of Science and Technology Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan430081 China

ISBN: (纸本)9798350331820

This article proposes a multi-agent deep reinforce-ment learning algorithm to control a fleet of unmanned surface vessels (USVs) that encircle and capture sea targets. First, a simulation environment for USVs is established based on a dynamic model;two-dimensional control variables are used to control movements in three directions. Second, the multi-agent deep deterministic policy gradient (MADDPG) algorithm is employed to achieve intelligent control of the USVs, using a reward function based on certain prior knowledge. Finally, centralized training and decentralized execution are used to complete the offline learning of multiple agents, and continuous action decisions are made based on the observations of the USV sensors. Simulations demonstrate that the method can capture stationary moving sea targets with any number of multi-agents under disturbances, and exhibits strong robustness and practicality. © 2023 IEEE.

关键词： Reinforcement learning

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Learning from Noisy Labels with Coarse-to-Fine Sample Credibility Modeling 17th

Learning from Noisy Labels with Coarse-to-Fine Sample Cred...

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17th European Conference on Computer Vision, ECCV 2022

作者： Zhang, Boshen Li, Yuxi Tu, Yuanpeng Peng, Jinlong Wang, Yabiao Wu, Cunlin Xiao, Yang Zhao, Cairong YouTu Lab Shenzhen China Tongji University Shanghai China Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9783031250620

Training deep neural network (DNN) with noisy labels is practically challenging since inaccurate labels severely degrade the generalization ability of DNN. Previous efforts tend to handle part or full data in a unified denoising flow via identifying noisy data with a coarse small-loss criterion to mitigate the interference from noisy labels, ignoring the fact that the difficulties of noisy samples are different, thus a rigid and unified data selection pipeline cannot tackle this problem well. In this paper, we first propose a coarse-to-fine robust learning method called CREMA, to handle noisy data in a divide-and-conquer manner. In coarse-level, clean and noisy sets are firstly separated in terms of credibility in a statistical sense. Since it is practically impossible to categorize all noisy samples correctly, we further process them in a fine-grained manner via modeling the credibility of each sample. Specifically, for the clean set, we deliberately design a memory-based modulation scheme to dynamically adjust the contribution of each sample in terms of its historical credibility sequence during training, thus alleviating the effect from noisy samples incorrectly grouped into the clean set. Meanwhile, for samples categorized into the noisy set, a selective label update strategy is proposed to correct noisy labels while mitigating the problem of correction error. Extensive experiments are conducted on benchmarks of different modality, including image classification (CIFAR, Clothing1M etc.) and text recognition (IMDB), with either synthetic or natural semantic noises, demonstrating the superiority and generality of CREMA. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep neural networks

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WATNet: A Wavelet-Aware Lightweight Hybrid Model for Fast Low-Light Enhancement 7

WATNet: A Wavelet-Aware Lightweight Hybrid Model for Fast Lo...

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7th Asian Conference on Artificial intelligence technology, ACAIT 2023

作者： Wang, Qiangqiang Huang, Peiliang Li, Lei Han, Longfei Anhui University AHU-IAI Ai Joint Laboratory Hefei China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei China Northwestern Polytechnical University School of Automation Xi'an China Science And Technology On Complex System Control And Intelligent Agent Cooperation Laboratory Beijing China Beijing Technology And Business University Beijing China University of Science And Technology of China School of Information Science And Technology Hefei China

ISBN: (纸本)9798350359145

Low-light enhancement task is an essential component of computer low-level visual tasks, which involves processing images captured under dim lighting conditions to make them appear as if they were taken under normal illumination. Currently, deep neural networks have become the mainstream approach for image processing. However, recent works have devoted considerable efforts to designing high-performance models, which often come with high computational complexity and inference time, making real-time processing unfeasible. We observed that some convolutional methods are due to the need for deep layers which results in a large number of parameters. Moreover, enhancing details and removing noise in low-light images remains an open challenge. In order to solve the above problems, we propose a lightweight baseline that combines CNN and sparse grid attention transformer blocks to enable the model to capture a global receptive field at an early stage. Specifically, we propose a High-Frequency Wavelet-aware Block(HFWB) that focuses on processing high-frequency information in the wavelet domain to refine details and suppress noise. With a processing time of only 10.6ms, the performance of our model outperforms that of the current state-of-the-art lightweight models on benchmark low-light datasets. Compared to state-of-the-art models in the LOL dataset, our model achieves a reduction in inference time of over 90% and requires only about 1% of the FLOPS. © 2023 IEEE.

关键词： Deep neural networks

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FastDrag: manipulate anything in one step 24

FastDrag: manipulate anything in one step

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Xuanjia Zhao Jian Guan Congyi Fan Dongli Xu Youtian Lin Haiwei Pan Pengming Feng College of Computer Science and Technology Harbin Engineering University Independent Researcher School of Intelligence Science and Technology Nanjing University State Key Laboratory of Space-Ground Integrated Information Technology

ISBN: (纸本)9798331314385

Drag-based image editing using generative models provides precise control over image contents, enabling users to manipulate anything in an image with a few clicks. However, prevailing methods typically adopt n-step iterations for latent semantic optimization to achieve drag-based image editing, which is time-consuming and limits practical applications. In this paper, we introduce a novel one-step drag-based image editing method, i.e., FastDrag, to accelerate the editing process. Central to our approach is a latent warpage function (LWF), which simulates the behavior of a stretched material to adjust the location of individual pixels within the latent space. This innovation achieves one-step latent semantic optimization and hence significantly promotes editing speeds. Meanwhile, null regions emerging after applying LWF are addressed by our proposed bilateral nearest neighbor interpolation (BNNI) strategy. This strategy interpolates these regions using similar features from neighboring areas, thus enhancing semantic integrity. Additionally, a consistency-preserving strategy is introduced to maintain the consistency between the edited and original images by adopting semantic information from the original image, saved as key and value pairs in self-attention module during diffusion inversion, to guide the diffusion sampling. Our FastDrag is validated on the DragBench dataset, demonstrating substantial improvements in processing time over existing methods, while achieving enhanced editing performance. Project page: https://***/.

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Bionic microchannels for step lifting transpiration

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International Journal of Extreme Manufacturing 2023年第2期5卷 244-252页

作者： Zhaolong Wang Qiu Yin Ziheng Zhan Wenhao Li Mingzhu Xie Huigao Duan Ping Cheng Ce Zhang Yongping Chen Zhichao Dong Interdisciplinary Research Center of Low-carbon Technology and Equipment College of Mechanical and Vehicle EngineeringHunan UniversityChangsha 410082People’s Republic of China MOE Key Laboratory for Power Machinery and Engineering School of Mechanical EngineeringShanghai Jiao Tong UniversityShanghai 200240People’s Republic of China Qian Xuesen Laboratory of Space Technology China Academy of Space Technology(CAST)Beijing 100094People’s Republic of China Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy Application School of Environmental Science and EngineeringSuzhou University of Science and TechnologySuzhouJiangsu 215009People’s Republic of China Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education School of Energy and EnvironmentSoutheast UniversityNanjing 210096People’s Republic of China Chinese Academy of Sciences Key Laboratory of Bio-inspired Materials and Interface Sciences Technical Institute of Physics and ChemistryChinese Academy of Sciences100190 BeijingPeople’s Republic of China

Those various cross-sectional vessels in trees transfer water to as high as 100 meters,but the traditional fabrication methods limit the manufacturing of those vessels,resulting in the non-availability of those bionic ***,we fabricate those bionic microchannels with various cross-sections by employing projection micro-stereolithography(PμSL)based 3D printing *** circumradius of bionic microchannels(pentagonal,square,triangle,and five-pointed star)can be as small as 100μm with precisely fabricated sharp ***'s more,those bionic microchannels demonstrate marvelous microfluidic performance with strong precursor effects enabled by their sharp *** significantly,those special properties of our bionic microchannels enable them outstanding step lifting performance to transport water to tens of millimeters,though the water can only be transported to at most 20 mm for a single bionic *** mimicked transpiration based on the step lifting of water from bionic microchannels is also *** precisely fabricated,low-cost,various cross-sectional bionic microchannels promise applications as microfluidic chips,long-distance unpowered water transportation,step lifting,mimicked transpiration,and so on.

关键词： bionic microchannel microfluidics water transportation step lifting 3D printing

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A Review of Rotation Mechanical Fault Diagnosis Research Based on Deep Domain Adaptation 9

A Review of Rotation Mechanical Fault Diagnosis Research Bas...

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9th IEEE International Conference on Big Data Computing Service and Applications, BigDataService 2023

作者： Zhang, Shun Xie, Gang Tian, Juan Shi, Hui Zhang, Xiaohong Nie, Xiaoyin Xie, Xinlin Shanxi Key Laboratory of Advanced Control and Equipment Intelligence Taiyuan China Taiyuan University of Science and Technology Dept. of Electronic and Information Engineering Taiyuan China Division of Industrial and System Engineering Taiyuan China

ISBN: (纸本)9798350333794

With the rapid development of computer technology and the Internet of Things (IoT), a large amount of data containing both healthy and faulty states can be collected from existing devices in factories. However, the usable faulty state data is scarce. Transfer learning makes it possible to train efficient and reliable neural networks with a small number of samples. Domain adaptation, as a branch of transfer learning, can effectively realize knowledge transfer between small sample data. Starting from three subcategories of deep learning-based domain adaptation methods, this article summarizes the latest research achievements and provides insights into future research directions. © 2023 IEEE.

关键词： Failure analysis

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Sustainable Mining in the Era of Artificial intelligence

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 1-4页

作者： Long Chen Yuting Xie Yutong Wang Shirong Ge Fei-Yue Wang IEEE the State Key Laboratory for Management and Control of Complex Systems at the Institute of Automation Chinese Academy of SciencesBeijing 100190China the National Laboratory of Pattern Recognition at the Institute of Automation and Waytous Ltd.China the School of Computer Science and Engineering Sun Yat-Sen UniversityGuangzhou 510275GuangdongChina the School of Mechanical Electronic and Information Engineering China University of Mining and Technology BeijingBejing 100083China the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100190China

The mining sector historically drove the global economy but at the expense of severe environmental and health repercussions,posing sustainability challenges[1]-[3].Recent advancements on artificial intelligence(AI)are revolutionizing mining through robotic and data-driven innovations[4]-[7].While AI offers mining industry advantages,it is crucial to acknowledge the potential risks associated with its widespread ***-reliance on AI may lead to a loss of human control over mining operations in the future,resulting in unpredictable consequences.

关键词： Sustainable mining consequences

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