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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zhida Feng Li Chen Jing Tian JiaXiang Liu Shikun Feng School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan China Baidu Inc. Institute of Systems Science National University of Singapore

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We introduced StyleEntity, a zero-shot image manipulation model that utilizes named entities as proxies during its training phase. This strategy enables our model to manipulate images using unseen textual descriptions during inference, all within a single training phase. Additionally, we proposed an inference technique termed Prompt Ensemble Latent Averaging (PELA). PELA averages the manipulation directions derived from various named entities during inference, effectively eliminating the noise directions, thus achieving stable manipulation. In our experiments, StyleEntity exhibited superior performance in a zero-shot setting compared to other methods. The code, model weights, and datasets are available at https://***/feng-zhida/StyleEntity.

关键词： Training Adaptation models Computer vision Codes Computational modeling Noise Stability analysis

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Population transfer via a finite temperature state

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Physical Review A 2020年第4期102卷 043714-043714页

作者： Wei Huang Baohua Zhu Wei Wu Shan Yin Wentao Zhang Chu Guo Guangxi Key Laboratory of Optoelectronic Information Processing Guilin University of Electronic Technology Guilin 541004 China School of Material Science and Engineering Guilin University of Electronic Technology Guilin 541004 China Department of Physics College of Liberal Arts and Sciences National University of Defense Technology Changsha 410073 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics and Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha 410081 China

We study quantum population transfer via a common intermediate state initially in thermal equilibrium with a finite temperature T, exhibiting a multilevel stimulated Raman adiabatic passage structure. We consider two situations for the common intermediate state, namely a discrete two-level spin and a bosonic continuum. In both cases we show that the finite temperature strongly affects the efficiency of the population transfer. We also show in the discrete case that strong coupling with the intermediate state, or a longer duration of the controlled pulse, would suppress the effect of finite temperature. In the continuous case, we adapt the thermofield-based chain-mapping matrix product states algorithm to study the time evolution of the system plus the continuum under time-dependent controlled pulses, which shows great potential to be used to solve open quantum system problems in quantum optics.

关键词： Coherent control Stimulated Raman adiabatic passage Density matrix methods

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Prescribed-time Cooperative Output Regulation of Linear Heterogeneous Multi-agent systems

arXiv

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arXiv 2024年

作者： Zuo, Gewei Zhu, Lijun Wang, Yujuan Chen, Zhiyong School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430072 China Key Laboratory of Imaging Processing and Intelligence Control Huazhong University of Science and Technology Wuhan430074 China The State Key Laboratory of Power Transmission Equipment & System Security and New Technology School of Automation Chongqing University Chongqing400044 China School of Engineering The University of Newcastle CallaghanNSW2308 Australia

A finite-time protocol for a multi-agent systems (MASs) can guarantee the convergence of every agent in a finite time interval in contrast to the asymptotic convergence, but the settling time depends on the initial condition and design parameters and is inconsistent across the agents. In this paper, we study the prescribed-time cooperative output regulation (PTCOR) problem for a class of linear heterogeneous MASs under a directed communication graph, where the settling time of every agent can be specified a priori and thus consistent. As a special case of PTCOR, the necessary and sufficient condition for prescribed-time output regulation of an individual system is first discussed. Then, the PTCOR problem is converted into two cascaded subsystem, where the first one composed of distributed estimate errors and local estimate errors and the second one is for local tracking errors. The criterion for prescribed-time stabilization of the cascaded system is proposed and is found to be different from that of traditional asymptotic stabilization of a cascaded system. Under the criterion and sufficient condition, the general PTCOR problem is studied in two scenarios including state feedback control and measurement output feedback control. In particular, a distributed prescribed-time observer for each subsystem is explicitly constructed to estimate the exosystem's state. Based on the observer, a distributed controller is proposed to achieve convergence of the regulated output to zero within a prescribed-time. Copyright © 2024, The Authors. All rights reserved.

关键词： Multi agent systems

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Erratum to “Consensus seeking over directed networks with limited information communication” [Automatica 49 (2) (2013) 610–618]

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Automatica 2014年第9期50卷 2430-2431页

作者： Dequan Li Qipeng Liu Xiaofan Wang Zongli Lin School of Science Anhui University of Science and Technology Huainan 232001 Anhui Province PR China Department of Automation Shanghai Jiao Tong University Shanghai 200240 PR China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 PR China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia P.O. Box 400743 Charlottesville VA 22904-4743 USA

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controllability of giant connected components in a directed network

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Physical Review E 2017年第4期95卷 042318-042318页

作者： Xueming Liu Linqiang Pan H. Eugene Stanley Jianxi Gao Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Automation Huazhong University of Science and Technology Wuhan 430074 Hubei China Center for Polymer Studies and Department of Physics Boston University Boston Massachusetts 02215 USA School of Electric and Information Engineering Zhengzhou University of Light Industry Zhengzhou 450002 Henan China Center for Complex Network Research and Department of Physics Northeastern University Boston Massachusetts 02115 USA

When controlling a complex networked system it is not feasible to control the full network because many networks, including biological, technological, and social systems, are massive in size and complexity. But neither is it necessary to control the full network. In complex networks, the giant connected components provide the essential information about the entire system. How to control these giant connected components of a network remains an open question. We derive the mathematical expression of the degree distributions for four types of giant connected components and develop an analytic tool for studying the controllability of these giant connected components. We find that for both Erdős-Rényi (ER) networks and scale-free (SF) networks with p fraction of remaining nodes, the minimum driver node density to control the giant component first increases and then decreases as p increases from zero to one, showing a peak at a critical point p=pm. We find that, for ER networks, the peak value of the driver node density remains the same regardless of its average degree 〈k〉 and that it is determined by pm〈k〉. In addition, we find that for SF networks the minimum driver node densities needed to control the giant components of networks decrease as the degree distribution exponents increase. Comparing the controllability of the giant components of ER networks and SF networks, we find that when the fraction of remaining nodes p is low, the giant in-connected, out-connected, and strong-connected components in ER networks have lower controllability than those in SF networks.

关键词： Network structure

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Acquisition and Analysis of Rotating Machinery Rotor Vibration Signal by Non-Contact Laser Sensing

SSRN

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SSRN 2024年

作者： Yang, Shangjun Wu, Jiali Ke, Chenghu Zhang, Feng Zhu, Chunhua Ke, Xizheng Key Laboratory of Grain Information Processing and Control [Henan University of Technology Ministry of Education Zhengzhou China College of Information Science and Engineering Henan University of Technology Zhengzhou China Henan Engineering Laboratory of Grain Condition Intelligent Detection and Application Henan University of Technology Zhengzhou China School of Information Engineering Xi’an University Xi’an China School of Electronic Information Engineering Xi’an Technological University Xi’an China School of Automation and Information Engineering Xi’an University of Technology Xi’an China

Conventional high-speed rotating machinery uses a contact structure to realize data transmission between rotating parts and fixed parts. Mechanical wear and vibration noise can interfere with the subsequent processing and fault diagnosis of vibration signals. This paper designs a contactless vibration signal transmission system for rotating machinery using laser sensing. It analyzes the non-coincidence error between the optical axis and the rotating axis, which causes the effective photosensitive area and practical power of the detector to change periodically with the frequency of the rotational speed, resulting in the vibration signal presenting various harmonics with the rotational speed angular frequency as the fundamental wave. A convenient time-frequency joint analysis method is proposed using fractional Fourier transform in time-frequency domain projection localization. Using mixed programming techniques of C# and Matlab, an upper computer interface for data storage, processing, network publishing, is developed to form a complete vibration signal processing system. © 2024, The Authors. All rights reserved.

关键词： Rotating machinery

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Artificial intelligence for geoscience:Progress,challenges,and perspectives

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The Innovation 2024年第5期5卷 136-160,135页

作者： Tianjie Zhao Sheng Wang Chaojun Ouyang Min Chen Chenying Liu Jin Zhang Long Yu Fei Wang Yong Xie Jun Li Fang Wang Sabine Grunwald Bryan MWong Fan Zhang Zhen Qian Yongjun Xu Chengqing Yu Wei Han Tao Sun Zezhi Shao Tangwen Qian Zhao Chen Jiangyuan Zeng Huai Zhang Husi Letu Bing Zhang Li Wang Lei Luo Chong Shi Hongjun Su Hongsheng Zhang Shuai Yin Ni Huang Wei Zhao Nan Li Chaolei Zheng Yang Zhou Changping Huang Defeng Feng Qingsong Xu Yan Wu Danfeng Hong Zhenyu Wang Yinyi Lin Tangtang Zhang Prashant Kumar Antonio Plaza Jocelyn Chanussot Jiabao Zhang Jiancheng Shi Lizhe Wang Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China School of Computer Science China University of GeosciencesWuhan 430078China State Key Laboratory of Mountain Hazards and Engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China Data Science in Earth Observation Technical University of Munich80333 MunichGermany The National Key Laboratory of Water Disaster Prevention Yangtze Institute for Conservation and DevelopmentHohai UniversityNanjing 210098China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of Geographical Sciences Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China Soil Water and Ecosystem Sciences DepartmentUniversity of FloridaPO Box 110290GainesvilleFLUSA Materials Science Engineering Program Cooperating Faculty Member in the Department of Chemistry and Department of Physics Astronomy University of CaliforniaCaliforniaRiversideCA 92521USA Institute of Remote Sensing and Geographical Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Key Laboratory of Computational Geodynamics University of Chinese Academy of SciencesBeijing 100049China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China College of Geography and Remote Sensing Hohai UniversityNanjing 211100China Department of Geography The University of Hong KongHong Kong 999077SARChina Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Nanjing 210044China School of Environmental Science and Engineering Nanjing University of Information Science&TechnologyNanjing 210044China Collaborative Inno

This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection *** models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical ***,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world *** contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical *** techniques have shown promise in addressing Earth science-related ***,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into *** integration of physics-based and data-driven methodologies into hybrid models presents an alternative *** models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data *** review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced AI techniques and *** examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of AI in *** paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.

关键词： Earth utilizing landscape

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In-Situ Joule-Heating Drives Rapid and On-Demand Catalytic Vocs Removal with Ultralow Energy Consumption

SSRN

引用

SSRN 2022年

作者： Du, Peng Wang, Ruyue Deng, Bohan He, Xian Long, Yuanzheng Yang, Cheng Wang, Zhiwei Ge, Binghui Huang, Kai Zhang, Ru Lei, Ming Wu, Hui State Key Laboratory of Information Photonics and Optical Communications School of Science Beijing University of Posts and Telecommunications Beijing100876 China State Key Laboratory of New Ceramics and Fine Processing School of Materials Science and Engineering Tsinghua University Beijing100084 China Xitucheng Road NO.10 Beijing100876 China Key Laboratory of Beijing on Regional Air Pollution Control Beijing Key Laboratory for Green Catalysis and Separation Center of Excellence for Environmental Safety and Biological Effects Beijing University of Technology Beijing100124 China Institutes of Physical Science and Information Technology Anhui University Hefei230601 China

The running of environmental purification usually employs massive consumption of energy, and it is of significant scientific importance and real impact to reduce the energy cost in environmental treatments. In particular, it is urgently required to minimalize the energy cost of the catalytic oxidation of gaseous pollutants represented by volatile organic compounds (VOCs). Herein, we develop a rapid in-situ Joule-heating configuration to drive the catalytic VOCs oxidation with ultra-low energy consumption. As a prototypical illustration, atomically dispersed Pt/CeO 2 catalyst was decorated on conductive Nickle foam substrate to act as both catalytic and electric components with minimal pressure drop and high compatibility. The catalytic system exhibited 100% conversion toward toluene (1000 ppm, 33 ml min -1 ) with an ultralow input power of 6.5 W, which was 87% lower than that of a conventional heating furnace. Moreover, the long-term stability, high universality for various VOCs, and responsiveness for real-time changing components were also demonstrated. The outstanding electro-thermal properties of the system can be extended to more gaseous catalytic reactions with much lower energy consumptions. © 2022, The Authors. All rights reserved.

关键词： Volatile organic compounds

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A Novel Person Re-ID Method based on Multi-Scale Feature Fusion

A Novel Person Re-ID Method based on Multi-Scale Feature Fus...

引用

Chinese control Conference (CCC)

作者： Yuanyuan Li Xiaofei Wang Zhiqin Zhu Xinghua Huang Penghua Li Guanqiu Qi Yujun Rong Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Complex System Safety and Control Ministry of Education Chongqing University Chongqing China Computer Information Systems Department Buffalo State College Buffalo NY USA China Mobile (Hangzhou) Information Technology Co. Ltd. Hangzhou China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

Person re-identification (Re-ID) is a challenging but significant research topic. Previous methods based on global features are not pay much attention to the fine-grained feature information. While the methods based on local features strictly rely on the accurate pedestrian detection bounding-boxes to align the image pairs. In this paper, the Multi-Scale Feature Fusion Network is proposed which can extract global and local features simultaneously and to learn jointly. The network structure consists of two branches. One is a global branch for global feature learning, in this branch, we extract the whole body feature information of human body to express the pedestrian image as a whole. The other is a local branch for local feature learning. In the local branch, according to the characteristics of pedestrian's human structure, we segment the image into 6 horizontal strips to bring the local feature representation of the pedestrian image. In addition, we use a novel integration of multiple loss functions to further improve the recognition accuracy of the network. Experiments on Market-1501 and DukeMTMC-reID datasets show that our proposed method can achieve state-of-the-art results.

关键词： Feature extraction Task analysis Training Measurement Smoothing methods Electronic mail Machine learning

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Deep Learning-Based Dimension Measurement system for Industrial Part Inspection

Deep Learning-Based Dimension Measurement System for Industr...

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2024 China Automation Congress, CAC 2024

作者： Li, Xiao-Di Fan, Xing-Rong Zhang, Xian-Ming Zhang, Han-Zhi Huang, Chun-Jie Xu, Cheng Chongqing Engineering Laboratory for Detection Control and Integrated System School of Artificial Intelligence Chongqing Technology and Business University Chongqing China Engineering Research Centre for Waste Oil Recovery Technology and Equipment Ministry of Education Chongqing Key Laboratory Chongqing Technology and Business University Chongqing China School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing China Chongqing Intelligent Supply Chain Engineering & Technology Research Center Chongqing Jiaotong University Chongqing China Chongqing Aerospace Bashan Motorcycle Manufacturing Co. Ltd. Chongqing China

ISBN: (纸本)9798350368604

This paper develops a deep learning-based system for the dimension measurement of industrial parts. The system employs a general self-supervised image segmentation network to perform dichotomous image segmentation (DIS) on the parts, effectively extracting binary images of the target components from complex backgrounds without requiring preprocessing steps such as filtering or denoising. Following segmentation, contour detection, and dimension measurement are executed. The system utilizes a CMOS area scan camera and was tested on a computer equipped with a GTX1050 graphics card. The tests, conducted on two typical thrust ball bearing components (i.e., the ball retainer and the shaft ring) ü yielded ten measurements per part. Comparison with a vernier caliper as the reference standard reveals that under normal operating conditions, the maximum standard deviation is 0.007 mm and the maximum absolute error is 0.017 mm. © 2024 IEEE.

关键词： Ball bearings

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