检索结果-内蒙古大学图书馆

Zero-Shot image Harmonization with Generative Model Prior

IEEE Transactions on Multimedia 2025年

作者： Chen, Jianqi Zhang, Yilan Zou, Zhengxia Chen, keyan Shi, Zhenwei Beihang University Image Processing Center School of Astronautics Beijing100191 China Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Astronautics Beijing100191 China Beihang University Department of Guidance Navigation and Control School of Astronautics Beijing100191 China

We propose a zero-shot approach to image harmonization, aiming to overcome the reliance on large amounts of synthetic composite images in existing methods. These methods, while showing promising results, involve significant training expenses and often struggle with generalization to unseen images. To this end, we introduce a fully modularized framework inspired by human behavior. Leveraging the reasoning capabilities of recent foundation models in language and vision, our approach comprises three main stages. Initially, we employ a pretrained vision-language model (VLM) to generate descriptions for the composite image. Subsequently, these descriptions guide the foreground harmonization direction of a text-to-image generative model (T2I). We refine text embeddings for enhanced representation of imaging conditions and employ self-attention and edge maps for structure preservation. Following each harmonization iteration, an evaluator determines whether to conclude or modify the harmonization direction. The resulting framework, mirroring human behavior, achieves harmonious results without the need for extensive training. We present compelling visual results across diverse scenes and objects, along with quantitative comparisons validating the effectiveness of our approach. Our code is available at https://***/WindVChen/Diff-Harmonization. © 1999-2012 IEEE.

关键词： Visual languages

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Generation Maintenance Scheduling for Power Systems Considering the Risk Quantification of Hybrid Uncertainty

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IEEE Transactions on Power Systems 2025年

作者： Yang, Xiao Zhao, Yong Li, Yuanzheng Huang, Cheng Ding, Qiang Key Laboratory of Image Information Processing and Intelligent Control of Ministry of Education of China Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan430074 China Power Science Research Institute of State Grid Jiangsu Electric Power Company China China Electric Power Research Institute Company China

The accurate quantification of risk caused by uncertainty forms a crucial foundation for formulating the generation maintenance scheduling (GMS) of power systems. However, the probability distribution functions (PDFs) of uncertain variables such as wind power and load are challenging to model accurately or are unknown, which makes it difficult to measure their economic risk and formulate appropriate GMS for power systems. To address this issue, we consider hybrid uncertainty from wind power and load, and propose a novel interval-probabilistic worst conditional value-at-risk (IP-WCVaR)-based generation maintenance scheduling method. Firstly, a novel IP-WCVaR method is proposed to measure the risk of the interval and probabilistic hybrid uncertainty, and the analytical mathematical model of the IP-WCVaR is derived through typical scenarios of probability correction. On this basis, the positive and negative spinning reserve models are established using the IP-WCVaR and then integrated into the GMS model, which enhances the resilience of the power system. Finally, the new risk-averse GMS model is formulated as the lower and upper boundary optimal models, which are transformed into tractable mixed integer linear programming problems based on the interval extreme value theory. The effectiveness and superiority of the proposed IP-WCVaR method are verified on the modified IEEE 24-bus and IEEE 118-bus power systems. © 1969-2012 IEEE.

关键词： Mixed-integer linear programming

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Surrogate-Assisted Multiobjective Gene Selection for Cell Classification from Large-Scale Single-Cell RNA Sequencing Data

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IEEE Transactions on Evolutionary Computation 2025年

作者： Lin, Jianqing He, Cheng Jiang, Hanjing Huang, Yabing Jin, Yaochu Huazhong University of Science and Technology Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Wuhan430074 China Huazhong University of Science and Technology State Key Laboratory of Advanced Electromagnetic Technology School of Electrical and Electronic Engineering Wuhan430074 China Renmin Hospital Department of Pathology Wuhan University Wuhan430060 China Westlake University Trustworth and General Artificial Intelligence Laboratory School of Engineering Hangzhou310030 China

Accurate cell classification is crucial but expensive for large-scale single-cell RNA sequencing (scRNA-seq) analysis. Gene selection (GS) emerges as a pivotal technique in identifying gene subsets of scRNA-seq for classification accuracy improvement and gene scale reduction. Nevertheless, the rising scale of scRNA-seq data presents challenges to existing GS methods regarding performance and computational time. Thus, we propose a surrogate-assisted evolutionary algorithm for multiobjective GS to address these deficiencies. An innovative two-phase initialization method is proposed to select sparse solutions to provide preliminary insights into gene contributions. Then, a binary competitive swarm optimizer is proposed for effective global search, where a local search method is embedded to eliminate irrelevant genes for efficiency consideration. Additionally, a surrogate model is adopted to forecast classification accuracy efficiently and substitutes part of the computationally expensive classification process. Experiments are conducted on eight large-scale scRNA-seq datasets with more than 20,000 genes. The effectiveness of the proposed GS method for scRNA-seq cell classification compared with eight state-of-the-art methods is validated. Gene expression analysis results of selected genes further validated the significance of the genes selected by the proposed method in the classification of scRNA-seq data. © 1997-2012 IEEE.

关键词： Gene expression

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Adaptively wavelet-based image denoising algorithm with edge preserving

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Chinese Optics Letters 2006年第2期4卷 80-83页

作者：谭毅华田金文柳健 State Key Laboratory of Education Ministry for Image Processing and Intelligent Control

A new wavelet-based image denoising algorithm, which exploits the edge information hidden in the corrupted image, is presented. Firstly, a canny-like edge detector identifies the edges in each subband. Secondly, multiplying the wavelet coefficients in neighboring scales is implemented to suppress the noise while magnifying the edge information, and the result is utilized to exclude the fake edges. The isolated edge pixel is also identified as noise. Unlike the thresholding method, after that we use local window filter in the wavelet domain to remove noise in which the variance estimation is elaborated to utilize the edge intbrmation. This method is adaptive to local image details, and can achieve bet, ter performance than the methods of state of the art.

关键词： Adaptive algorithms Detectors Edge detection Information technology Noise abatement Pattern recognition Wavelet transforms

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MindEye-OmniAssist: A Gaze-Driven LLM-Enhanced Assistive Robot System for Implicit Intention Recognition and Task Execution

arXiv

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arXiv 2025年

作者： Zhang, Zejia Yang, Bo Chen, Xinxing Shi, Weizhuang Wang, Haoyuan Luo, Wei Huang, Jian Hubei Key Laboratory of Brain-Inspired Intelligent Systems Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China State key laboratory of intelligent vehicle safety technology chongqing changan automobile co ltd Chongqing400023 China Science and technology innovation center China ship development and design centre Wuhan430060 China

A promising effective human-robot interaction in assistive robotic systems is gaze-based control. However, current gaze-based assistive systems mainly help users with basic grasping actions, offering limited support. Moreover, the restricted intent recognition capability constrains the assistive system’s ability to provide diverse assistance functions. In this paper, we propose an open implicit intention recognition framework powered by Large Language Model (LLM) and Vision Foundation Model (VFM), which can process gaze input and recognize user intents that are not confined to predefined or specific scenarios. Furthermore, we implement a gaze-driven LLM-enhanced assistive robot system (MindEye-OmniAssist) that recognizes user’s intentions through gaze and assists in completing task. To achieve this, the system utilizes open vocabulary object detector, intention recognition network and LLM to infer their full intentions. By integrating eye movement feedback and LLM, it generates action sequences to assist the user in completing tasks. Real-world experiments have been conducted for assistive tasks, and the system achieved an overall success rate of 41/55 across various undefined tasks. Preliminary results show that the proposed method holds the potential to provide a more user-friendly human-computer interaction interface and significantly enhance the versatility and effectiveness of assistive systems by supporting more complex and diverse task. Copyright © 2025, The Authors. All rights reserved.

关键词： Eye movements

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M3-CR: Multi-scale Multi-branch Mamba for SAR-assisted Optical image Thick Cloud Removal

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Pan, Linpeng Song, Xuedong Xie, Fengying Zhang, Xiaozhe Ji, Haolin Shi, Zhenwei Beihang University School of Astronautics Beijing100191 China Tianmushan Laboratory Hangzhou311115 China Shanghai Aerospace Control Technology Institute Shanghai201109 China Beihang University Image Processing Center School of Astronautics State Key Laboratory of Virtual Reality Technology and Systems Beijing100191 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China

SAR-assisted thick cloud removal from optical remote sensing images has long been a challenging task. Current mainstream methods face challenges in achieving an effective global receptive field, fully utilizing multi-scale features, and deeply integrating features from both modalities. To overcome these limitations, we propose the Multi-scale Multi-branch Mamba model(M3-CR) for SAR-assisted thick cloud removal. Specifically, we integrate the Mamba model into the task of SAR-assisted cloud removal, effectively modeling global dependencies within the images. Concurrently, a Multi-scale Multi-branch structure is introduced to extract and integrate multi-scale information, and in combination with a convolutional branch to fully exploit the global and local geographic proximities inherent in remote sensing images. Furthermore, we present a novel feature fusion module leveraging the Modal-Traversing 2D Selective Scan(MTSS2D) to enable deep interaction and integration of features from optical and SAR images. The experimental results on two benchmark databases show that the M3-CR achieves superior performance compared to state-of-theart cloud removal approaches, while requiring fewer parameters and reduced FLOPs. © 1980-2012 IEEE.

关键词： Optical remote sensing

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Dual-mode predictive control of a rotor suspension system

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Science China(Information Sciences) 2020年第1期63卷 178-189页

作者： Yue WU Gui-Ping REN Hai-Tao ZHANG Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and Technology State Key Laboratory of Digital Manufacturing Equipments and Technology

Rotor active magnetic bearing(rotor-AMB) systems are frequently used to alleviate vibrations for various applications such as in national defense, manufacturing industries, IC production, and aerospace engineering. One obstacle to improve machining efficiency and quality is the open-loop instability of rotorAMB systems during the machining process. We built a closed-loop processing platform using a spindle rotor installed with AMBs and thereby developed a rotor-AMB suspension system embedded with a dual-mode predictive controller(DMPC). The performance of the system is thus substantially improved. In the proposed DMPC, both model-based prediction and receding horizon optimization are utilized to guarantee the closedloop stability of the rotor-AMB suspension systems with input constraints. Finally, the effectiveness and superiority of the proposed method are examined through substantial levitation experiments on a machining platform with installed AMBs.

关键词： dual-mode predictive control system identification optimization control model predictive control suspension control

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image matching navigation based on fuzzy information

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Journal of Harbin Institute of Technology(New Series) 2003年第4期10卷 447-449页

作者：田玉龙吴伟仁田金文柳健 State Key Laboratory for Image Processing and Intelligence Control Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology

In conventional image matching methods, the image matching process is mostly based on image statistic information. One aspect neglected by all these methods is that there is much fuzzy information contained in these images. A new fuzzy matching algorithm based on fuzzy similarity for navigation is presented in this paper. Because the fuzzy theory is of the ability of making good description of the fuzzy information contained in images, the image matching method based on fuzzy similarity would look forward to producing good performance results. Experimental results using matching algorithm based on fuzzy information also demonstrate its reliability and practicability.

关键词： image matching fuzzy information similarity

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Dynamic modeling and simulation test of a 60 kW PEMFC generation system

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2011年第6期12卷 475-482页

作者： Ying-ying ZHANG Ji-chang SUN Ying ZHANG Xi LI Ouang-yi CAO Shandong Provincial Key Laboratory of Ocean Environment Monitoring Technology Shandong Academy of Sciences Institute of Oceanographic Instrumentation Qingdao 266001 China Control Science and Engineering Department Key Laboratory of Education Ministry for Image Processing and lntelligent Control Huazhong University of Science & Technology Wuhan 430074 China Fuel Cell Institute Department of Automation Shanghai Jiao Tong University Shanghai 200030 China

In this paper,a 60 kW proton exchange membrane fuel cell(PEMFC) generation system is modeled in order to design the system parameters and investigate the static and dynamic characteristics for control *** achieve an overall system model,the system is divided into five modules:the PEMFC stack(anode and cathode flows,membrane hydration,and stack voltage and power),cathode air supply(air compressor,supply manifold,cooler,and humidifier),anode fuel supply(hydrogen valve and humidifier),cathode exhaust exit(exit manifold and water return),and power conditioning(DC/DC and DC/AC) *** a combination of empirical and physical modeling techniques,the model is developed to set the operation conditions of current,temperature,and cathode and anode gas flows and pressures,which have major impacts on system *** current model is based on a 60 kW PEMFC power plant designed for residential applications and takes account of the electrochemical and thermal aspects of chemical reactions within the stack as well as flows of reactants across the *** simulation tests show that the system model can represent the static and dynamic characteristics of a 60 kW PEMFC generation system,which is mathematically simple for system parameters and control designs.

关键词： Proton exchange membrane fuel cell(PEMFC) Generation system Dynamic simulation model Reactant flow

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Structure-free containment control for uncertain underactuated multiple Euler-Lagrange systems

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Science China(Information Sciences) 2023年第11期66卷 150-168页

作者： Housheng SU Yali WU Liren ZHANG Xia CHEN Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology

This article studies the problem of structure-free distributed containment control for uncertain underactuated multiple Euler-Lagrange systems(MELSs) considering disturbances by using a layered approach. First, the second layer is the virtual layer constructed artificially for hierarchical control. We move all virtual nodes to the convex hull formed by leaders in the first layer by implementing containment control algorithms on all virtual nodes. Then, the third layer is the following layer, and we propose an adaptive robust tracking controller to ensure that each follower in the third layer tracks the corresponding virtual node in the second layer. So far, the underactuated MELSs can achieve containment control. Furthermore,through the theoretical derivation, sufficient conditions are obtained to achieve the objective of structure-free containment control. Finally, the effectiveness of the proposed stratified structure-free containment control method is verified by a simulation example.

关键词： Euler-Lagrange systems underactuated system containment control adaptive-robust control

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