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arXiv 2023年

作者： Chen, Hao Zhang, Haotian Chen, keyan Zhou, Chenyao Chen, Song Zou, Zhengxia Shi, Zhenwei The Image Processing Center School of Astronautics Beihang University Beijing100191 China The Beijing Key Laboratory of Digital Media Beihang University Beijing100191 China The State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China The Shanghai Artificial Intelligence Laboratory Shanghai200232 China The Department of Journalism and Communications Jeonuk National University Jeonju-Si54896 Korea Republic of The Department of Guidance Navigation and Control School of Astronautics Beihang University Beijing100191 China

Most contemporary supervised Remote Sensing (RS) image Change Detection (CD) approaches are customized for equal-resolution bitemporal images. Real-world applications raise the need for cross-resolution change detection, aka, CD based on bitemporal images with different spatial resolutions. Given training samples of a fixed bitemporal resolution difference (ratio) between the high-resolution (HR) image and the low-resolution (LR) one, current cross-resolution methods may fit a certain ratio but lack adaptation to other resolution differences. Toward continuous cross-resolution CD, we propose scale-invariant learning to enforce the model consistently predicting HR results given synthesized samples of varying resolution differences. Concretely, we synthesize blurred versions of the HR image by random downsampled reconstructions to reduce the gap between HR and LR images. We introduce coordinate-based representations to decode per-pixel predictions by feeding the coordinate query and corresponding multi-level embedding features into an MLP that implicitly learns the shape of land cover changes, therefore benefiting recognizing blurred objects in the LR image. Moreover, considering that spatial resolution mainly affects the local textures, we apply local-window self-attention to align bitemporal features during the early stages of the encoder. Extensive experiments on two synthesized and one real-world different-resolution CD datasets verify the effectiveness of the proposed method. Our method significantly outperforms several vanilla CD methods and two cross-resolution CD methods on the three datasets both in in-distribution and out-of-distribution settings. The empirical results suggest that our method could yield relatively consistent HR change predictions regardless of varying bitemporal resolution ratios. Our code is available at https://***/justchenhao/SILI_CD. Copyright © 2023, The Authors. All rights reserved.

关键词： Change detection

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A Game Theory-Reinforcement Learning Approach to Cooperation for UAVs

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IEEE Transactions on Vehicular Technology 2025年

作者： Tang, Changbing Pan, Linchao Chen, Jie Liu, Yang Lai, Jingang The College of Physics and Electronic Information Engineering Zhejiang Normal University Jinhua321004 China The School of Mathematical Sciences Zhejiang Normal University Jinhua321004 China The Hangzhou School of Automation Zhejiang Normal University Hangzhou311231 China The College of Electronic Engineering National University of Defense Technology Hefei230037 China The School of Artificial Intelligence and Automation the Key Laboratory of Image Processing and Intelligent Control Education Ministry of China China Huazhong University of Science and Technology Wuhan430074 China

To execute a variety of collaborative tasks, the cooperation for unmanned aerial vehicles (UAVs) with complicated interactions under dynamic environments is a challenging and critical issue. This paper studies the cooperation issue for UAVs under dynamic environments through an approach of game theory-reinforcement learning (GT-RL) approach, which combines the advantages of both game theory and reinforcement learning. First, to cope with the complicated interactions of UAVs, the cluster of UAVs is modeled as a public goods game with asymmetrical environmental feedback. Then, reinforcement learning is adopted to optimize decision-making of UAVs under unknown and dynamic environments through comparing the dynamic behaviors of UAVs, where a novel dynamics system offers a more comprehensive understanding on cooperative behavior among UAVs. Finally, the simulation results show that the GT-RL approach can effectively promote cooperation among UAVs in completing the collaborative tasks. © 1967-2012 IEEE.

关键词： Adversarial machine learning

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Named Entity Recognition Based on Anchor Span for Manufacturing Text Knowledge Extraction

SSRN

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SSRN 2024年

作者： Li, Yahui Sun, Qi Zhou, Chunjie Liu, Lu Tian, Yu-Chu School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Huazhong University of Science and Technology Hubei Wuhan430074 China School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Wuhan430074 China School of Computer Science Queensland University of Technology BrisbaneQLD4001 Australia

Intelligent industrial manufacturing heavily relies on structured knowledge. Named Entity Recognition (NER), an essential technique for extracting structured knowledge from text, has garnered significant research interest. However, current NER approaches face difficulties in handling multiple levels of entity nesting in knowledge extraction, especially within the manufacturing context. To address this issue, we present an Anchor Span-based NER (ASNER) approach for accurately and efficiently extracting manufacturing text knowledge. This method combines token and span classification. Initially, the head and tail features of the entity are extracted, with the corresponding anchor span of potential entities generated based on a boundary match neural network to localize the entities. Subsequently, token spatial contextual features are extracted using biaffine attention and convolutional neural networks, and classification categories are assigned to the anchor span-filtered entity features. Experimental studies demonstrate the effectiveness of the proposed ASNER approach. © 2024, The Authors. All rights reserved.

关键词： Character recognition

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Channel Reflection: Knowledge-Driven Data Augmentation for Eeg-Based Bcis

SSRN

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SSRN 2024年

作者： Wang, Ziwei Li, Siyang Luo, Jingwei Liu, Jiajing Wu, Dongrui 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 Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518063 China China Electronic System Technology Co. Ltd. Beijing100089 China School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan430074 China

A brain-computer interface (BCI) enables direct communication between the human brain and external devices. Electroencephalography (EEG) based BCIs are currently the most popular for able-bodied users. To increase user-friendliness, usually a small amount of user-specific EEG data are used for calibration, which may not be enough to develop a pure data-driven decoding model. To cope with this typical calibration data shortage challenge in EEG-based BCIs, this paper proposes a parameter-free channel reflection (CR) data augmentation approach that incorporates prior knowledge on the channel distributions of different BCI paradigms in data augmentation. Experiments on eight public EEG datasets across four different BCI paradigms (motor imagery, steady-state visual evoked potential, P300, and seizure classifications) using different decoding algorithms demonstrated that: 1) CR is effective, i.e., it can noticeably improve the classification accuracy;2) CR is robust, i.e., it consistently outperforms other data augmentation approaches in the literature;and, 3) CR is flexible, i.e., it can be combined with other data augmentation approaches to further increase the performance. We suggest that data augmentation approaches like CR should be an essential step in EEG-based BCIs. Our code is available online. © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

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Design and Visual Servoing control of a Hybrid Dual-Segment Flexible Neurosurgical Robot for Intraventricular Biopsy

arXiv

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arXiv 2024年

作者： Chen, Jian Chen, Mingcong Zhao, Qingxiang Wang, Shuai Wang, Yihe Xiao, Ying Hu, Jian Chan, Danny Tat Ming Yeung, Kam Tong Leo Chan, David Yuen Chung Liu, Hongbin The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Beijing100190 China Hong Kong Department of Biomedical Engineering City University of Hong Kong Hong Kong CAIR-HKISI-CAS HKSAR China The State Key Laboratory of Multimodal Artificial Intelligence Systems CASIA Beijing100190 China Department of Surgery The Chinese University of Hong Kong Hong Kong The State Key Laboratory of Management and Control for Complex Systems CASIA Beijing100190 China The School of Biomedical Engineering and Imaging Sciences King's College London LondonSE1 7EU United Kingdom

Traditional rigid endoscopes have challenges in flexibly treating tumors located deep in the brain, and low operability and fixed viewing angles limit its development. This study introduces a novel dual-segment flexible robotic endoscope MicroNeuro, designed to perform biopsies with dexterous surgical manipulation deep in the brain. Taking into account the uncertainty of the control model, an image-based visual servoing with online robot Jacobian estimation has been implemented to enhance motion accuracy. Furthermore, the application of model predictive control with constraints significantly bolsters the flexible robot's ability to adaptively track mobile objects and resist external interference. Experimental results underscore that the proposed control system enhances motion stability and precision. Phantom testing substantiates its considerable potential for deployment in neurosurgery. Copyright © 2024, The Authors. All rights reserved.

关键词： Visual servoing

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Endoatmospheric Energy Management Method for Launch Vehicle with Solid Rocket Boosters based on Changeable Launch Plane Strategy

Endoatmospheric Energy Management Method for Launch Vehicle ...

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第33届中国控制与决策会议

作者： Wenzhe Fu Wei Wang Lei Liu Bo Wang Zhongtao Cheng National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artificial Intelligence and AutomationHuazhong University of Science and Technology Beijing Aerospace Automatic Control Institute

Since the solid rocket boosters（SRB） can only be extinguished after fuel consumption,the energy management problem for the launch aircraft has received more and more attention *** paper comes up with a novel endoatmospheric energy management method based on the changeable launch plane（CLP） strategy for the SRB energy management *** transverse maneuvering energy management model is *** on the CLP theory,the height in the longitudinal plane and the displacement in the lateral plane are approximated by polynomial spline *** angle of attack（AOA） and the sideslip angle is calculated with the inverse of the dynamics ***,a simulation result for a three-stage solid rocket,called Vega,is given to demonstrate the effectiveness and efficiency of our proposed *** with the traditional energy management method,our proposed method has a 75.86% improvement in the capacity of energy consumption.

关键词： Solid rocket booster changeable launch plane strategy polynomial spline guidance method energy management

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Improved black-box attack based on query and perturbation distribution

Improved black-box attack based on query and perturbation di...

引用

International Workshop on Advanced Computational intelligence (IWACI)

作者： Weiwei Zhao Zhigang Zeng School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan China

Adversarial examples cause the deep neural network prediction error, which is a great threat to the deep neural network. How to generate more natural adversarial examples and improve the robustness of deep neural networks has received attention. In this paper, we propose an improved blackbox attack (IBBA) algorithm based on query and perturbation distribution. This algorithm only needs the top-l label of the attacked model to generate the adversarial examples. Based on the existing black-box attacks, we optimize the performance of the algorithm from two aspects: query distribution and perturbation distribution. In the aspect of query distribution, we adopt different strategies for nontargeted attack and targeted attack; in the aspect of perturbation distribution, we choose different low-frequency noise according to the difference between the targeted attack and nontargeted attack. The experimental results on ImageNet show that the proposed algorithm is better than the existing algorithms in low query number, and the targeted attack is better in each specified query number.

关键词： Perturbation methods Neural networks Prediction algorithms Robustness Low-frequency noise Discrete cosine transforms Computational intelligence

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Modality-aware triplet hard mining for zero-shot sketch-based image retrieval

arXiv

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arXiv 2021年

作者： Huang, Zongheng Sun, Yifan Han, Chuchu Gao, Changxin Sang, Nong Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Baidu Research United States

This paper tackles the Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) problem from the viewpoint of cross-modality metric learning. This task has two characteristics: 1) the zero-shot setting requires a metric space with good within-class compactness and the between-class discrepancy for recognizing the novel classes and 2) the sketch query and the photo gallery are in different modalities. The metric learning viewpoint benefits ZS-SBIR from two aspects. First, it facilitates improvement through recent good practices in deep metric learning (DML). By combining two fundamental learning approaches in DML, e.g., classification training and pairwise training, we set up a strong baseline for ZS-SBIR. Without bells and whistles, this baseline achieves competitive retrieval accuracy. Second, it provides an insight that properly suppressing the modality gap is critical. To this end, we design a novel method named Modality-Aware Triplet Hard Mining (MATHM). MATHM enhances the baseline with three types of pairwise learning, e.g., a cross-modality sample pair, a within-modality sample pair, and their combination. We also design an adaptive weighting method to balance these three components during training dynamically. Experimental results confirm that MATHM brings another round of significant improvement based on the strong baseline and sets up new state-of-the-art performance. For example, on the TU-Berlin dataset, we achieve 47.88+2.94% mAP@all and 58.28+2.34% Prec@100. Code will be publicly available. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep learning

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Fault Diagnosis Method for An Aircraft Attitude control System based on Deep Learning

Fault Diagnosis Method for An Aircraft Attitude Control Syst...

引用

第33届中国控制与决策会议

作者： Xiaoli Luo Wei Wang Hui Hu Zhongtao Cheng Maoqin Tang Bo Wang Lei Liu National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artificial Intelligence and AutomationHuazhong University of Science and Technology Beijing Aerospace Automatic Control Institute

This article proposes a deep-learning-based fault diagnosis method for an aircraft attitude control *** core idea is to construct two deep neural networks to approximate the mapping between actuator fault information and the real-time attitude generated by the *** proposed fault-diagnosis network consists of an input layer,a convolutional neural network（CNN） layer,a two-layer long and short-term memory network（LSTM）,and a fully connected layer in *** attitude quaternion and the attitude angular velocity are taken as the inputs of the networks,and the estimations of the bias fault size of each actuator are the ***,numerical simulations are carried out to verify the proposed method and to make a comparison with the existing diagnose *** simulation results show the effectiveness of the proposed method and the superiority over the compared method.

关键词： Aircraft attitude control system Bias fault CNN LSTM Fault diagnosis

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A Discrete Beetle Swarm Optimization Algorithm-based method for Multi-Targets Assignment for Multi-missiles Coordinated Attack

A Discrete Beetle Swarm Optimization Algorithm-based method ...

引用

第33届中国控制与决策会议

作者： Haojun Sun Wei Wang Gaoxiang Peng Zhongtao Cheng Bo Wang Lei Liu National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artificial Intelligence and AutomationHuazhong University of Science and Technology Beijing Aerospace Automatic Control Institute

Due to the nonlinearity and difficulty in the process of the cooperative target assignment,a discrete beetle swarm optimization algorithm is ***,the target assignment model is established based on the damage probability and target value of missile attacking ***,according to the constraint characteristics of the model,a particle integer coding method is proposed to reduce the complexity of the ***,the discrete beetle swarm optimization algorithm is designed and used to solve the problem,which is an improved particle swarm optimization algorithm by introducing beetle antennae search *** simulation results show that the algorithm combines the advantages of particle swarm optimization algorithm and beetle antennae search algorithm,and has fast convergence speed and is not easy to fall into local *** with other algorithms,the effectiveness of the algorithm is verified.

关键词： Weapon Target Assignment Particle Coding Discrete Beetle Swarm Optimization Algorithm

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