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International Conference on robotics and Automation engineering (ICRAE)

作者： Zijian Diao Shuai Cao Wenwei Li Yongjun Cao Lei Zhou Weixi Huang Hongan He Jianan Liang School of Information Engineering and Automation Kunming University of Science and Technology Kunming China Robotic Laboratory South China Robotics Innovation Research Institute Foshan China Guangdong Academy of Science Institute of Intelligent Manufacturing Guangzhou China South Glass Technology Co. LTD Foshan China

Aiming at the problem that the spatial features of pedestrian images are not aligned in current pedestrian re-identification and the network model cannot fully express the pedestrian information due to pose changes and occlusions, a method based on spatial transformation and multi-methods feature fusion is proposed. Firstly, for the pedestrian re-identification system, a processing method for enhancing the retrieval of pedestrians is provided, and the pedestrian images with more noise to be retrieved are denoised by means of side-window filtering; secondly, the spatial transformation network is improved. Channel attention and self-constrained branches are introduced to automatically align pedestrian spatial features to solve the problem of inconsistency in spatial semantic information caused by unaligned pedestrian image regions; then, multi-scale features are extracted from different deep layers of the backbone network, and coordinates attention and batch normalization of instances are integrated into different deep branches. Finally, the features of each branch are fused to obtain feature information with high representation ability. During the network training process, the dual loss function strategy is used to jointly optimize the model. Multiple experiments show that the proposed method has a higher recognition rate than other existing methods.

关键词： Training Adaptation models Automation Adaptive systems Filtering Semantics Feature extraction

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Tactile grasp stability classification based on graph convolutional networks

Tactile grasp stability classification based on graph convol...

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2021 IEEE International Conference on Real-Time Computing and robotics, RCAR 2021

作者： Mi, Tingting Que, Dashun Fang, Senlin Zhou, Zhenning Ye, Chaoxiang Liu, Chengliang Yi, Zhengkun Wu, Xinyu School of Information Engineering Wuhan University of Technology Wuhan430070 China Shenzhen Institute of Advanced Technology CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen Key Laboratory of Smart Sensing and Intelligent Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518055 China

ISBN: (纸本)9781665436786

One of the challenges for robots to grasp unknown objects is to predict whether objects will fall at the beginning of grasping. Evaluating robotic grasp state accurately and efficiently is a significant step to address this issue. In this paper, based on the different fusion approaches of multi-sensor tactile signals, we propose two novel methods based on Graph Convolution Network (GCN) for robotic stability classification. Specifically, we propose two deep learning methods including GCN based on data-level fusion (GCN-DF) and GCN based on feature-level fusion (GCN-FF). We explore the optimal parameters for transforming sensor signals into a graph structure. Furthermore, we verify the effectiveness of the proposed methods on the BioTac Grasp Stability (BiGS) dataset. The experimental results prove that the proposed approaches achieve higher classification accuracy than Support Vector Machine (SVM) and Long Short-Term Memory (LSTM). © 2021 IEEE.

关键词： Convolution

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Position Sensorless Control of Permanent Magnet Synchronous Motor Based on Adaptive Filtering Sliding Mode Observer

Position Sensorless Control of Permanent Magnet Synchronous ...

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第34届中国控制与决策会议

作者： Anna Wang Yingjie Xiong Tao Zhang College Of Information Science and Engineering Northeastern University School of Electrical Engineering Dalian University of Technology Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Key Laboratory of Networked Control Systems Chinese Academy of Sciences

In the position sensorless control strategy of permanent magnet synchronous motor(PMSM),the back-EMF method based on sliding mode observer(SMO) is favored because of its *** this paper,a SMO based on the piecewise sigmoid function is *** on the back-EMF observation error,the gain adaptation of the SMO is realized,which effectively reduces the high-frequency chattering problem of the conventional *** low-pass filter of the conventional SMO is improved,and an adaptive filter based on the back-EMF low-pass filter is *** to the back-EMF observation error,the cut-off frequency of the low-pass filter is changed in real-time,and the estimation of rotor position is *** Matlab/Simulink simulation verification,the control strategy is feasible and achieves a better control effect.

关键词： Sensorless control SMO Adaptive gain and Adaptive filter

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Natural Image Matting Based on Surrogate Model

SSRN

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SSRN 2022年

作者： Liang, Yihui Gou, Hongshan Feng, Fujian Liu, Guisong Huang, Han School of Computer Science Zhongshan Institute University of Electronic Science and Technology of China Zhongshan528400 China Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang550025 China School of Economic Information Engineering Southwestern University of Finance and Economics Chengdu610000 China Laboratory of Intelligent Algorithms and Intelligent Software School of Software Engineering South China University of Technology Guangzhou510006 China

Image matting is important in digital image processing. The pixel pair optimization-based methods are some of the image matting methods that have distinct advantages in spatially disconnected foreground. However, they cannot provide high-quality alpha mattes with limited computing resources which limits their computing resource-sensitive applications. To address this problem, we present a natural image matting method based on a surrogate model. The surrogate model for pixel pair optimization is established in the presented method to approximate pixel pair fitness, and its analytical optimal solution is used as the approximate optimal solution of pixel pair optimization problem, which saves a lot of computing resources. Experimental results show that the image matting method based on a surrogate model provides high-quality matting mattes with low computing resources and its image matting performance is competitive against state-of-the-art pixel pair optimization-based methods which require a lot of computational resources. © 2022, The Authors. All rights reserved.

关键词： Pixels

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Terahertz Image Super-Resolution Reconstruction Based on Complex Deconvolution Algorithm with Different Criteria 6

Terahertz Image Super-Resolution Reconstruction Based on Com...

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6th International Conference on information systems engineering, ICISE 2021

作者： Wang, Ying Qi, Feng Wang, Jinkuan School of Communication Science and Engineering Northeastern University Shenyang China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Science Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781450385220

Super-Resolution (SR) is one of the most fundamental issues in THz imaging processing. Deconvolution is widely used in THz imaging SR problems. Heavy-tailed distribution has good performance in deconvolution for its capability to preserve edges. By making use of the wave nature of THz waves, complex deconvolution is employed for solving THz images. A complex deconvolution algorithm based on criteria is proposed in this paper. And three criteria are discussed to get the most optimal result. Compared with the traditional methods, which are set the index as a fixed value, our method is more flexible for obtaining clear edges. The measured PSF is used to analyze the THz image by a complex image blurred model. Experiments illustrate the efficiency and accuracy of the proposed method. All three criteria can solve the LR problem well. Relative to Peak Signal to Noise Ratio (PSNR) and information entropy, the SR result of Structural Similarity Index Measure (SSIM) is better visual. Thus, we prefer SSIM as the criterion in our work. © 2021 ACM.

关键词： Deconvolution

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Hybrid saturation restoration for LDR images of HDR scenes

arXiv

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arXiv 2021年

作者： Zheng, Chaobing Li, Zhengguo Wu, Shiqian the Institute of Robotics and Intelligent Systems School of Information Science and Engineering Wuhan University of Science and Technology Wuhan430081 China the SRO Department Institute for Infocomm Research Singapore138632 Singapore

There are shadow and highlight regions in a low dynamic range (LDR) image which is captured from a high dynamic range (HDR) scene. It is an ill-posed problem to restore the saturated regions of the LDR image. In this paper, the saturated regions of the LDR image are restored by fusing model-based and data-driven approaches. With such a neural augmentation, two synthetic LDR images are first generated from the underlying LDR image via the model-based approach. One is brighter than the input image to restore the shadow regions and the other is darker than the input image to restore the high-light regions. Both synthetic images are then refined via a novel exposedness aware saturation restoration network (EASRN). Finally, the two synthetic images and the input image are combined together via an HDR synthesis algorithm or a multi-scale exposure fusion algorithm. The proposed algorithm can be embedded in any smart phones or digital cameras to produce an information-enriched LDR image. © 2021, CC0.

关键词： Restoration

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Potential Challenges and Food Security Responses to Climate Change in China and Mongolia in the Post-COP28 Era

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Journal of Resources and Ecology 2025年第1期16卷 22-35页

作者： CHEN Jiannan Faith Ka Shun CHAN WANG Jiayu FENG Meili WANG Zilin Ochir ALTANSUKH SHAO Yating LI Pengfei WANG School of Human Settlements and Civil Engineering Xi’an Eurasia UniversityXi’an 710054China College of Geomatics Xi’an University of Science and TechnologyXi’an 710054China School of Geographical Sciences University of Nottingham Ningbo ChinaNingboZhejiang 315100China Water@Leeds Research Institute and School of Geography University of LeedsLeeds LS29JTUK Research Centre for Intelligent Management&Innovation Development/Research Base for Shenzhen Municipal Policy&Development Southern University of Science and TechnologyShenzhenGuangdong 518000China Key Laboratory of Urban Environment and Health Institute of Urban EnvironmentChinese Academy of SciencesXiamenFujian 361021China Department of Environment and Forest Engineering School of Engineering and Technology and Environmental Engineering LaboratoryNational University of MongoliaUlaanbaatar 14201Mongolia State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China

After the COP28 Conference,many countries are increasingly concerned about their future practices regarding food *** North Asia,the Northeast China is the major food production base for the *** the border,the economy of Mongolia is heavily reliant on agricultural production and animal *** recent years,climatic extremes such as droughts and floods,combined with human-induced overgrazing,have posed alarming threats to food *** review illustrates the challenges and constraints these two countries are facing due to climate changes and summarizes the existing measures and established programs in both ***,we develop the“climate resilient agriculture”(CRA)framework for improving agricultural *** framework emphasizes the importance of international institutions,such as the World Bank,and developed countries to provide more financial and technological support to bolster climate resilience in Northern ***,we conclude by encouraging cross-border co-production and collaborations among governments to implement the CRA framework to tackle future climatic challenges.

关键词： agricultural sensitivity climatic extremes climate resilient agriculture(CRA) food security China and Mongolia

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FIRE: A Dataset for Feedback Integration and Refinement Evaluation of Multimodal Models

arXiv

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arXiv 2024年

作者： Li, Pengxiang Gao, Zhi Zhang, Bofei Yuan, Tao Wu, Yuwei Harandi, Mehrtash Jia, Yunde Zhu, Song-Chun Li, Qing Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China State Key Laboratory of General Artificial Intelligence BIGAI China State Key Laboratory of General Artificial Intelligence Peking University China Department of Electrical and Computer System Engineering Monash University Australia Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China Department of Automation Tsinghua University China

Vision language models (VLMs) have achieved impressive progress in diverse applications, becoming a prevalent research direction. In this paper, we build FIRE, a feedback-refinement dataset, consisting of 1.1M multi-turn conversations that are derived from 27 source datasets, empowering VLMs to spontaneously refine their responses based on user feedback across diverse tasks. To scale up the data collection, FIRE is collected in two components: FIRE-100K and FIRE-1M, where FIRE-100K is generated by GPT-4V, and FIRE-1M is freely generated via models trained on FIRE-100K. Then, we build FIRE-Bench, a benchmark to comprehensively evaluate the feedback-refining capability of VLMs, which contains 11K feedback-refinement conversations as the test data, two evaluation settings, and a model to provide feedback for VLMs. We develop the FIRE-LLaVA model by fine-tuning LLaVA on FIRE-100K and FIRE-1M, which shows remarkable feedback-refining capability on FIRE-Bench and outperforms untrained VLMs by 50%, making more efficient user-agent interactions and underscoring the significance of the FIRE dataset. Copyright © 2024, The Authors. All rights reserved.

关键词： Fireclay

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Design of an improved LADRC controller for a dual-joint robotic arm system

Design of an improved LADRC controller for a dual-joint robo...

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第40届中国控制会议

作者： Jiashun Dong Huaiyu Wu Zhihuan Chen Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology School of Information Science and Engineering Wuhan University of Science and Technology

This paper designs an improved linear active disturbance rejection controller（LADRC） for a dual-joint robotic arm system. Firstly, the model of the dual-joint manipulator was established;Secondly, the manipulator control system based on traditional linear active disturbance rejection controller（LADRC） was designed, and the stability and rapidity of the linear active disturbance rejection control were analyzed. Then, in order to improve the tracking speed and the robustness of the system, an improved LADRC controller with proportional-derivative term（ILADRC PD） is designed for the first joint of the manipulator,and an improved LADRC controller with adaptive gain（ILADRC AG） is designed for the second joint of the ***, numerical simulation is presented via comparing the improved LADRC with the traditional method, which demonstrates that the controller proposed in this study has better performance.

关键词： LADRC dual-joint robotic arm system proportional-derivative item adaptive gain

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Contrastive learning of Class-agnostic Activation Map for Weakly Supervised Object Localization and Semantic Segmentation

arXiv

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arXiv 2022年

作者： Xie, Jinheng Xiang, Jianfeng Chen, Junliang Hou, Xianxu Zhao, Xiaodong Shen, Linlin School of Computer Science & Software Engineering Shenzhen University China Dept. of Computer Science Wenzhou-Kean University Wenzhou China Shenzhen Institute of Artificial Intelligence of Robotics of Society Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University China

While class activation map (CAM) generated by image classification network has been widely used for weakly supervised object localization (WSOL) and semantic segmentation (WSSS), such classifiers usually focus on discriminative object regions. In this paper, we propose Contrastive learning for Class-agnostic Activation Map (C2AM) generation only using unlabeled image data, without the involvement of image-level supervision. The core idea comes from the observation that i) semantic information of foreground objects usually differs from their backgrounds;ii) foreground objects with similar appearance or background with similar color/texture have similar representations in the feature space. We form the positive and negative pairs based on the above relations and force the network to disentangle foreground and background with a class-agnostic activation map using a novel contrastive loss. As the network is guided to discriminate cross-image foreground-background, the class-agnostic activation maps learned by our approach generate more complete object regions. We successfully extracted from C2AM class-agnostic object bounding boxes for object localization and background cues to refine CAM generated by classification network for semantic segmentation. Extensive experiments on CUB-200-2011, ImageNet-1K, and PASCAL VOC2012 datasets show that both WSOL and WSSS can benefit from the proposed C2AM. Code will be available at https://***/CVISZU/CCAM. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantic Segmentation

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