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Semi-Autonomous Navigation Based on Local Semantic Map for Mobile robot

Journal of shanghai Jiaotong university(Science) 2025年第1期30卷 27-33页

作者： ZHAO Yanfei XIAO Peng WANG Jingchuan GUO Rui Department of Automation Institute of Medical RoboticsShanghai Jiao Tong UniversityShanghai200240China State Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240China State Grid Intelligence Technology Co. Ltd.Jinan250013China

Mobile robots represented by smart wheelchairs can assist elderly people with mobility *** paper proposes a multi-mode semi-autonomous navigation system based on a local semantic map for mobile robots,which can assist users to implement accurate navigation(e.g.,docking)in the environment without prior *** order to overcome the problem of repeated oscillations during the docking of traditional local path planning algorithms,this paper adopts a mode-switching method and uses feedback control to perform docking when approaching semantic *** last,comparative experiments were carried out in the real *** show that our method is superior in terms of safety,comfort and docking accuracy.

关键词： semi-autonomous navigation mobile robot semantic map voice interaction

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SAF: Local Shape-aware Face-based Garment Collision Handling via Neural SDFs

SAF: Local Shape-aware Face-based Garment Collision Handling...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Tang, Minzhe Yuan, Ruisheng Kou, Dongliang Sun, Mingyang Zhang, Lihua Academy for Engineering and Technology Fudan University Shanghai China Institute of Metaverse & Intelligent Medicine Fudan University Shanghai China Jilin Provincial Key Laboratory of Intelligence Science and Engineering Changchun China Engineering Research Center of AI and Robotics Ministry of Education Shanghai China AI and Unmanned Systems Engineering Research Center of Jilin Province Changchun China

ISBN: (纸本)9798350368741

Learning-based garment prediction presents an appealing alternative to physics-based methods owing to its high efficiency. However, the predicted garments can exhibit noticeable penetrations into the body. Many collision-handling methods operate in the point domain, which has an inherent limitation in addressing the penetration issues with the mesh faces. In light of this, we propose a local Shape-Aware, Face-based collision handling approach (SAF) that can be applied to garment prediction networks to achieve real-time collision handling. Considering the nature of garments, we design a compact formulation to model the continuity of the garment surface, and utilize neural Signed Distance Fields (SDFs) to accomplish penetration resolution. Recognizing the significant impact of the body's local shape on collision handling, we further propose using the angles between SDF gradients to characterize the sharpness of the body. Our approach can compensate for the inaccuracy of neural SDFs and preserve local smoothness and details. Extensive experiments demonstrate the outstanding performance and generalizability of our method. © 2025 IEEE.

关键词： collision handling computer graphics deep learning garment simulation

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ATTA: Anomaly-aware Test-Time Adaptation for Out-of-Distribution Detection in Segmentation 37

ATTA: Anomaly-aware Test-Time Adaptation for Out-of-Distribu...

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Gao, Zhitong Yan, Shipeng He, Xuming School of Information Science and Technology ShanghaiTech University China Shanghai Engineering Research Center of Intelligent Vision and Imaging China

ISBN: (纸本)9781713899921

Recent advancements in dense out-of-distribution (OOD) detection have primarily focused on scenarios where the training and testing datasets share a similar domain, with the assumption that no domain shift exists between them. However, in real-world situations, domain shift often exits and significantly affects the accuracy of existing out-of-distribution (OOD) detection models. In this work, we propose a dual-level OOD detection framework to handle domain shift and semantic shift jointly. The first level distinguishes whether domain shift exists in the image by leveraging global low-level features, while the second level identifies pixels with semantic shift by utilizing dense high-level feature maps. In this way, we can selectively adapt the model to unseen domains as well as enhance model’s capacity in detecting novel classes. We validate the efficacy of our method on several OOD segmentation benchmarks, including those with significant domain shifts and those without, observing consistent performance improvements across various baseline models. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Data handling

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Let's Rectify Step by Step: Improving Aspect-based Sentiment Analysis with Diffusion Models 30

Let's Rectify Step by Step: Improving Aspect-based Sentiment...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Liu, Shunyu Zhou, Jie Zhu, Qunxi Chen, Qin Bai, Qingchun Xiao, Jun He, Liang School of Computer Science and Technology East China Normal University Shanghai China Research Institute of Intelligent Complex Systems Fudan University Shanghai China Shanghai Engineering Research Center of Open Distance Education Shanghai Open University China

ISBN: (纸本)9782493814104

Aspect-Based Sentiment Analysis (ABSA) stands as a crucial task in predicting the sentiment polarity associated with identified aspects within text. However, a notable challenge in ABSA lies in precisely determining the aspects' boundaries (start and end indices), especially for long ones, due to users' colloquial expressions. We propose DiffusionABSA, a novel diffusion model tailored for ABSA, which extracts the aspects progressively step by step. Particularly, DiffusionABSA gradually adds noise to the aspect terms in the training process, subsequently learning a denoising process that progressively restores these terms in a reverse manner. To estimate the boundaries, we design a denoising neural network enhanced by a syntax-aware temporal attention mechanism to chronologically capture the interplay between aspects and surrounding text. Empirical evaluations conducted on eight benchmark datasets underscore the compelling advantages offered by DiffusionABSA when compared against robust baseline models. Our code is publicly available at https://***/Qlb6x/DiffusionABSA. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Sentiment analysis

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Generative artificial intelligence and its applications in materials science:Current situation and future perspectives

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Journal of Materiomics 2023年第4期9卷 798-816页

作者： Yue Liu Zhengwei Yang Zhenyao Yu Zitu Liu Dahui Liu Hailong Lin Mingqing Li Shuchang Ma Maxim Avdeev Siqi Shi School of Computer Engineering and Science Shanghai UniversityShanghai200444China State Key Laboratory of Advanced Special Steel School of Materials Science and EngineeringShanghai UniversityShanghai200444China Materials Genome Institute Shanghai UniversityShanghai200444China Shanghai Engineering Research Center of Intelligent Computing System Shanghai200444China Australian Nuclear Science and Technology Organisation Sydney2232Australia School of Chemistry The University of SydneySydney2006Australia

Generative Artificial Intelligence(GAI)is attracting the increasing attention of materials community for its excellent capability of generating required *** the introduction of Prompt paradigm and reinforcement learning from human feedback(RLHF),GAI shifts from the task-specific to general pattern gradually,enabling to tackle multiple complicated tasks involved in resolving the structure-activity ***,we review the development status of GAI comprehensively and analyze pros and cons of various generative models in the view of *** applications of task-specific generative models involving materials inverse design and data augmentation are also *** ChatGPT as an example,we explore the potential applications of general GAI in generating multiple materials content,solving differential equation as well as querying materials ***,we summarize six challenges encountered for the use of GAI in materials science and provide the corresponding *** work paves the way for providing effective and explainable materials data generation and analysis approaches to accelerate the materials research and development.

关键词： Machine learning Artificial intelligence Generative artificial intelligence Materials science Novel materials discovery Deep learning

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Anti-Disturbance Formation Control of Quadrotor UAVs Based on Terminal Sliding Mode Control

Anti-Disturbance Formation Control of Quadrotor UAVs Based o...

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2023 China Automation Congress, CAC 2023

作者： Xin, Ying Ni, Hao Huang, Zihao He, Yongning Xia, Sigang Shanghai Aerospace Control Technology Institute Intelligent Flight Control Technology Research and Development Center Shanghai China Shanghai Institute of Mechanical and Electrical Engineering Electrical System Laboratory Shanghai China

ISBN: (纸本)9798350303759

In this paper, the anti-disturbance formation flight control of quadrotor UAVs system is realized based on terminal sliding mode control. The generalized error state is designed for each quad-rotor UA V, based on which the formation control target is proposed. In order to achieve the goal of anti-disturbance formation control, a formation controller based on terminal sliding mode control is designed. Finally, the general error states of all quadrotor UAVs converge to zero to achieve the desired formation. Finally, a simulation example is given to verify the effectiveness of the proposed algorithm. © 2023 IEEE.

关键词： Multi agent systems

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Broad Federated Meta-Learning of Damaged Objects in Aerial Videos

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Computer Modeling in engineering & Sciences 2023年第12期137卷 2881-2899页

作者： Zekai Li Wenfeng Wang School of Aerospace Engineering Xi’an Jiaotong UniversityXi’an710049China Research Institute of Intelligent Engineering and Data Applications Shanghai Institute of TechnologyShanghai201418China Research Center for Ecology and Environment of Central Asia Chinese Academy of SciencesUrumqi830011China Applied Nonlinear Science Lab Anand International College of EngineeringJaipur391320India Department of Visual Engineering International Academy of Visual Art and EngineeringLondonCR26EQUK Institute of Intelligent Management and Technology Sino-Indian Joint Research Center of AI and RoboticsBhubaneswar752054India

We advanced an emerging federated learning technology in city intelligentization for tackling a real challenge-to learn damaged objects in aerial ***-learning system was integrated with the fuzzy broad learning system to further develop the theory of federated *** the mixed picture set of aerial video segmentation and the 3D-reconstructed mixed-reality data were employed in the performance of the broad federated meta-learning *** study results indicated that the object classification accuracy is up to 90%and the average time cost in damage detection is only 0.277 ***,the broad federated meta-learning system is efficient and effective in detecting damaged objects in aerial videos.

关键词： Fuzzy learning system mixed-reality 3D-reconstructed oblique photography

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intelligent Psyllid Monitoring Based on DiTs-YOLOv10-SOD

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2025年第1期3卷 286-294页

作者： Zhang, Li Liang, Qianyue John, Vijay Chen, Hong Li, Shanjun Li, Weifu Chen, Yaohui Huazhong Agricultural University College of Informatics Wuhan430070 China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Wuhan430070 China Huazhong Agricultural University College of Engineering Wuhan430070 China RIKEN Guardian Robot Project Kyoto619-0288 Japan

Citrus psyllids are common pests that feed on the sap of citrus trees, leading to yellowing, deformation, and potentially tree death in severe cases. Effective identification and monitoring of these pests are crucial for the health and sustainable development of the citrus industry. Rapid and accurate detection enables farmers to control citrus psyllid infestations promptly, thereby protecting their crops and ensuring industry sustainability. In this article, we utilize a custom-built pest-trapping device to capture the psyllids and upload the image to a server via the Internet of Things. We captured 420 images with a resolution of 3820 × 2160 using the device. These images, containing various types of pests, were utilized for model experimentation and training. On the server, the diffusion transformer (DiT) is utilized to increase the training data, addressing challenges such as limited sample size and class imbalance. A small object detection head is integrated into YOLOv10 to enhance the capture of shallow features in psyllid images. In addition, the soft nonmaximum suppression method is applied to resolve overlapping issues in counting the psyllids. Finally, the results are uploaded to an app, allowing users to stay informed about citrus pest conditions in real time. The experimental results indicate that DiTs-generated images achieved scores of 76.79, 0.29, and 1.68 in the Frechet inception distance, learned perceptual image patch similarity, and multiscale structural similarity metrics, respectively, outperforming the commonly used DDPM model by 8.51, 0.18, and 0.34, respectively. The improved YOLOv10 model, trained with the expanded DiTs dataset, reached a recall, F1-score, and precision of 90.55%, 92.18%, and 93.88%, respectively, demonstrating outstanding performance across all metrics. This approach enables fully automated recognition of citrus psyllids, facilitating real-time detection and contributing to the protection of citrus crops. © 2023 IEEE.

关键词： Forestry

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The influence of post-aging treatment on the microstructure and micromechanical behaviors of additively manufactured maraging steel investigated by in situ high-energy X-ray diffraction

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Journal of Materials Science & technology 2024年第33期200卷 1-12页

作者： Yang Li Jingyue Yu Shilei Li Shengjie Wang Yang Ren Ke Yang Yan-Dong Wang Beijing Advanced Innovation Center for Materials Genome Engineering State Key Laboratory for Advanced Metals and MaterialsUniversity of Science and Technology BeijingBeijing 100083China Department of Physics City University of Hong KongHong Kong 999077China Shanghai Synchrotron Radiation Facility Shanghai Advanced Research InstituteChinese Academy of SciencesShanghai 201204China Institute for Materials Intelligent Technology Liaoning Academy of MaterialsShenyang 110004China

The microstructure evolution and micromechanical behaviors of additively manufactured 18Ni_(3)00 marag-ing steel for both as-printed and aged one were investigated using the in situ high-energy X-ray diffrac-tion(HE-XRD)technique with uniaxial tensile *** investigations revealed that the volume frac-tion of reversed austenite increased as the annealing temperature *** maraging steel was strength-ened byη-Ni_(3)Ti precipitates,where the aged maraging steel had a higher UTS value of∼1860 MPa than∼1135 MPa in the as-printed one,but sacrificed more than half of ductility(from∼8.6%to∼4.0%).The austenite in aged steel presents more stability induced by the aging process than that in as-printed counterpart,which has a higher critical martensitic transformation stress of∼1200 MPa than that of∼780 MPa in as-printed *** austenite grains orientated with[200]//LD yield before the macro-yielding and preferential martensite transformation *** study provides further insight into the intricated micromechanical responses of additively manufactured 18Ni_(3)00 maraging steel,enlarging the scope of its adaptation and application.

关键词： Additive manufacturing Maraging steel Aging treatment High-energy X-ray diffraction

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Tackling Challenges of Low-texture and Illumination Variations for Endoscopy Self-supervised Monocular Depth Estimation

Tackling Challenges of Low-texture and Illumination Variatio...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Luyan Zhou Jingjing Luo Hongbo Wang Shizun Zhao Yuan Han Wenxian Li Academy for Engineering and Technology Fudan University Shanghai China Intelligent Robot Engineering Research Center of Ministry of Education Shanghai China Shanghai Intelligent Robot Engineering Technology Research Center Shanghai China Eye & ENT Hospital of Fudan University Shanghai China

Extracting 3D environmental insights from endoscopy images holds immense value for minimally invasive surgical procedures. The Self-supervised Monocular Depth Estimation (SMDE) framework is promising for achieving this objective. However, existing methods struggle with low-texture and drastic illumination fluctuations in endoscopic images. To tackle this, we incorporate Photometric Aliagnment method based on pixel-wise Color Offset, and propose a carefully designed Color Offset penalty based on Reconstruction Confidence. We furthur apply an Auto-Mask mechanism and a cutting-edge backbone to enhance the performance. Our experiments employ a dataset of airway intubation images captured with a low-resolution and near-field electronic bronchoscope. The experimental results unequivocally highlight the exceptional performance of our approach, achieving root mean square error of 1.64 ± 0.22 mm after point cloud registration, which decreases by 16% than the current SoTA. Furthermore, we introduce an innovative evaluation metric rooted in Photometric Alignment Symmetry, and conduct ablation experiments on this metric. The ablation experiments furthur validate the effectiveness of proposed modules. This study underscores the efficacy of the Reconstruction Confidence-based Color Offset penalty and symmetric alignment evaluations in extracting 3D information from low-resolution and near-field endoscopy images.

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