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4th International Computational Imaging Conference, CITA 2024

作者： Hu, Chun Li, Yanlin Zheng, Dezhi Cheng, Yufei Peng, Peng Nie, Sichong Advanced Research Institute of Multidisciplinary Sciences Beijing Institute of Technology Beijing100081 China MIIT Key Laboratory of Complex-field Intelligent Exploration Beijing Institute of Technology Beijing100081 China State Key Laboratory of CNS/ATM Beijing Institute of Technology Beijing100081 China Chongqing Caeri Robot Technology Co. Ltd. Certification Department Chongqing400722 China Innovation&Research Institute of HIWING Technology Academy Beijing100074 China

ISBN: (纸本)9781510688834

CO2 is one of the primary contributors to the greenhouse effect, and its monitoring and control are critical for achieving carbon peaking and neutrality goals. Therefore, large-scale distributed monitoring of CO2 concentrations in the environment is of significant importance. To investigate the diffusion patterns of CO2 in windless environments, this paper designs and implements a distributed CO2 monitoring system. The system adopts the three-layer architecture of the Internet of Things (IoT) as its overall framework, utilizing a self-developed CO2 sensor as the sensing layer node. The system employs the Narrowband Internet of Things (NB-IoT) protocol to achieve multi-node, wide-coverage, and low-power data transmission and reception. A custom database is developed as the application layer to store, classify, and manage data collected by the nodes. Experimental results provide CO2 monitoring data, which are analyzed to summarize the variation patterns of CO2 concentration over time and distance. By comparing these results with existing gas diffusion models, it is found that the trends are consistent, thereby demonstrating the reliability and stability of the proposed system. © 2025 SPIE.

关键词： Carbon capture and utilization

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Design and Analysis of a Novel Ring Truss Deployable Antenna Mechanism 16th

Design and Analysis of a Novel Ring Truss Deployable Antenna...

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16th International Conference on Intelligent robotics and Applications, ICIRA 2023

作者： Han, Bo Yuan, Zhantu Zhou, Yuanzhi Liu, Feng Yao, Jiantao Zhao, Yongsheng Hebei Provincial Key Laboratory of Parallel Robot and Mechatronic System Yanshan University Qinhuangdao066004 China Key Laboratory of Advanced Forging and Stamping Technology and Science Yanshan University Ministry of Education of China Qinhuangdao066004 China

ISBN: (纸本)9789819965038

With the development of astronautics, satellite antenna as an important communication equipment has also gradually developed to large scale. In this paper, a novel ring truss deployable antenna mechanism is proposed. The mechanism has three layers of installation points with height differences between them, which can be used to ensure that all the installation points will be distributed on the profile of the cable network, and this characteristic ensures the mechanism can be utilized to the maximum. Then the degrees of freedom (DOF) and kinematics of the ring truss deployable mechanism are analyzed using screw theory, where the analysis shows that the DOF of the overall ring truss deployable mechanism is equal to one. Following that, the dynamics model about the mechanism unit is established using the second type of Lagrange equations. Based on this dynamic model, the effect of torsion spring stiffness on the deployment process is discussed and the conclusion is verified by simulation. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Trusses

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Mechanism Design and Walking Stability Study of (2UPS-U) +R Series-Parallel Hybrid Bionic Wheel-Legged Quadruped robot*

Mechanism Design and Walking Stability Study of (2UPS-U) +R ...

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IEEE International Conference on robotics and Biomimetics

作者： Shanshan Li Hongbo Wang Dong Li Minghao Li Lianqing Li Yu Tian Jianye Niu Hebei Provincial Key Laboratory of Parallel Robot and Mechatronic System Yanshan University Qinhuangdao China Academy for Engineering & Technology Fudan University Shanghai China Hebei Provincial Key Laboratory of Parallel Robot and Mechatronic System and Key Laboratory of Advanced Forging & Stamping Technology and Science of Ministry of Education of China Yanshan University Qinhuangdao China

In this paper, a bionic-based design of a series-parallel hybrid wheel-legged quadruped robot that can realize wheel-foot transformations is presented to achieve footed stable walking, and a method of measuring the static stability of the robot by the evaluation of the torque of the supporting leg rotor axis is proposed with the consideration of the robot's leg mass. Firstly, the whole robot position is solved; Secondly, the force on the robot during static walking is analyzed, and the stability of its static walking is also analyzed from the moment perspective; Finally, the stabilizing support region for static walking is solved, and the stabilizing support region for the robot in such a motion state is calculated. This paper provides high reference significance and value for the mechanism design and walking stability research of series-parallel hybrid wheel-legged quadruped robots.

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Distance Transform Guided Medical Image Segmentation With Channel Information Exchange 6

Distance Transform Guided Medical Image Segmentation With Ch...

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6th International Conference on Algorithms, Computing and Artificial Intelligence, ACAI 2023

作者： Yang, Han Xu, Minxia Cai, Bin Liang, Pengpeng Sun, Zhiyong Song, Bo Cheng, Erkang Laboratory of Bionic Sensing and Advanced Robot Technology Institute of Intelligent Machines Hefei Institutes of Physical Science Chinese Academy of Sciences Anhui Hefei China University of Science and Technology of China Anhui Hefei China Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Zhejiang Hangzhou China School of Computer and Artificial Intelligence Zhengzhou University Henan Zhengzhou China

ISBN: (纸本)9798400709203

Medical image segmentation plays an important role in the computer-aided screening of many diseases. Different from popular deep learning-based segmentation methods that mainly focus on the whole target, recent studies pay more attention to target contour by making use of the distance transform of the object boundary. In this paper, to learn both the object content and the boundary information, we incorporate a regression task that estimates a distance map of target contour into a CNN-based segmentation network. The segmentation task and the regression task share most parts of the backbone network. Moreover, we introduce a channel information exchange module that dynamically exchanges feature maps between the segmentation and the regression tasks. The proposed information exchange module is parameter-free and controlled by the batch normalization scaling factor. In addition, the output feature maps of the two tasks are further integrated to provide fused segmentation *** evaluate our method on three popular datasets, including the 2D ultrasound thyroid nodule segmentation dataset, the 3D MRI left atrial segmentation dataset and 2017 MM-WHS whole heart segmentation Challenge datasets. Experimental results show that our method achieves promising performance compared to the state-of-the-art approaches. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Deep learning

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PM2.5 Concentration Prediction Based on Coupling Multivariable Parameters LSTM 6

PM2.5 Concentration Prediction Based on Coupling Multivariab...

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6th International Conference on Information Science, Electrical, and Automation Engineering, ISEAE 2024

作者： Zheng, Dezhi Zheng, Jinxiong Lu, Shijia Li, Zhongxiang Peng, Peng Liu, Bei Advanced Research Institute of Multidisciplinary Science Beijing Institute of Technology Beijing100081 China Yangtze Delta Region Academy Beijing Institute of Technology Jiaxing314019 China MIIT Key Laboratory of Complex-Field Intelligent Exploration Beijing Institute of Technology Beijing100081 China Chongqing Caeri Robot Technology Co. Ltd. Chongqing400722 China Beijing Research Institute of Telemetry Beijing100076 China

ISBN: (纸本)9781510682924

As one of the major air pollutants, PM2.5 poses serious threats to human health and daily life. Improving the accuracy of PM2.5 predictions is of significant importance for pollution control, as it can effectively reduce its harmful impact. Long Short-Term Memory (LSTM) networks are capable of handling long-term dependencies and are commonly used for modeling and remembering long sequence data, making them a key tool for PM2.5 prediction. Existing LSTM models often use a single parameter for PM2.5 prediction, without considering the coupling effects of multiple meteorological factors with different pollutants and their concentrations. Additionally, using a single optimization algorithm may lead to optimization parameters getting stuck in local optimal solutions, disrupting the optimization results. In this study, multiple meteorological factors highly correlated with PM2.5 data using Pearson correlation coefficients (PCCs) and pollutants with concentrations were selected to construct a coupled correlation model. Furthermore, to improve the reliability of optimization results, the Sparrow Search Algorithm (SSA) with strong local search capability and the Particle Swarm Optimization algorithm (PSO) with strong global search capability were employed to simultaneously optimize LSTM hyperparameters and obtain the optimal solution. Using hourly meteorological data from multiple stations in Beijing from March 1, 2013, to February 28, 2017, as the data source, the SSA-PSO algorithm was used to obtain the optimal solution for network hyperparameters. The coupled correlation model of multiple meteorological parameters with multiple pollutants and concentrations was established. The research results indicate that considering the coupled correlation model of meteorological parameters with pollutants and concentrations can effectively improve the prediction accuracy of PM2.5, and the more types of input parameters, the higher the model prediction accuracy. The R2 of the coupled

关键词： Health risks

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RGB-Based Category-Level Object Pose Estimation via Depth Recovery and Adaptive Refinement

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IEEE robotics and Automation Letters 2025年第6期10卷 5377-5384页

作者： Yang, Hui Sun, Wei Liu, Jian Zheng, Jin Zeng, Zhiwen Mian, Ajmal Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Changsha410082 China Hunan University State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Changsha410082 China Central South University School of Architecture and Art Changsha410082 China University of Western Australia Department of Computer Science and Software Engineering CrawleyWA6009 Australia

Category-level pose estimation methods have received widespread attention as they can be generalized to intra-class unseen objects. Although RGB-D-based category-level methods have made significant progress, reliance on depth image limits practical application. RGB-based methods offer a more practical and cost-effective solution. However, current RGB-based methods struggle with object geometry perception, leading to inaccurate pose estimation. We propose depth recovery and adaptive refinement for category-level object pose estimation from a single RGB image. We leverage DINOv2 to reconstruct the coarse scene-level depth from the input RGB image and propose an adaptive refinement network based on an encoder-decoder architecture to dynamically improve the predicted coarse depth and reduce its gap from the ground truth. We introduce a 2D–3D consistency loss to ensure correspondence between the point cloud obtained from depth projection and the objects in the 2D image. This consistency supervision enables the model to maintain alignment between the depth image and the point cloud. Finally, we extract features from the refined point cloud and feed them into two confidence-aware rotation regression branches and a translation and size prediction residual branch for end-to-end training. Decoupling the rotation matrix provides a more direct representation, which facilitates parameter optimization and gradient propagation. Extensive experiments on the REAL275 and CAMERA25 datasets demonstrate the superior performance of our method. Real-world estimation and robotic grasping experiments demonstrate our model robustness to occlusion, clutter environments, and low-textured objects. Our code and robotic grasping video are available at DA-Pose. © 2016 IEEE.

关键词： Depth perception

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Improvement of the spreading effect of atmospheric pressure microplasma jet treatment through shielding-gas-controlled focusing

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Plasma Science and technology 2022年第9期24卷 85-97页

作者： Li LV Jianhang CHEN Jiahao WANG Shengquan WANG Meng LI Deyu TU Liping SHI Tao WANG Anhui Province Engineering Laboratory of Intelligent Demolition Equipment Maanshan 243032People’s Republic of China School of Mechanical Engineering Anhui University of TechnologyMaanshan 243032People’s Republic of China Anhui Province Key Laboratory of Special Heavy Load Robot Anhui University of TechnologyMaanshan 243032People’s Republic of China Key Laboratory of Green Fabrication and Surface Technology of Advanced Metal Materials Ministry of EducationAnhui University of TechnologyMaanshan 243032People’s Republic of China

The spreading effect of atmospheric pressure microplasma jets(APμPJ)on the surface of materials will increase the etching area,and controlling the diameter of the jet can improve the precision of surface *** this work,a two-dimensional axisymmetric simulation model is established to analyze the effect of nitrogen(N_(2))shielding gas on helium(He)from gas *** addition,by etching the polyethylene terephthalate fllm,the relationship between the etching effect and aerodynamic analysis is *** simulation results are similar to the experimental results,indicating that N2 shielding gas has a focusing effect which is related to the N_(2)flow rate,distance difference between the inner and outer tubes,and outer tube nozzle *** is hoped that the results of this work can provide a certain reference for the use of shielding gas to control the jet flow of APμPJ.

关键词： microplasma jet nitrogen shielding gas focusing effect gas dynamic

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Multi-omics graph convolutional networks for digestive system tumour classification and early-late stage diagnosis

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CAAI Transactions on Intelligence technology 2024年第6期9卷 1572-1586页

作者： Lin Zhou Zhengzhi Zhu Hongbo Gao Chunyu Wang Muhammad Attique Khan Mati Ullah Siffat Ullah Khan School of Information Science and Technology University of Science and Technology of ChinaHefeiAnhuiChina Anhui Engineering Research Center on Information Fusion and Control of Intelligent Robot WuhuAnhuiChina Department of Breast Center West District of The Affiliated Hospital of University of Science and Technology of ChinaDivision of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefeiAnhuiChina Institute of Advanced Technology University of Science and Technology of ChinaHefeiAnhuiChina School of Electrical and Electronic Engineering Nanyang Technological UniversitySingaporeSingapore School of Biological and Environmental Engineering Chaohu UniversityChaohu Regional Collaborative Technology Service Center for Rural RevitalizationHefeiChina Department of Artificial Intelligence College of Computer Engineering and SciencePrince Mohammad Bin Fahd UniversityAl-KhobarSaudi Arabia School of Automation Northwestern Polytechnical UniversityXi'anShaanxiChina Institute of Engineering and Computing Science University of Science and Technology of BannuKPKBannuPakistan

The prevalence of digestive system tumours(DST)poses a significant challenge in the global crusade against *** neoplasms constitute 20%of all documented cancer diagnoses and contribute to 22.5%of cancer-related *** accurate diagnosis of DST is paramount for vigilant patient monitoring and the judicious selection of optimal *** this challenge,the authors introduce a novel methodology,denominated as the Multi-omics Graph Transformer Convolutional Network(MGTCN).This innovative approach aims to discern various DST tumour types and proficiently discern between early-late stage tumours,ensuring a high degree of *** MGTCN model incorporates the Graph Transformer Layer framework to meticulously transform the multi-omics adjacency matrix,thereby illuminating potential associations among diverse samples.A rigorous experimental evaluation was undertaken on the DST dataset from The Cancer Genome Atlas to scrutinise the efficacy of the MGTCN *** outcomes unequivocally underscore the efficiency and precision of MGTCN in diagnosing diverse DST tumour types and successfully discriminating between early-late stage DST *** source code for this groundbreaking study is readily accessible for download at https://***/bigone1/MGTCN.

关键词： digestive system tumors early-late stage multi-omics tumor types

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Development of Tendon-Driven Compliant Snake robot with Global Bending and Twisting Actuation

arXiv

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arXiv 2024年

作者： Kwon, Seongil Incekara, Serdar Kwon, Gangil Ha, Junhyoung The Advanced Research Team Endorobotics Co. Ltd. Seoul Korea Republic of The Department of AI Robot Korea Institute of Science and Technology Seoul Korea Republic of The Department of FAB Engineering and Operations Samsung Electronics Suwon Korea Republic of

Snake robots have been studied for decades with the aim of achieving biological snakes’ fluent locomotion. Yet, as of today, their locomotion remains far from that of the biological snakes. Our recent study suggested that snake locomotion utilizing partial ground contacts can be achieved with robots by using body compliance and lengthwise-globally applied body tensions. In this paper, we present the first hardware implementation of this locomotion principle. Our snake robot comprises serial tendon-driven continuum sections and is bent and twisted globally using tendons. We demonstrate how the tendons are actuated to achieve the ground contacts for forward and backward locomotion and sidewinding. The robot’s capability to generate snake locomotion in various directions and its steerability were validated in a series of indoor experiments. Copyright © 2024, The Authors. All rights reserved.

关键词： Tendons

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A method for obtaining user requirements based on NLTK and knowledge graph 5

A method for obtaining user requirements based on NLTK and k...

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5th World Conference on Mechanical Engineering and Intelligent Manufacturing, WCMEIM 2022

作者： Wang, Zhanfei Tan, Libin Yu, Xiaoliu Anhui University of Technology AnHui Province Key Laboratory of Special Heavy Load Robot Ma'Anshan243002 China Beijing Advanced Innovation Center for Intelligent Robots and Sy Stems Beijing Institute of Technology China

ISBN: (纸本)9781665473699

The traditional product design scheme can no longer fully meet the current user needs. To solve some problems that live in the period of small and medium batch products' design in traditional solutions, such as long cycles and a lot of differences between design direction and user needs as well as the high technical difficulty of current deep learning-assisted products. This paper proposes a rapid update design method for small batch products based on a knowledge graph, combining data mining and Python module methods. This method aims to reasonably reduce technical requirements, obtain user comments with intelligent crawler plug-ins, complete the extraction of demand information with two key modules of NLTK and Stanford CoreNlp, and realize visualization with a knowledge graph. © 2022 IEEE.

关键词： Product design

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