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arXiv 2023年

作者： Wang, Guangming Zheng, Yu Guo, Yanfeng Liu, Zhe Zhu, Yixiang Burgard, Wolfram Wang, Hesheng Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China China Department of Artificial Intelligence Shanghai Jiao Tong University Shanghai200240 China Electrical and Computer Engineering University of California Los Angeles United States Institute of Artificial Intelligence Shanghai Jiao Tong University Shanghai200240 China Department of Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Engineering University of Technology Nuremberg Germany

Robot localization using a previously built map is essential for a variety of tasks including highly accurate navigation and mobile manipulation. A popular approach to robot localization is based on image-to-point cloud registration, which combines illumination-invariant LiDAR-based mapping with economical image-based localization. However, the recent works for image-to-point cloud registration either divide the registration into separate modules or project the point cloud to the depth image to register the RGB and depth images. In this paper, we present I2PNet, a novel end-to-end 2D-3D registration network. I2PNet directly registers the raw 3D point cloud with the 2D RGB image using differential modules with a unique target. The 2D-3D cost volume module for differential 2D-3D association is proposed to bridge feature extraction and pose regression. 2D-3D cost volume module implicitly constructs the soft point-to-pixel correspondence on the intrinsic-independent normalized plane of the pinhole camera model. Moreover, we introduce an outlier mask prediction module to filter the outliers in the 2D-3D association before pose regression. Furthermore, we propose the coarse-to-fine 2D-3D registration architecture to increase localization accuracy. We conduct extensive localization experiments on the KITTI Odometry and nuScenes datasets. The results demonstrate that I2PNet outperforms the state-of-the-art by a large margin. In addition, I2PNet has a higher efficiency than the previous works and can perform the localization in real-time. Moreover, we extend the application of I2PNet to the camera-LiDAR online calibration and demonstrate that I2PNet outperforms recent approaches on the online calibration task. Copyright © 2023, The Authors. All rights reserved.

关键词： Calibration

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SliceProp: A Slice-Wise Bidirectional Propagation Model for Interactive 3D Medical Image Segmentation

SliceProp: A Slice-Wise Bidirectional Propagation Model for ...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Xin Xu Wenjing Lu Jiahao Lei Peng Qiu Hong-Bin Shen Yang Yang Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China Department of Vascular Surgery Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine Institute of Image Processing and Pattern Recognition and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China

Interactive medical image segmentation methods have become increasingly popular in recent years. These methods combine manual labeling and automatic segmentation, reducing the workload of annotation while maintaining high accuracy. However, most current interactive segmentation frameworks are limited to 2D image data, and are not suitable for 3D image data due to the large size and high complexity of 3D data, as well as the challenges posed by information asymmetry and sparse annotation. In this paper, we propose SliceProp, an interactive segmentation framework that implements slice-wise Label Bidirectional Propagation (LBP) for 3D medical image segmentation. SliceProp extends the interactive 2D image segmentation algorithm to 3D image segmentation, and can handle 3D data with large size and high complexity. Moreover, equipped with a Backtracking Feedback Check (BFC) module, SliceProp effectively addresses the issues of information asymmetry and spatial sparse annotation in 3D medical image segmentation. Additionally, we adopt an uncertainty-based criterion to pri-oritize the slices to be refined interactively, which enhances the efficiency of the interaction process by enabling the model to focus on the regions with the most unreliable predictions. SliceProp is evaluated on two datasets and achieves promising results compared to state-of-the-art methods.

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Power Loss Analysis of Three-Phase Four-leg Four-Wire Inverter Using SiC/Si HyS 2

Power Loss Analysis of Three-Phase Four-leg Four-Wire Invert...

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2nd IEEE China International Youth Conference on Electrical engineering, CIYCEE 2021

作者： Jing, Zhengting Fu, Yongsheng Chen, Jianfei Ren, Haipeng Xi'An Technological University School of Electrical Information Engineering Xi'an710021 China Xi'An Technological University School of Mechatronic Engineering And the School of Electrical Information Engineering Xi'an710021 China University of Maryland Department of Electrical and Computer Engineering College ParkMD20742 United States Xi'An Technological University School of Mechatronic Engineering Xi'an710021 China Shaanxi Key Laboratory of Comple System Control and Intelligent Information Xi an University of Technology Xi'an710048 China

ISBN: (纸本)9781665400640

Three-phase four-leg four-wire voltage source inverter (3P4L4W VSI) attracts more attention in many power electronics applications due to its flexibility for both balanced and unbalanced loads, where the power efficiency is a critical point. Conventional Si semiconductor devices suffer from large switching loss due to large parasitic capacitances and thus limit its switching frequency. As a result, it further limits the power density and efficiency improvement. The SiC/Si hybrid switch formed by SiC-MOSFET and Si-IGBT in parallel (SiC/Si HyS) can improve the switching frequency with high efficiency and low cost, which helps to design a power converter with high power density and high efficiency. However, the application of SiC/Si HyS in 3P4L4W faces some challenges, such as the drive circuit design, the proper delay selection, etc.. In this paper, a SiC/Si HyS based 3P4L4W VSI is introduced and the optimal turn-off time delay selection for the SiC/Si HyS used in 3P4L4W VSI is analyzed. A systematical way to calculate the power loss in the SiC/Si HyS is presented. A 5-kW/50kHz 3P4L4W VSI is taken as an example for validating the theory and method. It is found the peak efficiency of the 3P4L4W VSI is 97.5%, and the unbalanced phase load leads to more power loss and degrade the efficiency. The work in this paper provides practical guidance for a SiC/Si HyS based 3P4L4W VSI inverter design. © 2021 IEEE.

关键词： Silicon carbide

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An Efficient Route control Model of the Train-centric control system

An Efficient Route Control Model of the Train-centric Contro...

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第33届中国控制与决策会议

作者： Qi Wang Ming Chai Hongjie Liu Jidong Lv Xiwang Guo Department of Electronic Engineering Beijing Jiaotong University The National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Computer and Communication Engineering College Liaoning Shihua University

The Train-centric control system integrates the main functions of the whole system into the vehicle,so the train can control the route resources *** this system,the route control method which is mainly reflected in the control of the switch is the root to ensure driving *** paper focuses on the route control method of the Traincentric system,to find ways to assure the safety of the switch control process and compare track capacity of different control ***,we discuss the structural and compositional differences between the Train-centric control system and the traditional Communication Based Train control(CBTC) system,present the route control process of the new system,as route control method matters the security of the whole system,one of the key problem in this process is the logic to add resource lock to switches,we propose a model to calculate safe distance,and propose two route control approaches,using Simulink/Stateflow hybrid modeling approach to model for both of them,get the visual curve,then build math model to measure and compare the efficiency of the models.

关键词： Train-control T2T communication train autonomous route control Simulink/Stateflow

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Identifying common signatures and potential therapeutic biomarkers in COPD and lung cancer using miRNA-mRNA co-expression networks

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Informatics in Medicine Unlocked 2022年 34卷

作者： Fathinavid, Amirhossein Mousavian, Zaynab Najafi, Ali Nematzadeh, Sajjad Salimi, Mahdieh Masoudi-Nejad, Ali Department of Computer Engineering College of Engineering Hamedan Branch Islamic Azad University Hamedan Iran Laboratory of Systems Biology and Bioinformatics (LBB) Institute of Biochemistry and Biophysics University of Tehran Tehran Iran Department of Computer Science School of Mathematics Statistics and Computer Science University of Tehran Tehran Iran Molecular Biology Research Center System Biology and Poisoning Institute Tehran Iran Computer Engineering Department Architecture and Engineering Faculty Nisantasi University Istanbul Turkey Department of Medical Genetics Institute of Medical Biotechnology National Institute of Genetic Engineering and Biotechnology (NIGEB) Tehran Iran

The association between non-small cell lung cancer (NSCLC) and chronic obstructive pulmonary disease (COPD) is now well established, as people with COPD are more likely to develop lung carcinoma. However, the evidence for this relationship is inconclusive and there is currently little information on the underlying molecular mechanisms. MicroRNAs (miRNAs) are one of the regulatory factors in NSCLC and COPD that their functions are widely studied in many chronic diseases and cancers. In this study, we applied systems biology approaches to identify and predict miRNAs that potentially play regulatory roles between COPD and NSCLC. We performed differential expression analysis on public miRNA and mRNA expression datasets, for both of diseases, and then we reconstructed two miRNA-mRNA co-expression networks and merged these two co-expression networks into a community co-expression network. Results indicated the existence of very common miRNAs (ex. hsa-miR-326 and hsa-miR-1293) and mRNAs (such as FAT2, ALOX5AP, and LDB2) between the two mentioned diseases. Moreover, we discovered specific miRNAs (hsa-miR-574-3p) that targeted common mRNAs. We utilized drug-target interaction networks to identify candidate drugs for common mRNAs, which can be considered in the treatment of two diseases (e.g. iloperidone). Generally, our study highlighted common miRNAs between COPD and NSCLC that as new signatures or biomarkers for therapeutic purposes. Investigating the miRNA biomarkers in this study improves our understanding about the shared mechanisms between COPD and NSCLC. © 2022

关键词： Co-expression networks Community networks COPD miRNA-mRNA network Non-small cell lung cancer

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Challenges of Trajectory Planning with Integrator Models on Curved Roads ⁎

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IFAC-PapersOnLine 2020年第2期53卷 15588-15595页

作者： Jan Eilbrecht Olaf Stursberg Institute of Control and System Theory Department of Electrical Engineering and Computer Science University of Kassel 34121 Kassel Germany

In the context of trajectory planning for autonomous vehicles, a widely used vehicle model relies on linear integrator dynamics. We consider planning with this model type, with a focus on the requirement to account for curved road topologies. As our analysis reveals, this generally gives rise to non-convex, coupled constraints on the vehicle’s states and inputs, which impedes computationally efficient planning. We propose a method to resolve this issue by modification of the non-convex constraints. This modification is based on inner approximations of sub-level sets of nonlinear functions, which are obtained by quantifier elimination. The efficacy of the method is demonstrated in two example scenarios.

关键词： Autonomous driving trajectory planning vehicle dynamic systems constrained control

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Optimal convergence rates of totally asynchronous optimization

arXiv

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arXiv 2022年

作者： Wu, Xuyang Magnńsson, Sindri Feyzmahdavian, Hamid Reza Johansson, Mikael The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology StockholmSE-100 44 Sweden The Department of Computer and System Science Stockholm University StockholmSE-164 07 Sweden ABB Cooperate Research VästeråsSE-721 78 Sweden

Asynchronous optimization algorithms are at the core of modern machine learning and resource allocation systems. However, most convergence results consider bounded information delays and several important algorithms lack guarantees when they operate under total asynchrony. In this paper, we derive explicit convergence rates for the proximal incremental aggregated gradient (PIAG) and the asynchronous block-coordinate descent (Async-BCD) methods under a specific model of total asynchrony, and show that the derived rates are order-optimal. The convergence bounds provide an insightful understanding of how the growth rate of the delays deteriorates the convergence times of the algorithms. Our theoretical findings are demonstrated by a numerical example. © 2022, CC BY.

关键词： Machine learning

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The 2022 report of synergetic roadmap on carbon neutrality and clean air for China:Accelerating transition in key sectors

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Environmental Science and Ecotechnology 2024年第3期19卷 4-18页

作者： Yu Lei Zhicong Yin Xi Lu Qiang Zhang Jicheng Gong Bofeng Cai Cilan Cai Qimin Chai Huopo Chen Renjie Chen Shi Chen Wenhui Chen Jing Cheng Xiyuan Chi Hancheng Dai Xiangzhao Feng Guannan Geng Jianlin Hu Shan Hu Cunrui Huang Tiantian Li Wei Li Xiaomei Li Jun Liu Xin Liu Zhu Liu Jinghui Ma Yue Qin Dan Tong Xuhui Wang Xuying Wang Rui Wu Qingyang Xiao Yang Xie Xiaolong Xu Tao Xue Haipeng Yu Da Zhang Ning Zhang Shaohui Zhang Shaojun Zhang Xian Zhang Xin Zhang Zengkai Zhang Bo Zheng Yixuan Zheng Jian Zhou Tong Zhu Jinnan Wang Kebin He Center of Air Quality Simulation and System Analysis Chinese Academy of Environmental PlanningBeijing100041China Center for Carbon Neutrality Chinese Academy of Environmental PlanningBeijing100041China Key Laboratory of Meteorological Disaster Ministry of Education/Joint International Research Laboratory of Climate and Environment Change(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD)Nanjing University of Information Science&TechnologyNanjing210044China State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing100084China Institute for Carbon Neutrality Tsinghua UniversityBeijing100084China Ministry of Education Key Laboratory for Earth System Modelling Department of Earth System ScienceTsinghua UniversityBeijing100084China State Key Joint Laboratory for Environment Simulation and Pollution Control College of Environmental Sciences and Engineering and Center for Environment and HealthPeking UniversityBeijing100871China National Center for Climate Change Strategy and International CooperationBeijing100035China Nansen-Zhu International Research Centre Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing100029China School of Public Health Key Lab of Public Health Safety of the Ministry of Education and Key Lab of Health Technology Assessment of the Ministry of HealthFudan UniversityShanghai200032China School of Economics and Management Beijing University of Chemical TechnologyBeijing100029China Department of Earth System Science University of CaliforniaIrvineCA92697USA National Meteorological Center China Meteorological AdministrationBeijing100081China College of Environmental Sciences and Engineering Peking UniversityBeijing100871China Policy Research Center for Environment and Economy Ministry of Ecology and Environment of the People's Republic of ChinaBeijing100029China Jiangsu Key Laboratory of Atmospheric Environment Monitoring

China is now confronting the intertwined challenges of air pollution and climate *** the high synergies between air pollution abatement and climate change mitigation,the Chinese government is actively promoting synergetic control of these two *** Synergetic Roadmap project was launched in 2021 to track and analyze the progress of synergetic control in China by developing and monitoring key *** Synergetic Roadmap 2022 report is the first annual update,featuring 20 indicators across five aspects:synergetic governance system and practices,progress in structural transition,air pollution and associated weather-climate interactions,sources,sinks,and mitigation pathway of atmospheric composition,and health impacts and benefits of coordinated *** to the comprehensive review presented in the 2021 report,the Synergetic Roadmap 2022 report places particular emphasis on progress in 2021 with highlights on actions in key sectors and the relevant *** milestones include the proportion of non-fossil power generation capacity surpassing coal-fired capacity for the first time,a decline in the production of crude steel and cement after years of growth,and the surging penetration of electric ***,in 2022,China issued the first national policy that synergizes abatements of pollution and carbon emissions,marking a new era for China's pollution-carbon *** changes highlight China's efforts to reshape its energy,economic,and transportation structures to meet the demand for synergetic control and sustainable ***,the country has witnessed a slowdown in carbon emission growth,improved air quality,and increased health benefits in recent years.

关键词： Synergetic roadmap Carbon neutrality Clean air

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SI-YOLO: A Deep Learning Algorithm Suitable for Surgical Instrument Detection

SSRN

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SSRN 2022年

作者： Liu, Zhenzhong Zhou, Yifan Sun, Qiyuan Wang, Yan Polat, Kemal Nour, Majid Alenezi, Fayadh Alhudhaif, Adi Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China Tianjin300384 China Department of Electrical and Electronics Engineering Bolu Abant Izzet Baysal University Bolu14280 Turkey Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Department of Electrical Engineering College of Engineering Jouf University 72238 Saudi Arabia Department of Computer Science College of Computer Engineering and Sciences in Al-kharj Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj 11942 Saudi Arabia

Surgical instrument detection and tracking is one of the core technologies of surgical robots, which can assist clinicians in completing operations. However, existing methods need further improvement in terms of real-time performance and accuracy. We propose a deep learning algorithm, SI-YOLO, for detection of surgical instruments. SimAM attention mechanism is used to improve the feature extraction ability of YOLOv5. Replacing PANet with BiFPN in the neck network improves the feature fusion strategy. The α-IoU loss function is used to improve the regression accuracy of detection boxes. The mAP and FPS of the SI-YOLO reach 99.2% and 120 frames/sec, respectively. Aiming at the problem of ID transformation in the disappearance and occlusion of surgical instruments, the Inception-ResNet-v2 structure is added on the basis of the appearance feature extraction network Cosine of the DeepSort tracking algorithm. Combining SI-YOLO with the improved DeepSort implements the Tracking by Detection (TBD) algorithm. In the case of device disappearance or occlusion, the frequency of ID transitions is reduced by approximately 30%. Finally, we built a simulated surgery experimental platform, established our own data set, and realized the preliminary application of the proposed algorithm. © 2022, The Authors. All rights reserved.

关键词： Object detection

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Soybeannet: Transformer-Based Convolutional Neural Network for Soybean Pod Counting from Unmanned Aerial Vehicle (Uav) Images

SSRN

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SSRN 2023年

作者： Li, Jiajia Magar, Raju Thada Chen, Dong Lin, Feng Wang, Dechun Yin, Xiang Zhuang, Weichao Li, Zhaojian Department of Electrical and Computer Engineering Michigan State University East LansingMI United States Department of Plant Soil and Microbial Sciences Michigan State University East LansingMI United States Environmental Institute & Link Lab University of Virginia CharlottesvilleVA United States Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China School of Mechanical Engineering Southeast University Nanjing China Department of Mechanical Engineering Michigan State University East LansingMI48824 United States

Soybean is a critical source of food, protein, and oil, and thus has received extensive research aimed at enhancing their yield, refining cultivation practices, and advancing soybean breeding techniques. Within this context, soybean pod counting plays an essential role in understanding and optimizing production. Despite recent advancements, the development of a robust pod-counting algorithm capable of performing effectively in real-field conditions remains a significant challenge This paper presents a pioneering work of accurate soybean pod counting utilizing unmanned aerial vehicle (UAV) images captured from actual soybean fields in Michigan, USA. Specifically, this paper presents SoybeanNet, a novel point-based counting network that harnesses powerful transformer backbones for simultaneous soybean pod counting and localization with high accuracy. In addition, a new dataset of UAV-acquired images for soybean pod counting was created and open-sourced, consisting of 113 drone images with more than 260k manually annotated soybean pods captured under natural lighting conditions. Through comprehensive evaluations, SoybeanNet demonstrates superior performance over five state-of-the-art approaches when tested on the collected images. Remarkably, SoybeanNet achieves a counting accuracy of 84.51% when tested on the testing dataset, attesting to its efficacy in real-world scenarios. The publication also provides both the source at https://***/JiajiaLi04/Soybean-Pod-Counting-from-UAV-Images and the labeled soybean dataset at https://***/datasets/jiajiali/uav-based-soybean-pod-images, offering a valuable resource for future research endeavors in soybean pod counting and related fields. © 2023, The Authors. All rights reserved.

关键词： Statistical tests

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