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Adaptively temporal graph convolution model for epidemic prediction of multiple age groups

Fundamental Research 2022年第2期2卷 311-320页

作者： Yuejiao Wang Dajun Daniel Zeng Qingpeng Zhang Pengfei Zhao Xiaoli Wang Quanyi Wang Yin Luo Zhidong Cao The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Artificial Intelligence University of Chinese Academy of SciencesBeijingChina Shenzhen Artificial Intelligence and Data Science Institute(Longhua) ShenzhenChina School of Data Science City University of Hong Kong-Hong Kong SARChina Institute for Infectious Disease and Endemic Disease Control Beijing Center for Disease Prevention and Control BeijingChina

Introduction:Multivariate time series prediction of infectious diseases is significant to public health,and the deep learning method has attracted increasing attention in this research *** and methods:An adaptively temporal graph convolution(ATGCN)model,which leams the contact patterns of multiple age groups in a graph-based approach,was proposed for COVID-19 and influenza *** compared ATGCN with autoregressive models,deep sequence learning models,and experience-based ATGCN models in short-term and long-term prediction ***:Results showed that the ATGCN model performed better than the autoregressive models and the deep sequence learning models on two datasets in both short-term(12.5%and 10%improvements on RMSE)and longterm(12.4%and 5%improvements on RMSE)prediction *** the RMSE of ATGCN predictions fluctuated least in different age groups of COVID-19(0.029±0.003)and influenza(0.059±0.008).Compared with the Ones-ATGCN model or the Pre-ATGCN model,the ATGCN model was more robust in performance,with RMSE of 0.0293 and 0.06 on two datasets when horizon is ***:Our research indicates a broad application prospect of deep learning in the field of infectious disease *** characteristics and domain knowledge of infectious diseases should be further applied to the design of deep learning models and feature ***:The ATGCN model addressed the multivariate time series forecasting in a graph-based deep learning approach and achieved robust prediction on the confirmed cases of multiple age groups,indicating its great potentials for exploring the implicit interactions of multivariate variables.

关键词： Graph convolution model Infectious disease prediction Multiple age group Multivariate time series Public health

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A 3D Virtual Try-On Method with Global-Local Alignment and Diffusion Model

A 3D Virtual Try-On Method with Global-Local Alignment and D...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shougan Pan Zhengwentai Sun Chenxing Wang Junkai Zhang School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China The Hong Kong Polytechnic University Institute of Automation Chinese Academy of Sciences

3D virtual try-on has recently received more attention due to its great practical and commercial value. However, there remains the problems that the garment cannot accurately correspond to a human body by geometric transformation and abnormal textures may be produced in the synthesis result. To address these issues, a 3D virtual try-on method with global-local alignment and diffusion model is proposed. The global-local alignment module is designed for more accurate garment warping, combined with the guidance of an "after-try-on" semantic map alignment. Then, a diffusion model is introduced to design a try-on synthesizer that can not only avoid producing abnormal textures but also enhance the quality of textures produced in edge regions. Experiments on existing datasets show that our method outperforms the state-of-the-art methods. Code is available at https://***/Breaveh/VTON-GD.

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Lidar-Inertial and Downward Vision Based Mobile Robot SLAM in Digital Workshops 13

Lidar-Inertial and Downward Vision Based Mobile Robot SLAM i...

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13th IEEE Data Driven control and Learning systems Conference, DDCLS 2024

作者： Zhang, Yanchao Geng, Hao Liu, Bin Zhang, Yinlong Yuan, Xudong Zhang, Sichao School of Information Science and Engineering Shenyang University of Technology Shenyang110020 China Guangzhou Institute of Industrial Intelligence Guangzhou511458 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

ISBN: (纸本)9798350361674

In order to solve the problems of long-term cumulative error and the absence of absolute pose in the existing Lidar-Inertial Measurement Unit (IMU) fusion methods for Automated Guided Vehicle (AGV) Simultaneous Localization and Mapping (SLAM), we propose a new SLAM method for mobile robot, integrating Lidar, IMU, and a downward vision camera. Firstly, we tightly couple Lidar-IMU and downward vision camera using a multimodal fusion framework. Downward visual measurements are integrated as QR factors into the factor graph, providing absolute pose information to correct cumulative error in the Lidar-IMU system and enable the joint pose estimation of the AGV. Secondly, an enhanced Iterative Closest Point (ICP) point cloud matching algorithm has been developed to correct the incorrect pose. This approach achieves Scan-to-Map alignment by employing a coarse-to-fine matching strategy, thereby enhancing the accuracy of point cloud alignment. Finally, the extended Kalman filter is used to optimize the Lidar pose drift, as well as the Lidar mapping effectively by using the Lidar-IMU as the measurements and the downward visual measurements as the updates. The algorithm has been extensively evaluated on the developed AGV experimental platform. The experimental results show that the position error is less than 0.02 meters and the orientation error is less than 1°, which solves the problems of long-term error accumulation and the lack of absolute pose information for AGV navigation. © 2024 IEEE.

关键词： Extended Kalman filters

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Generalized-Extended-state-Observer and Equivalent-Input-Disturbance Methods for Active Disturbance Rejection: Deep Observation and Comparison

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 957-968页

作者： Jinhua She Kou Miyamoto Qing-Long Han Min Wu Hiroshi Hashimoto Qing-Guo Wang School of Engineering Tokyo University of TechnologyHachiojiTokyo 192-0982Japan K.Miyamoto is with the Institute of Technology Shimizu CorporationKotoTokyo 135-0044Japan School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia School of Automation China University of GeosciencesWuhan 430074 Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China School of Industrial Technology Advanced Institute of Industrial TechnologyTokyo 140-0011Japan Institute of Artificial Intelligence and Future Networks Beijing Normal UniversityZhuhai 519087 Guangdong Key Lab of AI and Multi-Modal Data Processing Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNUHKBU United International College Zhuhai 519087China

Active disturbance-rejection methods are effective in estimating and rejecting disturbances in both transient and steady-state *** paper presents a deep observation on and a comparison between two of those methods:the generalized extended-state observer(GESO)and the equivalent input disturbance(EID)from assumptions,system configurations,stability conditions,system design,disturbance-rejection performance,and extensibility.A time-domain index is introduced to assess the disturbance-rejection performance.A detailed observation of disturbance-suppression mechanisms reveals the superiority of the EID approach over the GESO method.A comparison between these two methods shows that assumptions on disturbances are more practical and the adjustment of disturbance-rejection performance is easier for the EID approach than for the GESO method.

关键词： Active disturbance-rejection control(ADRC) disturbance observer(DOB) equivalent input disturbance(EID) extendedstate observer(ESO) generalized extended-state observer(GESO)

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Manifestations of 9754 gout patients in a Chinese clinical center: A 10-year observational study

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Revue du Rhumatisme (Edition Francaise) 2022年第1期89卷 65-71页

作者： Gao, Qianhui Cheng, Xiaoyu Merriman, Tony R. Wang, Can Cui, Lingling Zhang, Hui Sun, Wenyan Wang, Jing Wang, Feiyue Li, Changgui Lu, Jie Shandong Provincial Key Laboratory of Metabolic Diseases and Qingdao Key Laboratory of Gout Department of Endocrinology and Metabolism the Affiliated Hospital of Qingdao University Qingdao 266003 China Institute of Metabolic Diseases Qingdao University Qingdao 266003 China Department of Biochemistry University of Otago Dunedin New Zealand Division of Clinical Immunology and Rheumatology University of Alabama Birmingham Alabama United States Institute of Smart Healthcare Systems Qingdao Academy of Intelligent Industries Qingdao 266109 China The State Key Laboratory for Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

Objectif: La prévalence de la goutte augmente, mais les études analysant ses caractéristiques cliniques dans de grands échantillons sont insuffisantes. Cette étude s'attache à clarifier les changements dans les manifestations cliniques de la goutte en Chine au cours des dix dernières années et à analyser les caractéristiques cliniques et les facteurs de risque de la goutte précoce. Méthodes: Les manifestations cliniques de la goutte ont été comparées chez 9754 patients reçus pour la première fois dans notre établissement entre 2008 et 2012 (premier groupe) ou entre 2013 et 2018 (dernier groupe). La goutte apparue ≤ 30 ans a été définie comme goutte précoce et > 30 ans, comme goutte tardive. Les caractéristiques cliniques et les facteurs de risque ont été comparés entre les groupes. Résultats: La goutte est apparue 4,14 ans plus tôt dans le dernier groupe (2013-2018, 5979 patients) et le pourcentage de goutte précoce (3827 patients) y était plus élevé. La durée de la maladie était significativement plus courte (5,72 ± 0,09 contre 6,01 ± 0,11, p < 0,05) et la proportion de patients présentant des tophi était plus faible dans le dernier groupe (20,0% contre 22,0%, p<0,05). Le dernier groupe comptait davantage de patients obèses (37,7% contre 32,6%, p < 0,001) et l'uricémie y était significativement plus élevée (485,5 ± 1,5 Μmol/l contre 465,0 ± 1,9 Μmol/l). Comme la goutte survenait à un âge plus jeune, nous avons appliqué une régression logistique multivariée et déterminé que des antécédents familiaux positifs, le sexe masculin, l'obésité, une uricémie élevée et la consommation de sodas sucrés étaient des facteurs prédictifs indépendants de goutte précoce. Conclusion: Les manifestations de la goutte en Chine ont évolué au cours des dix dernières années, avec notamment une tendance à une apparition plus précoce. © 2021

关键词： Apparition précoce Drop Early onset Facteurs de risque Goutte Risk factors

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AdvDiffuser: Generating Adversarial Safety-Critical Driving Scenarios via Guided Diffusion

AdvDiffuser: Generating Adversarial Safety-Critical Driving ...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Yuting Xie Xianda Guo Cong Wang Kunhua Liu Long Chen School of Computer Science and Engineering Sun Yat-Sen University School of Computer Science Wuhan University State Key Laboratory for Management and Control of Complex Systems at the Institute of Automation Chinese Academy of Sciences School of Mechanical and Automotive Engineering Qingdao University of Technology

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Safety-critical scenarios are infrequent in natural driving environments but hold significant importance for the training and testing of autonomous driving systems. The prevailing approach involves generating safety-critical scenarios automatically in simulation by introducing adversarial adjustments to natural environments. These adjustments are often tailored to specific tested systems, thereby disregarding their transferability across different systems. In this paper, we propose AdvDiffuser, an adversarial framework for generating safety-critical driving scenarios through guided diffusion. By incorporating a diffusion model to capture plausible collective behaviors of background vehicles and a lightweight guide model to effectively handle adversarial scenarios, AdvDiffuser facilitates transferability. Experimental results on the nuScenes dataset demonstrate that AdvDiffuser, trained on offline driving logs, can be applied to various tested systems with minimal warm-up episode data and outperform other existing methods in terms of realism, diversity, and adversarial performance.

关键词： Training Diffusion models Intelligent robots Autonomous vehicles Testing

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Finite-time coordinated path-following control of leader-following multi-agent systems

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Frontiers of Information Technology & Electronic Engineering 2022年第10期23卷 1511-1521页

作者： Weibin CHEN Yangyang CHEN Ya ZHANG School of Automation Southeast UniversityNanjing 210096China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationSoutheast UniversityNanjing 210096China Robot Department Jiangsu Automation Research InstituteLianyungang 222061China

This paper presents applications of the continuous feedback method to achieve path-following and a formation moving along the desired orbits within a finite *** is assumed that the topology for the virtual leader and followers is *** additional condition of the so-called barrier function is designed to make all agents move within a limited area.A novel continuous finite-time path-following control law is first designed based on the barrier function and *** a novel continuous finite-time formation algorithm is designed by regarding the path-following errors as *** settling-time properties of the resulting system are studied in detail and simulations are presented to validate the proposed strategies.

关键词： Finite-time Coordinated path-following Multi-agent systems Barrier function

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The Impact of Emotional Intelligence, Managerial Intelligence, and Transformational Leadership on Multidimensional Public Project Success

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IEEE Engineering management Review 2022年第4期50卷 111-126页

作者： Fareed, Muhammad Zeeshan Su, Qin Naqvi, Najam Abbas Xian Jiaotong University School of Management Xian710049 China State Key Laboratory for Manufacturing Systems Engineering Xi'an710049 China Key Laboratory of the Ministry of Education for Process Control and Efficiency Engineering Xi'an710049 China Institute of Space Technology National Center of GIS and Space Applications Islamabad44000 Pakistan

The project manager's role and influence on the project's overall outcome have fascinated many academicians, researchers, and practitioners. The project manager's leadership and human skills play a vital role in the project's outcome;therefore, it is imperative for these competencies to be identified, learned, and leadership styles to be adopted. This study aims to define the most relevant competencies to multidimensional project success and their interrelationship. The quantitative method was used by applying a survey to project managers and team members working in the public sector of Pakistan. The results suggest that a combination of emotional, managerial intelligence, and transformational leadership are stronger predictors of multidimensional public project success. This study has shown that management and leadership need to be integrated to achieve better results. Project managers need to combine soft and hard skills, leadership competencies, and management to engage with people and be effective in terms of processes, tasks, and objectives. © 1973-2011 IEEE.

关键词： Project management

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ReCUS: Reconvolution and Upsampling Network for Object Detection 7

ReCUS: Reconvolution and Upsampling Network for Object Detec...

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7th IEEE International Conference on Cloud Computing and Intelligence systems, CCIS 2021

作者： Li, Fudong Gao, Dongyang Yang, Yuequan Cao, Zhiqiang Wang, Wei Yangzhou225000 China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China Affiliated Hospital of Yangzhou University Yangzhou225012 China

ISBN: (纸本)9781665441490

Most of the mainstream object detection models such as RetinaNet, SSD, YOLO, and Faster RCNN hardly achieve a good balance between detection accuracy and speed. A major reason is rich deep feature semantic information of images is not fully exploited. To solve this problem, a novel deep convolutional network structure termed as reconvolution and upsampling network (ReCUS) is proposed. In the ReCUS, a modified path aggregation network(mPAN) is added after the backbone, which is beneficial to strengthen the foreground salient feature information and weaken background information. Moreover, two new spatial pyramid pooling (SPP) modules are embedded before output heads for multi-scale fusion of local and global features. The experiments show that the effectiveness of our proposed ReCUS. Furthermore, the better detectability of the ReCUS network is demonstrated for both small scale objects and large scale objects. © 2021 IEEE.

关键词： Object detection

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Data-Driven Model for Detection,Isolation and Accommodation of Faulty Angle of Attack Sensor Measurements in Fixed Winged Aircraft

Data-Driven Model for Detection,Isolation and Accommodation ...

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第34届中国控制与决策会议

作者： Bemnet Wondimagegnehu Mersha Hongbin Ma School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology

Ethiopian Airlines' Boeing 737-8 MAX nosedived and crashed shortly after takeoff on March 10,2019,at Ejere Town,south of Addis Ababa.A faulty angle of attack(AOA) sensor was the cause of the *** airplane accidents have been linked to faulty AOA sensors in the *** majority of the AOA sensor fault detection,isolation,and accommodation(SFDIA) literature relied on linear model-driven techniques,which are not suitable when the system's model is uncertain,complex,or *** multilayer perceptron(MLP) models have been employed in datadriven models in the literature and the effectiveness of deep learning-based data-driven models has not been *** this work,a data collection and processing method that ensures the collected data is not monotonous and a data-driven model for AOA SFDIA is *** proposed model uses a deep learning-based recurrent neural network(RNN) to accommodate for faulty AOA measurement under flight conditions with faulty AOA measurement,faulty total velocity measurement,and faulty pitch rate *** residual analysis with a fixed threshold is used to detect and isolate faulty AOA *** proposed and benchmark models are trained with the adaptive momentum estimation(Adam) *** show that the proposed model effectively detects,isolates,and accommodates faulty AOA measurements when compared to other data-driven benchmark *** method is able to detect and isolate faulty AOA sensors with a detection delay of 0.5 seconds for ramp failure and 0.1 seconds for step failure.

关键词： Data-driven modeling Intelligent transportation systems Faulty angle of attack measurement Sensor fault Detection Isolation and Accommodation(SFDIA) and Recurrent neural network

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