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

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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Pedestrian Trajectory Prediction by Modeling the Interactions using Social LSTM Extensions

Pedestrian Trajectory Prediction by Modeling the Interaction...

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Chinese automation Congress (CAC)

作者： Zeze Si Hongxia Zhao Haina Tang Fenghua Zhu Yisheng Lv Hao Lu State Key Laboratory for Management and Control of Complex Systems Institute of Autamation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academic of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China

In many driving situations, human mobility is an important topic in trajectory prediction. Considering the pedestrian trajectory as a sequence generative task, a prediction algorithm based on Social Long Short-Term Memory (Social LSTM) is implemented. In order to simulate the social interaction between pedestrians, Social Pooling (S-Pooling) is used to aggregate the hidden state of pedestrians, while the attention mechanism is utilized to aggregate information differently according to the importance of surrounding pedestrians. Furthermore, Convolutional Neural Networks (CNN) is introduced into Social LSTM model to consider both the interaction between people and the characteristic of scene scale in the prediction process. Experiments are carried out against baseline methods, and the results demonstrated that combining Social LSTM with attention mechanism or CNN can improve the performance of pedestrian trajectory prediction.

关键词： automation Aggregates Predictive models Prediction algorithms Trajectory Convolutional neural networks Task analysis

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A Survey of Multifingered Robotic Manipulation: Biological Results, Structural Evolvements, and Learning Methods

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FRONTIERS IN NEUROROBOTICS 2022年 16卷 843267页

作者： Li, Yinlin Wang, Peng Li, Rui Tao, Mo Liu, Zhiyong Qiao, Hong Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Chinese Acad Sci Hong Kong Inst Sci & Innovat Ctr Artificial Intelligence & Robot Hong Kong Peoples R China Chongqing Univ Sch Automation Chongqing Peoples R China Wuhan Second Ship Design & Res Inst Sci & Technol Thermal Energy & Power Lab Wuhan Peoples R China Beihang Univ Sch Automation Sci & Elect Engn Beijing Peoples R China

Multifingered robotic hands (usually referred to as dexterous hands) are designed to achieve human-level or human-like manipulations for robots or as prostheses for the disabled. The research dates back 30 years ago, yet, there remain great challenges to effectively design and control them due to their high dimensionality of configuration, frequently switched interaction modes, and various task generalization requirements. This article aims to give a brief overview of multifingered robotic manipulation from three aspects: a) the biological results, b) the structural evolvements, and c) the learning methods, and discuss potential future directions. First, we investigate the structure and principle of hand-centered visual sensing, tactile sensing, and motor control and related behavioral results. Then, we review several typical multifingered dexterous hands from task scenarios, actuation mechanisms, and in-hand sensors points. Third, we report the recent progress of various learning-based multifingered manipulation methods, including but not limited to reinforcement learning, imitation learning, and other sub-class methods. The article concludes with open issues and our thoughts on future directions.

关键词： multifingered hand visual-motor control multi-mode fusion hand structural evolution learning-based manipulation

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Comparing Multi-Dimensional fNIRS Features Using Bayesian Optimization-Based Neural Networks for Mild Cognitive Impairment (MCI) Detection

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IEEE TRANSACTIONS ON NEURAL systems AND REHABILITATION ENGINEERING 2023年 31卷 1019-1029页

作者： Zhang, Chutian Yang, Hongjun Fan, Chen-Chen Chen, Sheng Fan, Chenyu Hou, Zeng-Guang Chen, Jingyao Peng, Liang Xiang, Kexin Wu, Yi Xie, Hongyu Macau Univ Sci & Technol Macau Inst Syst Engn Macau Peoples R China Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Fudan Univ Huashan Hosp Dept Rehabil Med Shanghai 200040 Peoples R China Macau Univ Sci & Technol Inst Syst Engn CASIA MUST Joint Laboratoryof Intelligence Sci & T Macau Peoples R China

The diagnosis of mild cognitive impairment (MCI), a prodromal stage of Alzheimer's disease (AD), is essential for initiating timely treatment to delay the onset of AD. Previous studies have shown the potential of functional near-infrared spectroscopy (fNIRS) for diagnosing MCI. However, preprocessing fNIRS measurements requires extensive experience to identify poor-quality segments. Moreover, few studies have explored how proper multi-dimensional fNIRS features influence the classification results of the disease. Thus, this study outlined a streamlined fNIRS preprocessing method to process fNIRS measurements and compared multi-dimensional fNIRS features with neural networks in order to explore how temporal and spatial factors affect the classification of MCI and cognitive normality. More specifically, this study proposed using Bayesian optimization-based auto hyperparameter tuning neural networks to evaluate 1D channel-wise, 2D spatial, and 3D spatiotemporal features of fNIRS measurements for detecting MCI patients. The highest test accuracies of 70.83%, 76.92%, and 80.77% were achieved for 1D, 2D, and 3D features, respectively. Through extensive comparisons, the 3D time-point oxyhemoglobin feature was proven to be a more promising fNIRS feature for detecting MCI by using an fNIRS dataset of 127 participants. Furthermore, this study presented a potential approach for fNIRS data processing, and the designed models required no manual hyperparameter tuning, which promoted the general utilization of fNIRS modality with neural network-based classification to detect MCI.

关键词： Functional near-infrared spectroscopy Three-dimensional displays Feature extraction Convolutional neural networks Electroencephalography Task analysis Fans Convolutional neural networks (CNN) functional near-infrared spectroscopy (fNIRS) mild cognitive impairment (MCI) multi-dimensional feature evaluation multilayer perceptron (MLP)

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Data augmentation using image generation for change detection 1

Data augmentation using image generation for change detectio...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Li, Xuan Duan, Haibin Zhang, Hui Wang, Fei-Yue Peng Cheng Laboratory Shenzhen China Beihang University Beijing China Beijing Jiaotong University Beijing China Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex Systems Beijing China

ISBN: (纸本)9781665433372

Learning-based change detection (CD) in water scenarios is a key functionality for unmanned aerial vehicle (UAV). However, computer vision algorithms require large number of labeled datasets. Inspired by parallel intelligence, we propose a systematic framework for data generation. In this work, the framework consists of simulated scene and image generation network. In simulated scene, simulated images with pixel-level annotations are automatically generated. Then, image generation network uses paired images (real and simulated) to generate synthetic images. We use simulated and synthetic images in combination with publicly available real-world images to conduct experiments. The experimental results indicate that: 1) simulated images can be used in change detection research;2) synthetic images effectively improve the performance of supervised change detection model. © 2021 IEEE.

关键词： Change detection

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Pedestrian Trajectory Prediction by Modeling the Interactions using Social LSTM Extensions

Pedestrian Trajectory Prediction by Modeling the Interaction...

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2022 Chinese automation Congress, CAC 2022

作者： Si, Zeze Zhao, Hongxia Tang, Haina Zhu, Fenghua Lv, Yisheng Lu, Hao Institute of Autamation Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Qingdao Academy of Intelligent Industries Qingdao266000 China

ISBN: (纸本)9781665465335

关键词： Long short-term memory

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A comprehensive review of electrochemical hybrid power supply systems and intelligent energy managements for unmanned aerial vehicles in public services

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Energy and AI 2022年第3期9卷 148-171页

作者： Caizhi Zhang Yuqi Qiu Jiawei Chen Yuehua Li Zhitao Liu Yang Liu Jiujun Zhang Chan Siew Hwa College of Mechanical and Vehicle Engineering The State Key Laboratory of Mechanical TransmissionsChongqing Automotive Collaborative Innovation CentreChongqing UniversityChongqing400044China College of Automation Chongqing UniversityKey Laboratory of Complex System Safety and ControlMinistry of EducationChongqing UniversityChongqing400044China School of Mechanical Engineering University of Science and Technology BeijingBeijing100083China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang UniversityHangzhou310027China Institute for Sustainable Energy/College of Sciences Shanghai UniversityShanghai 200444China School of Mechanical and Aerospace Engineering Nanyang Technological University50 Nanyang Avenue639798Singapore Sichuan Energy Internet Research Institute Tsinghua UniversityChengdu 610213Sichuan ProvinceChina

The electric unmanned aerial vehicles (UAVs) are rapidly growing due to their abilities to perform some difficult or dangerous tasks as well as many public services including real-time monitoring, wireless coverage, search and rescue, wildlife surveys, and precision agriculture. However, the electrochemical power supply system of UAV is a critical issue in terms of its energy/power densities and lifetime for service endurance. In this paper, the current power supply systems used in UAVs are comprehensively reviewed and analyzed on the existing power configurations and the energy management systems. It is identified that a single type of electrochemical power source is not enough to support a UAV to achieve a long-haul flight;hence, a hybrid power system architecture is necessary. To make use of the advantages of each type of power source to increase the endurance and achieve good performance of the UAVs, the hybrid systems containing two or three types of power sources (fuel cell,battery, solar cell, and supercapacitor,) have to be developed. In this regard, the selection of an appropriate hybrid power structure with the optimized energy management system is critical for the efficient operation of a UAV. It is found that the data-driven models with artificial intelligence (AI) are promising in intelligent energy management. This paper can provide insights and guidelines for future research and development into the design and fabrication of the advanced UAV power systems.

关键词： Unmanned aerial vehicles(UAVs) Power supply system Fuel cell system Artificial intelligence(AI) Energy management systems

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

arXiv

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arXiv 2024年

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

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. © 2024, CC BY.

关键词： Automatic guided vehicles

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Design of a novel flexible assistive hand exoskeleton 37

Design of a novel flexible assistive hand exoskeleton

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37th Youth Academic Annual Conference of Chinese Association of automation, YAC 2022

作者： Li, Guotao Cheng, Long Gao, Zhengrun Lu, Jilong Zhao, Yanjiang Institute of Automation State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing100190 China Fudan University The National Clinical Research Center for Aging and Medicine Shanghai China Harbin University of Science and Technology Key Laboratory of Advanced Manufacturing and Intelligent Technology Ministry of Education Harbin150080 China Intelligent Machine Institute Harbin University of Science and Technology Harbin150080 China

ISBN: (纸本)9781665465366

Hand paralysis caused by stroke, spinal cord injury, or neurological trauma has a significant impact on the independence and quality of life of the patients. The hand exoskeleton can provide hand assistance and improve the patient's grasp. In this article, a novel wearable flexible hand exoskeleton is proposed to assist users in activities of daily life. To create a simple and lightweight assistive hand exoskeleton, 3D printing is used to manufacture most of the components, and a remote drive system is designed to reduce the weight of the hand exoskeleton. A ball pair device and a slider device are used to ensure the freedom of the thumb and the adaptability of the hand exoskeleton to different hand sizes. Finally, the experiments are conducted to verify that the hand exoskeleton can achieve motion assistance. © 2022 IEEE.

关键词： 3D printing

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Line-of-sight based three-dimensional path following control for an underactuated robotic dolphin

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Science China(Information Sciences) 2021年第1期64卷 215-226页

作者： Jincun LIU Zhenna LIU Junzhi YU State Key Laboratory for Turbulence and Complex System Department of Mechanics and Engineering ScienceBIC-ESATCollege of EngineeringPeking University Department of Electrical and Automation Shandong Labor Vocational and Technical College State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

This paper investigates the three-dimensional(3-D) path following control problem for an underactuated robotic dolphin. With a comprehensive consideration of the mechanical constraint and swimming principle of the robotic dolphin, a decoupling motion strategy is proposed to produce yaw and pitch maneuvers simultaneously. Then, kinematics and dynamics models for 3-D dolphin-like swimming are established,followed by simulations of the path following control. Furthermore, a novel lookahead based 3-D line-ofsight(LOS) guidance law is developed and implemented to obtain desired attitude angles with its simplicity,intuitiveness, and small computational footprint. Finally, simulation results illustrate the feasibility and effectiveness of the proposed path following control methods.

关键词： robotic dolphin path following 3-D LOS guidance law decoupling motion

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