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arXiv 2021年

作者： Cao, Siqi Fu, Di Yang, Xu Wermter, Stefan Liu, Xun Wu, Haiyan CAS Key Laboratory of Behavioral Science Institute of Psychology Chinese Academy of Sciences Beijing China Department of Psychology University of Chinese Academy of Sciences Beijing China Department of Experimental Psychology University of Oxford Oxford United Kingdom State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Department of Informatics University of Hamburg Hamburg Germany Centre for Cognitive and Brain Sciences Department of Psychology University of Macau Taipa China

Sensory and emotional experiences such as pain and empathy are essential for mental and physical health. Cognitive neuroscience has been working on revealing mechanisms underlying pain and empathy. Furthermore, as trending research areas, computational pain recognition and empathic artificial intelligence (AI) show progress and promise for healthcare or human-computer interaction. Although AI research has recently made it increasingly possible to create artificial systems with affective processing, most cognitive neuroscience and AI research do not jointly address the issues of empathy in AI and cognitive neuroscience. The main aim of this paper is to introduce key advances, cognitive challenges and technical barriers in computational pain recognition and the implementation of artificial empathy. Our discussion covers the following topics: How can AI recognize pain from unimodal and multimodal information? Is it crucial for AI to be empathic? What are the benefits and challenges of empathic AI? Despite some consensus on the importance of AI, including empathic recognition and responses, we also highlight future challenges for artificial empathy and possible paths from interdisciplinary perspectives. Furthermore, we discuss challenges for responsible evaluation of cognitive methods and computational techniques and show approaches to future work to contribute to affective assistants capable of empathy. © 2021, CC BY-NC-ND.

关键词： Cognitive systems

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The systematic error and the anomaly in the sensitivity and the probability of post-selection raised from the plural weak value

arXiv

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arXiv 2021年

作者： Huang, Jing-Hui Duan, Xue-Ying Wang, Guang-Jun Hu, Xiang-Yun Institute of Geophysics and Geomatics China University of Geosciences Wuhan430074 China School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China State Key Laboratory of Geological Processes and Mineral Resources China University of Geosciences Hubei Wuhan430074 China

Recently, weak value Aw derived in the pre- and post-selected weak measurement has been shown to be powerful in measuring minute physical effects. In principle, the decrease in the post-selection probability will increase the sensitivity. Besides, the sensitivity which characterizes the pointer position shift is proportional to the real part of Aw, and the sensitivity which characterizes the pointer shift in momentum space is proportional to the imaginary part of Aw. However, we find that the relationship between the post-selection probability and the sensitivity is true when Aw is a purely real number or a purely imaginary number. The plural Aw will lead to the abnormal behavior where the decrease in the post-selection probability reduce the sensitivity at certain pre- and post-selection. In addition, considering the device imperfections and the environmental instability in the WVA protocol, this anomaly raised from plural Aw will reduce the sensitivity and generate a systematic error of the measurement compared with the original scheme. Finally, three feasible methods are proposed to reduce these negative effects when the weak measurement inevitably changes into measurement with a plural weak value. © 2021, CC BY.

关键词： Probability

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Performance evaluation of faults in a photovoltaic array based on V-I and V-P characteristic curve 12

Performance evaluation of faults in a photovoltaic array bas...

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12th International Conference on Measuring Technology and Mechatronics automation, ICMTMA 2020

作者： Lodhi, Ehtisham Lina, Wang Pu, Yang Javed, M. Yaqoob Lodhi, Zeeshan Zhijie, Jiao Javed, Usman School of Automation Science and Electrical Engineering Beihang University Beijing100191 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Department of Electrical and Computer Engineering COMSATS University Islamabad Lahore54000 Pakistan Department of Electrical and Computer Engineering COMSATS University Islamabad Abbottabad22060 Pakistan Guo China-US Photonics Lab Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences Changchun130033 China

ISBN: (纸本)9781728170817

The prediction and forecasting of faults occurring in photovoltaic (PV) system is one of the important aspect to escalate the reliability, output power generation, proficiency, lifetime and effectiveness of overall system. In this article, the behavior of PV array is studied under various faulty circumstances and new approach has offered for the better performance assessment of PV array. This approach is established on the analysis of peculiarities displayed by voltage power (V-P) and voltage-current (V-I) characteristics curves of PV array under different faulty conditions. The proposed technique can be used to identify and classify the five common faults in PV system. These progressive faults (includes open circuit, line-to-line, partial shading, degradation and bridging faults) have been occurred at the basic components of PV cell and at the connection point between PV modules. The characteristic curves under normal and faulty conditions have been compared by utilizing Matlab/Simulink environment. Hence, the suggested method can be utilized to determine the important fact about the health PV array. © 2020 IEEE.

关键词： Solar panels

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Retraction Note: IoT-based 3D convolution for video salient object detection

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Neural Computing and Applications 2024年第23期36卷 14621-14621页

作者： Dong, Shizhou Gao, Zhifan Pirbhulal, Sandeep Bian, Gui-Bin Zhang, Heye Wu, Wanqing Li, Shuo Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen Shenzhen China Western University London Canada State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Sun Yat-Sen University Guangzhou China

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Dynamic fusion based federated learning for COVID-19 detection

arXiv

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arXiv 2020年

作者： Zhang, Weishan Zhou, Tao Lu, Qinghua Wang, Xiao Zhu, Chunsheng Sun, Haoyun Wang, Zhipeng Lo, Sin Kit Wang, Fei-Yue China Data61 CSIRO Australia State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China SUSTech Institute of Future Networks Southern University of Science and Technology China

Medical diagnostic image analysis (e.g., CT scan or X-Ray) using machine learning is an efficient and accurate way to detect COVID-19 infections. However, sharing diagnostic images across medical institutions is usually not allowed due to the concern of patients’ privacy. This causes the issue of insufficient datasets for training the image classification model. Federated learning is an emerging privacy-preserving machine learning paradigm that produces an unbiased global model based on the received updates of local models trained by clients without exchanging clients’ local data. Nevertheless, the default setting of federated learning introduces huge communication cost of transferring model updates and can hardly ensure model performance when data heterogeneity of clients heavily exists. To improve communication efficiency and model performance, in this paper, we propose a novel dynamic fusion-based federated learning approach for medical diagnostic image analysis to detect COVID-19 infections. First, we design an architecture for dynamic fusion-based federated learning systems to analyse medical diagnostic images. Further, we present a dynamic fusion method to dynamically decide the participating clients according to their local model performance and schedule the model fusion-based on participating clients’ training time. In addition, we summarise a category of medical diagnostic image datasets for COVID-19 detection, which can be used by the machine learning community for image analysis. The evaluation results show that the proposed approach is feasible and performs better than the default setting of federated learning in terms of model performance, communication efficiency and fault tolerance. Copyright © 2020, The Authors. All rights reserved.

关键词： Image analysis

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Dynamic horizon value estimation for model-based reinforcement learning

arXiv

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arXiv 2020年

作者： Wang, Junjie Zhang, Qichao Zhao, Dongbin Zhao, Mengchen Hao, Jianye State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Huawei Noah’s Ark Lab Beijing China

Existing model-based value expansion methods typically leverage a world model for value estimation with a fixed rollout horizon to assist policy learning. However, the fixed rollout with an inaccurate model has a potential to harm the learning process. In this paper, we investigate the idea of using the model knowledge for value expansion adaptively. We propose a novel method called Dynamic-horizon Model-based Value Expansion (DMVE) to adjust the world model usage with different rollout horizons. Inspired by reconstruction-based techniques that can be applied for visual data novelty detection, we utilize a world model with a reconstruction module for image feature extraction, in order to acquire more precise value estimation. The raw and the reconstructed images are both used to determine the appropriate horizon for adaptive value expansion. On several benchmark visual control tasks, experimental results show that DMVE outperforms all baselines in sample efficiency and final performance, indicating that DMVE can achieve more effective and accurate value estimation than state-of-the-art model-based methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Reinforcement learning

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A Feedback-based Print Quality Improving Strategy for FDM 3D Printing: An Optimal Design Approach

Research Square

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Research Square 2021年

作者： Tami, Tariku Sinshaw Xiong, Gang Fang, Qihang Dong, Xisong Shen, Zhen Wang, Fei-Yue The State Key Laboratory for Management and Control of Complex Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan523808 China

Fused deposition modelling (FDM) 3D printing, as a supporting technology in social manufacturing and cloud manufacturing, is a rapidly growing technology in the era of industry 4.0. It produces objects with the layer-by-layer material accumulation technique. However, qualitative uncertainties are the common challenges yet. In order to assure print quality, studying the error causing parameters and minimizing their effects is important. This paper presents a feedback-based error compensation strategy, which integrates fuzzy inference system and grey wolf optimization algorithm. The objectives are twofold. First, the possible errors in FDM 3D printing are discussed in detail and optimal error causing parameters are obtained in percentage. This is used to understand the effects of the printing errors in every phase of the 3D printing process. From the nine optimization configuration trials used, Config-6 that has 100 number of iterations and 60 wolves is selected due to its higher convergence speed and best fitness value. The integral absolute error (IAE) is used as an objective function and the global minimum is achieved in the iteration interval [86, 100]. The outputs of this optimization problem is used to achieve the next objective. Second, a closed-loop quality monitoring approach comprising of inner-loops and an outer-loop is utilized. The three inner-loops are used to monitor the errors during pre-printing, printing, and post-printing, respectively. The outer-loop, on the other hand, is responsible for monitoring the aggregated errors in all the three 3D printing phases. The error compensation system simulation in Matlab is run for 10 seconds, and the results show that the "normal" range deformation factors are reached within less than 2 seconds for the inner-loops, whereas the outer-loop deformation factor is achieved within 7 seconds. The responses are within the acceptable time range. © 2021, CC BY.

关键词： 3D printers

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Sliding sequential CVAE with time variant socially-aware rethinking for trajectory prediction

arXiv

引用

arXiv 2021年

作者： Zhou, Hao Ren, Dongchun Yang, Xu Fan, Mingyu Huang, Hai National Key Laboratory of Science and Technology of Underwater Vehicle Harbin Engineering University Harbin China State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Sciences Beijing China Research Center for Autonomous Vehicles Meituan Beijing China College of Computer Science Wenzhou University Wenzhou China National Key Laboratory of Science and Technology of Underwater Vehicle Harbin Engineering University Harbin China

Pedestrian trajectory prediction is a key technology in many applications such as video surveillance, social robot navigation, and autonomous driving, and significant progress has been made in this research topic. However, there remain two limitations of previous studies. First, with the continuation of time, the prediction error at each time step increases significantly, causing the final displacement error to be impossible to ignore. Second, the prediction results of multiple pedestrians might be impractical in the prediction horizon, i.e., the predicted trajectories might collide with each other. To overcome these limitations, this work proposes a novel trajectory prediction method called CSR, which consists of a cascaded conditional variational autoencoder (CVAE) module and a socially-aware regression module. The cascaded CVAE module first estimates the future trajectories in a sequential pattern. Specifically, each CVAE concatenates the past trajectories and the predicted points so far as the input and predicts the location at the following time step. Then, the socially-aware regression module generates offsets from the estimated future trajectories to produce the socially compliant final predictions, which are more reasonable and accurate results than the estimated trajectories. Moreover, considering the large model parameters of the cascaded CVAE module, a slide CVAE module is further exploited to improve the model efficiency using one shared CVAE, in a slidable manner. Experiments results demonstrate that the proposed method exhibits improvements over state-of-the-art method on the Stanford Drone Dataset (SDD) and ETH/UCY of approximately 38.0% and 22.2%, respectively. © 2021, CC BY.

关键词： Trajectories

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Impact of corticosteroids on initiation and half-year durability of humoral response in COVID-19 survivors

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Chinese Medical Journal Pulmonary and Critical Care Medicine 2024年第1期2卷 48-55页

作者： Yeming Wang Li Guo Guohui Fan Yang Han Qiao Zhang Lili Ren Hui Zhang Geng Wang Xueyang Zhang Tingxuan Huang Weiyang Wang Lan Chen Lixue Huang Xiaoying Gu Xinming Wang Jingchuan Zhong Ying Wang Hui Li Jiapei Yu Zhibo Liu Chaolin Huang Bin Cao Jianwei Wang National Center for Respiratory Medicine State Key Laboratory of Respiratory Health and MultimorbidityNational Clinical Research Center for Respiratory DiseasesInstitute of Respiratory MedicineChinese Academy of Medical SciencesChina-Japan Friendship HospitalBeijing 100029China National Health Commission Key Laboratory of Systems Biology of Pathogens and Christophe Mérieux Laboratory Institute of Pathogen BiologyChinese Academy of Medical Sciences&Peking Union Medical CollegeBeijing 102629China Key Laboratory of Respiratory Disease Pathogenomics Chinese Academy of Medical SciencesBeijing 100029China School of Population Medicine and Public Health Chinese Academy of Medical Sciences&Peking Union Medical CollegeState Key Laboratory of Respiratory Health and MultimorbidityKey Laboratory of Pathogen Infection Prevention and Control(Peking Union Medical College)Ministry of EducationBeijing 100730China National Center for Respiratory Medicine State Key Laboratory of Respiratory Health and MultimorbidityNational Clinical Research Center for Respiratory DiseasesInstitute of Respiratory MedicineChinese Academy of Medical SciencesDepartment of Clinical research and Data managementCenter of Respiratory MedicineChina-Japan Friendship HospitalBeijing 100029China Jin Yin-tan Hospital WuhanHubei 430023China Department of Pulmonary and Critical Care Medicine West China HospitalSichuan UniversityChengduSichuan 610041China Department of Rheumatology and Clinical Immunology Chinese Academy of Medical Sciences&Peking Union Medical CollegeNational Clinical Research Center for Dermatologic and Immunologic Diseases(NCRC-DID)Ministry of Science&TechnologyState Key Laboratory of Complex Severe and Rare DiseasesPeking Union Medical College Hospital(PUMCH)Key Laboratory of Rheumatology and Clinical ImmunologyMinistry of EducationBeijing 100730China Department of Pulmonary and Critical Care Medicine Center of Respiratory MedicineChina-Japan Friendship HospsitalInstitute of Respiratory MedicineChinese

Background:The impact of corticosteroids on humoral responses in coronavirus disease 2019(COVID-19)sur-vivors during the acute phase and subsequent 6-month period remains *** study aimed to determine how the use of corticosteroids influences the initiation and duration of humoral responses in COVID-19 survivors 6 months after infection ***:We used kinetic antibody data from the lopinavir-ritonavir trial conducted at Jin Yin-Tan Hospital in January 2020,which involved adults hospitalized with severe COVID-19(LOTUS,ChiCTR2000029308).Anti-body samples were collected from 192 patients during hospitalization,and kinetic antibodies were monitored at all available time points after ***,plasma samples were collected from 101 COVID-19 survivors for comprehensive humoral immune measurement at the half-year follow-up *** main focus was comparing the humoral responses between patients treated with systemic corticosteroid therapy and the non-corticosteroid ***:From illness onset to day 30,the median antibody titre areas under the receiver operating characteristic curve(AUCs)of nucleoprotein(N),spike protein(S),and receptor-binding domain(RBD)immunoglobulin G(IgG)were significantly lower in the corticosteroids *** AUCs of N-,S-,and RBD-IgM as well as neutralizing antibodies(NAbs)were numerically lower in the corticosteroids group compared with the non-corticosteroid ***,peak titres of N,S,RBD-IgM and-IgG and NAbs were not influenced by *** 6-month follow-up,we observed a delayed decline for most binding antibodies,except N-IgM(𝛽−0.05,95%CI[−0.10,0.00])in the corticosteroids group,though not reaching statistical *** significant difference was observed for ***,for the half-year seropositive rate,corticosteroids significantly accelerated the decay of IgA and IgM but made no difference to N-,S-,and RBD-IgG or ***,corticosteroids group showed a trend towards d

关键词： COVID-19 Corticosteroid Immunity Antibody Humoral response

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A Collision-Free Person-Following Approach Based on Path Planning

A Collision-Free Person-Following Approach Based on Path Pla...

引用

IEEE International Conference on Mechatronics and automation

作者： Lei Pang Zhiqiang Cao Junzhi Yu Weimin Zhang Xuechao Chen State Key Laboratory of Management and Control for Complex Systems Institute of Automation School of Artificial Intelligence University of Chinese Academy of Sciences BIC-ESAT College of Engineering Peking University Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

Person following is a crucial capability for mobile robots in tasks of human assistance and cooperation. In this paper, a person-following approach based on path planning is proposed for mobile robots, which takes person following and obstacle avoidance into account in a unified framework. The proposed approach is comprised of a sensing module and a planning module. The sensing module with a camera and a 3D LiDAR is employed to locate the target person and perceive obstacles. For the planning module, the combination of global planner and local planner effectively outputs collision-free paths and control commands for a mobile robot to keep following the target person. Experiments were conducted to evaluate the performance of the proposed approach.

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