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SSRN 2022年

作者： Gao, Pengli Hui, Hui Guo, Chanjuan Liu, Yu Su, Ya Huang, Xiazi Shang, Wenting Jiang, Jingying Tian, Jie Guo, Kunxiong CAS Key Laboratory of Molecular Imaging Beijing Key Laboratory of Molecular Imaging The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Biological Science and Medicine Engineering School of Engineering Medicine Beihang University No. 37 Xueyuan Road Beijing100191 China Key Laboratory of Big Data-Based Precision Medicine Beihang University Ministry of Industry and Information Technology of the People’s Republic of China No. 37 Xueyuan Road Beijing100191 China School of Engineering Medicine Beihang University No. 37 Xueyuan Road Beijing100191 China Department of Hepatobiliary Surgery Zhujiang Hospital Southern Medical University Guangzhou China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine China

Real-time renal imaging is indispensable for monitoring kidney-related disease processes and evaluating the nephrotoxicity of nanoprobes. Owing to their small size, tunable photoluminescence properties, and low biotoxicity, carbon dots (CDs) are widely employed as nanoprobes for imaging-guided disease diagnosis. However, the preparation of near-infrared CDs is a stupendous challenging. Here, CDs with near-infrared fluorescence (NIR-CDs ) were prepared by one-pot pyrolysis and were comprehensively evaluated for real-time renal imaging. The NIR-CDs possessed super-small size (1.8 ± 0.2 nm) and high quantum yield (14.3% in aqueous solution) at 696 nm. NIR-CDs exhibited high-efficiency renal clearance at 24 h post-treatment (97.47-99.27% ID) via glomerular filtration. The irreversible clearance of NIR-CDs enables effective real-time monitoring of kidney function in the normal and acute kidney injury (AKI) mouse models. Consequently, NIR-CDs can be utilized for imaging analyses of impaired kidney function, pioneering a serviceable guideline for neoapplications of NIR-CDs. © 2022, The Authors. All rights reserved.

关键词： Nanoprobes

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The Prediction of the Epidemic Trend of COVID-19 Using Neural Networks

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IFAC-PapersOnLine 2020年第5期53卷 857-862页

作者： Jing Yang Zhen Shen Xisong Dong Xiuqin Shang Wei Li Gang Xiong J. Yang is with the State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China and also with College of Information Sciences & Technology Beijing University of Chemical Technology Beijing 100029 China. Z. Shen is with the State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China and also with the Intelligent Manufacturing Center Qingdao Academy of Intelligent Industries Qingdao Shandong 266113 China. X. Dong X. Shang and W. Li are with the State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China. G. Xiong is with Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing 100190 China and also with the Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Dongguan Guangdong 523808 China.

In this paper, a BP neural network and an LSTM network are applied respectively to the prediction of Coronavirus Disease 2019 (COVID-19) in Wuhan, China and South Korea. The methods do not require specific theories of modelling and the predicted values can be obtained as long as the conventional parameters are set. The mean absolute percentage error (MAPE) of all the experiments are below 5% and the values of the determinable coefficient R are all larger than 0.9. The experiments show that the models can fit the actual values well and make relatively accurate predictions. As of March 29, 2020, the cumulative number of confirmed cases in Wuhan is expected to reach 50,068 using BP neural networks and 49,972 using LSTM network, respectively. As of April 13, 2020, the cumulative number of confirmed cases in South Korea is expected to reach 8,862 using BP neural networks and 8,716 using LSTM network, respectively. The models of neural networks are effective in predicting the trend of the COVID-19 epidemic, which is meaningful to prevent and control the epidemic.

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

arXiv

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

Research Square

引用

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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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 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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Graph Attention Model Embedded With Multi-Modal Knowledge For Depression Detection

Graph Attention Model Embedded With Multi-Modal Knowledge Fo...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Wenbo Zheng Lan Yan Chao Gou Fei-Yue Wang School of Software Engineering Xi’an Jiaotong University Xi’an China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Intelligent Systems Engineering Sun Yat-sen University

ISBN: (数字)9781728113319

ISBN: (纸本)9781728113326

With more than 300 million people depressed worldwide annually, depression is a global problem. The goal of depression detection is to improve diagnostic accuracy and availability, leading to faster intervention. The most important and challenging problem here is to design an effective and robust depression detection model. To this end, there are two challenges to overcome: 1) Multi-modal (audio, image, text, etc.) information must be jointly considered to make accurate inferences. 2) Existing deep learning-based work suffers from multi-modal data sufficiency problem. To address these issues, we propose a graph attention model embedded with multi-modal knowledge for depression detection. This approach learns not only reasonable embeddings for nodes in the knowledge graph, but also exploits medical knowledge to improve the performance of classification and prediction with the knowledge attention mechanism. Experimental results on the real-world datasets show that the proposed approach significantly improves the classification and prediction performance compared with other major state-of-the-art approaches, with guaranteed the robustness with each modality of multi-modal data. Overall, this paper shows how multi-modal knowledge attention mechanism and deep-learning-based networks can be combined to assist mental health patients and practitioners.

关键词： Biomedical imaging Robustness Data models Machine learning Task analysis Knowledge engineering

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A Parallel Medical Diagnostic and Treatment System for Chronic Diseases

A Parallel Medical Diagnostic and Treatment System for Chron...

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Chinese automation Congress (CAC)

作者： Jing Wang Xiao Wang Yuanyuan Guo Fei-Yue Wang The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China Institute of Smart Healthcare Systems Qingdao Academy of Intelligent Industries Qingdao China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

In order to improve the accuracy of diagnosis and the effectiveness of treatment on chronic diseases, a parallel medical diagnostic and treatment system for chronic diseases is proposed in this paper. The system consists of the artificial system, computational experiments and parallel execution. Generally, the actual medical system includes real doctors, real patients, and the process of real doctors diagnosing and creating a treatment plan. Based on the components in actual medical system, the virtual doctors and virtual patients are created in artificial system. The computational experiments are to exhaustively explore and evaluate reasonable diagnoses and treatment options, so that the most accurate diagnosis and the best treatment plan are configured. The parallel execution between the artificial and the actual system includes the interaction between virtual doctors and real doctors as well as that between the virtual patients and actual patients. The parallel execution, on the doctors' end, is to ensure the precise diagnosis of diseases and the effectiveness of the treatment plan. On the other hand, the parallel execution, on the patients' end, is to provide patients with more personal guidance on following the treatment and to assist sending feedback to the system regarding the treatment effectiveness. As the chronic diseases generally require a long-term treatment, this parallel system can better accommodate the process by continuously monitoring the treatment and sending feedback to the model so as to actively optimize the treatment plan.

关键词： Medical diagnostic imaging Medical services Diseases Medical diagnosis Deep learning Neural networks Knowledge engineering

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A vision-based path planning and following system for a miniature robotic fish

A vision-based path planning and following system for a mini...

引用

2019 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2019

作者： Lu, Yue Chen, Xingyu Wu, Zhengxing Yan, Yan Yu, Junzhi State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory for Turbulence and Complex System Department of Mechanics and Engineering Science BIC-ESAT College of Engineering Peking University Beijing100871 China

ISBN: (纸本)9781728137261

Most existing path following algorithms for middle- or large-sized underwater vehicles minimize yaw difference of the control object. Breaking the canonical manner, this paper presents a vision-based path planning and following system for miniature robotic fish. As for path planning, the proposed system finds a gird path based on the AStar algorithm and selects a series of target points in the grid path. By connecting these points in order, a curve path can be planned. In terms of path following, the proposed system forgoes to give a specific yaw and only uses a global camera. It obtains coordinates of the robotic fish in real time by KCF tracking algorithm to calculate an approximate motion vector. A PID controller is employed to drive the difference angle between the motion vector and the target vector (the vector from the robotic fish to the target point) to zero. By swimming to target points one by one, the robotic fish can move to the terminus following the planned path. Finally, aquatic experiments are conducted to verify the effectiveness of the vision-based path planning and following system. The results reveal that the developed system can generate a safe and smooth path, and the robotic fish can follow the path with acceptable deviation. © 2018 IEEE.

关键词： Motion planning

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Optimal and stable control for two-player zero-sum game using adaptive dynamic programming 8

Optimal and stable control for two-player zero-sum game usin...

引用

8th IEEE Data Driven control and Learning systems Conference, DDCLS 2019

作者： Zhu, Liao Song, Ruizhuo Wei, Qinglai Yang, Zhanyu Zhao, Bo State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China School of Systems Science Beijing Normal University Beijing100875 China

ISBN: (纸本)9781728114545

In this paper, an optimal and stable iteration learning scheme is developed for two-player zero-sum game (ZSG) in the discrete-time nonlinear systems. In the implementation of developed algorithm, the optimal and stable solution of Hamilton-Jacobi-Isaacs can be obtained based on adaptive dynamic programming (ADP). First, in order to obtain the optimal control policies, value iteration approach is employed with the proof of convergence given. Second, based on Lyapunov theory, an easy condition is proposed to get the stable control laws. Third, neural networks are used to implement the developed algorithm. Finally, a simulation example is included to verify the present method. © 2019 IEEE.

关键词： Neural networks

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