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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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iMAPeM: A New Paradigm for Implementing Intelligent Mining With Humans in the Loop

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2024年第12期54卷 7536-7547页

作者： Ai, Yunfeng Teng, Siyu Yang, Qingyuan Wu, Ye Li, Yuchen Gao, Yu Meng, Feng Yang, Shengli Tian, Bin Chen, Long Wang, Fei-Yue Univ Chinese Acad Sci Artificial Intelligence Dept Beijing 100049 Peoples R China Waytous Inc Qingdao 266109 Peoples R China Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China Guoneng Baorishile Energy Co Ltd Hulunbuir 021000 Peoples R China CHN Energy Investment Grp Co Ltd Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100045 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100190 Peoples R China

Since the 1990s, the automated process within open-pit mines has been rapidly advanced. However, mineral transportation, as the most costly and dangerous production process, has not witnessed efficient achievements. Adverse weather conditions and complex work environments are two critical bottlenecks that impede the development and deployment of autonomous transportation of open-pit mines. To alleviate this issue, this research proposes a novel paradigm, named IMAPeM, designed to enable safe and efficient autonomous transportation with humans in the loop. IMAPeM includes three categories of miners: 1) biological miners;2) digital miners;and 3) robotic miners, as well as three operational modes: 1) autonomous model (AM);2) parallel model (PM);and 3) expert/emergency model (EM). IMAPeM employs these miners and modes depend on the complexity of the task, optimizing the utilization of digital and robotic miners while reducing the workload for biological miners. In addition, we developed the YUGONG system based on IMAPeM. The empirical implementation demonstrates the exceptional performance of the YUGONG system in autonomous transportation across diverse open-pit mines. This system contributes to the advancement of sustainable mining practices, which also carries profound significance for achieving long-term environmentally responsible mining operations.

关键词： Autonomous transportation intelligent mining parallel driving operating system parallel systems theory

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Communication-Efficient Federated Edge Learning for NR-U-Based IIoT Networks

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IEEE INTERNET OF THINGS JOURNAL 2021年第14期9卷 12450-12459页

作者： Chen, Qimei Xu, Xiaoxia You, Zehua Jiang, Hao Zhang, Jun Wang, Fei-Yue Wuhan Univ Sch Elect Informat Wuhan 430072 Peoples R China Wuhan Univ Sch Elect Engn & Automat Wuhan 430072 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

As a key infrastructural technology, Industrial Internet of Things (IIoT) and its related techniques have emerged in the age of Industrial Internet. Among them, an increasing popular and attractive federated edge learning (FEL) mechanism, which performs data analysis and inference at the edge devices distributedly, and aggregates local FEL units at a centralized controller, is introduced to meet the stringent data privacy and low-latency requirements for high-stake IIoT devices. Due to the bandwidth limitation, only parts of the IIoT devices can be selected to transmit their local FEL models to the centralized controller at each learning step. However, the centralized controller prefers to collect all the local FEL models to generate the global FL model since each IIoT device has a differential data set. Existing works mainly focus on selecting an appropriate subset of IIoT devices through advanced scheduling mechanisms without extending the resource bandwidth. However, the new radio in unlicensed spectrum (NR-U) technology in the 5G network opens up new possibilities for FEL since it is a privately owned network with fruitful bandwidth resources. We thus propose a novel communication-efficient FEL mechanism for NR-U-based IIoT networks, which aims to select data importance IIoT devices for local training under relatively sufficient unlicensed resources. The objective function is formulated as a tradeoff between total FEL data importance and the transmission latency via joint learning, device selection, and resource management scheduling, which is a mixed-integer nonlinear programming (MINLP). To deal with this problem, an alternating direction method of multipliers and block coordinate update (ADMM-BCU) algorithm with low computational complexity has been used. Closed-form expressions for both optimal device selection and resource management are derived, which highlighted significant insights. Numerical results demonstrate the algorithmic advantages and structural

关键词： Industrial Internet of Things Wireless fidelity Servers Resource management Data models Wireless communication Data privacy Communication efficiency federated edge learning (FEL) Industrial Internet of Things (IIoT) new radio in unlicensed spectrum (NR-U) resource management user selection

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Two-Stage Co-Optimization for Utility-Social systems With Social-Aware P2P Trading

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2023年第4期10卷 1875-1887页

作者： Zhao, Pengfei Li, Shuangqi Hu, Paul Jen-Hwa Cao, Zhidong Gu, Chenghong Yan, Xiaohe Huo, Da Hernando-Gil, Ignacio Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100864 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100864 Peoples R China Univ Bath Dept Elect & Elect Engn Bath BA2 7AY Avon England Beijing Inst Technol Natl Engn Lab Elect Vehicles Beijing 100081 Peoples R China Univ Utah David Eccles Sch Business Salt Lake City UT 84112 USA North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing 102206 Peoples R China Cranfield Univ Sch Water Energy & Environm Bedford MK43 0AL England Univ Bordeaux ESTIA Inst Technol F-64210 Bidart France

Effective utility system management is fundamental and critical for ensuring the normal activities, operations, and services in cities and urban areas. In that regard, the advanced information and communication technologies underpinning smart cities enable close linkages and coordination of different subutility systems, which is now attracting research attention. To increase operational efficiency, we propose a two-stage optimal co-management model for an integrated urban utility system comprised of water, power, gas, and heating systems, namely, integrated water-energy hubs (IWEHs). The proposed IWEH facilitates coordination between multienergy and water sectors via close energy conversion and can enhance the operational efficiency of an integrated urban utility system. In particular, we incorporate social-aware peer-to-peer (P2P) resource trading in the optimization model, in which operators of an IWEH can trade energy and water with other interconnected IWEHs. To cope with renewable generation and load uncertainties and mitigate their negative impacts, a two-stage distributionally robust optimization (DRO) is developed to capture the uncertainties, using a semidefinite programming reformulation. To demonstrate our model's effectiveness and practical values, we design representative case studies that simulate four interconnected IWEH communities. The results show that DRO is more effective than robust optimization (RO) and stochastic optimization (SO) for avoiding excessive conservativeness and rendering practical utilities, without requiring enormous data samples. This work reveals a desirable methodological approach to optimize the water-energy-social nexus for increased economic and system-usage efficiency for the entire (integrated) urban utility system. Furthermore, the proposed model incorporates social participations by citizens to engage in urban utility management for increased operation efficiency of cities and urban areas.

关键词： Water heating Optimization Costs Cogeneration Water resources Resistance heating Peer-to-peer computing Distributionally robust optimization (DRO) peer-to-peer (P2P) trading smart city social-aware management water-energy-social nexus

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Could ChatGPT Imagine: Content control for Artistic Painting Generation Via Large Language Models

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JOURNAL OF INTELLIGENT & ROBOTIC systems 2023年第2期109卷 39-39页

作者： Lu, Yue Guo, Chao Dou, Yong Dai, Xingyuan Wang, Fei-Yue Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Macao Univ Sci & Technol Macao Inst Syst Engn Macau 999078 Peoples R China

Intelligent systems and human-machine interactions have consistently provided convenience in both work and daily life. Artificial Intelligence Generated Content (AIGC) can assist humans in artistic creation by generating painting images based on textual descriptions. However, the quality of generated painting images depends heavily on well-designed prompts, which are labor-intensive and time-consuming in painting creation. Large Language Models (LLMs) like ChatGPT have shown impressive performance in linguistic tasks such as question answering and logical inference, demonstrating strong linguistic intelligence. This paper proposes an assistant painting creation approach to provide precise content control for painting generation by combining LLMs with text-to-image generative models and evaluates the performance of the proposed approach on painting content generation and painting element arrangement. The experimental results show that our approach can provide clear guidance on rich painting content and reasonable arrangements of painting elements, demonstrating its ability of text-based painting scene imagination. In painting generation tasks, LLMs like ChatGPT can help the text-to-image models with precise control over the painting content and improve the overall painting results.

关键词： Intelligent systems Human-machine interactions Artistic painting generation Large language model ChatGPT Linguistic intelligence

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A Time-Series Augmentation Method Based on Empirical Mode Decomposition and Integrated LSTM Neural Network 44

A Time-Series Augmentation Method Based on Empirical Mode De...

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44th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Li, Chenguang Yang, Hongjun Cheng, Long Huang, Fubiao Chinese Acad Sci Inst Automat State Key Lab Control & Management Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Beijing Boai Hosp China Rehabil Res Ctr Beijing 100068 Peoples R China

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

Adequate patients' data have always been critical for disease assessment. However, large amounts of patient data are often difficult to collect, especially when patients are required to complete a series of assessment movements. For example, assessing the hand motor function of stroke patients or Parkinson's patients requires patients to complete a series of evaluation movements, and it is often difficult for patients to complete each group of actions multiple times, resulting in a small amount of data. To solve the problem of insufficient data quantity, this study proposes a data augmentation method based on empirical mode decomposition and integrated long short-term memory neural network (EMD-ILSTM). The method mainly consists of two parts: one is to decompose the raw signal by the method of EMD, and the other is to use LSTM for data augmentation of the decomposed signal. Then, the method is tested on the public dataset named Ninaweb, and the test results show that the classification accuracy can be improved by 5.2% by using the augmented data for classification tasks. Finally, clinical trials are conducted to verify that after dimensionality reduction, the augmented data and raw data have smaller intra-class distances and larger inter-class distances, indicating that data augmentation is effective.

关键词： Dimensionality reduction Empirical mode decomposition Stroke (medical condition) Clinical trials Biology Task analysis Biological neural networks

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Game-theoretical explorations of the mesoscale flow structure and regime transitions in bubble columns

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Particuology 2020年第1期18卷 100-108页

作者： Shuo Yuan Yifen Mu Xiaoping Guan Zhixin Liu Jianhua Chen Ning Yang Lin Zhang Key Lab of Systems and Control Institute of Systems SciencesAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China State Key Laboratory of Multiphase Complex Systems Institute of Process EngineeringChinese Academy of SciencesBeijing 100190China

Understanding the mesoscale structure and regime transition in bubble columns is of great significance for reactor design and *** on the energy-minimization multiscale(EMMS)model,a noncooperative game model with constraints is proposed to investigate the structural properties of gas-liquid systems in which small and large bubbles are chosen as players and the energy consumption form the objective *** conservation equations of the system can be regarded as the constraints of the *** the formulated noncooperative game model,the concept of the generalized Nash equilibrium(GNE)is used to characterize the *** algorithm is developed to numerically compute the GNE and some important structural parameters in the *** numerical results show the existence of the GNE for all values of the superficial gas velocity *** Ug varies,the trends in the state variables can be observed and the critical point of Ug *** overall trend of the flow regime transition agrees with the original EMMS model and experimental results,although the GNE calculation also reveals different single-bubble dominant mechanisms with increasing Ug.

关键词： Bubble column Mesoscale Regime transition Game theory Generalized Nash equilibrium EMMS model

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Development, Calibration, and Image Processing of Underwater Structured Light Vision System: A Survey

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷 1页

作者： Fan, Junfeng Wang, Xuan Zhou, Chao Ou, Yaming Jing, Fengshui Hou, Zengguang Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China China Univ Min & Technol Sch Mech Elect & Informat Engn Dept Mech Engn Beijing 100083 Peoples R China

The underwater structured light vision system has the characteristics of a large amount of information, high measurement accuracy, and good robustness, which has attracted the attention of researchers and is widely used in the underwater 3-D measurement. In this article, the state-of-the-art techniques of the underwater structured light vision system are comprehensively summarized, which provides valuable guidance for relevant researchers. First, underwater structured light vision systems are classified and introduced, and their advantages and disadvantages are analyzed. Then, the calibration methods of the underwater structured light vision system are introduced, including underwater camera calibration, underwater laser calibration, and underwater projector calibration. Besides, the image processing methods of the underwater structured light vision system are analyzed, including laser structured light image processing and coded structured light image processing. Moreover, the applications of the underwater structured light vision system in underwater archeology, seabed mapping, and underwater structure inspection are introduced. On this basis, the research directions of the underwater structured light vision system are prospected.

关键词： Machine vision Measurement by laser beam Three-dimensional displays Cameras Sea measurements Calibration Image processing Image processing system calibration system development underwater structured light vision system

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Improving University Faculty Evaluations via multi-view Knowledge Graph

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FUTURE GENERATION COMPUTER systems-THE INTERNATIONAL JOURNAL OF ESCIENCE 2021年 117卷 181-192页

作者： Lin, Qika Zhu, Yifan Lu, Hao Shi, Kaize Niu, Zhendong Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Pittsburgh Sch Comp & Informat Pittsburgh PA 15260 USA

University faculties generate a large amount of heterogeneous data in e-learning environments that online systems and toolkits have made widely available in all aspects of teaching and scientific researching activities. How to use the data efficiently and scientifically for faculty evaluations has recently become an important issue in university performance systems. However, it is still a challenge to comprehensively assess faculty members using multi-source and multi-modal data due to the lack of uniform representations and evaluation processes. To this end, this paper proposes a novel University Faculty Evaluation System based on a multi-view Knowledge Graph (UFES-KG) that integrates heterogeneous faculty data. Relevant data, collected both on the Internet and through university-administered internal systems, includes faculty information such as scientific research papers, patents, funds, monographs, awards, professional activities and teaching performance. Furthermore, we construct entity representations through knowledge graph embedding methods to retain their semantic information. In addition, by integrating the academic development status of scholars in the previous three years as well as student evaluation data, this paper proposes an academic development factor (ADF) for making predictions about faculty academic development. The experimental results show that this factor is closely related to the features of the knowledge graph and student evaluations. In a certain case study, this factor is superior to the traditional h-index, g-index, and RG score. Intuitively and scientifically, this multi-view approach can improve evaluations of university faculties. (C) 2020 Elsevier B.V. All rights reserved.

关键词： University faculty evaluation Knowledge graph Academic development prediction E-learning

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Process Monitoring, Diagnosis and control of Additive Manufacturing

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2024年第1期21卷 1041-1067页

作者： Fang, Qihang Xiong, Gang Zhou, MengChu Tamir, Tariku Sinshaw Yan, Chao-Bo Wu, Huaiyu Shen, Zhen Wang, Fei-Yue Chinese Acad Sci Inst Automat Beijing Engn Res Ctr Intelligent Syst & Technol State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Guangdong Engn Res Ctr 3D Printing & Intelligent M Cloud Comp Ctr Dongguan 523808 Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA Macau Univ Sci & Technol Inst Syst Engn Macau 999078 Peoples R China Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Fac Elect & Informat Engn Sch Automation Sci & Engn Xian 710049 Shaanxi Peoples R China

Additive manufacturing (AM) can build up complex parts in a layer-by-layer manner, which is a kind of novel and flexible production technology. The special manufacturing capability of AM shows great application potential in various fields. However, an open-loop control method cannot guarantee the reliability and repeatability of an AM process. Defects often occur to deteriorate product quality and lead to material and time waste, which hinders the development of AM industry. In this regard, a lot of efforts have been made to make an AM process more controllable. This work proposes an AM control framework that divides the related studies into three feedback loops, including the in-situ monitoring of process defects, fault diagnosis of 3-D printers, and closed-loop control of an AM process. These three loops constitute the inspection and control of AM from the machine level to product level. Specifically, the measurement requirements for monitoring techniques, defect detection, fault diagnosis, and closed-loop control are summarized. The challenges and future trends in realizing a more reliable and repeatable AM process are discussed. Note to Practitioners-This survey is motivated by urgent need to solve product quality problems in additive manufacturing (AM) caused by open-loop control. Three feedback loops can be established to solve them. The first one is defect detection that inspects part quality during fabrication. The second one is the fault diagnosis of a 3-D printer that monitors the health and operation conditions of its actuators. The last one is closed-loop control that improves AM process reliability and repeatability by regulating process variables in real time. These three loops are all based on the feedback signals of in-situ monitoring systems. This paper reviews the related studies and provides guidance for establishing the monitoring systems, performing defect detection and fault diagnosis, and designing closed-loop control systems, which helps real

关键词： Additive manufacturing (AM) in-situ monitoring defect detection fault diagnosis closed-loop control

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