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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Hongxin Zhang Jiaxin Liu Ying Tang Gang Xiong State Key Lab of CAD & CG Zhejiang University China College of Software Engineering Zhejiang University of Technology China State Key Laboratory of Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences

ISBN: (数字)9781728169040

ISBN: (纸本)9781728169057

Predicting traffic flow is one of the fundamental issue to smart cities. However it is still challenging in vehicular cyber-physical systems because of ever-increasing urban traffic data. Most previous models for traffic flow prediction suffers the problem with variance error depends on time and location. In this paper, a novel graph convolution network (GCN) approach is proposed for predicting citywide traffic flow. Our key idea is to introduce spatial and temporal attention to the GCN model to lessen the impact of urban data complexity. A framework of flow-based graph convolutional network is established to improve traffic flow prediction while investigating the spatial and temporal correlation of traffic flow. We further propose practical strategies that efficiently learn parameters of the model. Experimental results demonstrate that the proposed approach to traffic flow prediction outperforms state-of-the-art approaches.

关键词： Predictive models Data models Analytical models Convolution Urban areas Chebyshev approximation Roads

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Differentiated regional impacts of future meteorological changes and anthropogenic emission control on PM 2.5 concentrations in China

Sustainable Horizons

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Sustainable Horizons 2025年 16卷

作者： Yueqi Jiang Yisheng Sun Shengyue Li Dejia Yin Dian Ding Zhaoxin Dong Haotian Zheng Bin Zhao Shuxiao Wang State Key Laboratory of Regional Environment and Sustainability School of Environment Tsinghua University Beijing 100084 PR China Key Laboratory of Sources and Control of Air Pollution Complex Ministry of Ecology and Environment Beijing 100084 PR China Department of Meteorology and Atmospheric Science The Pennsylvania State University University Park PA 16802 USA Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control College of Environment and Energy South China University of Technology Guangzhou Higher Education Mega Center Guangzhou 510006 PR China Nanjing-Helsinki Institute in Atmospheric and Earth System Sciences Nanjing University Nanjing 210023 PR China

Despite great progress in past decades, mitigating ambient fine particulate matter (PM 2.5 ) pollution remains a long-term task for China in protecting human health. Effective strategies must account for the impacts of future meteorological changes and anthropogenic emission variations, which vary substantially across regions and climate change scenarios. In this study, we assessed meteorological conditions, anthropogenic emissions, and PM 2.5 concentrations in 2060 in key regions in mainland China under various climate change scenarios by coupling an integrated assessment model and a regional meteorology/air quality dynamic downscaling simulation system. The results show that surface temperature and humidity are projected to increase across mainland China under all scenarios (SSP126, SSP245, SSP370, SSP585), while other meteorological factors vary by region and scenario. In the Jing-Jin-Ji and Fenwei Plain, meteorological changes are expected to worsen air pollution, leading to PM 2.5 increases of over 3.5 and 1.5 μg/m³, respectively. Conversely, meteorological changes are likely to reduce PM 2.5 in the Yangtze River Delta, Pearl River Delta, and Sichuan Basin in most scenarios. The analysis of anthropogenic emission variations demonstrates that large anthropogenic emissions reductions will significantly decrease PM 2.5 concentrations in all key regions, but the extent of reductions varies due to regional differences in emission sources. Among key regions, the Pearl River Delta usually exhibits the smallest proportional reductions in emissions and the least decline in PM 2.5 concentration across various scenarios. The differentiated regional impact of meteorological changes and anthropogenic emission control on future PM 2.5 concentrations in China highlights the necessity of region-specific policies to mitigate future PM 2.5 pollution.

关键词： Regional air quality Climate change PM 2.5 China

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A Parallel Education Based Intelligent Tutoring systems Framework

A Parallel Education Based Intelligent Tutoring Systems Fram...

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2020 IEEE International Conference on Networking, Sensing and control, ICNSC 2020

作者： Jing, Sifeng Tang, Ying Liu, Xiwei Gong, Xiaoyan Cui, Wei Liang, Joleen State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao266044 China Department of Electrical and Computer Engineering Rowan University GlassboroNJ08028 United States Squirrel Ai Learning Shanghai200030 China

ISBN: (纸本)9781728168531

although online education has improved efficiency of learners' access to high-quality educational resources, realtime interaction between instructors and learners have not yet been achieved in online personalized learning. Intelligent Tutoring systems (ITS) provides a feasible way to realize realtime personalized learning guidance and resource recommendations by applying AI to capture and analyze online learners' characteristics and behaviors. In this paper, reviews and trends of ITS are discussed and three challenges of ITS research development are pointed out: learner model, guidance mechanism and human-computer interaction mechanism. In order to address these issues, parallel intelligence theory is introduced and a parallel intelligence education based ITS framework is proposed. © 2020 IEEE.

关键词： Computer aided instruction

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A Tracking control Method based on Event-Triggered Adaptive Dynamic Programming 38

A Tracking Control Method based on Event-Triggered Adaptive ...

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第三十八届中国控制会议

作者： Ziyang Wang Qinglai Wei Derong Liu School of Automation and Electrical Engineering University of Science and Technology Beijing The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Automation Guangdong University of Technology

ISBN: (纸本)9781728123295;9789881563972

In this paper, an event-triggered adaptive dynamic programming(ADP) algorithm is developed to solve optimal tracking control problems. The event-triggered control law will be updated only when the triggering conditions are ***, the computational cost can be reduced and the celerity of tracking can be improved. Compared to existing works,novel triggering conditions are designed in this paper. Besides, the stability is guaranteed with less assumption. Neural networks are used to implement the tracking control algorithm. Finally, an example is employed to show the effectiveness of the algorithm.

关键词： Adaptive dynamic programming event-triggered control optimal tracking control

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An Adaptive RBF Neural Network control Method for a Class of Nonlinear systems

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IEEE/CAA Journal of Automatica Sinica 2018年第2期5卷 457-462页

作者： Hongjun Yang Jinkun Liu State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Automation Science and Electrical Engineering Beihang University Beijing 100083 China

This paper focuses on designing an adaptive radial basis function neural network(RBFNN) control method for a class of nonlinear systems with unknown parameters and bounded disturbances. The problems raised by the unknown functions and external disturbances in the nonlinear system are overcome by RBFNN, combined with the single parameter direct adaptive control method. The novel adaptive control method is designed to reduce the amount of computations *** uniform ultimate boundedness of the closed-loop system is guaranteed by the proposed controller. A coupled motor drives(CMD) system, which satisfies the structure of nonlinear system,is taken for simulation to confirm the effectiveness of the *** show that the developed adaptive controller has favorable performance on tracking desired signal and verify the stability of the closed-loop system.

关键词： Index TermsbAdaptive control neural network （NN） nonlin-ear system radial basis function.

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A prototype-based generalized zero-shot learning framework for hand gesture recognition

arXiv

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arXiv 2020年

作者： Wu, Jinting Zhang, Yujia Zhao, Xiaoguang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

Hand gesture recognition plays a significant role in human-computer interaction for understanding various human gestures and their intent. However, most prior works can only recognize gestures of limited labeled classes and fail to adapt to new categories. The task of Generalized Zero-Shot Learning (GZSL) for hand gesture recognition aims to address the above issue by leveraging semantic representations and detecting both seen and unseen class samples. In this paper, we propose an end-to-end prototype-based GZSL framework for hand gesture recognition which consists of two branches. The first branch is a prototype-based detector that learns gesture representations and determines whether an input sample belongs to a seen or unseen category. The second branch is a zero-shot label predictor which takes the features of unseen classes as input and outputs predictions through a learned mapping mechanism between the feature and the semantic space. We further establish a hand gesture dataset that specifically targets this GZSL task, and comprehensive experiments on this dataset demonstrate the effectiveness of our proposed approach on recognizing both seen and unseen gestures. Copyright © 2020, The Authors. All rights reserved.

关键词： Palmprint recognition

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K-9 Artificial Intelligence Education in Qingdao: Issues, Challenges and Suggestions

K-9 Artificial Intelligence Education in Qingdao: Issues, Ch...

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2020 IEEE International Conference on Networking, Sensing and control, ICNSC 2020

作者： Gong, Xiaoyan Tang, Ying Liu, Xiwei Jing, Sifeng Cui, Wei Liang, Joleen Wang, Fei-Yue State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100084 China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao266044 China Department of Electrical and Computer Engineering Rowan University GlassboroNJ08028 United States Squirrel Ai Learning Shanghai200030 China

ISBN: (纸本)9781728168531

Nowadays, AI education from Kindergarten to 9th grade(K-9) is in full swing in China, but many challenges exist, such as fragmented AI curricula, ineffective teaching tools, and uneven educational resources in urban and rural areas, etc. So this paper presents an in-depth study of the current status of K-9 AI education in Qingdao area through questionnaires, expert discussions, and field visits to sort out existing problems and then put forward corresponding advice and suggestions. Collected data is then analyzed from the perspectives of government, schools, teachers, students and parents, respectively. Results show that AI-related educational activities have been carried out in Qingdao area to improve students' AI literacy. But efforts are still needed to make such education more systematic, standardized, and personalized. Finally the paper made a list of recommendations corresponding to these needed efforts. © 2020 IEEE.

关键词： Curricula

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Faster gradient-based NAS pipeline combining broad scalable architecture with confident learning rate

arXiv

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arXiv 2020年

作者： Ding, Zixiang Chen, Yaran Li, Nannan Zhao, Dongbin 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

In order to further improve the search efficiency of Neural Architecture Search (NAS), we propose B-DARTS, a novel pipeline combining broad scalable architecture with Confident Learning Rate (CLR). In B-DARTS, Broad Convolutional Neural Network (BCNN) is employed as the scalable architecture for DARTS, a popular differentiable NAS approach. On one hand, BCNN is a broad scalable architecture whose topology achieves two advantages compared with the deep one, mainly including faster single-step training speed and higher memory efficiency (i.e. larger batch size for architecture search), which are all contributed to the search efficiency improvement of NAS. On the other hand, DARTS discovers the optimal architecture by gradient-based optimization algorithm, which benefits from two superiorities of BCNN simultaneously. Similar to vanilla DARTS, B-DARTS also suffers from the performance collapse issue, where those weight-free operations are prone to be selected by the search strategy. Therefore, we propose CLR, that considers the confidence of gradient for architecture weights update increasing with the training time of over-parameterized model, to mitigate the above issue. Experimental results on CIFAR-10 and ImageNet show that 1) B-DARTS delivers state-of-the-art efficiency of 0.09 GPU day using first order approximation on CIFAR-10;2) the learned architecture by B-DARTS achieves competitive performance using state-of-the-art composite multiply-accumulate operations and parameters on ImageNet;and 3) the proposed CLR is effective for performance collapse issue alleviation of both B-DARTS and DARTS. Copyright © 2020, The Authors. All rights reserved.

关键词： Pipelines

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Renal Clearing Carbon Dots-Based Near-Infrared Fluorescent Super-Small Nanoprobe for Renal Imaging

SSRN

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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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Predicting Online News Authorship by an Authorship Embeddings Space Method

Predicting Online News Authorship by an Authorship Embedding...

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IEEE International Conference on Big Data Analysis (ICBDA)

作者： Wanting Wen Qiudan Li Junfeng Li Xu Zhang Daniel Zeng The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China National Internet Emergency Center CNCERT/CC Beijing China

ISBN: (数字)9781728141114

ISBN: (纸本)9781728141121

In this paper, we study the problem of authorship identification in online news data. Most of the existing approaches predict authorship via feature engineering, which cannot focus on important attributes. We designed an authorship identification method named Authorship Embeddings Space model (AES) to predict the online news authorship between online news and authors. First, we propose an authorship space to represent the deep semantic relationship of news content. Second, we use an embedding learning method to perform the relationship between authors and news. Finally, we formulated an authorship prediction algorithm to identify the news authors based on the authorship embeddings. Experimental results on the online news dataset reveal that the AES model outperforms the baseline models.

关键词： Organizations Stock markets Training Semantics Writing Support vector machines

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