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3rd IFAC Workshop pn Cyber-Physical and Human systems (CPHS)

作者： Chen, Bingyang Xu, Zhidong Wang, Xiao Xu, Long Zhang, Weishan China Univ Petr Sch Comp Sci & Technol Qingdao 266580 Peoples R China Natl Def Univ PLA China Sch Secur Beijing 100091 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Shandong Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

To improve text sentiments classification issues, such as information loss and insensitivity to spatial information, this paper proposes a text sentiment classification model based on the capsule network (T-Caps), which uses the Transformer to extract low-level text features. The method iteratively updates capsule network parameters through optimized dynamic routing algorithms and global parameter sharing, and it obtains the relationship between local features of the text and the overall emotional polarity to save the information integrity of text features. By comparing with multiple models, we find that the Transformer has the strongest feature extraction capability. The experimental results show that our model is capable of extracting more discriminative semantic features and yields a significant performance gain compared to other baseline methods. Copyright (C) 2020 The Authors.

关键词： Sentiment classification Capsule network Transformer Deep learning Attention

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INDReview on Facial Expression Analysis and its Application in Education

INDReview on Facial Expression Analysis and its Application ...

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Chinese Automation Congress (CAC)

作者： Li, Qiang Liu, Xiwei Gong, Xiaoyan Jing, Sifeng Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Beijing Peoples R China Qingdao Acad Intelligent Ind Inst Smart Educ Syst Beijing Peoples R China

ISBN: (纸本)9781728140940

In recent years, with the rapid development of computer technology, facial expression recognition technology has gradually been applied to primary and secondary schools. This article first introduces the application of facial expression recognition in education. Then the development of facial expression recognition and the four basic processes of facial expressions are described. After that, the methods and algorithms used in face detection and localization of face expression recognition and classification are summarized, feature extraction and classification are summarized. Finally, the current application of facial expression recognition technology in education and the existing problems and future development are pointed out.

关键词： Facial expression recognition smart educational enviornment emoticon coding face detection and localization feature extraction expression classification

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Learning from the Guidance: Knowledge Embedded Meta-learning for Medical Visual Question Answering 27th

Learning from the Guidance: Knowledge Embedded Meta-learning...

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27th International Conference on Neural Information Processing

作者： Zheng, Wenbo Yan, Lan Wang, Fei-Yue Gou, Chao Xi An Jiao Tong Univ Sch Software Engn Xian Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9783030638191;9783030638207

Traditional medical visual question answering approaches require a large amount of lab.led data for training, but still cannot jointly consider both image and text information. To address this issue, we propose a novel framework called Knowledge Embedded Meta-Learning. In particular, we present a deep relation network to capture and memorize the relation among different samples. First, we introduce the embedding approach to perform feature fusion representation learning. Then, we present the construction of our knowledge graph that relates image with text, as the guidance of our meta-learner. We design a knowledge embedding mechanism to incorporate the knowledge representation into our network. Final result is derived from our relation network by learning to compare the features of samples. Experimental results demonstrate that the proposed approach achieves significantly higher performance compared with other state-of-the-arts.

关键词： Visual question answering Meta-learning Knowledge graph

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Webly Supervised Knowledge Embedding Model for Visual Reasoning

Webly Supervised Knowledge Embedding Model for Visual Reason...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zheng, Wenbo Yan, Lan Gou, Chao Wang, Fei-Yue Xi An Jiao Tong Univ Sch Software Engn Xian Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9781728171685

Visual reasoning between visual image and natural language description is a long-standing challenge in computer vision. While recent approaches offer a great promise by compositionality or relational computing, most of them are oppressed by the challenge of training with datasets containing only a limited number of images with ground-truth texts. Besides, it is extremely time-consuming and difficult to build a larger dataset by annotating millions of images with text descriptions that may very likely lead to a biased model. Inspired by the majority success of webly supervised learning, we utilize readily-availab.e web images with its noisy annotations for learning a robust representation. Our key idea is to presume on web images and corresponding tags along with fully annotated datasets in learning with knowledge embedding. We present a two-stage approach for the task that can augment knowledge through an effective embedding model with weakly supervised web data. This approach learns not only knowledge-based embeddings derived from key-value memory networks to make joint and full use of textual and visual information but also exploits the knowledge to improve the performance with knowledge-based representation learning for applying other general reasoning tasks. Experimental results on two benchmarks show that the proposed approach significantly improves performance compared with the state-of-the-art methods and guarantees the robustness of our model against visual reasoning tasks and other reasoning tasks.

关键词： Embeddings

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Attention based Graph Covolution Networks for intelligent Traffic Flow Analysis

Attention based Graph Covolution Networks for Intelligent Tr...

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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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Optimal Elevator Group control via Deep Asynchronous Actor-Critic Learning

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systems 2020年第12期31卷 5245-5256页

作者： Wei, Qinglai Wang, Lingxiao Liu, Yu Polycarpou, Marios M. Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Univ Cyprus KIOS Res & Innovat Ctr Excellence CY-1678 Nicosia Cyprus Univ Cyprus Dept Elect & Comp Engn CY-1678 Nicosia Cyprus

In this article, a new deep reinforcement learning (RL) method, called asynchronous advantage actor-critic (A3C) method, is developed to solve the optimal control problem of elevator group control systems (EGCSs). The main contribution of this article is that the optimal control law of EGCSs is designed via a new deep RL method, such that the elevator system sends passengers to the desired destination floors as soon as possible. Deep convolutional and recurrent neural networks, which can update themselves during applications, are designed to dispatch elevators. Then, the structure of the A3C method is developed, and the training phase for the learning optimal law is discussed. Finally, simulation results illustrate that the developed method effectively reduces the average waiting time in a complex building environment. Comparisons with traditional algorithms further verify the effectiveness of the developed method.

关键词： Elevators Optimal control Neural networks Backpropagation Machine learning Learning (artificial intelligence) Actor– critic adaptive dynamic programming deep learning (DL) elevator group control (EGC) optimal control reinforcement learning (RL)

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Social Education: Opportunities and Challenges in Cyber-Physical-Social Space

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2019年第2期6卷 191-196页

作者： Wang, Fei-Yue Tang, Ying Liu, Xiwei Yuan, Yong Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Rowan Univ Coll Engn Dept Elect & Comp Engn Glassboro NJ 08028 USA Qingdao Acad Intelligent Ind Qingdao 266109 Shandong Peoples R China

WE are making good progresses in our impact and reputation over the last year. According to the latest data released by Scopus on February 11, 2019, our CiteScore hits its historical high to 3.94, and TCSS ranks 8th out of the 226 journals (top 3.54%) in the field of social sciences. This is a solid improvement compared with the corresponding data in 2017 (CiteScore: 2.36, Rank: 17/226, top 8%). Thanks and congratulations to our authors, reviewers, and members of our editorial board. The current issue includes 17 regular papers and a brief discussion on social education.

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Social Transportation: Social Signal and Technology for Transportation Engineering

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2019年第1期6卷 2-7页

作者： Wang, Fei-Yue Wang, Pu Li, Juanjuan Yuan, Yong Wang, Xiao Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Cent S Univ Sch Traff & Transportat Engn Changsha 410083 Hunan Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China

Welcome to the first issue of the IEEE Transactions on Computational Social systems (TCSS) this year, and Happy New Year to everyone. We would like to take this opportunity to express sincere gratitude to our editors, reviewers, authors, and readers for your support and great efforts devoted to TCSS. Also, we want to express the best wishes to you all, and hope you have a happy, healthy, and fruitful 2019.

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Moving from mass customization to social manufacturing: a footwear industry case study

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2019年第2期32卷 194-205页

作者： Shang, Xiuqin Shen, Zhen Xiong, Gang Wang, Fei-Yue Liu, Sheng Nyberg, Timo R. Wu, Huaiyu Guo, Chao Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Chinese Acad Sci Beijing Engn Res Ctr Intelligent Syst & Technol Inst Automat Beijing Peoples R China Qingdao Acad Intelligent Ind Qingdao Peoples R China Aalto Univ Sch Sci Aalto Finland

A social manufacturing (SM) system for the footwear industry is proposed to improve customisation accuracy and efficiency through the use of big data and crowdsourcing. SM is a novel manufacturing paradigm for distributed, collab.rative, service-oriented, crowdsourcing-based, intelligent, and customised footwear production. First, the definition and process of footwear SM are introduced. Next, key supporting technologies are discussed, including 3D scanning, an evaluation mechanism for supply-demand matching, and optimised allocation of socialised manufacturing resources. Finally, the system development and an application case are described. In SM, a prosumer is defined as someone who is both a producer and consumer at the same time, and can be involved in the full life cycle of a personalised product, including the design, manufacture, service, and consumption processes. This allows the experience of every prosumer to be enhanced through participation, potential demands for personalised product to be released, and the production efficiency of customisation to be improved.

关键词： Collab.rative manufacturing mass customisation optimisation SM resources

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An Enhanced Fault Diagnosis Method with Uncertainty Quantification Using Bayesian Convolutional Neural Network 16

An Enhanced Fault Diagnosis Method with Uncertainty Quantifi...

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16th IEEE International Conference on Automation Science and Engineering (CASE)

作者： Fang, Qihang Xiong, Gang Shang, Xiuqin Liu, Sheng Hu, Bin Shen, Zhen Chinese Acad Sci Inst Automat 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 Inst Automat Beijing Engn Res Ctr Intelligent Syst & Technol Beijing Peoples R China Chinese Acad Sci Guangdong Engn Res Ctr 3D Printing & Intelligent Cloud Comp Ctr Beijing Peoples R China

ISBN: (纸本)9781728169040

Fault diagnosis is a vital technique to pinpoint the machine malfunctions in manufacturing systems. In recent years, the deep learning techniques greatly improve the fault detection accuracy, but there still remain some problems. If one fault is absent in the training data or the fault signal is disturbed by severe noise interference, the fault classifier may misjudge the health state. This problem limits the reliability of the fault diagnosis in real applications. In this paper, we enhance the fault diagnosis method by using Bayesian Convolutional Neural Network (BCNN). A Shannon entropy-based method is presented to quantify the prediction uncertainty. The BCNN turns the deterministic predictions to probabilistic distributions and enhances the robustness of the fault diagnosis. The uncertainty quantification method helps to indicate the wrong predictions, detect unknown faults, and discover the strong disturbances. Then, a fine-tuning strategy is applied to enhance the model performance further. The potential usability of the proposed method in monitoring the motors of 3D printers is studied. And the experiment is conducted on a motor bearing dataset provided by Case Western Reserve University. The proposed BCNN achieves 99.82% fault classification accuracy over nine health conditions. Its robustness is verified by comparing the testing accuracy with three other methods on the noisy datasets. And the uncertainty quantification method successfully detects the outlier inputs.

关键词： Failure analysis

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