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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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Corrections to “A High-Speed Seam Extraction Method Based on the Novel Structured-Light Sensor for Arc Welding Robot: A Review”

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IEEE Sensors Journal 2019年第4期19卷 1590-1590页

作者： Lei Yang En Li Teng Long Junfeng Fan Zize Liang University of Chinese Academy of Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

there was an error in the title of [1] ; it is a regular paper, not a review paper. The right title of this article is “A High-Speed Seam Extraction Method Based on the Novel Structured-Light Sensor for Arc Welding ... 详细信息

关键词： Welding Robot sensing systems Fans complex systems

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Blockchain based Consensus Checking in Cloud Storage

Blockchain based Consensus Checking in Cloud Storage

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International Symposium on Autonomous Decentralized systems (ISADAS)

作者： Guanqiu Qi Zhiqin Zhu Matthew Haner Jaesung Sim Jian Sun Yi Chai Yinong Chen Li Yongfu Mathematics and Computer Information Science Mansfield University of Pennsylvania Mansfield PA 16933 USA College of Automation Chongqing University of Posts and Telecommunications Chongqing 400065 China School of Electronic and Information Engineering Southwest University Chongqing 400715 China Ministry of Education Key Laboratory of Complex System Safety and Control (Chongqing University) Chongqing 400044 China School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ 85287 USA

ISBN: (数字)9781728116723

ISBN: (纸本)9781728116730

In cloud computing, data is duplicated to prevent data loss. One way to achieve data consistency in such a distributed computing systems is to use a blockchain. Based on practical Byzantine fault tolerance (PBFT), a specific type of blockchain, this paper proposes a synchronous Byzantine fault tolerance (SBFT) algorithm that not only maintains data consistency, but also has much higher efficiency than other general blockchain algorithms. We provide experimental results that demonstrate the algorithm's data consistency, efficiency, and reliability.

关键词： Cloud computing Fault tolerance Fault tolerant systems Peer-to-peer computing

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Multi-view hybrid embedding: A divide-and-conquer approach

arXiv

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arXiv 2018年

作者： Xu, Jiamiao Yu, Shujian You, Xinge Leng, Mengjun Jing, Xiao-Yuan Chen, C. L. Philip School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States Department of Computer Science University of Houston HoustonTX77204 United States State Key Laboratory of Software Engineering School of Computer Wuhan University China Department of Computer and Information Science Faculty of Science and Technology University of Macau Macau99999 China Dalian Maritime University Dalian116026 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100080 China

We present a novel cross-view classification algorithm where the gallery and probe data come from different views. A popular approach to tackle this problem is the multiview subspace learning (MvSL) that aims to learn a latent subspace shared by multi-view data. Despite promising results obtained on some applications, the performance of existing methods deteriorates dramatically when the multi-view data is sampled from nonlinear manifolds or suffers from heavy outliers. To circumvent this drawback, motivated by the Divideand-Conquer strategy, we propose Multi-view Hybrid Embedding (MvHE), a unique method of dividing the problem of cross-view classification into three subproblems and building one model for each subproblem. Specifically, the first model is designed to remove view discrepancy, whereas the second and third models attempt to discover the intrinsic nonlinear structure and to increase discriminability in intra-view and interview samples respectively. The kernel extension is conducted to further boost the representation power of MvHE. Extensive experiments are conducted on four benchmark datasets. Our methods demonstrate overwhelming advantages against the stateof-the-art MvSL based cross-view classification approaches in terms of classification accuracy and robustness. Copyright © 2018, The Authors. All rights reserved.

关键词： Embeddings

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A process simulation-based quantitative HAZOP analysis method

A process simulation-based quantitative HAZOP analysis metho...

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IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Jianlong Chen Xiwei Liu Sifeng Jing Gang Xiong Xiuqin Shang Changjian Cheng The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Chemical Engineering and the Environment Beijing Institute of Technology Beijing China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao China Cloud Computing Center Chinese Academy of Sciences Dongguan China Beijing Engineering Research Center for Intelligent Systems and Technology Chinese Academy of Science Beijing China

ISBN: (纸本)9781509029280

Hazard and operability (HAZOP) methodology is an important Process Hazard Analysis (PHA) technique for ensuring safety production in chemical industry. Due to the lacks of accuracy and reliability in manual HAZOP analysis, the quantitative HAZOP analysis has gained more and more attention. In this work, a process simulation-based HAZOP analysis method and its flow chart are proposed. And the proposed method is applied to a steam generation subsystem in an ethylene pyrolysis unit on a dynamic process simulation platform. All deviation parameters of system can be calculated and described quantitatively. The case study shows the proposed method is useful for the effective risk prevention and control measures in a chemical process.

关键词： Analytical models Hazards Manuals Process control Boilers

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Design of AI + Curriculum for Primary and Secondary Schools in Qingdao

Design of AI + Curriculum for Primary and Secondary Schools ...

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Chinese Automation Congress (CAC)

作者： Xiao Han Fang Hu Gang Xiong Xiwei Liu Xiaoyan Gong Xiaojie Niu Wanruo Shi Xinzhu Wang Qingdao Institute of Education Sciences Qingdao China Qingdao No.2 Middle School of Shandong Province Qingdao China The Cloud Computing Center Chinese Academy of Sciences Dongguan China The State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao China Faculty of Education Beijing Normal University Beijing China

We are entering a new AI era, and the development of AI technology and popularization of AI education has become the national strategy of China. The primary and secondary school students are the builder of future society, and have strong curiosity and passion to learn new things; therefore, we should make a good preparation of AI education for them and cultivate their basic AI literacy and computational thinking to adapt to the future. In this paper, from the viewpoint of thinking mode and literacy and skill training, a curriculum including basic, project, and practical types is designed. Integrating with the iSTREAM educational system proposed by Chinese Academy of Sciences, exploratory works were shown with some case studies. The applications show that AI education is helpful to enrich the study of primary and secondary school students and to prompt comprehensive quality.

关键词： Artificial intelligence Education Industries Technological innovation Programming profession Task analysis

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A New Approach for Face Anti-Spoofing Using Handcrafted and Deep Network Features

A New Approach for Face Anti-Spoofing Using Handcrafted and ...

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IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Polash Kumar Das Bin Hu Chang Liu Kaixin Cui Prabhat Ranjan Gang Xiong School of Electronic and Information Engineering South China University of Technology Guangzhou China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Science Beijing China College of Computer Science Sichuan University Chengdu China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China The Cloud Computing Center Chinese Academy of Science Dongguan China

In biometrics, face recognition methods are achieving momentum with recent progress in the computer vision(CV). Face recognition is widely used in the identification of an individual's identity. Unfortunately, in recent research work has revealed this face biometrics system is unprotected to spoofing attacks using by very low price instrument such as printed 2D photos attack, 3D masking attack and taking videos using smart devices (reply attack). Therefore, a Liveness Attack Detection (LAD) approach is needed to improve the high-quality security of face recognition system. Most of the earlier worked LAD methods for face anti-spoofing methods have highlight on using the handcrafted features, which are developed by expert knowledge of researcher. As example Gabor filter, Histogram of Oriented Gradients, local ternary pattern, and the Local Binary Pattern. Because of that, the extracted features consider limited factors of the problem, yielding a capture accuracy that is very low and changes with the point of presentation in attack face images. The deep learning method has developed in the computer vision research community, which is proven to be suitable for automatically training. In this article, we approach to mix or combine the handcrafted features and deep neural network features to design the discriminant face spoofing detection. The handcrafted features were based on LBP analysis. We examine the features information from the brightness and the chrominance channels using LBP descriptor. In deep features, we present an approach based on pre-trained convolutional neural network VGG-16 model using static features to recognize video and printed(2D) photo attacks. By attaching this two types of image features on our dataset and public databases, we get good results to identify real and attack images feature, called hybrid features, which has better discrimination ability to understand spoofing image feature.

关键词： Face Videos Feature extraction Databases Face recognition Password

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The paralleleye dataset: Constructing large-scale artificial scenes for traffic vision research

arXiv

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arXiv 2017年

作者： Li, Xuan Wang, Kunfeng Tian, Yonglin Yan, Lan Wang, Fei-Yue School of Automation Beijing Institute of Technology Beijing100081 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Academy of Intelligent Industries Qingdao266000 China Department of Automation University of Science and Technology of China Hefei230027 China Research Center for Computational Experiments and Parallel Systems Technology National University of Defense Technology Changsha410073 China

Video image datasets are playing an essential role in design and evaluation of traffic vision algorithms. Nevertheless, a longstanding inconvenience concerning image datasets is that manually collecting and annotating large-scale diversified datasets from real scenes is time-consuming and prone to error. For that virtual datasets have begun to function as a proxy of real datasets. In this paper, we propose to construct large-scale artificial scenes for traffic vision research and generate a new virtual dataset called "ParallelEye". First of all, the street map data is used to build 3D scene model of Zhongguancun Area, Beijing. Then, the computer graphics, virtual reality, and rule modeling technologies are utilized to synthesize large-scale, realistic virtual urban traffic scenes, in which the fidelity and geography match the real world well. Furthermore, the Unity3D platform is used to render the artificial scenes and generate accurate ground-truth labels, e.g., semantic/instance segmentation, object bounding box, object tracking, optical flow, and depth. The environmental conditions in artificial scenes can be controlled completely. As a result, we present a viable implementation pipeline for constructing large-scale artificial scenes for traffic vision research. The experimental results demonstrate that this pipeline is able to generate photorealistic virtual datasets with low modeling time and high accuracy labeling. Copyright © 2017, The Authors. All rights reserved.

关键词： Pipelines

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BNAS: An efficient neural architecture search approach using broad scalable architecture

arXiv

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arXiv 2020年

作者： Ding, Zixiang Chen, Yaran Li, Nannan Zhao, Dongbin Sun, Zhiquan Philip Chen, C.L. 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 School of Automation and Electric Engineering University of Science and Technology Beijing Beijing100083 China School of Computer Science & Engineering South China University of Technology Guangzhou Guangdong510006 China College of Navigation Dalian Maritime University Dalian116026 China

Efficient Neural Architecture Search (ENAS) achieves novel efficiency for learning architecture with high-performance via parameter sharing and reinforcement learning. In the phase of architecture search, ENAS employs deep scalable architecture as search space whose training process consumes most of search cost. Moreover, time consuming of model training is proportional to the depth of deep scalable architecture. Through experiments using ENAS on CIFAR-10, we find that layer reduction of scalable architecture is an effective way to accelerate the search process of ENAS but suffers from prohibitive performance drop in the phase of architecture estimation. In this paper, we propose Broad Neural Architecture Search (BNAS) where we elaborately design broad scalable architecture dubbed Broad Convolutional Neural Network (BCNN) to solve the above issue. On one hand, the proposed broad scalable architecture has fast training speed due to its shallow topology. Moreover, we also adopt reinforcement learning and parameter sharing used in ENAS as the optimization strategy of BNAS. Hence, the proposed approach can achieve higher search efficiency. On the other hand, the broad scalable architecture extracts multi-scale features and enhancement representations, and feeds them into global average pooling layer to yield more reasonable and comprehensive representations. Therefore, the performance of broad scalable architecture can be promised. In particular, we also develop two variants for BNAS who modify the topology of BCNN. In order to verify the effectiveness of BNAS, several experiments are performed and experimental results show that 1) BNAS delivers 0.19 days which is 2.37x less expensive than ENAS who ranks the best in reinforcement learning-based NAS approaches, 2) compared with small-size (0.5 millions parameters) and medium-size (1.1 millions parameters) models, the architecture learned by BNAS obtains state-of-the-art performance (3.58% and 3.24% test error) on CIFAR-1

关键词： Topology

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The Multi-Blockchain Based Data Trading Markets with Novel Pricing Mechanisms

SSRN

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SSRN 2022年

作者： Li, Juanjuan Li, Junqing Wang, Xiao Qin, Rui Yuan, Yong Wang, Fei-Yue The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Qingdao Academy of Intelligent Industries Qingdao266109 China School of Mathematics Renmin University of China Beijing100872 China Macao Institute of Systems Engineering Macau University of Science and Technology Macau 999078 China

Aiming to deal with the challenge of data security and data pricing in data trading markets, we propose the novel multi-blockchain-based framework and design the related pricing mechanisms. Specifically, data recording and trading are conducted separately in the Data Blockchain (DChain) and the Value Blockchain (VChain). Based on these blockchains, two-layer data trading markets are established to manage the initial data trading in the primary market and the data resale in the secondary market. Pricing mechanisms are proposed to protect the formulated data trading markets against strategic trading behaviors and balance the payoffs of both suppliers and users. In the regular data trading on VChain-S2D, two auction models are used to deal with users' strategic bidding in terms of the demand scale, where the incentive-compatible Vickrey-Clarke-Groves (VCG) model is dedicated to the low-demand trading scenario, while the nearly incentive-compatible Monopolistic Price (MP) model is for the high-demand trading scenario. In the temporary data trading on VChain-D2S, a reverse auction mechanism named Two-Stage Obscure Selection (TSOS) is designed to regulate both suppliers' quoting and users' valuation strategies. Moreover, experiments are carried out to validate our theoretical analysis. Our work is expected to enhance the data security and improve the trading efficiency. © 2022, The Authors. All rights reserved.

关键词： Blockchain

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