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Has “Intelligent Manufacturing” Promoted the Productivity of Manufacturing Sector?--Evidence from China’s Listed Firms

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Procedia Computer Science 2018年 139卷 299-305页

作者： Yi Qu Yong Shi Kun Guo Yuanchun Zheng School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing 100190 China College of Information Science and Technology University of Nebraska at Omaha NE 68182 USA School of Computing and Control Engineering University of Chinese Academy of Sciences Beijing 100190 China

Intelligent Manufacturing has attracted global and continuous attention recent years, with more and more intelligent devices and systems applied in production. In this paper, we take China’s manufacturing listed firms to investigate the productivity difference between intelligent and general manufacturing firms. By the Cobb-Douglas production function, we built a Coefficient-varying Model and used Seemingly Unrelated Regression (SUR) to estimate the time-varying trend of productivity from 2011 to 2017. The empirical results show that “Intelligent Manufacturing” has obviously promoted the Labor factor utilization efficiency and the Total Factor Productivity (TFP) through the advances in technology. But it doesn’t have universally enhancing effect of all industries. The impact of “Intelligent Manufacturing” still remains to be observed overtime.

关键词： Intelligent Manufacturing Cobb-Douglas Production Function Total Factor Productivity (TFP) Coefficient-varying Model Seemingly Unrelated Regression (SUR)

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The Novel Sensor Network Structure for Classification Processing Based on the Machine Learning Method of the ACGAN

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Sensors (Switzerland) 2019年第14期19卷 3145页

作者： Chen, Yuantao Tao, Jiajun Wang, Jin Chen, Xi Yang, Kai School of Computer and Communication Engineering & Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportatio Changsha University of Science and Technology Changsha410114 China School of Information Science and Engineering Fujian University of Technology Fuzhou350118 China Hunan Institute of Scientific and Technical Information Changsha410001 China Electronics & Information School Yangtze University Jingzhou434023 China Technical Quality Department Hunan ZOOMLION Heavy Industry Intelligent Technology Corporation Limited Changsha410005 China

To address the problem of unstable training and poor accuracy in image classification algorithms based on generative adversarial networks (GAN), a novel sensor network structure for classification processing using auxiliary classifier generative adversarial networks (ACGAN) is proposed in this paper. Firstly, the real/fake discrimination of sensor samples in the network has been canceled at the output layer of the discriminative network and only the posterior probability estimation of the sample tag is outputted. Secondly, by regarding the real sensor samples as supervised data and the generative sensor samples as labeled fake data, we have reconstructed the loss function of the generator and discriminator by using the real/fake attributes of sensor samples and the cross-entropy loss function of the label. Thirdly, the pooling and caching method has been introduced into the discriminator to enable more ekective extraction of the classification features. Finally, feature matching has been added to the discriminative network to ensure the diversity of the generative sensor samples. Experimental results have shown that the proposed algorithm (CP-ACGAN) achieves better classification accuracy on the MNIST dataset, CIFAR10 dataset and CIFAR100 dataset than other solutions. Moreover, when compared with the ACGAN and CNN classification algorithms, which have the same deep network structure as CP-ACGAN, the proposed method continues to achieve better classification effects and stability than other main existing sensor solutions. © Sensors (Switzerland) 2019. All Rights Reserved.

关键词： Generative adversarial networks

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The Impact of Information on System Operations

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Journal of Physics: Conference Series 2020年第1期1646卷

作者： Tianchao Guan Shumin Zhou Tongfei Li Duo Xu Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong University Beijing 100044 China. China Railway Fifth Survey And Design Institute Group CO. LTD. Beijing 102600 China. Beijing Key Laboratory of Traffic Engineering Beijing University of Technology Beijing 100124 China. Postgraduate School People's Public Security University of China Beijing 100038 China.

The operating system is a complex system that includes supply and demand. Changes in external information have an impact on the operation of the system, especially on both supply and demand. This paper analyzes the supply and demand and establishes the nonlinear programming problem of system operation under the social optimal goal. With the increase of external information intervention, there will be more suitable travel fares for the demand side. However, the supply side's related operating indicators (for example, frequency, bus fleet, and vehicle capacity, etc.) have shown a trend of first increasing and then decreasing due to existing resource constraints. It can be seen that under the same information interference, there are differences in the law of change between the supply and demand sides. Besides, an example is used to verify the effectiveness of the model. Through the model and calculation examples, we can know that the supplier and the demander can meet their own needs under the social optimal goal. This provides certain theoretical support for future research information on system operation.

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How it outsourcing impacts on value creation of manufacturer? 2

How it outsourcing impacts on value creation of manufacturer...

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2nd International Conference on Precision Machinery and Manufacturing Technology, ICPMMT 2017

作者： Chen, Li-Shan Tseng, Yen-Ming School of Management Fujian University of Technology China Key Laboratory of Big Data Mining and Application School of Information Science and Engineering Fujian University of Technology China

A growing concern among the manufacturers who are actively involved in Information Technology outsourcing is post-contract management and the ensuing development of what many practitioners have coined the 'outsourcing partnership'. Over the past few decades, information technology outsourcing has become a widely used and researched means for manufacturers to enhance their performance. Increasing attention has been paid to building successful partnerships in information systems outsourcing. This study employs strategic social positioning, customer participation, customer orientation, and communication culture to develop research model. The quantitative method will be used to validate the research model and test hypotheses developed. This study uses survey method to collect data, and analysis of the reliability, validity and test the proposed hypotheses by Smart Partial Least Squares. The purpose of this study is to use above four factors to produce value co-creation between outsourcing vendors and manufacturers. The final aim is for the future collaboration. This combination will allow manufacturers and information technology outsourcing vendors to increase their value co-creations in the fields of these factors. © 2017 The Authors, published by EDP Sciences.

关键词： Outsourcing

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Machine learning dynamical phase transitions in complex networks

arXiv

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arXiv 2019年

作者： Ni, Qi Tang, Ming Liu, Ying Lai, Ying-Cheng School of Information Science and Technology East China Normal University Shanghai200241 China School of Mathematical Sciences Shanghai Key Laboratory of PMMP East China Normal University Shanghai200241 China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai200241 China School of Computer Science Southwest Petroleum University Chengdu610500 China Big Data Research center University of Electronic Science and Technology of China Chengdu610054 China School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States

Recent years have witnessed a growing interest in using machine learning to predict and identify critical dynamical phase transitions in physical systems (e.g., many body quantum systems). The underlying lattice structures in these applications are generally regular. While machine learning has been adopted to complex networks, most existing works concern about the structural properties. To use machine learning to detect phase transitions and accurately identify the critical transition point associated with dynamical processes on complex networks thus stands out as an open and significant problem. Here we develop a framework combining supervised and unsupervised learning, incorporating proper sampling of training data set. In particular, using epidemic spreading dynamics on complex networks as a paradigmatic setting, we start from supervised learning alone and identify situations that degrade the performance. To overcome the difficulties leads to the idea of exploiting confusion scheme, effectively a combination of supervised and unsupervised learning. We demonstrate that the scheme performs well for identifying phase transitions associated with spreading dynamics on homogeneous networks, but the performance deteriorates for heterogeneous networks. To strive to meet this challenge leads to the realization that sampling the training data set is necessary for heterogeneous networks, and we test two sampling methods: one based on the hub nodes together with their neighbors and another based on k-core of the network. The end result is a general machine learning framework for detecting phase transition and accurately identifying the critical transition point, which is robust, computationally efficient, and universally applicable to complex networks of arbitrary size and topology. Extensive tests using synthetic and empirical networks verify the virtues of the articulated framework, opening the door to exploiting machine learning for understanding, detection, prediction, a

关键词： Complex networks

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Dual flow immunochromatographic assay for rapid and simultaneous quantitative detection of ochratoxin A and zearalenone in corn, wheat, and feed samples

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Journal of Zhejiang University-Science B(Biomedicine & Biotechnology) 2018年第11期19卷 871-883页

作者： Xian ZHANG Ke HE Yun FANG Tong CAO Narayan PAUDYAL Xiao-feng ZHANG Hou-hui SONG Xiao-liang LI Wei-huan FANG China-Australian Joint Laboratory for Animal Health Big Data Analytics Zhejiang Provincial Engineering Laboratory for Animal Health Inspection and Internet TechnologyCollege of Animal Science and TechnologyZhejiang A&F UniversityLin'an 311300China Zhejiang University Institute of Preventive Veterinary Medicine Zhejiang Provincial Key Laboratory of Preventive Veterinary MedicineHangzhou 310058China Zhejiang Academy of Science and Technology for Inspection and Quarantine Hangzhou 310012China

A one-step dual flow immunochromatographic assay （DICGA）, based on a competitive format, was de- veloped for simultaneous quantification of ochratoxin A （OTA） and zearalenone （ZEN） in corn, wheat, and feed sam- ples. The limit of detection for OTA was 0.32 ng/ml with a detection range of 0.53-12.16 ng/ml, while for ZEN it was 0.58 ng/ml with a detection range of 1.06-39.72 ng/ml. The recovery rates in corn, wheat, and feed samples ranged from 77.3% to 106.3% with the coefficient of variation lower than 15%. Naturally contaminated corn, wheat, and feed samples were analyzed using both DICGA and liquid chromatography-tandem mass spectrometry （LC-MS/MS） and the correlation between the two methods was evaluated using a regression analysis. The DICGA method shows great potential for simple, rapid, sensitive, and cost-effective quantitative detection of OTA and ZEN in food safety control.

关键词： Immunochromatographic assay Gold nanoparticles Ochratoxin A Zearalenone Quantification

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Intelligence Quotient and Intelligence Grade of Artificial Intelligence

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Annals of data Science 2017年第2期4卷 179-191页

作者： Liu, Feng Shi, Yong Liu, Ying Research Center on Fictitious Economy and Data Science The Chinese Academy of Sciences Beijing100190 China The Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China

Although artificial intelligence (AI) is currently one of the most interesting areas in scientific research, the potential threats posed by emerging AI systems remain a source of persistent controversy. To address the issue of AI threat,this study proposes a "standard intelligence model" that unifies AI and human characteristics in terms of four aspects of knowledge, i.e., input, output, mastery, and creation. Using this model, we observe three challenges, namely, expanding of the von Neumann architecture;testing and ranking the intelligence quotient (IQ) of naturally and artificially intelligent systems, including humans, Google, Microsoft’s Bing, Baidu, and Siri;and finally, the dividing of artificially intelligent systems into seven grades from robots to Google Brain. Based on this, we conclude that Google’s AlphaGo belongs to the third grade. © 2017, Springer-Verlag Berlin Heidelberg.

关键词： Intelligent systems

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Emergence of a complete heavy-quark spin symmetry multiplet: Seven molecular pentaquarks in light of the latest LHCb analysis

arXiv

引用

arXiv 2019年

作者： Liu, Ming-Zhu Pan, Ya-Wen Peng, Fang-Zheng Sánchez, Mario Sánchez Geng, Li-Sheng Hosaka, Atsushi Valderrama, Manuel Pavon School of Physics and Nuclear Energy Engineering Beihang University Beijing100191 China Centre d'Études Nucléaires CNRS /IN2P3 Université de Bordeaux Gradignan33175 France Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beijing Advanced Innovation Center for Big Data-based Precision Medicine Beihang University Beijing100191 China Osaka University Ibaraki567-0047 Japan Tokai319-1195 Japan International Research Center for Nuclei and Particles in the Cosmos Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang University Beijing100191 China

A recent analysis by the LHCb collaboration suggests the existence of three narrow pentaquark-like states - the Pc(4312), Pc(4440) and Pc(4457) - instead of just one in the previous analysis (the Pc(4450)). The closeness of the Pc(4312) to the DΣcthreshold and the Pc(4440)/Pc(4457) to the D∗Σcone suggests a molecular interpretation of these resonances. We show that these three pentaquark-like resonances can be naturally accommodated in a contact-range effective field theory description that incorporates heavy-quark spin symmetry. This description leads to the prediction of all the seven possible S-wave heavy antimeson-baryon molecules (that is, there should be four additional molecular pentaquarks in addition to the Pc(4312), Pc(4440) and Pc(4457)), providing the first example of a heavy-quark spin symmetry molecular multiplet that is complete. If this is confirmed, it will not only give us an impressive example of the application of heavy-quark symmetries and effective field theories in hadron physics: it will also uncover a clear and powerful ordering principle for the molecular spectrum, reminiscent of the SU(3)-flavor multiplets to which the light hadron spectrum conforms. Copyright © 2019, The Authors. All rights reserved.

关键词： Hadrons

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Soft-gated warping-GAN for pose-guided person image synthesis

arXiv

引用

arXiv 2018年

作者： Dong, Haoye Liang, Xiaodan Gong, Ke Lai, Hanjiang Zhu, Jia Yin, Jian School of Data and Computer Science Sun Yat-sen University Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China School of Intelligent Systems Engineering Sun Yat-sen University School of Computer Science South China Normal University

Despite remarkable advances in image synthesis research, existing works often fail in manipulating images under the context of large geometric transformations. Synthesizing person images conditioned on arbitrary poses is one of the most representative examples where the generation quality largely relies on the capability of identifying and modeling arbitrary transformations on different body parts. Current generative models are often built on local convolutions and overlook the key challenges (e.g. heavy occlusions, different views or dramatic appearance changes) when distinct geometric changes happen for each part, caused by arbitrary pose manipulations. This paper aims to resolve these challenges induced by geometric variability and spatial displacements via a new Soft-Gated Warping Generative Adversarial Network (Warping-GAN), which is composed of two stages: 1) it first synthesizes a target part segmentation map given a target pose, which depicts the region-level spatial layouts for guiding image synthesis with higher-level structure constraints;2) the Warping-GAN equipped with a soft-gated warping-block learns feature-level mapping to render textures from the original image into the generated segmentation map. Warping-GAN is capable of controlling different transformation degrees given distinct target poses. Moreover, the proposed warping-block is lightweight and flexible enough to be injected into any networks. Human perceptual studies and quantitative evaluations demonstrate the superiority of our Warping-GAN that significantly outperforms all existing methods on two large datasets. Copyright © 2018, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Fault Detection of Liquid-Propellant Rocket Engines Based on LSSVM

Fault Detection of Liquid-Propellant Rocket Engines Based on...

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2018 International Conference on Information, Electronic and Communication engineering(IECE 2018)

作者： Ning-ning LI Wei XUE Xiang GUO Song-bo ZHAO Liang XU School of Electrical and Electronic Engineering and Tianjin Key Laboratory for Control Theory &Applications in Complicated Systems Tianjin University of Technology Beijing Aerospace Propulsion Institute Big data Center PICC Property and Casualty Company Limited

In terms of fault diagnosis of liquid-propellant rocket engine, the fault diagnosis accuracy of the traditional method is low the characteristics of engine fault data are small sample and nonlinear variation. In order to improve the accuracy of sensor fault diagnosis and overcome the scarcity of related samples, the particle swarm optimization(PSO) with strong global searching ability is used to optimize the LS-SVM parameters, and the LSSVM optimal parameter values are obtained by iteration to improve the model fitting accuracy and generalization ability. At the same time, the traditional support vector machine and BP neural network model are used for the detection. The simulation results show that the least squares support vector machine(SVM) detection method based on particle swarm optimization has the advantages of high precision and high speed. It has certain effect and positive significance for improving the safety of liquid-propellant rocket engine test and engine failure loss.

关键词： Liquid-propellant rocket engine Fault detection LSSVM PSO

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