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2nd International Conference on Urban Intelligence and Applications, ICUIA 2020

作者： Lu, Qiang Chen, Chao Teng, Jinfeng Zhang, Chunyuan Huang, Yi Xuan, Shanli Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei230009 China School of Computer and Information Hefei University of Technology Hefei230009 China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei230009 China

ISBN: (纸本)9789813346000

How to make robots understand the point cloud data which is collected from the 3D sensor and complete the recognition has become a hot research direction in recent years. In this paper, we propose a new approach to improve the critical robotic capability, semantic understanding of the environment (i.e., 3D object recognition). The convolutional neural network (CNN) method has a very good recognition result in the 2D image domain, but it has certain difficulty in applying irregular and unordered 3D point clouds data. The network for point cloud data generally uses the convolution to realize the extraction of point cloud features by finding the neighborhood features on the point set. Due to the different neighborhood scales caused by the irregularity of 3D point cloud data, we propose a CNN structure that combines multi-scale features. By finding multiple neighborhoods of the point set and establishing local graph extraction features, the stable expression of the local neighborhood is obtained. At the same time, the key point calibration method is added, so that the network can dynamically focus on key point features to improve the recognition result. In a series of analytical experiments, we demonstrate competing results that demonstrate the effectiveness of the network structure. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Convolutional neural networks

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Development of on-line detection system for simultaneous assessment of edible quality and internal defect in apple by NIR transmittance spectroscopy

Development of on-line detection system for simultaneous ass...

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2017 ASABE Annual International Meeting

作者： Guo, Zhiming Chen, Quansheng Wu, Jingzhu Ouyang, Qin Chen, Hua Zhao, Jiewen School of Food and Biological Engineering Jiangsu University Zhenjiang212013 China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business University Beijing100048 China

In order to simultaneous nondestructive on-line inspect edible quality and internal defect of apple, this work presents the development of an on-line detection prototype system using near infrared transmittance technology as a novel approach for on-line detect quality attributes without sample destructiveness. The on-line detection system was designed and developed to improve spectra signal quality, lower heat damage, reduce mechanical damage. Special detection software was developed for real-time inspection based on multithread programming technology. This prototype on-line system needed 80 ms capture transmittance spectra of an apple. Linear discriminant analysis (LDA) model was developed to identify internal defects samples. The results obtained from LDA models, in validation, gave a positive predictive value of classification about 90.14%. Moreover, established model strategy is first selection characteristic spectral ranges by synergy interval partial least squares (siPLS), then on the extraction of feature wavelength by stepwise multiple linear regression (SMLR). Calibration models were performed applying siPLS-SMLR algorithm to predict the soluble solid content (SSC) in apple. Results of the siPLS-SMLR model using 9 selected feature wavelengths were correlation coefficient (R) of 0.8764 and root mean square error of prediction (RMSEP) of 0.8649 Brix, respectively.. The results showed that the e on-line detection prototype based on NIR transmittance technique was feasible to simultaneous inspect the edible quality and internal defect of apple. The present research provides the foundation for the future development of an automatic system based on transmittance spectroscopy which is extremely important from the economic point of view.

关键词： Infrared devices

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A New Type Wireless data Center of Comb Topology 6th

A New Type Wireless Data Center of Comb Topology

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6th International Conference on data Science, ICDS 2019

作者： Zhang, Qingfang Han, Zhijie Du, Xiaoyu School of Computer and Information Engineering Henan University KaifengHenan475004 China Institute of Data and Knowledge Engineering Henan University KaifengHenan475004 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Network NanjingJiangsu210003 China

ISBN: (纸本)9789811528095

In recent years, group communication has become more and more popular. In the current data center network, multicast plays an extremely important role in group communication. Traditional data center network is designed based on wires. Researchers have proposed Dcell, Bcube and other data center topologies successively. However, in the traditional data center network, the wired topology structure has the disadvantages of high overhead, complex deployment, high maintenance cost, slow heat dissipation and so on. Moreover, the existing wireless data center Flyways structure and Graphite structure have the problems of low connectivity and poor scalability. So this paper proposes and designs a new data center network topology Comb structure (Comb model is a multi-layer cellular structure model, which is a multi-layer structure composed of single-layer honeycomb-like structure interlaced with each other). The structure can effectively improve the connectivity of the data center, shorten the routing path, reduce the probability of node failure in the network, and ensure the smooth network. © Springer Nature Singapore Pte Ltd 2020.

关键词： Topology

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Research on Intelligent Mobile Commerce Transaction Security Mechanisms Based on Mobile Agent

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Computers, Materials & Continua 2020年第12期65卷 2543-2555页

作者： Weijin Jiang Wei Liu Haolong Xia Yuhui Xu Dongbo Cao Guo Liang College of Computer and Information Engineering Hunan University of Technology and BusinessChangsha410205China Institute of Big Data and Internet Innovation Mobile E-business Collaborative Innovation Center of Hunan ProvinceHunan University of Technology and BusinessChangsha410205China Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Technology and BusinessChangsha410205China School of Bioinformatics University of MinnesotaTwin CitiesUSA

In networked mobile commerce network transactions,trust is the prerequisite and key to a smooth *** measurement of trust between entities involves factors such as transaction amount,transaction time,personal income of consumer entities and their risk attitude towards trust,etc.,so it is difficult to accurately calculate *** order to find out the essential characteristics of this trust relationship,based on the research background of mobile commerce in the mobile network environment,a dynamic trust mechanism is proposed through the research of trust in the mobile network environment,trust influencing factors and trust *** calculation model of mobile interactive services based on mobile service business *** model calculates feedback credibility through feedback deviation and feedback robustness,and combines transaction context factors and trust mapping mechanism to judge the seller’s *** model better reflects the degree of influence of subjective factors such as personal preferences and risk attitudes on trust calculations,And the sensitivity of trust algorithms and transaction attributes has been greatly *** a large number of experiments and theoretical analysis,this mechanism provides an effective explanation for solving the problem of network trust *** provides valuable new ideas for the study of secure transactions in the mobile Internet environment.

关键词： Mobile Internet mobile commerce dynamic trust model reputation management Mobile Agent System(MAS)

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An infrastructure with user-centered presentation data model for integrated management of materials data and services

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npj Computational Materials 2021年第1期7卷 779-786页

作者： Shilong Liu Yanjing Su Haiqing Yin Dawei Zhang Jie He Haiyou Huang Xue Jiang Xuan Wang Haiyan Gong Zhuang Li Hao Xiu Jiawang Wan Xiaotong Zhang Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology BeijingBeijingChina School of Computer and Communication Engineering University of Science and Technology BeijingBeijingChina Beijing Key Laboratory of Knowledge Engineering for Materials Science BeijingChina Corrosion and Protection Center University of Science and Technology BeijingBeijingChina Collaborative Innovation Center of Steel Technology University of Science and Technology BeijingBeijingChina National Materials Corrosion and Protection Data Center Institute for Advanced Materials and TechnologyUniversity of Science and Technology BeijingBeijingChina Institute for Advanced Materials and Technology University of Science and Technology BeijingBeijingChina Beijing Key Laboratory of Materials Genome Engineering University of Science and Technology BeijingBeijingChina

With scientific research in materials science becoming more data intensive and collaborative after the announcement of the Materials Genome Initiative,the need for modern data infrastructures that facilitate the sharing of materials data and analysis tools is compelling in the materials *** this paper,we describe the challenges of developing such infrastructure and introduce an emerging architecture with high *** call this architecture the Materials Genome engineering databases(MGED).MGED provides cloud-hosted services with features to simplify the process of collecting datasets from diverse data providers,unify data representation forms with user-centered presentation data model,and accelerate data discovery with advanced search *** also provides a standard service management framework to enable finding and sharing of tools for analyzing and processing *** describe MGED’s design,current status,and how MGED supports integrated management of shared data and services.

关键词： services service centered

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China’s NTB Market: A Liquidity Dilemma

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Journal of Systems Science & Complexity 2018年第2期31卷 493-507页

作者： LI Yumeng MENG Xiangying ZHANG Yueyan WEI Xianhua School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Key Laborutory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China Sino-Danish Center for Education and Research University of Chinese Academy of Sciences Beijing 100190 China The State Key Laboratory of Automotive Safety and Energy Tsinghua University Beijing 100084 China Department of Automotive Engineering Tsinghua University Beijing 100084 China

This paper introduces China＇s New Third Board Market（the NTB market） and tests the market-making system in the role of improving market liquidity. The NTB Market is the main part of China＇s OTC market, established in 2006. In comparing with China＇s Main Board Market and the Second Board Market, it attracts a lot of start-up companies that need financing with less strict listing requirements. Meanwhile, it is full of opportunities and challenges that appeal to numerous securities traders and investors with the rapid development momentum. Since its establishment, the NTB Market has been stuck in the lack of liquidity. The authority of the NTB Market introduced market maker system, so as to solve the problem of liquidity. Using the difference-in-difference method（DID method）, the market maker system has been proved to make little contribution to improving the stock liquidity. Although the problem of liquidity is difficult to solve in short term, the NTB Market is full of opportunities for all kinds of participants definitely.

关键词： China＇s stock market DID method liquidity market makers.

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data dimensionality reduction method combining intra-class and inter-class distance 2019

Data dimensionality reduction method combining intra-class a...

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2019 International Conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2019

作者： Zhang, Suzhi Chen, Xiaoni Li, Penghui Cai, Qiang Zhengzhou University of Light Industry School of Computer and Communication Engineering Zhengzhou China Beijng Technology and Business University Beijing Key Laboratory of Big Data Technology for Food Safety Beijing China

ISBN: (纸本)9781450372022

Traditional principal component analysis method has the problems of low computational efficiency and large memory consumption when faces high-dimension data. After the information entropy is introduced, although it reduces the memory and running time, but it cannot meet the requirements of the actual classification effect. In order to extract low-dimensional features with good discrimination ability, the idea of intra-class and inter-class distance is introduced, the principal component analysis data dimension reduction algorithm based on intra-class and inter-class distance is proposed. In this algorithm, firstly, the entropy of attribute information is calculated, and then the threshold value of information entropy is compared to it, which to realize the feature screening of data matrix. Then, the improved PCA algorithm based on the ideas of intra-class distance maximization and inter-class distance minimization is used to reduce the dimension of data. Finally, the data after dimension reduction is classified by KNN and SVM algorithms. Compared with the PCA, principal component analysis based on information entropy (E-PCA) and LDA algorithms, the experimental results show that the proposed algorithm can not only improve the result of dimension reduction, but also significantly improve the discrimination performance of low-dimensional data after dimension reduction. © 2019 Association for Computing Machinery.

关键词： Principal component analysis

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Research on data Fusion Method Based on Compressed Sensing

Research on Data Fusion Method Based on Compressed Sensing

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2019 IEEE International Conference on Artificial Intelligence and Computer Applications, ICAICA 2019

作者： Zhang, Suzhi Chen, Xiaoni Yang, Rui Li, Penghui Cai, Qiang School of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou China Beijing Key Laboratory of Big Data Technology for Food Safety Beijng Technology and Business University Beijing China

ISBN: (纸本)9781728112220

In order to improve the efficiency and fusion effect of data fusion, according to the characteristics of compression sensing theory, this paper proposes a data fusion method based on independent data region division and compression sensing. In the method of this paper, the compressed sensing theory is used to sample the data to obtain the measured values, and the sample data is divided by independent region to form a joint region, then the weight coefficient of mutual information fusion is calculated, and the fused data are obtained according to the compressed sensing coefficient reconstruction method. Simulation results show that compared with the traditional fusion method, this method not only improves the data fusion effect, but also has good stability. © 2019 IEEE.

关键词： Compressed sensing

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Does web news media have opinions? Evidence from real estate market prediction 18

Does web news media have opinions? Evidence from real estate...

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18th Pacific Asia Conference on Information Systems, PACIS 2014

作者： Sun, Daoyuan Zhang, Ce Xu, Wei Zuo, Meiyun Zhou, Junjie Du, Yudie Department of Information Systems School of Computing National University of Singapore Singapore Singapore School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China School of Information Beijing China School of E-Commerce and Logistics Management Henan University of Economics and Law Zhengzhou China

In this paper, we propose a novel method for real estate price prediction using web new media sentiments by incorporating human searching behaivor on the web. By combining online daily news' sentiments and Google search engine query data, we construct a web news content and online search behavior-based integrated model for real estate prediction. Besides these factors, real estate price time series data are also considered into the model in order to improve the forecasting performance. Furthermore, we make a comparison between the integrated model and the baseline model without search engine query data. Experimental results indicate that the integrated model outperforms the non-integrated model, which suggests that online user searching behavior is of great value in enhancing the prediction performance. These findings imply that the proposed integrated model is effective and feasible for real estate market prediction.

关键词： Search engines

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Hierarchical features-based targeted aspect extraction from online reviews

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Intelligent data Analysis 2021年第1期25卷 205-223页

作者： He, Jin Li, Lei Wang, Yan Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei Anhui China School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China Department of Computing Macquarie University Sydney Australia MiningLamp Academy of Sciences Mininglamp Techonologies Beijing China

With the prevalence of online review websites, large-scale data promote the necessity of focused analysis. This task aims to capture the information that is highly relevant to a specific aspect. However, the broad scope of the aspects of the various products makes this task overarching but challenging. A commonly used solution is to modify the topic models with additional information to capture the features for a specific aspect (referred to as a targeted aspect). However, the existing topic models, either perform the full analysis to capture features as many as possible or estimate the similarity to capture features as coherent as possible, overlook the fine-grained semantic relations between the features, resulting in the captured features coarse and confusing. In this paper, we propose a novel Hierarchical Features-based Topic Model (HFTM) to extract targeted aspects from online reviews, then to capture the aspect-specific features. Specifically, our model can not only capture the direct features posing target-to-feature semantics but also capture the latent features posing feature-to-feature semantics. The experiments conducted on real-world datasets demonstrate that HFTMl outperforms the state-of-the-art baselines in terms of both aspect extraction and document classification. © 2021 - IOS Press. All rights reserved.

关键词： Aspect extraction Focused analysis Online reviews Text mining Topic modeling

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