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Tool orientation optimization for 5-axis machining with C-space method

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2017年第5-8期88卷 1243-1255页

作者： Mi, Zhenpeng Yuan, Chun-Ming Ma, Xiaohui Shen, Li-Yong Chinese Acad Sci Acad Math & Syst Sci KLMM Beijing Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China

Collision avoidance is a fundamental problem in five-axis tool path planning. A two-step frame is widely used in tool path generation, that is, to determine C-spaces and then to design collision free pathes in the C-spaces. We present a feasible C-space computation algorithm for triangular mesh models based on collision-cone computation and stereographic projection. Then we sample points in the free area at each CC point and generate a tool orientation using the graph-based method. We also introduce a difference graph to find a smoother tool orientation. Experimental results show that the accelerations and velocities of the rotation axes are much smoother than those given by Plakhotnik and Lauwers (Int J Adv Manuf Technol 74:307-318, 2014).

关键词： Five-axis machining C-space Dijkstra's algorithm Difference graph Tool orientation

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Relationship between Capital Operation and Market Value management of Listed Companies Based on Random Forest Algorithm

Relationship between Capital Operation and Market Value Mana...

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International Conference on Computational Science (ICCS)

作者： Long, Wen Song, Linqiu Cui, Lingxiao Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing Peoples R China UCAS Sch Econ & Management Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China

This paper analyzes the influence of capital operations on the performance of listed companies under different market conditions by combining various capital operations with market value management. Random Forest algorithm is adopted and other machine learning methods are used to compare. We find that capital operation is significantly related to market value management and different capital operations have different effects on companies in various conditions. The result shows Random Forest algorithm has the highest classification accuracy and is more stable under different thresholds. Our findings will help to establish a market or industry benchmark which provides a scientific basis and decision support to the target companies when they operate their capitals. (C) 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the International Conference on Computational Science

关键词： Random Forest capital operation market value management financial strategy

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Image Class Prediction by Joint Object, Context, and Background Modeling

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2018年第2期28卷 428-438页

作者： Zhang, Chunjie Zhu, Guibo Liang, Chao Zhang, Yifan Huang, Qingming Tian, Qi Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Comp & Control Engn Beijing 100049 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Brain Inspired Intelligence Inst Automat Beijing 100190 Peoples R China Wuhan Univ Natl Engn Res Ctr Multimedia Software Sch Comp Wuhan 430072 Hubei Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China Univ Texas San Antonio Dept Comp Sci San Antonio TX 78249 USA

State-of-the-art image classification methods often use spatial pyramid matching or its variants to make use of the spatial layout of visual features. However, objects may appear at various places with different scales and orientations. Besides, traditionally object-centric-based methods only consider objects and the background without fully exploring the context information. To solve these problems, in this paper we propose a novel image classification method by jointly modeling the object, context, and background information (OCB). OCB consists of three components: 1) locate the positions of objects;2) determine the context areas of objects;and 3) treat the other areas as the background. We use objectness proposal techniques to select candidate bounding boxes. Boxes with high confidence scores are combined to determine objects' positions. To select the context areas, we use candidate boxes that have relatively lower confidence scores compared with boxes for object location selection. The other areas are viewed as the background. We jointly combine the object, context, and background for image representation and classification. Experiments on six data sets well demonstrate the superiority of the proposed OCB method over other spatial partition methods.

关键词： Background modeling context modeling image class prediction object modeling

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Improved New Word Detection Method Used in Tourism Field

Improved New Word Detection Method Used in Tourism Field

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International Conference on Computational Science (ICCS)

作者： Li, Wei Guo, Kun Shi, Yong Zhu, Luyao Zheng, Yuanchun Chinese Acad Sci Fictitious Econ & Data Sci Res Ctr Beijing Peoples R China UCAS Sch Econ & Management Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China

Chinese segmentation has attracted amounts of attention in natural language processing in recent years and is the basis of web text mining The article improved statistics-based method EMI, then we use improved approach to detect new words in tourism field. The result demonstrates that our method can detect new words significantly, especially in detecting proper nouns and sentiment words which will be helpful in subsequent tasks such as sentiment analysis and word embedding. In additional, this paper analyze parameters which are influential on the effects of new words detection. At last, the article discussed possible application of new word detection in sentiment analysis. (C) 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the International Conference on Computational Science

关键词： EMI PMI online travel new word detection

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Ramp loss K-Support Vector Classification-Regression;a robust and sparse multi-class approach to the intrusion detection problem

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knowledge-BASED SYSTEMS 2017年 126卷 113-126页

作者： Bamakan, Seyed Mojtaba Hosseini Wang, Huadong Shi, Yong Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Univ Nebraska Coll Informat Sci & Technol Omaha NE 68182 USA

Network intrusion detection problem is an ongoing challenging research area because of a huge number of traffic volumes, extremely imbalanced data sets, multi-class of attacks, constantly changing the nature of new attacks and the attackers' methods. Since the traditional network protection methods fail to adequately protect the computer networks, the need for some sophisticated methodologies has been felt. In this paper, we develop a precise, sparse and robust methodology for multi-class intrusion detection problem based on the Ramp Loss K-Support Vector Classification-Regression, named "Ramp-KSVCR". The main objectives of this research are to address the following issues;1) Highly imbalanced and skewed attacks' distribution;hence, we utilized the K-SVCR model as a core of our model;2) Sensitivity of SVM and its extensions to the presence of noises and outliers in the training sets, to cope with this problem, Ramp loss function is implemented to our model;3) and since the proposed Ramp-KSVCR model is a non-differentiable non-convex optimization problem, we took Concave-Convex Procedure (CCCP) to solve this model. Furthermore, we introduced Alternating Direction Method of Multipliers (ADMM) procedure to make our model well-adapted to be applicable in the large-scale setting and to reduce the training time. The performance of the proposed method has been evaluated by some artificial data and also by conducting some experiments with the NSL-KDD data set and UNSW-NB15 as a recently published intrusion detection data set. Experimental results not only demonstrate the superiority of the proposed method over the traditional approaches tested against it in terms of generalization power and sparsity but also saving a considerable amount of computational time. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Multi-Class classification Intrusion detection K-Support Vector Classification-Regression Ramp loss function Alternating Direction Method of Multipliers (ADMM) Concave-Convex Procedure (CCCP)

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The impact of the change of trade pattern on China's energy industry-Based on the measurement of the upstreamness of production of the value chains 6th

The impact of the change of trade pattern on China's energy ...

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6th International Conference on Information Technology and Quantitative management (ITQM)

作者： Wang, Xinran Guo, Xiaoxian Song, Yan Cao, Shasha Wu, Tian Tian, Xin Li, Zhenping Shanghai Pudong Dev Bank Shnaghai 200000 Peoples R China China Petr Technol & Dev Corp Beijing 100028 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Chinese Acad Sci Acad Math & Syst Sci NCMIS Beijing 100190 Peoples R China Beijing Wuzi Univ Sch Informat Beijing 101149 Peoples R China

The natural monopoly of China's upstream industry and the vertical structure of industrial chain's upstream and downstream competition is an important feature of China's industrial economic structure, and the impact of the change of trade pattern on the energy industry, which located at the upstream of the China's value chain, has become an important issue in the industrial economy. Since China has entered into the WTO in 2001, we use it for event analysis based on 1987-2007 China input-output data. The study calculates the upstreamness of production of the value chain in various industrial sectors of China's energy industry every five years, and make correlation analysis of upstreamness of production and other economic indicators to check the characteristics of periodic inspection of China's industrial structure with the development of industrial economy and the change of trade pattern. (C) 2018 The Authors. Published by Elsevier B.V.

关键词： Energy industry industrial economic structure upstreamness of production trade pattern

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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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Learning to Diversify Search Results via Subtopic Attention 17

Learning to Diversify Search Results via Subtopic Attention

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40th International ACM SIGIR conference on research and development in Information Retrieval

作者： Jiang, Zhengbao Wen, Ji-Rong Dou, Zhicheng Zhao, Wayne Xin Nie, Jian-Yun Yue, Ming Renmin Univ China Sch Informat Beijing Peoples R China Beijing Key Lab Big Data Management & Anal Metho Beijing Peoples R China MOE Key Lab Data Engn & Knowledge Engn Beijing Peoples R China Univ Montreal DIRO Montreal PQ Canada

ISBN: (纸本)9781450350228

Search result diversification aims to retrieve diverse results to satisfy as many different information needs as possible. Supervised methods have been proposed recently to learn ranking functions and they have been shown to produce superior results to unsupervised methods. However, these methods use implicit approaches based on the principle of Maximal Marginal Relevance (MMR). In this paper, we propose a learning framework for explicit result diversification where subtopics are explicitly modeled. Based on the information contained in the sequence of selected documents, we use attention mechanism to capture the subtopics to be focused on while selecting the next document, which naturally fits our task of document selection for diversification. The framework is implemented using recurrent neural networks and max-pooling which combine distributed representations and traditional relevance features. Our experiments show that the proposed method significantly outperforms all the existing methods.

关键词： search result diversification subtopics attention

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Detecting Difference Between Coefficients in Linear Model Using Jackknife Empirical Likelihood

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Journal of Systems Science & Complexity 2016年第2期29卷 542-556页

作者： WU Xinqi ZHANG Qingzhao ZHANG Sanguo School of Mathematical Sciences University of Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences

Empirical likelihood has been found very useful in many different occasions. It usually runs into serious computational difficulties while jackknife empirical likelihood （JEL） is shown to be effective when applied to some complicated statistics. In this paper, to test the difference between coefficients of two linear regression models, the authors apply JEL to construct the confidence regions. Based on the 3EL ratio test, a version of Wilks＇ theorem is developed. Furthermore, to improve the coverage accuracy of confidence regions, a Bartlett correction is applied. Simulation studies are carried out to show the effectiveness of the proposed method in aspects of coverage accuracy. A real data set is analyzed with the proposed method as an example.

关键词： Bartlett correction coverage accuracy Jackknife empirical likelihood linear regression model.

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A comparison of 17 article-level bibliometric indicators of institutional research productivity: Evidence from the information management literature of China

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INFORMATION PROCESSING & management 2017年第5期53卷 1156-1170页

作者： Li, Jing Wu, Dengsheng Li, Jianping Li, Minglu Anhui Univ Sch Management Hefei 230601 Anhui Peoples R China Chinese Acad Sci Inst Policy & Management Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Natl Nat Sci Fdn China Bur Planning Beijing 100085 Peoples R China

Periodically ranking institutional research productivity is necessary not only to understand the status of the development of related fields but also to identify gaps and take appropriate corrective steps. Many bibliometric indicators contribute to the assessment of institutional research productivity, but the appropriateness of the indicator and the relationships between different indicators are topics that have not been addressed. For this reason, an indicator framework for the ranking of institutional research performance, which consists of a count of published articles and quality weighted dimensions, is developed. Based on the literature review, 17 indicators in the framework are chosen for study. Based on these indicators, experiments are conducted to rank Chinese institutions in the field of Information management. Kendall's tau rank correlation coefficient (tau) is calculated, and all pair wise correlations are between 0.34 and 0.98. There are three primary findings: (1) among the article count indicators, the Straight count indicator is significantly different than others;(2) the rankings based on the indicators which are weighted by quality are consistent with those based on the indicators using article count;and (3) the Paper citation weighted indicator is sensitive to the procedure used for institutional ranking. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Research productivity Research assessment Institutional assessment Bibliometric indicators Information management

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