检索结果-内蒙古大学图书馆

Information processing structures and decision making delays in MRP and JIT

INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2017年 188卷 41-49页

作者： Wang, Hui Gong, Qiguo Wang, Shouyang Univ Chinese Acad Sci Sch Econ & Management Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China Chinese Acad Sci Acad Math & Syst Sci Beijing Peoples R China

Prior literature has asserted that higher supply chain visibility, concerned with better information flow and determining more accurate demand levels within the supply chain at a given time, improves decision-making efficiency. Decision-making efficiency denoted by decision-making delay is in turn dependent on the information processing structure. Different production control systems have different information processing structures. This paper considers the relations between the production control system (MRP and JIT) and the organization structure of information processing that determines the decision making delay. We compare MRP and JIT across different information processing structures and decision efficiency, and find that JIT is suitable for small lot size and large variety production and MRP for large lot size and small variety production. In addition, an optimized organization structure of information processing will surely reduce the decision making delay.

关键词： Production control system Information processing structure Decision making delay MRP JIT

来源：评论

学校读者我要写书评

暂无评论

Nonsmooth Penalized Clustering via l_p Regularized Sparse Regression

引用

IEEE TRANSACTIONS ON CYBERNETICS 2017年第6期47卷 1423-1433页

作者： Niu, Lingfeng Zhou, Ruizhi Tian, Yingjie Qi, Zhiquan Zhang, Peng Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Univ Technol Sydney Ctr QCIS Sydney NSW 2007 Australia

Clustering has been widely used in data analysis. A majority of existing clustering approaches assume that the number of clusters is given in advance. Recently, a novel clustering framework is proposed which can automatically learn the number of clusters from training data. Based on these works, we propose a nonsmooth penalized clustering model via l(p)( 0 < p < 1) regularized sparse regression. In particular, this model is formulated as a nonsmooth nonconvex optimization, which is based on over-parameterization and utilizes an l(p)norm-based regularization to control the tradeoff between the model fit and the number of clusters. We theoretically prove that the new model can guarantee the sparseness of cluster centers. To increase its practicality for practical use, we adhere to an easy-to-compute criterion and follow a strategy to narrow down the search interval of cross validation. To address the non-smoothness and nonconvexness of the cost function, we propose a simple smoothing trust region algorithm and present its convergent and computational complexity analysis. Numerical studies on both simulated and practical data sets provide support to our theoretical results and demonstrate the advantages of our new method.

关键词： l(p)-norm clustering analysis nonconvex optimization nonsmooth optimization penalized regression

来源：评论

学校读者我要写书评

暂无评论

Research on the Correlation of Monthly Electricity Consumption in Different Industries: A Case Study of Bazhou County

引用

Procedia Computer Science 2018年 139卷 496-503页

作者： Luhua Zhang Zili Huang Zhengze Li Kun Guo Jibei Electric Power Company Limited Metering Centre Beijing 102208 China China University of Political Science and Law Beijing 102449 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Research Centre on Fictitious Economy & Data Science UCAS Beijing 100190 China CAS Key Laboratory of Big Data Mining and Knowledge Management Beijing 100190 Chin

In recent years, with the rapid economic growth, the electric power consumption has led to a continuous increase. Besides, to adapt to the new supply and demand relationships, industrial structure has changed dramatically, and electricity consumption in different industries has changed accordingly, such as in Hebei province that boasts a large amount of traditional industries. Hence, the study of the change of electricity consumption in different industries and the correlation between them have great practical significance for monitoring changes in macroeconomic structure and aiding decision-making in power companies. This paper selected a typical county in Hebei Province and used the method of complex network and sensitive coefficient to analyze the correlation of electricity load among different industries. This paper found that the industrial structure in Bazhou county show a strong relationship with the traditional industry classification. The correlation between each industry in the same traditional industry is stronger and more stable, and it is weaker and more changeable regarding different traditional industries.

关键词： Power Consumption key Industry Complex Networks Sensetive Analysis

来源：评论

学校读者我要写书评

暂无评论

Automatic Chinese Multiple-Choice Question Generation for Human Resource Performance Appraisal

引用

Procedia Computer Science 2018年 139卷 165-172页

作者： Pei Quan Yong Shi Lingfeng Niu Ying Liu Tianlin Zhang School of Computer and Control Engineering University of Academy of Sciences Beijing 100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China College of Information Science and Technology University of Nebraska at Omaha NE 68182 USA

Performance appraisal has always been an important research topic in human resource management. A reasonable performance appraisal plan lays a solid foundation for the development of an enterprise. Especially as globalization and technology advance, in order to meet the fast-changing strategic goals and increasing cross-functional tasks, enterprises face new challenges in performance appraisal. How to improve employees’ ability to accept new knowledge efficiently and constantly has been an urgent problem for enterprises. In this paper, we propose an automatic method which generation multiple-choice questions by utilizing the relations between different terminology. Graphical model is used to extract core concept from different corpus while word embedding technology is used to indicate the relevant relations. Experimental results demonstrate that the proposed question generation method outperforms the traditional manual method in both efficiency and confusion.

关键词： Human resource management multiple-choice question generation natural language processing word embedding

来源：评论

学校读者我要写书评

暂无评论

SALIENCY DETECTION WITH TWO-LEVEL FULLY CONVOLUTIONAL NETWORKS

SALIENCY DETECTION WITH TWO-LEVEL FULLY CONVOLUTIONAL NETWOR...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Yi, Yang Su, Li Huang, Qingming Wu, Zhe Wang, Chunfeng Univ Chinese Acad Sci Beijing Peoples R China Univ Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China

ISBN: (纸本)9781509060672

This paper proposes a deep architecture for saliency detection by fusing pixel-level and superpixel-level predictions. Different from the previous methods that either make dense pixel-level prediction with complex networks or region-level prediction for each region with fully-connected layers, this paper investigates an elegant route to make two-level predictions based on a same simple fully convolutional network via seamless transformation. In the transformation module, we integrate the low level features to model the similarities between pixels and superpixels as well as superpixels and superpixels. The pixel-level saliency map detects and highlights the salient object well and the superpixel-level saliency map preserves sharp boundary in a complementary way. A shallow fusion net is applied to learn to fuse the two saliency maps, followed by a CRF post-refinement module. Experiments on four benchmark data sets demonstrate that our method performs favorably against the state-of-art methods.

关键词： Saliency detection Pixel-level network Superpixel-level network Fully Convolutional network

来源：评论

学校读者我要写书评

暂无评论

A data-Driven Dynamic Programming Model for Research Position Demand Forecasting

引用

Annals of data Science 2017年第1期4卷 19-30页

作者： Xie, Yongjia Wu, Dengsheng Chen, Yuanping Jiao, Wenbin Li, Jianping Institute of Policy and Management Chinese Academy of Sciences Beijing100190 China Computer Network Information Center Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China

It has been worthy of notice that the number of scientific researchers has experienced a rapid growth in China. Meanwhile, the strict restriction to the total number and the position structure of researchers has exerted great pressure on the Chinese researchers. The decision makers have noticed this dilemma and a quantitative predicting result for decision support is in need. This paper puts forward a data-driven dynamic programming model to estimate the research position demand gap based on the thought of dynamic programming. This model fully considers the real practice of human resource management in scientific management in China. In the empirical study, the personnel data from 2006 to 2014, which are abstracted from the Academia Resource Planning system of the Chinese Academy of Sciences, are applied to the empirical analysis to estimate the human resource demand gap in the 13th Five Year Plan. The results show that there is a big demand gap of the research position on the whole in the next five years. © 2017, Springer-Verlag Berlin Heidelberg.

关键词： Dynamic programming

来源：评论

学校读者我要写书评

暂无评论

Support vector machine classifier with truncated pinball loss

引用

PATTERN RECOGNITION 2017年 68卷 199-210页

作者： Shen, Xin Niu, Lingfeng Qi, Zhiquan Tian, Yingjie Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

Feature noise, namely noise on inputs is a long-standing plague to support vector machine(SVM). Conventional SVM with the hinge loss(C-SVM) is sparse but sensitive to feature noise. Instead, the pinball loss SVM(pin-SVM) enjoys noise robustness but loses the sparsity completely. To bridge the gap between C-SVM and pin-SVM, we propose the truncated pinball loss SVM((pin) over bar -SVM) in this paper. It provides a flexible framework of trade-off between sparsity and feature noise insensitivity. Theoretical properties including Bayes rule, misclassification error bound, sparsity, and noise insensitivity are discussed in depth. To train (pin) over bar -SVM, the concave-convex procedure(CCCP) is used to handle non-convexity and the decomposition method is used to deal with the subproblem of each CCCP iteration. Accordingly, we modify the popular solver LIBSVM to conduct experiments and numerical results validate the properties of (pin) over bar -SVM on the synthetic and real-world data sets. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Pinball loss Feature noise Sparsity Support vector machine

来源：评论

学校读者我要写书评

暂无评论

Research on the Quality Evaluation Strategy of Multi-source Heterogeneous Aggregation data

引用

Journal of Physics: Conference Series 2020年第1期1616卷

作者： Gaofeng Zhan Yong Jiang Xiaofeng Wang Yuyang Zhao Tong Zhao The first Research Institute of the Ministry of Public Security Beijing 100048 P.R. China Beijing Langrun Yihua Technology Co. Ltd Beijing 100429 P.R. China University of Chinese Academy of Sciences Beijing 100049 P.R. China Key Laboratory of big data mining and knowledge management Chinese Academy of Sciences Beijing 100049 P.R. China

In the era of big data, the application of multi-source heterogeneous aggregation data is more and more extensive. If the quality of aggregation data is uneven, it will bring a lot of troubles to the subsequent data mining, and then lead to inaccurate decision-making. A comprehensive quality evaluation method for aggregation data is proposed in this paper, based on factor analysis and multivariate variance analysis which is from the perspective of multivariate statistical inference. The case study shows that the method proposed in this paper is feasible and adaptive for the long-term evaluation of the quality of multi-source heterogeneous aggregation data.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Intelligence Quotient and Intelligence Grade of Artificial Intelligence

引用

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

来源：评论

学校读者我要写书评

暂无评论

Global and local metric learning via eigenvectors

引用

knowledge-BASED SYSTEMS 2017年第Jan.15期116卷 152-162页

作者： Li, Dewei Tian, Yingjie Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

Distance metric plays a significant role in machine learning methods(classification, clustering, etc.), especially in k-nearest neighbor classification(kNN), where the Euclidean distances are computed to decide the labels of unknown points. But Euclidean distance ignores the statistical structure which may help to measure the similarity of different inputs better. In this paper, we construct an unified framework, including two eigenvalue related methods, to learn data-dependent metric. Both methods aim to maximize the difference of intra-class distance and inter-class distance, but the optimization is considered in global view and local view respectively. Different from previous work in metric learning, our methods straight seek for equilibrium between inter-class distance and intra-class distance, and the linear transformation decomposed from the metric is to be optimized directly instead of the metric. Then we can effectively adjust the data distribution in transformed space and construct favorable regions for kNN classification. The problems can be solved simply by eigenvalue-decomposition, much faster than semi-definite programming. After selecting the top eigenvalues, the original data can be projected into low dimensional space, and then insignificant information will be mitigated or eliminated to make the classification more efficiently. This makes it possible that our novel methods make metric learning and dimension reduction simultaneously. The numerical experiments from different points of view verify that our methods can improve the accuracy of kNN classification and make dimension reduction with competitive performance. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Metric learning Global and local Dimension reduction Classification

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：