Time filter

Source Type

Nanchang, China

The Jiangxi University of Finance and Economics ) is a public, coeducational research university located in Nanchang, Jiangxi province, China. As one of the six business schools affiliated to the Ministry of Finance, of the People's Republic of China, the university offers programs in business and management. Wikipedia.

Wan S.-P.,Jiangxi University of Finance and Economics | Li D.-F.,Fuzhou University
IEEE Transactions on Fuzzy Systems | Year: 2014

The aim of this paper is to develop a new Atanassov's intuitionistic fuzzy (A-IF) programming method to solve heterogeneous multiattribute group decision-making problems with A-IF truth degrees in which there are several types of attribute values such as A-IF sets (A-IFSs), trapezoidal fuzzy numbers, intervals, and real numbers. In this method, preference relations in comparisons of alternatives with hesitancy degrees are expressed by A-IFSs. Hereby, A-IF group consistency and inconsistency indices are defined on the basis of preference relations between alternatives. To estimate the fuzzy ideal solution (IS) and weights, a new A-IF programming model is constructed on the concept that the A-IF group inconsistency index should be minimized and must be not larger than the A-IF group consistency index by some fixed A-IFS. An effective method is developed to solve the new derived model. The distances of the alternatives to the fuzzy IS are calculated to determine their ranking order. Moreover, some generalizations or specializations of the derived model are discussed. Applicability of the proposed methodology is illustrated with a real supplier selection example. © 1993-2012 IEEE. Source

Zhang N.,Jiangxi University of Finance and Economics | Choi Y.,Inha University
Energy Economics | Year: 2013

This paper proposes the metafrontier non-radial Malmquist CO2 emission performance index (MNMCPI) for measuring dynamic changes in total-factor CO2 emission performance over time. The MNMCPI method allows for the incorporation of group heterogeneity and non-radial slack into the previously introduced Malmquist CO2 emission performance index (MCPI). We derive the MNMCPI by solving several non-radial data envelopment analysis (DEA) models. We decompose the MNMCPI into an efficiency change (EC) index, a best-practice gap change (BPC) index, and a technology gap change (TGC) index, and based on the proposed indices, we examine the dynamic changes in CO2 emission performance and its decomposition of fossil fuel power plants in China for the 2005-2010 period. The empirical results show a 0.38% increase in total-factor CO2 emission performance as a whole and a U-shaped MNMCPI curve for the sample period. Because companies owned by the central government lack innovation and technological leadership, the results suggest a missing link in the role of the central government in promoting CO2 emission performance. © 2013 Elsevier B.V. Source

Zhang N.,Jiangxi University of Finance and Economics | Choi Y.,Inha University
Renewable and Sustainable Energy Reviews | Year: 2014

Recently, a relatively new methodology named directional distance function (DDF) has been attracting positive attention in the field of energy and environmental (E&E) modeling. However, there is still no literature review on the application of DDF in E&E studies. This paper is intended to fill this gap. First, the most widely used DDF techniques and its extensions are briefly introduced. Second, this article attempts a classification of typical publications in this field. The main issues raised by the previous studies are discussed. Some guidelines for model selection and future directions are proposed for DDF related research in E&E studies. © 2014 Elsevier Ltd. Source

Traditional methods for video smoke detection can easily achieve very low training errors but their generalization performances are not good due to arbitrary shapes of smoke, intra-class variations, occlusions and clutters. To overcome these problems, a double mapping framework is proposed to extract partition based features with AdaBoost. The first mapping is from an original image to block features. A feature vector is presented by concatenating histograms of edge orientation, edge magnitude and Local Binary Pattern (LBP) bit, densities of edge magnitude, LBP bit, color intensity and saturation. Each component of the feature vector produces a feature image. To obtain shape-invariant features, a detection window is partitioned into a set of small blocks called a partition, many multi-scale partitions are generated by changing block sizes and partition schemes. The sum of each feature image within each block of each partition is computed to generate block features. The second mapping is from the block features to statistical features. The statistical features of the block features, such as, mean, variance, skewness, kurtosis and Hu moments, are computed on all partitions to form a feature pool. AdaBoost is used to select discriminative shape-invariant features from the feature pool. Experiments show that the proposed method has better generalization performance and less insensitivity to geometry transform than traditional methods. © 2012 Elsevier Ltd. Source

Yuan F.,Jiangxi University of Finance and Economics
Fire Safety Journal | Year: 2011

Video surveillance systems are widely applied in a variety of fields. Hence, video-based smoke detection is regarded as an effective and inexpensive way for fire detection in an open or large spaces. In order to improve the efficiency of the video-based smoke detection, a novel video-based smoke detection method is proposed by using a histogram sequence of pyramids. The method involves four steps. Firstly, through multi-scale analysis, a 3-level image pyramid is constructed. Secondly, local binary patterns (LBP), which are insensitive to image rotation and illumination conditions, are extracted at each level of the image pyramid with uniform pattern, rotation invariance pattern and rotation invariance uniform pattern to generate an LBP pyramid. Thirdly, local binary patterns based on variance (LBPV) with the same patterns are also adopted in the same way to generate an LBPV pyramid. And fourthly, histograms of the LBP and LBPV pyramids are computed, and then all the histograms are concatenated into an enhanced feature vector. A neural network classifier is trained and used for discrimination of smoke and non-smoke objects. Experimental results show that the features are insensitive to rotation and illumination, and that the method is feasible and effective for video-based smoke detection at interactive frame rates. © 2010 Elsevier Ltd All rights reserved. Source

Discover hidden collaborations