Jiangxi University of Finance and Economics

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.

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Li X.,Jiangxi University of Finance and Economics
Journal of Chemical and Pharmaceutical Research | Year: 2013

K-means algorithm is wildly used in medical image segmentation for its powerful fuzzy information process ability but the algorithm has some shortages such as low efficiency in calculation which limited the usage of the algorithm. Some measures are advanced to overcome the shortages of original K-means algorithm and a new medical volume image segmentation algorithm is presented. Firstly, according to the physical means of the medical data, the volume data field is preprocessed to speed up succeed clustering processing; Secondly, the improved K-means algorithm is deduced and analyzed through improving cluster seed selection method and calculation flow and redesigning pixel processing and operational principle of original K-means algorithm. Finally, the experimental results show that the algorithm has high accuracy when used to segment 3D medical images and can improve calculation speed greatly.

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.

Wan S.,Jiangxi University of Finance and Economics | Dong J.,Jiangxi University of Finance and Economics
Journal of Computer and System Sciences | Year: 2014

The ranking of interval-valued intuitionistic fuzzy sets (IVIFSs) is very important for the interval-valued intuitionistic fuzzy decision making. From the probability viewpoint, the possibility degree of comparison between two interval-valued intuitionistic fuzzy numbers (IVIFNs) is defined by using the notion of 2-dimensional random vector, and a new method is then developed to rank IVIFNs. Hereby the ordered weighted average operator and hybrid weighted average operator for IVIFNs are defined based on the Karnik-Mendel algorithms and employed to solve multi-attribute group decision making problems with IVIFNs. The individual overall attribute values of alternatives are obtained by using the weighted average operator for IVIFNs. By using the hybrid weighted average operator for IVIFNs, we can obtain the collective overall attribute values of alternatives, which are used to rank the alternatives. A numerical example is examined to illustrate the effectiveness and flexibility of the proposed method in this paper. © 2013 Elsevier Inc.

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.

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.

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.

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.

Wan S.-P.,Jiangxi University of Finance and Economics
Knowledge-Based Systems | Year: 2013

The focus of this paper is on multi-attribute group decision making (MAGDM) problems in which the attribute values, attribute weights, and expert weights are all in the form of 2-tuple linguistic information, which are solved by developing a new decision method based on 2-tuple linguistic hybrid arithmetic aggregation operator. First, the operation laws for 2-tuple linguistic information are defined and the related properties of the operation laws are studied. Hereby some hybrid arithmetic aggregation operators with 2-tuple linguistic information are developed, involving the 2-tuple hybrid weighted arithmetic average (THWA) operator, the 2-tuple hybrid linguistic weighted arithmetic average (T-HLWA) operator, and the extended 2-tuple hybrid linguistic weighted arithmetic average (ET-HLWA) operator. In the proposed decision method, the individual overall preference values of alternatives are derived by using the extended 2-tuple weighted arithmetic average operator (ET-WA). Utilized the ET-HLWA operator, all the individual overall preference values of alternatives are further integrated into the collective ones of alternatives, which are used to rank the alternatives. A real example of personnel selection is given to illustrate the developed method and the comparison analyses demonstrate the universality and flexibility of the method proposed in this paper. © 2013 Elsevier B.V. All rights reserved.

Xie H.,Jiangxi University of Finance and Economics
International journal of environmental research and public health | Year: 2012

Ecological land is like the "liver" of a city and is very useful to public health. Ecological land change is a spatially dynamic non-linear process under the interaction between natural and anthropogenic factors at different scales. In this study, by setting up natural development scenario, object orientation scenario and ecosystem priority scenario, a Cellular Automation (CA) model has been established to simulate the evolution pattern of ecological land in Beijing in the year 2020. Under the natural development scenario, most of ecological land will be replaced by construction land and crop land. But under the scenarios of object orientation and ecosystem priority, the ecological land area will increase, especially under the scenario of ecosystem priority. When considering the factors such as total area of ecological land, loss of key ecological land and spatial patterns of land use, the scenarios from priority to inferiority are ecosystem priority, object orientation and natural development, so future land management policies in Beijing should be focused on conversion of cropland to forest, wetland protection and prohibition of exploitation of natural protection zones, water source areas and forest parks to maintain the safety of the regional ecosystem.

Yuan F.,Jiangxi University of Finance and Economics
Digital Signal Processing: A Review Journal | Year: 2014

Local Binary Pattern (LBP) only encodes the first order directional derivatives of a center pixel but it does not consider higher order derivatives. This paper proposes a rotation and scale invariant local binary pattern by jointly taking into account high order directional derivatives, circular shift sub-uniform, and scale space. Each order directional derivatives are independently encoded in a similar way of the first order derivatives to generate a code for the center pixel. Different order derivatives produce different codes that result in several histograms over an image, and then all the histograms multiplied by weights are concatenated together to fully utilize information of different order derivatives. To further improve performance, circular shift sub-uniform and scale space techniques are used to obtain rotation and scale invariant local binary patterns. Extensive experiments show that the high order derivatives based LBP can achieve good performance and obviously outperforms existing methods. © 2013 Elsevier Inc.

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