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Wuhan University Journal of Natural Sciences 2019年第6期24卷 515-518页

作者： LIU Jicheng FU Xiaoxu XU Fangqiu WANG Zhenzhen LUO Cuicui School of Economics and Management North China Electric Power UniversityBeijing 102206China Beijing Key Laboratory of New Energy and Low-Carbon Development North China Electric Power UniversityBeijing 102206China Department of Mathematics University of TorontoToronto M5S 1A1ONCanada

With the development of photovoltaic(PV) industry, installing small-scale PV systems which are integrated into the buildings becomes popular. Therefore, it is important to make optimal investment decisions for investors and consumers. This paper proposes an improved group decision-making method which integrates the cumulative prospect theory and choquet integral for the investment options of small-scale PV systems. From the perspective of sustainability, the alternatives are evaluated by four criteria, including economic benefits, solar energy condition, carbon emissions and social benefits. Since the performances of criteria are given by decision makers as linguistic variables, the proposed method measures the criteria values by intuitionistic trapezoidal fuzzy numbers. Then the alternatives are evaluated and ranked to determine the optimal option. Finally, the proposed method is implemented in a case study to illustrate its feasibility and effectiveness.

关键词： investment options small-scale photovoltaic systems cumulative prospect theory choquet integral intuitionistic trapezoidal fuzzy numbers

Smoke root detection from video sequences based on multi-feature fusion

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Journal of Forestry Research 2022年第6期33卷 1841-1856页

作者： Liming Lou Feng Chen Pengle Cheng Ying Huang School of Technology Beijing Forestry University100083 BeijingPeople’s Republic of China School of Nature Conservation Beijing Forestry University100083 BeijingPeople’s Republic of China Department of Civil Constructionand Environmental EngineeringNorth Dakota State UniversityFargoND 58102USA

Smoke detection is the most commonly used method in early warning of fire and is widely used in forest detection.Most existing smoke detection methods contain empty spaces and obstacles which interfere with detection and extract false smoke roots.This study developed a new smoke roots search algorithm based on a multi-feature fusion dynamic extraction strategy.This determines smoke origin candidate points and region based on a multi-frame discrete confidence level.The results show that the new method provides a more complete smoke contour with no background interference,compared to the results using existing methods.Unlike video-based methods that rely on continuous frames,an adaptive threshold method was developed to build the judgment image set composed of non-consecutive frames.The smoke roots origin search algorithm increased the detection rate and significantly reduced false detection rate compared to existing methods.

关键词： Smoke detection Multi-feature fusion Search strategy ViBe choquet

基于choquet模糊积分算子的多指标属性船型方案优选模型

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大连理工大学学报 2015年第4期55卷 380-386页

作者：张维英陈静张光发林力张光耀大连海洋大学航海与船舶工程学院辽宁大连116023

船型方案排序及优选是一个多指标属性、多层次且指标属性相互关联的决策问题.考虑船型方案评价中各评价指标间存在的模糊性和相互关联性,利用模糊测度和choquet模糊积分算子对船型方案进行优选.此方法的优点是考虑了方案各指标属性的关... 详细信息

船型方案排序及优选是一个多指标属性、多层次且指标属性相互关联的决策问题.考虑船型方案评价中各评价指标间存在的模糊性和相互关联性,利用模糊测度和choquet模糊积分算子对船型方案进行优选.此方法的优点是考虑了方案各指标属性的关联关系,而且决策过程不用确定各指标属性的权重,减少了决策过程中决策者的主观臆断性,使排序结果更具客观性和科学性.计算实例说明:基于choquet模糊积分方法建立的船舶方案优选模型正确,方法简单,结果合理,为船型方案选择提供了一种可靠的参考.

关键词： choquet 模糊积分算子多属性多层次结构模型船型方案排序与优选

基于粗糙集和choquet积分的区域协同创新能力评价

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统计与决策 2015年第16期31卷 39-42页

作者：尹彦天津财经大学商学院天津300222

文章借助区域发展相关理论和聚类分析方法,构建区域协同创新能力的结构模型,在厘清影响区域协同创新能力关键因素的同时,梳理各种关键因素对区域协同创新能力的影响机理。运用粗糙集约简得到区域协同创新能力评价指标体系的基础上,应用C... 详细信息

文章借助区域发展相关理论和聚类分析方法,构建区域协同创新能力的结构模型,在厘清影响区域协同创新能力关键因素的同时,梳理各种关键因素对区域协同创新能力的影响机理。运用粗糙集约简得到区域协同创新能力评价指标体系的基础上,应用choquet模糊积分探究区域协同创新能力以认清区域协同发展中存在的问题,为改进创新行为提供理论依据和政策参考。

关键词：区域协同创新能力评价粗糙集 choquet

The representations of two types of functionals on L^∞(Ω,F) and L^∞(Ω,F,P)

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Science China Mathematics 2006年第10期49卷 1376-1382页

作者： SONG Yongsheng YAN Jia'an Institute of Applied Mathematics Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100080 China Institute of Applied Mathematics Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100080 China

This article gives the representations of two types of real functionals on L∞(Ω,F)or L∞(Ω,F,P) in terms of choquet integrals. These functionals are comonotonically subadditive and comonotonically convex, respectiv... 详细信息

关键词： choquet integral, comonotonically subadditive, comonotonically convex.

An overview of representation theorems for static risk measures

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Science China Mathematics 2009年第7期52卷 1412-1422页

作者： SONG YongSheng YAN JiaAn Center for Financial Engineering and Risk Management Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China

In this paper,we give an overview of representation theorems for various static risk measures:coherent or convex risk measures, risk measures with comonotonic subadditivity or convexity, law-invariant coherent or conv... 详细信息

关键词： choquet integral (concave) distortion law-invariant risk measure stochastic orders

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choquet定理在一般可测空间的推广

科学通报 1986年第18期31卷 1361-1365页

作者：张连文汪培庄北京师范大学数学系

一、预备知识与主要结果设X是一集合,其上一子集类B称为交系,如果它对集合交运算封闭。含X和(φ)的交系称为富交系。给定X上的富交系B,函数Bel:B→[0,1]称为其上一信任函数,如果(1)Bel(φ)=0;

关键词：定理 Be 概率 choquet 信任函数

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脑电信号共空间模式模糊融合的研究

生物医学工程学杂志 2015年第6期32卷 1173-1178页

作者：徐鲁强肖光灿黎茂锋西南交通大学信息学院成都610031 西南科技大学计算机学院绵阳621010

共空间模式(CSP)是脑电信号特征提取的主要方法,但它存在较严重的过拟合问题。本文提出对多通道脑电数据划分多个区域,分别用CSP算法提取各区域的脑电数据特征,对得到的各特征分别进行线性分类,用choquet模糊积分融合各线性分类结果,有... 详细信息

共空间模式(CSP)是脑电信号特征提取的主要方法,但它存在较严重的过拟合问题。本文提出对多通道脑电数据划分多个区域,分别用CSP算法提取各区域的脑电数据特征,对得到的各特征分别进行线性分类,用choquet模糊积分融合各线性分类结果,有助于克服脑电信号处理的过拟合问题和提高脑电信号识别准确度,从而给出了脑电数据处理的一种新框架。采用2005年国际脑机接口(BCI)竞赛数据验证该处理框架,获得的识别准确率显著提高,并且在一定程度上解决了CSP的过拟合问题,显示了本框架处理脑电信息的有效性。

关键词：脑电共空间模式信息融合脑机接口线性识别 choquet 模糊积分

Using Non-Additive Measure for Optimization-Based Nonlinear Classification

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American Journal of Operations Research 2012年第3期2卷 364-373页

作者： Nian Yan Zhengxin Chen Yong Shi Zhenyuan Wang Guimin Huang College of Information Science and Technology University of Nebraska at Omaha Omaha NE USA Department of Mathematics University of Nebraska at Omaha Omaha NE USA 68182 Guilin University of Electronic Technology Guilin China

Over the past few decades, numerous optimization-based methods have been proposed for solving the classification problem in data mining. Classic optimization-based methods do not consider attribute interactions toward classification. Thus, a novel learning machine is needed to provide a better understanding on the nature of classification when the interaction among contributions from various attributes cannot be ignored. The interactions can be described by a non-additive measure while the choquet integral can serve as the mathematical tool to aggregate the values of attributes and the corresponding values of a non-additive measure. As a main part of this research, a new nonlinear classification method with non-additive measures is proposed. Experimental results show that applying non-additive measures on the classic optimization-based models improves the classification robustness and accuracy compared with some popular classification methods. In addition, motivated by well-known Support Vector Machine approach, we transform the primal optimization-based nonlinear classification model with the signed non-additive measure into its dual form by applying Lagrangian optimization theory and Wolfes dual programming theory. As a result, 2n – 1 parameters of the signed non-additive measure can now be approximated with m (number of records) Lagrangian multipliers by applying necessary conditions of the primal classification problem to be optimal. This method of parameter approximation is a breakthrough for solving a non-additive measure practically when there are relatively small number of training cases available (mn-1). Furthermore, the kernel-based learning method engages the nonlinear classifiers to achieve better classification accuracy. The research produces practically deliverable nonlinear models with the non-additive measure for classification problem in data mining when interactions among attributes are considered.

关键词： Nonlinear Programming Nonlinear Classification Non-Additive Measure choquet Integral Support Vector Machines

Monotone Measures Defined by Pan-Integral

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Advances in Pure Mathematics 2018年第6期8卷 535-547页

作者： Peipei Wang Minhao Yu Jun Li Property and Finance Department National Museum of China Beijing China School of Sciences Communication University of China Beijing China

Given a pan-space ?and a nonnegative measurable function f on measurable space (X, A), the pan-integral of f with respect to monotone measure μ and pan-operation ?determines a new monotone measure ?on?(X, A). Such the new monotone measure ?is absolutely continuous with respect to the monotone measure μ. We show that the new monotone measure preserves some important structural characteristics of the original monotone measures, such as continuity from below, subadditivity, null-additivity, weak null-additivity and (S) property. Since the pan-integral based on a pair of pan-operations ?covers the Sugeno integral (based on ) and the Shilkret integral (based on ), therefore, the previous related results for the Sugeno integral are covered by the results presented here, in the meantime, some special results related the Shilkret integral are also obtained.

关键词： Monotone Measure Pan-Integral Sugeno Integral choquet Integral Shilkret Integral