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Attribute Reduction of Hybrid Decision Information Systems Based on Fuzzy Conditional Information Entropy 被引量:1
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作者 Xiaoqin Ma Jun Wang +1 位作者 Wenchang Yu Qinli Zhang 《Computers, Materials & Continua》 SCIE EI 2024年第5期2063-2083,共21页
The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attr... The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attribute importance,Skowron discernibility matrix,and information entropy,struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values,and attributes with fuzzy boundaries and abnormal values.In order to address the aforementioned issues,this paper delves into the study of attribute reduction withinHDISs.First of all,a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring the differences between nominal attribute values.The newly introduced distance metric has been christened the supervised distance that can effectively quantify the differences between the nominal attribute values.Then,based on the newly developed metric,a novel fuzzy relationship is defined from the perspective of“feedback on parity of attribute values to attribute sets”.This new fuzzy relationship serves as a valuable tool in addressing the challenges posed by abnormal attribute values.Furthermore,leveraging the newly introduced fuzzy relationship,the fuzzy conditional information entropy is defined as a solution to the challenges posed by fuzzy attributes.It effectively quantifies the uncertainty associated with fuzzy attribute values,thereby providing a robust framework for handling fuzzy information in hybrid information systems.Finally,an algorithm for attribute reduction utilizing the fuzzy conditional information entropy is presented.The experimental results on 12 datasets show that the average reduction rate of our algorithm reaches 84.04%,and the classification accuracy is improved by 3.91%compared to the original dataset,and by an average of 11.25%compared to the other 9 state-of-the-art reduction algorithms.The comprehensive analysis of these research results clearly indicates that our algorithm is highly effective in managing the intricate uncertainties inherent in hybrid data. 展开更多
关键词 Hybrid decision information systems fuzzy conditional information entropy attribute reduction fuzzy relationship rough set theory(RST)
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Fusing Supervised and Unsupervised Measures for Attribute Reduction
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作者 Tianshun Xing Jianjun Chen +1 位作者 Taihua Xu Yan Fan 《Intelligent Automation & Soft Computing》 SCIE 2023年第7期561-581,共21页
It is well-known that attribute reduction is a crucial action of rough set.The significant characteristic of attribute reduction is that it can reduce the dimensions of data with clear semantic explanations.Normally,t... It is well-known that attribute reduction is a crucial action of rough set.The significant characteristic of attribute reduction is that it can reduce the dimensions of data with clear semantic explanations.Normally,the learning performance of attributes in derived reduct is much more crucial.Since related measures of rough set dominate the whole process of identifying qualified attributes and deriving reduct,those measures may have a direct impact on the performance of selected attributes in reduct.However,most previous researches about attribute reduction take measures related to either supervised perspective or unsupervised perspective,which are insufficient to identify attributes with superior learning performance,such as stability and accuracy.In order to improve the classification stability and classification accuracy of reduct,in this paper,a novel measure is proposed based on the fusion of supervised and unsupervised perspectives:(1)in terms of supervised perspective,approximation quality is helpful in quantitatively characterizing the relationship between attributes and labels;(2)in terms of unsupervised perspective,conditional entropy is helpful in quantitatively describing the internal structure of data itself.In order to prove the effectiveness of the proposed measure,18 University of CaliforniaIrvine(UCI)datasets and 2 Yale face datasets have been employed in the comparative experiments.Finally,the experimental results show that the proposed measure does well in selecting attributes which can provide distinguished classification stabilities and classification accuracies. 展开更多
关键词 Approximation quality attribute reduction conditional entropy neighborhood rough set
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Preparation of FeCoNi medium entropy alloy from Fe^(3+)-Co^(2+)-Ni^(2+)solution system
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作者 Zongyou Cheng Qing Zhao +3 位作者 Mengjie Tao Jijun Du Xingxi Huang Chengjun Liu 《International Journal of Minerals,Metallurgy and Materials》 SCIE EI CAS 2025年第1期92-101,共10页
In recent years,medium entropy alloys have become a research hotspot due to their excellent physical and chemical performances.By controlling reasonable elemental composition and processing parameters,the medium entro... In recent years,medium entropy alloys have become a research hotspot due to their excellent physical and chemical performances.By controlling reasonable elemental composition and processing parameters,the medium entropy alloys can exhibit similar properties to high entropy alloys and have lower costs.In this paper,a FeCoNi medium entropy alloy precursor was prepared via sol-gel and coprecipitation methods,respectively,and FeCoNi medium entropy alloys were prepared by carbothermal and hydrogen reduction.The phases and magnetic properties of FeCoNi medium entropy alloy were investigated.Results showed that FeCoNi medium entropy alloy was produced by carbothermal and hydrogen reduction at 1500℃.Some carbon was detected in the FeCoNi medium entropy alloy prepared by carbothermal reduction.The alloy prepared by hydrogen reduction was uniform and showed a relatively high purity.Moreover,the hydrogen reduction product exhibited better saturation magnetization and lower coercivity. 展开更多
关键词 medium entropy alloy SOL-GEL CO-PRECIPITATION carbothermal hydrogen reduction
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Comparison of Kernel Entropy Component Analysis with Several Dimensionality Reduction Methods
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作者 马西沛 张蕾 孙以泽 《Journal of Donghua University(English Edition)》 EI CAS 2017年第4期577-582,共6页
Dimensionality reduction techniques play an important role in data mining. Kernel entropy component analysis( KECA) is a newly developed method for data transformation and dimensionality reduction. This paper conducte... Dimensionality reduction techniques play an important role in data mining. Kernel entropy component analysis( KECA) is a newly developed method for data transformation and dimensionality reduction. This paper conducted a comparative study of KECA with other five dimensionality reduction methods,principal component analysis( PCA),kernel PCA( KPCA),locally linear embedding( LLE),laplacian eigenmaps( LAE) and diffusion maps( DM). Three quality assessment criteria, local continuity meta-criterion( LCMC),trustworthiness and continuity measure(T&C),and mean relative rank error( MRRE) are applied as direct performance indexes to assess those dimensionality reduction methods. Moreover,the clustering accuracy is used as an indirect performance index to evaluate the quality of the representative data gotten by those methods. The comparisons are performed on six datasets and the results are analyzed by Friedman test with the corresponding post-hoc tests. The results indicate that KECA shows an excellent performance in both quality assessment criteria and clustering accuracy assessing. 展开更多
关键词 dimensionality reduction kernel entropy component analysis(KECA) kernel principal component analysis(KPCA) CLUSTERING
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Variance Reduction Technique for Estimating Value-at-Risk based on the Cross - Entropy
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作者 Mykhailo Pupashenko 《Journal of Mathematics and System Science》 2014年第1期37-48,共12页
Value-at-Risk (VaR) estimation via Monte Carlo (MC) simulation is studied here. The variance reduction technique is proposed in order to speed up MC algorithm. The algorithm for estimating the probability of high ... Value-at-Risk (VaR) estimation via Monte Carlo (MC) simulation is studied here. The variance reduction technique is proposed in order to speed up MC algorithm. The algorithm for estimating the probability of high portfolio losses (more general risk measure) based on the Cross - Entropy importance sampling is developed. This algorithm can easily be applied in any light- or heavy-tailed case without an extra adaptation. Besides, it does not loose in the performance in comparison to other known methods. A numerical study in both cases is performed and the variance reduction rate is compared with other known methods. The problem of VaR estimation using procedures for estimating the probability of high portfolio losses is also discussed. 展开更多
关键词 VALUE-AT-RISK Monte Carlo simulation Cross - entropy method variance reduction importance sampling stratifiedsampling.
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Conditional Entropy of Partitions on Quantum Logic 被引量:1
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作者 ZHAO Yue-Xu Institute of Applied Mathematics and Engineering Computation,Hangzhou Dianzi University,Hangzhou 310018,ChinaMA Zhi-Hao Department of Mathematics,Shanghai Jiao Tong University,Shanghai 200240,China 《Communications in Theoretical Physics》 SCIE CAS CSCD 2007年第7期11-13,共3页
A construction of conditional entropy of partitions on quantum logic is given,and the properties ofconditional entropy are investigated.
关键词 STATE density operator PROJECTOR conditional entropy quantum logic LATTICE
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Attribute reduction based on background knowledge and its application in classification of astronomical spectra data 被引量:2
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作者 张继福 Li Yinhua Zhang Sulan 《High Technology Letters》 EI CAS 2007年第4期422-427,共6页
To improve the efficiency of the attribute reduction, we present an attribute reduction algorithm based on background knowledge and information entropy by making use of background knowledge from research fields. Under... To improve the efficiency of the attribute reduction, we present an attribute reduction algorithm based on background knowledge and information entropy by making use of background knowledge from research fields. Under the condition of known background knowledge, the algorithm can not only greatly improve the efficiency of attribute reduction, but also avoid the defection of information entropy partial to attribute with much value. The experimental result verifies that the algorithm is effective. In the end, the algorithm produces better results when applied in the classification of the star spectra data. 展开更多
关键词 rough set theory background knowledge intbrmation entropy attribute reduction astronomical spectra data
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Asymmetric acidic/alkaline N_(2)electrofixation accelerated by high-entropy metal-organic framework derivatives 被引量:2
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作者 Yuntong Sun Wenqiang Wu +10 位作者 Lei Yu Shuaishuai Xu Yuxiang Zhang Licheng Yu Baokai Xia Shan Ding Ming Li LiLi Jiang Jingjing Duan Junwu Zhu Sheng Chen 《Carbon Energy》 SCIE CSCD 2023年第3期126-137,共12页
High-entropy materials are composed of five or more metal elements with equimolar or near-equimolar concentrations within one crystal structure,which offer remarkable structural properties for many applications.Despit... High-entropy materials are composed of five or more metal elements with equimolar or near-equimolar concentrations within one crystal structure,which offer remarkable structural properties for many applications.Despite previously reported entropy-driven stabilization mechanisms,many high-entropy materials still tend to decompose to produce a variety of derivatives under operating conditions.In this study,we use transition-metal(Ni,Co,Ni,Zn,V)-based high-entropy metal-organic frameworks(HE-MOFs)as the precursors to produce different derivatives under acidic/alkaline treatment.We have shown that HE-MOFs and derivatives have shown favorable kinetics for N_(2)electrofixation in different pH electrolytes,specifically cathodic nitrogen reduction reaction in acidic media and anodic oxygen evolution reaction in alkaline media.To buffer the pH mismatch,we have further constructed an asymmetric acidic/alkaline device prototype by using bipolar membranes.As expected,the prototype showed remarkable activities,with an NH_(3)yield rate of 42.76μg h^(−1)mg^(−1),and Faradaic efficiency of 14.75%and energy efficiency of 2.59%,which are 14.4 and 4.4 times larger than those of its symmetric acidic and alkaline counterparts,respectively. 展开更多
关键词 bifunctional catalysts bipolar membrane high‐entropy materials nitrogen reduction reaction oxygen evolution reaction
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Entropy of Partitions on Quantum Logic
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作者 YUANHe-Jun 《Communications in Theoretical Physics》 SCIE CAS CSCD 2005年第3期437-439,共3页
Partition and entropy of partitions in quantum logic are introduced and their properties are investigated.The results are generalized to the general case of T-norm and T-conorm.
关键词 quantum logic entropy of partitions STATE
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A new knowledge reduction algorithm for information system
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作者 PENG Guan-ming BIAN Bing-chuan 《通讯和计算机(中英文版)》 2009年第7期35-39,共5页
关键词 通信系统 矩阵 布尔代数 信号处理
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“双碳”背景下主产区粮食生产减污降碳综合效益评价 被引量:2
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作者 刘畅 柳圩 马国巍 《农林经济管理学报》 CSSCI 北大核心 2024年第3期357-367,共11页
基于2002—2021年中国13个粮食主产区的面板数据,采用熵权TOPSIS法、障碍因子诊断模型与Dagum基尼系数,实证分析主产区粮食生产减污降碳综合效益及其时空演化。结果表明:2002—2021年粮食主产区综合效益指数由0.388提高至0.878,整体为... 基于2002—2021年中国13个粮食主产区的面板数据,采用熵权TOPSIS法、障碍因子诊断模型与Dagum基尼系数,实证分析主产区粮食生产减污降碳综合效益及其时空演化。结果表明:2002—2021年粮食主产区综合效益指数由0.388提高至0.878,整体为上升态势;主产区综合效益水平呈现“东高西低”的空间分布特征;核心障碍因素存在由粮食产出效率与粮食产出水平向粮食生产投入集约化水平与粮食生产碳排放强度集中的转变过程;总体差异水平未出现进一步缓解的趋势,空间差异主要来源于区域间差异,样本期贡献率均值为56.89%,东西部间差异最大,东部、中部地区区域内的基尼系数水平也存在上升趋势,说明缓解区域内差异同样不容忽视。据此,建议切实提高资源投入集约化程度,统筹协调各区域综合效益均衡发展,建立绿色粮食生产体系,强化绿色低碳收益,进而实现农业高质量发展。 展开更多
关键词 减污降碳 粮食生产 熵权TOPSIS法 时空演化
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高熵合金在电催化氧还原反应中的应用及发展 被引量:1
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作者 何小波 丁露 +1 位作者 银凤翔 李国儒 《常州大学学报(自然科学版)》 CAS 2024年第1期27-38,共12页
开发用于氧气还原反应(ORR)的高效催化剂是提高燃料电池和金属-空气电池性能的关键。然而,ORR是动力学缓慢反应,存在着很高的过电势,从而降低了燃料电池和金属-空气电池的能量转换效率。高熵合金是由5种或5种以上金属元素等(近)物质的... 开发用于氧气还原反应(ORR)的高效催化剂是提高燃料电池和金属-空气电池性能的关键。然而,ORR是动力学缓慢反应,存在着很高的过电势,从而降低了燃料电池和金属-空气电池的能量转换效率。高熵合金是由5种或5种以上金属元素等(近)物质的量比形成的一种新型合金材料。凭借其独特的组分与结构优势,高熵合金能够高效加速ORR、降低ORR过电势,表现出对ORR的显著催化作用。文章主要综述了高熵合金的结构、性能特点、制备方法以及在催化ORR方面的应用,并提出了挑战和发展展望。 展开更多
关键词 催化剂 电催化 高熵合金 氧还原反应
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“双减”政策执行阻滞:发生逻辑与消解策略 被引量:2
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作者 丁煌 王巍熹 《湘潭大学学报(哲学社会科学版)》 CSSCI 北大核心 2024年第2期17-26,共10页
“减负”政策执行难是我国基础教育事业发展过程中的“老大难”问题,在“双减”政策实践中表现为:基层政策表演式执行、选择式执行以及抵抗式执行。从“利益结构-社会文化-参与主体”三重维度对政策执行阻滞的发生逻辑进行诠释发现:政... “减负”政策执行难是我国基础教育事业发展过程中的“老大难”问题,在“双减”政策实践中表现为:基层政策表演式执行、选择式执行以及抵抗式执行。从“利益结构-社会文化-参与主体”三重维度对政策执行阻滞的发生逻辑进行诠释发现:政策执行的利益结构:成本分散-利益集中,社会文化:不平等的职业观与功利化的教育观,参与主体:行政吸纳社会、利益主体失语,这三重因素共同导致了政策执行阻滞的生成。本研究基于“嵌入理论”,从价值观念嵌入、执行方式嵌入、社会参与嵌入和监管模式嵌入等四个方面提出执行阻滞的消解策略。 展开更多
关键词 “双减”政策 执行阻滞 发生逻辑 消解策略 嵌入理论
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直觉模糊系统下的属性约简算法及其应用 被引量:1
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作者 李瑞 李美芳 《中央民族大学学报(自然科学版)》 2024年第2期88-96,共9页
信息熵是研究信息粒不确定性度量的一种重要方法,并且可以应用于信息系统的属性约简和综合评估中。本文提出了比较两个直觉模糊值大小的新方法,定义了直觉模糊集中香农熵形式的熵度量。以直觉模糊决策系统为背景,结合直觉模糊集理论,基... 信息熵是研究信息粒不确定性度量的一种重要方法,并且可以应用于信息系统的属性约简和综合评估中。本文提出了比较两个直觉模糊值大小的新方法,定义了直觉模糊集中香农熵形式的熵度量。以直觉模糊决策系统为背景,结合直觉模糊集理论,基于直觉模糊相似关系分别构造了直觉模糊信息熵和条件熵,利用直觉模糊条件熵给出属性重要度的计算公式,设计了基于直觉模糊条件熵的启发式属性约简算法。在此基础上,通过属性重要度得到了属性指标的权重,进而提出了一种综合评估方法,并将其应用于安全审计风险的研究中,得到可接受的安全风险评估方法,通过结果对比分析,验证了该方法的可靠性和有效性。 展开更多
关键词 直觉模糊熵 属性约简 综合评价
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中国降碳-减污-扩绿-增长协同发展空间关联网络特征及影响因素研究 被引量:1
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作者 崔新蕾 王冉冉 《环境科学研究》 CAS CSCD 北大核心 2024年第7期1446-1457,共12页
协同推进降碳-减污-扩绿-增长已成为我国经济社会发展全面绿色转型的必然选择。基于我国30个省份面板数据(不包含港澳台地区以及西藏自治区数据),运用熵值法、耦合协调度模型和社会网络分析方法,分析各省份间降碳-减污-扩绿-增长协同演... 协同推进降碳-减污-扩绿-增长已成为我国经济社会发展全面绿色转型的必然选择。基于我国30个省份面板数据(不包含港澳台地区以及西藏自治区数据),运用熵值法、耦合协调度模型和社会网络分析方法,分析各省份间降碳-减污-扩绿-增长协同演变趋势及空间关联网络特征。结果表明:①各省份降碳-减污-扩绿-增长协同效应的变化趋势基本一致,但在空间上呈现东部>东北>西部>中部的区域不均衡特征。②降碳-减污-扩绿-增长协同效应呈现以东部地区为核心的复杂空间网络结构,省际间空间关联性呈上升态势,但网络结构稳定性还有待提高。③北京市、天津市和上海市等地区凭借优越区位,在关联网络中处于主导地位,而宁夏回族自治区、黑龙江省和新疆维吾尔自治区等地区对其他地区的影响较小。④北京市、天津市和上海市等地区属于“主受益”板块,浙江省、广东省等地区属于“经纪人板块”,安徽省、江西省和湖北省等地区属于“净溢出”板块。⑤降碳-减污-扩绿-增长协同效应的空间关联网络受多种因素共同影响,人力资本水平、科技投入、市场化水平和数字经济发展均有利于空间关联关系的建立。研究显示,中国降碳-减污-扩绿-增长协同效应存在空间关联性,需进一步加强省份间的绿色合作与交流,共同推动降碳-减污-扩绿-增长协同发展。 展开更多
关键词 降碳-减污-扩绿-增长 协同效应 熵值法 耦合协调度模型 社会网络分析
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基于多核模糊条件熵的多类型混合数据属性约简算法
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作者 李俊霞 田勇 汤安 《电子器件》 CAS 2024年第2期483-489,共7页
对数据进行有效属性约简是数据挖掘中一个具有挑战性的任务。当前,粗糙集理论是构造属性约简的一种常用方法。然而,现有的属性约简方法都侧重于单类型的数据,对现实环境下多类型混合的数据并不适用。为了解决这一问题,提出一种多核模糊... 对数据进行有效属性约简是数据挖掘中一个具有挑战性的任务。当前,粗糙集理论是构造属性约简的一种常用方法。然而,现有的属性约简方法都侧重于单类型的数据,对现实环境下多类型混合的数据并不适用。为了解决这一问题,提出一种多核模糊条件熵的多类型混合数据属性约简算法。首先,针对标记型、数值型、区间型和集值型混合的多类型数据,提出了一种多核模糊相似关系。然后,基于这种多核模糊相似关系,定义了一种多核模糊条件熵模型,并讨论了它的单调性和有界性。最后,利用多核模糊条件熵的单调性提出了一种多类型混合数据的属性约简算法。通过UCI数据集的实验分析验证了该算法的有效性。 展开更多
关键词 粗糙集 属性约简 混合型数据 模糊关系 多核模糊条件熵
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一种基于粗糙熵的改进K-modes聚类算法
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作者 刘财辉 曾雄 谢德华 《南京理工大学学报》 CAS CSCD 北大核心 2024年第3期335-341,共7页
K-modes聚类算法被广泛应用于人工智能、数据挖掘等领域。传统的K-modes聚类算法有不错的聚类效果,但是存在迭代次数多、计算量大、容易受到冗余属性的干扰等问题,且仅采用简单的0-1匹配的方法来定义2个样本属性值之间的距离,没有充分... K-modes聚类算法被广泛应用于人工智能、数据挖掘等领域。传统的K-modes聚类算法有不错的聚类效果,但是存在迭代次数多、计算量大、容易受到冗余属性的干扰等问题,且仅采用简单的0-1匹配的方法来定义2个样本属性值之间的距离,没有充分考虑每个属性对聚类结果的影响。针对上述问题,该文将粗糙熵引入K-modes算法。首先利用粗糙集属性约简算法消除冗余属性,确定各属性的重要程度;然后利用粗糙熵确定每个属性的权重,从而定义新的类内距离。将该文所提算法与传统的K-modes聚类算法分别在4组公开数据集上进行对比试验。试验结果表明,该文所提算法聚类准确率比传统的K-modes聚类算法更高。 展开更多
关键词 聚类 K-modes算法 粗糙集 粗糙熵 属性约简 权重
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减税降费规模与地方政府债务风险治理——机制与证据
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作者 刘骅 王浚丞 刘梦娜 《工业技术经济》 CSSCI 北大核心 2024年第6期92-102,共11页
减税降费是中国为积极应对经济下行压力、维护全局经济稳定的重要政策手段,但其规模的不断扩大会加剧地方政府债务风险。本文通过构建包含“借”、“用”、“还”3个环节的风险指标体系,采用熵权-TOPSIS法测算2015~2022年中国省级地方... 减税降费是中国为积极应对经济下行压力、维护全局经济稳定的重要政策手段,但其规模的不断扩大会加剧地方政府债务风险。本文通过构建包含“借”、“用”、“还”3个环节的风险指标体系,采用熵权-TOPSIS法测算2015~2022年中国省级地方政府债务风险指数,使用双向固定效应模型、面板门槛模型实证分析减税降费规模对地方政府债务风险的影响。研究发现,减税降费规模与地方政府债务风险总体上呈正相关关系,并且存在经济发展水平的门槛效应,当经济发展水平跨过门槛值时,地方政府债务风险增长速度将显著降低。同时,减税降费规模与地方政府债务风险间存在非线性关系,随着减税降费规模不断扩大,地方政府债务风险增速也将被遏制。 展开更多
关键词 减税降费 地方政府债务 熵权-TOPSIS 双向固定效应 面板门槛效应 风险治理
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一种灰色关联分析优化ICEEMDAN的VP倾斜仪信号降噪模型
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作者 庞聪 孙海洋 +3 位作者 刘天龙 姚瑶 李忠亚 马武刚 《大地测量与地球动力学》 CSCD 北大核心 2024年第6期654-660,共7页
VP倾斜仪固体潮信号受仪器监测复杂环境限制,多含有大量环境噪声。为获得真实固体潮曲线,提出一种基于灰色关联分析优化改进的自适应噪声完备集合经验模态分解(ICEEMDAN)VP倾斜仪信号降噪模型(GRA-ICEEMDAN)。该方法首先将含噪信号进行I... VP倾斜仪固体潮信号受仪器监测复杂环境限制,多含有大量环境噪声。为获得真实固体潮曲线,提出一种基于灰色关联分析优化改进的自适应噪声完备集合经验模态分解(ICEEMDAN)VP倾斜仪信号降噪模型(GRA-ICEEMDAN)。该方法首先将含噪信号进行ICCEMDAN处理,得到若干个固有模态函数(IMF),并依次排列与标记;然后基于这些IMF分别计算相关系数、互信息、R^(2)、Adj-R^(2)、MSE、SSE、RMSE、MAE、MAPE、样本熵等10个评价指标值,构建IMF可信度评价指标矩阵;最后借助灰色关联分析(GRA)计算各评价指标与不同IMF之间的关联系数和关联度,依据关联度大小对各个IMF进行排序,将排名靠前的IMF进行线性重构,即可完成信号降噪。仿真去噪实验和实测去噪实验均表明,GRA-ICEEMDAN模型优于卡尔曼滤波、70阶低通FIR滤波、Savitzky-Golay等经典降噪模型,能显著区分噪声成分和有效成分,原始信号分解后的重构误差与信号损失极小,可推广至其他仪器的复杂信号降噪中。 展开更多
关键词 VP倾斜仪 信号降噪 改进的自适应噪声完备集合经验模态分解 灰色关联分析 固有模态函数 样本熵 互信息
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中国农业碳中和评价——以2010—2020年为例
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作者 师帅 周林庆 《浙江农业学报》 CSCD 北大核心 2024年第8期1920-1933,共14页
精准评估农业碳中和水平是实现我国“双碳”目标的前提。在厘清学术界对农业碳中和研究脉络的基础上,阐释农业碳中和的内涵,并从碳源与碳汇两个维度论述农业碳中和评价的理论架构,设置农业碳中和评价指标体系,采用熵权-TOPSIS法实证评... 精准评估农业碳中和水平是实现我国“双碳”目标的前提。在厘清学术界对农业碳中和研究脉络的基础上,阐释农业碳中和的内涵,并从碳源与碳汇两个维度论述农业碳中和评价的理论架构,设置农业碳中和评价指标体系,采用熵权-TOPSIS法实证评价中国30个省份2010—2020年的农业碳中和水平。结果表明:样本期内农业碳中和水平总体经历了“下降-上升”两个阶段的变化,2010—2016年农业碳中和水平年均下降6.97%,2017—2020年年均增长19.43%。农业碳中和水平呈现出明显的区域差异性,具体表现为东北地区>西部地区>中部地区>东部地区。据此建议各地结合区域农业碳源、碳汇的结构特征、自然特性与经济发展水平,统筹兼顾,从“减排”与“增汇”两手发力,协同推进农业碳中和进程。 展开更多
关键词 农业碳中和 熵权-TOPSIS法 减排增汇
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