Formula for calculating spatial similarity degrees between point clouds on multi-scale maps taking map scale change as the only independent variable

The degree of spatial similarity plays an important role in map generalization, yet there has been no quantitative research into it. To fill this gap, this study first defines map scale change and spatial similarity degree/relation in multi-scale map spaces and then proposes a model for calculating the degree of spatial similarity between a point cloud at one scale and its gener- alized counterpart at another scale. After validation, the new model features 16 points with map scale change as the x coordinate and the degree of spatial similarity as the y coordinate. Finally, using an application for curve fitting, the model achieves an empirical formula that can calculate the degree of spatial similarity using map scale change as the sole independent variable, and vice versa. This formula can be used to automate algorithms for point feature generalization and to determine when to terminate them during the generalization.

Rough similarity degree and rough close degree in rough fuzzy sets and the applications

Based on rough similarity degree of rough sets and close degree of fuzzy sets, the definitions of rough similarity degree and rough close degree of rough fuzzy sets are given, which can be used to measure the similar degree between two rough fuzzy sets. The properties and theorems are listed. Using the two new measures, the method of clustering in the rough fuzzy system can be obtained. After clustering, the new fuzzy sample can be recognized by the principle of maximal similarity degree.

Static rough similarity degree and its applications

The definition of rough similarity degree is given based on the axiomatic similarity degree, and the properties of rough similarity degree are listed. Using the properties of rough similarity degree, the method of clustering in rough systems can be obtained. After clustering, a new sample can be recognized by the principle of maximal rough similarity degree.

A New Definition of Intuitionistic Fuzzy Similarity Degree

As far as the problem of intuitionistic fuzzy cluster analysis is concerned, this paper proposes a new formula of similarity degree with attribute weight of each index. We conduct a fuzzy cluster analysis based on the new intuitionistic fuzzy similarity matrix, which is constructed via this new weighted similarity degree method and can be transformed into a fuzzy similarity matrix. Moreover, an example is given to demonstrate the feasibility and validity of this method.

MULTIVARIATE ABSOLUTE DEGREE OF GREY INCIDENCE BASED ON DISTRIBUTION CHARACTERISTICS OF POINTS

The analysis result of absolute degree of grey incidence for multivariate time series is often inconsistent with the qualitative analysis. To overcome this shortage, a multivariate absolute degree of grey incidence based on distribution characteristics of points is proposed. Based on the geometric description of multivariate time se- ries, the neighborhood extrema are extracted in the different regions, and a characteristic point set is constructed. Then according to the distribution of the characteristic point set, a characteristic point sequence reflecting the ge- ometric features of multivariate time series is obtained. The incidence analysis between multivariate time series is transformed into the relational analysis between characteristic point sequences, and a grey incidence model is established. The model possesses the properties of translational invariance, transpose and rank transform invari- ance, and satisfies the grey incidence analysis axioms. Finally, two cases are studied and the results prove the ef- fectiveness of the model.

Similarity measure on intuitionistic fuzzy sets

Study of fuzzy entropy and similarity measure on intuitionistic fuzzy sets (IFSs) was proposed and analyzed. Unlike fuzzy set, IFSs contain uncertainty named hesitance, which is contained in fuzzy membership function itself. Hence, designing fuzzy entropy is not easy because of many entropy definitions. By considering different fuzzy entropy definitions, fuzzy entropy on IFSs is designed and discussed. Similarity measure was also presented and its usefulness was verified to evaluate degree of similarity.

Design of similarity measure for discrete data and application to multi-dimension

Similarity measure design for discrete data group was proposed. Similarity measure design for continuous membership function was also carried out. Proposed similarity measures were designed based on fuzzy number and distance measure, and were proved. To calculate the degree of similarity of discrete data, relative degree between data and total distribution was obtained. Discrete data similarity measure was completed with combination of mentioned relative degrees. Power interconnected system with multi characteristics was considered to apply discrete similarity measure. Naturally, similarity measure was extended to multi-dimensional similarity measure case, and applied to bus clustering problem.

基于灰色关联分析的断层突水阶段化感知方法

为提升煤矿断层水害感知水平,提出一种以断层活化演变机制和关键控制因素识别的断层突水阶段化感知方法,通过断层突水演化过程相似模拟试验研究工作面底板与断层破坏的演化特征,以底板破坏带应力、断层裂隙带应力、导水通道内水压等为阶段监测参数,揭示各监测参数与涌水量变化的阶段特征;结合灰色关联性分析法,判定断层突水阶段转化的关键控制因素,进而根据断层突水分析与研究过程,提出断层突水阶段化感知方法。结果表明:底板破坏带、断层裂隙带的应力,导水通道内的水压等监测参数的数值变化特征在断层活化突水过程中呈现出明显的阶段特征,控制因素与涌水量的灰色关联度排序为:底板破坏带应力>断层裂隙带应力>导水通道水压,通过对关键控制因素的识别,可实现断层突水阶段的感知,揭示基于灰色关联分析的感知方法在原理上具备可行性。

基于直觉模糊相似关系的三支决策模型

针对一些直觉模糊相似关系使直觉模糊集中对象间的相似度过于集中或者相异度过高,导致不合理的分类结果的问题,以及在构造直觉模糊相似关系时,对象间的相似度和相异度容易受到不重要属性条件信息影响的问题,本文在直觉模糊集和可能性理论基础上,提出一种基于直觉模糊相似关系的三支决策模型。首先,给出可能性测度和必要性测度的定义,结合Hausdorff度量,构造一种距离公式,并证明其性质,定义了直觉模糊集中对象间的相似度和相异度,构造了新的直觉模糊相似关系;然后定义了直觉模糊相似关系下的(λ_(1),λ_(2))-截集及其直觉模糊(λ_(1),λ_(2))-截集下的相似类,并进一步求出目标集的正域、负域和边界域;最后通过UCI数据集和实例验证了该模型的合理性和有效性。

基于节点交互度的社会网络链路预测

链路预测是通过已知的网络拓扑和节点属性挖掘未来时刻节点潜在关系的重要手段,是预测缺失链路和识别虚假链路的有效方法,在研究社会网络结构演化中具有现实意义.传统的链路预测方法基于节点信息或路径信息相似性进行预测,然而,前者考虑指标单一导致预测精度受限,后者由于计算复杂度过高不适合在规模较大网络中应用.通过对网络拓扑结构的分析,本文提出一种基于节点交互度(interacting degree of nodes,IDN)的社会网络链路预测方法.该方法首先根据网络中节点间的路径特征,引入了节点效率的概念,从而提高对于没有公共邻居节点之间链路预测的准确性;为了进一步挖掘节点间共同邻居的相关属性,借助分析节点间共同邻居的拓扑结构,该方法还创新性地整合了路径特征和局部信息,提出了社会网络节点交互度的定义,准确刻画出节点间的相似度,从而增强网络链路的预测能力;最后,本文借助6个真实网络数据集对IDN方法进行验证,实验结果表明,相比于目前的主流算法,本文提出的方法在AUC和Precision两个评价指标上均表现出更优的预测性能,预测结果平均分别提升22%和54%.因此节点交互度的提出在链路预测方面具有很高的可行性和有效性.

车辆前纵梁封板冲压的聚集度自适应粒子群优化

为了提高车辆前纵梁封板的冲压件质量,提出了基于聚集度自适应粒子群算法的冲压工艺优化方法。介绍了前纵梁封板的三维模型、坯料设计结果和冲压成形原理。基于成形极限曲线,以减小冲压件的减薄率、增厚率、回弹量为目标,建立了多目标优化模型。使用复合中心实验法设计了5因素5水平的32组实验,基于Autoform软件获得了实验数据。使用BP神经网络拟合了输入输出间的回归关系,并验证了回归模型的精度。在粒子群算法基础上,依据粒子相似度和种群聚集度,将Levy飞行融入到粒子群算法中,提高粒子多样性和算法优化能力,从而并提出了聚集度自适应粒子群算法。将改进粒子群算法应用于多优化模型求解,改进粒子群算法收敛速度早于传统算法,且改进粒子群算法搜索的目标函数值比传统算法减小了3.71%,说明了改进算法的优越性。经验证,优化后的试制件外观合格,减薄率、增厚率及回弹量均满足质量要求,可以进行批量生产。

n值R_(0)命题逻辑系统中公式的条件随机真度

基于条件概率的思想和赋值集的随机化方法,在n值R_(0)命题系统中提出公式的条件随机真度,证明条件随机真度的MP规则和HS规则。其次,引入公式间的条件随机相似度和条件随机伪距离,建立条件随机逻辑度量空间,进一步给出在条件Γ下的近似推理理论。

基于灰色关联分析的矿用机器人焊接工艺设计

矿用机器人焊接工艺设计过于依赖人工经验而导致设计效率不高,若能实现以前相似实例的重用可有效提高设计效率。通过灰色关联分析完成焊接工艺相似实例的匹配,并采用其他相似度算法验证其计算结果的有效性,结果表明基于灰色关联分析的相似实例匹配方法具有可行性。

安徽省绿色物流绩效评价及障碍因子分析

构建以经济水平、资金投入、产业规模、基础设施、环境与资源5个方面指标的安徽省绿色物流绩效指标评价体系,选取安徽省2014—2021年的相关数据,运用熵权TOPSIS(逼近理想解排序)法对其绿色物流绩效水平进行实证分析,并借助障碍度模型找出制约安徽省绿色物流发展的障碍因子。研究结果显示:安徽省绿色物流绩效水平整体呈现稳步上升的趋势;经济水平和基础设施发展较好,资金投入有所增长,产业规模增长放缓;物流业能源消费总量和碳排放量持续增加,环境与资源方面尚有不足,需要进一步提升;2018—2021年制约绿色物流发展的主要障碍因子是产业规模;从长期来看,环境与资源会逐步成为阻碍绿色物流发展的主要因素。

基于数据挖掘和聚类分析的协同过滤推荐算法

为了提高推荐系统的可扩展性和用户满意度,设计基于数据挖掘和聚类分析的协同过滤推荐算法。基于双向关联规则原理,构建标签资源矩阵,利用K-means聚类算法对标签进行聚类。结合用户偏好标签,算法能计算标签与资源的紧密程度,实现基本推荐。通过标签计算用户与资源的兴趣度,实现个性化推荐。将基本推荐和个性化推荐线性组合,得出最终结果。实验表明,该算法不仅能保持数据集的平衡状态,准确性也高。通过聚类捕捉更复杂的用户兴趣模式,显著提高了推荐结果的命中率和NDCG值,为用户提供更符合个性化需求的资源。

基于专家动态赋权的离散模糊语言群决策方法

针对专家评价信息为离散模糊数、权重未知的群决策问题,给出了一种专家动态权重确定方法,并将其和逼近理想解排序法(TOPSIS)结合提出了一种新的模糊语言群决策方法。专家通过基于离散模糊数的模糊语言值表示模型得到对备选方案的主观评价;综合考虑主观评价的不确定性和相似度,通过线性加权得到专家的动态组合权重;给出了基于专家动态权重和TOPSIS的模糊语言群决策方法的具体步骤,用于实现备选方案的排序;通过一个具体的应用案例证明所提方法是可行和有效的。

基于RTOPSIS的集成学习的综合评价研究

Stacking集成学习被认为是一种“黑盒”模型,采用多个基学习器的预测结果输入,通过元学习器来生成最终的预测。这种复杂性使得难以准确了解每个基学习器对最终结果的贡献。为解决此问题,该文提出了RTOPSIS方法。该方法结合了灰色关联度系数计算和优劣解距离方法,为决策者提供了一种有效的工具,以清晰地揭示每个基学习器在Stacking模型中对最终结果的贡献程度。具体而言:采用RTOPSIS算法替代传统的判别方法,综合考虑基学习器和元学习器之间的关系,提供更客观和合理的模型排名结果;应用灰色关联分析算法计算各个基学习器在Stacking模型中的权重,并反映其对最终结果的贡献程度。实验证明,相对于单一指标如Accuracy、AUC和F1-score等,在Stacking模型综合评价中,RTOPSIS算法为该文6个模型提供了更为合适的排名,且与经典优劣解距离算法的排序结果基本一致。因此,RTOPSIS算法在Stacking模型评价中展现出更全面的评价效果。

k度匿名社交网络隐私保护方法

随着社交网络的迅速发展,社交网络积累了大量的数据,它们在一定程度上反映了社会规律。社交网络分析人员研究这些数据可得到有用的信息,但在该过程中,往往伴随着用户隐私泄露。针对如何在挖掘有效知识的问题中保证隐私安全,提出了k度匿名社交网络隐私保护方法(k degree anonymity social network privacy protection method,KDSNP)。首先,将社交网络转化为度序列,采用聚类的方法生成超点,再对超点进行划分得出至少包含k个节点的超点,以满足k度匿名的要求保证用户隐私,最后通过邻域中心概念和结构相似度相结合,对节点的边进行删除和增加,能有效减少信息损失和提高数据的效用性。在不同的数据集上的试验结果表明,与其他类似技术相比,该方法在匿名图和原始图间的平均相对性能较好。

An investigation of weed seed banks reveals similar potential weed community diversity among three different farmland types in Anhui Province, China

Crop type is one of main factors influencing weed community structure. However, the identity of weed communities associated with the cultivation of different crops in farmlands remains largely unclear. A field survey of weed seed banks was conducted in 2 280 fields at 228 sites of 62 locations representing three different types of farmland(95 paddy, 73 summer-ripe, and 60 autumn-ripe farmlands) along the bank of the Yangtze River in Anhui Province, China. A total of 43 families and 174 species of weeds were found in these weed seed banks. A comparison of the composition of weed groups in the seed banks showed that the species number and density percentage of grass, sedge and broadleaf weed groups were similar among the different types of farmland. The seed banks of all three farmland types shared 71 common weed species, accounting for 40.80% of the total number of species. These common weeds, which were both associated and not associated with crops, accounted for 91.71% of the total dominance degree among all farmland types. The crop-associated weed species were distributed in all soil layers of each farmland type. The Shannon-Wiener index H′(description of species diversity which is more sensitive to dense species) and Pielou's evenness index J(description of species evenness) in summer-ripe farmland were similar to those in autumn-ripe farmland but differed from those in paddy farmland. However, the Simpson's index D(description of species diversity which is more sensitive to sparse species) was similar among all three farmland types. The results of similarity comparison indicated that although the aboveground weed community differed among the different cropping patterns, the weed species composition in the soil seed bank was still similar. Consequently, our results demonstrate that after the implementation of long-term monoculture patterns, weed species compositions in the soil seed bank in different farmlands become homogenized regardless of the crop type.

Application of fuzzy optimal selection of similar slopes to the evaluation of slope stability

The numerical calculation method is widely used in the evaluation of slope stability,but it cannot take the randomness and fuzziness into account that exist in rock and soil engineering objectively.The fuzzy optimization theory is thus introduced to the evaluation of slope stability by this paper and a method of fuzzy optimal selection of similar slopes is put forward to analyze slope stability.By comparing the relative membership degrees that the evaluated object sample of slope is similar to the source samples of which the stabilities are detected clearly,the source sample with the maximal relative membership degree will be chosen as the best similar one to the object sample,and the stability of the object sample can be evaluated by that of the best similar source sample.In the process many uncertain influential factors are considered and characteristics and knowledge of the source samples are obtained.The practical calculation indicates that it can achieve good results to evaluate slope stability by using this method.