New Ranking of Generalized Quadrilateral Shape Fuzzy Number Using Centroid Technique

The output of the fuzzy set is reduced by one for the defuzzification procedure.It is employed to provide a comprehensible outcome from a fuzzy inference process.This page provides further information about the defuzzifica-tion approach for quadrilateral fuzzy numbers,which may be used to convert them into discrete values.Defuzzification demonstrates how useful fuzzy ranking systems can be.Our major purpose is to develop a new ranking method for gen-eralized quadrilateral fuzzy numbers.The primary objective of the research is to provide a novel approach to the accurate evaluation of various kinds of fuzzy inte-gers.Fuzzy ranking properties are examined.Using the counterexamples of Lee and Chen demonstrates the fallacy of the ranking technique.So,a new approach has been developed for dealing with fuzzy risk analysis,risk management,indus-trial engineering and optimization,medicine,and artificial intelligence problems:the generalized quadrilateral form fuzzy number utilizing centroid methodology.As you can see,the aforementioned scenarios are all amenable to the solution pro-vided by the generalized quadrilateral shape fuzzy number utilizing centroid methodology.It’s laid out in a straightforward manner that’s easy to grasp for everyone.The rating method is explained in detail,along with numerical exam-ples to illustrate it.Last but not least,stability evaluations clarify why the Gener-alized quadrilateral shape fuzzy number obtained by the centroid methodology outperforms other ranking methods.

Weakness Ranking Method for Subsystems of Heavy-Duty Machine Tools Based on FMECA Information

Heavy-duty machine tools are composed of many subsystems with different functions,and their reliability is governed by the reliabilities of these subsystems.It is important to rank the weaknesses of subsystems and identify the weakest subsystem to optimize products and improve their reliabilities.However,traditional ranking methods based on failure mode effect and critical analysis(FMECA)does not consider the complex maintenance of products.Herein,a weakness ranking method for the subsystems of heavy-duty machine tools is proposed based on generalized FMECA information.In this method,eight reliability indexes,including maintainability and maintenance cost,are considered in the generalized FMECA information.Subsequently,the cognition best worst method is used to calculate the weight of each screened index,and the weaknesses of the subsystems are ranked using a technique for order preference by similarity to an ideal solution.Finally,based on the failure data collected from certain domestic heavy-duty horizontal lathes,the weakness ranking result of the subsystems is obtained to verify the effectiveness of the proposed method.An improved weakness ranking method that can comprehensively analyze and identify weak subsystems is proposed herein for designing and improving the reliability of complex electromechanical products.

一种基于潜变量的Ranking模型构造算法

现有的Ranking算法获得的模型全部来自训练数据,因为很多模型的有用信息并不能完全从训练数据中得到,因此这样得到的模型不够精确,对此,提出一种基于潜变量的Ranking算法。该算法以结构化SVM为学习工具,将除训练数据外的其他有用信息以潜变量形式引入算法的框架中,并在此基础上定义了面向NDCG的目标函数。针对该目标函数非凸非平滑,首先使用"凹-凸过程"进行逼近,然后用"近似Bundle法"展开优化计算。基准数据集上的实验结果表明:相比完全依靠训练数据的Ranking算法,本文算法获得的模型更为精确。

基于pairwise的改进ranking算法

传统基于pairwise的ranking算法,学习后得到的模型在用NDCG这样的ranking标准评价时效果并不好,对此提出了一种新型ranking算法。该算法也是使用样本对作为训练数据,但定义了一个面向NDCG评估标准的目标函数。针对此目标函数非平滑、难以直接优化的特点,提出使用割平面算法进行学习,不仅解决了上述问题,而且使算法迭代的次数不再依赖于训练样本对数。最后基于基准数据集的实验证明了算法的有效性。

Ranking environmental projects model based on multicriteria decision-making and the weight sensitivity analysis

With the fast growth of Chinese economic, more and more capital will be invested in environmental projects. How to select the environmental investment projects （alternatives） for obtaining the best environmental quality and economic benefits is an important problem for the decision makers. The purpose of this paper is to develop a decision-making model to rank a finite number of alternatives with several and sometimes conflicting criteria. A model for ranking the projects of municipal sewage treatment plants is proposed by using exports＇ information and the data of the real projects. And, the ranking result is given based on the PROMETHEE method. Furthermore, by means of the concept of the weight stability intervals （WSI）, the sensitivity of the ranking results to the size of criteria values and the change of weights value of criteria are discussed. The result shows that some criteria, such as “proportion of benefit to project cost”, will influence the ranking result of alternatives very strong while others not. The influence are not only from the value of criterion but also from the changing the weight of criterion. So, some criteria such as “proportion of benefit to project cost” are key critera for ranking the projects. Decision makers must be cautious to them.

Shuffled complex evolution coupled with stochastic ranking for reservoir scheduling problems

This paper introduces an optimization method(SCE-SR)that combines shuffled complex evolution(SCE)and stochastic ranking(SR)to solve constrained reservoir scheduling problems,ranking individuals with both objectives and constrains considered.A specialized strategy is used in the evolution process to ensure that the optimal results are feasible individuals.This method is suitable for handling multiple conflicting constraints,and is easy to implement,requiring little parameter tuning.The search properties of the method are ensured through the combination of deterministic and probabilistic approaches.The proposed SCE-SR was tested against hydropower scheduling problems of a single reservoir and a multi-reservoir system,and its performance is compared with that of two classical methods(the dynamic programming and genetic algorithm).The results show that the SCE-SR method is an effective and efficient method for optimizing hydropower generation and locating feasible regions quickly,with sufficient global convergence properties and robustness.The operation schedules obtained satisfy the basic scheduling requirements of reservoirs.

Ranking important nodes in complex networks by simulated annealing

In this paper, based on simulated annealing a new method to rank important nodes in complex networks is presented.First, the concept of an importance sequence（IS） to describe the relative importance of nodes in complex networks is defined. Then, a measure used to evaluate the reasonability of an IS is designed. By comparing an IS and the measure of its reasonability to a state of complex networks and the energy of the state, respectively, the method finds the ground state of complex networks by simulated annealing. In other words, the method can construct a most reasonable IS. The results of experiments on real and artificial networks show that this ranking method not only is effective but also can be applied to different kinds of complex networks.

Random Low Patch⁃rank Method for Interpolation of Regularly Missing Traces

Assuming seismic data in a suitable domain is low rank while missing traces or noises increase the rank of the data matrix,the rank⁃reduced methods have been applied successfully for seismic interpolation and denoising.These rank⁃reduced methods mainly include Cadzow reconstruction that uses eigen decomposition of the Hankel matrix in the f⁃x(frequency⁃spatial)domain,and nuclear⁃norm minimization(NNM)based on rigorous optimization theory on matrix completion(MC).In this paper,a low patch⁃rank MC is proposed with a random⁃overlapped texture⁃patch mapping for interpolation of regularly missing traces in a three⁃dimensional(3D)seismic volume.The random overlap plays a simple but important role to make the low⁃rank method effective for aliased data.It shifts the regular column missing of data matrix to random point missing in the mapped matrix,where the missing data increase the rank thus the classic low⁃rank MC theory works.Unlike the Hankel matrix based rank⁃reduced method,the proposed method does not assume a superposition of linear events,but assumes the data have repeated texture patterns.Such data lead to a low⁃rank matrix after the proposed texture⁃patch mapping.Thus the methods can interpolate the waveforms with varying dips in space.A fast low⁃rank factorization method and an orthogonal rank⁃one matrix pursuit method are applied to solve the presented interpolation model.The former avoids the singular value decomposition(SVD)computation and the latter only needs to compute the large singular values during iterations.The two fast algorithms are suitable for large⁃scale data.Simple averaging realizations of several results from different random⁃overlapped texture⁃patch mappings can further increase the reconstructed signal⁃to⁃noise ratio(SNR).Examples on synthetic data and field data are provided to show successful performance of the presented method.

Low-complexity soft-output signal detector based on adaptive pre-conditioned gradient descent method for uplink multiuser massive MIMO systems

In multiuser massive Multiple Input Multiple Output(MIMO)systems,a large amount of antennas are deployed at the Base Station(BS).In this case,the Minimum Mean Square Error(MMSE)detector with soft-output can achieve the near-optimal performance at the cost of a large-scale matrix inversion operation.The optimization algorithms such as Gradient Descent(GD)method have received a lot of attention to realize the MMSE detection efficiently without a large scale matrix inversion operation.However,they converge slowly when the condition number of the MMSE filtering matrix(the coefficient matrix)increases,which can compromise the efficiency of their implementation.Moreover,their soft information computation also involves a large-scale matrix-matrix multiplication operation.In this paper,a low-complexity soft-output signal detector based on Adaptive Pre-conditioned Gradient Descent(APGD-SOD)method is proposed to realize the MMSE detection with soft-output for uplink multiuser massive MIMO systems.In the proposed detector,an Adaptive Pre-conditioner(AP)matrix obtained through the Quasi-Newton Symmetric Rank One(QN-SR1)update in each iteration is used to accelerate the convergence of the GD method.The QN-SR1 update supports the intuitive notion that for the quadractic problem one should strive to make the pre-conditioner matrix close to the inverse of the coefficient matrix,since then the condition number would be close to unity and the convergence would be rapid.By expanding the signal model of the massive MIMO system and exploiting the channel hardening property of massive MIMO systems,the computational complexity of the soft information is simplified.The proposed AP matrix is applied to the GD method as a showcase.However,it also can be used by Conjugate Gradient(CG)method due to its generality.It is demonstrated that the proposed detector is robust and its convergence rate is superlinear.Simulation results show that the proposed detector converges at most four iterations.Simulation results also show that the proposed approach achieves a better trade-off between the complexity and the performance than several existing detectors and achieves a near-optimal performance of the MMSE detector with soft-output at four iterations without a complicated large scale matrix inversion operation,which entails a big challenge for the efficient implementation.

基于视觉信息加工的草原公路行车安全性分析

为探究草原公路各典型行车环境驾驶人视觉信息加工模式的差异性,从视觉层面解析草原公路行车安全,基于眼动仪开展草原公路实车实验,提取驾驶人在自由行驶、跟驰、对向来车、后车切入、路侧风险、交叉口6种真实行车环境下的眼动数据。首先,从视觉分配和视觉搜索两个维度构建评价指标体系,采用动态聚类法将驾驶人的视觉兴趣区域划分为5类,将信息熵与马尔可夫链平稳分布理论结合,构建视点分布信息熵量化驾驶人视觉注意分配的复杂度,并引入扫视幅度、扫视速度分析驾驶人对视觉信息的搜索过程。最后,构建基于CRITIC法的加权秩和比评价模型,对不同行车环境下驾驶人的视觉信息加工强度进行了综合评价。结果显示:驾驶人的视点分布在不同行车环境中存在差异,但当前车道始终是驾驶人获取视觉信息的主视区;各视觉评价指标随行车环境的变化呈显著的统计学差异;驾驶人遇到路侧风险时的视觉信息加工强度最大,行车安全性相对更低。

基于有限差分低秩分解策略的黏声各向异性纯qP波正演模拟方法

地下介质的黏弹性和各向异性特征会导致地震波出现相位频散和振幅衰减,如果在地震数据处理中忽略这些影响,那么成像结果会出现同相轴畸变和偏移假象。常规黏声TTI介质拟声波方程可用于模拟地震波在黏声各向异性介质中的传播特征,但存在伪横波干扰和受模型参数限制(各向异性参数ε必须大于δ)等问题。为了解决上述问题,将基于声学近似的纯qP波频散关系与常Q衰减模型相结合,推导了一种黏声TTI介质纯qP波方程。该方程包含解耦的相位频散和振幅衰减项,便于实现衰减补偿逆时偏移。基于新推导的方程,发展了有限差分低秩分解求解策略,实现了黏声TTI介质纯qP波正演模拟。数值模拟结果表明,该方程克服了黏声TTI介质拟声波方程的局限,可较为准确且稳定地模拟地震波在黏声各向异性介质中的传播过程。同时,该有限差分低秩分解求解策略继承了有限差分求解方法高效的优势,相比于传统低秩分解法具有更高的计算效率。

乘型模糊判断矩阵排序向量的递推方法

文章首先在模糊判断矩阵乘型一致性以及矩阵元素和权重之间关系的基础上,结合模糊判断矩阵的上三角矩阵元素,构建了一个关于权重和矩阵上三角元素的方程组,并证明了该方程组存在唯一的正解。随后指出方程组的证明过程就是模糊判断矩阵排序向量的求解过程,从而给出了乘型一致性模糊判断矩阵排序向量的一种递推方法。然后,在偏差函数基础上,通过构造并求解一个优化模型,求出了非乘型一致性模糊判断矩阵的排序向量,结果显示,其解的形式与采用乘型一致性模糊判断矩阵递推方法得到的排序向量完全一样。最后,通过实例以及相关方法对比说明排序向量递推方法是可行有效的。

基于分数阶全变分和低秩正则化的彩色图像去模糊方法

针对现有的彩色图像去模糊过程中存在色彩失衡、阶梯效应和伪影等现象,提出了一种基于分数阶全变分和低秩正则的图像去模糊优化方法。首先,将传统的RGB彩色图像转换到YCbCr颜色空间,利用其亮度通道特征解决色彩失衡问题;其次,利用分数阶全变分的特征消除图像恢复任务中出现的阶梯效应,并且引入加权核范数低秩正则进一步抑制伪影及噪声;最后,利用交替方向乘子法设计出高效的求解方法,通过迭代优化得到纯净图像的最优估计。对彩色图像测试的实验结果表明,所提出的方法对图像去模糊任务取得较好的视觉恢复效果,客观评价指标良好。

天山北坡经济带城乡聚落格局变化与影响因素分析

以天山北坡经济带城乡聚落为研究对象,采用景观格局指数、地理信息系统(GIS)空间分析、位序-规模法等分析1980-2020年间城乡聚落格局的变化,结合地理探测器从自然、区位及社会经济因素探究城乡聚落格局的影响因素。结果表明:1980-2020年间,城乡聚落数量、规模持续扩张且趋于集聚,前20 a扩张以耕地、草地为主,后20 a以未利用地为主;城乡聚落密度变化显著,高密度区主要集中在各绿洲呈现“多核集中分布”特征,中密度区沿高密度区周围扩张,低密度区沿绿洲边缘分布并在非绿洲区有扩张;首位聚落规模发展较好,趋于位序-规模曲线,中型聚落略微发育,小型聚落数量持续减少;交通等区位因素是天山北坡城乡聚落格局的首位影响因素,社会经济因素次之,自然地理因素影响较小,各影响因素差异显著。

中国城市经济韧性的水平、耦合协调度与空间分布特征

中国经济的强韧性是防范风险的最有力支撑。以全国282个城市为研究样本,采用熵权法与加权秩和比法测算中国城市经济韧性水平,在此基础上利用耦合协调模型、空间自相关模型研究中国城市经济韧性三大子系统的耦合协调度,分析其空间分布的整体特征。研究发现:2011—2019年,中国城市经济韧性整体处在中等水平且呈现缓慢上升趋势,同时区域差异较大;中国城市经济韧性总体处于中级协调阶段,但各地区三大子系统耦合协调度及其增长情况有所不同;中国城市经济韧性的耦合协调度在空间分布上具有正相关性,且呈现显著的空间集聚特征和方向性。由此建议采取措施扎实推进城市经济韧性及城市经济韧性耦合协调度的全方位提升,提高区域经济的整体发展水平。

辽宁省农村三产融合综合评价研究

农村一二三产融合是实现乡村振兴和农业农村现代化的重要手段,有利于农村经济的高质量发展.辽宁省农村一二三产融合在空间上分布不均衡,运用熵权法和秩和比综合评价法研究农村三产融合综合水平,对于辽宁省三产融合高质量发展具有理论与现实意义.首先分析了辽宁省2012—2021年农村三产融合水平的时序变化,然后以2021年为例对辽宁省14地市农村三产融合水平的空间差异进行研究.结果表明:从时间特征来看,辽宁省2012—2021年农村一二三产融合综合水平总体呈上升趋势,受2016年中国经济呈现的放缓态势的影响,2016年融合水平出现明显下降;从空间特征来看,2021年辽宁省农村一二三产融合综合水平各地存在空间差异,其中大连排名第一,沈阳次之,抚顺、营口、辽阳排名靠后,前后存在较大差距.采用障碍度模型计算障碍因子,各地应立足自身优势,弥补不足,关注生产效率和产业链延伸的同时更要注重社会效益和绿色可持续发展,推进辽宁农村三产业融合整体发展.

基于AHP-TOPSIS的土壤重金属污染修复植物适宜性评价

目的筛选出适应性强、处理效果好的植物,用于解决困扰重金属污染土壤植物修复的选种难题。方法应用层次分析法构建以生态适应性、技术可行性、经济可行性为准则、下含8个影响因素的指标体系,建立判断矩阵,并对其构造进行改进,通过逼近理想解的排序法筛选出最适合的修复植物;把该模型用于沈阳市某化工厂被重金属严重污染的土壤修复,对15种植物进行评价、筛选。结果构造的改进判断矩阵评价误差更小;通过计算发现,在污染土壤植物修复工程中,耐旱性是影响修复效果的主要因素;筛选结果表明,狼尾草等适合用于该场地的植物修复,接近度为0.838,去除率可达82%。结论该植物筛选模型能综合考虑各评价指标的影响,可以客观、准确地筛选出修复植物。

突发公共卫生事件下各省医疗物资供给与医疗服务综合效率评价--基于DEA-RSR模型

文章对2020年新冠疫情下我国31个省份医院服务效率进行综合评价,并进行防疫前后综合效率对比,以帮助各省医疗系统提高弹性,在后疫情时代有效应对突发公共卫生事件。从提高不良输出DMU识别能力的角度,将“数据包络分析法(DEA)”和“秩和比综合比较法(RSR)”相结合,利用DEA和反向DEA模型分别从乐观和悲观的角度计算综合效率,在此基础上采用RSR法进行排序、分档。各省医院综合效率划分为四个等级,其中山东、四川、广东3个省份位于第四等级,青海和西藏位于第一等级;青海和西藏的医院在防疫期间的综合实力还有待加强,部分地区医疗资源出现挤兑,各省应调整医疗资源分配、扩大医疗服务功能。

高、低阶煤孔隙结构差异性及其对甲烷吸附特性的影响研究

采集沁水盆地高阶煤、二连盆地低阶煤,采用氮气、二氧化碳吸附法对煤基质纳米级吸附孔隙进行测试,分析高低阶煤的储气特征,从而确定影响高、低阶煤煤层气开发的关键因素。研究结果发现,高、低阶煤的比表面积相似,高阶煤的比表面积均值为215.33 m~2/g,可吸附甲烷49.53 cm~3/g;低阶煤的比表面积均值为212.17 m~2/g,可吸附甲烷48.96 cm~3/g。高、低阶煤储气主要为吸附气,占总储气量的99%以上,高阶煤储气主要是微孔吸附气,占总储气量的99.71%;低阶煤不同孔隙均有贡献,微孔最多,占比47.33%,其次是中孔,占比32.644%,大孔最少,占比19.69%。地层水侵入对低阶煤储气量影响大于高阶煤,平衡水条件下,高阶煤可储气量43.46 cm~3/g,低阶煤可储气量33.89 cm~3/g;受微孔特性影响,高阶煤储层储气量较高,但易储难逸,因此煤层气开采的关键问题是疏导微孔吸附气;受中、大孔及亲水性强影响,低阶煤储气量较低,且易储易散,勘察储气聚集区是低阶煤煤层气开发的首要工作。

基于加权密切值法的山东省健康管理服务评价研究

目的:以山东省三级综合医院为切入点,研究目前健康管理服务实施现状,探索其中存在的问题,为出台有关政策措施、提升健康管理服务能力提供依据。方法:在山东各地市随机抽取25家设有健康管理中心或体检中心的三级综合医院,对其健康管理服务现状进行问卷调查;采用加权密切值法和秩和比法两种方法对三级综合医院健康管理服务能力进行评价分档,并根据不同分档医院的健康管理服务能力绘制雷达图,探索薄弱环节。结果:(1)综合评价分档排序中2家医院处于优档,13家医院为中档,3家医院为差档;分别约占11.11%、72.22%、16.67%。(2)同一地市不同三级综合医院健康管理服务能力差异较大。(3)通过“雷达图”直观反映,三档中差档在投入、过程、产出、效果指标方面均与其他两档具有明显差异。结论:政府应加强顶层设计,提升区域内健康管理服务标准化建设;激励三级综合医院发挥骨干带头作用,与医联体各成员单位多项联动,合理构建全域健康管理服务新体系。