Some Results for Pythagorean Fuzzy Sets

Pythagorean fuzzy sets (PFSs), originally proposed by Yager (Yager, Abbasov. Int J Intell Syst 2013;28:436–452), are a new tool to deal with vagueness considering the membership grades are pairs satisfying the condition . As a generalized set, PFSs have close relationship with intuitionistic fuzzy sets (IFSs). PFSs can be reduced to IFSs satisfying the condition . However, the related operations of PFSs do not take different conditions into consideration. To better understand PFSs, we propose two operations: division and subtraction, and discuss their properties in detail. Then, based on Pythagorean fuzzy aggregation operators, their properties such as boundedness, idempotency, and monotonicity are investigated. Later, we develop a Pythagorean fuzzy superiority and inferiority ranking method to solve uncertainty multiple attribute group decision making problem. Finally, an illustrative example for evaluating the Internet stocks performance is given to verify the developed approach and to demonstrate its practicality and effectiveness.     

Analysis of thermo-mechanical wear problems for reciprocal punch sliding

The relative sliding motion of two elastic bodies in contact induces wear process and contact shape evolution. In the case of a punch sliding on a substrate the transient process tends to a steady state for which the fixed contact stress and strain distribution develops in the contact zone. This state usually corresponds to a minimum of the wear dissipation power. The optimality conditions of the wear dissipation functional provide the contact stress distribution and the wear rate compatible with the rigid body punch motion. The present paper is aimed to extend the previous analyses [1], [2], [3], [4], [5] of steady state conditions to cases of periodic sliding of punch, assuming cyclic steady state conditions for both mechanical and thermal fields.     

基于自适应特征融合的粒子滤波目标跟踪算法

提出了一种基于自适应特征融合的粒子滤波跟踪算法,用于解决传统的粒子滤波跟踪方法在复杂背景下容易跟踪失败的问题。该算法选取颜色特征和边缘特征来描述目标,并通过粒子滤波进行特征融合,根据可靠性因子调整各特征的权值系数;在跟踪过程中,随着目标自身形变,自适应更新目标模板。实验结果表明,在复杂背景下以及受到遮挡时,本算法能够准确稳健地跟踪目标。     

基于Landsat 8影像和能量平衡的西安市热场格局研究!

基于2014年8月15日的Landsat 8影像,通过劈窗算法反演西安中心城区地表温度,定量测算热岛中心范围。估算多种地表能量分量,分析热环境格局与地表能量分量的关系。结果表明:(1)西安中心城区城市热岛集中分布在人口、居住、商业密集区、经济技术开发区以及植被覆盖较差的区域;(2)感热、波文比与地表温度呈正相关,人为热与温度呈不显著正相关,净辐射、潜热与地表温度呈显著负相关;(3)城市热岛的地表能量结构中感热与潜热差异是构成城市热岛差异的主要原因。     

面向对象的黑河下游河岸林植被覆盖信息分类!

地表植被覆盖是描述区域生态系统的基础数据,也是全球及区域陆面过程、生态与水文众多模型中所需的重要地表参数。对于黑河下游额济纳绿洲,以Landsat 30m分辨率为主的遥感影像难以真实提取下游绿洲河岸林植被覆盖信息,而高分辨率影像目标地物轮廓清晰、空间细节信息丰富,有利于干旱背景下景观破碎、异质性强的植被覆盖信息分类。基于黑河下游额济纳绿洲QuickBird影像,通过面向对象的分类方法提取耕地、胡杨、柽柳、草地和裸地等主要植被覆盖类型,分类总体精度和Kappa系数分别为84.71%和0.7986。结果表明:利用面向对象分类方法对高分辨率影像进行植被覆盖信息分类,分类结果较好,能够满足精度要求。     

基于双EnKF的土壤水分与土壤属性参数同时估计

为提高土壤水分数据同化结果的精度,将基于双集合卡尔曼滤波(Dual Ensemble Kalman Filter,DEnKF)的状态-参数估计方案与简单生物圈模型(simple biosphere model 2,SiB2)相结合,同时更新土壤水分和优化模型参数(土壤属性参数)。选用2008年6月1日~10月29日黑河上游阿柔冻融观测站为参考站,开展了同化表层土壤水分观测数据的实验。研究结果表明:DEnKF可同时优化土壤属性参数和改进土壤水分估计,该方法对表层土壤水分估计的精度0.04高于EnKF算法的精度0.05。当观测数据稀少时,DEnKF算法仍然可以得到较高精度的土壤水分估计,3层土壤水分的估计精度在0.02~0.05之间。     

基于变步长阵发混沌的ZPW-2000低频信号检测方法研究

针对常规混沌阵列检测轨道移频信号所带低频信息存在复杂度高和精确性低的问题,对常规方法进行改进,提出了一种基于变步长阵发混沌的低频信号检测方法,该方法只运用一个Duffing振子,通过设置一组步长可以覆盖整个待检测低频信号所在频段的序列,来实现对轨道低频信号的检测。结合Duffing混沌系统检测信号的优越性,分析了利用变步长阵发混沌法检测低频信号的可行性,给出了此方法检测低频信号的步骤,并从理论上计算了可发生阵发混沌的步长序列,然后利用Matlab/Simulink搭建仿真模型进行仿真验证。结果表明,可用变步长阵法混沌法对低频信号进行检测。最后再利用阵发混沌周期法对仿真结果进行进一步研究,得出精确的低频信号。变步长阵发混沌法的检测性能在精确性及系统复杂度方面都优于传统的轨道低频信号检测方法,并且可以实现低信噪比下低频信号的检测。     

基于邻域关系模糊粗糙集的医学图像分类研究

对医学图像进行分类时,特征选择是影响分类准确率的非常重要的因素。针对医学图像的特殊性,以及目前提出的特征选择算法在应用于医学图像分类时效果不够理想等问题,提出一种基于邻域关系的模糊粗糙集模型,基于该模型给出特征选择算法,并将其应用于乳腺X光图像。实验结果表明,同已有的算法相比,该方法能有效选择特征,分类精度有较大的提升。     

A detection problem: Sensitivity and uncertainty analysis of a land surface temperature approach to detecting dynamics of water use by groundwater-dependent vegetation

Sustainable management of groundwater-dependent vegetation (GDV) requires the accurate identification of GDVs, characterisation of their water use dynamics and an understanding of associated errors. This paper presents sensitivity and uncertainty analyses of one GDV mapping method which uses temperature differences between time-series of modelled and observed land surface temperature (LST) to detect groundwater use by vegetation in a subtropical woodland. Uncertainty in modelled LST was quantified using the Jacobian method with error variances obtained from literature. Groundwater use was inferred where modelled and observed LST were significantly different using a Student's t-test. Modelled LST was most sensitive to low-range wind speeds (<1.5 m s⁻¹), low-range vegetation height (<=0.5 m), and low-range leaf area index (<=0.5 m² m⁻²), limiting the detectability of groundwater use by vegetation under such conditions. The model-data approach was well-suited to detection of GDV because model-data errors were lowest for climatic conditions conducive to groundwater use.