自适应外部环境的电网安全稳定智能防御系统应用

基于江西自适应外部环境的电网智能防御系统实际运行,结合实际调度运行的案例阐述了智能防御系统的作用。系统实现了电网拓扑关系、地理信息、自然环境信息、电网运行状态信息的多时间尺度多维一体可视化展示。基于灾害机理,系统实现了自然环境引发设备故障的概率量化评估和自适应预想故障集调整。结合雷电、山火实例介绍了考虑自然灾害信息的电网安全预警与辅助决策和相继故障风险评估等功能。最后,介绍了离线研究子系统在编制短、长期调度预案方面的积极作用。     

复合行星轮系均载特性仿真研究

针对复合行星轮系均载特性的问题,以Ravigneaux式复合行星轮系统的典型结构为研究对象,基于ADAMS建立了系统的虚拟样机模型,通过对齿轮动态啮合力进行仿真分析,计算出了各啮合副的均载系数,分析了齿轮偏心误差、负载以及输入转速对于系统均载性能的影响。研究结果表明:增加偏心误差、提高转速会降低系统的均载性能;当行星轮偏心误差呈辐射状对称时,系统的均载性能会得到极大提高;太阳轮s1偏心误差对于系统均载性能的影响较s2偏心误差的影响更为明显;增加负载能提高系统的均载性能。     

L1 Graph联合转换学习多观测样本分类算法

不同分布多观测样本分类问题中,训练样本和测试样本来自不同的域,针对如何利用转换学习提高不同分布多观测样本分类性能问题,提出L1 Graph联合转换学习的多观测样本分类算法。首先基于转换学习构建一种非负矩阵三因子分解框架,将其中不变信息作为源域到目标域的转换桥梁;其次,基于稀疏表示思路构造L1 Graph,自适应寻找数据近邻,保留样本及特征几何结构;最后,将两个互补目标函数联合到统一优化问题中,然后利用迭代算法解决优化问题,进而估计出测试样本类别。在USPS Binary数字数据库、Three Domain Object Benchmark数据库和ALOI数据库上进行对比实验,实验结果表明该方法的有效性,既提高了识别精度又保证了算法鲁棒性。     

A framework for reasoning with soft information

In order to provide for the representation and manipulation of human sourced soft information we turn to the fuzzy set based theory of approximate reasoning. We describe how approximate reasoning provides a framework for representing and manipulating a wide body linguistically expressed information. We then suggest a number of extensions of the theory to enhance its representational capacity. One such extension focuses on the ability to model imprecise variables as well as imprecise values for the variable. We consider the representation of possible qualified propositions. We look at the issue of deduction in the face of conflict in our knowledge base and suggest an approach compatible with human behavior.     

Generalized lower and upper approximations in a ring

In this paper, the concepts of set-valued homomorphism and strong set-valued homomorphism of a ring are introduced, and related properties are investigated. The notions of generalized lower and upper approximation operators, constructed by means of a set-valued mapping, which is a generalization of the notion of lower and upper approximation of a ring, are provided. We also propose the notion of generalized lower and upper approximations with respect to an ideal of a ring which is an extended notation of rough ideal introduced lately by Davvaz [B. Davvaz, Roughness in rings, Information Science 164 (2004) 147-163] in a ring and discuss some significant properties of them.     

SVM-FuzCoC: A novel SVM-based feature selection method using a fuzzy complementary criterion

An efficient filter feature selection (FS) method is proposed in this paper, the SVM-FuzCoC approach, achieving a satisfactory trade-off between classification accuracy and dimensionality reduction. Additionally, the method has reasonably low computational requirements, even in high-dimensional feature spaces. To assess the quality of features, we introduce a local fuzzy evaluation measure with respect to patterns that embraces fuzzy membership degrees of every pattern in their classes. Accordingly, the above measure reveals the adequacy of data coverage provided by each feature. The required membership grades are determined via a novel fuzzy output kernel-based support vector machine, applied on single features. Based on a fuzzy complementary criterion (FuzCoC), the FS procedure iteratively selects features with maximum additional contribution in regard to the information content provided by previously selected features. This search strategy leads to small subsets of powerful and complementary features, alleviating the feature redundancy problem. We also devise different SVM-FuzCoC variants by employing seven other methods to derive fuzzy degrees from SVM outputs, based on probabilistic or fuzzy criteria. Our method is compared with a set of existing FS methods, in terms of performance capability, dimensionality reduction, and computational speed, via a comprehensive experimental setup, including synthetic and real-world datasets.     

Developing a Fuzzy Risk Allocation Model for PPP Projects in China

Equitable allocation of risks between the government and the private sector in concession agreement is essential to the success of public-private partnership (PPP) projects. The decision-making process, based on the established risk allocation principles expressed in linguistic terms, requires qualitative judgment and experiential knowledge of construction experts. However, it is subjective, partial, and implicit in actual application. This paper aims to develop a fuzzy synthetic evaluation model for determining an equitable risk allocation between the government and the private sector. By doing so, it assists the PPP project practitioners to transform the risk allocation principles in linguistic terms into a more usable and systematic quantitative-based analysis using fuzzy set. Twenty-three principles and influencing factors for risk allocation were identified through a comprehensive literature review. Nine critical risk allocation criteria (RACs) that evaluate the risk carrying capability of project participants were further identified, validated, and compiled based on the experts’ knowledge via face-to-face interviews. On the other hand, the weighting for each critical risk allocation criterion was determined through a two-round Delphi questionnaire survey. A set of knowledge-based fuzzy inference rules was then established to set up the membership function for the nine RACs. Based on the research findings, a fuzzy synthetic evaluation model was finally established to determine an equitable risk allocation between the government and the private sector.     

Fuzzy Genetic Approach for Modeling Reference Evapotranspiration

This study investigates the ability of fuzzy genetic (FG) approach in modeling of reference evapotranspiration (ET0). The daily climatic data, solar radiation, air temperature, relative humidity, and wind speed from three stations, Windsor, Oakville and Santa Rosa, in central California, are used as inputs to the FG models to estimate ET0 obtained using the FAO-56 Penman-Monteith equation. A comparison is made between the estimates provided by the FG and those of the following empirical models: the California Irrigation Management System Penman, Hargreaves, Ritchie, and Turc methods. The FG results are also compared with the artificial neural networks. Root-mean-square errors (RMSE), mean-absolute errors (MAE), and correlation coefficient statistics are used as comparing criteria for the evaluation of the models’ performances. The comparison results reveal that the FG models are superior to the ANN and empirical models in modeling ET0 process. For the Windsor, Oakville, and Santa Rosa stations, it was found that the FG models with RMSEW = 0.138, MAEW = 0.098, and RW = 0.999; RMSEO = 0.144, MAEO = 0.102, and RO = 0.999; and RMSES = 0.167, MAES = 0.115, and RS = 0.998 in test period is superior in modeling daily ET0 than the other models, respectively.     

平板电视可靠性MTBF的管控论述

越来越多平板电视厂商意识到要通过提高产品的可靠性来提高品牌美誉度和降低售后三包成本.因此,该文论述了可靠性的衡量指标MTBF和统计方法,并且研究如何在开发阶段进行MTBF鉴定,以及到量产阶段仍然进行有效监控.可以直观地指导厂商进行平板电视可靠性管控.