基于Membership的高可用性Linux集群

介绍了Ｌｉｎｕｘ集群高可用性（ＨＡ）技术的应用背景和体系结构，重点讲述了集群Ｍｅｍｂｅｒｓｈｉｐ技术可以显著提高集群系统的可用性，并且提出了可配置的Ｍｅｍｂｅｒｓｈｉｐ服务（ＭＢＬＣ）来实现Ｌｉｎｕｘ集群的高可用性的方案。

基于Membership拓展的权限机制设计

给出一种基于ASP.NET中的Membership拓展与传统的RBAC机制相结合形成的一种权限机制,它能够实现在Web页面中对于角色与权限的有效控制访问,并且改进了Membership的对权限控制力度不够的缺陷以及传统RBAC机制不能有效利用ASP.NET控件的劣势。最后,在"研究生信息管理系统"项目中实现这种权限机制,充分验证了其优势以及可行性和有效性。

Idealized Numerical Simulation Study of the Potential Vorticity Banners over a Mesoscale Mountain: Dry Adiabatic Process

Topography-induced potential vorticity （PV） banners over a mesoscale topography （Dabie Mountain, hereafter DM） in eastern China, under an idealized dry adiabatic flow, are studied with a mesoscale numerical model, ARPS. PV banners generate over the leeside of the DM with a maximal intensity of ～1.5 PVU, and extend more than 100 km downstream, while the width varies from several to tens of kilometers, which contrasts with the half-width of the peaks along the ridge of the DM. Wave breaking occurs near the leeside surface of the DM, and leads to a strong PV generation. Combining with the PV generation, due to the friction and the flow splitting upstream, the PV is advected downstream, and then forms the PV banners over the DM. The PV banners are sensitive to the model resolution, Coriolis force, friction, subgrid turbulent mixing, stratification, the upstream wind speed and wind direction. The negative PV banners have a more compact connection with the low level turbulent kinetic energy. The PV banners are built up by the baroclinic and barotropic components. The barotropic-associated PV can identify the distribution of the PV banners, while the baroclinic one only has important contributions on the flanks and on the leeside near the topography. PV fluxes are diagnosed to investigate the influence of friction on the PV banners. Similar patterns are found between the total PV flux and the advective PV flux, except near the surface and inside the dipole of the PV banners, where the nonadvective PV flux associated with the friction has a net negative contribution.

Genome-wide association study identifies QTL for eight fruit traits in cultivated tomato (Solanum lycopersicum L.)

Genome-wide association study(GWAS)is effective in identifying favorable alleles for traits of interest with high mapping resolution in crop species.In this study,we conducted GWAS to explore quantitative trait loci(QTL)for eight fruit traits using 162 tomato accessions with diverse genetic backgrounds.The eight traits included fruit weight,fruit width,fruit height,fruit shape index,pericarp thickness,locule number,fruit firmness,and brix.Phenotypic variations of these traits in the tomato collection were evaluated with three replicates in field trials over three years.We filtered 34,550 confident SNPs from the 51K Axiom®tomato array based on<10%of missing data and>5%of minor allele frequency for association analysis.The 162 tomato accessions were divided into seven clusters and their membership coefficients were used to account for population structure along with a kinship matrix.To identify marker-trait associations(MTAs),four phenotypic data sets representing each of three years and combined were independently analyzed in the multilocus mixed model(MLMM).A total of 30 significant MTAs was detected over data sets for eight fruit traits at P<0.0005.The number of MTA per trait ranged from one(brix)to seven(fruit weight and fruit width).Two SNP markers on chromosomes 1 and 2 were significantly associated with multiple traits,suggesting pleiotropic effects of QTL.Furthermore,16 of 30 MTAs suggest potential novel QTL for eight fruit traits.These results facilitate genetic dissection of tomato fruit traits and provide a useful resource to develop molecular tools for improving fruit traits via marker-assisted selection and genomic selection in tomato breeding programs.

Fuzzy Set-Membership Filtering for Discrete-Time Nonlinear Systems

In this article,the problem of state estimation is addressed for discrete-time nonlinear systems subject to additive unknown-but-bounded noises by using fuzzy set-membership filtering.First,an improved T-S fuzzy model is introduced to achieve highly accurate approximation via an affine model under each fuzzy rule.Then,compared to traditional prediction-based ones,two types of fuzzy set-membership filters are proposed to effectively improve filtering performance,where the structure of both filters consists of two parts:prediction and filtering.Under the locally Lipschitz continuous condition of membership functions,unknown membership values in the estimation error system can be treated as multiplicative noises with respect to the estimation error.Real-time recursive algorithms are given to find the minimal ellipsoid containing the true state.Finally,the proposed optimization approaches are validated via numerical simulations of a one-dimensional and a three-dimensional discrete-time nonlinear systems.

Feature Selection Based on Adaptive Fuzzy Membership Functions

Neuro-fuzzy(NF)networks are adaptive fuzzy inference systems(FIS)and have been applied to feature selection by some researchers.However,their rule number will grow exponentially as the data dimension increases.On the other hand,feature selection algorithms with artificial neural networks(ANN)usually require normalization of input data,which will probably change some characteristics of original data that are important for classification.To overcome the problems mentioned above,this paper combines the fuzzification layer of the neuro-fuzzy system with the multi-layer perceptron(MLP)to form a new artificial neural network.Furthermore,fuzzification strategy and feature measurement based on membership space are proposed for feature selection. Finally,experiments with both natural and artificial data are carried out to compare with other methods,and the results approve the validity of the algorithm.

Multi-attribute decision making model based on optimal membership and relative entropy

To study the problems of multi-attribute decision making in which the attribute values are given in the form of linguistic fuzzy numbers and the information of attribute weights are incomplete, a new multi-attribute decision making model is presented based on the optimal membership and the relative entropy. Firstly, the definitions of the optimal membership and the relative entropy are given. Secondly, for all alternatives, a set of preference weight vectors are obtained by solving a set of linear programming models whose goals axe all to maximize the optimal membership. Thirdly, a relative entropy model is established to aggregate the preference weight vectors, thus an optimal weight vector is determined. Based on this optimal weight vector, the algorithm of deviation degree minimization is proposed to rank all the alternatives. Finally, a decision making example is given to demonstrate the feasibility and rationality of this new model.

FEW-NNN: A Fuzzy Entropy Weighted Natural Nearest Neighbor Method for Flow-Based Network Traffic Attack Detection

Attacks such as APT usually hide communication data in massive legitimate network traffic, and mining structurally complex and latent relationships among flow-based network traffic to detect attacks has become the focus of many initiatives. Effectively analyzing massive network security data with high dimensions for suspicious flow diagnosis is a huge challenge. In addition, the uneven distribution of network traffic does not fully reflect the differences of class sample features, resulting in the low accuracy of attack detection. To solve these problems, a novel approach called the fuzzy entropy weighted natural nearest neighbor(FEW-NNN) method is proposed to enhance the accuracy and efficiency of flowbased network traffic attack detection. First, the FEW-NNN method uses the Fisher score and deep graph feature learning algorithm to remove unimportant features and reduce the data dimension. Then, according to the proposed natural nearest neighbor searching algorithm(NNN_Searching), the density of data points, each class center and the smallest enclosing sphere radius are determined correspondingly. Finally, a fuzzy entropy weighted KNN classification method based on affinity is proposed, which mainly includes the following three steps: 1、 the feature weights of samples are calculated based on fuzzy entropy values, 2、 the fuzzy memberships of samples are determined based on affinity among samples, and 3、 K-neighbors are selected according to the class-conditional weighted Euclidean distance, the fuzzy membership value of the testing sample is calculated based on the membership of k-neighbors, and then all testing samples are classified according to the fuzzy membership value of the samples belonging to each class;that is, the attack type is determined. The method has been applied to the problem of attack detection and validated based on the famous KDD99 and CICIDS-2017 datasets. From the experimental results shown in this paper, it is observed that the FEW-NNN method improves the accuracy and efficiency of flow-based network traffic attack detection.

Rough function model and rough membership function

Two pairs of approximation operators, which are the scale lower and upper approximations as well as the real line lower and upper approximations, are defined. Their properties and antithesis characteristics are analyzed. The rough function model is generalized based on rough set theory, and the scheme of rough function theory is made more distinct and complete. Therefore, the transformation of the real function analysis from real line to scale is achieved. A series of basic concepts in rough function model including rough numbers, rough intervals, and rough membership functions are defined in the new scheme of the rough function model. Operating properties of rough intervals similar to rough sets are obtained. The relationship of rough inclusion and rough equality of rough intervals is defined by two kinds of tools, known as the lower （upper） approximation operator in real numbers domain and rough membership functions. Their relative properties are analyzed and proved strictly, which provides necessary theoretical foundation and technical support for the further discussion of properties and practical application of the rough function model.

On Income Distribution Mechanism of Farmer Cooperatives in the Context of Membership Heterogeneity

With reference to several possible solutions to the issue of two subject allocation,using the Accumulation Point analysis method in Game Theory,this paper analyzed the income distribution mechanism between large farmers and small farmers in farmer cooperatives in the context of membership heterogeneity. It found that,in the practice of the income distribution in farmer cooperatives,there possibly exists equalization solution,pure utility solution,Nash solution and Kalai-Smorodinsky solution and it will be affected by social conventions. Finally,it made an empirical analysis using five cases of farmer cooperatives.

The Theory of Membership Degree of Γ-Conclusion in Several n-Valued Logic Systems

Based on the analysis of the properties of Γ-conclusion by means of deduction theorems, completeness theorems and the theory of truth degree of formulas, the present papers introduces the concept of the membership degree of formulas A is a consequence of Γ (or Γ-conclusion) in Lukasiewicz n-valued propositional logic systems, Godel n-valued propositional logic system and the R0 n-valued propositional logic systems. The condition and related calculations of formulas A being Γ-conclusion were discussed by extent method. At the same time, some properties of membership degree of formulas A is a Γ-conclusion were given. We provide its algorithm of the membership degree of formulas A is a Γ-conclusion by the constructions of theory root.

Evaluation of Membership Degree on Conventional Chemical Components in Tobacco from Tobacco Leaf Bases in Yunnan Province Based on Cigarette Brands

[Objective] This study was conducted to investigate the quality condition of chemical components of tobacco in Yunnan. [Method] The C3F samples were collected from 76 base units in Yunnan Province and their conventional chemical components were analyzed, and the evaluation was carded out based on the membership function and hierarchical cluster analysis of cigarette brands H1 and H2 in Yunnan. [Result] The results showed that： （1） Major chemical components of 76 base units were coordinated overall. （2） Total nitrogen of brand H1 was higher than that of the high-quality tobacco leaves by 12.08%; raw tobacco of brands H3 and H2 satisfied the quality requirements of high-quality tobacco in Yunnan; and the total sugar and reducing sugar in flue-cured tobacco of brand H4 were higher those of the high-quality tobacco leaves by 0.76% and 10.3%, respectively. （3） The total sugar, reducing sugar and potassium of tobacco leaves from bases of G1 group were higher than those of tobacco from bases of G2 group by 38.1%, 2.27% and 7.34%, respectively; and total nitrogen and chlorine were lower by 4.69% and 11.11%, respectively; and nicotine contents in tobacco of the two groups were similar. （4） H2 and H3 were not significantly different in main chemical components; H3 and H4 were significantly different in total nitrogen, while other main chemical components were not significant different; and there were no significant differences between H4 and H2. [Conclusion] The quality of tobacco leaves from tobacco base units of G1 and G2 groups was better. Therefore, the evaluation provides theoretical reference for construction of tobacco base units.

Fuzzy logic controller design with unevenly-distributed membership function for high performance chamber cooling system

Fuzzy logic controller adopting unevenly-distributed membership function was presented with the purpose of enhancing performance of the temperature control precision and robustness for the chamber cooling system.Histogram equalization and noise detection were performed to modify the evenly-distributed membership functions of error and error change rate into unevenly-distributed membership functions.Then,the experimental results with evenly and unevenly distributed membership functions were compared under the same outside environment conditions.The experimental results show that the steady-state error is reduced around 40% and the noise disturbance is rejected successfully even though noise range is 60% of the control precision range.The control precision is improved by reducing the steady-state error and the robustness is enhanced by rejecting noise disturbance through the fuzzy logic controller with unevenly-distributed membership function.Moreover,the system energy efficiency and lifetime of electronic expansion valve(EEV) installed in chamber cooling system are improved by adopting the unevenly-distributed membership function.

Rock burst laws in deep mines based on combined model of membership function and dominance-based rough set

Rock bursts are spontaneous, violent fracture of rock that can occur in deep mines, and the likelihood of rock bursts occurring increases as depth of the mine increases. Rock bursts are also affected by the compressive strength, tensile strength, tangential strength, elastic energy index, etc. of rock, and the relationship between these factors and rock bursts in deep mines is difficult to analyze from quantitative point. Typical rock burst instances as a sample set were collected, and membership function was introduced to process the discrete values of these factors with the discrete factors as condition attributes and rock burst situations as decision attributes. Dominance-based rough set theory was used to generate preference rules of rock burst, and eventually rock burst laws analysis in deep mines with preference relation was taken. The results show that this model for rock burst laws analysis in deep mines is more reasonable and feasible, and the prediction results are more scientific.

Membership Proof and Verification in Authenticated Skip Lists Based on Heap

How to keep cloud data intact and available to users is a problem to be solved. Authenticated skip list is an important data structure used in cloud data integrity verification. How to get the membership proof of the element in authenticated skip list efficiently is an important part of authentication. Kaouthar Blibech and Alban Gabillon proposed a head proof and a tail proof algorithms for the membership proof of elements in the authenticated skip list. However, the proposed algorithms are uncorrelated each other and need plateau function. We propose a new algorithm for computing the membership proof for elements in the authenticated skip list by using two stacks, one is for storing traversal chain of leaf node, the other is for storing authentication path for the leaf. The proposed algorithm is simple and effective without needing plateau function. It can also be applicable for other similar binary hash trees.

An Approach to Formulation of FNLP with Complex Piecewise Linear Membership Functions

Traditionally, extra binary variables are demanded to formulate a fuzzy nonlinear programming(FNLP) problem with piecewise linear membership functions(PLMFs). However, this kind of methodology usually suffers increasing computational burden associated with formulation and solution, particularly in the face of complex PLMFs. Motivated by these challenges, this contribution introduces a novel approach free of additional binary variables to formulate FNLP with complex PLMFs, leading to superior performance in reducing computational complexity as well as simplifying formulation. A depth discussion about the approach is conducted in this paper, along with a numerical case study to demonstrate its potential benefits.

On Development of Fuzzy Controller: The Case of Gaussian and Triangular Membership Functions

In recent years, the use of Fuzzy set theory has been popularised for handling overlap domains in control engineering but this has mostly been within the context of triangular membership functions. In actual practice however, such domains are hardly triangular and in fact for most engineering applications the membership functions are usually Gaussian and sometimes cosine. In an earlier paper, we derived explicit Fourier series expressions for systematic and dynamic computation of grade of membership in the overlap and non-overlap regions of triangular Fuzzy sets. In another paper, we extended the methodology to cover cases of cosine, exponential and Gaussian Fuzzy sets by presenting explicit Fourier series representation for encoding fuzziness in the overlap and non-overlap domains of Fuzzy sets. This current paper presents the development of a “Fuzzy Controller” device, which incorporates the formal mathematical representation for computing grade of membership of Gaussian and triangular Fuzzy sets. It is shown that triangular approximation of Gaussian membership function in Fuzzy control can lead to wrong linguistic classification which may have adverse effects on operational and control decisions. The development of the Fuzzy controller demonstrates that the proposed technique can indeed be incorporated in engineering systems for dynamic and systematic computation of grade of membership in the overlap and non-overlap regions of Fuzzy sets;and thus provides a basis for the design of embedded Fuzzy controller for mission critical applications.

Life-cycle cost model and evaluation system for power grid assets based on fuzzy membership degree

Life-cycle cost(LCC)theory can be effectively applied to improve the efficiency and quality of power plant equipment and asset management.However,specific aspects of the LCC calculation and evaluation model require further research for practical application.This paper proposes an LCC assessment model for the management of electric power plant equipment during its service life.A membership function method based on fuzzy logic is used to improve the allocation of modernization and overhaul projects to multiple equipment assets.An LCC assessment model and evaluation system for power equipment are proposed and successfully applied to the equipment and project management of a Guangzhou power plant in the China Southern Power Grid,providing a decision-making mechanism that facilitates efficient operation and optimal utilization of power plant equipment and assets.

New Methods of Fitting the Membership Function of Oceanic Water Masses

After reviewing the analytical theories of T S curve, some methods of T S relationship, and fuzzy sets for studying water masses, new methods of fitting the membership function of oceanic water masses are presented based on the characteristics of T S curve family of oceanic water masses. The membership functions of oceanic Subsurface Water Mass with high salinity and Intermediate Water Mass with low salinity are fitted and discussed using the new methods. The proposed methods are useful in analyzing the mixing and modifying processes of these water masses, especially in tracing their sources. The principles and formulae of the new methods and examples are given.

Incremental learning of the triangular membership functions based on single-pass FCM and CHC genetic model

In order to improve the efficiency of learning the triangular membership functions( TMFs) for mining fuzzy association rule( FAR) in dynamic database,a single-pass fuzzy c means( SPFCM)algorithm is combined with the real-coded CHC genetic model to incrementally learn the TMFs. The cluster centers resulting from SPFCM are regarded as the midpoint of TMFs. The population of CHC is generated randomly according to the cluster center and constraint conditions among TMFs. Then a new population for incremental learning is composed of the excellent chromosomes stored in the first genetic process and the chromosomes generated based on the cluster center adjusted by SPFCM. The experiments on real datasets show that the number of generations converging to the solution of the proposed approach is less than that of the existing batch learning approach. The quality of TMFs generated by the approach is comparable to that of the batch learning approach. Compared with the existing incremental learning strategy,the proposed approach is superior in terms of the quality of TMFs and time cost.