Informatica

The 2-tuple linguistic computational model is an important tool to deal with linguistic information. To extend the application of hesitant fuzzy linguistic term sets and avoid information loss, this paper introduces hesitant fuzzy 2-tuple linguistic term sets that are expressed by using several symbolic numbers in $[0\mathrm{,}1]$. Considering the order relationship between hesitant fuzzy 2-tuple linguistic term sets, measures of expected value and variance are defined. Meanwhile, several induced generalized hesitant fuzzy 2-tuple linguistic aggregation operators are defined, by which the comprehensive attribute values of alternatives can be obtained. Then, models for the optimal weight vector on a decision maker set, on an attribute set and on their ordered sets are constructed, respectively. Furthermore, an approach to multi-granularity group decision making with hesitant fuzzy linguistic information is developed. Finally, an example is selected to illustrate the feasibility and practicality of the proposed procedure.

This paper presents minimum mean square error (MMSE) estimators for mean life and failure rate of Exponential distribution model based on failure censored step-stress accelerated life-testing (SSALT) data. The MMSE estimators are drived by revising the corresponding unbiased estimators in terms of mean square error (MSE). Two theorems prove mathematically the fact that MSE of the resulting MMSE estimators are smaller than that of the corresponding unbiased estimators. The results show that the MMSE estimators are more efficient than the unbiased estimators and maximum likelihood estimators (MLEs) in small and moderate sample size.

In the fierce global competition, cost, quality and customer satisfaction appears to be utmost significant. Flexible manufacturing systems (FMS) have a great potential in manufacturing both cost effective and customer based products. These systems bring us flexibility, but this flexibility accompanies cost and time. Thus, selecting suitable FMS necessitates excessive attention. The problem of FMS selection and evaluation becomes more difficult when facing multi FMSs selection problem. In this paper, we propose an integrated approach to find a suitable combination of FMSs in a multi FMSs decision making problem. Each FMS has several alternatives. Therefore, there are many possible solutions for this problem. We first identify the objective and subjective attributes. Second, Grey system theory is applied to deal with the incomplete and uncertain information of subjective data, and the objective data are extracted from simulation modelling. A goal-programming model is then utilized to formulate the problem and to assign priorities to the objectives. Finally, a genetic algorithm (FA) based model is applied to solve the combination problem, as the formulated problem is difficult to be solved. The model proposed in this paper determines the most appropriate FMSs combination and facilitates decision making of such a hard problem.

Following the White Paper HCE that Google released in August 2014, it was expected that NFC mobile phone payment would cause a wave of security discussion. After all, Android HCE will allow anyone to develop his own payment service on the Android platform to get rid of restriction from telecommunications, financial industries, or third party trust centers. On this security mechanism observation period, we propose a lightweight authentication protocol on NFC mobile sensors payment. Through introducing this security agreement, it not only allows individuals to have privacy protected, but also can prevent malicious attackers from the track, which will make legitimate Tags verified, while effectively preventing an illegal Tag from being forged as an authenticated Tag. Therefore, constructing full security mechanisms will benefit to the development of mobile NFC payment.

Our research is devoted to development of information infrastructure for e-service semi-automatic provision by using distributed data warehouses (DWs) of water protection domain. Development of software for semi-automatic service provision is based on artificial planner and structure of goals adapted for specialized needs of end users. Such e-service preparation mechanism can work under the unified coherent framework for solving the environment protection problems by evaluation of the processes of water consumption and contamination. The possibilities of integration of distributed DWs of water management sector into web portal meeting the requirements of conceptual interoperability are presented. Design approach is based on development of decision support system (DSS) that is designed as multilayered system with multi–componential, interoperable structure of databases (DBs), which are under responsibility of different public administration institutions such as European Environment Information and Observation Network (EIONET) and national environment protection agencies. The infrastructure of EIONET is used for supporting and improving data and information flows. The Water resource management information system (WRMIS) became the kernel component of DSS. WRMIS prototype facilitates data flows between the institutions and gives access to data for relevant institutions and the public providing e-services using proposed DSS. The research investigations are made according to the requirements of European Union Water Framework Directive, Sustainable Development Strategy and ReportNet as the EIONET infrastructure for supporting and improving data and information flows. Additional means are integrated in the structures of DSS as knowledge representation techniques based on conceptual schemas, data flow diagrams, and decision-making rules. The on-line management techniques are based on assurance of interoperability by using Open Web Platform W3C standards for web service development, such as XML, SOAP, HTTP, etc.

In this paper, we focus on group decision making problems with uncertain preference ordinals, in which the weight information of decision makers is completely unknown or partly unknown. First of all, the consistency and deviation measures between two uncertain preference ordinals are defined. Based on the two measures, a multi-objective optimization model which aims to maximize the deviation of each decision maker’s judgements and the consistency among different decision makers’ judgements is established to obtain the weights of decision makers. The compromise solution method, i.e. the VIKOR method is then extended to derive the compromise solution of alternatives for group decision making problems with uncertain preference ordinals. Finally, three examples are utilized to illustrate the feasibility and effectiveness of the proposed approach.

This paper presents the corpus-driven approach in building the computational model of fundamental frequency, or ${\mathit{F}}_0$, for Lithuanian language. The model was obtained by training the HMM-based speech synthesis system HTS on six hours of speech coming from multiple speakers. Several gender specific models, using different parameters and different contextual factors, were investigated. The models were evaluated by synthesizing ${\mathit{F}}_0$ contours and by comparing them to the original ${\mathit{F}}_0$ contours using criteria of root mean square error (RMSE) and voicing classification error. The HMM-based models showed an improvement of the RMSE over the mean-based model that predicted ${\mathit{F}}_0$ of the vowel on the basis of its average normalized pitch.

The aim of this paper is twofold. Firstly, to discuss a clustering of a given set of the European banks into groups based on their performance during 1999–2013. Secondly, to compare different dissimilarity measures and to determine which of them suits best for clustering banking ratios. Six ratios that reveal profitability, efficiency, stability and loan portfolio quality of the banks were used. The similarity/dissimilarity between banks was estimated using measures that are based on time series or functional data properties. Two dissimilarity measures that are not commonly used in the literature are proposed and two measures are extended from univariate into multivariate case. The results of our study show that there is no dissimilarity measure which would provide the best clustering results for all ratios. However, dissimilarity measures based on functional data properties in many cases outperfomed measures based on time series properties. The choice of the number of clusters is not that clear. According to different banking ratios, it is found that banks could be grouped into 6–12 clusters.

Long-term planning for energy systems is often based on deterministic economic optimization and forecasts of fuel prices. When fuel price evolution is underestimated, the consequence is a low penetration of renewables and more efficient technologies in favour of fossil alternatives. This work aims at overcoming this issue by assessing the impact of uncertainty on energy planning decisions.

The paper presents analytic and stochastic methods of structure parameters estimation for a model selection problem. Structure parameters are covariance matrices of parameters of linear and non-linear regression models. To optimize model parameters and structure parameters we maximize a model evidence, a convolution of a data likelihood with a prior distribution of model parameters. The analytic methods are based on the derivatives computation of the approximated model evidence. The stochastic methods are based on the model parameters sampling and data cross-validation. The proposed methods are tested and compared on the synthetic and real data.