Informatica

In the discrete form of multi-criteria decision-making (MCDM) problems, we are usually confronted with a decision-matrix formed from the information of some alternatives on some criteria. In this study, a new method is proposed for simultaneous evaluation of criteria and alternatives (SECA) in an MCDM problem. For making this type of evaluation, a multi-objective non-linear programming model is formulated. The model is based on maximization of the overall performance of alternatives with consideration of the variation information of decision-matrix within and between criteria. The standard deviation is used to measure the within-criterion, and the correlation is utilized to consider the between-criterion variation information. By solving the multi-objective model, we can determine the overall performance scores of alternatives and the objective weights of criteria simultaneously. To validate the proposed method, a numerical example is used, and three analyses are made. Firstly, we analyse the objective weights determined by the method, secondly, the stability of the performance scores and ranking results are examined, and finally, the ranking results of the proposed method are compared with those of some existing MCDM methods. The results of the analyses show that the proposed method is efficient to deal with MCDM problems.

A relevant challenge introduced by decentralized installations of photo-voltaic systems is the mismatch between green energy production and the load curve for domestic use. We advanced an ICT solution that maximizes the self-consumption by an intelligent scheduling of appliances. The predictive approach is complemented with a reactive one to minimize the short term effects due to prediction errors and to unforeseen loads. Using real measures, we demonstrated that such errors can be compensated modulating the usage of continuously running devices such as fridges and heat-pumps. Linear programming is used to dynamically compute in real-time the optimal control of these devices.

Linguistic hesitant fuzzy sets (LHFSs) permit the decision maker to apply several linguistic terms with each having several membership degrees to denote his/her preference of one thing. This type of fuzzy sets can well address the qualitative and quantitative cognitions of the decision maker as well as reflect his/her hesitancy, uncertainty and inconsistency. This paper introduces a distance measure between any two LHFSs and then defines a correlation coefficient of LHFSs. Considering the application of LHFSs, the weighted distance measure and the weighted correlation coefficient of LHFSs are defined. To address the interactions between elements in a set, the Shapley weighted distance measure and the Shapley weighted correlation coefficient are presented. It is worth noting that when the elements are independent, they degenerate to the associated weighted distance measure and the weighted correlation coefficient, respectively. After that, their application to pattern recognition is studied. Furthermore, an approach to multi-attribute decision making under linguistic hesitant fuzzy environment is developed. Meanwhile, numerical examples are offered to show the concrete application of the developed procedure.

As a means of supporting quality of service guarantees, aggregate multiplexing has attracted a lot of attention in the networking community, since it requires less complexity than flow-based scheduling. However, contrary to what happens in the case of flow-based multiplexing, few results are available for aggregate-based multiplexing. In this paper, we consider a server multiplexer fed by several flows and analyze the impact caused by traffic aggregation on the flows at the output of the server. No restriction is imposed on the server multiplexer other than the fact that it must operate in a work-conserving fashion. We characterize the best arrival curves that constrain the number of bits that leave the server, in any time interval, for each individual flow. These curves can be used to obtain the delays suffered by packets in complex scenarios where multiplexers are interconnected, as well as to determine the maximum size of the buffers in the different servers. Previous results provide tight delay bounds for networks where servers are of the FIFO type. Here, we provide tight bounds for any work-conserving scheduling policy, so that our results can be applied to heterogeneous networks where the servers (routers) can use different work-conserving scheduling policies such as First-In First-Out (FIFO), Earliest Deadline First (EDF), Strict Priority (SP), Guaranteed Rate scheduling (GR), etc.

Fingerprint ridge frequency is a global feature, which is most prominently different in fingerprints of men and woman, and it also changes within the maturing period of a person. This paper proposes the method of fingerprint pre-classification, based on the ridge frequency replacement by the density of edge points of the ridge boundary. This method is to be used after applying the common steps in most fingerprint matching algorithms, namely the fingerprint image filtering, binarization and marking of good/bad image areas. The experimental performance evaluation of fingerprint pre-classification is presented. We have found that fingerprint pre-classification using the fingerprint ridge edges density is possible, and it enables to preliminary reject part of the fingerprints without heavy loss of the recognition quality. The paper presents the evaluation of two sources of fingerprint ridge edges density variability: a) different finger pressure during the fingerprint scanning, b) different distance between the geometrical center of the fingerprint and position of the fingerprint fragment.