Current Issue : July - September Volume : 2019 Issue Number : 3 Articles : 5 Articles
As companies operate in a competitive environment, where the struggle for survival on\nthe market is rather tough, the top management face new challenges to identify methods, and even\ntechniques, which allows it to select from the market those assets that provide an optimal ratio\nbetween the acquisition cost and the economic performance. In this context, a fuzzy logic managerial\ndecision tool for the assets acquisition is proposed with the paper. The algorithm has three main\ncomponents: the matrix of the membership degree of the existing bids to asset selection criteria,\nusing fuzzy triangular numbers; the vector of the global membership degree of the bids to the\nselection criteria and the maximum of the global membership degree as an inference operator for\nestablishing the validated bids by the algorithm. Two scenarios of asset acquisition were tested.\nAfter simulations, it was determined that the proposed fuzzy logic managerial decision tool\ncombines, with very good results, the acquisition cost of the assets with their economic performance....
The safety computer in the train control system is designed to be the double two-vote-two\narchitecture. If safety-critical multi-input data are inconsistent, this may cause non-strict multi-sensor\ndata problems in the output. These kinds of problems may directly affect the decision making\nof the safety computer and even pose a serious threat to the safe operation of the train. In this\npaper, non-strict multi-sensor data problems that exist in traditional safety computers are analyzed.\nThe input data are classified based on data features and safety computer features. Then, the input\ndata that cause non-strict multi-sensor data problems are modeled. Fuzzy theory is used in the safety\ncomputer to process multi-sensor data and to avoid the non-strict multi-sensor problems. The fuzzy\nprocessing model is added into the onboard double two-vote-two architecture safety computer\nplatform. The fuzzy processing model can be divided into two parts: improved fuzzy decision tree\nand improved fuzzy weighted fusion. Finally, the model is verified based on two kinds of data.\nVerification results indicate that the fuzzy processing model can effectively reduce the non-strict\nidentical problems and improve the system efficiency on the premise of ensuring the data reliability....
Location information is very critical to VANETs such as navigation, routing, network management, and road congestion. In this\npaper, the vehicle location problem under urban road conditions is investigated by employing the GPS,WiFi, and Cellular Network\n(CN) positioning systems and by developing neighbor vehicle utilization in VANETs. Since GPS is possibly affected by satellite\nsignal in real urban environment, whileWiFi is only suitable for urban and CN is affected by the number of Base Stations (BSs) and\nsignal strength, then a fuzzy-based hybrid location algorithm is developed.The algorithm has some advantages that it can enhance\nthese positioning features by establishing a new fuzzy-weighting location mechanism (FLM) and also can adjust dynamically the\nmeasurement noise covariance by making use of a novel fuzzy Kalman filtering method. Finally, experiment results are given to\nshow effectiveness and merit of the proposed approach....
The classical Holder inequality shows an interesting upper bound for Lebesgue integral of the product of two functions.\nThis paper proposes Holder type inequalities and reverse Holder type inequalities for Sugeno integrals under usual multiplication\noperations for nonincreasing concave or convex functions. One of the interesting results is that the inequality,..................
In this paper, we introduce the concept of fuzzy ordered hyperideals of ordered semihyperrings, which is a generalization of the\nconcept of fuzzy hyperideals of semihyperrings to ordered semihyperring theory, and we investigate its related properties.We show\nthat every fuzzy ordered quasi-hyperideal is a fuzzy ordered bi-hyperideal, and, in a regular ordered semihyperring, fuzzy ordered\nquasi-hyperideal and fuzzy ordered bi-hyperideal coincide....
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