Shear wave velocity (Vs) data are key information for petrophysical, geophysical and geomechanical studies. Although compressional wave velocity (Vp) measurements exist in almost all wells, shear wave velocity is not recorded for most of elderly wells due to lack of technologic tools. Furthermore, measurement of shear wave velocity is to some extent costly. This study proposes a novel methodolo...

As grow as the data-intensive applications in cloud computing day after day, data popularity in this environment becomes critical and important. Hence to improve data availability and efficient accesses to popular data, replication algorithms are now widely used in distributed systems. However, most of them only replicate the static number of replicas on some requested chosen sites and it is ob...

In this paper, we investigate the algebraic properties of intuitionistic fuzzy matrices under the new operations and . The properties of intuitionistic fuzzy matrices are also obtained in the case where these new operations are combined with the well known operations of intuitionistic fuzzy matrices.

The developed software is a web application with open access and is aimed on forecasting of time series stored in database. We proposed approach of time series forecasting, combined ARIMA models with fuzzy techniques: three fuzzy time series models, fuzzy transformation (F-transform) and ACL-scale. Applications of a proposed web service have demonstrated efficiency in practical time series pred...

In this paper, we incorporate fuzzy reasoning in fractional-order PD controllers. The resulting fuzzy fractional PD controller is investigated in terms of its digital implementation and robustness. The combined features of both algorithms will result in a better control system performance. Also, the effectiveness and robustness of the new methodology is illustrated through an application exampl...

Prediction of cutting forces is very important for the design of cutting tools and for process planning. This paper presents a fuzzy modelling method of cutting forces based on subtractive clustering. The subtractive clustering combined with the least-square algorithm identifies the fuzzy prediction model directly from the information obtained from the sensors. In the micro-milling experimental...

Unsupervised competitive learning algorithms for clustering of sensor nodes in wireless sensor networks are evaluated with a large scale data set in this paper. The Centroid Neural Network (CNN) is compared with Fuzzy c-Means (FCM) algorithm in determining cluster heads among given sensor nodes. The cluster heads are combined with Low Energy Adaptive Clustering Hierarchy (LEACH) for minimizing ...