an artificial neural network (ann) was used to analyse the capillary rise in porous media. wetting experiments were performed with fifteen liquids and fifteen different powders. the liquids covered a wide range  of  surface  tension ( 15.45-71.99  mj/m2 )  and  viscosity (0.25-21 mpa.s). the powders also provided an acceptable range of particle size (0.012-45 μm) and surface free energy (25.54-...

The accurate prediction of business process performance during its design phase can facilitate the assessment of existing processes and the generation of alternatives. In this paper, an approximation method to estimate the cycle time of a business process is introduced. First, we propose a process execution scheme, with which Business Process Management Systems (BPMS) can control the execution ...

The process of deformation and fracture structural alloys under low-cycle fatigue in conditions uniaxial loading with axial strain control complex cycle shape block has been studied. obtained results experimental studies were used to assess the possibility using nonlinear Marco - Starkey damage accumulation model. processing nickel alloy cyclic tests a simple form carried out. A combination exp...

Model-based predictive control (MPC) is one of the most efficient techniques that is widely used in industrial applications. In such controllers, increasing the prediction horizon results in better selection of the optimal control signal sequence. On the other hand, increasing the prediction horizon increase the computational time of the optimization process which make it impossible to be imple...

A number of techniques currently in use for predicting solar activity on a solar cycle timescale are tested with historical data. Some techniques, e.g., regression and curve fitting, work well as solar activity approaches maximum and provide a month~by-month description of future activity, while others, e.g., geomagnetic precursors, work well near solar minimum but only provide an estimate of t...

Abstract In this paper, a novel technique based on fuzzy method is presented for chaotic nonlinear time series prediction. Fuzzy approach with the gradient learning algorithm and methods constitutes the main components of this method. This learning process in this method is similar to conventional gradient descent learning process, except that the input patterns and parameters are stored in mem...