With the rise of distributed generation, such as wind power and photovoltaic (PV), it is necessary to consider the effect of distributed generation’s output randomness. Using the method of Latin hypercube sampling (LHS) can effectively fit output scenario. Considering the sampling number of conventional LHS (CLHS) must be fixed in advance, LHS(ELHS) can be Extended to predict wind power. The sa...

The soil acidification model SMART2 requires 25 input variables for each point support location it is run. For the regional modelling of soil acidification, the model is run at a dense grid of point locations, and point support model output (aluminium concentration below the root zone) is spatially aggregated to values for 10 km × 10 km blocks. Monte Carlo analysis was used to assess the uncert...

Production planning of an open-pit mine is a procedure during which the rock blocks are assigned to different production periods in a way that leads to the highest net present value (NPV) subject to some operational and technical constraints. This process becomes much more complicated by incorporation of the uncertainty existing in the input parameters. The commodity price uncertainty is among ...

Due to correlation coefficient matrix of initialized samples are not always positive definite, this paper presents the improved Latin Hypercube Sampling (LHS) methods with Evolutionary Algorithm (EA) to control correlation and handle power system probability analysis problem. To deal with the non-positive definite correlation matrix, an improved median Latin hypercube sampling with evolutionary...

Uncertainty and sensitivity analysis results obtained with random and Latin hypercube sampling are compared. The comparison uses results from a model for two-phase fluid flow obtained with three independent random samples of size 100 each and three independent Latin hypercube samples (LHSs) of size 100 each. Uncertainty and sensitivity analysis results with the two sampling procedures are simil...

McKay, Conover and Beckman (1979) introduced Latin hypercube sampling (LHS) for reducing variance of Monte Carlo simulations. More recently Owen (1992a) and Tang (1993) generalized LHS using orthogonal arrays. In the Owen's class of generalized LHS, we de ne extended Latin hypercube sampling of strengthm (henceforth denoted as ELHS(m)), such that ELHS(1) reduces to LHS. We rst derive explicit f...

In this paper, a neural network model is presented to characterize the thickness and the uniformity of the cellgap process for flexible liquid crystal display (LCD). Input factors are explored via a D-optimal design with 15 runs and used as training data in the neural network. In order to verify the fitness of the model, three more runs are added as test data. Latin hypercube sampling and error...

We have programmed a Monte Carlo simulation of the Q-cycle model of electron transport in cytochrome b(6)f complex, an enzyme in the photosynthetic pathway that converts sunlight into biologically useful forms of chemical energy. Results were compared with published experiments of Kramer and Crofts (Biochim. Biophys. Acta 1183:72-84, 1993). Rates for the simulation were optimized by constructin...