The study of long term stability in particle accelerators has long been served by a group of widely circulated computer programs. The progress in these programs has mirrored the growth and versatility in accelerator size, complexity, and purpose, as well as evolving technologies in computing...

The study of long term stability in particle accelerators has long been served by a group of widely circulated computer programs. The progress in these programs has mirrored the growth and versatility in accelerator size, complexity, and purpose, as well as evolving technologies in computing...

The degradation of the material in critical components is shown to be an effective measure which can be used to compute the risk adjusted economic penalty associated with different maintenance decisions. The approach of estimating the probability, with confidence interval, of the time that a...

The efficient characterization of nonlinear systems is an important goal of vibration and model testing. The authors build a nonlinear system model based on the acceleration time series response of a single input, multiple output system. A series of local linear models are used as a template to...

In most probabilistic risk assessments, there is a subset of accident scenarios that involves physical challenges to the system, such as high heat rates and/or accelerations. The system`s responses to these challenges may be complicated, and their prediction may require the use of long-running...

This paper reports on the developments and findings of the Distribution Short-Term Load Forecaster (DSTLF) research activity. The objective of this research is to develop a distribution short-term load forecasting technology consisting of a forecasting method, development methodology, theories...

In this paper the authors discuss several complexity aspects pertaining to neural networks, commonly known as the curse of dimensionality. The focus will be on: (1) size complexity and depth-size tradeoffs; (2) complexity of learning; and (3) precision and limited interconnectivity. Results have...

Forecasting network data traffic is an important part of the function of planning and managing information systems. However, the contents of network data are so stochastic and complex that it is very difficult to establish stable functions to describe the mapping relationship between data flows...

Prediction of stock prices is an issue of interest to financial markets. Many prediction techniques have been reported in stock forecasting. Neural networks are viewed as one of the more suitable techniques. In this study, an experiment on the forecasting of the Stock Exchange of Thailand (SET)...

Stock markets are affected by many interrelated factors such as economics and politics at both national and international levels. Predicting stock indices and determining the set of relevant factors for making accurate predictions are complicated tasks. Neural networks are one of the popular...