Improving an inverse model of sheet metal forming by neural network based regression

An inverse model for a sheet meta l forming process aims to determine the initial parameter levels required to form the final formed shape. This is a difficult problem that is usually approached by traditional methods such as finite element analysis. Formulating the problem as a classification problem makes it possible to use well established classification algorithms, such as decision trees. Classification is, however, generally based on a winner-takes-all approach when associating the output value with the corresponding class. On the other hand, when formulating the problem as a regression task, all the output values are combined to produce the corresponding class value. For a multi-class problem, this may result in very different associations compared with classification between the output of the model and the corresponding class. Such formulation makes it possible to use well known regression algorithms, such as neural networks. In this paper, we develop a neural network based inverse model of a sheet forming process, and compare its performance with that of a linear model. Both models are used in two modes, classification mode and a function estimation mode, to investigate the advantage of re-formulating the problem as a function estimation. This results in large improvements in the recognition rate of set-up parameters of a sheet metal forming process for both models, with a neural network model achieving much more accurate parameter recognition than a linear model.

A performance model for analysis of heterogeneous multi-cluster systems

This paper addresses the problem of performance modeling for large-scale heterogeneous distributed systems with emphases on multi-cluster computing systems. Since the overall performance of distributed systems is often depends on the effectiveness of its communication network, the study of the interconnection networks for these systems is very important. Performance modeling is required to avoid poorly chosen components and architectures as well as discovering a serious shortfall during system testing just prior to deployment time. However, the multiplicity of components and associated complexity make performance analysis of distributed computing systems a challenging task. To this end, we present an analytical performance model for the interconnection networks of heterogeneous multi-cluster systems. The analysis is based on a parametric family of fat-trees, the m-port n-tree, and a deterministic routing algorithm, which is proposed in this paper. The model is validated through comprehensive simulation, which demonstrated that the proposed model exhibits a good degree of accuracy for various system organizations and under different working conditions.

Application of SWAT to model the water balance of the Woady Yaloak River catchment, Australia

The widespread land use changes that are expected to occur across the Corangamite region in southwest Victoria, Australia, have the potential to significantly alter the water balance of catchments. Adoption of the Soil and Water Assessment Tool (SWAT), which is a long-term water balance model, as a tool for predicting land use change impacts on catchment water balance for the Corangamite region is currently being considered. This paper describes the initial application of SWAT to the Woady Yaloak River catchment, located within the Corangamite region, to carry out an evaluation of its abilities for simulating the long-term water balance dynamics of the catchment. The performance of the model for predicting runoff at annual and monthly time scales was found to be very good. The excessive recharge of the shallow aquifer that occurred during winter, despite the subsoil being relatively impermeable, ultimately contributed to overestimation of baseflow and underestimation of interflow. The actual evapotranspiration from hydrologic response units (HRU s) containing eucalyptus trees was significantly less than that from HRUs containing pasture, a problem attributed to the incorrect simulation of Leaf Area Index (LAI) and biomass by the model for mature stands of eucalyptus trees and also to assigning inadequate values for two parameters that directly influence evapotranspiration. SWAT has very good potential for being used as tool to study land use change impacts across the Corangamite region provided that several modifications are made to the model to overcome some of the shortcomings and deficiencies that were identified in this initial application.

A comparative analysis of decision trees vis-à-vis other computational data mining techniques in automotive insurance fraud detection

The development and application of computational data mining techniques in financial fraud detection and business failure prediction has become a popular cross-disciplinary research area in recent times involving financial economists, forensic accountants and computational modellers. Some of the computational techniques popularly used in the context of - financial fraud detection and business failure prediction can also be effectively applied in the detection of fraudulent insurance claims and therefore, can be of immense practical value to the insurance industry. We provide a comparative analysis of prediction performance of a battery of data mining techniques using real-life automotive insurance fraud data. While the data we have used in our paper is US-based, the computational techniques we have tested can be adapted and generally applied to detect similar insurance frauds in other countries as well where an organized automotive insurance industry exists.