Towards an intelligent linear winding process through sensor integration and machine learning techniques

Towards Intelligent Machine Learning Algorithms

Towards Practical Machine Learning Techniques

Most research on the application of machine learning to engineering problems have solved artiicial problems. While research claimed to have reached results that would improve practice, these results have never been put to work by engineers themselves in solving their problems. A diierent approach of doing research on machine learning application is presented and a system design that may result ...

Efficient Program Compilation Through Machine Learning Techniques

The wealth of available compiler optimizations leads to the dual problems of finding the best set of optimizations and the best heuristic parameters to tune each optimization. We describe how machine learning techniques, such as logistic regression, can be used to address these problems. We focus on decreasing the compile time for a static commercial compiler, while preserving the execution tim...

Towards Intelligent Interpretation of Low Strain Pile Integrity Testing Results Using Machine Learning Techniques

Low strain pile integrity testing (LSPIT), due to its simplicity and low cost, is one of the most popular NDE methods used in pile foundation construction. While performing LSPIT in the field is generally quite simple and quick, determining the integrity of the test piles by analyzing and interpreting the test signals (reflectograms) is still a manual process performed by experienced experts on...

Towards Adaptive Heuristic Planning through Machine Learning

In domain independent heuristic planning there is a number of planning systems with very good performance on some problems and very poor on others. Few attempts have been made in the past to explain this phenomenon. In this paper we use machine learning techniques to discover knowledge hidden in the dynamics of the planning process that would relate specific characteristics of a planning proble...