This report demonstrates the use of a genetic algorithm search in the solution of an inverse problem. The genetic algorithm is used to solve the one-dimensional inverse heat conduction problem using numerical data generated by solution of the corresponding direct problem. Both “pure” and noisy data are considered. If used with regularization, the method is shown to yield reasonable results even in the face of noisy data. Although only a simple one-dimensional case is presented to demonstrate the method, the genetic algorithm approach is expected to excel in the solution of complicated multidimensional inverse problems.

The inverse heat conduction problem (IHCP) has been identified as a good candidate for solution using artificial neural networks (ANNs) [5, 9]. Reasons that have been cited include the ability of ANNs to represent complex mappings of input/output vectors and the availability of a virtually unlimited amount of training data for the IHCP from solutions of the corresponding forward problem. This paper presents a synopsis of efforts to achieve successful solution of the IHCP via ANNs. Two basic approaches are presented: whole domain (whole history mapping) and sequential (local-time mapping). The experiences recounted here show some promise for the approach to the solution of inverse problems using ANNs and will be useful to others interested in application of these methods to this class of problems.

By analyzing the highlights of the major activities reported by the technology transfer offices (TTOs) of twenty US major universities, the performances of TTO activities are quantitatively assessed and the associated scores are compared with each other. The key performance indicators, which govern the success of the university technology transfer, are specifically selected and examined. Two normalized metrics, overall performance metric (OPM) and patenting control ratio (PCR), which are the representing combined indicators for the TTO performance, are developed and demonstrated. The two metrics are evaluated for each university selected and compared to specifically provide a comprehensive overview of how good is the TTO of a university as compare to those of its peers. Finally, the factors for a successful TTO are described and the major unsolved issues are also discussed.