Hidden Markov Mode l Based Tigrigna Speech Recognition
| dc.contributor.advisor | Meshesha, Million (PhD) | |
| dc.contributor.author | Abera, Hafte | |
| dc.date.accessioned | 2020-06-05T10:04:26Z | |
| dc.date.accessioned | 2023-11-18T12:45:15Z | |
| dc.date.available | 2020-06-05T10:04:26Z | |
| dc.date.available | 2023-11-18T12:45:15Z | |
| dc.date.issued | 2009-11 | |
| dc.description.abstract | Conventional method of assessing the performance of gas turbine engine involves analyzing different engine parameters manually and comparing them with their respective acceptable limits in engines maintenance manuals . The method takes lengthy and complicated processes that demand personnel with many years of experience and allows subjective judgment of the personnel involved in evaluation process. Technological advances in design and constructions of gas turbine engines adds more eng in e parameters thereby setting more hurdles on the process of engines performance evaluation . This paper reports on the finding of a research that had the objective to build a model that classify the performance, either accepting or rejecting, of PT6A-27 model turboprop pas turbine engine . The engines cons id erred were those undergo ing evaluation for performance after they went through repair or overhaul. The data used to build the mode first passed through different data preprocessing and analyzing techniques . the model employed neural network built using back propagation algorithm on a neural network tool box found in MATLAB 6.5 . The model built classifies gas turbine engines by their performances into their respective classes . The classification accuracy found was encouraging for the model to be adapted in real problem. The outcome of this research can also put a corner stone for further researches in using data mining technique for gas turbine engine maintenance . | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/12345678/21445 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Information Science | en_US |
| dc.title | Hidden Markov Mode l Based Tigrigna Speech Recognition | en_US |
| dc.type | Thesis | en_US |