No abstract

No abstract

Hard Disk Drive (HDD) manufacturing is one real-world application area that machine learning has been extensively adopted for problem solving. However, most problem solving activities in HDD industry tackle on failure root-cause analysis task. Machine learning is rarely applied in a task of yield prediction. This research presents the application of machine learning and statistical techniques to select appropriate features to be used in yield prediction for the HDD manufacturing process. The seven well-known algorithms are used in the feature selection step. These algorithms are decision tree (C5 and CART), Support Vector Machine (SVM), stepwise regression, Genetic Algorithm (GA), chi-square and information gain. The two prominent learning algorithms, Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN), are used in the yield prediction modeling step. Yield prediction performance has been assessed based on the two evaluation metrics: Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Yield prediction with MLR shows higher accuracy than yield estimation traditionally performed by human engineers. Resulting to conclusion that the proposed novel learning steps can help HDD process engineers to predict yield with the better performance, especially on applying GA as feature selection tool, the MAE is reduced from 0.014 (yield estimated by human engineer) to 0.0059 (yield predicted by MLR). That means error reduction is about 60%.

At present fingerprint recognition has been used widely, such as an authentication means of mobile phone usage and a monitoring for working hours. But the recognition performance of existing system low. We thus propose techniques to improve the recognition. We notice that edge detection techniques applied to the fingerprint images can enhance the quality of images and cause the improvement in image recognition We thus study the four edges detection techniques: sobel, prewitt, robert and canny. For faster classification we also apply two dimensionality reduction techniques: principal component analysis and linear discriminant analysis.Then, we identify fingerprint images with the algorithm support vector machine using linear kernel function. Experimental results showed that the pre-processing fingerprint images using canny edge detection with principal component analysis can increased the recognition rate from 64.3% to 88%. On using canny edge detection with linear discriminant analysis, the fingerprint image recognition can be improved from 73.8% to 88%

No abstract