• Journal of Biosystems Engineering
• /
• v.24 no.4
• /
• pp.325-334
• /
• 1999

The aim of this study was to present a groundwork for development of a sorting system of peeled garlics using machine vision. Images of various garlic samples such as sound, partially defective, discolored, rotten and un-peeled were obtained with a B/W machine vision system. Sorting factors which were based on normalized histogram and statistical analysis(STEPDISC Method) had good separability for various garlic samples. Bayes discriminant function and neural network sorting algorithms were developed with the sample images and were experimented on various garlic samples. It was showed that garlic samples could be classified by sorting algorithm with average sorting accuracies of 88.4% by Bayes discriminant function and 93.2% by neural network.

• MALSORI
• /
• no.44
• /
• pp.105-113
• /
• 2002

In Korean, each digit is monosyllable and some pairs are known to have high confusability, causing performance degradation of connected digit recognition systems. To improve the performance, in this paper, we employ various discriminant analyses (DA) including Linear DA (LDA), Weighted Pairwise Scatter LDA WPS-LDA), Heteroscedastic Discriminant Analysis (HDA), and Maximum Likelihood Linear Transformation (MLLT). We also examine several combinations of various DA for additional performance improvement. Experimental results show that applying any DA mentioned above improves the string accuracy, but the amount of improvement of each DA method varies according to the model complexity or number of mixtures per state. Especially, more than 20% of string error reduction is achieved by applying MLLT after WPS-LDA, compared with the baseline system, when class level of DA is defined as a tied state and 1 mixture per state is used.

• KSTLE International Journal
• /
• v.2 no.1
• /
• pp.35-39
• /
• 2001

In this paper, statistical approach is undertaken to investigate the classification of wear debris which is the key function of objective assessment of wear debris morphology. Wear tests are run to produce various kinds of wear debris. The images of wear debris from wear tests are captured with image acquisition equipment. By thresholding, two-dimensional binary images of wear debris are made and, then, morphological parameters are used to quantify the images of debris. Parametric and nonparametric discriminant method are employed to classify wear debris into predefined wear conditions. It is demonstrated that classification accuracy of parametric and nonparametric discriminant method is similar. The selected use of morphological parameters by stepwise discriminant analysis can generally improve the classification accuracy of parametric and nonparametric discriminant method.

• Management Science and Financial Engineering
• /
• v.3 no.1
• /
• pp.39-56
• /
• 1997

This paper applies the binary tree classifier and discriminant analysis methods to predicting failures of banks and insurance companies. In this study, discriminant analysis is generally better than the binary tree classifier in the classification of bank defaults; the binary tree is generally better than discriminant analysis in the classification of insurance company defaults. This situation can be explained that the performance of a classifier depends on the characteristics of the data. If the data are dispersed appropriately for the classifier, the classifier will show a good performance. Otherwise, it may show a poor performance. The two data sets (bank and insurance) are analyzed to explain the better performance of the binary tree in insurance and the worse performance in bank; the better performance of discriminant analysis in bank and the worse performance in insurance.

• Journal of Biomedical Engineering Research
• /
• v.10 no.2
• /
• pp.185-190
• /
• 1989

In this paper, the discriminant analysis was performed on parameter for detection of cardiac arrythmia. The parameters used for discriminant analysis was two group. One group consist of 05 width and Heart rate, and the other Morphology and Heart Rate. For this study, we designed data acquisition system for EKG signals. The parameters pre-processed by this system was heart rate, 05 width and Morphology. And then, we analyzed the discriminancy of two group and extracted the quantity of discriminancy. The analysis results showed first that the group with morphology and heart rate is better discriminant than with 05 width and heart rate : next, that it can quantify the discriminany of each group of diseases.

• Korean Journal of Construction Engineering and Management
• /
• v.11 no.5
• /
• pp.84-94
• /
• 2010

Partnering concept has been mentioned as an innovative arrangement that helps to reduce many of the disadvantages of the traditional arrangement. Partnering in construction has been widely applied in Vietnam from late 1990s. The application of the new has arrangement spread thanks to anecdotal proofs. This concept is quite new to Vietnamese practitioners. It is necessary to conduct study as a lesson-learn of the industry to encourage the partnering implementation. This paper attempts to develop a model, using discriminant analysis, which classifies the partnering in construction projects into success levels. Dedication, teamwork, sufficiency, and balance are the four significant components in discriminant model. The proposed model is helpful to practitioners in developing, adjusting and improving their strategy for partnering implementation.

• Proceedings of the IEEK Conference
• /
• 2008.06a
• /
• pp.717-718
• /
• 2008

Fisher Linear Discriminant(FLD) is a sample and intuitive linear feature extraction method in pattern recognition. But in some special cases, such as un-separable case, one class data dispersed into several clustering case, FLD doesn't work well. In this paper, a new discriminant named K-means Fisher Linear Discriminant, which combines FLD with K-means clustering is proposed. It could deal with this case efficiently, not only possess FLD's global-view merit, but also K-means' local-view property. Finally, the simulation results also demonstrate its advantage against K-means and FLD individually.

• Management Science and Financial Engineering
• /
• v.12 no.2
• /
• pp.71-85
• /
• 2006

Collaborative filtering, among other recommender systems, has been known as the most successful recommendation technique. However, it requires the user-item rating data, which may not be easily available. As an alternative, some collaborative filtering algorithms have been developed recently by utilizing the market basket data in the form of the binary user-item matrix. Viewing the recommendation scheme as a two-class classification problem, we proposed a new collaborative filtering scheme using a regularized discriminant analysis applied to the binary user-item data. The proposed discriminant model was built in terms of the major principal components and was used for predicting the probability of purchasing a particular item by an active user. The proposed scheme was illustrated with two modified real data sets and its performance was compared with the existing user-based approach in terms of the recommendation precision.

• Communications for Statistical Applications and Methods
• /
• v.9 no.1
• /
• pp.87-100
• /
• 2002

Poorly-posed problems in the balanced discriminant analysis was considered. We restrict consideration to the case of observations and the number of variables are the same and small. When these problems exist, we do not use a maximum likelihood estimates(MLE) to estimate covariance matrices. Instead of MLE, an alternative estimate for the covariance matrices are proposed. This alternative method make good use of two regularization parameters, $\lambda$} and $\gamma$. A new test rule for the discriminant function is suggested and examined via limited hut informative simulation study. From the simulation study, it is shown that the suggested test rule gives better test result than other previously suggested method in terms of error rate criterion.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.14 no.3
• /
• pp.467-473
• /
• 2011

The contractor management for the effective defense project is essential factor in the modern defense acquisition task. The occurrence of unlawful company causes hastiness for project manager and setback to the deployment of defense weapon system. In this paper, we develop a prediction model for the effective defense project by using the discriminant analysis and analyse the variables that discriminate the unlawful company in many variables. It is expected that our model can be used to improve the project management capability of defense acquisition and contribute to the establishment of efficient procurement procedure through entry of the reliable defense manufacturer.