• Korean Journal of Food Science and Technology
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• v.28 no.2
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• pp.298-304
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• 1996

Thirty-nine samples of roasted sesame seed oils were sensorially evaluated in terms of nutty odor, burnt odor and overall desirability, and their volatile compounds quantitatively analysed using direct sampling capillary GLC. Five volatile compounds were appeared to be significant for the sensory Properties of sesame oils through the multivariate analytical techniques such as stepwise discriminant analysis. canonical discriminant analysis, discriminant analysis and principal component analysis. The most important compounds were 2,5-dimethylpyrazine and 2-methylpyrazine which could be effectively used as chemical indicators related to nutty and burnt odor of sesame oils, respectively. The sesame oils which have represented a good grade of overall desirability have been always kept $35.82{\sim}4.43$ ppm of 2,5-dimethylpyrazine and also $28.90{\sim}6.35$ ppm of 2-methylpyrazine.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.30 no.1
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• pp.9-16
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• 2019

Objectives: The purpose of this study was to analyze the discriminant validity and the clinical cut off scores of the Child Behavior Checklist 1.5-5 (CBCL 1.5-5) in the diagnosis of autism spectrum disorder (ASD) and non-ASD. Methods: In total, 104 ASD and 441 non-ASD infants were included in the study. T-test, discriminant analysis, receiver operating characteristic (ROC) curve analysis, and odds ratio analysis were performed on the data. Results: The discriminant validity was confirmed by mean differences and discriminant analysis on the subscales of Emotionally reactive, Somatic complaints, Withdrawn, Sleep problems, Attention problems, Aggressive behavior, Internalizing problems, Externalizing problems, and Total problems, along with the Diagnostic and Statistical Manual of Mental Disorders (DSM)-oriented scales between the two groups. ROC analysis showed that the following subscales significantly separated ASD from normal infants: Emotionally reactive, Somatic complaints, Withdrawn, Sleep problems, Attention problems, Aggressive behavior, Internalizing problems, Externalizing problems, Total problems, and DSM pervasive developmental problems. Moreover, the clinical cut off score criteria adopted in the Korean-CBCL 1.5-5 were shown to be valid for the subscales Withdrawn, Internalizing problems, Externalizing problems, Total problems, and DSM pervasive developmental problems. Conclusion: The subscales of Withdrawn, Internalizing problems, Externalizing problems, Total problems, and DSM pervasive developmental problems significantly discriminated infants with ASD.

• Korean Journal of Child Studies
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• v.32 no.3
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• pp.19-41
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• 2011

This paper was an examination of gender-specific behaviors in children and the types of bullying behavior among 1,181 fifth and sixth grade elementary schools student identified were then classified. Differences were identified in individual variables, family variables, and school variables. The data thus collected were subjected to descriptive and comparative statistical analysis using the SPSS software program. Our results showed that multiple discriminant analysis yielded a function of individual, family and school variables that proved effective in classifying bully, reinforcer, assistant, victim, outsider and defender types in boys. In girls, multiple discriminant analysis yielded a function of individual variables that was effective in classifying bully, reinforcer, assistant, victim, outsider and defender types.

• Journal of Korean Institute of Industrial Engineers
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• v.32 no.2
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• pp.133-140
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• 2006

Large signal type data sets are difficult to classify, especially if the data sets are non-stationary. In this paper, large signal type and non-stationary data sets are wavelet transformed so that distinct features of the data are extracted in wavelet domain rather than time domain. For the classification of the data, a few wavelet coefficients representing class properties are employed for statistical classification methods : Linear Discriminant Analysis, Quadratic Discriminant Analysis, Neural Network etc. The application of our wavelet-based feature selection method to a mass spectrometry data set for ovarian cancer diagnosis resulted in 100% classification accuracy.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.6
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• pp.622-627
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• 2014

This paper proposes a new method which improves recognition rate on the gait recognition system using wavelet transform, linear discriminant analysis and genetic algorithm. We use wavelet transform to obtain the four sub-bands from the gait energy image. In order to extract feature data from sub-bands, we use linear discriminant analysis. Distance values between training data and four sub-band data are calculated and four weights which are calculated by genetic algorithm is assigned at each sub-band distance. Based on a new fusion distance value, we conducted recognition experiments using k-nearest neighbors algorithm. Experimental results show that the proposed weight fusion method has higher recognition rate than conventional method.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.3
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• pp.250-255
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• 2012

Face recognition has wide applications in security and surveillance systems as well as in robot vision and machine interfaces. Conventional challenges in face recognition include pose, illumination, and expression, and face recognition at a distance involves additional challenges because long-distance images are often degraded due to poor focusing and motion blurring. This study investigates the effectiveness of applying photon-counting linear discriminant analysis (Pc-LDA) to face recognition in harsh environments. A related technique, Fisher linear discriminant analysis, has been found to be optimal, but it often suffers from the singularity problem because the number of available training images is generally much smaller than the number of pixels. Pc-LDA, on the other hand, realizes the Fisher criterion in high-dimensional space without any dimensionality reduction. Therefore, it provides more invariant solutions to image recognition under distortion and degradation. Two decision rules are employed: one is based on Euclidean distance; the other, on normalized correlation. In the experiments, the asymptotic equivalence of the photon-counting method to the Fisher method is verified with simulated data. Degraded facial images are employed to demonstrate the robustness of the photon-counting classifier in harsh environments. Four types of blurring point spread functions are applied to the test images in order to simulate long-distance acquisition. The results are compared with those of conventional Eigen face and Fisher face methods. The results indicate that Pc-LDA is better than conventional facial recognition techniques.

• Journal of Korean Society for Quality Management
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• v.24 no.4
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• pp.168-189
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• 1996

In order for the corporation to get a good image from the customers it should consider several variables, but especially important are corproate ethics and customer satisfaction through quality improvement. Standard multivariate data analysis can be applied to find out the importance of customer satisfaction and corporate ethics as influence factors in the corporate competitive strategy. When applying this Methodology, multivariate normal distributions density function and the identical covariance between groups assumptions have to be satisfied. By using the evaluation result from a small number of specialists in an attempt to decide on the strategical factors that will create a better company image than its competitor, if it chooses to use statistical discriminant analysis method, it would be difficult to satisfy the two assumptions mentioned above. This thesis introduces discriminant analysis method that uses LP/GP effectively which is applicable to this particular situation.

• The Korean Journal of Applied Statistics
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• v.30 no.1
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• pp.83-93
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• 2017

A typical approach to visualizing k (${\geq}2$)-group multidimensional data is to use Fisher's canonical discriminant analysis (CDA). CDA finds the best low-dimensional subspace that accommodates k group centroids in the Mahalanobis space. This paper proposes an alternative visualization procedure functioning in the Euclidean space, which finds the primary dimension with maximum discrimination of k group centroids and the secondary dimension with maximum dispersion of all observational units. This hybrid procedure is especially useful when the number of groups k is two.

• Journal of Distribution Science
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• v.20 no.10
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• pp.61-66
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• 2022

Purpose: Predicting the financial distress distribution of an enterprise is important to warn enterprises about their future. Predicting the possibility of financial distress helps companies have action plans to avoid the possibility of bankruptcy. In this study, the author conducted a forecast of the financial distress distribution of enterprises. Research design, data and methodology: The forecasting method is based on Logit and Discriminant analysis models. The data was collected from companies listed on Vietnam Stock Exchange from 2012 to 2020. In which there are both companies suffer from financial distress and non-financial distress. Results: The forecast analysis results show that the Logistic model has better predictability than the Discriminant analysis model. At the same time, the results also indicate three main factors affecting the financial distress of enterprises at all three research stages: (1) Liquidity, (2) Interest payment, and (3) firm size. In addition, at each stage, the impact of factors on financial distress differs. Conclusions: From the results of this study, the author also made several recommendations to help companies better control company operations to avoid falling into financial distress. Adjustments to current assets, debt, and company expansion considerations are the most important factors for companies.

• Korean Journal of Child Studies
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• v.32 no.5
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• pp.121-134
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• 2011

The purposes of this study were to test the differences in communicative competence and conflict-resolving strategies between both popular and rejected children, and to thereby verify the discriminance of communicative competence and conflict-resolving strategies for both types of children. 52 popular children and 41 rejected children from among a pool of 202 6th grade elementary students were selected, and the data were analyzed by means of independent sample t-test and discriminant analysis. The research findings are as follows : First, listen up (sub-factors of perceiving), self-presentation, planning, and coding revealed statistically significant differences between the popular and the rejected children. Second, only negotiation and cooperation strategies revealed any statistically significant differences between the popular and the rejected children, while other sub-factors of conflict-resolving strategies indicated broad indifference between them. Third, it was only the factor of planning among 5 factors of communicative competence and 4 factors within conflict-resolving strategies which indicated that it was the most discriminant predictor between the popular and the rejected children. These results suggest that a comprehensive program is needed to improve the communicative competence and conflict-resolving strategies of rejected children.