• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.4
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• pp.332-339
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• 2014

Face classification has wide applications in security and surveillance. However, this technique presents various challenges caused by pose, illumination, and expression changes. Face recognition with long-distance images involves additional challenges, owing to focusing problems and motion blurring. Multiple frames under varying spatial or temporal settings can acquire additional information, which can be used to achieve improved classification performance. This study investigates the effectiveness of multi-frame decision-level fusion with photon-counting linear discriminant analysis. Multiple frames generate multiple scores for each class. The fusion process comprises three stages: score normalization, score validation, and score combination. Candidate scores are selected during the score validation process, after the scores are normalized. The score validation process removes bad scores that can degrade the final output. The selected candidate scores are combined using one of the following fusion rules: maximum, averaging, and majority voting. Degraded facial images are employed to demonstrate the robustness of multi-frame decision-level fusion in harsh environments. Out-of-focus and motion blurring point-spread functions are applied to the test images, to simulate long-distance acquisition. Experimental results with three facial data sets indicate the efficiency of the proposed decision-level fusion scheme.

• Journal of Korean Society for Atmospheric Environment
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• v.14 no.6
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• pp.631-642
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• 1998

Fall-out particles were collected by the modified British deposit gauges at 35 sampling sites in Suwon area from January to November, 1996. Twenty chemical species (Al. Ba, Cd, Cr, K, Pb, Sb, Zn, Cu, Fe, Ni, V, F-, Cl-, NO3-, 5042-, Na+, NH4+, Mg2+, and Ca2+) were analyzed by AAS and If. The purposes of this study were to estimate qualitatively various emission sources of the fell-out particle by applying multivariate statistical techniques such as factor analysis, multiple regression analysis, and discriminant analysis. During the study, outlier sites were determined by a z-score method. Cl-, Na+, Mg2+, and SO42- were highly correlated due to their common marine related source. Wind speed was the most influential factor for the deposition fluxes of the particle itself and all the chemical species as well. When applying the factor analysis, 8 source patterns were qualitatively obtained, such as marine source, soil source, oil burning source, Cr related source, tire source, Cd related source, agriculture source, and F- related source. As a result of the multiple regression analysis, we could suggest that some chemical compounds may possibly exist in the form of CaSO4, NaN03, NaCl, MgC12, (NH4)2SO4, NaF, and CaCl2 in the fall-out particles. Finally, spatial and seasonal classification study performed by a discriminant analysis showed th.at SO42-, Ca2+, Cl-, and Fe were dominant in the group of spatial pattern; however, SO42-, Cl-, Al, and V were in the group of seasonal pattern.

• Journal of the Korean Society of Clothing and Textiles
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• v.29 no.7 s.144
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• pp.959-967
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• 2005

The color of apparels has the interaction of the face skin colors of the wearers. This study was carried out to classify the face skin colors of Korean males into several similar face skin colors in order to extract favorable colors which flatter to their face skin colors. The criterion that select the new subjects who have the classified face skin colors have to be decided. With color spectrometer, JX-777, face skin colors of subjects were measured quantitatively and classified into three clusters that had similar hue, value and chroma with Munsell Color System. Sample size was 418 Korean males and other 15 of new males subjects. Data were analyzed by K-means cluster analysis, ANOVA, Duncan multiple range test, Stepwise discriminant analysis using SPSS Win. 12. Findings were as follows: 1. 418 subjects who have YR colors were clustered into 3 kinds of face skin color groups. 2. Discriminant variables of face skin colors was 4 variables : L value of forehead, v value of cheek, c value of forehead, and b value of cheek from standardized canonical discriminant function coefficient 1 and c value of forehead, L value of forehead, b value of cheek. and L value of cheek from standardized canonical discriminant function coefficient 2. 3. Hit ratio of type 1 was $92.3\%$, of type 2 was $96.5\%$ and of type 3 was $92.6\%$ by the canonical discriminant function of 4 variables. 4. The canonical discriminant function equation 1 and 2 were calculated with the unstandardized canonical discriminant function coefficient and constant, the cutting score, and range of the score were computed. 5. The criterion that select the new subjects who have the classified face skin colors was decided.

• Economic and Environmental Geology
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• v.27 no.3
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• pp.237-246
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• 1994

Tin mineralizations in South Korea have been found only in the Ulchin and Sangdong areas. They appear to be in close spatial association with the Wangpiri granitoid in the UlChin area, and the Nonggeori and Naedeogri granites in the Sangdong area. However, previous works have revealed that there are considerable differences in geological setting, mineralogical and geochemical compositions among these granitoids concerned. The roles of discriminant and multiple regression analysis have been examed to establish geochemical differences among the tin-granitoids and to identify elements relating to tin mineralizations. The data set used in this study consists of 60 observations with 29 elements which are cited from pre-existing publications. A stepwise discriminant analysis determined the group of variables that differentiate between samples from four training sets; Buncheon, Wangpiri, Nonggeori and Naedeogri granitoids. These granitoids were most effectively discriminated on the basis of major elements FeO, CaO and $P_2O_5$ and also by the trace elements Rb and Zr. Results of the multiple regression analysis shows that the level of Sn in granitoids depends positively on ones of MnO, Rb and FeO and negatively $P_2O_5$. Graphical representation of discriminant scores on sampling locations greatly aid recognition of differences in the geochemical characteristics in terms of spatial distribution of granitoids examed. The application of the discriminant analysis provides a potential means of identifying and comparing geochemical characteristics.

• Journal of the Korean Society of Clothing and Textiles
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• v.29 no.7 s.144
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• pp.978-986
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• 2005

The color of apparel products have a close relationship with the face skin colors of consumers. In order to extract the favorable colors which flatter to consumer's face skin colors, this study was carried our to classify the face skin colors of Korean females. The criteria that select new subjects who have the classified face skin colors have to be decided. With color spectrometer, JX-777, face skin colors of subjects were measured and classified into three clusters that had similar hue, value and chroma with Munsell Color System. Sample size was 324 Korean females and other new 10 college girls. Data were analyzed by K-means cluster analysis, ANOVA, Duncan multiple range test, Stepwise discriminant analysis using SPSS Win. 12. Findings were as follows: 1. 324 subjects who have YR colors were clustered into 3 face skin color groups. 2. Discriminant variables of face skin colors were 5 variables : b value of cheek, V value of forehead, L value of cheek, C value of forehead and H value of cheek by the standardized canonical discriminant function coefficient 1. 3. Hit ratio of type 1 was $96.8\%$, of type 2 was $94.9\%$, of type 3 was $100.0\%$ and mean of hit ratio was $96.9\%$ by canonical discriminant function of 5 variables. 4. With the unstandardized canonical discriminant function coefficient and constant, canonical discriminant function equation 1 and 2 were calculated. And cutting score and range of score of the classified types were computed. The criteria that select the new subjects were decided.

• MALSORI
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• no.66
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• pp.61-72
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• 2008

This paper proposes a method to improve pathological and normal voice classification performance by combining multiple features such as auditory-based and higher-order features. Their performances are measured by Gaussian mixture models (GMMs) and linear discriminant analysis (LDA). The combination of multiple features proposed by the frame-based LDA method is shown to be an effective method for pathological and normal voice classification, with a 87.0% classification rate. This is a noticeable improvement of 17.72% compared to the MFCC-based GMM algorithm in terms of error reduction.

• Smart Media Journal
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• v.12 no.10
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• pp.9-18
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• 2023

Harvest forecasting is the great demand of multiple aspects like temperature, rain, environment, and their relations. The existing study investigates the climate conditions and aids the cultivators to know the harvest yields before planting in farms. The proposed study uses federated learning. In addition, the additional widespread techniques such as bagging classifier, extra tees classifier, linear discriminant analysis classifier, quadratic discriminant analysis classifier, stochastic gradient boosting classifier, blending models, random forest regressor, and AdaBoost are utilized together. These presented nine algorithms achieved exemplary satisfactory accuracies. The powerful contributions of proposed algorithms can create exact harvest forecasting. Ultimately, we intend to compare our study with the earlier research's results.

• Journal of the Korean Society of Tobacco Science
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• v.22 no.2
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• pp.184-189
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• 2000

This study was conducted to develop the objective evaluation method for tobacco smoke. The evaluation was carried out by using the data of cut or blended tobacco components, smoke components, electric nose system (ENS), and sensory test. By using the statistical methods, such as cluster analysis, discriminant analysis, factor analysis, correlation analysis, and multiple regression analysis, the relationship among the data of tobacco, smoke, ENS, and sensory evaluation was studied. By the results of cluster analysis, the data from smoke analysis by GC and ENS were able to select the difference of tobacco leaf characteristics. As the results of discriminant analysis, grouping by the components of tobacco leaves and smoke was possible and the results of GC analysis of smoke could be used for discrimination of tobacco leaves. In the results of factor analysis, nicotine, tar, CO, puff No and pH in the smoke were the factors effecting on the tobacco leaf characteristics. From the correlation analysis, aroma, taste, irritation, and smoke volume of sensory test had high relation to tar, p-cresol threonolatone, levoglucosane, and quinic acid- ${\gamma}$ -lactone of smoke. The ENS data showed high efficiency for discriminant analysis and cluster analysis, but it was not good for factor analysis, and correlation analysis. It was possible to estimate tobacco leaves and their blending characteristics by the analytical data of tobacco leaves, smoke, ENS, and sensory test results. By the multiple regression analysis, some correlation among selected chemical components and sensory evaluation were found. This study strongly indicated that the some chemical analysis data was available for the objective evaluation of tobacco sensory attributes.

• Health Policy and Management
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• v.15 no.4
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• pp.81-109
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• 2005

According to many recent studies suggesting that cash flow analysis method tends to be more effective than traditional financial index analysis method to predict corporate bankruptcy, this study applies the cash flow analysis method to hospital business to identify the significant variables which can distinguish between superior hospitals and bankruptcy hospitals. The author analyzed recent 3 years, i.e. from the year of 2000 to the year of 2002, financial statements of 31 bankrupt hospitals In 2003, and the same number of superior hospitals through using Multiple Discriminant Analysis and Logit Analysis. The results are belows; First, the study releases that Logit Analysis is more likely to be effective than Multiple Discriminant Analysis. Second, this research also shows that traditional financial index analysis method is more superior compare to cash flow analysis method for hospital bankruptcy predict model. Finally, this study suggest that the significant variables, which can distinguish superior hospitals from bankrupt hospitals, are Operating/Current Liabilities$(Y_2)$, CFO/Equity$(Y_5)$ for cash flow analysis method and Net Worth to Total Assets Ratio$(X_1)$, Quick Ratio $(X_3)$, Return on Assets$(X_6)$, Growth Rate of Patient Revenues$(X_{16})$ for traditional financial index analysis method.

• Geomechanics and Engineering
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• v.13 no.5
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• pp.861-879
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• 2017

Coal and gas outburst is a serious dynamic disaster that occurs during coal mining and threatens the lives of coal miners. Currently, coal and gas outburst is commonly predicted using single indicator and its critical value. However, single indicator is unable to fully reflect all of the factors impacting outburst risk and has poor prediction accuracy. Therefore, a more accurate prediction method is necessary. In this work, we first analyzed on-site impacting factors and precursors of coal and gas outburst; then, we constructed a Fisher discriminant analysis (FDA) index system using the gas adsorption index of drilling cutting ${\Delta}h_2$, the drilling cutting weight S, the initial velocity of gas emission from borehole q, the thickness of soft coal h, and the maximum ratio of post-blasting gas emission peak to pre-blasting gas emission $B_{max}$; finally, we studied an FDA-based multiple indicators discriminant model of coal and gas outburst, and applied the discriminant model to predict coal and gas outburst. The results showed that the discriminant model has 100% prediction accuracy, even when some conventional indexes are lower than the warning criteria. The FDA method has a broad application prospects in coal and gas outburst prediction.