• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.47-60
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• 2012

Video data comes in the form of the unstructured and the complex structure. As the importance of efficient management and retrieval for video data increases, studies on the video parsing based on the visual features contained in the video contents are researched to reconstruct video data as the meaningful structure. The early studies on video parsing are focused on splitting video data into shots, but detecting the shot boundary defined with the physical boundary does not cosider the semantic association of video data. Recently, studies on structuralizing video shots having the semantic association to the video scene defined with the semantic boundary by utilizing clustering methods are actively progressed. Previous studies on detecting the video scene try to detect video scenes by utilizing clustering algorithms based on the similarity measure between video shots mainly depended on color features. However, the correct identification of a video shot or scene and the detection of the gradual transitions such as dissolve, fade and wipe are difficult because color features of video data contain a noise and are abruptly changed due to the intervention of an unexpected object. In this paper, to solve these problems, we propose the Scene Detector by using Color histogram, corner Edge and Object color histogram (SDCEO) that clusters similar shots organizing same event based on visual features including the color histogram, the corner edge and the object color histogram to detect video scenes. The SDCEO is worthy of notice in a sense that it uses the edge feature with the color feature, and as a result, it effectively detects the gradual transitions as well as the abrupt transitions. The SDCEO consists of the Shot Bound Identifier and the Video Scene Detector. The Shot Bound Identifier is comprised of the Color Histogram Analysis step and the Corner Edge Analysis step. In the Color Histogram Analysis step, SDCEO uses the color histogram feature to organizing shot boundaries. The color histogram, recording the percentage of each quantized color among all pixels in a frame, are chosen for their good performance, as also reported in other work of content-based image and video analysis. To organize shot boundaries, SDCEO joins associated sequential frames into shot boundaries by measuring the similarity of the color histogram between frames. In the Corner Edge Analysis step, SDCEO identifies the final shot boundaries by using the corner edge feature. SDCEO detect associated shot boundaries comparing the corner edge feature between the last frame of previous shot boundary and the first frame of next shot boundary. In the Key-frame Extraction step, SDCEO compares each frame with all frames and measures the similarity by using histogram euclidean distance, and then select the frame the most similar with all frames contained in same shot boundary as the key-frame. Video Scene Detector clusters associated shots organizing same event by utilizing the hierarchical agglomerative clustering method based on the visual features including the color histogram and the object color histogram. After detecting video scenes, SDCEO organizes final video scene by repetitive clustering until the simiarity distance between shot boundaries less than the threshold h. In this paper, we construct the prototype of SDCEO and experiments are carried out with the baseline data that are manually constructed, and the experimental results that the precision of shot boundary detection is 93.3% and the precision of video scene detection is 83.3% are satisfactory.

• The Journal of Korean Medicine
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• v.17 no.2 s.32
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• pp.337-393
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• 1996

The purpose of this study is to evaluate and standardize the four scales of Questionnaire for the Sasang Constitution Classification 　II (QSCCII). QSCCII is newly prepared by statistical item analysis and is designed to examine its diagnostic discriminability. QSCCII is administered to 1366 random informants. From the survey, we could get the data for the standardization. The criteria of standardization are based on the data from 265 informants who are examined by professionals. Collectted data are analyzed by internal consistency, variation analysis(ANOVA), Duncan test and discrimination analysis of SPSS PC+ V4.0 program. The results are as follows reliability of four scales for QSCCII is relatively valid. The internal consistency of Tae-yang(太陽) (太陽) scale is Cronbach's a=0.5708. That of So-yang(少陽) scale is a=0.5708. That of Tae-eum(太陰) scale is a =0.5922. That of So-eum(少陰) scale is a=0.6319. 2. There is a significant difference between each group through variation analysis of four scales. 3. The process of standardization is based on the average value and standard deviation with respect to age and sex difference of each criteria 4. This study suggests a source of standardization of Sasang Constitution Classification by providing norms in which the differences of age, sex, and number of items are taken into deep consideration. QSCC Ⅱ, therefore, can be applied to every age(the 10's to the 60's) and sex groups. 5. The recalculation of the raw-score to standard value (T-score) shows that the diagnostic discriminability (Hit-ratio: 70.08%) of QSCC Ⅱ brings about 37% improvement than proportional chance criteria (33.33%). Especially, Hit-ratios of Tae-eum In(74.5%) and So-eum In(70.8%) are higher than that of So-yang In(60.0%). 6. QSCC has discriminability only to male informants. Compared with QSCC, however, QSCC II has relatively efficient discriminability both to male and female informants. 7. These results would be a demonstration of the fact that the QSCC II could be used as a tool for sasang constitution classification.

• Journal of Sasang Constitutional Medicine
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• v.8 no.1
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• pp.187-246
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• 1996

The purpose of this study is to evaluate and standardize the four scales of Questionnaire for the Sasang Constitution ClassificationII (QSCCII). QSCCII is newly prepared by statistical item analysis and is designed to examine its diagnostic discriminability. QSCCII is administered to 1366 random informants. From the survey, we could get the data for the standardization. The criteria of standardization are based on the data from 265 informants who are examined by professionals. Collected data are analyzed by internal consistency, variation analysis(ANOVA), Duncan test and discrimination analysis of SPSS PC+ V4.0 program. The results are as follows 1) The reliability of four scales for QSCCII is relatively valid. The internal consistency of Tae-yang(太陽) scale is Cronbach's ${\alpha}=0.5708$. That of So-yang(少陽) scale is ${\alpha}=0.5708$. That of Tae-eum(太陰) scale is ${\alpha}=0.5922$. That of So-eum(少陰) scale is ${\alpha}=0.6319$. 2) There is a significant difference between each group through variation analysis of four scales. 3) The process of standardization is based on the average value and standard deviation with respect to age and sex difference of each criteria. 4) This study suggests a source of standardization of Sasang Constitution Classification by providing norms in which the differences of age, sex, and number of items are taken into deep consideration. QSCCII, therefore, can be applied to every age(the 10's to the 60's) and sex groups. 5) The recalculation of the raw-score to standard value (T-score) shows that the diagnostic discriminability (Hit-ratio : 70.08%) of QSCCII brings about 37% improvement than proportional chance criteria(33.33%). Especially, Hit-ratios of Tae-eum In(74.5%) and So-eum In(70.8%) are higher than that of So-yang In(60.0%). 6) QSCC has discriminability only to male informants. Compared with QSCC, however, QSCCII has relatively efficient discriminability both to male and female informants. 7) These results would be a demonstration of the fact that the QSCCII could be used as a tool for sasang constitution classification.

• Food Science and Preservation
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• v.21 no.4
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• pp.512-519
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• 2014

This study was carried to investigate the physicochemical and microbiological characteristics of seasonal salted-Kimchi cabbage order to provide basic data for optimal salting and storage condition of seasonal Kimchi cabbage. Generally, fall season samples had slightly higher pH and acidity value than the other seasonal salted Kimchi cabbage. The soluble solids content of spring, summer, fall and winter samples were 5.95%, 6.18%, 6.29% and 7.76%, respectively. The salt content of all the seasonal salted Kimchi cabbage samples were insignificant. The number of microbial bacteria in the summer samples were generally much more significant than spring and winter samples. There was no significant difference in the color of seasonal salted Kimchi cabbage. As for the texture properties, the firmest samples in the surface rupture test were the spring samples (force: 4.92 kg), and the hardest samples in the puncture test were the summer samples (force: 11.71 kg). In the correlation analysis of the quality characteristics of seasonal samples, the soluble solids content and hardness of the seasonal salted Kimchi cabbage was significantly correlated at 1% significance level. Also, in the principal component analysis, F1 and F2 were shown to explain 27.28% and 35.59% of the total variance (62.87%), respectively. The hierarchical cluster analysis of the quality characteristics of seasonal samples, the samples were divided into three groups: spring cabbage group, summer cabbage group and fall and winter cabbage group.