• Korean Journal of Poultry Science
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• v.11 no.2
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• pp.109-114
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• 1984

The experiment was made to study the effect of levels of metabolizable energy of basal diets on apparent metabolizable energy (AME) and true metabolizable energy (TME) values of corn and soybean meals. The test materials, corn and soybean meals, were substituted with basal diet at 50% and 30%, respectively. The excreta of fed md unfed birds were collected for 30 hours. The results obtained were as follows; 1. The AME values of corn were not significantly different among treatments (P＞0.05) except for 2400 Kcal/13% treatment, The AME values of soybean meals differed significantly between 2,400 Kcal/13% and 2,800 Kcal/15% or 3,000 Kcal/16%, but were not different between 2,400 Kcal/13% and 2,600 Kcal/14 % (probability at 5% level). 2. The energy levels of basal diets did not affect the AME values of corn and soybean meals (P＞0.05) except 2,400 Kcal/13% treatment. This fact indicates that it is not necessary to change energy levels of basal diet according to test materials. 3. That the values of standard error of soybean meals were higher than those of corn was resulted from its low level of substitution with basal diet. 4. The TME values of corn showed significant differences (P＜0.05) between 2,400Kcal/13% treatment and other treatments but those of soybean meals were not different among all treatments (P＞0.05). 5. The reason that the AME values of corn and soybean meals and the TME values of corn reduced significantly in 2,400 Kcal/13% could be explained by the effect of interaction among ingredients in the diet.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.1
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• pp.1-12
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• 2008

In this paper, we present a real-time algorithm for recognizing the vehicle color from the indoor and outdoor vehicle images based on GPU (Graphics Processing Unit) acceleration. In the preprocessing step, we construct feature victors from the sample vehicle images with different colors. Then, we combine the feature vectors for each color and store them as a reference texture that would be used in the GPU. Given an input vehicle image, the CPU constructs its feature Hector, and then the GPU compares it with the sample feature vectors in the reference texture. The similarities between the input feature vector and the sample feature vectors for each color are measured, and then the result is transferred to the CPU to recognize the vehicle color. The output colors are categorized into seven colors that include three achromatic colors: black, silver, and white and four chromatic colors: red, yellow, blue, and green. We construct feature vectors by using the histograms which consist of hue-saturation pairs and hue-intensity pairs. The weight factor is given to the saturation values. Our algorithm shows 94.67% of successful color recognition rate, by using a large number of sample images captured in various environments, by generating feature vectors that distinguish different colors, and by utilizing an appropriate likelihood function. We also accelerate the speed of color recognition by utilizing the parallel computation functionality in the GPU. In the experiments, we constructed a reference texture from 7,168 sample images, where 1,024 images were used for each color. The average time for generating a feature vector is 0.509ms for the $150{\times}113$ resolution image. After the feature vector is constructed, the execution time for GPU-based color recognition is 2.316ms in average, and this is 5.47 times faster than the case when the algorithm is executed in the CPU. Our experiments were limited to the vehicle images only, but our algorithm can be extended to the input images of the general objects.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2004.11a
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• pp.250-255
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• 2004

To analyze delay, Seoul - Daegu line of Korea High Speed Railway was divided into three sections and analyzed independently by the business characteristics. The analysis on the project delay reasons was performed on macro and micro scales. This analytic method was named as 'Macro-Micro Delay Analysis Method (MMDAM)'. The macro scale analysis has three approaches, which are (1) scheduling, (3) structural characteristic, (3) and responsibility of project administrative works. Micro analysis also has three, methodologies which are (1) As Planned Method, (2) As Built method, (3) Modified Time Impact Analysis for analyzing the most influential section which the largest delay occurred. Using elicited project delay reasons from above analysis, the questionnaire was carried out for analyzing the influence of project delay reason. The reasons of the delay were driven from two different aspects (1) structural characteristic and (2) responsibility of the people involved in the project. The reasons that were identified from aforementioned three sections are the factors of the delay of the large-scale government driven projects. Finally, the author suggested the methodology of identifying the project delaying factors. The author also analyzed delay reasons in both the overseas and domestic cases of high rapid railway construction and has elicited some benchmarks for the future projects.

• Journal of Internet Computing and Services
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• v.21 no.4
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• pp.97-107
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• 2020

This study aims to conduct a comprehensive meta-study from the perspective of content analysis to explore trends in Korean academic research on the sharing economy by using the big data analytics. Comprehensive meta-analysis methodology can examine the entire set of research results historically and wholly to illuminate the tendency or properties of the overall research trend. Academic research related to the sharing economy first appeared in the year in which Professor Lawrence Lessig introduced the concept of the sharing economy to the world in 2008, but research began in earnest in 2013. In particular, between 2006 and 2008, research improved dramatically. In order to grasp the overall flow of domestic academic research of trends, 8 years of papers from 2013 to the present have been selected as target analysis papers, focusing on titles, keywords, and abstracts using database of electronic journals. Big data analysis was performed in the order of cleaning, analysis, and visualization of the collected data to derive research trends and insights by year and type of literature. We used Python3.7 and Textom analysis tools for data preprocessing, text mining, and metrics frequency analysis for key word extraction, and N-gram chart, centrality and social network analysis and CONCOR clustering visualization based on UCINET6/NetDraw, Textom program, the keywords clustered into 8 groups were used to derive the typologies of each research trend. The outcomes of this study will provide useful theoretical insights and guideline to future studies.

• Journal of the Korean Society for information Management
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• v.33 no.3
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• pp.63-83
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• 2016

As the usage of information resources produced in various media and forms has been increased, the importance of metadata as a tool of information organization to describe the information resources becomes increasingly crucial. The purposes of this study are to analyze and to demonstrate the intellectual structure in the field of metadata through co-word analysis. The data set was collected from the journals which were registered in the Core collection of Web of Science citation database during the period from January 1, 1998 to July 8, 2016. Among them, the bibliographic data from 727 journals was collected using Topic category search with the query word 'metadata'. From 727 journal articles, 410 journals with author keywords were selected and after data preprocessing, 1,137 author keywords were extracted. Finally, a total of 37 final keywords which had more than 6 frequency were selected for analysis. In order to demonstrate the intellectual structure of metadata field, network analysis was conducted. As a result, 2 domains and 9 clusters were derived, and intellectual relations among keywords from metadata field were visualized, and proposed keywords with high global centrality and local centrality. Six clusters from cluster analysis were shown in the map of multidimensional scaling, and the knowledge structure was proposed based on the correlations among each keywords. The results of this study are expected to help to understand the intellectual structure of metadata field through visualization and to guide directions in new approaches of metadata related studies.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.10
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• pp.101-107
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• 2018

Recently, Korea's defense industry has advanced highly, and defense R&D budget is gradually increasing in defense budget. However, without objective analysis of defense industry technology, effective defense R&D activities are limited and defense budgets can be used inefficiently. Therefore, in addition to analyzing the defense industry technology quantitatively reflecting the opinions of the experts, this paper aims to analyze the defense industry technology objectively by quantitative methods, and to make efficient use of the defense budget. In addition, we propose a patent analysis method to grasp the characteristics of the defense industry technology and the vacant technology objectively and systematically by applying the big data analysis method, which is one of the keywords of the 4th industrial revolution, to the defense industry technology. The proposed method is applied to the technology of the firepower industry among several defense industrial technologies and the case analysis is conducted. In the process, the patents of 10 domestic companies related to firepower were collected through the Kipris in the defense industry companies' classification of the Korea Defense Industry Association(KDIA), and the data matrix was preprocessed to utilize IPC codes among them. And then, we Implemented association rule mining which can grasp the relation between each item in data mining technique using R program. The results of this study are suggested through interpretation of support, confidence lift index which were resulted from suggested approach. Therefore, this paper suggests that it can help the efficient use of massive national defense budget and enhance the competitiveness of defense industry technology.

• Food Science and Preservation
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• v.13 no.6
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• pp.686-690
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• 2006

Quality properties of legumes (Seoritae and red bean) with soaking of 3% NaCl solution and microwave dying were evaluated. The samples were soaking with 3% NaCl at $25^{\circ}C$ for 6 hr(Seoritae) and 12 hr(red bean). Moisture content of Seoritae and red bean after soaking are 35.8%, and 35.1% respectively. The samples were dried with microwave treatment and cooling with microwave treatment combination secondly to $12{\sim}14%$ for moisture content Hardness of Seoritae was decreased from $12,863g_f$ to $3,309g_f$. There was a difference between varieties on color value. Hardness of cooked rice with ratio of milled rice and legumes(7:3) was $3,165g_f$ which is lower value compared to regular cooked mixed rice. Sensory evaluation of cooked mixed rice showed that treated samples have higher scores on color, flavor, taste, texture and overall acceptability values than those of control.

• Journal of agriculture & life science
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• v.46 no.6
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• pp.147-156
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• 2012

This study evaluated the effects of presoaking apple slices in 2 or 5% brine solution prior to air-drying at $50^{\circ}C$. Several quality factors of the dried apples were measured including moisture content (MC), water activity ($a_w$), shear force, color values, appearance and consumer likability. Except at time 0, the $a_w$ of presoaked samples was lower than those of control during and after drying. The relationship between MC and water activity during drying was well-fit by an exponential model. During drying, the moisture contents of presoaked samples were slightly higher than control samples with no presoaking. However, the $a_w$ of presoaked slices were lower than control at a given drying time. At a given $a_w$, presoaked slices had higher moisture content. The shear force was lower for samples presoaked in brine solution, particularly at shorter drying times. Presoaked apple slices also were lighter in color after drying than controls. Sensory evaluations suggest that presoaking of apple slices before drying may help enhance palatability. Samples presoaked in 2% brine solution had the highest scores for taste, texture and overallacceptability, while those presoaked in 5% brine solution had the highest scores for appearance.

• Journal of the Society of Disaster Information
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• v.17 no.2
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• pp.245-253
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• 2021

Purpose: This study develops a risk prediction model that predicts the risk of a fire site by using initial information such as building information and reporter acquisition information, and supports effective mobilization of fire fighting resources and the establishment of damage minimization strategies for appropriate responses in the early stages of a disaster. Method: In order to identify the variables related to the fire damage scale on the fire statistics data, a correlation analysis between variables was performed using a machine learning algorithm to examine predictability, and a learning data set was constructed through preprocessing such as data standardization and discretization. Using this, we tested a plurality of machine learning algorithms, which are evaluated as having high prediction accuracy, and developed a risk prediction model applying the algorithm with the highest accuracy. Result: As a result of the machine learning algorithm performance test, the accuracy of the random forest algorithm was the highest, and it was confirmed that the accuracy of the intermediate value was relatively high for the risk class. Conclusion: The accuracy of the prediction model was limited due to the bias of the damage scale data in the fire statistics, and data refinement by matching data and supplementing the missing values was necessary to improve the predictive model performance.

• Journal of the East Asian Society of Dietary Life
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• v.23 no.5
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• pp.645-653
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• 2013

The results of examining total soluble solid, reducing sugar, antioxidant and sensory properties regarding LTD (Liriope Tuber Dried), LTSD (Liriope Tuber Steamed and Dried), LTASD (Liriope Tuber Alcohol-Steamed and Dried), LTDR (LTD Roasted), LTSDR (LTSD Roasted) and LTASDR (LTASD Roasted) are as follows : Total soluble solid content of the roasted samples (LTDR, LTSDR and LTASDR) was more than those of all dried samples (LTD, LTSD and LTASD). According to roasting conditions, total sugar and reducing sugar are significantly greater than the raw and dried sample (LTD) in all heat-treated samples. The browning index was significantly higher in all roasted samples. In particular, LTASDR had a high browning index. Further, the antioxidative activity of the roasted LT samples were higher than that of all dried LT samples. In particular, the LTASDR sample showed significantly high figures in DPPH scavenging activity, ABTS scavenging activity, Nitrite scavenging activity and xanthine oxidase inhibitory activity. Sensory properties showed an increased acceptance in all evaluation items among roasted samples. In this study, hot water extracts of steamed or alcohol-steamed roasted LT samples had a higher antioxidative effect than that of LTD or LTDR and attained positive results by getting high scores in the overall sensory evaluation. Therefore, when using Liriope tuber in making beverages or herbal recipes, it is appropriate to dry and roast before steaming or spreading with alcohol when treating LT.