• Korean Journal of Breeding Science
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• v.43 no.3
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• pp.165-171
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• 2011

A new rice variety 'Jinbo' is a japonica rice (Oryza sativa L.) with good eating quality, lodging tolerance, and resistance to rice stripe virus (RSV) and bacterial blight disease (BB). It was developed by the rice breeding team of Yeongdeog Substation, National Institute of Crop Science (NICS), RDA in 2009. This variety was derived from a cross between 'Yeongdeog26' with good grain quality and wind tolerance and 'Koshihikari' with good eating quality in 1998 summer season. A promising line, YR21324-56-1-1, selected by pedigree breeding method, was designated as the name of 'Yeongdeog45' in 2005. After the local adaptability test was carried out at nine locations from 2006 to 2008, 'Yeongdeog45' was released as the name of 'Jinbo' in 2009. 'Jinbo' has short culm length as 74 cm and medium maturating growth duration. This variety is resistant to $K_1$, $K_2$, and $K_3$ races of bacterial blight and stripe virus and moderately resistant to leaf blast disease with durable resistance, and also it has tolerance to unfavorable environments such as cold and dried wind. 'Jinbo' has translucent and clear milled rice kernel without white core and white belly rice, and good eating quality as a result of panel test. The yield potential of 'Jinbo' in milled rice is about 5.65 MT/ha at ordinary fertilizer level in local adaptability test. This cultivar would be adaptable to middle plain, mid-west costal area, east-south coastal area, and south mid-mountainous area.

• Korean Journal of Breeding Science
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• v.42 no.6
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• pp.638-644
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• 2010

A new rice variety 'Haeoreumi' is a japonica rice (Oryza sativa L.) with lodging tolerance, resistance to rice stripe virus (RSV) and bacterial leaf blight (BLB), and high grain quality. It was developed by the rice breeding team of Yeongdeog Substation, National Institute of Crop Science (NICS), RDA in 2008. This variety was derived from a cross between 'Milyang165' with good grain quality and lodging resistance, and 'Haepyeongbyeo' with wind tolerance in winter season of 2000/2001. A promising line, YR22375-B-B-1, selected by pedigree breeding method, was designated as the name of 'Yeongdeog46' in 2005. 'Yeongdeog46' was released as the name of 'Haeoreumi' in 2008 after the local adaptability test that was carried out at nine locations from 2006 to 2008. 'Haeoreumi' has 74 cm short culm length as and medium maturating growth duration. This variety showed resistance to $K_1,\;K_2$, and $K_3$ races of bacterial blight, and stripe virus and moderate resistant to leaf blast disease with durable resistance, and also has tolerance to unfavorable environment such as cold, dry and cold salty wind. 'Haeoreumi' has translucent and clear milled rice kernel without white core and white belly rice, and good eating quality as a result of panel test. The yield potential of 'Haeoreumi' in milled rice is about 5.58MT/ha at ordinary fertilizer level of local adaptability test. This cultivar would be adaptable to Middle plain, mid-west costal area, and east-south coastal area.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.97-117
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• 2020

Product evaluation criteria is an indicator describing attributes or values of products, which enable users or manufacturers measure and understand the products. When companies analyze their products or compare them with competitors, appropriate criteria must be selected for objective evaluation. The criteria should show the features of products that consumers considered when they purchased, used and evaluated the products. However, current evaluation criteria do not reflect different consumers' opinion from product to product. Previous studies tried to used online reviews from e-commerce sites that reflect consumer opinions to extract the features and topics of products and use them as evaluation criteria. However, there is still a limit that they produce irrelevant criteria to products due to extracted or improper words are not refined. To overcome this limitation, this research suggests LDA-k-NN model which extracts possible criteria words from online reviews by using LDA and refines them with k-nearest neighbor. Proposed approach starts with preparation phase, which is constructed with 6 steps. At first, it collects review data from e-commerce websites. Most e-commerce websites classify their selling items by high-level, middle-level, and low-level categories. Review data for preparation phase are gathered from each middle-level category and collapsed later, which is to present single high-level category. Next, nouns, adjectives, adverbs, and verbs are extracted from reviews by getting part of speech information using morpheme analysis module. After preprocessing, words per each topic from review are shown with LDA and only nouns in topic words are chosen as potential words for criteria. Then, words are tagged based on possibility of criteria for each middle-level category. Next, every tagged word is vectorized by pre-trained word embedding model. Finally, k-nearest neighbor case-based approach is used to classify each word with tags. After setting up preparation phase, criteria extraction phase is conducted with low-level categories. This phase starts with crawling reviews in the corresponding low-level category. Same preprocessing as preparation phase is conducted using morpheme analysis module and LDA. Possible criteria words are extracted by getting nouns from the data and vectorized by pre-trained word embedding model. Finally, evaluation criteria are extracted by refining possible criteria words using k-nearest neighbor approach and reference proportion of each word in the words set. To evaluate the performance of the proposed model, an experiment was conducted with review on '11st', one of the biggest e-commerce companies in Korea. Review data were from 'Electronics/Digital' section, one of high-level categories in 11st. For performance evaluation of suggested model, three other models were used for comparing with the suggested model; actual criteria of 11st, a model that extracts nouns by morpheme analysis module and refines them according to word frequency, and a model that extracts nouns from LDA topics and refines them by word frequency. The performance evaluation was set to predict evaluation criteria of 10 low-level categories with the suggested model and 3 models above. Criteria words extracted from each model were combined into a single words set and it was used for survey questionnaires. In the survey, respondents chose every item they consider as appropriate criteria for each category. Each model got its score when chosen words were extracted from that model. The suggested model had higher scores than other models in 8 out of 10 low-level categories. By conducting paired t-tests on scores of each model, we confirmed that the suggested model shows better performance in 26 tests out of 30. In addition, the suggested model was the best model in terms of accuracy. This research proposes evaluation criteria extracting method that combines topic extraction using LDA and refinement with k-nearest neighbor approach. This method overcomes the limits of previous dictionary-based models and frequency-based refinement models. This study can contribute to improve review analysis for deriving business insights in e-commerce market.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.55 no.1
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• pp.76-82
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• 2010

Experiments were conducted to investigate seed persistence of Chinese milk vetch(CMV) in naturally reseeded rice field in 2007~2009. The seed and pods with seeds were buried in rice field at 0, 5 and 10 cm depths and retrieved from the field at one to three month intervals from July to the following year March and determined change of seed dormancy and viability. In the second experiment, persistence of the CMV seeds in the naturally reseeded rice field at different tillage methods and soil depths were also investigated after rice harvest in autumn. Burial depths and durations affected recovery rate, dormancy and viability of CMV seed. The viability loss was faster and greater in the seed than the pod with seeds and on the soil surface than the 5 or 10 cm burial depths. The recovery rate of CMV seed was decreased starting from one month as seed burial and it was significantly decreased to 52~65% for the seed in September. However, unlike the seed burial, the nearly 100% CMV seeds were recovered for burial as pod with seeds even after four months burial in both 0 and 5 cm depths. However, the recovery rate was sharply declined to below 30% at October in 2007 in both seed and pods with seeds and in the 2008/2009 experiment. the 15~47% of CMV seeds still remained even after October. The CMV had high seed dormancy of 95%, showing only 4~5% germination at the beginning in June but the seed germination increased to 25 to 35% in seed and 55 to 61% in pod with seeds in September due to breakage of hard seed dormancy. The viability loss was faster in the seed than in the pod with seeds regardless of depths of placement in the soil base on decayed seeds. Also the seed placed on the soil surface lost viability faster than the 5~10 burial depths. On the other hand, field observation in the naturally reseeded CMV rice field showed that as many as 917~2,185 CMV seeds $m^2$ were from the 0~15 cm soil depth in the rotary tillage and 250~10,105 CMV seeds in minimum tillage treatmints. The recovered seed germinated 25~33%, 23~43% but still had high percentage of hard seed having 64~72% and 51~77% even after rice harvest in autumn. These results indicate that freshly harvested CMV seeds had high level of primary dormancy and the dormancy was gradually broken in soil with time during rice cultivation periods and appreciable number of CMV seeds remained even 4 month after burial in soil. CMV plant regenerated naturally from the remained seed bank at rice harvest time in autumn. The CMV seedling still emerged even after 2 years of continuous destructive killing of emerged CMV plant by rotary tillage in naturally reseeded CMV plant in rice field, indicating that CMV seeds do persistent as least two years in soil.

• Sleep Medicine and Psychophysiology
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• v.9 no.1
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• pp.34-40
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• 2002

Objectives: The gold standard for diagnosing obstructive sleep apnea syndrome (OSAS) is nocturnal polysomnography (NPSG). This is rather expensive and somewhat inconvenient, however, and consequently simpler and cheaper alternatives to NPSG have been proposed. Oximetry is appealing because of its widespread availability and ease of application. In this study, we have evaluated whether oximetry alone can be used to diagnose or screen OSAS. The diagnostic performance of an analysis algorithm using arterial oxygen saturation ($SaO_2$) base on 'dip index', mean of $SaO_2$, and CT90 (the percentage of time spent at $SaO_2$<90%) was compared with that of NPSG. Methods: Fifty-six patients referred for NPSG to the Division of Sleep Studies at Seoul National University Hospital, were randomly selected. For each patient, NPSG with oximetry was carried out. We obtained three variables from the oximetry data such as the dip index most linearly correlated with respiratory disturbance index (RDI) from NPSG, mean $SaO_2$, and CT90 with diagnosis from NPSG. In each case, sensitivity, specificity and positive and negative predictive values of oximetry data were calculated. Results: Thirty-nine patients out of fifty-six patients were diagnosed as OSAS with NPSG. Mean RDI was 17.5, mean $SaO_2$ was 94.9%, and mean CT90 was 5.1%. The dip index [4%-4sec] was most linearly correlated with RDI (r=0.861). With dip index [4%-4sec]${\geq}2$ as diagnostic criteria, we obtained sensitivity of 0.95, specificity of 0.71, positive predictive value of 0.88, and negative predictive value of 0.86. Using mean $SaO_2{\leq}97%$, we obtained sensitivity of 0.95, specificity of 0.41, positive predictive value of 0.79, and negative predictive value of 0.78. Using $CT90{\geq}5%$, we obtained sensitivity of 0.28, specificity of 1.00, positive predictive value of 1.00, and negative predictive value of 0.38. Conclusions: The dip index [4%-4sec] and mean $SaO_2{\leq}97%$ obtained from nocturnal oximetry data are helpful in diagnosis of OSAS. CT90${\leq}$5% can be also used in excluding OSAS.

• Journal of Dairy Science and Biotechnology
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• v.30 no.2
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• pp.93-109
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• 2012

This study was performed to investigate the effects of added whey protein concentrates (WPC) and whey powder (WP) on the quality and shelf life of Tofu, a traditional food in Korea. Combined whey powder and whey protein concentrates were obtained at drainage after the casein was separated by using rennet enzyme or acidification of milk. We manufactured whey Tofu and evaluated its nutritional quality by testing, the general composition for yield, moisture, pH, crude protein, crude fat, carbohydrate, rheology, sensory properties, and change during storage. 1. The general compositions of WPC and WP were as follows: (a) WPC: moisture, 5.9%; crude protein, 56.2%; crude fat, 0.1%; carbohydrate, 32.6%; ash, 5.2%; and pH 5.93 and (b) WP: moisture, 3.7%; crude protein, 13.2%; crude fat, 1.6%; carbohydrate, 74.4%; ash, 7.1%; and pH, 6.65. 2. The yield of Tofu was as follows: (a) in WPC, the content was $CaCl_2$:GDL=6:4 > $CaCl_2$:GDL=9:1 > $CaCl_2$:GDL=7:3 > $CaCl_2$:GDL=8:2 and (b) in WP, 2% addition was the highest (265%) at $13.3g/cm^2$, but with 4% addition WP was the lowest (184%) at $22.2g/cm^2$. 3. The moisture content of Tofu was as follows: (a) in WPC, the content was $CaCl_2$:GDL = 6:4 > $CaCl_2$:GDL=9:1 > $CaCl_2$:GDL=7:3 > $CaCl_2$:GDL=8:2 and (b) in WP, 2% addition was the highest at 79.82% ($13.3g/cm^2$), but 4% was the lowest at 75.18% ($22.2g/cm^2$). 4. The pH of Tofu was as follows: (a) in WPC, the value was WPC 6% > WPC 4% > WPC 2% > control and $CaCl_2$:GDL=6:4 > $CaCl_2$:GDL=8:2 > $CaCl_2$:GDL=9:1 > $CaCl_2$:GDL=7:3 and (b) in WP, WP 4% > WP 2% > control. 5. The ash content of Tofu was as follows: (a) in WPC, the content was $CaCl_2$:GDL=8:2 > $CaCl_2$:GDL=7:3 > $CaCl_2$:GDL=6:4 > $CaCl_2$:GDL=9:1 and (b) in WP, there was no difference between 2% and 4% addition. 6. The crude protein content of Tofu was as follows: (a) in WPC, the content was $CaCl_2$:GDL=8:2 > $CaCl_2$:GDL=7:3 > $CaCl_2$:GDL=9:1 > $CaCl_2$:GDL=6:4 and (b) in WP, there was no difference between 2% and 4% addition. 7. The crude fat content of Tofu was as follows: (a) in WPC, the content was $CaCl_2$:GDL=8:2 > $CaCl_2$:GDL=7:3 > $CaCl_2$:GDL=9:1 > $CaCl_2$:GDL=6:4 and (b) in WP, values decreased with increasing pressed weight. 8. The carbohydrate content of Tofu was as follows: (a) in WPC, the content was $CaCl_2$:GDL=8:2 > $CaCl_2$:GDL=7:3 > $CaCl_2$:GDL=6:4 > $CaCl_2$:GDL=9:1 and (b) in WP, values increased with increasing pressed weight. 9. The rheology test results of Tofu were as follows: (a) in WPC, hardness and brittleness was highest with $CaCl_2$:GDL=8:2 and 6% added WPC. Cohesiveness was highest with $CaCl_2$:GDL=6:4 and 2% added WPC. Elasticity was the highest with $CaCl_2$:GDL=7:3 and the added WPC control. (b) in WP, hardness was the highest with $22.2g/cm^2$ and added WP control. Cohesiveness was the highest with $17.8g/cm^2$ and added WP 2%. Elasticity was the highest with $17.8g/cm^2$ and added WP 4%. Brittleness was the highest with $17.8g/cm^2$ and added WP control. 10. The sensory test results of Tofu were as follows: (a) in WPC, the texture, flavor, color, and smell were the highest with $CaCl_2$:GDL=6:4 and 6% added WPC. (b) in WP, the texture was the highest in the control with $22.2g/cm^2$. Flavor and smell were the highest in WP 2% and $22.2g/cm^2$. Color was the highest in WP 2% and $17.8g/cm^2$. 11. The quality change of Tofu during storage was as follows: (a) in WPC, after 60 h, all samples began to get spoiled and their color changed, and mold began to germinate. (b) in WP, the result was similar, but the rate of spoilage was more rapid than that in the control.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.57-78
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• 2020

With the development of the Internet, consumers have had an opportunity to check product information easily through E-Commerce. Product reviews used in the process of purchasing goods are based on user experience, allowing consumers to engage as producers of information as well as refer to information. This can be a way to increase the efficiency of purchasing decisions from the perspective of consumers, and from the seller's point of view, it can help develop products and strengthen their competitiveness. However, it takes a lot of time and effort to understand the overall assessment and assessment dimensions of the products that I think are important in reading the vast amount of product reviews offered by E-Commerce for the products consumers want to compare. This is because product reviews are unstructured information and it is difficult to read sentiment of reviews and assessment dimension immediately. For example, consumers who want to purchase a laptop would like to check the assessment of comparative products at each dimension, such as performance, weight, delivery, speed, and design. Therefore, in this paper, we would like to propose a method to automatically generate multi-dimensional product assessment scores in product reviews that we would like to compare. The methods presented in this study consist largely of two phases. One is the pre-preparation phase and the second is the individual product scoring phase. In the pre-preparation phase, a dimensioned classification model and a sentiment analysis model are created based on a review of the large category product group review. By combining word embedding and association analysis, the dimensioned classification model complements the limitation that word embedding methods for finding relevance between dimensions and words in existing studies see only the distance of words in sentences. Sentiment analysis models generate CNN models by organizing learning data tagged with positives and negatives on a phrase unit for accurate polarity detection. Through this, the individual product scoring phase applies the models pre-prepared for the phrase unit review. Multi-dimensional assessment scores can be obtained by aggregating them by assessment dimension according to the proportion of reviews organized like this, which are grouped among those that are judged to describe a specific dimension for each phrase. In the experiment of this paper, approximately 260,000 reviews of the large category product group are collected to form a dimensioned classification model and a sentiment analysis model. In addition, reviews of the laptops of S and L companies selling at E-Commerce are collected and used as experimental data, respectively. The dimensioned classification model classified individual product reviews broken down into phrases into six assessment dimensions and combined the existing word embedding method with an association analysis indicating frequency between words and dimensions. As a result of combining word embedding and association analysis, the accuracy of the model increased by 13.7%. The sentiment analysis models could be seen to closely analyze the assessment when they were taught in a phrase unit rather than in sentences. As a result, it was confirmed that the accuracy was 29.4% higher than the sentence-based model. Through this study, both sellers and consumers can expect efficient decision making in purchasing and product development, given that they can make multi-dimensional comparisons of products. In addition, text reviews, which are unstructured data, were transformed into objective values such as frequency and morpheme, and they were analysed together using word embedding and association analysis to improve the objectivity aspects of more precise multi-dimensional analysis and research. This will be an attractive analysis model in terms of not only enabling more effective service deployment during the evolving E-Commerce market and fierce competition, but also satisfying both customers.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.109-125
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• 2019

As interest on intelligent search engines increases, various studies have been conducted to extract and utilize the features related to products intelligencely. In particular, when users search for goods in e-commerce search engines, the 'color' of a product is an important feature that describes the product. Therefore, it is necessary to deal with the synonyms of color terms in order to produce accurate results to user's color-related queries. Previous studies have suggested dictionary-based approach to process synonyms for color features. However, the dictionary-based approach has a limitation that it cannot handle unregistered color-related terms in user queries. In order to overcome the limitation of the conventional methods, this research proposes a model which extracts RGB values from an internet search engine in real time, and outputs similar color names based on designated color information. At first, a color term dictionary was constructed which includes color names and R, G, B values of each color from Korean color standard digital palette program and the Wikipedia color list for the basic color search. The dictionary has been made more robust by adding 138 color names converted from English color names to foreign words in Korean, and with corresponding RGB values. Therefore, the fininal color dictionary includes a total of 671 color names and corresponding RGB values. The method proposed in this research starts by searching for a specific color which a user searched for. Then, the presence of the searched color in the built-in color dictionary is checked. If there exists the color in the dictionary, the RGB values of the color in the dictioanry are used as reference values of the retrieved color. If the searched color does not exist in the dictionary, the top-5 Google image search results of the searched color are crawled and average RGB values are extracted in certain middle area of each image. To extract the RGB values in images, a variety of different ways was attempted since there are limits to simply obtain the average of the RGB values of the center area of images. As a result, clustering RGB values in image's certain area and making average value of the cluster with the highest density as the reference values showed the best performance. Based on the reference RGB values of the searched color, the RGB values of all the colors in the color dictionary constructed aforetime are compared. Then a color list is created with colors within the range of ${\pm}50$ for each R value, G value, and B value. Finally, using the Euclidean distance between the above results and the reference RGB values of the searched color, the color with the highest similarity from up to five colors becomes the final outcome. In order to evaluate the usefulness of the proposed method, we performed an experiment. In the experiment, 300 color names and corresponding color RGB values by the questionnaires were obtained. They are used to compare the RGB values obtained from four different methods including the proposed method. The average euclidean distance of CIE-Lab using our method was about 13.85, which showed a relatively low distance compared to 3088 for the case using synonym dictionary only and 30.38 for the case using the dictionary with Korean synonym website WordNet. The case which didn't use clustering method of the proposed method showed 13.88 of average euclidean distance, which implies the DBSCAN clustering of the proposed method can reduce the Euclidean distance. This research suggests a new color synonym processing method based on RGB values that combines the dictionary method with the real time synonym processing method for new color names. This method enables to get rid of the limit of the dictionary-based approach which is a conventional synonym processing method. This research can contribute to improve the intelligence of e-commerce search systems especially on the color searching feature.