• Korean Journal of Breeding Science
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• v.40 no.3
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• pp.308-313
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• 2008

"Jeokjoong", a white winter wheat (Triticum aestivum L.) variety was developed from the cross "Keumkang"/"Tapdong". "Jeokjoong" is an awned, semi-dwarf and soft white winter wheat, similar to "Keumkang" (check variety). The heading and maturing date of "Jeokjoong" were similar to "Keumkang". Culm and spike length of "Jeokjoong" were 78 cm and 7.5 cm, similar to "Keumkang". "Jeokjoong" had lower test weight (800 g) and lower 1,000-grain weight (40.1 g) than "Keumkang" (811 g and 44.0 g, respectively). It had resistance to winter hardiness, wet-soil tolerance and lodging tolerance. "Jeokjoong" showed moderate to scab in test of specific character although "Keumkang" is susceptible to scab. "Jeokjoong" had lower flour yield (69.2%) and ash content (0.36%) than "Keumkang" (72.0% and 0.41%, respectively) and similar flour color to "Keumkang". It showed lower protein content (8.9%) and SDS-sedimentation volume (36.8 ml) and shorter mixograph mixing time (3.5 min) than "Keumkang" (11.0%, 59.7 ml and 4.5 min, respectively). Amylose content and pasting properties of "Jeokjoong" were similar to "Keumkang". "Jeokjoong" had softer and more elastic texture of cooked noodles than "Keumkang". Average yield of "Jeokjoong" in the regional adaptation yield trial was 6.19 MT ha-1 in upland and 5.33 MT/ha in paddy field, which was 19% and 16% higher than those of "Keumkang" (5.21 MT/ha and 4.58 MT/ha, respectively). "Jeokjoong" would be suitable for the area above the daily minimum temperature of $-10^{\circ}C$ in January in Korean peninsula.

• Korean Journal of Breeding Science
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• v.41 no.1
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• pp.44-50
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• 2009

"Sukang", a winter wheat (Triticum aestivum L.) cultivar was developed by the National Institute of Crop Science, RDA. It was derived from the cross "Suwon266" / "Asakaze" during 1994. "Sukang" was evaluated as "Iksan312" in Advanced Yield Trial Test in 2005. It was tested in the regional yield trial test between 2006 and 2008. "Sukang" is an awned, semi-dwarf and hard winter wheat, similar to "Keumkang" (check cultivar). The heading and maturing date of "Sukang" were similar to "Keumkang". Culm and spike length of "Sukang" were 90 cm and 8.1 cm, longer culm length and similar spike length compared to "Keumkang" (80 cm and 7.9 cm, respectively). "Sukang" had similar test weight (819 g/L) and lower 1,000-grain weight (40.2 g) than "Keumkang" (813 g/L and 44.9 g, respectively). "Sukang" showed resistance to winter hardiness and pre-harvest sprouting, which lower withering rate on the high ridge (4.5%) and rate of pre-harvest sprouting (0.2%) than "Keumkang" (21.9% and 30.4%, respectively). "Sukang" had lower flour yield (71.1%) and higher ash content (0.45%) than "Keumkang" (74.1% and 0.42%, respectively). "Sukang" showed lower lightness (89.13) and higher yellowness (10.93) in flour color than "Keumkang" (90.02 and 9.28, respectively). It showed higher protein content (12.8%) and gluten content (11.1%) and lower SDS-sedimentation volume (56.8 ml) and mixing time of mixograph (2.6 min) than "Keumkang" (11.9%, 10.2%, 62.3 ml and 4.7 min, respectively). Fermentation properties, amylose content and pasting properties of "Sukang" were similar to "Keumkang". "Sukang" showed different compositions in high molecular weight glutenin subunits (HMW-GS, $2^{\ast}$, 13+16, 2+12) and puroindolines (pina-1b/pinb-1a) compared to "Keumkang" ($2^{\ast}$, 7+8, 5+10 in HMW-GS and Pina-1a/Pinb-1b in puroindolines, respectively). "Sukang" showed lower hardness (4.53 N) and similar springiness and cohesiveness of cooked noodles (0.94 and 0.63) compared to "Keumkang" (4.65 N, 0.93 and 0.64, respectively). Average yield of "Sukang" in the regional adaptation yield trial was 5.34 MT/ha in upland and 4.72 MT/ha in paddy field, which was 4% and 1% lower than those of "Keumkang" (5.55 MT/ha and 4.77 MT/ha, respectively). "Sukang" would be suitable for the area above $-10^{\circ}C$ of daily minimum temperature in January in Korean peninsula.

• Korean Journal of Breeding Science
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• v.41 no.2
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• pp.130-136
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• 2009

"Hanbaek", a white winter wheat (Triticum aestivum L.) cultivar was developed by the National Institute of Crop Science, RDA. It was derived from the cross "Shan7859/Keumkang"//"Guamuehill" during 1996. "Hanbaek" was evaluated as "Iksan314" in Advanced Yield Trial Test in 2005. It was tested in the regional yield trial between 2006 and 2008. "Hanbaek" is an awned, semi-dwarf and hard winter wheat, similar to "Keumkang" (check cultivar). The heading and maturing date of "Hanbaek" were similar to that of "Keumkang". Culm and spike length of "Hanbaek" were 89 cm and 9.0 cm, which longer culm length and spike length than "Keumkang" (80 cm and 7.9 cm, respectively). "Hanbaek" had lower test weight (797 g) and higher 1,000-grain weight (47.7 g) than "Keumkang" (813 g and 44.9 g, respectively). "Hanbaek" showed resistance to winter hardiness and susceptible to pre-harvest sprouting, which lower withering rate on the high ridge (4.4%) and higher rate of pre-harvest sprouting (47.9%) than "Keumkang" (21.9% and 30.4%, respectively). "Hanbaek" had similar flour yield (74.4%) to "Keumkang" (74.1%) and higher ash content (0.45%) than "Keumkang" (0.42%). "Hanbaek" showed lower lightness (89.13) and similar redness and yellowness (-0.87 and 10.93) in flour color than "Keumkang" (90.02, -1.23 and 9.28, respectively). It showed similar protein content (12.8%) SDS-sedimentation volume (63.0 ml) and gluten content (10.8%) to those of "Keumkang" (11.9%, 62.3 ml and 10.2%, respectively). It showed lower water absorption (59.6%) and mixing time (3.8 min) in mixograph and higher fermentation volume (1,350 ml) than those of "Keumkang" (60.6%, 4.7 min and 1,290 ml, respectively). Amylose content and pasting properties of "Hanbaek " were similar to those of "Keumkang". "Hanbaek" showed same compositions in high molecular weight glutenin subunits (HMW-GS, 2*, 13+16, 2+12), granule bound starch synthase (Wx-A1a, Wx-B1a, and Wx-D1a) and puroindolines (Pina-D1a/Pinb-D1b) compared to "Keumkang". "Hanbaek" showed lower hardness (4.22N) and similar springiness and cohesiveness of cooked noodles (0.94 and 0.63) to those of "Keumkang" (4.65N, 0.93 and 0.64, respectively). Average yield of "Hanbaek" in the regional adaptation yield trial was 5.98 MT/ha in upland and 5.05 MT/ha in paddy field, which was 8% and 6% higher than those of "Keumkang" (5.55 MT/ha and 4.77 MT/ha, respectively). "Hanbaek" would be suitable for the area above the daily minimum temperature of $-10^{\circ}C$ in January in Korean peninsula.

• Korean Journal of Breeding Science
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• v.40 no.2
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• pp.153-158
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• 2008

"Baegjoong", a white winter wheat (Triticum aestivum L.) cultivar was developed by the National Institute of Crop Science, RDA. It was derived from the cross "Keumkang"/"Olgeuru" during 1996. "Baegjoong" was evaluated as "Iksan307" in Advanced Yield Trial Test in 2004. It was tested in the regional yield trial test between 2005 and 2007. "Baegjoong" is an awned, semi-dwarf and soft white winter wheat, similar to "Keumkang" (check cultivar). The heading and maturing date of "Baegjoong" were similar to "Keumkang". Culm and spike length of "Baegjoong" were 77 cm and 7.5 cm, similar to "Keumkang". "Baegjoong" had lower test weight (802 g) and lower 1,000-grain weight (39.8 g) than "Keumkang" (811 g and 44.0 g, respectively). It had resistance to winter hardiness, wet-soil tolerance and lodging tolerance. "Baegjoong" showed moderate to pre-harvest sprouting (23.9%) although "Keumkang" is susceptible to pre-harvest sprouting (38.9%). "Baegjoong" had similar flour yield (72.4%) and ash content (0.41%) to "Keumkang" (72.0% and 0.41%, respectively) and similar flour color to "Keumkang". It showed lower protein content (8.8%) and SDS-sedimentation volume (35.3 ml) and shorter mixograph mixing time (3.8 min) than "Keumkang" (11.0%, 59.7 ml and 4.5 min, respectively). Amylose content and pasting properties of "Baegjoong" were similar to "Keumkang". "Baegjoong" had softer and more elastic texture of cooked noodles than "Keumkang". Average yield of "Baegjoong" in the regional adaptation yield trial was $5.88\;MT\;ha^{-1}$ in upland and 5.35 MT ha-1 in paddy field, which was 13% and 17% higher than those of "Keumkang" ($5.21\;MT\;ha^{-1}$ and $4.58\;MT\;ha^{-1}$, respectively). "Baegjoong" would be suitable for the area above the daily minimum temperature of $-10^{\circ}C$ in January in Korean peninsula.

• Korean Journal of Environment and Ecology
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• v.36 no.6
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• pp.592-602
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• 2022

This study aimed to check habitat distribution and analyze influencing factors by analyzing the mating calls of Auritibicen intermedius inhabiting limited locations in South Korea by applying bioacoustic detection techniques. The study sites were 20 protection areas nationwide. The mating call analysis period was 4 years from 2017 to 2021, excluding 2020. The bioacoustic recording system installed at each study site collected recordings of mating calls every day for 1 minute per hour. Climate data received from the Meteorological Agency, such as temperature, humidity, rainfall, cloudiness, and sunshine, were analyzed. The results of this study identified A. intermedius habitat only in four national parks in the highlands of Gangwon Province (Mt. Seorak, Mt. Odae, Mt. Chiak, and Mt. Taebak) out of 20 study sites. During the four years of study, the mating call period of A. intermedius was between August 5 and September 28, and the duration of the mating call was 31 to 52 days. The temperature analysis during the appearance period of A. intermedius showed that A. intermedius mainly produced mating calls at temperatures between 13.1℃ and 35.3℃, and the average temperature during the circadian cycle of mating calls (09:00 to 16:00) was 24.4 to 24.9℃. The analysis of the circadian cycle of mating calls at four study sites where A. intermedius appeared in 2019 showed that A. intermedius produced mating calls from 06:00 to 16:00 and that they peaked around 11:00 to 12:00. During the appearance period of A. intermedius, four species appeared in common: Hyalessa maculaticollis, Meimuna opalifera, Graptopsaltria nigrofuscata, and Suisha coreana. A logistic regression analysis confirmed that sunlight was the environmental factor affecting the mating call of A. intermedius. Regarding interspecific influence, it was confirmed that A. intermedius exchanged interspecific influence with 4 other common species (H. maculaticollis, M. opalifera, G. nigrofuscata, and S. coreana). The above results confirmed that A. intermedius habitats were limited in the highlands of Gangwon Province highlands in Korea and produced mating calls at a lower temperature compared to other species. These results can be used as basic data for future research on A. intermedius in Korea.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.287-310
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• 2022

Companies' interest in developing AI-based intelligent new products is increasing. Recently, the main concern of companies is to innovate customer experience and create new values by developing new products through the effective use of Artificial intelligence technology. However, due to the nature of products based on radical technologies such as artificial intelligence, intelligent products differ from existing products and development methods, so it is clear that there is a limitation to applying the existing development methodology as it is. This study proposes a new research method based on KANO-TOPSIS for the successful development of AI-based intelligent new products by using car voice assistants as an example. Using the KANO model, select and evaluate functions that customers think are necessary for new products, and use the TOPSIS method to derives priorities by finding the importance of functions that customers need. For the analysis, major categories such as vehicle condition check and function control elements, driving-related elements, characteristics of voice assistant itself, infotainment elements, and daily life support elements were selected and customer demand attributes were subdivided. As a result of the analysis, high recognition accuracy should be considered as a top priority in the development of car voice assistants. Infotainment elements that provide customized content based on driver's biometric information and usage habits showed lower priorities than expected, while functions related to driver safety such as vehicle condition notification, driving assistance, and security, also showed as the functions that should be developed preferentially. This study is meaningful in that it presented a new product development methodology suitable for the characteristics of AI-based intelligent new products with innovative characteristics through an excellent model combining KANO and TOPSIS.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.93-108
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• 2014

To support business decision making, interests and efforts to analyze and use transaction data in different perspectives are increasing. Such efforts are not only limited to customer management or marketing, but also used for monitoring and detecting fraud transactions. Fraud transactions are evolving into various patterns by taking advantage of information technology. To reflect the evolution of fraud transactions, there are many efforts on fraud detection methods and advanced application systems in order to improve the accuracy and ease of fraud detection. As a case of fraud detection, this study aims to provide effective fraud detection methods for auction exception agricultural products in the largest Korean agricultural wholesale market. Auction exception products policy exists to complement auction-based trades in agricultural wholesale market. That is, most trades on agricultural products are performed by auction; however, specific products are assigned as auction exception products when total volumes of products are relatively small, the number of wholesalers is small, or there are difficulties for wholesalers to purchase the products. However, auction exception products policy makes several problems on fairness and transparency of transaction, which requires help of fraud detection. In this study, to generate fraud detection rules, real huge agricultural products trade transaction data from 2008 to 2010 in the market are analyzed, which increase more than 1 million transactions and 1 billion US dollar in transaction volume. Agricultural transaction data has unique characteristics such as frequent changes in supply volumes and turbulent time-dependent changes in price. Since this was the first trial to identify fraud transactions in this domain, there was no training data set for supervised learning. So, fraud detection rules are generated using outlier detection approach. We assume that outlier transactions have more possibility of fraud transactions than normal transactions. The outlier transactions are identified to compare daily average unit price, weekly average unit price, and quarterly average unit price of product items. Also quarterly averages unit price of product items of the specific wholesalers are used to identify outlier transactions. The reliability of generated fraud detection rules are confirmed by domain experts. To determine whether a transaction is fraudulent or not, normal distribution and normalized Z-value concept are applied. That is, a unit price of a transaction is transformed to Z-value to calculate the occurrence probability when we approximate the distribution of unit prices to normal distribution. The modified Z-value of the unit price in the transaction is used rather than using the original Z-value of it. The reason is that in the case of auction exception agricultural products, Z-values are influenced by outlier fraud transactions themselves because the number of wholesalers is small. The modified Z-values are called Self-Eliminated Z-scores because they are calculated excluding the unit price of the specific transaction which is subject to check whether it is fraud transaction or not. To show the usefulness of the proposed approach, a prototype of fraud transaction detection system is developed using Delphi. The system consists of five main menus and related submenus. First functionalities of the system is to import transaction databases. Next important functions are to set up fraud detection parameters. By changing fraud detection parameters, system users can control the number of potential fraud transactions. Execution functions provide fraud detection results which are found based on fraud detection parameters. The potential fraud transactions can be viewed on screen or exported as files. The study is an initial trial to identify fraud transactions in Auction Exception Agricultural Products. There are still many remained research topics of the issue. First, the scope of analysis data was limited due to the availability of data. It is necessary to include more data on transactions, wholesalers, and producers to detect fraud transactions more accurately. Next, we need to extend the scope of fraud transaction detection to fishery products. Also there are many possibilities to apply different data mining techniques for fraud detection. For example, time series approach is a potential technique to apply the problem. Even though outlier transactions are detected based on unit prices of transactions, however it is possible to derive fraud detection rules based on transaction volumes.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.143-156
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• 2012

People easily believe that news and stock index are closely related. They think that securing news before anyone else can help them forecast the stock prices and enjoy great profit, or perhaps capture the investment opportunity. However, it is no easy feat to determine to what extent the two are related, come up with the investment decision based on news, or find out such investment information is valid. If the significance of news and its impact on the stock market are analyzed, it will be possible to extract the information that can assist the investment decisions. The reality however is that the world is inundated with a massive wave of news in real time. And news is not patterned text. This study suggests the stock-index invest model based on "News Big Data" opinion mining that systematically collects, categorizes and analyzes the news and creates investment information. To verify the validity of the model, the relationship between the result of news opinion mining and stock-index was empirically analyzed by using statistics. Steps in the mining that converts news into information for investment decision making, are as follows. First, it is indexing information of news after getting a supply of news from news provider that collects news on real-time basis. Not only contents of news but also various information such as media, time, and news type and so on are collected and classified, and then are reworked as variable from which investment decision making can be inferred. Next step is to derive word that can judge polarity by separating text of news contents into morpheme, and to tag positive/negative polarity of each word by comparing this with sentimental dictionary. Third, positive/negative polarity of news is judged by using indexed classification information and scoring rule, and then final investment decision making information is derived according to daily scoring criteria. For this study, KOSPI index and its fluctuation range has been collected for 63 days that stock market was open during 3 months from July 2011 to September in Korea Exchange, and news data was collected by parsing 766 articles of economic news media M company on web page among article carried on stock information>news>main news of portal site Naver.com. In change of the price index of stocks during 3 months, it rose on 33 days and fell on 30 days, and news contents included 197 news articles before opening of stock market, 385 news articles during the session, 184 news articles after closing of market. Results of mining of collected news contents and of comparison with stock price showed that positive/negative opinion of news contents had significant relation with stock price, and change of the price index of stocks could be better explained in case of applying news opinion by deriving in positive/negative ratio instead of judging between simplified positive and negative opinion. And in order to check whether news had an effect on fluctuation of stock price, or at least went ahead of fluctuation of stock price, in the results that change of stock price was compared only with news happening before opening of stock market, it was verified to be statistically significant as well. In addition, because news contained various type and information such as social, economic, and overseas news, and corporate earnings, the present condition of type of industry, market outlook, the present condition of market and so on, it was expected that influence on stock market or significance of the relation would be different according to the type of news, and therefore each type of news was compared with fluctuation of stock price, and the results showed that market condition, outlook, and overseas news was the most useful to explain fluctuation of news. On the contrary, news about individual company was not statistically significant, but opinion mining value showed tendency opposite to stock price, and the reason can be thought to be the appearance of promotional and planned news for preventing stock price from falling. Finally, multiple regression analysis and logistic regression analysis was carried out in order to derive function of investment decision making on the basis of relation between positive/negative opinion of news and stock price, and the results showed that regression equation using variable of market conditions, outlook, and overseas news before opening of stock market was statistically significant, and classification accuracy of logistic regression accuracy results was shown to be 70.0% in rise of stock price, 78.8% in fall of stock price, and 74.6% on average. This study first analyzed relation between news and stock price through analyzing and quantifying sensitivity of atypical news contents by using opinion mining among big data analysis techniques, and furthermore, proposed and verified smart investment decision making model that could systematically carry out opinion mining and derive and support investment information. This shows that news can be used as variable to predict the price index of stocks for investment, and it is expected the model can be used as real investment support system if it is implemented as system and verified in the future.