• The Korean Journal of Food And Nutrition
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• v.25 no.2
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• pp.368-382
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• 2012

This paper investigated the system that is relevant to Jang(fermented soybean paste or solution), the relief of hunger-stricken people by Jang, 33 kinds of Jang, and its consumption in the documents, such as the annals of the Chosun Dynasty, Ihlseong-document, Seungjeongwon daily, Uigwe(record of national ceremony), official documents on the basis of Kyujanggak institute for the Korean studies and data base of Korean classics. There are lots of Jang named after the place of particular soybean's production from the ancient times. Jang, soybean, salt and Meju(source of Jang), during the Dynasty, were collected as taxation or tribute. In the 5th year of Hyeonjong(1664), the storage amount of soybean in Hojo(ministry of finance) was 16,200 $k{\ell}$, and its consumption was 7,694 $k{\ell}$ a year. In the 32nd year of Yongjo(1756), the 1,800 $k{\ell}$ of soybean was distributed to the people at the time of disaster, and in his 36th year(1756), the 15,426 $k{\ell}$ of soybean was reduced from the soybean taxation nationwide. The offices managing Jang are Naejashi, Saseonseo, Sadoshi, Yebinshi and Bongsangshi. Chongyoongcheong(Gyeonggi military headquarters) stored the 175.14 $k{\ell}$ of Jang, and the 198 $k{\ell}$ of Jang in Yebinshi. There are such posts managing Jang as Jangsaek, Jangdoo, and Saseonsikjang. In the year of Jeongjong(1777~1800), the royal family distributed the 3.6 $k{\ell}$ of Meju to Gasoon-court, Hygyeong-court, queen's mother-court, queen's court, royal palace. The 13.41 $k{\ell}$ of Gamjang(fermented soybean solution) was distributed to the Gasoon-court, 17.23 $k{\ell}$ to Hegyeong-court, 17.09 $k{\ell}$ to the queen's mother-court, and the 17.17 $k{\ell}$ to the queen's court each. There are 112 Jang-storing pots in the royal storages, and the 690 are in Namhan-hill, where the 2.7 $k{\ell}$ of fermented Jang was made and brought back by them each year. At the time of starvation, Jang relieved the starving people. There are 20 occasions of big reliefs, according to the annals of the Chosun Dynasty. In the 5th year of Sejong(1423), the 360 $k{\ell}$ of Jang was given to the hunger-stricken people. In his 6th year(1424), the 8,512.92 $k{\ell}$ of rice, bean, and Jang was provided and in the 28th year(1446), the 8,322.68 $k{\ell}$ of Jang was also provided to them. In the Dynasty, Jang was given as a salary. In case that when they were bereaved, they didn't eat Jang patiently for its preservation. They were awarded for their filial piety. In the annals of the Chosun Dynasty, there are 19 kinds of Jang. They are listed in the order of Jang(108), Yeomjang(90), Maljang(11), Yookjang(5), Gamjang(4), and etc.,. In Seungjeongwon daily, there are 11 kinds of Jang. Jang(6), Cheongjang (5), Maljang(5), and Tojang(3) are listed in order. In the Ihlseong-document, there are 5 kinds of Jang. They are listed in Jang(15), Maljang(2), Gamjang(2), and etc.,. There are 13 kinds of Jang in Uigwe, and the official documents, in the order of Gamjang(59), Ganjang(37), Jang(28), Yeomjang(7), Maljang(6), and Cheongjang(5). In addition, shi are Jeonshi(7), and Dooshi(4). All these are made of only soybean except, for Yookjang. The most-frequently recorded Jang among anthology, cookbook, the annals of the Chosun Dynasty, Ihlseong-document, Seoungjeongwon daily, Uigwe, or official document is Jang(372), and then Yeomjang(194), Gamjang(73), Cheongjang(46), Ganjang(46), Soojang(33), and Maljang(26), which were made of soybean. Jang from China in cookbook is not in anthology and royal palace documents. Thus, traditional Jang made of soybean was used in the daily food life in the royal court, and in the public during the Chosun period.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.113-125
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• 2013

Every company wants to know customer's requirement and makes an effort to meet them. Cause that, communication between customer and company became core competition of business and that important is increasing continuously. There are several strategies to find customer's needs, but VOC (Voice of customer) is one of most powerful communication tools and VOC gathering by several channels as telephone, post, e-mail, website and so on is so meaningful. So, almost company is gathering VOC and operating VOC system. VOC is important not only to business organization but also public organization such as government, education institute, and medical center that should drive up public service quality and customer satisfaction. Accordingly, they make a VOC gathering and analyzing System and then use for making a new product and service, and upgrade. In recent years, innovations in internet and ICT have made diverse channels such as SNS, mobile, website and call-center to collect VOC data. Although a lot of VOC data is collected through diverse channel, the proper utilization is still difficult. It is because the VOC data is made of very emotional contents by voice or text of informal style and the volume of the VOC data are so big. These unstructured big data make a difficult to store and analyze for use by human. So that, the organization need to automatic collecting, storing, classifying and analyzing system for unstructured big VOC data. This study propose an intelligent VOC analyzing system based on opinion mining to classify the unstructured VOC data automatically and determine the polarity as well as the type of VOC. And then, the basis of the VOC opinion analyzing system, called domain-oriented sentiment dictionary is created and corresponding stages are presented in detail. The experiment is conducted with 4,300 VOC data collected from a medical website to measure the effectiveness of the proposed system and utilized them to develop the sensitive data dictionary by determining the special sentiment vocabulary and their polarity value in a medical domain. Through the experiment, it comes out that positive terms such as "칭찬, 친절함, 감사, 무사히, 잘해, 감동, 미소" have high positive opinion value, and negative terms such as "퉁명, 뭡니까, 말하더군요, 무시하는" have strong negative opinion. These terms are in general use and the experiment result seems to be a high probability of opinion polarity. Furthermore, the accuracy of proposed VOC classification model has been compared and the highest classification accuracy of 77.8% is conformed at threshold with -0.50 of opinion classification of VOC. Through the proposed intelligent VOC analyzing system, the real time opinion classification and response priority of VOC can be predicted. Ultimately the positive effectiveness is expected to catch the customer complains at early stage and deal with it quickly with the lower number of staff to operate the VOC system. It can be made available human resource and time of customer service part. Above all, this study is new try to automatic analyzing the unstructured VOC data using opinion mining, and shows that the system could be used as variable to classify the positive or negative polarity of VOC opinion. It is expected to suggest practical framework of the VOC analysis to diverse use and the model can be used as real VOC analyzing system if it is implemented as system. Despite experiment results and expectation, this study has several limits. First of all, the sample data is only collected from a hospital web-site. It means that the sentimental dictionary made by sample data can be lean too much towards on that hospital and web-site. Therefore, next research has to take several channels such as call-center and SNS, and other domain like government, financial company, and education institute.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.177-193
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• 2014

Over the past decade, there has been a rapid diffusion of electronic commerce and a rising number of interconnected networks, resulting in an escalation of security threats and privacy concerns. Electronic commerce has a built-in trade-off between the necessity of providing at least some personal information to consummate an online transaction, and the risk of negative consequences from providing such information. More recently, the frequent disclosure of private information has raised concerns about privacy and its impacts. This has motivated researchers in various fields to explore information privacy issues to address these concerns. Accordingly, the necessity for information privacy policies and technologies for collecting and storing data, and information privacy research in various fields such as medicine, computer science, business, and statistics has increased. The occurrence of various information security accidents have made finding experts in the information security field an important issue. Objective measures for finding such experts are required, as it is currently rather subjective. Based on social network analysis, this paper focused on a framework to evaluate the process of finding experts in the information security field. We collected data from the National Discovery for Science Leaders (NDSL) database, initially collecting about 2000 papers covering the period between 2005 and 2013. Outliers and the data of irrelevant papers were dropped, leaving 784 papers to test the suggested hypotheses. The co-authorship network data for co-author relationship, publisher, affiliation, and so on were analyzed using social network measures including centrality and structural hole. The results of our model estimation are as follows. With the exception of Hypothesis 3, which deals with the relationship between eigenvector centrality and performance, all of our hypotheses were supported. In line with our hypothesis, degree centrality (H1) was supported with its positive influence on the researchers' publishing performance (p<0.001). This finding indicates that as the degree of cooperation increased, the more the publishing performance of researchers increased. In addition, closeness centrality (H2) was also positively associated with researchers' publishing performance (p<0.001), suggesting that, as the efficiency of information acquisition increased, the more the researchers' publishing performance increased. This paper identified the difference in publishing performance among researchers. The analysis can be used to identify core experts and evaluate their performance in the information privacy research field. The co-authorship network for information privacy can aid in understanding the deep relationships among researchers. In addition, extracting characteristics of publishers and affiliations, this paper suggested an understanding of the social network measures and their potential for finding experts in the information privacy field. Social concerns about securing the objectivity of experts have increased, because experts in the information privacy field frequently participate in political consultation, and business education support and evaluation. In terms of practical implications, this research suggests an objective framework for experts in the information privacy field, and is useful for people who are in charge of managing research human resources. This study has some limitations, providing opportunities and suggestions for future research. Presenting the difference in information diffusion according to media and proximity presents difficulties for the generalization of the theory due to the small sample size. Therefore, further studies could consider an increased sample size and media diversity, the difference in information diffusion according to the media type, and information proximity could be explored in more detail. Moreover, previous network research has commonly observed a causal relationship between the independent and dependent variable (Kadushin, 2012). In this study, degree centrality as an independent variable might have causal relationship with performance as a dependent variable. However, in the case of network analysis research, network indices could be computed after the network relationship is created. An annual analysis could help mitigate this limitation.

• Korean Journal of Animal Reproduction
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• v.24 no.1
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• pp.1-18
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• 2000

Eighty dairy farms in Palmers ton North area in New Zealand were surveyed on 1) general characteristics (10 Questions), 2) milk yield and feed supplementary (7 questions), 3) reproductive efficiencies (12 questions) and 4) reproductive disorders (12 questions) by mail questions from February to July, 1998. Among those 4 items from 38 dairy farms (47.5%), especially in items 1) and 2), overall dairy farming situation, supplementary feeding and milk yields were surveyed and analyzed for Korean dairy farmers (especially in Cheju island) to have better understanding or higher economical gains. The results were as follows. 1. In dairy experience, 21 (45%) among 38 dairy farms surveyed were answered that farming less than 15 years, 15~19 year, 20~25 years and over 26 years experience were 3 (7.9%), 7 (18.4%), 6 (15.8%) and 5 (13.2%) which generally showed longer experience compare to Korean dairy farming situation. In survey of labour input and business goal of dairy farming, self-managing farms, sharemilkers, unpaid family manpowering farms, manager running farms, farms with hired worker, farms with part time helper and other type was 21 (55.3%), 10 (26.3%), 2 (3.5%), 3 (5.3%), 18 (31.6%), 2 (3.5%), and 1 (1.8%), respectively. 2. Analyzing pasture and tillable land, pasture according to feeding scale (200, 300 and 400 heads) were 56, 90 and 165.3 ha, and tillable lands were 51, 78 and 165 ha which showed some differences among feeding scale. In recording methods in 38 farms replied, 36 (95%) dairy handbook and 23 (70%) dual methods taking farms were higher than that of 10 (26.3%) computer and 15(39.5%) well-recorder methods. 3. Dairy waste processing facilities in environmental field were almost perfect except of metropolitan area, and so no problem was developed in its control so far. Hence, 26 farm (68.4%) of pond system was higher rather than those in 8 (21.2%) of using as organic manure after storing feces of dairy cattle, 1(2.6%) bunker system and 3 (7.9%) other type farms. 4. In milking facilities, 33 farms (86.9%) of Harringbone types were higher than those in 3 (7.9%) of Walkthrough types, 1 (2.6%) of Rotary system and other types. Although the construction facilities was not enough, this system show the world-leveled dairy country to attempted to elevate economic gains using the advantage of climatic condition. 5. In milking day and yearly yield per head, average 275 milking days and 87 drying days were longer than that of 228 average milking days in New Zealand. Annual total milk yield per head and milk solid (ms) was 3,990 kg and approximately 319 kg. Dairy milk solid (ms) per head, milk yield, fat percentage was 1.2 kg, 15.5 kg and average 4.83% which was much higher than in other country, and milk protein was average 3.75%. 6. In coclusion, Palmerstone North has been a center of dairy farming in New Zealand for the last 21 years. Their dairy farming history is 6~9 year longer than ours and the average number of milking cows per farm is 355, which is much greater than that (35) of Korea. They do not have dairy barn, but only milking parlors. Cows are taken care of by family 0.5 persons), are on a planned calving schedule in spring (93%) and milked for 240~280 days a year, avoiding winter. Cows are dried according to milk yield and body condition score. This management system is quite different from that of Korean dairy farms. Cows are not fed concentrates, relying entirely on pasture forages and the average milk yield per cow is 3,500 kg, which is about 1/2 milk yield of Korean dairy farms. They were bred to produce high fat milk with an average of 4.5%. Their milk production cost is the lowest in the world and the country's economy relies heavily on milk production. We Korean farmers may try to increase farming size, decreasing labor and management costs.