• The KIPS Transactions:PartB
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• v.19B no.2
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• pp.123-126
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• 2012

This paper proposed a method to separate a liver into left and right liver lobes for simple and exact volumetry of the river graft at abdominal MDCT(Multi-Detector Computed Tomography) image before the living donor liver transplantation. A medical team can evaluate an accurate river graft with minimized interaction between the team and a system using this algorithm for ensuring donor's and recipient's safe. On the image of segmented liver, 2 points(PMHV: a point in Middle Hepatic Vein and PPV: a point at the beginning of right branch of Portal Vein) are selected to separate a liver into left and right liver lobes. Middle hepatic vein is automatically segmented using PMHV, and the cutting line is decided on the basis of segmented Middle Hepatic Vein. A liver is separated on connecting the cutting line and PPV. The volume and ratio of the river graft are estimated. The volume estimated using 2 points are compared with a manual volume that diagnostic radiologist processed and estimated and the weight measured during surgery to support proof of exact volume. The mean ${\pm}$ standard deviation of the differences between the actual weights and the estimated volumes was $162.38cm^3{\pm}124.39$ in the case of manual segmentation and $107.69cm^3{\pm}97.24$ in the case of 2 points method. The correlation coefficient between the actual weight and the manually estimated volume is 0.79, and the correlation coefficient between the actual weight and the volume estimated using 2 points is 0.87. After selection the 2 points, the time involved in separation a liver into left and right river lobe and volumetry of them is measured for confirmation that the algorithm can be used on real time during surgery. The mean ${\pm}$ standard deviation of the process time is $57.28sec{\pm}32.81$ per 1 data set ($149.17pages{\pm}55.92$).

• KIPS Transactions on Software and Data Engineering
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• v.3 no.10
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• pp.441-450
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• 2014

The stock market is constantly changing and sometimes the stock prices unaccountably plummet or surge. So, the stock market is recognized as a complex system and the change on the stock prices is unpredictable. Recently, many researchers try to understand the stock market as the network among individual stocks and to find a clue about the change of the stock prices from big data being created in real time from Internet. We focus on the correlation between the stock prices and the human interactions in Internet especially in the stock message boards. To uncover this correlation, we collected and investigated the articles concerning with 57 target companies, members of KOSPI200. From the analysis result, we found that there is no significant correlation between the stock prices and the article volume, but the strength of correlation between the article volume and the stock prices is relevant to the stock return. We propose a new method for recommending stock portfolio base on the result of our analysis. According to the simulated investment test using the article data from the stock message boards in 'Daum' portal site, the returns of our portfolio is about 1.55% per month, which is about 0.72% and 1.21% higher than that of the Markowitz's efficient portfolio and that of the KOSPI average respectively. Also, the case using the data from 'Naver' portal site, the stock returns of our proposed portfolio is about 0.90%, which is 0.35%, 0.40%, and 0.58% higher than those of our previous portfolio, Markowitz's efficient portfolio, and KOSPI average respectively. This study presents that collective human behavior on Internet stock message board can be much helpful to understand the stock market and the correlation between the stock price and the collective human behavior can be used to invest in stocks.

• The Journal of Korean Society for Radiation Therapy
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• v.30 no.1_2
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• pp.177-183
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• 2018

Purpose : Volumetric Modulated Arc Therapy(VMAT) has the advantage of uniformly and precisely irradiating the tumor to the shape of the tumor while reducing the risk of radiation damage to normal tissues. such as brain cancer, head and neck cancer and prostate cancer, It is being used for treatment. The purpose of this study is to evaluate the usefulness of the Jaw-Tracking technique(JTT) in VMAT for brain and head and neck cancer. Materials and Methods : We selected eight patients with brain and head and neck cancer(4 Brain, 4 head and neck) who were treated with the VMAT treatment technique. Contouring information of the patient's tumor and normal organ was fused to the Rando phantom using the deformable registration of Velocity(Varian, USA). A treatment plan was developed using the Varian Eclipse(ver 15.5, Varian, USA) with the same patient actual beam parameters except for the use of jaw-tracking. As the evaluation index, the maximum dose and mean dose of target and OAR were compared and a portal dosimetry was performed for the treatment plan verification. Results : When using JTT, the relative dose of OAR decreased by 5.24 % and the maximum dose by 7.05 %, respectively, compared with the Static-Jaw technique(SJT). In the various OARs, the mean dose and maximum dose reduction ranges ranged from 0.01 to 3.16 Gy and from 0.12 to 6.27 Gy, respectively. In the case of the target, the maximum dose of GTV, CTV, PTV decreased by 0.17 %, 0.43 %, and 0.37 % in JTT, and the mean dose decreased by 0.24 %, 0.47 % and 0.47 %, respectively. Gamma analysis The JTT and SJT passing rates were $98{\pm}1.73%$ and $97{\pm}1.83%$ on the basis of 3 % / 3 mm, respectively. Comparing the doses of all OARs applied to the experiment, it was found that the use of JTT resulted in a significant decrease in dose due to additional jaw shielding besides MLC than SJT. Conclusion : In radiation therapy using VMAT treatment plan, we can apply JTT in the case of adjacent tumor and normal organs such as brain cancer and head and neck cancer, and in radiotherapy required large field and high energy caused increase leakage dose through MLC. It is considered that the target dose of PTV can be increased by lowering the dose of normal tissue surrounding the tumor.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.137-152
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• 2012

In the generation of Web 2.0, as many users start to make lots of web contents called user created contents by themselves, the World Wide Web is overflowing by countless information. Therefore, it becomes the key to find out meaningful information among lots of resources. Nowadays, the information retrieval is the most important thing throughout the whole field and several types of search services are developed and widely used in various fields to retrieve information that user really wants. Especially, the legal information search is one of the indispensable services in order to provide people with their convenience through searching the law necessary to their present situation as a channel getting knowledge about it. The Office of Legislation in Korea provides the Korean Law Information portal service to search the law information such as legislation, administrative rule, and judicial precedent from 2009, so people can conveniently find information related to the law. However, this service has limitation because the recent technology for search engine basically returns documents depending on whether the query is included in it or not as a search result. Therefore, it is really difficult to retrieve information related the law for general users who are not familiar with legal terms in the search engine using simple matching of keywords in spite of those kinds of efforts of the Office of Legislation in Korea, because there is a huge divergence between everyday words and legal terms which are especially from Chinese words. Generally, people try to access the law information using everyday words, so they have a difficulty to get the result that they exactly want. In this paper, we propose a term mapping methodology between everyday words and legal terms for general users who don't have sufficient background about legal terms, and we develop a search service that can provide the search results of law information from everyday words. This will be able to search the law information accurately without the knowledge of legal terminology. In other words, our research goal is to make a law information search system that general users are able to retrieval the law information with everyday words. First, this paper takes advantage of tags of internet blogs using the concept for collective intelligence to find out the term mapping relationship between everyday words and legal terms. In order to achieve our goal, we collect tags related to an everyday word from web blog posts. Generally, people add a non-hierarchical keyword or term like a synonym, especially called tag, in order to describe, classify, and manage their posts when they make any post in the internet blog. Second, the collected tags are clustered through the cluster analysis method, K-means. Then, we find a mapping relationship between an everyday word and a legal term using our estimation measure to select the fittest one that can match with an everyday word. Selected legal terms are given the definite relationship, and the relations between everyday words and legal terms are described using SKOS that is an ontology to describe the knowledge related to thesauri, classification schemes, taxonomies, and subject-heading. Thus, based on proposed mapping and searching methodologies, our legal information search system finds out a legal term mapped with user query and retrieves law information using a matched legal term, if users try to retrieve law information using an everyday word. Therefore, from our research, users can get exact results even if they do not have the knowledge related to legal terms. As a result of our research, we expect that general users who don't have professional legal background can conveniently and efficiently retrieve the legal information using everyday words.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.57-71
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• 2013

Over a billion people in the world generate new news minute by minute. People forecasts some news but most news are from unexpected events such as natural disasters, accidents, crimes. People spend much time to watch a huge amount of news delivered from many media because they want to understand what is happening now, to predict what might happen in the near future, and to share and discuss on the news. People make better daily decisions through watching and obtaining useful information from news they saw. However, it is difficult that people choose news suitable to them and obtain useful information from the news because there are so many news media such as portal sites, broadcasters, and most news articles consist of gossipy news and breaking news. User interest changes over time and many people have no interest in outdated news. From this fact, applying users' recent interest to personalized news service is also required in news service. It means that personalized news service should dynamically manage user profiles. In this paper, a content-based news recommendation system is proposed to provide the personalized news service. For a personalized service, user's personal information is requisitely required. Social network service is used to extract user information for personalization service. The proposed system constructs dynamic user profile based on recent user information of Facebook, which is one of social network services. User information contains personal information, recent articles, and Facebook Page information. Facebook Pages are used for businesses, organizations and brands to share their contents and connect with people. Facebook users can add Facebook Page to specify their interest in the Page. The proposed system uses this Page information to create user profile, and to match user preferences to news topics. However, some Pages are not directly matched to news topic because Page deals with individual objects and do not provide topic information suitable to news. Freebase, which is a large collaborative database of well-known people, places, things, is used to match Page to news topic by using hierarchy information of its objects. By using recent Page information and articles of Facebook users, the proposed systems can own dynamic user profile. The generated user profile is used to measure user preferences on news. To generate news profile, news category predefined by news media is used and keywords of news articles are extracted after analysis of news contents including title, category, and scripts. TF-IDF technique, which reflects how important a word is to a document in a corpus, is used to identify keywords of each news article. For user profile and news profile, same format is used to efficiently measure similarity between user preferences and news. The proposed system calculates all similarity values between user profiles and news profiles. Existing methods of similarity calculation in vector space model do not cover synonym, hypernym and hyponym because they only handle given words in vector space model. The proposed system applies WordNet to similarity calculation to overcome the limitation. Top-N news articles, which have high similarity value for a target user, are recommended to the user. To evaluate the proposed news recommendation system, user profiles are generated using Facebook account with participants consent, and we implement a Web crawler to extract news information from PBS, which is non-profit public broadcasting television network in the United States, and construct news profiles. We compare the performance of the proposed method with that of benchmark algorithms. One is a traditional method based on TF-IDF. Another is 6Sub-Vectors method that divides the points to get keywords into six parts. Experimental results demonstrate that the proposed system provide useful news to users by applying user's social network information and WordNet functions, in terms of prediction error of recommended news.

• Korean Journal of Agricultural and Forest Meteorology
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• v.15 no.2
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• pp.109-117
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• 2013

The Global Framework on Climate Services (GFCS) will guide the development of climate services that link science-based climate information and predictions with climate-risk management and adaptation to climate change. GFCS structure is made up of 5 pillars; Observations/Monitoring (OBS), Research/ Modeling/ Prediction (RES), Climate Services Information System (CSIS) and User Interface Platform (UIP) which are all supplemented with Capacity Development (CD). Corresponding to each GFCS pillar, the Commission for Agricultural Meteorology (CAgM) has been proposing "Global Initiatives in AgroMeteorology" (GIAM) in order to facilitate GFCS implementation scheme from the perspective of AgroMeteorology - Global AgroMeteorological Outlook System (GAMOS) for OBS, Global AgroMeteorological Pilot Projects (GAMPP) for RES, Global Federation of AgroMeteorological Society (GFAMS) for UIP/RES, WAMIS next phase for CSIS/UIP, and Global Centers of Research and Excellence in AgroMeteorology (GCREAM) for CD, through which next generation experts will be brought up as virtuous cycle for human resource procurements. The World AgroMeteorological Information Service (WAMIS) is a dedicated web server in which agrometeorological bulletins and advisories from members are placed. CAgM is about to extend its service into a Grid portal to share computer resources, information and human resources with user communities as a part of GFCS. To facilitate ICT resources sharing, a specialized or dedicated Data Center or Production Center (DCPC) of WMO Information System for WAMIS is under implementation by Korea Meteorological Administration. CAgM will provide land surface information to support LDAS (Land Data Assimilation System) of next generation Earth System as an information provider. The International Society for Agricultural Meteorology (INSAM) is an Internet market place for agrometeorologists. In an effort to strengthen INSAM as UIP for research community in AgroMeteorology, it was proposed by CAgM to establish Global Federation of AgroMeteorological Society (GFAMS). CAgM will try to encourage the next generation agrometeorological experts through Global Center of Excellence in Research and Education in AgroMeteorology (GCREAM) including graduate programmes under the framework of GENRI as a governing hub of Global Initiatives in AgroMeteorology (GIAM of CAgM). It would be coordinated under the framework of GENRI as a governing hub for all global initiatives such as GFAMS, GAMPP, GAPON including WAMIS II, primarily targeting on GFCS implementations.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.199-219
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• 2018

With the everyday use of the Internet and the spread of various smart devices, users have been able to communicate in real time and the existing communication style has changed. Due to the change of the information subject by the Internet, data became more massive and caused the very large information called big data. These Big Data are seen as a new opportunity to understand social issues. In particular, text mining explores patterns using unstructured text data to find meaningful information. Since text data exists in various places such as newspaper, book, and web, the amount of data is very diverse and large, so it is suitable for understanding social reality. In recent years, there has been an increasing number of attempts to analyze texts from web such as SNS and blogs where the public can communicate freely. It is recognized as a useful method to grasp public opinion immediately so it can be used for political, social and cultural issue research. Text mining has received much attention in order to investigate the public's reputation for candidates, and to predict the voting rate instead of the polling. This is because many people question the credibility of the survey. Also, People tend to refuse or reveal their real intention when they are asked to respond to the poll. This study collected comments from the largest Internet portal site in Korea and conducted research on the 19th Korean presidential election in 2017. We collected 226,447 comments from April 29, 2017 to May 7, 2017, which includes the prohibition period of public opinion polls just prior to the presidential election day. We analyzed frequencies, associative emotional words, topic emotions, and candidate voting rates. By frequency analysis, we identified the words that are the most important issues per day. Particularly, according to the result of the presidential debate, it was seen that the candidate who became an issue was located at the top of the frequency analysis. By the analysis of associative emotional words, we were able to identify issues most relevant to each candidate. The topic emotion analysis was used to identify each candidate's topic and to express the emotions of the public on the topics. Finally, we estimated the voting rate by combining the volume of comments and sentiment score. By doing above, we explored the issues for each candidate and predicted the voting rate. The analysis showed that news comments is an effective tool for tracking the issue of presidential candidates and for predicting the voting rate. Particularly, this study showed issues per day and quantitative index for sentiment. Also it predicted voting rate for each candidate and precisely matched the ranking of the top five candidates. Each candidate will be able to objectively grasp public opinion and reflect it to the election strategy. Candidates can use positive issues more actively on election strategies, and try to correct negative issues. Particularly, candidates should be aware that they can get severe damage to their reputation if they face a moral problem. Voters can objectively look at issues and public opinion about each candidate and make more informed decisions when voting. If they refer to the results of this study before voting, they will be able to see the opinions of the public from the Big Data, and vote for a candidate with a more objective perspective. If the candidates have a campaign with reference to Big Data Analysis, the public will be more active on the web, recognizing that their wants are being reflected. The way of expressing their political views can be done in various web places. This can contribute to the act of political participation by the people.

• Asia pacific journal of information systems
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• v.18 no.1
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• pp.25-51
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• 2008

Researchers have been continuously interested in the adoption of information technology (IT) since it is of great importance to the information systems success and it is also an important stage to the success. Adoption alone, however, does not ensure information systems success because it does not necessarily lead to achieving organizational or individual objectives. When an organization or an individual decide to adopt certain information technologies, they have objectives to accomplish by using those technologies. Adoption itself is not the ultimate goal. The period after adoption is when users continue to use IT and intended objectives can be accomplished. Therefore, continued IT use in the post-adoption period accounts more for the accomplishment of the objectives and thus information systems success. Previous studies also suggest that continued IT use in the post-adoption period is one of the important factors to improve long-term productivity. Despite the importance there are few empirical studies focusing on the user behavior of continued IT use in the post-adoption period. User behavior in the post-adoption period is different from that in the pre-adoption period. According to the technology acceptance model, which explains well about the IT adoption, users decide to adopt IT assessing the usefulness and the ease of use. After adoption, users are exposed to new experiences and they shape new beliefs different from the thoughts they had before. Users come to make decisions based on their experiences of IT use whether they will continue to use it or not. Most theories about the user behaviors in the pre-adoption period are limited in describing them after adoption since they do not consider user's experiences of using the adopted IT and the beliefs formed by those experiences. Therefore, in this study, we explore user's experiences and beliefs in the post-adoption period and examine how they affect user's intention to continue to use IT. Through deep literature reviews on the construction of subjective beliefs by experiences, we draw three meaningful constructs which theoretically have great impacts on the continued use of IT: perceived habit, confirmation, and computer self-efficacy. Then, we examine the role of the subjective beliefs on the cognitive/affective attitudes and intention to continue to use that IT. We set up a research model and conducted survey research. Since IT use implies interactions among a user, IT, and a task, we carefully selected the sample of users using same/similar IT to perform same/similar tasks, to exclude unwanted influences of other factors than subjective beliefs on the IT use. We also considered that the sample of users were able to make decisions to continue to use IT volitionally or at least quasi-volitionally. For each construct, we used measurement items recognized for reliability and widely used in the previous research. We slightly modified some items proper to the research context and a pilot test was carried out for forty users of a portal service in a university. We performed a full-scale survey after verifying the reliability of the measurement. The results show that the intention to continue to use IT is strongly influenced by cognitive/affective attitudes, perceived habits, and computer self-efficacy. Confirmation affects the intention to continue indirectly through cognitive/affective attitudes. All the constructs representing the subjective beliefs built by the experiences of IT use have direct and/or indirect impacts on the intention of users. The results also show that the attitudes in the post-adoption period are formed, at least partly, by the experiences of IT use and newly shaped beliefs after adoption. The findings suggest that subjective beliefs built by the experiences have deep impacts on the continued use. The results of the study signify that while experiencing IT in the post-adoption period users form new beliefs, attitudes, and intentions which may be different from those of the pre-adoption period. The results of this study partly demonstrate that the beliefs shaped by the behaviors, those are the experiences of IT use, influence users' attitudes and intention. The results also suggest that behaviors (experiences) also change attitudes while attitudes shape behaviors. If we combine the findings of this study with the results of the previous research on IT adoption, we can propose a cycle of IT adoption and use where behavior shapes attitude, the attitude forms new behavior, and that behavior shapes new attitude. Different from the previous research, the study focused on the user experience after IT adoption and empirically demonstrated the strong influence of the subjective beliefs formed in the post-adoption period on the continued use. This partly confirms the differences between attitudes in the pre-adoption and in the post-adoption period. Users continuously change their attitudes and intentions while experiencing (using) IT. Therefore, to make users adopt IT and to make them use IT after adoption is a different problem. To encourage users to use IT after adoption, experiential variables such as perceived habit, confirmation, and computer self-efficacy should be managed properly.

• Journal of the Korean Society for Library and Information Science
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• v.58 no.2
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• pp.177-198
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• 2024

Article 22(1) of the Library Act, which was completely revised in December 2006, stipulated that regional representative libraries are statutory organizations, and Article 25(1) of the Library Act, which was revised again in late 2021, renamed them as metropolitan representative libraries and expanded their duties. The reason why cities and provinces are required to specify or establish and operate metropolitan representative libraries is that in addition to their role as public libraries for public information use, cultural activities, and lifelong learning as stipulated in Article 23 of the Act, they are also responsible for the legal works of metropolitan representative libraries as stipulated in Article 26, and lead the development of libraries and knowledge culture by serving as policy libraries, comprehensive knowledge information centers, support and cooperation centers, research centers, and joint preservation libraries for all public libraries in the city or province. Therefore, it is necessary to analyze and diagnose whether the metropolitan representative library has been faithfully fulfilling its legal works for the past 15 years(2009-2023), and whether it is properly providing the results of its statutory planning and implementation on its website to meet the digital and mobile era. Therefore, this study investigated and analyzed the performance of the metropolitan representative library for the last two years based on the current statutory tasks and evaluated the extent to which it provides them through its website, and suggested complementary measures to strengthen its web services. As a result, it was analyzed that the web services for legal works that the metropolitan representative library should perform are quite insufficient and inadequate, so it suggested complementary measures such as building a website for legal works on the homepage, enhancing accessibility and visibility through providing an independent website, providing various policy information and web services (portal search, inter-library loan, one-to-one consultation, joint DB construction, data transfer and preservation, etc.), and ensuring digital accessibility of knowledge information for the vulnerable.

• 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.