• Korean Journal of Environment and Ecology
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• v.37 no.2
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• pp.140-150
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• 2023

The Eurasian otter (Lutra lutra) is an apex predator of the riparian ecosystem. It is a keystone and an indicator species; consequently, its presence suggests a sustainable water environment. Otter is a keystone species as a predator at the top of the food web in the aquatic environment and an indicator species representing the health of the aquatic environment. Although Eurasian otters disappeared from the Han River urban water system because of anthropogenic activities like habitat destruction, poaching, and environmental pollution in the 1980s, the species were sighted in the Cheonggye Stream, Jungrang Stream, and Seongnae Stream, which are urban sections of the Han River, in 2016 and 2021. Therefore, it is pertinent to assess the habitat potential in the area for conservation and management measures to ensure its permanent presence. However, existing studies on otter habitats focused on natural rivers and reservoirs, and there is a limit to applying them to habitats artificially confined habitats in narrow spaces such as tributaries in urban areas of the Han River. This study selected the Cheonggye Stream, an artificially restored urban stream, to evaluate its potential as a habitat for Eurasian otters in urban water environments using the habitat suitability index (HSI). The HSI was calculated with selected environment attributes, such as the cover, food, and threat, that best describe the L. lutra habitat. According to the results, the confluence area of Seongbuk Stream and Cheonggye Stream and the confluence area of Cheonggye Stream and Jungnang Stream were suitable otter habitats, requiring appropriate conservation efforts. The HSI model suggests a valuable method to assess the habitat quality of Eurasian otters in urban water environments. The study is crucial as it can help rehabilitate the species' populations by identifying and managing potential Eurasian otter habitats in highly urbanized areas of the Han River basin and its tributaries.

• Korean Journal of Ecology and Environment
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• v.54 no.4
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• pp.346-362
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• 2021

A sediment control dam is an artificial structure built to prolong sedimentation in the main dam by reducing the inflow of suspended solids. These dams can affect changes in dissolved organic matter (DOM) in the water body by changing the river flow regime. The main DOM component for Yeongju Dam sediment control of the Naeseongcheon River was analyzed through 3D excitation-emission matrix (EEM) and parallel factor (PARAFAC) analyses. As a result, four humic-like components (C1~C3, C5), and three proteins, tryptophan-like components (C2, C6~C7) were detected. Among DOM components, humic-like components (autochthonous: C1, allochthonous: C2~C3) were found to be dominant during the sampling period. The total amount of DOM components and the composition ratio of each component did not show a difference for each depth according to the amount of available light (100%, 12%, and 1%). Throughout the study period, the allochthonous organic matter was continuously decomposing and converting into autochthonous organic matter; the DOM indices (fluorescence index, humification index, and freshness index) indicated the dominance of autochthonous organic matter in the river. Considering the relative abundance of cyanobacteria and that the number of bacteria cells and rotifers increased as autochthonous organic matter increased, it was suggested that the algal bloom and consequent activation of the microbial food web was affected by the composition of DOM in the water body. Research on DOM characteristics is important not only for water quality management but also for understanding the cycling of matter through microbial food web activity.

• 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.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.111-136
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• 2018

In this paper, we propose a methodology to extract answer information about queries from various types of unstructured documents collected from multi-sources existing on web in order to expand knowledge base. The proposed methodology is divided into the following steps. 1) Collect relevant documents from Wikipedia, Naver encyclopedia, and Naver news sources for "subject-predicate" separated queries and classify the proper documents. 2) Determine whether the sentence is suitable for extracting information and derive the confidence. 3) Based on the predicate feature, extract the information in the proper sentence and derive the overall confidence of the information extraction result. In order to evaluate the performance of the information extraction system, we selected 400 queries from the artificial intelligence speaker of SK-Telecom. Compared with the baseline model, it is confirmed that it shows higher performance index than the existing model. The contribution of this study is that we develop a sequence tagging model based on bi-directional LSTM-CRF using the predicate feature of the query, with this we developed a robust model that can maintain high recall performance even in various types of unstructured documents collected from multiple sources. The problem of information extraction for knowledge base extension should take into account heterogeneous characteristics of source-specific document types. The proposed methodology proved to extract information effectively from various types of unstructured documents compared to the baseline model. There is a limitation in previous research that the performance is poor when extracting information about the document type that is different from the training data. In addition, this study can prevent unnecessary information extraction attempts from the documents that do not include the answer information through the process for predicting the suitability of information extraction of documents and sentences before the information extraction step. It is meaningful that we provided a method that precision performance can be maintained even in actual web environment. The information extraction problem for the knowledge base expansion has the characteristic that it can not guarantee whether the document includes the correct answer because it is aimed at the unstructured document existing in the real web. When the question answering is performed on a real web, previous machine reading comprehension studies has a limitation that it shows a low level of precision because it frequently attempts to extract an answer even in a document in which there is no correct answer. The policy that predicts the suitability of document and sentence information extraction is meaningful in that it contributes to maintaining the performance of information extraction even in real web environment. The limitations of this study and future research directions are as follows. First, it is a problem related to data preprocessing. In this study, the unit of knowledge extraction is classified through the morphological analysis based on the open source Konlpy python package, and the information extraction result can be improperly performed because morphological analysis is not performed properly. To enhance the performance of information extraction results, it is necessary to develop an advanced morpheme analyzer. Second, it is a problem of entity ambiguity. The information extraction system of this study can not distinguish the same name that has different intention. If several people with the same name appear in the news, the system may not extract information about the intended query. In future research, it is necessary to take measures to identify the person with the same name. Third, it is a problem of evaluation query data. In this study, we selected 400 of user queries collected from SK Telecom 's interactive artificial intelligent speaker to evaluate the performance of the information extraction system. n this study, we developed evaluation data set using 800 documents (400 questions * 7 articles per question (1 Wikipedia, 3 Naver encyclopedia, 3 Naver news) by judging whether a correct answer is included or not. To ensure the external validity of the study, it is desirable to use more queries to determine the performance of the system. This is a costly activity that must be done manually. Future research needs to evaluate the system for more queries. It is also necessary to develop a Korean benchmark data set of information extraction system for queries from multi-source web documents to build an environment that can evaluate the results more objectively.

• 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.

• Journal of Physiology & Pathology in Korean Medicine
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• v.17 no.1
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• pp.1-24
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• 2003

This study was done to compile 104 experimental theses which are related to the antitumor and immuno-activating therapies between February 1990 through February 2002. Master's and doctoral theses were dassified by schools, degrees, materials, effects, experimental methods of antitumor and immunoactivity, and results. The following results were obtained from this study : 1. Classifying the theses by the school, 34.6% were presented by Daejeon University, 29.8% by Kyung-hee University and 11.5% by Won-kwang University. Of all theses, 51.0% were aimed for the doctoral degree and 43.3% were for the master's degree. All of three universities have their own cancer centers. 2. Classifying the theses by herb materials, complex prescription accounted for 60.3%, single herb accounted for 24.8% and herbal acupuncture accounted for 14.2%. Considering the key principles of the traditional medicine, complex prescription was much more thoroughly studied than single herb prescription. The results showed that the complex prescription had both antitumor activity and immuno-activating activity, which might reflects on multi-activation mechanisms by complex components. 3. Classifying the theses by the efficacy of herbs examined, in single herb, invigorating spleen and supplementing was 35.5%, expelling toxin and cooling was 29.0%, activating blood flow and removing blood stasis was 12.9%. In herbal acupuncture, invigorating spleen and supplementing was 52.9%, expelling toxin and cooling was 29.4%. In complex prescription, pathogen-free status was 41.9%, strengthening healthy qi to eliminate pathogen was 35.5%, strengthening healthy qi was 22.6%. It is presumed that the antitumor and immunoactivating therapy based on syndrome differentiation is the best way to develop oriental oncology. 4. Classifying the theses by antitumor experiments, cytotoxic effect was 48.1 %, survival time was 48.1 % and change of tumor size was 42.3%. Survival rate was not necessarily correlated with cytotoxicity. These data reflect the characteristic, wholistic nature of the oriental medicine which is based on BRM (biological response modifier). 5. Classifying the theses by immunoactivating experiments, hemolysin titer was 51.0%, hemagglutinin titer was 46.2% and NK cell's activity was 44.2%. In the future studies, an effort to elucidate specific molecular and cellular mechanisms of cytokine production in the body would be crucial. 6. Classifying the theses according to the data in terms of antitumor activity, 50% was evaluated good, 24.0% was excellent, and 15.5% have no effect. In an evaluation of immuno-activating activity, 35.9% was excellent and 18.0% showed a little effect. The index point, as described here, may helps to use experimental data for clinical trials. Changes in index points by varying dosage implicate the importance of oriental medical theory for prescription. 7. In 167 materials, IIP (immunoactivating index point, mean : 3.12±0.07) was significantly higher than AIP(antitumor index point, mean : 2.83±0.07). These data demonstrate that the effect of herb medicine on tumor activity depends more on immunoactivating activity than antitumor activity. This further implies that the development of herbal antitumor drugs must be preceded by the mechanistic understanding of immunoactivating effect. 8. After medline-searching tumor and herb-related articles from NCBI web site, we conclude that most of the studies are primarily focused on biomolecular mechanisms and/or pathways. Henceforth, we need to define the biomolecular mechanisms and/or pathways affected by herbs or complicated prescriptions. 9. Therefore, the most important point of oriental medical oncology is to conned between experimental results and clinical trials. For the public application of herbal therapy to cancer, it is critical to present the data to mass media. 10. To develop the relationship of experimental results and clinical trials, university's cancer clinic must have a long-range plan related to the university laboratories and, at the same time, a regular consortium for this relationship is imperative. 11. After all these efforts, a new type herbal medicine for cancer therapy which is to take care of the long-term administering and safety problem must be developed. Then, it would be expected that anti-tumor herbal acupuncture can improve clinical symptoms and quality of life (QOL) for cancer patients. 12. Finally, oriental medical cancer center must be constructed in NCC (National Cancer Center) or government agency for the development of oriental medical oncology which has international competitive power.

• Spatial Information Research
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• v.23 no.4
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• pp.57-66
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• 2015

At these day, owing to functional limitations and cost issues, used image map in web service have a problem which can not make exactly meet the user needs. This study aims to create an individual map for user suitable purposes using HTML5 technology that implement the vector map creation and its functions with services. The results of this study, the invisible problems of the tilting and rotation functions in image-based map utilizing the existing web environment were solved in HTML5 vector map. And to access the map information, by implementing the function of expressing the background and name data to selectively derive, various results were expressed in the map. Also, as a result of a comparison of performance the time required was measured at 0.88sec which comes in the range of the first loading time between 0.78sec and 7.56sec in a commercial map service, which showed the possibility of its commercialization. compared to 180GB of image maps for the production of a national map index system, the volume to produce a vector map was 2.5GB, a decrease by over 90%, which solved the issue of costs for a storage space. As a result, this study of HTML5 vector map design and implementation presented a plan for providing information suitable for the requirements of the users who use spatial information through utilizing a variety of information and expanding functions.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.4
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• pp.259-267
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• 2013

GIS-based web map service is all the more accessible to the public. Among others, location query services are most frequently utilized, which are currently restricted to only one keyword search. Although there increases the demand for the service for querying multiple keywords corresponding to sequential activities(banking, having lunch, watching movie, and other activities) in various locations POI, such service is yet to be provided. The objective of the paper is to develop the k-IPS algorithm for quickly and accurately querying multiple POIs that internet users input and locating the search outcomes on a web map. The algorithm is developed by utilizing hierarchical tree structure of $R^*$-tree indexing technique to produce overlapped geometric regions. By using recursive $R^*$-tree index based spatial join process, the performance of the current spatial join operation was improved. The performance of the algorithm is tested by applying 2, 3, and 4 multiple POIs for spatial query selected from 159 keyword set. About 90% of the test outcomes are produced within 0.1 second. The algorithm proposed in this paper is expected to be utilized for providing a variety of location-based query services, of which demand increases to conveniently support for citizens' daily activities.

• Journal of Information Management
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• v.43 no.3
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• pp.23-41
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• 2012

Citation analysis studies have reported many problems associated with data coverage problems common to popular citation databases such as Web of Science(WoS). In addition, the studies that analyzed citation patterns of Korean publications found that up to 75% of references in Korean publications were to international publications. As a first step in investigating the international coverage of WoS database, the study investigated the effect of adding citation data from Korea Science Citation Database(KSCD) to the impact factors and journal rankings of the journals listed in Journal Citation Reports. Specifically, the study mined the reference data from top 5 Korean Library and Information Science(KLIS) journals to recompute the impact factors reported in JCR 2009. Since the resulting journal rankings did not significantly differ from JCR 2009 rankings except for minor ranking changes, we analyzed additional citation data from 45 computer science and electrical engineering journals. Although the overall ranking difference was not statistically significant, one of the ranking partitions showed significant change. Such study findings despite its limited data sample suggest the potential impact of non-Western citation databases such as KSCD to bibliometric indicators provided by popular citation databases like WoS.

• Journal of the Korean Society of Food Science and Nutrition
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• v.32 no.2
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• pp.287-294
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• 2003

The purpose of this study was to develop a web-based internet program for nutritional assessment and diet management of patient having hyperlipidemia. Hyperlipidermia were classified by hypercholesterolemia and hypertriglyceridemia. The program consisted of four parts according to their functions and contents. The first part explained the metabolism of lipids and defined the hyperchotesterolemia and hypertriglyceridemia. The second part is to assess the general health status such as body weight, obesity index, basal metabolic rate and total energy requirement by the input of age, sex, height, weight and degree of activity. This part also provides the Patient with menus lists and 1 day menu suitable to his weight, activity and the status of hyperlipidemia and offers the information for food selection, snacks, convenience foods, dine-out, behavioral modification, cooking methods, food exchange lists, and information on energy and nutrients of foods and drinks, and top 20 foods classified by nutrients. The third part is designed to investigate diet history of patient, that is, to find out his inappropriate dietary habit and give him some suggestions for appropriate dietary behavior. This part also offers on-line counseling and frequently asked Questions. The fourth part is evaluating their energy and nutrients intake by comparing with recommended dietary allowance for Koreans or standardized data for patient with hyperlipidemia. In this part, it is also analyzing energy and nutrients of food consumed by food group and meals, and evaluating the status of nutrient intake. These results are finally displayed as tabular forms and graphical forms on the computer screen.