• Korean Journal of Environment and Ecology
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• v.37 no.2
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• pp.151-162
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• 2023

This study investigated the characteristics of fish communities and inhabiting status of the endangered species, Rhodeus pseudosericeus, in the Geumdang Stream in Korea from March to October 2021. A total of 1,698 fish in 5 families and 25 species were collected from 7 survey stations during the survey period. The dominant species was Zacco platypus (relative abundance, 46.5%), and the subdominant species was Squalidus gracilis majimae (16.7%), followed by Rhynchocypris oxycephalus (12.0%), Z. koreanus (5.7%), Pungtungia herzi (3.2%), R. pseudosericeus (2.0%), R. notatus (1.9%), and Acheilognathus rhombeus (1.8%). Nine Korean endemic species (36.0%) were collected, including R. pseudosericeus, R. uyekii, Sarcocheilichthys variegatus wakiyae, Microphysogobio yaluensis, S. gracilis majimae, Z. koreanus, Cobitis nalbanti, Iksookimia koreensis, and Odontobutis interrupta. An exotic species, Micropterus salmoides, designated as an invasive alien species (IAS), was collected downstream. The investigation of the habitat patterns of the endangered species (class II), Rhodeus pseudosericeus, showed a habitat range of about 6 to 7 km in the middle of Geumdang Stream (RP-1 to RP-4), and this species inhabited the edge with water depths of 0.3 through 1.0 m with slow water flow and many aquatic plants. According to the community analysis results, the overall dominance and evenness indexes were low, while diversity and richness indexes were high, and the cluster structure was largely divided into upstream and middle-downstream areas. The river health (fish assessment index) evaluated using fish was assessed as good (3 stations), normal (3 stations), and bad (1 station), and water quality was evaluated as good both upstream and downstream. Compared to previous studies, the number of species was relatively similar, and among the species that appeared in the past, 13 species did not appear in this survey, while 6 species appeared for the first time in this survey. Disturbance factors included river construction, many weirs, and the appearance of the ecosystem-disturbing species, M. salmoides. Since Geumdang Strem has high conservation value because it is home to many species in the Acheilognathinae subfamily, including the endangered species R. pseudosericeus, continuous attention and systematic conservation measures are required.

• Korean Journal of Environment and Ecology
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• v.38 no.1
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• pp.55-66
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• 2024

This study conducted a survey to investigate the characteristics of fish communities and the inhabiting status of endangered species in the Byekgye Stream, Korea from April to September 2020. A total of 3,415 fish of 9 families and 31 species were collected from 7 survey stations during the survey period. The dominant species was Zacco koreanus (relative abundance of 31.2%), and the subdominant species was Z. platypus (15.0%), followed by Pungtungia herzi (11.7%), Acheilognathus yamatsutae (5.4%), A. lanceolata intermedia (4.8%), Rhinogobius brunneus (4.4%), and Pseudopungtungia tenuicorpa (4.3%). Among the fish species collected, 19 (61.3%) were identified as Korean endemic species, and two cold-water fish species sensitive to climate change (Rhynchocypris kumgangensis and Cottus koreanus) were collected. Four species were designated as class II endangered wildlife by the Ministry of Environment: A. signifer, P. tenuicorpa, Rhodeus pseudosericeus, and C. koreanus. A. signifer and P. tenuicorpa mainly inhabited the mid to lower streams, R. pseudosericeus in the midstream, and R. pseudosericeus in the upstream. P. tenuicorpa inhabited in large numbers, and estimating the age by total length-frequency distribution in July, the total length of the 26-35 mm group was estimated as 0 years old, the 54-75 mm group as 1 year old, 82-97 mm group as 2 years old, 104-109 mm group as 3 years or older. The cluster analysis showed that the dominance index decreased from upstream to downstream, but the diversity, evenness, and richness index increased. The water quality of Byekgye Stream was evaluated as good overall since the river health (fish assessment index, FAI) using fish was evaluated as excellent (5 stations) and good (2 stations). Byekgye Stream has relatively well-preserved habitats, but conservation measures are required as habitats are disturbed by river repair work in some parts of the midstream and downstream areas where many endangered species inhabit.

• Journal of Korea Water Resources Association
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• v.57 no.7
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• pp.437-449
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• 2024

Rainfall-runoff prediction studies using deep learning while considering catchment attributes have been gaining attention. In this study, we selected two models: the Transformer model, which is suitable for large-scale data training through the self-attention mechanism, and the LSTM-based multi-state-vector sequence-to-sequence (LSTM-MSV-S2S) model with an encoder-decoder structure. These models were constructed to incorporate catchment attributes and predict the inflow of 10 multi-purpose dam watersheds in South Korea. The experimental design consisted of three training methods: Single-basin Training (ST), Pretraining (PT), and Pretraining-Finetuning (PT-FT). The input data for the models included 10 selected watershed attributes along with meteorological data. The inflow prediction performance was compared based on the training methods. The results showed that the Transformer model outperformed the LSTM-MSV-S2S model when using the PT and PT-FT methods, with the PT-FT method yielding the highest performance. The LSTM-MSV-S2S model showed better performance than the Transformer when using the ST method; however, it showed lower performance when using the PT and PT-FT methods. Additionally, the embedding layer activation vectors and raw catchment attributes were used to cluster watersheds and analyze whether the models learned the similarities between them. The Transformer model demonstrated improved performance among watersheds with similar activation vectors, proving that utilizing information from other pre-trained watersheds enhances the prediction performance. This study compared the suitable models and training methods for each multi-purpose dam and highlighted the necessity of constructing deep learning models using PT and PT-FT methods for domestic watersheds. Furthermore, the results confirmed that the Transformer model outperforms the LSTM-MSV-S2S model when applying PT and PT-FT methods.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.17-35
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• 2015

Demand forecasting is the activity of estimating the quantity of a product or service that consumers will purchase for a certain period of time. Developing precise forecasting models are considered important since corporates can make strategic decisions on new markets based on future demand estimated by the models. Many studies have developed market growth curve models, such as Bass, Logistic, Gompertz models, which estimate future demand when a market is in its early stage. Among the models, Bass model, which explains the demand from two types of adopters, innovators and imitators, has been widely used in forecasting. Such models require sufficient demand observations to ensure qualified results. In the beginning of a new market, however, observations are not sufficient for the models to precisely estimate the market's future demand. For this reason, as an alternative, demands guessed from those of most adjacent markets are often used as references in such cases. Reference markets can be those whose products are developed with the same categorical technologies. A market's demand may be expected to have the similar pattern with that of a reference market in case the adoption pattern of a product in the market is determined mainly by the technology related to the product. However, such processes may not always ensure pleasing results because the similarity between markets depends on intuition and/or experience. There are two major drawbacks that human experts cannot effectively handle in this approach. One is the abundance of candidate reference markets to consider, and the other is the difficulty in calculating the similarity between markets. First, there can be too many markets to consider in selecting reference markets. Mostly, markets in the same category in an industrial hierarchy can be reference markets because they are usually based on the similar technologies. However, markets can be classified into different categories even if they are based on the same generic technologies. Therefore, markets in other categories also need to be considered as potential candidates. Next, even domain experts cannot consistently calculate the similarity between markets with their own qualitative standards. The inconsistency implies missing adjacent reference markets, which may lead to the imprecise estimation of future demand. Even though there are no missing reference markets, the new market's parameters can be hardly estimated from the reference markets without quantitative standards. For this reason, this study proposes a case-based expert system that helps experts overcome the drawbacks in discovering referential markets. First, this study proposes the use of Euclidean distance measure to calculate the similarity between markets. Based on their similarities, markets are grouped into clusters. Then, missing markets with the characteristics of the cluster are searched for. Potential candidate reference markets are extracted and recommended to users. After the iteration of these steps, definite reference markets are determined according to the user's selection among those candidates. Then, finally, the new market's parameters are estimated from the reference markets. For this procedure, two techniques are used in the model. One is clustering data mining technique, and the other content-based filtering of recommender systems. The proposed system implemented with those techniques can determine the most adjacent markets based on whether a user accepts candidate markets. Experiments were conducted to validate the usefulness of the system with five ICT experts involved. In the experiments, the experts were given the list of 16 ICT markets whose parameters to be estimated. For each of the markets, the experts estimated its parameters of growth curve models with intuition at first, and then with the system. The comparison of the experiments results show that the estimated parameters are closer when they use the system in comparison with the results when they guessed them without the system.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.1-23
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• 2013

To discover significant social issues such as unemployment, economy crisis, social welfare etc. that are urgent issues to be solved in a modern society, in the existing approach, researchers usually collect opinions from professional experts and scholars through either online or offline surveys. However, such a method does not seem to be effective from time to time. As usual, due to the problem of expense, a large number of survey replies are seldom gathered. In some cases, it is also hard to find out professional persons dealing with specific social issues. Thus, the sample set is often small and may have some bias. Furthermore, regarding a social issue, several experts may make totally different conclusions because each expert has his subjective point of view and different background. In this case, it is considerably hard to figure out what current social issues are and which social issues are really important. To surmount the shortcomings of the current approach, in this paper, we develop a prototype system that semi-automatically detects social issue keywords representing social issues and problems from about 1.3 million news articles issued by about 10 major domestic presses in Korea from June 2009 until July 2012. Our proposed system consists of (1) collecting and extracting texts from the collected news articles, (2) identifying only news articles related to social issues, (3) analyzing the lexical items of Korean sentences, (4) finding a set of topics regarding social keywords over time based on probabilistic topic modeling, (5) matching relevant paragraphs to a given topic, and (6) visualizing social keywords for easy understanding. In particular, we propose a novel matching algorithm relying on generative models. The goal of our proposed matching algorithm is to best match paragraphs to each topic. Technically, using a topic model such as Latent Dirichlet Allocation (LDA), we can obtain a set of topics, each of which has relevant terms and their probability values. In our problem, given a set of text documents (e.g., news articles), LDA shows a set of topic clusters, and then each topic cluster is labeled by human annotators, where each topic label stands for a social keyword. For example, suppose there is a topic (e.g., Topic1 = {(unemployment, 0.4), (layoff, 0.3), (business, 0.3)}) and then a human annotator labels "Unemployment Problem" on Topic1. In this example, it is non-trivial to understand what happened to the unemployment problem in our society. In other words, taking a look at only social keywords, we have no idea of the detailed events occurring in our society. To tackle this matter, we develop the matching algorithm that computes the probability value of a paragraph given a topic, relying on (i) topic terms and (ii) their probability values. For instance, given a set of text documents, we segment each text document to paragraphs. In the meantime, using LDA, we can extract a set of topics from the text documents. Based on our matching process, each paragraph is assigned to a topic, indicating that the paragraph best matches the topic. Finally, each topic has several best matched paragraphs. Furthermore, assuming there are a topic (e.g., Unemployment Problem) and the best matched paragraph (e.g., Up to 300 workers lost their jobs in XXX company at Seoul). In this case, we can grasp the detailed information of the social keyword such as "300 workers", "unemployment", "XXX company", and "Seoul". In addition, our system visualizes social keywords over time. Therefore, through our matching process and keyword visualization, most researchers will be able to detect social issues easily and quickly. Through this prototype system, we have detected various social issues appearing in our society and also showed effectiveness of our proposed methods according to our experimental results. Note that you can also use our proof-of-concept system in http://dslab.snu.ac.kr/demo.html.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.7 no.1
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• pp.31-39
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• 2004

Purpose: Although acute nonspecific mesenteric lymphadenitis (ANML) is probably common cause of abdominal pain in children, which can be severe enough to be an abdominal emergency, the clinical features of mesenteric lymphadenitis are not clear. Also, a differential diagnosis with acute appendicitis (APPE) is indispensable to avoid serious complications. The clinical features of ANML were determined, and the risk factors for differential diagnosis with APPE were analyzed. Methods: Between November 2000 and May 2001, data from 26 patients (aged 1 to 11 years) with ANML and 21 patients (aged 2 to 13 years) with APPE were reviewed. ANML was defined as a cluster of five or more lymph nodes measuring 10 mm or greater in their longitudinal diameter in the right lower quadrant (RLQ) without an identifiable specific inflammatory process on the ultrasonographic examination. There were risk factors on patient's history, physical examination, and laboratory examination; the location of abdominal pain, abdominal rigidity, rebound tenderness, fever, nocturnal pain, the vomiting intensity, the diarrhea intensity, the symptom duration, and the peripheral blood leukocytes count. Results: Of the 26 ANML patients and 21 APPE patients, abdominal pain was noted on periumbilical (76.9% vs 14.2%), on RLQ (11.5% vs 71.4%), with abdomen rigidity (7.6% vs 80.9%), with rebound tenderness (0.0% vs 76.1%)(p<0.05), in the lower abdomen (11.5% vs 14.2%), and at night (80.8% vs 100.0%) (p>0.05). The clinical symptoms were vomiting (38.4% vs 90.4%), the vomiting intensity ($1.5{\pm}0.7$ [1~3]/day vs $4.5{\pm}2.9$ [1~10]/day), diarrhea (65.3% vs 28.5%) (p<0.05), and fever (61.5% vs 76.2%)(p>0.05). The period to the subsidence of abdominal pain in the ANMA patients was $2.5{\pm}0.5$ (2~3) days. The laboratory data showed a significant difference in the peripheral blood leukocytes count ($8,403{\pm}1,737[5,900{\sim}12,300]/mm^3\;vs\;15,471{\pm}3,749[5,400{\sim}20,800]/mm^3$)(p<0.05). Discriminant analysis between ANML and APPE showed that the independent discriminant factors were a vomiting intensity and the peripheral blood leukocytes count and the discriminant power was 95.7%. Conclusion: The clinical characteristics of ANML were abrupt onset of periumbilical pain without rigidity or rebound tenderness, a mild vomiting intensity, normal peripheral leukocytes count, and relatively short clinical course. If the abdominal pain persist for more than 3 days, and/or the vomiting intensity is more than 3 times/day, and/or the peripheral leukocytes count is over $13,500/mm^3$, abdominal ultrasonography is recommended to rule out APPE.

• Korean Journal of Environmental Agriculture
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• v.22 no.4
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• pp.278-283
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• 2003

This study was carried out to examine the nitrogen removal rate of a subsurface-flow treatment wetland system which was constructed on floodplain of the Kwangju River from May to June 2001. Its dimensions were 29m in length, 9m in width and 0.65m in depth. A bottom layer of 45cm in depth was filled with crushed granite with about $15{\sim}30\;mm$ in diameter and a middle layer of 10cm in depth had pea pebbles with about 10 mm in diameter. An upper layer of 5 cm in depth contained course sand. Reeds (Phragmites australis) were transplanted on the surface of the system. They were dug out of natural wetlands and stems were cut at about 40 cm height from their bottom ends. Water of the Kwangju River flowed into it via a pipe by gravity flow and its effluent was funneled back into the river. The height of reed stems was 44.2 cm in July 2001 and 75.3cm in September 2001. The number of stems was increased from $80\;stems/m^2$ in July 2001 to $136\;stems/m^2$ in September 2001. Volume and water quality of inflow and outflow were analyzed from July 2001 through December 2001. Inflow and outflow averaged 40.0 and $39.2\;m^3/day$, respectively. Hydraulic detention time was about 1.5 days. Average nitrogen uptake by reeds was $69.31\;N\;mg/m^2/day$. Removal rate of $NO_3-N$, $NH_3-N$, T-N averaged 195.58, 53.65, and $628.44\;mg/m^2/day$, respectively. Changes of $NO_3-N$ and $NH_3-N$ abatement rates were closely related to those of wetland temperatures. The lower removal rate of nitrogen species compared with that of subsurface-flow wetlands operating in North America could be attributed to the initial stage of the system and inclusion of two cold months into the six-month monitoring period. Increase of standing density of reeds within a few years will develop both root zones suitable for the nitrification of ammonia and surface layer substrates beneficial to the denitrification of nitrates into nitrogen gases, which may lead to increment in the nitrogen retention rate.

• 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 agricultural medicine and community health
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• v.19 no.2
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• pp.141-158
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• 1994

To provide better health care services to the rural population, the government has made the Community Health Practitioner(CHP) a regular government official from April 1, 1992. This study was carried out to study the impact of officialization of CHP on the activities and management system of Primary Health Post(PHP). Fifty PHPs were selected by two stage sampling, cluster and simple random, from 595 PHPs in Kyungnam and Kyungpook provinces. Data were collected by a personal interview with CHPs and review of records and reports kept in the PHPs. The study was done for the periods of January 1-March 31, 1992 (before officialization) and January 1-March 31, 1993 (after officialization). Ninety-six percent of the CHPs wanted to become a regular government official in the hope of better job security and higher salary. The proportion of CHPs who were proud of their iob was increased from 24% to 46% after officialization. Those CHPs who felt insecure for their job decreased from 30% to 10%. Monthly salary was increased by 34% from 802,600 Won to 1,076,000 Won and 90% of the CHPs were satisfied with their salary, also more CHPs responded that they have autonomy in their work planning, implementation of plan, management of the post, and evaluation of their activity. There were no appreciable changes in such CHPs' activities as assessment of local health resources, drawing map for the catchment area, utilization of community organization, grasping the current population structure in the catchment area, keeping the family health records, individual and group health education, and school health service. However, the number of home visits was increased from 13.6 times on the average per month per CHP to 27.5 times. More mothers and children were referred to other medical facilities for the immunization and family planning services. Average number of patients of hypertension, cancer, and diabetes in three months period was decreased from 12.7 to 11.6, from 1.5 to 1.2, and 4.3 to 3.4, respectively. Records for the patient care, drug management, and equipment were well kept but not for other records. The level of record keeping was not changed after officialization. The proportion of PHPs which had support from the health center was increased for drug supply from 14.0% to 30.0%, for consumable commodities from 22.0% to 52.0%, for maintenance of PHP from 54.0% to 68.0%, for supply of health education materials from 34.0% to 44.0%, and supply of equipment from 54.0% to 58.0%. Total monthly revenue of a PHP was increased by about 50,000 Won; increased by 22,000 Won in patient care and 34,700 Won in the government subsidy but decreased in the membership due and donation. However, there was no remarkable changes in the expenditure. The proportion of PHPs which had received official notes from the health center for the purpose of guidance and supervision of the CHPs was increased from 20% to 38% during three months period and the average number of telephone call for supervision from the health center per PHP was increased from 1.8 to 2.1 times(p<0.01). However, the proportion of PHPs that had supervisory visit and conference was reduced from 79% to 62%, and from 88% to 74%, respectively. The proportion of CHPs who maintained a cooperative relationship with Myun Health Workers was reduced from 42% to 36%, that with the director of health center from 46% to 24%, that with the chief of public health administration section from 56% to 36%, and that with the chairman of PHP management council from 62% to 38%. Most of the CHPs (92% before and 82% after officialization) stated that the PHP management council is not helpful for the PHP. CHPs who considered the PHP management council unnecessary increased from 4% to 16%(p<0.05). Suggestions made by the CHPs for the improvement of CHP program included emphasis on health education, assurance of autonomy for PHP management, increase of the kind of drugs that can be dispensed by CHPs, and appointment of an experienced CHP in the health center as the supervisor of CHPs. The results of this study revealed that the role and function of CHPs as reflected in their activities have not been changed after officialization. However, satisfaction in job security and salary was improved as well as the autonomy. Support of health center to the PHP was improved but more official notes were sent to the PHPs which required the CHPs more paper works. Number of telephone calls for supervision was increased but there was little administrative and technical guidance for the CHP activities.