• Journal of the Korea Society of Computer and Information
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• v.8 no.2
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• pp.51-56
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• 2003

The structuring of Web information supports a strong user side viewpoint that a user wants his/her own needs on snooping a specific Web site. Not only the depth first algorithm or the breadth-first algorithm, but also the Web information is abstracted to a hierarchical structure. A prototype system is suggested in order to visualize and to represent a semantic significance. As a motivating example, the Web test site is suggested and analyzed with respect to several keywords. As a future research, the Web site model should be extended to the whole WWW and an accurate assessment function needs to be devised by which several suggested models should be evaluated.

• The Korean Journal of Archival Studies
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• no.59
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• pp.207-275
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• 2019

This archiving project of the survivors of suicide was done with the survivor supporting team of the Seoul Suicide Prevention Center. The survivor supporting team was operating a Self-help Support Group for the emotional support of the survivors of suicide. A Self-help Support Group is a place for the survivors of suicide to regularly meet and share their suffering by talking of topics hard to discuss elsewhere. As the Self-help Support Group progressed members who acted as the leader of the group appeared. They formed an essay group that writes together. Two fathers who lost their sons, two mothers who lost their daughters, a mother who lost her son, a wife who lost his husband. The essay group met each week in a place facing Sajik Park. Through the windows that took up the whole side of the room, evening was coming in. The things that happened during the day went away towards Inwang mountain following the setting sun. Ten people (six members of the essay group, three from the survivor support team, a historian for unique conversation) sat around a table, facing each other. "Now, what shall we do?" History for unique conversation is a time that archives life by sharing conversations. At times a complete stranger, and other times people who share their ordinary lives sit around together (3-9 people, sometimes about 15). On the table there is coffee, bread, fruits and salads, and sometimes a dish someone heartily prepared. When a bottle of wine is placed on the table, each takes a glass. Morning, afternoon, the time the evening is welcomed in, late night. It does not matter which. For six months, 3 hours when meeting every week, 6 hours when at every other week. A room where the ambience is like that of a kitchen where sunlight enters, or a cozy living room is the best location. However, there are many times when it is held in a multipurpose room in the suburbs where many meetings are held, or in a classroom of a school. The meeting place is decided according to different situations of the time. There are no participation requirements as it is said to be for themselves to write down according to archiving form while looking back their lives thoroughly, and they are the only ones to stop themselves. The archives landscape from far away would seem like trying to do some talking. However, when going into a microscopic situation one must leave themselves to the emotional dynamics. It is because it archives the frustration and failures one experienced through life. A participator of history for unique conversation must face the sufferings of their life. The archiving project took place in 2013 to 2014. Many years have passed. Has the objective distance for archiving the situation of that time been secured? That may be uncertain, but I will speak of a few stray thoughts on archiving while depicting the process and method of operation.

• 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.

• Journal of Korean Society of Transportation
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• v.34 no.5
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• pp.437-448
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• 2016

Some previous researches suggested a transit trip destination inference method by constructing trip chains with incomplete(missing destination) smart card dataset obtained on the entry fare control systems. To explore the feasibility of the transit trip destination inference method, the transit trip chains are constructed from the pre-paid smart card tagging data collected in Busan on October 2014 weekdays by tracing the card IDs, tagging times(boarding, alighting, transfer), and the trip linking distances between two consecutive transit trips in a daily sequences. Assuming that most trips in the transit trip chains are linked successively, the individual transit trip destination zones are inferred as the consecutive linking trip's origin zones. Applying the model to the complete trips with observed OD reveals that about 82% of the inferred trip destinations are the same as those of the observed trip destinations and the inference error defined as the difference in distance between the inferred and observed alighting stops is minimized when the trip linking distance is less than or equal to 0.5km. When applying the model to the incomplete trips with missing destinations, the overall destination missing rate decreases from 71.40% to 21.74% and approximately 77% of the destination missing trips are the single transit trips for which the destinations can not be inferable. In addition, the model remarkably reduces the destination missing rate of the multiple incomplete transit trips from 69.56% to 6.27%. Spearman's rank correlation and Chi-squared goodness-of-fit tests showed that the ranks for transit trips of each zone are not significantly affected by the inferred trips, but the transit trip distributions only using small complete trips are significantly different from those using complete and inferred trips. Therefore, it is concluded that the model should be applicable to derive a realistic transit trip patterns in cities with the incomplete smart card data.

• Journal of the Korean Institute of Landscape Architecture
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• v.48 no.4
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• pp.19-28
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• 2020

The Urban Heat Island (UHI) Effect has intensified due to urbanization and heat management at the urban level is treated as an important issue. Green space improvement projects and environmental policies are being implemented as a way to alleviate Urban Heat Islands. Several studies have been conducted to analyze the correlation between urban green areas and heat with linear regression models. However, linear regression models have limitations explaining the correlation between heat and the multitude of variables as heat is a result of a combination of non-linear factors. This study evaluated the Heat Island alleviating effects in Seoul during the summer by using a deep neural network model methodology, which has strengths in areas where it is difficult to analyze data with existing statistical analysis methods due to variable factors and a large amount of data. Wide-area data was acquired using Landsat 8. Seoul was divided into a grid (30m × 30m) and the heat island reduction variables were enter in each grid space to create a data structure that is needed for the construction of a deep neural network using ArcGIS 10.7 and Python3.7 with Keras. This deep neural network was used to analyze the correlation between land surface temperature and the variables. We confirmed that the deep neural network model has high explanatory accuracy. It was found that the cooling effect by NDVI was the greatest, and cooling effects due to the park size and green space proximity were also shown. Previous studies showed that the cooling effects related to park size was 2℃-3℃, and the proximity effect was found to lower the temperature 0.3℃-2.3℃. There is a possibility of overestimation of the results of previous studies. The results of this study can provide objective information for the justification and more effective formation of new urban green areas to alleviate the Urban Heat Island phenomenon in the future.

• Economic and Environmental Geology
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• v.54 no.1
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• pp.77-89
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• 2021

In this study, a method has been proposed to improve the performance of hydraulic property estimation model developed by Jeong et al. (2020). In their study, low-dimensional features of the annual groundwater level (GWL) fluctuation patterns extracted based on a Denoising autoencoder (DAE) was used to develop a regression model for predicting hydraulic properties of an aquifer. However, low-dimensional features of the DAE are highly dependent on the precipitation pattern even if the GWL is monitored at the same location, causing uncertainty in hydraulic property estimation of the regression model. To solve the above problem, a process for generating the GWL fluctuation pattern for conditioning the precipitation is proposed based on a conditional variational autoencoder (CVAE). The CVAE trains a statistical relationship between GWL fluctuation and precipitation pattern. The actual GWL and precipitation data monitored on a total of 71 monitoring stations over 10 years in South Korea was applied to validate the effect of using CVAE. As a result, the trained CVAE model reasonably generated GWL fluctuation pattern with the conditioning of various precipitation patterns for all the monitoring locations. Based on the trained CVAE model, the low-dimensional features of the GWL fluctuation pattern without interference of different precipitation patterns were extracted for all monitoring stations, and they were compared to the features extracted based on the DAE. Consequently, it can be confirmed that the statistical consistency of the features extracted using CVAE is improved compared to DAE. Thus, we conclude that the proposed method may be useful in extracting a more accurate feature of GWL fluctuation pattern affected solely by hydraulic characteristics of the aquifer, which would be followed by the improved performance of the previously developed regression model.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.5 no.1
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• pp.37-46
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• 2000

We investigated the temporal and spatial variations in heterotrophic dinoflagellates (hereafter HTD) and ciliates from June to September 1997 in the waters off Kohung, Korea where red tides dominated by harmful dinoflagellates had occurred from August to October since 1995. We took water samples five times from 5-7 depths at 3 stations in this study period. A total of 17 HTD species were present and of these species in the genus Protoperidinium were 11. The species number of tintinnids (hereafter TIN) present totalled 15 and several naked ciliate (hereafter NC) species were observed. The species numbers of HTD and TIN rapidly increased between August 1st and 21st and then reached to the maximum numbers of 13 and 10, respectively, on August 27 when red tides dominated by Gyrodinium impudicum were first observed in the study area. However the species numbers drastically decreased on September 22. The maximum densities of HTD, TIN, and NC were 45, 39, 57 cells $ml^{-1}$, respectively. ADAS, calculated by averaging the densities of a certain species in the all samples collected from all depths and stations at a sampling period, most increased between August 1st and 21st and then reached to the maximum density of f cells $ml^{-1}$ on August 27 for HTD, while did between August 21st and 27th and up to 7 cells $ml^{-1}$ for TIN. Unlike ADAS of HTD and TIN, that of NC did not change much with the maximum of 8 cells $ml^{-1}$ on August 27th. The pattern of the temperal variation in the species number and ADAS of HTD was similar to that of diatoms and the distributions of Protoperidinium spp. and diatoms had a strong positive correlation. This evidence suggests that HTD, in particular Protoperidinium spp. be a grazer on diatom. In general, the densities of HTD, TIN, and NC decreased with going to stations located in the outer bay. Therefore, the availability of suitable prey and distance from the coastal line might be responsible for the distribution of HTD, TIN, and NC. The results of the present study provide a basis for further experiments for the feeding by dominant HTD, TIN, and NC on dominant phytoplankton including red tide species and for understanding food webs in the planktonic community before, during, and after the red tide outbreak.