• Clinical and Experimental Pediatrics
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• v.48 no.8
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• pp.832-838
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• 2005

Purpose : Mycoplasma pneumoniae infection is relatively common in childhood. Its extrapulmonary manifestations have been reported so much, but hepatitis associated with it has been reported rarely in Korea. Methods : A clinical study was performed on 556 patients of M. pneumoniae pneumonia diagnosed serologically at Gil hospital from January 2001 to December 2004. We reviewed 65 cases among these patients, who had elevated level of serum AST and ALT greater than 50 IU/L respectively without evidence of hepatitis A, B, C, Cytomegalovirus and Ebstein-Barr virus infections. Results : Hepatitis occurred in 11.7% of Mycoplasma pneumoniae pneumonia, especially in fall and winter times. Male to female ratio was 1.2 : 1 and the mean age was 4 years and 3 months. Besides hepatitis, cough(95.4%), sputum(52.3%) and dyspnea(12.3%) were common as pulmonary manifestations. And among gastrointestinal manifestations, nausea/vomiting(26.2%) was the most common symptom, followed by poor oral intake(12.3%), diarrhea(12.3%) and abdominal pain(6.2%). In addition to hepatomegaly(4.6%) and splenomegaly(4.6%), coarse breathing sound was the most common physical manifestation, followed by rale(63.1%), pharyngeal injection(26.2%), and rash(10.8%). Anemia was noted in 20.0%, neutrophilia in 10.8%, eosinphilia in 38.5% and thrombocytosis in 6.2%, respectively. Mean level of ESR and CRP was 32.02 mm/hr and 6.69 mg/dL, respectively. Mean level of AST and ALT was 293.80 IU/L and 181.48 IU/L, respectively. Hyperbilirubinemia was noted in 7.7% and hypoalbuminemia was noted in 58.5%. Lobar or lobular pneumonia(78.5%) was the most common finding in chest X-ray and left lower lobe(39.2%) was most commonly affected. Pleural effusion was noted in 26.2%. Mean duration of hospitalization was 9.91 days. Serum AST/ALT level was normalized within 9.94 days and pulmonary consolidation resolved within 14.29 days. Conclusion : The prognosis of M. pneumoniae hepatitis is good. However, liver function should be considerately checked in M. pneumoniae infection because its incidence is not so low.

• Journal of Astronomy and Space Sciences
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• v.22 no.2
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• pp.147-174
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• 2005

To understand the physical processes that control the high-latitude lower thermospheric dynamics, we quantify the forces that are mainly responsible for maintaining the high-latitude lower thermospheric wind system with the aid of the National Center for Atmospheric Research Thermosphere-Ionosphere Electrodynamics General Circulation Model (NCAR-TIEGCM). Momentum forcing is statistically analyzed in magnetic coordinates, and its behavior with respect to the magnitude and orientation of the interplanetary magnetic field (IMF) is further examined. By subtracting the values with zero IMF from those with non-zero IMF, we obtained the difference winds and forces in the high-latitude 1ower thermosphere(<180 km). They show a simple structure over the polar cap and auroral regions for positive($B_y$ > 0.8|$\overline{B}_z$ |) or negative($B_y$ < -0.8|$\overline{B}_z$|) IMF-$\overline{B}_y$ conditions, with maximum values appearing around -80$^{\circ}$ magnetic latitude. Difference winds and difference forces for negative and positive $\overline{B}_y$ have an opposite sign and similar strength each other. For positive($B_z$ > 0.3125|$\overline{B}_y$|) or negative($B_z$ < -0.3125|$\overline{B}_y$|) IMF-$\overline{B}_z$ conditions the difference winds and difference forces are noted to subauroral latitudes. Difference winds and difference forces for negative $\overline{B}_z$ have an opposite sign to positive $\overline{B}_z$ condition. Those for negative $\overline{B}_z$ are stronger than those for positive indicating that negative $\overline{B}_z$ has a stronger effect on the winds and momentum forces than does positive $\overline{B}_z$ At higher altitudes(>125 km) the primary forces that determine the variations of tile neutral winds are the pressure gradient, Coriolis and rotational Pedersen ion drag forces; however, at various locations and times significant contributions can be made by the horizontal advection force. On the other hand, at lower altitudes(108-125 km) the pressure gradient, Coriolis and non-rotational Hall ion drag forces determine the variations of the neutral winds. At lower altitudes(<108 km) it tends to generate a geostrophic motion with the balance between the pressure gradient and Coriolis forces. The northward component of IMF By-dependent average momentum forces act more significantly on the neutral motion except for the ion drag. At lower altitudes(108-425 km) for negative IMF-$\overline{B}_y$ condition the ion drag force tends to generate a warm clockwise circulation with downward vertical motion associated with the adiabatic compress heating in the polar cap region. For positive IMF-$\overline{B}_y$ condition it tends to generate a cold anticlockwise circulation with upward vertical motion associated with the adiabatic expansion cooling in the polar cap region. For negative IMF-$\overline{B}_z$ the ion drag force tends to generate a cold anticlockwise circulation with upward vertical motion in the dawn sector. For positive IMF-$\overline{B}_z$ it tends to generate a warm clockwise circulation with downward vertical motion in the dawn sector.

• Asian Journal of Turfgrass Science
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• v.17 no.4
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• pp.129-146
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• 2003

Research was initiated to compare establishment vigor, uniformity, rooting potential and turf quality in sods of cool-season grasses (CSG). Several turfgrasses grown under pure sand soil were tested. Establishment vigor, uniformity, rooting potential and turf quality were evaluated in the study. Turfgrass entries were comprised of three blends from Kentucky bluegrass (KB, Poa pratensis L.), perennial ryegrass (PR, Lolium perenne L.), and tall fescue (TF, Festuca arundinacea Schreb.), respectively and three mixtures among them. Differences by treatments were significantly observed in establishment vigor, uniformity, rooting potential and turf quality. Early establishment vigor was mainly influenced by germination speed, being fastest with PR, intermediate with TF and slowest with KB. In a late stage of growth, however, it was affected more by growth habit, resulting in highest with KB and slowest with TF. There were considerable variations in sod uniformity among turfgrasses. Best uniformity among monostand sods was associated with KB, while poorest one with TF. PR sod produced intermediate uniformity between KB and TF. The uniformity of polystand sods of CSG mixtures was inferior to that of monostands of KB, PR and TF, due to characteristics of mixtures comprised of a variety of color, density, texture and growth habit. The greatest potential of sod rooting was found with PR and the poorest with KB. Intermediate potential between PR and KB was associated with TF. In CSG mixtures, it was variable, depending on turfgrass mixing rates. Generally, the higher the PR in mixtures, the greater the sod rooting potential. At the time of sod harvest, however, turfgrass quality of KB was superior to that of PR. because of its characteristics of uniform surface, high density and good mowing quality. These results suggest that a careful expertise based on turf quality as well as sod characteristics like establishment vigor, uniformity and rooting potential be strongly required for the success of golf course or athletic field in establishment.

• The Journal of Korean Society for Radiation Therapy
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• v.27 no.2
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• pp.97-106
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• 2015

Purpose : This study evaluate the thermal changes caused by use of the chemoport for drug administration and blood sampling during radiofrequency hyperthermia. Materials and Methods : 20cm size of the electrode radio frequency hyperthermia (EHY-2000, Oncotherm KFT, Hungary) was used. The materials of the chemoport in our hospital from currently being used therapy are plastics, metal-containing epoxy and titanium that were made of the diameter 20 cm, height 20 cm insertion of the self-made cylindrical Agar phantom to measure the temperature. Thermoscope(TM-100, Oncotherm Kft, Hungary) and Sim4Life (Ver2.0, Zurich, Switzerland) was compared to the actual measured temperature. Each of the electrode measurement position is the central axis and the central axis side 1.5 cm, 0 cm(surface), 0.5 cm, 1.8 cm, 2.8 cm in depth was respectively measured. The measured temperature is $24.5{\sim}25.5^{\circ}C$, humidity is 30% ~ 32%. In five-minute intervals to measure the output power of 100W, 60 min. Results : In the electrode central axis 2.8 cm depth, the maximum temperature of the case with the unused of the chemoport, plastic, epoxy and titanium were respectively $39.51^{\circ}C$, $39.11^{\circ}C$, $38.81^{\circ}C$, $40.64^{\circ}C$, simulated experimental data were $42.20^{\circ}C$, $41.50^{\circ}C$, $40.70^{\circ}C$, $42.50^{\circ}C$. And in the central axis electrode side 1.5 cm depth 2.8 cm, mesured data were $39.37^{\circ}C$, $39.32^{\circ}C$, $39.20^{\circ}C$, $39.46^{\circ}C$, the simulated experimental data were $42.00^{\circ}C$, $41.80^{\circ}C$, $41.20^{\circ}C$, $42.30^{\circ}C$. Conclusion : The thermal variations were caused by radiofrequency electromagnetic field surrounding the chemoport showed lower than in the case of unused in non-conductive plastic material and epoxy material, the titanum chemoport that made of conductor materials showed a slight differences. This is due to the metal contents in the chemoport and the geometry of the chemoport. And because it uses a low radio frequency bandwidth of the used equipment. That is, although use of the chemoport in this study do not significantly affect the surrounding tissue. That is, because the thermal change is insignificant, it is suggested that the hazard of the chemoport used in this study doesn't need to be considered.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.93-112
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• 2015

Depending on the change in consumer's consumption pattern, existing retail shop has evolved in hypermarket or convenience store offering grocery and daily products mostly. Therefore, it is important to maintain the inventory levels and proper product configuration for effectively utilize the limited space in the retail store and increasing sales. Accordingly, this study proposed proper product configuration and inventory level strategy based on RFM(Recency, Frequency, Monetary) model and SOM(self-organizing map) for manage the retail shop effectively. RFM model is analytic model to analyze customer behaviors based on the past customer's buying activities. And it can differentiates important customers from large data by three variables. R represents recency, which refers to the last purchase of commodities. The latest consuming customer has bigger R. F represents frequency, which refers to the number of transactions in a particular period and M represents monetary, which refers to consumption money amount in a particular period. Thus, RFM method has been known to be a very effective model for customer segmentation. In this study, using a normalized value of the RFM variables, SOM cluster analysis was performed. SOM is regarded as one of the most distinguished artificial neural network models in the unsupervised learning tool space. It is a popular tool for clustering and visualization of high dimensional data in such a way that similar items are grouped spatially close to one another. In particular, it has been successfully applied in various technical fields for finding patterns. In our research, the procedure tries to find sales patterns by analyzing product sales records with Recency, Frequency and Monetary values. And to suggest a business strategy, we conduct the decision tree based on SOM results. To validate the proposed procedure in this study, we adopted the M-mart data collected between 2014.01.01~2014.12.31. Each product get the value of R, F, M, and they are clustered by 9 using SOM. And we also performed three tests using the weekday data, weekend data, whole data in order to analyze the sales pattern change. In order to propose the strategy of each cluster, we examine the criteria of product clustering. The clusters through the SOM can be explained by the characteristics of these clusters of decision trees. As a result, we can suggest the inventory management strategy of each 9 clusters through the suggested procedures of the study. The highest of all three value(R, F, M) cluster's products need to have high level of the inventory as well as to be disposed in a place where it can be increasing customer's path. In contrast, the lowest of all three value(R, F, M) cluster's products need to have low level of inventory as well as to be disposed in a place where visibility is low. The highest R value cluster's products is usually new releases products, and need to be placed on the front of the store. And, manager should decrease inventory levels gradually in the highest F value cluster's products purchased in the past. Because, we assume that cluster has lower R value and the M value than the average value of good. And it can be deduced that product are sold poorly in recent days and total sales also will be lower than the frequency. The procedure presented in this study is expected to contribute to raising the profitability of the retail store. The paper is organized as follows. The second chapter briefly reviews the literature related to this study. The third chapter suggests procedures for research proposals, and the fourth chapter applied suggested procedure using the actual product sales data. Finally, the fifth chapter described the conclusion of the study and further research.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.63-77
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• 2011

The World Wide Web is a very large distributed digital information space. From its origins in 1991, the web has grown to encompass diverse information resources as personal home pasges, online digital libraries and virtual museums. Some estimates suggest that the web currently includes over 500 billion pages in the deep web. The ability to search and retrieve information from the web efficiently and effectively is an enabling technology for realizing its full potential. With powerful workstations and parallel processing technology, efficiency is not a bottleneck. In fact, some existing search tools sift through gigabyte.syze precompiled web indexes in a fraction of a second. But retrieval effectiveness is a different matter. Current search tools retrieve too many documents, of which only a small fraction are relevant to the user query. Furthermore, the most relevant documents do not nessarily appear at the top of the query output order. Also, current search tools can not retrieve the documents related with retrieved document from gigantic amount of documents. The most important problem for lots of current searching systems is to increase the quality of search. It means to provide related documents or decrease the number of unrelated documents as low as possible in the results of search. For this problem, CiteSeer proposed the ACI (Autonomous Citation Indexing) of the articles on the World Wide Web. A "citation index" indexes the links between articles that researchers make when they cite other articles. Citation indexes are very useful for a number of purposes, including literature search and analysis of the academic literature. For details of this work, references contained in academic articles are used to give credit to previous work in the literature and provide a link between the "citing" and "cited" articles. A citation index indexes the citations that an article makes, linking the articleswith the cited works. Citation indexes were originally designed mainly for information retrieval. The citation links allow navigating the literature in unique ways. Papers can be located independent of language, and words in thetitle, keywords or document. A citation index allows navigation backward in time (the list of cited articles) and forwardin time (which subsequent articles cite the current article?) But CiteSeer can not indexes the links between articles that researchers doesn't make. Because it indexes the links between articles that only researchers make when they cite other articles. Also, CiteSeer is not easy to scalability. Because CiteSeer can not indexes the links between articles that researchers doesn't make. All these problems make us orient for designing more effective search system. This paper shows a method that extracts subject and predicate per each sentence in documents. A document will be changed into the tabular form that extracted predicate checked value of possible subject and object. We make a hierarchical graph of a document using the table and then integrate graphs of documents. The graph of entire documents calculates the area of document as compared with integrated documents. We mark relation among the documents as compared with the area of documents. Also it proposes a method for structural integration of documents that retrieves documents from the graph. It makes that the user can find information easier. We compared the performance of the proposed approaches with lucene search engine using the formulas for ranking. As a result, the F.measure is about 60% and it is better as about 15%.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.1-23
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• 2018

From the 21st century, various high-quality services have come up with the growth of the internet or 'Information and Communication Technologies'. Especially, the scale of E-commerce industry in which Amazon and E-bay are standing out is exploding in a large way. As E-commerce grows, Customers could get what they want to buy easily while comparing various products because more products have been registered at online shopping malls. However, a problem has arisen with the growth of E-commerce. As too many products have been registered, it has become difficult for customers to search what they really need in the flood of products. When customers search for desired products with a generalized keyword, too many products have come out as a result. On the contrary, few products have been searched if customers type in details of products because concrete product-attributes have been registered rarely. In this situation, recognizing texts in images automatically with a machine can be a solution. Because bulk of product details are written in catalogs as image format, most of product information are not searched with text inputs in the current text-based searching system. It means if information in images can be converted to text format, customers can search products with product-details, which make them shop more conveniently. There are various existing OCR(Optical Character Recognition) programs which can recognize texts in images. But existing OCR programs are hard to be applied to catalog because they have problems in recognizing texts in certain circumstances, like texts are not big enough or fonts are not consistent. Therefore, this research suggests the way to recognize keywords in catalog with the Deep Learning algorithm which is state of the art in image-recognition area from 2010s. Single Shot Multibox Detector(SSD), which is a credited model for object-detection performance, can be used with structures re-designed to take into account the difference of text from object. But there is an issue that SSD model needs a lot of labeled-train data to be trained, because of the characteristic of deep learning algorithms, that it should be trained by supervised-learning. To collect data, we can try labelling location and classification information to texts in catalog manually. But if data are collected manually, many problems would come up. Some keywords would be missed because human can make mistakes while labelling train data. And it becomes too time-consuming to collect train data considering the scale of data needed or costly if a lot of workers are hired to shorten the time. Furthermore, if some specific keywords are needed to be trained, searching images that have the words would be difficult, as well. To solve the data issue, this research developed a program which create train data automatically. This program can make images which have various keywords and pictures like catalog and save location-information of keywords at the same time. With this program, not only data can be collected efficiently, but also the performance of SSD model becomes better. The SSD model recorded 81.99% of recognition rate with 20,000 data created by the program. Moreover, this research had an efficiency test of SSD model according to data differences to analyze what feature of data exert influence upon the performance of recognizing texts in images. As a result, it is figured out that the number of labeled keywords, the addition of overlapped keyword label, the existence of keywords that is not labeled, the spaces among keywords and the differences of background images are related to the performance of SSD model. This test can lead performance improvement of SSD model or other text-recognizing machine based on deep learning algorithm with high-quality data. SSD model which is re-designed to recognize texts in images and the program developed for creating train data are expected to contribute to improvement of searching system in E-commerce. Suppliers can put less time to register keywords for products and customers can search products with product-details which is written on the catalog.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.

• 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 the Korean Institute of Landscape Architecture
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• v.43 no.6
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• pp.73-85
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• 2015

This study investigated the extent and magnitude of the woodland edge effects on users' thermal environments according to distance from woodland border. A series of experiments to measure air temperature, relative humidity, wind velocity, MRT and UTCI were conducted over six days between July 31 and August 5, 2015, which corresponded with extremely hot weather, at the south-facing edge of Hadongsongrim(pure Pinus densiflora stands, tree age: $100{\pm}33yr$, tree height: $12.8{\pm}2.7m$, canopy closure: 75%, N $35^{\circ}03^{\prime}34.7^{{\prime}{\prime}}$, E $127^{\circ}44^{\prime}43.3^{{\prime}{\prime}}$, elevation 7~10m) and east-facing edge of Hamyangsangrim (Quercus serrata-Carpinus tschonoskii community, tree age: 102~125yr/58~123yr, tree height: tree layer $18.6{\pm}2.3m/subtree$ layer $5.9{\pm}3.2m/shrub$ layer $0.5{\pm}0.5m$, herbaceous layer coverage ratio 60%, canopy closure: 96%, N $35^{\circ}31^{\prime}28.1^{{\prime}{\prime}}$, E $127^{\circ}43^{\prime}09.8^{{\prime}{\prime}}$, elevation 170~180m) in rural villages of Hadong and Hamyang, Korea. The minus result value of depth means woodland's outside. The depth of edge influence(DEI) on the maximum air temperature, minimum relative humidity and wind speed at maximum air temperature time during the daytime(10:00~17:00) were detected to be $12.7{\pm}4.9$, $15.8{\pm}9.8$ and $23.8{\pm}26.2m$, respectively, in the mature evergreen conifer woodland of Hadongsongrim. These were detected to be $3.7{\pm}2.2$, $4.9{\pm}4.4$ and $2.6{\pm}7.8m$, respectively, in the deciduous broadleaf woodland of Hamyansangrim. The DEI on the maximum 10 minutes average MRT, UTCI from the three-dimensional environment absorbed by the human-biometeorological reference person during the daytime(10:00~17:00) were detected to be $7.1{\pm}1.7$ and $4.3{\pm}4.6m$, respectively, in the relatively sparse woodland of Hadongsongrim. These were detected to be $5.8{\pm}4.9$ and $3.5{\pm}4.1m$, respectively, in the dense and closed woodland of Hadongsongrim. Edge effects on the thermal environments of air temperature, relative humidity, wind speed, MRT and UTCI in the sparse woodland of Hadongsongrim were less pronounced than those recorded in densed and closed woodland of Hamyansangrim. The gradient variation was less steep for maximum 10 minutes average UTCI with at least $4.3{\pm}4.6m$(Hadongsongrim) and $3.5{\pm}4.1m$(Hamyansangrim) being required to stabilize the UTCI at mature woodlands. Therefore it is suggested that the woodlands buffer widths based on the UTCI values should be 3.5~7.6 m(Hamyansangrim) and 4.3~8.9(Hadongsongrim) m on each side of mature woodlands for users' thermal comfort environments. The woodland edge structure should be multi-layered canopies and closed edge for the buffer effect of woodland edge on woodland users' thermal comfort.