• Korean Journal of Environment and Ecology
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• v.32 no.5
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• pp.469-477
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• 2018

Hanabusaya asiatica is an endemic species whose distribution is limited in the mid-eastern part of the Korean peninsula. Due to its narrow range and small population, it is necessary to protect its habitats by identifying it as Key Biodiversity Areas (KBAs) adopted by the International Union for Conservation of Nature (IUCN). In this paper, we estimated potential natural habitats for H. asiatica using maximum entropy model (MaxEnt) and identified candidate sites for KBA based on the model results. MaxEnt is a machine learning algorithm that can predict habitats for species of interest unbiasedly with presence-only data. This property is particularly useful for the study area where data collection via a field survey is unavailable. We trained MaxEnt using 38 locations of H. asiatica and 11 environmental variables that measured climate, topography, and vegetation status of the study area which encompassed all locations of the border region between South and North Korea. Results showed that the potential habitats where the occurrence probabilities of H. asiatica exceeded 0.5 were $778km^2$, and the KBA candidate area identified by taking into account existing protected areas was $1,321km^2$. Of 11 environmental variables, elevation, annual average precipitation, average precipitation in growing seasons, and the average temperature in the coldest month had impacts on habitat selection, indicating that H. asiatica prefers cool regions at a relatively high elevation. These results can be used not only for identifying KBAs but also for the reference to a protection plan for H. asiatica in preparation of Korean reunification and climate change.

• Journal of Korean Society of Forest Science
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• v.105 no.1
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• pp.149-156
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• 2016

Forest museums can be defined as facilities for the collection, exhibition, and education of the forest or forest related artifacts or data. This study was performed to measure the educational value of Gyeongnam state forest museum's forest and its environment. The tool used was the Contingent Valuation Methods (CVM) which is well known as a value estimation tool of environmental goods. The study for the value estimation is performed from April, 2014 to October of the same year through selection of the subject, decision of proposed price, and orientation of the survey staffs and total of 386 surveys were used in analysis. The value estimation tool used the DBDC logit model and the input parameters were number of visit (time), degree of environmental education (contri), the environment conservation effort of the respondent (execu), the education level of the respondent (edu), and income of the respondent (inc) and trimmed mean (WTPtruncated) was used. The estimated value of flora and environment education per each person per visit is 23,338 won. When applied to the average annual visitors deducted from 2010 to 2014, which is 430,000 per year, the environmental value that Gyeongnam state forest museum is providing to visitors each year is about 10 billion won. The result of this study is significant to propose the value of forest education and environment that the forest museum is offering to the visitors in the current currency. This is an evidence to directly determine the value of the forest museum and therefore proposing an opportunity change the recognition toward the forest and environment education.

• Korean Journal of Environment and Ecology
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• v.24 no.2
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• pp.209-221
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• 2010

This study was carried out to apply basic data of the native trees for planting in the salinity area by the vegetation ecological selection. Which focused on native woody species to the tolerances of saline soil and salt spray drift on the coastal forests in the West-Sea, Korea. The soil salinity($EC_{1:5}$) was 0.11dS$m^{-1}$, ranging of 0.00dS$m^{-1}$~0.68dS$m^{-1}$. The soil salinity was gradually decreasing from Belt I to Belt Ⅳ except the Belt I in some coastal windbreaks. The order of decreasing soil salinity was Belt I>Belt II>Belt III>Belt Ⅳ and the soil salinity was $EC_{1:5}$ 0.14dS$m^{-1}$, 0.11dS$m^{-1}$, 0.10dS$m^{-1}$, and 0.08dS$m^{-1}$, respectively. The total 181 taxa consisted of 52 families, 104 genus, 157 species, and 24 varieties were recorded as the trees tolerating to both soil salinity and salt spray drift. The trees emerged in the highest degree of salinity($EC_{1:5}$ 0.51dS$m^{-1}$) was nothing but appearanced Pinus thunbergii Parl., Smilax china L., Quercus dentata Thunb. ex Murray, Quercus serrata Thunb. ex Murray and so on at the level of singular and ideal value. The emerged trees in the high salinity of $EC_{1:5}$0.41dS$m^{-1}$~0.50dS$m^{-1}$ were Albizia kalkora Prain, Melia azedarach L., Paederia scandens (Lour.) Merr. var. scandens These species were trees of tolerance to saline soil. The emerged woody species in all belts were Pinus rigida Mill., Pinus densiflora Siebold & Zucc., Pinus thunbergii Parl., Juniperus rigida Siebold & Zucc. and so on. The woody species with high important value(I.V.) were Pinus densiflora Siebold & Zucc., Pinus thunbergii Parl., Pseudosasa japonica (Siebold & Zucc. ex Steud.) Makino, Smilax china L., Platycarya strobilacea Siebold & Zucc. var. strobilacea for. strobilacea and so on, which can be classified as highly tolerant native trees to salt spray drift.

• Tuberculosis and Respiratory Diseases
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• v.44 no.6
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• pp.1366-1381
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• 1997

Background : Uvulopalatopharyngoplasty(UPPP) has become the most common surgical treatment for obstructive sleep apnea syndrome(OSAS). However, the results of this therapeutic modality have been quite variable with successful results by several authors and poor results by others. Until recently, in Korea, there is only a few reports about the clinical efficacy of UPPP. A prospective study was undertaken to evaluate the effectiveness and complications of UPPP. Method : Twenty-six OSAS patients who had undergone UPPP with preoperative and postoperative polysomnographic studies were included in this study. Two definitions of surgical success were used. The responder was defined, using a conventional criteria, as a 50% or more reduction in apnea index(AI) or apneahypopnea index(AHI) after UPPP, or a postoperative AI of <10 or AHI of <20. The initial cure was defined, using our own criteria, as a postoperative AI of <5 or AHI of <10. Complications were categorized in two groups : early(disorders during the first 10 postoperative days) and late. Results : Eighteen patients(69.2%) were responders, and ten patients(38.5%) were considered as initial cure. On the other hand, in five patients (19.2%), postoperative polysomnographic data demonstrated deterioration compared with preoperative data. Reduction rate of AI or AHI following UPPP was not significantly related to the preoperative body mass index, AI or AHI. There was no significant change of sleep architecture before and after UPPP in responder and initial cure groups. Early complications such as pain, dyspnea, bleeding, nasal reflux, dysphagia or wound disruption were observed in all patients. Late complications such as nasal reflux, voice change, dysphagia, loss of taste, pharyngeal dryness or foreign body sensation were discovered in 22 patients (84.6%). However, all early and late complications were of minor importance. Conclusion : The response to UPPP was favorable in approximately 70% of OSAS patient. However, the initial Cure rate of UPPP was relatively low. We suggest that selection of more appropriate surgical candidates and adequate surgical protocol is necessary to obtain a more successful result with UPPP.

• Korean Journal of Plant Resources
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• v.32 no.4
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• pp.338-343
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• 2019

This study was conducted to nominate optimal ground cover plants eventually enhancing Aronia production in the highland rolling plains. Total number of 17 weed species were observed in Aronia field when no cover plant was applied. Meanwhile, 12, 14, 15 and 16 weed species were observed when kentucky bluegrass, white clover, rattail fescue and ground ivy were used, respectively. Untreated native weed species were 73.6 cm tall before cut, and kentucky bluegrass, white clover, Rattail fescue and ground ivy were 57.5, 36.8, 48.3 and 40.9 cm, respectively. Based on plant height before cut, two shortest plants, white clover and ground ivy, were considered effective as ground cover plants in Aronia field. Coverage at $3^{rd}$ year by cover plants ranged from 85% to 100%. Coverage of uncovered Aronia field by native weed species was 95% while coverage by 4 treatments, kentucky bluegrass, white clover, rattail fescue and ground ivy were 100, 87, 85 and 100%, respectively. Aronia yield per plant at $3^{rd}$ year was 1,916 g with white clover cover followed by 1,770 g with Rattail fescue, 1,766 g with ground ivy, 1,098 g without cover plants and 931 g with Kentucky Bluegrass. Out results indicated that ground ivy was the best among all treatments based on 3 criteria, (1) short plant architecture, (2) rapid ground covering and (3) better weed control. In addition, ground ivy cover appeared to secure better yield.

• Land and Housing Review
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• v.14 no.4
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• pp.77-93
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• 2023

The COVID-19 pandemic has brought about significant social changes through government prevention and control measures, changes in people's risk perceptions, and lifestyle changes. In response, urban inhabitants changed their behaviors significantly, including their preferences for transportation modes and urban spaces in response to government quarantine policies and concerns over the potential risk of infection in urban spaces. These changes may have long-lasting effects on urban spaces beyond the COVID-19 pandemic or they may evolve and develop new forms. Therefore, this study aims to explore the potential for urban spaces to adapt to the present and future pandemics by examining changes in urban residents' preferences in travel modes and urban space use due to the COVID-19 pandemic. This study found that overall preferences for travel modes and urban spaces significantly differ between the pre-pandemic, pandemic, and post-pandemic periods. During the pandemic, preferences for travel modes and urban spaces has decreased, except for privately owned vehicles and green spaces, which are perceived to be safe from transmission, show more favorable than others. Post-pandemic preferences for travel modes and urban spaces are less favorable than pre-pandemic with urban spaces being five times less favorable than transportation. Although green spaces and medical facilities that were positively perceived during the pandemic are expected to return to the pre-pandemic preference level, other factors of urban spaces are facing a new-normal. The findings suggest that the COVID-19 pandemic has had a significant impact on urban residents' preferences for travel modes and urban space use. Understanding these changes is crucial for developing strategies to adapt to present and future pandemics and improve urban resilience.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.