• Journal of Korean Ophthalmic Optics Society
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• v.13 no.4
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• pp.25-30
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• 2008

Purpose: In this paper, we have analyzed the problems of the Oh's report which is used to the basic data for supply and demand of medical technicians and studied a proposal for improvement to control system and supply and demand of korean optometrists. Methods: We have analyzed errors of Oh's report including supply and demand for medical technicians and management policy, expecting number for future optician, inaccurate estimation by limited data (employment rate, retirement rate, mortality rate) and an incorrect method of measurement for future supply and demand. Results: Oh's report showed the 18% error for estimation of supply which exclude the irregular entrance students. The estimation of supply was calculated by graduation rate 62.6% (college and University of Technology are 78.9% and 85.98% respectively), employment rate 65.8% (the average employment between 2002 and 2007 is 73.96%) and retirement rate is 2.3% (the retirement of pharmacists is 1.3%) but it showed the significant differences to objective data. For estimate the suitable ratio of optometrists to the population, the ratio use of medical facilities by an age group was used, and suggested spectacle wearers 1,280 persons (populations 2,928 persons) per optometrist but the different from reference of Germany (4,706 persons), America (1,789 persons) and Korea (1,825 persons/an optometrist) are applied to estimation on supply. This report applied the low employment rate and argued that maintain the present situation, but claimed that utilize unemployment persons. The above result has induced double weighting effect on estimation of supply. Conclusions: To solve the related problems of supply and demand, we have to make a search for exact data and optimum application model, have to take an example of nation similar job category as Germany and the research result of the job satisfaction into consideration. After we get the integrated research result, we must carried out the policy with fairness and balance for the estimation of supply and demand. Therefore exact research is required prior to beginning policy establishment, government and related group have to make a clear long-term plan and permanent organization for medical technician to establish supply and demand of medical technician.

• Journal of Environmental Impact Assessment
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• v.28 no.4
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• pp.387-399
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• 2019

This research aimed to prepare the classification of the damage types and the damage rating system of noise barriers for expressway noise barriers and to develop deterioration evaluation method of noise barriers by reflecting them. The noise barrier consists of soundproof panels, foundations and posts and the soundproof panels with 10 different types of materials are used in a single or mixed form.In this paper, damage of soundproof panel shows a single or composite damage, and thus a evaluation model of deterioration has been developed for noise barriers that can reflect the characteristic of noise barriers. Materials used mainly for soundproof walls were divided into material types for metal, plastic, timber, transparent and concrete. And damage types for noise barrier were classified into corrosion, discoloration, deformation, spalling and dislocation and damage types were subdivided according to the noise barrier's components and materials. Damage rating was divided into good, minor, normal and severe for each major part of noise barrier to assess damage rating of soundproof panel, foundation and post. The deterioration degree of noise barrier was evaluated comprehensively by using the deterioration evaluation method of whole noise barrier using weighted average. Deterioration evaluation method that can be systematically assessed has been developed for noise barrier using single or mixed soundproof panel and noise barrier with single or complex damage types. Through such an evaluation system, it is deemed that the deterioration status of noise barrier installed can be systematically understood and utilized for efficient maintenance planning and implementation for repair and improvement of noise barriers.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.127-137
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• 2019

Recidivism prediction has been a subject of constant research by experts since the early 1970s. But it has become more important as committed crimes by recidivist steadily increase. Especially, in the 1990s, after the US and Canada adopted the 'Recidivism Risk Assessment Report' as a decisive criterion during trial and parole screening, research on recidivism prediction became more active. And in the same period, empirical studies on 'Recidivism Factors' were started even at Korea. Even though most recidivism prediction studies have so far focused on factors of recidivism or the accuracy of recidivism prediction, it is important to minimize the prediction misclassification cost, because recidivism prediction has an asymmetric error cost structure. In general, the cost of misrecognizing people who do not cause recidivism to cause recidivism is lower than the cost of incorrectly classifying people who would cause recidivism. Because the former increases only the additional monitoring costs, while the latter increases the amount of social, and economic costs. Therefore, in this paper, we propose an XGBoost(eXtream Gradient Boosting; XGB) based recidivism prediction model considering asymmetric error cost. In the first step of the model, XGB, being recognized as high performance ensemble method in the field of data mining, was applied. And the results of XGB were compared with various prediction models such as LOGIT(logistic regression analysis), DT(decision trees), ANN(artificial neural networks), and SVM(support vector machines). In the next step, the threshold is optimized to minimize the total misclassification cost, which is the weighted average of FNE(False Negative Error) and FPE(False Positive Error). To verify the usefulness of the model, the model was applied to a real recidivism prediction dataset. As a result, it was confirmed that the XGB model not only showed better prediction accuracy than other prediction models but also reduced the cost of misclassification most effectively.

• Journal of Korea Water Resources Association
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• v.54 no.6
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• pp.419-431
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• 2021

In Seoul, it has been confirmed that the duration of rainfall is shortened and the frequency and intensity of heavy rains are increasing with a changing climate. In addition, due to high population density and urbanization in most areas, floods frequently occur in flood-prone areas for the increase in impermeable areas. Furthermore, the Seoul City is pursuing various projects such as structural and non-structural measures to resolve flood-prone areas. A disaster prevention performance target was set in consideration of the climate change impact of future precipitation, and this study conducted to reduce the overall flood damage in Seoul for the long-term. In this study, 29 GCMs with RCP4.5 and RCP8.5 scenarios were used for spatial and temporal disaggregation, and we also considered for 3 research periods, which is short-term (2006-2040, P1), mid-term (2041-2070, P2), and long-term (2071-2100, P3), respectively. For spatial downscaling, daily data of GCM was processed through Quantile Mapping based on the rainfall of the Seoul station managed by the Korea Meteorological Administration and for temporal downscaling, daily data were downscaled to hourly data through k-nearest neighbor resampling and nonparametric temporal detailing techniques using genetic algorithms. Through temporal downscaling, 100 detailed scenarios were calculated for each GCM scenario, and the IDF curve was calculated based on a total of 2,900 detailed scenarios, and by averaging this, the change in the future extreme rainfall was calculated. As a result, it was confirmed that the probability of rainfall for a duration of 100 years and a duration of 1 hour increased by 8 to 16% in the RCP4.5 scenario, and increased by 7 to 26% in the RCP8.5 scenario. Based on the results of this study, the amount of rainfall designed to prepare for future climate change in Seoul was estimated and if can be used to establish purpose-wise water related disaster prevention policies.

• Korean Journal of Environment and Ecology
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• v.37 no.4
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• pp.289-301
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• 2023

Forests within watersheds are essential in maintaining ecosystems and are the central infrastructure for constructing an ecological network system. However, due to indiscriminate development projects carried out over past decades, forest fragmentation and land use changes have accelerated, and their original functions have been lost. Since a forest's structural pattern directly impacts ecological processes and functions in understanding forest ecosystems, identifying and analyzing change patterns is essential. Therefore, this study analyzed structural changes in the forest landscape according to the time-series land cover changes using the FRAGSTATS model for the dam watershed of the Geum River upstream. Land cover changes in the dam watershed of the Geum River upstream through land cover change detection showed an increase of 33.12 square kilometers (0.62%) of forests and 67.26 square kilometers (1.26%) of urbanized dry areas and a decrease of 148.25 square kilometers (2.79%) in agricultural areas from the 1980s to the 2010s. The results of no-sampling forest landscape analysis within the watershed indicated landscape percentage (PLAND), area-weighted proximity index (CONTIG_AM), average central area (CORE_MN), and adjacency index (PLADJ) increased, and the number of patches (NP), landscape shape index (LSI), and cohesion index (COHESION) decreased. Identification of structural change patterns through a moving window analysis showed the forest landscape in Sangju City, Gyeongsangbuk Province, Boeun County in Chungcheongbuk Province, and Jinan Province in Jeollabuk Province was relatively well preserved, but fragmentation was ongoing at the border between Okcheon County in Chungcheongbuk Province, Yeongdong and Geumsan Counties in Chungcheongnam Province, and the forest landscape in areas adjacent to Muju and Jangsu Counties in Jeollabuk Province. The results indicate that it is necessary to establish afforestation projects for fragmented areas when preparing a future regional forest management strategy. This study derived areas where fragmentation of forest landscapes is expected and the results may be used as basic data for assessing the health of watershed forests and establishing management plans.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.11
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• pp.471-480
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• 2023

The Korean construction order volume in South Korea grew significantly from 91.3 trillion won in public orders in 2013 to a total of 212 trillion won in 2021, particularly in the private sector. As the size of the domestic and overseas markets grew, the scale and complexity of EPC (Engineering, Procurement, Construction) projects increased, and risk management of project management and ITB (Invitation to Bid) documents became a critical issue. The time granted to actual construction companies in the bidding process following the EPC project award is not only limited, but also extremely challenging to review all the risk terms in the ITB document due to manpower and cost issues. Previous research attempted to categorize the risk terms in EPC contract documents and detect them based on AI, but there were limitations to practical use due to problems related to data, such as the limit of labeled data utilization and class imbalance. Therefore, this study aims to develop an AI model that can categorize the contract terms based on the FIDIC Yellow 2017(Federation Internationale Des Ingenieurs-Conseils Contract terms) standard in detail, rather than defining and classifying risk terms like previous research. A multi-text classification function is necessary because the contract terms that need to be reviewed in detail may vary depending on the scale and type of the project. To enhance the performance of the multi-text classification model, we developed the ELECTRA PLM (Pre-trained Language Model) capable of efficiently learning the context of text data from the pre-training stage, and conducted a four-step experiment to validate the performance of the model. As a result, the ensemble version of the self-developed ITB-ELECTRA model and Legal-BERT achieved the best performance with a weighted average F1-Score of 76% in the classification of 57 contract terms.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.23-46
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• 2017

Although there have been cases of evaluating the value of specific companies or projects which have centralized on developed countries in North America and Europe from the early 2000s, the system and methodology for estimating the economic value of individual technologies or patents has been activated on and on. Of course, there exist several online systems that qualitatively evaluate the technology's grade or the patent rating of the technology to be evaluated, as in 'KTRS' of the KIBO and 'SMART 3.1' of the Korea Invention Promotion Association. However, a web-based technology valuation system, referred to as 'STAR-Value system' that calculates the quantitative values of the subject technology for various purposes such as business feasibility analysis, investment attraction, tax/litigation, etc., has been officially opened and recently spreading. In this study, we introduce the type of methodology and evaluation model, reference information supporting these theories, and how database associated are utilized, focusing various modules and frameworks embedded in STAR-Value system. In particular, there are six valuation methods, including the discounted cash flow method (DCF), which is a representative one based on the income approach that anticipates future economic income to be valued at present, and the relief-from-royalty method, which calculates the present value of royalties' where we consider the contribution of the subject technology towards the business value created as the royalty rate. We look at how models and related support information (technology life, corporate (business) financial information, discount rate, industrial technology factors, etc.) can be used and linked in a intelligent manner. Based on the classification of information such as International Patent Classification (IPC) or Korea Standard Industry Classification (KSIC) for technology to be evaluated, the STAR-Value system automatically returns meta data such as technology cycle time (TCT), sales growth rate and profitability data of similar company or industry sector, weighted average cost of capital (WACC), indices of industrial technology factors, etc., and apply adjustment factors to them, so that the result of technology value calculation has high reliability and objectivity. Furthermore, if the information on the potential market size of the target technology and the market share of the commercialization subject refers to data-driven information, or if the estimated value range of similar technologies by industry sector is provided from the evaluation cases which are already completed and accumulated in database, the STAR-Value is anticipated that it will enable to present highly accurate value range in real time by intelligently linking various support modules. Including the explanation of the various valuation models and relevant primary variables as presented in this paper, the STAR-Value system intends to utilize more systematically and in a data-driven way by supporting the optimal model selection guideline module, intelligent technology value range reasoning module, and similar company selection based market share prediction module, etc. In addition, the research on the development and intelligence of the web-based STAR-Value system is significant in that it widely spread the web-based system that can be used in the validation and application to practices of the theoretical feasibility of the technology valuation field, and it is expected that it could be utilized in various fields of technology commercialization.

• The Korean Journal of Nuclear Medicine
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• v.32 no.6
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• pp.490-496
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• 1998

Purpose: We evaluated brain perfusion SPECT findings of MELAS syndrome and mitochondrial myopathy in correlation with MR imaging in search of specific imaging features. Materials and Methods: Subjects were five patients (four females and one male; age range, 1 to 25 year) who presented with repeated stroke-like episodes, seizures or developmental delay or asymptomatic but had elevated lactic acid in CSF and serum. Conventional non-contrast MR imaging and Tc-99m-ethyl cysteinate dimer (ECD) brain perfusion SPECT were Performed and imaging features were analyzed. Results: MRI demonstrated increased T2 signal intensities in the affected areas of gray and white matters mainly in the parietal (4/5) and occipital lobes (4/5) and in the basal ganglia (1/5), which were not restricted to a specific vascular territory. SPECT demonstrated decreased perfusion in the corresponding regions of MRI lesions. In addition, there were perfusion defects in parietal (1 patient), temporal (2), and frontal (1) lobes and basal ganglia (1) and thalami (2). In a patient with mitochondrial myopathy who had normal MRI, decreased perfusion was noted in left parietal area and bilateral thalami. Conclusion: Tc-99m ECD SPECT imaging in patients with MELAS syndrome and mitochondrial myopathy showed hypoperfusion of parieto-occipital cortex, basal ganglia, thalamus and temporal cortex, which were not restricted to a specific vascular territory. There were no specific imaging features on SPECT. The significance of abnormal perfusion on SPECT without corresponding MR abnormalities needs to be evaluated further in larger number of patients.