• Korean Journal of Clinical Laboratory Science
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• v.50 no.4
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• pp.511-524
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• 2018

The purpose of this study is to provide policy recommendations for manpower planning by forecasting the supply and demand of Medical Technologists. Supply was estimated using an in-and-out movement method with a demographic method based on a baseline projection model. Demand was projected according to a demand-based method using the number of clinico-pathologic examinations taken for Medical Technologists. Over- or undersupply of Medical Technologists will depend on the productivity scenario and assumptions and ultimately on governmental policy direction. In other words, whether the production of Medical Technologists is higher or lower than the current level depends on the government policy to consider insurance finances. In this study, we assessed 'productivity scenario 3' based on the productivity as of 2012, when the government's policy direction was not considered. Based on the demand scenario using the ARIMA model, the supply of Medical Technologists is expected to be excessive. This oversupply accounts for less than 10% of the total and therefore should not be a big problem. However, given that the employment rate of Medical Technologists is 60%, it is necessary to consider policies to utilize the unemployed. These measures should expand the employment opportunities for the unemployed. To this end, it is necessary to strengthen the functions of laboratories in the public health center, to increase the quota of Medical Technologists, to assure their status, to establish a permanent inspection system for outpatient patients, and to expand the export of Medical Technologists overseas.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.155-170
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• 2018

Predicting a company's financial bankruptcy is traditionally one of the most crucial forecasting problems in business analytics. In previous studies, prediction models have been proposed by applying or combining statistical and machine learning-based techniques. In this paper, we propose a novel intelligent prediction model based on the simulated annealing which is one of the well-known optimization techniques. The simulated annealing is known to have comparable optimization performance to the genetic algorithms. Nevertheless, since there has been little research on the prediction and classification of business decision-making problems using the simulated annealing, it is meaningful to confirm the usefulness of the proposed model in business analytics. In this study, we use the combined model of simulated annealing and machine learning to select the input features of the bankruptcy prediction model. Typical types of combining optimization and machine learning techniques are feature selection, feature weighting, and instance selection. This study proposes a combining model for feature selection, which has been studied the most. In order to confirm the superiority of the proposed model in this study, we apply the real-world financial data of the Korean companies and analyze the results. The results show that the predictive accuracy of the proposed model is better than that of the naïve model. Notably, the performance is significantly improved as compared with the traditional decision tree, random forests, artificial neural network, SVM, and logistic regression analysis.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.4
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• pp.545-550
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• 2019

The frequency of natural disasters and the scale of damage are increasing due to the abnormal weather phenomenon occurring all over the world. As a result, as the hydrological aspect of the urban watershed changes, the increase in impervious area leads to serious domestic flood damage due to increased rainfall. In order to minimize the damage of life and property, domestic flooding prediction system is needed. In this study, we developed a flood nomogram capable of predicting flooding only by rainfall intensity and duration. This study suggests a method to set the internal water immersion alarm criterion by analyzing the characteristics of the flooding damage in the flooded area in the metropolitan area where flooding is highly possible and the risk of flooding is high. In addition, based on the manhole and the pipe, the water level was set as follows under the four conditions. 1) When manhole overflows, 2) when manhole is full, 3) when 70% of the pipe is reached, and 4) when 60% of the pipe is reached. Therefore, it can be used as a criterion and a predictive measure to cope with the pre-preparation before the flooding starts, through the rainfall that causes the flooding and the flooding damage.

• The Journal of the Korea Contents Association
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• v.19 no.5
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• pp.104-114
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• 2019

In the global market competition, countries and businesses are actively engaged in technology prediction activities to maximize their profits by attempting to enter and preempting the core technology of the future. In this paper, we propose a growth model based on patent application trends to predict the time to replace a product with a promising new technology to dominate the market. Although the Fisher-Pry model that Bhargava generalized to predict the emergence of promising alternative technologies was relatively satisfactory compared to the original Fisher-Pry model, it was difficult to predict the replacement rate behavior properly due to a parameter problem. The application of the Fisher-Pry Modification Model in the form of a quadratic equation through the patent trend analysis of the optical storage system for the purpose of verifying the time alternative to the light storage technology has resulted in satisfactory verification results. It is expected that small and medium-sized companies and individual researchers will apply this model and use it more easily to predict the time to replace the market for promising replacement technologies.

• Journal of the Korea Society for Simulation
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• v.31 no.3
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• pp.35-44
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• 2022

In this study, we analyze and forecast quantum computer technology trends. Previous research has been mainly focused on application fields centered on technology for quantum computer technology trends analysis. Therefore, this paper analyzes important quantum computer technologies and performs future signal detection and prediction, for a more market driven technical analysis and prediction. As analyzing words used in news articles to identify rapidly changing market changes and public interest. This paper extends conference presentation of Cha & Chang (2022). The research is conducted by collecting domestic news articles from 2019 to 2021. First, we organize the main keywords through text mining. Next, we explore future quantum computer technologies through analysis of Term Frequency - Inverse Document Frequency(TF-IDF), Key Issue Map(KIM), and Key Emergence Map (KEM). Finally, the relationship between future technologies and supply and demand is identified through random forests, decision trees, and correlation analysis. As results of the study, the interest in artificial intelligence was the highest in frequency analysis, keyword diffusion and visibility analysis. In terms of cyber-security, the rate of mention in news articles is getting overwhelmingly higher than that of other technologies. Quantum communication, resistant cryptography, and augmented reality also showed a high rate of increase in interest. These results show that the expectation is high for applying trend technology in the market. The results of this study can be applied to identifying areas of interest in the quantum computer market and establishing a response system related to technology investment.

• International journal of advanced smart convergence
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• v.11 no.1
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• pp.19-27
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• 2022

Across the world, 'housing' comprises a significant portion of wealth and assets. For this reason, fluctuations in real estate prices are highly sensitive issues to individual households. In Korea, housing prices have steadily increased over the years, and thus many Koreans view the real estate market as an effective channel for their investments. However, if one purchases a real estate property for the purpose of investing, then there are several risks involved when prices begin to fluctuate. The purpose of this study is to design a real estate price 'return rate' prediction model to help mitigate the risks involved with real estate investments and promote reasonable real estate purchases. Various approaches are explored to develop a model capable of predicting real estate prices based on an understanding of the immovability of the real estate market. This study employs the LSTM method, which is based on artificial intelligence and deep learning, to predict real estate prices and validate the model. LSTM networks are based on recurrent neural networks (RNN) but add cell states (which act as a type of conveyer belt) to the hidden states. LSTM networks are able to obtain cell states and hidden states in a recursive manner. Data on the actual trading prices of apartments in autonomous districts between January 2006 and December 2019 are collected from the Actual Trading Price Disclosure System of the Ministry of Land, Infrastructure and Transport (MOLIT). Additionally, basic data on apartments and commercial buildings are collected from the Public Data Portal and Seoul Metropolitan Government's data portal. The collected actual trading price data are scaled to monthly average trading amounts, and each data entry is pre-processed according to address to produce 168 data entries. An LSTM model for return rate prediction is prepared based on a time series dataset where the training period is set as April 2015~August 2017 (29 months), the validation period is set as September 2017~September 2018 (13 months), and the test period is set as December 2018~December 2019 (13 months). The results of the return rate prediction study are as follows. First, the model achieved a prediction similarity level of almost 76%. After collecting time series data and preparing the final prediction model, it was confirmed that 76% of models could be achieved. All in all, the results demonstrate the reliability of the LSTM-based model for return rate prediction.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.481-481
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• 2021

서울시는 '10년, '11년, '18년의 기록적인 호우로 인해 막대한 재산피해를 기록하였다. 이로 인해 서울시는 수재해 최소화 대책의 필요성을 인지하여 방재시설물 확충 등의 구조적 대책과 함께 침수지역 예측, 호우 영향 예보와 관련된 비구조적 대책 수립을 위해 노력하고 있다. 그 일환으로 2018~2019년 『서울시 강한 비구름 유입경로 및 침수위험도 예측 용역』 수행을 통해 레이더 실황강우 기반의 강한 비구름 이동경로 추정 기술, 강우시나리오 기반의 침수위험지역추정 기술이 적용된 서울시 도시침수 예측시스템을 개발하였다. 또한, 침수피해에 선제적으로 대응하기 위해 2019~2020년 『서울시 내수침수 위험지역 실시간 예측기술 개발』을 통하여 이류모델 기반의 예측강우정보 추정 기술, 예측강우정보 기반의 실시간 침수위험지역 추정기술을 적용하였다. 현재 서울시 도시침수 예측시스템은 서울시 전역의 강우 및 침수정보를 제공하며, 관로 113,286개(전체 385,768개), 맨홀 106,097개(전체 272,133개), 빗물펌프장 117개소(전체 121개소)가 반영되어 있다. 서울시 도시침수 예측시스템에서는 서울시 25개 자치구를 대상으로 실황 및 예측 강우정보, 강한 비구름에 대한 이동경로정보, 시나리오 및 실시간 침수정보를 제공하고 있다. 강우정보는 10분 및 1시간 단위 AWS 실황정보와 10분 단위 이류모델 기반 예측정보, 1시간 단위 LDAPS 기반 예측정보를 제공한다. 또한, 레이더 실황정보를 통해 판별된 강한 비구름에 대해 10분 단위 1시간 예측경로를 제공한다. 침수정보는 총강우량, 강우지속기간, 빗물받이효율 조건을 반영한 강우시나리오 기반의 6m 고해상도 격자단위 침수시나리오 정보와 자치구별 침수위험정보를 제공한다. 또한, 이류모델 기반의 레이더 예측정보를 이용하여 실시간 침수 예측정보를 제공한다. 향후 서울시 내 모든 수방시설물의 적용, 관로 유출구별 기점수위 반영, 관측자료를 이용한 도시유출 및 도시침수 모델 최적화 등 지속적으로 고도화를 수행하고자 하며, 서울시 도시침수 예측시스템을 통해 서울시 및 자치구 풍수해 담당자가 침수피해를 대비, 대응할 수 있을 것으로 기대된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.341-341
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• 2020

서울시는 '10년, '11년, '18년의 기록적인 호우로 인해 막대한 재산피해를 기록하였다. 이로 인해 서울시는 수재해 최소화 대책의 필요성을 인지하여 방재시설물 확충 등의 구조적 대책과 함께 침수지역 예측, 호우 영향 예보와 관련된 비구조적 대책 수립을 위해 노력하고 있다. 그 일환으로 '18년에 『서울시 강한 비구름 유입경로 및 침수위험도 예측 용역』을 수행하였으며 이를 통해 레이더 기반의 비구름 이동경로 추정 기술, 침수시나리오 기반의 침수위험지역 추정기술 등을 적용한 서울시 도시침수 예측시스템을 개발하였다. 그러나 침수피해에 선제적으로 대응하기 위해서는 실시간으로 예측강우정보를 생산하고 이를 통해 침수위험지역을 추정하는 기술이 필요하다. 이에 본 연구를 통해 예측강우정보 생산 기술 적용, 예측강우정보를 이용한 실시간 침수위험지역 추정 기술 개발을 수행하여 서울시 도시침수 예측시스템을 고도화하였다. 예측강우정보의 경우 현재 기상청에서 광역 단위 호우특보 및 읍면동 단위 동네예보를 통해 제공되고 있지만, 풍수해 업무에 적용하기에는 제한적이며, 실시간 침수위험지역 추정의 경우 침수해석모델의 모의시간, 라이센스 등의 문제로 인해 한계를 보이고 있는 실정이다. 따라서 본 연구에서는 레이더 실황강우정보를 활용한 이류모델 기반의 예측강우정보 생산 기술을 적용하여 풍수해 업무 적용이 용이하도록 하였으며, 예측강우정보를 이용한 최적 침수시나리오 추정 기술 개발을 통해 실시간 침수위험지역 추정이 가능하도록 하였다. 서울시 도시침수 예측시스템은 25개 자치구를 대상으로 강우량, 호우이동경로, 침수 정보를 제공하고 있다. 강우정보는 기상청 및 SK-TechX 기반의 10분 및 1시간 단위 AWS 관측정보, 이류모델 기반 10분 단위 레이더 예측정보, 국지예보모델 기반 1시간 단위 LDAPS 예측정보를 제공하며. 호우이동경로는 레이더 실황강우정보와 LDAPS 바람장을 이용하여 서울시 및 수도권 지역의 10분 단위 1시간 예측경로를 제공한다. 침수정보는 실시간으로 레이더 예측강우정보를 이용하여 최적의 침수시나리오를 추정하여 격자 단위 상세 침수정보와 시군구 단위 침수위험지도를 제공한다. 본 시스템을 통해 실시간 침수위험지역 확인이 가능해짐에 따라 서울시의 효율적인 풍수해 업무 지원이 가능할 것으로 판단된다.

• Journal of Navigation and Port Research
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• v.47 no.3
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• pp.167-173
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• 2023

The manufacturing industry is the backbone of the Korean economy. Among them, the petrochemical industry is a strategic growth industry, which makes a profit through reexports based on eminent technology in South Korea which imports all of its crude oil. South Korea imports whole amount of crude oil, which is the raw material for many manufacturing industries, by sea transportation. Therefore, it must respond swiftly to a highly volatile tanker freight market. This study aimed to make an early warning model of crude oil shipping market using a signal approach. The crisis of crude oil shipping market is defined by BDTI. The overall leading index is made of 38 factors from macro economy, financial data, and shipping market data. Only leading correlation factors were chosen to be used for the overall leading index. The overall leading index had the highest correlation coefficient factor of 0.499 two months ago. It showed a significant correlation coefficient five months ago. The QPS value was 0.13, which was found to have high accuracy for crisis prediction. Furthermore, unlike other previous time series forecasting model studies, this study quantitatively approached the time lag between economic crisis and the crisis of the tanker ship market, providing workers and policy makers in the shipping industry with an framework for strategies that could effectively deal with the crisis.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.5
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• pp.30-43
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• 2023

Green infrastructure(GI) is considered a key strategy in establishing sustainable communities. However, research on GI from the perspective of urban system dynamics and resilience lacks depth, as does its integration with physical design. This research addresses two primary causes. First, there is a gap in methods between existing GI planning, which considers static variables, and urban modeling research, which addresses dynamic variables. Second, there is a gap in information between landscape design and urban modeling research. To address these issues, this study proposes an integrated modeling approach in consideration of design decision-making. By combining the LEAM model and MCDA model, this study evaluates the relationship between GI services and socioeconomic growth, while spatially forecasting the geographies of GI demand in 2050. The resulting information reveals a potential degradation in ecosystem services over the region due to Chicago's sub-urbanization. This indicates that there would be a spatial shift in GI demand, emphasizing the need for comprehensive, dynamic GI strategies. This study further discusses the applications of evidence-based design in a studio environment. This study aims to contribute to the GeoDesign literature in terms of the creation of a more resilient urban environment by facilitating efficient evidence-based decision-making.