• Journal of Preventive Medicine and Public Health
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• v.40 no.4
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• pp.259-264
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• 2007

Effective communicable disease surveillance systems are the basis of the national disease prevention and control. Following the increase in emerging and re-emerging infectious diseases since late 1990s, the Korean government has strived to enhance surveillance and response system. Since 2000, sentinel surveillance, such as influenza sentinel surveillance, pediatric sentinel surveillance, school-based sentinel surveillance and ophthalmological sentinel surveillance, was introduced to improve the surveillance activities. Electronic reporting system was developed in 2000, enabling the establishment of national database of reported cases. Disweb, a portal for sharing communicable disease information with the public and health care workers, was developed. In general, the survey results on usefulness and attributes of the system, such as simplicity, flexibility, acceptability, sensitivity, timeliness, and representa-tiveness, received relatively high recognition. Compared to the number of paid cases of national health insurance, reported cases by national notifiable disease surveillance system, and various sentinel surveillance system, the result of the correlation analysis was high. According to the research project conducted by KCDC, the reporting rate of physicians in 2004 has also greatly improved, compared with that in 1990s. However, continuous efforts are needed to further improve the communicable disease surveillance system. Awareness of physicians on communicable disease surveillance system must be improved by conducting education and information campaigns on a continuous basis. We should also devise means for efficient use of various administrative data including cause of death statistics and health insurance. In addition, efficiency of the system must be improved by linking data from various surveillance system.

• 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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.203-215
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• 2019

This study was conducted to identify the utilization of public health centers, as well as the individual characteristics and regional characteristics that affect their utilization based on data from the 2016 Community Health Survey, National Statistical Portal, and National Institute of Environmental Research. Independent samples t-tests, variance analysis, and multiple logistic regression analysis were used for analysis. Hierarchical multiple regression was used to analyze individual and regional characteristics. The results of hierarchical multiple regressions revealed that aged regions, women, older age individuals, respondents with lower education level and income level, walking practitioners, nutrition label readers, individuals experiencing depression, those who have received health checkups, those who are not covered by essential care, those who have spouses, and basic livelihood beneficiaries have increased use of public health centers. However, the use of public health centers decreased in stressors, and regions in which the population per 1,000, number of health care workers, health and welfare budget, fiscal independence, and unemployment rate were above the national average. As above, the central government and local governments need to analyze not only individual characteristics such as health behavior and psychological factors, but also regional characteristics, when establishing local health care policy.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.3
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• pp.78-84
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• 2017

Because the 'Internet of Things' and sensor network technology have become a new generation industry competitiveness with a development of Information Communication Technology, each local autonomous entity is trying to adopt a Smart City quickly. This requires an integrated platform inside of a smart city operation center. Established Smart City platform provides various services using CCTV information and ITS transportation information based on a two-dimensional map. The provision of advanced Smart City services will necessitate three-dimensional map information, building and facilities unit information, linked information with public data portal for service to the public. In this paper, the authors reviewed development trends of Smart City integrated platform and proposed mashup methods between BIM/GIS interoperability platform and external open data. BIM/GIS platform can provide spatial information services for indoor and outdoor seamlessly because it was developed based on GIS spatial data with BIM data. The linked external open data are V-World data, Seoul Open Data, and Architectural Data Open. Finally, the authors proposed the direction of development for BIM/GIS integrated platform to provide advanced Smart City services.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.12
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• pp.7364-7371
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• 2014

Construction post-evaluation (CPE) boosts the efficiency of public construction projects by assessing the performance results of public construction projects and using the results to pursue similar projects. This evaluation comprehensively analyzes and assesses the planning of construction work, the estimated and actual construction costs, the construction period, the demand for construction projects, and the effects of construction projects after the construction work to ensure efficiency. The data was used to prevent lax project execution and enhance the quality and efficiency of similar construction projects. On the other hand, CPE results information must be managed systematically to refer to and use CPE data in similar construction projects. Therefore, in this study, the CPE system was developed as a measure of the systematic and comprehensive management and use of the CPE results information of individual ordering agencies. Therefore, the groundwork was laid for the assurance of quality enhancement and efficient project execution in public construction projects. This system is expected to serve as a useful tool for comprehensively analyzing and assessing construction works.

• Journal of Microbiology and Biotechnology
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• v.16 no.2
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• pp.178-183
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• 2006

Microbes are one of the most important bioresources in bioindustry and provide high economic values. Although there are currently about 6,000 bacterial species with validly published names, microbiologists generally assume that the number may account for less than 1% of the bacterial species present on Earth. To discover the remaining species, studies of metagenomes, metabolomes, and proteomes related to microbes have recently been carried out in various fields. We have constructed an information system that integrates various data on microbial resources and manages bioinformation to support efficient research of microorganisms. We have designated this system 'Bio-Meta Information System (Bio-MIS).' Bio-MIS consists of an integrated microbial resource database, a microbial resource input system, an integrated microbial resource search engine, a microbial resource online distribution system, a portal service, and management via the Internet. In the future, this system is expected to be connected with various public databases. We plan to implement useful bioinformatics software for analyzing microbial genome resources. The Web site is accessible at http://biomis.probionic.com.

• Journal of Preventive Medicine and Public Health
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• v.57 no.4
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• pp.370-378
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• 2024

Objectives: Comorbidities increase susceptibility to severe coronavirus disease 2019 (COVID-19) infections, but limited information has been published regarding human immunodeficiency virus (HIV) and COVID-19 co-infections. This study explored the relationships among socioeconomic characteristics, sexual behaviors, and COVID-19 infection rates among Korean men who have sex with men (MSM) who are also living with HIV. Methods: Data were collected through a web survey aimed at members of the largest gay portal site in Korea, supported by the National Research Foundation of Korea (n=1005). The primary independent variables included COVID-19-related vaccinations and sexual behaviors. The dependent variable was the incidence of COVID-19 infection among respondents during the pandemic. For statistical analysis, hierarchical multiple logistic regression was performed, controlling for potential confounding variables. Results: Model I indicated that older MSM were less likely to contract COVID-19 (adjusted odds ratio [aOR], 0.98; 95% confidence interval [CI], 0.96 to 0.99). Model II demonstrated that HIV-positive MSM were nearly twice as likely to be infected with COVID-19 compared to their HIV-negative counterparts (aOR, 1.97; 95% CI, 1.14 to 3.41). Furthermore, even after accounting for COVID-19 vaccination status in model III, HIV-positive MSM continued to show a higher risk of infection (aOR, 1.93; 95% CI, 1.12 to 3.35). Conclusions: The findings of this study indicate that HIV-positive MSM are at an increased risk of contracting COVID-19, even when their vaccination status is considered. Therefore, it is essential to prioritize the prevention of COVID-19 infections in HIV-positive individuals by administering appropriate antiretroviral therapy and ensuring adherence to public health guidelines.

• The Journal of Bigdata
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• v.5 no.1
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• pp.181-188
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• 2020

As the size of the airport terminal grows in line with the rapid growth of aviation passengers, the advanced baggage handling system that combines various data technologies has become an essential element in order to handle the baggage carried by passengers swiftly and accurately. Therefore, this study introduces the method of analyzing the baggage handling capacity of domestic airports through the latest data analysis methodology from the process point of view to advance the operation of the airport BHS and the main points based on event log data. By presenting an accurate load prediction method, it can lead to advanced BHS operation strategies in the future, such as the preemptive arrangement of resources and optimization of flight-carrousel scheduling. The data used in the analysis utilized the APIs that can be obtained by searching for "Korea Airports Corporation" in the public data portal. As a result of applying the method to the domestic airport BHS simulation model, it was possible to confirm a high level of predictive performance.

• International Journal of Advanced Culture Technology
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• v.10 no.1
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• pp.274-283
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• 2022

Korea is facing a number difficulties arising from rising housing prices. As 'housing' takes the lion's share in personal assets, many difficulties are expected to arise from fluctuating housing prices. The purpose of this study is creating housing price prediction model to prevent such risks and induce reasonable real estate purchases. This study made many attempts for understanding real estate instability and creating appropriate housing price prediction model. This study predicted and validated housing prices by using the LSTM technique - a type of Artificial Intelligence deep learning technology. LSTM is a network in which cell state and hidden state are recursively calculated in a structure which added cell state, which is conveyor belt role, to the existing RNN's hidden state. The real sale prices of apartments in autonomous districts ranging from January 2006 to December 2019 were collected through the Ministry of Land, Infrastructure, and Transport's real sale price open system and basic apartment and commercial district information were collected through the Public Data Portal and the Seoul Metropolitan City Data. The collected real sale price data were scaled based on monthly average sale price and a total of 168 data were organized by preprocessing respective data based on address. In order to predict prices, the LSTM implementation process was conducted by setting training period as 29 months (April 2015 to August 2017), validation period as 13 months (September 2017 to September 2018), and test period as 13 months (December 2018 to December 2019) according to time series data set. As a result of this study for predicting 'prices', there have been the following results. Firstly, this study obtained 76 percent of prediction similarity. We tried to design a prediction model of real estate transaction price with the LSTM Model based on AI and Bigdata. The final prediction model was created by collecting time series data, which identified the fact that 76 percent model can be made. This validated that predicting rate of return through the LSTM method can gain reliability.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.346-352
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• 2022

Mountain meteorology in South Korea that is covered mountains with complex terrain is important for understanding and managing the forest disaster and forest ecosystems. In particular, recent changes in dryness and/or rainfall intensity due to climate change may cause an increase in the possibility of forest disasters. Therefore, accurate monitoring of mountain meteorology is needed for efficient forest management. Korea Forest Service (KFS) is establishing the Automatic Mountain Meteorology Observation Stations (AMOS) in the mountain regions since 2012. 464 AMOSs are observing various meteorological variables such as air temperature, relative humidity, wind speed and direction, precipitation, soil temperature, and air pressure for every minute, which is conducted the quality control (QC) to retain data reliability. QC process includes the physical limit test, step test, internal consistency test, persistence test, climate range test, and median filter test. All of AMOS observations are open to use, which can be found from the Korean Mountain Meteorology Information System (KoMIS, http://mtweather.nifos.go.kr/) of the National Institute of Forest Science and the Public Data Portal (https://public.go.kr/). AMOS observations with guaranteed quality can be used in various forest fields including the public safety, forest recreation, forest leisure activities, etc., and can contribute to the advancement of forest science and technology. In this paper, a series of processes are introduced to collect and use the AMOS dataset in the mountain region in South Korea.