• Journal of Korea Water Resources Association
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• v.53 no.11
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• pp.1025-1037
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• 2020

This study attempted to evaluate the spatial characteristics and applicability of the predicted ensemble rainfall data used for heavy rain alarms. Limited area ENsemble prediction System (LENS) has 13 rainfall ensemble members, so it is possible to use a probabilistic method in issuing heavy rain warnings. However, the accessibility of LENS data is very low, so studies on the applicability of rainfall prediction data are insufficient. In this study, the evaluation index was calculated by comparing one point value and the area average value with the observed value according to the heavy rain warning system used for each administrative district. In addition, the accuracy of each ensemble member according to the LENS issuance time was evaluated. LENS showed the uncertainty of over or under prediction by member. Area-based prediction showed higher predictability than point-based prediction. In addition, the LENS data that predicts the upcoming 72-hour rainfall showed good predictive performance for rainfall events that may have an impact on a water disaster. In the future, the predicted rainfall data from LENS are expected to be used as basic data to prepare for floods in administrative districts or watersheds.

• Journal of the Korean Arthroscopy Society
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• v.4 no.2
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• pp.148-153
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• 2000

Purpose : This study was conducted to analyze the results of arthroscopic ankle arthrodesis and to verify the advantages of the technique compared to open ankle arthrodesis. Materials and Methods : Between October 1992 and August 1996, the arthroscopic ankle arthrodesis had been performed in five patients(six ankle joints): two patients with seropositive rheumatoid arthritis(one patient surgically treated bilaterally), two with osteoarthritis and one with tuberculous arthritis. There were one man and 4 women. Average age was 48 years ranging from 38 to 65 years. Follow up period was average 45 months(range, $12\~80$). Results : All patients were successfully treated with ankle joint arthrodesis under arthroscopic control. The mean time to fusion was 10 weeks(range, $6\~15$). There was a $100\%$ fusion rate without any complication. Conclusion : The arthroscopic ankle arthrodesis was successful in all cases with less morbidity and short hospital stay. It was technically feasible with excellent predictability.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1047-1056
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• 2022

Sea ice, frozen sea water, in the Artic is a primary indicator of global warming. Due to its importance to the climate system, shipping-route navigation, and fisheries, Arctic sea ice prediction has gained increased attention in various disciplines. Recent advances in artificial intelligence (AI), motivated by a desire to develop more autonomous and efficient future predictions, have led to the development of new sea ice prediction models as alternatives to conventional numerical and statistical prediction models. This study aims to evaluate the performance of the two-stream convolutional long-and short-term memory (TS-ConvLSTM) AI model, which is designed for learning both global and local characteristics of the Arctic sea ice changes, for the minimum September Arctic sea ice from 2001 to 2021, and to show the possibility for an operational prediction system. Although the TS-ConvLSTM model generally increased the prediction performance as training data increased, predictability for the marginal ice zone, 5-50% concentration, showed a negative trend due to increasing first-year sea ice and warming. Additionally, a comparison of sea ice extent predicted by the TS-ConvLSTM with the median Sea Ice Outlooks (SIOs) submitted to the Sea Ice Prediction Network has been carried out. Unlike the TS-ConvLSTM, the median SIOs did not show notable improvements as time passed (i.e., the amount of training data increased). Although the TS-ConvLSTM model has shown the potential for the operational sea ice prediction system, learning more spatio-temporal patterns in the difficult-to-predict natural environment for the robust prediction system should be considered in future work.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.1
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• pp.209-220
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• 2022

With the worldwide spread of African swine fever, interest in livestock epidemics has increased. Livestock transport vehicles are the main cause of the spread of livestock epidemics, but there are no empirical quarantine procedures and standards related to the mobility of livestock transport vehicles in South Korea. This study extracted the trajectory of livestock-related vehicles using the facility-visit history data from the Korea Animal Health Integrated System and the DTG (Digital Tachograph) data from the Korea Transportation Safety Authority. The results are presented as exposure indices aggregating the link-time occupancy of each vehicle. As a result, 274,519 livestock-related vehicle trajectories were extracted, and the exposure values by link and zone were derived quantitatively. This study highlights the need for prior monitoring of livestock transport vehicles and the establishment of post-disaster prevention policies.

• Journal of Life Science
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• v.33 no.6
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• pp.490-497
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• 2023

Pregnant women may need to take medications to treat preexisting diseases or diseases that develop during pregnancy. However, some drugs may be fetotoxic and lead to, for example, teratogenicity and growth retardation. Predicting the fetotoxicity of drugs is thus important for the health of the mother and fetus. The fetotoxicity of many drugs has not been established because various challenges hinder the ability of researchers to determine their fetotoxicity. The need exists for in silico-based fetotoxicity assessment models, as they can modernize the testing paradigm, improve predictability, and reduce the use of animals and the costs of fetotoxicity testing. In this study, we collected data on the fetotoxicity of drugs and constructed fetotoxicity prediction models based on various machine learning algorithms. We optimized the models for more precise predictions by tuning the hyperparameters. We then performed quantitative performance evaluations. The results indicated that the constructed machine learning-based models had high performance (AUROC >0.85, AUPR >0.9) in fetotoxicity prediction. We also analyzed the feature importance of our model's predictions, which could be leveraged to identify the specific features of drugs that are strongly associated with fetotoxicity. The proposed model can be used to prescreen drugs and drug candidates at a lower cost and in less time. It provides a predictive score for fetotoxicity risk, which may be beneficial in the design of studies on fetotoxicity in human pregnancy.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.85-100
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• 2023

Corporate ESG performance (environmental, social, and corporate governance) reflecting a company's strategic sustainability has emerged as one of the main factors in today's investment decisions. The traditional ESG performance rating process is largely performed in a qualitative and subjective manner based on the institution-specific criteria, entailing limitations in reliability, predictability, and timeliness when making investment decisions. This study attempted to predict the corporate ESG rating through automated machine learning based on quantitative and disclosed corporate financial information. Using 12 types (21,360 cases) of market-disclosed financial information and 1,780 ESG measures available through the Korea Institute of Corporate Governance and Sustainability during 2019 to 2021, we suggested a deep neural network prediction model. Our model yielded about 86% of accurate classification performance in predicting ESG rating, showing better performance than other comparative models. This study contributed the literature in a way that the model achieved relatively accurate ESG rating predictions through an automated process using quantitative and publicly available corporate financial information. In terms of practical implications, the general investors can benefit from the prediction accuracy and time efficiency of our proposed model with nominal cost. In addition, this study can be expanded by accumulating more Korean and international data and by developing a more robust and complex model in the future.

• Journal of the Korean earth science society
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• v.44 no.6
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• pp.540-556
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• 2023

This study analyzed rawinsonde soundings observed during the summer and early fall seasons (June, July, August and September) on the Korean peninsula to examine the utility of the Convective Available Potential Energy (CAPE) and Convective Inhibition (CIN) in predicting the occurrence of deep moist convection and precipitation. Rawinsonde soundings are categorized into two groups based on thermodynamic criteria: high CAPE and low CIN represent a high potential for deep moist convection; low CAPE and high CIN indicate conditions unfavorable for deep convection. A statistical hypothesis test is conducted to determine whether the two groups are significantly different in terms of 12-hour cumulative precipitation, 12-hour mean cloud base, and 12-hour mean mid-level cloud cover. The results, in the case of no-precipitation, reveal statistically significant differences between the two groups, except for the 12-hour mean cloud base during the 21:01-09:00 KST time period. This suggests that the group characterized by high CAPE and low CIN is more conducive to the occurrence of deep moist convection and precipitation than the group with low CAPE and high CIN.

• Survey Research
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• v.13 no.1
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• pp.135-158
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• 2012

Korean telephone surveys have been based on landline telephone directory or RDD(Random Digit Dialing) method. These days, however, there has been an increase of the households with no landline, or households with the line but not willing to register in the directory. Moreover, it is hard to contact young people or office workers who are usually staying out of home in the daytime. Due to these issues above, the predictability of election polls gets weaker. Especially, low accessibility to those who stay out of home when the poll's done, results in predictions with positive inclination toward conservatism. A solution to resolve this problem is to contact respondents by using both mobile and landline phones-via landline phone to those who are at home and via mobile phone to those who are out of home in the daytime(Mixed Mode Survey, hereafter MMS). To conduct MMS, 1) we need to obtain the sampling frames for the landline and mobile surveys, and 2) we need to decide the proportion of sample size of both. In this paper, we propose a heuristic method for conducting MMS. The method uses RDD for the landline phone survey, and the access panel list for the mobile phone survey. The proportion of sample sizes between landline and mobile phones are determined based on the 'Lifestyle and Time Use Study' conducted by Statistics Korea. As a case study, 4 election polls were conducted in the periods of the special election for the mayor of Seoul on Oct 26th, 2011. From the initial 3 polls, reactions and responses regarding the issues raised during the survey period were appropriately covered, and the final poll showed a very close prediction to the real election result.

• Journal of Bio-Environment Control
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• v.29 no.2
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• pp.161-170
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• 2020

There is systematic spatial variations in environmental properties due to sensitive reaction to external conditions at plastic greenhouse occupied 99.2% of domestic agricultural facilities. In order to construct 3 dimensional distribution of temperature, relative humidity, CO₂ and illuminance, measurement matrix as 3 by 3 by 5 in direction of width, height and length, respectively, dividing indoor space of greenhouse was designed and tested at experimental site. Linear regression analysis was conducted to evaluate optimal estimation method in terms with horizontal and vertical variations. Even though sole measurement point for temperature and relative humidity could be feasible to assess indoor condition, multiple measurement matrix is inevitably required to improve spatial precision at certain time domain such as period of sunrise and sunset. In case with CO₂, multiple measurement matrix could not successfully improve the spatial predictability during a whole experimental period. In case with illuminance, prediction performance was getting smaller after a time period of sunrise due to systematic interference such as indoor structure. Thus, multiple sensing methodology was proposed in direction of length at higher height than growing bed, which could compensate estimation error in spatial domain. Appropriate measurement matrix could be constructed considering the transition of stability in indoor environmental properties due to external variations. As a result, optimal measurement matrix should be carefully designed considering flexibility of construction relevant with the type of property, indoor structure, the purpose of crop and the period of growth. For an instance, partial cooling and heating system to save a consumption of energy supplement could be successfully accomplished by the deployment of multiple measurement matrix.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.6
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• pp.915-936
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• 2017

In this study, current status of Korean hazard mitigation guideline for tunnel operation is summarized. It shows that requirement for CCTV installation has been gradually stricted and needs for tunnel incident detection system in conjunction with the CCTV in tunnels have been highly increased. Despite of this, it is noticed that mathematical algorithm based incident detection system, which are commonly applied in current tunnel operation, show very low detectable rates by less than 50%. The putative major reasons seem to be (1) very weak intensity of illumination (2) dust in tunnel (3) low installation height of CCTV to about 3.5 m, etc. Therefore, an attempt in this study is made to develop an deep-learning based tunnel incident detection system, which is relatively insensitive to very poor visibility conditions. Its theoretical background is given and validating investigation are undertaken focused on the moving vehicles and person out of vehicle in tunnel, which are the official major objects to be detected. Two scenarios are set up: (1) training and prediction in the same tunnel (2) training in a tunnel and prediction in the other tunnel. From the both cases, targeted object detection in prediction mode are achieved to detectable rate to higher than 80% in case of similar time period between training and prediction but it shows a bit low detectable rate to 40% when the prediction times are far from the training time without further training taking place. However, it is believed that the AI based system would be enhanced in its predictability automatically as further training are followed with accumulated CCTV BigData without any revision or calibration of the incident detection system.