• Journal of Advanced Navigation Technology
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• v.23 no.1
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• pp.84-89
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• 2019

This is a pilot study of machine learning based structural health monitoring system using flight data of composite aircraft. In this study, the most suitable machine learning algorithm for structural health monitoring was selected and dimensionality reduction method for application on the actual flight data was conducted. For these tasks, impact test on the cantilever beam with added mass, which is the simulation of damage in the aircraft wing structure was conducted and classification model for damage states (damage location and level) was trained. Through vibration test of cantilever beam with fiber bragg grating (FBG) sensor, data of normal and 12 damaged states were acquired, and the most suitable algorithm was selected through comparison between algorithms like tree, discriminant, support vector machine (SVM), kNN, ensemble. Besides, through neighborhood component analysis (NCA) feature selection, dimensionality reduction which is necessary to deal with high dimensional flight data was conducted. As a result, quadratic SVMs performed best with 98.7% for without NCA and 95.9% for with NCA. It is also shown that the application of NCA improved prediction speed, training time, and model memory.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.3
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• pp.155-163
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• 2022

The optimization of long-range ensemble climate prediction for rice phenology model with advanced bias correction method is conducted. The daily long-range forecast(6-month) of mean/ minimum/maximum temperature and observation of January to October during 1991-2021 is collected for rice phenology prediction. In this study, the concept of "buffer period" is newly introduced to reduce the problem after bias correction by quantile mapping with constructing the transfer function by month, which evokes the discontinuity at the borders of each month. The four experiments with different lengths of buffer periods(5, 10, 15, 20 days) are implemented, and the best combinations of buffer periods are selected per month and variable. As a result, it is found that root mean square error(RMSE) of temperatures decreases in the range of 4.51 to 15.37%. Furthermore, this improvement of climatic variables quality is linked to the performance of the rice phenology model, thereby reducing RMSE in every rice phenology step at more than 75~100% of Automated Synoptic Observing System stations. Our results indicate the possibility and added values of interdisciplinary study between atmospheric and agriculture sciences.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.284-301
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• 2023

The changes in rice climatic yield potential (CYP) across the Korean Peninsula are evaluated based on the new climate change scenario produced by the National Institute of Agricultural Sciences with 18 ensemble members at 1 km resolution under a Shared Socioeconomic Pathway (SSP) and Representative Concentration Pathways (RCP) emission scenarios. To overcome the data availability, we utilize solar radiation f or CYP instead of sunshine duration which is relatively uncommon in the climate prediction f ield. The result show that maximum CYP(CYPmax) decreased, and the optimal heading date is progressively delayed under warmer temperature conditions compared to the current climate. This trend is particularly pronounced in the SSP5-85 scenario, indicating faster warming, except for the northeastern mountainous regions of North Korea. This shows the benef its of lower emission scenarios and pursuing more efforts to limit greenhouse gas emissions. On the other hand, the CYPmax shows a wide range of feasible futures, which shows inherent uncertainties in f uture climate projections and the risks when analyzing a single model or a small number of model results, highlighting the importance of the ensemble approach. The f indings of this study on changes in rice productivity and uncertainties in temperature and solar radiation during the 21st century, based on climate change scenarios, hold value as f undamental information for climate change adaptation efforts.

• Journal of Convergence for Information Technology
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• v.8 no.1
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• pp.201-206
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• 2018

Recently, as technologies for realizing artificial intelligence have become more common, machine learning is widely used. Machine learning provides insight into collecting large amounts of data, batch processing, and taking final action, but the effects of the work are not immediately integrated into the learning process. In this paper proposed an adaptive learning model to improve the performance of real-time stream analysis as a big business issue. Adaptive learning generates the ensemble by adapting to the complexity of the data set, and the algorithm uses the data needed to determine the optimal data point to sample. In an experiment for six standard data sets, the adaptive learning model outperformed the simple machine learning model for classification at the learning time and accuracy. In particular, the support vector machine showed excellent performance at the end of all ensembles. Adaptive learning is expected to be applicable to a wide range of problems that need to be adaptively updated in the inference of changes in various parameters over time.

• Journal of Korean Society for Quality Management
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• v.48 no.3
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• pp.493-510
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• 2020

Purpose: As of July 31, there were 14,336 confirmed cases of COVID-19 in South Korea, including 301 deaths. Since the daily confirmed number of cases hit 909 on February 29, the spread of the disease had gradually decreased due to the active implementation of preventive control interventions, and the daily confirmed number had finally recorded a single digit on April 19. Since May, however, the disease has re-emerged and retaining after June. In order to eradicate the disease, it is necessary to suggest suitable forward preventive strategies by predicting future infectivity of the disease based on the cases so far. Therefore, in this study, we aim to evaluate the transmission potential of the disease in early phases by estimating basic reproduction number and assess the preventive control measures through effective reproduction number. Methods: We used publicly available cases and deaths data regarding COVID-19 in South Korea as of July 31. Using ensemble model integrated stochastic linear birth model and deterministic linear growth model, the basic reproduction number and the effective reproduction number were estimated. Results: Estimated basic reproduction number is 3.1 (95% CI: 3.0-3.2). Effective reproduction number was the highest with 7 on February 15, decreased as of April 20. Since then, the value is gradually increased to more than unity. Conclusion: Preventive policy such as wearing a mask and physical distancing campaigns in the early phase of the outbreak was fairly implemented. However, the infection potential increased due to weakening government policy on May 6. Our results suggest that it seems necessary to implement a stronger policy than the current level.

• Korean Journal of Health Education and Promotion
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• v.23 no.4
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• pp.13-28
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• 2006

Objectives: This study used the characteristics of the knowledge discovery and data mining algorithms to develop hypertension predictive model for hypertension management using the Korea National Health Insurance Corporation database(the insureds' screening and health care benefit data). Methods: This study validated the predictive power of data mining algorithms by comparing the performance of logistic regression, decision tree, and ensemble technique. On the basis of internal and external validation, it was found that the model performance of logistic regression method was the best among the above three techniques. Results: Major results of logistic regression analysis suggested that the probability of hypertension was: - lower for the female(compared with the male)(OR=0.834) - higher for the persons whose ages were 60 or above(compared with below 40)(OR=4.628) - higher for obese persons(compared with normal persons)(OR= 2.103) - higher for the persons with high level of glucose(compared with normal persons)(OR=1.086) - higher for the persons who had family history of hypertension(compared with the persons who had not)(OR=1.512) - higher for the persons who periodically drank alcohol(compared with the persons who did not)$(OR=1.037{\sim}1.291)$ Conclusions: This study produced several factors affecting the outbreak of hypertension using screening. It is considered to be a contributing factor towards the nation's building of a Hypertension Management System in the near future by bringing forth representative results on the rise and care of hypertension.

• Atmosphere
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• v.27 no.2
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• pp.133-150
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• 2017

This study evaluates CMIP5 model performance on rainy season evolution in the East Asian summer monsoon. Historical (1986~2005) simulation is analyzed using ensemble mean of CMIP5 19 models. Simulated rainfall amount is underestimated than the observed and onset and termination of rainy season are earlier in the simulation. Compared with evolution timing, duration of the rainy season is uncertain with large model spread. This area-averaged analysis results mix relative differences among the models. All model show similarity in the underestimated rainfall, but there are quite large difference in dynamic and thermodynamic processes. The model difference is shown in horizontal distribution analysis. BEST and WORST group is selected based on skill score. BEST shows better performance in northward movement of the rain band, summer monsoon domain. Especially, meridional gradient of equivalent potential temperature and low-level circulation for evolving frontal system is quite well captured in BEST. According to RCP8.5, CMIP5 projects earlier onset, delayed termination and longer duration of the rainy season with increasing rainfall amount at the end of 21st century. BEST and WORST shows similar projection for the rainy season evolution timing, meanwhile there are large discrepancy in thermodynamic structure. BEST and WORST in future projection are different in moisture flux, vertical structure of equivalent potential temperature and the subsequent unstable changes in the conditional instability.

• Polymer(Korea)
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• v.35 no.6
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• pp.513-519
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• 2011

Molecular simulations via the molecular dynamics method have been carried out to investigate the dynamic collision properties of penetrable-sphere model fluids. The collision frequencies, the mean free paths, the angle distributions of the hard-type reflection and the soft-type penetration, and the effective packing fractions are computed over a wide range of the packing fraction ${\phi}$ and the repulsive energy ${\varepsilon}^*$. The soft-type collisions are dominated for lower repulsive energy systems, while the hardtype collisions for higher repulsive energy systems. Very interestingly, the ratio of the soft-type (or, the hard-type) collision frequency to the total collision frequency is directly related with the Boltzmann factor of acceptance (or rejection) probabilities in the canonical ensemble Monte Carlo calculations. Such dynamic collision properties are shown to be restricted for highly repulsive and dense systems of ${\varepsilon}^*{\geqq}3.0 $and ${\phi}{\geqq}0.7$, indicating the cluster forming structures in the penetrable-sphere model.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.145-145
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• 2018

Small islands rely heavily on groundwater resources in addition to rainwater as the source of freshwater since surface water bodies are often absent. The groundwater resources are vulnerable to sea level rise, coastal flooding, saltwater intrusion, irregular pattern of precipitation resulting in long droughts and flash floods. Increase in population increases the demand for the limited groundwater resources, thus aggravating the problem. In this study, the effects of climate change on Tongatapu Island, Kingdom of Tonga, a small island in Pacific Ocean, are investigated using a sharp interface transient groundwater flow model. Twenty nine downscaled General Circulation Model(GCM) predictions are input to a water balance model to estimate the groundwater recharge. The temporal variation in recharge is predicted over the period of 2010 to 2099. A set of GCM models are selected to represent the ensemble of 29 models based on cumulative recharge at the end of the century. This set of GCM model predictions are then used to simulate a total of six climate scenarios, three each (2010-2039, 2040-2069, and 2070-2099) under RCP 4.5 and RCP 8.5. The impacts of predicted climate change on groundwater resources is evaluated in terms of freshwater volume changes and saltwater ratios in pumping wells compared to present conditions. Though the cumulative recharge at the end of the century indicates a wetter climate compared to the present conditions the large variability in rainfall pattern results in frequent periods of groundwater drought leading to saltwater intrusion in pumping wells. Thus for sustaining the limited groundwater resources in small islands, implementation of timely assessment and management practices are of utmost importance.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.4
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• pp.1-13
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• 2023

This study develops an optimal mobile-communication-based National Highway traffic volume estimation model using an ensemble-based machine learning algorithm. Based on information such as mobile communication data and VDS data, the LightGBM model was selected as the optimal model for estimating traffic volume. As a result of evaluating traffic volume estimation performance from 96 points where VDS was installed, MAPE was 8.49 (accuracy 91.51%). On the roads where VDS was not installed, traffic estimation accuracy was 92.6%.