• Communications for Statistical Applications and Methods
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• v.30 no.2
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• pp.119-133
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• 2023

With the rapid growth of the economy and fossil fuel consumption, the concentration of air pollutants has increased significantly and the air pollution problem is no longer limited to small areas. We conduct statistical analysis with the actual data related to air quality that covers the entire of South Korea using R and Python. Some factors such as SO₂, CO, O₃, NO₂, PM₁₀, precipitation, wind speed, wind direction, vapor pressure, local pressure, sea level pressure, temperature, humidity, and others are used as covariates. The main goal of this paper is to predict air quality index (AQI) spatio-temporal data. The observations of spatio-temporal big datasets like AQI data are correlated both spatially and temporally, and computation of the prediction or forecasting with dependence structure is often infeasible. As such, the likelihood function based on the spatio-temporal model may be complicated and some special modelings are useful for statistically reliable predictions. In this paper, we propose several methods for this big spatio-temporal AQI data. First, random effects with spatio-temporal basis functions model, a classical statistical analysis, is proposed. Next, neural networks model, a deep learning method based on artificial neural networks, is applied. Finally, random forest model, a machine learning method that is closer to computational science, will be introduced. Then we compare the forecasting performance of each other in terms of predictive diagnostics. As a result of the analysis, all three methods predicted the normal level of PM_2.5 well, but the performance seems to be poor at the extreme value.

• Journal of Electrical Engineering and Technology
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• v.8 no.6
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• pp.1615-1625
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• 2013

Modelling and simulating the wind power intermittent behaviour are the basis of the planning and scheduling studies concerning wind power integration. The wind power outputs are evidently correlated in space and time and bring challenges in characterizing their behaviour. This paper provides a methodology to model and simulate the clustered wind power considering its spatio-temporal correlations using the theory of copula. The sampling approach captures the complex spatio-temporal connections among the wind farms by employing a conditional density function calculated using multidimensional copula function. The empirical study of real wind power measurement shows how the wind power outputs are correlated and how these correlations affect the overall uncertainty of clustered wind power output. The case study validates the simulation technique by comparing the simulated results with the real measurements.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.26 no.2
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• pp.72-80
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• 2014

The approximately highest high water(AHHW), which has been used frequently as a basis of the design sea level, has not only ambiguous return period but also spatio-temporal problems induced by sea level rise and the spatial variability of tidal characteristics. The ratios of 4 major constituents with other constituents were investigated. In addition, tidal data were analyzed by probability density function. The temporal variability may be cured by using the latest tidal data. And the AHHW at summer was examined to lessen the spatial variability. The results show that the design sea levels need to increase by 10 cm or more at the Southern Coast and by 15~25 cm at the East Coast.

• Physical Therapy Rehabilitation Science
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• v.7 no.3
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• pp.114-118
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• 2018

Objective: The left and right sides of the brain has different roles. This study investigated the differences in cognitive driving ability between stroke survivors with damage to the left brain and right brain. Therefore, the purpose of this study was to compare the driving cognitive ability of left and right hemispheric drivers following stroke. Design: Cross-sectional study. Methods: The Stroke Drivers' Screening Assessment (SDSA) from the UK was translated to the Korean Stroke Drivers' Screening Assessment (K-SDSA) to meet the specific traffic environments of Korea. The SDSA is composed of 4 tasks :1) a dot cancellation task that measures concentration and visuospatial abilities necessary for driving, 2) a directional matrix task to measure spatio-temporal executive function required for driving, 3) a compass matrix task to measure accurate direction determination ability required for driving, and 4) recognition of traffic signs and reasoning ability to understanding traffic situation. The SDSA assessment time is about 30 minutes. The K-SDSA was used to compare the cognitive driving abilities between 15 stroke survivors with left and 15 stroke survivors with right brain damage. Results: There were significant differences between the persons with stroke patients with left brain lesions (right hemiplegia) compared to the persons with stroke with right brain lesions (left hemiplegia) (p<0.05). It was found that the cognitive driving ability of those with right brain damage was lower than that of the group of left brain damage. Conclusions: This research investigated the driving cognitive ability of persons with stroke. The therapists can use this information as basis for the driving test and training purposes. It could also be used as a basis to understanding if the cognitive ability of not only stroke survivors but also those with brain damage is adequate to actually drive.