• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.16 no.5
• /
• pp.338-346
• /
• 2023

During isometric elbow flexion, forearm length should be an important factor to determine not only joint torque but also maximum endurance time (MET), when the forearm is perpendicular to the direction of the force. The purpose of this paper is to examine the effect of forearm length as an additional factor on empirical models of MET such as an exponential model and a power model during isometric elbow flexion. Thirty volunteers participated in our experiment to measure factor variables such as circumferences and lengths of their upper and lower arms. Their METs were measured according to the percent of maximum voluntary contraction intensity (%MVC). For the multiple linear regression model of ln(MET) using these measurements, significant variables could be observed in %MVC and forearm lengths (P<0.05). The empirical models were assessed by these models using forearm length as the additional factor. Mean absolute deviations (MAD) between the measured METs amd the two empirical models were about 19.4 [s], but MAD using models applied forearm lengths were reduced to about 16.2 [s]. The correlation coefficients and intraclass correlation coefficients were about 0.87, but those applied forearm lengths were increased to about 0.91. These results demonstrated that forearm length was a significant additional factor to the empirical model.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.7
• /
• pp.57-65
• /
• 2023

Various studies to enhance the accuracy of rice production forecasting are focused on improving the accuracy of the models. In contrast, there is a relative lack of research regarding the data itself, which the prediction models are applied to. When applying the same dependent variable and prediction model to two different sets of rice production data composed of distinct features, discrepancies in results can occur. It is challenging to determine which dataset yields superior results under such circumstances. To address this issue, by identifying potential influential features within the data before applying the prediction model and centering the modeling around these, it is possible to achieve stable prediction results regardless of the composition of the data. In this study, we propose a method to adjust the composition of the data's features in order to select optimal base variables, aiding in achieving stable and consistent predictions for rice production. This method makes use of the Korea Meteorological Administration's ASOS data. The findings of this study are expected to make a substantial contribution towards enhancing the utility of performance evaluations in future research endeavors.

• Analytical Science and Technology
• /
• v.24 no.6
• /
• pp.544-555
• /
• 2011

The acute toxicity in the rainbow trout (Oncorhynchus mykiss) was analyzed and predicted using quantitative structure-activity relationships (QSAR). The aquatic toxicity, 96h $LC_{50}$ (median lethal concentration) of 275 organic pesticides, was obtained from EU-funded project DEMETRA. Prediction models were derived from 558 2D molecular descriptors, calculated in PreADMET. The linear (multiple linear regression) and nonlinear (support vector machine and artificial neural network) learning methods were optimized by taking into account the statistical parameters between the experimental and predicted p$LC_{50}$. After preprocessing, population based forward selection were used to select the best subsets of descriptors in the learning methods including 5-fold cross-validation procedure. The support vector machine model was used as the best model ($R^2_{CV}$=0.677, RMSECV=0.887, MSECV=0.674) and also correctly classified 87% for the training set according to EU regulation criteria. The MLR model could describe the structural characteristics of toxic chemicals and interaction with lipid membrane of fish. All the developed models were validated by 5 fold cross-validation and Y-scrambling test.

• Journal of Korean Society of Water and Wastewater
• /
• v.34 no.6
• /
• pp.503-512
• /
• 2020

Identifying the water circulation status is one of the indispensable processes for watershed management in an urban area. Recently, various water circulation models have been developed to simulate the water circulation, but it takes a lot of time and cost to make a water circulation model that could adapt the characteristics of the watershed. This paper aims to develop a water circulation state estimation model that could easily calculate the status of water circulation in an urban watershed by using multiple linear regression analysis. The study watershed is a watershed in Seoul that applied the impermeable area ratio in 1962 and 2000. And, It was divided into 73 watersheds in order to consider changes in water circulation status according to the urban characteristic factors. The input data of the SHER(Similar Hydrologic Element Response) model, a water circulation model, were used as data for the urban characteristic factors of each watershed. A total of seven factors were considered as urban characteristic factors. Those factors included annual precipitation, watershed area, average land-surface slope, impervious surface ratio, coefficient of saturated permeability, hydraulic gradient of groundwater surface, and length of contact line with downstream block. With significance probabilities (or p-values) of 0.05 and below, all five models showed significant results in estimating the water circulation status such as the surface runoff rate and the evapotranspiration rate. The model that was applied all seven urban characteristics factors, can calculate the most similar results such as the existing water circulation model. The water circulation estimation model developed in this study is not only useful to simply estimate the water circulation status of ungauged watersheds but can also provide data for parameter calibration and validation.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.25 no.3
• /
• pp.74-99
• /
• 2022

Surface ozone is produced by photochemical reactions of nitrogen oxides(NOx) and volatile organic compounds(VOCs) emitted from vehicles and industrial sites, adversely affecting vegetation and the human body. In South Korea, ozone is monitored in real-time at stations(i.e., point measurements), but it is difficult to monitor and analyze its continuous spatial distribution. In this study, surface ozone concentrations were interpolated to have a spatial resolution of 1.5km every hour using the stacking ensemble technique, followed by a 5-fold cross-validation. Base models for the stacking ensemble were cokriging, multi-linear regression(MLR), random forest(RF), and support vector regression(SVR), while MLR was used as the meta model, having all base model results as additional input variables. The results showed that the stacking ensemble model yielded the better performance than the individual base models, resulting in an averaged R of 0.76 and RMSE of 0.0065ppm during the study period of 2020. The surface ozone concentration distribution generated by the stacking ensemble model had a wider range with a spatial pattern similar with terrain and urbanization variables, compared to those by the base models. Not only should the proposed model be capable of producing the hourly spatial distribution of ozone, but it should also be highly applicable for calculating the daily maximum 8-hour ozone concentrations.

• International Journal of Highway Engineering
• /
• v.6 no.4 s.22
• /
• pp.109-121
• /
• 2004

This study was performed to investigate the characteristics of drying shrinkage for concrete slabs as a project for Korean pavement design procedure. According to the volume-surface ratios and aggregate types, the experiments have been executed for 252 days. In order to simulate the volume-surface ratio of a real concrete pavement slab, three-layer epoxy coating and wrapping were used to prevent the evaporation at the part of specimen surfaces. As a result of preliminary test, coating and wrapping method was identified as reliable for three months. According to the volume-surface ratio, the drying shrinkage of the concrete specimen using sandstone was measured 1.32 to 1.8 times higher than that of the limestone specimen. Comparing to the measured drying shrinkage strains and established ACI and CEB-FIP model equations, it turned out that those model equations were underestimated. Finally, considering the age and volume-surface ratios, the prediction equations of the drying shrinkage of concrete specimen were proposed through a multiple nonlinear regression analysis.

• Smart Media Journal
• /
• v.11 no.4
• /
• pp.19-29
• /
• 2022

Recently, with the development of IoT technology, the number of farms using smart farms is increasing. Smart farms monitor the environment and optimise internal environment automatically to improve crop yield and quality. For optimized crop cultivation, researches on predict crop productivity are actively studied, by using collected agricultural digital data. However, most of the existing studies are based on statistical models based on existing statistical data, and thus there is a problem with low prediction accuracy. In this paper, we use various predition models for predicting the production and sales profits, and compare the performance results through models by using the agricultural digital data collected in the facility horticultural smart farm. The models that compared the performance are multiple linear regression, support vector machine, artificial neural network, recurrent neural network, LSTM, and ConvLSTM. As a result of performance comparison, ConvLSTM showed the best performance in R2 value and RMSE value.

• Journal of The Korean Society of Grassland and Forage Science
• /
• v.41 no.1
• /
• pp.47-55
• /
• 2021

The objective of this study was to access the effect of climate and soil factors on alfalfa dry matter yield (DMY) by the contribution through constructing the yield prediction model in a general linear model considering climate and soil physical variables. The processes of constructing the yield prediction model for alfalfa was performed in sequence of data collection of alfalfa yield, meteorological and soil, preparation, statistical analysis, and model construction. The alfalfa yield prediction model used a multiple regression analysis to select the climate variables which are quantitative data and a general linear model considering the selected climate variables and soil physical variables which are qualitative data. As a result, the growth degree days(GDD) and growing days(GD), and the clay content(CC) were selected as the climate and soil physical variables that affect alfalfa DMY, respectively. The contributions of climate and soil factors affecting alfalfa DMY were 32% (GDD, 21%, GD 11%) and 63%, respectively. Therefore, this study indicates that the soil factor more contributes to alfalfa DMY than climate factor. However, for examming the correct contribution, the factors such as other climate and soil factors, and the cultivation technology factors which were not treated in this study should be considered as a factor in the model for future study.

• Atmosphere
• /
• v.25 no.4
• /
• pp.669-683
• /
• 2015

In this study, we investigated the prediction skills of four multiple linear regression methods for monthly air temperature over South Korea. We used simulation results from four regional climate models (RegCM4, SNURCM, WRF, and YSURSM) driven by two boundary conditions (NCEP/DOE Reanalysis 2 and ERA-Interim). We selected 15 years (1989~2003) as the training period and the last 5 years (2004~2008) as validation period. The four regression methods used in this study are as follows: 1) Homogeneous Multiple linear Regression (HMR), 2) Homogeneous Multiple linear Regression constraining the regression coefficients to be nonnegative (HMR+), 3) non-homogeneous multiple linear regression (EMOS; Ensemble Model Output Statistics), 4) EMOS with positive coefficients (EMOS+). It is same method as the third method except for constraining the coefficients to be nonnegative. The four regression methods showed similar prediction skills for the monthly air temperature over South Korea. However, the prediction skills of regression methods which don't constrain regression coefficients to be nonnegative are clearly impacted by the existence of outliers. Among the four multiple linear regression methods, HMR+ and EMOS+ methods showed the best skill during the validation period. HMR+ and EMOS+ methods showed a very similar performance in terms of the MAE and RMSE. Therefore, we recommend the HMR+ as the best method because of ease of development and applications.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2015.05a
• /
• pp.641-641
• /
• 2015

수자원 관리의 새로운 패러다임으로 스마트워터그리드가 이슈화되고 있다. 스마트워터그리드는 현재 직면해 있는 물 문제를 ICT기술을 활용하여 효율적으로 관리 및 운영할 수 있는 기술이다. 가뭄과 홍수 등을 대응하기 위한 분산형 수자원, 그리고 수량과 수질등의 통합 관리를 지향하는 물관리 패러다임과 ICT와 물산업을 융복합화하여 양방향 실시간 운영을 통한 네트워크 구성, 그리고 대규모 수원개발과 장거리 수송방식으로부터 지역단위의 부존된 수자원인 다중수원을 효율적으로 활용하고, 이들 수원의 최적 블랜딩 기술 및 멀티워터루프를 이용한 수요자 중심의 물관리에 초점을 맞추고 있다. 이에 본 연구에서는 지역의 부존된 수자원을 효율적으로 관리하기 위한 운영지수를 산정함으로써 지역의 평상시 물관리 운영과 상수공급차단, 다중수원에 오염물질 유입, 가뭄발생 등 비상시 상황에 물관리가 원활히 이루어지도록 하였다. 스마트워터그리드에서 사용되는 물관리 운영지수는 비상시 지역간 플랫폼에 정상적으로 물공급이 어렵거나, 예상되어 유량 확보가 필요시 원활하게 유량 확보가 가능하도록 주변 지역간의 운영지수를 산정하여 최적의 조건을 도출하여 우선순위를 시나리오에 나타내어 사용자가 플랫폼에 물공급이 가능하도록 제시한 운영지수이다. 물관리 운영지수는 중회귀분석 기법을 이용하여 종속변수에 선형적인 영향을 미치는 유량, 수질, 이송거리, 전력비용, 경제적인 영향 등을 고려하여 종속변수의 값을 예측하고 통계적인 기법으로 유량, 수질, 이송거리, 전력비용, 경제적인 영향 등에 대한 장래추정치를 부여하여 회귀모델을 구축하여 물관리 운영지수를 산정하였다. 이를 활용하여 평상시에는 물수요 공급량을 정확히 관리하여 지역간의 수자원 불균형을 해소하고 비상시에는 상황을 대처하여 지역의 물부족 문제를 해소할 수 있을 것으로 판단된다.