• Journal of Bio-Environment Control
• /
• v.31 no.4
• /
• pp.325-331
• /
• 2022

One of the most important factors in predicting tomato growth and yield is the leaf area. Estimating leaf area accurately is the beginning of an effective tomato plant growth assessment model. To this end, this study was conducted to identify the most effective model for estimating plant leaf area through the measurement of tomato plant leaves. Leaf area (LA), leaf length (L), leaf width (W), and lamina length (La) were measured for all leaves of 5 plants at two-week intervals. The correlation between LA and tomato-leaf-independent variables showed a strong positive relationship with the formulas La × W, L × W, La + W, and L + W. For LA estimation, a linear model using the formula LA = a + b (La² + W²) gave the most accurate estimation (R² = 0.867, RMSE = 88.76). After examining the positions of upper, middle, and lower leaves from September to December, the coefficient of determination (R²) values for each model were 0.878, 0.726, and 0.794 respectively. The most accurate estimation came from the model that used the upper leaves of the plants. The high accuracy of the upper-leaf-based model is judged by the 50% defoliation performed by farmers after October.

• 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.

• Therapeutic Science for Rehabilitation
• /
• v.12 no.1
• /
• pp.51-63
• /
• 2023

Objective : This study aimed to analyze the association between lifestyle and medical expenses of older adults with mental illness using claims data. Methods : We conducted secondary data analysis using the older adult cohort database provided by the Korea National Health Insurance Service. The lifestyle and medical expense variables were extracted from the cohort database. We used a generalized linear model to examine the association between lifestyle and medical expenses. Results : In total, 32,853 records were extracted. The results showed that smokers had medical expenses (estimate = -218,255, p = .037). As the number of days of walking increased, medical expenses significantly decreased (estimate = -58,843, p < .0001). Furthermore, as the number of days of drinking decreased, medical expenses increased (estimate = 692,289, p < .0001). Conclusion : This study analyzed the estimates of medical expenses according to lifestyle among older adults with mental illness. Smoking and exercise were negatively associated with medical expenses. These results suggest the importance of a healthy lifestyle for older adults with mental illness. In addition, this study can be used as clinical evidence for lifestyle management programs to improve physical and mental health.

• Journal of Korea Water Resources Association
• /
• v.56 no.3
• /
• pp.211-224
• /
• 2023

In this study, a soil moisture estimation was performed using the Water Cloud Model (WCM), a backscatter model that considers vegetation based on SAR (Synthetic Aperture Radar). Sentinel-1 SAR and Sentinel-2 MSI (Multi-Spectral Instrument) images of a 40 × 50 km² area including the Yongdam Dam watershed of the Geum River were collected for this study. As vegetation descriptor of WCM, Sentinel-1 based vegetation index RVI (Radar Vegetation Index), depolarization ratio (DR), and Sentinel-2 based NDVI (Normalized Difference Vegetation Index) were used, respectively. Forward modeling of WCM was performed by 3 groups, which were divided by the characteristics between backscattering coefficient and soil moisture. The clearer the linear relationship between soil moisture and the backscattering coefficient, the higher the simulation performance. To estimate the soil moisture, the simulated backscattering coefficient was inverted. The simulation performance was proportional to the forward modeling result. The WCM simulation error showed an increasing pattern from about -12dB based on the observed backscattering coefficient.

• KIPS Transactions on Software and Data Engineering
• /
• v.12 no.3
• /
• pp.133-140
• /
• 2023

The performance of lithium ion batteries depends on the usage environment and the combination ratio of cathode materials. In order to develop a high-performance lithium-ion battery, it is necessary to manufacture the battery and measure its performance while varying the cathode material ratio. However, it takes a lot of time and money to directly develop batteries and measure their performance for all combinations of variables. Therefore, research to predict the performance of a battery using an artificial intelligence model has been actively conducted. However, since measurement experiments were conducted with the same battery in the existing published battery data, the cathode material combination ratio was fixed and was not included as a data attribute. In this paper, we define a training data model required to develop an artificial intelligence model that can predict battery performance according to the combination ratio of cathode materials. We analyzed the factors that can affect the performance of lithium-ion batteries and defined the mass of each cathode material and battery usage environment (cycle, current, temperature, time) as input data and the battery power and capacity as target data. In the battery data in different experimental environments, each battery data maintained a unique pattern, and the battery classification model showed that each battery was classified with an error of about 2%.

• Journal of the Korean Institute of Gas
• /
• v.27 no.1
• /
• pp.70-77
• /
• 2023

Air environment regulations have been strengthened due to increasing air pollutant emissions, the target of reducing emissions has increased and interest in gas measurement methods is also increasing. The sampling method is mainly used, but due to the spatial and temporal measurement limitations, the laser absorption spectroscopy which is a real-time and in-situ method is in the spotlight. In this study, we studied the wavelength modulation spectroscopy and described the calibration-free algorithm. The developed algorithm was modified to reflect 46 multi-absorption lines and was applied to light absorption signal analysis in visible and mid-infrared regions. In addition, the difference between the modulation parameters of laser was analyzed. As a result of reviewing the performance through O₂ and NO gas measurement experiments of various concentration conditions, the linearity was R²_O2=0.99999 and R²_NO=0.99967.

• Journal of Industrial Convergence
• /
• v.21 no.6
• /
• pp.61-71
• /
• 2023

The objective of this research is to examine the mediating roles of nursing professional values and occupational stress in the relationship between self-leadership and patient-centered nursing among nurses employed at COVID-19 designated hospitals. This study were 160 nurses at a COVID-19 designated hospitals, and the data were collected from January 10 to February 30, 2022. The collected data were analyzed by independent t-test, one-way ANOVA, correlation analysis, multiple linear regression analysis, and SPSS PROCESS Macro model No 4 bootstrapping method. The average score for self-leadership was 61.3±8.55, nursing professional values was 95.5±11.66, occupational stress was 51.3±4.76, and patient-centered nursing was 59.3±7.63. The mediating effect of nursing professional values and occupational stress was confirmed in the influence relationship between self-leadership and patient-centered nursing of nurses at COVID-19 designated hospitals. This result suggests that the content related to improve nursing professional values and reduce occupational stress should be considered when applying the patient-centered nursing enhancement program.

• Journal of Cadastre & Land InformatiX
• /
• v.52 no.2
• /
• pp.189-210
• /
• 2022

Cities around the world have paid attention to public transportation as an alternative to reducing traffic congestion caused by automobile usage, excessive energy consumption, and environmental pollution. This study measures accessibility to subway stations in Seoul using a supply-demand-based accessibility technique. Then, the impacts were analyzed through land prices by use and segment. As a result of analysis using the multilevel hedonic price models, accessibility considering both supply and demand for the subway had a positive effect on both residential and non-residential land prices. The effect was stronger for residential than for non-residential. Further, among the accessibility measured by the three functions, the accessibility by the Exponential function was most suitable for the residential land price, and the accessibility measured by the Power function for the non-residential land price had the highest explanatory power. Also, looking at the impacts by land price segments, it was found that higher access to metro stations had the greatest positive impacts on the most expensive segment of residential and non-residential land prices. The results of this study can be applied not only to identify the impacts of public investment on neighborhoods, but also to support real estate valuation.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2020.06a
• /
• pp.261-261
• /
• 2020

물관리의 기본이 되는 연속적인 유량 자료 확보를 위해서는 정확도 높은 수위-유량 관계 곡선식 개발이 필수적이다. 수위-유량 관계곡선식은 모든 수문시설 설계의 기초가 되며 홍수, 가뭄 등 물재해 대응을 위해서도 중요한 의미를 가지고 있다. 그러나 일반적으로 유량 측정은 많은 비용과 시간이 들고, 식생성장, 단면변화 등의 통제특성(control)이 변함에 따라 구간분리, 기간분리와 같은 비선형적인 양상이 나타나 자료 해석에 어려움이 존재한다. 특히, 국내 하천의 경우 자연적 및 인위적인 환경 변화가 다양하여 지점 및 기간에 따라 세밀한 분석이 요구된다. 머신러닝(Machine Learning)이란 데이터를 통해 컴퓨터가 스스로 학습하여 모델을 구축하고 성능을 향상시키는 일련의 과정을 뜻한다. 기존의 수위-유량 관계곡선식은 개발자의 판단에 의해 데이터의 종류와 기간 등을 설정하여 회귀식의 파라미터를 산출한다면, 머신러닝은 유효한 전체 데이터를 이용해 스스로 학습하여 자료 간 상관성을 찾아내 모델을 구축하고 성능을 지속적으로 향상 시킬 수 있다. 머신러닝은 충분한 수문자료가 확보되었다는 전제 하에 복잡하고 가변적인 수자원 환경을 반영하여 유량 추정의 정확도를 지속적으로 향상시킬 수 있다는 이점을 가지고 있다. 본 연구는 머신러닝의 대표적인 알고리즘들을 활용하여 유량을 추정하는 모델을 구축하고 성능을 비교·분석하였다. 대상지역은 안정적인 수량을 확보하고 있는 한강수계의 거운교 지점이며, 사용자료는 2010~2018년의 시간, 수위, 유량, 수면폭 등 이다. 프로그램은 파이썬을 기반으로 한 머신러닝 라이브러리인 사이킷런(sklearn)을 사용하였고 알고리즘은 랜덤포레스트 회귀, 의사결정트리, KNN(K-Nearest Neighbor), rgboost을 적용하였다. 학습(train) 데이터는 입력자료 종류별로 조합하여 6개의 세트로 구분하여 모델을 구축하였고, 이를 적용해 검증(test) 데이터를 RMSE(Roog Mean Square Error)로 평가하였다. 그 결과 모델 및 입력 자료의 조합에 따라 3.67~171.46로 다소 넓은 범위의 값이 도출되었다. 그 중 가장 우수한 유형은 수위, 연도, 수면폭 3개의 입력자료를 조합하여 랜덤포레스트 회귀 모델에 적용한 경우이다. 비교를 위해 동일한 검증 데이터를 한국수문조사연보(2018년) 내거운교 지점의 수위별 수위-유량 곡선식을 이용해 유량을 추정한 결과 RMSE가 3.76이 산출되어, 머신러닝이 세분화된 수위-유량 곡선식과 비슷한 수준까지 성능을 내는 것으로 확인되었다. 본 연구는 양질의 유량자료 생산을 위해 기 구축된 수문자료를 기반으로 머신러닝 기법의 적용 가능성을 검토한 기초 연구로써, 국내 효율적인 수문자료 측정 및 수위-유량 곡선 산출에 도움이 될 수 있을 것으로 판단된다. 향후 수자원 환경 및 통제특성에 영향을 미치는 다양한 영향변수를 파악하기 위해 기상자료, 취수량 등의 입력 자료를 적용할 필요가 있으며, 머신러닝 내 비지도학습인 딥러닝과 같은 보다 정교한 모델에 대한 추가적인 연구도 수행되어야 할 것이다.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.28 no.6D
• /
• pp.809-817
• /
• 2008

This paper deals with the development of ViscoElastic Continuum Damage Finite Element Program (VECD-FEP++) and its verification with the results from both field and laboratory accelerated pavement tests. Damage characteristics of asphalt concrete mixture have been defined by Schapery's work potential theory, and uniaxial constant crosshead rate tests were carried out to be used for damage model implementation. VECD-FEP++ predictions were compared with strain responses (longitudinal and transverse strains) under moving wheel loads running at different constant speeds. To this end, an asphalt pavement section (A5) of Korea Expressway Corporation Test Road (KECTR) instrumented with strain gauges were loaded with a dump truck. Also, a series of accelerated pavement fatigue tests have been conducted at pavement sections surfaced with four asphalt concrete mixtures (Dense-graded, SBS, Terpolymer, CR-TB). Planar strain responses were in good agreement with field measurements at base layers, whereas strains at both surface and intermediate layers were found different from simulation results due to the complexity of tire-road contact pressures. Finally, fatigue characteristics of four asphalt mixtures were reasonably described with VECD-FEP++.