• Journal of Biosystems Engineering
• /
• v.37 no.1
• /
• pp.51-57
• /
• 2012

Purpose: The compression strength of corrugated fiberboard containers used to package agricultural products rapidly decreases owing to various environmental factors encountered during the distribution of unitized products. The main factors affecting compression strength are moisture absorption, long-term top load, and fatigue caused by shock and vibration during transport. This study characterized the durability of corrugated fiberboard containers for packaging fruits and vegetables under simulated transportation conditions. Methods: Compression tests were done after corrugated fiberboard containers containing fruit were vibrated by an electro-dynamic vibration test system using the power spectral density of routes typically traveled to transport fruits and vegetables in South Korea. Results: To predict loss of compression strength owing to vibration fatigue, a multiple nonlinear regression equation ($r^2=0.9217$, $RMSE=0.6347$) was developed using three independent variables of initial container compression strength, namely top stacked weight, loading weight, and vibration time. To test the applicability of our model, we compared our experimental results with those obtained during a road test in which peaches were transported in corrugated containers. Conclusions: The comparison revealed a highly significant ($p{\leq}0.05$) relationship between the experimental and road-test results.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.24 no.3
• /
• pp.271-279
• /
• 2006

Acoustic Emission (AE) technology is an effective nondestructive testing for continuous monitoring of defect formation and failures in structural materials. This paper presents a source location model using Acoustic Emission (AE) sensors in a Pre-Stressed Concrete (PSC) beam and the evaluation of the model was performed through lab experiments. 54 AE events were made on the surface of the 5m-PSC beam using a Schmidt Hammer and arrival times were measured with 7AE sensors. The source location f3r each event was estimated using least squares. The results were compared with actual positions and the RMSE (Root Mean Square Errors) was about 2cm.

• Journal of Korean Society of Water and Wastewater
• /
• v.30 no.2
• /
• pp.179-185
• /
• 2016

In this study, the RDII predictions were compared using two methodologies, i.e., the RTK-based and regression methods. Long-term (1/1/2011~12/31/2011) monitoring data, which consists of 10-min interval streamflow and the amount of precipitation, were collected at the domestic study area (1.36 km2 located in H county), and used for the construction of the RDII prediction models. The RTK method employs super position of tri-triangles, and each triangle (called, unit hydrograph) is defined by three parameters (i.e., R, T and K) determined/optimized using Genetic Algorithm (GA). In regression method, the MovingAverage (MA) filtering was used for data processing. Accuracies of RDII predictions from these two approaches were evaluated by comparing the root mean square error (RMSE) values from each model, in which the values were calculated to 320.613 (RTK method) and 420.653 (regression method), respectively. As a results, the RTK method was found to be more suitable for RDII prediction during extreme rainfall event, than the regression method.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2005.05b
• /
• pp.65-69
• /
• 2005

SWAT 모형은 우리나라 논 지역에서의 수리관행을 이행할 수 있도록 관개용수의 공급과 담수지역에 대한 성분해석에 대한 기본적인 틀을 갖추고 있으며, 각종 물관리 체계의 조작이 가능한 우수한 모형이다. 그러나, 실제 우리나라 농업지역에 관개용수를 공급하고 담수상태의 논 지역을 충분히 모사하지 못하고 있다. 따라서, 이러한 결함을 제거하기 위해서는 SWAT 모형의 관개용수공급과 관련한 내부 모듈의 전면적인 수정$\cdot$보완과 교체작업이 필요하였다. 이러한 SWAT 모형의 문제점 해결을 위해 우리나라 는 농사의 특징인 담수를 고려하고, 관개량 및 관개일정을 제어하여 담수 논에 대한 수문성분이 전체 하천유출을 포함하는 수문순환 과정에 미치는 영향을 정량적으로 평가할 수 있는 SWAT-AGRIMAN(SWAT-AGRIculture MANagement) 모형을 개발하였다. 개발된 SWAT-AGRIMAN 모형을 보청천 기대교 상류유역에 적용하였고, 적용결과를 SWAT 모형에 의한 모의결과와 비교$\cdot$평가하였다. 관개용수의 수원의 제기능 여부 및 담수상태의 유지여부와 논의 침투량 및 증발산량, 유역 출구에서의 유출량의 변화특성을 살펴보았으며, 실제 관측유량과의 유출율, 평균제곱근오차(RMSE), 모형효율계수(ME), 상관계수(CORRL), 결정계수($R^2$)의 비교$\cdot$분석을 통해 모형을 평가하였다.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2009.05a
• /
• pp.1321-1326
• /
• 2009

강우는 수자원 확보 측면에서 근원이 되는 요소이다. 그러므로 정확한 확률강우량 산정은 미래의 가용 수자원량을 예측하는데 있어 중요한 사항중 하나이며 무엇보다 신중한 결정이 요구된다. 또한 하천의 범람에 의한 침수를 예방하는 수공구조물 등의 설계에 있어서는 신뢰할 수 있는 확률강우량 산정이 선행되어야 한다. 본 연구에서는 최근 우리나라 극치강우확률분포로서 많은 연구가 이루어지고 있는 GEV 분포(GEV-O)를 기반으로 위치 매개변수에 시간의 함수를 고려한 개선된 GEV 분포(GEV-A)를 이용하여 서울지점에 적용함으로서 GEV-O 분포에 의한 확률강우량과 GEV-A 분포로 산정된 확률강우량을 비교 검토하였다. 먼저 임의의 난수 발생을 통해 최우도추정법과 확률가중모멘트법으로 매개변수를 추정한 GEV-O 분포와 최우도추정법으로 매개변수를 추정한 GEV-A 분포의 상대평균제곱근오차 (R-RMSE)를 계산하여 비교함으로서 GEV-A 분포의 효율성을 판단하였다. 사례연구는 1961년부터 2008년까지 서울강우관측소에서 측정된 연최대 1일 강우량으로 하였으며 $X^2$-검정, PPCC-검정으로 적합도 검정을 실시하였다. 강우빈도분석 결과 GEV-A 분포가 GEV-O 분포로 산정된 결과 보다 대체로 재현기간 200년 이상일 경우, 과다 산정되는 경향을 보였다. 추후 개선된 GEV 분포를 서울 인근 지점에 적용함으로서 지역빈도해석(Regional Frequency Analysis)을 실행하기 위한 연구가 진행되어야 할 것이다. 또한 확률홍수량 산정 등에도 개선된 GEV 분포를 이용함으로서 보다 정확하고 신뢰성 있는 확률수문량을 예측하여야 할 것이다.

• Computers and Concrete
• /
• v.10 no.6
• /
• pp.649-662
• /
• 2012

This paper presents the results of an investigation on the compressive strength and weight loss of mortars containing three types of fillers as cement replacements; Limestone Filler (LF), Silica Fume (SF) and Trass (TR), subjected to elevated temperatures including $400^{\circ}C$, $600^{\circ}C$, $800^{\circ}C$ and $1000^{\circ}C$. Results indicate that addition of TR to blended cements, compared to SF addition, leads to higher compressive strength and lower weight loss at elevated temperatures. In order to model the influence of the different parameters on the compressive strength and the weight loss of specimens, artificial neural networks (ANNs) were adopted. Different diagrams were plotted based on the predictions of the most accurate networks to study the effects of temperature, different fillers and cement content on the target properties. In addition to the impressive RMSE and $R^2$ values of the best networks, the data used as the input for the prediction plots were chosen within the range of the data introduced to the networks in the training phase. Therefore, the prediction plots could be considered reliable to perform the parametric study.

• Computers and Concrete
• /
• v.26 no.3
• /
• pp.293-307
• /
• 2020

The aim of this paper is to develop design-oriented models for the prediction of the ultimate strength and ultimate axial strain for concrete confined with glass fiber-reinforced polymer (GFRP) wraps. Twenty of most used and recent design-oriented models developed to predict the strength and strain of GFRP-confined concrete in circular sections are selected and evaluated basing on a database of 163 test results of concrete cylinders confined with GFRP wraps subjected to uniaxial compression. The evaluation of these models is performed using three statistical indices namely the coefficient of the determination (R²), the root mean square error (RMSE), and the average absolute error (AAE). Based on this study, new strength and strain models for GFRP-wrapped concrete are developed using regression analysis. The obtained results show that the proposed models exhibit better performance and provide accurate predictions over the existing models.

• Proceedings of the Korean Society of Agricultural Engineers Conference
• /
• 2001.10a
• /
• pp.93-98
• /
• 2001

This study was performed to analyse the rainfall and the rainfall-runoff characteristics of a rural watershed. The Sangwha basin($105.9km^{2}$) in the Geum river system was selected for this study. The arithmetic mean method, the Thiessen's weighing method, and the isohyetal method were used to analyse areal rainfall distribution and the Huff's quartile method was used to analyse temporal rainfall distribution. In addition, daily runoff analyses were peformed using the DAWAST and tank model. In the model calibration, the data from June through November, 1999 were used. In the model calibration, the observed runoff depth was 513.7mm and runoff rate was 45.2%, and the DAWAST model simulated runoff depth was 608.6mm and runoff rate was 53.5%, and the tank model runoff depth was 596.5mm and runoff rate was 52.5%, respectively. In the model test, the data from June through November, 2000 were used. In the model test, the observed runoff depth was 1032.3mm and runoff rate was 72.5%, and the DAWAST model simulated runoff depth was 871.6mm and runoff rate was 61.3%, and the tank model runoff depth was 825.4mm and runoff rate was 58%, respectively. The DAWAST and tank model's $R^{2}$ and RMSE were 0.85, 3.61mm, and 0.85, 2.77mm in 1999, and 0.83, 5.73mm, and 0.87, 5.39mm in 2000, respectively. Both models predicted low flow runoff better than flood runoff.

• Journal of Environmental Science International
• /
• v.21 no.6
• /
• pp.695-704
• /
• 2012

The application of disinfection models on the plasma process was investigated. Nine empirical models were used to find an optimum model. The variation of parameters in model according to the operating conditions (first voltage, second voltage, air flow rate, pH) were investigated in order to explain the disinfection model. In this experiment, the DBD (dielectric barrier discharge) plasma reactor was used to inactivate Ralstonia Solanacearum which cause wilt in tomato plantation. Optimum disinfection models were chosen among the nine models by the application of statistical SSE (sum of squared error), RMSE (root mean sum of squared error), $r^2$ values on the experimental data using the GInaFiT software in Microsoft Excel. The optimum model was shown as Weibull+talil model followed by Log-linear+ Shoulder+Tail model. Two models were applied to the experimental data according to the variation of the operating conditions. In Weibull+talil model, Log10($N_o$), Log10($N_{res}$), ${\delta}$ and p values were examined. And in Log-linear+Shoulder+Tail model, the Log10($N_o$), Log10($N_{res}$), $k_{max}$, Sl values were calculated and examined.

• Smart Structures and Systems
• /
• v.26 no.1
• /
• pp.117-134
• /
• 2020

Over recent years, the interest for vegetables and fruits in all seasons and places has much increased, from where diverse countries have directed to the commercial production in greenhouse. In this article, we propose an algorithm based on wireless sensor network technologies that monitor the microclimate inside a greenhouse and linear equations model for optimization plant production and material cost. Moreover, we also suggest a novel design of an intelligent greenhouse. We validate our algorithms with simulations on a benchmark based on experimental data made at lNRA of Montfavet in France. Finally, we calculate the statistical estimators RMSE, TSSE, MAPE, EF and R². The results obtained are promising, which shows the efficiency of our proposed system.