• Journal of Korean Society of Transportation
• /
• v.22 no.1 s.72
• /
• pp.43-62
• /
• 2004

Conventionally the estimation method of the origin-destination Matrix has been developed by implementing the expansion of sampled data obtained from roadside interview and household travel survey. In the survey process, the bigger the sample size is, the higher the level of limitation, due to taking time for an error test for a cost and a time. Estimating the O-D matrix from observed traffic count data has been applied as methods of over-coming this limitation, and a gradient model is known as one of the most popular techniques. However, in case of the gradient model, although it may be capable of minimizing the error between the observed and estimated traffic volumes, a prior O-D matrix structure cannot maintained exactly. That is to say, unwanted changes may be occurred. For this reason, this study adopts a conjugate gradient algorithm to take into account two factors: estimation of the O-D matrix from the conjugate gradient algorithm while reflecting the prior O-D matrix structure maintained. This development of the O-D matrix estimation model is to minimize the error between observed and estimated traffic volumes. This study validates the model using the simple network, and then applies it to a large scale network. There are several findings through the tests. First, as the consequence of consistency, it is apparent that the upper level of this model plays a key role by the internal relationship with lower level. Secondly, as the respect of estimation precision, the estimation error is lied within the tolerance interval. Furthermore, the structure of the estimated O-D matrix has not changed too much, and even still has conserved some attributes.

• KDI Journal of Economic Policy
• /
• v.14 no.1
• /
• pp.121-145
• /
• 1992

This paper examines characteristics of time series data related to the construction investment(stationarity and time series components such as secular trend, cyclical fluctuation, seasonal variation, and random change) and surveys predictibility, fitness, and explicability of independent variables of various models to build a short-term construction investment forecasting model suitable for current economic circumstances. Unit root test, autocorrelation coefficient and spectral density function analysis show that related time series data do not have unit roots, fluctuate cyclically, and are largely explicated by lagged variables. Moreover it is very important for the short-term construction investment forecasting to grasp time lag relation between construction investment series and leading indicators such as building construction permits and value of construction orders received. In chapter 3, we explicate 7 forecasting models; Univariate time series model (ARIMA and multiplicative linear trend model), multivariate time series model using leading indicators (1st order autoregressive model, vector autoregressive model and error correction model) and multivariate time series model using National Accounts data (simple reduced form model disconnected from simultaneous macroeconomic model and VAR model). These models are examined by 4 statistical tools that are average absolute error, root mean square error, adjusted coefficient of determination, and Durbin-Watson statistic. This analysis proves two facts. First, multivariate models are more suitable than univariate models in the point that forecasting error of multivariate models tend to decrease in contrast to the case of latter. Second, VAR model is superior than any other multivariate models; average absolute prediction error and root mean square error of VAR model are quitely low and adjusted coefficient of determination is higher. This conclusion is reasonable when we consider current construction investment has sustained overheating growth more than secular trend.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.37 no.1
• /
• pp.49-56
• /
• 2024

An automated dynamic structural analysis module stands as a crucial element within a structural integrated mitigation system. This module must deliver prompt real-time responses to enable timely actions, such as evacuation or warnings, in response to the severity posed by the structural system. The finite element method, a widely adopted approximate structural analysis approach globally, owes its popularity in part to its user-friendly nature. However, the computational efficiency and accuracy of results depend on the user-provided finite element mesh, with the number of elements and their quality playing pivotal roles. This paper introduces a computationally efficient adaptive mesh generation scheme that optimally combines the h-method of node movement and the r-method of element division for mesh refinement. Adaptive mesh generation schemes automatically create finite element meshes, and in this case, representative strain values for a given mesh are employed for error estimates. When applied to dynamic problems analyzed in the time domain, meshes need to be modified at each time step, considering a few hundred or thousand steps. The algorithm's specifics are demonstrated through a standard cantilever beam example subjected to a concentrated load at the free end. Additionally, a portal frame example showcases the generation of various robust meshes. These examples illustrate the adaptive algorithm's capability to produce robust meshes, ensuring reasonable accuracy and efficient computing time. Moreover, the study highlights the potential for the scheme's effective application in complex structural dynamic problems, such as those subjected to seismic or erratic wind loads. It also emphasizes its suitability for general nonlinear analysis problems, establishing the versatility and reliability of the proposed adaptive mesh generation scheme.

• Journal of the KSME
• /
• v.27 no.6
• /
• pp.504-514
• /
• 1987

복잡한 전자부품의 조립시에 필요한 열적 디자인에 관한 정보는 오래전부터 실험을 통하여 얻어지고 있다. 실험적 데이터를 이용하여 무차원 파라미터로 표시된 실험결과는 꼭 같지는 않지만 현상적으로는 비슷한 상황에 응용될 수 있다. 여기서는 학술문헌에 나타나 있는 자연대류에 관한 실험적인 상관관계식들과 프레임에 수직으로 꽂혀있는 균일가열 전자회로기판의 모델에서 얻어진 무차원 자료들을 비교하고자 한다. 대부분의 자료들은 수정채널 Rayleigh수(Ra")가 15~100범위에 속하며, 이러한 범위는 부품이 조밀하게 배치된 기관이 서로 좁은 채널을 이루고 있으며, 동시에 상당한 전력을 소비하고 있는 경우에 해당한다. Wirt와 Stutzman, Bar-Cohen과 Rohsenow의 일반상관관계식은 AT'||'&'||'T Bell 연구소에서 개발된 전자기기를 이용하여 수집한 실험데이터를 잘 표현하고 있으며 10 < Ra" <1,000범위에서 추천될 수 있다. 두개의 유사한 상관관계식과 비교할 때 상당히 좋은 예측을 보였으며 또한 Sparrow와 Gregg의 연구결과와도 잘 일치하므로 Ra" < 10인 경우에 Aung의 완전발달층류의 채널유동방식, Ra" > 1,000인 경우에는 Aung등의 단일 수직평판 근사식이 추천될 수 있다. Coyne의 알고리즘에 의한 계산치는 10

• Journal of the KSME
• /
• v.21 no.1
• /
• pp.21-31
• /
• 1981

원자력발전소의 원자로냉각재 압력경계의 건전성과 안정성을 확보하기 위하여 법적 요구조건을 설정함에 있어 파괴역학이 어떻게 적용되었는 가를 설명하였다. 이를 요약하면 다음과 같다. 1) 압력경계에 사용되는 재료의 $RT_{NDT}$를 정의하였다. 이는 무연성천이온도와 같은 개 념의 것으로, 앞으로 재료의 파괴인성은 이 $RT_{NDT}$에 대한 상대온도의 함수로 주어진다. 2)비연성파괴를 방지하기 위한 설계조건으로서 선형탄성 파괴역학에 근거한 조건식을 인용하였다. 여기서 조건식이란 능력확대계수의 합계가 어떠한 조건에서도 이러한 조건식을 만족한다는 것을 해석적으로 확인하고 규제당국의 승인을 받아야 한다. 3) 가동중검사에 발견된 결함으로 합격수준을 초과하는 것은 파괴역학적으로 해석하여 구조적 으로 안전하다는 것은 파괴역학적으로 해석하여 구조적으로 안전하다는 것을 입증하여야 한다. 이때 결함은 원자로의 가동과 더불어 성장하므로 수명기간중 피로파괴에 이를 것인지의 여부도 평가하여야 한다. 이때의 대조균열성장률은 Paris의 power law에 따른다. 4) 고속중성자 (E>1. 0MeV)에 의한 조사취화를 감시하기 위하여 감시시험계획을 사전에 수립 하고 이에 따라 감시시험을 수행하여 조사에 수립하고 이에 따라 감시시험을 수행하여 조사에 의한 원자로용기 재료의 파괴인성의 저하를 평가하여 이를 고려한 충분한 안전여유를 갖는 운 전조건 즉, 압력-온도 한계곡선을 산출하여야 한다. 이때의 취화 정도는 DELTA. $RT_{NDT}$ 와 Upper Shelf Energy의 감소로 나타낸다. 또한, 압력-온도 한계곡선은 선형관성 파괴역학에 입각한 조건식을 이용하여 해당 온도에서의 압력을 산출한다. System을 개발 사용하기 위하여 기존 전자계산소를 이용하는 방법이 바람직하며 System의 도입은 자체운영을 결정하기 전에 경제적인 여건 등 여러가지 문제를 검토하여야 한다. 특히 Turn Key Base로 System를 도입할 경우에는 System의 도입목 적과 사용빈도, 앞으로의 확장성 현재 설계및 생산 과정과의 마찰가능성, 유지보수문제 등을 신 중히 검토하여야 한다. 이제 기계공업도 전자계산기를 이해하고 사용하므로 서 발전할 수 있는 단계가 되었다. 예로부터 좋은 공구를 개발하여 적절히 사용하는 것이 기계공업 발전의 첩경이 었다. 전자계산기는 현대 기술이 개발한 가장 강력하고 사용하기 좋은 공구이다.점에서 피로구열의 안정성장을 논하고, 과거 10여년간의 피로 crack문제에 대한 연구방법, 실험방법 등을 소개하는 방향으로 고 를 진행시켜 나가겠다.에 그 효과가 증대됨을 알 수 있었다.적용한 임상실험이 수행되어야 할 것이다. 또한 위치결정에서 획득한 좌표값의 정확성을 알아보기 위해서 팬톰을 이용한 방사선조사 실험이 추후에 실행되어져야 할 것이다. 그리고 제작된 프레임에 Rotating X선 시스템과 내부 장기의 움직임을 계량화하고 PTV에서의 최적 여유폭을 설정함으로써 정위 방사선수술 및 3 차원 업체 방사선치료에 대한 병소 위치측정과 환자의 자세에 대한 setup 오차측정 결정에 도움이 될 수 있을 것이라고 사료된다. 상대적으로 우수한 것으로 나타났으며, 혼합충전재는 암모니아의 경우 코코넛과 펄라이트의 비율이 7：3인 혼합 재료 3번과 소나무수피와 펄라이트의 비율이 7：3인 혼합 재료 6번에서 다른 혼합 재료에 비하여 우수한 것으로 나타났다. 4. 코코넛과 소나무수피의 경우 암모니아 가스에 대한 흡착 능력은 거의 비슷한 것으로 사료되며,

• Korean Journal of Environmental Biology
• /
• v.41 no.4
• /
• pp.370-385
• /
• 2023

Cyanobacterial harmful algal blooms (Cyano-HABs) are an international environmental problem that negatively affects the ecosystem as well as the safety of water resources by discharging cyanotoxins. In particular, the discharge of microcystins (MCs), a highly toxic substance, has been studied most actively, and various water treatment methods have been proposed for this purpose. In this paper, we reviewed adsorption technology, which is recognized as the most feasible, economical, and efficient method among suggested treatment methods for removing MCs. Activated carbons (AC) are widely used adsorbents for MCs removal, and excellent MCs adsorption performance has been reported. Research on alternative adsorption materials for AC such as biochar and biosorbents has been conducted, however, their performance was lower compared to activated carbon. The impacts of adsorbent properties(characteristics of pore surface chemistry) and environmental factors (solution pH, temperature, natural organic matter, and ionic strength) on the MCs adsorption performance were also discussed. In addition, toward effective control of MCs, the possibility of the direct removal of harmful cyanobacteria as well as the removal of dissolved MCs using adsorption strategy was examined. However, to fully utilize the adsorption for the removal of MCs, the application and optimization under actual environmental conditions are still required, thereby meeting the environmental and economic standards. From this study, crucial insights could be provided for the development and selection of effective adsorbent and subsequent adsorption processes for the removal of MCs from water resources.

• Journal of The Korean Society of Grassland and Forage Science
• /
• v.32 no.3
• /
• pp.301-308
• /
• 2012

Near infrared reflectance spectroscopy (NIRS) has become increasingly used as a rapid and accurate method of evaluating some chemical compositions in forages. This study was carried out to explore the accuracy of near infrared spectroscopy (NIRS) for the prediction of chemical parameters of Italian ryegrass silages. A population of 267 Italian ryegrass silages representing a wide range in chemical parameters and fermentative characteristics was used in this investigation. Samples of silage were scanned at 2 nm intervals over the wavelength range 680~2,500 nm and the optical data recorded as log 1/Reflectance (log 1/R) and scanned in intact fresh condition. The spectral data were regressed against a range of chemical parameters using partial least squares (PLS) multivariate analysis in conjunction with spectral math treatments to reduced the effect of extraneous noise. The optimum calibrations were selected on the basis of the highest coefficients of determination in cross validation ($R^2$) and the lowest standard error of cross validation (SECV). The results of this study showed that NIRS predicted the chemical parameters with very high degree of accuracy. The $R^2$ and SECV were 0.98 (SECV 1.27%) for moisture, 0.88 (SECV 1.26%) for ADF, 0.84 (SECV 2.0%), 0.93 (SECV 0.96%) for CP and 0.78 (SECV 0.56), 0.81 (SECV 0.31%), 0.88 (SECV 1.26%) and 0.82 (SECV 4.46) for pH, lactic acid, TDN and RFV on a dry matter (%), respectively. Results of this experiment showed the possibility of NIRS method to predict the chemical composition and fermentation quality of Italian ryegrass silages as routine analysis method in feeding value evaluation and for farmer advice.

• Journal of The Korean Society of Grassland and Forage Science
• /
• v.36 no.1
• /
• pp.50-57
• /
• 2016

This study was conducted to determine the effect of mathematical transformation on near infrared spectroscopy (NIRS) calibrations for the prediction of chemical composition and fermentation parameters in corn silage. Corn silage samples (n=407) were collected from cattle farms and feed companies in Korea between 2014 and 2015. Samples of silage were scanned at 1 nm intervals over the wavelength range of 680~2,500 nm. The optical data were recorded as log 1/Reflectance (log 1/R) and scanned in intact fresh condition. The spectral data were regressed against a range of chemical parameters using partial least squares (PLS) multivariate analysis in conjunction with several spectral math treatments to reduce the effect of extraneous noise. The optimum calibrations were selected based on the highest coefficients of determination in cross validation ($R^2{_{cv}}$) and the lowest standard error of cross validation (SECV). Results of this study revealed that the NIRS method could be used to predict chemical constituents accurately (correlation coefficient of cross validation, $R^2{_{cv}}$, ranging from 0.77 to 0.91). The best mathematical treatment for moisture and crude protein (CP) was first-order derivatives (1, 16, 16, and 1, 4, 4), whereas the best mathematical treatment for neutral detergent fiber (NDF) and acid detergent fiber (ADF) was 2, 16, 16. The calibration models for fermentation parameters had lower predictive accuracy than chemical constituents. However, pH and lactic acids were predicted with considerable accuracy ($R^2{_{cv}}$ 0.74 to 0.77). The best mathematical treatment for them was 1, 8, 8 and 2, 16, 16, respectively. Results of this experiment demonstrate that it is possible to use NIRS method to predict the chemical composition and fermentation quality of fresh corn silages as a routine analysis method for feeding value evaluation to give advice to farmers.