• 한국습지학회지
• /
• 제16권1호
• /
• pp.73-83
• /
• 2014

최근 정부에서는 영산강 하구역 수질환경의 현재 상황 파악 및 미래 상황 예측을 위하여 모니터링 및 모델 연구를 진행 중에 있다. 그러나 모니터링 및 모델 자료는 대부분 수치 및 문자 형태로 이루어져 있어 사용자들의 이해도가 떨어지는 실정이다. 따라서 본 연구에서는 하구역 수질환경의 현재 상황 파악 및 미래 상황 예측을 지원할 수 있는 GIS기반 통합정보시스템을 개발하였다. 시스템 개발을 지원하기 위하여 모니터링 및 모델 DB 수집, 모델 연계 방안 마련, 시스템 GUI 및 개발환경 정의, 시스템 구성 등을 수행하였다. 모니터링 자료는 2010 ~ 2012년 영산강 하구역을 대상으로 실시된 관측값을 사용하였으며, 모델 자료는 유역 지역을 모의하기 위한 HSPF(Hydrological Simulation Program-Fortran) 모델과 하천 및 하구 지역을 모의하기 위한 EFDC(Environmental Fluid Dynamics Code) 모델 자료를 사용하였다. 최종적으로 모니터링 및 모델 자료를 시스템에 적용하여 관리 및 표출 방안에 대하여 제시하였다. 본 연구를 통해 개발된 시스템은 영산강 하구역 수질환경을 정량적으로 파악 및 예측하는데 지원할 수 있으며, 지도 기반 환경에 모니터링 및 모델 자료를 표출함으로써 사용자의 공간적 이해도를 높였다. 향후에는 영산강 하구역 수질환경 문제점에 대처 가능한 의사결정지원시스템으로 고도화하여 환경 평가 및 정책 수립에 지원할 수 있을 것으로 기대된다.

• 한국안전학회지
• /
• 제23권4호
• /
• pp.59-66
• /
• 2008

Recently, the deterioration of reinforced concrete structures, primarily due to corrosion of steel reinforcement, has become a major concern. Chloride-induced deterioration is the most important deterioration phenomenon in reinforced concrete structures in harsh environments. For the realistic prediction of chloride penetration into concrete, a mathematical model was developed in which the effects of diffusion, chloride binding and convection due to water movement can be taken into account. The aim of this research was to reach a better understanding on the physical mechanisms underlying the deterioration process of reinforced concrete associated with chloride-induced corrosion and to propose a reliable method for estimating these effects. Chloride concentrations coming from de-icing salts are significantly influenced by the exposure conditions such as salt usage, ambient temperature and repeated wet-dry cycles.

• 한국군사과학기술학회지
• /
• 제4권2호
• /
• pp.189-201
• /
• 2001

For a design of multi-layer armor, the extensive full scale or sub-scale penetration test data are required. In generally, the collection of penetration data is in need of time-consuming and expensive processes. However, the application of numerical or analytical method is very limited due to poor understanding about penetration mechanics. In this paper, we have developed a neural network analyzer which can be used as a design tool for a new armor. Calculation results show that the developed neural network analyzer can predict relatively exact penetration depth of a new armor through the effective analysis of the pre-existing penetration database.

• 국제학술발표논문집
• /
• The 2th International Conference on Construction Engineering and Project Management
• /
• pp.653-661
• /
• 2007

Overseas construction projects tend to be more complex than domestic projects, being exposed to more external risks, such as politics, economy, society, and culture, as well as more internal risks from the project itself. It is crucial to have an early understanding of the project condition, in order to be well prepared in various phases of the project. This study compares a structural equation model and multiple regression analysis, in their capacity to predict cost performance of international construction projects. The structural equation model shows a more accurate prediction of cost performance than does regression analysis, due to its intrinsic capability of considering various cost factors in a systematic way.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제8권11호
• /
• pp.4016-4027
• /
• 2014

Understanding network latency is important for providing consistent and acceptable levels of services in network-based applications. However, due to the difficulty of estimating applications' network demands and the difficulty of network latency modeling the management of network resources has often been ignored. We expect that, since network latency repeats cycles of congested states, a systematic classification method for network status would be helpful to simplify issues in network resource managements. This paper presents a simple empirical method to classify network status with a real operational network. By observing oscillating behavior of end-to-end latency we determine networks' status in run time. Five typical network statuses are defined based on a long-term stability and a short-term burstiness. By investigating prediction accuracies of several simple numerical models we show the effectiveness of the network status classification. Experimental results show that around 80% reduction in prediction errors depending on network status.

• 대한기계학회논문집B
• /
• 제30권8호
• /
• pp.818-824
• /
• 2006

The hydrodynamic performance analysis of an axial-flow main coolant pump for the modular nuclear reactor has been carried out using a commercial computational fluid dynamics (CFD) software. The prediction capability of the CFD software adopted in the present study was validated in comparison with the experimental data. Predicted performance curves agree satisfactorily well with the experimental results for the main coolant pump over the normal operating range. π Ie prediction method presented herein can be used effectively as a tool for the hydrodynamic design optimization and assist the understanding of the operational characteristics of general purpose axial-flow pumps.

• 응용통계연구
• /
• 제22권2호
• /
• pp.425-433
• /
• 2009

Spatial models suitable for describing the evolving random fields in climate and environmental systems have been developed by many researchers. In general, rainfall in South Korea is highly variable in intensity and amount across space. This study characterizes the monthly and regional variation of rainfall fields using the spatial modeling. The main objective of this research is spatial prediction with the Bayesian hierarchical modeling (kriging) in order to further our understanding of water resources over space. We use the Bayesian approach in order to estimate the parameters and produce more reliable prediction. The Bayesian kriging also provides a promising solution for analyzing and predicting rainfall data.

• Journal of Biosystems Engineering
• /
• 제30권6호통권113호
• /
• pp.360-365
• /
• 2005

Precision agriculture attempts to improve cropping efficiency by variable application of crop treatments such as fertilizers and pesticides, within field on a point-by-point basis. Therefore, a more complete understanding of the relationships between yield and soil properties is of critical importance in precision agriculture. In this study, the functional relationships between measured soil properties and rice yield were investigated. A supervised back-propagation neural network model was employed to relate soil chemical properties and rice yields on a point-by point basis, within individual site-years. As a results, a positive correlation was found between practical yields and predicted yields in 1999, 2000, 2001, and 2002 are 0.916, 0.879, 0.800 and 0.789, respectively. The results showed that significant overfitting for yields with only the soil chemical properties occurred so that more of environmental factors, such as climatological data, variety, cultivation method etc., would be required to predict the yield more accurately.

• Journal of Information Processing Systems
• /
• 제8권2호
• /
• pp.241-262
• /
• 2012

An understanding of quality attributes is relevant for the software organization to deliver high software reliability. An empirical assessment of metrics to predict the quality attributes is essential in order to gain insight about the quality of software in the early phases of software development and to ensure corrective actions. In this paper, we predict a model to estimate fault proneness using Object Oriented CK metrics and QMOOD metrics. We apply one statistical method and six machine learning methods to predict the models. The proposed models are validated using dataset collected from Open Source software. The results are analyzed using Area Under the Curve (AUC) obtained from Receiver Operating Characteristics (ROC) analysis. The results show that the model predicted using the random forest and bagging methods outperformed all the other models. Hence, based on these results it is reasonable to claim that quality models have a significant relevance with Object Oriented metrics and that machine learning methods have a comparable performance with statistical methods.

• 한국자동차공학회논문집
• /
• 제16권5호
• /
• pp.164-170
• /
• 2008

A typical clutch system for automotive manual transmissions transfers hydraulic pressure generated by driver's pedal manipulation to the clutch diaphragm spring. The foot effort history during the period of push is different than the period of the clutch pedal's return. The effort or load difference is called clutch foot effort hysteresis. It is known that the hysteresis is caused by friction. The frictional force and moment are produced between various component contact points such as between the rubber seal and the inner wall inside the hydraulic cylinder and between the diaphragm spring and the pressure plate, etc. Understanding the clutch pedal foot effort hysteresis is essential for a clutch release system design and analysis. The dynamic model for a clutch release system is developed for the foot effort hysteresis prediction and a simulation analysis is performed to propose a tool for analysing a clutch system.