• 상하수도학회지
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• 제25권4호
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• pp.581-589
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• 2011

Generally, treated water or raw water is transported into storage reservoirs which are receiving facilities of local governments from multi-regional water supply systems. A water supply control and operation center is operated not only to manage the water facilities more economically and efficiently but also to mitigate the shortage of water resources due to the increase in water consumption. To achieve the goal, important information such as the flow-rate in the systems, water levels of storage reservoirs or tanks, and pump-operation schedule should be considered based on the resonable water demand forecasting. However, it is difficult to acquire the pattern of water demand used in local government, since the operating information is not shared between multi-regional and local water systems. The pattern of water demand is irregular and unpredictable. Also, additional changes such as an abrupt accident and frequent changes of electric power rates could occur. Consequently, it is not easy to forecast accurate water demands. Therefore, it is necessary to introduce a short-term water demands forecasting and to develop an application of the forecasting models. In this study, the forecasting simulator for water demand is developed based on mathematical and neural network methods as linear and non-linear models to implement the optimal water demands forecasting. It is shown that MLP(Multi-Layered Perceptron) and ANFIS(Adaptive Neuro-Fuzzy Inference System) can be applied to obtain better forecasting results in multi-regional water supply systems with a large scale and local water supply systems with small or medium scale than conventional methods, respectively.

• 한국지능시스템학회논문지
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• 제23권1호
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• pp.72-79
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• 2013

본 논문에서는 태양광 발전시스템의 최대전력점을 추종하기 위해, 개방회로전압과 가까운 첫 번째 지역극대전력점(local peak power point)의 전압 및 전류값이 특정한 범위 내에 있을 경우, 첫 번째 지역극대점이 전역극대전력점(global peak power point)인지 판단할 수 있도록 패턴을 분석하였다. 직-병렬 어레이로 연결된 태양전지 모듈에 부분그늘문제(partial shading problem)가 발생할 경우 다수의 지역극대전력점이 관찰될 수 있어, 전역극대전력점을 찾는데 어려움이 있다. 부분선형 태양전지 모델을 이용한 태블로 해석(Tableau analysis)으로 태양전지 어레이 회로의 V-I 특성을 시뮬레이션하여 지역극대전력점과 전역극대전력점을 확인하고, 그에 해당하는 전압 및 전류 값과 V-I 특성곡선의 패턴을 분석하였다. 분석된 패턴을 통해 특정한 영역을 설정하여 첫 번째 지역극대전력점이 전역극대전력점 인지 판단하여 발전하는 경우, 첫 번째 지역극대전력점으로만 발전했을 때에 비해 효율이 향상되었다.

• Computers and Concrete
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• 제22권6호
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• pp.527-538
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• 2018

Buckling of longitudinal bars is a brittle failure mechanism, often recorded in reinforced concrete (RC) structures after an earthquake. Studies in the literature highlights that it often occurs when steel is in the post elastic range, by inducing a modification of the engineered stress-strain law of steel in compression. A proper evaluation of this effect is of fundamental importance for correctly evaluating capacity and ductility of structures. Significant errors can be obtained in terms of ultimate bending moment and curvature ductility of an RC section if these effects are not accounted, as well as incorrect evaluations are achieved by non-linear static analyses. This paper presents a numerical investigation aiming to evaluate the engineered stress-strain law of reinforcing steel in compression, including second order effects. Non-linear FE analyses are performed under the assumption of local buckling. A role of key parameters is evaluated, making difference between steel with strain hardening or with perfectly plastic behaviour. Comparisons with experimental data available in the literature confirm the accuracy of the achieved results and make it possible to formulate recommendations for design purposes. Finally, comparisons are made with analytical formulations available in the literature and based on obtained results, a modification of the stress-strain law model of Dhakal and Maekawa (2002) is proposed for fitting the numerical predictions.

• 한국대기환경학회지
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• 제23권1호
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• pp.14-28
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• 2007

This study presented the selection of ozone ($O_3$) potential factors and designed and assessed its potential prediction model using multiple-linear regression equations in Ulsan area during the springtime from April to June, $2000{\sim}2004$. $O_3$ potential factors were selected by analyzing the relationship between meterological parameters and surface $O_3$ concentrations. In addition, cluster analysis (e.g., average linkage and K-means clustering techniques) was performed to identify three major synoptic patterns (e.g., $P1{\sim}P3$) for an $O_3$ potential prediction model. P1 is characterized by a presence of a low-pressure system over northeastern Korea, the Ulsan was influenced by the northwesterly synoptic flow leading to a retarded sea breeze development. P2 is characterized by a weakening high-pressure system over Korea, and P3 is clearly associated with a migratory anticyclone. The stepwise linear regression was performed to develop models for prediction of the highest 1-h $O_3$ occurring in the Ulsan. The results of the models were rather satisfactory, and the high $O_3$ simulation accuracy for $P1{\sim}P3$ synoptic patterns was found to be 79, 85, and 95%, respectively ($2000{\sim}2004$). The $O_3$ potential prediction model for $P1{\sim}P3$ using the predicted meteorological data in 2005 showed good high $O_3$ prediction performance with 78, 75, and 70%, respectively. Therefore the regression models can be a useful tool for forecasting of local $O_3$ concentration.

• 한국지능시스템학회논문지
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• 제16권5호
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• pp.608-615
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• 2006

본 논문에서는 퍼지 클러스터 기법을 이용하여 구간 분할된 퍼지 모델트리의 제안과 이를 이용한 데이터 모델링 기법을 다룬다. 제안된 방법은 먼저 입력과 출력변수의 속성을 고려한 퍼지 클러스터링에 의해 중심벡터를 계산한 후, 중심벡터들과 입력속성간의 소속도를 이용하여 구간 분할된 영역별로 각각의 선형모델을 구축한다. 노드의 확장은 부모노드(parent node)에서 만들어진 모델에서 계산된 오차값과 자식노드(child node)에서 계산된 오차값을 비교하여 이루어진다. 출력값 예측 단계에서는 입력된 데이터와 잎노드에서 계산된 클러스터 중심값과 비교하여 소속도가 높은 선형모델을 선택하여 데이터에 대한 출력값을 예측하게 된다. 제안된 방법의 우수성을 보이기 위해 다양한 데이터를 대상으로 실험한 결과, 기존의 모델트리방식 및 뉴럴 네트워크 기반의 신경회로망 보다 향상된 성능을 보임을 알 수 있었다.

• Bulletin of the Korean Chemical Society
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• 제33권2호
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• pp.613-619
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• 2012

We applied several machine learning methods for developing QSAR models for prediction of acute toxicity to fathead minnow. The multiple linear regression (MLR) and artificial neural network (ANN) method were applied to predict 96 h $LC_{50}$ (median lethal concentration) of 555 chemical compounds. Molecular descriptors based on 2D chemical structure were calculated by PreADMET program. The recursive partitioning (RP) model was used for grouping of mode of actions as reactive or narcosis, followed by MLR method of chemicals within the same mode of action. The MLR, ANN, and two RP-MLR models possessed correlation coefficients ($R^2$) as 0.553, 0.618, 0.632, and 0.605 on test set, respectively. The consensus model of ANN and two RP-MLR models was used as the best model on training set and showed good predictivity ($R^2$=0.663) on the test set.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2003년도 추계 학술발표회 논문집
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• pp.179-182
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• 2003

This study describes a new graphical method for assessing and characterizing effect modification by a matching covariate in matched case-control studies. This method to understand effect modification is based on a semiparametric model using a varying coefficient model. The method allows for nonparametric relationships between effect modification and other covariates, or can be useful in suggesting parametric models. This method can be applied to examining effect modification by any ordered categorical or continuous covariates for which cases have been matched with controls. The method applies to effect modification when causality might be reasonably assumed. An example from veterinary medicine is used to demonstrate our approach. The simulation results show that this method, when based on linear, quadratic and nonparametric effect modification, can be more powerful than both a parametric multiplicative model fit and a fully nonparametric generalized additive model fit.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.320-325
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• 1998

Underwater robotic vehicles (URVs) have been an important tool for various underwater tasks because they have greater speed, endurance, depth capability, and safety than human divers. As the use of such vehicles increases, the vehicle control system becomes one of the most critical subsytems to increase autonomy of the vehicle. The vehicle dynamics are nonlinear and their hydrodynamic coefficients are often difficult to estimate accurately. In this paper a new type of fuzzy model-based controller based on Takagi-Sugeno-Kang fuzzy model is designed and applied to the control of of an underwater robotic vehicle. The proposed fuzzy controller : 1) is a nonlinear controller, but a linear state feedback controller in the consequent of each local fuzzy control rule ; 2) can guarantee the stability of the closed-loop fuzzy system ; 3) is relatively easy to implement. Its good performance as well as its robustness to the change of parameters have been shown and compared with the re ults of conventional linear controller by simulation.

• 동력기계공학회지
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• 제2권2호
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• pp.47-54
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• 1998

Underwater robotic vehicles(URVs) have been an important tool for various underwater tasks such as pipe-lining, data collection, hydrography mapping, construction, maintenance and repairing of undersea equipment, etc because they have greater speed, endurance, depth capability, and safety than human divers. As the use of such vehicles increases, the vehicle control system is one of the most critical subsystems to increase autonomy of the vehicle. The vehicle dynamics are nonlinear and their hydrodynamic coefficients are often difficult to estimate accurately. It is desirable to have an intelligent vehicle control system because the fixed-parameter linear controller such as PID may not be able to handle these changes promptly and result in poor performance. In this paper we described and analyzed a new type of fuzzy model-based controller which is designed for underwater robotic vehicles and based on Takagi-Sugeno-Kang(TSK) fuzzy model. The proposed fuzzy controller: 1) is a nonlinear controller, but a linear state feedback controller in the consequent of each local fuzzy control rule; 2) can guarantee the stability of the closed-loop fuzzy system; 3) is relatively easy to implement. Its good performance as well as its robustness to parameter changes will be shown and compared with those of the PID controller by simulation.

• Journal of Mechanical Science and Technology
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• 제19권spc1호
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• pp.343-349
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• 2005

There are many things in common between hemodynamics in arterial systems and multibody dynamics in mechanical systems. Hemodynamics is concerned with the forces generated by the heart and the resulting motion of blood through the multi-branched vascular system. The conventional hemodynamics model has been intended to show the general behavior of the body arterial system with the frequency domain based linear model. The need for detailed models to analyze the local part like coronary arterial tree and cerebral arterial tree has been required recently. Non-linear analysis techniques are well-developed in multibody dynamics. In this paper, the studies of hemodynamics are summarized from the view of multibody dynamics. Computational algorithms of arterial tree analysis is derived, and proved by experiments on animals. The flow and pressure of each branch are calculated from the measured flow data at the ascending aorta. The simulated results of the carotid artery and the iliac artery show in good accordance with the measured results.