• Proceedings of the Korean Society of Applied Pharmacology
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• 1995.04a
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• pp.35-36
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• 1995

To gain insight into the etiological mechanism of dementia and to develop clinically effective congnitive enhancers, it is required to prepare animal models with symptoms and mechanism resemble to that in human. Dementia is mainly classified into two types : senile type of Alzheimer's disease (STAD) and cerebral ischemia-induced one. As animal models of cerebral ischemia, a couple of types in rats have been introduced : one is the occlusion of bilateral carotid arteries-induced forebrain/global ischemia and the other is the occlusion of middle cerebral arteries-induced focal/regional ischemia.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.55
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• pp.33-42
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• 2000

It is difficult to estimate the effective factors upon Cumulative Trauma Disorders in real workplace because those are developed by combination of various risk factors for time. The purpose of this paper was to evaluate relative level of CTDs risk factors such as task-related factors, anthropometric factors, joint deviation factors and personal factors using fuzzy linear regression models. And the models are built corresponding to each category with the survey data from telephone operators. The coefficient of fuzzy models are described as the relative level of variable to present risk factors upon CTDs.

• Smart Structures and Systems
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• v.15 no.6
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• pp.1439-1461
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• 2015

Design criteria, modeling rules, and analysis principles of seismic isolation systems have already found place in important building codes and standards such as the Uniform Building Code and ASCE/SEI 7-05. Although real behaviors of isolation systems composed of high damping or lead rubber bearings are nonlinear, equivalent linear models can be obtained using effective stiffness and damping which makes use of linear seismic analysis methods for seismic-isolated buildings possible. However, equivalent linear modeling and analysis may lead to errors in seismic response terms of multi-story buildings and thus need to be assessed comprehensively. This study investigates the accuracy of equivalent linear modeling via numerical experiments conducted on generic five-story three dimensional seismic-isolated buildings. A wide range of nonlinear isolation systems with different characteristics and their equivalent linear counterparts are subjected to historical earthquakes and isolation system displacements, top floor accelerations, story drifts, base shears, and torsional base moments are compared. Relations between the accuracy of the estimates of peak structural responses from equivalent linear models and typical characteristics of nonlinear isolation systems including effective period, rigid-body mode period, effective viscous damping ratio, and post-yield to pre-yield stiffness ratio are established. Influence of biaxial interaction and plan eccentricity are also examined.

• Journal of Information Technology Application
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• v.1
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• pp.173-211
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• 1999

The task of classification permeates all walks of life, from business and economics to science and public policy. In this context, nonlinear techniques from artificial intelligence have often proven to be more effective than the methods of classical statistics. The objective of knowledge discovery and data mining is to support decision making through the effective use of information. The automated approach to knowledge discovery is especially useful when dealing with large data sets or complex relationships. For many applications, automated software may find subtle patterns which escape the notice of manual analysis, or whose complexity exceeds the cognitive capabilities of humans. This paper explores the utility of a collaborative learning approach involving integrated models in the preprocessing and postprocessing stages. For instance, a genetic algorithm effects feature-weight optimization in a preprocessing module. Moreover, an inductive tree, artificial neural network (ANN), and k-nearest neighbor (kNN) techniques serve as postprocessing modules. More specifically, the postprocessors act as second0order classifiers which determine the best first-order classifier on a case-by-case basis. In addition to the second-order models, a voting scheme is investigated as a simple, but efficient, postprocessing model. The first-order models consist of statistical and machine learning models such as logistic regression (logit), multivariate discriminant analysis (MDA), ANN, and kNN. The genetic algorithm, inductive decision tree, and voting scheme act as kernel modules for collaborative learning. These ideas are explored against the background of a practical application relating to financial fraud management which exemplifies a binary classification problem.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1993.10a
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• pp.134-152
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• 1993

Many business forecasting problems are characterized by infrequent occurences, a large number of variables, presence of error, and great complexity. Because no forecasting models and tools are effective in handing these problems, managers often use the outcomes of past analogous cases to predict the outcome of the current one. They (1) observe significant attributes in describing a case, (2) identify the past cases similar in these attributes to the current case, and (3) predict the outcome of the current case based on those of the analogous cases identified through some mental simulation and adjustment. This process of forecasting can be termed forecasting-by-analogy. In spite of fairly frequent use of this forecasting process in practice, however, if has not been recognized as a primary forecasting tool, nor applied on a regular basis. In this paper, by automatizing this process using computer models, we develop a case-based forecasting system(CBFS), which identifies relevant cases and applies their outcomes to generate a forecast. We demonstrate the effectiveness of the CBFS in terms of its accuracy in predicting the outcome of the current problem based on the similar cases identified. We compare the forecasting accuracy of the CBFS with that of regression models developed by stepwise procedure under varied simulated problem conditions. The CBFS outperforms regression models in most comparisons. The CBFS could be used as an effective forecasting tool.

• Journal of the Korean Operations Research and Management Science Society
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• v.19 no.2
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• pp.199-215
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• 1994

Many business forecasting problems are characterized by infrequent occurrences, a large number of variables, presence of error, and great complexity. Because no forecasting models and tools are effective in handing these problems, managers often use the outcomes of past analogous cases to predict the outcome of the current one. They (1) observe significant attributes in describing a case, (2) identify the past cases similar in these attributes to the current case, and (3) predict the outcome of the current case based on those of the analogous cases identified through some mental simulation and adjustment. This process of forecasting can be termed forecasting-by-analogy. In spite of fairly frequent use of this forecasting process in practice, however, it has not been recognized as a primary forecasting tool, nor applied on a regular basis. In this paper, by automatizing this process using computer models, we develop a case-based forecasting system (CBFS), which identifies relevant cases and applies their coutcomes to generate a forecast. We demonstrate the effectiveness of the CBFS in terms of its accuracy in predicting the outcome of the current problem based on the similar cases identified. We compare the forecasting accuracy of the CBFS with that of regression models developed by stepwise procedure under varied simulated problem conditions. The CBFS outperforms regression models in most comparisons. The CBFS could be used as an effective forecasting tool.

• Journal of Computing Science and Engineering
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• v.2 no.2
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• pp.200-219
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• 2008

The effective management of uncertainty is one of the most fundamental problems in medical decision making. According to the literatures review, most medical decision models rely on point estimates for input parameters. However, it is natural that they should be interested in the relationship between changes in those values and subsequent changes in model output. Therefore, the purpose of this study is to identify the ranges of numerical values for which each option will be most efficient with respect to the input parameters. The Nonhomogeneous Poisson Process(NHPP) was used for describing the behavior of aging chronic diseases. Three kind of failure models (linear, exponential, and power law) were considered, and each of these failure models was studied under the assumptions of unknown scale factor and known aging rate, known scale factor and unknown aging rate, and unknown scale factor and unknown aging rate, respectively. In addition, this study illustrated developed method with an analysis of data from a trial of immunotherapy in the treatment of chronic Granulomatous disease. Finally, the proposed design of Bayesian value of information analysis facilitates the effective use of the computing capability of computers and provides a systematic way to integrate the expert's opinions and the sampling information which will furnish decision makers with valuable support for quality medical decision making.

• Structural Engineering and Mechanics
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• v.49 no.3
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• pp.395-410
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• 2014

Fiber Reinforced Elastomeric Bearings (FREBs) are a relatively new type of laminated bearings that can be used as seismic/vibration isolators or bridge bearings. In an unbonded (U)-FREB, the bearing is placed between the top and bottom supports with no bonding or fastening provided at its contact surfaces. Under shear loads the top and bottom faces of a U-FREB roll off the contact supports and the bearing exhibits rollover deformation. As a result of rollover deformation, the horizontal response characteristics of U-FREBs are significantly different than conventional elastomeric bearings that are employed in bonded application. Current literature lacks an efficient analytical horizontal stiffness solution for this type of bearings. This paper presents two simplified analytical models for horizontal stiffness evaluation of U-FREBs. Both models assume that the resistance to shear loads is only provided by an effective region of the bearing that sustains significant shear strains. The presented models are different in the way they relate this effective region to the horizontal bearing displacements. In comparison with experimental results and finite element analyses, the analytical models that are presented in this paper are found to be sufficiently accurate to be used in the preliminary design of U-FREBs.

• Journal of the Korean Society of Hazard Mitigation
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• v.4 no.3 s.14
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• pp.61-72
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• 2004

In this research, multi reservoir operation methods for reservoirs in Han River are proposed based on the single dam operation models using fuzzy control techniques. The result of fuzzy controled single dam operation has shown that it can improve flood controllability at the downstream of dams. Among the many control rules of fuzzy operation, a rule that shows the most effective flood control rate at the downstream has been selected as Ike operation rule. The simulated results for 1990 and 1995 flood events are compared with historical ones. As the results, it is founded that suggested models can reduce the inundation of upstream and keep the water elevation lower at downstream, which make the proposed models as the effective methods in multi reservoir operation.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.31-39
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• 2007

In this study we developed a turbidity management system to support the operation for effective turbid water management. The decision-making system includes various models for prediction of turbid water inflow, effective reservoir operation using the selective withdrawal facility, analysis of turbid water discharge in the downstream. The system is supported by the intensive monitoring devices installed in the upstream rivers, reservoirs, and downstream rivers. SWAT and HSPF models were constructed to predict turbid water flows in the Imha and Andong catchments. CE-QUAL-W2 models were constructed for turbid water behavior prediction, and various analyses were conducted to examine the effects of the selective withdrawal operation for efficient high turbid water discharge, turbid water distribution under differing amount and locations of turbid water discharge. A 1-dimensional dynamic water quality model was built using Ko-Riv1 for simulation of turbidity propagation in the downstream of the reservoirs, and 2-dimensional models were developed to investigate the mixing phenomena of two waters discharged from the Andong and Imha reservoirs with different temperature and turbidity conditions during joint dam operation for reducing the impacts of turbid water.