• Journal of Information Technology Applications and Management
• /
• v.23 no.1
• /
• pp.45-59
• /
• 2016

An ensemble classifier is a method that combines output of multiple classifiers. It has been widely accepted that ensemble classifiers can improve the prediction accuracy. Recently, ensemble techniques have been successfully applied to the bankruptcy prediction. Bagging and random subspace are the most popular ensemble techniques. Bagging and random subspace have proved to be very effective in improving the generalization ability respectively. However, there are few studies which have focused on the integration of bagging and random subspace. In this study, we proposed a new hybrid ensemble model to integrate bagging and random subspace method using genetic algorithm for improving the performance of the model. The proposed model is applied to the bankruptcy prediction for Korean companies and compared with other models in this study. The experimental results showed that the proposed model performs better than the other models such as the single classifier, the original ensemble model and the simple hybrid model.

• Journal of Educational Research in Mathematics
• /
• v.8 no.2
• /
• pp.605-620
• /
• 1998

This paper suggested a geometry learning model which relates an exploratory learning model with GSP applications, Such a model adopts GSP's capability of visualizing dynamic geometric figures and exploratory learning method's advantages of discovering properties and relations of geometric problem proving and concepts associated with geometric inferencing of students. The research was conducted for 3 middle school students by applying the proposed model for 6times at computer laboratory. The overall procedure was videotaped so that the collected data was later analyzed by qualitative methodology. The analysis indicated that the students with less than van Hiele 4 level took advantages of adoption our proposed model to gain concrete understandings of geometric principles and concepts with GSP. One of the lessons learned from this study suggested that the roles of students and a teacher who want to employ the proposed model need to change their roles respectively.

• Journal of the Korea Society of Computer and Information
• /
• v.22 no.10
• /
• pp.129-135
• /
• 2017

This research is to identify an easy and effective method of teaching-learning plan. The teaching-learning plan is a blue_print applied for designing effective lessons. However, most of the teachers regard it as a difficult and inefficient job. This study proposed the concrete instructional model framework as a tool to develop the teaching-learning plan easily and effectively. The concrete instructional model framework will represent a decomposed instructional strategy applied for each step of the instructional model developed by educational researchers. This method is applied to develop a computer teaching-learning plan. Therefore, the proposed method will expand an easier teaching-learning plan. Furthermore, the proposed method develops a teaching-learning plan with fluent content in detail based on low-level instruction strategies applied in the concrete instruction model framework.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.26 no.3
• /
• pp.39-52
• /
• 2001

Recently, the assessment of a bank efficiency focusing on its branches has been conceived as important in developing a competitive strategy. DEA (Data Envelopment Analysis) model can be employed as an effective analysis model for such an assessment. Therefore, this paper proposes an analysis process using DEA model to conduct an efficiency assessment of bank branches. The proposed process includes a segmentation of branches considering their competitive environment and strategy for target market : this approach can help to develop effective strategies for each group of branches. The proposed DEA model can analyze efficiency in terms of not only cost but also marketing. Finally, a real case is analyzed, demonstrating the effectiveness of the proposed model and process.

• The Transactions of the Korean Institute of Electrical Engineers P
• /
• v.63 no.4
• /
• pp.307-311
• /
• 2014

Due to the increasing of power consumption, it is difficult to construct accurate prediction model for daily peak power demand. It is very important work to know power demand in next day for manager and control power system. In this research, we develop a daily peak power demand prediction method considering of characteristics of day of week. The proposed method is composed of liner model based on AR model and nonlinear model based on ELM to resolve the limitation of a single model. Using data sets between 2006 and 2010 in Korea, the proposed method has been intensively tested. As the prediction results, we confirm that the proposed method makes it possible to effective estimate daily peak power demand than conventional methods.

• Proceedings of the Korea Society for Simulation Conference
• /
• 2000.11a
• /
• pp.174-179
• /
• 2000

Proposed in the paper is a V-FMS (Virtual Flexible Manufacturing System) model to be used as a prototyping tool for FMS design. The proposed V-FMS framework follows an object-oriented modeling (OOM) paradigm and is based on a set of user requirements for FMS prototyping. The V-FMS model consists of four types of object: virtual device, transfer handler, state manager and flow controller. A virtual device model, which corresponds to a static model in OOM, consists of two parts, shell and core, for reusability. A transfer handler corresponds to a functional model of OOM and it stores low level device commands required to perform job flow operations between giving and taking devices. The state manager and the flow controller constitute a dynamic model of OOM. The proposed V-FMS model has been implemented for a couple of linear type FMS-lines

• Journal of Applied Reliability
• /
• v.18 no.2
• /
• pp.104-113
• /
• 2018

Purpose: This paper proposes the non-periodic preventive maintenance policy based on the level of cumulative hazard intensity. We aim to construct a cost-effectiveness on the proposed model with relaxing the constraint on reliability. Methods: We use the level of cumulative hazard intensity as a condition variable, instead of reliability. Such a level of cumulative hazard intensity can derive the reliability which decreases as the frequency of preventive maintenance action increases. We also model the imperfect preventive maintenance action using the proportional age setback model. Conclusion: We provide a numerical example to illustrate the proposed model. We also analyze how the parameters of our model affect the optimal preventive maintenance policy. The results show that as long as high reliability is guaranteed, the inefficient preventive maintenance action is performed reducing the system operation time. Moreover, the optimal value of the proposed model is sensitive to changes in preventive maintenance cost and replacement cost.

• Journal of information and communication convergence engineering
• /
• v.19 no.3
• /
• pp.136-141
• /
• 2021

Research on wireless sensor networks has focused on the monitoring and characterization of large-scale physical environments and the tracking of various environmental or physical conditions, such as temperature, pressure, and wind speed. We propose a stochastic mobility model that can be applied to a MANET (Mobile Ad-hoc NETwork). environment, and apply this mobility model to a newly proposed clustering-based routing protocol. To verify its stability and durability, we compared the proposed stochastic mobility model with a random model in terms of energy efficiency. The FND (First Node Dead) was measured and compared to verify the performance of the newly designed protocol. In this paper, we describe the proposed mobility model, quantify the changes to the mobile environment, and detail the selection of cluster heads and clusters formed using a fuzzy inference system. After the clusters are configured, the collected data are sent to a base station. Studies on clustering-based routing protocols and stochastic mobility models for MANET applications have shown that these strategies improve the energy efficiency of a network.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2018.05a
• /
• pp.176-176
• /
• 2018

This study presents a regional, probabilistic framework for estimating streamflow via spatial scaling in the Great Lakes basin, which is the largest lake system in the world. The framework follows a two-fold strategy including (1) a quadratic-programming based optimization model a priori to explore the model structure, and (2) a time-varying hierarchical Bayesian model based on insights found in the optimization model. The proposed model is developed to explore three innovations in hierarchical modeling for reconstructing historical streamflow at ungaged sites: (1) information of physical characteristics is utilized in spatial scaling, (2) a time-varying approach is introduced based on climate information, and (3) heteroscedasticity in residual errors is considered to improve streamflow predictive distributions. The proposed model is developed and calibrated in a hierarchical Bayesian framework to pool regional information across sites and enhance regionalization skill. The model is validated in a cross-validation framework along with four simpler nested formulations and the optimization model to confirm specific hypotheses embedded in the full model structure. The nested models assume a similar hierarchical Bayesian structure to our proposed model with their own set of simplifications and omissions. Results suggest that each of three innovations improve historical out-of-sample streamflow reconstructions although these improvements vary corrsponding to each innovation. Finally, we conclude with a discussion of possible model improvements considered by additional model structure and covariates.

• Proceedings of the Korean Geotechical Society Conference
• /
• 1999.10a
• /
• pp.351-356
• /
• 1999

An elasto-plastic constitutive model was Proposed, in which the behavior at small-to-large strain level can be modeled. From a mathematical approach it was proved that the model includes the previous successful models. The experimental results of a series of resonant column tests, torsional shear tests and triaxial tests were verified and as a result the proposed model could predict small-to-large strain behavior more consistently and accurately than the hyperbolic model and the Ramberg-Osgood model for a weathered granitic soil.