• The Journal of Information Systems
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• v.27 no.3
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• pp.63-80
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• 2018

Purpose The purpose of this study is to build a machine learning-based customer classification model to promote customer expansion effect of the free sample promotion. Specifically, the proposed model classifies potential target customers who are expected to purchase the products included in the free sample promotion after receiving the free samples. Design/methodology/approach This study proposes to build a customer classification model for determining customers suitable for providing free samples by using various machine learning techniques such as logistic regression, multiple discriminant analysis, case-based reasoning, decision tree, artificial neural network, and support vector machine. To validate the usefulness of the proposed model, we apply it to a real-world free sample-based target marketing case of a Korean major cosmetic retail company. Findings Experimental results show that a machine learning-based customer classification model presents satisfactory accuracy ranging from 70% to 75%. In particular, support vector machine is found to be the most effective machine learning technique for free sample-based target marketing model. Our study sheds a light on customer relationship management strategies using free sample promotions.

• Structural Engineering and Mechanics
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• v.61 no.4
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• pp.463-473
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• 2017

Reinforced concrete is a complex material to be modeled in finite element domain. A proper material model is necessary to represent the nonlinear behaviour accurately. Though the nonlinear analysis of RC structures evolved long back, still an accurate and reliable model to predict the realistic behaviour of components are limited. It is observed from literature that there are three well-known models to represent the nonlinear behaviour of concrete. These models include Chu model (1985), Hsu model (1994) and Saenz model (1964).A new stress-strain model based on Weibull distribution has been proposed in the present study. The objective of the present study is to analyze a reinforced concrete beam under flexural loading by employing all the models. Nonlinear behaviour of concrete is considered in terms of stress vs. strain, damage parameter, tension stiffening behaviour etc. The ductility of the RC beams is computed by using deflection based and energy based concepts. Both deflection ductility and energy based ductility is compared and energy based concept is found to be in good correlation with the experiments conducted. The behavior of RC beam predicted using ABAQUS has been compared with the corresponding experimental observations. Comparison between numerical and experimental results confirms that these four constitutive models are reliable in predicting the behaviour of RC structures and any of the models can be employed for analysis.

• Journal of Drive and Control
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• v.19 no.1
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• pp.51-61
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• 2022

This paper presents a model-free system based on a framework of a backstepping sliding mode control (BSMC) with a radial basis function neural network (RBFNN) and adaptive mechanism for electro-hydraulic systems (EHSs). First, an EHS mathematical model was dedicatedly derived to understand the system behavior. Based on the system structure, BSMC was employed to satisfy the output performance. Due to the highly nonlinear characteristics and the presence of parametric uncertainties, a model-free approximator based on an RBFNN was developed to compensate for the EHS dynamics, thus addressing the difficulty in the requirement of system information. Adaptive laws based on the actor-critic neural network (ACNN) were implemented to suppress the existing error in the approximation and satisfy system qualification. The stability of the closed-loop system was theoretically proven by the Lyapunov function. To evaluate the effectiveness of the proposed algorithm, proportional-integrated-derivative (PID) and improved PID with ACNN (ACPID), which are considered two complete model-free methods, and adaptive backstepping sliding mode control, considered an ideal model-based method with the same adaptive laws, were used as two benchmark control strategies in a comparative simulation. The simulated results validated the superiority of the proposed algorithm in achieving nearly the same performance as the ideal adaptive BSMC.

• Smart Structures and Systems
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• v.32 no.1
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• pp.49-59
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• 2023

A bridge transportation network supplies products from various source nodes to destination nodes through bridge structures in a target region. However, recent frequent earthquakes have caused damage to bridge structures, resulting in extreme direct damage to the target area as well as indirect damage to other lifeline structures. Therefore, in this study, a surrogate model-based comprehensive framework to estimate the seismic resilience of bridge transportation networks is proposed. For this purpose, total system travel time (TSTT) is introduced for accurate performance indicator of the bridge transportation network, and an artificial neural network (ANN)-based surrogate model is constructed to reduce traffic analysis time for high-dimensional TSTT computation. The proposed framework includes procedures for constructing an ANN-based surrogate model to accelerate network performance computation, as well as conventional procedures such as direct Monte Carlo simulation (MCS) calculation and bridge restoration calculation. To demonstrate the proposed framework, Pohang bridge transportation network is reconstructed based on geographic information system (GIS) data, and an ANN model is constructed with the damage states of the transportation network and TSTT using the representative earthquake epicenter in the target area. For obtaining the seismic resilience curve of the Pohang region, five epicenters are considered, with earthquake magnitudes 6.0 to 8.0, and the direct and indirect damages of the bridge transportation network are evaluated. Thus, it is concluded that the proposed surrogate model-based framework can efficiently evaluate the seismic resilience of a high-dimensional bridge transportation network, and also it can be used for decision-making to minimize damage.

• Journal of Korean Home Economics Education Association
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• v.31 no.2
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• pp.155-175
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• 2019

This study aimed to present a concrete and systematic lesson design model for practical problem-based learning in Practical Arts based on the backward design model. To this purpose, this study was conducted in the following four stages: analysis, design, development, and evaluation. In the analysis stage, the literature related to the practical-problem-based curriculum and the instructional design model were reviewed as the theoretical foundations, and the 2015 revision of Practical Arts curriculum was analyzed. In the design stage, based on the literature review, the stages and sub-elements of practical-problem-based learning in Practical Arts were determined, and a template of instructional design was constructed. In the development stage, the design model was drafted based on the stages and sub-elements determined at the design stage. In the evaluation stage, the drafted design model from the development stage was evaluated, modified, and enhanced through expert verification. Based on the suggestions from the experts, the 'practical problem setting' stage was added to the original three-stage model, resulting in a model composed of the following four stages: practical problem setting, goal setting, evaluation planning, and learning activity planning. The experts appraised that the developed model of practical-problem-based instructional design in Practical Arts to be a systematic template that would serve as a concrete guideline for teachers who are trying to design classes based on practical problems.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.87.2-87
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• 2001

By the state transformation including independent outputs functions, a nonlinear process model can be decomposed into two subsystems; the one(design model) is described in output variables as new states and used for control system synthesis and the other(disturbance model) is described in the original unavailable states and its couplings with the design model are treated as uncertain time-varying parameters in the design model. Its existence with respect to the design model is ignored. So, the design model is an uncertain time-variant system. Control synthesis based on a reduced design model is a combined ...

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.23-27
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• 2000

In this paper, we propose an emotional evaluation model based on information fusion. This model can transform the physical features of a color pattern to the emotional features. Our proposed model consists of the fuzzy logic system and neural network model. The evaluation values produced by them were fused. The model shows comparable performances to the neural network and fuzzy logic system for the approximation of the nonlinear transforms. We believe the evaluated results of a color pattern can be used to the emotion-based color image retrievals.

• Archives of Pharmacal Research
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• v.10 no.4
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• pp.250-257
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• 1987

The development of physiologically based pharmacokinetic model for drug distribution and excretion is described. The physiological modeling procedure is useful in animal and clinical applications to obtain fundamental knowledge of the transport and metabolism of a substance in vivo. In this paper a review of physiologically based pharmacokinetics is presented in the hope of understanding and increasing the use of this modelling technique. The method of model development and the composition of equations based on the different models are explained. For the better understanding a physiological pharmacokinetic model of tenoxicam disposition in the rat is presented as an example of flow limited model.

• Korean Management Science Review
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• v.14 no.1
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• pp.87-105
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• 1997

There have been lots of research on temporal data management for the past two decades. Most of them are based on some logical data model, especially on the relational data model, although there are some conceptual data models which are independent of logical data models. Also, many properties or issues regarding temporal data models and temporal query languages have been studied. But some of them were shown to be incompatible, which means there could not be a complete temporal data model, satisfying all the desired properties at the same time. Many modeling issues discussed in the papers, do not have to be done so, if they take object-oriented data model as a base model. Therefore, this paper proposes a temporal data model, which is based on the object-oriented data model, mainly discussing the most essential issues that are common to many temporal data models. Our new temporal data model and query language will be illustrated with a small database, created by a set of sample transaction.

• Journal of the Korean Operations Research and Management Science Society
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• v.14 no.1
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• pp.87-87
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• 1989

There have been lots of research on temporal data management for the past two decades. Most of them are based on some logical data model, especially on the relational data model, although there are some conceptual data models which are independent of logical data models. Also, many properties or issues regarding temporal data models and temporal query languages have been studied. But some of them were shown to be incompatible, which means there could not be a complete temporal data model, satisfying all the desired properties at the same time. Many modeling issues discussed in the papers, do not have to be done so, if they take object-oriented data model as a base model. Therefore, this paper proposes a temporal data model, which is based on the object-oriented data model, mainly discussing the most essential issues that are common to many temporal data models. Our new temporal data model and query language will be illustrated with a small database, created by a set of sample transaction.