• Journal of Korean Society of Forest Science
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• v.77 no.4
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• pp.389-400
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• 1988

The recent trend in multiple-use land management planning is using Model I and Model II formulations designed for timber activity scheduling problems. Numerous models hate been developed, with MUSYC(Johnson and Jones, 1979) being the first to incorporate both model structures. Currently the most popular computer program using both Model I and Model II is FORPLAN(Johnson and others, 1986). A Model I formulation requires fewer rows and provides more direct information on what happens to an acre from rotation to rotation. In some problems, Model II provides a much more compact problem matrix with much fewer columns and only a moderate increase in row number. In this paper we examined and evaluated their usefulness in comprehensive multiresource forest management planning.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.125-140
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• 2013

We call a data set in which the number of records belonging to a certain class far outnumbers the number of records belonging to the other class, 'imbalanced data set'. Most of the classification techniques perform poorly on imbalanced data sets. When we evaluate the performance of a certain classification technique, we need to measure not only 'accuracy' but also 'sensitivity' and 'specificity'. In a customer churn prediction problem, 'retention' records account for the majority class, and 'churn' records account for the minority class. Sensitivity measures the proportion of actual retentions which are correctly identified as such. Specificity measures the proportion of churns which are correctly identified as such. The poor performance of the classification techniques on imbalanced data sets is due to the low value of specificity. Many previous researches on imbalanced data sets employed 'oversampling' technique where members of the minority class are sampled more than those of the majority class in order to make a relatively balanced data set. When a classification model is constructed using this oversampled balanced data set, specificity can be improved but sensitivity will be decreased. In this research, we developed a hybrid model of support vector machine (SVM), artificial neural network (ANN) and decision tree, that improves specificity while maintaining sensitivity. We named this hybrid model 'hybrid SVM model.' The process of construction and prediction of our hybrid SVM model is as follows. By oversampling from the original imbalanced data set, a balanced data set is prepared. SVM_I model and ANN_I model are constructed using the imbalanced data set, and SVM_B model is constructed using the balanced data set. SVM_I model is superior in sensitivity and SVM_B model is superior in specificity. For a record on which both SVM_I model and SVM_B model make the same prediction, that prediction becomes the final solution. If they make different prediction, the final solution is determined by the discrimination rules obtained by ANN and decision tree. For a record on which SVM_I model and SVM_B model make different predictions, a decision tree model is constructed using ANN_I output value as input and actual retention or churn as target. We obtained the following two discrimination rules: 'IF ANN_I output value <0.285, THEN Final Solution = Retention' and 'IF ANN_I output value ${\geq}0.285$, THEN Final Solution = Churn.' The threshold 0.285 is the value optimized for the data used in this research. The result we present in this research is the structure or framework of our hybrid SVM model, not a specific threshold value such as 0.285. Therefore, the threshold value in the above discrimination rules can be changed to any value depending on the data. In order to evaluate the performance of our hybrid SVM model, we used the 'churn data set' in UCI Machine Learning Repository, that consists of 85% retention customers and 15% churn customers. Accuracy of the hybrid SVM model is 91.08% that is better than that of SVM_I model or SVM_B model. The points worth noticing here are its sensitivity, 95.02%, and specificity, 69.24%. The sensitivity of SVM_I model is 94.65%, and the specificity of SVM_B model is 67.00%. Therefore the hybrid SVM model developed in this research improves the specificity of SVM_B model while maintaining the sensitivity of SVM_I model.

• The Journal of Korean Academy of Prosthodontics
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• v.34 no.3
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• pp.415-430
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• 1996

The purpose of this study was to evaluate the stress distributions in the surrounding tissues of the teeth seated by indirect retainers in three different teeth of unilateral distal extension partial denture when the dislodging forces were applied on denture bases. Three dimensional photoelastic models were made. The teeth on which indirect retainers were seated were mandibular left lateral incisor (Model I), canine (Model II), and first premolar (Model III). The dislodging force with 860mg at $45^{\circ}$ angulation to occlusal plane was applied to each model. Three dimensional photoelastic stress analysis was done, and the records were diagramed and analysed. The results were as follows : The compressive stresses were shown the most on neck portions of buccal, mesial, and distal sides in all three models. Slight tensile stresses were shown on neck portions of lingual sides in all three models. The compressive stresses on buccal side were shown in strength in such order as model I, model II, and model III. The compressive stresses were shown on neck portion of mesial and distal sides of model I and mode II, with model I more than Model II. The compressive stresses were shown only on neck portion of mesial side on Model III. The general overall magnitude of compressive stresses were shown in strength in such order as Model I, Model II, and Model III.

• Journal of the Korean Applied Science and Technology
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• v.38 no.4
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• pp.1156-1164
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• 2021

This study sought to provide basic data on the soccer shoe brand strategy method by verifying the effectiveness of the soccer shoe brand 3i model to derive the importance and implications of the brand 3i model. Therefore, the sample was selected for those who have purchased soccer shoes for the past three years, and 421 copies of the data were applied to the final analysis. Data processing performed frequency analysis, internal consistency, confirmatory factor analysis, correlation analysis, and structural equation model analysis, all of which utilized SPSS (ver. 21.0) and AMOS (ver. 20.0) programs. The conclusions of this study are as follows. First, soccer shoe brand 3i model had a significant influence on brand trust. Second, brand trust had a significant influence on consumer purchasing behavior. Third, soccer shoe brand 3i model had a significant influence on consumer purchasing behavior. Lastly, brand trust between soccer shoe brand 3i model and consumer purchasing behavior showed partial mediated effect.

• Korean Journal of Construction Engineering and Management
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• v.15 no.4
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• pp.119-127
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• 2014

This study aims to determine whether or not the input output life cycle assessment (I-O LCA) model can be used to assess the carbon dioxide (CO2) emission of buildings in initial planning phase. To ensure this end, this study proposed I-O LCA model which is the simplified LCA model and Hybrid LCA model which is the detailed LCA model, and then assessed and compared the CO2 emission of six case projects (three apartment complexes and three educational facilities) using the two LCA model. The results of the case study showed that the CO2 emissions assessed by the I-O LCA is significantly similar to the CO2 emission assessed by the Hybrid LCA model. The similarity of results from both LCA models was 78.2-86.3% in apartment complexes and 59.9-84.8% in educational facilities. However, the CO2 emissions from I-O LCA model were smaller than the CO2 emissions from Hybrid LCA model in case study. Nevertheless, the case study showed that the I-O LCA model was capable of assessing the CO2 emission of buildings quite appropriately although the I-O LCA model is the simplified LCA model which considers only the construction cost. The I-O LCA model is expected to be a useful tool for assessing the CO2 emission of buildings in initial planning phase.

• 한국HCI학회:학술대회논문집
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• 2008.02b
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• pp.158-164
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• 2008

This study was conducted for raising the considering the dynamical context of mobile phone use environment in the mobile-phone research. For this I identified the characteristic of the mobile phone use. The first characteristic is that the mobile phone is the context sensitive device. Also, it reflects the user's life pattern because it is the very personal device. I defined the context of mobile phone use with the basis on this identification of those characteristics. I referenced the definition, 'situational context', defining the mobile phone use context. Also, I set up the research scope within the user task that is influential from the situational context, I named this kind of task as the 'contextual task'. I developed the Contextual Task Model in this study. I named the model as the 'ESR Model'. The reason that I developed this contextual task model is that this model can help novice designers and designers unfamiliar with an application domain understand the user behavior and user centered design. And also this model can be effective to communicate each other, I identified the user's contextual tasks three kinds of model. First, the Extend Model includes user tasks that related to extending from user physical working space to the virtual level. Second model is Synchronize Model, which includes issues that lesson the blocking when use several functions at a time or sequentially. Third model is Replace Model, which is come from the characteristic of user life pattern to use the mobile phone. Finally, I proposed an application of this model, CIQ. Through the process to make CIQ I proved the effectiveness of this ESR Model.

• Korean Journal of Mathematics
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• v.31 no.1
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• pp.17-23
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• 2023

For a locus with two alleles (I^A and I^B), the frequencies of the alleles are represented by $$p=f(I^A)={\frac{2N_{AA}+N_{AB}}{2N}},\;q=f(I^B)={\frac{2N_{BB}+N_{AB}}{2N}}$$ where N_AA, N_AB and N_BB are the numbers of I^AI^A, I^AI^B and I^BI^B respectively and N is the total number of populations. The frequencies of the genotypes expected are calculated by using p², 2pq and q². Choi defined the density and operator for the value of the frequency of one gene and found the adapted partial differential equation as a follow-up for the frequency of alleles and applied this adapted partial differential equation to several diploid model [1]. In this paper, we find adapted equations for the model for selection against recessive homozygotes and in case that the alley frequency changes after one generation of selection when there is no dominance. Also we consider the triploid model with three alleles I^A, I^B and i and determine whether six genotypes observed are in Hardy-Weinburg for equilibrium.

• Journal of Power Electronics
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• v.4 no.4
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• pp.217-227
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• 2004

A robust controller that combines the merits of the feed-back, feed-forward and model-following control for induction motor drives utilizing field orientation control is designed in this paper. The proposed controller is a two-degrees-of­freedom (2DOF) integral plus proportional & rate feedback (I-PD) speed controller combined with a model-following (2DOF I-PD MFC) speed controller. A systematic mathematical procedure is derived to find the parameters of the 2DOF I-PD MFC speed controller according to certain specifications for the drive system. Initially, we start with the I-PD feed­back controller design, then we add the feed-forward controller. These two controllers combine to form the 2DOF I-PD speed controller. To realize high dynamic performance for disturbance rejection and set point tracking characterisitics, a MFC controller is designed and added to the 2DOF I-PD controller. This combination is called a 2DOF I-PD MFC speed controller. We then study the effect of the 2DOF I-PD MFC speed controller on the performance of the drive system under different operating conditions. A computer simulation is also run to demonstrate the effectiveness of the proposed controller. The results verify that the proposed 2DOF I-PD MFC controller is more accurate and more reliable in the presence of load disturbance and motor parameter variations than a 2DOF I-PD controller without a MFC. Also, the proposed controller grants rapid and accurate responses to the reference model, regardless of whether a load disturbance is imposed or the induction machine parameters vary.

• Journal of Distribution Research
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• v.15 no.4
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• pp.61-85
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• 2010

Introduction: Diffusion is process by which an innovation is communicated through certain channel overtime among the members of a social system(Rogers 1983). Bass(1969) suggested the Bass model describing diffusion process. The Bass model assumes potential adopters of innovation are influenced by mass-media and word-of-mouth from communication with previous adopters. Various expansions of the Bass model have been conducted. Some of them proposed a third factor affecting diffusion. Others proposed multinational diffusion model and it stressed interactive effect on diffusion among several countries. We add a spatial factor in the Bass model as a third communication factor. Because of situation where we can not control the interaction between markets, we need to consider that diffusion within certain market can be influenced by diffusion in contiguous market. The process that certain type of retail extends is a result that particular market can be described by the retail life cycle. Diffusion of retail has pattern following three phases of spatial diffusion: adoption of innovation happens in near the diffusion center first, spreads to the vicinity of the diffusing center and then adoption of innovation is completed in peripheral areas in saturation stage. So we expect spatial effect to be important to describe diffusion of domestic discount store. We define a spatial diffusion model using multinational diffusion model and apply it to the diffusion of discount store. Modeling: In this paper, we define a spatial diffusion model and apply it to the diffusion of discount store. To define a spatial diffusion model, we expand learning model(Kumar and Krishnan 2002) and separate diffusion process in diffusion center(market A) from diffusion process in the vicinity of the diffusing center(market B). The proposed spatial diffusion model is shown in equation (1a) and (1b). Equation (1a) is the diffusion process in diffusion center and equation (1b) is one in the vicinity of the diffusing center. $$\array{{S_{i,t}=(p_i+q_i{\frac{Y_{i,t-1}}{m_i}})(m_i-Y_{i,t-1})\;i{\in}\{1,{\cdots},I\}\;(1a)}\\{S_{j,t}=(p_j+q_j{\frac{Y_{j,t-1}}{m_i}}+{\sum\limits_{i=1}^I}{\gamma}_{ij}{\frac{Y_{i,t-1}}{m_i}})(m_j-Y_{j,t-1})\;i{\in}\{1,{\cdots},I\},\;j{\in}\{I+1,{\cdots},I+J\}\;(1b)}}$$ We rise two research questions. (1) The proposed spatial diffusion model is more effective than the Bass model to describe the diffusion of discount stores. (2) The more similar retail environment of diffusing center with that of the vicinity of the contiguous market is, the larger spatial effect of diffusing center on diffusion of the vicinity of the contiguous market is. To examine above two questions, we adopt the Bass model to estimate diffusion of discount store first. Next spatial diffusion model where spatial factor is added to the Bass model is used to estimate it. Finally by comparing Bass model with spatial diffusion model, we try to find out which model describes diffusion of discount store better. In addition, we investigate the relationship between similarity of retail environment(conceptual distance) and spatial factor impact with correlation analysis. Result and Implication: We suggest spatial diffusion model to describe diffusion of discount stores. To examine the proposed spatial diffusion model, 347 domestic discount stores are used and we divide nation into 5 districts, Seoul-Gyeongin(SG), Busan-Gyeongnam(BG), Daegu-Gyeongbuk(DG), Gwan- gju-Jeonla(GJ), Daejeon-Chungcheong(DC), and the result is shown