• Journal of Industrial Technology
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• v.28 no.A
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• pp.185-189
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• 2008

Practical pattern recognition systems should overcome very large class problem. Sometimes it is almost impossible to build every model for every class due to memory and time constraints. For this case, grouping similar models will be helpful. In this paper, we propose GMC(Group Model Clustering) to build a large class Chinese character recognition system. We built hidden Markov models for 10% of total classes, then classify the rest of classes into already trained group classes. Finally group models are trained using group model clustered data. Recognition is performed using only group models, in order to achieve reduced model size and improved recognition speed.

• English Language & Literature Teaching
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• v.10 no.1
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• pp.53-76
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• 2004

This paper introduces a class model based on a course, Internet English, offered by an English department at a university. The course has dual purposes of developing students I English skills and Internet using skills at the same time. In support of using the Internet for language learning, the advantages of project-based language learning and constructivist learning in relation to CALL are explored. The activities in this course, which are basically project-based under the paradigm of constructivist learning perspective, are explained in detail to show the relationship between second language learning theory and teaching application. The way how the four language skills - speaking, listening, reading, and writing - are integrated in this class is described as well. Finally, judgmental evaluation of the course by the students is noted. The results show that a project-based CALL class could be a promising class model to realize an integrative, constructivist, and authentic learning.

• Asia pacific journal of information systems
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• v.33 no.1
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• pp.22-38
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• 2023

This study empirically investigates e-market consumers' responses to monthly platform discount coupons. Specifically, based on an archival data set obtained from a leading e-marketplace in Korea, our hidden Markov model reveals that there are two different types of consumers on the e-market, those who purchase relatively less but seek temporal seller discounts (Class 1) and those who buy relatively more but are less attracted by such discounts (Class 2). Class 1 consumers purchase products when platform coupons are available but are less likely to buy when platform coupons are all redeemed. On the other hand, Class 2 consumers are willing to purchase products even without platform coupons. Our latent groups demonstrate that the effect of platform promotion is not unidirectional but may depend on the consumer state and class. We discuss the theoretical contributions and managerial implications of our findings.

• Journal of Korean Academy of Nursing
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• v.36 no.2
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• pp.286-298
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• 2006

Purpose: This study was to develop an online 'alternative therapy and health promotion' class for university students and to evaluate its changes. Method: The online class was developed based on the Instructional Systems Development(ISD) model and model of Web-Based Instruction(WBI) developmental process. This was a quasi-experimental, one group pretest-posttest design. The subjects of this study were 130 students in 3 universities, and they were provided the cyber class for 16 weeks. Data was analyzed by descriptive and plural answer statistics, and paired t-test. Results: The cyber class was developed in five steps : analysis, design, data collection and reconstruction, programing and publishing, and evaluation. The results of program evaluation were positive, which included learning 3.47. system 3.57, and learning satisfaction 3.64 on the scale of 5. The posttest scores of cognition and reliability of alternative therapy were higher than pretest scores. The posttest score of health promoting lifestyle(t=-5.051, p=.000) and perceived health status(t=2.979, p=.003) were significantly higher than those of the pretest. Conclusion: These results suggest that the cyber class is a positive method in increasing a cognition, reliability of alternative therapy, and is effective to improve a health promotion lifestyle and perceived health status for the university students.

• Communications for Statistical Applications and Methods
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• v.28 no.6
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• pp.595-610
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• 2021

Recently a few articles have derived robust first-order rotatable and D-optimal designs for the lifetime response having distributions gamma, lognormal, Weibull, exponential assuming errors that are correlated with different correlation structures such as autocorrelated, intra-class, inter-class, tri-diagonal, compound symmetry. Practically, a first-order model is an adequate approximation to the true surface in a small region of the explanatory variables. A second-order model is always appropriate for an unknown region, or if there is any curvature in the system. The current article aims to extend the ideas of these articles for second-order models. Invariant (free of the above four distributions) robust (free of correlation parameter values) second-order rotatable designs have been derived for the intra-class and inter-class correlated error structures. Second-order rotatability conditions have been derived herein assuming the response follows non-normal distribution (any one of the above four distributions) and errors have a general correlated error structure. These conditions are further simplified under intra-class and inter-class correlated error structures, and second-order rotatable designs are developed under these two structures for the response having anyone of the above four distributions. It is derived herein that robust second-order rotatable designs depend on the respective error variance covariance structure but they are independent of the correlation parameter values, as well as the considered four response lifetime distributions.

• Journal of the Korean Data and Information Science Society
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• v.11 no.2
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• pp.173-179
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• 2000

Interval prediction based on the empirical distribution function for the class of time series with time varying coefficients is discussed. To this end, strong mixing property of the model is shown and results due to Fotopoulos et. al.(1994) are employed. A simulation study is presented to assess the accuracy of the proposed interval predictor.

• The Transactions of the Korea Information Processing Society
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• v.7 no.3
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• pp.818-830
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• 2000

Automation tools for program analysis are needed in order to program understand and maintain, extract the characteristics of object-oriented program such as class name, member function and data member. In this paper, we carried out design and implementation of the automation tool for effective maintenance of object-oriented software. Being based on Reverse Engineering, this approach extracts class relationship information from C++ source code and generates object-oriented model of class diagram using UML as the standard object-oriented methodology. Therefore, this paper provides developers visualized including class information, definitions of classes, inheritance relationships, set relationships, and simple reference relationships. Finally in this paper, we propose a method that construct class relationship information to table in analysis state and make form of table construction to link form so tat developers can perform understanding and maintaining program efficiently. And this method enable to restructure and reuse in object-oriented model.

• Journal of Information Technology Applications and Management
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• v.29 no.3
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• pp.43-55
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• 2022

In this study, with the goal of developing a deep learning-based product recommendation model for effective matching of influencers and products, a deep learning model with a collaborative filtering model combined with generalized matrix decomposition(GMF), a collaborative filtering model based on multi-layer perceptron (MLP), and neural collaborative filtering and generalized matrix Factorization (NeuMF), a hybrid model combining GMP and MLP was developed and tested. In particular, we utilize one-class problem free boosting (OCF-B) method to solve the one-class problem that occurs when training is performed only on positive cases using implicit feedback in the deep learning-based collaborative filtering recommendation model. In relation to model selection based on overall experimental results, the MLP model showed highest performance with weighted average precision, weighted average recall, and f1 score were 0.85 in the model (n=3,000, term=15). This study is meaningful in practice as it attempted to commercialize a deep learning-based recommendation system where influencer's promotion data is being accumulated, pactical personalized recommendation service is not yet commercially applied yet.

• Communications of the Korean Mathematical Society
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• v.13 no.2
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• pp.393-403
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• 1998

We consider $M_1,M_2/M/1$ retrial queues with two classes of customers in which the service rates depend on the total number or the customers served since the beginning of the current busy period. In the case that arriving customers are bloced due to the channel being busy, the class 1 customers are queued in the priority group and are served as soon as the channel is free, whereas the class 2 customers enter the retrical group in order to try service again after a random amount of time. For the first $N(N \geq 1)$ exceptional services model which is a special case of our model, we derive the joint generating function of the numbers of customers in the two groups. When N = 1 i.e., the first exceptional service model, we obtain the joint generating function explicitly and if the arrival rate of class 2 customers is 0, we show that the results for our model coincide with known results for the M/M/1 queues with smart machine.

• The Korean Journal of Applied Statistics
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• v.18 no.1
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• pp.1-13
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• 2005

Latent Class model has been considered recently by many researchers and practitioners as a tool for identifying heterogeneous segments or groups in a population, and grouping objects into the segments. In this paper we consider data on prostate cancer patients from Korean National Cancer Institute and propose a method for grouping prostate cancer patients by using latent class Poisson model. A Bayesian approach equipped with a Markov chain Monte Carlo method is used to overcome the limit of classical likelihood approaches. Advantages of the proposed Bayesian method are easy estimation of parameters with their standard errors, segmentation of objects into groups, and provision of uncertainty measures for the segmentation. In addition, we provide a method to determine an appropriate number of segments for the given data so that the method automatically chooses the number of segments and partitions objects into heterogeneous segments.