• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.7
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• pp.1247-1253
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• 2008

A design method of a T-S fuzzy model based reduced order nonlinear unknown input observer(NUIO) is presented. The fuzzy NUIO is designed based on the parallel distributed compensation(PDC) concept. It consists of a number of the linear UIOs, each of which is designed for each local linear model in the T-S fuzzy model of a class of nonlinear systems. The fuzzy NUIO provides not only the state estimates insensitive to the unknown inputs, for example, disturbances and faults etc., but also the estimates of the unknown inputs. Therefore, It can be employed in the state feedback control and disturbance rejection control of a class of nonlinear systems with unknown disturbances. It also applied to the robust residual generation for the fault detection and isolation systems and to the design of fault tolerant control systems. As an example, the NUIO is applied to an inverted pendulum system to show the state and disturbance estimation performance and to illustrate the fuzzy reduced order NUIO design method.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04a
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• pp.670-672
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• 2001

소프트웨어 산업계에서 재사용 단위가 객체보다 더 큰 컴포넌트 기반의 개발에 관심이 집중되고 있다. 그래서 모델링 언어인 UML과 컴포넌트가 운용되는 유연하고 확장성 높은 기반 아키텍처인 EJB를 이용한 기업형 시스템 개발이 요즘 기업에서 활발해지고 있다. UML과 EJB 각각에 대한 연구는 많이 진행되었지만, UML Model을 이용한 EJB Model 구현시의 mapping 기법에 관한 연구는 아직 미흡한 실정이다. 그래서 본 논문에서는 UML Modeling을 통해 Class diagram에서 추출된 Class들이 EJB로 구현될 때 실제로 어떤 Bean으로 Mapping 되는지에 대해 제시한다.

• The Korean Journal of Ecology
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• v.9 no.1
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• pp.41-50
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• 1986

The net primary production in the Korean peninsula was estimated by Miami model, Montreal model and Kira's model, based on 148 meteorological data. The modes in frequency distribution of the values calculated by Montreal and Miami model were found at 1,500g/m2/yr. class and at one step high class in 100g. interval, while by Kira's madel at 1,700g/m2/yr. class. The relationships between values by Miami model(X) and those by Motreal model (Ym) and Kira's model(Yk) can be expressed as follows: Ym=0.365X+944.7, Yk=0.462 X+1006.9 and Yk=1.282Ym-211.5. The total amount of the net primary production in 218,583.4km2, 98.9% of the whole area(220,951 km2) of the Korean Peninsula, was estimated as 290,691,407 tons/yr. by Miami model, 310,751,566 tons/yr by Montreal model and 352,071,901 tons/yr by Kira's model. Therefore, it is reasonable that the organic substance over 300 million-tons is added yearly in the Korean Peninsula, because only 1.1% of the whole area no calculated. In additiion, the net primary production amount of Han-river basin was estimated as ca. 38 million-tons, whether calculated with the meteorological data in level of the Korean Peninsula or with more detail data.

• 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.

• Journal of the Korea Society of Computer and Information
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• v.21 no.7
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• pp.93-100
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• 2016

This paper shows what kind of influence the learning motivation factors have on the effectiveness of Flipped Learning Model through the case of operating a JAVA programming subject. The Flipped Learning Approach consisting of Before Class, Before or At Start of Class, and In Class provides the students with learning motivation as well as satisfies Keller's ARCS(Attention, Relevance, Confidence, Satisfaction) to keep them studying steadily. This research conducts the operation of Flipped Learning and gets Exploratory Factor Analysis and Reliability Analysis from the result of the course experience questionnaire at the end of the class. Given this survey result, Flipped Learning approach improves the learners' satisfaction in class and the effectiveness in the fields of understanding learning context more than does the previous lecture-based learning approach by pacing learning procedure and conducting self-directed learning.

• 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.

• The Journal of the Korea Contents Association
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• v.20 no.3
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• pp.462-469
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• 2020

This study investigated high school students' perception of English extensive reading (ER) after introducing ER to general high school students in a metropolitan area. In addition, as the free semester system was expanded in high schools as well as middle schools, we developed a class model for English extensive reading. 91.4 percent of the students who participated in the study said that they wanted to try using the English extensive reading method. Also 35 high school students who experienced English extensive reading chose the 'Five Finger rule' and 'Graded Readers' Series' as the most helpful factors in their extensive reading experiences. In interviews with English teachers, teachers expressed their demand for the development of a model of English extensive reading suitable for free semesters in general high schools. This study proposes an English extensive reading class model utilizing library resources that can be used in the free semester system as well as a performance assessment-oriented English extensive reading class model.

• Journal of the Korean Statistical Society
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• v.36 no.3
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• pp.435-445
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• 2007

This paper addresses for the first time the use of complete data information criteria in unsupervised learning of the Naive-Bayes model. A Monte Carlo study sets a large experimental design to assess these criteria, unusual in the Bayesian network literature. The simulation results show that complete data information criteria underperforms the Bayesian information criterion (BIC) for these Bayesian networks.

• International Journal of Internet, Broadcasting and Communication
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• v.11 no.4
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• pp.37-42
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• 2019

In this paper, we explore the details of three classic data augmentation methods and two generative model based oversampling methods. The three classic data augmentation methods are random sampling (RANDOM), Synthetic Minority Over-sampling Technique (SMOTE), and Adaptive Synthetic Sampling (ADASYN). The two generative model based oversampling methods are Conditional Generative Adversarial Network (CGAN) and Wasserstein Generative Adversarial Network (WGAN). In imbalanced data, the whole instances are divided into majority class and minority class, where majority class occupies most of the instances in the training set and minority class only includes a few instances. Generative models have their own advantages when they are used to generate more plausible samples referring to the distribution of the minority class. We also adopt CGAN to compare the data augmentation performance with other methods. The experimental results show that WGAN-based oversampling technique is more stable than other approaches (RANDOM, SMOTE, ADASYN and CGAN) even with the very limited training datasets. However, when the imbalanced ratio is too small, generative model based approaches cannot achieve satisfying performance than the conventional data augmentation techniques. These results suggest us one of future research directions.

• Journal of Korean Society for Quality Management
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• v.48 no.4
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• pp.553-566
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• 2020

Purpose: The purpose of this study was to design a framework for generating one-class classification algorithm based on Hyper-Rectangle(H-RTGL) in a distributed environment connected by network. Methods: At first, we devised one-class classifier based on H-RTGL which can be performed by distributed computing nodes considering model and data parallelism. Then, we also designed facilitating components for execution of distributed processing. In the end, we validate both effectiveness and efficiency of the classifier obtained from the proposed framework by a numerical experiment using data set obtained from UCI machine learning repository. Results: We designed distributed processing framework capable of one-class classification based on H-RTGL in distributed environment consisting of physically separated computing nodes. It includes components for implementation of model and data parallelism, which enables distributed generation of classifier. From a numerical experiment, we could observe that there was no significant change of classification performance assessed by statistical test and elapsed time was reduced due to application of distributed processing in dataset with considerable size. Conclusion: Based on such result, we can conclude that application of distributed processing for generating classifier can preserve classification performance and it can improve the efficiency of classification algorithms. In addition, we suggested an idea for future research directions of this paper as well as limitation of our work.