• Journal of IKEEE
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• v.21 no.2
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• pp.136-141
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• 2017

In this paper, we propose an algorithm for memory optimization to apply deep learning to PC. The proposed algorithm minimizes the memory and computation processing time by reducing the amount of computation processing and data required in the conventional deep learning structure in a general PC. The algorithm proposed in this paper consists of three steps: a convolution layer configuration process using a random filter with discriminating power, a data reduction process using PCA, and a CNN structure creation using SVM. The learning process is not necessary in the convolution layer construction process using the discriminating random filter, thereby shortening the learning time of the overall deep learning. PCA reduces the amount of memory and computation throughput. The creation of the CNN structure using SVM maximizes the effect of reducing the amount of memory and computational throughput required. In order to evaluate the performance of the proposed algorithm, we experimented with Yale University's Extended Yale B face database. The results show that the algorithm proposed in this paper has a similar performance recognition rate compared with the existing CNN algorithm. And it was confirmed to be excellent. Based on the algorithm proposed in this paper, it is expected that a deep learning algorithm with many data and computation processes can be implemented in a general PC.

• The Journal of Society for e-Business Studies
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• v.25 no.3
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• pp.1-13
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• 2020

Machine learning refers to a model generation technique that can solve specific problems from the generalization process for given data. In order to generate a high performance model, high quality training data and learning algorithms for generalization process should be prepared. As one way of improving the performance of model to be learned, the Ensemble technique generates multiple models rather than a single model, which includes bagging, boosting, and stacking learning techniques. This paper proposes a new Ensemble technique with multiple stacking that outperforms the conventional stacking technique. The learning structure of multiple stacking ensemble technique is similar to the structure of deep learning, in which each layer is composed of a combination of stacking models, and the number of layers get increased so as to minimize the misclassification rate of each layer. Through experiments using four types of datasets, we have showed that the proposed method outperforms the exiting ones.

• School Mathematics
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• v.17 no.4
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• pp.555-570
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• 2015

General and subject guideline for new curriculum are confirmed and plan to announced on September 2015. The development of instructional materials according to them will proceed. Because textbooks have the role and function to determine and control the direction and scope of teaching and learning, Textbook plays a very important role in the situation where teachers and students meet. Thus we get a significant effect depending on the structure of textbook. In this study, we analyzed differences of detail learning content according to high school textbooks focused on utilizing of definite integral. After creating the questionnaire based on it, The survey was conducted targeting for 369 high school mathematics teachers belong to 14 education offices of cities and provinces are polled out by accidental sampling method. Analyzing the results of the survey, We searched various teaching and learning method that arise due to differences of detail learning content according to high school textbooks and thereby it explored the impact on students' mathematics learning. Through this, Our intention is to offer implication about the structure of textbook detail learning contents and to derive the improvements about textbook compilation system.

• Korean Medical Education Review
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• v.10 no.2
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• pp.19-24
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• 2008

Purpose: The 'learning cycle' proposed by Guilbert in 1981 has been accredited as an effective and useful model for curriculum design. Three components of learning cycle, learning objective, instructional method, and assessment are connected organically and form basic structure of curriculum. In this study, we intend to analyze how the learning cycle and its three components are applied to present medical curriculum and examine the points at issue of the learning cycle in medical education. Also, we try to identify the educational significance of the leaning cycle in medical education. Results: First, concerning the learning objective, it was identified that impractical and abstract expressions are major controversial points. Also, there is a need to make learning objectives covering entire medical curriculum. Second, because of various structural problems, it is hard to practice new and various instructional methods. Third, even though there is a growing need for medical curriculum to develop and utilize more various and detailed assessment and evaluation, it was revealed that only are standardized and traditional assessments mainly used. Conclusion: Synthetically, we have some suggestions as follows. First, it is necessary to specify and actualize the learning objectives. Also, instructional methods and assessments should be diversified. And finally, there is a need to build organic and delicate medical curriculum by applying the learning cycle to medical education more actively.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.6
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• pp.1182-1189
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• 1994

In this paper, we evaluate the performance of a fuzzy controller with a self-learning structure. The fuzzy controller is based on a fuzzy logic that approximates and effectively represents the uncertain phenomena of the real world. The fuzzy controller has control of a plant with a fuzzy inference logic. However, it is not easy to decide the membership function of a fuzzy controller and its controlrule. This problem can be solved by designing a self-learning controller that improves its own contropllaw to its goal with a performance table. The fuzzy controller is implemented with a 386PC, an interface board, a D/A converter, a PWM(Pulse Width Modulation) motor drive-circuit, and a sensing circuit, for error and differential of error. Since a Ball and Beam System is used in the experiment, the validity of the fuzzy controller with the self-learning structure can be evaluated through the actual experiment and the computer simulation of the real plant. The self-learning fuzzy controller reduces settling time by just under 10%.

• Smart Structures and Systems
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• v.30 no.6
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• pp.687-701
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• 2022

Inevitable response loss under complex operational conditions significantly affects the integrity and quality of measured data, leading the structural health monitoring (SHM) ineffective. To remedy the impact of data loss, a common way is to transfer the recorded response of available measure point to where the data loss occurred by establishing the response mapping from measured data. However, the current research has yet addressed the structural condition changes afterward and response mapping learning from a small sample. So, this paper proposes a novel data driven structural response reconstruction method based on a sophisticated designed generating adversarial network (UAGAN). Advanced deep learning techniques including U-shaped dense blocks, self-attention and a customized loss function are specialized and embedded in UAGAN to improve the universal and representative features extraction and generalized responses mapping establishment. In numerical validation, UAGAN efficiently and accurately captures the distinguished features of structural response from only 40 training samples of the intact structure. Besides, the established response mapping is universal, which effectively reconstructs responses of the structure suffered up to 10% random stiffness reduction or structural damage. In the experimental validation, UAGAN is trained with ambient response and applied to reconstruct response measured under earthquake. The reconstruction losses of response in the time and frequency domains reached 16% and 17%, that is better than the previous research, demonstrating the leading performance of the sophisticated designed network. In addition, the identified modal parameters from reconstructed and the corresponding true responses are highly consistent indicates that the proposed UAGAN is very potential to be applied to practical civil engineering.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2424-2429
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• 2003

The importance of e-Learning, which supports to study anywhere and anytime, has been pointed out for improving education. There have been various research papers on e-Learning system for educations. Most of literatures have focused on guiding the student or measuring understanding their level etc. Design method of e-Learning system has not been discussed based on structure and analysis of the class. In this paper, scheme of the class is proposed by analyzing and structuring class, then design method of e-Learning system is discussed based on it.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10a
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• pp.589-591
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• 2003

현재 대부분 e-Learning에서 이루어지고 있는 교육은 학습(Loaming)이 아닌 단순 훈련(Trainning)만이 이루어지고 있다. e-Learning에서 진정한 학습이 이루어지기 위해서는 학습자의 수준에 맞는 적응적(Adaptive), 적시적(Just-in-Time) 학습이 단편적이 아닌 연속적, 통합적으로 이루어져야 한다. 이를 위해서는 기술적 관점뿐만 아니라, 발견적 학습(heuristic learning)관점에서 학습자원이 기술되고, 컴퓨터(에이전트)가 학습자원의 구성요소인 학습목표(Goal), 학습내용(Content), 학습맥락(Context), 학습구조(Structure), 학습전략(Strategy)의 의미(Semantic)와 관계(Relation)를 이해해 학습자에게 필요한 정보만을 검색, 추론해주고 이를 학습자 수준에 맞게 재가공해 학습자에게 지식(Knowledge)을 적응적(Adaptive), 적시적(Just-in-Time)으로 전달해주는 e-Learning 학습 환경이 필수적이다. 메타데이터(RDF), 온톨로지(Ontology), 에이전트(Agent) 매커니즘의 시멘틱 웹을 e-Learning 환경에 적용함으로써 학습자원의 구성요소의 의미와 관계를 파악해 적응적(Adaptive)으로 지식을 전달해 주어 자기 주도적 학습(Self-directed Loaming)을 실현해 줄 수 있다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.6B
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• pp.1120-1126
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• 2000

Recently, in order to analyze the time series problems that occur in the nature word, and analyzing method using a neural electric network is being studied more than a typical statistical analysis method. A neural electric network has a generalization performance that is possible to estimate and analyze about non-learning data through the learning of a population. In this paper, after collecting weather datum that was collected from 1987 to 1996 and learning a population established, it suggests the weather forecasting system for an estimation and analysis the future weather. The suggested weather forecasting system uses 28*30*1 neural network structure, raises the total learning numbers and accuracy letting the selecting concentration learning about the pattern, that is not collected, using the descending epsilon learning method. Also, the weather forecasting system, that is suggested through a comparative experiment of the typical time series analysis method shows more superior than the existing statistical analysis method in the part of future estimation capacity.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.91-93
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• 2009

In this paper, we present the Q-learning method for adaptive traffic signal control on the basis of In this paper, we present the Q-learning method for adaptive traffic signal control on the basis of multi-agent technology. The structure is composed of sixphase agents and one intersection agent. Wireless communication network provides the possibility of the cooperation of agents. As one kind of reinforcement learning, Q-learning is adopted as the algorithm of the control mechanism, which can acquire optical control strategies from delayed reward; furthermore, we adopt dynamic learning method instead of static method, which is more practical. Simulation result indicates that it is more effective than traditional signal system.