• 전기의세계
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• v.39 no.12
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• pp.21-32
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• 1990

퍼지 논리를 이용한 제어시스템에 관하여 핵심 개념을 중심으로 기술하고자 한다. 요약컨데 이 퍼지제어기의 특징은 1) Parallel(distributed) control 2) logic control 3) linguistic control등이며 퍼지 제어가 효과적일 수 있는 제어대상(plant)로서는 수학적 모델을 적용하기 힘든 시스템으로서 경험적으로 또는 수동적인 방법으로 제어가 잘되고 있는 대상을 들 수 있다. 그 뿐만 아니라 간단한 제어기가 필요한 경우로서 보다 효과적인 제어측 Software를 쓰거나 센서 또는 필터없이 사용가능하고, Inverted Penedulum의 자세 제어처럼 정확성보다는 속도 응답 제어가 요구되는 경우 등에 효과적으로 쓸 수 있는 것으로 알려지고 있다. Fuzzy 제어는 지식 베이스의 규모에서 인공지능형 Expert System보다 Compact하고 선형.비선형 플랜트에 공히 이용될 수 있으며, 설계자는 오퍼레이터와의 접촉을 통해 룰을 구축하므로 사용자가 시스템을 이해하기 쉬운 잇점등이 있기도 한다. 그러나 가장 큰 문제는 구축해 놓은 시스템의 안전성(Stability)를 이론적으로 사전에 검증하기가 어렵고, 같은 제어대상이라 할지라도 추론방법, 소속함수의 형태선택, 룰수 등에 따라 제어성능이 바뀔수 있으나, 무엇이 어떤 영향을 주는지 규명되지 않은점 등 여러가지 연구되어야 할 내용이 많이 있다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.409-412
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• 2019

본 논문에서는 인공지능과 빅데이터 클라우드 등 다양한 4차 산업혁명시대의 기술과 교육을 융합한 에듀테크를 기초로 하여 에듀테크에 대한 교사의 학습 방향을 제시하며 전체적인 클라우드의 개념 및 분류체계, 교육의 활용을 제시하였고 클라우드 기반 교육용 소셜 플랫폼과 R. M. Gagne(1985)의 9가지 이론을 토대로 정보교과 추상화 단원의 학습 지도안을 설계 후 성취도 평가를 제시하였다. 연구 내용 분석 결과 기술의 발전성과 교육현장에서의 개인정보 교육 및 정보보안 교육의 필요성이 강조되며 확고한 플랫폼 구축과 빅데이터 확보 및 분석하여 개인에게 맞춤형 서비스 제공이 필요하다. 또한 사용자 편의성 극대화 서비스 및 UX 간결이 요구된다. 본 논문을 토대로 에듀테크의 일부분인 클라우드 기반 소셜러닝의 다양하고 체계적인 선행연구 활성화에 시발점이 되었으면 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.484-485
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• 2013

스마트폰은 현대인에게 없어서는 안 될 필수품이 되었다. 기존의 휴대용 전화처럼 단순 연락 수단이라는 의미를 넘어 컴퓨터가 할 수 있는 거의 모든 기능을 내장하고 있으며, 여러 가지 센서들을 내장하여 그 활용 가능성이 높고, 수많은 종류의 어플리케이션들을 이용하여 추가적인 편의 서비스도 사용할 수 있다. 이렇듯 개개인의 생활과 밀착되어 사용되는 스마트폰은 사용자의 정보를 수집할 수 있는 최적의 기기이다. 스마트폰에서 얻은 사용자 정보, 즉 각종 센서를 이용한 행위 인지 정보, GPS를 이용한 이동 경로 정보, 음성 정보, 텍스트 정보 등을 활용하여 사용자의 감성정보를 추출하고, 이러한 감성정보에 기반하여 각 사용자에게 알맞은 서비스(어플리케이션)를 추천하는 맞춤형 지능서비스 제공기술의 개발이 가능하다. 이러한 기술 개발의 핵심사항은 사용자의 감성을 정확히 추출하는 것이다. 본 논문에서는 MCRDR 이론을 적용하여 보다 정확한 감성 추출 기법을 제안하고자 한다.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.432-439
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• 2022

This paper introduces deliberative behavior tree using utility theory. The proposed approach combine the strengths of behavior trees and utility theory to implement complex behavior of AI agents in an easier and more concise way. To achieve this goal, we devised and implemented three types of additional behavior tree nodes, which evaluate utility values of its own node or its subtree while traversing and selecting its child nodes based on the evaluated values. In order to validate our approach, we implemented a sample scenario using conventional behavior tree and our proposed deliberative tree respectively. And then we compared and analyzed the simulation results.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• The Journal of Society for e-Business Studies
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• v.8 no.4
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• pp.113-128
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• 2003

A Semantic Web based service description is to enable intelligent matchmaking of services by improving representational flexibility and expressiveness in service description for Web Services. Flexibility concerns the way of how a computer handles the discrepancies in the granularity and the structure of service description between different participants of web services. Expressiveness means richness in the semantic description of services (or ontological representation of each service as a concept). The main shortcoming of currently available industry standard framework for e-commerce such as UDDI and ebXML is that they do not allow much flexibility and expressiveness in the service description. In our research we analyze the requirements of extending service description based on the Semantic Web, especially in the context of DAML-S. We also propose a method to be applied to implement intelligent web services by implementing a virtual auction site, testAuction.com. Finally, we consider some theoretical issues regarding the framework for web services using ontology.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.4 s.316
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• pp.8-14
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• 2007

Social participation of the elderly and people with disabilities continuously becomes more active due to improvement of social systems and technological development. Various assistive systems, such as intelligence robots and intelligence home systems, have been developed to support the social participation, and obviously contributed to independent lives of the elderly and people with disabilities. These systems, however, usually require expensive special hardware. To relieve a financial burden software-oriented approaches, which utilizes existing hardware such as laptops, can be an alternative. The software should be developed considering users with physical limitation and without much knowledge of computers. This paper suggests software-oriented approaches to solve these problems and describes an actual development procedure of the software with related theories. We also introduce an unified user interface for a special work chair as a real application.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.736-741
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• 2006

The design of a classification system generally involves data acquisition module, learning module and decision module, considering their functions and it is often an important component of intelligent systems. The learning module provides a priori information and it has been playing a key role for the classification. The conventional learning techniques for classification are based on a winner take all fashion which does not reflect the description of real data where boundarues might be fuzzy Moreover they need all data for the learning of its problem domain. Generally, in many practical applications, it is not possible to prepare them at a time. In this paper, we design an adaptive classification model using incremental training fuzzy neural networks, FNN-I. To have a more useful information, it introduces the representation and membership degree by fuzzy theory. And it provides an incremental learning algorithm for continuously gathered data. We present tie experimental results on computer virus data. They show that the proposed system can learn incrementally and classify new viruses effectively.

• Journal of the Korean earth science society
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• v.40 no.6
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• pp.654-670
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• 2019

From the perspective of science gifted education, the successful intelligence theory is a means to understand how the gifted education curriculum reflects the characteristics of science gifted students. The purpose of this study is to investigate if the successful intelligence is fully reflected in the teaching materials of two gifted education centers (GECDOE: Gifted Education Center affiliated with District Office of Education, GSEIU: Gifted Science Education Institute attached to University). For this study, we selectively used 143 (GECDOE) and 134 questions (GSEIU) from the teaching materials of two gifted education centers. Those questions is analyzed through the semantic network analysis method. The results are as follow. First, the teaching materials of two gifted education centers are not evenly reflected in the successful intelligence, such as analytical ability, creative ability, and practical ability. Second, the teaching materials of two gifted education centers intensively demands analytical ability for students such as 'identify problem', 'represent and organize information', and 'additional prompts for analytical thinking'. Third, the teaching materials of two gifted education centers are presented to students without linking each frame of successful intelligence to one another. As the gifted students are quick to learn and show a preference for more complex thinking, it is necessary to develop teaching materials to experience the various abilities and promote integrated thinking according to the level of the gifted students. In this respect, this study is expected to be used as useful information for developing teaching materials to support customized education for gifted students.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.207-228
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• 2023

After COVID-19, organizations have widely adopted platforms such as zoom or developed their proprietary online real-time systems for remote work, with recent forays into incorporating the metaverse for meetings and publicity. While ongoing studies investigate the impact of avatar customization, expansive virtual environments, and past virtual experiences on participant satisfaction within virtual reality or metaverse settings, the utilization of the metaverse as a dedicated workspace is still an evolving area. There exists a notable gap in research concerning the factors influencing the performance of the metaverse as a workspace, particularly in non-immersive work-type metaverses. Unlike studies focusing on immersive virtual reality or metaverses emphasizing immersion and presence, the majority of contemporary work-oriented metaverses tend to be non-immersive. As such, understanding the factors that contribute to the success of these existing non-immersive metaverses becomes crucial. Hence, this paper aims to empirically analyze the factors impacting personal outcomes in the non-immersive metaverse workspace and derive implications from the results. To achieve this, the study adopts the Embodied Social Presence (ESP) model as a theoretical foundation, modifying and proposing a research model tailored to the non-immersive metaverse workspace. The findings validate that the impact of presence on task engagement and task involvement exhibits a moderating effect based on the metaverse platform used. Following interviews with participants engaged in non-immersive metaverse workplaces (specifically Gather Town and Ifland), a survey was conducted to gather comprehensive insights.