• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.693-695
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• 1997

This paper deals with the tracking control problem of robotic manipulators with unknown or changing dynamics. The torque input applied to the joint actuators is determined at every instance by the identification model that best approximates the robot dynamics. The best of the identified model is chosen by the proposed switching mechanism with fuzzy inference of the manipulator in an indirect adaptive controller architecture. Simulation results are also included to demonstrate the improvement in the tracking performance when the proposed method is used.

• Korean Journal of Construction Engineering and Management
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• v.16 no.4
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• pp.107-117
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• 2015

As the demand for urban business hotel has increased in the recent years due to the sharp rise in overseas tourists, the potential of business hotel development has gained in financial attention for the real estate investment. However, its concept and business domain has not been defined, and none of investment guidance for the development has not been clarified. Accordingly, this report intends to define the concept of urban business hotel in perspective of real estate investment through prior case studies and literature review, and provide the structure of determinants which control its investment decisions. Furthermore, this study develops the weight of determinants depending on the type of investment, and evaluate the two actual projects in urban area of Seoul, differed from investment type: direct and indirect, based on AHP and Fuzzy methodologies. Research finding indicate that the financial factors affecting the sales and tour business condition factors are critical determinants regardless of investment type. And it is notable that the direct investment perspective pays more attention on the finance and investment associated determinants while the indirect one more focuses on the regional and environmental ones. From the analysis of case evaluation, it is also noteworthy that the direct investors turn out to be more conservative than indirect investors due to the risk of equity capital investment.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.79-82
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• 1997

The current from the feed motor of a machine tool contains substantial information about the machining state. There have been many researches that investigated the current as a measure for the cutting forces. However it has not been reported that indirect measurement of the cutting forces from the current of the feed motor at rest is possible. The cutting force normal to the machined surface influences the machined surface of the workpiece, which makes it necessary to estimate this force to control the roughness of the machined surface. But the unpredictable behavior of the current prevents applying the current to prediction of the cutting state. In this paper, empirical approach was conducted to resolve the problem. Also parametric adaptive and fuzzy logic control strategies are applied to the force regulation problem. As a result, the current is shown to be related to the accumulation of the infinitesimal rotation of the motor, and besides the unpredictable behavior of the current is shown to be caused by the relationship. Subsequently the relationship between the current and the cutting force is identified, and it is presented that control of machined surface using the current of the feed motor at rest is possible.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.1
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• pp.60-68
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• 1999

This paper deals with the tracking control problem of industrial robotic manipulators with unknown or changing dynamics. The proposed method makes use of multiple moodels and switching mechanism by fuzzy inference of the manipulator in an indirect adaptive controller architecture. The models used for the indmtification of the manipliator are identical, except for the initial estimates of the unknown inertial pararmeters of the manipulator and its load. The torque input that is applied to the joint actuators is determined at every instant by the identification model that best approximates the robot dynamics. Simulation results are also included to dermnstrate the improvement in the tracking perfermance when the proposed method is used.s used.

• 한국지구물리탐사학회:학술대회논문집
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• 2003.11a
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• pp.723-730
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• 2003

In general, well log and core data have been utilized for reservoir characterization. These well data can provide valuable information on reservoir properties with high vertical resolution at well locations. While the seismic surveys cover large areas of field but give only indirect features about reservoir properties. Therefore it is possible to estimate the reservoir properties guided by seismic data on entire area if a relationship of seismic data and well data can be defined. Seismic attributes calculated from seismic surveys contain the particular reservoir features, so that they should be extracted and used properly according to the purpose of study. The method to select the suitable seismic attributes among enormous ones is needed. The stepwise regression and fuzzy curve analysis based on fuzzy logics are used for selecting the best attributes. The relationship can be utilized to estimate reservoir properties derived from seismic attributes. This methodology is applied to a synthetic seismogram and a sonic log acquired from velocity model. Seismic attributes calculated from the seismic data are reflection strength, instantaneous phase, instantaneous frequency and pseudo sonic logging data as well as seismic trace. The fuzzy curve analysis is used for choosing the best seismic attributes compared to sonic log as well data, so that seismic trace, reflection strength, instantaneous frequency, and pseudo sonic logging data are selected. The relationship between the seismic attribute and well data is found out by the statistical regression method and estimates the reliable well data at a specific field location derived from only seismic attributes. For a future work in this study, the methodology should be checked an applicability of the real fields with more complex and various reservoir features.

• Korean Journal of Construction Engineering and Management
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• v.21 no.1
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• pp.70-77
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• 2020

Public conflict in domestic project has been increased and it results in direct and indirect damages. In order to reduce the loss, there have been diverse researches on the public conflict. However, most of researches tend to mainly focus on characteristics of the conflict. Considering the conflict occurred in SOC project, a research of conflict with project characteristics is necessary. In response to consider the relationships between characteristics of project and conflict, this paper aims to present the relative importance of the relationships. We conducted literature reviews and interviews with experts to select main characteristics of projects and public conflicts. Based on the selected four main characteristics each, we analyzed the relationships between them using Fuzzy AHP. The results show that SOC construction project type among project characteristics exerts the most important effect on overall public conflict. Especially conflict factors and conflict phases among public characteristics are critically influenced by project types. The findings provide a foundation for a conflict management reflecting varying public conflicts due to project characteristics.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.650-652
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• 2004

본 논문에서는 간접 벡터 제어 방식의 유도전동기를 위한 하이브리드 퍼지 속도제어기를 설계한다. 제안한 하이브리드 퍼지 속도제어기는 유도 전동기의 속도 응답 성능을 향상시키기 위하여 응답 상태에 따라 PI(비계적분) 제어기와 퍼지 제어기를 선택하여 사용하는 형태이다. 정상상태에서는 PI 제어기를 사용하고 속도 오차값이 크면 퍼지 제어기를 사용한다. 또한 사용된 퍼지 제어기는 퍼지 입력의 파라미터를 튜닝하여 응답 성능을 높였다. 본 논문에서 제안한 하이브리드 퍼지속도 제어기와 기존의 PI 제어기의 성능을 실험을 통하여 비교 검증한다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.1
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• pp.31-36
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• 2008

This paper presents adaptive learning data of evolvable neural networks (ENNs) for time series prediction of nonlinear dynamic systems. ENNs are a special class of neural networks that adopt the concept of biological evolution as a mechanism of adaptation or learning. ENNs can adapt to an environment as well as changes in the enviromuent. ENNs used in this paper are L-system and DNA coding based ENNs. The ENNs adopt the evolution of simultaneous network architecture and weights using indirect encoding. In general just previous data are used for training the predictor that predicts future data. However the characteristics of data and appropriate size of learning data are usually unknown. Therefore we propose adaptive change of learning data size to predict the future data effectively. In order to verify the effectiveness of our scheme, we apply it to chaotic time series predictions of Mackey-Glass data.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.2
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• pp.125-130
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• 2001

This paper presents an indirect field-oriented (IFO) induction motor position servo drives which uses the model following adaptive controller with the artificial neural network(ANN)-based rotor resistance estimator. The model reference adaptive system(MRAS)-based 2-layer ANN estimates the rotor resistance on-line and a linear model-following position controller is designed by using the estimated the rotor resistance value. At the end, a fuzzy logic system(FLS) is added to make the position controller robust to the external disturbances and the parameter variations. The simulation results show the effectiveness of the proposed method.

• Geomechanics and Engineering
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• v.27 no.5
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• pp.511-525
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• 2021

In the design of underground excavation, the shear strength (SS) is a key characteristic. It describes the way the rock material resists the shear stress-induced deformations. In general, the measurement of the parameters related to rock shear strength is done through laboratory experiments, which are costly, damaging, and time-consuming. Add to this the difficulty of preparing core samples of acceptable quality, particularly in case of highly weathered and fractured rock. This study applies rock index test to the indirect measurement of the SS parameters of shale. For this aim, two efficient artificial intelligence methods, namely (1) adaptive neuro-fuzzy inference system (ANFIS) implemented by subtractive clustering method (SCM) and (2) support vector regression (SVR) optimized by Harmony Search (HS) algorithm, are proposed. Note that, it is the first work that predicts the SS parameters of shale through ANFIS-SCM and SVR-HS hybrid models. In modeling processes of ANFIS-SCM and SVR-HS, the results obtained from the rock index tests were set as inputs, while the SS parameters were set as outputs. By reviewing the obtained results, it was found that both ANFIS-SCM and SVR-HS models can provide acceptable predictions for interlocking and friction angle parameters, however, ANFIS-SCM showed a better generalization capability.