• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 추계학술대회
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• pp.21-22
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• 2021

Early academic warning is considered as an inherent problem in education data mining. Early and timely concern and guidance can save a student's university career. It is widely assumed as a multi-class classification system in view of machine learning. Therefore, An accurate and precise methodical solution is a complicated task to accomplish. For this issue, we present a hybrid model employing rough set theory with a back-propagation neural network to ameliorate the predictive capability of the system with an illustrative example. The experimental results show that it is an effective early academic warning model with an escalating improvement in predictive accuracy.

• 융합신호처리학회논문지
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• 제3권1호
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• pp.54-60
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• 2002

본 논문에서는 신경망제어기의 출력을 보상하는 퍼지보상기를 갖는 신경망-퍼지 하이브리드 제어기에 관하여 제안하였다. 학습이 완료된 신경망제어기를 사용하더라도 예상치 못한 외란으로 인해 플랜트의 출력이 좋지 못한 경우가 있는데, 이것을 적절하게 조절해 주기 위해 퍼지보상기를 사용하여 원하는 결과를 얻을 수 있도록 하였다. 그리고 플랜트의 역모델 신경망을 학습시킨 결과를 이용하여 주 신경망의 가중치를 변경시킴으로서 원하는 플랜트의 동적 특성을 얻게 된다. 2차 플랜트를 통한 모의실험 결과, 제안한 속도제어기가 신경망제어기보다 더 좋은 응답 특성을 가짐을 확인할 수 있다. 제안한 속도제어기의 성능을 확인하기 위해 유압 서보시스템을 대상으로 DSP 프로세서를 사용하여 구현한 후 실험 결과를 관찰하였다.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회/대한산업공학회 2005년도 춘계공동학술대회 발표논문
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• pp.960-968
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• 2005

The high cost in maintaining complex manufacturing process makes it necessary to enhance an efficient maintenance system. For the effective maintenance of manufacturing process, precise fault diagnosis should be performed and an appropriate maintenance action should be executed. This paper suggests an intelligent fault diagnosis system using hybrid data mining. In this system, the rules for the fault diagnosis are generated by hybrid decision tree/genetic algorithm and the most effective maintenance action is selected by decision network and AHP. To verify the proposed intelligent fault diagnosis system, we compared the accuracy of the hybrid decision tree/genetic algorithm with one of the general decision tree learning algorithm(C4.5) by data collected from a coil-spring manufacturing process.

• ETRI Journal
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• 제36권6호
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• pp.1016-1022
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• 2014

This paper presents a hybrid model solution for user motion recognition. The use of a single classifier in motion recognition models does not guarantee a high recognition rate. To enhance the motion recognition rate, a hybrid model consisting of decision trees and artificial neural networks is proposed. We define six user motions commonly performed in an indoor environment. To demonstrate the performance of the proposed model, we conduct a real field test with ten subjects (five males and five females). Experimental results show that the proposed model provides a more accurate recognition rate compared to that of other single classifiers.

• 전력전자학회논문지
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• 제25권6호
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• pp.503-510
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• 2020

The importance of DC breakers as key protection equipment is increasing in accordance with growing concerns on MVDC distribution network systems without DC/AC conversion. Different from the situation in AC systems, no natural zero-crossing point exists in DC systems. Thus, DC breaker technology is more difficult than AC breaker technology. The solutions for DC breakers can be divided into three types: mechanical, power electronics, and hybrid. In this study, the operating principles of several topologies of hybrid circuit breakers and that of the proposed DC breaker are analyzed and simulated by sorting two types. The breakers are compared in terms of the type and number of semiconductors, volume, power loss, auxiliary components, isolation, and other aspects. The advantages and disadvantages of the breakers are also analyzed.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 International Symposium on GPS/GNSS Vol.2
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• pp.101-106
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• 2006

The geoidal undulations are needed for determining the orthometric heights from the Global Positioning System GPS-derived ellipsoidal heights. There are several methods for geoidal undulation determination. The paper presents a method employing a simple architecture Two Layer Multiquadric-Biharmonic Artificial Neural Network (TLMB-ANN) to approximate an area of 4200 square kilometres quasigeoid surface with GPS-levelling data. Hardy’s Multiquadric-Biharmonic functions is used as the hidden layer neurons’ activation function and Levenberg-Marquardt algorithm is used to train the artificial neural network. In numerical examples five surfaces were compared: the gravimetric geometry hybrid quasigeoid, Support Vector Machine (SVM) model, Hybrid Fuzzy Neural Network (HFNN) model, Traditional Three Layer Artificial Neural Network (ANN) with tanh activation function and TLMB-ANN surface approximation. The effectiveness of TLMB-ANN surface approximation depends on the number of control points. If the number of well-distributed control points is sufficiently large, the results are similar with those obtained by gravity and geometry hybrid method. Importantly, TLMB-ANN surface approximation model possesses good extrapolation performance with high precision.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1995년도 추계학술대회 논문집
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• pp.538-541
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• 1995

Joint flexibilities and frictional uncertainties are known to be a major cause of performance degration in motion control systems. This paper investigates the modeling and compensation of these undesired effects. A hybrid controller, which consists of a predictive controller and a neural network controller, is designed to overcome these undesired effects. Also learning scheme for friction uncertainies, which don't interfere with feedback controller dynamics, is discussed. Through simulation works with two inetia-torsional spring system having Coulomb friction, the effectiveness of the proposed hybrid controller was tested. The proposed predictive & neural network hybrid controller shows better performance over one when only predictive controller used.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1995년도 춘계학술대회 논문집
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• pp.273-277
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• 1995

Many hudraulic components have nonlinearities to some extent. These nonlinearities often cause the time delay, thus degrading the performance of the hydraulic control systems and making it difficult to modelthem. In this paper, a new vibration isolation control algorithm that eliminates the necessity of a sophiscated modeling of hydraulic system was proposed. The algotithm is a hybrid type control shecheme consisting of a linear controller and a hetero-synaptic neural network controller. Using this control scheme, simulations and experiments were performed for 1 DOF(Degree of freedom) and 2 DOF vibration isolation. The hybrid type control algorithm can isolate the base vibration signifcantly rather than linear control algorithm. And from the weights in hetero-synaptic neural network, we can get the 2nd equivalent differentialmodel of the hydraulic control system with on-line control operation. This equivalent model provides us with much information, such as stability and the characteristics of the control system.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 하계종합학술대회 논문집(3)
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• pp.13-16
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• 2000

In this paper, we propose hybrid control method of neural network using the 3-point search algorithm. Proposed control method is searched the weight using the 3-point search algorithm for off-line then control the on-line. In order to verify the usefulness of the proposed method, we simulated the proposed control method with one link manipulator system and confirmed the excellency.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1080-1085
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• 2005

A hybrid control scheme is proposed for the stabilization of backward movement along simple paths for a vehicle composed of a truck and six trailers. The hybrid comprises the combination of a linear quadratic regulator (LQR) and a neurocontroller (NC) that is trained by a genetic algorithm (GA). Acting singly, either the NC or the LQR are unable to perform satisfactorily over the entire range of the operation required, but the proposed hybrid is shown to be capable of providing good overall system performance. The evaluation function of the NC in the hybrid design has been modified from the conventional type to incorporate both the squared errors and the running steps errors. The reverse movement of the trailer-truck system can be modeled as an unstable nonlinear system, with the control problem focusing on the steering angle. Achieving good backward movement is difficult because of the restraints of physical angular limitations. Due to these constraints the system is impossible to globally stabilize with standard smooth control techniques, since some initial states necessarily lead to jack-knife locks. This paper demonstrates that a hybrid of neural networks and LQR can be used effectively for the control of nonlinear dynamical systems. Results from simulated trials are reported.