• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.43.4-43
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• 2001

This paper deals with motion identification using artificial neural network (ANN) and its application to automatic ship berthing. As ship motions are expressed by multi-term non-linear model, it is very difficult to find optimal methods for automatic ship berthing especially under environmental disturbances. In this paper, metier identification was used to estimate the effect of environmental disturbances and then the differences between values of identification and state variables are used to estimate the effect of environmental disturbances. A rule based-algorithm using the difference is suggested to cope with the effect of the disturbances. The algorithm adjusts the value of input units of ANN, which control a ship to keep desired route ...

• The Journal of the Acoustical Society of Korea
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• v.27 no.5
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• pp.252-255
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• 2008

In this paper, we apply a discriminative weight training to a support vector machine (SVM) based gender identification. In our approach, the gender decision rule is expressed as the SVM of optimally weighted mel-frequency cepstral coefficients (MFCC) based on a minimum classification error (MCE) method which is different from the previous works in that different weights are assigned to each MFCC filter bank which is considered more realistic. According to the experimental results, the proposed approach is found to be effective for gender identification using SVM.

• Journal of the Korean Society of Marine Environment & Safety
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• v.23 no.3
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• pp.224-230
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• 2017

The purpose of this study is to classify the deficient abilities of seafarers into SRK (Skill, Rule, and Knowledge) and analyze and identify the SRK by the type of accident and ship. Experimental data used the SRK cumulative frequency for 1,606 marine accident records and two-way ANOVA and t-test were used for the analysis tools. The results of two-way ANOVA showed that it is possible to identify the deficient abilities by using the cumulative frequency of SRK in both accident and ship types. As a result of the t-test, the adoption of the null hypothesis (H=0) that the mean of two pairs is equal and the rejection of the null hypothesis (H=1) were 29.2 % and 70.8 %, respectively. For the ship type, H=0 is 33.3 % and H=1 is 66.7 %. Through this study, it was found that about 70 % of the deficient abilities of seafarers inherent in maritime accidents can be identified using the proposed method.

• Journal of Advanced Marine Engineering and Technology
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• v.27 no.1
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• pp.42-48
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• 2003

In general many engineers can diagnose the fault condition using the abnormal ones among data monitored from a diesel engine, but they don't need the system modelling or identification for the work. They check the abnormal data and the relationship and then catch the fault condition of the engine. This paper proposes the construction of a fault diagnosis engine through malfunction data gained from the data fault detection system of neural networks for diesel generator engine, and the rule inference method to induce the rule for fuzzy inference from the malfunction data of diesel engine like a site engineer with a fuzzy system. The proposed fault diagnosis system is constructed in the sense of the Malfunction Diagnosis Engine(MDE) and Hierarchy of Malfunction Hypotheses(HMH). The system is concerned with the rule reduction method of knowledge base for related data among the various interactive data.

• KSBB Journal
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• v.6 no.1
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• pp.15-25
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• 1991

The optimal glucose concentration for the high-density culture of recombinant yeasts was obtained using dynamic simulation. An adaptive and predictive algoritilm complimented by the rule base was proposed for the control of the fed-batch fermentation process. The measurement of process variables has relatively long sampling period and relatively long time delay characteristics. As one of the solution on these problems, prediction techniques and rule bases were added to a classical recursive identification and control algorithm. Rule bases were used in the determination of control input considering the difference between the predicted value and the measured value. A mathelnatical model was used in the estimation and interpretation of the changes of state variables and parameters. Better performances were obtained by employing the control algorithm proposed in the present study compared to the conventional adaptive control method.

• Journal of Information Technology Application
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• v.1 no.3_4
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• pp.69-90
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• 1999

본 논문은 시계열자료의 ARMA 모형화를 위해 계층적(Hierarchical) 문제해결 방식인 인공신경망 기초 의상결정트리분류기상의 인공신경망 구조를 개선하여 지역문제(Local Problem)를 해결하는 복수개의 인공신경망 결과를 Dempster's rule of combination을 이용하여 종합하는 병행적인 (Parallel) ARMA 모형활르 위한 방법론을 제시함으로써 의사결정트리분류기에 근거한 방법론의 단점을 보완하였다. 본 논문에서 제시한 ARMA 모형화를 위한 방법론은 세 단계로 구성되어 있다: 1) ESACF 특성 벡터 추출단계; 2) 개별 인공신경망에 의한 부분적 모델링 단계; 3) Conflict Resolution 단계, 제시한 방법론을 검증하기 위해 모의실험용 자료와 실제 시계열자료를 이용하여 제시된 방법론을 검증하였으며 실험결과 기존 연구에 비해 ARMA 모형화와 정확도가 높은 것으로 나타났다.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.36-39
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• 1996

This paper proposes identification and control algorithm of nonlinear systems and the proposed fuzzy-neural network has following characteristics. The network is roughly divided into premise and consequence. The consequence function is nonlinear function which consists of three parameters and the membership function in the premise contains of two parameters. The parameters in premise and consequence are learned by the extended back-propagation algorithm which has a modified form of the generalized delta rule. Simulation results on the identification show that this method is more effective than that of Narendra [3]. The indirect fuzzy-neural control is made of the fuzzy-neural identification and controller. Result on the indirect fuzzy-neural control shows that the proposed fuzzy-neural network can be efficiently applied to nonlinear systems.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.6
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• pp.1311-1322
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• 1997

Performance management of ATM network is urgently required because a different quality of service should be guaranteed on each connection. A lot of performance measurement data increase the burden on agent systems and on managment stations. In this paper, an effective OAM-based performance monitoring and faulty node identification technique is proposed. A proposed VP Selection Algorithm reduces management-related traffic and when the indication of hard or soft failure state is detected, failed node is identified by Fault Identification Rule.

• Speech Sciences
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• v.13 no.2
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• pp.107-120
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• 2006

The main purpose of this study is to investigate whether Korean learners can use the vowel duration cue to distinguish voicing contrasts in word-final consonants in English. Given that the Korean group's performance on the auditory task was much better than their performance on the identification task or on the production task, we conclude that the AX discrimination task makes contact with a different layer of perception. In particular, the AX discrimination task can be done at the auditory or phonetic level, where differences in vowel length are still encoded in the representation. In contrast, the identification and production tasks are probing the mental representation of vowel length and voicing. It was also founded that Korean speakers stored neither vowel length nor voicing in memorized representations and did not internalize the lengthening of the preceding vowel as a rule to differentiate the voicing contrasts of final consonants, even though they were able to detect the acoustic differences in vowel duration provided that they were tested in an appropriate task.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.578-580
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• 1998

The paper presents an approach to the structure identification based on the CART (Classification And Regression Tree) algorithm and to the parameter identification by hybrid learning method in neuro-fuzzy system. By using the CART algorithm, the proposed method can roughly estimate the numbers of membership function and fuzzy rule using the centers of decision regions. Then the parameter identification is carried out by the hybrid learning scheme using BP (Back-propagation) and RLSE (Recursive Least Square Estimation) from the numerical data. Finally, we will show it's usefulness for fuzzy modeling to truck backer upper control.