• Journal of Mechanical Science and Technology
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• 제18권11호
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• pp.1916-1922
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• 2004

A robust position control with the bound function of neural network structure is proposed for uncertain robot manipulators. The uncertain factors come from imperfect knowledge of system parameters, payload change, friction, external disturbance, and etc. Therefore, uncertainties are often nonlinear and time-varying. The neural network structure presents the bound function and does not need the concave property of the bound function. The robust approach is to solve this problem as uncertainties are included in a model and the controller can achieve the desired properties in spite of the imperfect modeling. Simulation is performed to validate this law for four-axis SCARA type robot manipulator.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1996년도 추계학술대회발표논문집; 고려대학교, 서울; 26 Oct. 1996
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• pp.31-34
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• 1996

The extensive researches have compared the performance of neural networks(NN) with those of various statistical techniques for the classification problem. The empirical results of these comparative studies have indicated that the neural networks often outperform the traditional statistical techniques. Moreover, there are some efforts that try to combine various classification methods, especially multivariate discriminant analysis with neural networks. While these efforts improve the performance, there exists a problem violating robust assumptions of multivariate discriminant analysis that are multivariate normality of the independent variables and equality of variance-covariance matrices in each of the groups. On the contrary, cluster analysis alleviates this assumption like neural networks. We propose a new approach to classification problems by combining the cluster analysis with neural networks. The resulting predictions of the composite model are more accurate than each individual technique.

• Steel and Composite Structures
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• 제9권5호
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• pp.445-455
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• 2009

This study proposes a system identification technique for a fiber-reinforced polymer deck with neural networks. Neural networks are trained for system identification and the identified structure gives training data in return. This process is repeated until the identified parameters converge. Hence, the proposed algorithm is called an iterative neural network scheme. The proposed algorithm also relies on recent developments in the experimental design of the response surface method. The proposed strategy is verified with known systems and applied to a fiber-reinforced polymer bridge deck with experimental data.

• 전자공학회논문지B
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• 제32B권12호
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• pp.1545-1551
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• 1995

As the modern industrial processes become more complex, it is getting more difficult to model and control the processes. Naturally, an advanced type of DCS(Distributed Control System) with higher level functions is being sought. Advanced DCS is a DCS with advanced functions such as fault diagnosis, GPC(Generalized Predictive Control), NN(Neural Network), and Fuzzy Control. In this thesis, we have studied a fuzzy control algorithm for realizing an advanced DCS. Its algorithm is implemented in a form of function code which is a process control language, being used by the industrial engineers. To verify the realized function code of the fuzzy control, the function code is applied to a continuous casting process of the Pohang Iron & Steel Works in Kwangyang. The rules of the fuzzy control were collected via interviews of the field operators and their operation documents. Finally under a real-time operating system environment, usability of the function code of the fuzzy control is shown via simulation for the continuous casting process.

• 전력전자학회:학술대회논문집
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• 전력전자학회 1998년도 전력전자학술대회 논문집
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• pp.384-387
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• 1998

This paper presents a newly developed speed sensorless drive using RLS(Recursive Least Squares) based on Neural Network Training Algorithm. The proposed algorithm based on the RLS has just the time-varying learning rate, while the well-known back-propagation (or generalized delta rule) algorithm based on gradient descent has a constant learning rate. The number of iterations required by the new algorithm to converge is less than that of the back-propagation algorithm. The RLS based on NN is used to adjust the motor speed so that the neural model output follows the desired trajectory. This mechanism forces the estimated speed to follow precisely the actual motor speed. In this paper, a flux estimation strategy using filter concept is discussed. The theoretical analysis and experimental results to verify the effectiveness of the proposed analysis and the proposed control strategy are described.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2001년도 봄 학술발표논문집 Vol.28 No.1 (B)
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• pp.253-255
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• 2001

인터넷을 통한 사용자의 선호도를 분석하고 협력적 여과 및 내용기반 여과 기술을 결합 이용하여 유머문서를 추천하는 MrHumor 시스템을 구축하였다. 유머문서 추천 기술은 다양한 아이템에 대한 여과 및 추천 기술로 확장되어 인터넷을 통한 과다 정보 시대에 필요한 소프트봇 혹은 지능형 에이전트 기술에 적용될 수 있다. MrHumor 추천시스템은 적응형 학습 시스템으로서 새로운 사용자의 선호도에 대한 학습량과 추천시기에 따라 이용할 추천방식이 다른 성능을 보이는데 여러 가지 상황에서도 적절한 동작을 보이기 위하여 MrHumor에서는 은닉변수 모델을 이용하여 사용자의 인구통계적 정보와 문서의 내용적 특징간의 관계를 학습하여 초기 추천을 행하고 SVM을 이용하여 개인의 선호도를 학습한 내용 기반의 여과와 적응형 k-NN모델을 이용한 협력적 여과를 결합하여 추천을 수행한다. 제안된 방식에 의한 추천 성능은 3방식이 각각 이용된 경우에 비해 안정적이고 높은 예측 정확도를 보인다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제10권1호
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• pp.37-42
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• 2010

Final year students are normally encountering high pressing in their study. In view of this fact, this research focuses on determining mental states condition of college student in final year based on the psycho-physiological information. The experiments were conducted in two times, i.e., prior- and post- graduation seminar examination. The early results indicated that the student profile of mood states (POMS) in prior final graduation seminar showed higher scores than students in post final graduation seminar. Thus, in this research, relation between biosignal representing by heart rate variability (HRV) and questionnaire responses were evaluated by hidden Markov model (HMM) and neural networks (NN).

• 한국전산구조공학회논문집
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• 제23권6호
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• pp.667-673
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• 2010

Recently, structures are becoming longer and higher because of the developments of new materials and construction techniques. However, such modern structures are more susceptible to excessive structural vibrations which cause deterioration in serviceability and structural safety. A modified probabilistic neural network(MPNN) approach is proposed to reduce the structural vibration. In this study, the global probability density function(PDF) of MPNN is reflected by summing the heterogeneous local PDFs automatically determined in the individual standard deviation of each variable. The proposed algorithm is applied for the vibration control of a three-story shear building model under Northridge earthquake. When the control results of the MPNN are compared with those of conventional PNN to verify the control performance, the MPNN controller proves to be more effective than PNN methods in decreasing the structural responses.

• 전기학회논문지
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• 제60권2호
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• pp.293-300
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• 2011

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and stator resistance and adaptive fuzzy learning contrroller(AFLC) for speed control in IPMSM Drives. AFLC is chaged fuzzy rule base by rule base modifier for robust control of IPMSM. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator and AFLC is confirmed by comparing to conventional algorithm.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2006년도 추계학술발표대회
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• pp.397-400
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• 2006

본 논문에서는 위치기반 서비스에서 도로 네트워크 상을 움직이는 객체들의 현재 위치를 효율적으로 갱신하는 색인기법을 제안한다. 효율적인 시공간 질의 처리를 위하여 도로를 적절하게 분할하여 색인하게 된다. 기존의 도로 분할 방식은 연결정보를 포함하지 않기 때문에 객체가 현재의 도로 영역을 벗어났을 때 색인구조를 처음부터 검색해야 하는 단점이 있다. 제안하는 기법은 도로를 분할할 때 교차점을 포함하도록 분할하여 연결 정보가 유지되도록 한다. 이로 인해 제안하는 알고리즘은 객체가 색인된 현재의 도로 영역을 벗어났을 때 연결 정보와 NN(Nearest Neighbor) 검색 기법을 이용하여 벗어난 도로를 바로 찾아갈 수 있으므로 향상된 갱신 성능을 보인다.