• Proceedings of the KIEE Conference
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• 2006.04b
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• pp.146-148
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• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications. This paper proposes efficiency optimization control of IPMSM drive using AFLC-FNN(Adaptive Fuzzy Learning Control Fuzzy Neural Network)controller. In order to maximize the efficiency in such applications, this paper proposes the optimal control method of the armature current. The optimal current can be decided according to the operating speed and the load conditions. This paper proposes speed control of IPMSM using AFLC-FNN and estimation of speed using ANN controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled AFLC-FNN controller, the operating characteristics controlled by efficiency optimization control are examined in detail.

• Journal of the Korean Data and Information Science Society
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• v.9 no.2
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• pp.345-355
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• 1998

The back propagation(BP) algorithm allows multilayer feedforward neural networks to learn input-output mappings from training samples. It iteratively adjusts the network parameters(weights) to minimize the sum of squared approximation errors using a gradient descent technique. However, the mapping acquired through the BP algorithm may be corrupt when errorneous training data are employed. In this paper two types of robust backpropagation algorithms are discussed both from a theoretical point of view and in the case studies of nonlinear regression function estimation and handwritten Korean character recognition. For future research we suggest Bayesian learning approach to neural networks and compare it with two robust backpropagation algorithms.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.2 s.173
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• pp.455-463
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• 2000

In order to develop nondestructive techniques for the quantitative estimation of creep damage a series of crept copper samples were prepared and their ultrasonic velocities were measured. Velocities measured in three directions with respect to the loading axis decreased nonlinearly and their anisotropy increased as a function of creep-induced porosity. A progressive damage model was described to explain the void-velocity relationship, including the anisotropy. The comparison of modeling study showed that the creep voids evolved from sphere toward flat oblate spheroid with its minor axis aligned along the stress direction. This model allowed us to determine the average aspect ratio of voids for a given porosity content. A novel technique, the back propagation neural network (BPNN), was applied for estimating the porosity content due to the creep damage. The measured velocities were used to train the BP classifier, and its accuracy was tested on another set of creep samples containing 0 to 0.7 % void content. When the void aspect ratio was used as input parameter together with the velocity data, the NN algorithm provided much better estimation of void content.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1265-1271
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• 2009

본 연구에서는 강우의 시 공간적 분포의 불규칙한 변동성을 고려한 강우-유출예측모형을 위해 인공신경망(Artificial Neural Networks: ANNs)의 기법의 일종인 자기조직화(Self Organizing Map: SOM) 이론과 역전파 학습 알고리즘(Back Propagation Algorithm: BPA) 이론을 복합적으로 이용하였다. 기존의 인공신경망 연구에서 야기된 저 갈수기의 유출량에 대한 과대평가, 홍수기의 유출량에 대한 과소평가, 예측값이 연속적으로 선행 유출량을 나타내는 Persistence 현상을 해결하기 위하여 패턴분류 성능을 지닌 SOM 이론을 예측모형의 전처리 과정으로 이용하였다. 먼저, 본 연구에서 제안한 방법은 SOM에 의해 강우-유출 관계를 분류하고, SOM에 의한 분류에 따라 각각의 모형을 구성한다. 개별적으로 구축된 모형은 유출량의 예측을 위해 각각의 양상에 따라 분류된 자료를 이용한다. 결과적으로 본 연구에서 제안한 방법은 과거의 인공신경망의 일반적인 적용에 의한 결과보다 더 나은 예측능력을 보여주었으며, 더불어 유출량의 과소 및 과대추정과 Persistence 현상과 같은 문제점이 나타나지 않았다. 또한 강우량 및 유출량의 범위에 제한을 받지 않는 강우-유출예측 모형의 개발 및 홍수기로부터 갈수기까지의 보다 넓은 범위의 유출량의 예측에 기여할 것으로 기대된다.

• Journal of the Korean Society of Safety
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• v.17 no.1
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• pp.11-17
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• 2002

The purpose of this study was to present the method to choose the optimization machining condition for the wire electric machine. This was completed by examining the ever-changing quality of the material and by improving the function of the wire electric discharge machine. Precision metal mold products and the unmanned wire electric discharge machining system were used and then applied in industrial fields. This experiment uses the wire electric discharge machine with brass wire electrode of 0.25mm. To measure the precision of the machining surface, average values are obtained from 3 samples of measures of center-line average roughness by using a third dimension gauge and a stylus surface roughness gauge. In this experiment, we changed no-node voltage to 7 and 9, pulse-on-time to $6{\mu}s,\;8{\mu}s$ and $10{\mu}s$, pulse-off-time to $8{\mu}s,\;10{\mu}s$ and $13{\mu}s$, and experimented on wire tension at room temperature by 1000gf, 1200gf, and 1400gf, respectively.

• International Journal of Contents
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• v.7 no.1
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• pp.14-22
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• 2011

Temporal Sign Language Recognition (TSLR) from hand motion is an active area of gesture recognition research in facilitating efficient communication with deaf people. TSLR systems consist of two stages: a motion sensing step which extracts useful features from signers' motion and a classification process which classifies these features as a performed sign. This work focuses on two of the research problems, namely unknown time varying signal of sign languages in feature extraction stage and computing complexity and time consumption in classification stage due to a very large sign sequences database. In this paper, we propose a combination of Dynamic Time Warping (DTW) and application of the Single hidden Layer Feedforward Neural networks (SLFNs) trained by Extreme Learning Machine (ELM) to cope the limitations. DTW has several advantages over other approaches in that it can align the length of the time series data to a same prior size, while ELM is a useful technique for classifying these warped features. Our experiment demonstrates the efficiency of the proposed method with the recognition accuracy up to 98.67%. The proposed approach can be generalized to more detailed measurements so as to recognize hand gestures, body motion and facial expression.

• Computers and Concrete
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• v.10 no.6
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• pp.557-573
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• 2012

This paper discusses the combined application of two different techniques, Neural Networks (NN) and Principal Component Analysis (PCA), for improved prediction of concrete properties. The combination of these approaches allowed the development of six neural networks models for predicting slump and compressive strength of concrete with mineral additives such as blast furnace slag, fly ash and silica fume. The Back-Propagation Multi-Layer Perceptron (BPMLP) with Bayesian regularization was used in all these models. They are produced to implement the complex nonlinear relationship between the inputs and the output of the network. They are also established through the incorporation of a huge experimental database on concrete organized in the form Mix-Property. Thus, the data comprising the concrete mixtures are much correlated to each others. The PCA is proposed for the compression and the elimination of the correlation between these data. After applying the PCA, the uncorrelated data were used to train the six models. The predictive results of these models were compared with the actual experimental trials. The results showed that the elimination of the correlation between the input parameters using PCA improved the predictive generalisation performance models with smaller architectures and dimensionality reduction. This study showed also that using the developed models for numerical investigations on the parameters affecting the properties of concrete is promising.

• Environmental Engineering Research
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• v.22 no.3
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• pp.302-311
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• 2017

Nowadays, with the burgeoning development of economy, $CO_2$ emissions increase rapidly in China. It has become a common concern to seek effective methods to forecast $CO_2$ emissions and put forward the targeted reduction measures. This paper proposes a novel hybrid model combined principal component analysis (PCA) with regularized extreme learning machine (RELM) to make $CO_2$ emissions prediction based on the data from 1978 to 2014 in China. First eleven variables are selected on the basis of Pearson coefficient test. Partial autocorrelation function (PACF) is utilized to determine the lag phases of historical $CO_2$ emissions so as to improve the rationality of input selection. Then PCA is employed to reduce the dimensionality of the influential factors. Finally RELM is applied to forecast $CO_2$ emissions. According to the modeling results, the proposed model outperforms a single RELM model, extreme learning machine (ELM), back propagation neural network (BPNN), GM(1,1) and Logistic model in terms of errors. Moreover, it can be clearly seen that ELM-based approaches save more computing time than BPNN. Therefore the developed model is a promising technique in terms of forecasting accuracy and computing efficiency for $CO_2$ emission prediction.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.258-261
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• 2007

This paper presents techniques to recognize music using back propagation network, one of the neural network algorithms, and to preprocess technique for music image. Music symbols and music notes are segmented by preprocessing such as binarization, slope correction, staff line removing, etc. Segmented music symbols and music notes are recognized by music note recognizing network and non-music note recognizing network. We proved correctness of proposed music recognition algorithm through experiments and analysis with various kind of musics.

• Proceedings of the KIEE Conference
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• 2005.11b
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• pp.162-164
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• 2005

This paper presents an hour-ahead System Marginal Price (SMP) forecasting framework based on a neural network. Recently, the deregulation in power industries has impacted on the power system operational problems. The bidding strategy of market participants in energy market is highly dependent on the short-term price levels. Therefore, short-term SMP forecasting is a very important issue to market participants to maximize their profits. and to market operator who may wish to operate the electricity market in a stable sense. The proposed hybrid neural network is composed of tow parts. First part of this scheme is pattern classification to input data using Kohonen Self-Organizing Map (SOM) and the second part is SMP forecasting using back-propagation neural network that has three layers. This paper compares the forecasting results using classified input data and unclassified input data. The proposed technique is trained, validated and tested with historical date of Korea Power Exchange (KPX) in 2002.