• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.4 no.3
• /
• pp.57-62
• /
• 2005

Neural networks, which have learning and self-organizing abilities, can be advantageously used in the pattern recognition. Neural network techniques have been widely used in monitoring and diagnosis, and compare favourable with traditional statistical pattern recognition algorithms, heuristic rule-based approaches, and fuzzy logic approaches. In this study the fault diagnosis of the face-milling using the artificial neural network was investigated. After training, the sample which measure load current was monitored by constant output results.

• Proceedings of the KIEE Conference
• /
• 1990.07a
• /
• pp.143-146
• /
• 1990

The human brain has the most powerful capabilities in thinking, interpreting, remembering, and problem-solving. Artificial neural network is appeared by scientists who have tried to simulate such a human brain. The artificial neural network has the capability of learning, massive parallelism capability and robustness for disturbance which are necessary for power system application. In this paper, We reviewed the typical topologies and learning algorithms of artifical neural networks which can be used for pattern classification. And we surveyed for the applications of artifical neural network to the power system.

• Proceedings of the KIEE Conference
• /
• 2007.07a
• /
• pp.1422-1423
• /
• 2007

This study deals with various neural network algorithms for the on-site partial discharge pattern recognition. For the purpose, the pattern recognition has been carried out on partial discharge data for the typical artificial defect using 9 different neural network models. In order to enhance on-site applicability, artificial defects were installed in the insulation joint box of extra-high voltage xLPE cables and partial discharges were measured by use of the metal foil sensor and a HFCT as a sensor. As the result, it is found out that the accuracy of pattern recognition could be enhanced through the application of the Sigmoid function, the Momentum algorithm and the Genetic algorism on the artificial neural networks. Although Multilayer Perceptron (MLP) algorism showed the best result among 9 neural network algorisms, it is thought that more researches on others would be needed in consideration of on-site application.

• Journal of Biosystems Engineering
• /
• v.37 no.4
• /
• pp.271-278
• /
• 2012

Worldwide trends in animal welfare have resulted in an increased interest in individual management of sows housed in groups within hog barns. Estrus detection has been shown to be one of the greatest determinants of sow productivity. Purpose: We conducted this study to develop a method that can automatically detect the estrus state of a sow by selecting optimal texture parameters from images of a sow's pudendum and by optimizing the number of neurons in the hidden layer of an artificial neural network. Methods: Texture parameters were analyzed according to changes in a sow's pudendum in estrus such as mucus secretion and expansion. Of the texture parameters, eight gray level co-occurrence matrix (GLCM) parameters were used for image analysis. The image states were classified into ten grades for each GLCM parameter, and an artificial neural network was formed using the values for each grade as inputs to discriminate the estrus state of sows. The number of hidden layer neurons in the artificial neural network is an important parameter in neural network design. Therefore, we determined the optimal number of hidden layer units using a trial and error method while increasing the number of neurons. Results: Fifteen hidden layers were determined to be optimal for use in the artificial neural network designed in this study. Thirty images of 10 sows were used for learning, and then 30 different images of 10 sows were used for verification. Conclusions: For learning, the back propagation neural network (BPN) algorithm was used to successful estimate six texture parameters (homogeneity, angular second moment, energy, maximum probability, entropy, and GLCM correlation). Based on the verification results, homogeneity was determined to be the most important texture parameter, and resulted in an estrus detection rate of 70%.

• The Bulletin of The Korean Astronomical Society
• /
• v.39 no.2
• /
• pp.107.1-107.1
• /
• 2014

We apply a machine learning algorithm, artificial neural network, to the search for gravitational-wave signals associated with short gamma-ray bursts. The multi-dimensional samples consisting of data corresponding to the statistical and physical quantities from the coherent search pipeline are fed into the artificial neural network to distinguish simulated gravitational-wave signals from background noise artifacts. Our result shows that the data classification efficiency at a fixed false alarm probability is improved by the artificial neural network in comparison to the conventional detection statistic. Therefore, this algorithm increases the distance at which a gravitational-wave signal could be observed in coincidence with a gamma-ray burst. We also evaluate the gravitational-wave data within a few seconds of the selected short gamma-ray bursts' event times using the trained networks and obtain the false alarm probability. We suggest that artificial neural network can be a complementary method to the conventional detection statistic for identifying gravitational-wave signals related to the short gamma-ray bursts.

• Journal of the Korea Institute of Building Construction
• /
• v.13 no.1
• /
• pp.66-74
• /
• 2013

In the early stages of a construction project, the most important thing is to predict construction costs in a rational way. For this reason, many studies have been performed on the estimation of construction costs for apartment housing and office buildings at early stage using artificial intelligence, statistics, and the like. In this study, cost data held by a provincial Office of Education on elementary schools constructed from 2004 to 2007 were used to compare the multiple regression model with an artificial neural network model. A total of 96 historical data were classified into 76 historical data for constructing models and 20 historical data for comparing the constructed regression model with the artificial neural network model. The results of an analysis of predicted construction costs were that the error rate of the artificial neural network model is lower than that of the multiple regression model.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.29 no.3C
• /
• pp.97-104
• /
• 2009

In this study, artificial neural network was performed using the data of soils characteristic value, standard penetration test, and field permeability test of the 12 embankment that are located in the near Nak-dong and Kum-ho river to estimate the coefficient of field permeability of river embankment. The 89 data of total 108, 82% was used in learning step, and the other 19 data was used in estimation step. Also the results of generally used empirical equations were compared with those of artificial neural network for evaluation of application. As results, all of the coefficient of field permeability by empirical equation showed below 0.4 in terms of the coefficient of correlation with the measured values, but the coefficient of correlation of the predicted results by artificial neural network was up 0.8 in the all case. Therefore artificial neural network could predict more the precise field permeability well than the empirical equations.

• Speech Sciences
• /
• v.11 no.1
• /
• pp.21-29
• /
• 2004

In this paper we examined the effect of normalization on discriminating the pathological voice into normal and abnormal classes using artificial neural network. Average values per each parameter were used to normalize each set of parameter values. Artificial neural networks were used as classifiers. And the effect of normalization was evaluated by comparing the discrimination results between original and normalized parameter sets.

• Journal of information and communication convergence engineering
• /
• v.16 no.1
• /
• pp.1-5
• /
• 2018

In radio-over-fiber (RoF) systems, nonlinear compensation is very important to meet the error vector magnitude (EVM) requirement of the mobile network standards. In this study, a nonlinear compensation technique based on an artificial neural network (ANN) is proposed for RoF systems. This technique is based on a backpropagation neural network (BPNN) with one hidden layer and three neuron units in this study. The BPNN obtains the inverse response of the system to compensate for nonlinearities. The EVM of the signal is measured by changing the number of neurons and the hidden layers in a RoF system modeled by a measured data. Based on our simulation results, it is concluded that one hidden layer and three neuron units are adequate for the RoF system. Our results showed that the EVMs were improved from 4.027% to 2.605% by using the proposed ANN compensator.

• The Journal of Korean Association of Computer Education
• /
• v.21 no.1
• /
• pp.51-58
• /
• 2018

Recently artificial neural networks have shown excellent performance in various fields. However, there is a problem that it is difficult for a person to understand what is the knowledge that artificial neural network trained. One of the methods to solve these problems is an algorithm for extracting rules from trained neural network. In this paper, we extracted rules from artificial neural networks using ordered-attribute search(OAS) algorithm, which is one of the methods of extracting rules, and analyzed result to improve extracted rules. As a result, we have found that the distribution of output values of the hidden layer unit affects the accuracy of rules extracted by using OAS algorithm, and it is suggested that efficient rules can be extracted by binarizing hidden layer output values using hidden unit clarification.