• Journal of the Korea Convergence Society
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• v.8 no.10
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• pp.45-52
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• 2017

Drifter is an equipment for observing the characteristics of seawater in the ocean, and it can be used to predict effluent oil diffusion and to observe ocean currents. In this paper, we design models or the prediction of drifter trajectory using machine learning. We propose methods for estimating the trajectory of drifter using support vector regression, radial basis function network, Gaussian process, multilayer perceptron, and recurrent neural network. When the propose mothods were compared with the existing MOHID numerical model, performance was improve on three of the four cases. In particular, LSTM, the best performed method, showed the imporvement by 47.59% Future work will improve the accuracy by weighting using bagging and boosting.

• Journal of the Korea Convergence Society
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• v.8 no.10
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• pp.1-8
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• 2017

Accurate forecasting enables to effectively prepare for future phenomenon. Especially, meteorological phenomenon is closely related with human life, and it can prevent from damage such as human life and property through forecasting of weather and disaster that can occur. To respond quickly and effectively to oil spill accidents, it is important to accurately predict the movement of oil spills and the weather in the surrounding waters. In this paper, we selected four representative machine learning techniques: support vector machine, Gaussian process, multilayer perceptron, and radial basis function network that have shown good performance and predictability in the previous studies related to oil spill detection and prediction in meteorology such as wind, rainfall and ozone. we suggest the applicability of oil spill prediction model based on machine learning.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.7
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• pp.63-70
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• 2006

This paper describes research of using area of artificial mark on vision based head tracker system. A head tracker system consists of the translational and rotational motions which are detected by web camera. Results of the motion are taken from image processing and neural network. Because of the characteristics of cockpit, the specific color on the helmet is tracked for translational motion. And rotational motion is tracked via neural network. Ratio of two different colored area on the helmet is used as input of network. Neural network algorithms used, such as back-propagation and RBFN (Radial Basis Function Network). Both back-propagation using a characteristic of feedback and RBFN using a characteristic of statistics have a good performances for the tracking of nonlinear system such as a head motion. Finally, this paper analyzes and compares with tracking performance.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.3
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• pp.57-67
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• 2018

The ensemble is a unified approach used for getting better performance by using multiple algorithms in machine learning. In this paper, we introduce boosting and bagging, which have been widely used in ensemble techniques, and design a method using support vector regression, radial basis function network, Gaussian process, and multilayer perceptron. In addition, our experiment was performed by adding a recurrent neural network and MOHID numerical model. The drifter data used for our experimental verification consist of 683 observations in seven regions. The performance of our ensemble technique is verified by comparison with four algorithms each. As verification, mean absolute error was adapted. The presented methods are based on ensemble models using bagging, boosting, and machine learning. The error rate was calculated by assigning the equal weight value and different weight value to each unit model in ensemble. The ensemble model using machine learning showed 61.7% improvement compared to the average of four machine learning technique.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.2
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• pp.173-178
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• 2014

In this study, we introduce ASM-based face recognition classifier and its design methodology with the aid of 2-dimensional 2-directional hybird preprocessing algorithm. Since the image of face recognition is easily affected by external environments, ASM(active shape model) as image preprocessing algorithm is used to resolve such problem. In particular, ASM is used widely for the purpose of feature extraction for human face. After extracting face image area by using ASM, the dimensionality of the extracted face image data is reduced by using $(2D)^2$hybrid preprocessing algorithm based on LDA and PCA. Face image data through preprocessing algorithm is used as input data for the design of the proposed polynomials based radial basis function neural network. Unlike as the case in existing neural networks, the proposed pattern classifier has the characteristics of a robust neural network and it is also superior from the view point of predictive ability as well as ability to resolve the problem of multi-dimensionality. The essential design parameters (the number of row eigenvectors, column eigenvectors, and clusters, and fuzzification coefficient) of the classifier are optimized by means of ABC(artificial bee colony) algorithm. The performance of the proposed classifier is quantified through yale and AT&T dataset widely used in the face recognition.