• Journal of the Korean Institute of Intelligent Systems
• /
• v.23 no.5
• /
• pp.473-478
• /
• 2013

In this paper, we develop the Heavy Rain Advisory Decision Model based on intelligent neuro-fuzzy algorithm RBFNNs by using KLAPS(Korea Local Analysis and Prediction System) Reanalysis data. the prediction ability of existing heavy rainfall forecasting systems is usually affected by the processing techniques of meteorological data. In this study, we introduce the heavy rain forecast method using the pre-processing techniques of meteorological data are in order to improve these drawbacks of conventional system. The pre-processing techniques of meteorological data are designed by using point conversion, cumulative precipitation generation, time series data processing and heavy rain warning extraction methods based on KLAPS data. Finally, the proposed system forecasts cumulative rainfall for six hours after future t(t=1,2,3) hours and offers information to determine heavy rain advisory. The essential parameters of the proposed model such as polynomial order, the number of rules, and fuzzification coefficient are optimized by means of Differential Evolution.

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

• The Journal of Engineering Geology
• /
• v.10 no.2
• /
• pp.189-214
• /
• 2000

Three study areas of Kwanak campus(Seoul National University), Gapyung and Boeun were selected and classified according to bedrock types in order to investigate soil-gas radon concentrations. Several soil-gas samples showed relatively high radon concentrations in the residual soils which derived from granite bedrock. It also showed that water content of soil and the degree of radioactivity disequilibrium was a secondary factor governing radon emanation and distribution of radon radioactivity. The results of radon concentrations and working levels for forty rooms in Kwanak campus, Seoul National University, showed that indoor basement rooms under poor ventilation condition can be classified as high radon risk zone having more than EPA guideline(4 pCi/L). Some results of section analysis which was surveyed in the fault zone of Kyungju and Gapyung area confirmed the existence of fault-associated radon anomalies with a meaning of radon risk zone.

• Journal of the Korean Society of Radiology
• /
• v.16 no.5
• /
• pp.603-609
• /
• 2022

The dose distribution in the human body was evaluated and analyzed through dosimetry data using water phantom, ionization chamber and simulated by Monte Carlo simulation for ^99mTc and ¹⁸F sources, which are frequently used in the nuclear medicine in this study. As a result of this study, it was found that the dose decreased exponentially as the distance from the radioisotope increased, and it particularly showed a tendency to decrease sharply when the radioisotope was separated by 5 cm. It means that a large amount of dose is delivered to an organ located within 4 cm of source's movement path when a source uptake in the human body. Numerically, it was formed in the rage of 0.16 to 2.16 pC/min for ^99mTc and 0.49 to 9.29 pC/min for ¹⁸F. In addition, the energy transfer coefficient calculated using the result was found to be similar to the measured value and the simulation value in the range of 0.240 to 0.260. Especially, when the measured data and the simulation value were compared, there was a difference is within 2%, so the reliability of the data was secured. In this study, the distribution of radiation generated from a source was calculated to quantitatively evaluate the internal dose by radioisotopes. It presented reliable results through comparative analysis of the measurement value and simulation value. Above all, it has a great significance to the point that it was presented by directly measuring the distribution of radiation in the human body.

• Journal of Institute of Control, Robotics and Systems
• /
• v.17 no.8
• /
• pp.731-738
• /
• 2011

In this paper, we introduce an advanced architecture of K-Means clustering-based polynomial Radial Basis Function Neural Networks (p-RBFNNs) designed with the aid of SSOA (Space Search Optimization Algorithm) and develop a comprehensive design methodology supporting their construction. In order to design the optimized p-RBFNNs, a center value of each receptive field is determined by running the K-Means clustering algorithm and then the center value and the width of the corresponding receptive field are optimized through SSOA. The connections (weights) of the proposed p-RBFNNs are of functional character and are realized by considering three types of polynomials. In addition, a WLSE (Weighted Least Square Estimation) is used to estimate the coefficients of polynomials (serving as functional connections of the network) of each node from output node. Therefore, a local learning capability and an interpretability of the proposed model are improved. The proposed model is illustrated with the use of nonlinear function, NOx called Machine Learning dataset. A comparative analysis reveals that the proposed model exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.61 no.1
• /
• pp.135-142
• /
• 2012

In this paper, we proposed a new architecture called radial basis function-based polynomial neural networks classifier that consists of heterogeneous neural networks such as radial basis function neural networks and polynomial neural networks. The underlying architecture of the proposed model equals to polynomial neural networks(PNNs) while polynomial neurons in PNNs are composed of Fuzzy-c means-based radial basis function neural networks(FCM-based RBFNNs) instead of the conventional polynomial function. We consider PNNs to find the optimal local models and use RBFNNs to cover the high dimensionality problems. Also, in the hidden layer of RBFNNs, FCM algorithm is used to produce some clusters based on the similarity of given dataset. The proposed model depends on some parameters such as the number of input variables in PNNs, the number of clusters and fuzzification coefficient in FCM and polynomial type in RBFNNs. A multiobjective particle swarm optimization using crowding distance (MoPSO-CD) is exploited in order to carry out both structural and parametric optimization of the proposed networks. MoPSO is introduced for not only the performance of model but also complexity and interpretability. The usefulness of the proposed model as a classifier is evaluated with the aid of some benchmark datasets such as iris and liver.

• Journal of the Korea Convergence Society
• /
• v.8 no.10
• /
• pp.1-8
• /
• 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 Institute of Intelligent Systems
• /
• v.15 no.6
• /
• pp.720-729
• /
• 2005

In this paper, we design the indirect adaptive controller using Wavelet Neural Network(WNN) for unknown nonlinear systems. The proposed indirect adaptive controller using WNN consists of identification model and controller. Here, the WNN is used in both Identification model and controller The WNN has advantage of indicating the location in both time and frequency simultaneously, and has faster convergence than MLPN and RBFN. There are several training methods for WNN, such as GD, GA, DNA, etc. In this paper, we present the Extended Kalman Filter(EKF) based training method. Although it is computationally complex, this algorithm updates parameters consistent with previous data and usually converges in a few iterations. Finally, ore illustrate the effectiveness of our method through computer simulations for the Buffing system and the one-link rigid robot manipulator. From the simulation results, we show that the indirect adaptive controller using the EKF method has better performance than the GD method.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.30 no.1
• /
• pp.17-22
• /
• 2017

In this study, we designed the metallic reception part of a patch antenna using the phase field design method. The design object function is formulated with the S-parameter value which represent the return loss so that it is targeted to maximize radiation efficiency at a target frequency. The initial model of a patch antenna was designed via the ordinary theory based approach and its performance was enhanced by changing the structural configuration of the metallic part using the phase field design method combined with the double well potential functions. The final shape was proposed by removing the gray scale area along the structural boundary by employing a cut-off method. The proposed shape shows that the radiation efficiency at target frequency is significantly improved compared with the initial patch shape. The finite element analysis and optimization precess was performed using the commercial package COMSOL and Matlab programming.

• Composites Research
• /
• v.37 no.1
• /
• pp.32-39
• /
• 2024

Solid-state hydrogen storage is gaining prominence as a crucial subject in advancing the hydrogen-based economy and innovating energy storage technology. This storage method shows superior characteristics in terms of safety, storage, and operational efficiency compared to existing methods such as compression and liquefied hydrogen storage. In this study, we aim to evaluate the solid hydrogen storage performance on the nanotube surface by various structural design factors. This is accomplished through molecular dynamics simulations (MD) with the aim of uncovering the underlying ism. The simulation incorporates diverse carbon nanotubes (CNTs) - encompassing various diameters, multi-walled structures (MWNT), single-walled structures (SWNT), and boron-nitrogen nanotubes (BNNT). Analyzing the storage and effective release of hydrogen under different conditions via the radial density function (RDF) revealed that a reduction in radius and the implementation of a double-wall configuration contribute to heightened solid hydrogen storage. While the hydrogen storage capacity of boron-nitrogen nanotubes falls short of that of carbon nanotubes, they notably surpass carbon nanotubes in terms of effective hydrogen storage capacity.