• Bulletin of the Korean Space Science Society
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• 2011.04a
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• pp.28.3-28.3
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• 2011

Halo coronal mass ejections (HCMEs) are mainly responsible for the most severe geomagnetic storms. To minimize the projection effect of the HCMEs observed by coronagraphs, several cone models have been suggested. These models allow us to determine the geometrical and kinematic parameters of HCMEs : radial speed, source location, angular width, and the angle between the central axis of the cone and the plane of the sky. In this study, we compare these parameters form two representative cone models (the ice-cream cone model and the asymmetric cone model) using well-observed HCMEs from 2001 to 2002. And we obtain the root mean square error (rms error) between observed projection speeds and calculated projection speeds for both cone models. It is found that the average rms speed error (89 km/s) of the asymmetric cone model is a little smaller than that (107 km/s) of the ice-cream cone models, implying that the radial speeds from both models are reasonably estimated. We also find that the radial speeds obtained from two models are similar to each other with the correlation coefficient of about 0.8.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.77-87
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• 2021

In this study, a novel improved second order Radial Basis Function Neural Network based method with excellent scheduling capabilities is used for the dynamic prediction of short and long-term energy required applications. The effectiveness and the reliability of the algorithm are evaluated using training operations with New England-ISO database. The dynamic prediction algorithm is implemented in Matlab and the computation of mean absolute error and mean absolute percent error, and training time for the forecasted load, are determined. The results show the impact of temperature and other input parameters on the accuracy of solar Photovoltaic load forecasting. The mean absolute percent error is found to be between 1% to 3% and the training time is evaluated from 3s to 10s. The results are also compared with the previous studies, which show that this new method predicts short and long-term load better than sigmoidal neural network and bagged regression trees. The forecasted energy is found to be the nearest to the correct values as given by England ISO database, which shows that the method can be used reliably for short and long-term load forecasting of any electrical system.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.669-676
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• 2018

Transmission system has been well studied since long time and power system techniques of distribution system are more or less derived from transmission system. However, unlike transmission systems, many practical issues are encountered in the distribution system. Considerable amount of error is observed in voltage obtained from the Feeder Remote Terminal Units (FRTUs) measured by the pole mounted PTs along the distribution feeder. Load uncertainty is also an issue in distribution system. Further, penetration of Distributed Generators (DGs) creates voltage variations in the system. Hybrid radial/ loop distribution system also make it complicated to handle distribution system. How these constraints to be handled under Distribution Automation (DAS) environment in order to obtain error free voltage is described in this paper and therefore, a new approach of voltage error correction technique has been proposed. The proposed technique utilizes reliable data from substation and the FRTUs installed in DAS. The proposed technique adopts an iterative process for voltage error correction. It has been tested and proved accurate not only for conventional radial systems but also for loop distribution systems.

• Transactions of the Korean Society of Automotive Engineers
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• v.10 no.5
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• pp.29-34
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• 2002

This study presents the process of the continuous-time system identification for unknown nonlinear systems. The Radial Basis Function(RBF) error filtering identification model is introduced at first. This identification scheme includes RBF network to approximate unknown function of nonlinear system which is structured by affine form. The neural network is trained by the adaptive law based on Lyapunov synthesis method. The identification scheme is applied to engine and the performance of RBF error filtering Identification model is verified by the simulation with a three-state engine model. The simulation results have revealed that the values of the estimated function show favorable agreement with the real values of the engine model. The introduced identification scheme can be effectively applied to model-based nonlinear control.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.243-248
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• 2001

A radial artery pulse wave is well known as a good mans to diagnose human body condition in th field of Chinese medical science. Information about constitution as well as organs can be obtained by detecting the artery pulse wave. Recently, the information about the human body constitution may be utilized in accelerating the recovery process of the patient on the basis of comprehensive diagnosis. A radial artery pulse wave is considered as one of promising means in examining the human body constitution. Since the examination has been conducted by the feeling of finger, the diagnosis may occasionally have uncertainty or fatal error. In this paper, a new measuring system is suggested and developed to examine the pattern of a pulse wave correctly. The system is composed of four pressure vessels, pressure sensors and air supplying pumps. One of them is utilized for appropriately pressing the radial artery, three of them for detecting pressure change in several mmHg level. The detected data is shown and discussed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.5C
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• pp.452-460
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• 2003

In this paper, an equalizer(RNE) using nonlinear multilayer combining techniques in Bayesian equalizer with a structure of radial basis function network is proposed in order to simplify the structure and enhance the performance of the equalizer(RE) using a radial basis function network. The conventional RE Produces its output using linear combining the outputs of the basis functions in the hidden layer while the proposed RNE produces its output using nonlinear combining the outputs of the basis function in the first hidden layer. The nonlinear combiner is implemented by multilayer perceptrons(MLPs). In addition, as an infinite impulse response structure, the RNE with decision feedback equalizer (RNDFE) is proposed. The proposed equalizer has simpler structure and shows better performance than the conventional RE in terms of bit error probability and mean square error.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.336-342
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• 1994

This paper presents an useful technique for assessing the volumetric error in multi_axis machine tools using the kinematic double ball bar and 3 dimensional spherical contouring. The developed system proposes the 3 dimensional spherical contour for the error analysis. The developed system input the measured radial data, analysing the volumetric errors such as positional, strightness, angle, and squareness errors, etc. The developed system has been tested in a practical machine tool, and showed high

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2007.11a
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• pp.33-34
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• 2007

• Journal of Magnetics
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• v.21 no.3
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• pp.437-441
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• 2016

Dipole model based analytical expression is proposed to estimate the length and depth of the rectangular defect on ferromagnetic pipe. Among the three leakage profiles of Magnetic Flux Leakage (MFL), radial and axial leakage profiles are considered in this work. Permeability variation of the specimen is ignored by considering the flux density as close to saturation level of the inspected specimen. Comparing the profile of both the components, radial leakage profile furnishes the better estimation of defect parameter. This is evident from the results of error percentage of length and depth of the defect. Normalized pattern of the proposed analytical model radial leakage profile is good agreement with the experimentally obtained profile support the performance of proposed expression.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1165-1168
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• 1996

The neural network approach has been shown to be a general scheme for nonlinear dynamical system identification. Unfortunately the error surface of a Multilayer Neural Network(MNN) that widely used is often highly complex. This is a disadvantage and potential traps may exist in the identification procedure. The objective of this paper is to identify a nonlinear dynamical systems based on Radial Basis Function Networks(RBFN). The learning with RBFN is fast and precise. This paper discusses RBFN as identification procedure is based on a nonlinear dynamical systems. and A design method of model follow control system based on RBFN controller is developed. As a result of applying this method to inverted pendulum, the simulation has shown that RBFN can be used as identification and control of nonlinear dynamical systems effectively.