• The Korean Journal of Applied Statistics
• /
• v.22 no.1
• /
• pp.221-235
• /
• 2009

This manuscript illustrates the comparative study between ARIMA and Exponential Smoothing modeling to develop forest fire forecasting system using different weather parameters. In this paper, authors have developed the most suitable and closest forecasting models like ARIMA and Exponential Smoothing techniques using different weather parameters. Authors have considered the extremes of the Wind speed, Radiation, Maximum Temperature and Deviation Temperature of the Summer Season form March to June month for the Ranchi Region in Jharkhand. The data is taken by own resource with the help of Automatic Weather Station. This paper consists a deep study of the effect of extreme values of the different parameters on the weather fluctuations which creates forest fires in the region. In this paper, the numerical illustration has been incorporated to support the present study. Comparative study of different suitable models also incorporated and best fitted model has been tested for these parameters.

• Phonetics and Speech Sciences
• /
• v.2 no.1
• /
• pp.87-95
• /
• 2010

In this paper, we address issues associated with mixed-lingual text-to-speech based on context-dependent HMMs, where there are multiple sets of HMMs corresponding to each individual language. In particular, we propose smoothing techniques of synthesis parameters at the boundaries between different languages to obtain more natural quality of speech. In other words, mel-frequency cepstral coefficients (MFCCs) at the language boundaries are smoothed by applying several linear and nonlinear approximation techniques. It is shown from an informal listening test that synthesized speech smoothed by a modified version of linear least square approximation (MLLSA) and a quadratic interpolation (QI) method is preferred than that without using any smoothing technique.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.22 no.1
• /
• pp.185-192
• /
• 1997

We propose a method of paramete estimation using order-of-magnitude analysis for optimal boundary smoothing in Mean Field Annealing(MFA) technique in this paper. We previously proposed two boundary smoothing methods for consistent object representation in the previous paper, one is using a constratined regulaization(CR) method and the other is using a MFA method. The CR method causes unnecessary smoothing effects at corners. On the other hand, the MFA method method smooths our the noise without losing sharpness of corners. The MFA algorithm is influenced by several parameters such as standard deviation of the noise, the relativemagnitude of prior ter, initial temperature and final temperature. We propose a general parameter esimation method for optimal boundary smoothing using order-of-magnitude analysis to be used for consistent object representation in this paper. In addition, we prove the effectiveness of our parameter estimation and also show the temperature parameter sensitivities of the algorithm.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2015.10a
• /
• pp.55-57
• /
• 2015

The tracking system plays a key role in accurate estimation and prediction of maneuvering vessel's position and velocity in a bid to enhance safety by taking avoiding action against collision. Therefore, in order to achieve this, many ocean- going vessels are equipped with radar and the ARPA system. However, the accuracy of prediction highly depends on the choice of the gain parameters, ${\alpha}$, ${\beta}$ and ${\gamma}$ employed in the tracking filter. P revious research of this paper was based on theoretically developing an algorithm for a tracking module. This research paper is hence a continuation by the authors to determine the optimal values of the gain parameters used in the tracking module. A tracking algorithm is developed using the ${\alpha}-{\beta}-{\gamma}$ filter to carry out prediction and smoothing of the positions and velocities. Numerical simulations are then performed to evaluate the optimal values of the smoothing parameters that will improve the performance of the tracking module and reduce measurement noise. The twice distance root mean square (2drms) is then calculated to determine error variation.

• Journal of the Korean Data and Information Science Society
• /
• v.27 no.4
• /
• pp.993-1000
• /
• 2016

Semi-supervised learning makes it easy to use an unlabeled data in the supervised learning such as classification. Applying the semi-supervised learning on the regression analysis, we propose two methods for a better regression function estimation. The proposed methods have been assumed different marginal densities of independent variables and different smoothing parameters in unlabeled and labeled data. We shows that the overfitted pilot estimator should be used to achieve the fastest convergence rate and unlabeled data may help to improve the convergence rate with well estimated smoothing parameters. We also find the conditions of smoothing parameters to achieve optimal convergence rate.

• Journal of the Korean Geophysical Society
• /
• v.4 no.3
• /
• pp.175-182
• /
• 2001

We consider the problem of determining smoothing parameters of Gibbs priors for Bayesian methods used in the medical imaging application of emission tomographic reconstruction. We address a simple smoothing prior (membrane) whose global hyperparameter (the smoothing parameter) controls the bias/variance tradeoff of the solution. We base our maximum-likelihood (ML) estimates of hyperparameters on observed training data, and argue the motivation for this approach. Good results are obtained with a simple ML estimate of the smoothing parameter for the membrane prior.

• The Journal of Engineering Research
• /
• v.4 no.1
• /
• pp.47-54
• /
• 2002

We consider the problem of determining smoothing parameters of Gibbs priors for Bayesian methods used in the medical imaging application of emission tomographic reconstruction. We address a simple smoothing prior (membrane) whose global hyperparameter (the smoothing parameter) controls the bias/variance tradeoff of the solution. We base our maximum-likelihood(ML) estimates of hyperparameters on observed training data, and argue the motivation for this approach. Good results are obtained with a simple ML estimate of the smoothing parameter for the membrane prior.

• International Journal of Naval Architecture and Ocean Engineering
• /
• v.12 no.1
• /
• pp.85-101
• /
• 2020

The optimal parameters for the fluid-structure interaction analysis using the Smoothed Particle Hydrodynamics (SPH) for fluids and finite elements for structures, respectively, are explored, and the effectiveness of the simulations with those parameters is validated by solving several open surface fluid problems. For the optimization of the Equation of State (EOS) and the simulation parameters such as the time step, initial particle spacing, and smoothing length factor, a dam-break problem and deflection of an elastic plate is selected, and the least squares analysis is performed on the simulation results. With the optimal values of the pivotal parameters, the accuracy of the simulation is validated by calculating the exerted force on a moving solid column in the open surface fluid. Overall, the SPH-FEM coupled simulation is very effective to calculate the fluid-structure interaction. However, the relevant parameters should be carefully selected to obtain accurate results.

• The Korean Journal of Applied Statistics
• /
• v.29 no.4
• /
• pp.581-594
• /
• 2016

We introduce innovations state space exponential smoothing models (ISS-ESM) that can analyze time series with multiple seasonal patterns. Especially, in order to control complex structure existing in the multiple patterns, the model equations use a matrix consisting of seasonal updating parameters. It enables us to group the seasonal parameters according to their similarity. Because of the grouped parameters, we can accomplish the principle of parsimony. Further, the ISS-ESM can potentially accommodate any number of multiple seasonal patterns. The models are applied to predict electricity demand in Korea that is observed on hourly basis, and we compare their performance with that of the traditional exponential smoothing methods. It is observed that the ISS-ESM are superior to the traditional methods in terms of the prediction and the interpretability of seasonal patterns.

• Journal of KIISE
• /
• v.42 no.6
• /
• pp.769-780
• /
• 2015

Smoothing a digital curve by averaging its connected points is widely employed to minimize sharp changes of the curve that are generally introduced by noise. An appropriate degree of smoothing is critical since the area or features of the original shape can be distorted at a higher degree while the noise is insufficiently removed at a lower degree. In this paper, we provide a mathematical relationship between the parameters, such as the number of iterations, average distance between neighboring points, weighting factors for averaging and the moving distance of the point on the curve after smoothing. Based on these findings, we propose to control the smoothed curve such that its deviation is bounded particular error level as well as to significantly expedite smoothing for a pixel-based digital curve.