• Journal of applied mathematics & informatics
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• v.14 no.1_2
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• pp.377-386
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• 2004

A technique that provides prediction intervals based on a model called an empirical linear model is discussed. The technique, high-dimensional empirical linear prediction (HELP), involves principal component analysis, factor analysis and model selection. HELP can be viewed as a technique that provides prediction (and confidence) intervals based on a factor analysis models do not typically have justifiable theory due to nonidentifiability, we show that the intervals are justifiable asymptotically.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.5
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• pp.851-856
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• 2016

Wind speed is heavily fluctuated and quite local than other weather elements. It is difficult to improve the accuracy of prediction only in a numerical prediction model. An MOS (Model Output Statistics) technique is used to correct the systematic errors of the model using a statistical data analysis. The Most of previous MOS has used a linear regression model for weather prediction, but it is hard to manage an irregular nature of prediction of wind speed. In order to solve the problem, a nonlinear regression method using SVR (Support Vector Regression) is introduced for a development of MOS for wind speed prediction. Experiments are performed for KLAPS (Korea Local Analysis and Prediction System) re-analysis data from 2007 to 2013 year for Jeju Island and Busan area in South Korea. The MLR and SVR based linear and nonlinear methods are compared to each other for prediction accuracy of wind speed. Also, the comparison experiments are executed for the variation in the number of UM elements.

• Journal of the Korean Operations Research and Management Science Society
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• v.27 no.2
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• pp.1-13
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• 2002

In this paper, we compares the business failure prediction accuracy among Linear Programming Discriminant Analysis(LPDA) model, Multivariate Discriminant Analysis (MDA) model and logit analysis model. The Data for 417 companies analyzed were gathered from KIS-FAS Published by Korea Information Service in 1999. The result of comparison for four time horizons shows that LPDA Is advantageous in prediction accuracy over the other two models when over all tilt ratio and business failure accuracy are considered simultaneously.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.5
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• pp.477-482
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• 2015

Linear regressions and evolutionary nonlinear regression based compensation techniques for the short-range prediction of wind speed are investigated. Development of an efficient MOS(Model Output Statistics) is necessary to correct systematic errors of the model, but a linear regression based MOS is hard to manage an irregular nature of weather prediction. In order to solve the problem, a nonlinear and symbolic regression method using GP(Genetic Programming) is suggested for a development of MOS for wind speed prediction. The proposed method is compared to various linear regression methods for prediction of wind speed. Also, statistical analysis of distribution for UM elements for each method is executed. experiments are performed for KLAPS(Korea Local Analysis and Prediction System) re-analysis data from 2007 to 2013 year for Jeju Island and Busan area in South Korea.

• Proceedings of the Korean Geotechical Society Conference
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• 2009.03a
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• pp.146-153
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• 2009

One dimensional site response analysis is widely used in prediction of the ground motion that is induced by earthquake. Equivalent linear analysis is the most widely used method due to its simplicity and ease of use. However, the equivalent linear method has been known to be unreliable since it approximates the nonlinear soil behavior within the linear framework. To consider the nonlinearity of the ground at frequency domain, frequency dependent algorithms that can simulate shear strain - frequency dependency have been proposed. In this study, the results of the modified equivalent linear analysis are compared to evaluate the degree of improvement and the applicability of the modified algorithms. Results show the novel smoothed curve that is proposed by this study indicates the most stable prediction and can enhance the accuracy of the prediction.

• Genomics & Informatics
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• v.12 no.4
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• pp.254-260
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• 2014

Best linear unbiased prediction (BLUP) has been used to estimate the fixed effects and random effects of complex traits. Traditionally, genomic relationship matrix-based (GRM) and random marker-based BLUP analyses are prevalent to estimate the genetic values of complex traits. We used three methods: GRM-based prediction (G-BLUP), random marker-based prediction using an identity matrix (so-called single-nucleotide polymorphism [SNP]-BLUP), and SNP-SNP variance-covariance matrix (so-called SNP-GBLUP). We used 35,675 SNPs and R package "rrBLUP" for the BLUP analysis. The SNP-SNP relationship matrix was calculated using the GRM and Sherman-Morrison-Woodbury lemma. The SNP-GBLUP result was very similar to G-BLUP in the prediction of genetic values. However, there were many discrepancies between SNP-BLUP and the other two BLUPs. SNP-GBLUP has the merit to be able to predict genetic values through SNP effects.

• Agricultural and Biosystems Engineering
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• v.2 no.1
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• pp.15-23
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• 2001

On the assumption that the voices of the cows are produced by the linear prediction filter, we characterized the cows’voices. The order of this filter was determined by examining the voice characteristics both in time and frequency domains. The proposed order of the linear prediction filter is 15 for modeling voice production of the cow. The characteristics of the amplitude envelope of the voice signal was investigated by analyzing the sequence of the short time variance both in time and frequency domains, and the new parameters were defined. One of the coefficients o the linear prediction filter generating the voice signal, the fundamental frequency, the slope of the straight line regressed from the log-log spectra of the short time variance and the coefficients of the linear prediction filter generating the sequence of the short time variance of the voice signal can differentiate the two cows.

• Journal of the Society of Naval Architects of Korea
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• v.58 no.5
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• pp.281-293
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• 2021

Oil and steel prices, which are major pricescosts in the shipbuilding industry, were predicted. Firstly, the error of the moving average line (N=3-5) was examined, and in all three error analyses, the moving average line (N=3) was small. Secondly, in the linear prediction of data through existing theory, oil prices rise slightly, and steel prices rise sharply, but in reality, linear prediction using existing data was not satisfactory. Thirdly, we identified the limitations of linear prediction methods and confirmed that oil and steel price prediction was somewhat similar to actual moving average line prediction methods. Due to the high volatility of major price flows, large errors were inevitable in the forecast section. Through the time series analysis method at the end of this paper, we were able to achieve not bad results in all analysis items relative to artificial intelligence (Prophet). Predictive data through predictive analysis using eight predictive models are expected to serve as a good research foundation for developing unique tools or establishing evaluation systems in the future. This study compares the basic settings of artificial intelligence programs with the results of core price prediction in the shipbuilding industry through time series prediction theory, and further studies the various hyper-parameters and event effects of Prophet in the future, leaving room for improvement of predictability.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.4
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• pp.337-342
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• 2016

In this paper, we studied a modified linear prediction method to estimate target signal correctly. Linear prediction method estimate direction-of-arrival to linear combination for any antenna element and other antenna elements. Modified linear prediction used optimal weight and posterior probability method. Through simulation, we are comparative analysis about the performance of proposed, bartlett and MUSIC method. From simulation, Bartlett and MUSIC method was estimation 3 targets signal, and proposed method estimated 4 targets. We showed the superior performance of the proposed algorithm relative to the classical method in order to estimate of target signals.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11b
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• pp.196-203
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• 2000

On the assumption that the voices of the cows are produced by the linear prediction filter, we characterized the cows' voices. The order of this filter is determined by examining the voices characteristics both in time and frequency domains. The proposed order of the linear prediction filter is 15 for modeling voice production of the cow. The combination of the two parameters of the fundamental frequency, the slope of the straight line regressed from the log-log spectra of the amplitude-envelope and the only one coefficient involved in the linear prediction filter can differentiate the two cows.