• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.4
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• pp.281-285
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• 2005

Learning and evolving are two basics for data mining. As compared with classical learning theory based on objective function with minimizing training errors, the recently evolutionary computing has had an efficient approach for constructing optimal model without the minimizing training errors. The global search of evolutionary computing in solution space can settle the local optima problems of learning models. In this research, combining co-evolving algorithm into statistical learning theory, we propose an co-evolutionary computing for statistical learning theory for overcoming local optima problems of statistical learning theory. We apply proposed model to classification and prediction problems of the learning. In the experimental results, we verify the improved performance of our model using the data sets from UCI machine learning repository and KDD Cup 2000.

• Journal of applied mathematics & informatics
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• v.9 no.1
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• pp.289-301
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• 2002

This paper considers the Bayesian analysis of the regression model wish autoregressive errors. The Bayesian approach for finding the order p of autoregressive error is proposed and the proposed method can be simplified by generalized Savage-Dicky density ratio(Verdinelli and Wasser-man, [18]). And the Markov chain Monte Carlo method(Gibbs sample, [7]) is used in order to overcome the difficulty of Bayesian computations. Final1y, several examples are used to illustrate our proposed methodology.

• Communications for Statistical Applications and Methods
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• v.22 no.4
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• pp.349-359
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• 2015

We study a semiparametric Bayesian approach to small area estimation under a nested error linear regression model with area level covariate subject to measurement error. Consideration is given to radial basis functions for the regression spline and knots on a grid of equally spaced sample quantiles of covariate with measurement errors in the nested error linear regression model setup. We conduct a hierarchical Bayesian structural measurement error model for small areas and prove the propriety of the joint posterior based on a given hierarchical Bayesian framework since some priors are defined non-informative improper priors that uses Markov Chain Monte Carlo methods to fit it. Our methodology is illustrated using numerical examples to compare possible models based on model adequacy criteria; in addition, analysis is conducted based on real data.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.2
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• pp.251-256
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• 2005

Up to now, a number of models have been proposed and discussed to describe a wide range of inelastic behaviors of materials. The fetal problem of using such models is however the existence of model errors, and the problem remains inevitably as far as a material model is written explicitly. In this paper, the authors define the implicit constitutive model and propose an implicit viscoplastic constitutive model using neural networks. In their modeling, inelastic material behaviors are generalized in a state space representation and the state space form is constructed by a neural network using input output data sets. A technique to extract the input-output data from experimental data is also described. The proposed model was first generated from pseudo-experimental data created by one of the widely used constitutive models and was found to replace the model well. Then, having been tested with the actual experimental data, the proposed model resulted in a negligible amount of model errors indicating its superiority to all the existing explicit models in accuracy.

• Journal of Korean Society of Water and Wastewater
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• v.22 no.1
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• pp.121-129
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• 2008

Major factors affecting water quality in rivers are transportation, input of pollutant loads and kinetic transformation of pollutants. Government level decision makings on water quality management are based on steady state water quality modeling. However, it is more than often that such a steady state assumption is far from real situations in rivers. Therefore, it is unavoidable to have modeling errors in water quality modeling especially for steady state modeling for longer period of time. Authors attempted to identify sources of errors in results of steady state models and thus tried to find out ways to minimize those errors. Three water quality models, QUAL2E (Brown et al., 1983), QUAL2K (Chapra et al., 2006) and CAP (Seo and Lee, 2000) were applied to the lower stream of the Geum River. $BOD_5$ and COD tend to underestimate observed data while TN and TP showed relatively smaller errors. QUAL2E model provided best calibration results for BOD5 and TP and QUAL2K model showed best calibration results for TN. Since these errors are only relative values, it was difficult to conclude which model is better performing in certain situations. The most probable reasons for errors in water quality modeling are; 1) inappropriate consideration on flow characteristics, 2) lack of information on incoming pollutant load and 3) inappropriate location of sampling for water quality analysis.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.5
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• pp.56-63
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• 2002

A new model utilizing a transfer function is introduced in the present paper for analizing motion errors of hydrostatic tables. Relationship between film reaction force in a single hydrostatic pad and form error of a guide rail is derived at various spacial frequencies by finite element analysis, and it is expressed as a transfer function. This transfer function clarifies so called averaging effect of the oil film quantitively. For example, it is found that the amplitide of the film reaction farce is reduced as the spacial frequency increases or relative width of the pocket is reduced. Motion errors of a multiple pad table is estimated from transfer function, geomatric relationship between each pads and form errors of a guide rail, which is named as Transfer Function Method(TFM). Calculated motion errors by TFM show good agreement with motion errors calculated by Multi Pad Method, which is considered entire table as an analysis object. From the results, it is confirmed that the proposed TFM is very effective to analyze the motion errors of hydrostatic tables.

• Korean Journal of Remote Sensing
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• v.33 no.4
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• pp.445-454
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• 2017

The performance of spatial downscaling models depends on the quality of input coarse scale products. Thus, the impact of intrinsic errors contained in coarse scale satellite products on predictive performance should be properly assessed in parallel with the development of advanced downscaling models. Such an assessment is the main objective of this paper. Based on a synthetic satellite precipitation product at a coarse scale generated from rain gauge data, two synthetic precipitation products with different amounts of error were generated and used as inputs for spatial downscaling. Geographically weighted regression, which typically has very high explanatory power, was selected as the trend component estimation model, and area-to-point kriging was applied for residual correction in the spatial downscaling experiment. When errors in the coarse scale product were greater, the trend component estimates were much more susceptible to errors. But residual correction could reduce the impact of the erroneous trend component estimates, which improved the predictive performance. However, residual correction could not improve predictive performance significantly when substantial errors were contained in the input coarse scale data. Therefore, the development of advanced spatial downscaling models should be focused on correction of intrinsic errors in the coarse scale satellite product if a priori error information could be available, rather than on the application of advanced regression models with high explanatory power.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.11
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• pp.3549-3559
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• 1996

In this study, the effect of parameter and modal filter errors on the vibration control characteristics of flexible structures is analyzed for IMSC ( Independent Modal Space Control). If the control force is designed on the basis of the mathematical model with the parameter and modal filter errors, the closed-loop performance of the vibration control system will be degraded depending on the magnitude of the errors. An asymptotic stability condition of the system with parameter and modal filter errors has more significant effect on the stability condition of the system with parameter and modal filter errors has been drived using Lyapunov approach. It has been found that modal filter error has more significant effect on the stability of closed-loop system than parameter error does. The extent of the response deviation of the closed-loop system is also derived and evaluated using operator thchniques.

• Journal of the Ergonomics Society of Korea
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• v.23 no.4
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• pp.9-21
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• 2004

As menu structure of household appliance is complicated, user's cognitive workload frequently occurs errors. In existing studies, errors didn't present that interpretation for cognitive factors and alternatives, but are only considered as statistical frequency. Therefore, error classification and analysis in tasks is inevitable in usability evaluation. This study classified human error throughout information process model and navigation behavior. Human error is defined as incorrect decision and behavior reducing performance. And navigation is defined as unrelated behavior with target item searching. We searched and analyzed human errors and its causes as a case study, using mobile phone which could control appliances in near future. In this study, semantic problems in menu structure were elicited by SAT. Scenarios were constructed by those. Error analysis tests were performed twice to search and analyze errors. In 1st prototype test, we searched errors occurred in process of each scenario. Menu structure was revised to be based on results of error analysis. Henceforth, 2nd Prototype test was performed to compare with 1st. Error analysis method could detect not only mistakes, problems occurred by semantic structure, but also slips by physical structure. These results can be applied to analyze cognitive causes of human errors and to solve their problems in menu structure of electronic products.

• Journal of the Korean earth science society
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• v.39 no.5
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• pp.473-482
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• 2018

During the period of 2002 to 2017, the Gravity Recovery And Climate Experiment (GRACE) had observed time-varying gravity changes with unprecedented accuracy. The GRACE science data centers provide the monthly gravity solutions after removing the sub-monthly mass fluctuation using geophysical models. However, model misfit makes the solutions to be contaminated by aliasing errors, which exhibits peculiar north-south stripes. Two conventional filters are used to reduce the errors, but signals with similar spatial patterns to the errors are also removed during the filtering procedure. This would be particularly problematic for estimating the ice mass changes in Western Antarctic Ice Sheet (WAIS) and Antarctic Peninsula (AP) due to their similar spatial pattern to the elongated north-south direction. In this study, we introduce an alternative filter to remove aliasing errors using the Empirical Orthogonal Functions (EOF) analysis. EOF can decompose data into different modes, and thus is useful to separate signals from noise. Therefore, the aliasing errors are effectively suppressed through EOF method. In particular, the month-to-month mass changes in WAIS and AP, which have been significantly contaminated by aliasing errors, can be recovered using EOF method.