• Structural Engineering and Mechanics
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• v.45 no.2
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• pp.159-168
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• 2013

The effect of structural uncertainties or measurement errors on damage detection results makes the robustness become one of the most important features during identification. Due to the wide use of vibration signatures on damage detection, the development of vibration-based techniques has attracted a great interest. In this work, a review on vibration-based robust detection techniques is presented, in which the robustness is considerably improved through modeling error compensation, environmental variation reduction, denoising, or proper sensing system design. It is hoped that this study can give help on structural health monitoring or damage mitigation control.

• Journal of Information Processing Systems
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• v.14 no.4
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• pp.904-915
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• 2018

We demonstrate how social media content can be used to predict the unemployment rate, a real-world indicator. We present a novel method for predicting the unemployment rate using social media analysis based on natural language processing and statistical modeling. The system collects social media contents including news articles, blogs, and tweets written in Korean, and then extracts data for modeling using part-of-speech tagging and sentiment analysis techniques. The autoregressive integrated moving average with exogenous variables (ARIMAX) and autoregressive with exogenous variables (ARX) models for unemployment rate prediction are fit using the analyzed data. The proposed method quantifies the social moods expressed in social media contents, whereas the existing methods simply present social tendencies. Our model derived a 27.9% improvement in error reduction compared to a Google Index-based model in the mean absolute percentage error metric.

• Journal of the Korean Society of Combustion
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• v.19 no.1
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• pp.39-44
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• 2014

A short reaction mechanism for premixed $CH_4/CHF_3/Air$ flames was developed with a reduction method of the combined application of simulation error minimization (SEM) which included connectivity method and principal component analysis. It consisted of 43 species and 403 elementary reactions at the condition of less than 5% of maximum error. The calculation time operated with a short mechanism was over 5 times faster than one with a detailed reaction mechanism. Good agreement was found between the flame speeds calculated by the short reaction mechanism and those by the detailed reaction mechanism for the entire range of $CHF_3/CH_4$ mole ratios and equivalence ratios. In addition excellent agreements were determined for the profiles of temperature, species concentration, and the production rates of the various species. So the short reaction mechanism was able to accurately predict the flame structure for premixed $CH_4/CHF_3/Air$ flames.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.4
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• pp.158-164
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• 2003

The suspension system of cars is composed of dampers and springs, which usually have nonlinear characteristics. The nonlinear characteristics make the differences in the results of analytical models and experiments. In this study, the nonlinear system identification method which does not assume a special form for nonlinear dynamic systems and minimize the error by calculating the error reduction ratio is devised to estimate the nonlinear parameters of the suspension system of an EF-SONATA car from the field running test data. The results show that the spring has a cubic nonlinear term and the damper has a coupled nonlinear term. Also, the numerical results with the estimated nonlinear parameters agree well with the field test data for the different running speeds.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.429-431
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• 2003

The induced learning retardation method involves the temporary inhibition of the artificial neural network’s active units from participating in the error reduction process during training. This stimulates the less active units to contribute significantly to reduce the network error. However, some less active units are not sensitive to stimulation making them almost useless. The network can then be pruned by removing the less active units to make it smaller and more efficient. This study focuses on making the network more efficient and accurate by developing the induced learning retardation and pruning sequence training method. The developed procedure results to faster learning and more accurate artificial neural network for satellite image classification.

• Proceedings of the KIEE Conference
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• 2005.10c
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• pp.334-336
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• 2005

This paper proposes a simple MPPT control scheme of a Current- Control-Loop Error system Based that can be obtains a lot of advantage to compare with another digital control method, P&O and IncCond algorithm, that is applied mostly a PV system. An existent method is needed an expensive processor such as DSP that calculated to change the measure power of a using current and voltage sensor at the once. But, a proposed method is easy to solve the cost reduction and power unbalance problems that it is used by control scheme to limit error of a current control of common sensor. This proposed algorithm had verified through a simulation and an experiment on battery charger using PIC that is the microprocessor of a low price.

• MALSORI
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• no.54
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• pp.63-72
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• 2005

The performance of speech recognition system is degraded by the mismatch between training and test environments. Many studies have been presented to compensate for noise components in the cepstral domain. Recently, higher order cepstral moment normalization method has been introduced to improve recognition accuracy. In this paper, we present real-time high order moment normalization method with post-processing smoothing filter to reduce the parameter estimation error in higher order moment computation. In experiments using Aurora2 database, we obtained error rate reduction of 44.7% with proposed algorithm in comparison with baseline system.

• Communications for Statistical Applications and Methods
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• v.28 no.4
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• pp.351-368
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• 2021

For a probabilistic model with positively skewed data, a lognormal distribution is one of the key distributions that play a critical role. Several lognormal models can be found in various areas, such as medical science, engineering, and finance. In this paper, we propose a new estimator for a lognormal mean and depict the performance of the proposed estimator in terms of the relative mean squared error (RMSE) compared with Shen's estimator (Shen et al., 2006), which is considered the best estimator among the existing methods. The proposed estimator includes a tuning parameter. By finding the optimal value of the tuning parameter, we can improve the average performance of the proposed estimator over the typical range of σ². The bias reduction of the proposed estimator tends to exceed the increased variance, and it results in a smaller RMSE than Shen's estimator. A numerical study reveals that the proposed estimator has performance comparable with Shen's estimator when σ² is small and exhibits a meaningful decrease in the RMSE under moderate and large σ² values.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.7
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• pp.43-52
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• 2022

Design of a controller for a high-precision servo control system has been a popular topic while finding optimal parameters for multiple controllers is still a challenging subject. In this paper, we propose a practical scheme to optimize multi-parameters for the robust servo controller design by introducing a new cost function and optimization scheme. The proposed design method provides a simple and practical tool for the systematic servo design to reduce the control error with guaranteeing robust stability of the overall system. The reduction of the position error by 24% along with a faster convergence rate is demonstrated using a typical hard disk drive servo controller with 41 parameters.

• International Journal of Advanced Culture Technology
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• v.10 no.3
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• pp.25-32
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• 2022

This study analyzes the impact of occupational accidents on economic growth and labor productivty losses in Korea between January 2008 and July 2018, using the Vector Error-Correction Model (VECM). According to the analysis, the occurrence of occupational accidents was revealed to reduce the number of employed workers and also hinder economic growth. This can be reinterpreted as the reduction of occupational accidents does not cause labor losses in the industry, rather may induce economic growth. Also, the findings discovered that an increase in the number of workers may lead to increase in the probability of occupational accidents in the short term. This suggests that greater number of work-related accidents may occur during the early stages- due to new employees' lack of knowledge related to safety at workplace.