• Journal of Mechanical Science and Technology
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• v.15 no.11
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• pp.1482-1489
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• 2001

Thermally-induced errors originating from machine tool errors have received significant attention recently because high speed and precise machining is now the principal trend in manufacturing proce sses using CNC machine tools. Since the thermal error model is generally a function of temperature, the thermal error compensation system contains temperature sensors with the same number of temperature variables. The minimization of the number of variables in the thermal error model can affect the economical efficiency and the possibility of unexpected sensor fault in a error compensation system. This paper presents a thermal error model with minimum number of variables using a fuzzy logic strategy. The proposed method using a fuzzy logic strategy does not require any information about the characteristics of the plant contrary to numerical analysis techniques, but the developed thermal error model guarantees good prediction performance. The proposed modeling method can also be applied to any type of CNC machine tool if a combination of the possible input variables is determined because the error model parameters are only calculated mathematically-based on the number of temperature variables.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.129-130
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• 2010

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.11
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• pp.1725-1731
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• 2012

In this paper, experiments were conducted to validate the importance and necessity of modeling. The modeling was performed using 120Hz and 280Hz noise of KTX main interior noise frequency. After the modeling, In order to solve the system instability by the additional path that exists between the control speaker and the error microphone, the secondary path was estimated. Next, simulations were performed to verify the modeling's necessity and importance. Thought the simulation results, we confirmed that the system with the modeling is more effective for noise reduction than without the modeling.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.05a
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• pp.81-82
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• 2012

• Journal of Electrical Engineering and Technology
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• v.11 no.5
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• pp.1475-1485
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• 2016

• Journal of the Korean Society for Precision Engineering
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• v.13 no.1
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• pp.98-107
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• 1996

The use of composite hyperpatch model is proposed to predict a machine tool positional error over the entire work space. This is an appropriate representation of the distorted work space. This model is valid for any configuration of 3-axis machine tool. Tool position, which is given NC data or CL data, contains error vector in actual work space. In this study, off-line compensation scheme was investigated for tool position error due to inaccuracy in machine tool structure. The error vector in actual work space is corrected by the error model using Newton-Raphson method. The proposed error compensation method shows the possibility of improving machine accuracy at a low cost.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.6
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• pp.221-227
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• 2002

In this paper, the system model for the compensation of the low-cost personal navigation system is derived and the error compensation method using GPS is also proposed. WNS(Walking Navigation System) is a kind of personal navigation system using the number of a walk, stride and azimuth. Because the accuracy of these variables determines the navigation performance, computational methods have been investigated. The step is detected using the walking patterns, stride is determined by neural network and azimuth is calculated with gyro output. The neural network filters off unnecessary motions. However, the error compensation method is needed, because the error of navigation information increases with time. In this paper, the accumulated error due to the step detection error, stride error and gyro bias is compensated by the integrating with GPS. Loosely coupled Kalman filter is used for the integration of WNS and GPS. It is shown by simulation that the error is bounded even though GPS signal is blocked.

• Journal of Korea Water Resources Association
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• v.53 no.6
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• pp.437-450
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• 2020

This study presents a probabilistic distributed hydrological model for Ephemeral catchment, where zero flow often occurs due to the influence of distinct climate characteristics in South Korea. The gridded hydrological model is developed by combining the Sacramento Soil Moisture Accounting Model (SAC-SMA) runoff model with a routing model. In addition, an error model is employed to represent a probabilistic hydrologic model. To be specific, the hydrologic model is coupled with a censoring error model to properly represent the features of ephemeral catchments. The performance of the censoring error model is evaluated by comparing it with the Gaussian error model, which has been utilized in a probabilistic model. We first address the necessity to consider ephemeral catchments through a review of the extensive research conducted over the recent decade. Then, the Yongdam Dam catchment is selected for our study area to confirm the usefulness of the hydrologic model developed in this study. Our results indicate that the use of the censored error model provides more reliable results, although the two models considered in this study perform reliable results. In addition, the Gaussian model delivers many negative flow values, suggesting that it occasionally offers unrealistic estimations in hydrologic modeling. In an in-depth analysis, we find that the efficiency of the censored error model may increase as the frequency of zero flow increases. Finally, we discuss the importance of utilizing the censored error model when the hydrologic model is applied for ephemeral catchments in South Korea.

• The Journal of the Acoustical Society of Korea
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• v.22 no.1
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• pp.23-27
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• 2003

Recently attempts to we the syllable as the recognition unit to enhance performance in continuous speech recognition hate been reported. However, syllables are worse in their trainability than phones and the former have a disadvantage in that contort-dependent modeling is difficult across the syllable boundary since the number of models is much larger for syllables than for phones. In this paper, we propose a method to enhance the trainability for the syllables in Korean and phoneme-context dependent syllable modeling across the syllable boundary. An experiment in which the proposed method is applied to word recognition shows average 46.23% error reduction in comparison with the common syllable modeling. The right phone dependent syllable model showed 16.7% error reduction compared with a triphone model.

• Computers and Concrete
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• v.16 no.5
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• pp.741-757
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• 2015

In the last decade, several modeling approaches have been proposed and applied to estimate the high-performance concrete (HPC) slump flow. While HPC is a highly complex material, modeling its behavior is a very difficult issue. Thus, the selection and application of proper modeling methods remain therefore a crucial task. Like many other applications, HPC slump flow prediction suffers from noise which negatively affects the prediction accuracy and increases the variance. In the recent years, ensemble learning methods have introduced to optimize the prediction accuracy and reduce the prediction error. This study investigates the potential usage of bagging (Bag), which is among the most popular ensemble learning methods, in building ensemble models. Four well-known artificial intelligence models (i.e., classification and regression trees CART, support vector machines SVM, multilayer perceptron MLP and radial basis function neural networks RBF) are deployed as base learner. As a result of this study, bagging ensemble models (i.e., Bag-SVM, Bag-RT, Bag-MLP and Bag-RBF) are found superior to their base learners (i.e., SVM, CART, MLP and RBF) and bagging could noticeable optimize prediction accuracy and reduce the prediction error of proposed predictive models.