• Journal of International Society for Simulation Surgery
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• v.2 no.1
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• pp.7-9
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• 2015

Purpose In the many face-related application such as head pose estimation, 3D face modeling, facial appearance manipulation, the robust and fast facial feature extraction is necessary. We present the facial feature extraction method based on shape regression and feature selection for real-time facial feature extraction. Materials and Methods The facial features are initialized by statistical shape model and then the shape of facial features are deformed iteratively according to the texture pattern which is selected on the feature pool. Results We obtain fast and robust facial feature extraction result with error less than 4% and processing time less than 12 ms. The alignment error is measured by average of ratio of pixel difference to inter-ocular distance. Conclusion The accuracy and processing time of the method is enough to apply facial feature based application and can be used on the face beautification or 3D face modeling.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.56 no.4
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• pp.179-185
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• 2007

In this paper, the method of computing demand rate with respect to a transformer capacity is proposed and addressed to predict a future demand rate. The simulation data are taken from switchgears of a real medium voltage transformer. Data taken from the electrical instrument at 22.9 kVY power receiving panels are employed to evaluate the correlation between demand rate and power usage of transformer. It is verified a usefulness with respect to an proposed index of demand rate for transformer by using a least square error of regressive modeling, As a result of investigation and simulation on the spot to a few buildings, it is considered that there is necessity to make a partial amendment of demand rate being applicable currently for electrical energy saving in domestic.

• Proceedings of the KSME Conference
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• 2001.06b
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• pp.335-340
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• 2001

It is important to model the mechanical structure precisely and reasonably in predicting the dynamic characteristics, controlling the vibration, and designing the structure dynamics. In the finite element modeling, the errors can be contained from the physical parameters, the approximation of the boundary conditions, and the element modeling. From the dynamic test, more precise dynamic characteristics can be obtained. Model updating using parameter modification is appropriate when the design parameter is used to analyze the input parameter like finite element method. Finite element analysis for cantilever and simply supported beams with uniform area and shape change are carried out as model updating examples. Mass and stiffness matrices are updated by comparing test and analytical modal frequencies. The result shows that the updated frequencies become closer to the test frequencies.

• JSTS:Journal of Semiconductor Technology and Science
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• v.10 no.1
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• pp.11-19
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• 2010

Register transfer level power macromodeling is well known as a promising technique for accurate and efficient power estimation. This paper proposes effective approaches based on the tablebased method for the RTL power macro-modeling. The new parameter SD, which characterizes the distribution of switching activities for each gate in the circuit, is one of the contributions. The new parameter SD has strong correlation with power consumption. We also propose an accurate table reference method considering the circuit characteristics. The table reference method is applicable for every table-based method and outputs more accurate power value. The experimental results show that the combination of the proposed methods reduces max error 30.36% in the best case, comparing conventional methods. The RMS error is also improved 1.70% in the best case.

• Structural Engineering and Mechanics
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• v.12 no.6
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• pp.633-656
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• 2001

In this paper, modal control with direct output feedback is formulated in a systematic manner for easy implementation. Its application to the seismic protection of structural systems is verified by a shaking table test, which involves a full-scale building model and an active bracing system as the control device. Two modal control cases, namely, one full-state feedback and one direct output feedback control were tested and compared. The experimental result shows that in mitigating the seismic response of building structures, modal control with direct output feedback can be as effective and efficient as that with full-state feedback control. For practical concerns, the control performance of the proposed method in the presence of sensor noise and stiffness modeling error was also investigated. The numerical result shows that although the control force may be increased, the maximum floor displacements of the controlled structure are very insensitive to sensor noise and modeling error.

• 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.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2019.11a
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• pp.108-109
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• 2019

Top-down construction is a useful method of utilizing the working space, economic benefits and shorten the construction period. Precision construction of the elevator core is very important for safety of the top-down structure. In this study, the layout system for the field-BIM(Building Information Modeling) was used to precisely construct the elevator core in the basement and the ground. Through the layout system, it was possible to process the construction status, review the design results and construction errors, and confirm whether there is or not within the construction error range for elevator installation.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.4
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• pp.110-118
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• 1997

Multi-layer neural networks with error back-propagation algorithm have a great potential for identifying nonlinear systems with unknown characteristics. However, because they have a demerit that the speed of convergence is too slow, various methods for improving the training characteristics of backpropagition networks have been proposed. In this paper, a fuzzy division method is proposed to improve the convergence speed, which can find out an effective fuzzy division by the tuning of membership function and independently train each neural network after dividing the network model into several parts. In the simulations, the proposed method showed that the optimal fuzzy partitions could be found from the arbitray initial ones and that the convergence speed was faster than the traditional method without the fuzzy division.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.23 no.11
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• pp.831-836
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• 2010

In this paper, Plasma Enhanced Chemical Vapor Deposition (PECVD) $SiO_2$ film properties are modeled using statistical analysis and neural networks. For systemic analysis, Box-Behnken's 3 factor design of experiments (DOE) with response surface method are used. For characterization, deposited film thickness and film stress are considered as film properties and three process input factors including plasma RF power, flow rate of $N_2O$ gas, and flow rate of 5% $SiH_4$ gas contained at $N_2$ gas are considered for modeling. For film thickness characterization, regression based model showed only 0.71% of root mean squared (RMS) error. Also, for film stress model case, both regression model and neural prediction model showed acceptable RMS error. For sensitivity analysis, compare to conventional fixed mid point based analysis, proposed sensitivity analysis for entire range of interest support more process information to optimize process recipes to satisfy specific film characteristic requirements.