• Journal of the Korea Computer Graphics Society
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• v.3 no.2
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• pp.27-34
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• 1997

3-D image modeling is in high demand for automated visual inspection and non-destructive testing. It also can be useful in biomedical research, medical therapy, surgery planning, and simulation of critical surgery (i.e. cranio-facial). Image processing and image analysis are used to enhance and classify medical volumetric data. Analyzing medical volumetric data is very difficult In this paper, we propose a new image modeling method based on tetrahedrization to improve the visualization of three-dimensional medical volumetric data. In this method, the trivariate piecewise linear interpolation is applied through the constructed tetrahedral domain. Also, visualization methods including iso-surface, color contouring, and slicing are discussed. This method can be useful to the correct and speedy analysis of medical volumetric data, because it doesn't have the ambiguity problem of Marching Cubes algorithm and achieves the data reduction. We expect to compensate the degradation of an accuracy by using an adaptive sub-division of tetrahedrization based on least squares fitting.

• Journal of Korea Game Society
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• v.7 no.4
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• pp.63-70
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• 2007

Since the fisheye lens has a wide field of view, it can capture the scene and illumination from all directions from far less number of omnidirectional images. Due to these advantages of the omnidirectional camera, it is widely used in surveillance and reconstruction of 3D structure of the scene In this paper, we present a new self-calibration algorithm of omnidirectional camera from uncalibrated images by considering the inlier distribution. First, one parametric non-linear projection model of omnidirectional camera is estimated with the known rotation and translation parameters. After deriving projection model, we can compute an essential matrix of the camera with unknown motions, and then determine the camera information: rotation and translations. The standard deviations are used as a quantitative measure to select a proper inlier set. The experimental results showed that we can achieve a precise estimation of the omnidirectional camera model and extrinsic parameters including rotation and translation.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.2
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• pp.26-36
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• 2013

Purpose of this study is to develop virtual components and environment for developing a controller of an Active Air Suspension System in laboratory that slough off existing development environment using real vehicle test. This paper presents an air spring modeling and analysis of air suspension system for a commercial vehicle. Preferentially, It was performed vehicle test for pneumatic system and an air spring for characteristic analysis of system. Each component of an air spring suspension system was developed through emulations and modeling of system for pressure and height sensors in the basis on test results in SILS environment. Non-linear characteristics of air spring are accounted for using the measured data. Also, pressure and volume relations for vehicle hight control is considered. After performance verification of virtual model was performed, we developed virtual environment based on HILS for an Active Air Suspension System. We studied estimation and verification technology for control algorithm that developed.

• Industrial Engineering and Management Systems
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• v.4 no.1
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• pp.54-67
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• 2005

This paper provides an evaluation of an optimization-based, multiple-input double-output (MIDO) run-to-run (R2R) control scheme for general semiconductor manufacturing processes. The controller in this research, termed adaptive dual response optimizing controller (ADROC), can serve as a process optimizer as well as a recipe regulator between consecutive runs of wafer fabrication. In evaluation, it is assumed that the equipment model could be appropriately described by a pair of second-order polynomial functions in terms of a set of controllable variables. Of practical relevance is to consider a drifting effect in the equipment model since in common semiconductor practice the process tends to drift due to machine aging and tool wearing. We select a typical application of R2R control to chemical mechanical planarization (CMP) in semiconductor manufacturing in this evaluation, and there are five different CMP process scenarios demonstrated, including mean shift, variance increase, and IMA disturbances. For the controller, ADROC, an on-line estimation technique is implemented in a self-tuning (ST) control manner for the adaptation purpose. Subsequently, an ad hoc global optimization algorithm based on the dual response approach, arising from the response surface methodology (RSM) literature, is used to seek the optimum recipe within the acceptability region for the execution of next run. The main components of ADROC are described and its control performance is assessed. It reveals from the evaluation that ADROC can provide excellent control actions for the MIDO R2R situations even though the process exhibits complicated, nonlinear interaction effects between control variables, and the drifting disturbances.

• Korean Journal of Remote Sensing
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• v.26 no.2
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• pp.143-151
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• 2010

We carried out studies on InSAR techniques for time-series deformation monitoring using multi-temporal SAR. The PSInSAR method using permanent scatterer is much more complicate than the SBAS because it includes many non-linear equation due to the input of wrapped phase. It is conformed the PS algorithm is very sensitive to even PSC selection. On the other hand, the SBAS method using interferogram of small baseline subset is simple but sensitive to the accuracy of unwrapped phase. The SBAS is better method for expecting not significant unwrapping error while PSInSAR is more proper method for expecting local deformation within very limited area. We used 51 ERS-1/2 SAR data during 1992-2000 over Las Vegas, USA for the comparison between PSInSAR and SBAS. Both PSInSAR and SBAS show similar ground deformation value although local deformation seems to be detected in the PSInSAR method only.

• Korean Journal of Remote Sensing
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• v.15 no.2
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• pp.107-117
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• 1999

A comparison of a neural network approach with the conventional statistical methods, multiple regression and band ratio analyses, for the estimation of water quality parameters in presented in this paper. The Landsat TM image of Lake Daechung acquired on March 18, 1996 and the thirty in-situ sampling data sets measured during the satellite overpass were used for the comparison. We employed a three-layered and feedforward network trained by backpropagation algorithm. A cross validation was applied because of the small number of training pairs available for this study. The neural network showed much more successful performance than the conventional statistical analyses, although the results of the conventional statistical analyses were significant. The superiority of a neural network to statistical methods in estimating water quality parameters is strictly because the neural network modeled non-linear behaviors of data sets much better.

• Nuclear Engineering and Technology
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• v.54 no.1
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• pp.36-48
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• 2022

As a valid numerical method to obtain a high-resolution result of a flow field, computational fluid dynamics (CFD) have been widely used to study coolant flow and heat transfer characteristics in fuel rod bundles. However, the time-consuming, iterative calculation of Navier-Stokes equations makes CFD unsuitable for the scenarios that require efficient simulation such as sensitivity analysis and uncertainty quantification. To solve this problem, a reduced-order model (ROM) based on proper orthogonal decomposition (POD) and machine learning (ML) is proposed to simulate the flow field efficiently. Firstly, a validated CFD model to output the flow field data set of the rod bundle is established. Secondly, based on the POD method, the modes and corresponding coefficients of the flow field were extracted. Then, an deep feed-forward neural network, due to its efficiency in approximating arbitrary functions and its ability to handle high-dimensional and strong nonlinear problems, is selected to build a model that maps the non-linear relationship between the mode coefficients and the boundary conditions. A trained surrogate model for modes coefficients prediction is obtained after a certain number of training iterations. Finally, the flow field is reconstructed by combining the product of the POD basis and coefficients. Based on the test dataset, an evaluation of the ROM is carried out. The evaluation results show that the proposed POD-ROM accurately describe the flow status of the fluid field in rod bundles with high resolution in only a few milliseconds.

• Geomechanics and Engineering
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• v.30 no.3
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• pp.259-272
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• 2022

Rockburst is a dynamic, multivariate, and non-linear phenomenon that occurs in underground mining and civil engineering structures. Predicting rockburst is challenging since conventional models are not standardized. Hence, machine learning techniques would improve the prediction accuracies. This study describes decision based uncertainty models to predict rockburst in underground engineering structures using gradient boosting algorithms (GBM). The model input variables were uniaxial compressive strength (UCS), uniaxial tensile strength (UTS), maximum tangential stress (MTS), excavation depth (D), stress ratio (SR), and brittleness coefficient (BC). Several models were trained using different combinations of the input variables and a 3-fold cross-validation resampling procedure. The hyperparameters comprising learning rate, number of boosting iterations, tree depth, and number of minimum observations were tuned to attain the optimum models. The performance of the models was tested using classification accuracy, Cohen's kappa coefficient (k), sensitivity and specificity. The best-performing model showed a classification accuracy, k, sensitivity and specificity values of 98%, 93%, 1.00 and 0.957 respectively by optimizing model ROC metrics. The most and least influential input variables were MTS and BC, respectively. The partial dependence plots revealed the relationship between the changes in the input variables and model predictions. The findings reveal that GBM can be used to anticipate rockburst and guide decisions about support requirements before mining development.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.187-192
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• 2022

Even the announcing date for the staring date of "With Corona" has been decided, still many people have not completed vaccination, the most important condition for starting the With Corona, because of concerns for its side effects. In addition, although the economy may can be recovered by the With Corona, but the number of infected people may can be surged. In this paper, in order to awaken the people for the awareness of Corona 19 in advance of the With Corona, the Corona 19 is predicted through a non-linear probability process. Here, among the deep learning RNN, BI-LSTM, which is a bidirectional LSTM, and GRU, gates decreased than LSTM have been used. And this has been compared and analyzed through train set, test set, loss function, residual analysis, normal distribution, and autocorrelation, and compared and predicted for which has a better performance.

• The Journal of the Acoustical Society of Korea
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• v.27 no.7
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• pp.386-393
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• 2008

This paper proposes a new approach for speech enhancement in highly nonstationary noisy environments. The spectral subtraction (SS) is a well known technique for speech enhancement in stationary noisy environments. However, in real world, noise is mostly nonstationary. The proposed method uses an auto control parameter for an adaptive threshold to work well in highly nonstationary noisy environments. Especially, the auto control parameter is affected by a linear function associated with an a posteriori signal to noise ratio (SNR) according to the increase or the decrease of the noise level. The proposed algorithm is combined with spectral subtraction (SS) using a hangover scheme (HO) for speech enhancement. The performances of the proposed method are evaluated ITU-T P.835 signal distortion (SIG) and the segment signal to-noise ratio (SNR) in various and highly nonstationary noisy environments and is superior to that of conventional spectral subtraction (SS) using a hangover (HO) and SS using a minimum statistics (MS) methods.