• Journal of the Korean Wood Science and Technology
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• v.24 no.2
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• pp.46-54
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• 1996

This study was carried out to investigate the vacuum drying characteristics of 50mm- and 75mm- thick tree disks of some domestic softwoods for substituting the conventional hardwoods as the materials for wood crafts. Though the elapsed drying times from green to in-use moisture content(MC) were largely shortened by vacuum drying, the tree disks treated by EWS couldn't be dried to in-use MC, and so sapwood stains also were occurred severely. We suggest EWS treatment is undesirable for the species with very high sapwood portion or vacuum drying with hot water circulation heating system. Heart checks were slight, but sapwood checks, which have never been trouble in drying process of tree disks, were severe. For the reasonable explanation it is suggested MC differences between sapwood and heartwood were large and most of tree disks had already no barks before drying test. Ginkgo was vacuum-dried with very slight drying defects such as heart checks, sapwood checks. V-cracks and sapwood stains. In Korean red pine and pitch pine V-cracks were severely occurred. And it was found the special feature that most of these defected tree disks contained several V-cracks within one tree disk. It can be considered as the causes that the region of sapwood was defected by the several checks at the early drying stage because of the steep MC gradient along the radial direction, and then at the later drying stage the drying stresses due to differential shrinkage were concentrated on these brittle spots.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.7
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• pp.259-266
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• 2018

In this paper, we propose a method to classify a document using a Recurrent Neural Network by extracting features considering word sense and contexts. Word2vec method is adopted to include the order and meaning of the words expressing the word in the document as a vector. Doc2vec is applied for considering the context to extract the feature of the document. RNN classifier, which includes the output of the previous node as the input of the next node, is used as the document classification method. RNN classifier presents good performance for document classification because it is suitable for sequence data among neural network classifiers. We applied GRU (Gated Recurrent Unit) model which solves the vanishing gradient problem of RNN. It also reduces computation speed. We used one Hangul document set and two English document sets for the experiments and GRU based document classifier improves performance by about 3.5% compared to CNN based document classifier.

• Korean Journal of Crystallography
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• v.8 no.1
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• pp.15-19
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• 1997

Single crystals of Co-doped quartz (blue quartz) were grown hydrothermally from the $Na_2CO_3$ solution. The size of as-grown crystal was $100{\times}50{\times}35mm^3$ and the growth rate was 0.55 mm/day under the growth condition of $5wt.\%\;Na_2CO_3$ mineralizer, growth temperature of $343^{\circ}C$ and temperature gradient of $22^{\circ}C$. Visible spectrum showed a typical absorption feature of the synthetic blue quartz near 545, 570 and 643 nm. The concentration of color of the as-grown blue quartz related not to the concentration of cobalt in raw material but to the growth temperature.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.8
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• pp.1698-1706
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• 1997

In this paper, we envisioned a multimedia object recognition system processing and combinig information from all available sources, such as 2-D, 3-D, color and sound data. Out of the overall system, we proposed 3-D information extraction and object recognition methods. Firstly, surfaces are classified by z-gradient from the range data, surface features are extracted using the intersection of normal vectors. Also feature relationship such as intersection angle and distance is established between the surfaces. Secondly, recognition is accomplished by matching process which is improtant step in the image understanding systems. Matching process is very improtant procedures because of more general and more efficient method is needed in the field of multimedia sytem. Therefore, we focused the proposal of matching process and in this article, first of all, we deal with the matching process of the 3-D object. Similarity measures are calculated.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.10
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• pp.2163-2170
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• 2009

We present an automated, energy minimized-based method for Lung parenchyma segmenting Chest Computed Tomography(CT) datasets. Deformable model is used for energy minimized segmentation. Quantitative knowledge including expected volume, shape of Chest CT provides more feature constrain to diagnosis or surgery operation planning. Segmentation subdivides an lung image into its consistent regions or objects. Depends on energy-minimizing, the level detail image of subdivision is carried. Segmentation should stop when the objects or region of interest in an application have been detected. The deformable model that has attracted the most attention to date is popularly known as snakes. Snakes or deformable contour models represent a special case of the general multidimensional deformable model theory. This is used extensively in computer vision and image processing applications, particularly to locate object boundaries, in the mean time a new type of external force for deformable models, called gradient vector flow(GVF) was introduced by Xu. Our proposed algorithm of deformable model is new external energy of GVF for exact segmentation. In this paper, Clinical material for experiments shows better results of proposal algorithm in Lung parenchyma segmentation on Chest CT.

• Earthquakes and Structures
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• v.17 no.2
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• pp.143-162
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• 2019

Motivated by a simpler and more compact hybrid active tuned mass damper (ATMD) system with wide frequency spacing (i.e., high robustness) but not reducing the effectiveness using the least number of ATMD units, the active tuned tandem mass dampers (ATTMD) have been proposed to attenuate undesirable oscillations of structures under the ground acceleration. Likewise, it is expected that the frequency spacing of the ATTMD is comparable to that of the active multiple tuned mass dampers (AMTMD) or the multiple tuned mass dampers (MTMD). In accordance with the mode generalised system in the specific vibration mode being controlled (simply referred herein to as the structure), the closed-form expression of the dimensionless displacement variances has been derived for the structure with the attached ATTMD. The criterion for the optimum searching may then be determined as minimization of the dimensionless displacement variances. Employing the gradient-based optimization technique, the effects of varying key parameters on the performance of the ATTMD have been scrutinized in order to probe into its superiority. Meanwhile, for the purpose of a systematic comparison, the optimum results of two active tuned mass dampers (two ATMDs), two tuned mass dampers (two TMDs) without the linking damper, and the TTMD are included into consideration. Subsequent to work in the frequency domain, a real-time Simulink implementation of dynamic analysis of the structure with the ATTMD under earthquakes is carried out to verify the findings of effectiveness and stroke in the frequency domain. Results clearly show that the findings in the time domain support the ones in the frequency domain. The whole work demonstrates that ATTMD outperforms two ATMDs, two TMDs, and TTMD. Thereinto, a wide frequency spacing feature of the ATTMD is its highlight, thus deeming it a high robustness control device. Furthermore, the ATTMD system only needs the linking dashpot, thus embodying its simplicity.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.6
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• pp.682-689
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• 2019

In part-based image representation, the partial shapes of an object are represented as basis vectors, and an image is decomposed as a linear combination of basis vectors where the coefficients of those basis vectors represent the partial (or local) feature of an object. In this paper, a face recognition for occluded faces is proposed in which face images are represented using non-negative matrix factorization(NMF), one of part-based representation techniques, and recognized using an artificial neural network technique. Standard NMF, projected gradient NMF and orthogonal NMF were used in part-based representation of face images, and their performances were compared. Learning vector quantizer were used in the recognizer where Euclidean distance was used as the distance measure. Experimental results show that proposed recognition is more robust than the conventional face recognition for the occluded faces.

• Journal of Information Technology Services
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• v.21 no.3
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• pp.63-72
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• 2022

The extinction crisis of local cities, caused by a population density increase phenomenon in capital regions, directly causes the increase of vacant houses in local cities. According to population and housing census, Gunsan-si has continuously shown increasing trend of vacant houses during 2015 to 2019. In particular, since Gunsan-si is the city which suffers from doughnut effect and industrial decline, problems regrading to vacant house seems to exacerbate. This study aims to provide a foundation of a system which can predict and deal with the building that has high risk of becoming vacant house through implementing a data driven vacant house prediction machine learning model. Methodologically, this study analyzes three types of machine learning model by differing the data components. First model is trained based on building register, individual declared land value, house price and socioeconomic data and second model is trained with the same data as first model but with additional POI(Point of Interest) data. Finally, third model is trained with same data as the second model but with excluding water usage and electricity usage data. As a result, second model shows the best performance based on F1-score. Random Forest, Gradient Boosting Machine, XGBoost and LightGBM which are tree ensemble series, show the best performance as a whole. Additionally, the complexity of the model can be reduced through eliminating independent variables that have correlation coefficient between the variables and vacant house status lower than the 0.1 based on absolute value. Finally, this study suggests XGBoost and LightGBM based machine learning model, which can handle missing values, as final vacant house prediction model.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.42.1-42.1
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• 2021

In this study, we present a visual explanation of a deep learning solar flare forecast model and its relationship to physical parameters of solar active regions (ARs). For this, we use full-disk magnetograms at 00:00 UT from the Solar and Heliospheric Observatory/Michelson Doppler Imager and the Solar Dynamics Observatory/Helioseismic and Magnetic Imager, physical parameters from the Space-weather HMI Active Region Patch (SHARP), and Geostationary Operational Environmental Satellite X-ray flare data. Our deep learning flare forecast model based on the Convolutional Neural Network (CNN) predicts "Yes" or "No" for the daily occurrence of C-, M-, and X-class flares. We interpret the model using two CNN attribution methods (guided backpropagation and Gradient-weighted Class Activation Mapping [Grad-CAM]) that provide quantitative information on explaining the model. We find that our deep learning flare forecasting model is intimately related to AR physical properties that have also been distinguished in previous studies as holding significant predictive ability. Major results of this study are as follows. First, we successfully apply our deep learning models to the forecast of daily solar flare occurrence with TSS = 0.65, without any preprocessing to extract features from data. Second, using the attribution methods, we find that the polarity inversion line is an important feature for the deep learning flare forecasting model. Third, the ARs with high Grad-CAM values produce more flares than those with low Grad-CAM values. Fourth, nine SHARP parameters such as total unsigned vertical current, total unsigned current helicity, total unsigned flux, and total photospheric magnetic free energy density are well correlated with Grad-CAM values.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.6
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• pp.88-95
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• 2011

An embedded mobile device mostly has lower computation power than a general purpose computer because of its relatively lower system specifications. Consequently, conventional face tracking and face detection methods, requiring complex algorithms for higher recognition rates, are unsuitable in a mobile environment aiming for real time detection. On the other hand, by applying a real-time tracking and detecting algorithm, we would be able to provide a two-way interactive multimedia service between an user and a mobile device thus providing a far better quality of service in comparison to a one-way service. Therefore it is necessary to develop a real-time face and eye tracking technique optimized to a mobile environment. For this reason, in this paper, we proposes a method of tracking horizontal face position of a user on a T-DMB device for enhancing the quality of 3D DMB content. The proposed method uses the orientation of edges to estimate the left and right boundary of the face, and by the color edge information, the horizontal position and size of face is determined finally to decide the horizontal face. The sobel gradient vector is projected vertically and candidates of face boundaries are selected, and we proposed a smoothing method and a peak-detection method for the precise decision. Because general face detection algorithms use multi-scale feature vectors, the detection time is too long on a mobile environment. However the proposed algorithm which uses the single-scale detection method can detect the face more faster than conventional face detection methods.