• ETRI Journal
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• v.35 no.6
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• pp.969-979
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• 2013

Various simulation applications for hair, clothing, and makeup of a 3D avatar can provide more useful information to users before they select a hairstyle, clothes, or cosmetics. To enhance their reality, the shapes, textures, and colors of the avatars should be similar to those found in the real world. For a more realistic 3D avatar color reproduction, this paper proposes a spectrum-based color reproduction algorithm and color management process with respect to the implementation of the algorithm. First, a makeup color reproduction model is estimated by analyzing the measured spectral reflectance of the skin samples before and after applying the makeup. To implement the model for a makeup simulation system, the color management process controls all color information of the 3D facial avatar during the 3D scanning, modeling, and rendering stages. During 3D scanning with a multi-camera system, spectrum-based camera calibration and characterization are performed to estimate the spectrum data. During the virtual makeup process, the spectrum data of the 3D facial avatar is modified based on the makeup color reproduction model. Finally, during 3D rendering, the estimated spectrum is converted into RGB data through gamut mapping and display characterization.

• Journal of Korea Society of Industrial Information Systems
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• v.12 no.5
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• pp.47-53
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• 2007

On account of supporting the full spectrum of content types in enterprises, customers recognize the problems of the conventional way, that is, point solutions for content management. And then they increase the requirement for ECM (Enterprise Content Management) solutions. In this paper, we survey the spectrum of enterprise content, the components, core value, the market landscape, and application cases of ECM, in order to suggesting the prospective customers the approach for enterprise-wide content management.

• ETRI Journal
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• v.36 no.3
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• pp.514-517
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• 2014

In this paper, alternative dynamic features for speech recognition are proposed. The goal of this work is to improve speech recognition accuracy by deriving the representation of distinctive dynamic characteristics from a speech spectrum. This work was inspired by two temporal dynamics of a speech signal. One is the highly non-stationary nature of speech, and the other is the inter-frame change of a speech spectrum. We adopt the use of a sub-frame spectrum analyzer to capture very rapid spectral changes within a speech analysis frame. In addition, we attempt to measure spectral fluctuations of a more complex manner as opposed to traditional dynamic features such as delta or double-delta. To evaluate the proposed features, speech recognition tests over smartphone environments were conducted. The experimental results show that the feature streams simply combined with the proposed features are effective for an improvement in the recognition accuracy of a hidden Markov model-based speech recognizer.

• Journal of Biosystems Engineering
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• v.23 no.6
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• pp.561-570
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• 1998

The MLR(Multiple Linear Regression) models to estimate soluble solids content non-destructively were presented to make a selection of optimal photosensor utilized to measure the soluble solids content of apples. Visible and NIR absorbance in the 400 to 2498 nanometer(nm) wavelength region, soluble solids content(sugar content), hardness, and weight were measured for 400 apples(gala). Spectrophotometer with fiber optic probe was utilized for spectrum measurement and digital refractometer was used for soluble solids content. Correlation between absorbance spectrum and soluble solids content was analyzed to pick out the optimal wavelengths and to develop corresponding prediction model by means of MLR. For the coefficient of determination($R^2$) to be over 0.92, the MLR models out of the original absorbance were built based on 7 wavelengths of 992, 904, 1096, 1032, 880, 824, 1048nm, and the ones of the second derivative absorbance based on 5 wavelengths of 784, 1056, 992, 808, 872nm. The best model of the second derivative absorbance spectrum had $R^2$=0.91, bias= -0.02bx, SEP=0.28bx for unknown samples.

• Journal of Biosystems Engineering
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• v.27 no.2
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• pp.117-124
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• 2002

This research was conducted to develop a method to remove the effect of surface temperature of Shingo pears for sugar content measurement. Sugar content was measured by a near-infrared spectrum analysis technique. Reflected spectrum and sugar content of a pear were used for developing regression models. For the model development, reflected spectrums having wavelengths in the range of 654 to 1,052nm were used. To remove the effect of surface temperature, special sample preparation techniques and partial least square (PLS) regression models were proposed and tested. 71 Shingo pears stored in a cold storage, which had 2$^{\circ}C$ inside temperature, were taken out and left in a room temperature for a while. Temperature and reflected spectrum of each pear was measured. To increase the temperature distribution of samples, temperature and reflected spectrum of each pear was measured four times with one hour twenty minutes interval. During the experiment, temperature of pears increased up to 17 $^{\circ}C$. The total number of measured spectrum was 284. Three groups of spectrum data were formed according to temperature distribution. First group had surface temperature of 14$^{\circ}C$ and total number of 51. Second group consisted of the first and the fourth experiment data which contained the minimum and the maximum temperatures. Third group consisted of 155 data with normal temperature-distribution. The rest data set were used for model evaluation. Results shelved that PLS model I, which was developed by using the first data group, was inadequate for measuring sugar content of pears which had different surface temperatures from 14$^{\circ}C$. After temperature compensation, sugar content predictions became close to the measured values. Since using many data which had wide range of surface temperatures, PLS model II and III were able to predict sugar content of pears without additional temperature compensation. PLS model IV, which included the surface temperatures as an independent variable. showed slightly improved performance(R$^2$=0.73). Performance of the model could be enhanced by using samples with more wide range of temperatures and sugar contents.

• Journal of Biosystems Engineering
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• v.22 no.2
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• pp.247-255
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• 1997

This study was conducted to develop regression models predicting sugar and acid contents in intact fruits nondestructively by using the second derivative of absorbance spectrum measured with a spectrophotometer wavelength range of 400nm to 2, 400nm. The correlation analysis was made in wavelength range of 600nm to 1, 100nm and 600nm to 2, 400nm respectively, in order to examine the feasibility of using a real time spectrophotometer, which covers the former range, in predicting sugar and acid contents. The second derivative data of the spectrum were obtained by varying smoothing size and derivative size of the original absorbance spectrum. SAS statistical package program was used for the regression analysis. The sugar contents of Fuji apple, Shingo pear md Yumyung peach could be predicted with SEPs of 0.40, 1.17 and 0.77 respectively, in the spectrum range of 600 to 1, 100nm. The highest correlation coefficient of the titratible acidity of apple was -0.45 at 2, 346nm and regression models indicated determination coefficient less than 0.47.

• Journal of Biosystems Engineering
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• v.33 no.4
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• pp.269-275
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• 2008

This study was conducted to design an impedance sensor that can measure soil water content of soils. Partial least square regression (PLSR) was applied to soil impedance data preprocessed with a smoothing method. An optimal sub-spectrum size and wavelength range were determined by comparing the coefficient of determination ($R^2$) and root mean square error (RMSE) of the PLSR models obtained using soil impedance data. various PLS analysis. Based on the PLSR analysis, it would be concluded that the optimal spectrum measurement range was $32.0{\sim}50.0\;MHz$ with the optimal sub-spectrum size of about 18.5 MHz.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.325-333
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• 1996

This study was conducted to develop neural networks of predicting the sugar content of fruits based on the optical densities obtained from a spectrophotometer. Pear, apple and peach were used in investigating the feasbility of the developed neural networks as a nondestructive measurement. A spectrophotometer was used to measure the optical densities of test fruits. The neural networks suggested in this study consisted of multi-layers having one hidden layer and one output layer. The correlation coefficients between the predicted and the measured sugar content for most fruits were high. The neural networks using 2nd derivatives of optical density spectrum produced a better results in predicting the sugar content of fruits. This study contributed to develop a method for nondestructively predicting the sugar content of fruits.

• Journal of Biosystems Engineering
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• v.41 no.3
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• pp.273-280
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• 2016

Purpose: This experiment was conducted to detect water stress in terms of the moisture content of cucumber seedlings under water stress condition using a hyperspectral image acquisition system, linear regression analysis, and partial least square regression (PLSR) to achieve a non-destructive measurement procedure. Methods: Changes in the reflectance spectrum of cucumber seedlings under water stress were measured using hyperspectral imaging techniques. A model for estimating moisture content of cucumber seedlings was constructed through a linear regression analysis that used the moisture content of cucumber seedlings and a normalized difference vegetation index (NDVI). A model using PLSR that used the moisture content of cucumber seedlings and reflectance spectrum was also created. Results: In the early stages of water stress, cucumber seedlings recovered completely when sub-irrigation was applied. However, the seedlings suffering from initial wilting did not recover when more than 42 h passed without irrigation. The reflectance spectrum of seedlings under water stress decreased gradually, but increased when irrigation was provided, except for the seedlings that had permanently wilted. From the results of the linear regression analysis using the NDVI, the model excluding wilted seedlings with less than 20% (n=97) moisture content showed a precision ($R^2$ and $R^2_{\alpha}$) of 0.573 and 0.568, respectively, and accuracy (RE) of 4.138% and 4.138%, which was higher than that for models including all seedlings (n=100). For PLS regression analysis using the reflectance spectrum, both models were found to have strong precision ($R^2$) with a rating of 0.822, but accuracy (RMSE and RE) was higher in the model excluding wilted seedlings as 5.544% and 13.65% respectively. Conclusions: The estimation model of the moisture content of cucumber seedlings showed better results in the PLSR analysis using reflectance spectrum than the linear regression analysis using NDVI.

• Earthquakes and Structures
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• v.15 no.4
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• pp.443-452
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• 2018

In this paper the new intensity measures (IMs) for probabilistic seismic analysis of RC high-rise buildings with core wall structural system are proposed. The existing IMs are analysed and the new optimal ones are presented. The newly proposed IMs are based on the existing ones which: 1) comprise a wider range of frequency velocity spectrum content and 2) are defined as the integral along the velocity spectrum. In analysis characteristics of optimal IMs such as: efficiency, practicality, proficiency and sufficiency are considered. As prototype buildings, RC high-rise buildings with core wall structural system and with characteristic heights: 20-storey, 30-storey and 40-storey, are selected. The non-linear 3D models of the prototype buildings are constructed. 720 non-linear time-history analyses are conducted for 60 ground motion records with a wide range of magnitudes, distances to source and various soil types. Statistical processing of results and detailed regression analysis are performed and appropriate demand models which relate IMs to demand measures (DMs), are obtained. The conducted analysis has shown that the newly proposed IMs can efficiently predict the DMs with minimum dispersion and satisfactory practicality as compared to the other commonly used IMs (e.g., PGA and $S_a(T_1)$). The newly proposed IMs overcome difficulties in calculating of integral along the velocity spectrum and present adequate replacement for IMs which comprise a wider range of frequency velocity spectrum content.