• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.207-209
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• 2022

In the IoT environment, it is possible to collect life pattern data by recognizing human physical activity from smart devices. In this paper, the proposed model consists of a prediction stage and a recommendation stage. The prediction stage predicts the scale of anxiety and depression by using logistic regression and k-nearest neighbor algorithm through machine learning on the dataset collected from life pattern data. In the recommendation step, if the symptoms of anxiety and depression are classified, the principal component analysis algorithm is applied to recommend food and light exercise that can improve them. It is expected that the proposed anxiety/depression prediction and food/exercise recommendations will have a ripple effect on improving the quality of life of individuals.

• Communications for Statistical Applications and Methods
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• v.31 no.2
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• pp.213-234
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• 2024

In recent decades, increasing research attention has been directed toward predicting the price of stocks in financial markets using deep learning methods. For instance, recurrent neural network (RNN) is known to be competitive for datasets with time-series data. Long short term memory (LSTM) further improves RNN by providing an alternative approach to the gradient loss problem. LSTM has its own advantage in predictive accuracy by retaining memory for a longer time. In this paper, we combine both supervised and unsupervised dimension reduction methods with LSTM to enhance the forecasting performance and refer to this as a dimension reduction based LSTM (DR-LSTM) approach. For a supervised dimension reduction method, we use methods such as sliced inverse regression (SIR), sparse SIR, and kernel SIR. Furthermore, principal component analysis (PCA), sparse PCA, and kernel PCA are used as unsupervised dimension reduction methods. Using datasets of real stock market index (S&P 500, STOXX Europe 600, and KOSPI), we present a comparative study on predictive accuracy between six DR-LSTM methods and time series modeling.

• Journal of Korean Society of Transportation
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• v.26 no.3
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• pp.53-66
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• 2008

This research is to the establishment of a conceptual framework that supports road characteristic classification from a new point of view in order to complement of the existing road functional classification and examine of traffic pattern. The road characteristic classification(RCC) is expected to use important performance criteria that produced a policy guidelines for transportation planning and operational management. For this study, the traffic data used the permanent traffic counters(PTCs) located within the national highway between 2002 and 2006. The research has described for a systematic review and assessment of how exploratory factor analysis should be applied from 12 explanatory variables. The optimal number of components and clusters are determined by interpretation of the factor analysis results. As a result, the scenario including all 12 explanatory variables is better than other scenarios. The four components is produced the optimal number of factors. This research made contributions to the understanding of the exploratory factor analysis for the road characteristic classification, further applying the objective input data for various analysis method, such as cluster analysis, regression analysis and discriminant analysis.

• Journal of Biosystems Engineering
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• v.34 no.3
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• pp.197-203
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• 2009

This study was performed to develop a nondestructive determination technology for sugar content in sweet persimmons, and the main research results included the following. In order to determine sugar content in sweet persimmons, a dual side reflex was adopted, and the study was to measure sugar content using a reflectance spectrum for 2 parts because it was difficult to determine representative sugar content due to a great deviation in sugar content according to the part of sweet persimmons. To predict sugar contents of sweet persimmon, PLSR and PCR models were compared with a few preprocess methods. As a result, PLSR had $R^2$=0.67, SEP=0.42 brix, LV=11, and PCR had $R^2$=0.65, SEP=0.41 brix, PC=16. SNV method was the best among preprocess methods for predicting sugar contents.

• Journal of the Korean Institute of Landscape Architecture
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• v.22 no.3
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• pp.151-162
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• 1994

This is a comparative study on the use pattern and satisfaction of skiers between the suburban skiing ground and the resort one. The purpose of this study is to provide basic data for the planning and the management of skiing ground. The sites of case study are Yong Pyung Ski Resort (Resort type) and Bears Town(Suburban type). Data were collected from questionaire. A total of 420 questionaires were completed. And data were subjected to following analysis: First, the descriptive statistics(mean, chi-square analysis etc.) were used to compare the characteristics of the users and the use pattern of two sites. Second, factor analysis was utilized to reduce 22 satisfaction items into the smaller number of factors. Third, regression analysis was used to find the factors affecting users' overall satisfaction in each skiing ground. The findings of this study are as follows: First, it was proved that the characteristics of users between tow sites were different in terms of age, income, and skill level. Second, it was proved that the use pattern between two sites were different in terms of travel distance from home, traffic mode, length of stay, accommodation type, and the money spent per day. Third, By a principal component factor analysis several factors of satisfaction are found: In physical terms, they are 'slope and life facilities', 'recreation and lodge facilities', 'accessibility', 'crowding', and 'landscape'. In psychological terms, they are 'skiing skills and thrills', and 'relaxation and freedom'. Forth, As the result of the stepwise regression analysis, it was yielded that 'relaxation/and freedom' was most important factor to predict the overall satisfaction in both skiing ground. And it was proved that not only physical factors but also phychological(need gratifying) factors were important sources of the satisfaction.

• Journal of Biosystems Engineering
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• v.41 no.3
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• pp.208-220
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• 2016

Purpose: Fusel oil is a potent volatile aroma compound found in many alcoholic beverages. At low concentrations, it makes an essential contribution to the flavor and aroma of fermented alcoholic beverages, while at high concentrations, it induced an off-flavor and is thought to cause undesirable side effects. In this work, we introduce Fourier transform near-infrared (FT-NIR) spectroscopy as a rapid and nondestructive technique for the quantitative determination of fusel oil in the Korean alcoholic beverage "soju". Methods: FT-NIR transmittance spectra in the 1000-2500 nm region were collected for 120 soju samples with fusel oil concentrations ranging from 0 to 1400 ppm. The calibration and validation data sets were designed using data from 75 and 45 samples, respectively. The net analyte signal (NAS) was used as a preprocessing method before the application of the partial least-square regression (PLSR) and principal component regression (PCR) methods for predicting fusel oil concentration. A novel variable selection method was adopted to determine the most informative spectral variables to minimize the effect of nonmodeled interferences. Finally, the efficiency of the developed technique was evaluated with two different validation sets. Results: The results revealed that the NAS-PLSR model with selected variables ($R^2_{\upsilon}=0.95$, RMSEV = 100ppm) did not outperform the NAS-PCR model (($R^2_{\upsilon}=0.97$, RMSEV = 7 8.9ppm). In addition, the NAS-PCR shows a better recovery for validation set 2 and a lower relative error for validation set 3 than the NAS-PLSR model. Conclusion: The experimental results indicate that the proposed technique could be an alternative to conventional methods for the quantitative determination of fusel oil in alcoholic beverages and has the potential for use in in-line process control.

• Journal of the Korean Society of Tobacco Science
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• v.17 no.2
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• pp.177-183
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• 1995

This study was conducted to develop the most precise NIR(near infrared spectrometric) calibration for rapid determination of chemical composition in ground samples of toasted burley tobacco using stepwise, stepup, principal component regression(PCR), partial least square(PLS) and modified partial least square(MPLS) calibration method. The number of wavelength(W) selected by stepup multiple linear regression using: second derivative spectra was as follows: total sugar(TS)-4 W, nicotine-9 W, total nitrogen(TN)-2 W, ash-8 W, total volatile base(TVB)-5 W, chlorine4 W, L of color-6 W, a of color-6 W and b of color-7 W. Comparing the calibration equations followed by each chemical components, the most precise calibration equation was MPLS for 75, a and b of color, PLS for nicotine, ash, TVB, chlorine and L of color and stepup for TN. The standard error of calibration(SEC) and standard error of performance(SEP) between result of near infrared analysis and standard laboratory analysis were 0.18, 0.40% for 75, 0.06, 0.08% for nicotine, 0.18, 0.16% for TN, 0.33, 0.46% for ash, 0.04, 0.03% for TVB, 0.08, 0.06% for chlorine, 0.54, 0.58 for L of color, 0.22, 0.22 for a of color and 0.27, 0.27 for b of color, respectively. The SEC and SEP of ash and TVB were within allowable error of standard laboratory analysis, nicotine, TN and chlorine were 1.2-2.0 times and 75 were 2.1-4.0 times larger than allowable error of standard laboratory analysis. The ratio of SEC and SEP to mean were 1.5, 1.6% for L of color, 3.7, 3.8% for a of color and 1.8, 1.8% for b of color, respectively. Key words : burley tobacco chemistry, near infrared spectroscopy.

• Journal of the East Asian Society of Dietary Life
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• v.22 no.6
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• pp.836-850
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• 2012

This study was conducted to identify the sensory characteristics of the Korean traditional confectionery, dasik, prepared under different conditions and to compare their consumer acceptance in Korea. To accomplish this, descriptive analysis of eight samples prepared using two types of rice cake powder, dasik (Rflour, Rflour_Omija), brown rice powder red ginseng dasik (Brice_Ginseng_P), pinepollen dasik (PineP), black sesame dasik (BSesame), bean dasik (Rbean), and two types of mungbean starch dasik (Starch_Omija, Starch_Greentea), was conducted by ten trained panelists. In addition, 81 consumers evaluated the overall acceptance (OL), acceptance of appearance (APPL), odor (ODL), flavor (FLL), and texture (TXTL) of the samples using a 9-point hedonic scale, as well as the perceived intensities of sesame flavor, sweetness, and hardness using a 9-point just-about-right (JAR) scale. Partial least square- regression (PLSR) indicated that the BSesame and Rbean samples, which had significantly (p<0.05) high roasted sesame, burnt, greasy, glossy, and cooked chestnut flavor scores, had the highest acceptability and consumer desire scores. Additionally, the PineP and Rflour_Omija samples, which had relatively high particle size, transparency, roughness, spoiled tofu, fermentation and raw rice flavor scores, were the least preferred samples. Therefore, roasted sesame, burnt, greasy, glossy, and cooked chestnut flavor attributes were considered drivers of "liking" whereas particle size, transparent, roughness, spoiled tofu, fermentation, and raw rice flavor attributes acted as drivers of "disliking" among consumers.

• Journal of IKEEE
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• v.19 no.4
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• pp.541-547
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• 2015

In current semiconductor manufacturing, as the feature size of integrated circuit (IC) devices continuously shrinks, detecting endpoint in plasma etching process is more difficult than before. For endpoint detection, various kinds of sensors are installed in semiconductor manufacturing equipments, and sensor data are gathered with predefined sampling rate. Generally, detecting endpoint is performed using OES data of by-product. In this study, OES data of both by-product and etchant gas are used to improve reliability of endpoint detection. For the OES data pre-processing, a combination of Signal to Noise Ratio (SNR) and Principal Component Analysis (PCA),are used. Polynomial Regression and Expanded Hidden Markov model (eHMM) technique are applied to pre-processed OES data to detect endpoint.

• Journal of Broadcast Engineering
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• v.24 no.2
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• pp.264-272
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• 2019

In this paper, we propose a 3D human body reconstruction and refinement method from the frames extracted from a video to obtain natural and smooth motion in temporal domain. Individual frames extracted from the video are fed into convolutional neural network to estimate the location of the joint and the silhouette of the human body. This is done by projecting the parameter-based 3D deformable model to 2D image and by estimating the value of the optimal parameters. If the reconstruction process for each frame is performed independently, temporal consistency of human pose and shape cannot be guaranteed, yielding an inaccurate result. To alleviate this problem, the proposed method analyzes and interpolates the principal component parameters of the 3D morphable model reconstructed from each individual frame. Experimental result shows that the erroneous frames are corrected and refined by utilizing the relation between the previous and the next frames to obtain the improved 3D human reconstruction result.