• Journal of Global Scholars of Marketing Science
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• v.19 no.2
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• pp.17-26
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• 2009

As the lifestyle of consumers changes and the need for various products increases, new products are being developed in the market. Each household owns various home appliances which are purchased through the choice of a decision maker. These appliances include not only large-sized products such as TV, refrigerator, and washing machine, but also small-sized products such as microwave oven and air cleaner. There exists latent correlation among possession of home appliances, even though they are purchased independently. The purpose of this research is to analyze the effect of demographic factors on the purchase and possession of each home appliances, and to derive some relationships among various appliances. To achieve this purpose, the present status on the possession of each home appliances are investigated through consumer survey data on the electric and energy product. And a multivariate probit(MVP) model is applied for the empirical analysis. From the estimation results, some appliances show a substitutive or complementary pattern as expected, while others which look apparently unrelated have correlation by co-incidence. This research has several advantages compared to previous literatures on home appliances. First, this research focuses on the various products which are purchased by each household, while previous researches such as Matsukawa and Ito(1998) and Yoon(2007) focus just on a particular product. Second, the methodology of this research can consider a choice process of each product and correlation among products simultaneously. Lastly, this research can analyze not only a substitutive or complementary relationship in the same category, but also the correlation among products in the different categories. As the data on the possession of home appliances in each household has a characteristic of multiple choice, not a single choice, a MVP model are used for the empirical analysis. A MVP model is derived from a random utility model, and has an advantage compared to a multinomial logit model in that correlation among error terms can be derive(Manchanda et al., 1999; Edwards and Allenby, 2003). It is assumed that the error term has a normal distribution with zero mean and variance-covariance matrix ${\Omega}$. Hence, the sign and value of correlation coefficients means the relationship between two alternatives(Manchanda et al., 1999). This research uses the data of 'TEMEP Household ICT/Energy Survey (THIES) 2008' which is conducted by Technology Management, Economics and Policy Program in Seoul National University. The empirical analysis of this research is accomplished in two steps. First, a MVP model with demographic variables is estimated to analyze the effect of the characteristics of household on the purchase of each home appliances. In this research, some variables such as education level, region, size of family, average income, type of house are considered. Second, a MVP model excluding demographic variables is estimated to analyze the correlation among each home appliances. According to the estimation results of variance-covariance matrix, each households tend to own some appliances such as washing machine-refrigerator-cleaner-microwave oven, and air conditioner-dish washer-washing machine and so on. On the other hand, several products such as analog braun tube TV-digital braun tube TV and desktop PC-portable PC show a substitutive pattern. Lastly, the correlation map of home appliances are derived using multi-dimensional scaling(MDS) method based on the result of variance-covariance matrix. This research can provide significant implications for the firm's marketing strategies such as bundling, pricing, display and so on. In addition, this research can provide significant information for the development of convergence products and related technologies. A convergence product can decrease its market uncertainty, if two products which consumers tend to purchase together are integrated into it. The results of this research are more meaningful because it is based on the possession status of each household through the survey data.

• Spatial Information Research
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• v.22 no.1
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• pp.55-63
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• 2014

Near surface air temperature data which are one of the essential factors in hydrology, meteorology and climatology, have drawn a substantial amount of attention from various academic domains and societies. Meteorological observations, however, have high spatio-temporal constraints with the limits in the number and distribution over the earth surface. To overcome such limits, many studies have sought to estimate the near surface air temperature from satellite image data at a regional or continental scale with simple regression methods. Alternatively, we applied various Kriging methods such as ordinary Kriging, universal Kriging, Cokriging, Regression Kriging in search of an optimal estimation method based on near surface air temperature data observed from automatic weather stations (AWS) in South Korea throughout 2010 (365 days) and MODIS land surface temperature (LST) data (MOD11A1, 365 images). Due to high spatial heterogeneity, auxiliary data have been also analyzed such as land cover, DEM (digital elevation model) to consider factors that can affect near surface air temperature. Prior to the main estimation, we calculated root mean square error (RMSE) of temperature differences from the 365-days LST and AWS data by season and landcover. The results show that the coefficient of variation (CV) of RMSE by season is 0.86, but the equivalent value of CV by landcover is 0.00746. Seasonal differences between LST and AWS data were greater than that those by landcover. Seasonal RMSE was the lowest in winter (3.72). The results from a linear regression analysis for examining the relationship among AWS, LST, and auxiliary data show that the coefficient of determination was the highest in winter (0.818) but the lowest in summer (0.078), thereby indicating a significant level of seasonal variation. Based on these results, we utilized a variety of Kriging techniques to estimate the surface temperature. The results of cross-validation in each Kriging model show that the measure of model accuracy was 1.71, 1.71, 1.848, and 1.630 for universal Kriging, ordinary Kriging, cokriging, and regression Kriging, respectively. The estimates from regression Kriging thus proved to be the most accurate among the Kriging methods compared.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.2
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• pp.87-95
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• 2016

This study examined the characteristics of the hourly demand of city gas in Korea and proposed multiple regression models to obtain precise estimates of the hourly demand of city gas. Forecasting the hourly demand of city gas with accuracy is essential in terms of safety and cost. If underestimated, the pipeline pressure needs to be increased sharply to meet the demand, when safety matters. In the opposite case, unnecessary inventory and operation costs are incurred. Data analysis showed that the hourly demand of city gas has a very high autocorrelation and that the 24-hour demand pattern of a day follows the previous 24-hour demand pattern of the same day. That is, there is a weekly cycle pattern. In addition, some conditions that temperature affects the hourly demand level were found. That is, the absolute value of the correlation coefficient between the hourly demand and temperature is about 0.853 on average, while the absolute value of the correlation coefficient on a specific day improves to 0.861 at worst and 0.965 at best. Based on this analysis, this paper proposes a multiple regression model incorporating the hourly demand ahead of 24 hours and the hourly demand ahead of 168 hours, and another multiple regression model with temperature as an additional independent variable. To show the performance of the proposed models, computational experiments were carried out using real data of the domestic city gas demand from 2009 to 2013. The test results showed that the first regression model exhibits a forecasting accuracy of MAPE (Mean Absolute Percentage Error) around 4.5% over the past five years from 2009 to 2013, while the second regression model exhibits 5.13% of MAPE for the same period.

• Journal of radiological science and technology
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• v.36 no.2
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• pp.123-129
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• 2013

This study investigated the rate of setup variance by the rotating unbalance of gantry in image-guided radiation therapy. The equipments used linear accelerator(Elekta Synergy TM, UK) and a three-dimensional volume imaging mode(3D Volume View) in cone beam computed tomography(CBCT) system. 2D images obtained by rotating $360^{\circ}$and $180^{\circ}$ were reconstructed to 3D image. Catpan503 phantom and homogeneous phantom were used to measure the setup errors. Ball-bearing phantom was used to check the rotation axis of the CBCT. The volume image from CBCT using Catphan503 phantom and homogeneous phantom were analyzed and compared to images from conventional CT in the six dimensional view(X, Y, Z, Roll, Pitch, and Yaw). The variance ratio of setup error were difference in X 0.6 mm, Y 0.5 mm Z 0.5 mm when the gantry rotated $360^{\circ}$ in orthogonal coordinate. whereas rotated $180^{\circ}$, the error measured 0.9 mm, 0.2 mm, 0.3 mm in X, Y, Z respectively. In the rotating coordinates, the more increased the rotating unbalance, the more raised average ratio of setup errors. The resolution of CBCT images showed 2 level of difference in the table recommended. CBCT had a good agreement compared to each recommended values which is the mechanical safety, geometry accuracy and image quality. The rotating unbalance of gentry vary hardly in orthogonal coordinate. However, in rotating coordinate of gantry exceeded the ${\pm}1^{\circ}$ of recommended value. Therefore, when we do sophisticated radiation therapy six dimensional correction is needed.

• Korean Journal of Remote Sensing
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• v.35 no.6_2
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• pp.1117-1132
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• 2019

Forest covers 30% of the Earth's land area and plays an important role in global carbon flux through its ability to store much greater amounts of carbon than other terrestrial ecosystems. The Gross Primary Production (GPP) represents the productivity of forest ecosystems according to climate change and its effect on the phenology, health, and carbon cycle. In this study, we estimated the daily GPP for a forest ecosystem using remote-sensed data from Moderate Resolution Imaging Spectroradiometer (MODIS) and machine learning algorithms Support Vector Machine (SVM). MODIS products were employed to train the SVM model from 75% to 80% data of the total study period and validated using eddy covariance measurement (EC) data at the six flux tower sites. We also compare the GPP derived from EC and MODIS (MYD17). The MODIS products made use of two data sets: one for Processed MODIS that included calculated by combined products (e.g., Vapor Pressure Deficit), another one for Unprocessed MODIS that used MODIS products without any combined calculation. Statistical analyses, including Pearson correlation coefficient (R), mean squared error (MSE), and root mean square error (RMSE) were used to evaluate the outcomes of the model. In general, the SVM model trained by the Unprocessed MODIS (R = 0.77 - 0.94, p < 0.001) derived from the multi-sites outperformed those trained at a single-site (R = 0.75 - 0.95, p < 0.001). These results show better performance trained by the data including various events and suggest the possibility of using remote-sensed data without complex processes to estimate GPP such as non-stationary ecological processes.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.32 no.1
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• pp.65-73
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• 2019

In order to increase the production efficiency of the ship and shorten the production cycle, it is important to evaluate the accuracy of the ship components efficiently during the drying cycle. The accuracy control of the block is important for shortening the ship process, reducing the cost, and improving the accuracy of the ship. Some systems have been developed and used mainly in large shipyards, but in some cases, they are measured and managed using conventional measuring instruments such as tape measure and beam, optical instruments as optical equipment, In order to perform accuracy control, these tools and equipment as well as equipment for recording measurement data and paper drawings for measuring the measurement position are inevitably combined. The measured results are managed by the accuracy control system through manual input or recording device. In this case, the measurement result is influenced by the work environment and the skill level of the worker. Also, in the measurement result management side, there are a human error about the lack of the measurement result creation, the lack of the management sheet management, And costs are lost in terms of efficiency due to consumption. The purpose of this study is to improve the working environment in the existing accuracy management process by using the augmented reality technology to visualize the measurement information on the actual block and to obtain the measurement information And a smart management system based on augmented reality that can effectively manage the accuracy management data through interworking with measurement equipment. We confirmed the applicability of the proposed system to the accuracy control through the prototype implementation.

• Journal of the Korean Applied Science and Technology
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• v.36 no.3
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• pp.995-1007
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• 2019

The purpose of this study was to investigate the effects of 12-week elastic band exercise on body composition, blood lipids and AMPK in 24 elderly female volunteers aged 65-75 years, and they were divided into the combined exercise group(n=12) and the control group(n=12). The elastic band exercise method was to do exercise 3 times a week for 60 minutes per session, 1-4 weeks for low intensity of OMNI-RES 3-4, 5-8 weeks for medium intensity of OMNI-RES 5-6, 9-12 weeks for OMNI-RES 7-8 of high intensity. In order to compare the differences in the groups before and after the elastic band exercise, two-way repeated measures ANOVA was used to verify the interaction between group and time. The difference in the groups of the measured data was paired t-test, the difference between the groups was paired independent t-test, and analysis of covariance ANCOVA was performed to minimize the inter-group error. The statistical significance level of each item was set to .05. As a result, body fat percentage of exercise group significantly decreased (p<.05), and skeletal muscle volume was significantly increased (p<.01). TC, TG and LDL-C were not significantly different between the exercise and control groups, and HDL-C was significantly decreased in the control group (p<.05). AMPK was significantly decreased in the exercise group (p<.001), but there was no significant difference in the control group. According to the covariance analysis to minimize the error of difference between the pre-exercise groups (p<.05), there was significant difference in AMPK of groups after exercise. These results suggest that the 12-week elastic band exercise has a positive effect on the body composition and AMPK of the elderly women.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.1-15
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• 2021

If it is difficult for us to make decisions, we ask for advice from friends or people around us. When we decide to buy products online, we read anonymous reviews and buy them. With the advent of the Data-driven era, IT technology's development is spilling out many data from individuals to objects. Companies or individuals have accumulated, processed, and analyzed such a large amount of data that they can now make decisions or execute directly using data that used to depend on experts. Nowadays, the recommender system plays a vital role in determining the user's preferences to purchase goods and uses a recommender system to induce clicks on web services (Facebook, Amazon, Netflix, Youtube). For example, Youtube's recommender system, which is used by 1 billion people worldwide every month, includes videos that users like, "like" and videos they watched. Recommended system research is deeply linked to practical business. Therefore, many researchers are interested in building better solutions. Recommender systems use the information obtained from their users to generate recommendations because the development of the provided recommender systems requires information on items that are likely to be preferred by the user. We began to trust patterns and rules derived from data rather than empirical intuition through the recommender systems. The capacity and development of data have led machine learning to develop deep learning. However, such recommender systems are not all solutions. Proceeding with the recommender systems, there should be no scarcity in all data and a sufficient amount. Also, it requires detailed information about the individual. The recommender systems work correctly when these conditions operate. The recommender systems become a complex problem for both consumers and sellers when the interaction log is insufficient. Because the seller's perspective needs to make recommendations at a personal level to the consumer and receive appropriate recommendations with reliable data from the consumer's perspective. In this paper, to improve the accuracy problem for "appropriate recommendation" to consumers, the recommender systems are proposed in combination with context-based deep learning. This research is to combine user-based data to create hybrid Recommender Systems. The hybrid approach developed is not a collaborative type of Recommender Systems, but a collaborative extension that integrates user data with deep learning. Customer review data were used for the data set. Consumers buy products in online shopping malls and then evaluate product reviews. Rating reviews are based on reviews from buyers who have already purchased, giving users confidence before purchasing the product. However, the recommendation system mainly uses scores or ratings rather than reviews to suggest items purchased by many users. In fact, consumer reviews include product opinions and user sentiment that will be spent on evaluation. By incorporating these parts into the study, this paper aims to improve the recommendation system. This study is an algorithm used when individuals have difficulty in selecting an item. Consumer reviews and record patterns made it possible to rely on recommendations appropriately. The algorithm implements a recommendation system through collaborative filtering. This study's predictive accuracy is measured by Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Netflix is strategically using the referral system in its programs through competitions that reduce RMSE every year, making fair use of predictive accuracy. Research on hybrid recommender systems combining the NLP approach for personalization recommender systems, deep learning base, etc. has been increasing. Among NLP studies, sentiment analysis began to take shape in the mid-2000s as user review data increased. Sentiment analysis is a text classification task based on machine learning. The machine learning-based sentiment analysis has a disadvantage in that it is difficult to identify the review's information expression because it is challenging to consider the text's characteristics. In this study, we propose a deep learning recommender system that utilizes BERT's sentiment analysis by minimizing the disadvantages of machine learning. This study offers a deep learning recommender system that uses BERT's sentiment analysis by reducing the disadvantages of machine learning. The comparison model was performed through a recommender system based on Naive-CF(collaborative filtering), SVD(singular value decomposition)-CF, MF(matrix factorization)-CF, BPR-MF(Bayesian personalized ranking matrix factorization)-CF, LSTM, CNN-LSTM, GRU(Gated Recurrent Units). As a result of the experiment, the recommender system based on BERT was the best.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1423-1444
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• 2022

In this study, the sensitivity of the mid-infrared radiance to atmospheric and surface factors was analyzed using the radiative transfer model, MODerate resolution atmospheric TRANsmission (MODTRAN6)'s simulation data. The possibility of retrieving the land surface temperature (LST) using only the mid-infrared bands at night was evaluated. Based on the sensitivity results, the LST retrieval algorithm that reflects various factors for night was developed, and the level of the LST retrieval algorithm was evaluated using reference LST and observed LST. Sensitivity experiments were conducted on the atmospheric profiles, carbon dioxide, ozone, diurnal variation of LST, land surface emissivity (LSE), and satellite viewing zenith angle (VZA), which mainly affect satellite remote sensing. To evaluate the possibility of using split-window method, the mid-infrared wavelength was divided into two bands based on the transmissivity. Regardless of the band, the top of atmosphere (TOA) temperature is most affected by atmospheric profile, and is affected in order of LSE, diurnal variation of LST, and satellite VZA. In all experiments, band 1, which corresponds to the atmospheric window, has lower sensitivity, whereas band 2, which includes ozone and water vapor absorption, has higher sensitivity. The evaluation results for the LST retrieval algorithm using prescribed LST showed that the correlation coefficient (CC), the bias and the root mean squared error (RMSE) is 0.999, 0.023K and 0.437K, respectively. Also, the validation with 26 in-situ observation data in 2021 showed that the CC, bias and RMSE is 0.993, 1.875K and 2.079K, respectively. The results of this study suggest that the LST can be retrieved using different characteristics of the two bands of mid-infrared to the atmospheric and surface conditions at night. Therefore, it is necessary to retrieve the LST using satellite data equipped with sensors in the mid-infrared bands.

• Journal of Korea Port Economic Association
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• v.37 no.4
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• pp.161-173
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• 2021

The shipping industry plummeted and was depressed due to the global economic crisis caused by the bankruptcy of Lehman Brothers in the US in 2008. In 2020, the shipping market also suffered from a collapse in the unstable global economic situation due to the COVID-19 pandemic, but unexpectedly, it changed to an upward trend from the end of 2020, and in 2021, it exceeded the market of the boom period of 2008. According to the Clarksons report published in May 2021, the decrease in cargo volume due to the COVID-19 pandemic in 2020 has returned to the pre-corona level by the end of 2020, and the tramper bulk carrier capacity of 103~104% of the Panamax has been in the ports due to congestion. Earnings across the bulker segments have risen to ten-year highs in recent months. In this study, as factors affecting BDI, the capacity and congestion ratio of Cape and Panamax ships on the supply side, iron ore and coal seaborne tonnge on the demand side and Granger causality test, IRF(Impulse Response Function) and FEVD(Forecast Error Variance Decomposition) were performed using VAR model to analyze the impact on BDI by congestion caused by strengthen quarantine at the port due to the COVID-19 pandemic and the loading and discharging operation delay due to the infection of the stevedore, etc and to predict the shipping market after the pandemic. As a result of the Granger causality test of variables and BDI using time series data from January 2016 to July 2021, causality was found in the Fleet and Congestion variables, and as a result of the Impulse Response Function, Congestion variable was found to have significant at both upper and lower limit of the confidence interval. As a result of the Forecast Error Variance Decomposition, Congestion variable showed an explanatory power upto 25% for the change in BDI. If the congestion in ports decreases after With Corona, it is expected that there is down-risk in the shipping market. The COVID-19 pandemic occurred not from economic factors but from an ecological factor by the pandemic is different from the past economic crisis. It is necessary to analyze from a different point of view than the past economic crisis. This study has meaningful to analyze the causality and explanatory power of Congestion factor by pandemic.