• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• The Korean Journal of Nuclear Medicine
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• v.39 no.3
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• pp.174-181
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• 2005

Purpose: Reduction of respiratory motion artifacts in PET images was studied using respiratory-gated PET (RGPET) with moving phantom. Especially a method of generating simulated helical CT images from 4D-CT datasets was developed and applied to a respiratory specific RGPET images for more accurate attenuation correction. Materials and Methods: Using a motion phantom with periodicity of 6 seconds and linear motion amplitude of 26 mm, PET/CT (Discovery ST: GEMS) scans with and without respiratory gating were obtained for one syringe and two vials with each volume of 3, 10, and 30 ml respectively. RPM (Real-Time Position Management, Varian) was used for tracking motion during PET/CT scanning. Ten datasets of RGPET and 4D-CT corresponding to every 10% phase intervals were acquired. from the positions, sizes, and uptake values of each subject on the resultant phase specific PET and CT datasets, the correlations between motion artifacts in PET and CT images and the size of motion relative to the size of subject were analyzed. Results: The center positions of three vials in RGPET and 4D-CT agree well with the actual position within the estimated error. However, volumes of subjects in non-gated PET images increase proportional to relative motion size and were overestimated as much as 250% when the motion amplitude was increased two times larger than the size of the subject. On the contrary, the corresponding maximal uptake value was reduced to about 50%. Conclusion: RGPET is demonstrated to remove respiratory motion artifacts in PET imaging, and moreover, more precise image fusion and more accurate attenuation correction is possible by combining with 4D-CT.

• Korean Journal of Remote Sensing
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• v.13 no.2
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• pp.99-120
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• 1997

Solar radiation is scattered and absorbed atmospheric compositions in the atmosphere before it reaches the surface and, then after reflected at the surface, until it reaches the satellite sensor. Therefore, consideration of the radiative transfer through the atmosphere is essential for the quantitave analysis of the satellite sensed data, specially at shortwave region. This study examined a feasibility of using radiative transfer code for estimating the atmospheric effects on satellite remote sensing data. To do this, the flux simulated by LOWTRAN7 is compared with CAGEX data in shortwave region. The CAGEX (CERES/ARM/GEWEX Experiment) data provides a dataset of (1) atmospheric soundings, aerosol optical depth and albedo, (2) ARM(Aerosol Radiation Measurement) radiation flux measured by pyrgeometers, pyrheliometer and shadow pyranometer and (3) broadband shortwave flux simulated by Fu-Liou's radiative transfer code. To simulate aerosol effect using the radiative transfer model, the aerosol optical characteristics were extracted from observed aerosol column optical depth, Spinhirne's experimental vertical distribution of scattering coefficient and D'Almeida's statistical atmospheric aerosols radiative characteristics. Simulation of LOWTRAN7 are performed on 31 sample of completely clear days. LOWTRAN's result and CAGEX data are compared on upward, downward direct, downward diffuse solar flux at the surface and upward solar flux at the top of the atmosphere(TOA). The standard errors in LOWTRAN7 simulation of the above components are within 5% except for the downward diffuse solar flux at the surface(6.9%). The results show that a large part of error in LOWTRAN7 flux simulation appeared in the diffuse component due to scattering mainly by atmispheric aerosol. For improving the accuracy of radiative transfer simulation by model, there is a need to provide better information about the radiative charateristrics of atmospheric aerosols.

• Korean Journal of Remote Sensing
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• v.17 no.1
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• pp.33-44
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• 2001

The paper presents several satellite models and satellite image decomposition methods for inaccessible area where ground control points can hardly acquired in conventional ways. First, 10 different satellite sensor models, which were extended from collinearity condition equations, were developed and then behavior of each sensor model was investigated. Secondly, satellite images were decomposed and also pseudo images were generated. The satellite sensor model extended from collinearity equations was represented by the six exterior orientation parameters in $1^{st}$, $2^{nd}$ and $3^{rd}$ order function of satellite image row. Among them, the rotational angle parameters such as $\omega$(omega) and $\Phi$(phi) correlated highly with positional parameters could be assigned to constant values. For inaccessible area, satellite images were decomposed, which means that two consecutive images were combined as one image, The combined image consists of one satellite image with ground control points and the other without ground control points. In addition, a pseudo image which is an imaginary image, was prepared from one satellite image with ground control points and the other without ground control points. In other words, the pseudo image is an arbitrary image bridging two consecutive images. For the experiments, SPOT satellite images exposed to the similar area in different pass were used. Conclusively, it was found that 10 different satellite sensor models and 5 different decomposed methods delivered different levels of accuracy. Among them, the satellite camera model with 1st order function of image row for positional orientation parameters and rotational angle parameter of kappa, and constant rotational angle parameter omega and phi provided the best 60m maximum error at check point with pseudo images arrangement.

• Journal of Korea Water Resources Association
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• v.54 no.10
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• pp.835-848
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• 2021

In Jeju Island which has peculiarity for its geological features and hydrology system, hydrological factor analysis for the effective water management is necessary. Because in-situ hydro-meteorological data is affected by surrounding environment, the in-situ dataset could not be the spatially representative for the study area. For this reason, remote sensing data may be used to overcome the limit of the in-situ data. In this study, applicability assessment of MOD16 evapotranspiration data, Globas Land Data Assimilation System (GLDAS) based evapotranspiration/soil moisture data, and Advanced SCATterometer (ASCAT) soil moisture product which were evaluated their applicability on other study areas was conducted. In the case of evapotranspiration, comparison with total precipitation and flux-tower based evapotranspiration were conducted. And for soil moisture, 6 in-situ data and ASCAT soil moisture product were compared on each site. As a result, 57% of annual precipitation was calculated as evapotranspiration, and the correlation coefficient between MOD16 evapotranspiration and GLDAS evapotranspiration was 0.759, which was a robust value. The correlation coefficient was 0.434, indicating a relatively low fit. In the case of soil moisture, in the case of the GLDAS data, the RMSE value was less than 0.05 at all sites compared to the in-situ data, and a statistically significant result was obtained as a result of the significance test of the correlation coefficient. However, for satellite data, RMSE over than 0.05 were found at Wolgak and there was no correlation at Sehwa and Handong points. It is judged that the above results are due to insufficient quality control and spatial representation of the evapotranspiration and soil moisture sensors installed in Jeju Island. It is estimated as the error that appears when adjacent to the coast. Through this study, the necessity of improving the existing ground observation data of hydrometeorological factors is emphasized.

• Journal of Korea Water Resources Association
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• v.54 no.10
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• pp.819-833
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• 2021

This study is to evaluate applicability of linkage modeling using PHABSIM (Physical Habitat Simulation System) and SWAT (Soil and Water Assessment Tool) and to estimate ecological flow for target fishes of Andong downstream (4,565.7 km²). The SWAT was established considering 2 multi purpose dam (ADD, IHD) and 1 streamflow gauging station (GD). The SWAT was calibrated and validated with 9 years (2012 ~ 2020) data of 1 stream (GD) and 2 multi-purpose dam (ADD, IHD). For streamflow and dam inflows (GD, ADD and IHD), R², NSE and RMSE were 0.52 ~ 0.74, 0.48 ~ 0.71, and 0.92 ~ 2.51 mm/day respectively. As a result of flow duration analysis for 9 years (2012 ~ 2020) using calibrated streamflow, the average Q185 and Q275 were 36.5 m³/sec (-1.4%) and 23.8 m³/sec (0%) respectively compared with the observed flow duration and were applied to flow boundary condition of PHABSIM. The target stream was selected as the 410 m section where GD is located, and stream cross-section and hydraulic factors were constructed based on Nakdong River Basic Plan Report and HEC-RAS. The dominant species of the target stream was Zacco platypus and the sub-dominant species was Puntungia herzi Herzenstein, and the HSI (Habitat Suitability Index) of target species was collected through references research. As the result of PHABSIM water level and velocity simulation, error of Q185 and Q275 were analyzed -0.12 m, +0.00 m and +0.06 m/s, +0.09 m/s respectively. The average WUA (Weighted Usable Area) and ecological flow of Zacco platypus and Puntungia herzi Herzenstein were evaluated 76,817.0 m²/1000m, 20.0 m³/sec and 46,628.6 m²/1000m, 9.0 m³/sec. This results indicated Zacco platypus is more adaptable to target stream than Puntungia herzi Herzenstein.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1165-1178
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• 2018

Collapse of an Antarctic ice shelf and its flow velocity changes has the potential to reduce the restraining stress to the seaward flow of the Antarctic Ice Sheet, which can cause sea level rising. In this study, variations in ice velocity from 2000 to 2017 for the Nansen Ice Shelf in East Antarctica that experienced a large-scale collapse in April 2016 were analyzed using Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) images. To extract ice velocity, image matching based on orientation correlation was applied to the image pairs of blue, green, red, near-infrared, panchromatic, and the first principal component image of the Landsat multispectral data, from which the results were combined. The Landsat multispectral image matching produced reliable ice velocities for at least 14% wider area on the Nansen Ice Shelf than for the case of using single band (i.e., panchromatic) image matching. The ice velocities derived from the Landsat multispectral image matching have the error of $2.1m\;a^{-1}$ compared to the in situ Global Positioning System (GPS) observation data. The region adjacent to the Drygalski Ice Tongue showed the fastest increase in ice velocity between 2000 and 2017. The ice velocity along the central flow line of the Nansen Ice Shelf was stable before 2010 (${\sim}228m\;a^{-1}$). In 2011-2012, when a rift began to develop near the ice front, the ice flow was accelerated (${\sim}255m\;a^{-1}$) but the velocity was only about 11% faster than 2010. Since 2014, the massive rift had been fully developed, and the ice velocity of the upper region of the rift slightly decreased (${\sim}225m\;a^{-1}$) and stabilized. This means that the development of the rift and the resulting collapse of the ice front had little effect on the ice velocity of the Nansen Ice Shelf.

• Journal of the Korea Organic Resources Recycling Association
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• v.26 no.4
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• pp.21-29
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• 2018

The contents of total nitrogen(T-N), phosphate($T-P_2O_5$), and potash($T-K_2O$) are important factors to determine the application rate of the livestock compost to prevent nutrients accumulation and maintain their appropriate levels in arable lands. The concentrations of nutrient, organic matter, salt, water content, heavy metal in livestock compost in circulation were investigated with 659 samples from 2016 to 2017. In order to investigate the fluctuation nutrient contents of livestock composts with the same product name, 19 samples were collected and analyzed T-N, and $T-P_2O_5$, and $T-K_2O$ concentration during two years. The mean levels of T-N, $T-P_2O_5$, and $T-K_2O$ in livestock composts of from 2016 to 2017 were 1.73%, 1.88%, and 1.66%, respectively. The average contents of organic matter, water, and salt were 38.9%, 40.9%, and 1.2%, respectively. There were found that the maximum concentrations of Cr, Ni, Cu, and Zn in some livestock composts were exceeded the criteria of the official standard of commercial fertilizer. The maximum variation coefficient of T-N, $T-P_2O_5$ and $T-K_2O$ content of livestock composts was found to be 24%, 27%, and 50% on average, respectively. In order to manage the nutrients in agricultural soils, it will be reasonable that the error range of T-N and $T-P_2O_5$ content in livestock composts should be recommended to be 27% in mean as variation coefficient in case of displaying the nutrient element in liverstock compost.

• Analytical Science and Technology
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• v.31 no.6
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• pp.240-246
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• 2018

The present study analyzed standard samples of three types of aluminum matrix certified reference materials (CRM) using GD-MS. Calibration curves were constructed for 13 elements (Mg, Si, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, Ga, Sn, and Pb), with the slope representing the relative sensitivity factor (RSF). The x- and y-axes of the calibration curve represented ion beam ratio (IBR) and the authenticated value of the standard sample, respectively. In order to evaluate precision and linearity of the calibration curve, RSD and the coefficient of determination were calculated. Curve RSD for every element reflected high precision (within 10 %). For most elements, the coefficient of determination was ${\geq}0.99$, indicating excellent linearity. However, vanadium, nickel, and gallium curves exhibited relatively low linearity (0.90~0.95), likely due to their narrow concentration ranges. Standard RSF was calculated using the slope of the curve generated for three types of CRM. Despite vanadium, nickel, and gallium exhibiting low coefficients of determination, their standard RSF resembled that of the three types of CRM. Therefore, the RSF method may be used for element quantitation. Standard iron matrix samples were analyzed to verify the applicability of the aluminum matrix standard RSF, as well as to calculate the RSD-estimated error of the measured value relative to the actual standard value. Six elements (Al, Si, V, Cr, Mn, and Ni) exhibited an RSD of approximately 30 %, while the RSD of Cu was 77 %. In general, Cu isotopes are subject to interference: $^{63}Cu$ to $^{54}Fe^{2+}-^{36}Ar$ and $^{65}Cu$ to $^{56}Fe-Al^{3+}$ interference. Thus, the influence of these impurities may have contributed to the high RSD value observed for Cu. To reliably identify copper, the resolution should be set at ${\geq}8000$. However, high resolutions are inappropriate for analyzing trace elements, as it lowers ion permeability. In conclusion, quantitation of even relatively low amounts of six elements (Al, Si, V, Cr, Mn, and Ni) is possible using this method.

• The Journal of Korean Society for Radiation Therapy
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• v.31 no.1
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• pp.17-24
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• 2019

Purpose: The present study aims to assess the level of coherency and the accuracy of Point dose of the Isocenter of VERO, a linear accelerator developed for the purpose of the Stereotactic Body Radiation Therapy(SBRT). Materials and Method: The study was conducted randomly with 10 treatment plans among SBRT patients in Kyungpook National University Chilgok Hospital, using VERO, a linear accelerator between June and December, 2018. In order to assess the equipment's power stability level, we measured the output constancy by using PTW-LinaCheck, an output detector. We also attempted to measure the level of accuracy of the equipment's Laser, kV(Kilo Voltage) imaging System, and MV(Mega Voltage) Beam by using Tofu Phantom(BrainLab, Germany) to assess the accuracy level of geometrical Isocenter. We conducted a comparative analysis to assess the accuracy level of the dose by using an acrylic Phantom($30{\times}30{\times}20cm$), a calibrated ion chamber CC-01(IBA Dosimetry), and an Electrometer(IBA, Dosimetry). Results: The output uniformity of VERO was calculated to be 0.66 %. As for geometrical Isocenter accuracy, we analyzed the error values of ball Isocenter of inner Phantom, and the results showed a maximum of 0.4 mm, a minimum of 0.0 mm, and an average of 0.28 mm on X-axis, and a maximum of -0.4 mm, a minimum of 0.0 mm, and an average of -0.24 mm on Y-axis. A comparison and evaluation of the treatment plan dose with the actual measured dose resulted in a maximum of 0.97 % and a minimum of 0.08 %. Conclusion: The equipment's average output dose was calculated to be 0.66 %, meeting the ${\pm}3%$ tolerance, which was considered as a much uniform fashion. As for the accuracy assessment of the geometric Isocenter, the results met the recommended criteria of ${\pm}1mm$ tolerance, affirming a high level of reproducibility of the patient's posture. The difference between the treatment plan dose and the actual measurement dose was calculated to be 0.52 % on average, significantly less than the 3 % tolerance, confirming that it obtained predicted does. The current study suggested that VERO equipment is suitable for SBRT, and would result in notable therapeutic effect.