• 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.37 no.2
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• pp.321-335
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• 2021

Particulate matter (PM10 and PM2.5 with a diameter less than 10 and 2.5 ㎛, respectively) can be absorbed by the human body and adversely affect human health. Although most of the PM monitoring are based on ground-based observations, they are limited to point-based measurement sites, which leads to uncertainty in PM estimation for regions without observation sites. It is possible to overcome their spatial limitation by using satellite data. In this study, we developed machine learning-based retrieval algorithm for ground-level PM10 and PM2.5 concentrations using aerosol parameters from Geostationary Ocean Color Imager (GOCI) satellite and various meteorological parameters from a numerical weather prediction model during January to December of 2019. Gradient Boosted Regression Trees (GBRT) and Light Gradient Boosting Machine (LightGBM) were used to estimate PM concentrations. The model performances were examined for two types of feature sets-all input parameters (Feature set 1) and a subset of input parameters without meteorological and land-cover parameters (Feature set 2). Both models showed higher accuracy (about 10 % higher in R2) by using the Feature set 1 than the Feature set 2. The GBRT model using Feature set 1 was chosen as the final model for further analysis(PM10: R2 = 0.82, nRMSE = 34.9 %, PM2.5: R2 = 0.75, nRMSE = 35.6 %). The spatial distribution of the seasonal and annual-averaged PM concentrations was similar with in-situ observations, except for the northeastern part of China with bright surface reflectance. Their spatial distribution and seasonal changes were well matched with in-situ measurements.

• Journal of the Korean earth science society
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• v.43 no.5
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• pp.604-617
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• 2022

Sea surface temperature (SST) is a key variable that can be used to understand ocean-atmosphere phenomena and predict climate change. Satellite microwave remote sensing enables the measurement of SST despite the presence of clouds and precipitation in the sensor path. Therefore, considering the high utilization of microwave SST, it is necessary to continuously verify its accuracy and analyze its error characteristics. In this study, the validation of the microwave global precision measurement (GPM)/GPM microwave imager (GMI) SST around the Northwest Pacific and Korean Peninsula was conducted using surface drifter temperature data for approximately eight years from March 2014 to December 2021. The GMI SST showed a bias of 0.09K and an average root mean square error of 0.97K compared to the actual SST, which was slightly higher than that observed in previous studies. In addition, the error characteristics of the GMI SST were related to environmental factors, such as latitude, distance from the coast, sea wind, and water vapor volume. Errors tended to increase in areas close to coastal areas within 300 km of land and in high-latitude areas. In addition, relatively high errors were found in the range of weak wind speeds (<6 m s^-1) during the day and strong wind speeds (>10 m s^-1) at night. Atmospheric water vapor contributed to high SST differences in very low ranges of <30 mm and in very high ranges of >60 mm. These errors are consistent with those observed in previous studies, in which GMI data were less accurate at low SST and were estimated to be due to differences in land and ocean radiation, wind-induced changes in sea surface roughness, and absorption of water vapor into the microwave atmosphere. These results suggest that the characteristics of the GMI SST differences should be clarified for more extensive use of microwave satellite SST calculations in the seas around the Korean Peninsula, including a part of the Northwest Pacific.

• Journal of the Korean Geotechnical Society
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• v.38 no.7
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• pp.5-24
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• 2022

The current performance evaluation of slope anchors qualitatively determines the physical bonding between the anchor head and ground as well as cracks or breakage of the anchor head. However, such performance evaluation does not measure these primary factors quantitatively. Therefore, the time-dependent management of the anchors is almost impossible. This study is an evaluation of the 3D numerical model by SfM which combines UAS images with terrestrial LiDAR to collect numerical data on the damage factors. It also utilizes the data for the quantitative maintenance of the anchor system once it is installed on slopes. The UAS 3D model, which often shows relatively low precision in the z-coordinate for vertical objects such as slopes, is combined with terrestrial LiDAR scan data to improve the accuracy of the z-coordinate measurement. After validating the system, a field test is conducted with ten anchors installed on a slope with arbitrarily damaged heads. The damages (such as cracks, breakages, and rotational displacements) are detected and numerically evaluated through the orthogonal projection of the measurement system. The results show that the introduced system at the resolution of 8K can detect cracks less than 0.3 mm in any aperture with an error range of 0.05 mm. Also, the system can successfully detect the volume of the damaged part, showing that the maximum damage area of the anchor head was within 3% of the original design guideline. Originally, the ground adhesion to the anchor head, where the z-coordinate is highly relevant, was almost impossible to measure with the UAS 3D numerical model alone because of its blind spots. However, by applying the combined system, elevation differences between the anchor bottom and the irregular ground surface was identified so that the average value at 20 various locations was calculated for the ground adhesion. Additionally, rotation angle and displacement of the anchor head less than 1" were detected. From the observations, the validity of the 3D numerical model can obtain quantitative data on anchor damage. Such data collection can potentially create a database that could be used as a fundamental resource for quantitative anchor damage evaluation in the future.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.1
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• pp.17-27
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• 2023

Weeds are one of the factors that reduce crop yield through nutrient and photosynthetic competition. Quantification of weed density are an important part of making accurate decisions for precision weeding. In this study, we tried to quantify the density of weeds in images of maize fields taken by unmanned aerial vehicle (UAV). UAV image data collection took place in maize fields from May 17 to June 4, 2021, when maize was in its early growth stage. UAV images were labeled with pixels from maize and those without and the cropped to be used as the input data of the semantic segmentation network for the maize detection model. We trained a model to separate maize from background using the deep learning segmentation networks DeepLabV3+, U-Net, Linknet, and FPN. All four models showed pixel accuracy of 0.97, and the mIOU score was 0.76 and 0.74 in DeepLabV3+ and U-Net, higher than 0.69 for Linknet and FPN. Weed density was calculated as the difference between the green area classified as ExGR (Excess green-Excess red) and the maize area predicted by the model. Each image evaluated for weed density was recombined to quantify and visualize the distribution and density of weeds in a wide range of maize fields. We propose a method to quantify weed density for accurate weeding by effectively separating weeds, maize, and background from UAV images of maize fields.

• Journal of Korea Water Resources Association
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• v.56 no.1
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• pp.53-62
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• 2023

Recently, due to the influence of climate change, the occurrence of damage to heavy rain is increasing around the world, and the frequency of heavy rain with a large amount of rain in a short period of time is also increasing. Heavy rains generate a large amount of outflow in a short time, causing flooding in the downstream part of the mountainous area before joining the small and medium-sized rivers. In order to reduce damage to downstream areas caused by flooding, it is very important to calculate the outflow of mountainous areas due to torrential rains. However, the sewage network flooding analysis, which is currently conducting the most analysis in Korea, uses the time and area method using the existing data rather than calculating the rainfall outflow in the mountainous area, which is difficult to determine that the soil characteristics of the region are accurately applied. Therefore, if the rainfall is analyzed for mountainous areas that can cause flooding in the downstream area in a short period of time due to large outflows, the accuracy of the analysis of flooding characteristics that can occur in the downstream area can be improved and used as data for evacuating residents and calculating the extent of damage. In order to calculate the rainfall outflow in the mountainous area, the rainfall outflow in the mountainous area was calculated using MIKE SHE among the MIKE series, and the flooding analysis in the downstream area was conducted through MIKE 21 FM (Flood model). Through this study, it was possible to confirm the amount of outflow and the time to reach downstream in the event of rainfall in the mountainous area, and the results of this analysis can be used to protect human and material resources through pre-evacuation in the downstream area in the future.

• The Journal of Korean Society for Radiation Therapy
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• v.28 no.1
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• pp.7-16
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• 2016

Purpose : This study aimed to compare and evaluate between the efficiency of two respective devices, 3D-bolus and step-bolus when the devices were used for the treatment of patients whose chest walls were required to undergo the electron beam therapy after the surgical procedure of modified radical mastectomy, MRM. Materials and Methods : The treatment plan of reverse hockey stick method, using the photon beam and electron beam, had been set for six breast cancer patients and these 6 breast cancer patients were selected to be the subjects for this study. The prescribed dose of electron beam for anterior chest wall was set to be 180 cGy per treatment and both the 3D-bolus, produced using 3D printer(CubeX, 3D systems, USA) and the self-made conventional step-bolus were used respectively. The surface dose under 3D-bolus and step-bolus was measured at 5 measurement spots of iso-center, lateral, medial, superior and inferior point, using GAFCHROMIC EBT3 film (International specialty products, USA) and the measured value of dose at 5 spots was compared and analyzed. Also the respective treatment plan was devised, considering the adoption of 3D-bolus and stepbolus and the separate treatment results were compared to each other. Results : The average surface dose was 179.17 cGy when the device of 3D-bolus was adopted and 172.02 cGy when step-bolus was adopted. The average error rate against the prescribed dose of 180 cGy was -(minus) 0.47% when the device of 3D-bolus was adopted and it was -(minus) 4.43% when step-bolus was adopted. It was turned out that the maximum error rate at the point of iso-center was 2.69%, in case of 3D-bolus adoption and it was 5,54% in case of step-bolus adoption. The maximum discrepancy in terms of treatment accuracy was revealed to be about 6% when step-bolus was adopted and to be about 3% when 3D-bolus was adopted. The difference in average target dose on chest wall between 3D-bolus treatment plan and step-bolus treatment plan was shown to be insignificant as the difference was only 0.3%. However, to mention the average prescribed dose for the part of lung and heart, that of 3D-bolus was decreased by 11% for lung and by 8% for heart, compared to that of step-bolus. Conclusion : It was confirmed through this research that the dose uniformity could be improved better through the device of 3D-bolus than through the device of step-bolus, as the device of 3D-bolus, produced in consideration of the contact condition of skin surface of chest wall, could be attached to patients' skin more nicely and the thickness of chest wall can be guaranteed more accurately by the device of 3D-bolus. It is considered that 3D-bolus device can be highly appreciated clinically because 3D-bolus reduces the dose on the adjacent organs and make the normal tissues protected, while that gives no reduction of dose on chest wall.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.97-117
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• 2020

Product evaluation criteria is an indicator describing attributes or values of products, which enable users or manufacturers measure and understand the products. When companies analyze their products or compare them with competitors, appropriate criteria must be selected for objective evaluation. The criteria should show the features of products that consumers considered when they purchased, used and evaluated the products. However, current evaluation criteria do not reflect different consumers' opinion from product to product. Previous studies tried to used online reviews from e-commerce sites that reflect consumer opinions to extract the features and topics of products and use them as evaluation criteria. However, there is still a limit that they produce irrelevant criteria to products due to extracted or improper words are not refined. To overcome this limitation, this research suggests LDA-k-NN model which extracts possible criteria words from online reviews by using LDA and refines them with k-nearest neighbor. Proposed approach starts with preparation phase, which is constructed with 6 steps. At first, it collects review data from e-commerce websites. Most e-commerce websites classify their selling items by high-level, middle-level, and low-level categories. Review data for preparation phase are gathered from each middle-level category and collapsed later, which is to present single high-level category. Next, nouns, adjectives, adverbs, and verbs are extracted from reviews by getting part of speech information using morpheme analysis module. After preprocessing, words per each topic from review are shown with LDA and only nouns in topic words are chosen as potential words for criteria. Then, words are tagged based on possibility of criteria for each middle-level category. Next, every tagged word is vectorized by pre-trained word embedding model. Finally, k-nearest neighbor case-based approach is used to classify each word with tags. After setting up preparation phase, criteria extraction phase is conducted with low-level categories. This phase starts with crawling reviews in the corresponding low-level category. Same preprocessing as preparation phase is conducted using morpheme analysis module and LDA. Possible criteria words are extracted by getting nouns from the data and vectorized by pre-trained word embedding model. Finally, evaluation criteria are extracted by refining possible criteria words using k-nearest neighbor approach and reference proportion of each word in the words set. To evaluate the performance of the proposed model, an experiment was conducted with review on '11st', one of the biggest e-commerce companies in Korea. Review data were from 'Electronics/Digital' section, one of high-level categories in 11st. For performance evaluation of suggested model, three other models were used for comparing with the suggested model; actual criteria of 11st, a model that extracts nouns by morpheme analysis module and refines them according to word frequency, and a model that extracts nouns from LDA topics and refines them by word frequency. The performance evaluation was set to predict evaluation criteria of 10 low-level categories with the suggested model and 3 models above. Criteria words extracted from each model were combined into a single words set and it was used for survey questionnaires. In the survey, respondents chose every item they consider as appropriate criteria for each category. Each model got its score when chosen words were extracted from that model. The suggested model had higher scores than other models in 8 out of 10 low-level categories. By conducting paired t-tests on scores of each model, we confirmed that the suggested model shows better performance in 26 tests out of 30. In addition, the suggested model was the best model in terms of accuracy. This research proposes evaluation criteria extracting method that combines topic extraction using LDA and refinement with k-nearest neighbor approach. This method overcomes the limits of previous dictionary-based models and frequency-based refinement models. This study can contribute to improve review analysis for deriving business insights in e-commerce market.

• Radiation Oncology Journal
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• v.26 no.4
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• pp.237-246
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• 2008

Purpose: Three-dimensional conformal radiation therapy (3DCRT) and intensity-modulated radiation therapy (IMRT) were found to reduce the incidence of acute and late rectal toxicity compared with conventional radiation therapy (RT), although acute and late urinary toxicities were not reduced significantly. Acute urinary toxicity, even at a low-grade, not only has an impact on a patient's quality of life, but also can be used as a predictor for chronic urinary toxicity. With bladder filling, part of the bladder moves away from the radiation field, resulting in a small irradiated bladder volume; hence, urinary toxicity can be decreased. The purpose of this study is to evaluate the impact of bladder volume on acute urinary toxicity during RT in patients with prostate cancer. Materials and Methods: Forty two patients diagnosed with prostate cancer were treated by 3DCRT and of these, 21 patients made up a control group treated without any instruction to control the bladder volume. The remaining 21 patients in the experimental group were treated with a full bladder after drinking 450 mL of water an hour before treatment. We measured the bladder volume by CT and ultrasound at simulation to validate the accuracy of ultrasound. During the treatment period, we measured bladder volume weekly by ultrasound, for the experimental group, to evaluate the variation of the bladder volume. Results: A significant correlation between the bladder volume measured by CT and ultrasound was observed. The bladder volume in the experimental group varied with each patient despite drinking the same amount of water. Although weekly variations of the bladder volume were very high, larger initial CT volumes were associated with larger mean weekly bladder volumes. The mean bladder volume was $299{\pm}155\;mL$ in the experimental group, as opposed to $187{\pm}155\;mL$ in the control group. Patients in experimental group experienced less acute urinary toxicities than in control group, but the difference was not statistically significant. A trend of reduced toxicity was observed with the increase of CT bladder volume. In patients with bladder volumes greater than 150 mL at simulation, toxicity rates of all grades were significantly lower than in patients with bladder volume less than 150 mL. Also, patients with a mean bladder volume larger than 100 mL during treatment showed a slightly reduced Grade 1 urinary toxicity rate compared to patients with a mean bladder volume smaller than 100 mL. Conclusion: Despite the large variability in bladder volume during the treatment period, treating patients with a full bladder reduced acute urinary toxicities in patients with prostate cancer. We recommend that patients with prostate cancer undergo treatment with a full bladder.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.45-58
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• 2014

The Korean Culture Contents spread out to Worldwide, because the Korean wave is sweeping in the world. The contents stand in the middle of the Korean wave that we are used it. Each country is ongoing to keep their Culture industry improve the national brand and High added value. Performing contents is important factor of arousal in the enterprise industry. To improve high arousal confidence of product and positive attitude by populace is one of important factor by advertiser. Culture contents is the same situation. If culture contents have trusted by everyone, they will give information their around to spread word-of-mouth. So, many researcher study to measure for person's arousal analysis by statistical survey, physiological response, body movement and facial expression. First, Statistical survey has a problem that it is not possible to measure each person's arousal real time and we cannot get good survey result after they watched contents. Second, physiological response should be checked with surround because experimenter sets sensors up their chair or space by each of them. Additionally it is difficult to handle provided amount of information with real time from their sensor. Third, body movement is easy to get their movement from camera but it difficult to set up experimental condition, to measure their body language and to get the meaning. Lastly, many researcher study facial expression. They measures facial expression, eye tracking and face posed. Most of previous studies about arousal and interest are mostly limited to reaction of just one person and they have problems with application multi audiences. They have a particular method, for example they need room light surround, but set limits only one person and special environment condition in the laboratory. Also, we need to measure arousal in the contents, but is difficult to define also it is not easy to collect reaction by audiences immediately. Many audience in the theater watch performance. We suggest the system to measure multi-audience's reaction with real-time during performance. We use difference image analysis method for multi-audience but it weaks a dark field. To overcome dark environment during recoding IR camera can get the photo from dark area. In addition we present Multi-Audience Engagement Index (MAEI) to calculate algorithm which sources from sound, audience' movement and eye tracking value. Algorithm calculates audience arousal from the mobile survey, sound value, audience' reaction and audience eye's tracking. It improves accuracy of Multi-Audience Engagement Index, we compare Multi-Audience Engagement Index with mobile survey. And then it send the result to reporting system and proposal an interested persons. Mobile surveys are easy, fast, and visitors' discomfort can be minimized. Also additional information can be provided mobile advantage. Mobile application to communicate with the database, real-time information on visitors' attitudes focused on the content stored. Database can provide different survey every time based on provided information. The example shown in the survey are as follows: Impressive scene, Satisfied, Touched, Interested, Didn't pay attention and so on. The suggested system is combine as 3 parts. The system consist of three parts, External Device, Server and Internal Device. External Device can record multi-Audience in the dark field with IR camera and sound signal. Also we use survey with mobile application and send the data to ERD Server DB. The Server part's contain contents' data, such as each scene's weights value, group audience weights index, camera control program, algorithm and calculate Multi-Audience Engagement Index. Internal Device presents Multi-Audience Engagement Index with Web UI, print and display field monitor. Our system is test-operated by the Mogencelab in the DMC display exhibition hall which is located in the Sangam Dong, Mapo Gu, Seoul. We have still gotten from visitor daily. If we find this system audience arousal factor with this will be very useful to create contents.