• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.2
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• pp.135-145
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• 2020

Wave observation is widely used to direct observation method for observing the water surface elevation using wave buoy or pressure gauge and remote-sensing wave observation method. The wave buoy and pressure gauge can produce high-quality wave data but have disadvantages of the high risk of damage and loss of the instrument, and high maintenance cost in the offshore area. On the other hand, remote observation method such as radar is easy to maintain by installing the equipment on the land, but the accuracy is somewhat lower than the direct observation method. This study investigates the data quality of MIROS Wave and Current Radar (MWR) installed at Dokdo and improve the data quality of remote wave observation data using the wave buoy (CWB) observation data operated by the Korea Meteorological Administration. We applied and developed the three types of wave data quality control; 1) the combined use (Optimal Filter) of the filter designed by MIROS (Reduce Noise Frequency, Phillips Check, Energy Level Check), 2) Spike Test Algorithm (Spike Test) developed by OOI (Ocean Observatories Initiative) and 3) a new filter (H-Ts QC) using the significant wave height-period relationship. As a result, the wave observation data of MWR using three quality control have some reliability about the significant wave height. On the other hand, there are still some errors in the significant wave period, so improvements are required. Also, since the wave observation data of MWR is different somewhat from the CWB data in high waves of over 3 m, further research such as collection and analysis of long-term remote wave observation data and filter development is necessary.

• Journal of Genetic Medicine
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• v.4 no.1
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• pp.45-52
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• 2007

Purpose : A simple, rapid, and highly sensitive analytical method for Gb3 in plasma was developed without labor-ex tensive pre-treatment by electrospray ionization MS/ MS (ESI-MS/MS). Measurement of globotriaosy lceramide (Gb3, ceramide trihex oside) in plasma has clinical importance for monitoring after enzyme replacement therapy in Fabry disease patients. The disease is an X-linked lipid storage disorder that results from a deficiency of the enzyme ${\alpha}$-galactosidase A (${\alpha}$-Gal A). The lack of ${\alpha}$-Gal A causes an intracellular accumulation of glycosphingolipids, mainly Gb3. Methods : Only simple 50-fold dilution of plasma is necessary for the extraction and isolation of Gb3 in plasma. Gb3 in diluted plasma was dissolved in dioxane containing C17:0 Gb3 as an internal standard. After centrifugation it was directly injected and analyzed through guard column by in combination with multiple reaction monitoring mode of ESI-MS/MS. Results : Eight isoforms of Gb3 were completely resolved from plasma matrix. C16:0 Gb3 occupied 50% of total Gb3 as a major component in plasma. Linear relationship for Gb3 isoforms w as found in the range of 0.001-1.0 ${\mu}g$/mL. The limit of detection (S/N=3) was 0.001 ${\mu}g$/mL and limit of quantification was 0.01 ${\mu}g$/mL for C16:0 Gb3 with acceptable precision and accuracy. Correlation coefficient of calibration curves for 8 Gb3 isoforms ranged from 0.9678 to 0.9982. Conclusion : This quantitative method developed could be useful for rapid and sensitive 1st line Fabry disease screening, monitoring and/or diagnostic tool for Fabry disease.

• Korean Journal of Remote Sensing
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• v.25 no.4
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• pp.383-389
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• 2009

The main objective of the research is a feasibility study on the intertidal zone using a X-band radar satellite, TerraSAR-X. The TerraSAR-X data have been acquired in the west coast of Korea where large tidal flats, Ganghwa and Yeongjong tidal flats, are developed. Investigations include: 1) waterline and backscattering characteristics of the high resolution X-band images in tidal flats; 2) polarimetric signature of halophytes (or salt marsh plants), specifically Suaeda japonica; and 3) phase and coherence of interferometric pairs. Waterlines from TerraSAR-X data satisfy the requirement of horizontal accuracy of 60 m that corresponds to 20 cm in average height difference while current other spaceborne SAR systems could not meet the requirement. HH-polarization was the best for extraction of waterline, and its geometric position is reliable due to the short wavelength and accurate orbit control of the TerraSAR-X. A halophyte or salt marsh plant, Suaeda japonica, is an indicator of local sea level change. From X-band ground radar measurements, a dual polarization of VV/VH-pol. is anticipated to be the best for detection of the plant with about 9 dB difference at 35 degree incidence angle. However, TerraSAR-X HH/TV dual polarization was turned to be more effective for salt marsh monitoring. The HH-HV value was the maximum of about 7.9 dB at 31.6 degree incidence angle, which is fairly consistent with the results of X-band ground radar measurement. The boundary of salt marsh is effectively traceable specifically by TerraSAR-X cross-polarization data. While interferometric phase is not coherent within normal tidal flat, areas of salt marsh where the landization is preceded show coherent interferometric phases regardless of seasons or tide conditions. Although TerraSAR-X interferometry may not be effective to directly measure height or changes in tidal flat surface, TanDEM-X or other future X-band SAR tandem missions within one-day interval would be useful for mapping tidal flat topography.

• Journal of the Korean Society of Radiology
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• v.15 no.6
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• pp.781-788
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• 2021

Due to the high sensitivity to radiation, excessive exposure needs to be prevented by accurately measuring the dose irradiated to the skin during radiation therapy. Although clinical trials use dosimeters such as film, OSLD, TLD, glass dosimeter, etc. to measure skin dose, these dosimeters have difficulty in accurate dosimetry on skin curves. In this study, to solve these problems, we developed a skin dosimeter that can be attached according to human flexion and evaluated its response characteristics. For the manufacture of the dosimeter, lead oxide (PbO) with high atomic number (Z_Pb: 82, Z_O: 8) and density (9.53 g/cm³) and silicon binders that can bend according to human flexion were used. In the case of a dosimeter made of PbO material, the performance degradation has been prevented by using parylene and others due to the presence of degradation due to oxidation, but the previously used parylene is affected by bending, so a new form of passive layer was produced and applied to the skin dosimeter. The characteristic evaluation of the skin dosimeter was evaluated by analyzing SEM, reproducibility, and linearity. Through SEM analysis, bending was evaluated, reproducibility and linearity at 6 MeV energy were evaluated, and applicability was assessed with a skin dosimeter. As a result of observing the dosimeter surface through SEM analysis, the parylene passive layer PbO dosimeter with the positive layer raised to the parylene produced cracks on the surface when bent. On the other hand, no crack was observed in the silicon passive layer PbO dosimeter, which was raised to silicon passive layer. In the reproducibility measurement results, the RSD of the silicon passive layer PbO dosimeter was 1.47% which satisfied the evaluation criteria RSD 1.5% and the linearity evaluation results showed the R² value of 0.9990, which satisfied the evaluation criteria R² 9990. The silicon passive layer PbO dosimeter was evaluated to be applicable to skin dosimeters by demonstrating high signal stability, precision, and accuracy in reproducibility and linearity, without cracking due to bending.

• 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 radiological science and technology
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• v.40 no.1
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• pp.27-34
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• 2017

The author investigated interventional radiology patient doses in several other countries, assessed accuracy of DAP meters embedded in intervention equipments in domestic country, conducted measurement of patient doses for 13 major interventional procedures with use of Dose Area Product(DAP) meters from 23 hospitals in Korea, and referred to 8,415 cases of domestic data related to interventional procedures by radiation exposure after evaluation the actual effectives of dose reduction variables through phantom test. Finally, dose reference level for major interventional procedures was suggested. In this study, guidelines for patient doses were $237.7Gy{\cdot}cm^2$ in TACE, $17.3Gy{\cdot}cm^2$ in AVF, $114.1Gy{\cdot}cm^2$ in LE PTA & STENT, $188.5Gy{\cdot}cm^2$ in TFCA, $383.5Gy{\cdot}cm^2$ in Aneurysm Coil, $64.6Gy{\cdot}cm^2$ in PTBD, $64.6Gy{\cdot}cm^2$ in Biliary Stent, $22.4Gy{\cdot}cm^2$ in PCN, $4.3Gy{\cdot}cm^2$ in Hickman, $2.8Gy{\cdot}cm^2$ in Chemo-port, $4.4Gy{\cdot}cm^2$ in Perm-Cather, $17.1Gy{\cdot}cm^2$ in PCD, and $357.9Gy{\cdot}cm^2$ in Vis, EMB. Dose referenece level acquired in this study is considered to be able to use as minimal guidelines for reducing patient dose in the interventional radiology procedures. For the changes and advances of materials and development of equipments and procedures in the interventional radiology procedures, further studies and monitorings are needed on dose reference level Korean DAP dose conversion factor for the domestic procedures.

• Journal of Bio-Environment Control
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• v.31 no.4
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• pp.401-408
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• 2022

As research on a controlled environment system based on crop growth environment sensing for sustainable production of horticultural crops and its industrial use has been important, research on how to properly utilize soil moisture sensors for outdoor cultivation is being actively conducted. This experiment was conducted to suggest the proper method of utilizing the TEROS 12, an FDR (frequency domain reflectometry) sensor, which is frequently used in industry and research fields, for each orchard soil in three regions in Korea. We collected soils from each orchard where fruit trees were grown, investigated the soil characteristics and soil water retention curve, and compared TEROS 12 sensor calibration equations to correlate the sensor output to the corresponding soil volumetric water content through linear and cubic regressions for each soil sample. The estimated value from the calibration equation provided by the manufacturer was also compared. The soil collected from all three orchards showed different soil characteristics and volumetric water content values by each soil water retention level across the soil samples. In addition, the cubic calibration equation for TEROS 12 sensor showed the highest coefficient of determination higher than 0.95, and the lowest RMSE for all soil samples. When estimating volumetric water contents from TEROS 12 sensor output using the calibration equation provided by the manufacturer, their calculated volumetric water contents were lower than the actual volumetric water contents, with the difference up to 0.09-0.17 m³·m^-3 depending on the soil samples, indicating an appropriate calibration for each soil should be preceded before FDR sensor utilization. Also, there was a difference in the range of soil volumetric water content corresponding to the soil water retention levels across the soil samples, suggesting that the soil water retention information should be required to properly interpret the volumetric water content value of the soil. Moreover, soil with a high content of sand had a relatively narrow range of volumetric water contents for irrigation, thus reducing the accuracy of an FDR sensor measurement. In conclusion, analyzing soil water retention characteristics of the target soil and the soil-specific calibration would be necessary to properly quantify the soil water status and determine their adequate irrigation point using an FDR sensor.

• Korean Journal of Remote Sensing
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• v.40 no.2
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• pp.229-241
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• 2024

With the advancement of big data processing technology using cloud platforms, access, processing, and analysis of large-volume data such as satellite imagery have recently been significantly improved. In this study, the Change Detection Method, a relatively simple technique for retrieving soil moisture, was applied to the backscattering coefficient values of pre-processed Sentinel-1 synthetic aperture radar (SAR) satellite imagery product based on Google Earth Engine (GEE), one of those platforms, to estimate the surface soil moisture for six observatories within the Yongdam Dam watershed in South Korea for the period of 2015 to 2023, as well as the watershed average. Subsequently, a correlation analysis was conducted between the estimated values and actual measurements, along with an examination of the applicability of GEE. The results revealed that the surface soil moisture estimated for small areas within the soil moisture observatories of the watershed exhibited low correlations ranging from 0.1 to 0.3 for both VH and VV polarizations, likely due to the inherent measurement accuracy of the SAR satellite imagery and variations in data characteristics. However, the surface soil moisture average, which was derived by extracting the average SAR backscattering coefficient values for the entire watershed area and applying moving averages to mitigate data uncertainties and variability, exhibited significantly improved results at the level of 0.5. The results obtained from estimating soil moisture using GEE demonstrate its utility despite limitations in directly conducting desired analyses due to preprocessed SAR data. However, the efficient processing of extensive satellite imagery data allows for the estimation and evaluation of soil moisture over broad ranges, such as long-term watershed averages. This highlights the effectiveness of GEE in handling vast satellite imagery datasets to assess soil moisture. Based on this, it is anticipated that GEE can be effectively utilized to assess long-term variations of soil moisture average in major dam watersheds, in conjunction with soil moisture observation data from various locations across the country 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.