• Smart Structures and Systems
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• v.32 no.4
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• pp.267-279
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• 2023

Data modelling and interpretation for structural health monitoring (SHM) field data are critical for evaluating structural performance and quantifying the vulnerability of infrastructure systems. In order to improve the data modelling accuracy, and extend the application range from data regression analysis to out-of-sample forecasting analysis, an improved most likely heteroscedastic Gaussian process (iMLHGP) methodology is proposed in this study by the incorporation of the outof-sample forecasting algorithm. The proposed iMLHGP method overcomes this limitation of constant variance of Gaussian process (GP), and can be used for estimating non-stationary typhoon-induced response statistics with high volatility. The first attempt at performing data regression and forecasting analysis on structural responses using the proposed iMLHGP method has been presented by applying it to real-world filed SHM data from an instrumented cable-stay bridge during typhoon events. Uncertainty quantification and correlation analysis were also carried out to investigate the influence of typhoons on bridge strain data. Results show that the iMLHGP method has high accuracy in both regression and out-of-sample forecasting. The iMLHGP framework takes both data heteroscedasticity and accurate analytical processing of noise variance (replace with a point estimation on the most likely value) into account to avoid the intensive computational effort. According to uncertainty quantification and correlation analysis results, the uncertainties of strain measurements are affected by both traffic and wind speed. The overall change of bridge strain is affected by temperature, and the local fluctuation is greatly affected by wind speed in typhoon conditions.

• Korean Journal of Veterinary Research
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• v.36 no.3
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• pp.681-692
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• 1996

The present study was conducted to isolate Babesia gibsoni by culture method of the microaerophilous stationary phase(MASP) and analyse the antigenic properties of the parasite by SDS-PAGE and immunoblot. The results obtained were summarized as follows. The protozoan parasite Babesia gibsoni multiplied in canine erythrocytes in RPMI 1640 medium(pH7.0) containing 20 40% normal canine serum under the MASP condition of 5% CO2 and 95% air at $37^{\circ}C$ incubator. The levels of parasitaemia in the erythrocytes were shown more higher by exchanging the medium at 24 hours interval. Under the above condition of MASP, the percentage of parasitized erythrocytes(PPE) after incubation for 8 days increased about 14 times more than that in the initiation of the 1% infected canine erythrocyte culture. The parasites were purely isolated from the MASP culture of red blood cells collected from dogs infected with Babesia gibsoni naturally or artificially. Among the total of 36 canine(Pit-bullterier) blood samples the parasites were isolated from 17 cases(47.2%) in the MASP culture while the parasites were detected from 20 cases(56%) and 12 cases(33.3%), respectively, by indirect fluorescent antibody(IFA) test and direct light microscopy(DLM). On the other hand, Babesia gibsoni was isolated by MASP culture from 15 cases(75%) and 11 cases(92%) of positive cases of IFA and DLM, respectively. In the analysis of the erythrocytic merozoite(AEOM) antigen derived from infected dog approximately 11 antigenic bands in molecular weight of 130, 120, 97.4, 92, 80, 52, 50, 42, 36, 30 and 29 KDa were observed on SDS-PAGE. Antigenic bands in the endoerythrocytic merozoite(CEOM) antigen derived from infected erythrocyte (sediment) in MASP culture were much similar to those of AEOM bands. In the exoerythrocytic merozoite(CEEM) antigen derived from supernatant of the infected erythrocyte culture approximately 20 antigenic bands were observed and the molecular weight of the major bands among these were 140, 120, 114, 105, 96, 93, 92, 80, 60, 52, 50, 38, 36, 30, 24, 18.5 and 16 KDa. In the protein patterns of AEOM and CEOM antigen by immunoblot 15 bands were observed and these patterns were much similar between each other. The molecular weight of the major bands in the both antigens were 130, 120, 80, 60, 52, 50, 42, 30, 29, 18.5 and 16 KDa. Approximately 21 bands were observed in CEEM antigen and the molecular weight of the major bands were 140, 120, 96, 92, 85, 80, 76, 60, 52, 50, 37, 30, 24, 16 and 15 KDa. The specific antigenic bands in the artificially infected dogs were firstly observed at 3 weeks afrer inoculation of infected blood and these antigenic bands were maintained up to 18 months after inoculation. In the immunoblot of the sera of the splenectomized dogs the specific antigenic bands with the molecular weight of 93 KDa and 52 KDa, respectively, were observed weakly comparing to those of non-splenectomized dog. In immunoblot of the sera collected from the naturally infected dogs the antigenic bands were observed as same as those of artificially infected dogs while antigenic band of 29 KDa in some individual dog showed strongly. In comparison of immunoblot of the sera collected from dogs non-treated and treated with diminazene aceturate(7mg/kg, IM) after artificial infection no differences of antigenic bands were observed. In analysis of antigenic bands by digoxigenin glycan/protein double labeling, antigenic bands in the molecular weight of 106, 60 58, 36, 30 and 29 KDa were determined as glycoproteins.

• Economic and Environmental Geology
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• v.56 no.6
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• pp.661-674
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• 2023

From the seafloor of Taean, Chungcheongnamdo Province, a ship of the Joseon Dynasty was discovered for the first time in the history of underwater excavations in Korea in 2014 and was named Mado Shipwreck No. 4. A total of 27 unused whetstones loaded as tribute were discovered on the hull of Mado No. 4, which revealed that Mado Shipwreck No. 4 was a Grain transport ship that sank while carrying tribute from Naju to Hanyang between 1417 and 1425 (King Taejong to King Sejong). All of the 27 whetstones are in the shape of narrow and long sticks. The average values of length, width, thickness, and weight are 161.5 mm, 36.1 mm, 22.7 mm, and 253.2 g, respectively. The result of X-ray diffraction analysis shows that the constituent minerals are quartz, alkali feldspar, and plagioclase, which is similar to that of the high-resolution digital stereomicroscope analysis. The average porosity of Mado-2672 and 2673 is 2.69% and 1.78%, respectively, and the average surface hardness is 807.2HLD and 834.5HLD, respectively. It is interpreted that if the porosity increases beyond a certain level, it affects the decrease in surface hardness. All of these are made of feldspathic sandstones with an average SiO₂ content of 74.51% and were confirmed to be suitable as grindstones. They are all medium whetstones when classified based on the SiO₂ content. These whetstones are small in size and weight and are convenient to carry, so they are presumed to be a type of non-stationary whetstone, and are estimated to have been mainly used in the fields such as weapon polishing and craft production during the Joseon Dynasty.

• Journal of the Korean earth science society
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• v.45 no.3
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• pp.203-213
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• 2024

This study aimed to investigate the seasonal patterns of multiple bar formation in summer and flattening in winter on the macrotidal Sinduri beach in Taean, and to understand the processes their formation and subsequent flattening. Beach profiling has been conducted regularly over the last four years using a VRS-GPS system. Surface sediment samples were collected seasonally along the transectline, and grain size analyses were performed. Tidal current data were acquired using a TIDOS current observation system during both winter and summer. The Sinduri macrotidal beach consists of two geomorphic units: an upper high-gradient beach face and a lower gentler sloped intertidal zone. High berms and beach cusps did not develop on this beach face. The approximately 400-m-wide intertidal zone comprises distinct 2-5 lines of multiple bars. Mean grain sizes of sand bars range from 2.0 to 2.75 phi, corresponding to fine sands. Mean sizes show shoreward coarsening trend. Regular beach-profiling survey revealed that the summer profile has a multi-barred morphology with a maximum of five bar lines, whereas, the winter profile has a non-barred, flat morphology. The non-barred winter profiles likely result from flattening by scour-and-fill processes during winter. The growth of multiple bars in summer is interpreted to be formed by a break-point mechanism associated with moderate waves and the translation of tide levels, rather than the standing wave hypothesis, which is stationary at high tide. The break-point hypothesis for multi-bars is supported by the presence of the largest bar at mean sea-level, shorter bar spacing toward the shore, irregular bar spacing, strong asymmetry of bars, and the 10-30 m shoreward migration of multi-bars.

• The Journal of the Acoustical Society of Korea
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• v.20 no.2E
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• pp.30-45
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• 2001

This study considers the environmental factors affecting propagation loss and sonar performance in the continental regions of the East Coast Sea of Korea. Water mass distributions appear to change dramatically in a few weeks. Simple calculation with the case when the NKCW (North Korean Cold Water) develops shows that the difference in propagation loss may reach in the worst up to 10dB over range 5km. Another factor, an eddy, has typical dimensions of 100-200km in diameter and 150-200m in thickness. Employing a typical eddy and assuming frequency to be 100Hz, its effects on propagation loss appear to make lower the normal formation of convergence zones with which sonars are possible to detect long-range targets. The change of convergence zones may result in 10dB difference in received signals in a given depth. Thermal fronts also appear to be critical restrictions to operating sonars in shallow waters. Assuming frequency to be 200Hz, thermal fronts can make 10dB difference in propagation loss between with and without them over range 20km. An observation made in one site in the East Coast Sea of Korea reveals that internal waves may appear in near-inertial period and their spectra may exist in periods 2-17min. A simulation employing simple internal wave packets gives that they break convergence zones on the bottom, causing the performance degradation of FOM as much as 4dB in frequency 1kHz. An acoustic experiment, using fixed source and receiver at the same site, shows that the received signals fluctuate tremendously with time reaching up to 6.5dB in frequencies 1kHz or less. Ambient noises give negative effects directly on sonar performance. Measurements at some sites in the East Coast Sea of Korea suggest that the noise levels greatly fluctuate with time, for example noon and early morning, mainly due to ship traffics. The average difference in a day may reach 10dB in frequency 200Hz. Another experiment using an array of hydrophones gives that the spectrum levels of ambient noises are highly directional, their difference being as large as 10dB with vertical or horizontal angles. This fact strongly implies that we should obtain in-situ information of noise levels to estimate reasonable sonar performance. As one of non-stationary noise sources, an eel may give serious problems to sonar operation on or under the sea bottoms. Observed eel noises in a pier of water depth 14m appear to have duration time of about 0.4 seconds and frequency ranges of 0.2-2.8kHz. The 'song'of an eel increases ambient noise levels to average 2.16dB in the frequencies concerned, being large enough to degrade detection performance of the sonars on or below sediments. An experiment using hydrophones in water and sediment gives that sensitivity drops of 3-4dB are expected for the hydrophones laid in sediment at frequencies of 0.5-1.5kHz. The SNR difference between in water and in sediment, however, shows large fluctuations rather than stable patterns with the source-receiver ranges.

• 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.

• 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.