• 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.