• 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 Construction Engineering and Management
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• v.15 no.1
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• pp.39-50
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• 2014

After the global financial crisis, domestic construction industry has gone through a rapid recession. This resulted in gradual market shift towards architectural remodeling. Architectural remodeling not only improves residential environment but it has many advantages such as increase of each unit's exclusive area, free space within the horizontal or extension of an annex building, and increase number of household through splitting the household of bigger pyeong, etc. However, in case of the Korean market for apartment remodeling, due to various regulations and problem with business promotion procedures, majority of business is slow despite the figure that remodeling volume is not that small. Also, feasibility study which decides to push ahead public house remodeling business will have a flaw using net present value's law; it has a flaw of not considering properties of each phase of remodeling business and future's uncertainty. Hence, this research will improve the problem of traditional value assessment method of net present value's law. It will also consider one of the real options such as binomial model in order to supplement NPV which is used in current feasibility study. This research was based on real successful cases of public house remodeling and it was possible for feasibility study which was more realistic and valid. This research provided foundation for development of Korean public house remodeling market. There is high anticipation of increasing the validity by improving the problems of current feasibility study and economic efficiency assessment.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.19 no.5
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• pp.355-363
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• 2007

The Korean traditional 'Ondol' system has been a target for innovation to meet the requirements of sustainable domestic building and low carbon emission energy utilization. Simulation techniques provide designers and researchers with powerful tools to predict heating load and thermal behaviour of Ondol systems installed in various contexts. However, there are few studies on Ondol models, especially associated with multi-stories buildings of which type covers about 50% of Korean housing stock. In this study, we analyzed the double floor Ondol system on the multi-stories buildings using the ESP-r program. On the basis of the double floor Ondol system, we suggested the new modelling method that is composed of the Vent zone and Ondol zone. Using the this model, sensitivity analysis was carried out to refine the applicability of the model taking account of control conditions, constructions, air change and air flow network method and CFD analysis using the FLUENT. The air layer has enough temperature to use in heating zone. It is suggested that the simplicity of the model will allow building designers and mechanical engineers easily to implement scenario-based assessments of design options as well as control strategies. Later, we will simulate the real buildings and analyze the air distributions using the Fluent according to the various conditions.

• Environmental and Resource Economics Review
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• v.28 no.1
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• pp.39-59
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• 2019

The Korean Demilitarized Zone (DMZ) has the great ecosystem as all the artificial activities in DMZ have been prohibited over half a century. The ecosystem should be conserved even after the reunification of Korea and hence the conservation plan should be established not after the reunification but before it. It requires a considerable budget to conserve DMZ, considering management of ecology resource, recovery, and research. The objective of this paper is to analyze a fund-raising measure for DMZ conservation, using economic incentives mechanism when multiple developers participate in the auction to get the right to develop North Korean regions, have private information about their sunk costs and pay a part of their profits for the fund. First, we analyze the real option model to decide the optimal investment time. Second, we construct the auction for bidders not to misrepresent their private information, based on Bayesian Nash equilibrium.

• Environmental and Resource Economics Review
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• v.13 no.4
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• pp.735-761
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• 2004

As a possible alternative to Traditional Discounted Cash Flow Method, "Option Pricing Model" has drawn academic attentions for the last a few decades. However, it has failed to replace traditional DCF method practically due to its mathematical complexity. This paper introduces an option pricing valuation model specifically adjusted for the natural resource development projects. We add market information and industry-specific features into the model so that the model remains objective as well as realistic after the adjustment. The following two features of natural resource development projects take central parts in model construction; product price is a unique source of cash flow's uncertainty, and the projects have cost structure from capital-intense industry, in which initial capital cost takes most part of total cost during the projects. To improve the adaptability of Option Pricing Model specifically to the natural resource development projects, we use Two-Factor Model and Long-term Asset Model for the analysis. Although the model introduced in this paper is still simple and reflects limited reality, we expect an improvement in applicability of option pricing method for the evaluation of natural resource development projects can be made through the process taken in this paper.

• Journal of Veterinary Science
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• v.21 no.3
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• pp.39.1-39.15
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• 2020

Background: There are various Helicobacter species colonizing the stomachs of animals. Although Helicobacter species usually cause asymptomatic infection in the hosts, clinical signs can occur due to gastritis associated with Helicobacter in animals. Among them, Helicobacter pylori is strongly associated with chronic gastritis, gastric ulcers, and gastric cancers. As the standard therapies used to treat H. pylori have proven insufficient, alternative options are needed to prevent and eradicate the diseases associated with this bacterium. Cheonwangbosim-dan (CBD), a traditional herbal formula that is popular in East Asia, has been commonly used for arterial or auricular flutter, neurosis, insomnia, and cardiac malfunction-induced disease. Objectives: The present study investigated the antimicrobial effect of CBD on H. pylori-infected human gastric carcinoma AGS cells and model mice. Methods: AGS cells were infected with H. pylori and treated with a variety of concentrations of CBD or antibiotics. Mice were given 3 oral inoculations with H. pylori and then dosed with CBD (100 or 500 mg/kg) for 4 weeks or with standard antibiotics for 1 week. One week after the last treatment, gastric samples were collected and examined by histopathological analysis, real-time quantitative polymerase chain reaction, and immunoblotting. Results: Our results showed that CBD treatment of AGS cells significantly reduced the H. pylori-induced elevations of interleukin-8, inducible nitric oxide synthase (iNOS), and cyclooxygenase-2 (COX-2). In the animal model, CBD treatment inhibited the colonization of H. pylori and the levels of malondialdehyde, inflammation, proinflammatory cytokines, iNOS, and COX-2 in gastric tissues. CBD also decreased the phosphorylation levels of p38 mitogen-activated protein kinase family. Conclusions: This study suggests that CBD might be a prospective candidate for treating H. pylori-induced gastric injury.

• Korean Journal of Construction Engineering and Management
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• v.15 no.1
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• pp.111-121
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• 2014

The Clean Development Mechanism (CDM) allows New & Renewable Energy projects to make additional income by selling CER's, which represent the amount of Green House Gases(GHG) that is reduced in the project. However, forward contracts used to hedge fluctuating market prices does not allow projects to sell CER's at a premium. As an alternate approach to maximize CER revenue, CER's are modeled as a 'real option', in which CER's are sold only above the desired sales price. Using the Binomial Option Pricing model, the resultant lattices are used to determine whether to sell, defer or abandon the option at individual nodes. Overlaying Pascal's Triangle on the lattices also enabled the calculation of the annual probabilities for deferring CER sales without incurring downside losses. Application to an actual Landfill Gas project showed increased overall NPV, and that CER sales could be deferred at a maximum of 2 years. The proposed framework allows transparency in the analysis and provides valuable and strategical information when making investment decisions related to CER sales of CDM projects.

• Journal of Internet of Things and Convergence
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• v.10 no.4
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• pp.9-19
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• 2024

In this paper, a real-time public transportation monitoring system is proposed. The proposed system was implemented by developing a public transportation app and utilizing optical sensors, pressure sensors, and an object detection algorithm. Additionally, a bus model was created to verify the system's functionality. The proposed real-time public transportation monitoring system has three key features. First, the app can monitor congestion levels within public transportation by detecting seat occupancy and the total number of passengers based on changes in optical and pressure sensor readings. Second, to prevent errors in the optical sensor that can occur when multiple passengers board or disembark simultaneously, we explored the possibility of using the YOLO object detection algorithm to verify the number of passengers through CCTV footage. Third, convenience is enhanced by displaying occupied seats in different colors on a separate screen. The system also allows users to check their current location, available public transportation options, and remaining time until arrival. Therefore, the proposed system is expected to offer greater convenience to public transportation users.

• Journal of Korean Society of Environmental Engineers
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• v.34 no.6
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• pp.373-381
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• 2012

An automatic flow and water quality monitoring system was applied to estimate pollutant loads to an urban stream during storm events in DTV (Daeduk Techno Valley), Daejeon, Korea. The monitoring system consists of rainfall gage, ultrasonic water level meter, water quality sensors for DO, temperature, pH, conductivity, turbidity and automatic water sampler for further laboratory analysis. All data are transmitted through on-line system and the monitoring system is designed to be controlled manually in the field and remotely from laboratory computer. Flow rates were verified with field measurements during storm events and showed good agreements. Automatic sampler was used to collect real time samples and analyzed for BOD, COD, TN, TP, SS and other pollutant concentrations in the laboratory. SWMM (Storm Water Management Model) urban watershed model was applied and calibrated using the observed flow and water quality data for the study area. While flow modeling results showed good agreement for all events, water quality modeling results showed variable levels of agreement. These results indicate that current options in the SWMM model to predict pollutant build up and wash-off effects are not sufficient to satisfy modeling of all the rainfall events under study and thus need further modification. This study showed the automatic monitoring system can be used to provide data to assist further refinement of modeling accuracy. This automatic stormwater monitoring and modeling system can be used to develop basin scale water quality management strategies of urban streams in storm events.

• Asia Marketing Journal
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• v.15 no.3
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• pp.159-172
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• 2013

In the present study, we apply the multiple cue probability learning (MCPL) paradigm to examine consumer learning from feedback in repeated trials. This paradigm is useful in investigating consumer learning, especially learning the relationships between the overall quality and attributes. With this paradigm, we can analyze what people learn from repeated trials by using the lens model, i.e., whether it is knowledge or consistency. In addition to introducing this paradigm, we aim to demonstrate that knowledge people gain from repeated trials with feedback is robust enough to weaken one of the most often examined contextual effects, the asymmetric dominance effect. The experiment consists of learning session and a choice task and stimuli are sport rafting boats with motor engines. During the learning session, the participants are shown an option with three attributes and are asked to evaluate its overall quality and type in a number between 0 and 100. Then an expert's evaluation, a number between 0 and 100, is provided as feedback. This trial is repeated fifteen times with different sets of attributes, which comprises one learning session. Depending on the conditions, the participants do one (low) or three (high) learning sessions or do not go through any learning session (no learning). After learning session, the participants then are provided with either a core or an extended choice set to make a choice to examine if learning from feedback would weaken the asymmetric dominance effect. The experiment uses a between-subjects experimental design (2 × 3; core set vs. extended set; no vs. low vs. high learning). The results show that the participants evaluate the overall qualities more accurately with learning. They learn the true trade-off rule between attributes (increase in knowledge) and become more consistent in their evaluations. Regarding the choice task, there is a significant decrease in the percentage of choosing the target option in the extended sets with learning, which clearly demonstrates that learning decreases the magnitude of the asymmetric dominance effect. However, these results are significant only when no learning condition is compared either to low or high learning condition. There is no significant result between low and high learning conditions, which may be due to fatigue or reflect the characteristics of learning curve. The present study introduces the MCPL paradigm in examining consumer learning and demonstrates that learning from feedback increases both knowledge and consistency and weakens the asymmetric dominance effect. The latter result may suggest that the previous demonstrations of the asymmetric dominance effect are somewhat exaggerated. In a single choice setting, people do not have enough information or experience about the stimuli, which may lead them to depend mostly on the contextual structure among options. In the future, more realistic stimuli and real experts' judgments can be used to increase the external validity of study results. In addition, consumers often learn through repeated choices in real consumer settings. Therefore, what consumers learn from feedback in repeated choices would be an interesting topic to investigate.