• 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 Child Education & Care
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• v.19 no.2
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• pp.177-196
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• 2019

Objective: This study aims to analyze research trends related to software education for young children, focusing on studies published in Korea from 2016 to 2019 March. Methods: A total of 26 research publications on software education for young children, searched from Korea Citation Index and Research Information Sharing Service were identified for the analysis. The trend in these publications was classified and examined respectively by publication dates, types of publications, and the fields of study. To investigate a means of research, the analysis included key topics, types of research methods, and characteristics of the study variables. Results: The results of the analysis show that the number of publications on the topic of software education for young children has increased over the three years, of which most were published as a scholarly journal article. Among the 26 research studies analyzed, 16 (61.5%) are related to the field of early childhood education or child studies. Key topics and target subjects of the most research include the curriculum development of software education for young children or the effectiveness of software education on 4- and 5-year-old children. Most of the analyzed studies are experimental research designs or in the form of literature reviews. The most frequently studied research variable is young children's cognitive characteristics. For the studies that employ educational programs, the use of a physical computing environment is prevalent, and the most frequently used robot as a programming tool is "Albert". The duration of the program implementation varies, ranging from 5 weeks to 48 weeks. In the analyzed research studies, computational thinking is conceptualized as a problem-solving skill that can be improved by software education, and assessed by individual instruments measuring sub-factors of computational thinking. Conclusion/Implications: The present study reveals that, although the number of research publications in software education for young children has increased, the overall sufficiency of the accumulated research data and a variety of research methods are still lacking. An increased interest in software education for young children and more research activities in this area are needed to develop and implement developmentally appropriate software education programs in early childhood settings.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.4
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• pp.28-34
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• 2010

In this paper, we proposed an algorithm for utilizing visual information for non-contact predicting method of friction coefficient. Coefficient of friction is very important in driving on road and traversing over obstacle. Our algorithm is based on terrain classification for visual image. The proposed method, non-contacting approach, has advantage over other methods that extract material characteristic of road by sensors contacting road surface. This method is composed of learning group(experiment, grouping material) and predicting friction coefficient group(Bayesian classification prediction function). Every group include previous work of vision. Advantage of our algorithm before entering such terrain can be very useful for avoiding slippery areas. We make experiment on measurement of friction coefficient of terrain. This result is utilized real friction coefficient as prediction method. We show error between real friction coefficient and predicted friction coefficient for performance evaluation of our algorithm.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.8
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• pp.87-96
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• 2014

In this paper, we propose a directionally adaptive aliasing and noise removal using dictionary learning based on space-frequency analysis. The proposed aliasing and noise removal algorithm consists of two modules; i) aliasing and noise detection using dictionary learning and analysis of frequency characteristics from the combined wavelet-Fourier transform and ii) aliasing removal with suppressing noise based on the directional shrinkage in the detected regions. The proposed method can preserve the high-frequency details because aliasing and noise region is detected. Experimental results show that the proposed algorithm can efficiently reduce aliasing and noise while minimizing losses of high-frequency details and generation of artifacts comparing with the conventional methods. The proposed algorithm is suitable for various applications such as image resampling, super-resolution image, and robot vision.

• The KIPS Transactions:PartB
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• v.17B no.1
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• pp.47-54
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• 2010

Old archived film shows two major defects: line scratch and blobs. In this paper, we present a design and implementation of an automatic video restoration system for line scratches observed in archived film. We use autoregressive (AR) image model because we can make stochastic and specifically autoregressive image generation process with our PAST-PRESENT model and Sampling Pattern. We designed locality maximizing scanning pattern, which can generate nearly stationary time-like series of pixels, which is a strong requirement for a stochastic series to be autoregressive. The sampled pixel series undergoes filtering and model fitting using Durbin-Levinson algorithm before interpolation process. We designed three-stage film restoration system, which includes (1) film acquisition from VHS tapes, (2) simple line scratch detection and restoration, and (3) manual blob identification and sophisticated inpainting scheme. We implemented film acquisition and simple inpainting scheme on Texas Instruments DSP board TMS320DM642 EVM, and implemented our AR inpainting scheme on PC for sophisticated restoration. We experimented our scheme with two old Korean films: "Viva Freedom" and "Robot Tae-Kwon-V", and the experimental results show that our scheme improves Bertalmio's scheme for subjective quality (MOS), objective quality (PSNR), and especially restoration ratio (RR), which reflects how much similar to the manual inpainting results.

• Korean Journal of Construction Engineering and Management
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• v.17 no.3
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• pp.32-42
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• 2016

In Korea, the absence of BIM use in existing civil structures and buildings is driving a demand for as-built BIM. As-built BIMs are often created using laser scanners that provide dense 3D point cloud data. Conventional static laser scanning approaches often suffer from limitations in their operability due to the difficulties in moving the equipment, the selection of scanning location, and the requirement of placing targets or extracting tie points for registration of each scanned point cloud. This paper aims at reducing the manual effort using a kinematic 3D laser scanning system based on graph-based simultaneous localization and mapping (SLAM) for continuous indoor mapping. The robotic platform carries three 2D laser scanners: the front scanner is mounted horizontally to compute the robot's trajectory and to build the SLAM graph; the other two scanners are mounted vertically to scan the profiles of surrounding environments. To reduce the accumulated error in the trajectory of the platform through loop closures, the graph-based SLAM system incorporates AdaBoost loop closure approach, which is particularly suitable for the developed multi-scanner system providing more features than the single-scanner system for training. We implemented the proposed method and evaluated it in two indoor test sites. Our experimental results show that the false positive rate was reduced by 13.6% and 7.9% for the two dataset. Finally, the 2D and 3D mapping results of the two test sites confirmed the effectiveness of the proposed graph-based SLAM.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.3
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• pp.82-89
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• 2012

This paper presents the intelligent tracking algorithm for maneuvering target using the positional error compensation of the maneuvering target. The difference between measured point and predict point is separated into acceleration and noise. Fuzzy c-mean clustering and predicted impact point are used to get the optimal acceleration value. The membership function is determined for acceleration and noise which are divided by fuzzy c-means clustering and the characteristics of the maneuvering target is figured out. Divided acceleration and noise are used in the tracking algorithm to compensate computational error. The filtering process in a series of the algorithm which estimates the target value recognize the nonlinear maneuvering target as linear one because the filter recognize only remained noise by extracting acceleration from the positional error. After filtering process, we get the estimates target by compensating extracted acceleration. The proposed system improves the adaptiveness and the robustness by adjusting the parameters in the membership function of fuzzy system. To maximize the effectiveness of the proposed system, we construct the multiple model structure. Procedures of the proposed algorithm can be implemented as an on-line system. Finally, some examples are provided to show the effectiveness of the proposed algorithm.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.8
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• pp.132-139
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• 2015

Visual SLAM(Simultaneous Localization And Mapping) has been used widely to estimate a mobile robot's location. Visual SLAM estimates relative motions with static visual features over image sequence. Because visual SLAM methods assume generally static features in the environment, we cannot obtain precise results in dynamic situation including many moving objects: cars and human beings. This paper presents a stereo vision based SLAM method in dynamic environment. First, we extract disparity map with stereo vision and compute optical flow. We then compute disparity change that is the estimated flow field between stereo views. After examining the disparity change value, we detect ROIs(Region Of Interest) in disparity space to determine dynamic scene objects. In indoor environment, many structural planes like walls may be determined as false dynamic elements. To solve this problem, we segment the scene into planar structure. More specifically, disparity values by the stereo vision are projected to X-Z plane and we employ Hough transform to determine planes. In final step, we remove ROIs nearby the walls and discriminate static scene elements in indoor environment. The experimental results show that the proposed method can obtain stable performance in dynamic environment.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.1
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• pp.44-49
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• 2016

This paper proposed the method to improve performance of a pixel, which has low accuracy, by applying image segmentation methods based on color and multi-threshold of brightness. Stereo matching is the process to find the corresponding point on the right image with the point on the left image. For this process, distance(depth) information in stereo images is calculated. However, in the case of a region which has textureless, stereo matching has low accuracy and bad pixels occur on the disparity map. In the proposed method, the relationship between adjacent pixels is considered for compensating bad pixels. Generally, the object has similar color and brightness. Therefore, by considering the relationship between regions based on segmented regions by means of color and multi-threshold of brightness respectively, the region which is considered as parts of same object is re-segmented. According to relationship information of segmented sets of pixels, bad pixels in the disparity map are compensated efficiently. By applying the proposed method, the results show a decrease of nearly 28% in the number of bad pixels of the image applied the method which is established.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.10 no.1
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• pp.47-57
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• 2016

In this paper, we measured human body signals in order to verify a active harness system that we developed for gait and balance training. The experimental procedure was validated by tests with 20 healthy male subjects. They conducted motions of Activities of Daily Living(ADL)(Normal Walking, Stand-to-Sit, Sit-to-Stand, Stair Walking Up, and Stair Walking Down) according to body weight support rates (0%, 30%, 50% of subjects' body weight). The effectiveness of the active harness system is verified by using the results of foot pressure distribution. In normal walking, the decrease of fore-foot pressure, lateral soleus muscle and biceps femoris muscle were remarkable. The result of stand-to-sit results motion indicated that the rear-foot pressure and tibialis anterior muscle activities exceptionally decreased according to body weight support. The stair walking down show the marked drop of fore-foot pressure and rectus femoris muscle activities. The sit-to-stand and stair walking up activities were inadequate about the effect of body weight support because the velocity of body weight support system was slower than male's activity.