• Journal of Digital Convergence
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• v.12 no.1
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• pp.359-364
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• 2014

In this paper, we propose a method for extracting a road area by using the mean-shift method and connected-component method. Mean-shift method is very effective to divide the color image by the method of non-parametric statistics to find the center mode. Generally, the feature points of road are extracted by using the information located in the middle and bottom of the road image. And it is possible to extract a road region by using this feature-point and the partitioned color image. However, if a road region is extracted with only the color information and the position information of a road image, it is possible to detect not only noise but also off-road regions. This paper proposes the method to determine the road region by eliminating the noise with the closing / opening operation of the morphology, and by extracting only the portion of the largest area using a connected-components method. The proposed method is simulated and verified by applying the captured road images.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.11
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• pp.2413-2418
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• 2010

We propose a retouching method that converts a photography taken by smartphone to a watercolor painting image using bilateral filtering and mean shift segmentation which are mostly used in image processing. The first step is to convert an input image to fit the screen resolution of smartphone. And next step is to weaken high frequency components of the image, while preserving the edge of image using the bilateral filtering. And after that we perform mean shift segmentation from the bilateral filtered image. We apply parameters of mean shift segmentation considering the processing speed of smartphone. Experimental result shows that our method can be applied to various types of image and bring better result.

• Journal of the Korean Society for information Management
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• v.36 no.4
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• pp.83-105
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• 2019

This study aims to suggest an effective method for the automatic classification of keywords with similar patterns by calculating pattern similarity of temporal data. For this, large scale news on the Web were collected and time series data composed of 120 time segments were built. To make training data set for the performance test of the proposed model, 440 representative keywords were manually classified according to 8 types of trend. This study introduces a Dynamic Time Warping(DTW) method which have been commonly used in the field of time series analytics, and proposes an application model, MA-DTW based on a Moving Average(MA) method which gives a good explanation on a tendency of trend curve. As a result of the automatic classification by a k-Nearest Neighbor(kNN) algorithm, Euclidean Distance(ED) and DTW showed 48.2% and 66.6% of maximum micro-averaged F1 score respectively, whereas the proposed model represented 74.3% of the best micro-averaged F1 score. In all respect of the comprehensive experiments, the suggested model outperformed the methods of ED and DTW.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.206-208
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• 2010

We propose a retouching method that converts a photography taken by smartphone to a watercolor painting image using bilateral filtering and mean shift segmentation which are mostly used in image processing. The first step is to convert an input image to fit the screen resolution of smartphone. And next step is to weaken high frequency components of the image, while preserving the edge of image using the bilateral filtering. And after that we perform mean shift segmentation from the bilateral filtered image. We apply parameters of mean shift segmentation considering the processing speed of smartphone. Experimental result shows that our method can be applied to various types of image and bring better result.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.5
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• pp.117-121
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• 2014

In the orthogonal frequency division multiplexing system, the prefix inserted between data symbols should be eliminated to apply the Fourier transform on the valid symbol interval. This functional procedure should be based on the accurate symbol timing detection. The symbol timing detection at the receiver side provides the reference for determining the beginning time index of each symbol whose initial point is located at the boundary between the preamble and the payload part. Also, the detection error is one of the main factors in the overall system performance. In this paper the effect of the bandwidth of the moving average filter on the symbol timing detection is discussed. Simulations are carried out to analyze the detection performance for the varying values of the window size of the moving average filter which is related to the filter bandwidth.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1065-1074
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• 2011

During several decades, many researchers in the field of finance have studied Value at Risk (VaR) to measure the market risk. VaR indicates the worst loss over a target horizon such that there is a low, pre-specified probability that the actual loss will be larger (Jorion, 2006, p.106). In this paper, we compare conditional copula method with two conventional VaR forecasting methods based on simple moving average and exponentially weighted moving average for measuring the risk of the portfolio, consisting of two domestic stock indices. Through real data analysis, we conclude that the conditional copula method can improve the accuracy of portfolio VaR forecasting in the presence of high kurtosis and strong correlation in the data.

• Journal of the Korea Convergence Society
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• v.12 no.4
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• pp.249-258
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• 2021

Bitcoin prices have been soaring recently as investors flock to cryptocurrency exchanges. The purpose of this study is to predict the Bitcoin price using a deep learning model and analyze whether Bitcoin is profitable through investment strategy. LSTM is utilized as Bitcoin prediction model with nonlinearity and long-term memory and the profitability of MA cross-over strategy with predicted prices as input variables is analyzed. Investment performance of Bitcoin strategy using LSTM forecast prices from 2013 to 2021 showed return improvement of 5.5% and 46% more than market price MA cross-over strategy and benchmark Buy & Hold strategy, respectively. The results of this study, which expanded to recent data, supported the inefficiency of the cryptocurrency market, as did previous studies, and showed the feasibility of using the deep learning model for Bitcoin investors. In future research, it is necessary to develop optimal prediction models and improve the profitability of Bitcoin investment strategies through performance comparison of various deep learning models.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.556-560
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• 2010

자갈하상 산지하천의 주요 관심사는 소류사 크기 분포와 연계하여 하상재료의 이송에 대한 광범위의 기초자료 확보하는 것이다. 자갈 하상재료가 주를 이루는 강원영동지역 양양남대천의 산지 하천을 대상으로 하상재료를 조사하였다. 산지하천의 소류사 크기를 고려한 소류사 채집기를 설치하고, 호우 발생 이후 측정기에 포획된 소류사의 크기를 파악하기 위해 입도분포를 분석하였다. 두 개의 소류사 측정기는 지남교 지점에 유속이 빠른 우안과 유속이 상대적으로 느린 좌안에 각각 설치하였다. 산지하천 시험구간의 하상재료 분포도는 그림 1과 같으며, 하상재료 평균입경은 184.65mm 이였다. 연구단에서 운영중인 법수치 우량관측소의 관측자료에 따르면, 2009년 11월 1~13일 185mm 강우가 발생하였으며, 11월 11일 7:00경에 최대수위에 따른 첨두유출량 107.23$m^3/s$가 발생하였다. 첨두유출 발생당시 이동한 우안 소류사의 평균입경은 102.88mm 이였고, 좌안 소류사의 평균입경은 66.53mm 이였다(그림 2). 하상재료의 평균입경에 대한 소류사 평균입경을 비교하였을 때, 소류사는 하상재료의 $45.87{\pm}13.92%$ 크기 범위에서 이동함을 확인하였다.

• Journal of the Institute of Convergence Signal Processing
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• v.15 no.1
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• pp.9-14
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• 2014

The path loss data was re-calculated according to the distance between the base station and a mobile station in the mobile telecommunications network. In this paper, the averaged path loss data was plotted with the conventional path loss models(free space, plane earth, Hata model ${\ldots}$). The standard deviations for the 2 Km, 1 Km, 0.5 Km-interval averaged path loss were 2.29 dB, 3.39 dB, 4.75 dB, respectively. Additionally, the derivative values for the 2 Km, 1 Km, 0.5 Km-interval averaged path loss were evaluated to find the positions with more than 1 times or 2times of the standard deviation. The situations with the sharply fluctuated path loss were calculated to 5 positions in the 2 Km interval, to 7 positions in the 1 Km interval, to 19 positions in the 0.5 Km interval, respectively. And, the exact distances between the base station and a mobile station were found with the sharply fluctuated path loss.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.11
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• pp.1463-1471
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• 2016

The indoor localization algorithm using the Difference Means based on Fingerprint (DMFPA) to improve the performance of indoor localization in moving Wi-Fi environment is proposed in this paper. In addition to this, the performance of the proposed algorithm is also compared with the Original Fingerprint Algorithm (OFPA) and the Gaussian Distribution Fingerprint Algorithm (GDFPA) by our developed indoor localization simulator. The performance metrics are defined as the accuracy of the average localization accuracy; the average/maximum cumulative distance of the occurred errors and the average measurement time in each reference point.