• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.1
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• pp.47-58
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• 2018

The heterogeneous cellular network (HCN) is most significant as a key technology for future fifth generation (5G) wireless networks. The heterogeneous network considered consists of randomly macrocell base stations (MBSs) overlaid with femtocell base stations (BSs). The stochastic geometry has been shown to be a very powerful tool to model, analyze, and design networks with random topologies such as wireless ad hoc, sensor networks, and multi- tier cellular networks. The HCNs can be energy-efficiently designed by deploying various BSs belonging to different networks, which has drawn significant attention to one of the technologies for future 5G wireless networks. In this paper, we propose switching off/on systems enabling the BSs in the cellular networks to efficiently consume the power by introducing active/sleep modes, which is able to reduce the interference and power consumption in the MBSs and FBSs on an individual basis as well as improve the energy efficiency of the cellular networks. We formulate the minimization of the power onsumption for the MBSs and FBSs as well as an optimization problem to maximize the energy efficiency subject to throughput outage constraints, which can be solved the Karush Kuhn Tucker (KKT) conditions according to the femto tier BS density. We also formulate and compare the coverage probability and the energy efficiency in HCNs scenarios with and without coordinated multi-point (CoMP) to avoid coverage holes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.4
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• pp.765-773
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• 2009

Currently Telematics traffic information services have been various because we can collect real-time traffic information through Intelligent Transport System. In this paper, we proposed and implemented a short-term traffic information prediction model for giving to guarantee the traffic information with high quality in the near future. A Short-term prediction model is for forecasting traffic flows of each segment in the near future. Our prediction model gives an average speed on the each segment from 5 minutes later to 60 minutes later. We designed a Bayesian network for each segment with some casual nodes which makes an impact to the road situation in the future and found out its joint probability density function on the supposition of GMM(Gaussian Mixture Model) using EM(Expectation Maximization) algorithm with training real-time traffic data. To validate the precision of our prediction model we had conducted various experiments with real-time traffic data and computed RMSE(Root Mean Square Error) between a real speed and its prediction speed. As the result, our model gave 4.5, 4.8, 5.2 as an average value of RMSE about 10, 30, 60 minutes later, respectively.

• Journal of the Korean GEO-environmental Society
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• v.21 no.4
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• pp.5-17
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• 2020

In recent years, the frequency of heavy rainfall associated with high rainfall intensity has been continuously increasing due to the effects of climate change; and thus also causes an increase in watershed soil erosion. The existing estimation techniques, used for the prediction of soil erosion in Korea have limitations in predicting the: average soil erosion in watersheds, and the soil erosion associated with abnormal short-term rainfall events. Therefore, it is necessary to consider the characteristics of torrential rainfall, and utilize physics-based model to accurately determine the soil erosion characteristics of a watershed. In this study, the rainfall kinetic energy equation, in the form of power function, is proposed by applying the probability density function, to analyze the rainfall particle distribution. The distributed rainfall-erosion model, which utilizes the proposed rainfall kinetic energy equation, was utilized in this study to determine the soil erosion associated with various typhoon events that occurred at Cheoncheon watershed. As a result, the model efficiency parameters of the model for NSE and RMSE are 0.036 and 4.995 ppm, respectively. Therefore, the suggested soil erosion model, coupled with the proposed rainfall-energy estimation, shows accurate results in predicting soil erosion in a watershed due to short-term rainfall events.

• 한국가스학회:학술대회논문집
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• 2005.10a
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• pp.37-43
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• 2005

The questionnaires about the safety of the urban gas have been carried out for the end users. about 8 of 10 persons said that the urban gas Is safe to use, whereas $35\%$ of them said there exists a hazard of an accident in thier residences. There cannot be found the clear evidences that the understandings on the safety of the urban gas have no relations to their ages, sex, and monthly incomes, while the safety is less confidential to the highly educated, the accident-experienced, or the mans who are poor at the safety inspections. Most of the questioned man know the inspection knacks for the gas utilities, but only $60\%$ of them carry out it. They said that they do not feel the necessity of the inspection because they are inspected routinely by the suppliers or the inspection companies. This says that the end user does not concern the safety inspections, and in order to improve the dependency of the user for the self-inspections, the inspection staff should educate the user for the necessity and the knack of inspections to encourage the self-inspection of the gas utilities.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.4
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• pp.53-63
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• 2008

Reader collision may occur when neighboring RFID readers use the same channel at the same time. Especially when the readers are operated in dense mode, even though many channels are available, because of frequent reader collisions we can not guarantee the performance of RFID readers. Conventional solutions such as FH(Frequency Hopping) or LBT(Listen Before Talk) are not effective in this situation because they can not schedule RFID readers effectively when RFID readers are operated in multi-channel, dense reader mode, In this paper, we propose a new RFID reader anti-collision algorithm which employs LBT, random backoff before channel access, and probabilistic channel hopping at the same time. While LBT and Random backoff before channel access reduces collisions between competing readers, probabilistic channel hopping increases channel utilization by adaptively changing the hopping probability by reflecting the reader density and utilization. Simulation results shows that our algorithm outperforms conventional methods.

• The Bulletin of The Korean Astronomical Society
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• v.42 no.2
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• pp.59.5-60
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• 2017

We present the relationship between vector magnetic field parameters and solar major flare occurrence rate. Based on this, we are developing a forecast model of major flare (M and X-class) occurrence rate within a day using hourly vector magnetic field data of Space-weather HMI Active Region Patch (SHARP) from May 2010 to April 2017. In order to reduce the projection effect, we use SHARP data whose longitudes are within ${\pm}60$ degrees. We consider six SHARP magnetic parameters (the total unsigned current helicity, the total photospheric magnetic free energy density, the total unsigned vertical current, the absolute value of the net current helicity, the sum of the net current emanating from each polarity, and the total unsigned magnetic flux) with high F-scores as useful predictors of flaring activity from Bobra and Couvidat (2015). We have considered two cases. In case 1, we have divided the data into two sets separated in chronological order. 75% of the data before a given day are used for setting up a flare model and 25% of the data after that day are used for test. In case 2, the data are divided into two sets every year in order to reduce the solar cycle (SC) phase effect. All magnetic parameters are divided into 100 groups to estimate the corresponding flare occurrence rates. The flare identification is determined by using LMSAL flare locations, giving more numbers of flares than the NGDC flare list. Major results are as follows. First, major flare occurrence rates are well correlated with six magnetic parameters. Second, the occurrence rate ranges from 0.001 to 1 for M and X-class flares. Third, the logarithmic values of flaring rates are well approximated by two linear equations with different slopes: steeper one at lower values and lower one at higher values. Fourth, the sum of the net current emanating from each polarity gives the minimum RMS error between observed flare rates and predicted ones. Fifth, the RMS error for case 2, which is taken to reduce SC phase effect, are smaller than those for case 1.

• Korean Journal of Remote Sensing
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• v.36 no.5_2
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• pp.975-988
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• 2020

Change detection is the process of identifying changes by observing the multi-temporal images at different times, and it is an important technique in remote sensing using satellite images. Among the change detection methods, the unsupervised change detection technique has the advantage of extracting rapidly the change area as a binary image. However, it is difficult to understand the changing pattern of land cover in binary images. This study used grid points generated from seamless digital map to evaluate the satellite image change detection results. The land cover change results were extracted using multi-temporal KOMPSAT-3A (K3A) data taken by Gimje Free Trade Zone and change detection algorithm used Spectral Angle Mapper (SAM). Change detection results were presented as binary images using the methods Otsu, Kittler, Kapur, and Tsai among the automated threshold selection algorithms. To consider the seasonal change of vegetation in the change detection process, we used the threshold of Differenced Normalized Difference Vegetation Index (dNDVI) through the probability density function. The experimental results showed the accuracy of the Otsu and Kapur was the highest at 58.16%, and the accuracy improved to 85.47% when the seasonal effects were removed through dNDVI. The algorithm generated based on this research is considered to be an effective method for accuracy assessment and identifying changes pattern when applied to unsupervised change detection.

• The Korean Journal of Applied Statistics
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• v.30 no.2
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• pp.243-257
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• 2017

In this paper, we compare several methods to approximate option prices: Edgeworth expansion, A-type and C-type Gram-Charlier expansions, a method using normal inverse gaussian (NIG) distribution, and an asymptotic method using nonlinear regression. We used two different types of approximation. The first (called the RNM method) approximates the risk neutral probability density function of the log return of the underlying asset and computes the option price. The second (called the OPTIM method) finds the approximate option pricing formula and then estimates parameters to compute the option price. For simulation experiments, we generated underlying asset data from the Heston model and NIG model, a well-known stochastic volatility model and a well-known Levy model, respectively. We also applied the above approximating methods to the KOSPI200 call option price as a real data application. We then found that the OPTIM method shows better performance on average than the RNM method. Among the OPTIM, A-type Gram-Charlier expansion and the asymptotic method that uses nonlinear regression showed relatively better performance; in addition, among RNM, the method of using NIG distribution was relatively better than others.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.5B
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• pp.449-457
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• 2011

Recently, increasing heavy rainfalls due to climate change and/or variability result in hydro-climatic disasters being accelerated. To cope with the extreme rainfall events in the future, hydrologic frequency analysis is usually used to estimate design rainfalls in a design target year. The rainfall data series applied to the hydrologic frequency analysis is assumed to be stationary. However, recent observations indicate that the data series might not preserve the statistical properties of rainfall in the future. This study incorporated the residual analysis and the hydrologic frequency analysis to estimate design rainfalls in a design target year considering the non-stationarity of rainfall. The residual time series were generated using a linear regression line constructed from the observations. After finding the proper probability density function for the residuals, considering the increasing or decreasing trend, rainfalls quantiles were estimated corresponding to specific design return periods in a design target year. The results from applying the method to 14 gauging stations indicate that the proposed method provides appropriate design rainfalls and reduces the prediction errors compared with the conventional rainfall frequency analysis which assumes that the rainfall data are stationary.

• Journal of Bio-Environment Control
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• v.1 no.1
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• pp.1-13
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• 1992

This study was attemped to provide some fundamental data for the safety structural design of biological production facility. Wind speed and snow depth according to recurrence intervals for design load estimation were calculated by frequency analysis using the weather data of 60 stations in Korea. The following results were obtained : 1. Type-I extremal distribution was selected for the probability density function of yearly maximum wind speed and snow depth and result of Chi-square goodness of fit showed highly significance at most regions. 2. Design frequency factors for given number of samples and recurrence intervals were calculated, and also design wind speed and snow depth as shown in Table 5-Table 6 and Fig.3-Fig.4 were derived. 3. About 46.4% of the winds having maximum wind speed at every station was analyzed to be same direction, and the consideration of this fact may improve the structural safety. 4. Considering wind speed and snow depth, protected cultivation is very difficult in Ullungdo and the Youngdong districts, and strong structural design is needed in the Chungnam and Junbuk west seaside against snow depth and the west-south seaside against wind speed in Korea.