• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.9
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• pp.1856-1864
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• 2015

In this paper, the localization algorithm for improving the accuracy of the positioning using the Wi-Fi fingerprint using the normal distribution probability and the built-in typed accelerometer sensor, the gyroscope sensor of smartphone in the indoor environment is proposed. The experiments for analyzing the performance of the proposed algorithm were carried out at the region of the horizontal and vertical 20m * 10m in the engineering school building of our university, and the performance of the proposed algorithm is compared with the fingerprint and the DR (dead reckoning) while user is moving according to the assigned region. As a result, the maximum error distance in the proposed algorithm was decreased to 2cm and 36cm compared with two algorithms, respectively. In addition to this, the maximum error distance was also less than compared with two algorithms as 16.64cm and 36.25cm, respectively. It can be seen that the fingerprint map searching time of the proposed algorithm was also reduced to 0.15 seconds compared with two algorithms.

• Journal of the Korean Data and Information Science Society
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• v.28 no.5
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• pp.1077-1085
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• 2017

In this study, we have found the major factors which affect Korean women's wage analysing the data provided by 2015 Korea Labor Panel Survey (KLIPS). In general, wage data is difficult to analyze because random sampling is infeasible. Heckman sample selection model is the most widely used method for analysing the data with sample selection. Heckman proposed two kinds of selection models: the one is the model with maximum likelihood method and the other is the Heckman two stage model. Heckman two stage model is known to be robust to the normal assumption of bivariate error terms. Recently, Marchenko and Genton (2012) proposed the Heckman selectiont model which generalizes the Heckman two stage model and concluded that Heckman selection-t model is more robust to the error assumptions. Employing the two models, we carried out the analysis of the data and we compared those results.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.6
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• pp.121-130
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• 2009

In this paper, Gaussian probability distribution model based multi-sensor data fusion algorithm is proposed for a vehicular location awareness system. Conventional vehicular location awareness systems are operated by GPS (Global Positioning System). However, the conventional system is not working in the indoor of building or urban area where the receiver is difficult to receive the signal from satellites. A method which is combined GPS and UWB (Ultra Wide-Band) has developed to improve this problem. However, vehicular is difficult to receive seamless location information since the measurement systems by both GPS and UWB convert the vehicle's movement information separately at each sensor. In this paper, normalized probability distribution model based Hybrid UWB/GPS is proposed by utilizing GPS location data and UWB sensor data. Therefore the proposed system provides information with seamless and location flexible properties. The proposed system tested by Ubisense and Asen GPS in the $12m{\times}8m$ outdoor environments. As a result, the proposed system has improved performance for accurateness and connection ability between devices to support various CNS (Car Navigation System).

• 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 Earthquake Engineering Society of Korea
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• v.16 no.2
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• pp.1-13
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• 2012

Seismic fragility functions are derived for probabilistic evaluation of seismic control performance of energy dissipation devices installed in reinforced concrete structures. Displacement-dependent dampers are added to the nonlinear single-degree-of-freedom systems with different natural periods and hysteretic characteristics of which stiffness and strength has uncertainty. Nonlinear time history analysis is conducted for those SDOF systems and the result is processed statistically to obtain seismic fragility functions in the form of log normal distribution. Variation of seismic fragility functions for different parameters of SDOF systems and dampers are investigated and the seismic control performance is assessed probabilistically.

• Journal of the Korean Data and Information Science Society
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• v.10 no.1
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• pp.29-36
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• 1999

We propose a new method of asymptotic inference on the odds ratio (or cross-product ratio) in $2{\times}2$ contingency table. Saddlepoint approximations to the conditional tail probability we used in this procedure. We assess the accuracy of the suggested method by comparing with the exact one. To obtain the exact values, we need very complicated calculations containing the cumulative probabilities of non-central hypergeometric distribution. The suggested method in this paper is very accurate even for small or moderate sample sizes as well as simple and easy to use. Example with a real data is also considered.

• Journal of the Korea Concrete Institute
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• v.23 no.4
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• pp.421-430
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• 2011

As a fundamental study to introduce the reliability-based design code, a statistical study is conducted for the material strength data collected from domestic construction sites. In order to develop a rational design code based on statistics and reliability theory, it is essential to obtain the statistical properties of material strength. Material strength data for concrete, reinforcing bars, and prestressing strands which are used in domestic construction sites are collected and statistically analyzed. Then, the statistical properties are compared with those used in the process of the reliability-based calibration of internationally leading design codes. The statistical properties of the domestic data are such that the bias factor is relatively uniform between 1.13 and 1.20 and the coefficient of variation is below 0.10. Reinforcing bar data show difference among different manufacturers but there is not much difference among re-bar diameters. In the case of tendons, which are high strength materials, both of the domestic and foreign data show smaller values of the bias factor and the coefficient of variation than those of concrete and re-bar. Statistical distribution of all the material strength can be properly assumed as normal, log-normal, or Gumbel distribution after analyzing the classified data by individual construction site and manufacturer rather than the mixed data obtained from different sources in order to express the individual distribution of each structure.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.1
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• pp.232-238
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• 2013

This paper study the position estimation of MBK system according to the non-linear filter for non-Gaussian noise in underwater sensor networks. In the filter to estimate location, recently, the extended Kalman filter (EKF) and particle filter are getting attention. EKF is widely used due to the best algorithm in the Gaussian noise environment, but has many restrictions on the usage in non-Gaussian noise environment such as in underwater. In this paper, we propose the improved One-Dimension Particle Filter (ODPF) using the distribution re-interpretation techniques based on the maximum likelihood. Through the simulation, we compared and analyzed the proposed particle filter with the EKF in non-Gaussian underwater sensor networks. In the case of both the sufficient statistical sample and the sufficient calculation capacity, we confirm that the ODPF's result shows more accurate localization than EKF's result.

• The Journal of the Acoustical Society of Korea
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• v.29 no.4
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• pp.278-285
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• 2010

The difference between training and test environments is one of the major performance degradation factors in noisy speech recognition and many silence feature normalization methods were proposed to solve this inconsistency. Conventional silence feature normalization method represents higher classification performance in higher SNR, but it has a problem of performance degradation in low SNR due to the low accuracy of speech/silence classification. On the other hand, cepstral distance represents well the characteristic distribution of speech/silence (or noise) in low SNR. In this paper, we propose a Cepstral distance and Log-energy based Silence Feature Normalization (CLSFN) method which uses both log-energy and cepstral euclidean distance to classify speech/silence for better performance. Because the proposed method reflects both the merit of log energy being less affected with noise in high SNR and the merit of cepstral distance having high discrimination accuracy for speech/silence classification in low SNR, the classification accuracy will be considered to be improved. The experimental results showed that our proposed CLSFN presented the improved recognition performances comparing with the conventional SFN-I/II and CSFN methods in all kinds of noisy environments.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.5
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• pp.134-143
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• 2017

In the case of highway, there may be a large number of travel time groups when there are a bus exclusive lane, a rest area, a sleeping shelter, etc. in the corresponding section. In most of the conventional travel time estimation studies, one representative travel time (assuming normal distribution) group is assumed in the low sample collection state, and if it is out of the specified range, it is determined as outliers and then the travel time is estimated. However, if there is a bus exclusive lane, a rest area, or a sleeping shelter in the relevant section, such as the highway, the distribution of travel time will be in the form of a bi-modal or a multi-modal, rather than a regular distribution. Therefore, applying the existing estimation methodology may result in distorted results. To solve this problem, first, it should be reliable even in the case of insufficient number of samples. Second, we propose a methodology to select the representative time group among a number of time groups and to estimate the representative time using individual time data of the selected time group.