• Journal of Korea Water Resources Association
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• v.48 no.7
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• pp.567-579
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• 2015

This study performed the non-stationary flood frequency analysis considering time-varying parameters of a probability density function. Also, return period and risk under non-stationary condition were estimated. A stationary model and three non-stationary models using Generalized Extreme Value(GEV) were developed. The only location parameter was assumed as time-varying parameter in the first model. In second model, the only scale parameter was assumed as time-varying parameter. Finally, the both parameters were assumed as time varying parameter in the last model. Relative likelihood ratio test and Akaike information criterion were used to select appropriate model. The suggested procedure in this study was applied to eight multipurpose dams in South Korea. Using relative likelihood ratio test and Akaike information criterion it is shown that the inflow into the Hapcheon dam and the Seomjingang dam were suitable for non-stationary GEV model but the other six dams were suitable for stationary GEV model. Also, it is shown that the estimated return period under non-stationary condition was shorter than those estimated under stationary condition.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.868-874
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• 2018

In this paper, we propose a new jammer suppression algorithm that uses orthogonal waveform space projection (OWSP) processing for a multiple input multiple output (MIMO) radar system exposed to a jamming signal. Generally, a conventional suppression algorithm based on adaptive beamforming (ABF) needs a covariance matrix composed of the jammer and noise only. By exploiting the orthogonality of the transmitting waveforms of MIMO, we can construct a transmitting waveform space (TWS). Then, using the OWSP processing, we can build a space orthogonal to the TWS that contains no SOI. By excluding the SOI from the received signal, even in the case that contains the SOI and jamming signal, the proposed algorithm makes it possible to evaluate the covariance matrix for ABF. We applied the proposed OWSP processing to suppressing the jamming signal in bistatic MIMO radar. We verified the performance of the proposed algorithm by comparing the SINR loss to that of the ideal covariance matrix composed of the jammer and noise only. We also derived the computational complexity of the proposed algorithm and compared the estimation of the DOD and DOA using the SOI with those using the generalized likelihood ratio test (GLRT) algorithm.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.3 no.4
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• pp.49-69
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• 2010

In this paper, we propose a class of spectrum sensing schemes for cognitive radio with receive diversity. By considering the generalized likelihood ratio test detector in each branch and exploiting non-linear diversity combining strategy, the proposed scheme exhibits reasonable performance for spectrum sensing even in the environment of heavier-tailed noise. From the results of analysis and computer simulations, it is observed that the proposed scheme provides significant performance gain and performance stability over the conventional schemes, especially in impulsive noise environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.3
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• pp.428-451
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• 2010

Traffic matrix-based anomaly detection and DDoS attacks detection in networks are research focus in the network security and traffic measurement community. In this paper, firstly, a new type of unidirectional flow called IF flow is proposed. Merits and features of IF flows are analyzed in detail and then two efficient methods are introduced in our DDoS attacks detection and evaluation scheme. The first method uses residual variance ratio to detect DDoS attacks after Recursive Least Square (RLS) filter is applied to predict IF flows. The second method uses generalized likelihood ratio (GLR) statistical test to detect DDoS attacks after a Kalman filter is applied to estimate IF flows. Based on the two complementary methods, an evaluation formula is proposed to assess the seriousness of current DDoS attacks on router ports. Furthermore, the sensitivity of three types of traffic (IF flow, input link and output link) to DDoS attacks is analyzed and compared. Experiments show that IF flow has more power to expose anomaly than the other two types of traffic. Finally, two proposed methods are compared in terms of detection rate, processing speed, etc., and also compared in detail with Principal Component Analysis (PCA) and Cumulative Sum (CUSUM) methods. The results demonstrate that adaptive filter methods have higher detection rate, lower false alarm rate and smaller detection lag time.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.21 no.3
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• pp.241-253
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• 2009

This study performs the investigation of a long-term variation of annual maximum surge heights(AMSH) and main characteristics of high surge events, and the statistical evaluation of the AMSH using sea level data at Yeosu and Tongyeong tidal stations over more than 30 years. It is found that the long-term uptrends based on the linear regression in the AMSH are 34.5 cm/34 yr at Yeosu and 33.6 cm/31 yr at Tongyeong, which are relatively much higher than those at Sokcho and Mukho in the Eastern Coast. 71% and 68% of the AMSH occur during typhoon's event in Yeosu and Tongyeong tidal stations, respectively, and the highest surge records are mostly produced by the typhoon. The generalized extreme value distribution taking into account of the time variable is applied to detect time trend in annual maximum surge heights. In addition, Gumbel distribution is checked to find which one is best fitted to the data using likelihood ratio test. The return level and its 90% confidence interval are obtained for the statistical prediction of the future trend. The prevention of the growing storm surge damage by the intensified typhoon requires the steady analysis and prediction of the surge events associated with the climate change.

• Journal of Korean Society of Transportation
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• v.29 no.3
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• pp.61-72
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• 2011

In the benefits of ITS, there are intangible gains from real-time traffic information as well as classical gains such as travel time saving. These intangible gains are difficult to be estimated by existing transportation investment appraisal commonly used in SOC investment. The major reason is not because of the absence of methodology but because of the absence of generalized values of particular benefits from real time traffic information. This research explores the value of real-time traffic information on VMS that is the most representative of ITS services, by using CVM with Double Bounded Dichotomous Choice Question. Willingness-To-Pay (WTP) functions of drivers are built with survival functions using various types of probability distribution functions such as Exponential, Log-logistic, and Weibull functions. The results reveal that Log-logistic distribution is the most appropriate distribution model to estimate WTP, and the estimated coefficients are stable through LR (Likelihood Ratio) test. For the further study, it is recommended to perform statistical tests of temporal and spatial transferability that is not examined in this research due to the lack of data.

• Journal of the Korea Society for Simulation
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• v.18 no.3
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• pp.103-112
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• 2009

In a nuclear power plant (NPP), periodic sensor calibrations are required to assure sensors are operating correctly. However, only a few faulty sensors are found to be calibrated. For the safe operation of an NPP and the reduction of unnecessary calibration, on-line calibration monitoring is needed. In this paper, principal component-based Auto-Associative support vector regression (PCSVR) was proposed for the sensor signal validation of the NPP. It utilizes the attractive merits of principal component analysis (PCA) for extracting predominant feature vectors and AASVR because it easily represents complicated processes that are difficult to model with analytical and mechanistic models. With the use of real plant startup data from the Kori Nuclear Power Plant Unit 3, SVR hyperparameters were optimized by the response surface methodology (RSM). Moreover the statistical techniques are integrated with PCSVR for the failure detection. The residuals between the estimated signals and the measured signals are tested by the Shewhart Control Chart, Exponentially Weighted Moving Average (EWMA), Cumulative Sum (CUSUM) and generalized likelihood ratio test (GLRT) to detect whether the sensors are failed or not. This study shows the GLRT can be a candidate for the detection of sensor drift.