• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.5
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• pp.1861-1870
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• 2013

A climate change-driven increased hydrological variability has been widely acknowledged over the past decades. In this regards, rainfall simulation techniques are being applied in many countries to consider the increased variability. This study proposed a Homogeneous Hidden Markov Chain(HMM) designed to recognize rather complex patterns of rainfall with discrete hidden states and underlying distribution characteristics via mixture probability density function. The proposed approach was applied to Seoul and Jeonju station to verify model's performance. Statistical moments(e.g. mean, variance, skewness and kurtosis) derived by daily and seasonal rainfall were compared with observation. It was found that the proposed HMM showed better performance in terms of reproducing underlying distribution characteristics. Especially, the HMM was much better than the existing Markov Chain model in reproducing extremes. In this regard, the proposed HMM could be used to evaluate a long-term runoff and design flood as inputs.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.5
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• pp.409-416
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• 2017

In this paper, we consider a framework of compressed sensing over finite fields. One measurement sample is obtained by an inner product of a row of a sensing matrix and a sparse signal vector. A recovery algorithm proposed in this study for sparse signals based probabilistic decoding is used to find a solution of compressed sensing. Until now compressed sensing theory has dealt with real-valued or complex-valued systems, but for the processing of the original real or complex signals, the loss of the information occurs from the discretization. The motivation of this work can be found in efforts to solve inverse problems for discrete signals. The framework proposed in this paper uses a parity-check matrix of low-density parity-check (LDPC) codes developed in coding theory as a sensing matrix. We develop a stochastic algorithm to reconstruct sparse signals over finite field. Unlike LDPC decoding, which is published in existing coding theory, we design an iterative algorithm using probability distribution of sparse signals. Through the proposed recovery algorithm, we achieve better reconstruction performance as the size of finite fields increases. Since the sensing matrix of compressed sensing shows good performance even in the low density matrix such as the parity-check matrix, it is expected to be actively used in applications considering discrete signals.

• Journal of Korean Society of Transportation
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• v.21 no.3
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• pp.15-22
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• 2003

In general, the linear regression model has been used to estimate trip generation in the travel demand forecasting procedure. However, the model suffers from several methodological limitations. First, trips as a dependent variable with non-negative integer show discrete distribution but the model assumes that the dependent variable is continuously distributed between -$\infty$ and +$\infty$. Second, the model may produce negative estimates. Third, even if estimated trips are within the valid range, the model offers only forecasted trips without discrete probability distribution of them. To overcome these limitations, a poisson model with a assumption of equidispersion has frequently been used to analyze count data such as trip frequencies. However, if the variance of data is greater than the mean. the poisson model tends to underestimate errors, resulting in unreliable estimates. Using overdispersion test, this study proved that the poisson model is not appropriate and by using Vuong test, zero inflated negative binomial model is optimal. Model reliability was checked by likelihood test and the accuracy of model by Theil inequality coefficient as well. Finally, marginal effect of the change of socio-demographic characteristics of households on trips was analyzed.

• Journal of Korean Society of Disaster and Security
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• v.13 no.4
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• pp.25-36
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• 2020

An increasing frequency and intensity of natural disasters have been observed due to climate change. To better prepare for these, the MOIS (ministry of the interior and safety) announced a comprehensive plan for minimizing damages associated with natural disasters, including drought and heavy snowfall. The spatial-temporal pattern of snowfall is greatly influenced by temperature and geographical features. Heavy snowfalls are often observed in Gangwon-do, surrounded by mountains, whereas less snowfall is dominant in the southern part of the country due to relatively high temperatures. Thus, snow depth data often contains zeros that can lead to difficulties in the selection of probability distribution and estimation of the parameters. A generalized mixture distribution approach to a maximum snow depth series over the southern part of Korea (i.e., Changwon, Tongyeoung, Jinju weather stations) are located is proposed to better estimate a threshold (𝛿) classifying discrete and continuous distribution parts. The model parameters, including the threshold in the mixture model, are effectively estimated within a Bayesian modeling framework, and the uncertainty associated with the parameters is also provided. Comparing to the Daegwallyeong weather station, It was found that the proposed model is more effective for the regions in which less snow depth is observed.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.5
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• pp.464-473
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• 2001

Since DCT algorithm divides an image into blocks uniformly in both the spatial domain and the frequency domain, it has a weak point that it can not reflect HVS(Human Visual System) efficiently To avoid this problem, we propose a new algorithm, which combines only the merits of DCT and wavelet transform. The proposed algorithm uses the high compaction efficiency of DCT, and applies wavelet transform mode to DCT coefficients, so that the algorithm can utilize interband and intraband correlations of wavelet simultaneously After that, the proposed algorithm quantizes each coefficient based on the characteristic of each coefficient's band. In terms of coding method, the quantized coefficients of important DCT coefficients have symmetrical distribution, the bigger that value Is, the smaller occurrence probability is. Using the characteristic, we propose a new still image coding algorithm of symmetric and bidirectional tree structure with simple algorithm and fast decoding time. Comparing the proposed method with JPEG, the proposed method yields better image quality both objectively and subjectively at the same bit rate.

• Wind and Structures
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• v.18 no.6
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• pp.693-715
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• 2014

Stochastic processes are used to represent phenomena in many diverse fields. Numerical simulation method is widely applied for the solution to stochastic problems of complex structures when alternative analytical methods are not applicable. In some practical applications the stochastic processes show non-Gaussian properties. When the stochastic processes deviate significantly from Gaussian, techniques for their accurate simulation must be available. The various existing simulation methods of non-Gaussian stochastic processes generally can only simulate super-Gaussian stochastic processes with the high-peak characteristics. And these methodologies are usually complicated and time consuming, not sufficiently intuitive. By revealing the inherent coupling effect of the phase and amplitude part of discrete Fourier representation of random time series on the non-Gaussian features (such as skewness and kurtosis) through theoretical analysis and simulation experiments, this paper presents a novel approach for the simulation of non-Gaussian stochastic processes with the prescribed amplitude probability density function (PDF) and power spectral density (PSD) by amplitude modulation and phase reconstruction. As compared to previous spectral representation method using phase modulation to obtain a non-Gaussian amplitude distribution, this non-Gaussian phase reconstruction strategy is more straightforward and efficient, capable of simulating both super-Gaussian and sub-Gaussian stochastic processes. Another attractive feature of the method is that the whole process can be implemented efficiently using the Fast Fourier Transform. Cases studies demonstrate the efficiency and accuracy of the proposed algorithm.

• Wind and Structures
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• v.31 no.3
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• pp.269-285
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• 2020

The statistical characteristics of typhoon wind speed records tend to have a considerable time-varying trend; thus, the stationary wind model may not be appropriate to estimate the wind characteristics of typhoon events. Several nonstationary wind speed models have been proposed by pioneers to characterize wind characteristics more accurately, but comparative studies on the applicability of the different wind models are still lacking. In this study, three landfall typhoons, Ampil, Jongdari, and Rumbia, recorded by ultrasonic anemometers atop the Shanghai World Financial Center (SWFC), are used for the comparative analysis of stationary and nonstationary wind characteristics. The time-varying mean is extracted with the discrete wavelet transform (DWT) method, and the time-varying standard deviation is calculated by the autoregressive moving average generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) model. After extracting the time-varying trend, the longitudinal wind characteristics, e.g., the probability distribution, power spectral density (PSD), turbulence integral scale, turbulence intensity, gust factor, and peak factor, are comparatively analyzed based on the stationary wind speed model, time-varying mean wind speed model and time-varying standard deviation wind speed model. The comparative analysis of the different wind models emphasizes the significance of the nonstationary considerations in typhoon events. The time-varying standard deviation model can better identify the similarities among the different typhoons and appropriately describe the nonstationary wind characteristics of the typhoons.

• Structural Engineering and Mechanics
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• v.68 no.6
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• pp.747-760
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• 2018

In the present study, a methodology for developing fragilities of arch concrete dams to assess their performance against seismic hazards is introduced. Firstly, the probability risk and fragility curves are presented, followed by implementation and representation of the way this method is used. Amirkabir arch concrete dam was subjected to non-linear dynamic analyses. A modified three dimensional rotating smeared crack model was used to take the nonlinear behavior of mass concrete into account. The proposed model considers major characteristics of mass concrete. These characteristics are pre-softening behavior, softening initiation criteria, fracture energy conservation, suitable damping mechanism and strain rate effect. In the present analysis, complete fluid-structure interaction is included to account for appropriate fluid compressibility and absorptive reservoir boundary conditions. In this study, the Amirkabir arch concrete dam is subjected to a set of 8 three-component earthquakes each scaled to 10 increasing intensity levels. Using proposed nonlinear smeared crack model, nonlinear analysis is performed where the structure is subjected to a large set of scaled and un-scaled ground motions and the maximum responses are extracted for each one and plotted. Based on the results, fragility curves were plotted according to various and possible damages indexes. Discrete damage probabilities were calculated using statistical methods for each considered performance level and incremental nonlinear analysis. Then, fragility curves were constructed based on the lognormal distribution assumption. Two damage indexes were introduced and compared to one another. The results indicate that the dam has a proper stability under earthquake conditions at MCE level. Moreover, displacement damages index is more conservative and impractical in the fragility analysis than tensional damage index.