• Title/Summary/Keyword: Probability Density function

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Failure Probability Calculation Method Using Kriging Metamodel-based Importance Sampling Method (크리깅 근사모델 기반의 중요도 추출법을 이용한 고장확률 계산 방안)

  • Lee, Seunggyu;Kim, Jae Hoon
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.41 no.5
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    • pp.381-389
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    • 2017
  • The kernel density was determined based on sampling points obtained in a Markov chain simulation and was assumed to be an important sampling function. A Kriging metamodel was constructed in more detail in the vicinity of a limit state. The failure probability was calculated based on importance sampling, which was performed for the Kriging metamodel. A pre-existing method was modified to obtain more sampling points for a kernel density in the vicinity of a limit state. A stable numerical method was proposed to find a parameter of the kernel density. To assess the completeness of the Kriging metamodel, the possibility of changes in the calculated failure probability due to the uncertainty of the Kriging metamodel was calculated.

Clustering In Tied Mixture HMM Using Homogeneous Centroid Neural Network (Homogeneous Centroid Neural Network에 의한 Tied Mixture HMM의 군집화)

  • Park Dong-Chul;Kim Woo-Sung
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.31 no.9C
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    • pp.853-858
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    • 2006
  • TMHMM(Tied Mixture Hidden Markov Model) is an important approach to reduce the number of free parameters in speech recognition. However, this model suffers from a degradation in recognition accuracy due to its GPDF (Gaussian Probability Density Function) clustering error. This paper proposes a clustering algorithm, called HCNN(Homogeneous Centroid Neural network), to cluster acoustic feature vectors in TMHMM. Moreover, the HCNN uses the heterogeneous distance measure to allocate more code vectors in the heterogeneous areas where probability densities of different states overlap each other. When applied to Korean digit isolated word recognition, the HCNN reduces the error rate by 9.39% over CNN clustering, and 14.63% over the traditional K-means clustering.

Study of New Approach of Performance Analysis for OADF Relay Systems over Rayleigh Fading channels (레일리 페이딩 채널에서의 OADF 릴레이 시스템에 대한 새로운 성능분석 기법에 관한 연구)

  • Ko, Kyun-Byoung;Seo, Jeong-Tae
    • Journal of the Institute of Convergence Signal Processing
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    • v.12 no.3
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    • pp.188-193
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    • 2011
  • In this letter, we have derived another exact performance analysis for the OADF(opportunistic adaptive decode-and-forward) relay systems over Rayleigh fading channels. Based on error-events at relay nodes, the received instantaneous SNR(signal-to-noise ratio) is presented and its PDF(probability density function) is expressed as a more tractable form in which the number of summations and the length of each summation are specified. Then, exact average error rate, outage probability, and average channel capacity are obtained as general forms. Simulation results are finally presented to validate that the proposed analytical expressions can be a unified frame work covering all Rayleigh fading channel conditions. Furthermore, it is confirmed that OADF schemes can outperform the other schemes on the average error rate, outage probability, and average channel capacity.

New Chaos Map for BER Performance Improvement in Chaos Communication System Using CDSK (상관지연편이변조 방식의 혼돈(Chaos) 통신 방식에서 비트오류율 성능 향상을 위한 새로운 혼돈 지도)

  • Lee, Jun-Hyun;Ryu, Heung-Gyoon
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.38A no.8
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    • pp.629-637
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    • 2013
  • Chaos communication systems have the characteristics such as non-periodic, wide-band, non-predictability of signals and easy implementation. There have been many studies about chaos communication systems because of these advantages. But, chaos communication systems have low BER(Bit Error Rate) compare to general digital communication system. Existing researches on chaos communication systems only analyze BER performance according to various chaos maps. There are no studies on analysis of BER performance according to PDF(Probability Density Function) of chaos maps. In this paper, we analyze the BER performance according to changing parameter, equation, and initial values of chaos map's PDF. In addition, we propose new chaos map to improve BER performance. Simulation results show that BER performance of CDSK(Correlation Delay Shift Keying) is changed when PDF of chaos map changed. And the proposed chaos map has a better BER performance compare to previous chaos maps such as Tent map, Logistic map, and Henon map.

Forest Fire Severity Classification Using Probability Density Function and KOMPSAT-3A (확률밀도함수와 KOMPSAT-3A를 활용한 산불피해강도 분류)

  • Lee, Seung-Min;Jeong, Jong-Chul
    • Korean Journal of Remote Sensing
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    • v.35 no.6_4
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    • pp.1341-1350
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    • 2019
  • This research deals with algorithm for forest fire severity classification using multi-temporal KOMPSAT-3A image to mapping forest fire areas. The recent satellite of the KOMPSAT series, KOMPSAT-3A, demonstrates high resolution and multi-spectral imagery with infrared and high resolution electro-optical bands. However, there is a lack of research to classify forest fire severity using KOMPSAT-3A. Therefore, the purpose of this study is to analyze forest fire severity using KOMPSAT-3A images. In addition, this research used pre-fire and post-fire Sentinel-2 with differenced Normalized Burn Ratio (dNBR) to taking for burn severity distribution map. To test the effectiveness of the proposed procedure on April 4, 2019, Gangneung wildfires were considered as a case study. This research used the probability density function for the classification of forest fire damage severity based on R software, a free software environment of statistical computing and graphics. The burn severities were estimated by changing NDVI before and after forest fire. Furthermore, standard deviation of probability density function was used to calculate the size of each class interval. A total of five distribution of forest fire severity were effectively classified.

Random Variable State and Response Variability (확률변수상태와 응답변화도)

  • Noh, Hyuk-Chun;Lee, Phill-Seung
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.26 no.6A
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    • pp.1001-1011
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    • 2006
  • It is a general agreement that exact statistical solutions can be found by a Monte Carlo technique. Due to difficulties, however, in the numerical generation of random fields, which satisfy not only the probabilistic distribution but the spectral characteristics as well, it is recognized as relatively difficult to find an exact response variability of a structural response. In this study, recognizing that the random field assumes a constant over the domain under consideration when the correlation distance tends to infinity, a semi-theoretical solution of response variability is proposed for general structures. In this procedure, the probability density function is directly used. It is particularly noteworthy that the proposed methodology provides response variability for virtually any type of probability density function, and has capability of considering correlations between multiple random variables.

Seafloor Sediment Classification Using Nakagami Probability Density Function of Acoustic Backscattered Signals (음향후방산란신호의 나카가미 확률밀도함수를 이용한 해저퇴적물 분류)

  • Bok, Tae-Hoon;Paeng, Dong-Guk;Park, Yo-Sup;Kong, Gee-Soo;Park, Soo-Chul
    • The Journal of the Acoustical Society of Korea
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    • v.28 no.3
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    • pp.165-173
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    • 2009
  • The physical properties of a seafloor sediment have been used as a basic data for the ocean survey. Conventional methods such as a coring, a drilling, and a grabbing have been used to explore the physical properties but these methods have a number of shortcomings as it is time consuming, expensive and spatially limited. To overcome these limitations, seafloor sediment classification using acoustic signals has been studied actively. In this paper, we obtained the backscattered signal from the seafloor sediment using an echo sounder which is one kind of seafloor topography equipment. Nakagami probability density function of the backscattered signals from the seafloor sediment was computed and a Nakagami parameter was compared with the physical properties of the seafloor sediment. We have confirmed that Nakagami parameter, m is correlated with the physical properties of a seafloor sediment. This study will be utilized as a basic data of the seafloor sediment research.

Characteristics of wind loads on roof cladding and fixings

  • Ginger, J.D.
    • Wind and Structures
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    • v.4 no.1
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    • pp.73-84
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    • 2001
  • Analysis of pressures measured on the roof of the full-scale Texas Tech building and a 1/50 scale model of a typical house showed that the pressure fluctuations on cladding fastener and cladding-truss connection tributary areas have similar characteristics. The probability density functions of pressure fluctuations on these areas are negatively skewed from Gaussian, with pressure peak factors less than -5.5. The fluctuating pressure energy is mostly contained at full-scale frequencies of up to about 0.6 Hz. Pressure coefficients, $C_p$ and local pressure factors, $K_l$ given in the Australian wind load standard AS1170.2 are generally satisfactory, except for some small cladding fastener tributary areas near the edges.

THE STUDY OF FLOOD FREQUENCY ESTIMATES USING CAUCHY VARIABLE KERNEL

  • Moon, Young-Il;Cha, Young-Il;Ashish Sharma
    • Water Engineering Research
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    • v.2 no.1
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    • pp.1-10
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    • 2001
  • The frequency analyses for the precipitation data in Korea were performed. We used daily maximum series, monthly maximum series, and annual series. For nonparametric frequency analyses, variable kernel estimators were used. Nonparametric methods do not require assumptions about the underlying populations from which the data are obtained. Therefore, they are better suited for multimodal distributions with the advantage of not requiring a distributional assumption. In order to compare their performance with parametric distributions, we considered several probability density functions. They are Gamma, Gumbel, Log-normal, Log-Pearson type III, Exponential, Generalized logistic, Generalized Pareto, and Wakeby distributions. The variable kernel estimates are comparable and are in the middle of the range of the parametric estimates. The variable kernel estimates show a very small probability in extrapolation beyond the largest observed data in the sample. However, the log-variable kernel estimates remedied these defects with the log-transformed data.

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Estimation of sewer deterioration by Weibull distribution function (와이블 분포함수를 이용한 하수관로 노후도 추정)

  • Kang, Byongjun;Yoo, Soonyu;Park, Kyoohong
    • Journal of Korean Society of Water and Wastewater
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    • v.34 no.4
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    • pp.251-258
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    • 2020
  • Sewer deterioration models are needed to forecast the remaining life expectancy of sewer networks by assessing their conditions. In this study, the serious defect (or condition state 3) occurrence probability, at which sewer rehabilitation program should be implemented, was evaluated using four probability distribution functions such as normal, lognormal, exponential, and Weibull distribution. A sample of 252 km of CCTV-inspected sewer pipe data in city Z was collected in the first place. Then the effective data (284 sewer sections of 8.15 km) with reliable information were extracted and classified into 3 groups considering the sub-catchment area, sewer material, and sewer pipe size. Anderson-Darling test was conducted to select the most fitted probability distribution of sewer defect occurrence as Weibull distribution. The shape parameters (β) and scale parameters (η) of Weibull distribution were estimated from the data set of 3 classified groups, including standard errors, 95% confidence intervals, and log-likelihood values. The plot of probability density function and cumulative distribution function were obtained using the estimated parameter values, which could be used to indicate the quantitative level of risk on occurrence of CS3. It was estimated that sewer data group 1, group 2, and group 3 has CS3 occurrence probability exceeding 50% at 13th-year, 11th-year, and 16th-year after the installation, respectively. For every data groups, the time exceeding the CS3 occurrence probability of 90% was also predicted to be 27th- to 30th-year after the installation.