• Journal of Environmental Science International
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• v.12 no.3
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• pp.265-274
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• 2003

The Pasquill-Gifford stability category is a very important scheme of the Gaussian type dispersion model defined the complex turbulence state of the atmosphere by A grade(very unstable) to F grade(very stable). But there has been made a point out that this stability category might decrease the predictability of the model because it was each covers a broad range of stability conditions, and that they were very site specific. The APSM (Air Pollution Simulation Model) was composed of the turbulent parameters, i.e. friction velocity(${\mu}$$\_$*/), convective velocity scale($\omega$$\_$*/) and Monin-Obukhov length scale(Ｌ) for the purpose of the performance increasing on the case of the unstable atmospheric conditions. And the PDF (Probability Density Function)model was used to express the vertical dispersion characteristics and the profile method was used to calculate the turbulent characteristics. And the performance assessment was validated between APSM and EPA regulatory models(TEM, ISCST), tracer experiment results. There were very good performance results simulated by APSM than that of TEM, ISCST in the short distance (<1415 m) from the source, but increase the simulation error(%) to stand off the source in others. And there were differences in comparison with the lateral dispersion coefficient($\sigma$$\_$y/) which was represent the horizontal dispersion characteristics of a air pollutant in the atmosphere. So the different calculation method of $\sigma$$\_$y/ which was extrapolated from a different tracer experiment data might decrease the simulation performance capability. In conclusion, the air pollution simulation model showed a good capability of predict the air pollution which was composed of the turbulent parameters compared with the results of TEM and ISCST for the unstable atmospheric conditions.

• Coupled systems mechanics
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• v.7 no.2
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• pp.197-209
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• 2018

Fiber-reinforced concrete (FRC) is a material with increasing application in civil engineering. Here it is assumed that the material consists of a great number of rather small fibers embedded into the concrete matrix. It would be advantageous to predict the mechanical properties of FRC using nondestructive testing; unfortunately, many testing methods for concrete are not applicable to FRC. In addition, design methods for FRC are either inaccurate or complicated. In three-point bending tests of FRC prisms, it has been observed that fiber reinforcement does not break but simply pulls out during specimen failure. Following that observation, this work is based on an assumption that the main components of a simple and rather accurate FRC model are mechanical properties of the concrete matrix and fiber pullout force. Properties of the concrete matrix could be determined from measurements on samples taken during concrete production, and fiber pullout force could be measured on samples with individual fibers embedded into concrete. However, there is no clear relationship between measurements on individual samples of concrete matrix with a single fiber and properties of the produced FRC. This work presents an inverse model for FRC that establishes a relation between parameters measured on individual material samples and properties of a structure made of the composite material. However, a deterministic relationship is clearly not possible since only a single beam specimen of 60 cm could easily contain over 100000 fibers. Our inverse model assumes that the probability density function of individual fiber properties is known, and that the global sample load-displacement curve is obtained from the experiment. Thus, each fiber is stochastically characterized and accordingly parameterized. A relationship between fiber parameters and global load-displacement response, the so-called forward model, is established. From the forward model, based on Levenberg-Marquardt procedure, the inverse model is formulated and successfully applied.

• Journal of applied mathematics & informatics
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• v.18 no.1_2
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• pp.497-503
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• 2005

This paper presents characterizations on the independence of the exponential and Pareto distributions by record values. Let ${X_{n},\;n {\ge1}$ be a sequence of independent and identically distributed(i.i.d) random variables with a continuous cumulative distribution function(cdf) F(x) and probability density function(pdf) f(x). $Let{\;}Y_{n} = max{X_1, X_2, \ldots, X_n}$ for n \ge 1. We say $X_{j}$ is an upper record value of ${X_{n},{\;}n\ge 1}, if Y_{j} > Y_{j-1}, j > 1$. The indices at which the upper record values occur are given by the record times {u(n)}, n \ge 1, where u(n) = $min{j|j > u(n-1), X_{j} > X_{u(n-1)}, n \ge 2}$ and u(l) = 1. Then F(x) = $1 - e^{-\frac{x}{a}}$, x > 0, ${\sigma} > 0$ if and only if $\frac {X_u(_n)}{X_u(_{n+1})} and X_u(_{n+1}), n \ge 1$, are independent. Also F(x) = $1 - x^{-\theta}, x > 1, {\theta} > 0$ if and only if $\frac {X_u(_{n+1})}{X_u(_n)}{\;}and{\;} X_{u(n)},{\;} n {\ge} 1$, are independent.

• Atmosphere
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• v.22 no.3
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• pp.287-298
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• 2012

The predictability of heavy precipitation over the Korean Peninsula is studied using THORPEX Interactive Grand Global Ensemble (TIGGE) data. The performance of the six ensemble models is compared through the inconsistency (or jumpiness) and Root Mean Square Error (RMSE) for MSLP, T850 and H500. Grand Ensemble (GE) of the three best ensemble models (ECMWF, UKMO and CMA) with equal weight and without bias correction is consisted. The jumpiness calculated in this study indicates that the GE is more consistent than each single ensemble model. Brier Score (BS) of precipitation also shows that the GE outperforms. The GE is used for a case study of a heavy rainfall event in Korean Peninsula on 9 July 2009. The probability forecast of precipitation using 90 members of the GE and the percentage of 90 members exceeding 90 percentile in climatological Probability Density Function (PDF) of observed precipitation are calculated. As the GE is excellent in possibility of potential detection of heavy rainfall, GE is more skillful than the single ensemble model and can lead to a heavy rainfall warning in medium-range. If the performance of each single ensemble model is also improved, GE can provide better performance.

• International Journal of Control, Automation, and Systems
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• v.5 no.3
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• pp.251-268
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• 2007

A Gaussian sum filter (GSF) is proposed in this paper on simultaneous localization and mapping (SLAM) for mobile robot navigation. In particular, the SLAM problem is tackled here for cases when only bearing measurements are available. Within the stochastic mapping framework using an extended Kalman filter (EKF), a Gaussian probability density function (pdf) is assumed to describe the range-and-bearing sensor noise. In the case of a bearing-only sensor, a sum of weighted Gaussians is used to represent the non-Gaussian robot-landmark range uncertainty, resulting in a bank of EKFs for estimation of the robot and landmark locations. In our approach, the Gaussian parameters are designed on the basis of minimizing the representation error. The computational complexity of the GSF is reduced by applying the sequential probability ratio test (SPRT) to remove under-performing EKFs. Extensive experimental results are included to demonstrate the effectiveness and efficiency of the proposed techniques.

• Earthquakes and Structures
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• v.8 no.4
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• pp.935-956
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• 2015

Structural Health Monitoring (SHM) of steel towers has become a hot research topic. From the literature, it is impractical and impossible to develop a "general" method that can detect all kinds of damages for all types of structures. A practical method should make use of the characteristics of the type of structures and the kind of damages. This paper reports a feasibility study on the use of measured modal parameters for the detection of damaged braces of tower structures following the Bayesian probabilistic approach. A substructure-based structural model-updating scheme, which groups different parts of the target structure systematically and is specially designed for tower structures, is developed to identify the stiffness distributions of the target structure under the undamaged and possibly damaged conditions. By comparing the identified stiffness distributions, the damage locations and the corresponding damage extents can be detected. By following the Bayesian theory, the probability model of the uncertain parameters is derived. The most probable model of the steel tower can be obtained by maximizing the probability density function (PDF) of the model parameters. Experimental case studies were employed to verify the proposed method. The contributions of this paper are not only on the proposal of the substructure-based Bayesian model updating method but also on the verification of the proposed methodology through measured data from a scale model of transmission tower under laboratory conditions.

• Journal of IKEEE
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• v.15 no.3
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• pp.198-204
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• 2011

In this letter, another analytical approach for the opportunistic amplify-and-forward (OAF) relay systems is proposed over Rayleigh fading channels. Based on set-operation at the selected relay node, its selection probability as the best relay is derived and then, the probability density function (PDF) of the received instantaneous signal-to-noise ratio (SNR) is expressed as a more tractable form in which the number of summations and the length of each summation are specified. Then, the average error rate, outage probability, and average channel capacity are obtained as approximated closed-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 OAF schemes can outperform the other non-selective schemes on the average error rate, outage probability, and average channel capacity.

• International Journal of Concrete Structures and Materials
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• v.19 no.1E
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• pp.11-16
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• 2007

Recently, probabilistic neural network (PNN) has been proposed to predict the compressive strength of concrete for the known effect of improvement on PNN by the iteration method. However, an empirical method has been incorporated in the PNN technique to specify its smoothing parameter, which causes significant uncertainty in predicting the compressive strength of concrete. In this study, a modified probabilistic neural network (MPNN) approach is hence proposed. The global probability density function (PDF) of variables is reflected by summing the heterogeneous local PDFs which are automatically determined by the individual standard deviation of each variable. The proposed MPNN is applied to predict the compressive strength of concrete using actual test data from a concrete company. The estimated results of MPNN are compared with those of the conventional PNN. MPNN showed better results than the conventional PNN in predicting the compressive strength of concrete and provided promising results for the probabilistic approach to predict the concrete strength by using the individual standard deviation of a variable.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.12
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• pp.2317-2323
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• 2015

In this paper, we analyze the performance of dual-hop amplify-and-forward (AF) relaying in cooperative networks with partial relay selection. An AF relay gain considered in this paper includes channel-noise-assisted relay gain. Leveraging a received signal-to-noise ratio (SNR) model, we derive exact closed-form expressions for the probability density function (pdf) and cumulative distribution function (cdf) of the end-to-end SNR. Moreover, an exact closed-form expression of the ergodic capacity for dual-hop AF relaying with channel-noise-assisted relay gain and partial relay selection is investigated. The analytical results shown in this paper are confirmed by Monte-Carlo simulations.

• Journal of Korean Society of Transportation
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• v.17 no.5
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• pp.33-42
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• 1999

본 논문은 도로투자 사업성분석시 사업주체의 현금흐름을 결정하는 항목들을 고정값(Deterministic Value)이 아닌 확률적으로 추정함으로써, 사업의 재무적 변동으로 인한 위험도를 민간사업자의 견지에서 사업성분석과정에 내재화하는 모형을 개발하는 것이다. 즉, 확률적 비용추정기법으로 국소적으로 활용되던 위험도분석을 재무모형에 내재화함으로써 사업의 재무적 변동을 보다 포괄적으로 분석할 수 있는 틀을 제공한다. 본 연구에서는 몬테카를로 시뮬레이션기법을 이용한 위험도분석(Risk Analysis)을 적용하여 사업성 평가지표와 비용의 확률밀도함수(Probability Density Function : PDF), 누적확률분포함수(Cumulative Distribution Function : CDF)를 산출하고, 그 결과로 해당 사업의 위험도를 고려하여 사업성을 평가한다. 이 모형은 사업의 모든 변동요인을 복합적으로 추정하여 사업기간 내 사업주체의 현금흐름을 분석할 수 있다. 따라서 사업주체는 효용에 따라 합리적인 위험도 관리 목표값(Target Value)을 선정하고, 사업의 위험도를 고려하여 건설비, 예비비를 결정할 수 있다. 본 연구에서 정립된 모형을 서울외곽순환고속도로(일산-퇴계원 구간)와 대전당진고속도로를 대상으로 사례분석을 수행하였다. 그 결과, 대전당진고속도로의 경우 사업성이 없으며, 서울외곽순환고속도로의 경우, 일부 위험도 발생변수를 합리적으로 관리한다면, 사업성이 충분한 것으로 분석되었다. 본 사례분석은 사업의 위험도를 반영한 사업성분석 방법으로 우리나라 민자유치대상고속도로의 사업성분석의 하나의 지침이 될 것이다.