• Computers and Concrete
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• v.20 no.3
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• pp.275-282
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• 2017

Grouting is an operation often carried out to consolidate and seal the rock mass in dam sites and tunnels. One of the important parameters in this operation is grouting pressure. In this paper, analytical models used to estimate pressure are investigated. To validate these models, grouting data obtained from Seymareh and Aghbolagh dams were used. Calculations showed that P-3 model from Groundy and P-25 model obtained from the results of grouting in Iran yield the most accurate predictions of the pressure and measurement errors compared to the real values in P-25 model in this dams are 12 and 14.33 Percent and in p-3 model are 12.25 and 16.66 respectively. Also, SPSS software was applied to define the optimum relation for pressure estimation. The results showed a high correlation between the pressure with the depth of the section, the amount of water take, rock quality degree and grout volume, so that the square of the multiple correlation coefficient among the parameters in this dams were 0.932 and 0.864, respectively. This indicates that regression results can be used to predict the amount of pressure. Eventually, the relationship between the parameters was obtained with the correlation coefficient equal to 0.916 based on the data from both dams generally and shows that there is a desirable correlation between the parameters. The outputs of the program led to the multiple linear regression equation of P=0.403 Depth+0.013 RQD+0.011 LU-0.109 V+0.31 that can be used in estimating the pressure.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.2
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• pp.73-78
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• 2004

By modeling the block matching algorithm as a function of the correlation of image blocks, we derive search patterns for fast block matching motion estimation. The proposed approach provides an analytical support lot the diamond-shape search pattern, which is widely used in fast block matching algorithms. We also propose a new fast motion estimation algorithm using adaptive search patterns and statistical properties of the object displacement. In order to select an appropriate search pattern, we exploit the relationship between the motion vector and the block differences. By changing the search pattern adaptively, we improve motion prediction accuracy while reducing required computational complexity compared to other fast block matching algorithms.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.5
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• pp.95-102
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• 2008

This paper presents an appropriate approach for modeling noise in Computed Radiography(CR) images of high strength materials. The approach is specifically designed for types of noise with the statistical and nonlinear properties. CR images Ere degraded even before they are encoded by computer process. Various types of noise often contribute to contaminate radiography image, although they are detected on digitalization. Quantum noise, which is Poisson distributed, is a shot noise, but the photon distribution on Image Plate(IP) of CR system is not always Poisson process. The statistical properties are relative and case-dependant due to its material characteristics. The usual assumption of a distribution of Poisson, binomial and Gaussian statistics are considered. Nonlinear effect is also represented in the process of statistical noise model. It leads to estimate the noise variance in regions from high to low intensity, specifying analytical model. The analysis approach is tested on a database of steel tube step-wedge CR images. The results are available for the comparative parameter studies which measure noise coherence, distribution, signal/noise ratios(SNR) and nonlinear interpolation.

• Journal of KIBIM
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• v.10 no.4
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• pp.1-10
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• 2020

While the importance of barrier-free and fire evacuation design is highlighted in architectural design education, systemic analysis, and examination on such performances are still challenging due to methodological lacks. The present study investigates the effectiveness of BIM-based human behavior simulation for architecture major students' analytical examinations to promote barrier-free and fire evacuation performances. To achieve such an aim, quasi-experiments were conducted, which compare 50 students' analysis and examination scores according to the use and non-use of the simulation, and the data were collected via participants' survey and interview. As a result, T-Test and MANOVA analyses indicate that, compared with its non-use counterpart, the use of human behavior simulation better facilitates the students' (1) examination of the physical properties and dimensions for the disabled's accessibility and evacuation, (2) understanding of the bodily capacity and handicap of the disabled, (3) examination on the spatial layouts and locations of exits, (4) understanding on evacuees' urgent behaviors, and (5) responsibility as an architect. Based on previous studies, the reasons of statistical results are interpreted as the explicit observation and analytical measures of multiple numbers of virtual-evacuees and direct-experience from body range of the disabled responding to the populated occupants as what they face in authentic reality.

• Advances in aircraft and spacecraft science
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• v.9 no.5
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• pp.367-375
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• 2022

Modeling the properties of complex alloys such as nickel superalloys is an extremely challenging scientific and engineering task. The model should take into account a large number of uncorrelated factors, for many of which information may be missing or vague. The individual contribution of one or another chemical element out of a dozen possible ligants cannot be determined by traditional methods. Moreover, there are no general analytical models describing the influence of elements on the characteristics of alloys. Artificial neural networks are one of the few statistical modeling tools that can account for many implicit correlations and establish correspondences that cannot be identified by other more familiar mathematical methods. However, such networks require careful tuning to achieve high performance, which is time-consuming. Data preprocessing can make model training much easier and faster. This article focuses on combining physics-based deep network configuration and input data engineering to simulate the solvus temperature of nickel superalloys. The used deep artificial neural network shows good simulation results. Thus, this method of numerical simulation can be easily applied to such problems.

• Steel and Composite Structures
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• v.33 no.4
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• pp.583-594
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• 2019

In the areas highly exposed to earthquakes, concrete-filled steel tube columns (CFSTCs) are known to provide superior structural aspects such as (i) high strength for good seismic performance (ii) high ductility (iii) enhanced energy absorption (iv) confining pressure to concrete, (v) high section modulus, etc. Numerous studies were reported on behavior of CFSTCs under axial compression loadings. This paper presents an analytical model to predict ultimate load capacity of CFSTCs with circular sections under axial load by using multivariate adaptive regression splines (MARS). MARS is a nonlinear and non-parametric regression methodology. After careful study of literature, 150 comprehensive experimental data presented in the previous studies were examined to prepare a data set and the dependent variables such as geometrical and mechanical properties of circular CFST system have been identified. Basically, MARS model establishes a relation between predictors and dependent variables. Separate regression lines can be formed through the concept of divide and conquers strategy. About 70% of the consolidated data has been used for development of model and the rest of the data has been used for validation of the model. Proper care has been taken such that the input data consists of all ranges of variables. From the studies, it is noted that the predicted ultimate axial load capacity of CFSTCs is found to match with the corresponding experimental observations of literature.

• Communications for Statistical Applications and Methods
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• v.26 no.5
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• pp.445-461
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• 2019

It is possible that data are not always fitted with sufficient precision by the existing distributions; therefore this article presents a methodology that enables the use of families of asymmetric distributions as alternative probabilistic models for survival analysis, with censorship on the right, different from those usually studied (the Exponential, Gamma, Weibull, and Lognormal distributions). We use a more flexible parametric model in terms of density behavior, assuming that data can be fit by a distribution of stable distribution families considered unconventional in the analyses of survival data that are appropriate when extreme values occur, with small probabilities that should not be ignored. In the methodology, the determination of the analytical expression of the risk function h(t) of the $L{\acute{e}}vy$ distribution is included, as it is not usually reported in the literature. A simulation was conducted to evaluate the performance of the candidate distribution when modeling survival times, including the estimation of parameters via the maximum likelihood method, survival function ${\hat{S}}$(t) and Kaplan-Meier estimator. The obtained estimates did not exhibit significant changes for different sample sizes and censorship fractions in the sample. To illustrate the usefulness of the proposed methodology, an application with real data, regarding the survival times of patients with colon cancer, was considered.

• Preventive Nutrition and Food Science
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• v.8 no.4
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• pp.419-424
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• 2003

The physical properties of food playa significant role in the modeling and computation of the heat and mass transfers in basic food processing operations. With the advent of improved analytical techniques, statistical experiment design applications, computing ability and knowledge of the food physical properties of food, there have been significant advances in our ability to predict the impact of processing on the physical properties of food. This article briefly reviews our current ability to predict the influence of processing on the physical properties of food, such as water activity, moisture, color, and rheological characteristics.

• Asian-Australasian Journal of Animal Sciences
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• v.15 no.8
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• pp.1222-1226
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• 2002

Estimates of genetic correlation between direct and maternal effects for weaning weight of beef cattle are often negative in field data. The biological existence of this genetic antagonism has been the point at issue. Some researchers perceived such negative estimate to be an artifact from poor modeling. Recent studies on sources affecting the genetic correlation estimates are reviewed in this article. They focus on heterogeneity of the correlation by sex, selection bias caused from selective reporting, selection bias caused from splitting data by sex, sire by year interaction variance, and sire misidentification and inbreeding depression as factors contributing sire by year interaction variance. A biological justification of the genetic antagonism is also discussed. It is proposed to include the direct-maternal genetic covariance in the analytical models.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1997.04a
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• pp.545-550
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• 1997

It is well known that Statistical Energy Analysis(SEA) is one of very attractive analytical methods to solve shipboard noise problems. With reasonable successes, many applications of SEA to shipboard noise prediction have been reported. However when one wishes to obtain theoretical predictions by using SEA in practical systems, he will find difficulty in modeling of source systems, that is, foundations where to place main engine, generator, compressor, and so on. Also, he will find that it is hard to determine the amount of power flow from machinery to structures. In this paper, SEA of a simple foundation model was carried out using the estimated amount of power flow from source; the estimated mobility method. The comparison between the estimated and measured results is presented. That comparison shows a method to get structure-borne noise power from the combination of machinery and foundation. This prediction method gave a good results for a air-compressor mounted on a model foundation. The method is expected to give a reasonable power output in practical problems.