• 아태비즈니스연구
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• 제6권2호
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• pp.1-17
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• 2015

This study compares the most widely used parametric and non-parametric techniques to measure cost and profit efficiency of banks, namely the Stochastic Frontier Analysis (SFA) and Data Envelopment Analysis (DEA). We formulate the specification form of both stochastic cost and profit frontier models and constant return to scale Cost DEA and Profit DEA models and provide an empirical assessment of the cost and profit frontiers based on a panel dataset of National Commercial Banks (NCBs) and Private Banks (PBs) in Bangladesh over the 2001-2010 period. The cost inefficiency and profit efficiency are slightly higher for PBs than NCBs in case of both SFA and DEA. The coefficients of advance and off-balance sheet items are significant that positively influence the banks in stochastic cost frontier model while the advance, other earning assets, price of borrowed fund are significant and negative effects on the banks in stochastic profit frontier model. The average cost inefficiency and average profit efficiency are recorded with 16.3% and 91% respectively. The highest and lowest cost inefficiency are observed for Janata Bank and United Commercial Bank Limited whilst the highest and lowest profit efficiency are recorded for Eastern Bank Limited and Janata Bank respectively. The average technical and allocative efficiency are 68.8% and 35.9%, respectively in case of CRS cost-DEA model whereas they are 70.3% and 31.8% in case of CRS profit-DEA model. The average cost inefficiency is recorded 6.3% by SFA whereas it is 24.5% by DEA. The average profit efficiency is found 91% by SFA while it is 22.1% by DEA, and SFA method shows better bank efficiency than DEA.

• 경영과학
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• 제11권1호
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• pp.107-115
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• 1994

Stochastic manufacturing systems are generally formulated as performance models of discrete event systems. In this paper, logical models(as opposed to performance models) of kanban systems are presented which are deterministic and untimed but not stochastic and timed. As a result, the first and second order properties of kanban systems are showed which can be fruitfully applied to the analysis and design of kanban systems.

• Structural Engineering and Mechanics
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• 제75권3호
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• pp.311-325
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• 2020

In applying the spectral stochastic finite element methods to the frequency response analysis, the conventional methods are known to give unstable and inaccurate results near the natural frequencies. To address this issue, a new sensitivity based stabilized formulation for stochastic frequency response analysis is proposed in this paper. The main difference over the conventional spectral methods is that the polynomials of random variables are applied to both numerator and denominator in approximating the harmonic response solution. In order to reflect the resonance behavior of the structure, the denominator polynomials is constructed by utilizing the natural frequency sensitivity and the random mode superposition. The numerator is approximated by applying a polynomial chaos expansion, and its coefficients are obtained through the Galerkin or the spectral projection method. Through various numerical studies, it is seen that the proposed method improves accuracy, especially in the vicinities of structural natural frequencies compared to conventional spectral methods.

• 수산해양기술연구
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• 제32권4호
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• pp.432-446
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• 1996

A stochastic Hamilton variational principle(SHVP) is formulated for dynamic problems of linear continuum. The SHVP allows incorporation of probabilistic distributions into the finite element analysis. The formulation is simplified by transformation of correlated random variables to a set of uncorrelated random variables through a standard eigenproblem. A procedure based on the Fourier analysis and synthesis is presented for eliminating secularities from the perturbation approach. In addition to, a method to analyse stochastic design sensitivity for structural dynamics is present. A combination of the adjoint variable approach and the second order perturbation method is used in the finite element codes. An alternative form of the constraint functional that holds for all times is introduced to consider the time response of dynamic sensitivity. The algorithms developed can readily be adapted to existing deterministic finite element codes. The numerical results for stochastic analysis by proceeding approach of cantilever, 2D-frame and 3D-frame illustrates in this paper.

• Smart Structures and Systems
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• 제7권2호
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• pp.153-167
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• 2011

The Subspace based System Identification Techniques (SSIT) have been very popular within the research circles in the last decade due to their proven superiority over the other existing system identification techniques. For operational (output only) modal analysis, the stochastic SSIT and for operational modal analysis in the presence of exogenous inputs, the combined deterministic stochastic SSIT have been used in the literature. This study compares the application of the two alternative techniques on a typical school building in Istanbul using 100 Monte Carlo simulations. The study clearly shows that the combined deterministic stochastic SSIT performs superior to the stochastic SSIT when the techniques are applied on noisy data from low to mid rise stiff structures.

• Steel and Composite Structures
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• 제11권2호
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• pp.115-130
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• 2011

Considering the randomness of material parameters in the laminated composite plate, a scheme of stochastic finite element method to analyze the displacement response variability is suggested. In the formulation we adopted the concept of the weighted integral where the random variable is defined as integration of stochastic field function multiplied by a deterministic function over a finite element. In general the elastic modulus of composite materials has distinct value along an individual axis. Accordingly, we need to assume 5 material parameters as random. The correlations between these random parameters are modeled by means of correlation functions, and the degree of correlation is defined in terms of correlation coefficients. For the verification of the proposed scheme, we employ an independent analysis of Monte Carlo simulation with which statistical results can be obtained. Comparison is made between the proposed scheme and Monte Carlo simulation.

• 한국건설관리학회논문집
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• 제6권1호
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• pp.73-79
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• 2005

This paper, introduces a Stochastic Project Scheduling Simulation system (SPSS III) developed by the author to predict a project completion probability in a certain time. The system integrates deterministic CPM, probabilistic PERT, and stochastic Discrete Event Simulation (DES) scheduling methods into one system. It implements automated statistical analysis methods for computing the minimum number of simulation runs, the significance of the difference between independent simulations, and the confidence interval for the mean project duration as well as sensitivity analysis method in What-if analyzer component. The SPSS 111 gives the several benefits to researchers in that it (1) complements PERT and Monte Carlo simulation by using stochastic activity durations via a web based JAVA simulation over the Internet, (2) provides a way to model a project network having different probability distribution functions, (3) implements statistical analyses method which enable to produce a reliable prediction of the probability of completing a project in a specified time, and (4) allows researchers to compare the outcome of CPM, PERT and DES under different variability or skewness in the activity duration data.

• Structural Engineering and Mechanics
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• 제56권5호
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• pp.767-785
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• 2015

The stochastic finite element method is employed to obtain a stochastic dynamic model of angled beams subjected to impact loads when uncertain material properties are described by random fields. Using the perturbation technique in conjunction with a precise time integration method, a random analysis approach is developed for efficient analysis of random elastic waves. Formulas for the mean, variance and covariance of displacement, strain and stress are introduced. Statistics of displacement and stress waves is analyzed and effects of bend angle and material stochasticity on wave propagation are studied. It is found that the elastic wave correlation in the angled section is the most significant. The mean, variance and covariance of the stress wave amplitude decrease with an increase in bend angle. The standard deviation of the beam material density plays an important role in longitudinal displacement wave covariance.

• Structural Engineering and Mechanics
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• 제59권1호
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• pp.187-207
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• 2016

Practical ambient excitations of engineering structures usually do not comply with the stationary-white-noise assumption in traditional operational modal analysis methods due to heavy traffic, wind guests, and other disturbances. In order to eliminate spurious modes induced by non-white noise inputs, the improved stochastic subspace identification based on a delay index is proposed in this paper for a representative kind of stationary non-white noise ambient excitations, which have nonzero autocorrelation values near the vertical axis. It relaxes the stationary-white-noise assumption of inputs by avoiding corresponding unqualified elements in the Hankel matrix. Details of the improved stochastic subspace identification algorithms and determination of the delay index are discussed. Numerical simulations on a four-story frame and laboratory vibration experiments on a simply supported beam have demonstrated the accuracy and reliability of the proposed method in eliminating spurious modes under non-white noise ambient excitations.

• Structural Engineering and Mechanics
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• 제78권5호
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• pp.529-543
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• 2021

Inevitable source-uncertainties in geometry configuration, boundary condition, and material properties may deviate the structural dynamics from its expected responses. This paper aims to examine the influence of these uncertainties on the vibration of functionally graded beams. Finite element procedures are presented for Timoshenko beams and utilized to generate reliable datasets. A prerequisite to the uncertainty quantification of the beam vibration using Monte Carlo simulation is generating large datasets, that require executing the numerical procedure many times leading to high computational cost. Utilizing artificial neural networks to model beam vibration can be a good approach. Initially, the optimal network for each beam configuration can be determined based on numerical performance and probabilistic criteria. Instead of executing thousands of times of the finite element procedure in stochastic analysis, these optimal networks serve as good alternatives to which the convergence of the Monte Carlo simulation, and the sensitivity and probabilistic vibration characteristics of each beam exposed to randomness are investigated. The simple procedure presented here is efficient to quantify the uncertainty of different stochastic behaviors of composite structures.