• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.507-517
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• 2021

The research objective was to analyze the effect of work behavior on organizational citizenship behavior (OCB) and organizational commitment. For this, we analyze the effect of organizational commitment on OCB and analyze organizational commitment as a mediating variable in the relationship between workplace behavior and OCB. This study employed survey data for 80 employees of 12 village credit institutions in the Badung Regency. Observation is one way to obtain primary data. It is done by observing the object, which is the primary source of data, and conducting direct observations of the research location to determine the circumstances and problems. This research uses data collection techniques: 1) interviews conducted with employees working in Badung Regency both in managers and staff positions. 2) Questionnaire, namely, by using several closed questions given to the Badung Regency employees regarding workplace spirituality, organizational commitment, and OCB. The analysis technique used the partial least squares (PLS) approach and the Sobel test. The results show that workplace spirituality does not affect OCB but positively affects organizational commitment. Also, organizational commitment has a positive effect on OCB. The major findings are organizational commitment acts as a mediating variable in the relationship between workplace spirituality and OCB.

• Journal of Distribution Science
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• v.20 no.12
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• pp.81-87
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• 2022

Purpose: This study aims to explore the relationship between stock liquidity and skewness risk-tail risk (stock price crash risk) in an emerging market, in which problems on liquidity are more severe than in developed markets. Research design, data, and methodology: Based on the Thai market stock exchange over the period of 2000 to 2019, our sample include 13,462 firm-period observations. We employ a panel regression models regarding to five liquidity measures. These five liquidity measures cover three dimensions of liquidity namely the volume-based, price-based, and transaction cost-based measures for the liquidity-tail risk relationship. Results: We find a positively significant relationship between stock liquidity and tail risk in all cases. The finding here shows that the higher the stock liquidity, the larger the tail risk is. Conclusion: As the prior studies show inconclusive effect of stock liquidity on stock price crash risk, we demonstrate that mixed results found in prior studies are probably driven from the type of liquidity measure. The stock liquidity-tail risk association is present in the Stock Exchange of Thailand. The results remain the same regardless of the definition of tail risk and liquidity factors. An endogeneity issue is addressed by employing the two-stage least squares regression.

• Steel and Composite Structures
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• v.39 no.5
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• pp.511-528
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• 2021

There are recently some advances in solving numerically topology optimization problems for large-scaled trusses based on ground structure approach. A disadvantage of this approach is that the final design usually includes many bars, which is difficult to be produced in practice. One of efficient tools is a so-called filter scheme for the ground structure to reduce this difficulty and determine several distinct bars. In detail, this technique is valuable for practical uses because unnecessary bars are filtered out from the ground structure to obtain a well-defined structure during the topology optimization process, while it still guarantees the global equilibrium condition. This process, however, leads to a singular system of equilibrium equations. In this case, the minimization of least squares with Tikhonov regularization is adopted. In this paper, a proposed algorithm in controlling optimal Tikhonov parameter is considered in combination with the filter scheme due to its crucial role in obtaining solution to remove numerical singularity and saving computational time by using sparse matrix, which means that the discrete optimal topology solutions depend on choosing the Tikhonov parameter efficiently. Several numerical examples are investigated to demonstrate the efficiency of the filter parameter control algorithm in terms of the large-scaled optimal topology designs.

• Journal of Astronomy and Space Sciences
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• v.26 no.1
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• pp.75-88
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• 2009

The main purpose of the current research is to introduce the alternative algorithm of the non-recursive batch filter based on the unscented transformation in which the linearization process is unnecessary. The presented algorithm is applied to the orbit determination of a low earth orbiting satellite and compared its results with those of the well-known Bayesian batch least squares estimation and the iterative UKF smoother (IUKS). The system dynamic equations consist of the Earth's geo-potential, the atmospheric drag, solar radiation pressure and the lunar/solar gravitational perturbations. The range, azimuth and elevation angles of the satellite measured from ground stations are used for orbit determination. The characteristics of the non recursive unscented batch filter are analyzed for various aspects, including accuracy of the determined orbit, sensitivity to the initial uncertainty, measurement noise and stability performance in a realistic dynamic system and measurement model. As a result, under large non-linear conditions, the presented non-recursive batch filter yields more accurate results than the other batch filters about 5% for initial uncertainty test and 12% for measurement noise test. Moreover, the presented filter exhibits better convergence reliability than the Bayesian least squares. Hence, it is concluded that the non-recursive batch filter based on the unscented transformation is effectively applicable for highly nonlinear batch estimation problems.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.32 no.4
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• pp.215-222
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• 2019

The heat conduction problem with discontinuous material coefficients generally consists of the conservative equation, boundary condition, and interface condition, which should be additionally satisfied in the solution procedure. This feature often makes the development of new numerical schemes difficult as it induces a layered singularity in the solution fields; thus, a special approximation is required to capture the singular behavior. In addition to the approximation, the construction of a total system of equations is challenging. In this study, a wedge function is devised for enriching the approximation, and the interface condition itself is embedded in the moving least squares(MLS) derivative approximation to consistently satisfy the interface condition. The heat conduction problem is then discretized in a strong form using the developed derivative approximation, which is named as the interface immersed MLS difference method. This method is able to efficiently provide a numerical solution for such interface problems avoiding both numerical quadrature as well as extra difference equations related to the interface condition enforcement. Numerical experiments proved that the developed numerical method was highly accurate and computationally efficient at solving the heat conduction problem with interfacial jump as well as the problem with a geometrically induced interfacial singularity.

• Journal of Ocean Engineering and Technology
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• v.28 no.2
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• pp.93-101
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• 2014

In general, particle simulation methods such as the MPS(Moving Particle Simulation) or SPH(Smoothed Particle Hydrodynamics) methods have some serious drawbacks for pressure solutions. The pressure field shows spurious high fluctuations both temporally and spatially. It is well known that pressure fluctuation primarily occurs because of the numerical approximation of the partial differential operators. The MPS and SPH methods employ a pre-defined kernel function in the approximation of the gradient and Laplacian operators. Because this kernel function is constructed artificially, an accurate solution cannot be guaranteed, especially when the distribution of particles is irregular. In this paper, we propose a particle simulation method based on the moving least-square technique for solving the partial differential operators using a Taylor-series expansion. The developed method was applied to the hydro-static pressure and dam-broken problems to validate it.

• Journal of Korea Multimedia Society
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• v.17 no.9
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• pp.1041-1051
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• 2014

In this paper, we propose a new image alignment technique based on CUDA SURF in order to solve the initial image alignment problem that frequently occurs in machine vision applications. Machine vision systems using multi-spectral images have recently become more common for solving various decision problems that cannot be performed by the human vision system. These machine vision systems mostly use markers for the initial image alignment. However, there are some applications where the markers cannot be used and the alignment techniques have to be changed whenever their markers are changed. In order to solve these problems, we propose a new image alignment method for multi-spectral machine vision applications based on SURF extracting image features without depending on markers. In this paper, we propose an image alignment method that obtains a sufficient number of feature points from multi-spectral images using SURF and removes outlier iteratively based on a least squares method. We further propose an effective preliminary scheme for removing mismatched feature point pairs that may affect the overall performance of the alignment. In addition, we reduce the execution time by implementing the proposed method using CUDA based on GPGPU in order to guarantee real-time operation. Simulation results show that the proposed method is able to align images effectively in applications where markers cannot be used.

• Journal of Korea Society of Industrial Information Systems
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• v.15 no.2
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• pp.83-90
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• 2010

Sensor is a key element in various fields of applications such as sensor networks. However, they could not be easily developed because of several factors such as temperature dependence of output characteristics and/or nonlinearity. Calibration of sensor is also needed to solve these problems. Conventional calibration process required a lot of time and expenses. Therefore, it is important to develop sensor systems which can shorten development time and minimize expense. In this paper, we develop CO and $CO_2$ Sensor modules and propose a multiple sensor calibration system to resolve problems of conventional calibration process. A proposed system is composed of sensor module, system board and monitor program. Regression analysis method based on the least mean squares is used for calibration. We introduced the structure of calibration systems and experimental results. Calibration results can be used to confirm the effectiveness of the proposed system.

• The Korean Journal of Applied Statistics
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• v.36 no.6
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• pp.501-514
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• 2023

When analyzing high dimensional data such as text data, if we input all the variables as explanatory variables, statistical learning procedures may suffer from over-fitting problems. Furthermore, computational efficiency can deteriorate with a large number of variables. Dimensionality reduction techniques such as feature selection or feature extraction are useful for dealing with these problems. The sparse principal component analysis (SPCA) is one of the regularized least squares methods which employs an elastic net-type objective function. The SPCA can be used to remove insignificant principal components and identify important variables from noisy observations. In this study, we propose a dimension reduction procedure for text data based on the SPCA. Applying the proposed procedure to real data, we find that the reduced feature set maintains sufficient information in text data while the size of the feature set is reduced by removing redundant variables. As a result, the proposed procedure can improve classification accuracy and computational efficiency, especially for some classifiers such as the k-nearest neighbors algorithm.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.20 no.3
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• pp.311-319
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• 2007

A Meshfree is a method used to establish algebraic equations of system for the whole problem domain without the use of a predefined mesh for the domain discretization. A point interpolation method is based on combining radial and polynomial basis functions. Involvement of radial basis functions overcomes possible singularity Furthermore, the interpolation function passes through all scattered points in an influence domain and thus shape functions are of delta function property. This makes the implementation of essential boundary conditions much easier than the meshfree methods based on the moving least-squares approximation. This study aims to investigate a stress analysis of structural element between a meshfree method and the finite element method. Examples on cantilever type plate, hollow cylinder and stress concentration problems show that the accuracy and convergence rate of the meshfree methods are high.