• Structural Engineering and Mechanics
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• v.91 no.6
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• pp.539-549
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• 2024

In this paper, thermal buckling analysis was conducted using trigonometric shear deformation theory, which employs only four unknowns instead of five. This present theory is variationally consistent, and accounts for a trigonometric variation of the transverse shear strains across the thickness and satisfies the zero traction boundary conditions on the top and bottom surfaces of the plate without using shear correction factors. The grading is provided along the thickness of the plate as per power law volume fraction variation of metal-matrix ceramic reinforced composite. The non-linear governing equation problem was solved for simply supported boundary conditions. Three types of thermal loads are assumed in this work: uniform, linear and non-linear distribution through-the-thickness. It is well known that material properties change with temperature variations and so the analysis was performed for both the cases: temperature-dependent (TD) and temperature-independent (TID) material properties. The impact on thermal buckling for both linear and non-linear temperature variation was considered. The results were validated for the TID case with other theories and were found to be in good agreement. Furthermore, a comprehensive analysis was performed to study the impact of grading indices and geometrical parameters, such as aspect ratio (a/b) and side-to-thickness ratio (a/h), on the thermal buckling of the FG plate.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.631-634
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• 2004

In this paper, the Bayesian recurrent neural network (BRNN) is proposed to predict time series data. Among the various traditional prediction methodologies, a neural network method is considered to be more effective in case of non-linear and non-stationary time series data. A neural network predictor requests proper learning strategy to adjust the network weights, and one need to prepare for non-linear and non-stationary evolution of network weights. The Bayesian neural network in this paper estimates not the single set of weights but the probability distributions of weights. In other words, we sets the weight vector as a state vector of state space method, and estimates its probability distributions in accordance with the Bayesian inference. This approach makes it possible to obtain more exact estimation of the weights. Moreover, in the aspect of network architecture, it is known that the recurrent feedback structure is superior to the feedforward structure for the problem of time series prediction. Therefore, the recurrent network with Bayesian inference, what we call BRNN, is expected to show higher performance than the normal neural network. To verify the performance of the proposed method, the time series data are numerically generated and a neural network predictor is applied on it. As a result, BRNN is proved to show better prediction result than common feedforward Bayesian neural network.

• Journal of Information Processing Systems
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• v.12 no.4
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• pp.591-611
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• 2016

High dimensional space is the biggest problem when classification process is carried out, because it takes longer time for computation, so that the costs involved are also expensive. In this research, the facial space generated from homogeneous and non-homogeneous polynomial was proposed to extract the facial image features. The homogeneous and non-homogeneous polynomial-based eigenspaces are the second opinion of the feature extraction of an appearance method to solve non-linear features. The kernel trick has been used to complete the matrix computation on the homogeneous and non-homogeneous polynomial. The weight and projection of the new feature space of the proposed method have been evaluated by using the three face image databases, i.e., the YALE, the ORL, and the UoB. The experimental results have produced the highest recognition rate 94.44%, 97.5%, and 94% for the YALE, ORL, and UoB, respectively. The results explain that the proposed method has produced the higher recognition than the other methods, such as the Eigenface, Fisherface, Laplacianfaces, and O-Laplacianfaces.

• Structural Engineering and Mechanics
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• v.12 no.6
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• pp.657-668
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• 2001

Fiber reinforced cementitious composites are nowadays widely applied in civil engineering. The postcracking performance of this material depends on the interaction between a steel fiber, which is obliquely across a crack, and its surrounding matrix. While the partly debonded steel fiber is subjected to pulling out from the matrix and simultaneously subjected to transverse force, it may be modelled as a Bernoulli-Euler beam partly supported on an elastic foundation with non-linearly varying modulus. The fiber bridging the crack may be cut into two parts to simplify the problem (Leung and Li 1992). To obtain the transverse displacement at the cut end of the fiber (Fig. 1), it is convenient to directly solve the corresponding differential equation. At the first glance, it is a classical beam on foundation problem. However, the differential equation is not analytically solvable due to the non-linear distribution of the foundation stiffness. Moreover, since the second order deformation effect is included, the boundary conditions become complex and hence conventional numerical tools such as the spline or difference methods may not be sufficient. In this study, moment equilibrium is the basis for formulation of the fundamental differential equation for the beam (Timoshenko 1956). For the cantilever part of the beam, direct integration is performed. For the non-linearly supported part, a transformation is carried out to reduce the higher order differential equation into one order simultaneous equations. The Runge-Kutta technique is employed for the solution within the boundary domain. Finally, multi-dimensional optimization approaches are carefully tested and applied to find the boundary values that are of interest. The numerical solution procedure is demonstrated to be stable and convergent.

• Journal of Korean Institute of Industrial Engineers
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• v.15 no.2
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• pp.93-101
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• 1989

The classic machine repair problem is extended to the case where a number of different machines are arranged in the sequence of operation. The steady-state availability of the system with a series of operating machines is maximized under some constraints such as total cost, available space. In order to find the optimal numbers of spare units and repair channels for each operating machine, the problem is formulated as non-linear integer programming(NLIP) problem and an efficient algorithm, which is a natural extension of the new Lawler-Bell algorithm of Sasaki et al., is used to solve the NLIP problem. A numerical example is given to illustrate the algorithm.

• IE interfaces
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• v.11 no.3
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• pp.65-75
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• 1998

This research considers the problem of scheduling jobs on parallel machines with non-common due dates and additional resource constraints. The objective is to minimize the total absolute deviation of job completion times about the due dates. Job processing times are assumed to be the same. This problem is motivated by restrictions that occur in the handling and processing of jobs in certain phases of semiconductor manufacturing and other production systems. We examine two problems. For the first of these, the number of different types of additional: resources and resource requirements per job are arbitrary. The problem is formulated as a zero-one integer linear programming and the Lagrangian relaxation approach is used. For the second case, there exists one single type of additional resource and the resource requirements per job are zero or one. We show how to formulate the problem as an assignment problem.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.315-318
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• 1995

The optimum design problem of a coat-hanger die is solved by the inverse formulation. The flow in the die is analyzed using three-dimensional model. The new model for the manifold geometry is developed for the inverse formulation. The inverse problem for the optimum die geometry is formed as the optimization problem whose objective function is the linear combination of the square sum of pressure gradient deviation at die exit and the penalty function relating to the measure of non-smoothness of solution. From the several iterative solutions of the optimization problem, the optimum solution can be obtained automatically while producing the uniform flow rate distribution at die exit.

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.127-130
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• 2003

In this paper, a hypothesis in order that the impulse response of a stable linear system does not change sign is suggested. For fixed zeros of the systems, the problem of synthesizing such a system is reduced to the problem of finding a proper denominator polynomial so that the step response of the overall system will not overshoot. The hypothesis is associated with the generalized time constant by Kim[5]. Under the hypothesis, we propose several methods that allow to compose a continuous time LTI systems achieving non-negative impulse response.

• Phonetics and Speech Sciences
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• v.8 no.3
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• pp.31-38
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• 2016

Speech inversion (acoustic-to-articulatory mapping) is not a trivial problem, despite the importance, due to the highly non-linear and non-unique nature. This study aimed to investigate the performance of Deep Neural Network (DNN) compared to that of traditional Artificial Neural Network (ANN) to address the problem. The Wisconsin X-ray Microbeam Database was employed and the acoustic signal and articulatory pellet information were the input and output in the models. Results showed that the performance of ANN deteriorated as the number of hidden layers increased. In contrast, DNN showed lower and more stable RMS even up to 10 deep hidden layers, suggesting that DNN is capable of learning acoustic-articulatory inversion mapping more efficiently than ANN.

• Transactions of the Korean Society of Mechanical Engineers
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• v.13 no.1
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• pp.161-169
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• 1989

A stability problem on wave instability of natural convection flow along isothermal vertical cylindrical surfaces has been formulated, accounting for the non-parallelism of the basic flow and thermal fields. Then the problem is solved numerically under the simplifying assumption of the parallelism of the basic flow quantities. It is shown that the flow corresponding to the same characteristic boundary layer thickness becomes more stable as the value of the curvature parameter increases. The stability characteristics for Pr=0.7 appear to be more sensitive to the curvature parameter than those for Pr=7.