• Journal of Biomedical Engineering Research
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• v.22 no.3
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• pp.223-229
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• 2001

In this paper, we have compared the direct fan-beam reconstruction method with the rebinning method in terms of computation time and spatial resolution using computer simulation. As a result, the direct fan-beam method is superior to the rebinning method in the spatial resolution though the former needs longer computation time. However, if we adopt the quarter-detector-offset technique to improve the spatial resolution, the rebinning method outperforms the direct fan-beam method. The computation times have been evaluated using the fast algorithms optimized to reduce the number of interpolation calculations at the back-projection, and the spatial resolutions have been compared using the computer generated phantoms.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2001.05a
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• pp.94-97
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• 2001

An inverse finite element approach is employed for more capability to design the optimum blank shape from the desired final shape with small amount of computation time and effort. For multi-stage deep-drawing processes, numerical analysis is extremely difficult to carry out due to its complexities and convergence problem as well as tremendous computation time. In this paper, multi-stage finite element inverse analysis is applied to multi-stage rectangular cup drawing processes to calculate intermediate blank shapes and strain distributions in each stages. Deformation history of the previous stage is considered in the computation. Finite element patches are used to describe arbitrary intermediate sliding constraint surfaces.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.7
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• pp.579-583
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• 2001

In the area of data clustering in high dimensional space, one of the difficulties is the time-consuming process for computing vector similarities. It becomes worse in the case of the agglomerative algorithm with the group-average link and mean centroid method, because the cluster similarity must be recomputed whenever the cluster center moves after the merging step. As a solution of this problem, we present an incremental method of similarity computation, which substitutes the scalar calculation for the time-consuming calculation of vector similarity with several measures such as the squared distance, inner product, cosine, and minimum variance. Experimental results show that it makes clustering speed significantly fast for very high dimensional data.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.1017-1020
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• 1999

Recently, a new learning algorithm for multilayer neural networks has been proposed 〔1〕. In the new learning algorithm, each output neuron is considered as a function of weights and the weights are adjusted so that the output neurons produce desired outputs. And the adjustment is accomplished by taking gradients. However, the gradient computation was performed numerically, resulting in a long computation time. In this paper, we derive the all necessary equations so that the gradient computation is performed analytically, resulting in a much faster learning time comparable to the backpropagation. Since the weight adjustments are accomplished by summing the gradients of the output neurons, we will call the new learning algorithm “multi-gradient.” Experiments show that the multi-gradient consistently outperforms the backpropagation.

• Journal of Mechanical Science and Technology
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• v.14 no.7
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• pp.789-795
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• 2000

Free-surface flows with an arbitrary deformation, induced by a submerged hydrofoil, are simulated numerically, considering two-fluid flows of both water and air. The computation is performed by a finite volume method using unstructured meshes and an interface capturing scheme to determine the shape of the free surface. The method uses control volumes with an arbitrary number of faces and allows cell wise local mesh refinement. The integration in space is of second order, based on midpoint rule integration and linear interpolation. The method is fully implicit and uses quadratic interpolation in time through three time levels. The linear equations are solved by conjugate gradient type solvers, and the non-linearity of equations is accounted for through Picard iterations. The solution method is of pressure-correction type and solves sequentially the linearized momentum equations, the continuity equation, the conservation equation of one species, and the equations for two turbulence quantities. Finally, a comparison is quantitatively made at the same speed between the computation and experiment in which the grid sensitivity is numerically checked.

• Transactions of Materials Processing
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• v.10 no.5
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• pp.389-395
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• 2001

An inverse finite element approach is employed for more capability to design the optimum blank shape from the desired final shape with small amount of computation time and effort. For multi-stage deep-drawing processes with large aspect ratio, numerical analysis is extremely difficult to carry out due to its complexities and convergence problem. as well as tremendous computation time. In this paper, multi-stage finite element inverse analysis is applied to multi-stage rectangular cup drawing processes to calculate intermediate blank shapes and strain distributions in each stages. Deformation history of the previous stage is considered in the computation. Finite element patches are used to describe arbitrary intermediate sliding constraint surfaces.

• 한국전산유체공학회:학술대회논문집
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• 2003.08a
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• pp.43-48
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• 2003

When the Eulerian-Lagrangian method is used to analyze the particle laden two-phase flow, a large number of particles should be used to obtain statistically meaningful solutions. Then it takes too much time to track the particles and to average the particle properties in the numerical analysis of two-phase flow. The purpose of this paper is to reduce the computation time by means of a set of particle gird separate to the flow grid. Particle motion equation here is the simplified B-B-O equation, which is integrated to get the particle trajectories. Particle turbulent dispersion, wall collision, and wall roughness effects are considered but the two-way coupling effects between gas and particles are neglected. Particle laden 2-D channel flow is solved and it is shown that the computational efficiency is indeed improved by using the current method

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.149-154
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• 2022

In recent years, the application of deep learning method to computer vision has shown to achieve great performances. Thus, many research projects have also applied deep learning technology to railroad defect detection. In this paper, we have reviewed the researches that applied computer vision based deep learning method to railroad defect detection and inspection, and have discussed the current trend and the direction of those researches. Many research projects were targeted to operate automatically without visual inspection of human and to work in real-time. Therefore, methods to speed up the computation were also investigated. The reduction of the number of learning parameters was considered important to improve computation efficiency. In addition to computation speed issue, the problem of annotation was also discussed in some research projects. To alleviate the problem of time consuming annotation, some kinds of automatic segmentation of the railroad defect or self-supervised methods have been suggested.

• Journal of Information Processing Systems
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• v.20 no.2
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• pp.226-238
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• 2024

Recently, multi-access edge computing (MEC) has emerged as a promising technology to alleviate the computing burden of vehicular terminals and efficiently facilitate vehicular applications. The vehicle can improve the quality of experience of applications by offloading their tasks to MEC servers. However, channel conditions are time-varying due to channel interference among vehicles, and path loss is time-varying due to the mobility of vehicles. The task arrival of vehicles is also stochastic. Therefore, it is difficult to determine an optimal offloading with resource allocation decision in the dynamic MEC system because offloading is affected by wireless data transmission. In this paper, we study computation offloading with resource allocation in the dynamic MEC system. The objective is to minimize power consumption and maximize throughput while meeting the delay constraints of tasks. Therefore, it allocates resources for local execution and transmission power for offloading. We define the problem as a Markov decision process, and propose an offloading method using deep reinforcement learning named deep deterministic policy gradient. Simulation shows that, compared with existing methods, the proposed method outperforms in terms of throughput and satisfaction of delay constraints.

• The KIPS Transactions:PartA
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• v.18A no.4
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• pp.143-150
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• 2011

This paper presents an EDF based scheduling algorithm for scheduling imprecise computation model where each task consists of mandatory part and optional part. Imprecise computation is useful to manage overload condition. In overload situation, some optional parts should be removed. The proposed DOP algorithm removes optional parts of earlier deadline tasks to enhance flexibly for newly arriving tasks. A simulation result shows that DOP has better performance than other algorithms.