• Proceedings of the KSME Conference
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• 2001.06a
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• pp.680-685
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• 2001

In this paper, RKPM is extended for solving moderately thick and thin structures. General Timoshenko beam and Mindlin plate theory are used far formulation. Shear locking is the main difficulty in analysis of these kinds of structures. Shear relaxation factor, which is formulated using the difference between bending and shear strain energy, is introduced to overcome shear locking. Analysis results obtained reveal that RKPM using introduced method is free of locking and very effectively applicable to deeply as well as shallowly beams and plates.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.12 no.5
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• pp.73-79
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• 2003

In this paper, RKPM is extended for solving moderately thick and thin beams. General Timoshenko beam theory is used for formulation. Shear locking is the main difficulty in analysis of these kinds of structures. Shear relaxation factor, which is formulated using the difference between bending and shear strain energy, and corrected shear rigidity are introduced to overcome shear locking. Analysis results obtained reveal that RKPM using introduced methods is free of locking and very effectively applicable to deep beams as well as shallow beams.

• Journal of Biosystems Engineering
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• v.17 no.1
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• pp.79-90
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• 1992

Viscoelastic characteristics of agricultural products may be determined through three basic tests ; stress relaxation, creep, and dynamic test. Considering the changeability of living materials, dynamic test in which information is derived in a relatively short time appears to be highly desirable, in which either cyclic stress or cyclic strain is imposed and the remaining quantity (strain or stress) is measured. The periodically varying stress will also result in periodically varying strain which in a viscoelastic material should theoretically be out of phase with the stress, because part of the energy subjected to sample is stored in the material as potential energy and part is dissipated as heat. This behavior results in a complex frequency-dependent compliance denoted by J($i{\omega}$). The complex compliance and therefore the storage compliance, the loss compliance, the phase angle, and percent energy loss for the sample should be obtainable with a given static viscoelastic property of the material under static load. The complex compliance of the rough rice kernel were computed from the Burger's model describing creep behavior of the material which were obtained in the previous study. Also, the effects of cyclic load and moisture content of grain on the dynamic viscoelastic behavior of the samples were analyized. The results obtained from this study were summarized as follows ; 1. The storage compliance of the rough rice kernel slightly decreased with the frequency applied but at above the frequency of 0.1 Hz it was nearly constant with the frequency, and the loss compliance of the sample very rapidly decreased with increase in the frequency on those frequency ranges. 2. It was shown that the storage compliance and the loss compliance of the sample increased with increase in grain moisture content. Effect of grain moisture content on the storage compliance of the sample was highly significant than effect of the frequency applied, but effect of the frequency on the loss compliance of the sample was more significant than effect of grain moisture content. 3. In low moisture content, the percent energy loss of Japonica-type rough rice was much higher than that of Indica-type rough rice, but, in high moisture content, vice versa.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.673-676
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• 2003

This paper deals with the problem of moving object detection and location in computer vision. We describe a new object-dependent motion analysis method for tracking target in an image sequence taken from a moving platform. We tackle these tasks with three steps. First, we make an active contour model of a target in order to build some of low-energy points, which are called kernels. Then we detect interest points in two windows called tracking windows around a kernel respectively. At the third step, we decide the correspondence of those detected interest points between tracking windows by the probabilistic relaxation method In this algorithm, the detecting process is iterative and begins with the detection of all potential correspondence pair in consecutive image. Each pair of corresponding points is then iteratively recomputed to get a globally optimum set of pairwise correspondences.