• Korean Journal of Applied Biomechanics
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• v.16 no.4
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• pp.105-113
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• 2006

The purpose of this study was to analyze the joint torque of triple segmental system in golf driver swing. For this purpose, joint torque were calculated. In order to determine the load on the lumbar region, a triple segmental system was set for wrist, left shoulder and lumbar, torque working on the lumbar region were estimated. For this study, a total of 7 professional golfers were sampled, and then, their driver swings were recorded with two high-speed digital video cameras (180 frames/sec.) to be synthesized into 3-dimensional images and coordinated. Then, Eular's equation was used to produce some kinematic data, which were used to calculate joint torque with Newton's function. All data were calculated using LabVIEW 6.1 graphic program. The results of this study can be summarized as follows; It was found that the joint torque was generated in the direction opposite the target on wrist and shoulder during down swing, while in the direction towards the target on the lumbar region. During impact and release, the torque on the wrist joint was converted from the direction opposite the target to the direction towards the target, while the torque on the lumbar region was generated vice versa. The joints on the club-arm-shoulder were generated in the opposite direction at the beginning of down swing when the torque on the thorax-pelvis began to be generated, and then, the torque on the thorax-pelvis began to lower, while that on the club-arm-shoulder began to increase. Thus, a rapid decrease of the torque on the lumbar region linked to the low trunk acted to increase moment and joint torque on the arm-club region.

• Korean Journal of Applied Biomechanics
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• v.15 no.2
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• pp.57-67
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• 2005

The purpose of this study was to analyze the kinetic factors of the golf driver swing using the Inverse Dynamics function. For this purpose, joint force were calculated. In order to test the possibility of Inverse Dynamics function(motion-dependent interaction), a triple segmental system was set for wrist, left shoulder and lumbar and joint force working on the anatomical joint region was estimated. For this study, 7 professional golfers were sampled, and then, their driver swings were recorded with two high-speed digital video cameras (180 frames/sec.) to be synthesized into 3-dimensional images and coordinated. Then, Eular's equation was used to produce some kinematic data, which were used to calculate joint force and torque with Newton's function. All data were calculated using LabVIEW 6.1 graphic program. The results of this study can be summarized as follows; It was found that the joint force was generated on wrist, shoulder and lumbar joints in the direction of the target, and that the joint force was stronger in the direction of target immediately before impact. The joint force was generated towards the target to activate the nodes, and then, it was generated in the reverse direction to increase the speed during impact.

• Journal of Multimedia Information System
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• v.8 no.1
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• pp.31-34
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• 2021

The classical HMM is defined by a parameter triple �� = (��, A, B), where each parameter represents a collection of probability distributions: initial state, state transition and output distributions in order. This paper proposes a new stationary parameter e = (e1, e2, …, eN) where N is the number of states and et = P(|xt = i, y) for describing how an input pattern y ends in state xt = i at time t followed by nothing. It is often said that all is well that ends well. We argue here that all should end well. The paper sets the framework for the theory and presents an efficient inference and training algorithms based on dynamic programming and expectation-maximization. The proposed model is applicable to analyzing any sequential data with two or more finite segmental patterns are concatenated, each forming a context to its neighbors. Experiments on online Hangul handwriting characters have proven the effect of the proposed augmentation in terms of highly intuitive segmentation as well as recognition performance and 13.2% error rate reduction.