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RISS 인기검색어
- 검색키워드
- 저자 : Seiyed Hamid Mousavi (검색결과 2 건)
- 무료
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Seiyed Hamid Mousavi 대한기계학회 2020 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.34 No.9
Semi-active systems with variable stiffness and damping have demonstrated excellent performance. The aim of this study is to investigate the new configuration of a semiactive single-stage nonlinear vibration isolation system with a Magneto-Rheological (MR) damper to reduce the magnitude of force transmissibility over the both resonant and nonresonant regions. The magnitude of force transmissibility is widely used to performance measurement for the isolation system. In current study, to achieve this reduction, two horizontal springs and one MR damper were added to the isolator. Theoretical analysis reveals that the nonlinear system with MR damper can produce ideal vibration isolation. However, due to the nonlinear characteristics behavior of the MR damper, conventional control algorithms to reach the desired are cumbersome. To address this issue, an artificial intelligent strategy using wavelet network and fuzzy logic controller is considered to be constructed to copy the inverse dynamics of the MR damper and the nonlinear isolator. Accordingly, simulation results demonstrated that the intelligent algorithm has acceptable performance.
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Seiyed Hamid Mousavi,Hassan Sayyaadi,Navid Arjmand 대한기계학회 2020 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.34 No.8
Spinal posture including thorax/pelvis orientations as well as loads on the intervertebral discs are crucial parameters in biomechanical models and ergonomics to evaluate the risk of low back injury. In vivo measurement of spinal posture toward estimation of spine loads requires the common motion capture techniques and laboratory instruments that are costly and time-consuming. Hence, a closed loop algorithm including an artificial neural network (ANN) and fuzzy logic is proposed here to predict the L5-S1 segment loads and thorax/pelvis orientations in various 3D reaching activities. Two parts namely a fuzzy logic strategy and an ANN from this algorithm; the former, developed based on the measured postures of 20 individuals, is to determine 3D orientations of the thorax/pelvis during the various activities while the latter, developed based on the predicted L5-S1 loads by a detailed musculoskeletal model of the spine, is to estimate compression/shear forces at the L5-S1 disc. The fuzzy logic rules are extracted based on Sugeno inference engine and the ANN is trained by LevenbergMarquardt algorithm. To evaluate the performance of the proposed strategy, the comparison between the predicted values, the target values and the presented values in the literature are reviewed. The comparison demonstrated that the proposed algorithm had a promising performance. The maximum relative error for all predictions was ~19 % and with respect to the target values while this error for the literature’s values was ~37 %. Also, the average improvement of the proposed strategy was ~17 % with respect to the presented strategy in the literature.
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