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RISS 인기검색어
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- 저자 : M. Khazaei (검색결과 2 건)
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A linear mixed model for analyzing longitudinal skew-normal responses with random dropout
M. Ganjali,T. Baghfalaki,M. Khazaei 한국통계학회 2013 Journal of the Korean Statistical Society Vol.42 No.2
In this paper, a linear mixed effects model is used to fit skewed longitudinal data in the presence of dropout. Two distributional assumptions are considered to produce background for heavy tailed models. One is the linear mixed model with skew-normal random effects and normal errors and the other one is the linear mixed model with skewnormal errors and normal random effects. An ECM algorithm is developed to obtain the parameter estimates. Also an empirical Bayes approach is used for estimating random effects. A simulation study is implemented to investigate the performance of the presented algorithm. Results of an application are also reported where standard errors of estimates are calculated using the Bootstrap approach.
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M. H. Korayem,S. Nosoudi,S. Khazaei Far,A. K. Hoshiar 대한기계학회 2018 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.32 No.2
Nanomanipulation plays a significant role in nanotechnology research. The process of Atomic force microscopy (AFM) based manipulation is complex and time-consuming, which can be improved using a path-planning algorithm to reduce its manipulation time and time complexity. Due to real-time monitoring limitation in AFM based manipulations, Virtual reality (VR) environments have been developed. One such developed VR environment, however, is limited to point to point manipulation and lacks any path information. Therefore, we propose using a hybrid Improved particle swarm optimization (IPSO), a cellular automata-based algorithm for path planning during manipulation of micro/nanoparticles. In this technique, the critical time-force diagram, representing the AFM based manipulation dynamic is considered as a constraint, and is subsequently used to find the best path. The main path is divided into several segments and is optimized. Used as an algorithm for manipulation, this technique provides a more precise path in the AFM-based manipulation. Finally, the ability of this technique was compared to the other path planner algorithms based on its efficiency in reducing time-complexity parameters.
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