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Full-Text Articles in Engineering
Mixture Model Segmentation For Gait Recognition, Matthew Field, David A. Stirling, Fazel Naghdy, Zengxi Pan
Mixture Model Segmentation For Gait Recognition, Matthew Field, David A. Stirling, Fazel Naghdy, Zengxi Pan
Faculty of Engineering - Papers (Archive)
Modeling of human motion through a discrete sequence of motion primitives, retaining elements of skillful or unique motion of an individual is addressed. Using wireless inertial motion sensors, a skeletal model of the fluid human gait was gathered. The posture of the human model is described by sets of Euler angles for each sample. An intrinsic classification algorithm known as minimum message length encoding (MML) is deployed to segment the stream of data and subsequently formulate certain Gaussian mixture models (GMM) that contain a plausible range of motion primitives. The removal of certain less seemingly important modes has been shown …
An Effective Permeability Model To Predict Field-Dependent Modulus Of Magnetorheological Elastomers, X. Z. Zhang, W.H. Liu, Kosasih, X.C. Zhang, X.L Gong, Q.P. Zhang
An Effective Permeability Model To Predict Field-Dependent Modulus Of Magnetorheological Elastomers, X. Z. Zhang, W.H. Liu, Kosasih, X.C. Zhang, X.L Gong, Q.P. Zhang
Faculty of Engineering - Papers (Archive)
Magnetorheological elastomers (MREs) are composites where magnetic particles are suspended in a non-magnetic solid or a gel-like matrix. MREs are shown to have a controllable, field-dependent shear modulus. Most of conventional MREs models are based on magnetic dipole interactions between two adjacent particles of the chain. These models can predict the field-dependent properties of MREs with simple chain-like structures. In this paper, an effective permeability model is proposed to predict the field-induced modulus of MREs. Based on the effective permeability rule and taking into account the particle’s saturation, the model is proposed to predict the mechanical performances of MREs with …