21,653 research outputs found
Orbit Characterization, Stabilization and Composition on 3D Underactuated Bipedal Walking via Hybrid Passive Linear Inverted Pendulum Model
A Hybrid passive Linear Inverted Pendulum (H-LIP) model is proposed for characterizing, stabilizing and composing periodic orbits for 3D underactuated bipedal walking. Specifically, Period-l (P1) and Period -2 (P2) orbits are geometrically characterized in the state space of the H-LIP. Stepping controllers are designed for global stabilization of the orbits. Valid ranges of the gains and their optimality are derived. The optimal stepping controller is used to create and stabilize the walking of bipedal robots. An actuated Spring-loaded Inverted Pendulum (aSLIP) model and the underactuated robot Cassie are used for illustration. Both the aSLIP walking with PI or P2 orbits and the Cassie walking with all 3D compositions of the PI and P2 orbits can be smoothly generated and stabilized from a stepping-in-place motion. This approach provides a perspective and a methodology towards continuous gait generation and stabilization for 3D underactuated walking robots
"Virus hunting" using radial distance weighted discrimination
Motivated by the challenge of using DNA-seq data to identify viruses in human
blood samples, we propose a novel classification algorithm called "Radial
Distance Weighted Discrimination" (or Radial DWD). This classifier is designed
for binary classification, assuming one class is surrounded by the other class
in very diverse radial directions, which is seen to be typical for our virus
detection data. This separation of the 2 classes in multiple radial directions
naturally motivates the development of Radial DWD. While classical machine
learning methods such as the Support Vector Machine and linear Distance
Weighted Discrimination can sometimes give reasonable answers for a given data
set, their generalizability is severely compromised because of the linear
separating boundary. Radial DWD addresses this challenge by using a more
appropriate (in this particular case) spherical separating boundary.
Simulations show that for appropriate radial contexts, this gives much better
generalizability than linear methods, and also much better than conventional
kernel based (nonlinear) Support Vector Machines, because the latter methods
essentially use much of the information in the data for determining the shape
of the separating boundary. The effectiveness of Radial DWD is demonstrated for
real virus detection.Comment: Published at http://dx.doi.org/10.1214/15-AOAS869 in the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Imaging crystal orientations in multicrystalline silicon wafers via photoluminescence
We present a method for monitoring crystal orientations in chemically polished and unpassivated multicrystalline silicon wafers based on band-to-band photoluminescence imaging. The photoluminescence intensity from such wafers is dominated by surface recombination, which is crystal orientation dependent. We demonstrate that a strong correlation exists between the surface energy of different grain orientations, which are modelled based on first principles, and their corresponding photoluminescence intensity. This method may be useful in monitoring mixes of crystal orientations in multicrystalline or so-called “cast monocrystalline” wafers.H. C. Sio acknowledges scholarship support from
BT Imaging and the Australian Solar Institute, and the
Centre for Advanced Microscopy at ANU for SEM access.
This work has been supported by the Australian Research
Council
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