Design and Testing of an Underwater Microscopewith Variable Objective Lens for the Study of Benthic Communities

Monitoring the ecology and physiology of corals, sediments, planktons, and microplastic at a suitable spatial resolution is of great importance in oceanic scien...     

A two-stage-training support vector machine approach to predicting unintentional vehicle lane departure

Advanced driver assistance systems, such as unintentional lane departure warning systems, have recently drawn much attention and efforts. In this study, we explored utilizing the nonlinear binary support vector machine (SVM) technique to predict unintentional lane departure, which is innovative, as the SVM methodology has not previously been attempted for this purpose in the literature. Furthermore, we developed a two-stage training scheme to improve SVM's prediction performance in terms of minimization of the number of false positive prediction errors. Experiment data generated by VIRTTEX, a hydraulically powered, 6-degrees-of-freedom moving base driving simulator at Ford Motor Company, were used. All the vehicle variables were sampled at 50 Hz and there were 16 drowsy drivers (about 3 hours of driving per subject) and six control drivers (approximately 20 minutes f driving each). In total, 3,508 unintentional lane departures occurred for the drowsy drivers and 23 for the control drivers. Our study involving these 22 drivers with a total of more than 7.5 million prediction decisions demonstrates that (a) excellent SVM prediction performance, measured by numbers of false positives (i.e., falsely predicted lane departures) and false negatives (i.e., lane departures failed to be predicted), was achieved when the prediction horizon was 0.6 seconds or less, (b) lateral position and lateral velocity worked the best as SVM input variables among the nine variable sets that we explored, and (c) the radial basis function performed the best as the SVM kernel function.     

CFD simulation of fixed and variable pitch vertical axis tidal turbine

In this paper, hydrodynamic analysis of vertical axis tidal turbine (both fixed pitch & variable pitch) is numerically analyzed. Two-dimensional numerical modeling & simulation of the unsteady flow through the blades of the turbine is performed using ANSYS CFX, hereafter CFX, which is based on a Reynolds-Averaged Navier-Stokes (RANS) model. A transient simulation is done for fixed pitch and variable pitch vertical axis tidal turbine using a Shear Stress Transport turbulence (SST) scheme. Main hydrodynamic parameters like torque T, combined moment C _M , coefficients of performance C _P and coefficient of torque C _T , etc. are investigated. The modeling and meshing of turbine rotor is performed in ICEM-CFD. Moreover, the difference in meshing schemes between fixed pitch and variable pitch is also mentioned. Mesh motion option is employed for variable pitch turbine. This article is one part of the ongoing research on turbine design and developments. The numerical simulation results are validated with well reputed analytical results performed by Edinburgh Design Ltd. The article concludes with a parametric study of turbine performance, comparison between fixed and variable pitch operation for a four-bladed turbine. It is found that for variable pitch we get maximum C _P and peak power at smaller revolution per minute N and tip sped ratio λ.