Pattern recognition from light delivery vehicle crash characteristics

2021 ◽  
pp. 1-19
Author(s):  
Subasish Das ◽  
Anandi Dutta ◽  
M. Ashifur Rahman
Keyword(s):  
Pattern Recognition ◽  
Delivery Vehicle ◽  
Light Delivery ◽  
Vehicle Crash ◽  
Crash Characteristics

scholarly journals Modelling and analysis of vehicle crash system integrated with different VDCS under high speed impacts

2012 ◽  
Vol 2 (4) ◽  
Author(s):  
Mustafa Elkady ◽  
Ahmed Elmarakbi
Keyword(s):  
Control Systems ◽  
Vehicle Dynamics ◽  
High Speed ◽  
Dynamic Responses ◽  
High Speeds ◽  
Vehicle Crash ◽  
Proposed Model ◽  
High Speed Collision ◽  
Crash Characteristics ◽  
Vehicle Dynamics Control

AbstractThe behaviour of a vehicle at high-speed crashes is enhanced by using active vehicle dynamics control systems. A 6-Degree-of-Freedom (6-DOF) mathematical model is developed to carry out this study. In this model, vehicle dynamics is studied together with vehicle crash structural dynamics. Validation of the vehicle crash structure of the proposed model is achieved to ensure that the modelling of the crumble zone and the dynamic responses are reliable. Five different speeds are selected to investigate the robustness of control system and its effect on the vehicle crash characteristics at low and high speeds with full and offset collision scenarios. A great improvement of vehicle pitch and yaw angels and accelerations at high speed collision are obtained from this analysis.

Motor vehicle crash characteristics and medical outcomes among older drivers in Utah, 1992-1995

2000 ◽  
Vol 35 (6) ◽  
pp. 585-591 ◽  
Author(s):  
Lawrence J. Cook ◽  
Stacey Knight ◽  
Lenora M. Olson ◽  
Patricia J. Nechodom ◽  
J.Michael Dean
Keyword(s):  
Motor Vehicle ◽  
Motor Vehicle Crash ◽  
Older Drivers ◽  
Medical Outcomes ◽  
Vehicle Crash ◽  
Crash Characteristics

Motor vehicle crash characteristics and medical outcomes among older drivers in Utah, 1992-1995

2000 ◽  
Vol 35 (6) ◽  
pp. 0585-0591
Author(s):  
Patrick E. McKinney ◽  
Lawrence J. Cook ◽  
Stacey Knight ◽  
Lenora M. Olson ◽  
Patricia J. Nechodom
Keyword(s):  
Motor Vehicle ◽  
Motor Vehicle Crash ◽  
Older Drivers ◽  
Medical Outcomes ◽  
Vehicle Crash ◽  
Crash Characteristics

The accuracy of medical records and police reports in determining motor vehicle crash characteristics

1998 ◽  
Vol 2 (1) ◽  
pp. 23-28 ◽  
Author(s):  
Robert J. Grant ◽  
Mary Ann Gregor ◽  
Ronald F. Maio ◽  
Sham S. Huang
Keyword(s):  
Medical Records ◽  
Motor Vehicle ◽  
Motor Vehicle Crash ◽  
Vehicle Crash ◽  
Police Reports ◽  
Crash Characteristics

Software pattern recognition applied to nanodiffraction patterns from a STEM instrument

1986 ◽  
Vol 44 ◽  
pp. 694-695
Author(s):  
G.Y. Fan ◽  
J.M. Cowley
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Computer Software ◽  
Processing System ◽  
Light Level ◽  
Host Computer ◽  
Low Light ◽  
Image Processing System ◽  
Recent Developments ◽  
On Line

In recent developments, the ASU HB5 has been modified so that the timing, positioning, and scanning of the finely focused electron probe can be entirely controlled by a host computer. This made the asynchronized handshake possible between the HB5 STEM and the image processing system which consists of host computer (PDP 11/34), DeAnza image processor (IP 5000) which is interfaced with a low-light level TV camera, array processor (AP 400) and various peripheral devices. This greatly facilitates the pattern recognition technique initiated by Monosmith and Cowley. Software called NANHB5 is under development which, instead of employing a set of photo-diodes to detect strong spots on a TV screen, uses various software techniques including on-line fast Fourier transform (FFT) to recognize patterns of greater complexity, taking advantage of the sophistication of our image processing system and the flexibility of computer software.

Bayesian removal of noise for increased sensitivity in vector pattern recognition lattice imaging of interfaces

1993 ◽  
Vol 51 ◽  
pp. 994-995
Author(s):  
L. Fei ◽  
P. Fraundorf
Keyword(s):  
Pattern Recognition ◽  
Perfect Crystal ◽  
Unit Cell ◽  
Ion Milling ◽  
Beam Radiation ◽  
Major Interest ◽  
Unit Cells ◽  
Mapping Process ◽  
Increased Sensitivity ◽  
Electron Microscopes

Interface structure is of major interest in microscopy. With high resolution transmission electron microscopes (TEMs) and scanning probe microscopes, it is possible to reveal structure of interfaces in unit cells, in some cases with atomic resolution. A. Ourmazd et al. proposed quantifying such observations by using vector pattern recognition to map chemical composition changes across the interface in TEM images with unit cell resolution. The sensitivity of the mapping process, however, is limited by the repeatability of unit cell images of perfect crystal, and hence by the amount of delocalized noise, e.g. due to ion milling or beam radiation damage. Bayesian removal of noise, based on statistical inference, can be used to reduce the amount of non-periodic noise in images after acquisition. The basic principle of Bayesian phase-model background subtraction, according to our previous study, is that the optimum (rms error minimizing strategy) Fourier phases of the noise can be obtained provided the amplitudes of the noise is given, while the noise amplitude can often be estimated from the image itself.

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