scholarly journals Riders' Perception of Symmetrical Pressure on the Ischial Tuberosities and Rein Contact Whilst Sitting on a Static Object

2016 ◽  
Vol 48 ◽  
pp. 9-9 ◽  
Keyword(s):  
Static Object ◽  
Ischial Tuberosities

Riders’ perception of symmetrical pressure on their ischial tuberosities and rein contact tension whilst sitting on a static object

2017 ◽  
Vol 13 (1) ◽  
pp. 7-12 ◽  
Author(s):  
R. Guire ◽  
H. Mathie ◽  
M. Fisher ◽  
D. Fisher
Keyword(s):  
Standard Deviation ◽  
Solid Wall ◽  
Static Model ◽  
Ischial Tuberosity ◽  
Static Object ◽  
Left And Right ◽  
And Performance ◽  
The Right ◽  
Ischial Tuberosities ◽  
Paired T Test

The horse-rider system is of great interest in understanding the mechanics involved in optimising locomotor function and performance in the ridden horse. Adult riders (n=30) attending a rider conference volunteered to take part in the study. Riders were asked to mimic riding position by positioning themselves symmetrically on their seat bones (ischial tuberosities) sitting on a (Pliance) pressure mat which was placed on a static platform. Riders were also asked to mimic even rein contact using reins with gauges which were attached to a solid wall. When satisfied that they were sitting symmetrically and had an even rein contact, pressure and rein measurements were captured for 5 s and repeated three times. A paired T Test was carried out to determine differences between left and right ischial tuberosities and rein pressures. Using a static model, this study found that the riders had significantly more pressure beneath the left ischial tuberosity (mean ± standard deviation, 3.22±1.43 N/cm2) compared to the right (2.65±1.49 N/cm2) (P=0.04) and no significant differences were observed between left (6.37±2.42 N) and right rein pressure (6.38±2.66 N) (P=0.95). Whilst sitting on a static platform, differences in ischial tuberosity pressure in adult riders were observed despite these riders’ perception that their seat was symmetrically weighted. These differences observed need to be investigated further, dynamically, to determine if there is a similar trend in the ridden situation.

THE REALIZATION OF THE ITERATIVE METHOD OF THE LEAST SQUARES FOR THE ESTIMATION OF STATIC OBJECT PARAMETERS IN MATLAB ENVIRONMENT

2017 ◽  
pp. 28-36
Author(s):  
Galina Vasil’evna Troshina ◽  
Alexander Aleksandrovich Voevoda
Keyword(s):  
Parameter Estimation ◽  
Iterative Method ◽  
Least Squares ◽  
Input Signal ◽  
Layer Structure ◽  
Estimation Procedure ◽  
Parameter Estimation Procedure ◽  
Noise Simulation ◽  
Static Object ◽  
Object Parameters

It was suggested to use the system model working in real time for an iterative method of the parameter estimation. It gives the chance to select a suitable input signal, and also to carry out the setup of the object parameters. The object modeling for a case when the system isn't affected by the measurement noises, and also for a case when an object is under the gaussian noise was executed in the MatLab environment. The superposition of two meanders with different periods and single amplitude is used as an input signal. The model represents the three-layer structure in the MatLab environment. On the most upper layer there are units corresponding to the simulation of an input signal, directly the object, the unit of the noise simulation and the unit for the parameter estimation. The second and the third layers correspond to the simulation of the iterative method of the least squares. The diagrams of the input and the output signals in the absence of noise and in the presence of noise are shown. The results of parameter estimation of a static object are given. According to the results of modeling, the algorithm works well even in the presence of significant measurement noise. To verify the correctness of the work of an algorithm the auxiliary computations have been performed and the diagrams of the gain behavior amount which is used in the parameter estimation procedure have been constructed. The entry conditions which are necessary for the work of an iterative method of the least squares are specified. The understanding of this algorithm functioning principles is a basis for its subsequent use for the parameter estimation of the multi-channel dynamic objects.

Interprocedural compatibility analysis for static object preallocation

2003 ◽  
Vol 38 (1) ◽  
pp. 273-284 ◽  
Author(s):  
Ovidiu Gheorghioiu ◽  
Alexandru Salcianu ◽  
Martin Rinard
Keyword(s):  
Compatibility Analysis ◽  
Static Object

End-to-end Learning Improves Static Object Geo-localization from Video

2021 ◽  
Author(s):  
Mohamed Chaabane ◽  
Lionel Gueguen ◽  
Ameni Trabelsi ◽  
Ross Beveridge ◽  
Stephen O'Hara
Keyword(s):  
Static Object ◽  
End To End

Detecting Common Modules in Java Packages Based on Static Object Trace Birthmark

2009 ◽  
Vol 54 (1) ◽  
pp. 108-124 ◽  
Author(s):  
H. Park ◽  
H.-i. Lim ◽  
S. Choi ◽  
T. Han
Keyword(s):  
Static Object

scholarly journals Real-time Foreground Object Detection Combining the PBAS Background Modelling Algorithm and Feedback from Scene Analysis Module

2014 ◽  
Vol 60 (1) ◽  
pp. 53-64 ◽  
Author(s):  
Tomasz Kryjak ◽  
Mateusz Komorkiewicz ◽  
Marek Gorgon
Keyword(s):  
Object Detection ◽  
Detection Algorithm ◽  
Scene Analysis ◽  
Foreground Object ◽  
Connected Component ◽  
Hardware Platform ◽  
Static Object ◽  
Consecutive Frame ◽  
Sobel Edge Detection ◽  
Edge Similarity

Abstract The article presents a hardware implementation of the foreground object detection algorithm PBAS (Pixel-Based Adaptive Segmenter) with a scene analysis module. A mechanism for static object detection is proposed, which is based on consecutive frame differencing. The method allows to distinguish stopped foreground objects (e.g. a car at the intersection, abandoned luggage) from false detections (so-called ghosts) using edge similarity. The improved algorithm was compared with the original version on popular test sequences from the changedetection.net dataset. The obtained results indicate that the proposed approach allows to improve the performance of the method for sequences with the stopped objects. The algorithm has been implemented and successfully verified on a hardware platform with Virtex 7 FPGA device. The PBAS segmentation, consecutive frame differencing, Sobel edge detection and advanced one-pass connected component analysis modules were designed. The system is capable of processing 50 frames with a resolution of 720 × 576 pixels per second

scholarly journals A novel object detection technique for dynamic scene and static object

2016 ◽  
Vol 54 ◽  
pp. 08003
Author(s):  
Xiu Li ◽  
Liansheng Chen ◽  
Zhixiong Yang ◽  
Huimin Wang
Keyword(s):  
Object Detection ◽  
Detection Technique ◽  
Dynamic Scene ◽  
Novel Object ◽  
Static Object

Keller's Gender/Science System: Is the Philosophy of Science to Science as Science is to Nature?

Hypatia ◽  
1988 ◽  
Vol 3 (3) ◽  
pp. 137-148
Author(s):  
Kelly Oliver
Keyword(s):  
Philosophy Of Science ◽  
Open Science ◽  
Object Model ◽  
Dynamic Object ◽  
Middle Ground ◽  
Science System ◽  
Static Object

I argue that although in “The Gender/Science System,” Keller intends to formulate a middle ground position in order to open science to feminist criticisms without forcing it into relativism, she steps back into objectivism. While she endorses the dynamic-object model for science, she endorses the static-object model for philosophy of science. I suggest that by modeling her methodology for philosophy on her methodology for science her philosophy would better serve her feminist goals.

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