Fitting a Precise Levelling Network to Control Points Using a Modified Robust Huber’s Mean Error Function

2017 ◽  
Vol 143 (1) ◽  
pp. 06016003 ◽  
Author(s):  
Edward Osada ◽  
Andrzej Borkowski ◽  
Grzegorz Kurpiński ◽  
Marcin Oleksy ◽  
Mateusz Seta
Keyword(s):  
Error Function ◽  
Control Points ◽  
Precise Levelling ◽  
Mean Error

An Effective Approach to Approximating 2-D Free-Form Curve Offsets for B-Spline NC Tool Paths With Offset Error Globally Bounded

2010 ◽  
Author(s):  
Maqsood A. Khan ◽  
Zezhong C. Chen
Keyword(s):  
Computer Aided Design ◽  
Tool Path ◽  
Error Function ◽  
Industrial Applications ◽  
Cnc Machining ◽  
Free Form ◽  
Control Points ◽  
Base Function ◽  
B Spline ◽  
Tool Paths

The topic of representing the offset of a 2-D B-spline curve in the same form has been under research for a long time, and it has many industrial applications, especially, in NC tool path generation for pocketing. For B-spline tool paths, it is often required that the tool paths have fewer control points, lower base function degree, and higher geometric accuracy. However, the existing methods often generate the offsets of 2-D free-form curves in the form of B-spline curves with high function degree and many control points. Although these offsets are useful in computer-aided design, they are inappropriate for the use of CNC machining. To address the problems in order to generate high quality B-spline tool paths, this original work formulates an error function of the offset approximation and then constructs a NURBS curve to globally bound the errors. By checking the maximum coefficient of the bounding curve, the upper bound of all the approximated offset errors is found and the errors can be reduced by adding more offset points at the appropriate locations. The proposed new approach is more efficient, and the resulting offsets in B-spline are more accurate with fewer control points and lower function degree. It is useful to generate B-spline tool paths for CNC pocketing, and has potential for other applications in industry.

Practical Realization of a Reference System for Earth Dynamics by Satellite Methods

1975 ◽  
Vol 26 ◽  
pp. 341-380 ◽  
Author(s):  
R. J. Anderle ◽  
M. C. Tanenbaum
Keyword(s):  
Reference Frame ◽  
Polar Motion ◽  
Pole Position ◽  
Control Points ◽  
Significant Information ◽  
Crustal Motion ◽  
Average Reference ◽  
Future Data ◽  
Existing Data

AbstractObservations of artificial earth satellites provide a means of establishing an.origin, orientation, scale and control points for a coordinate system. Neither existing data nor future data are likely to provide significant information on the .001 angle between the axis of angular momentum and axis of rotation. Existing data have provided data to about .01 accuracy on the pole position and to possibly a meter on the origin of the system and for control points. The longitude origin is essentially arbitrary. While these accuracies permit acquisition of useful data on tides and polar motion through dynamio analyses, they are inadequate for determination of crustal motion or significant improvement in polar motion. The limitations arise from gravity, drag and radiation forces on the satellites as well as from instrument errors. Improvements in laser equipment and the launch of the dense LAGEOS satellite in an orbit high enough to suppress significant gravity and drag errors will permit determination of crustal motion and more accurate, higher frequency, polar motion. However, the reference frame for the results is likely to be an average reference frame defined by the observing stations, resulting in significant corrections to be determined for effects of changes in station configuration and data losses.

On-farm udder health monitoring

2011 ◽  
Vol 39 (02) ◽  
pp. 95-100
Author(s):  
J. C. van Veersen ◽  
O. Sampimon ◽  
R. G. Olde Riekerink ◽  
T. J. G. Lam
Keyword(s):  
Management Practices ◽  
Health Management ◽  
Action Plan ◽  
Real Life ◽  
Health Data ◽  
Control Points ◽  
Udder Health ◽  
Critical Control Points ◽  
On Farm ◽  
Clinical Problems

SummaryIn this article an on-farm monitoring approach on udder health is presented. Monitoring of udder health consists of regular collection and analysis of data and of the regular evaluation of management practices. The ultimate goal is to manage critical control points in udder health management, such as hygiene, body condition, teat ends and treatments, in such a way that results (udder health parameters) are always optimal. Mastitis, however, is a multifactorial disease, and in real life it is not possible to fully prevent all mastitis problems. Therefore udder health data are also monitored with the goal to pick up deviations before they lead to (clinical) problems. By quantifying udder health data and management, a farm is approached as a business, with much attention for efficiency, thought over processes, clear agreements and goals, and including evaluation of processes and results. The whole approach starts with setting SMART (Specific, Measurable, Acceptable, Realistic, Time-bound) goals, followed by an action plan to realize these goals.

The Pricing Error Function

2008 ◽  
Author(s):  
Huntley Schaller ◽  
Lynda Khalaf
Keyword(s):  
Error Function ◽  
Pricing Error

Critical Control Points in DPR: Quantifying the Multi-Barrier Approach to Treatment

2015 ◽  
Vol 2015 (14) ◽  
pp. 5477-5488
Author(s):  
Ben Stanford ◽  
Troy Walker ◽  
Stuart Khan ◽  
Shane Snyder ◽  
Cedric Robillot
Keyword(s):  
Control Points ◽  
Critical Control Points

Measurements of the excess volume of benzene-methanol, benzene-acetonitrile and methanol-acetonitrile mixtures by a vibrating-tube densimeter

1979 ◽  
Vol 44 (2) ◽  
pp. 295-306 ◽  
Author(s):  
Ivan Cibulka ◽  
Vladimír Hynek ◽  
Robert Holub ◽  
Jiří Pick
Keyword(s):  
Excess Volume ◽  
Density Measurement ◽  
Indirect Measurement ◽  
Measuring System ◽  
Mean Error ◽  
Shaped Tube ◽  
Underlying Principle ◽  
The Mean ◽  
Vibrating Tube Densimeter

A digital vibrating-tube densimeter was constructed for measuring the density of liquids at several temperatures. The underlying principle of the apparatus is the measurement of the period of eigen-vibrations of a V-shaped tube; the second power of the period of the vibrations is proportional to the density of the liquid in the tube. The temperature of the measuring system is controlled by an electronic regulator. The mean error in the density measurement is approximately ±1 . 10-5 g cm-3 at 25 °C and ±2 . 10-5 g cm-3 at 40 °C. The apparatus was used for an indirect measurement of the excess volume, tested with the benzene-cyclohexane system and further used for determining the excess volume of the benzene-methanol, benzene-acetonitrile and methanol-acetonitrile systems at 25 and 40 °C.

scholarly journals A High-Precision Registration Technology Based on Bundle Adjustment in Structured Light Scanning System

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Jianying Yuan ◽  
Qiong Wang ◽  
Xiaoliang Jiang ◽  
Bailin Li
Keyword(s):  
High Precision ◽  
Large Scale ◽  
Structured Light ◽  
Bundle Adjustment ◽  
Scanning System ◽  
Control Points ◽  
Registration Method ◽  
Global Control ◽  
3D Data ◽  
Structured Light Scanning

The multiview 3D data registration precision will decrease with the increasing number of registrations when measuring a large scale object using structured light scanning. In this paper, we propose a high-precision registration method based on multiple view geometry theory in order to solve this problem. First, a multiview network is constructed during the scanning process. The bundle adjustment method from digital close range photogrammetry is used to optimize the multiview network to obtain high-precision global control points. After that, the 3D data under each local coordinate of each scan are registered with the global control points. The method overcomes the error accumulation in the traditional registration process and reduces the time consumption of the following 3D data global optimization. The multiview 3D scan registration precision and efficiency are increased. Experiments verify the effectiveness of the proposed algorithm.

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