This document provides instructions for setting up and adjusting a GPS survey project in MOVE3 software. It includes steps for:
1. Setting the projection and dimension, standard deviations, loading options from other projects, and additional GPS parameters.
2. Adjusting the network as a free network with inner constraint and analyzing results and rejections.
3. Rescaling the GPS baseline correlation matrix, importing known coordinates, and combining terrestrial and GPS observations and adjustments.
4. Connecting the network to the local coordinate system by setting the adjustment phase and getting an accepted F-test.
Using FME for Topographical Data Generalization at Natural Resources CanadaSafe Software
To meet increasing and diversified user needs for geographic information, Natural Resources Canada (NRCan) must produce and maintain geographic data at multiple scales. To automate the generalization process NRCan is using an approach based on FME and MetaAlgorithms.
Presentation by the author of the CompNet Least Square Adjustment Software on the optimal setup and conditions in order to produce the most accurate resection traverse.
Horizontal axis wind turbine blade- 1way FSI analysisVishnu R
Combination of CFD and FEA analyses to assess the mechanical response of a wind turbine blade spinning clockwise as a consequence of wind blowing along the -z direction
Using FME for Topographical Data Generalization at Natural Resources CanadaSafe Software
To meet increasing and diversified user needs for geographic information, Natural Resources Canada (NRCan) must produce and maintain geographic data at multiple scales. To automate the generalization process NRCan is using an approach based on FME and MetaAlgorithms.
Presentation by the author of the CompNet Least Square Adjustment Software on the optimal setup and conditions in order to produce the most accurate resection traverse.
Horizontal axis wind turbine blade- 1way FSI analysisVishnu R
Combination of CFD and FEA analyses to assess the mechanical response of a wind turbine blade spinning clockwise as a consequence of wind blowing along the -z direction
The part is axisymmetrically modeled in solidworks(2D) before importing to ansys workbench where the boundary zones are identified and appropriate mesh settings is applied. The model is then imported in Fluent for analysis . Significant setting changes are Density based solver , Enhanced Eddy viscosity model with near wall treatment , solution steering , FMG initialization etc.
Analytical Evaluation of Generalized Predictive Control Algorithms Using a Fu...inventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Estimation of global solar radiation by using machine learning methodsmehmet şahin
In this study, global solar radiation (GSR) was estimated based on 53 locations by using ELM, SVR, KNN, LR and NU-SVR methods. Methods were trained with a two-year data set and accuracy of the mentioned methods was tested with a one-year data set. The data set of each year was consisting of 12 months. Whereas the values of month, altitude, latitude, longitude, vapour pressure deficit and land surface temperature were used as input for developing models, GSR was obtained as output. Values of vapour pressure deficit and land surface temperature were taken from radiometry of NOAA-AVHRR satellite. Estimated solar radiation data were compared with actual data that were obtained from meteorological stations. According to statistical results, most successful method was NU-SVR method. The RMSE and MBE values of NU-SVR method were found to be 1,4972 MJ/m2 and 0,2652 MJ/m2, respectively. R value was 0,9728. Furthermore, worst prediction method was LR. For other methods, RMSE values were changing between 1,7746 MJ/m2 and 2,4546 MJ/m2. It can be seen from the statistical results that ELM, SVR, k-NN and NU-SVR methods can be used for estimation of GSR.
Cross-Validation and Big Data Partitioning Via Experimental Designdans_salford
Trident is an innovation in data partitioning that offers the data analyst superior methods for self-testing predictive models via cross-validation (CV), novel methods for combining the CV-fold specific models into high-performance ensembles, and more accurate estimates of the generalization performance of any of the predictive models generated as part of the Trident-cross-validation process. In addition to providing data partitioning plans for the observations of a typical training data set for predictive modeling, Trident can also be used to partition predictors into optimally balanced overlapping subsets so that problems with very large numbers of predictors (say in the hundreds of thousands or millions) can be managed via the post-model analysis of the performances of many models each built in parallel on relatively modest numbers of predictors. This introduction provides a succinct overview of the Trident methodology and is written for the general practitioner. Detailed technical expositions are available in separate documents and in our Patent filings.
The part is axisymmetrically modeled in solidworks(2D) before importing to ansys workbench where the boundary zones are identified and appropriate mesh settings is applied. The model is then imported in Fluent for analysis . Significant setting changes are Density based solver , Enhanced Eddy viscosity model with near wall treatment , solution steering , FMG initialization etc.
Analytical Evaluation of Generalized Predictive Control Algorithms Using a Fu...inventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Estimation of global solar radiation by using machine learning methodsmehmet şahin
In this study, global solar radiation (GSR) was estimated based on 53 locations by using ELM, SVR, KNN, LR and NU-SVR methods. Methods were trained with a two-year data set and accuracy of the mentioned methods was tested with a one-year data set. The data set of each year was consisting of 12 months. Whereas the values of month, altitude, latitude, longitude, vapour pressure deficit and land surface temperature were used as input for developing models, GSR was obtained as output. Values of vapour pressure deficit and land surface temperature were taken from radiometry of NOAA-AVHRR satellite. Estimated solar radiation data were compared with actual data that were obtained from meteorological stations. According to statistical results, most successful method was NU-SVR method. The RMSE and MBE values of NU-SVR method were found to be 1,4972 MJ/m2 and 0,2652 MJ/m2, respectively. R value was 0,9728. Furthermore, worst prediction method was LR. For other methods, RMSE values were changing between 1,7746 MJ/m2 and 2,4546 MJ/m2. It can be seen from the statistical results that ELM, SVR, k-NN and NU-SVR methods can be used for estimation of GSR.
Cross-Validation and Big Data Partitioning Via Experimental Designdans_salford
Trident is an innovation in data partitioning that offers the data analyst superior methods for self-testing predictive models via cross-validation (CV), novel methods for combining the CV-fold specific models into high-performance ensembles, and more accurate estimates of the generalization performance of any of the predictive models generated as part of the Trident-cross-validation process. In addition to providing data partitioning plans for the observations of a typical training data set for predictive modeling, Trident can also be used to partition predictors into optimally balanced overlapping subsets so that problems with very large numbers of predictors (say in the hundreds of thousands or millions) can be managed via the post-model analysis of the performances of many models each built in parallel on relatively modest numbers of predictors. This introduction provides a succinct overview of the Trident methodology and is written for the general practitioner. Detailed technical expositions are available in separate documents and in our Patent filings.
References1. HCS 2010 online manuals.2. Data Data provi.docxdebishakespeare
References:
1. HCS 2010 online manuals.
2. Data: Data provided for Lab 6
3. Software: HCS 2010
Objectives: The objective of this exercise is to become familiar with operational-level signal timing software. The software you will use is the implementation of the Highway Capacity Manual 2010. General notes regarding this lab:
· The intersection you will be using for analysis is Lake Mary Rd. and High Country Trail. Take a look at this intersection on Google Maps. Draw a rough sketch of the intersection, showing lane usage. Be sure to include a North Arrow, as well as movements (with numbers….use movement 2 for Northbound through). (2)
· Operational data for this intersection is provided in the file ‘Lab6data.xlsx’ open this file.
· The timing data given is for pretimed 2-phase operation. What is the cycle length given? (1)
Background: The HCS2010 software does not exactly duplicate the HCM 2010 methods but it is reasonably close. One difference is in the optimization algorithms, which are specific to the software and are not specified in the HCM 2010.
The software will not design your timing plan for you (It can, but we will not be using that option). It is used as a calculation tool to help you determine which combination of green interval lengths and cycle length provide the best LOS (or lowest delay) with safe operation.
A proper phase plan is a critical aspect of signal timing design. Once the phase plan has been developed, most of the signal timing can be systematically treated in a deterministic fashion. There are several important considerations you need to keep in mind when establishing a phase plan for your intersection:
1. Safety: Phasing can be used to minimize accident risks by separating the competing movement. A traffic signal always eliminates the basic through crossing conflicts present at the intersections.
2. Lost time per cycle: In addition, left-turn protection can also be used to eliminate the conflicts between left-turning movements and the opposing through movement. However, additional phasing can also lead to more lost time per cycle and therefore additional delay.
3. Sat flow rates for LTs: While increasing the number of phases also increases the total lost time in the cycle, a benefit is that this also increases the affected left-turn saturation flow rates. This in turn can lead to less delay.
Lab Steps:
1. Open the HCS2010 software on a PC in Rm. 114 or 113. Open the traffic signals module.
2. When you start a new traffic signal file, a dialog box will appear asking you to verify settings of the intersection. The only setting you need to adjust is that of the forward direction, which is used for arterial analysis. Even though we will only be working with one intersection, you should still choose NB for this. All other settings can be left at their default.
3. You will be entering data in the following sections:
a. Primary Input Data (All Sections), shown in Figure 1
b. Detailed I ...
Experiment5Physics with Calculators 5 - 1Picket Fe.docxgitagrimston
Experiment
5
Physics with Calculators 5 - 1
Picket Fence Free Fall
We say an object is in free fall when the only force acting on it is the earth’s gravitational force.
No other forces can be acting; in particular, air resistance must be either absent or so small as to
be ignored. When the object in free fall is near the surface of the earth, the gravitational force on
it is nearly constant. As a result, an object in free fall accelerates downward at a constant rate.
This acceleration is usually represented with the symbol g.
Physics students measure the acceleration due to gravity using a wide variety of timing methods.
In this experiment, you will have the advantage of using a very precise timer connected to the
calculator and a Photogate. The Photogate has a beam of infrared light that travels from one side
to the other. It can detect whenever this beam is blocked. You will drop a piece of clear plastic
with evenly spaced black bars on it, called a Picket Fence. As the Picket Fence passes through
the Photogate, the LabPro or CBL 2 interface will measure the time from the leading edge of one
bar blocking the beam until the leading edge of the next bar blocks the beam. This timing
continues as all eight bars pass through the Photogate. From these measured times, the program
will calculate the velocities and accelerations for this motion and graphs will be plotted.
Picket
fence
Figure 1
OBJECTIVE
• Measure the acceleration of a freely falling body (g) to better than 0.5% precision using a
Picket Fence and a Photogate.
MATERIALS
LabPro or CBL 2 interface Vernier Photogate
TI Graphing Calculator Picket Fence
DataGate program clamp or ring stand to secure Photogate
Modified from and reported with permission
of the publisher Copyright (2000),
Vernier Software & Technology
Experiment 5
5 - 2 Physics with Calculators
PRELIMINARY QUESTIONS
1. Inspect your Picket Fence. You will be dropping it through a Photogate to measure g. The
distance, measured from one edge of a black band to the same edge of the next band, is
5.0 cm. What additional information will you need to determine the average speed of the
Picket Fence as it moves through the Photogate?
2. If an object is moving with constant acceleration, what is the shape of its velocity vs. time
graph?
3. Does the initial velocity of an object have anything to do with its acceleration? For example,
compared to dropping an object, if you throw it downward would the acceleration be
different after you released it?
PROCEDURE
1. Fasten the Photogate rigidly to a ring stand so the arms extend horizontally, as shown in
Figure 1. The entire length of the Picket Fence must be able to fall freely through the
Photogate. To avoid damaging the Picket Fence, make sure it has a soft landing surface.
2. Connect the Photogate to the DIG/SONIC 1 input of the LabPro or the DIG/SONIC input on the
CBL 2. Use the black link cable to connect the interface to the TI Graphing Calculator.
Firmly pr ...
Adaptive Hyper-Parameter Tuning for Black-box LiDAR Odometry [IROS2021]KenjiKoide1
Adaptive Hyper-Parameter Tuning for Black-box LiDAR Odometry
Kenji Koide, Masashi Yokozuka, Shuji Oishi, and Atsuhiko Banno
Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2021), pp. 7708-7714, Prague, Czech Republic, Sep., 2021
https://staff.aist.go.jp/k.koide/
FAA Flight Landing Distance Forecasting and AnalysisQuynh Tran
The overall goal of this project is to get an ideal model to forecast landing distance based on variables given in the dataset. To be able to come up with a good model that fits the dataset, we need to go through some certain steps to explore, clean, visualize, and analyze values in the dataset.
Precise Attitude Determination Using a Hexagonal GPS PlatformCSCJournals
In this paper, a method of precise attitude determination using GPS is proposed. We use a hexagonal antenna platform of 1 m diameter (called the wheel) and post-processing algorithms to calculate attitude, where we focus on yaw to prove the concept. The first part of the algorithm determines an initial absolute position using single point positioning. The second part involves double differencing (DD) the carrier phase measurements for the received GPS signals to determine relative positioning of the antennas on the wheel. The third part consists of Direct Computation Method (DCM) or Implicit Least Squares (ILS) algorithms which, given sufficiently accurate knowledge of the fixed body frame coordinates of the wheel, takes in relative positions of all the receivers and produces the attitude. Field testing results presented in this paper will show that an accuracy of 0.05 degrees in yaw can be achieved. The results will be compared with a theoretical error, which is shown by Monte Carlo simulation to be < 0.001 degrees. The improvement to the current state-of-the-art is that current methods require either very large baselines of several meters to achieve such accuracy or provide errors in yaw that are orders of magnitude greater.
2. New Job (or Project-New)
Setting the Projection & Dimension switch (Automatically appears)
Options | General | Geometry
In the Geometry tab sheet the dimension of the solution can be changed. If you are working
only in 2D make sure the dimension switch is correctly set.
Setting the Projection
One of the first things to be done is Set the Projection this is important to work in your
Local XY coordinates.
OPTIONS – GEOMETRY – MORE
Select which MGA94 zone you are in.
3. (Note: If you want to work with ellipsoidal coordinates you can use the Projection: None. If
you want to work Easting Northing (XY) set the appropriate MGA 94 zone projection.
OPTIONS – GEOMETRY – MORE Australia
5. Setting the Parameters for Standard deviations for observations and for
stations
OPTIONS – STANDARD DEVIATIONS
6. OPTIONS-LOAD
Use this command to LOAD options from another project file (*.PRJ) or a previous saved
options file (*.OPT). This way your options, parameter settings are set for another project
and can be used instead of setting the same options all over again using the options tab
sheets. The Options LOAD command opens a File Open dialog box where an option or project
file can be selected.
8. Check OPTIONS – GENERAL – PROJECT the terrestrial Observations and Coordinates and
GPS Observations & Coordinates. If applicable set the Geoid model SGEOID09
9. Set the Phase to Free Network OPTIONS – GENERAL – ADJUSTMENT and check the box
Inner Constraint. With the Inner Constraint option it is not necessary to provide any known
points for a Free network adjustment. With the Inner Constraint checked MOVE3 will solve
the rank deficiency by adding additional constraints (Centre of Gravity, orientation and
scale). If the item is not checked adding the minimum number of known stations or the
selected base stations will solve the rank deficiency.
Adjust the free network using COMPUTE – MOVE3 and have a look at the results. Try to find
out what’s the reason to the largest rejections in the network.
Results - Report
10. MOVE3 has a number of tools to analyse the rejections. The output file contains a summary
of the largest rejections.
Largest W-tests: 1D-test (test for an error in a single baseline component);
Largest T-tests: 3D-test (test for an error in a complete baseline);
Largest tests for antenna height errors: 1D- test in the direction of the height system.
This test value will only be shown if only the height component of the baseline is
rejected.
The test value with the largest factor is the most suspect observation. This specific
observation can be deselected (Do not remove!) and then do a re-adjustment.
Rejections after the re-adjustment are also analysed.
In case a lot of rejections appear, it’s time to find out what’s wrong with the network.
Have a close look at the adjustment resulted and also make use of:
PRERUN3 to do a pre-analyses of the observations;
LOOPS3: to detect loops in the network and to compute closing errors in the loops.
Results – Test Summary
After running a MOVE3 adjustment computation, rejected items (observations and
coordinates) can be viewed and edited with this command. In the Test Summary dialog box
the type of the rejected item, the type of test, the factor and the station(s) are shown.
To edit a rejected item select one of the listed items and use View to jump to the Edit
observation dialog box (for observations) or the Edit station dialog box (for coordinates).
This specific observation can be deselected (Do not remove!)
11. Possible Problem
If you have a lot of rejected GPS baselines this may be caused by a too optimistic estimate
for the covariance matrix:
Usually the covariance matrix of the computed GPS baselines has overoptimistic values for
the standard deviations of the baseline components. When using the correlation matrix for
the precision of the GPS baselines this will result in rejections for almost all baselines, as in
this particular network. Standard deviations are about 1mm, which is rather optimistic. The
variance component analysis can be used to estimate the scaling factor for the stochastic
model. The variance component (from the F-test) is 122.606. Rescale the correlation matrix
with the factor SQRT (122.606) = 11.
Use VIEW – OBSERVATIONS – TOOLS change St. deviations and rescale the correlation for
the GPS baselines with a factor 11. Most of the rejections have disappeared. The remaining
rejections need to be solved. Record 23, baseline 1009-1005 is the largest rejection. From
the factors one can conclude that it is most likely an antenna height error. Deselect the
baseline record 23. The network is now accepted.
12. Import known coordinates
After the scaling of the correlation matrices of the baselines and a new adjustment the
network will be connected to known stations in the local MGA94 Zone.
Set the Check under OPTIONS – GENERAL – PROJECT for the terrestrial coordinates.
Import the known coordinates from the project with “KNOWN COORDINATES”
Via IMPORT/EXPORT – MOVE3 and adjust the network with COMPUTE – MOVE3
Combine adjustment of terrestrial and GPS observations
Terrestrial observations will be added to the GPS observations for a combined adjustment
Set the Check under OPTIONS – GENERAL – PROJECT for the terrestrial observations
13. Import the terrestrial observations using IMPORT/EXPORT
Compute all approximate coordinates from the network one by one until the F-test is
accepted.
Connection to the local system
After removing the rejections in the observations, the network can be connected to the
local system. Rejections that appear in this step will be caused by errors in the known
coordinates.
Phase
This determines the type of adjustment that will be performed:
free network for a free network adjustment with minimum constraint;
Pseudo constrained a pseudo least squares adjustment with pseudo least
squares precision (coordinates and standard deviations of
the known stations do not change in the adjustment);
weighted constrained for a weighted constrained adjustment (coordinates and
standard deviations of the known stations get a
correction);
absolute constrained for an absolute constrained adjustment (coordinates of
the known stations do not change, standard deviations of
these stations are fixed at zero).
14. Set the Phase to e.g. Pseudo Constrained to test the connection to the known
coordinates and compute the final adjusted coordinates.
Try and get an accepted F-test.
Setting the Phase Options – General – adjustment
Or via the Compute - Adjustment
15. Other options to viewing Base Lines
Click on Base Line to view and edit.
Wayne Pappas September 2012
MOVE3 ICT Helpdesk GIS ICT Australia
wtpappas@xs4all.nl