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Applied science and mathematics for engineering
Published in Mike Tooley, Engineering GNVQ: Intermediate, 2012
We often use graphs to plot experimental data. The shape of the graph helps us to understand how the two quantities are related. Where two quantities are directly proportional to one another the graph plotted to show their relationship will take the form of a straight line. Putting this the other way round, if we plot a graph and it takes the form of a straight line we can infer that the two plotted quantities are directly proportional to one another. If this is beginning to sound a little complex the following example may help.
Advanced Project Planning and Risk Managemen
Published in Timothy J. Havranek, Modern Project Management Techniques for the Environmental Remediation Industry, 2017
Probability theory is used in describing experimental data (i.e., outcome events). Describing experimental data involves the use of graphing techniques as well as a number of calculated descriptive statistics which measure the central tendency of data and their degree of dispersion. Familiarity with common techniques for describing experimental data is important in understanding the results of a stochastic project model.
The inter-laboratory equivalence for lower limb kinematics and kinetics during unplanned sidestepping
Published in Sports Biomechanics, 2021
Cyril J. Donnelly, Gillian Weir, Chris Jackson, Jacqueline Alderson, Radin Rafeeuddin, Raihana Sharir, Jos Vanrenterghem, Mark A. Robinson
The participants in this study were the Australian Women’s National Field Hockey team. Their competition in the Glasgow 2014 Commonwealth games provided a unique opportunity for our group to test these athletes between continents, and unique laboratory set-ups. One test session was conducted at the University of Western Australia (UWA) in either November, 2013 or August, 2014, and a second session at Liverpool John Moores University (LJMU) in July, 2014. Sixteen athletes deemed fit and healthy by the team’s medical staff were eligible to take part in this study. Whilst we initially expected a drop-out rate of around 20% only eight participants (1.68 ± 0.10 m, 64.0 ± 9.2 kg) were able to complete both testing sessions due to de-selection, injury or availability. All participants provided informed consent prior to experimental data collections, which were approved by the Human Research Ethics Committees at The University of Western Australia (UWA) (RA/4/1/5333) and Liverpool John Moores University (LJMU) (12/SPS/022).
Effects of a priori parameter selection in minimum relative entropy method on inverse electrocardiography problem
Published in Inverse Problems in Science and Engineering, 2018
Onder Nazim Onak, Yesim Serinagaoglu Dogrusoz, Gerhard Wilhelm Weber
Simultaneously recorded body surface potentials and the corresponding epicardial potentials were not available at the time that this study was carried out; therefore, we could not use true body surface potential measurements in this study. However, in order for this (or any other) inverse ECG solution method to be valuable in clinical studies, these methods need to be tested with real data, in addition to simulated data. On the other hand, using simulated body surface potentials has its own advantages. Evaluation of inverse problem solutions with true body surface potential measurements still involves an ongoing discussion in the inverse ECG community. In this study, our purpose was to assess the applicability of the MRE method to the solution of inverse ECG problem, and using simulated data based on true epicardial potential measurements enabled numerical comparison of our MRE inverse solutions with these true potentials. Recently, as part of CEI, researchers have released their data-sets for use by others working on ECGI [2]. These data-sets include simulated data, experimental data and data from human subjects. Our goal in the near future is to apply our methods to these data-sets and compare the performances of our algorithms with the results of other groups using the same data-sets.
Performance evaluation of a non-equilibrium model for low temperature grain drying and simulation of seasonal dryer operation
Published in Drying Technology, 2022
Adam Epstein, William Lubitz, Greg Dineen, James Dyck
There is no standardized measure to evaluate the validity of a grain drying model. Often, the model is compared graphically to experimental data.[17,18,23] An early review of low temperature drying models found that evaluation of many models had only been completed for high temperature scenarios.[22] A challenge of evaluating low temperature models is that deep bed experiments can take days or weeks to run, with inlet conditions that vary with the ambient weather conditions. In the following section the model is evaluated against the full-scale experimental data both graphically and by quantifying overall prediction errors.