rafeglan taleabina carylah
Violet Mcdow
Milliman AccuRate Fleet is a usage-based insurance score designed to enable more accurate pricing of fleet exposure and driving behavior risk. Developed by Milliman actuaries and data scientists, AccuRate Fleet is based on over a billion miles of commercial auto driving data and thousands of crashes.
Use our extensive commercial telematics data to get accurate pricing that is approved by regulators. AccuRate Fleet can enable more refined fleet pricing and risk selection, as well as encourage safer driving behaviors that are most directly relevant to commercial fleets.
Today, we are helping organizations take on some of the world's most critical and complex issues, including retirement funding and healthcare financing, risk management and regulatory compliance, data analytics and business transformation.
I realized after posting that you may have been talking about elevation. There are a specific number of terrain values in each import. If you were to do an 18X import of the center of the area, then add imagery to get the surrounding 18X areas, that will give you more detailed terrain.
Actually I think it is satellite data. Our National Survey has made its aerial Lidar data public and compared to that the Digital Globe and before that, Google data is vastly inaccurate. Even the National Survey data is not accurate enough to place building levels, with points about 2 m apart. They are actually doing a new scan of the whole country at a resolution of something like 3 points/m. That would probably be quite usable.
I have spent all afternoon looking for a way to even check altitude at a point. The results were at best poor. I did find one discussion on the data used to create the height maps and it also said that the data is about to be updated to be more accurate. Trimble should put a warning on it so people are aware of the issue.
New with PlaceMaker version 3: import high quality detailed terrain models for almost any location on the globe. This exciting feature allows for great accuracy with some locations boasting 1m or even better terrain data. With a simple ability to...
I found after a little research online that it seems to be a common problem with many differing CAD software and printing, and there were many differing suggestions and ideas, but very little in the way of conclusive solutions.
The thing is that most printers rely on feeding paper through on different systems of rollers, and that there is always some paper slippage plus the calibration of the roller outer diameter to the linear paper feed. So, while in general they are reasonably accurate and repeatable in the width (carriage movement) direction, they are not nearly as accurate in the length (paper feed) direction.
This is why systems which need accurate registration of printed material and, say, cutouts made afterwards, rely on printing, then scanning the print back in (reference marks) and then scaling it internally as necessary before cutting.
In years past, I used to plot out full scale framing for boat building onto Mylar with ink pens on a large format HP plotter. EXPENSIVE! YIKES! but did save the builder from having to loft from numerical offsets. Now, I just email all the drawings and they can be dealt with full size on their plotter!
Even flat bed scanners except for very expensive ones no doubt, cant do 100% which is even more of a pain. Had an Epson A3 at work, had to scale down by different amounts the resulting scan, my A4 Epson is no better.
In simpler terms, given a statistical sample or set of data points from repeated measurements of the same quantity, the sample or set can be said to be accurate if their average is close to the true value of the quantity being measured, while the set can be said to be precise if their standard deviation is relatively small.
In the fields of science and engineering, the accuracy of a measurement system is the degree of closeness of measurements of a quantity to that quantity's true value.[2] The precision of a measurement system, related to reproducibility and repeatability, is the degree to which repeated measurements under unchanged conditions show the same results.[2][3] Although the two words precision and accuracy can be synonymous in colloquial use, they are deliberately contrasted in the context of the scientific method.
The field of statistics, where the interpretation of measurements plays a central role, prefers to use the terms bias and variability instead of accuracy and precision: bias is the amount of inaccuracy and variability is the amount of imprecision.
A measurement system can be accurate but not precise, precise but not accurate, neither, or both. For example, if an experiment contains a systematic error, then increasing the sample size generally increases precision but does not improve accuracy. The result would be a consistent yet inaccurate string of results from the flawed experiment. Eliminating the systematic error improves accuracy but does not change precision.
In addition to accuracy and precision, measurements may also have a measurement resolution, which is the smallest change in the underlying physical quantity that produces a response in the measurement.
In numerical analysis, accuracy is also the nearness of a calculation to the true value; while precision is the resolution of the representation, typically defined by the number of decimal or binary digits.
In military terms, accuracy refers primarily to the accuracy of fire (justesse de tir), the precision of fire expressed by the closeness of a grouping of shots at and around the centre of the target.[4]
In industrial instrumentation, accuracy is the measurement tolerance, or transmission of the instrument and defines the limits of the errors made when the instrument is used in normal operating conditions.[5]
Ideally a measurement device is both accurate and precise, with measurements all close to and tightly clustered around the true value. The accuracy and precision of a measurement process is usually established by repeatedly measuring some traceable reference standard. Such standards are defined in the International System of Units (abbreviated SI from French: Systme international d'units) and maintained by national standards organizations such as the National Institute of Standards and Technology in the United States.
This also applies when measurements are repeated and averaged. In that case, the term standard error is properly applied: the precision of the average is equal to the known standard deviation of the process divided by the square root of the number of measurements averaged. Further, the central limit theorem shows that the probability distribution of the averaged measurements will be closer to a normal distribution than that of individual measurements.
A common convention in science and engineering is to express accuracy and/or precision implicitly by means of significant figures. Where not explicitly stated, the margin of error is understood to be one-half the value of the last significant place. For instance, a recording of 843.6 m, or 843.0 m, or 800.0 m would imply a margin of 0.05 m (the last significant place is the tenths place), while a recording of 843 m would imply a margin of error of 0.5 m (the last significant digits are the units).
A reading of 8,000 m, with trailing zeros and no decimal point, is ambiguous; the trailing zeros may or may not be intended as significant figures. To avoid this ambiguity, the number could be represented in scientific notation: 8.0 103 m indicates that the first zero is significant (hence a margin of 50 m) while 8.000 103 m indicates that all three zeros are significant, giving a margin of 0.5 m. Similarly, one can use a multiple of the basic measurement unit: 8.0 km is equivalent to 8.0 103 m. It indicates a margin of 0.05 km (50 m). However, reliance on this convention can lead to false precision errors when accepting data from sources that do not obey it. For example, a source reporting a number like 153,753 with precision +/- 5,000 looks like it has precision +/- 0.5. Under the convention it would have been rounded to 150,000.
In engineering, precision is often taken as three times Standard Deviation of measurements taken, representing the range that 99.73% of measurements can occur within.[6] For example, an ergonomist measuring the human body can be confident that 99.73% of their extracted measurements fall within 0.7 cm - if using the GRYPHON processing system - or 13 cm - if using unprocessed data.[7]
A shift in the meaning of these terms appeared with the publication of the ISO 5725 series of standards in 1994, which is also reflected in the 2008 issue of the BIPM International Vocabulary of Metrology (VIM), items 2.13 and 2.14.[2]
According to ISO 5725-1,[1] the general term "accuracy" is used to describe the closeness of a measurement to the true value. When the term is applied to sets of measurements of the same measurand, it involves a component of random error and a component of systematic error. In this case trueness is the closeness of the mean of a set of measurement results to the actual (true) value and precision is the closeness of agreement among a set of results.
ISO 5725-1 and VIM also avoid the use of the term "bias", previously specified in BS 5497-1,[8] because it has different connotations outside the fields of science and engineering, as in medicine and law.
When computing accuracy in multiclass classification, accuracy is simply the fraction of correct classifications:[13] Accuracy = correct classifications all classifications \displaystyle \textAccuracy=\frac \textcorrect classifications\textall classifications This is usually expressed as a percentage. For example, if a classifier makes ten predictions and nine of them are correct, the accuracy is 90%.
Accuracy is also called top-1 accuracy to distinguish it from top-5 accuracy, common in convolutional neural network evaluation. To evaluate top-5 accuracy, the classifier must provide relative likelihoods for each class. When these are sorted, a classification is considered correct if the correct classification falls anywhere within the top 5 predictions made by the network. Top-5 accuracy was popularized by the ImageNet challenge. It is usually higher than top-1 accuracy, as any correct predictions in the 2nd through 5th positions will not improve the top-1 score, but do improve the top-5 score.
c01484d022