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A random number is a number chosen from a pool of limited or unlimited numbers that has no discernible pattern for prediction. The pool of numbers is almost always independent from each other. However, the pool of numbers may follow a specific distribution. For example, the height of the students in a school tends to follow a normal distribution around the median height. If the height of a student is picked at random, the picked number has a higher chance to be closer to the median height than being classified as very tall or very short. The random number generators above assume that the numbers generated are independent of each other, and will be evenly spread across the whole range of possible values.
A random number generator, like the ones above, is a device that can generate one or many random numbers within a defined scope. Random number generators can be hardware based or pseudo-random number generators. Hardware based random-number generators can involve the use of a dice, a coin for flipping, or many other devices.
A pseudo-random number generator is an algorithm for generating a sequence of numbers whose properties approximate the properties of sequences of random numbers. Computer based random number generators are almost always pseudo-random number generators. Yet, the numbers generated by pseudo-random number generators are not truly random. Likewise, our generators above are also pseudo-random number generators. The random numbers generated are sufficient for most applications yet they should not be used for cryptographic purposes. True random numbers are based on physical phenomena such as atmospheric noise, thermal noise, and other quantum phenomena. Methods that generate true random numbers also involve compensating for potential biases caused by the measurement process.
Most amine regenerators have 20-22 trays. I design it by modeling the column in ProMax and making sure I have an appropriate temperature profile, as well as CO2 stripping in the column, not the reboiler.
All simulated CO2 removal systems I've seen have been totally WRONG in predicting excessive number of trays required to regenerate amine solutions - especially MEA solutions. I know this to be factually true because I've had the advantage of designing, building, installing, and operating these units out in the field. I have reported these factual, field experiences many times in many prior threads on the same subject here in our Forums in the past and these can be found in our SEARCH engine.
The LastPass password generator creates random passwords based on parameters set by you. Parameters include password length, whether the password should be easy to say or read, and whether the password should have uppercase letters, lowercase letters, numbers, and symbols.
The regenerator is considered hung when:
1. It reports that it is executing sql for a PDT build/ trigger check and that sql is not executing on the database.
2. The regenerator process has not begun since the previous regenerator process has completed within the timeframe dictated by the "PDT And Datagroup Maintenance Schedule".
The source of a hung regenerator is usually difficult to track down, as it typically involves something on the network or database side interrupting the regeneration process. We have seen a number of reports of hung regenerators coinciding with Redshift database maintenance, including a mass maintenance Redshift performed that caused a critical because of the large number of customers whose regenerators were hung.
(Pseudo)Random numbers in MATLAB come from the rand, randi, and randn functions. Many other functions call those three, but those are the fundamental building blocks. All three depend on a single shared random number generator that you can control using rng.
It's important to realize that "random" numbers in MATLAB are not unpredictable at all, but are generated by a deterministic algorithm. The algorithm is designed to be sufficiently complicated so that its output appears to be an independent random sequence to someone who does not know the algorithm, and can pass various statistical tests of randomness. The function that is introduced here provides ways to take advantage of the determinism to
If you look at the output from rand, randi, or randn in a new MATLAB session, you'll notice that they return the same sequences of numbers each time you restart MATLAB. It's often useful to be able to reset the random number generator to that startup state, without actually restarting MATLAB. For example, you might want to repeat a calculation that involves random numbers, and get the same result.
When you first start a MATLAB session or call rng("default"), MATLAB initializes the random number generator using the default algorithm and seed. Starting in R2023b, you can set the default algorithm and seed in MATLAB preferences. If you do not change these preferences, then rng uses the factory value of "twister" for the Mersenne Twister generator with seed 0, as in previous releases. For more information, see Default Settings for Random Number Generator and Reproducibility for Random Number Generator.
What are the "default" random number settings that MATLAB starts up with, or that rng("default") gives you? Before R2023b, if you call rng with no inputs, you can see that it is the Mersenne Twister generator algorithm, seeded with 0.
You'll see in more detail below how to use the above output, including the State field, to control and change how MATLAB generates random numbers. For now, it serves as a way to see what generator rand, randi, and randn are currently using.
Each time you call rand, randi, or randn, they draw a new value from their shared random number generator, and successive values can be treated as statistically independent. But as mentioned above, each time you restart MATLAB those functions are reset and return the same sequences of numbers. Obviously, calculations that use the same "random" numbers cannot be thought of as statistically independent. So when it's necessary to combine calculations done in two or more MATLAB sessions as if they were statistically independent, you cannot use the default generator settings.
One simple way to avoid repeating the same random numbers in a new MATLAB session is to choose a different seed for the random number generator. rng gives you an easy way to do that, by creating a seed based on the current time.
"shuffle" is a very easy way to reseed the random number generator. You might think that it's a good idea, or even necessary, to use it to get "true" randomness in MATLAB. For most purposes, though, it is not necessary to use "shuffle" at all. Choosing a seed based on the current time does not improve the statistical properties of the values you'll get from rand, randi, and randn, and does not make them "more random" in any real sense. While it is perfectly fine to reseed the generator each time you start up MATLAB, or before you run some kind of large calculation involving random numbers, it is actually not a good idea to reseed the generator too frequently within a session, because this can affect the statistical properties of your random numbers.
So far, you've seen how to reset the random number generator to its default settings, and reseed it using a seed that is created using the current time. rng also provides a way to reseed it using a specific seed.
As with "shuffle" there is a caveat when reseeding MATLAB's random number generator, because it affects all subsequent output from rand, randi, and randn. Unless you need repeatability or uniqueness, it is usually advisable to simply generate random values without reseeding the generator. If you do need to reseed the generator, that is usually best done at the command line, or in a spot in your code that is not easily overlooked.
Not only can you reseed the random number generator as shown above, you can also choose the type of random number generator that you want to use. Different generator types produce different sequences of random numbers, and you might, for example, choose a specific type because of its statistical properties. Or you might need to recreate results from an older version of MATLAB that used a different default generator type.
One other common reason for choosing the generator type is that you are writing a validation test that generates "random" input data, and you need to guarantee that your test can always expect exactly the same predictable result. If you call rng with a seed before creating the input data, it reseeds the random number generator. But if the generator type has been changed for some reason, then the output from rand, randi, and randn will not be what you expect from that seed. Therefore, to be 100% certain of repeatability, you can also specify a generator type.
However, because the default random number generator settings may change between MATLAB releases, using "default" does not guarantee predictable results over the long-term. "default" is a convenient way to reset the random number generator, but for even more predictability, specify a generator type and a seed.
The third field, State, contains a copy of the generator's current state vector. This state vector is the information that the generator maintains internally in order to generate the next value in its sequence of random numbers. Each time you call rand, randi, or randn, the generator that they share updates its internal state. Thus, the state vector in the settings structure returned by rng contains the information necessary to repeat the sequence, beginning from the point at which the state was captured.
While just being able to see this output is informative, rng also accepts a settings structure as an input, so that you can save the settings, including the state vector, and restore them later to repeat calculations. Because the settings contain the generator type, you'll know exactly what you're getting, and so "later" might mean anything from moments later in the same MATLAB session, to years (and multiple MATLAB releases) later. You can repeat results from any point in the random number sequence at which you saved the generator settings. For example
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