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As performance is part of the specification of a program – a program that is unusably slow is not fit for purpose: a video game with 60 Hz (frames-per-second) is acceptable, but 6 frames-per-second is unacceptably choppy – performance is a consideration from the start, to ensure that the system is able to deliver sufficient performance, and ...
Exponential smoothing or exponential moving average (EMA) is a rule of thumb technique for smoothing time series data using the exponential window function.Whereas in the simple moving average the past observations are weighted equally, exponential functions are used to assign exponentially decreasing weights over time.
In C and C++, volatile is a type qualifier, like const, and is a part of a type (e.g. the type of a variable or field). The behavior of the volatile keyword in C and C++ is sometimes given in terms of suppressing optimizations of an optimizing compiler: 1- don't remove existing volatile reads and writes, 2- don't add new volatile reads and writes, and 3- don't reorder volatile reads and writes.
Here's a question: Why is my computer so slow? The answer to this age-old question could be any number of things. So, I'm going to identify a few steps you can take to hopefully remedy the ...
Temporary variables, along with XOR swaps and arithmetic operators, are one of three main ways to exchange the contents of two variables. To swap the contents of variables "a" and "b" one would typically use a temporary variable temp as follows, so as to preserve the data from a as it is being overwritten by b: temp := a a := b b := temp
Another downside of one-hot encoding is that it causes multicollinearity between the individual variables, which potentially reduces the model's accuracy. [citation needed] Also, if the categorical variable is an output variable, you may want to convert the values back into a categorical form in order to present them in your application. [10]
Dependent variables in these processes are other measurements that represent either control objectives or process constraints. MPC uses the current plant measurements, the current dynamic state of the process, the MPC models, and the process variable targets and limits to calculate future changes in the dependent variables.
This is done by introducing fast-scale and slow-scale variables for an independent variable, and subsequently treating these variables, fast and slow, as if they are independent. In the solution process of the perturbation problem thereafter, the resulting additional freedom – introduced by the new independent variables – is used to remove ...