Regression
This dataset was generated using high-fidelity air combat simulations to develop and evaluate Weapon Engagement Zone (WEZ) prediction models. It contains data for various Beyond Visual Range (BVR) air combat scenarios, capturing diverse conditions and configurations between a shooter aircraft and a target.
The dataset is split into factorial and random design datasets, with outputs representing critical WEZ parameters, including the maximum range (Rmax) and the no-escape zone (Rnez).
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This dataset contains the input and output data from an industrial case study aiming to detect undesired non-intuitive behavior in an engineered complex system (an Autonomous Surface Vessel (ASV) on a Search and Rescue (SAR) mission). We used the Taguchi method to set up experiments, conducted the experiments in a case company specific test arena, and performed different forms of regression analysis.
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Transistor models are crucial for circuit simulation. Reliable design of high-performance circuits requires that transistor characteristics are adequately represented, which makes accurate and fast models indispensable. Scattering (or S-)parameters are perhaps the most widely used RF characteristics, employed in the design and analysis of linear devices and circuits for calculation of the input and output impedance, isolation, gain, as well as stability, all being important performance figures for small-signal or low-noise amplifiers.
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The development of electronic nose (e-nose) for a rapid, simple, and low-cost meat assessment system becomes the concern of researchers in recent years. Hence, we provide time-series datasets that were recorded from e-nose for beef quality monitoring experiment. This dataset is originated from 12 type of beef cuts including round (shank), top sirloin, tenderloin, flap meat (flank), striploin (shortloin), brisket, clod/chuck, skirt meat (plate), inside/outside, rib eye, shin, and fat.
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1591 Views
Characteristic impedance Result of Microstrip Transmission lines with 3D EM simulation tool
These data had been donated by Peyman Mahouti in 2019.
Donators note:
Please cite the following paper if you use this data set:
[1] Mahouti P, Gunes F, Belen MA, Demirel S. Symbolic Regression for Derivation of an Accurate Analytical Formulation Using Big Data : An Application Example. ACES JOURNAL 2017; 32(5): 574-591.
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