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The upper bound can be derived following the same steps as Appendix B. It turns out that at high SNR, the gap rate is upper bounded by, Using Lemma 2, the expected gap rate is upper bounded by, (45).
This paper shows that the values predicted with the ANFIS can be used to predict damper gap rate of HVAC system quite accurately.
In order to derive the gap rate with the full CSIT, we characterize in Lemma 2 the angle between the normalized vector vector and its quantized vector.
After the bottom gap rate β is over 25%%, the vortex B completely disappears and the separation position III merges with the reattachment position II.
The temperature fields around heat pipes were given in the paper in different containing water rate of the soil, gap rate of the soil and different disposal of heat pipes.
Ambiguous and saturated aligned regions were removed using the BMGE software 1.1 (parameter gap rate set to 0.5) [ 66].
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The aim of this study is to design a HVAC system which damper gap rates have been controlled by PID controller.
This model is an efficient and robust tool to predict damper gap rates and fan motor speed to minimize energy consumption of the HVAC system.
The fan motor speed and damper gap rates were predicted using wavelet packet decomposition (WPD), entropy, and neural network (NN) techniques.
Besides, the damper gap rates of a HVAC system with only one zone were predicted by using Artificial Neural Fuzzy Interface System (ANFIS) method.
Its fan motor speed and damper gap rates were controlled by two controllers (i.e. a PID controller and an intelligent controller) in real time to minimize its energy consumption.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com