Abstract: A novel nonlinear method for area-wide near surface air temperature precision retrieval is described. The steps include: First, construct the 1st sub-model modelVEC1 to the f-th sub-model modelVECf. Establish and normalize raw data vectors of each gridded pixel sBlkVEC in the targeted area. Calculate the retrieved full maps (surfTf) of near surface air temperatures using each sub-model. Then, identify abnormal samples and define their near-range regions in surfTf Apply a selective arithmetic mean (SAM) approach to achieve precision temperature map surfT. And finally apply further modification to the pixels of surfT where pixlf?badsurfT?f is true to all f=1, 2, 3, . . . . Using the super nonlinear algorithm, this invention provides a solution of retrieving near surface air temperature based on combinations of various factors (information fusion) to achieve satisfied prediction errors, which are independent of cloud levels and topographic characteristics.

Abstract: A novel nonlinear method for area-wide near surface air temperature precision retrieval is described. The steps include: First, construct the 1st sub-model modelVEC1 to the f-th sub-model modelVECf. Establish and normalize raw data vectors of each gridded pixel sBlkVEC in the targeted area. Calculate the retrieved full maps (surfTf) of near surface air temperatures using each sub-model. Then, identify abnormal samples and define their near-range regions in surfTf Apply a selective arithmetic mean (SAM) approach to achieve precision temperature map surfT. And finally apply further modification to the pixels of surfT where pixlf?badsurfT?f is true to all f=1, 2, 3, . . . . Using the super nonlinear algorithm, this invention provides a solution of retrieving near surface air temperature based on combinations of various factors (information fusion) to achieve satisfied prediction errors, which are independent of cloud levels and topographic characteristics.

Abstract: A method of temperature control is applied to a cabinet in which a fan is disposed and the fan runs based on an input signal. The method includes obtaining a temperature signal corresponding to the cabinet, calculating a deviation signal between the input signal and the temperature signal, executing fuzzy learning to obtain a first proportional parameter, a first integral parameter and a first derivative parameter based on the deviation signal, generating a driver signal to execute a proportional-integral-derivative (PID) control based on the first proportional parameter, the first integral parameter and the first derivative parameter, wherein the driver signal is for driving the fan to adjust a temperature of the cabinet.

Abstract: A method of temperature control is applied to a cabinet in which a fan is disposed and the fan runs based on an input signal. The method includes obtaining a temperature signal corresponding to the cabinet, calculating a deviation signal between the input signal and the temperature signal, executing fuzzy learning to obtain a first proportional parameter, a first integral parameter and a first derivative parameter based on the deviation signal, generating a driver signal to execute a proportional-integral-derivative (PID) control based on the first proportional parameter, the first integral parameter and the first derivative parameter, wherein the driver signal is for driving the fan to adjust a temperature of the cabinet.