Abstract: A SiC ohmic contact preparation method is provided and includes: selecting a SiC substrate; preparing a graphene/SiC structure by forming a graphene on a Si-face of the SiC substrate; depositing an Au film on the graphene of the graphene/SiC structure; forming a first transfer electrode pattern on the Au film by a first photolithography; etching the Au film uncovered by the first transfer electrode pattern through a wet etching; etching the graphene uncovered by the Au film through a plasma etching after the wet etching; forming a second transfer electrode pattern on the SiC substrate by a second photolithography; depositing an Au material on the Au film exposed by the second transfer electrode pattern and forming an Au electrode and then annealing. The graphene reduces potential barrier associated with the SiC interface, specific contact resistance of ohmic contact reaches the order of 10?7˜10?8 ?·cm2, and the method has high repeatability.

Abstract: An operation control system for a gas-steam combined cycle generator unit-includes a real-time operation data acquisition module, to collect the operation parameter data and power generation data of a power plant unit, an operation status evaluation index mining module, to mine and analyze the operation parameter data of a power plant unit to get key parameters, an operation status evaluation index extraction module, to obtain characteristic variables, an operation characteristic parameter prediction module, to predict the said characteristic variables and obtain the predicted value and the corresponding change trend, and an operation intelligent control module, to realize the intelligent control of parameters. The operation control system can guide the optimal operation of power plant units, and improve their operation reliability and economy.

Abstract: A method comprises performing data pre-processing of initial signals to obtain pre-processed initial signals; building a first machine learning model based on the pre-processed initial signals; generating output signals using the first machine learning model; computing ranks of the output signals; computing classifications of the output signals; and building a set of stacked machine learning models based on the ranks and the classifications. The set of stacked machine learning models may be used to generate subsurface well log data, NMR data, or other data.

Abstract: A method comprises performing data pre-processing of initial signals to obtain pre-processed initial signals; building a first machine learning model based on the pre-processed initial signals; generating output signals using the first machine learning model; computing ranks of the output signals; computing classifications of the output signals; and building a set of stacked machine learning models based on the ranks and the classifications. The set of stacked machine learning models may be used to generate subsurface well log data, NMR data, or other data.