Intelligent Forecasting on Composite Regeneration Endpoint of DPF in the Vehicle
A mathematical model based on fuzzy least squares support vector machines has been developed to forecast the composite regeneration endpoint of the diesel particulate filter in Vehicle. The results show that the relative error of forecasting model of composite regeneration endpoint is less than 3.5%, and the oxygen concentration of exhaust gas has the biggest effect on the endpoint of composite regeneration, followed by the mass flow rate of exhaust gas, the microwave power, the temperature of exhaust gas and the amount of cerium-based additive.
Identification on rock and soil parameters for vibration drilling rock in metal mine based on fuzzy least square support vector machine
A single freedom degree model of drilling bit-rock was established according to the vibration mechanism and its dynamic characteristics. Moreover, a novel identification method of rock and soil parameters for vibration drilling based on the fuzzy least squares (FLS)-support vector machine (SVM) was developed, in which the fuzzy membership function was set by using linear distance, and its parameters, such as penalty factor and kernel parameter, were optimized by using adaptive genetic algorithm. And FLS-SVM identification on rock and soil parameters for vibration drilling was made by changing the input/output data from single freedom degree model of drilling bit-rock. The results of identification simulation and resonance column experiment show that relative error of natural frequency for some hard sand from identification simulation and resonance column experiment is 1.1% and the identification precision based on the fuzzy least squares-support vector machine is high.
Unidirectional hexagonal rare-earth disilicide nanowires on vicinal Si(100)-2×1
Rare-earth disilicide nanowires grown on vicinal Si(100) with a miscut of 2–2.5° toward the [110] azimuth at 600 °C were studied by scanning tunneling microscopy and compared with those grown on flat Si(001). In contrast to rare-earth disilicide nanowires grown on flat Si(100) surfaces, the nanowires grow unidirectionally along the [01̄1] direction of the vicinal Si(100) surface. Rare-earth disilicide nanowires form bundles composed of single nanowire units on both flat and vicinal surfaces. Yet, on the vicinal surface, the bundle width is comparable to the width of the terrace. The average nanowire length on the vicinal substrate is longer than that on the flat substrate. Scanning tunneling spectroscopy shows that the rare-earth disilicide nanowires have metallic properties.