65 results about "Assignment function" patented technology

The invention concerns methods for the wavelength calibration of spectrometers and in particular secondary spectrometers which achieve considerably better calibration accuracies than the conventional peak search methods especially for spectrometers with a relatively large bandwidth. The methods according to the invention are based on the principle of a stepwise relative shift of corresponding measured-value blocks of a model and calibration spectrum where a correlation value is calculated for each shift step. A shift value is determined for each measured-value block at which the correlation value reaches an optimum. A value pair consisting of a position marker of the measured-value block and the associated shift value is determined for each measured-value block. These value pairs represent the design points for fitting to a suitable assignment function. The coefficients obtained in this manner can be used directly as coefficients of a wavelength assignment or be combined with the coefficients of an existing first wavelength assignment in that they for example replace these coefficients or are offset against the coefficients of an existing first wavelength assignment.

The invention provides a reservoir adaptive scheduling method based on the D-S evidence theory, which is characterized by including the following steps: S1, predicting the temperature and precipitation within a future research time through GCMs (Global Cycle Model) and a downscaling technology, taking the prediction result as the input predicted runoff of a hydrological model, and taking the runoff prediction results output by GCMs as multiple future climate change scenarios; S2, constituting a recognition framework with each potential climate change scenario as a focal element Theta in the D-S evidence theory; S3, defining corresponding basic probability assignment functions m1, m2 and m3; S4, synthesizing three evidences based on a modified D-S synthesis rule, and calculating the synthesis probability m123 of the scenarios; S5, letting the weight Alpha of each scenario be equal to a trust function; and S6, constructing a reservoir optimal scheduling model with the maximum weighted average of the average benefits over the years as an optimization objective, setting a linear scheduling function, and optimizing the parameters of the scheduling function through a simulated optimization approach to get an adaptive scheduling scheme.