No abstract

No abstract

Multi-spectral thermometry plays an important role in the field of high temperature measurement. The data processing is the focus of multi-spectral thermometry, which usually requires an hypothetical emissivity model.　The deviation between the hypothetical model and the true emissivity can inevitably affect the accuracy of the true temperature inversion. In order to eliminate the influence of hypothetical model on data processing results, a ridge estimation-sequence quadratic programming constraint optimization algorithm for data processing in multi-spectral thermometry is proposed. The algorithm can simultaneously calculate the true temperature and spectral emissivity of the target without hypothetical emissivity model in advance. The efficiency and superiority of proposed algorithm can be confirmed by the simulation results.

No abstract

The data mining methodology for identification and detection of bugs is an important application. Especially separating bugs from non-bugs is a general challenge. When software developers classify bug reports, they may misclassify bug reports with bias and errors. All