Biblio
The aim of this paper is to present a fresh methodology of improved evidence synthesis for assessing software trustworthiness, which can unwind collisions stemming from proofs and these proofs' own uncertainties. To achieve this end, the paper, on the ground of ISO/IEC 9126 and web software attributes, models the indicator framework by factor analysis. Then, the paper conducts an calculation of the weight for each indicator via the technique of structural entropy and makes a fuzzy judgment matrix concerning specialists' comments. This study performs a computation of scoring and grade regarding software trustworthiness by using of the criterion concerning confidence degree discernment and comes up with countermeasures to promote trustworthiness. Relying on online accounting software, this study makes an empirical analysis to further confirm validity and robustness. This paper concludes with pointing out limitations.
In order to realize the accurate positioning and recognition effectively of the analog circuit, the feature extraction of fault information is an extremely important port. This arrival based on the experimental circuit which is designed as a failure mode to pick-up the fault sample set. We have chosen two methods, one is the combination of wavelet transform and principal component analysis, the other is the factorial analysis for the fault data's feature extraction, and we also use the extreme learning machine to train and diagnose the data, to compare the performance of these two methods through the accuracy of the diagnosis. The results of the experiment shows that the data which we get from the experimental circuit, after dealing with these two methods can quickly get the fault location.