In "Proposed model for change point estimation", the proposed ANN-based methodology consisting of two modules, fault detection/fault diagnosis module and change point estimation module, is described.
The study was conducted using an unprecedentedly large-scale survey-based methodology, consisting of respondents who practice ASD and who had experience practicing plan-driven software development in the past.
A novel methodology, consisting of a Bayesian hierarchical model, is used in this paper.
The error metrics methodology consists of objective measures based on theoretical models using computers to compare images/videos.
This methodology consists of two approaches based on an interaction force and behavior regulation rule for a robot.
The methodology consists of five stages: base-case process definition and characterization, pre-benchmarking, systems interactions analysis, implementation strategy and post-benchmarking.
To ensure the highest quality, it is essential that the methodology consists of appropriate analysis tools, an extensive validation base, and last but not least highly educated engineers applying the methodology.
The methodology consisted of a desk review followed by country visits and a debriefing workshop based on which the final report was prepared.
The 'best subsets' methodology consists of an initial filter removing models that omit (omission error = predicting absence in areas of known presence) heavily based on the extrinsic testing data, and a second filter based on an index of commission error (= predicting presence in areas of known absence), in which models predicting very large and very small areas are removed from consideration.
The methodology consists of several steps, including the building of a simulation model of the system, the development of regression model(s) of the system based on simulation and D-optimal design, and finally the development of a MINLP model.
The methodology consists of six indicators: academic reputation (40%), employer reputation (10%), faculty student ratio (20%), citations per faculty (20%), international students (5%), and international faculty (5%).
