Shift operator techniques for the classification of multipole‐phonon states. VII. Self‐consistent single step algorithm for R(5) O0l eigenstate and eigenvalue determination

1980 ◽  
Vol 21 (11) ◽  
pp. 2635-2639 ◽  
Author(s):  
H. E. De Meyer ◽  
G. Vanden Berghe
Keyword(s):  
Shift Operator ◽  
Single Step ◽  
Self Consistent ◽  
Step Algorithm

A single-step algorithm for oscillatory problems

1989 ◽  
Vol 5 (2) ◽  
pp. 113-120 ◽  
Author(s):  
R. M. Thomas ◽  
S. J. Evans
Keyword(s):  
Single Step ◽  
Step Algorithm

A LITERATURE REVIEW AND CLASSIFICATION OF ENTERPRISE SOFTWARE SELECTION APPROACHES

2009 ◽  
Vol 08 (02) ◽  
pp. 217-238 ◽  
Author(s):  
CEYDA GÜNGÖR ŞEN ◽  
HAYRİ BARAÇLI ◽  
SELÇUK ŞEN
Keyword(s):  
Decision Making ◽  
Literature Review ◽  
Selection Process ◽  
Single Step ◽  
Software Selection ◽  
Enterprise Software ◽  
Software Products ◽  
Functional Perspective ◽  
Comprehensive Classification

The evaluation and selection of enterprise software has become increasingly difficult for decision makers due to a large number of software products available for many applications. Therefore, systematic and repeatable approaches are needed in order to select the appropriate product that best meets the customer requirements. In this paper, we present a literature review and classification of enterprise software selection approaches from the period 1982–2007. In addition to classifying the selected approaches by functional perspective, the decision-making methods used by these approaches in the generic phases of software selection process are also presented. Results are summarized with comprehensive classification tables. This study is intended as a source for academics and practitioners alike interested in software selection problem, especially those who want to see alternative decision-making techniques that can be used to support the every single step of software selection process.

Rough Set Diagnostic Frameworks for Gas Turbine Fault Classification

2013 ◽  
Author(s):  
L. Wang ◽  
Y. G. Li ◽  
M. F. Abdul Ghafir
Keyword(s):  
Gas Turbine ◽  
Rough Set ◽  
Single Step ◽  
Fault Classification ◽  
Second Step ◽  
Turbine Engine ◽  
The Past ◽  
The Individual ◽  
Component Faults

Fault classification has become one of the main features in gas turbine health monitoring. Hence techniques such as gas path analysis, artificial neural networks, expert systems, fuzzy logic and many others have been developed for this purpose in the past. In this paper, an alternative rough set based diagnostic method using enhanced fault signatures combined with three fault classification frameworks for gas turbine fault classification have been introduced, i.e. Framework 1 with a single step to classify single and dual component faults, Framework 2 with the first step to identify weather it is a single or dual component faults and the second step to identify the individual faults, and Framework 3 with the first step to identify faults associated with component types and the second step to identify the individual faults. Such frameworks have been applied to the fault classification of a model two-spool turbofan gas turbine engine implemented with single and dual component faults to test the effectiveness of the frameworks. It has been demonstrated in the application that all three framework configurations can provide satisfactory fault classification and that Framework 1 has higher fault classification success rate than that of Frameworks 2 and 3. In addition, Frameworks 2 and 3 have better performance in identifying fault types than Framework 1.

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