Qualitative representation of positions in 2-D

2005 ◽  
pp. 25-54
Keyword(s):  
Qualitative Representation

Qualitative representation of spatial knowledge in two-dimensional space

1994 ◽  
Vol 3 (4) ◽  
pp. 479-516 ◽  
Author(s):  
Dimitris Papadias ◽  
Timos Sellis
Keyword(s):  
Dimensional Space ◽  
Spatial Knowledge ◽  
Two Dimensional ◽  
Qualitative Representation

scholarly journals Study on Non-Commutativity Measure of Quantum Discord

Mathematics ◽  
2019 ◽  
Vol 7 (6) ◽  
pp. 543 ◽  
Author(s):  
Jin Liang ◽  
Chengwei Zhang
Keyword(s):  
Explicit Expression ◽  
Quantum Discord ◽  
Dynamic Models ◽  
Quantum Correlations ◽  
Trace Distance ◽  
Qualitative Representation

In this paper, we are concerned with the non-commutativity measure of quantum discord. We first present an explicit expression of the non-commutativity measure of quantum discord in the two-qubit case. Then we compare the geometric quantum discords for two dynamic models with their non-commutativity measure of quantum discords. Furthermore, we show that the results conducted by the non-commutativity measure of quantum discord are different from those conducted by both or one of the Hilbert-Schmidt distance discord and trace distance discord. These intrinsic differences indicate that the non-commutativity measure of quantum discord is incompatible with at least one of the well-known geometric quantum discords in the quantitative and qualitative representation of quantum correlations.

scholarly journals Improving the delivery of physical activity services in lung cancer: A qualitative representation of the patient’s perspective

2018 ◽  
Vol 28 (1) ◽  
pp. e12946 ◽  
Author(s):  
Catherine L. Granger ◽  
Selina M. Parry ◽  
Lara Edbrooke ◽  
Shaza Abo ◽  
Nina Leggett ◽  
...  
Keyword(s):  
Physical Activity ◽  
Lung Cancer ◽  
Patient’S Perspective ◽  
Qualitative Representation

scholarly journals Gaussian Qualitative Trigonometric Functions in a Fuzzy Circle

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
M. Clement Joe Anand ◽  
Janani Bharatraj
Keyword(s):  
Particle Swarm Optimization ◽  
Unit Circle ◽  
Fuzzy Theory ◽  
Particle Swarm ◽  
Membership Functions ◽  
Trigonometric Functions ◽  
Swarm Optimization ◽  
Qualitative Representation ◽  
Trigonometric Identities

We build a bridge between qualitative representation and quantitative representation using fuzzy qualitative trigonometry. A unit circle obtained from fuzzy qualitative representation replaces the quantitative unit circle. Namely, we have developed the concept of a qualitative unit circle from the view of fuzzy theory using Gaussian membership functions, which play a key role in shaping the fuzzy circle and help in obtaining sharper boundaries. We have also developed the trigonometric identities based on qualitative representation by defining trigonometric functions qualitatively and applied the concept to fuzzy particle swarm optimization using α-cuts.

Qualitative representation of trends: an alternative approach to process diagnosis and control

2008 ◽  
Vol 57 (10) ◽  
pp. 1525-1532 ◽  
Author(s):  
K. Villez ◽  
C. Rosén ◽  
F. Anctil ◽  
C. Duchesne ◽  
P. A. Vanrolleghem
Keyword(s):  
Supervisory Control ◽  
Pilot Scale ◽  
Qualitative Description ◽  
Data Series ◽  
Multivariate Techniques ◽  
Case Based Reasoning ◽  
Statistical Process ◽  
Process Diagnosis ◽  
Qualitative Representation ◽  
And Control

The potential for qualitative representation of trends in the context of process diagnosis and control is evaluated in this paper. The technique for qualitative description of the data series is relatively new to the field of process monitoring and diagnosis and is based on the cubic spline wavelet decomposition of the data. It is shown that the assessed qualitative description of trends can be coupled easily with existing process knowledge and does not demand the user to understand the underlying technique in detail, in contrast to, for instance, multivariate techniques in Statistical Process Control. The assessed links can be integrated straightforwardly into the framework of supervisory control systems by means of look-up tables, expert systems or case-based reasoning frameworks. This in turn allows the design of a supervisory control system leading to fully automated control actions. The technique is illustrated by an application to a pilot-scale SBR.

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