Chemical analysis: Information contribution of results

1991 ◽  
Vol 56 (3) ◽  
pp. 505-559 ◽  
Author(s):  
Karel Eckschlager
Keyword(s):  
Analytical Chemistry ◽  
Information Theory ◽  
Set Theory ◽  
Fuzzy Set Theory ◽  
Information Gain ◽  
Analytical Signal ◽  
Point Of View ◽  
System A ◽  
Review Analysis ◽  
Theory Point

In this review, analysis is treated as a process of gaining information on chemical composition, taking place in a stochastic system. A model of this system is outlined, and a survey of measures and methods of information theory is presented to an extent as useful for qualitative or identification, quantitative and trace analysis and multicomponent analysis. It is differentiated between information content of an analytical signal and information gain, or amount of information, obtained by the analysis, and their interrelation is demonstrated. Some notions of analytical chemistry are quantified from the information theory and system theory point of view; it is also demonstrated that the use of fuzzy set theory can be suitable. The review sums up the principal results of the series of 25 papers which have been published in this journal since 1971.

FUZZY OPERATOR TREES FOR MODELING RATING FUNCTIONS

2009 ◽  
Vol 08 (04) ◽  
pp. 413-428 ◽  
Author(s):  
YU YI ◽  
THOMAS FOBER ◽  
EYKE HÜLLERMEIER
Keyword(s):  
Machine Learning ◽  
Experimental Study ◽  
Set Theory ◽  
Fuzzy Set Theory ◽  
Model Calibration ◽  
Main Idea ◽  
Aggregation Operators ◽  
Point Of View ◽  
Fuzzy Operator ◽  
Rating Criterion

We introduce a new method for modeling rating (utility) functions which employs techniques from fuzzy set theory. The main idea is to build a hierarchical model, called a fuzzy operator tree (FOT), by recursively decomposing a rating criterion into sub-criteria, and to combine the evaluations of these sub-criteria by means of suitable aggregation operators. Apart from the model conception itself, we propose an evolutionary method for model calibration that fits the parameters of an FOT to exemplary ratings. The possibility to adapt an FOT to a given set of data makes the approach also interesting from a machine learning point of view. The performance of the approach is evaluated by means of a suitable experimental study.

A GENERALIZED APPROACH TO POSSIBILISTIC CLUSTERING ALGORITHMS

2007 ◽  
Vol 15 (supp02) ◽  
pp. 117-138 ◽  
Author(s):  
JIAN ZHOU ◽  
CHIH-CHENG HUNG
Keyword(s):  
Set Theory ◽  
Fuzzy Clustering ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Clustering Algorithms ◽  
Infinite Family ◽  
Real Data ◽  
Point Of View ◽  
New Family ◽  
Possibilistic Clustering

Fuzzy clustering is an approach using the fuzzy set theory as a tool for data grouping, which has advantages over traditional clustering in many applications. Many fuzzy clustering algorithms have been developed in the literature including fuzzy c-means and possibilistic clustering algorithms, which are all objective-function based methods. Different from the existing fuzzy clustering approaches, in this paper, a general approach of fuzzy clustering is initiated from a new point of view, in which the memberships are estimated directly according to the data information using the fuzzy set theory, and the cluster centers are updated via a performance index. This new method is then used to develop a generalized approach of possibilistic clustering to obtain an infinite family of generalized possibilistic clustering algorithms. We also point out that the existing possibilistic clustering algorithms are members of this family. Following that, some specific possibilistic clustering algorithms in the new family are demonstrated by real data experiments, and the results show that these new proposed algorithms are efficient for clustering and easy for computer implementation.

scholarly journals Multiparticle Production Processes from the Information Theory Point of View

2006 ◽  
Vol 25 (1) ◽  
pp. 65-81 ◽  
Author(s):  
O.V. Utyuzh ◽  
O.V. Utyuzh ◽  
G. Wilk ◽  
G. Wilk ◽  
Z. Włodarczyk ◽  
...  
Keyword(s):  
Information Theory ◽  
Point Of View ◽  
Multiparticle Production ◽  
Production Processes ◽  
Theory Point

scholarly journals Fundamental Limits of Non-Orthogonal Multiple Access (NOMA) for the Massive Gaussian Broadcast Channel in Finite Block-Length

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 715
Author(s):  
Jean-Marie Gorce ◽  
Philippe Mary ◽  
Dadja Anade ◽  
Jean-Marc Kélif
Keyword(s):  
Information Theory ◽  
Multiple Access ◽  
Base Station ◽  
Point Of View ◽  
Block Length ◽  
Broadcast Channel ◽  
Superposition Coding ◽  
Fundamental Limits ◽  
Trade Offs ◽  
Theory Point

Superposition coding (SC) has been known to be capacity-achieving for the Gaussian memoryless broadcast channel for more than 30 years. However, SC regained interest in the context of non-orthogonal multiple access (NOMA) in 5G. From an information theory point of view, SC is capacity-achieving in the broadcast Gaussian channel, even when the number of users tends to infinity. However, using SC has two drawbacks: the decoder complexity increases drastically with the number of simultaneous receivers, and the latency is unbounded since SC is optimal only in the asymptotic regime. To evaluate these effects quantitatively in terms of fundamental limits, we introduce a finite time transmission constraint imposed at the base station, and we evaluate fundamental trade-offs between the maximal number of superposed users, the coding block-length and the block error probability. The energy efficiency loss due to these constraints is evaluated analytically and by simulation. Orthogonal sharing appears to outperform SC for hard delay constraints (equivalent to short block-length) and in low spectral efficiency regime (below one bit per channel use). These results are obtained by the association of stochastic geometry and finite block-length information theory.

Study of the Chemical Space of Selected Bacteriostatic Sulfonamides from an Information Theory Point of View

ChemPhysChem ◽  
2016 ◽  
Vol 17 (23) ◽  
pp. 4003-4010 ◽  
Author(s):  
Sheila López-Rosa ◽  
Moyocoyani Molina-Espíritu ◽  
Rodolfo O. Esquivel ◽  
Catalina Soriano-Correa ◽  
Jésus S. Dehesa
Keyword(s):  
Information Theory ◽  
Chemical Space ◽  
Point Of View ◽  
Theory Point

Fuzzy Reliability Assessment of Consecutive r Out of k From n G System Using UGF

2021 ◽  
pp. 181-191
Author(s):  
KM. Meenakshi ◽  
Akshay Kumar ◽  
S. B. Singh
Keyword(s):  
Set Theory ◽  
Generating Function ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Reliability Assessment ◽  
Fuzzy Reliability ◽  
Numerical Example ◽  
System A ◽  
Special Case ◽  
Universal Generating Function

This chapter considers a consecutive r-out of-k-from n: G system taken for the study. The considered system works if at least r elements out of k consecutive elements within n elements work. The consecutive r-out of-k-from n: G system is special case of linear consecutive k-out-of-r-from-n:G system. A technique is applied for analyzing fuzzy reliability of the system using universal generating function and fuzzy set theory. At the end of the chapter, the proposed technique is demonstrated from a numerical example.

scholarly journals Vendor Selection Methodology Based on Multi Criteria Decision Making

2010 ◽  
pp. 10-15
Author(s):  
Pratesh Jayaswal ◽  
M.K. Trivedi ◽  
Lalit Kumar
Keyword(s):  
Set Theory ◽  
Fuzzy Set Theory ◽  
Analytical Models ◽  
Vendor Selection ◽  
Linguistic Variables ◽  
Fuzzy Decision ◽  
System A ◽  
Selection For ◽  
Selection Methodology ◽  
Selection Of

Many analytical models have been developed for addressing the Vender selection for companies. There are a list of vendor for selecting materials. It is very difficult to select a genuine vendor. Since the selection of a vendor is influenced by several parameters which are in linguistic. Form also, therefore to quantity the linguistic variables fuzzy logic and set theory is used. The fuzzy set theory helps in vagueness of the system a fuzzy decision approach is developed where are resourcing of vendors to select suitable vendor for materials is made.

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