scholarly journals Quality Assessment of Pixel-Level ImageFusion Using Fuzzy Logic

2012 ◽  
Vol 3 (1) ◽  
pp. 11-23 ◽  
Author(s):  
Srinivasa Rao Dammavalam
Keyword(s):  
Fuzzy Logic ◽  
Quality Assessment

High Frequency Modeling of Smart Sensor for Precise Power Quality Assessment Using Fuzzy Logic

2016 ◽  
Vol 25 (06) ◽  
pp. 1650056 ◽  
Author(s):  
G. Sudha ◽  
K. R. Valluvan
Keyword(s):  
Fuzzy Logic ◽  
Quality Assessment ◽  
Power Quality ◽  
High Frequency ◽  
Wide Band ◽  
Electrical Equipment ◽  
Distribution Networks ◽  
Accurate Method ◽  
Energy Difference ◽  
Critical Issue

Power Quality Assessment (PQA) is a critical issue both in transmission and distribution networks. Therefore, it is necessary to precisely classify the disturbances in shortest possible time to prevent the malfunction or increase of losses in the electrical equipment through appropriate remedial techniques. This paper proposes a highly accurate method of PQA through data acquisition using smart sensors, the Rogowski coils (RCs). RCs with wide band width and linear characteristics allow faithful reproduction of high-frequency (HF) signals. In the proposed method, simulated disturbance signals are applied to RC. The output signals are subjected to multilevel wavelet decomposition and then computation of the energy difference in the detailed components between the disturbance signal and the pure sinusoidal waveform is performed to design a fuzzy logic Power Quality Classifier. The classifier is tested by varying the magnitude, frequency and duration of the disturbance and found to be accurate to 98.38%. The classification accuracy depends mainly on the performance of sensors at HFs. Thus, with RCs as sensors instead of conventional instrument transformers, it is found that the precision of power quality classification is greatly improved.

scholarly journals Quality Assessment Parameters for Iterative Image Fusion Using Fuzzy and Neuro Fuzzy Logic and Applications

2015 ◽  
Vol 19 ◽  
pp. 888-894 ◽  
Author(s):  
D. Srinivasa Rao ◽  
M. Seetha ◽  
M.H.M. Krishna Prasad
Keyword(s):  
Fuzzy Logic ◽  
Image Fusion ◽  
Quality Assessment ◽  
Neuro Fuzzy

scholarly journals Fuzzy Logic in Carbonate Reservoir Quality Assessment: A Case Study from Tarim Basin, China

2017 ◽  
Vol 10 (1) ◽  
pp. 195-203
Author(s):  
Weifu Liu
Keyword(s):  
Fuzzy Logic ◽  
Quality Assessment ◽  
Tarim Basin ◽  
Carbonate Reservoir ◽  
Carbonate Reservoirs ◽  
Reservoir Quality ◽  
Pore Throat ◽  
Throat Radius ◽  
Single Well ◽  
Factor Set

Introduction: To address reservoir quality assessment in highly complex and heterogeneous carbonate reservoirs, a methodology utilizing fuzzy logic is developed and presented in this paper. Based on carbonate reservoir characteristics, three parameters reflecting the macroscopic and microscopic of storage abundance, permeability, and median of pore throat radius were selected to establish the factor set and the evaluation criteria. After analysis of core and test data, a membership function is constructed by semi-drop trapezoid method and the weight formula is also determined by reservoir factor sub-index. The developed method then is used to evaluate a carbonate reservoir in the Tarim Basin in China. Based on the result of single well evaluation, the plane classification map of the carbonate reservoir quality is constructed. Results obtained from reservoir quality assessment in the K32 well show that I-level, II-level, and III-level reservoir qualities account for 58%, 37%, 5% of the reservoir, respectively. The results are consistent with the actual production data demonstrating reliability of the proposed method for reservoir quality assessment practices in usually very complex and heterogeneous carbonate reservoirs. Background: Carbonate reservoirs are complex and heterogeneous and this makes their evaluation a difficult task. Objective: To overcome the uncertainties associated with evaluation of complex carbonate reservoirs a reliable method to accurately evaluate carbonate reservoirs is presented. Methods: Fuzzy logic is used to evaluate a carbonate reservoir from Tarim Basin in China. Based on carbonate reservoir characteristics, three parameters reflecting the macroscopic and microscopic of storage abundance, permeability, and median of pore throat radius are selected to establish the factor set and to evaluate the criteria of carbonate reservoir. After the analysis of core and test data, a membership function is reasonably constructed by semi-drop trapezoid method and the weight formula is also determined by reservoir factor sub-index. Results: An effective methodology for the evaluation of reservoir quality in carbonate reservoirs is established by using fuzzy logic. In addition, an example reservoir from China is used to demonstrate the applicability of the developed method. Conclusion: Based on the result of single well evaluation, the plane classification map of the carbonate reservoir is constructed. Favorable zones in the reservoir are also delineated. Evaluation results are consistent with the actual production data of gas and oil which proves that the proposed method is instrumental in reservoir quality assessment.

Air Quality Assessment by Neural Networks

2011 ◽  
pp. 91-117
Author(s):  
Petr Hájek ◽  
Vladimír Olej
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Air Quality ◽  
Quality Assessment ◽  
Air Pollutants ◽  
Quality Indices ◽  
Feature Maps ◽  
Local Conditions ◽  
Mean Values ◽  
Air Quality Assessment

The chapter presents an overview of current methods for air quality assessment, i.e. air stress indices and air quality indices. Traditional air quality assessment is realized using air quality indices which are determined as mean values of selected air pollutants. Thus, air quality assessment depends on strictly given limits without taking into account specific local conditions and synergic relations between air pollutants and other meteorological factors. The stated limitations can be eliminated, e.g. using systems based on neural networks and fuzzy logic. Therefore, the chapter presents a design of a model for air quality assessment based on a combination of Kohonen’s self-organizing feature maps and fuzzy logic neural networks. The model makes it possible to analyze the structure of data, to find localities with similar air quality, and to interpret the classification results by means of fuzzy logic. Due to its generalization ability, it is also possible to classify unknown localities into classes assessing their air quality.

Cocoa bean quality assessment by using hyperspectral images and fuzzy logic techniques

2015 ◽  
Author(s):  
Juan Soto ◽  
Guillermo Granda ◽  
Flavio Prieto ◽  
William Ipanaque ◽  
Jorge Machacuay
Keyword(s):  
Fuzzy Logic ◽  
Quality Assessment ◽  
Hyperspectral Images ◽  
Cocoa Bean
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