Wavelet-based neural network with fuzzy-logic adaptivity for nuclear image restoration

1996 ◽  
Vol 84 (10) ◽  
pp. 1458-1473 ◽  
Author(s):  
Wei Qian ◽  
L.P. Clarke
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Image Restoration

Review of Improvement of Web Search Based on Web Log File

2012 ◽  
Vol 3 (2) ◽  
pp. 298-300 ◽  
Author(s):  
Soniya P. Chaudhari ◽  
Prof. Hitesh Gupta ◽  
S. J. Patil
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Web Search ◽  
Pattern Mining ◽  
Efficiency Improvement ◽  
Web Searching ◽  
Important Method ◽  
Web Log ◽  
Journal Paper ◽  
Log File

In this paper we review various research of journal paper as Web Searching efficiency improvement. Some important method based on sequential pattern Mining. Some are based on supervised learning or unsupervised learning. And also used for other method such as Fuzzy logic and neural network

scholarly journals Image Restoration by Learning Morphological Opening-Closing Network

2020 ◽  
Vol 4 (1) ◽  
pp. 87-107
Author(s):  
Ranjan Mondal ◽  
Moni Shankar Dey ◽  
Bhabatosh Chanda
Keyword(s):  
Neural Network ◽  
Image Restoration ◽  
State Of The Art ◽  
Source Code ◽  
Back Propagation ◽  
Image Features ◽  
Main Difficulty ◽  
The Right ◽  
Right Order ◽  
Morphological Opening

AbstractMathematical morphology is a powerful tool for image processing tasks. The main difficulty in designing mathematical morphological algorithm is deciding the order of operators/filters and the corresponding structuring elements (SEs). In this work, we develop morphological network composed of alternate sequences of dilation and erosion layers, which depending on learned SEs, may form opening or closing layers. These layers in the right order along with linear combination (of their outputs) are useful in extracting image features and processing them. Structuring elements in the network are learned by back-propagation method guided by minimization of the loss function. Efficacy of the proposed network is established by applying it to two interesting image restoration problems, namely de-raining and de-hazing. Results are comparable to that of many state-of-the-art algorithms for most of the images. It is also worth mentioning that the number of network parameters to handle is much less than that of popular convolutional neural network for similar tasks. The source code can be found here https://github.com/ranjanZ/Mophological-Opening-Closing-Net

A fuzzy logic neural network algorithm for compressor crankshaft-rolling bearing system optimization

2021 ◽  
pp. 1-7
Author(s):  
Zheng Zhang ◽  
Jianrong Zheng
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Dynamic Analysis ◽  
Weighted Average ◽  
Rolling Bearing ◽  
System Optimization ◽  
Simulation Software ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Bearing System

Taking the crankshaft-rolling bearing system in a certain type of compressor as the research objective, dynamic analysis software is used to conduct detailed dynamic analysis and optimal design under the rated power of the compressor. Using Hertz mathematical formula and the analysis method of the superstatic orientation problem, the relationship expression between the bearing force and deformation of the rolling bearing is solved, and the dynamic analysis model of the elastic crankshaft-rolling bearing system is constructed in the simulation software ADAMS. The weighted average amplitude of the center of the neck between the main bearings is used as the target, and the center line of the compressor cylinder is selected as the design variable. Finally, an example analysis shows that by introducing the fuzzy logic neural network algorithm into the compressor crankshaft-rolling bearing system design, the optimal solution between the design variables and the objective function can be obtained, which is of great significance to the subsequent compressor dynamic design.

scholarly journals Applicability of Fuzzy Logic and Artificial Neural Network for Unpaved Airfield Surface Bearing Strength Prediction

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3373
Author(s):  
Ludek Cicmanec
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Logic ◽  
Regression Function ◽  
Large Data ◽  
Soil Strength ◽  
Data Sets ◽  
Bearing Strength ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The main objective of this paper is to describe a building process of a model predicting the soil strength at unpaved airport surfaces (unpaved runways, safety areas in runway proximity, runway strips, and runway end safety areas). The reason for building this model is to partially substitute frequent and meticulous inspections of an airport movement area comprising the bearing strength evaluation and provide an efficient tool to organize surface maintenance. Since the process of building such a model is complex for a physical model, it is anticipated that it might be addressed by a statistical model instead. Therefore, fuzzy logic (FL) and artificial neural network (ANN) capabilities are investigated and compared with linear regression function (LRF). Large data sets comprising the bearing strength and meteorological characteristics are applied to train the likely model variations to be subsequently compared with the application of standard statistical quantitative parameters. All the models prove that the inclusion of antecedent soil strength as an additional model input has an immense impact on the increase in model accuracy. Although the M7 model out of the ANN group displays the best performance, the M3 model is considered for practical implications being less complicated and having fewer inputs. In general, both the ANN and FL models outperform the LRF models well in all the categories. The FL models perform almost equally as well as the ANN but with slightly decreased accuracy.

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