Evaluating the quality of argon‐arc welded joints using neural network models with regression input

2000 ◽  
Vol 14 (7) ◽  
pp. 559-564
Author(s):  
E A Gladkov ◽  
A V Maloletkov ◽  
R A Perkovskii ◽  
A I Gavrilov
Keyword(s):  
Neural Network ◽  
Welded Joints ◽  
Network Models ◽  
Neural Network Models

Predicting the quality of TIG butt welded joints using neural network models

1998 ◽  
Vol 12 (3) ◽  
pp. 215-219 ◽  
Author(s):  
E A Gladkov ◽  
A V Maloletkov ◽  
R A Perkovskii
Keyword(s):  
Neural Network ◽  
Welded Joints ◽  
Network Models ◽  
Neural Network Models

scholarly journals Demand Modelling in Telecommunications

10.14311/1121 ◽  
2009 ◽  
Vol 49 (2) ◽  
Author(s):  
M. Chvalina
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Network Models ◽  
Short Term ◽  
Neural Network Models ◽  
Term Forecast ◽  
Artificial Intelligence Methods ◽  
Demand Modelling

This article analyses the existing possibilities for using Standard Statistical Methods and Artificial Intelligence Methods for a short-term forecast and simulation of demand in the field of telecommunications. The most widespread methods are based on Time Series Analysis. Nowadays, approaches based on Artificial Intelligence Methods, including Neural Networks, are booming. Separate approaches will be used in the study of Demand Modelling in Telecommunications, and the results of these models will be compared with actual guaranteed values. Then we will examine the quality of Neural Network models. 

scholarly journals System of providing sustainability of tower cranes from overturn in extreme wind loads

2018 ◽  
Vol 224 ◽  
pp. 02086
Author(s):  
Pavel Sorokin ◽  
Alexey Mishin ◽  
Vitaliy Antsev ◽  
Alexey Red’kin
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Network Models ◽  
Wind Loads ◽  
Neural Network Models ◽  
Quality Of Work ◽  
Development Stages ◽  
The Neural Network ◽  
Extreme Wind

The article is devoted to the issues of ensuring stability of tower cranes from overturn. The development stages of devices for ensuring tower cranes safety are examined and their shortcomings are revealed. The system consisting of subsystems and drives is proposed and their interaction is presented. The article deals with a subsystem based on artificial intelligence methods. The neural network models of forecasting wind parameters are developed. The quality of work of neural network models is estimated. The ways of further topic development are suggested.

Implementation of Neural Network in Classifying the Product Quality for the ABS Metallization

2020 ◽  
Vol 305 ◽  
pp. 139-146
Author(s):  
Yuh Wen Chen ◽  
Sheng Chieh Wang ◽  
Pin Chuan Yao ◽  
Wen Tsung Lin ◽  
Aisyah Larasati ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Quality Control ◽  
Surface Treatment ◽  
Network Models ◽  
Acrylonitrile Butadiene Styrene ◽  
Operational Parameters ◽  
Neural Network Models ◽  
Quality Control Tool

The surface treatment conditions of a plastic surface are related to the quality of finished products. Usually, more than 20 causes dominate the success of electroplating for acrylonitrile butadiene styrene (ABS). Thus, the quality control is very complicated and challenging. Even nowadays, most of the production quality still relies on the operator's experience and intuition. This research takes a company of water hardware in Taiwan as the research object. We propose a revolutionary concept of quality management, combining artificial intelligence and surface treatment process altogether. We set up a parameter monitoring system during production to predict the quality of ABS metallization using neural network models such as artificial intelligence forms the basis of the intelligent manufacturing system. It can be used as a quality control tool to improve quality yield and industrial competitiveness. Totally 13 operational parameters (causes) and one quality parameter (consequence) of the electroplating tanks were collected from time to time to build the NN models. Interestingly, we finally find the fuzzy NN model performs better than the precise NN model. We conclude this is resulting from the limitation and vagueness of data.

Neural-network models for predicting the quality of roasted electrodes

2011 ◽  
Vol 41 (1) ◽  
pp. 23-30
Author(s):  
Z. G. Salikhov ◽  
A. L. Rutkovskii ◽  
S. V. Soshkin ◽  
G. S. Soshkin
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Neural Network Models

scholarly journals Adversarial Image Perturbation with a Genetic Algorithm

2021 ◽  
Author(s):  
Rok Kukovec ◽  
Špela Pečnik ◽  
Iztok Fister Jr. ◽  
Sašo Karakatič
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Prior Knowledge ◽  
Network Models ◽  
Visual Assessment ◽  
Training Process ◽  
Neural Network Models ◽  
The Neural Network ◽  
Statistical Metrics

The quality of image recognition with neural network models relies heavily on filters and parameters optimized through the training process. These filters are di˙erent compared to how humans see and recognize objects around them. The di˙erence in machine and human recognition yields a noticeable gap, which is prone to exploitation. The workings of these algorithms can be compromised with adversarial perturbations of images. This is where images are seemingly modified imperceptibly, such that humans see little to no di˙erence, but the neural network classifies t he m otif i ncorrectly. This paper explores the adversarial image modifica-tion with an evolutionary algorithm, so that the AlexNet convolutional neural network cannot recognize previously clear motifs while preserving the human perceptibility of the image. The ex-periment was implemented in Python and tested on the ILSVRC dataset. Original images and their recreated counterparts were compared and contrasted using visual assessment and statistical metrics. The findings s uggest t hat t he human eye, without prior knowledge, will hardly spot the di˙erence compared to the original images.

scholarly journals Optimization of parameters of neuro-fuzzy model

2020 ◽  
Vol 19 (1) ◽  
pp. 229
Author(s):  
Lyalya Bakievna Khuzyatova ◽  
Lenar Ajratovich Galiullin
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Fuzzy Model ◽  
Network Models ◽  
Knowledge Bases ◽  
Fuzzy Neural Networks ◽  
Neural Network Models ◽  
Neuro Fuzzy ◽  
Fuzzy Neural

<p>The need for increasing the efficiency of the neuron-fuzzy model in the formation of knowledge bases is being updated. The task is to develop methods and algorithms for presetting and optimizing the parameters of a fuzzy neural network. To solve difficult formalized tasks, it is necessary to develop decision support systems - expert systems based on a knowledge base. ES developers are constantly faced with the problems of “extraction” and formalization of knowledge, as well as the search for new ways to obtain it. To do this, use the extraction, acquisition and formation of knowledge. Currently, the formation of knowledge bases is relevant for the creation of hybrid technologies - fuzzy neural networks that combine the advantages of neural network models and fuzzy systems. The analysis of the efficiency of the fuzzy neural network carried out in the work showed that the quality of training of the NN largely depends on the choice of the number of fuzzy granules for input drugs. In addition, to use fuzzy information formalized by the mathematical apparatus of fuzzy logic, procedures are required for selecting optimal forms and presetting the parameters of the corresponding membership functions (MF).</p>

PREDICTING SOFTWARE QUALITY, DURING TESTING, USING NEURAL NETWORK MODELS: A COMPARATIVE STUDY

1994 ◽  
Vol 01 (03) ◽  
pp. 303-319 ◽  
Author(s):  
TAGHI M. KHOSHGOFTAAR ◽  
ROBERT M. SZABO
Keyword(s):  
Neural Network ◽  
Software Quality ◽  
Principal Components ◽  
Network Modeling ◽  
Network Models ◽  
Neural Nets ◽  
Neural Network Modeling ◽  
Neural Network Models ◽  
Predictive Quality

The application of statistical modeling techniques has been an intensely pursued area of research in the field of software engineering. The goal has been to model software quality and use that information to better understand the software development process. Neural network modeling methods have recently been applied to this field. The results reported indicate that neural network models have better predictive quality than some statistical models when predicting reliability and the number of faults. In this paper, we will investigate the application of principal components analysis to neural network modeling as a way of improving the predictive quality of neural network quality models. Using data we collected from a large commercial software system, we developed a multiple regression model using the principal components. Then, we trained two neural nets, one with raw data, and one with principal components. Then, we compare the predictive quality of the three competing models for a variety of quality measures.

scholarly journals EXPERIENCE OF NEURAL NETWORK MODELING IN MANAGING THE ACHIEVEMENT OF CRITERIA FOR A NEW MODEL OF A MEDICAL ORGANIZATION THAT USES LEAN TECHNOLOGIES

2020 ◽  
Vol 30 (5) ◽  
pp. 673-678
Author(s):  
S.B. Petrov ◽  
S.D. Mazunina
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Models ◽  
Performance Criteria ◽  
Neural Network Modeling ◽  
Neural Network Models ◽  
Medical Organization ◽  
New Model ◽  
Generalized Regression

Nowadays the scientific developments connected with increase of readiness of the medical institutions, rendering primary medical and sanitary aid, to work with application of methods and tools of lean technologies for increase of level of availability and quality of medical aid to the population of Russia acquire urgency. The aim of the study is to assess the prognostic importance of common neural network models to analyze the value components of the reception of a local therapist, affecting the level of satisfaction with the quality of medical care, from the position of management to achieve the criteria of a new model of a medical organization using lean technologies. The following types of neural network models were studied: based on a multilayer perceptron, a radial basis function, and a generalized regression neural network. Models based on multiple linear regression equations were used as a control group of networks. In total, 50 artificial neural networks were obtained and analyzed. The effectiveness of neural network models was evaluated based on the following parameters: the ratio of standard deviations of the forecast error and the source data, as well as the Pearson correlation between the observed and predicted indicators of the model. Among the studied neural network models, models based on a multi-layer perceptron and generalized regression neural networks have the highest quality of prediction, which makes them promising for use in systems that monitor and predict the structure of the value component of the main processes in medical organizations for patients. The proposed neural network models can become the basis for creating information management systems that monitor the achievement of performance criteria for a new model of a medical organization that uses lean technologies.

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