scholarly journals Supporting the decision during inter-operational inspection of the electrodes based on the ensemble of neural networks

Mechanik ◽  
2018 ◽  
Vol 91 (12) ◽  
pp. 1060-1063
Author(s):  
Halina Nieciąg ◽  
Rafał Kudelski ◽  
Krzysztof Zagórski
Keyword(s):  
Neural Networks ◽  
Flexible Manufacturing ◽  
Electro Discharge Machining ◽  
Tool Monitoring ◽  
Geometrical Quality ◽  
The Neural Networks ◽  
Physical Phenomena ◽  
Random Nature ◽  
Edm Process

In this paper the method based on the ensemble of artificial neural networks is presented for prediction of the geometrical quality of workpieces after electro-discharge machining (EDM). The complexity and random nature of physical phenomena accompanying the EDM process excluded the theoretical ways. The working electrodes were measured using CMM in flexible manufacturing system. The data obtained from inter-operational measurements were used for the neural networks training. Commonly used measures to express the tool wear turn out to be useless due to their large uncertainty. The tool monitoring and the ensemble method provided more stable diagnosis of the condition of the tool.

scholarly journals The Communication in Distributed Client - Server Systems Used for Management of Flexible Manufacturing Systems

2011 ◽  
Vol 6 (2) ◽  
pp. 297
Author(s):  
Valeriu Lupu ◽  
Doru E. Tiliute
Keyword(s):  
Neural Networks ◽  
Flexible Manufacturing ◽  
Manufacturing Systems ◽  
Labour Productivity ◽  
Necessary Condition ◽  
Client Server ◽  
Labour Productivity Growth ◽  
Economic Progress ◽  
The Neural Networks ◽  
Server Overload

<p>Labour productivity growth is a necessary condition for social and economic progress, in general, and to overcome the economic crisis facing most of the world, in special. <br />Applying innovative solutions, based on the ITC, is one of the straight ways for achieving that objective, both important and necessary. This paper presents a software solution applicable to industrial production based on numerically controlled machines. It involves a distributed client - server communication system, combined with MLP neural networks for the recognition of the 2D industrial objects, viewed from any angle. The information on prismatic and rotational parts to be processed by numerically controlled machine, are stored on a database server together with the corresponding processing programs. The client applications run on the numerically controlled machines and on the robots serving groups of machines. While the machines are fixed, the robots are mobile and can move from a machine to another. As a novelty of the proposed solution, in some well defined situations, the clients are allowed to change messages among them, in order to avoid the server overload. The neural networks are used to help robots to recognize the parts before and during manipulation.</p>

scholarly journals A comparative investigation of neural networks in solving differential equations

2021 ◽  
Vol 15 ◽  
pp. 174830262199860
Author(s):  
Enze Shi ◽  
Chuanju Xu
Keyword(s):  
Neural Networks ◽  
Differential Equations ◽  
Numerical Solutions ◽  
Comparative Investigation ◽  
Loss Functions ◽  
Approximation Properties ◽  
Function Fitting ◽  
Müntz Polynomials ◽  
The Neural Networks

Methods for solving differential equations based on neural networks have been widely proposed in recent years. However, limited open literature to date has reported the choice of loss functions and the hyperparameters of the network and how it influences the quality of numerical solutions. In the present work we intend to address this issue. Precisely we will focus on possible choices of loss functions and compare their efficiency in solving differential equations through a series of numerical experiments. In particular, a comparative investigation is performed between the natural neural networks and Ritz neural networks, with and without penalty for the boundary conditions. The sensitivity on the accuracy of the neural networks with respect to the size of training set, the number of nodes, and the penalty parameter is also studied. In order to better understand the training behavior of the proposed neural networks, we further investigate the approximation properties of the neural networks in function fitting. A particular attention is paid to approximating Müntz polynomials by neural networks.

Novel U-net based deep neural networks for transmission tomography

2021 ◽  
pp. 1-19
Author(s):  
Csaba Olasz ◽  
László G. Varga ◽  
Antal Nagy
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Tomographic Reconstruction ◽  
Similarity Index ◽  
Simulated Data ◽  
Electrical Noise ◽  
Projection Data ◽  
Beam Hardening ◽  
The Neural Networks

BACKGROUND: The fusion of computer tomography and deep learning is an effective way of achieving improved image quality and artifact reduction in reconstructed images. OBJECTIVE: In this paper, we present two novel neural network architectures for tomographic reconstruction with reduced effects of beam hardening and electrical noise. METHODS: In the case of the proposed novel architectures, the image reconstruction step is located inside the neural networks, which allows the network to be trained by taking the mathematical model of the projections into account. This strong connection enables us to enhance the projection data and the reconstructed image together. We tested the two proposed models against three other methods on two datasets. The datasets contain physically correct simulated data, and they show strong signs of beam hardening and electrical noise. We also performed a numerical evaluation of the neural networks on the reconstructed images according to three error measurements and provided a scoring system of the methods derived from the three measures. RESULTS: The results showed the superiority of the novel architecture called TomoNet2. TomoNet2 improved the quality of the images according to the average Structural Similarity Index from 0.9372 to 0.9977 and 0.9519 to 0.9886 on the two data sets, when compared to the FBP method. This network also yielded the best results for 79.2 and 53.0 percent for the two datasets according to Peak-Signal-to-Noise-Ratio compared to the other improvement techniques. CONCLUSIONS: Our experimental results showed that the reconstruction step used in skip connections in deep neural networks improves the quality of the reconstructions. We are confident that our proposed method can be effectively applied to other datasets for tomographic purposes.

scholarly journals Application of Neural Networks in Distribution of the Load in Cluster-Based Web Systems

2021 ◽  
Vol 12 (1) ◽  
pp. 79
Author(s):  
Waldemar Pokuta ◽  
Krzysztof Zatwarnicki
Keyword(s):  
Neural Networks ◽  
Quality Of Service ◽  
Load Distribution ◽  
Production Systems ◽  
Computing Systems ◽  
Resource Configuration ◽  
Artificial Neuron ◽  
The Neural Networks ◽  
Web Systems

Cloud computing systems revolutionized the Internet, and web systems in particular. Quality of service is the basis of resource configuration management in the cloud. Load balancing mechanisms are implemented in order to reduce costs and increase the quality of service. The usage of those methods with adaptive intelligent algorithms can deliver the highest quality of service. In this article, the method of load distribution using neural networks to estimate service times is presented. The discussed and conducted research and experiments include many approaches, among others, application of a single artificial neuron, different structures of the neural networks, and different inputs for the networks. The results of the experiments let us choose a solution that enables effective load distribution in the cloud. The best solution is also compared with other intelligent approaches and distribution methods often used in production systems.

Mathematical Cutting Model Based on Experimental Approach: Drilling Application

2011 ◽  
Vol 62 ◽  
pp. 77-84
Author(s):  
Jean Yves K'nevez ◽  
Olivier Cahuc ◽  
Philippe Darnis ◽  
Raynald Laheurte
Keyword(s):  
Cutting Tools ◽  
Geometrical Parameters ◽  
Geometrical Quality ◽  
Experimental Cutting ◽  
Three Stages ◽  
Set Up ◽  
Stage 1 ◽  
Physical Phenomena ◽  
Cutting Model

The object of this work research tasks relates to the improvement of the cutting tools in drilling within the industrial framework of the aeronautical assembly. The stakes of the study consist in optimizing the lifespan of the tools according to a criterion of respect of geometrical quality and surface quality of the bored holes. This optimization relates to the geometry of the cutting part of the drills. The discussion thread of work thus tends to set up methods which make it possible to bind the geometry of the tools to the final quality of borings carried out. The study was divided into three stages differentiated and complementary to modeling of the physical phenomena induced by the process of drilling. The first stage [1] lies in describing the real geometrical parameters according to the parameters of grinding of the tool. While being based on the modeling of the geometry, the experimental cutting model enables to identify the mechanical actions of cut along the edge. Lastly, the phenomenological [2] aspect of the process associates the parameters of cut the final quality of the bored holes. [3].

An Efficient Hybrid Forecasting Approach for Wind Speed Time Series

2017 ◽  
Vol 7 (9) ◽  
pp. 13
Author(s):  
Bhargavi Munnaluri ◽  
K. Ganesh Reddy
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Speed ◽  
Fossil Fuels ◽  
Renewable Energy Sources ◽  
Support Vector ◽  
Hybrid Forecasting ◽  
Artificial Neural ◽  
Future Utilization

Wind forecasting is one of the best efficient ways to deal with the challenges of wind power generation. Due to the depletion of fossil fuels renewable energy sources plays a major role for the generation of power. For future management and for future utilization of power, we need to predict the wind speed.  In this paper, an efficient hybrid forecasting approach with the combination of Support Vector Machine (SVM) and Artificial Neural Networks(ANN) are proposed to improve the quality of prediction of wind speed. Due to the different parameters of wind, it is difficult to find the accurate prediction value of the wind speed. The proposed hybrid model of forecasting is examined by taking the hourly wind speed of past years data by reducing the prediction error with the help of Mean Square Error by 0.019. The result obtained from the Artificial Neural Networks improves the forecasting quality.

Predictive performance modeling of Habesha brewery wastewater treatment plant using artificial neural networks

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Wastewater Treatment ◽  
Artificial Neural Networks ◽  
Performance Modeling ◽  
Predictive Performance ◽  
Mathematical Tool ◽  
Effluent Water ◽  
Artificial Neural

Recently, process control in wastewater treatment plants (WWTPs) is, mostly accomplished through examining the quality of the water effluent and adjusting the processes through the operator’s experience. This practice is inefficient, costly and slow in control response. A better control of WTPs can be achieved by developing a robust mathematical tool for performance prediction. Due to their high accuracy and quite promising application in the field of engineering, Artificial Neural Networks (ANNs) are attracting attention in the domain of WWTP predictive performance modeling. This work focuses on applying ANN with a feed-forward, back propagation learning paradigm to predict the effluent water quality of the Habesha brewery WTP. Data of influent and effluent water quality covering approximately an 11-month period (May 2016 to March 2017) were used to develop, calibrate and validate the models. The study proves that ANN can predict the effluent water quality parameters with a correlation coefficient (R) between the observed and predicted output values reaching up to 0.969. Model architecture of 3-21-3 for pH and TN, and 1-76-1 for COD were selected as optimum topologies for predicting the Habesha Brewery WTP performance. The linear correlation between predicted and target outputs for the optimal model architectures described above were 0.9201 and 0.9692, respectively.

Comparative analysis of intelligent planning methods

2012 ◽  
Vol 9 (2) ◽  
pp. 53-57 ◽  
Author(s):  
O.V. Darintsev ◽  
A.B. Migranov
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Comparative Analysis ◽  
Mobile Robots ◽  
Sensory Information ◽  
Working Environment ◽  
Planning Methods ◽  
Intelligent Planning

The main stages of solving the problem of planning movements by mobile robots in a non-stationary working environment based on neural networks, genetic algorithms and fuzzy logic are considered. The features common to the considered intellectual algorithms are singled out and their comparative analysis is carried out. Recommendations are given on the use of this or that method depending on the type of problem being solved and the requirements for the speed of the algorithm, the quality of the trajectory, the availability (volume) of sensory information, etc.

scholarly journals Tax Fraud Detection through Neural Networks: An Application Using a Sample of Personal Income Taxpayers

2019 ◽  
Vol 11 (4) ◽  
pp. 86 ◽  
Author(s):  
César Pérez López ◽  
María Delgado Rodríguez ◽  
Sonia de Lucas Santos
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Income Tax ◽  
Fraud Detection ◽  
Personal Income ◽  
Personal Income Tax ◽  
Tax Returns ◽  
The Neural Networks ◽  
Tax Fraud ◽  
Efficiency Rate

The goal of the present research is to contribute to the detection of tax fraud concerning personal income tax returns (IRPF, in Spanish) filed in Spain, through the use of Machine Learning advanced predictive tools, by applying Multilayer Perceptron neural network (MLP) models. The possibilities springing from these techniques have been applied to a broad range of personal income return data supplied by the Institute of Fiscal Studies (IEF). The use of the neural networks enabled taxpayer segmentation as well as calculation of the probability concerning an individual taxpayer’s propensity to attempt to evade taxes. The results showed that the selected model has an efficiency rate of 84.3%, implying an improvement in relation to other models utilized in tax fraud detection. The proposal can be generalized to quantify an individual’s propensity to commit fraud with regards to other kinds of taxes. These models will support tax offices to help them arrive at the best decisions regarding action plans to combat tax fraud.

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