Contributions to Ranking an Ergonomic Workstation, Considering the Human Effort and the Microclimate Parameters, Using Neural Networks

2013 ◽  
Vol 371 ◽  
pp. 812-816 ◽  
Author(s):  
Daniel Constantin Anghel ◽  
Nadia Belu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Experimental Results ◽  
Work Place ◽  
Feed Forward Neural Network ◽  
Linear Relationships ◽  
The Neural Network ◽  
Manufacture Industry ◽  
Human Effort ◽  
Hidden Layer

The paper presents a method to use a feed forward neural network in order to rank a working place from the manufacture industry. Neural networks excel in gathering difficult non-linear relationships between the inputs and outputs of a system. The neural network is simulated with a simple simulator: SSNN. In this paper, we considered as relevant for a work place ranking, 6 input parameters: temperature, humidity, noise, luminosity, load and frequency. The neural network designed for the study presented in this paper has 6 input neurons, 13 neurons in the hidden layer and 1 neuron in the output layer. We present also some experimental results obtained through simulations.

A Matlab Neural Network Application for the Study of Working Conditions

2013 ◽  
Vol 837 ◽  
pp. 310-315 ◽  
Author(s):  
Daniel Constantin Anghel ◽  
Alexandru Ene ◽  
Nadia Belu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Working Conditions ◽  
Experimental Results ◽  
Output Layer ◽  
Linear Relationships ◽  
The Neural Network ◽  
Manufacture Industry ◽  
The Neural Networks ◽  
Network Application

The paper presents a method based on the neural networks to study of working conditions, for the workstations from the manufacture industry. The neural networks were chosen because they excel in gathering difficult non-linear relationships between the inputs and outputs of a system. The neural network was simulated with Matlab. In this paper, we considered as relevant for the study of working conditions, 6 input parameters: temperature, humidity, noise, luminosity, load and frequency. The neural network designed for the study presented in this paper has 6 input neurons and 3 neurons in the output layer. Some experimental results obtained through simulations, are presented in the final part of the paper.

Contributions to Predict the Malfunction Probability of the Human-Machine-Environment System, Using Artificial Neural Networks

2015 ◽  
Vol 760 ◽  
pp. 771-776
Author(s):  
Daniel Constantin Anghel ◽  
Nadia Belu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Design Experiment ◽  
Manufacturing Processes ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Linear Relationships ◽  
The Neural Network ◽  
Artificial Neural

This paper presents the application of Artificial Neural Networks to predict the malfunction probability of the human-machine-environment system, in order to provide some guidance to designers of manufacturing processes. Artificial Neural Networks excel in gathering difficult non-linear relationships between the inputs and outputs of a system. We used, in this work, a feed forward neural network in order to predict the malfunction probability. The neural network is simulated with Matlab. The design experiment presented in this paper was realized at University of Pitesti, at the Faculty of Mechanics and Technology, Technology and Management Department.

How to Redesign Ergonomic Workstations, Using Neural Networks and the Rula Method in Catia V5

2014 ◽  
Vol 1036 ◽  
pp. 995-1000 ◽  
Author(s):  
Daniel Constantin Anghel ◽  
Nadia Belu ◽  
Nicoleta Rachieru
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Design Experiment ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Combined Application ◽  
Linear Relationships ◽  
The Neural Network ◽  
Non Linear

This paper presents the combined application of Artificial Neural Networks and the RULA method in the process of redesign ergonomic workstations.Artificial Neural Networks excel in gathering difficult non-linear relationships between the inputs and outputs of a system. We used, in this work, a feed forward neural network in order to ranking a workstation. The neural network is simulated with a simple simulator: SSNN. The design experiment presented in this paper was realized at University of Pitesti, at the Faculty of Mechanics and Technology, Technology and Management Department, using CATIA V5 software.

A New Type of Pulse Neural Network Based on FPGA

2014 ◽  
Vol 556-562 ◽  
pp. 6081-6084
Author(s):  
Qian Huang ◽  
Wen Long Li ◽  
Jian Kang ◽  
Jun Yang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Sigmoid Function ◽  
Nonlinear Excitation ◽  
The Neural Network ◽  
Xor Problem ◽  
Quartus Ii ◽  
New Type ◽  
Hidden Layer ◽  
Eda Tools

In this paper, based on the study analyzed on the basis of a variety of neural networks, a kind of new type pulse neural network is implemented based on the FPGA [1]. The neural network adopts the Sigmoid function as its hidden layer nonlinear excitation function, at the same time, to reduce ROM table storage space and improve the efficiency of look-up table [2], it also adopts the STAM algorithm based nonlinear storage. Choose Altera Corporation’s EDA tools Quartus II as compilation, simulation platform, Cyclone II series EP2C20F484C6 devices and realized the pulse neural networks finally. In the last, we use XOR problem as example to carry out the hardware simulation, and simulation results are consistent with the theoretical value. Neural network to improve the complex, nonlinear, time-varying, uncertainty about the system reliability and security provides a new way.

scholarly journals Using neural networks to describe tracer correlations

2004 ◽  
Vol 4 (1) ◽  
pp. 143-146 ◽  
Author(s):  
D. J. Lary ◽  
M. D. Müller ◽  
H. Y. Mussa
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Chemical Models ◽  
The Neural Network ◽  
Pressure Time ◽  
Fortran Code ◽  
Time Of Year ◽  
Hidden Layer ◽  
And Training

Abstract. Neural networks are ideally suited to describe the spatial and temporal dependence of tracer-tracer correlations. The neural network performs well even in regions where the correlations are less compact and normally a family of correlation curves would be required. For example, the CH4-N2O correlation can be well described using a neural network trained with the latitude, pressure, time of year, and CH4 volume mixing ratio (v.m.r.). In this study a neural network using Quickprop learning and one hidden layer with eight nodes was able to reproduce the CH4-N2O correlation with a correlation coefficient between simulated and training values of 0.9995. Such an accurate representation of tracer-tracer correlations allows more use to be made of long-term datasets to constrain chemical models. Such as the dataset from the Halogen Occultation Experiment (HALOE) which has continuously observed CH4  (but not N2O) from 1991 till the present. The neural network Fortran code used is available for download.

scholarly journals Diagnosing Diabetes Using Artificial Neural Networks

2020 ◽  
Vol 5 (2) ◽  
pp. 221-224
Author(s):  
Joy Oyinye Orukwo ◽  
Ledisi Giok Kabari
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Success Rate ◽  
Medical Diagnosis ◽  
Learning Algorithm ◽  
Diabetes Diagnosis ◽  
Feed Forward Neural Network ◽  
The Neural Network ◽  
Artificial Neural

Diabetes has always been a silent killer and the number of people suffering from it has increased tremendously in the last few decades. More often than not, people continue with their normal lifestyle, unaware that their health is at severe risk and with each passing day diabetes goes undetected. Artificial Neural Networks have become extensively useful in medical diagnosis as it provides a powerful tool to help analyze, model and make sense of complex clinical data. This study developed a diabetes diagnosis system using feed-forward neural network with supervised learning algorithm. The neural network is systematically trained and tested and a success rate of 90% was achieved.

scholarly journals Nearly Exponential Neural Networks Approximation in Lp Spaces

2017 ◽  
Vol 26 (1) ◽  
pp. 103-113
Author(s):  
Eman Samir Bhaya ‎ ◽  
Zahraa Mahmoud Fadel
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Continuous Function ◽  
Real Function ◽  
Convex Subset ◽  
Exponential Approximation ◽  
Lp Spaces ◽  
The Neural Network ◽  
Hidden Layer ◽  
Engineering Physics

In different applications, we can widely use the neural network approximation. They are being applied to solve many problems in computer science, engineering, physics, etc. The reason for successful application of neural network approximation is the neural network ability to approximate arbitrary function. In the last 30 years, many papers have been published showing that we can approximate any continuous function defined on a compact subset of the Euclidean spaces of dimensions greater than 1, uniformly using a neural network with one hidden layer. Here we prove that any real function in L_P (C) defined on a compact and convex subset  of can be approximated by a sigmoidal neural network with one hidden layer, that we call nearly exponential approximation.

scholarly journals Estimation of Waste Mobile Phones in the Philippines using Neural Networks

2018 ◽  
Vol 20 (4) ◽  
pp. 767-772
Keyword(s):  
Neural Network ◽  
Mobile Phone ◽  
Mobile Phones ◽  
Network Architecture ◽  
The Philippines ◽  
Strategy Formulation ◽  
Feed Forward Neural Network ◽  
The Neural Network ◽  
Mobile Industry ◽  
Hidden Layer

<p>Waste mobile phone is one of the subgroups of e-waste which is defined as discarded electronic products in the Philippine context. This study estimated current and projected quantities of waste mobile phones in the country using feed forward neural network. The neural network architecture used had three layers: (i) input layer, (ii) hidden layer, and (iii) output layer. Seven input factors were fed to the network: (i) population, (ii) literacy rate, (iii) mobile connections, (iv) mobile subscribers, (v) gross domestic product (GDP), (vi) GDP per capita, and (vii) US dollar to Philippine peso exchange rate. These input factors were selected based on the criteria provided in the study by the Groupe Spéciale Mobile Association (GSMA) Intelligence in 2015 on why the Philippines is an innovation hub in mobile industry and the availability of data from the sources. The structure was designed with five hidden layers which consisted of (i) six neurons for layer 1, (ii) five neurons for layer 2, (iii) four neurons for layer 3, (iv) three neurons for layer 4, and (v) two neurons for layer 5. The neural network was designed to initially calculate the sales of mobile phones before estimating waste mobile phone generation. Visual Gene Developer 1.7 Software was used which achieved a sum of squared error of 0.00001. Estimated values were found to be in good agreement with a calculated accuracy of 99%. This study can be used by policy makers as basis for strategy formulation and as guideline and baseline data for establishing a proper management system. Neural network performed better than the traditional linear extrapolation method for forecasting of data.</p>

scholarly journals Neural Network Identifiability for a Family of Sigmoidal Nonlinearities

2021 ◽  
Author(s):  
Verner Vlačić ◽  
Helmut Bölcskei
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Uniform Norm ◽  
Feed Forward Neural Network ◽  
The Subject ◽  
Hidden Layer ◽  
Arbitrary Precision ◽  
Necessary And Sufficient ◽  
Fully Connected

AbstractThis paper addresses the following question of neural network identifiability: Does the input–output map realized by a feed-forward neural network with respect to a given nonlinearity uniquely specify the network architecture, weights, and biases? The existing literature on the subject (Sussman in Neural Netw 5(4):589–593, 1992; Albertini et al. in Artificial neural networks for speech and vision, 1993; Fefferman in Rev Mat Iberoam 10(3):507–555, 1994) suggests that the answer should be yes, up to certain symmetries induced by the nonlinearity, and provided that the networks under consideration satisfy certain “genericity conditions.” The results in Sussman (1992) and Albertini et al. (1993) apply to networks with a single hidden layer and in Fefferman (1994) the networks need to be fully connected. In an effort to answer the identifiability question in greater generality, we derive necessary genericity conditions for the identifiability of neural networks of arbitrary depth and connectivity with an arbitrary nonlinearity. Moreover, we construct a family of nonlinearities for which these genericity conditions are minimal, i.e., both necessary and sufficient. This family is large enough to approximate many commonly encountered nonlinearities to within arbitrary precision in the uniform norm.

scholarly journals Deep Autoencoder-Based Image Compression using Multi-Layer Perceptrons

2020 ◽  
Vol 9 (6) ◽  
pp. 1-6
Keyword(s):  
Neural Network ◽  
Image Compression ◽  
Backpropagation Algorithm ◽  
Feed Forward Neural Network ◽  
The Neural Network ◽  
Proposed Model ◽  
Hidden Layer ◽  
Preprocessing Technique ◽  
Block Sizes ◽  
Normalization Process

The Artificial Neural Network is one of the heavily used alternatives for solving complex problems in machine learning and deep learning. In this research, a deep autoencoder-based multi-layer feed-forward neural network has been proposed to achieve image compression. The proposed neural network splits down a large image into small blocks and each block applies the normalization process as the preprocessing technique. Since this is an autoencoder-based neural network, each normalized block of pixels has been initialized as the input and the output of the neural network. The training process of the proposed network has been done for various block sizes and different saving percentages of various kinds of images by using the backpropagation algorithm. The output of the middle-hidden layer will be the compressed representation for each block of the image. The proposed model has been implemented using Python, Keras, and Tensorflow backend.

Sign in / Sign up

Export Citation Format

Share Document