Mechanical movement data acquisition method based on the multilayer neural networks and machine vision in a digital twin environment

Digital Twin ◽  
2021 ◽  
Vol 1 ◽  
pp. 6
Author(s):  
Hao Li ◽  
Gen Liu ◽  
Haoqi Wang ◽  
Xiaoyu Wen ◽  
Guizhong Xie ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Machine Vision ◽  
Mechanical Motion ◽  
Digital Twin ◽  
Movement Data ◽  
Multilayer Neural Networks ◽  
Visual Mark ◽  
Mechanical Movement ◽  
Acquisition Method

Background: Digital twin requires virtual reality mapping and optimization iteration between physical devices and virtual models. The mechanical movement data collection of physical equipment is essential for the implementation of accurate virtual and physical synchronization in a digital twin environment. However, the traditional approach relying on PLC (programmable logic control) fails to collect various mechanical motion state data. Additionally, few investigations have used machine visions for the virtual and physical synchronization of equipment. Thus, this paper presents a mechanical movement data acquisition method based on multilayer neural networks and machine vision. Methods: Firstly, various visual marks with different colors and shapes are designed for marking physical devices. Secondly, a recognition method based on the Hough transform and histogram feature is proposed to realize the recognition of shape and color features respectively. Then, the multilayer neural network model is introduced in the visual mark location. The neural network is trained by the dropout algorithm to realize the tracking and location of the visual mark. To test the proposed method, 1000 samples were selected. Results: The experiment results shows that when the size of the visual mark is larger than 6mm, the recognition success rate of the recognition algorithm can reach more than 95%. In the actual operation environment with multiple cameras, the identification points can be located more accurately. Moreover, the camera calibration process of binocular and multi-eye vision can be simplified by the multilayer neural networks. Conclusions: This study proposes an effective method in the collection of mechanical motion data of physical equipment in a digital twin environment. Further studies are needed to perceive posture and shape data of physical entities under the multi-camera redundant shooting.

Effects of Nonlinear Synapses on the Performance of Multilayer Neural Networks

1996 ◽  
Vol 8 (5) ◽  
pp. 939-949 ◽  
Author(s):  
G. Dündar ◽  
F-C. Hsu ◽  
K. Rose
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Multilayer Neural Networks ◽  
Circuit Simulations ◽  
Multiplier Circuit

The problems arising from the use of nonlinear multipliers in multilayer neural network synapse structures are discussed. The errors arising from the neglect of nonlinearities are shown and the effect of training in eliminating these errors is discussed. A method for predicting the final errors resulting from nonlinearities is described. Our approximate results are compared with the results from circuit simulations of an actual multiplier circuit.

scholarly journals SOC and SOH Monitoring Algorithms for Lithium Batteries Using Multilayer Neural Networks

10.29007/m89x ◽  
2020 ◽  
Author(s):  
Jong Hyun Lee ◽  
Hyun Sil Kim ◽  
In Soo Lee
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Monitoring System ◽  
Lithium Batteries ◽  
Lithium Battery ◽  
Discharge Voltage ◽  
State Of Charge ◽  
State Of Health ◽  
Soc Estimation ◽  
Multilayer Neural Networks

This paper presents a battery monitoring system using a multilayer neural network (MNN) for state of charge (SOC) estimation and state of health (SOH) diagnosis. In this system, the MNN utilizes experimental discharge voltage data from lithium battery operation to estimate SOH and uses present and previous voltages for SOC estimation. From experimental results, we know that the proposed battery monitoring system performs SOC estimation and SOH diagnosis well.

Complex-Valued Neural Network and Inverse Problems

2009 ◽  
pp. 27-55 ◽  
Author(s):  
Takehiko Ogawa
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Inverse Problems ◽  
Inversion Method ◽  
Complex Numbers ◽  
Original Network ◽  
Multilayer Neural Networks ◽  
Complex Valued ◽  
Inputs And Outputs

Network inversion solves inverse problems to estimate cause from result using a multilayer neural network. The original network inversion has been applied to usual multilayer neural networks with real-valued inputs and outputs. The solution by a neural network with complex-valued inputs and outputs is necessary for general inverse problems with complex numbers. In this chapter, we introduce the complex-valued network inversion method to solve inverse problems with complex numbers. In general, difficulties attributable to the ill-posedness of inverse problems appear. Regularization is used to solve this ill-posedness by adding some conditions to the solution. In this chapter, we also explain regularization for complex-valued network inversion.

ON THE CAPACITY OF MULTILAYER NEURAL NETWORKS TRAINED WITH BACKPROPAGATION

2000 ◽  
Vol 10 (04) ◽  
pp. 281-285
Author(s):  
E. N. MIRANDA
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Learning Ability ◽  
Sharp Change ◽  
Maximum Capacity ◽  
Multilayer Neural Networks ◽  
Hidden Neurons

The capacity of a layered neural network for learning hetero-associations is studied numerically as a function of the number M of hidden neurons. We find that there is a sharp change in the learning ability of the network as the number of hetero-associations increases. This fact allows us to define a maximum capacity C for a given architecture. It is found that C grows logarithmically with M.

A REGULARIZATION TERM TO AVOID THE SATURATION OF THE SIGMOIDS IN MULTILAYER NEURAL NETWORKS

1996 ◽  
Vol 07 (03) ◽  
pp. 257-262 ◽  
Author(s):  
LLUIS GARRIDO ◽  
SERGIO GÓMEZ ◽  
VICENS GAITÁN ◽  
MIQUEL SERRA-RICART
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Error Function ◽  
New Method ◽  
Quadratic Error ◽  
Regularization Term ◽  
Multilayer Neural Networks

In this paper we propose a new method to prevent the saturation of any set of hidden units of a multilayer neural network. This method is implemented by adding a regularization term to the standard quadratic error function, which is based on a repulsive action between pairs of patterns.

A MEMORY-BASED SELF-GENERATED BASIS FUNCTION NEURAL NETWORK

1999 ◽  
Vol 09 (01) ◽  
pp. 41-59 ◽  
Author(s):  
CHUN-SHIN LIN ◽  
CHIEN-KUO LI
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Basis Function ◽  
Convergence Property ◽  
High Dimensional ◽  
Memory Space ◽  
Dimensional Modeling ◽  
The Neural Network ◽  
Multilayer Neural Networks ◽  
Learning Convergence

The paper presents a novel memory-based Self-Generated Basis Function Neural Network (SGBFN) that is composed of small CMACs. The SGBFN requires much smaller memory space than the conventional CMAC and has an excellent learning convergence property compared to multilayer neural networks. Each CMAC in the new structure takes a subset of problem inputs as its inputs. Several CMACs that have different subsets of inputs form a submodule and a group of submodules form a neural network. The output of a submodule is the product of its CMACs' outputs. Each submodule implements a self-generated basis function, which is developed during the learning. The output of the neural network is the sum of the outputs from the submodules. Using only a subset of inputs in each CMAC significantly reduces the required memory space in high-dimensional modeling. With the same size of memory, the new structure is able to achieve a much smaller learning error compared to the conventional CMAC.

scholarly journals Construction of neural network classification of Fisher irises based on JavaScript

2019 ◽  
Author(s):  
A.М. Заяц ◽  
С.П. Хабаров
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Back Propagation ◽  
Data Set ◽  
Test Dataset ◽  
Neural Network Classification ◽  
The Neural Network ◽  
Multilayer Neural Networks ◽  
Hidden Layer

Рассматривается процедура выбора структуры и параметров нейронной сети для классификации набора данных, известного как Ирисы Фишера, который включает в себя данные о 150 экземплярах растений трех различных видов. Предложен подход к решению данной задачи без использования дополнительных программных средств и мощных нейросетевых пакетов с использованием только средств стандартного браузера ОС. Это потребовало реализации ряда процедур на JavaScript c их подгрузкой в разработанную интерфейсную HTML-страницу. Исследование большого числа различных структур многослойных нейронных сетей, обучаемых на основе алгоритма обратного распространения ошибки, позволило выбрать для тестового набора данных структуру нейронной сети всего с одним скрытым слоем из трех нейронов. Это существенно упрощает реализацию классификатора Ирисов Фишера, позволяя его оформить в виде загружаемой с сервера HTML-страницы. The procedure for selecting the structure and parameters of the neural network for the classification of a data set known as Iris Fisher, which includes data on 150 plant specimens of three different species, is considered. An approach to solving this problem without using additional software and powerful neural network packages using only the tools of the standard OS browser is proposed. This required the implementation of a number of JavaScript procedures with their loading into the developed HTML interface page. The study of a large number of different structures of multilayer neural networks, trained on the basis of the back-propagation error algorithm, made it possible to choose the structure of a neural network with only one hidden layer of three neurons for a test dataset. This greatly simplifies the implementation of the Fisher Iris classifier, allowing it to be formatted as an HTML page downloaded from the server.

scholarly journals Classification of Red Blood Cell Rigidity from Sequence Data of Blood Flow Simulations Using Neural Networks

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 938
Author(s):  
Katarína Bachratá ◽  
Katarína Buzáková ◽  
Michal Chovanec ◽  
Hynek Bachratý ◽  
Monika Smiešková ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Blood Flow ◽  
Red Blood Cells ◽  
Blood Cells ◽  
Sequence Data ◽  
Numerical Models ◽  
Microfluidic Devices ◽  
Movement Data ◽  
The Neural Network

Numerical models for the flow of blood and other fluids can be used to design and optimize microfluidic devices computationally and thus to save time and resources needed for production, testing, and redesigning of the physical microfluidic devices. Like biological experiments, computer simulations have their limitations. Data from both the biological and the computational experiments can be processed by machine learning methods to obtain new insights which then can be used for the optimization of the microfluidic devices and also for diagnostic purposes. In this work, we propose a method for identifying red blood cells in flow by their stiffness based on their movement data processed by neural networks. We describe the performed classification experiments and evaluate their accuracy in various modifications of the neural network model. We outline other uses of the model for processing data from video recordings of blood flow. The proposed model and neural network methodology classify healthy and more rigid (diseased) red blood cells with the accuracy of about 99.5% depending on the selected dataset that represents the flow of a suspension of blood cells of various levels of stiffness.

SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

2020 ◽  
Vol 2020 (10) ◽  
pp. 54-62
Author(s):  
Oleksii VASYLIEV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Statistical Data ◽  
Activation Function ◽  
Decision Making Process ◽  
Neural Network Architecture ◽  
Acceptable Accuracy ◽  
The Neural Network ◽  
Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

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