scholarly journals Comparison of Deep Neural Network Models and Effectiveness of EMG Signal Feature Value for Estimating Dorsiflexion

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2767
Author(s):  
Muhammad Akmal Bin Mohammed Zaffir ◽  
Praveen Nuwantha ◽  
Daiki Arase ◽  
Keiko Sakurai ◽  
Hiroki Tamura
Keyword(s):  
Neural Network ◽  
Average Rate ◽  
Network Models ◽  
Rate Of Change ◽  
Estimation Accuracy ◽  
Neural Network Models ◽  
Emg Signal ◽  
Minimum Number ◽  
Deep Learning Network ◽  
Simple Network

(1) Background: Robotic ankle–foot orthoses (AFO) are often used for gait rehabilitation. Our research focuses on the design and development of a robotic AFO with minimum number of sensor inputs. However, this leads to degradation of gait estimation accuracy. (2) Methods: To prevent degradation of accuracy, we compared a few neural network models in order to determine the best network when only two input channels are being used. Further, the EMG signal feature value of average rate of change was used as input. (3) Results: LSTM showed the highest accuracy. However, MLP with a small number of hidden layers showed results similar to LSTM. Moreover, the accuracy for all models, with the exception of LSTM for one subject (SD), increased with the addition of feature value (average rate of change) as input. (4) Conclusions: In conclusion, time-series networks work best with a small number of sensor inputs. However, depending on the optimizer being used, even a simple network can outrun a deep learning network. Furthermore, our results show that applying EMG signal feature value as an input tends to increase the estimation accuracy of the network.

scholarly journals Photovoltaic Panel Efficiency Estimation with Artificial Neural Networks: Samples of Adiyaman, Malatya, and Sanliurfa

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Yasin Icel ◽  
Mehmet Salih Mamis ◽  
Abdulcelil Bugutekin ◽  
Mehmet Ismail Gursoy
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Network Models ◽  
Estimation Accuracy ◽  
Neural Network Models ◽  
Photovoltaic Panels ◽  
Artificial Neural ◽  
Artificial Neural Network Models

The amount of electric energy produced by photovoltaic panels depends on air temperature, humidity rate, wind velocity, photovoltaic module temperature, and particularly solar radiation. Being aware of the behaviour patterns of the panels to be used in project and planning works regarding photovoltaic applications will set forth a realistic expense form; therefore, erroneous investments will be avoided, and the country budget will benefit from added value. The power ratings obtained from the photovoltaic panels and the environmental factors were measured and recorded for a year by the measurement stations established in three diverse regions (Adiyaman-Malatya-Sanliurfa). In the developed artificial neural network models, the estimation accuracy was 99.94%. Furthermore, by taking the data of the General Directorate of Meteorology as a reference, models of artificial neural networks were developed using the data from Adiyaman province for training; by using Malatya and Sanliurfa as test data, 99.57% estimation accuracy was achieved. With the artificial neural network models developed as a result of the study, the energy efficiency for the photovoltaic energy systems desired to be established by using meteorological parameters such as temperature, humidity, wind, and solar radiation of various regions anywhere in the world can be estimated with high accuracy.

Method of complex copper-zinc ore typification using neural network models

2020 ◽  
Vol 5 ◽  
pp. 140-147 ◽  
Author(s):  
T.N. Aleksandrova ◽  
◽  
E.K. Ushakov ◽  
A.V. Orlova ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Copper Zinc ◽  
Complex Copper

Digital twin of equipment as a basis for the consumer in digital production

2020 ◽  
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Chip Formation ◽  
Network Models ◽  
Machining Accuracy ◽  
Neural Network Models ◽  
Digital Twin ◽  
The Neural Network ◽  
Digital Production ◽  
Cyberphysical System

The neural network models series used in the development of an aggregated digital twin of equipment as a cyber-physical system are presented. The twins of machining accuracy, chip formation and tool wear are examined in detail. On their basis, systems for stabilization of the chip formation process during cutting and diagnose of the cutting too wear are developed. Keywords cyberphysical system; neural network model of equipment; big data, digital twin of the chip formation; digital twin of the tool wear; digital twin of nanostructured coating choice

scholarly journals Prediction of Stress in Power Transformer Winding Conductors Using Artificial Neural Networks: Hyperparameter Analysis

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4242
Author(s):  
Fausto Valencia ◽  
Hugo Arcos ◽  
Franklin Quilumba
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Power Transformer ◽  
Network Models ◽  
Activation Function ◽  
Neural Network Models ◽  
Transformer Winding ◽  
Artificial Neural ◽  
Femm Software

The purpose of this research is the evaluation of artificial neural network models in the prediction of stresses in a 400 MVA power transformer winding conductor caused by the circulation of fault currents. The models were compared considering the training, validation, and test data errors’ behavior. Different combinations of hyperparameters were analyzed based on the variation of architectures, optimizers, and activation functions. The data for the process was created from finite element simulations performed in the FEMM software. The design of the Artificial Neural Network was performed using the Keras framework. As a result, a model with one hidden layer was the best suited architecture for the problem at hand, with the optimizer Adam and the activation function ReLU. The final Artificial Neural Network model predictions were compared with the Finite Element Method results, showing good agreement but with a much shorter solution time.

scholarly journals The Effectiveness of Ensemble-Neural Network Techniques to Predict Peak Uplift Resistance of Buried Pipes in Reinforced Sand

2021 ◽  
Vol 11 (3) ◽  
pp. 908
Author(s):  
Jie Zeng ◽  
Panagiotis G. Asteris ◽  
Anna P. Mamou ◽  
Ahmed Salih Mohammed ◽  
Emmanuil A. Golias ◽  
...  
Keyword(s):  
Neural Network ◽  
Numerical Models ◽  
Correlation Coefficients ◽  
Network Models ◽  
Small Scale ◽  
Offshore Platforms ◽  
Neural Network Models ◽  
Buried Pipes ◽  
Ensemble Techniques ◽  
Uplift Resistance

Buried pipes are extensively used for oil transportation from offshore platforms. Under unfavorable loading combinations, the pipe’s uplift resistance may be exceeded, which may result in excessive deformations and significant disruptions. This paper presents findings from a series of small-scale tests performed on pipes buried in geogrid-reinforced sands, with the measured peak uplift resistance being used to calibrate advanced numerical models employing neural networks. Multilayer perceptron (MLP) and Radial Basis Function (RBF) primary structure types have been used to train two neural network models, which were then further developed using bagging and boosting ensemble techniques. Correlation coefficients in excess of 0.954 between the measured and predicted peak uplift resistance have been achieved. The results show that the design of pipelines can be significantly improved using the proposed novel, reliable and robust soft computing models.

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