Joint Torque Estimation Model of sEMG Signal for Arm Rehabilitation Device Using Artificial Neural Network Techniques

Oceanographic remote sensing, which is based on the sensitivity of reflected signals from the Global Navigation Satellite Systems (GNSS), so-called GNSS-Reflectometry (GNSS-R), is very useful for the observation of ocean wind speed. Wind speed estimation over the ocean is the core factor in maritime transportation management and the study of climate change. The main concept of the GNSS-R technique is using the different times between the reflected and the direct signals to measure the wind speed and wind direction. Accordingly, this research proposes a novel technique for wind speed estimation involving the integration of an artificial neural network and the particle filter based on a theoretical model. Moreover, particle swarm optimization was applied to find the optimal weight and bias of the artificial neural network, in order to improve the accuracy of the estimation result. The observation dataset of the reflected signal information from BeiDou Geostationary Earth Orbit (GEO) satellite number 4 was used as an input for the estimation model. The data consisted of two phases with I and Q components. Two periods of BeiDou data were selected, the first period was from 3 to 8 August 2013 and the second period was from 12 to 14 August 2013, which corresponded to events from the typhoon Utor. The in situ wind speed measurement collected from the buoy station was used to validate the results. A coastal experiment was conducted at the Yangjiang site located in the South China Sea. The results show the ability of the proposed technique to estimate wind speed with a root mean square error of approximately 1.9 m/s.

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The Added Value of Surface Data to Radar-Derived Rainfall-Rate Estimation Using an Artificial Neural Network

Journal of Atmospheric and Oceanic Technology ◽

10.1175/2010jtecha1361.1 ◽

2010 ◽

Vol 27 (9) ◽

pp. 1547-1554 ◽

Cited By ~ 3

Author(s):

B. Root ◽

T-Y. Yu ◽

M. Yeary ◽

M. B. Richman

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Radar Reflectivity ◽

Rainfall Rate ◽

Added Value ◽

Training Dataset ◽

Estimation Model ◽

Rate Estimation ◽

Surface Data ◽

Artificial Neural

Abstract Radar measurements are useful for determining rainfall rates because of their ability to cover large areas. Unfortunately, estimating rainfall rates from radar reflectivity data alone is prone to errors resulting from variations in drop size distributions, precipitation types, and other physics that cannot be represented in a simple, one-dimensional Z–R relationship. However, improving estimates is possible by utilizing additional inputs, thereby increasing the dimensionality of the model. The main purpose of this study is to determine the value of surface observations for improving rainfall-rate estimation. This work carefully designed an artificial neural network to fit a model that would relate radar reflectivity, surface temperature, humidity, pressure, and wind to observed rainfall rates. Observations taken over 13 years from the Oklahoma Mesonet and the KTLX WSR-88D radar near Oklahoma City, Oklahoma, were used for the training dataset. While the artificial neural network underestimated rainfall rates for higher reflectivities, it did have an overall better performance than the best-fit Z–R relation. Most importantly, it is shown that the surface data contributed significant value to an unaugmented radar-based rainfall-rate estimation model.

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Smart System for Prediction of Accurate Surface Electromyography Signals Using an Artificial Neural Network

Future Internet ◽

10.3390/fi11010025 ◽

2019 ◽

Vol 11 (1) ◽

pp. 25 ◽

Cited By ~ 1

Author(s):

Osama Dorgham ◽

Ibrahim Al-Mherat ◽

Jawdat Al-Shaer ◽

Sulieman Bani-Ahmad ◽

Stephen Laycock

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Electrical Potential ◽

Eccentric Contraction ◽

Surface Emg ◽

Mean Square ◽

Ann Model ◽

Semg Signal ◽

Artificial Neural ◽

Semg Signals

Bioelectric signals are used to measure electrical potential, but there are different types of signals. The electromyography (EMG) is a type of bioelectric signal used to monitor and recode the electrical activity of the muscles. The current work aims to model and reproduce surface EMG (SEMG) signals using an artificial neural network. Such research can aid studies into life enhancement for those suffering from damage or disease affecting their nervous system. The SEMG signal is collected from the surface above the bicep muscle through dynamic (concentric and eccentric) contraction with various loads. In this paper, we use time domain features to analyze the relationship between the amplitude of SEMG signals and the load. We extract some features (e.g., mean absolute value, root mean square, variance and standard deviation) from the collected SEMG signals to estimate the bicep’ muscle force for the various loads. Further, we use the R-squared value to depict the correlation between the SEMG amplitude and the muscle loads by linear fitting. The best performance the ANN model with 60 hidden neurons for three loads used (3 kg, 5 kg and 7 kg) has given a mean square error of 1.145, 1.3659 and 1.4238, respectively. The R-squared observed are 0.9993, 0.99999 and 0.99999 for predicting (reproduction step) of smooth SEMG signals.

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The Analytical Research on Construction Project Cost Estimation Model Which Is Based on Artificial Neural Network

Lecture Notes in Electrical Engineering - Advances in Automation and Robotics, Vol. 2 ◽

10.1007/978-3-642-25646-2_17 ◽

2011 ◽

pp. 129-134

Author(s):

Junming Hong

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Cost Estimation ◽

Construction Project ◽

Estimation Model ◽

Project Cost ◽

Analytical Research ◽

Artificial Neural ◽

Cost Estimation Model ◽

Construction Project Cost

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Simulation and optimization of genetic algorithm-artificial neural network based air quality estimator

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v19.i2.pp775-783 ◽

2020 ◽

Vol 19 (2) ◽

pp. 775

Author(s):

Shirish Pandey ◽

S. Hasan Saeed ◽

N. R. Kidwai

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Artificial Neural Network ◽

Air Quality ◽

Estimation Model ◽

Simulation And Optimization ◽

Artificial Neural ◽

Using Data ◽

One Year ◽

Intelligent Model

In this work intelligent model for estimation of the concentration of carbon monoxide in a polluted environment is developed on mat Lab platform. The results are validated using data collected from repository linked to University of California. The data records are over the duration of one year using E nose sensor placed in main city of Italy. The records are rectified and segmented at different length to extract the Base and Divergence Values features. An Artificial Neural Network Model (ANN) is developed and the result is validated manually. Another optimized Genetic Algorithm-Artificial Neural Network based air quality estimation model is developed which validate the result using artificial intelligence technique to get a better performance network.

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