scholarly journals Prediction of High Rate GPS Satellite Orbit Using Artificial Neural Networks

2021 ◽  
Author(s):  
Hamad Yousif
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Back Propagation ◽  
Satellite Orbit ◽  
Back Propagation Neural Network ◽  
High Rate ◽  
Trigonometric Interpolation ◽  
International Gnss Service ◽  
Feed Forward Back Propagation ◽  
Precise Ephemeris

Precise real-time GPS orbit at a high rate is required for a number of applications, including real-time Precise Point Positioning (PPP), long range RTK and weather forecasts. To support these applications, the International GNSS Service (IGS) has developed a precise orbital service. At present, users may take advantage of the predicted part of the IGS ultra-rapid orbit for real-time and near real-time applications. Unfortunately, however the data rate of such precise orbits is usually limited to 15 minutes. In addition, the precision of the predicted part of the IGS ultrarapid orbit is limited to about 10 cm. for the 24-hour predicted part, which may not be sufficient for the above applications, This research proposes algorithms for interpolation and prediction methods that are intended to reduce the effect of such limitations. This research examines the performance of four interpolation methods for IGS precise GPS orbits, nameley Lagrange, Newton Divided Difference, Bernese Polynomial, Cubic Spline and Trigonometric Interpolation. In addition, a comparison between this research and earlier studies were conducted. A new approach that utilizes the residuals between the broadcast and precise ephemeris to generate a high-density precise ephemeris is also introduced in this research. A three-step neural network-based model is then developed in this research to generate a 6-hour predicted orbital arc. First, an initial predicted orbit is generated by extrapolating a concentrated group of previous precise ephemeris for 5 days. GPS observations for 35 globally distributed tracking stations, corresponding to the 24-hour period preceding the predicted part, are then utilized within the Bernese software to further enhance the predicted orbit. FInally, the predicted orbit is refined by implementing a modular - three-layer feed-forward back-propagation neural network. A comparison is made between our predicted orbit and the IGS ultra-rapid orbit to verify the efficiency of the newly developed neural network-based model. It is shown that the newly developed neural network-based model improved the orbit prediction by 47%, 22% and 37% for three randomly selected satellites from Blocks IIA, IIR and IIR-M respectively.

scholarly journals Prediction of High Rate GPS Satellite Orbit Using Artificial Neural Networks

2021 ◽  
Author(s):  
Hamad Yousif
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Back Propagation ◽  
Satellite Orbit ◽  
Back Propagation Neural Network ◽  
High Rate ◽  
Trigonometric Interpolation ◽  
International Gnss Service ◽  
Feed Forward Back Propagation ◽  
Precise Ephemeris

Precise real-time GPS orbit at a high rate is required for a number of applications, including real-time Precise Point Positioning (PPP), long range RTK and weather forecasts. To support these applications, the International GNSS Service (IGS) has developed a precise orbital service. At present, users may take advantage of the predicted part of the IGS ultra-rapid orbit for real-time and near real-time applications. Unfortunately, however the data rate of such precise orbits is usually limited to 15 minutes. In addition, the precision of the predicted part of the IGS ultrarapid orbit is limited to about 10 cm. for the 24-hour predicted part, which may not be sufficient for the above applications, This research proposes algorithms for interpolation and prediction methods that are intended to reduce the effect of such limitations. This research examines the performance of four interpolation methods for IGS precise GPS orbits, nameley Lagrange, Newton Divided Difference, Bernese Polynomial, Cubic Spline and Trigonometric Interpolation. In addition, a comparison between this research and earlier studies were conducted. A new approach that utilizes the residuals between the broadcast and precise ephemeris to generate a high-density precise ephemeris is also introduced in this research. A three-step neural network-based model is then developed in this research to generate a 6-hour predicted orbital arc. First, an initial predicted orbit is generated by extrapolating a concentrated group of previous precise ephemeris for 5 days. GPS observations for 35 globally distributed tracking stations, corresponding to the 24-hour period preceding the predicted part, are then utilized within the Bernese software to further enhance the predicted orbit. FInally, the predicted orbit is refined by implementing a modular - three-layer feed-forward back-propagation neural network. A comparison is made between our predicted orbit and the IGS ultra-rapid orbit to verify the efficiency of the newly developed neural network-based model. It is shown that the newly developed neural network-based model improved the orbit prediction by 47%, 22% and 37% for three randomly selected satellites from Blocks IIA, IIR and IIR-M respectively.

scholarly journals A Framework for Prediction of Household Energy Consumption Using Feed Forward Back Propagation Neural Network

Technologies ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 30 ◽  
Author(s):  
Muhammad Fayaz ◽  
Habib Shah ◽  
Ali Aseere ◽  
Wali Mashwani ◽  
Abdul Shah
Keyword(s):  
Neural Network ◽  
Performance Evaluation ◽  
Energy Consumption ◽  
Data Acquisition ◽  
Residential Buildings ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Statistical Moments ◽  
Feed Forward ◽  
Feed Forward Back Propagation

Energy is considered the most costly and scarce resource, and demand for it is increasing daily. Globally, a significant amount of energy is consumed in residential buildings, i.e., 30–40% of total energy consumption. An active energy prediction system is highly desirable for efficient energy production and utilization. In this paper, we have proposed a methodology to predict short-term energy consumption in a residential building. The proposed methodology consisted of four different layers, namely data acquisition, preprocessing, prediction, and performance evaluation. For experimental analysis, real data collected from 4 multi-storied buildings situated in Seoul, South Korea, has been used. The collected data is provided as input to the data acquisition layer. In the pre-processing layer afterwards, several data cleaning and preprocessing schemes are applied to the input data for the removal of abnormalities. Preprocessing further consisted of two processes, namely the computation of statistical moments (mean, variance, skewness, and kurtosis) and data normalization. In the prediction layer, the feed forward back propagation neural network has been used on normalized data and data with statistical moments. In the performance evaluation layer, the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE) have been used to measure the performance of the proposed approach. The average values for data with statistical moments of MAE, MAPE, and RMSE are 4.3266, 11.9617, and 5.4625 respectively. These values of the statistical measures for data with statistical moments are less as compared to simple data and normalized data which indicates that the performance of the feed forward back propagation neural network (FFBPNN) on data with statistical moments is better when compared to simple data and normalized data.

scholarly journals Real-Time Stripe Width Computation Using Back Propagation Neural Network for Adaptive Control of Line Structured Light Sensors

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2618 ◽  
Author(s):  
Jingbo Zhou ◽  
Laisheng Pan ◽  
Yuehua Li ◽  
Peng Liu ◽  
Lijian Liu
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Real Time ◽  
Exposure Time ◽  
Control Method ◽  
Structured Light ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Stripe Width ◽  
Accuracy Result

A line structured light sensor (LSLS) is generally constituted of a laser line projector and a camera. With the advantages of simple construction, non-contact, and high measuring speed, it is of great perspective in 3D measurement. For traditional LSLSs, the camera exposure time is usually fixed while the surface properties can be varied for different measurement tasks. This would lead to under/over exposure of the stripe images or even failure of the measurement. To avoid these undesired situations, an adaptive control method was proposed to modulate the average stripe width (ASW) within a favorite range. The ASW is first computed based on the back propagation neural network (BPNN), which can reach a high accuracy result and reduce the runtime dramatically. Then, the approximate linear relationship between the ASW and the exposure time was demonstrated via a series of experiments. Thus, a linear iteration procedure was proposed to compute the optimal camera exposure time. When the optimized exposure time is real-time adjusted, stripe images with the favorite ASW can be obtained during the whole scanning process. The smoothness of the stripe center lines and the surface integrity can be improved. A small proportion of the invalid stripe images further proves the effectiveness of the control method.

scholarly journals Isobaric vapour-liquid equilibrium calculations of binary systems using neural network

2004 ◽  
Vol 69 (8-9) ◽  
pp. 669-674 ◽  
Author(s):  
Mehmet Bilgin
Keyword(s):  
Neural Network ◽  
Binary Systems ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Liquid Equilibrium ◽  
Chemical Structures ◽  
Feed Forward Back Propagation ◽  
The Neural Network ◽  
Classical Models ◽  
Vle Data

A model on a feed forward back propagation neural network was employed to calculate the isobaric vapour?liquid equilibrium (VLE) data at 40, 66.67, and 101.32 ??0.02 kPa for the methylcyclohexane ? toluene and isopropanol ? methyl isobutyl ketone binary systems, which are composed of different chemical structures (cyclic, aromatic, alcohol and ketone) and do not show azeotrope behaviour. Half of the experimental VLE data only were assigned into the designed framework as training patterns in order to estimate the VLE data over the whole composition range at the mentioned pressures. The results were compared with the data calculated by the two classical models used in this field, the UNIFAC and Margules models. In all cases the deviations the experimental activity coefficients and those calculated by the neural network model (NNET) were lower than those obtained using the Margules and UNIFAC models.

FFBPNN-BASED HIGH DROWSINESS CLASSIFICATION USING EMG AND WPT

2020 ◽  
Vol 32 (03) ◽  
pp. 2050023 ◽  
Author(s):  
Mousa Kadhim Wali
Keyword(s):  
Neural Network ◽  
Wavelet Packet ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Road Accidents ◽  
Mean Values ◽  
Physiological Signal ◽  
Feed Forward Back Propagation ◽  
Emg Signal ◽  
Amplitude Level

The detection of drowsiness level is important because it is the main reason for fatal road accidents. Electromyography of the upper arm and shoulder is an important physiological signal affected by drivers’ drowsiness, in which its amplitude level and frequency band of the sleep-deprived case are different than those of the alert state. Therefore depending on electromyography (EMG), its drowsiness frequency (80–100[Formula: see text]Hz) was detected in order to determine high drowsiness state based on wavelet packet transform (WPT) which decomposes the EMG signal into its approximation and detail coefficients up to level 4 using db2, db7, sym5 and coif5 wavelets. In this research after extraction, the two higher order statistical features, kurtosis and skewness, are computed from 3[Formula: see text]s window of the three EMG channels, and analysis of variance test is used to check whether their mean values are different for the different classes as both [Formula: see text]-values are less than 0.005 under db2 wavelet. Therefore, they were supplied to feed forward back propagation neural network (FFBPNN) as this type of neural network is used for distinguishing and classification purposes for different objects. They obtained an accuracy of 75% for detecting high levels among other levels of normal and low drowsiness with an average sensitivity of 78.63% and specificity of 75.97% because the spectrum of the EMG alert (non-drowsiness) signal of 80–100 Hz is different from that of drowsy 80–90[Formula: see text]Hz and high drowsy 78–95[Formula: see text]Hz signals.

Sign in / Sign up

Export Citation Format

Share Document