High-Speed Aircraft Single Channel SAR-GMTI Based on Neural Network

Background: Angular contact ball bearing is an important component of many high-speed rotating mechanical systems. Oil-air lubrication makes it possible for angular contact ball bearing to operate at high speed. So the lubrication state of angular contact ball bearing directly affects the performance of the mechanical systems. However, as bearing rotation speed increases, the temperature rise is still the dominant limiting factor for improving the performance and service life of angular contact ball bearings. Therefore, it is very necessary to predict the temperature rise of angular contact ball bearings lubricated with oil-air. Objective: The purpose of this study is to provide an overview of temperature calculation of bearing from many studies and patents, and propose a new prediction method for temperature rise of angular contact ball bearing. Methods: Based on the artificial neural network and genetic algorithm, a new prediction methodology for bearings temperature rise was proposed which capitalizes on the notion that the temperature rise of oil-air lubricated angular contact ball bearing is generally coupling. The influence factors of temperature rise in high-speed angular contact ball bearings were analyzed through grey relational analysis, and the key influence factors are determined. Combined with Genetic Algorithm (GA), the Artificial Neural Network (ANN) model based on these key influence factors was built up, two groups of experimental data were used to train and validate the ANN model. Results: Compared with the ANN model, the ANN-GA model has shorter training time, higher accuracy and better stability, the output of ANN-GA model shows a good agreement with the experimental data, above 92% of bearing temperature rise under varying conditions can be predicted using the ANNGA model. Conclusion: A new method was proposed to predict the temperature rise of oil-air lubricated angular contact ball bearings based on the artificial neural network and genetic algorithm. The results show that the prediction model has good accuracy, stability and robustness.

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Single Channel EEG signal for Automatic Detection of Absence Seizure using Convolutional Neural Network

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813666191122114608 ◽

2019 ◽

Vol 13 ◽

Author(s):

Niha Kamal Basha ◽

Aisha Banu Wahab

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Convolutional Neural Network ◽

Monitoring System ◽

Single Channel ◽

Confusion Matrix ◽

Sudden Change ◽

Automatic Detection ◽

Support Vector ◽

Absence Seizure

: Absence seizure is a type of brain disorder in which subject get into sudden lapses in attention. Which means sudden change in brain stimulation. Most of this type of disorder is widely found in children’s (5-18 years). These Electroencephalogram (EEG) signals are captured with long term monitoring system and are analyzed individually. In this paper, a Convolutional Neural Network to extract single channel EEG seizure features like Power, log sum of wavelet transform, cross correlation, and mean phase variance of each frame in a windows are extracted after pre-processing and classify them into normal or absence seizure class, is proposed as an empowerment of monitoring system by automatic detection of absence seizure. The training data is collected from the normal and absence seizure subjects in the form of Electroencephalogram. The objective is to perform automatic detection of absence seizure using single channel electroencephalogram signal as input. Here the data is used to train the proposed Convolutional Neural Network to extract and classify absence seizure. The Convolutional Neural Network consist of three layers 1] convolutional layer – which extract the features in the form of vector 2] Pooling layer – the dimensionality of output from convolutional layer is reduced and 3] Fully connected layer–the activation function called soft-max is used to find the probability distribution of output class. This paper goes through the automatic detection of absence seizure in detail and provide the comparative analysis of classification between Support Vector Machine and Convolutional Neural Network. The proposed approach outperforms the performance of Support Vector Machine by 80% in automatic detection of absence seizure and validated using confusion matrix.

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Tributary Mapping Multiplexing an Efficient Technique for High Speed Fiber Optic Communication

Pakistan Journal of Engineering Technology & Science ◽

10.22555/pjets.v6i2.1963 ◽

2018 ◽

Vol 6 (2) ◽

Author(s):

Usman Illahi ◽

Javed Iqbal ◽

Muhammad Ismail Sulaiman ◽

Muhammad Alam ◽

Mazliham Mohd Su'ud

Keyword(s):

Spectral Efficiency ◽

High Speed ◽

Fiber Optic ◽

Single Channel ◽

Efficient Technique ◽

High Dispersion ◽

Optical Filtering ◽

Fiber Optic Communication ◽

Optic Communication ◽

Dispersion Tolerance

<p>A novel technique of multiplexing called Tributary Mapping Multiplexing (TMM) is<br />applied to a single channel wavelength division multiplexing system and performance is monitored on the basis of simulation results. To elaborate the performance of TMM in this paper, a 4-User TMM system over single wavelength channel is demonstrated. TMM showed significant tolerance against narrow optical filtering as compared to that of conventional TDM at the rate of 40 Gbit/s. The above calculations are made by optical filter bandwidth and dispersion tolerance that was allowed at minimum. The spectral efficiency achieved by this TMM was 1 b/s/Hz and it was executed by using transmitters and receivers of 10 Gbit/s without polarized multiplexing. The high spectral efficiency, high dispersion tolerance and tolerance against strong optical filtering makes TMM an efficient technique for High<br />Speed Fiber Optic Communication.</p>

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Erratum: High speed opto-electronic neural network

Electronics Letters ◽

10.1049/el:19901116 ◽

1990 ◽

Vol 26 (20) ◽

pp. 1739

Author(s):

N.M. Barnes ◽

P. Healey ◽

P. McKee ◽

A.W. O'Neill ◽

M.A.Z. Rejmangreene ◽

...

Keyword(s):

Neural Network ◽

High Speed

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Algorithm-Aware Neural Network Based Image Compression for High-Speed Imaging

2020 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR) ◽

10.1109/aivr50618.2020.00040 ◽

2020 ◽

Author(s):

Reid Pinkham ◽

Tanner Schmidt ◽

Andrew Berkovich

Keyword(s):

Neural Network ◽

Image Compression ◽

High Speed ◽

High Speed Imaging

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Influence of hinge length and distribution number on the camber and cornering properties of a non-pneumatic tire

Advances in Mechanical Engineering ◽

10.1177/1687814020984687 ◽

2020 ◽

Vol 12 (12) ◽

pp. 168781402098468

Author(s):

Xianbin Du ◽

Youqun Zhao ◽

Yijiang Ma ◽

Hongxun Fu

Keyword(s):

Neural Network ◽

Network Model ◽

Adaptive Learning ◽

High Speed ◽

Back Propagation ◽

Lateral Force ◽

Learning Ability ◽

Vehicle Performance ◽

Neural Network Theory ◽

Bp Neural Network Model

The camber and cornering properties of the tire directly affect the handling stability of vehicles, especially in emergencies such as high-speed cornering and obstacle avoidance. The structural and load-bearing mode of non-pneumatic mechanical elastic (ME) wheel determine that the mechanical properties of ME wheel will change when different combinations of hinge length and distribution number are adopted. The camber and cornering properties of ME wheel with different hinge lengths and distributions were studied by combining finite element method (FEM) with neural network theory. A ME wheel back propagation (BP) neural network model was established, and the additional momentum method and adaptive learning rate method were utilized to improve BP algorithm. The learning ability and generalization ability of the network model were verified by comparing the output values with the actual input values. The camber and cornering properties of ME wheel were analyzed when the hinge length and distribution changed. The results showed the variation of lateral force and aligning torque of different wheel structures under the combined conditions, and also provided guidance for the matching of wheel and vehicle performance.

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