scholarly journals Star Image Prediction and Restoration under Dynamic Conditions

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1890 ◽  
Author(s):  
Liu ◽  
Chen ◽  
Liu ◽  
Shi
Keyword(s):  
Attitude Control ◽  
Attitude Determination ◽  
Back Propagation ◽  
Prediction Method ◽  
Back Propagation Neural Network ◽  
Attitude Measurement ◽  
Star Sensor ◽  
Dynamic Conditions ◽  
Star Image ◽  
Image Prediction

The star sensor is widely used in attitude control systems of spacecraft for attitude measurement. However, under high dynamic conditions, frame loss and smearing of the star image may appear and result in decreased accuracy or even failure of the star centroid extraction and attitude determination. To improve the performance of the star sensor under dynamic conditions, a gyroscope-assisted star image prediction method and an improved Richardson-Lucy (RL) algorithm based on the ensemble back-propagation neural network (EBPNN) are proposed. First, for the frame loss problem of the star sensor, considering the distortion of the star sensor lens, a prediction model of the star spot position is obtained by the angular rates of the gyroscope. Second, to restore the smearing star image, the point spread function (PSF) is calculated by the angular velocity of the gyroscope. Then, we use the EBPNN to predict the number of iterations required by the RL algorithm to complete the star image deblurring. Finally, simulation experiments are performed to verify the effectiveness and real-time of the proposed algorithm.

Region-confined restoration method for motion-blurred star image of the star sensor under dynamic conditions

2016 ◽  
Vol 55 (17) ◽  
pp. 4621 ◽  
Author(s):  
Liheng Ma ◽  
Franco Bernelli-Zazzera ◽  
Guangwen Jiang ◽  
Xingshu Wang ◽  
Zongsheng Huang ◽  
...  
Keyword(s):  
Star Sensor ◽  
Dynamic Conditions ◽  
Star Image ◽  
Restoration Method

scholarly journals Peramalan Jumlah Penumpang Kereta Api di Indonesia dengan Resilient Back-Propagation (Rprop) Neural Network

2018 ◽  
Vol 4 (2) ◽  
pp. 90-99
Author(s):  
Mertha Endah Ervina ◽  
Rini Silvi ◽  
Intaniah Ratna Nur Wisisono
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Prediction Method ◽  
Back Propagation Neural Network ◽  
Training Data ◽  
Percentage Error ◽  
Network Architectures ◽  
Forecasting Accuracy ◽  
Resilient Back Propagation ◽  
Very High

Train scheduling affects the level of customer satisfaction and profitability of the train service provider. The prediction method of Back-propagation Neural Network (BPNN) has relatively slow convergence. Therefore, this study uses Resilient Back-propagation (Rprop) because it has a more fast convergence and high accuracy. The model produced is a model for Jabodetabek, Java (non-Jabodetabek), Sumatra, and Indonesia. From the results of data analysis conducted, it can be concluded that the performance of neural network model with Resilient Back-propagation (Rprop) formed from training data gives very accurate prediction accuracy level with mean absolute percentage error (MAPE) less than 10% for each model. Then forecasting for the next 12 months conducted and the results compared with the data testing, Rprop provides a very high forecasting accuracy with MAPE value below 10%. The MAPE value for each forecasting the number of rail passengers is 7.50% for Jabodetabek, 5.89% for Java (non-Jabodetabek), 5.36% for Sumatra and 4.80% for Indonesia. That is, four neural network architectures with Rprop can be used for this case with very accurate forecasting results.

Prediction method for offset compensation on three-dimensional mandrel with spatial irregular shape

2019 ◽  
pp. 152808371985876 ◽  
Author(s):  
Meng Zhuo ◽  
Yao Lingling ◽  
Bu Jianqiu ◽  
Sun Yize
Keyword(s):  
Arbitrary Shape ◽  
Back Propagation ◽  
Three Dimensional ◽  
Prediction Method ◽  
Single Layer ◽  
Back Propagation Neural Network ◽  
Precise Control ◽  
Finite Element Software ◽  
Offset Compensation ◽  
Braiding Angle

In this paper, trajectory control of arbitrary shape mandrel in three-dimensional circular braiding is studied. To obtain accurate trajectory, offset of mandrel is predicted and compensated for trajectory of mandrel. Firstly, the equation of the force of all yarns on three-dimensional mandrel is given. Then offset of mandrel in single layer braiding machine is analyzed via finite element software. Learning these data via back propagation neural network algorithm, offset of mandrel at each moment is derived. The trajectory generation of three-dimensional mandrel based on offset compensation by roll pitch yaw transformation is given. Lastly, braiding angle for the mandrel is analyzed theoretically. In the practical engineering, this method is proven to effectively reduce the error of braiding angle and helpful for the precise control of the trajectory of arbitrary shape mandrel.

A New Restoration Algorithm for the Smeared Image of a SINS-aided Star Sensor

2014 ◽  
Vol 67 (5) ◽  
pp. 881-898 ◽  
Author(s):  
Kedong Wang ◽  
Chao Zhang ◽  
Yong Li ◽  
Xin Kan
Keyword(s):  
Inertial Navigation System ◽  
Attitude Determination ◽  
Angular Rate ◽  
Wiener Filter ◽  
Star Sensor ◽  
Tracking Method ◽  
Trace Length ◽  
Star Image ◽  
Restoration Algorithm ◽  
The Impact

The accuracy of attitude determination using a star sensor tends to be degraded if the host vehicle's manoeuvring smears the star image. In this paper, a new restoration algorithm with the aid of a Strap-down Inertial Navigation System (SINS) is proposed to reduce the effect of the smeared image. The smeared trace length is estimated with aid of the SINS angular rate. The restoration algorithm based on a Wiener filter is designed after the smeared zone is derived with the aid of the SINS coarse attitude. A tracking method is proposed to reject the stars of low centroid extraction accuracy. Simulations demonstrate that the success rate and the accuracy of attitude determination are improved significantly by the restoration algorithm even under a very fast rotation. The impact of the SINS error on the restoration is evaluated in the simulations.

scholarly journals Electric Vehicle Load Prediction Method Based on Back-Propagation Neural Network

2021 ◽  
Vol 804 (4) ◽  
pp. 042045
Author(s):  
Dunnan Liu ◽  
Lingxiang Wang ◽  
Hua Li ◽  
Yuan Gao ◽  
Xiaofeng Peng ◽  
...  
Keyword(s):  
Neural Network ◽  
Electric Vehicle ◽  
Back Propagation ◽  
Prediction Method ◽  
Back Propagation Neural Network ◽  
Load Prediction ◽  
Vehicle Load

scholarly journals Restoration Method of a Blurred Star Image for a Star Sensor Under Dynamic Conditions

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4127 ◽  
Author(s):  
Zhiya Mu ◽  
Jun Wang ◽  
Xin He ◽  
Zhonghui Wei ◽  
Jiawei He ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Kinematic Model ◽  
Motion Blur ◽  
Star Sensor ◽  
Signal To Noise ◽  
Dynamic Conditions ◽  
Star Image ◽  
Centroid Coordinates ◽  
Noise Ratio ◽  
Restoration Method

Under the dynamic working conditions of a star sensor, motion blur of the star will appear due to its energy dispersion during imaging, leading to the degradation of the star centroid accuracy and attitude accuracy of the star sensor. To address this, a restoration method of a blurred star image for a star sensor under dynamic conditions is presented in this paper. First, a kinematic model of the star centroid and the degradation function of blurred star image under different conditions are analyzed. Then, an improved curvature filtering method based on energy function is proposed to remove the noise and improve the signal-to-noise ratio of the star image. Finally, the Richardson Lucy algorithm is used and the termination condition of the iterative equation is established by using the star centroid coordinates in three consecutive frames of restored images to ensure the restoration effect of the blurred star image and the accuracy of the star centroid coordinates. Under the dynamic condition of 0~4°/s, the proposed algorithm can effectively improve the signal-to-noise ratio of a blurred star image and maintain an error of the star centroid coordinates that is less than 0.1 pixels, which meets the requirement for high centroid accuracy.

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