scholarly journals Image Prediction Method with Nonlinear Control Lines Derived from Kriging Method with Extracted Feature Points Based on Morphing

2013 ◽  
Vol 2 (1) ◽  
Author(s):  
Kohei Arai
Keyword(s):  
Nonlinear Control ◽  
Prediction Method ◽  
Feature Points ◽  
Kriging Method ◽  
Image Prediction

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.

scholarly journals Modeling and Prediction Method for CNC Machine Tools’ Errors Based on Spatial Feature Points

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Guohua Chen ◽  
Lin Zhang ◽  
Hua Xiang ◽  
Yong Chen
Keyword(s):  
Machine Tools ◽  
Prediction Method ◽  
Numerical Control ◽  
Cnc Machine Tools ◽  
Ratio Method ◽  
Feature Points ◽  
Cnc Machine ◽  
Spatial Feature ◽  
Whole Space ◽  
Easy Integration

In order to improve the precision of CNC machine tools effectively, a method for modeling and predicting their spatial errors based on spatial feature points was proposed. Taking three-axis vertical CNC machine tools as the research object, we think that the whole space formed by machine tools’ working can be seen as the combination of a number of cubes, whose vertices are considered to be feature points, and others in the cubes are called nonfeature points. So, each nonfeature point’s errors can be predicted by the cube’s eight vertices’ errors. Based on the above ideas, an approach including the installing instrument for measuring any spatial feature point’s errors was put forward. In this way, all data of the feature points’ errors could be obtained. Moreover, according to these error data, the prediction model of nonfeature points’ errors was established by using the internal division ratio method. The method has the advantages of small interpolation operation, easy integration in the numerical control system, and high compensation precision. Finally, an example was used to prove its effectiveness and feasibility.

scholarly journals Age prediction based on a small number of facial landmarks and texture features

2021 ◽  
pp. 1-11
Author(s):  
Mengjie Wang ◽  
Weiyang Chen
Keyword(s):  
Essential Feature ◽  
Texture Feature ◽  
Prediction Method ◽  
Texture Features ◽  
Facial Feature ◽  
Support Vector ◽  
Feature Points ◽  
Facial Landmarks ◽  
Geometric Point ◽  
Age Prediction

BACKGROUND: Age is an essential feature of people, so the study of facial aging should have particular significance. OBJECTIVE: The purpose of this study is to improve the performance of age prediction by combining facial landmarks and texture features. METHODS: We first measure the distribution of each texture feature. From a geometric point of view, facial feature points will change with age, so it is essential to study facial feature points. We annotate the facial feature points, label the corresponding feature point coordinates, and then use the coordinates of feature points and texture features to predict the age. RESULTS: We use the Support Vector Machine regression prediction method to predict the age based on the extracted texture features and landmarks. Compared with facial texture features, the prediction results based on facial landmarks are better. This suggests that the facial morphological features contained in facial landmarks can reflect facial age better than facial texture features. Combined with facial landmarks and texture features, the performance of age prediction can be improved. CONCLUSIONS: According to the experimental results, we can conclude that texture features combined with facial landmarks are useful for age prediction.

scholarly journals A new Sequential Image Prediction Method Based on LSTM and DCGAN

2020 ◽  
Vol 64 (1) ◽  
pp. 217-231 ◽  
Author(s):  
Wei Fang ◽  
Feihong Zhang ◽  
Yewen Ding ◽  
Jack Sheng
Keyword(s):  
Prediction Method ◽  
Image Prediction ◽  
Sequential Image

Selection of a leading edge noise prediction method for PNoise

2018 ◽  
pp. 214-223
Author(s):  
AM Faria ◽  
MM Pimenta ◽  
JY Saab Jr. ◽  
S Rodriguez
Keyword(s):  
Wind Turbine ◽  
Turbine Blades ◽  
Prediction Method ◽  
Leading Edge ◽  
Wind Farms ◽  
Broadband Noise ◽  
Turbulence Energy ◽  
Noise Prediction ◽  
Migration Routes ◽  
Turbulent Inflow

Wind energy expansion is worldwide followed by various limitations, i.e. land availability, the NIMBY (not in my backyard) attitude, interference on birds migration routes and so on. This undeniable expansion is pushing wind farms near populated areas throughout the years, where noise regulation is more stringent. That demands solutions for the wind turbine (WT) industry, in order to produce quieter WT units. Focusing in the subject of airfoil noise prediction, it can help the assessment and design of quieter wind turbine blades. Considering the airfoil noise as a composition of many sound sources, and in light of the fact that the main noise production mechanisms are the airfoil self-noise and the turbulent inflow (TI) noise, this work is concentrated on the latter. TI noise is classified as an interaction noise, produced by the turbulent inflow, incident on the airfoil leading edge (LE). Theoretical and semi-empirical methods for the TI noise prediction are already available, based on Amiet’s broadband noise theory. Analysis of many TI noise prediction methods is provided by this work in the literature review, as well as the turbulence energy spectrum modeling. This is then followed by comparison of the most reliable TI noise methodologies, qualitatively and quantitatively, with the error estimation, compared to the Ffowcs Williams-Hawkings solution for computational aeroacoustics. Basis for integration of airfoil inflow noise prediction into a wind turbine noise prediction code is the final goal of this work.

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