Robust Principal Component Algorithms for High-Order Fast-Sampled Systems

2019 ◽  
Author(s):  
Hao Yang ◽  
Rafael M. Morales ◽  
Matthew C. Turner
Keyword(s):  
Principal Component ◽  
High Order ◽  
Sampled Systems

scholarly journals An Efficient Ground Manoeuvring Target Refocusing Method Based on Principal Component Analysis and Motion Parameter Estimation

2020 ◽  
Vol 12 (3) ◽  
pp. 378
Author(s):  
Dong Li ◽  
Haining Ma ◽  
Hongqing Liu ◽  
Zhanye Chen ◽  
Jia Su ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Parameter Estimation ◽  
Principal Component ◽  
Component Analysis ◽  
High Order ◽  
Motion Parameter ◽  
Cubic Phase ◽  
Motion Parameter Estimation ◽  
Imaging Results ◽  
Motion Parameters

Refocusing ground manoeuvring targets with complex motions in synthetic aperture radar (SAR) remains a challenging objective because of the large range of cell migration (RCM) and time-varying Doppler frequency modulation (DFM). By exploiting the geometric information of RCM and two-dimensional (2-D) coherently integrated gain, a fast ground manoeuvring target refocusing method using principal component analysis (PCA) and high-order motion parameter estimation is proposed. First, an efficient phase difference (PD) method and PCA are utilized to correct the RCM, and then, the energy of the ground manoeuvring target is concentrated into the same range bin. Second, by utilizing the coherently integrated cubic phase function (CICPF) that was developed in our previous work, the motion parameters are obtained accurately, and the manoeuvring target is thus well refocused into a sharp peak point based on the estimated motion parameters. The proposed method is of low computational complexity because it avoids time-consuming search and interpolation operations and demonstrates an improved anti-noise performance due to fully exploiting the 2-D coherent accumulation characteristics for estimating motion parameters and enhanced refocused imaging results for manoeuvring targets due to adopting the high-order motion model. Finally, experiments are conducted using simulated and real SAR data to show the performance of the proposed method.

scholarly journals A Novel Probabilistic Power Flow Algorithm Based on Principal Component Analysis and High-Dimensional Model Representation Techniques

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3520 ◽  
Author(s):  
Hang Li ◽  
Zhe Zhang ◽  
Xianggen Yin
Keyword(s):  
Principal Component Analysis ◽  
Power System ◽  
Power Flow ◽  
Random Variables ◽  
Principal Component ◽  
Component Analysis ◽  
High Order ◽  
High Dimensional ◽  
High Dimensional Model Representation ◽  
Dimensional Model

Because the penetration level of renewable energy sources has increased rapidly in recent years, uncertainty in power system operation is gradually increasing. As an efficient tool for power system analysis under uncertainty, probabilistic power flow (PPF) is becoming increasingly important. The point-estimate method (PEM) is a well-known PPF algorithm. However, two significant defects limit the practical use of this method. One is that the PEM struggles to estimate high-order moments accurately; this defect makes it difficult for the PEM to describe the distribution of non-Gaussian output random variables (ORVs). The other is that the calculation burden is strongly related to the scale of input random variables (IRVs), which makes the PEM difficult to use in large-scale power systems. A novel approach based on principal component analysis (PCA) and high-dimensional model representation (HDMR) is proposed here to overcome the defects of the traditional PEM. PCA is applied to decrease the dimension scale of IRVs and eliminate correlations. HDMR is applied to estimate the moments of ORVs. Because HDMR considers the cooperative effects of IRVs, it has a significantly smaller estimation error for high-order moments in particular. Case studies show that the proposed method can achieve a better performance in terms of accuracy and efficiency than traditional PEM.

Digital Processing of STEM Images

1981 ◽  
Vol 39 ◽  
pp. 236-237
Author(s):  
A. V. Crewe ◽  
M. Ohtsuki
Keyword(s):  
High Resolution ◽  
Low Dose ◽  
Assembly Language ◽  
Processing System ◽  
Principal Component ◽  
Digital Processing ◽  
Image Analysis System ◽  
Black And White ◽  
Analysis System ◽  
Dose Imaging

We have assembled an image processing system for use with our high resolution STEM for the particular purpose of working with low dose images of biological specimens. The system is quite flexible, however, and can be used for a wide variety of images.The original images are stored on magnetic tape at the microscope using the digitized signals from the detectors. For low dose imaging, these are “first scan” exposures using an automatic montage system. One Nova minicomputer and one tape drive are dedicated to this task.The principal component of the image analysis system is a Lexidata 3400 frame store memory. This memory is arranged in a 640 x 512 x 16 bit configuration. Images are displayed simultaneously on two high resolution monitors, one color and one black and white. Interaction with the memory is obtained using a Nova 4 (32K) computer and a trackball and switch unit provided by Lexidata.The language used is BASIC and uses a variety of assembly language Calls, some provided by Lexidata, but the majority written by students (D. Kopf and N. Townes).

Determination of the Burgers vector of a dislocation in a Fe-Mn alloy by weak-beam image in HVEM

1978 ◽  
Vol 36 (1) ◽  
pp. 330-331
Author(s):  
Y. Ishida ◽  
H. Ishida ◽  
K. Kohra ◽  
H. Ichinose
Keyword(s):  
High Order ◽  
Simulation Technique ◽  
The Other ◽  
Image Simulation ◽  
Diffraction Vector ◽  
Burgers Vector ◽  
Weak Beam ◽  
Beam Image ◽  
Edge Component

IntroductionA simple and accurate technique to determine the Burgers vector of a dislocation has become feasible with the advent of HVEM. The conventional image vanishing technique(1) using Bragg conditions with the diffraction vector perpendicular to the Burgers vector suffers from various drawbacks; The dislocation image appears even when the g.b = 0 criterion is satisfied, if the edge component of the dislocation is large. On the other hand, the image disappears for certain high order diffractions even when g.b ≠ 0. Furthermore, the determination of the magnitude of the Burgers vector is not easy with the criterion. Recent image simulation technique is free from the ambiguities but require too many parameters for the computation. The weak-beam “fringe counting” technique investigated in the present study is immune from the problems. Even the magnitude of the Burgers vector is determined from the number of the terminating thickness fringes at the exit of the dislocation in wedge shaped foil surfaces.

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