A Simple Modelling Method for Deflection of Circular Plates Under Impulsive Loading using Dimensionless Analysis and Singular value Decomposition

2010 ◽  
Vol 26 (3) ◽  
pp. 355-361 ◽  
Author(s):  
H. Gharababaei ◽  
N. Nariman-zadeh ◽  
A. Darvizeh
Keyword(s):  
Experimental Data ◽  
Singular Value Decomposition ◽  
Impulsive Loading ◽  
Singular Value ◽  
Circular Plates ◽  
Dimensionless Analysis ◽  
Novel Approach ◽  
Complex Process ◽  
Value Decomposition ◽  
Svd Method

AbstractA novel approach of numerical modelling using input-output experimental data pairs is presented for deflection-thickness ratio of circular plates subjected to impulse loading. In this way, singular value decomposition (SVD) method is used in conjunction with dimensionless parameters incorporated in such complex process. The closed-form obtained model shows very good agreement with some testing experimental data pairs which have been unforeseen during the training process. Moreover, two modifications are consequently suggested for some similar models already proposed in previous works. The approach of this paper can generally be applied to model very complex real-world processes using appropriate experimental data.

scholarly journals Distributed Singular Value Decomposition Method for Fast Data Processing in Recommendation Systems

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2284
Author(s):  
Krzysztof Przystupa ◽  
Mykola Beshley ◽  
Olena Hordiichuk-Bublivska ◽  
Marian Kyryk ◽  
Halyna Beshley ◽  
...  
Keyword(s):  
Distributed Systems ◽  
Singular Value Decomposition ◽  
Data Processing ◽  
Message Passing ◽  
Message Passing Interface ◽  
Recommendation Systems ◽  
Singular Value ◽  
Singular Value Decomposition Method ◽  
Value Decomposition ◽  
Svd Method

The problem of analyzing a big amount of user data to determine their preferences and, based on these data, to provide recommendations on new products is important. Depending on the correctness and timeliness of the recommendations, significant profits or losses can be obtained. The task of analyzing data on users of services of companies is carried out in special recommendation systems. However, with a large number of users, the data for processing become very big, which causes complexity in the work of recommendation systems. For efficient data analysis in commercial systems, the Singular Value Decomposition (SVD) method can perform intelligent analysis of information. With a large amount of processed information we proposed to use distributed systems. This approach allows reducing time of data processing and recommendations to users. For the experimental study, we implemented the distributed SVD method using Message Passing Interface, Hadoop and Spark technologies and obtained the results of reducing the time of data processing when using distributed systems compared to non-distributed ones.

scholarly journals Satellite image classification using proposed singular value decomposition method

2019 ◽  
Vol 13 (28) ◽  
pp. 52-67
Author(s):  
Noor Zubair Kouder
Keyword(s):  
Singular Value Decomposition ◽  
Classification Accuracy ◽  
Satellite Image ◽  
Field Work ◽  
Mean Value ◽  
Singular Value ◽  
Singular Value Decomposition Method ◽  
Erdas Imagine ◽  
Value Decomposition ◽  
Svd Method

In this work, satellite images for Razaza Lake and the surrounding areadistrict in Karbala province are classified for years 1990,1999 and2014 using two software programming (MATLAB 7.12 and ERDASimagine 2014). Proposed unsupervised and supervised method ofclassification using MATLAB software have been used; these aremean value and Singular Value Decomposition respectively. Whileunsupervised (K-Means) and supervised (Maximum likelihoodClassifier) method are utilized using ERDAS imagine, in order to getmost accurate results and then compare these results of each methodand calculate the changes that taken place in years 1999 and 2014;comparing with 1990. The results from classification indicated thatwater and hills are decreased, while vegetation, wet land and barrenland are increased for years 1999 and 2014; comparable with 1990.The classification accuracy was done by number of random pointschosen on the study area in the field work and geographical data thencompared with the classification results, the classification accuracy forthe proposed SVD method are 92.5%, 84.5% and 90% for years1990,1999,2014, respectivety, while the classification accuracies forunsupervised classification method based mean value are 92%, 87%and 91% for years 1990,1999,2014 respectivety.

scholarly journals Non-Negative K-SVD as an element of the forecasting electricity demand system

2019 ◽  
Vol 84 ◽  
pp. 01003
Author(s):  
Marcin Drechny
Keyword(s):  
Singular Value Decomposition ◽  
Sparse Coding ◽  
Demand Forecasting ◽  
Singular Value ◽  
Demand System ◽  
Power Demand ◽  
Power Load ◽  
Fast Power ◽  
Value Decomposition ◽  
Svd Method

The article describes the NN-K-SVD method based on the use of sparse coding and the singular value decomposition to specific values. An example of using the method is the compression of load profiles. The experiment of compression of 125022 power load profiles has been carried out with the use of registered profiles in households and small offices. Two matrices: patterns (atoms) and scaling factors are the result of the discussed algorithm. Features of the created matrices, which can be used in the creation of fast power demand forecasting systems, have been characterized.

Computing Surface Parameter of Conic Aspheric Mirror with Singular Value Decomposition

2013 ◽  
Vol 631-632 ◽  
pp. 1363-1366
Author(s):  
Yong Luo
Keyword(s):  
Singular Value Decomposition ◽  
Singular Value ◽  
Radius Of Curvature ◽  
Surface Parameter ◽  
Aspheric Mirror ◽  
Value Decomposition ◽  
Svd Method

The result of null testing is usually used as the criterion in the fabricating process of aspheric mirrors. To ensure the accuracy of paraxial radius of curvature and conic constant is important when the null compensator emerges a problem. From the equation of conic aspheric mirror, we derive a set of algorithm from which the paraxial radius of curvature R and conic constant k can be obtained by using Singular Value Decomposition (SVD) method. The simulating result of an aspheric mirror with an aperture of 1229mm is presented and the solving precision reaches △R=0.1% and △k=0.14%. Thus the supplement to null testing of aspheric mirror is achieved effectively.

scholarly journals Receiver phase alignment using fitted SVD derived sensitivities from routine prescans

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256700
Author(s):  
Olivia W. Stanley ◽  
Ravi S. Menon ◽  
L. Martyn Klassen
Keyword(s):  
Singular Value Decomposition ◽  
Radio Frequency ◽  
Signal To Noise Ratio ◽  
Singular Value ◽  
7 Tesla ◽  
Signal To Noise ◽  
Phase Signal ◽  
Noise Ratio ◽  
Value Decomposition ◽  
Svd Method

Magnetic resonance imaging radio frequency arrays are composed of multiple receive coils that have their signals combined to form an image. Combination requires an estimate of the radio frequency coil sensitivities to align signal phases and prevent destructive interference. At lower fields this can be accomplished using a uniform physical reference coil. However, at higher fields, uniform volume coils are lacking and, when available, suffer from regions of low receive sensitivity that result in poor sensitivity estimation and combination. Several approaches exist that do not require a physical reference coil but require manual intervention, specific prescans, or must be completed post-acquisition. This makes these methods impractical for large multi-volume datasets such as those collected for novel types of functional MRI or quantitative susceptibility mapping, where magnitude and phase are important. This pilot study proposes a fitted SVD method which utilizes existing combination methods to create a phase sensitive combination method targeted at large multi-volume datasets. This method uses any multi-image prescan to calculate the relative receive sensitivities using voxel-wise singular value decomposition. These relative sensitivities are fitted to the solid harmonics using an iterative least squares fitting algorithm. Fits of the relative sensitivities are used to align the phases of the receive coils and improve combination in subsequent acquisitions during the imaging session. This method is compared against existing approaches in the human brain at 7 Tesla by examining the combined data for the presence of singularities and changes in phase signal-to-noise ratio. Two additional applications of the method are also explored, using the fitted SVD method in an asymmetrical coil and in a case with subject motion. The fitted SVD method produces singularity-free images and recovers between 95–100% of the phase signal-to-noise ratio depending on the prescan data resolution. Using solid harmonic fitting to interpolate singular value decomposition derived receive sensitivities from existing prescans allows the fitted SVD method to be used on all acquisitions within a session without increasing exam duration. Our fitted SVD method is able to combine imaging datasets accurately without supervision during online reconstruction.

scholarly journals Objects Detection by Singular Value Decomposition Technique in Hybrid Color Space: Application to Football Images

2010 ◽  
Vol 5 (2) ◽  
pp. 193
Author(s):  
Mourad Moussa Jlassi ◽  
Ali Douik ◽  
Hassani Messaoud
Keyword(s):  
Singular Value Decomposition ◽  
Color Space ◽  
Singular Value ◽  
Space Application ◽  
Color Information ◽  
Novel Approach ◽  
Computer Vision Applications ◽  
Value Decomposition ◽  
Objects Detection ◽  
Background Pixel

In this paper, we present an improvement non-parametric background modeling and foreground segmentation. This method is important; it gives the hand to check many states kept by each background pixel. In other words, generates the historic for each pixel, indeed on certain computer vision applications the background can be dynamic; several intensities were projected on the same pixel. This paper describe a novel approach which integrate both Singular Value Decomposition (SVD) of each image to increase the compactness density distribution and hybrid color space suitable to this case constituted by the three relevant chromatics levels deduced by histogram analysis. In fact the proposed technique presents the efficiency of SVD and color information to subtract background pixels corresponding to shadows pixels. This method has been applied on colour images issued from soccer video. In the other hand to achieve some statistics information about players ongoing of the match (football, handball, volley ball, Rugby...) as well as to refine their strategy coach and leaders need to have a maximum of technical-tactics information. For this reason it is prominent to elaborate an algorithm detecting automatically interests color regions (players) and solve the confusion problem between background and foreground every moment from images sequence.

scholarly journals A Star Sensor On-Orbit Calibration Method Based on Singular Value Decomposition

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3301 ◽  
Author(s):  
Liang Wu ◽  
Qian Xu ◽  
Janne Heikkilä ◽  
Zijun Zhao ◽  
Liwei Liu ◽  
...  
Keyword(s):  
Singular Value Decomposition ◽  
Singular Values ◽  
Calibration Method ◽  
Angular Distance ◽  
Singular Value ◽  
Optical Parameters ◽  
Star Sensor ◽  
Calibration Methods ◽  
Value Decomposition ◽  
Svd Method

The navigation accuracy of a star sensor depends on the estimation accuracy of its optical parameters, and so, the parameters should be updated in real time to obtain the best performance. Current on-orbit calibration methods for star sensors mainly rely on the angular distance between stars, and few studies have been devoted to seeking new calibration references. In this paper, an on-orbit calibration method using singular values as the calibration reference is introduced and studied. Firstly, the camera model of the star sensor is presented. Then, on the basis of the invariance of the singular values under coordinate transformation, an on-orbit calibration method based on the singular-value decomposition (SVD) method is proposed. By means of observability analysis, an optimal model of the star combinations for calibration is explored. According to the physical interpretation of the singular-value decomposition of the star vector matrix, the singular-value selection for calibration is discussed. Finally, to demonstrate the performance of the SVD method, simulation calibrations are conducted by both the SVD method and the conventional angular distance-based method. The results show that the accuracy and convergence speed of both methods are similar; however, the computational cost of the SVD method is heavily reduced. Furthermore, a field experiment is conducted to verify the feasibility of the SVD method. Therefore, the SVD method performs well in the calibration of star sensors, and in particular, it is suitable for star sensors with limited computing resources.

Observability Analysis of SINS/GPS during In-Motion Alignment Using Singular Value Decomposition

2012 ◽  
Vol 433-440 ◽  
pp. 5918-5923 ◽  
Author(s):  
Yao Li ◽  
Yong Li ◽  
Chris Rizos ◽  
Xiao Su Xu
Keyword(s):  
Singular Value Decomposition ◽  
Singular Value ◽  
Integrated System ◽  
Accelerated Motion ◽  
Observability Analysis ◽  
Attitude Error ◽  
Axis Rotation ◽  
Value Decomposition ◽  
Svd Method ◽  
Degree Of Observability

The in-motion alignment of a SINS/GPS integrated system uses the GPS velocity and position as observations, hence the SINS position and velocity errors are instantaneously observable. However the attitude error is not directly observable, it is important to find a way to improve the observability of attitude error because the degree-of-observability of the attitude error affects the accuracy of alignment. In this paper, the singular value decomposition (SVD) method is used to analyze the degree-of-observability of SINS/GPS during in-motion alignment. Simulations involving nine typical maneuvers indicate that both the yaw-axis rotation and the accelerated motion can improve the degree-of-observability of attitude error. Of the nine types of maneuvers, the triaxial rotation with acceleration is the most effective maneuver for improving the degree-of-observability of a SINS/GPS integrated system.

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