Identification and Observability Measure of a Basis Set of Error Parameters in Robot Calibration

1989 ◽  
Vol 111 (4) ◽  
pp. 513-518 ◽  
Author(s):  
Chia-Hsiang Menq ◽  
Jin-Hwan Borm ◽  
Jim Z. Lai
Keyword(s):  
Basis Set ◽  
Robot Calibration ◽  
Position Errors ◽  
Estimation Of Error ◽  
Observability Index ◽  
Value Decomposition ◽  
Svd Method ◽  
Observability Measure ◽  
Robot Configurations ◽  
Error Parameter

This paper presents a method of identifying a basis set of error parameters in robot calibration using the Singular Value Decomposition (SVD) method. With the method, the error parameter space can be separated into two: observable subspace and unobservable one. As a result, for a defined position error model, one can determine the dimension of the observable subspace, which is vital to the estimation of error parameters. The second objective of this paper is to study, when unmodeled error exists, the implications of measurement configurations in robot calibration. For selecting measurement configurations in calibration, and index is defined to measure the observability of the error parameters with respect to a set of robot configurations. As the observability index increases, the attribution of the position errors to the parameters becomes dominant and the effects of the measurement and unmodeled errors become less significant; consequently better estimation of the parameter errors can be obtained.

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.

A New Integrated Algorithm of Structure Determination and Parameter Estimation for Nonlinear Systems Identification

1999 ◽  
Author(s):  
Wang Xiao Wang ◽  
Jianyin Xie
Keyword(s):  
Parameter Estimation ◽  
Nonlinear Systems ◽  
Structure Determination ◽  
Model Identification ◽  
Identification Algorithm ◽  
Householder Transformation ◽  
Systems Identification ◽  
Value Decomposition ◽  
Svd Method ◽  
Nonlinear Systems Identification

Abstract A new integrated algorithm of structure determination and parameter estimation is proposed for nonlinear systems identification in this paper, which is based on the Householder Transformation (HT), Givens and Modified Gram-Schmidt (MGS) algorithms. While being used for the polynomial and rational NARMAX model identification, it can select the model terms while deleting the unimportant ones from the assumed full model, avoiding the storage difficulty as the CGS identification algorithm does which is proposed by Billings et. al., and is numerically more stable. Combining the H algorithm with the modified bidiagonalization least squares (MBLS) algorithm and the singular value decomposition (SVD) method respectively, two algorithms referred to as the MBLSHT and SVDHT ones are proposed for the polynomial and rational NARMAX model identification. They are all numerically more stable than the HT or Givens or MGS algorithm given in this paper, and the MBLSHT algorithm has the best performance. A higher precision for the parameter estimation can thus be obtained by them, as supported b simulation results.

scholarly journals Coordinate transformation parameters in Nepal by using neural network and SVD methods

2019 ◽  
Vol 9 (1) ◽  
pp. 22-28
Author(s):  
Kutubuddin Ansari ◽  
Prabin Gyawali ◽  
Prachand Man Pradhan ◽  
Kwan-Dong Park
Keyword(s):  
Neural Network ◽  
Coordinate Transformation ◽  
Data Sets ◽  
Extension Model ◽  
Global Positioning ◽  
Coordinate Measurements ◽  
Transformation Parameters ◽  
Artificial Neural Network Ann ◽  
Value Decomposition ◽  
Svd Method

Abstract The present study computes B-W extension model (extended Bursa-Wolf model) coordinate transformation parameters from World Geodetic System 1984 (WGS-84) to the Everest datum namely Everest (1830) and Everest (1956) using records of coordinate measurements from Global Positioning System (GPS) observable across Nepal region. Synthetic or modeled coordinates were determined by using the Artificial Neural Network (ANN) and Singular Value Decomposition (SVD) methods. We studied 9-transformation parameters with the help of the ANN technique and validated the outcomes with the SVD method. The comparative analysis of the ANN, as well as SVD methods, was done with the observed output following one way ANOVA test. The analysis showed that the null hypothesis for both datums were acceptable and suggesting all models statistically significantly equivalent to each other. The outcomes from this study would complement a relatively better understanding of the techniques for coordinate transformation and precise coordinate assignment while assimilating data sets from different resources.

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.

Reconciling Distance Metric Methods for Rigid Body Displacements

2009 ◽  
Author(s):  
Anurag Purwar ◽  
Qiaode Jeffrey Ge
Keyword(s):  
Rigid Body ◽  
Polar Decomposition ◽  
Fast Method ◽  
Robot Calibration ◽  
Mechanism Synthesis ◽  
Orthogonal Matrices ◽  
Higher Dimensional ◽  
Motion Approximation ◽  
Value Decomposition ◽  
And Control

In the last twenty years, researchers have proposed a few different methods to establish a norm (or, metric) for both planar and spatial rigid body displacements. Desire to meaningfully quantify a displacement composed of rotation and translation stems from a requirement to ascertain “distance” between two given displacements in applications, such as motion approximation and interpolation, mechanism synthesis, collision avoidance, positioning, and robot calibration and control. In this paper, we show that the various seemingly different shape independent norm calculation methods based on approximating displacements with higher dimensional rotations via orthogonal matrices, or polar decomposition (PD) and singular value decomposition (SVD) can be reconciled and unified in the mathematically compact and elegant framework of biquaternions. In the process, we also propose an elegant and fast method for such norm calculations.

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.

Theoretical and Experimental Determination of Proppant Crushing Rate and Fracture Conductivity

2020 ◽  
Vol 142 (10) ◽  
Author(s):  
Dali Guo ◽  
Yunxiang Zhao ◽  
Zixi Guo ◽  
Xianhui Cui ◽  
Bo Huang
Keyword(s):  
Oil And Gas ◽  
Search Algorithm ◽  
Mechanical Analysis ◽  
Experimental Conditions ◽  
Fracture Conductivity ◽  
The Monte Carlo Method ◽  
Physical Testing ◽  
Value Decomposition ◽  
Svd Method

Abstract Proppant is an important material for hydraulic fracturing that impacts the production and production cost of oil and gas wells. The key properties of proppant are crushing rate and fracture conductivity. The most common way to evaluate the key properties of proppant is physical testing, but this method is time-consuming and costly, and it may result in different results under the same experimental conditions. This paper presents a method for calculating proppant crushing rate and fracture conductivity, which are obtained by combining a series of simple and economical laboratory experiments with a significant amount of numerical calculations under various experimental conditions. First, the arrangement of proppant particles was simulated, and the location of particles was determined with the Monte Carlo method, the optimization model, and search algorithm in this process. Second, by mechanical analysis of proppant particles, a mathematical model of force was established, and the singular-value decomposition (SVD) method was used to calculate the force of each particle. Third, the crushing rate of proppant particles was calculated under irregular conditions using mathematical statistics. The Kozeny–Carman equation was improved on to establish a fracture conductivity model. Finally, the average fracture conductivity was calculated on the basis of the simulation results. The calculated fracture conductivity is consistent with the experimental results, which verifies the accuracy of the model.

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