scholarly journals A Novel Protection Algorithm for Phase to Phase Faults in Double Circuit Transmission Lines

2019 ◽  
Vol 8 (3S2) ◽  
pp. 486-488
Keyword(s):  
Mean Square Error ◽  
Transmission Lines ◽  
Selection Process ◽  
Research Direction ◽  
Mean Square ◽  
Decision Strategy ◽  
Squared Error ◽  
Industrial Needs ◽  
Robot Selection ◽  
Selection Of

The selection of robots used for industry purpose is a crucial practice where various parameters have to be considered during appropriate selection process. The decision strategy of robot selection has a potential research direction to justify the necessity of industrial needs. We have compared three different mathematical models and selected the best method for choosing the a targeted application. In addition to the mathematical methodologies applied here, the performance characteristics for selecting the robot is examined by assessment of statistical errors namely Mean Square Error (MSE), Root Mean Square Error (RMSE), and R-Squared Error (RSE)

scholarly journals Selection of Commercial Robots with Anticipated Cost and Design Specifications using Regression Models

2019 ◽  
Vol 8 (2S10) ◽  
pp. 834-839
Keyword(s):  
Mathematical Models ◽  
Mean Square Error ◽  
Selection Process ◽  
Industrial Robot ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Mean Square ◽  
Robot Selection ◽  
Selection Of

The selection of robots used for industry purpose is a crucial practice where various parameters have to be considered during appropriate selection process. The decision strategy of robot selection has a potential research direction to justify the necessity of industrial needs. We have compared three different mathematical models and selected the best method for choosing the industrial robot to provide a complete selection framework to the present article. Principal Component Regression (PCR), Partial Least Square Regression (PLSR) and Linear Regression using Feed Forward Neural Network (FNN) are the three mathematical models used to correlate input with output parameters. During the testing procedure, eleven numbers of distinct parameters are considered to estimate the best possible rank selection. The strata or rank of the robot is approximated by utilizing the proposed algorithm. However, the most approved rank has met the desired genuinity for a targeted application. In addition to the mathematical methodologies applied here, the performance characteristics for selecting the robot is examined by assessment of statistical errors namely Mean Square Error (MSE), Root Mean Square Error (RMSE), and R-Squared Error (RSE).

Segmenting Star Images with Complex Backgrounds Based on Correlation between Objects and 1D Gaussian Morphology

2021 ◽  
Vol 11 (9) ◽  
pp. 3763
Author(s):  
Yunlong Zou ◽  
Jinyu Zhao ◽  
Yuanhao Wu ◽  
Bin Wang
Keyword(s):  
Mean Square Error ◽  
Selection Process ◽  
Minimum Mean Square Error ◽  
Correlation Coefficients ◽  
False Alarms ◽  
Mean Square ◽  
Segmentation Method ◽  
Space Objects ◽  
High Earth Orbits ◽  
Star Images

Space object recognition in high Earth orbits (between 2000 km and 36,000 km) is affected by moonlight and clouds, resulting in some bright or saturated image areas and uneven image backgrounds. It is difficult to separate dim objects from complex backgrounds with gray thresholding methods alone. In this paper, we present a segmentation method of star images with complex backgrounds based on correlation between space objects and one-dimensional (1D) Gaussian morphology, and the focus is shifted from gray thresholding to correlation thresholding. We build 1D Gaussian functions with five consecutive column data of an image as a group based on minimum mean square error rules, and the correlation coefficients between the column data and functions are used to extract objects and stars. Then, lateral correlation is repeated around the identified objects and stars to ensure their complete outlines, and false alarms are removed by setting two values, the standard deviation and the ratio of mean square error and variance. We analyze the selection process of each thresholding, and experimental results demonstrate that our proposed correlation segmentation method has obvious advantages in complex backgrounds, which is attractive for object detection and tracking on a cloudy and bright moonlit night.

scholarly journals Subset selection of markers for the genome-enabled prediction of genetic values using radial basis function neural networks

2020 ◽  
Vol 43 ◽  
pp. e46307 ◽  
Author(s):  
Isabela de Castro Sant'Anna ◽  
Gabi Nunes Silva ◽  
Moysés Nascimento ◽  
Cosme Damião Cruz
Keyword(s):  
Neural Networks ◽  
Root Mean Square Error ◽  
Radial Basis Function ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Basis Function ◽  
Stepwise Regression ◽  
Subset Selection ◽  
Mean Square ◽  
Selection Of

This paper aimed to evaluate the effectiveness of subset selection of markers for genome-enabled prediction of genetic values using radial basis function neural networks (RBFNN). To this end, an F1 population derived from the hybridization of divergent parents with 500 individuals genotyped with 1000 SNP-type markers was simulated. Phenotypic traits were determined by adopting three different gene action models – additive, additive-dominant, and epistatic, representing two dominance situations: partial and complete with quantitative traits having a heritability (h2) of 30 and 60%; traits were controlled by 50 loci, considering two alleles per locus. Twelve different scenarios were represented in the simulation. The stepwise regression was used before the prediction methods. The reliability and the root mean square error were used for estimation using a fivefold cross-validation scheme. Overall, dimensionality reduction improved the reliability values for all scenarios, specifically with h2 =30 the reliability value from 0.03 to 0.59 using RBFNN and from 0.10 to 0.57 with RR-BLUP in the scenario with additive effects. In the additive dominant scenario, the reliability values changed from 0.12 to 0.59 using RBFNN and from 0.12 to 0.58 with RR-BLUP, and in the epistasis scenarios, the reliability values changed from 0.07 to 0.50 using RBFNN and from 0.06 to 0.47 with RR-BLUP. The results showed that the use of stepwise regression before the use of these techniques led to an improvement in the accuracy of prediction of the genetic value and, mainly, to a large reduction of the root mean square error in addition to facilitating processing and analysis time due to a reduction in dimensionality.

Fractional order calculus approach for drying modeling of eggplants

2019 ◽  
Vol 26 (5) ◽  
pp. 379-387
Author(s):  
Koksal Erenturk ◽  
Bircan Kose ◽  
Saliha Erenturk
Keyword(s):  
Regression Analysis ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Fractional Order ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Fractional Order Calculus ◽  
Squared Error ◽  
Drying Conditions

In order to determine the drying modeling of eggplants, fractional order calculus modeling was applied and compared to regression analysis in this study. The fractional order calculus based on the Caputo derivative approach was considered and applied for modeling of eggplant drying. The drying experiments were performed on three levels of drying air temperatures (60, 70, and 80 ℃), with two different air flow velocity levels (0.5 and 1 m/s), and three levels of thickness (3.5, 6.5, and 9.5 mm) to measure the effects of different drying conditions for eggplants in a convective dryer. Four different commonly used mathematical models from the literature were fitted to the experimental data with regression analysis. Based on obtained results, the coefficient of determination (R2) was found as 0.9956 for the values of 80 ℃, 0.5 m/s, and 3.5 mm, while sum squared error was 0.0061 and root mean square error was 0.0235 for the Page model. However, better and more accurate results were obtained using fractional order modeling with the values of R2 found as 0.9981, sum squared error as 0.0012, and root mean square error as 0.0099 for the considered case.

scholarly journals Model Regresi Semiparametrik Spline untuk D ata Longitudinal pada Kasus Demam Berdarah Dengue di Kota Makassar

2021 ◽  
Vol 3 (1) ◽  
pp. 20
Author(s):  
Syafruddin Side ◽  
Wahidah Sanusi ◽  
Mustati'atul Waidah Maksum
Keyword(s):  
Regression Model ◽  
Mean Square Error ◽  
Cross Validation ◽  
Semiparametric Regression ◽  
Model Estimation ◽  
Mean Square ◽  
Semiparametric Regression Model ◽  
Regression Spline ◽  
Generalized Cross Validation ◽  
Selection Of

Abstrak. Regresi semiparametrik merupakan model regresi yang memuat komponen parametrik dan komponen nonparametrik dalam suatu model. Pada penelitian ini digunakan model regresi semiparametrik spline untuk data longitudinal dengan studi kasus penderita Demam Berdarah Dengue (DBD) di Rumah Sakit Universitas Hasanuddin Makassar periode bulan  Januari sampai bulan Maret 2018. Estimasi model regresi terbaik didapat dari pemilihan titik knot optimal dengan melihat nilai Generalized Cross Validation (GCV) dan Mean Square Error (MSE) yang minimum. Komponen parametrik pada penelitian ini adalah hemoglobin (g/dL) dan umur (tahun), suhu tubuh ( ), trombosit ( ) sebagai komponen nonparametrik dengan nilai GCV minimum sebesar 221,67745153 dicapai pada titik knot yaitu 14,552; 14,987; dan 15,096; nilai MSE sebesar 199,1032; dan nilai koefisien determinasi sebesar 75,3% yang diperoleh dari model regresi semiparametrik spline linear dengan tiga titik knot..Kata Kunci: regresi semiparametrik, spline, knot, Generalized Cross Validation, Demam Berdarah Dengue.Abstract. Semiparametric regression is a regression model that includes parametric and nonparametric components in it. The regression model in this research is spline semiparametric regression with case studies of patients with Dengue Hemorrahagic Fever (DHF) at University of Hasanuddin Makassar Hospital during the period of January to March 2018. The best regression model estimation is obtained from the selection of optimal knot which has minimum Generalized Cross Validation (GCV) and Mean Square Error (MSE). Parametric component in this research is hemoglobin (g/dL) and age (years), body temperature ( ), platelets ( ) as a nonparametric components. The minimum value of GCV is 221,67745153 achieved at the point 14,552; 14,987; and 15,096 knot; MSE value of 199,1032; and the value of coefficient determination is 75,3% obtained from semiparametric regression model linear spline with third point of knots.Keywords: semiparametric regression, spline, knot, Generalized Cross Validation, Dengue Hemorrahagic Fever.

scholarly journals PEMILIHAN PARAMETER PENGHALUS DALAM REGRESI SPLINE LINIER

2011 ◽  
Vol 3 (1) ◽  
pp. 9
Author(s):  
Agustini Tripena Br. Sb.
Keyword(s):  
Mean Square Error ◽  
Cross Validation ◽  
Selection Method ◽  
Regression Estimation ◽  
Mean Square ◽  
Spline Regression ◽  
Selection Methods ◽  
The Mean ◽  
Smoothing Parameters ◽  
Selection Of

This paper discusses aselection of smoothing parameters for the linier spline regression estimation on the data of electrical voltage differences in the wastewater. The selection methods are based on the mean square errorr (MSE) and generalized cross validation (GCV). The results show that in selection of smooting paranceus the mean square error (MSE) method gives smaller value , than that of the generalized cross validatio (GCV) method. It means that for our data case the errorr mean square (MSE) is the best selection method of smoothing parameter for the linear spline regression estimation.

Comparison of methods for control allocation in multiple arm studies using response adaptive randomization

2019 ◽  
Vol 17 (1) ◽  
pp. 52-60 ◽  
Author(s):  
Kert Viele ◽  
Kristine Broglio ◽  
Anna McGlothlin ◽  
Benjamin R Saville
Keyword(s):  
Mean Square Error ◽  
Simulation Study ◽  
Control Allocation ◽  
Mean Square ◽  
Comparison Of Methods ◽  
Single Treatment ◽  
Multiple Control ◽  
Adaptive Randomization ◽  
Selection Of

Background/Aims: Response adaptive randomization has many polarizing properties in two-arm settings comparing control to a single treatment. The generalization of these features to the multiple arm setting has been less explored, and existing comparisons in the literature reach disparate conclusions. We investigate several generalizations of two-arm response adaptive randomization methods relating to control allocation in multiple arm trials, exploring how critiques of response adaptive randomization generalize to the multiple arm setting. Methods: We perform a simulation study to investigate multiple control allocation schemes within response adaptive randomization, comparing the designs on metrics such as power, arm selection, mean square error, and the treatment of patients within the trial. Results: The results indicate that the generalization of two-arm response adaptive randomization concerns is variable and depends on the form of control allocation employed. The concerns are amplified when control allocation may be reduced over the course of the trial but are mitigated in the methods considered when control allocation is maintained or increased during the trial. In our chosen example, we find minimal advantage to increasing, as opposed to maintaining, control allocation; however, this result reflects an extremely limited exploration of methods for increasing control allocation. Conclusion: Selection of control allocation in multiple arm response adaptive randomization has a large effect on the performance of the design. Some disparate comparisons of response adaptive randomization to alternative paradigms may be partially explained by these results. In future comparisons, control allocation for multiple arm response adaptive randomization should be chosen to keep in mind the appropriate match between control allocation in response adaptive randomization and the metric or metrics of interest.

Improved Relative Ratio Method for Robot Selection

2014 ◽  
Vol 685 ◽  
pp. 275-278
Author(s):  
Shao Liang Yuan
Keyword(s):  
Selection Process ◽  
Ratio Method ◽  
Manufacturing Firm ◽  
Relative Ratio ◽  
Important Concern ◽  
Decision Making Problem ◽  
Multi Attribute Decision Making ◽  
Real Robot ◽  
Robot Selection ◽  
Selection Of

The selection of a desirable robot is an important concern for the manufacturing firm. The selection process needs to consider few critical selection attributes and then given the ranking result from a number of candidate robots. Then the robot selection problem is actually a multi-attribute decision making problem. This paper will propose a new robot selection method based on the concept of relative ratio method. A real robot selection case is used to demonstrate that the proposed method is effectiveness and feasibility.

Metamodeling Time-Temperature Creep Parameters

2020 ◽  
Vol 142 (3) ◽  
Author(s):  
Mohammad Shafinul Haque ◽  
Calvin M. Stewart
Keyword(s):  
Mean Square Error ◽  
Creep Rupture ◽  
Mean Square ◽  
Hastelloy X ◽  
Matlab Code ◽  
Rupture Data ◽  
Mathematical Problems ◽  
Temperature Parameter ◽  
Parameter Function ◽  
Selection Of

Abstract There exist many time-temperature parameter (TTP) models for creep rupture prediction of components including the Larson–Miller (LM), Manson–Haferd (MH), Manson–Brown (MB), Orr–Sherby–Dorn (OSD), Manson–Succop (MS), Graham–Walles (GW), Chitty–Duval (CD), Goldhoff–Sherby (GS) models. It remains a challenge to determine which model is “best”, capable of accurate interpolation and physically realistic extrapolation of creep rupture data for a given material. In this study, metamodeling is applied to create a unified TTP metamodel that combines and regresses into twelve TTP models (eight existing and four newly derived). An analysis of the mathematical problems that exist in TTP models is provided. A matlab code is written that can: (1) calibrate the material constants of any of the twelve TTP models (using the metamodel); (2) determine the most suitable stress-parameter function; (3) and report the normalized mean square error (NMSE) of rupture predictions for a given material database. Using the metamodel, and code, a design engineer can make an intelligent selection of the “best” TTP model for creep resistant design. This process is demonstrated using four isotherms of alloy P91 creep rupture data. To assess the influence of material, further validation is performed on alloys Hastelloy X, 304SS, and 316SS. It is determined that the “best” model is dependent on material type and the quality and quantity of available data.

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