scholarly journals An Improved Relay Protection Simulation Method - Direction Distance Method

2017 ◽  
Author(s):  
Feng GAO ◽  
Dong-Hai KUANG ◽  
Xi-Lan ZHAO
Keyword(s):  
Relay Protection ◽  
Simulation Method ◽  
Distance Method ◽  
Direction Distance

Power of Modified Brown-Forsythe and Mixed-Model Approaches in Split-Plot Designs

Methodology ◽  
2017 ◽  
Vol 13 (1) ◽  
pp. 9-22 ◽  
Author(s):  
Pablo Livacic-Rojas ◽  
Guillermo Vallejo ◽  
Paula Fernández ◽  
Ellián Tuero-Herrero
Keyword(s):  
Repeated Measures ◽  
Statistical Power ◽  
Mixed Model ◽  
Covariance Structure ◽  
Simulation Method ◽  
Future Research ◽  
Repeated Measures Design ◽  
Fixed And Random Effects ◽  
Split Plot ◽  
High Level

Abstract. Low precision of the inferences of data analyzed with univariate or multivariate models of the Analysis of Variance (ANOVA) in repeated-measures design is associated to the absence of normality distribution of data, nonspherical covariance structures and free variation of the variance and covariance, the lack of knowledge of the error structure underlying the data, and the wrong choice of covariance structure from different selectors. In this study, levels of statistical power presented the Modified Brown Forsythe (MBF) and two procedures with the Mixed-Model Approaches (the Akaike’s Criterion, the Correctly Identified Model [CIM]) are compared. The data were analyzed using Monte Carlo simulation method with the statistical package SAS 9.2, a split-plot design, and considering six manipulated variables. The results show that the procedures exhibit high statistical power levels for within and interactional effects, and moderate and low levels for the between-groups effects under the different conditions analyzed. For the latter, only the Modified Brown Forsythe shows high level of power mainly for groups with 30 cases and Unstructured (UN) and Autoregressive Heterogeneity (ARH) matrices. For this reason, we recommend using this procedure since it exhibits higher levels of power for all effects and does not require a matrix type that underlies the structure of the data. Future research needs to be done in order to compare the power with corrected selectors using single-level and multilevel designs for fixed and random effects.

New Simplified Low Cost Simulation Method for SME Manufacturing System

2015 ◽  
Vol 9 (2) ◽  
pp. 206
Author(s):  
Tawfik Benabdallah ◽  
Nor Nait Sadi ◽  
Mustapha Kamel Abdi
Keyword(s):  
Manufacturing System ◽  
Low Cost ◽  
Simulation Method ◽  
Cost Simulation

Applying the Method of Lagrange Multipliers to Derive an Estimator for Unsampled Soil Properties

2014 ◽  
Vol 11 (1) ◽  
pp. 15
Author(s):  
Set Foong Ng ◽  
Pei Eng Ch’ng ◽  
Yee Ming Chew ◽  
Kok Shien Ng
Keyword(s):  
Soil Properties ◽  
Lagrange Multipliers ◽  
Unbiased Estimator ◽  
Soil Property ◽  
Minimum Variance ◽  
Distance Method ◽  
True Value ◽  
A Site ◽  
Method Of Lagrange Multipliers ◽  
Inverse Distance

Soil properties are very crucial for civil engineers to differentiate one type of soil from another and to predict its mechanical behavior. However, it is not practical to measure soil properties at all the locations at a site. In this paper, an estimator is derived to estimate the unknown values for soil properties from locations where soil samples were not collected. The estimator is obtained by combining the concept of the ‘Inverse Distance Method’ into the technique of ‘Kriging’. The method of Lagrange Multipliers is applied in this paper. It is shown that the estimator derived in this paper is an unbiased estimator. The partiality of the estimator with respect to the true value is zero. Hence, the estimated value will be equal to the true value of the soil property. It is also shown that the variance between the estimator and the soil property is minimised. Hence, the distribution of this unbiased estimator with minimum variance spreads the least from the true value. With this characteristic of minimum variance unbiased estimator, a high accuracy estimation of soil property could be obtained.

Applying analytical procedures for performance audit of customs authorities

2020 ◽  
Vol 19 (6) ◽  
pp. 1035-1055 ◽  
Author(s):  
S.V. Pankova ◽  
V.V. Popov
Keyword(s):  
Distance Method ◽  
Scientific Methods ◽  
Scientific Hypothesis ◽  
Financial Audit ◽  
Promising Area ◽  
History Of ◽  
Performance Audit ◽  
Analytical Procedures ◽  
And Performance ◽  
Customs Administration

Subject. The article considers the development of a set of methods and indicators of economic analysis, which can be used for performance audit of customs authorities, using the Volga Customs Administration case. Objectives. The aim is to justify the use of analytical procedures to rank the effectiveness of customs payments for the purpose of performance audit of customs authorities. Methods. We employ general scientific methods of research, i.e. dialectical and monographic methods, logical analysis, comparison, as well as the Euclidean distance method. Results. We reviewed works by Russian and foreign scholars on the history of customs audit development and internal financial control of customs authorities, gave scientific credence to attributing the system of customs payment and performance to the indicators of economic activity of customs authorities. Due to the lack of methods for assessing the performance of customs authorities, the use of analytical procedures during the performance audit seems to be a promising area. Conclusions. When verifying the scientific hypothesis put forward in the study, we established that the introduction and development of the ranking system for the performance of customs authorities related to the collection of customs duties can contribute to effective financial audit of customs authorities in general.

Combination of Support Vector Machine and K-Fold cross-validation for prediction of long-term degradation of the compressive strength of marine concrete

2018 ◽  
Vol 1 (1) ◽  
pp. 120-130 ◽  
Author(s):  
Chunxiang Qian ◽  
Wence Kang ◽  
Hao Ling ◽  
Hua Dong ◽  
Chengyao Liang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Environmental Factors ◽  
Cross Validation ◽  
Concrete Strength ◽  
Simulation Method ◽  
Support Vector ◽  
Svm Model ◽  
Artificial Neural Network Ann ◽  
Influence Degree ◽  
Fold Cross Validation

Support Vector Machine (SVM) model optimized by K-Fold cross-validation was built to predict and evaluate the degradation of concrete strength in a complicated marine environment. Meanwhile, several mathematical models, such as Artificial Neural Network (ANN) and Decision Tree (DT), were also built and compared with SVM to determine which one could make the most accurate predictions. The material factors and environmental factors that influence the results were considered. The materials factors mainly involved the original concrete strength, the amount of cement replaced by fly ash and slag. The environmental factors consisted of the concentration of Mg2+, SO42-, Cl-, temperature and exposing time. It was concluded from the prediction results that the optimized SVM model appeared to perform better than other models in predicting the concrete strength. Based on SVM model, a simulation method of variables limitation was used to determine the sensitivity of various factors and the influence degree of these factors on the degradation of concrete strength.

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