Simplified Entropy and Multidimensional Search for Test Points Selection

2016 ◽  
Vol 13 (10) ◽  
pp. 6803-6809 ◽  
Author(s):  
Huijie Zhang ◽  
Shulin Tian ◽  
Zhen Liu ◽  
Quan Zhou
Keyword(s):  
Local Optimum ◽  
Selection Algorithm ◽  
Fault Dictionary ◽  
Multidimensional Search ◽  
Statistical Experiments ◽  
Test Points ◽  
Test Points Selection ◽  
Selection Algorithms ◽  
Potential Test ◽  
Complex Entropy

In order to meet the demand of test points selection on higher speed, a new test points selection algorithm based on simplified entropy and multidimensional search is proposed. With the proposed algorithm, statistic results, instead of the complex entropy calculation, are utilized to estimate the order of entropy. In addition, multidimensional search method is adopted to find potential test points sets that can isolate the potential faults. The proposed method is also adaptive to the complexity of fault dictionary. In each iteration of the proposed algorithm, the dimension of multidimensional search could be changed according to the complexity of fault dictionary. Statistical experiments have shown that the proposed algorithm is more efficient in finding local optimum sets of test points compared with other test points selection algorithms.

Entropy-Based Optimum Test Points Selection for Analog Fault Dictionary Techniques

2004 ◽  
Vol 53 (3) ◽  
pp. 754-761 ◽  
Author(s):  
J.A. Starzyk ◽  
D. Liu ◽  
Z.-H. Liu ◽  
D.E. Nelson ◽  
J. Rutkowski
Keyword(s):  
Fault Dictionary ◽  
Test Points ◽  
Selection For ◽  
Test Points Selection

Test Points Selection for Analog Fault Dictionary Techniques

2009 ◽  
Vol 25 (2-3) ◽  
pp. 157-168 ◽  
Author(s):  
Chenglin Yang ◽  
Shulin Tian ◽  
Bing Long
Keyword(s):  
Fault Dictionary ◽  
Test Points ◽  
Selection For ◽  
Test Points Selection

A test points selection method for analog fault dictionary techniques

2009 ◽  
Vol 63 (2) ◽  
pp. 349-357 ◽  
Author(s):  
Chenglin Yang ◽  
Shulin Tian ◽  
Bing Long ◽  
Fang Chen
Keyword(s):  
Selection Method ◽  
Fault Dictionary ◽  
Test Points ◽  
Test Points Selection

An Enhancement of Feature Selection Algorithm for EDM: A Review

2018 ◽  
Vol 8 (5) ◽  
pp. 29
Author(s):  
Manpreet Kaur ◽  
Chamkaur Singh
Keyword(s):  
Feature Selection ◽  
Educational Data Mining ◽  
Problem Formulation ◽  
Research Area ◽  
Education Quality ◽  
Educational Institutions ◽  
Selection Algorithm ◽  
Positive Role ◽  
Data Set ◽  
Selection Algorithms

Educational Data Mining (EDM) is an emerging research area help the educational institutions to improve the performance of their students. Feature Selection (FS) algorithms remove irrelevant data from the educational dataset and hence increases the performance of classifiers used in EDM techniques. This paper present an analysis of the performance of feature selection algorithms on student data set. .In this papers the different problems that are defined in problem formulation. All these problems are resolved in future. Furthermore the paper is an attempt of playing a positive role in the improvement of education quality, as well as guides new researchers in making academic intervention.

Fast satellite selection algorithm for GNSS multisystems based on the Sherman-Morrison formula

2021 ◽  
Author(s):  
Junpeng SHI ◽  
Kezhao LI ◽  
Lin CHAI ◽  
Lingfeng LIANG ◽  
Chengdong TIAN ◽  
...  
Keyword(s):  
Optimal Estimation ◽  
Observation Data ◽  
Selection Algorithm ◽  
Maximum Volume ◽  
Adaptive Function ◽  
Satellite Selection ◽  
Geometric Dilution Of Precision ◽  
Value Decomposition ◽  
Selection Algorithms ◽  
Usage Efficiency

Abstract The usage efficiency of GNSS multisystem observation data can be greatly improved by applying rational satellite selection algorithms. Such algorithms can also improve the real-time reliability and accuracy of navigation. By combining the Sherman-Morrison formula and singular value decomposition (SVD), a smaller geometric dilution of precision (GDOP) value method with increasing number of visible satellites is proposed. Moreover, by combining this smaller GDOP value method with the maximum volume of tetrahedron method, a new rapid satellite selection algorithm based on the Sherman-Morrison formula for GNSS multisystems is proposed. The basic idea of the algorithm is as follows: first, the maximum volume of tetrahedron method is used to obtain four initial reference satellites; then, the visible satellites are co-selected by using the smaller GDOP value method to reduce the GDOP value and improve the accuracy of the overall algorithm. By setting a reasonable precise threshold, the satellite selection algorithm can be autonomously run without intervention. The experimental results based on measured data indicate that (1) the GDOP values in most epochs over the whole period obtained with the satellite selection algorithm based on the Sherman-Morrison formula are less than 2. Furthermore, compared with the optimal estimation results of the GDOP for all visible satellites, the results of this algorithm can meet the requirements of high-precision navigation and positioning when the corresponding number of selected satellites reaches 13. Moreover, as the number of selected satellites continues to increase, the calculation time increases, but the decrease in the GDOP value is not obvious. (2) The algorithm includes an adaptive function based on the end indicator of the satellite selection calculation and the reasonable threshold. When the reasonable precise threshold is set to 0.01, the selected number of satellites in most epochs is less than 13. Furthermore, when the number of selected satellites reaches 13, the GDOP value is less than 2, and the corresponding probability is 93.54%. These findings verify that the proposed satellite selection algorithm based on the Sherman-Morrison formula provides autonomous functionality and high-accuracy results.

Evaluation Method for Band Selection Algorithms of Hyperspectral Image

2013 ◽  
Vol 684 ◽  
pp. 495-498
Author(s):  
Bai He Wang ◽  
Shi Qi Huang ◽  
Yi Hong Li
Keyword(s):  
Data Fusion ◽  
Dimension Reduction ◽  
Evaluation Method ◽  
Hyperspectral Image ◽  
Band Selection ◽  
Selection Algorithm ◽  
Amount Of Information ◽  
Algorithm Evaluation ◽  
Common Band ◽  
Selection Algorithms

Band selection algorithm is most important in data dimension reduction of hyperspectral image. There are many algorithms of band selection, but there are only few methods to do algorithm evaluation. A method is put forward in this paper to evaluate the band selection algorithm of hyperspectral image. The amount of information, brightness, image contrast and definition are defined as 4 indexes to measure deferent data fusion based on various band selection results. Based on the measurement, the evaluation of band selection algorithm is realized. In the paper, the evaluation method is used in the compare of 4 common band selection algorithms, the result of measurement is analyzed and the feasibility is verified.

Design of Automatic Test Platform for the Gyroscope Group Based on Pertinence Matrix

2014 ◽  
Vol 556-562 ◽  
pp. 2567-2570
Author(s):  
He Jia Li ◽  
Xue Wang ◽  
Hai Feng Xu ◽  
Cheng Yao ◽  
Wen Ju Gao ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Performance Test ◽  
Gun Control ◽  
Automatic Test ◽  
Optimal Test ◽  
Fault Dictionary ◽  
Dependency Matrix ◽  
Optimal Criterion ◽  
Test Platform ◽  
Test Points

Aiming the problem of the armored vehicle's gun control system that there are many kinds of internal devices, complex fault reasons ,but no all-around and online fault diagnosis and state inspection mean, The automatic test platform for the gyroscope group with performance test and fault diagnosis for component and circuit is designed .The platform based on dependency matrix and optimal criterion of the maximum failure feature information entropy optimize test points ,choose optimal test points design. Performance test module is created and provides test result information for fault dictionary in fault diagnosis module. Automatic test platform is able to locate the circuit component failure.The platform is tested by actual vehicle experiment, and the results prove the reliability and validity of the platform.

scholarly journals A Pseudo-Label Guided Artificial Bee Colony Algorithm for Hyperspectral Band Selection

2020 ◽  
Vol 12 (20) ◽  
pp. 3456
Author(s):  
Chunlin He ◽  
Yong Zhang ◽  
Dunwei Gong
Keyword(s):  
Classification Accuracy ◽  
Artificial Bee Colony ◽  
Computational Cost ◽  
Band Selection ◽  
Selection Algorithm ◽  
Evolutionary Clustering ◽  
Bee Colony ◽  
Hyperspectral Remote Sensing Images ◽  
Selection Algorithms ◽  
Grid Division

Hyperspectral remote sensing images have characteristics such as high dimensionality and high redundancy. This paper proposes a pseudo-label guided artificial bee colony band selection algorithm with hypergraph clustering (HC-ABC) to remove redundant and noise bands. Firstly, replacing traditional pixel points by super-pixel centers, a hypergraph evolutionary clustering method with low computational cost is developed to generate high-quality pseudo-labels; Then, on the basis of these pseudo-labels, taking classification accuracy as the optimized objective, a supervised band selection algorithm based on artificial bee colony is proposed. Moreover, a noise filtering mechanism based on grid division is designed to ensure the accuracy of pseudo-labels. Finally, the proposed algorithm is applied in 3 real datasets and compared with 6 classical band selection algorithms. Experimental results show that the proposed algorithm can obtain a band subset with high classification accuracy for all the three classifiers, KNN, Random Forest, and SVM.

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