Grey Relational Evaluation of Electronic Equipment Effectiveness Based on Ideal Reference Sequence

Multipath Transmission Control Protocol (MPTCP), a transport layer protocol, proposed by the IETF working group in 2009, can provide multipath communication end to end. It also can improve the utilization of network resources and network transmission reliability. However, that how to select multiple paths to improve the end to end overall throughput, and how to avoid the throughput declining by the performance difference, become the focus of this study. We propose a path selection strategy based on improved gray relational analysis, and set the optimal values of the QoS parameters for the selected paths as the reference sequence. According to the value of improved grey relational degree (IGRD) which is compared with reference sequence, we select the paths with better performance, smaller difference for transmission.

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A Calibration-Free Method Based on Grey Relational Analysis for Heterogeneous Smartphones in Fingerprint-Based Indoor Positioning

Sensors ◽

10.3390/s19183885 ◽

2019 ◽

Vol 19 (18) ◽

pp. 3885 ◽

Cited By ~ 1

Author(s):

Shuai Zhang ◽

Jiming Guo ◽

Nianxue Luo ◽

Di Zhang ◽

Wei Wang ◽

...

Keyword(s):

Grey Relational Analysis ◽

Indoor Positioning ◽

Test Point ◽

Reference Sequence ◽

Fingerprint Matching ◽

Relational Analysis ◽

Grey Relational Degree ◽

Relational Degree ◽

Grey Relational Coefficient ◽

Grey Relational

The fingerprint method has been widely adopted in Wi-Fi indoor positioning because of its advantage in non-line-of-sight channels between access points (APs) and mobile users. However, the received signal strength (RSS) during the fingerprint positioning process generally varies due to the dissimilar hardware configurations of heterogeneous smartphones. This difference may degrade the accuracy of fingerprint matching between fingerprint and test data. Thus, this paper puts forward a fingerprint method based on grey relational analysis (GRA) to approach the challenge of heterogeneous smartphones and to improve positioning accuracy. Initially, the grey relational coefficient (GRC) between the RSS comparability sequence of each reference point (RP) and the RSS reference sequence of the test point (TP) is calculated. Subsequently, the grey relational degree (GRD) between each RP and TP is determined on the basis of GRC, and the K most relational RPs are selected in accordance with the value of GRD. Finally, the user location is determined by weighting the K most relational RPs that correspond to the coordinates. The main advantage of this GRA method is that it does not require device calibration when handling heterogeneous smartphone problems. We further carry out extensive experiments using heterogeneous Android smartphones in an office environment to verify the positioning performance of the proposed method. Experimental results indicate that the proposed method outperforms the existing ones no matter whether heterogeneous smartphones are used.

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Research on the Application of Weighted Grey Relational Analysis on the Failure Diagnosis of Complicated Electronic Equipment

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.333-335.1543 ◽

2013 ◽

Vol 333-335 ◽

pp. 1543-1547

Author(s):

Hong Yan Zhao ◽

Jun Zhang ◽

Guo Ping Hu ◽

Jian Qiang Zhang

Keyword(s):

Failure Mode ◽

Grey Relational Analysis ◽

Failure Diagnosis ◽

Reference Sequence ◽

Relational Analysis ◽

Grey Relational Grade ◽

Diagnosis Method ◽

Diagnosis Accuracy ◽

The Individual ◽

Grey Relational

Based on weighted grey relational analysis, a new failure diagnosis method for complicated electronic equipments is proposed. First, according to the typical failure samples and weight values to construct grey reference sequence. Secondly, calculating the individual relational coefficient and grade to form grey relational grade sequence. Finally, according to the maximal grey relational grade to choose the corresponding failure mode as the finally diagnosis result. The results of analyses show that the proposed method has higher diagnosis accuracy and reliability than the traditional grey relational method.

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Research on Grey Relational Clustering Model of Multiobjective Human Resources Based on Time Constraint

Scientific Programming ◽

10.1155/2021/5551255 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Xiaowen Wang ◽

Yijun Mu

Keyword(s):

Reference Sequence ◽

Multidimensional Data ◽

Clustering Method ◽

Correlation Clustering ◽

Grey Correlation ◽

Clustering Model ◽

Relational Clustering ◽

Correlation Degree ◽

Grey Correlation Degree ◽

Grey Relational

The existing grey relational clustering method has limitations in the application of multidimensional sequences and cannot directly calculate the grey correlation degree between unequal-length sequences. In this paper, by introducing the multidimensional dynamic DTW distance into the existing 3D grey relational model, a new grey relational analysis model that can be applied to multidimensional data is proposed, which is based on DTW distance. The model does not require one-to-one correspondence of data points but evaluates the similarity of its geometric curves by calculating the shortest distance between sequences. In addition, since the traditional grey correlation clustering method is implemented, the method first extracts the reference sequence from the observation sequence and then calculates the similarity between the observation objects by calculating the grey correlation degree between each sequence and the reference sequence, so each object only needs to be calculated once. The experimental results show that the multidimensional grey correlation degree based on DTW distance and the grey relational clustering model oriented to multidimensional data are more accurate than other existing methods. Finally, the grey relation clustering method of multidimensional data is used to analyze the multiobjective human resource grey relational clustering model under time constraints, and the validity of the model is verified.

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Find Point Correspondences on Contour between Template Image and its Target with SVD and Euclidean Distance

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.239-240.1152 ◽

2012 ◽

Vol 239-240 ◽

pp. 1152-1157

Author(s):

Lu Yun Zhang ◽

Qiang Wang ◽

Hai Yan Liu ◽

Gang Wang

Keyword(s):

Image Matching ◽

Affine Transformation ◽

Euclidean Distance ◽

Reference Sequence ◽

Target Image ◽

Relational Analysis ◽

Template Image ◽

Contour Point ◽

Value Decomposition ◽

Grey Relational

A new algorithm is presented in this paper to determinate the point correspondences on contour between template image and its target after affine transformation. In the algorithm, the singular value decomposition（SVD）is applied to the contour point sets of the template and target image respectively for eliminating the influences of the shear and scale in the affine transformation. The Euclidean distance between the contour point and the center of the shape are taken as the feature to form the reference sequence and comparative sequences, and then grey relational analysis (GRA) is used to find the best correlation sequence. After two contour sequences with the best correlation are found, the corresponding points between the two contours can be decided also. Finally the affine transformation parameter can be calculated and image matching can be realized by this way. Compared with the similar methods, experiments show that the proposed method has lower computational complexity and better accurate for image matching.

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Software solution of medical grey relational method based on SAS environment

Grey Systems Theory and Application ◽

10.1108/gs-06-2016-0011 ◽

2016 ◽

Vol 6 (3) ◽

pp. 309-321 ◽

Cited By ~ 1

Author(s):

Jin-Xiu Zhu ◽

Xue-Rui Tan ◽

Nan Lu ◽

Shao-Xing Chen ◽

Xiao-Jun Chen

Keyword(s):

Original Data ◽

Reference Sequence ◽

The Novel ◽

Data Set ◽

Content Type ◽

Relational Analysis ◽

Typical Distribution ◽

Grey Relational Coefficient ◽

Grey Relational ◽

Novel Algorithm

Purpose The purpose of this paper is to construct a new algorithm of program procedure for medical grey relational method based on SAS software. Design/methodology/approach Based on the SAS environment, the authors construct a new algorithm of program procedure through the following methods: the construction data set, confirmation of the comparison sequence and reference sequence, the original data transformation, calculation of the grey relational coefficient of reference sequence and comparison sequence and calculating the correlation. Findings The results show that the novel algorithm of program procedure for medical grey relational method based on SAS software satisfies the properties properly. It also fully confirmed the biggest advantage of the grey relational analysis is that its requirements are not too high for the amount of data, and it does not need to follow the typical distribution. Originality/value The paper succeeds in constructing a novel algorithm of program procedures for medical grey relational method and providing a valuable tool for solving similar problems.

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Integration of grey analysis with artificial neural network for classification of slope failure

E3S Web of Conferences ◽

10.1051/e3sconf/202132501008 ◽

2021 ◽

Vol 325 ◽

pp. 01008

Author(s):

Ashanira Mat Deris ◽

Badariah Solemon ◽

Rohayu Che Omar

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Grey Relational Analysis ◽

Slope Failure ◽

Reference Sequence ◽

Ann Model ◽

Relational Analysis ◽

Artificial Neural ◽

Grey Relational

With the advent of technology and the introduction of computational intelligent methods, the prediction of slope failure using the machine learning (ML) approach is rapidly growing for the past few decades. This study employs an “artificial neural network” (ANN) to predict the slope failures based on historical circular slope cases. Using the feed-forward back-propagation algorithm with a multilayer perceptron network, ANN is a powerful ML method capable of predicting the complex model of slope cases. However, the prediction result of ANN can be improved by integrating the statistical analysis method, namely grey relational analysis (GRA), to the ANN model. GRA is capable of identifying the influencing factors of the input data based on the correlation level of the reference sequence and comparability sequence of the dataset. This statistical machine learning model can analyze the slope data and eliminate the unnecessary data samples to improve the prediction performance. Grey relational analysis-artificial neural network (GRANN) prediction model was developed based on six slope factors: unit weight, friction angle, cohesion, pore pressure ratio, slope height, and slope angle, with the factor of safety (FOS) as the output factor. The prediction results were analyzed based on accuracy percentage and receiver operating characteristic (ROC) values. It shows that the GRANN model has outperformed the ANN model by giving 99% accuracy and 0.999 ROC value, compared with 91% and 0.929.

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