Separability of Histogram Based Features for Optical Performance Monitoring: An Investigation Using t-SNE Technique

Saif, Waddah S.; Alshawi, Tariq; Esmail, Maged Abdullah; Ragheb, Amr; Alshebeili, Saleh A.

doi:10.1109/jphot.2019.2913687

Cited by 24 publications

(18 citation statements)

References 27 publications

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“…Because t-SNE is capable of capturing the local structure of the high-dimensional data very well and revealing global structure such as the presence of clusters at several scales [51], it has been considered as a powerful visualizing approach to preserve both global and local structures of data in low-dimensional space [52]. In this study, t-SNE is employed for presenting visualization results of training datasets under different resampling approaches.…”

Section: ) Comparison Of Visulization Results Based On Different Resmentioning

confidence: 99%

An Integrated Framework Based on Latent Variational Autoencoder for Providing Early Warning of At-Risk Students

Yang

Hung

2020

IEEE Access

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The rapid development of learning technologies has enabled online learning paradigm to gain great popularity in both high education and K-12, which makes the prediction of student performance become one of the most popular research topics in education. However, the traditional prediction algorithms are originally designed for balanced dataset, while the educational dataset typically belongs to highly imbalanced dataset, which makes it more difficult to accurately identify the at-risk students. In order to solve this dilemma, this study proposes an integrated framework (LVAEPre) based on latent variational autoencoder (LVAE) with deep neural network (DNN) to alleviate the imbalanced distribution of educational dataset and further to provide early warning of at-risk students. Specifically, with the characteristics of educational data in mind, LVAE mainly aims to learn latent distribution of at-risk students and to generate at-risk samples for the purpose of obtaining a balanced dataset. DNN is to perform final performance prediction. Extensive experiments based on the collected K-12 dataset show that LVAEPre can effectively handle the imbalanced education dataset and provide much better and more stable prediction results than baseline methods in terms of accuracy and F 1.5 score. The comparison of t-SNE visualization results further confirms the advantage of LVAE in dealing with imbalanced issue in educational dataset. Finally, through the identification of the significant predictors of LVAEPre in the experimental dataset, some suggestions for designing pedagogical interventions are put forward. INDEX TERMS Performance prediction, early warning prediction, latent variational autoencoder, resampling methods, deep neural network, t-SNE.

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Section: ) Comparison Of Visulization Results Based On Different Resmentioning

confidence: 99%

An Integrated Framework Based on Latent Variational Autoencoder for Providing Early Warning of At-Risk Students

Yang

Hung

2020

IEEE Access

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“…Thus, it can be used to differentiate between different values of the same impairment. However, the presence of multiple signal impairments complicates the monitoring process, when AAH is being used [47].…”

Section: A Features Extractionmentioning

confidence: 99%

Optical Performance Monitoring in Mode Division Multiplexed Optical Networks

Saif

Ragheb

Alshawi

et al. 2021

J. Lightwave Technol.

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This paper considers, for the first time, optical performance monitoring (OPM) in few mode fiber (FMF)-based optical networks. One dimensional (1D) features vector, extracted by projecting a two-dimensional (2D) asynchronous in-phase quadrature histogram (IQH), and the 2D IQH are proposed to achieve OPM in FMF-based network. Three machine learning algorithms are employed for OPM and their performances are compared. These include support vector machine, random forest algorithm, and convolutional neural network. Extensive simulations are conducted to monitor optical to signal ratio (OSNR), chromatic dispersion (CD), and mode coupling (MC) for dual polarization-quadrature phase shift keying (DP-QPSK) at 10, 12, 16, 20, and 28 Gbaud transmission speeds. Besides, M-ary quadrature amplitude modulation (M = 8 and 16) is considered. Also, the OPM accuracy is investigated under different FMF channel conditions including phase noise and polarization mode dispersion. Simulation results show that the proposed 1D projection features vector provides better OPM results than those of the widely used asynchronous amplitude histogram (AAH) features. Furthermore, it has been found that the 2D IQH features outperform the 1D projection features but require larger number of features samples. Additionally, the effect of fiber nonlinearity on the OPM accuracy is investigated. Finally, OPM using the 2D IQH features has been verified experimentally for 10 Gbaud DP-QPSK signal. The obtained results show a good agreement between both simulation and experimental findings.

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“…Compared with the original SNE, it uses t-distribution to solve the probability distribution in low-dimensional situations to alleviate the data crowding problem. Simultaneously, this method uses joint probability instead of conditional probability to recalculate KLD to obtain symmetry [18], [19].…”

Section: Introductionmentioning

confidence: 99%

Induction Motor Fault Classification Based on ROC Curve and t-SNE

Lee

Lin

2021

IEEE Access

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This paper proposes a novel fault classification method with application to induction motors, which is based on integrating and combining with receiver operating characteristic (ROC) curve and tdistribution stochastic neighbor embedding (t-SNE). According to the feature selection methods of ReliefF, symmetrical uncertainty (SU), and fast correlation-based filter (FCBF), the significant features were verified. Additionally, support vector machine (SVM), k-nearest neighbor (KNN), and decision tree (DT) are also considered as classifiers to identify the simulation results. To begin with, the current signals obtained from distinctive four topologies of working conditions of the motor, which includes healthy, bearing damage, broken rotor bar, and short circuit in stator windings, respectively. The potential feature set is extracted by using Hilbert-Huang transform (HHT) technique. Then, three feature selection methods are adopted to select three optimal feature subsets from the original feature set. Finally, the classification accuracy (ACC) and ROC curve are used to demonstrate the capability of classifiers' recognition. The results showed that the optimal feature subsets significantly reduce the number of selected features and improve the classification ACC and area under the curve (AUC) compared with the original feature set. In conclusion, the proposed method can downgrade the data, demonstrate the scatter plot more intuitively, and identify various types of faults, unlike with other fault diagnosis literature.

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Separability of Histogram Based Features for Optical Performance Monitoring: An Investigation Using t-SNE Technique

Cited by 24 publications

References 27 publications

An Integrated Framework Based on Latent Variational Autoencoder for Providing Early Warning of At-Risk Students

An Integrated Framework Based on Latent Variational Autoencoder for Providing Early Warning of At-Risk Students

Optical Performance Monitoring in Mode Division Multiplexed Optical Networks

Induction Motor Fault Classification Based on ROC Curve and t-SNE

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