Underwater Sediments Echoes Recognition Based on KECCA + PLS

2013 ◽  
Vol 310 ◽  
pp. 629-633
Author(s):  
Bo Wen Luo ◽  
Bu Yan Wan ◽  
Wei Bin Qin ◽  
Ji Yu Xu
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Feature Fusion ◽  
Recognition Accuracy ◽  
Partial Least Square ◽  
Least Square ◽  
Classification Model ◽  
Nuclear Space ◽  
Nonlinear Feature

In order to solve the nonlinear feature fusion of underwater sediments echoes, the shortage of Enhanced Canonical Correlation Analysis (ECCA) was analyzed and made ECCA extend to Kernel ECCA (KECCA) in the nuclear space, a multi-feature nonlinear fusion classification model with KECCA combining with Partial Least-Square (PLS ) was put forward。In the process of identifying four types of underwater sediment such as Basalt, Volcanic breccia, Cobalt crusts and Mudstone, the results showed that the recognition accuracy could be further improved for the KECCA + PLS model.

scholarly journals Process Monitoring Using Canonical correlation analysis

10.31645/2021 ◽  
2019 ◽  
Author(s):  
Muahmmad Shakir ◽  
Keyword(s):  
Correlation Analysis ◽  
Process Monitoring ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Large Scale ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Process Data ◽  
Industrial Plants

Principal component analysis (PCA) and partial least square (PLS) used for fault diagnosis and process monitoring for systems. It is assumed that the data to be investigated is not self-correlated. However, the most large-scale chemical industrial plants are nonlinear in nature so these techniques do not cope with them, invalid in nature. To fulfil the gap, there is need to develop an algorithm which can manage these nonlinearities of the process. The demands of industrial products are increasing rapidly so different adaptable techniques are being proposed. Canonical Correlation Analysis (CCA) is multivariate data-driven methodology that takes input-output both process data into consideration. Most industrial systems assumed that the data to be analysed is Gaussian in nature. However, it is not due to the non-linearity’sreal systems in nature. In this work, an algorithm is developed that can monitor the system process using CCA with control limit that is achieved from the kernel density estimation by estimating probability density function (pdf).

A Dynamic Window Recognition Algorithm for SSVEP-Based Brain–Computer Interfaces Using a Spatio-Temporal Equalizer

2018 ◽  
Vol 28 (10) ◽  
pp. 1850028 ◽  
Author(s):  
Chen Yang ◽  
Xu Han ◽  
Yijun Wang ◽  
Rami Saab ◽  
Shangkai Gao ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Recognition Accuracy ◽  
Recognition Algorithm ◽  
Benchmark Dataset ◽  
Ease Of Use ◽  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
Spatio Temporal

The past decade has witnessed rapid development in the field of brain–computer interfaces (BCIs). While the performance is no longer the biggest bottleneck in the BCI application, the tedious training process and the poor ease-of-use have become the most significant challenges. In this study, a spatio-temporal equalization dynamic window (STE-DW) recognition algorithm is proposed for steady-state visual evoked potential (SSVEP)-based BCIs. The algorithm can adaptively control the stimulus time while maintaining the recognition accuracy, which significantly improves the information transfer rate (ITR) and enhances the adaptability of the system to different subjects. Specifically, a spatio-temporal equalization algorithm is used to reduce the adverse effects of spatial and temporal correlation of background noise. Based on the theory of multiple hypotheses testing, a stimulus termination criterion is used to adaptively control the dynamic window. The offline analysis which used a benchmark dataset and an offline dataset collected from 16 subjects demonstrated that the STE-DW algorithm is superior to the filter bank canonical correlation analysis (FBCCA), canonical variates with autoregressive spectral analysis (CVARS), canonical correlation analysis (CCA) and CCA reducing variation (CCA-RV) algorithms in terms of accuracy and ITR. The results show that in the benchmark dataset, the STE-DW algorithm achieved an average ITR of 134 bits/min, which exceeds the FBCCA, CVARS, CCA and CCA-RV. In off-line experiments, the STE-DW algorithm also achieved an average ITR of 116 bits/min. In addition, the online experiment also showed that the STE-DW algorithm can effectively expand the number of applicable users of the SSVEP-based BCI system. We suggest that the STE-DW algorithm can be used as a reliable identification algorithm for training-free SSVEP-based BCIs, because of the good balance between ease of use, recognition accuracy, ITR and user applicability.

scholarly journals Automated Atrial Fibrillation Detection Based on Feature Fusion Using Discriminant Canonical Correlation Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jingjing Shi ◽  
Chao Chen ◽  
Hui Liu ◽  
Yinglong Wang ◽  
Minglei Shu ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Expert Knowledge ◽  
Feature Fusion ◽  
Intraclass Correlation ◽  
Single Feature ◽  
Disability Rate ◽  
Parallel Feature

Atrial fibrillation (AF) is one of the most common cardiovascular diseases, with a high disability rate and mortality rate. The early detection and treatment of atrial fibrillation have great clinical significance. In this paper, a multiple feature fusion is proposed to screen out AF recordings from single lead short electrocardiogram (ECG) recordings. The proposed method uses discriminant canonical correlation analysis (DCCA) feature fusion. It fully takes intraclass correlation and interclass correlation into consideration and solves the problem of computation and information redundancy with simple series or parallel feature fusion. The DCCA integrates traditional features extracted by expert knowledge and deep learning features extracted by the residual network and gated recurrent unit network to improve the low accuracy of a single feature. Based on the Cardiology Challenge 2017 dataset, the experiments are designed to verify the effectiveness of the proposed algorithm. In the experiments, the F1 index can reach 88%. The accuracy, sensitivity, and specificity are 91.7%, 90.4%, and 93.2%, respectively.

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