Novel Nonlinear Approach for Real-Time Fatigue EEG Data: An Infinitely Warped Model of Weighted Permutation Entropy

Wu, Edmond Q.; Zhu, Limin; Zhang, Wen Ming; Deng, Ping-Yu; Jia, Bo; Chen, Shengdi; Ren, He; Zhou, Guowei

doi:10.1109/tits.2019.2918438

Cited by 20 publications

(3 citation statements)

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“…Next, 60 samples are randomly selected from each class to be used as a training classifier and the remaining samples are left to be used in testing the performance. Also, both EMD-EIMF-PE [ 21 ] and VMD-WPE-LTSA (the mode number of VMD is set as [ 4 , 20 ]) methods are considered. The classification outputs are shown in Figure 18 .…”

Section: Feature Extraction Of Ship-radiated Noise Based On the Proposed Methodsmentioning

confidence: 99%

“…To this end, due to the preamble weights, the weighted permutation entropy (WPE) [ 20 ] is further critical to the amplitude-coded information in the signal and has outperformed the PE in combating the distortion caused by noise. To the best of the authors’ knowledge, WPE has been widely used in uncertainty measurements in many fields [ 21 , 22 ], but is rarely used in the SRN feature extraction.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Stage Feature Extraction and Classification for Ship-Radiated Noise

Esmaiel

Xie²,

Qasem

et al. 2021

Sensors

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Due to the complexity and unique features of the hydroacoustic channel, ship-radiated noise (SRN) detected using a passive sonar tends mostly to distort. SRN feature extraction has been proposed to improve the detected passive sonar signal. Unfortunately, the current methods used in SRN feature extraction have many shortcomings. Considering this, in this paper we propose a new multi-stage feature extraction approach to enhance the current SRN feature extractions based on enhanced variational mode decomposition (EVMD), weighted permutation entropy (WPE), local tangent space alignment (LTSA), and particle swarm optimization-based support vector machine (PSO-SVM). In the proposed method, first, we enhance the decomposition operation of the conventional VMD by decomposing the SRN signal into a finite group of intrinsic mode functions (IMFs) and then calculate the WPE of each IMF. Then, the high-dimensional features obtained are reduced to two-dimensional ones by using the LTSA method. Finally, the feature vectors are fed into the PSO-SVM multi-class classifier to realize the classification of different types of SRN sample. The simulation and experimental results demonstrate that the recognition rate of the proposed method overcomes the conventional SRN feature extraction methods, and it has a recognition rate of up to 96.6667%.

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Section: Feature Extraction Of Ship-radiated Noise Based On the Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Stage Feature Extraction and Classification for Ship-Radiated Noise

Esmaiel

Xie²,

Qasem

et al. 2021

Sensors

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show abstract

“…Various learning-based methods are widely used for their flexibility of handling complex scenes in modern intelligent transport systems [2], [3], and recent deep learning techniques further add wings to the development of intelligent driving. They gain immense success in reinforcement learning, unsupervised learning, supervised learning and their hybrids [4].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning in Lane Marking Detection: A Survey

Zhang

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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Lane marking detection is a fundamental but crucial step in intelligent driving systems. It can not only provide relevant road condition information to prevent lane departure but also assist vehicle positioning and forehead car detection. However, lane marking detection faces many challenges, including extreme lighting, missing lane markings, and obstacle obstructions. Recently, deep learning-based algorithms draw much attention in intelligent driving society because of their excellent performance. In this paper, we review deep learning methods for lane marking detection, focusing on their network structures and optimization objectives, the two key determinants of their success. Besides, we summarize existing lane-related datasets, evaluation criteria, and common data processing techniques. We also compare the detection performance and running time of various methods, and conclude with some current challenges and future trends for deep learning-based lane marking detection algorithm.

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