Trajectory optimization by using EMD- and ICA-based processing method

Yang, Dandan; Wang, Lixin; Hu, Wenbin; Ding, Chenkang; Gan, Weibing; Liu, Fang

doi:10.1016/j.measurement.2019.03.063

Cited by 9 publications

(3 citation statements)

References 12 publications

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“…In the context of an experiment based on supervised learning, it was assumed that the change in the continuous deflection of bridge was only due to the result of structural damage. A metal pad (42.8L × 12.8W × 0.2H cm) with slope at both ends was used to simulate structural deformation caused by damage rather than physically destroying the structure [43]. The pad as an obstacle was placed on the movement path of the measuring device to simulate the deformation caused by structural damage.…”

Section: Experimental Design and Proceduresmentioning

confidence: 99%

Applying Deep Learning to Continuous Bridge Deflection Detected by Fiber Optic Gyroscope for Damage Detection

Zuo

et al. 2020

Sensors

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Improving the accuracy and efficiency of bridge structure damage detection is one of the main challenges in engineering practice. This paper aims to address this issue by monitoring the continuous bridge deflection based on the fiber optic gyroscope and applying the deep-learning algorithm to perform structural damage detection. With a scale-down bridge model, three types of damage scenarios and an intact benchmark were simulated. A supervised learning model based on the deep convolutional neural networks was proposed. After the training process under ten-fold cross-validation, the model accuracy can reach 96.9% and significantly outperform that of other four traditional machine learning methods (random forest, support vector machine, k-nearest neighbor, and decision tree) used for comparison. Further, the proposed model illustrated its decent ability in distinguishing damage from structurally symmetrical locations.

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Section: Experimental Design and Proceduresmentioning

confidence: 99%

Applying Deep Learning to Continuous Bridge Deflection Detected by Fiber Optic Gyroscope for Damage Detection

Zuo

et al. 2020

Sensors

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“…Overall decomposition process is repeated until ( ) is identified. This process is repeated for j=1 until ( ) is either a constant, a monotonic slope, or a function with only one extremum [31]. Finally, ( ) can be represented as…”

Section: Emg Modelling Using Empirical Mode Decomposition (Emd)mentioning

confidence: 99%

Quality assessment on muscle locations for speech representation

Safie

Rahim

2020

IJEECS

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<span lang="EN-US">There are more than 68 muscles, which are activated either simultaneously or sequentially during speech production. To monitor the signals from all these muscles at once, involve a lot of sensors and such system is very expensive. In the Quran therapeutic treatment applications, the use of specific muscles is very important, for the production of correct Arabic pronunciation. The proper pronunciation will improve the reader's understanding of what is being read, thus assisting the effectiveness of the therapy process. The objective of this study is to identify the most optimal muscle location, which is suitable for monitoring the quality of a recitation during the Quran’s therapeutic process, based on the information content embedded in their Electromyogram (EMG) signals. Empirical Mode Decomposition (EMD) technique was used in this study to extract features of the EMG while the combination of Hilbert Huang Spectral Entropy (HHSE) and Kullback Leibler Divergence (KLD) techniques were used to quantify the information content in each feature. Combination of these techniques managed to rank ten widely used speech muscles in the literature based upon their information content. Four muscle locations have been suggested, which is believed to be sufficient in developing a low-cost self-assessment system for monitoring Quran recitation.</span>

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“…In recent years, trajectory research is a hot area that attracts lots of scholars to pay their attention to the study of trajectory processing [1], [2], trajectory prediction [3], [4], trajectory classification [5], [6], and trajectory matching [7], [8].…”

Section: Introductionmentioning

confidence: 99%