A method based on musical-staff-inspired signal processing model for measuring rock moisture content

Zheng, Wei; Jiang, Jingyu; Tao, Kai

doi:10.1016/j.measurement.2018.05.047

Cited by 7 publications

(1 citation statement)

References 22 publications

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“…Kai Tao et al extracted six feature parameters from the original AE signals when compression tests were carried out on sandstone with different MC levels, and the identification of moisture was conducted using an SVM classifier [17]. Wei Zheng et al measured the effect of moisture on the AE characteristics of rocks and established a musical-staff-inspired MC detection model [18]. In [19], an AE multi-parameter analysis for dry and saturated sandstone with cracks under uniaxial compression was presented.…”

Section: Wireless Acoustic Emission Sensor Networkmentioning

confidence: 99%

A Non-Destructive Measurement of Trunk Moisture Content in Living Trees Based on Multi-Sensory Data Fusion

2023

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Water plays an important role in various physiological activities of living trees. Measuring trunk moisture content (MC) in real-time without damage has important guiding significance for transpiration research in forest ecosystems. However, existing standing tree MC detection methods are either too cumbersome to install or cause different degrees of damage. Here, we propose a novel Internet of Things (IoT) monitoring system that includes wireless acoustic emission sensor nodes (WASNs) and underground soil MC sensor nodes to efficiently detect and diagnose the MC level of living tree trunks. After the characteristic parameters were collected by the two sensors, a feature selection and multi-sensory global fusion method for MC diagnosis was designed and developed and several statistical parameters were selected as the input variables to predict the heartwood MC level with a support vector machine (SVM) model. Moreover, to achieve the highest prediction accuracy, an improved sparrow search algorithm (ISSA) is applied to ensure the most suitable parameter combinations in a two-objective optimization model. Extensive experiments result in a fusion of the environment, and AE signals show that the proposed mechanism has better diagnostic performance than state-of-the-art methods and is more adaptable to the fluctuation of working conditions.

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Section: Wireless Acoustic Emission Sensor Networkmentioning

confidence: 99%