Characterization and Classification of Coals and Rocks Using Terahertz Time-Domain Spectroscopy

Wang, Xin; Hu, Kexiang; Zhang, Lei; Yu, Xinbao; Ding, Enjie

doi:10.1007/s10762-016-0317-2

Cited by 40 publications

(15 citation statements)

References 23 publications

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“…Cong et al [31] performed EMD decomposition and kurtosis filtering on the vibration signal of the hydraulic support tail beam to realize the coal gangue interface identification. Wang et al [32], [33] used electromagnetic wave technology and terahertz timedomain spectroscopy to identify coal and rock. Huang et al [34] identified the coal rock interface during the cutting process of the shearer by clustering image recognition technology.…”

Section: Collision Contact Between the Coal Gangue And The Hydraulimentioning

confidence: 99%

Influence of Coal Gangue Volume Mixing Ratio on the System Contact Response When Multiple Coal Gangue Particles Impacting the Metal Plate and the Study of Coal Gangue Mixing Ratio Recognition Based on the Metal Plate Contact Response and the Multi-Information Fusion

et al. 2020

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Section: Collision Contact Between the Coal Gangue And The Hydraulimentioning

confidence: 99%

Influence of Coal Gangue Volume Mixing Ratio on the System Contact Response When Multiple Coal Gangue Particles Impacting the Metal Plate and the Study of Coal Gangue Mixing Ratio Recognition Based on the Metal Plate Contact Response and the Multi-Information Fusion

et al. 2020

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“…As the used materials have significantly different dielectric properties, high accuracy levels can be achieved with established methods. Most of the approaches perform an extraction of features either with statistical methods [5], [6] or linear feature mapping [7], [8]. Rather uncommon is the use of raw data [9], time-domain features [10], and filtered data.…”

Section: Introductionmentioning

confidence: 99%

Material Classification for Terahertz Images Based on Neural Networks

Kubiczek

Balzer

2022

IEEE Access

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Terahertz time-domain spectroscopy enables the extraction of electrical properties from materials. An extraction of the complex permittivity can be carried out with measurements in transmission or reflection geometry enabling the identification of materials. To perform an exact identification, the sample thickness and the angle of incidence of the terahertz radiation must be known. However, when those parameters are unknown and additionally the materials show strong absorbances, a precise differentiation between materials is challenging. A promising approach is the use of a neural network for automated material classification of terahertz images from different materials. Here, we show that a trained neural network can differentiate between 16 3D printed dielectric materials with a high accuracy of 98 % from measurements taken in transmission mode. As the constitution of the dataset has a big impact on the accuracy, various data preparations were investigated as well as the number of traces needed for achieving a well-trained network was determined. Finally, the trained neural network was evaluated with different sample thicknesses, revealing the huge impact of the materials absorbance on the extrapolation ability. This approach can be used in security application to classify harmful substances as well as for the automated generation of material maps.

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“…Therefore, accurate coal hardness detection can assure high coal mining efficiency [3,4]. In recent years, recognition methods have mostly focused on coal-rock image analysis [5,6], multi-sensor information fusion [7,8], acoustic signal analysis [9,10], etc. The idea of the image recognition of coal hardness and type is to compare image features of the working face with those in the database [11].…”

Section: Introductionmentioning

confidence: 99%

Coal and Rock Hardness Identification Based on EEMD and Multi-Scale Permutation Entropy

Liu

et al. 2021

Entropy

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This study offers an efficient hardness identification approach to address the problem of poor real-time performance and accuracy in coal and rock hardness detection. To begin, Ensemble Empirical Mode Decomposition (EEMD) was performed on the current signal of the cutting motor to obtain a number of Intrinsic Mode Functions (IMFs). Further, the target signal was selected among the IMFs to reconstruct the current signal according to the energy density and correlation coefficient criteria. After that, the Multi-scale Permutation Entropy (MPE) of the reconstructed signal was trained by the Adaboost improved Back Propagation (BP) neural network, in order to establish the hardness recognition model. Finally, the cutting arm’s swing speed and the cutting head’s rotation speed were adjusted based on the coal and rock hardness. The simulation results indicated that using the energy density and correlation criterion to reconstruct the signal can successfully filter out noise interference. Compared to the BP model, the relative root-mean-square error of the Adaboost-BP model decreased by 0.0633, and the prediction results were more accurate. Additionally, the speed control strategy based on coal and rock hardness can ensure the efficient cutting of the roadheader.

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Characterization and Classification of Coals and Rocks Using Terahertz Time-Domain Spectroscopy

Cited by 40 publications

References 23 publications

Influence of Coal Gangue Volume Mixing Ratio on the System Contact Response When Multiple Coal Gangue Particles Impacting the Metal Plate and the Study of Coal Gangue Mixing Ratio Recognition Based on the Metal Plate Contact Response and the Multi-Information Fusion

Influence of Coal Gangue Volume Mixing Ratio on the System Contact Response When Multiple Coal Gangue Particles Impacting the Metal Plate and the Study of Coal Gangue Mixing Ratio Recognition Based on the Metal Plate Contact Response and the Multi-Information Fusion

Material Classification for Terahertz Images Based on Neural Networks

Coal and Rock Hardness Identification Based on EEMD and Multi-Scale Permutation Entropy

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