Pattern recognition approach to identify loose particle material based on modified MFCC and HMMs

Zhai, Guofu; Chen, Jinbao; Li, Chao; Wang, Guotao

doi:10.1016/j.neucom.2014.12.039

Cited by 19 publications

(2 citation statements)

References 19 publications

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“…Then, the support vector machine algorithm was added to identify the loose particle materials, effectively distinguishing three main loose particle materials. Zhai et al [16] identified and classified the loose particle materials based on the hidden Markov model and the improved Mel frequency cepstral coefficient, realising the classification of four main loose particle materials. Chen et al [17] selected the particle diameter, particle material, vibration acceleration, and vibration frequency as parameters to evaluate their influences on the spectral distribution of sound signals through single-factor experiments.…”

Section: Introductionmentioning

confidence: 99%

Research on a synchronised classification method for loose particle detection signals of aerospace-sealed electronic components

Li,

Zhai,

Wang

et al. 2024

Meas. Sci. Technol.

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Aerospace-sealed electronic components exhibit favourable anti-interference capability and high reliability and are widely utilised in satellites, rockets, and missiles. Loose particle detection is crucial to ensure high reliability. However, the classification problem of loose particle detection signals based on the Particle Impact Noise Detection (PIND) method has been a challenge for the high reliability of aerospace-sealed electronic components. To address this issue, this paper systematically proposes a synchronised classification method based on PIND and validates its feasibility. The proposed approach combines a self-developed empirical model (referred to as Algorithm 1) with a classical machine learning model (referred to as Algorithm 2). The key findings are as follows: this method has a recognition rate for loose particle signals of 91.86%. Compared to a single algorithmic mode, the overall speed improves by 300%. The maximal recognition accuracy rates for component signals, mixed signals, and excessive signals are 90.03%, 81.04%, and 95.17%, respectively. The test results demonstrate that the method effectively balances the accuracy and speed of loose particle detection and leverages the complementary advantages of the two algorithms, thereby addressing the multi-classification issue of loose particle detection signals.

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Section: Introductionmentioning

confidence: 99%

Research on a synchronised classification method for loose particle detection signals of aerospace-sealed electronic components

Li,

Zhai,

Wang

et al. 2024

Meas. Sci. Technol.

Self Cite

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“…Hidden Markov model (HMM) has a strong capability of pattern classification due to its rich mathematical structure and proven accuracy on critical applications. It was widely used in signal recognition and classification [31,32]. HMM was used to recognize abnormal broiler sound in this study.…”

Section: Sound Classificationmentioning

confidence: 99%

A Novel Method for Broiler Abnormal Sound Detection Using WMFCC and HMM

Liu

Zhao

et al. 2020

Journal of Sensors

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Broilers produce abnormal sounds such as cough and snore when they suffer from respiratory diseases. The aim of this research work was to develop a method for broiler abnormal sound detection. The sounds were recorded in a broiler house for one week (24/7). There were 20 thousand white feather broilers reared on the floor in a building. Results showed that the developed recognition algorithm, using wavelet transform Mel frequency cepstrum coefficients (WMFCCs), correlation distance Fisher criterion (CDF), and hidden Markov model (HMM), provided an average accuracy, precision, recall, and F1 of 93.8%, 94.4%, 94.1%, and 94.2%, respectively, for broiler sound samples. The results indicate that sound analysis can be used in broiler respiratory assessment in a commercial broiler farm.

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