Dataset Information

Multi-disturbance identification from mine wind-velocity data based on MSSW and WPT-GBDT.

ABSTRACT: To overcome the false alarm problem that arises for mine wind-velocity sensors due to air-door and mine-car operation, a wind-velocity disturbance identification method based on the wavelet packet transform and gradient lifting decision tree is proposed. In this method, a multi-scale sliding window discretizes continuous wind-velocity monitoring data, the wavelet packet transform extracts the hidden features of discrete data, and a gradient lifting decision tree multi-disturbance classification model is established. Based on the overlap degree rule, the disturbance identification results are merged, modified, combined, and optimized. In accordance with a least absolute shrinkage and selection operator regression, the air-door operation information is further extracted. A similarity experiment is performed to verify the method performance. For the disturbance identification task, the recognition accuracy, accuracy, and recall of the proposed method are 94.58%, 95.70% and 92.99%, respectively, and for the task involving further extraction of disturbance information related to air-door operation, those values are 72.36%, 73.08%, and 71.02%, respectively. This algorithm gives a new recognition method for abnormal time series data.

SUBMITTER: Shang W

PROVIDER: S-EPMC10112794 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

Multi-disturbance identification from mine wind-velocity data based on MSSW and WPT-GBDT.

Shang Wentian W Deng Lijun L Liu Jian J Zhou Yukai Y

PloS one 20230418 4

To overcome the false alarm problem that arises for mine wind-velocity sensors due to air-door and mine-car operation, a wind-velocity disturbance identification method based on the wavelet packet transform and gradient lifting decision tree is proposed. In this method, a multi-scale sliding window discretizes continuous wind-velocity monitoring data, the wavelet packet transform extracts the hidden features of discrete data, and a gradient lifting decision tree multi-disturbance classification ...[more]

PMID: 37071602

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Project description:Pattern identification (PI) is a diagnostic method used in Traditional East Asian medicine (TEAM) to select appropriate and personalized acupuncture points and herbal medicines for individual patients. Developing a reproducible PI model using clinical information is important as it would reflect the actual clinical setting and improve the effectiveness of TEAM treatment. In this paper, we suggest a novel deep learning-based PI model with feature extraction using a deep autoencoder and k-means clustering through a cross-sectional study of sleep disturbance patient data. The data were obtained from an anonymous electronic survey in the Republic of Korea Army (ROKA) members from August 16, 2021, to September 20, 2021. The survey instrument consisted of six sections: demographics, medical history, military duty, sleep-related assessments (Pittsburgh sleep quality index (PSQI), Berlin questionnaire, and sleeping environment), diet/nutrition-related assessments [dietary habit survey questionnaire and nutrition quotient (NQ)], and gastrointestinal-related assessments [gastrointestinal symptom rating scale (GSRS) and Bristol stool scale]. Principal component analysis (PCA) and a deep autoencoder were used to extract features, which were then clustered using the k-means clustering method. The Calinski-Harabasz index, silhouette coefficient, and within-cluster sum of squares were used for internal cluster validation and the final PSQI, Berlin questionnaire, GSRS, and NQ scores were used for external cluster validation. One-way analysis of variance followed by the Tukey test and chi-squared test were used for between-cluster comparisons. Among 4,869 survey responders, 2,579 patients with sleep disturbances were obtained after filtering using a PSQI score of >5. When comparing clustering performance using raw data and extracted features by PCA and the deep autoencoder, the best feature extraction method for clustering was the deep autoencoder (16 nodes for the first and third hidden layers, and two nodes for the second hidden layer). Our model could cluster three different PI types because the optimal number of clusters was determined to be three via the elbow method. After external cluster validation, three PI types were differentiated by changes in sleep quality, dietary habits, and concomitant gastrointestinal symptoms. This model may be applied to the development of artificial intelligence-based clinical decision support systems through electronic medical records and clinical trial protocols for evaluating the effectiveness of TEAM treatment.

Dataset Information

Multi-disturbance identification from mine wind-velocity data based on MSSW and WPT-GBDT.

Publications

Multi-disturbance identification from mine wind-velocity data based on MSSW and WPT-GBDT.

Similar Datasets

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