Wind turbine condition monitoring (WTCM) is important to reduce the operation and maintenance cost of wind turbine. This paper proposes a WTCM approach based on autoencoder (AE) and K-means cluster. After data preproc...
详细信息
ISBN:
(纸本)9781665413558
Wind turbine condition monitoring (WTCM) is important to reduce the operation and maintenance cost of wind turbine. This paper proposes a WTCM approach based on autoencoder (AE) and K-means cluster. After data preprocessing, we firstly build an AE model with long short-term memory layer, and the construction of the model is determined by cross-validation experiment. Use the bottleneck layer of the AE model as the feature vector and establish the feature vector space of normal data. Secondly, the K-means cluster is employed. We gather the features of normal data into a cluster, then the cluster center and Euclidian distance are used to set the threshold. Thirdly, obtain the feature of testing data and calculate the Euclidian distance between the feature and the cluster center of normal data. The calculated Euclidian distance is used as the evaluation basis. Two cases with typical faults have been studied to demonstrate the feasibility of the proposed method.
Regulatory elements in DNA sequences, such as promoters, enhancers, terminators and so on, are essential for gene expression in physiological and pathological processes. A promoter is the specific DNA sequence that is...
详细信息
Regulatory elements in DNA sequences, such as promoters, enhancers, terminators and so on, are essential for gene expression in physiological and pathological processes. A promoter is the specific DNA sequence that is located upstream of the coding gene and acts as the "switch" for gene transcriptional regulation. Lots of promoter predictors have been developed for different bacterial species, but only a few are designed for Pseudomonas aeruginosa, a widespread Gram-negative conditional pathogen in nature. In this work, an ensemble model named SPREAD is proposed for the recognition of promoters in Pseudomonas aeruginosa. In SPREAD, the DNA sequence autoencoder model LSTM is employed to extract potential sequence information, and the mean output probability value of CNN and RF is applied as the final prediction. Compared with G4PromFinder, the only state-of-the-art classifier for promoters in Pseudomonas aeruginosa, SPREAD improves the prediction performance significantly, with an accuracy of 0.98, recall of 0.98, precision of 0.98, specificity of 0.97 and F1-score of 0.98.
暂无评论