Purpose The purpose of this study is to analyze the intelligent semisupervised fault diagnosis method of aeroengine. Design/methodology/approach A semisupervised fault diagnosis method based on denoising autoencoder (...
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Purpose The purpose of this study is to analyze the intelligent semisupervised fault diagnosis method of aeroengine. Design/methodology/approach A semisupervised fault diagnosis method based on denoising autoencoder (DAE) and deep belief network (DBN) is proposed for aeroengine. Multiple state parameters of aeroengine with long time series are processed to form high-dimensional fault samples and corresponding fault types are taken as sample labels. DAE is applied for unsupervised learning of fault samples, so as to achieve denoised dimension-reduction features. Subsequently, the extracted features and sample labels are put into DBN for supervised learning. Thus, the semisupervised fault diagnosis of aeroengine can be achieved by the combination of unsupervised learning and supervised learning. Findings The JT9D aeroengine data set and simulated aeroengine data set are applied to test the effectiveness of the proposed method. The result shows that the semisupervised fault diagnosis method of aeroengine based on DAE and DBN has great robustness and can maintain high accuracy of fault diagnosis under noise interference. Compared with other traditional models and separate deep learning model, the proposed method also has lower error and higher accuracy of fault diagnosis. Originality/value Multiple state parameters with long time series are processed to form high-dimensional fault samples. As a typical unsupervised learning, DAE is used to denoise the fault samples and extract dimension-reduction features for future deep learning. Based on supervised learning, DBN is applied to process the extracted features and fault diagnosis of aeroengine with multiple state parameters can be achieved through the pretraining and reverse fine-tuning of restricted Boltzmann machines.
Long non-coding RNAs (lncRNAs) are recent listing in RNA Bioinformatics, which is getting more popular due to their important functional roles. According to the available research, lncRNAs play an essential role in mu...
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Long non-coding RNAs (lncRNAs) are recent listing in RNA Bioinformatics, which is getting more popular due to their important functional roles. According to the available research, lncRNAs play an essential role in multiple complex diseases. Determining the function of lncRNAs in diseases will help to comprehend many missing links in the disease mechanism. Predicting lncRNAdisease association (LDA) is a crucial stage in this process which is getting at most research interest nowadays. The developments in machine learning and deep learning technologies influenced recent research on LDA models. Most of the methods analyse the interactions of lncRNA with other molecules such as microRNA (miRNA), messenger RNA(mRNA), and proteins. Deep learning models, specifically from autoencoder classes, used extensively in unsupervised learning of features from these associations. This research paper proposes a denoising autoencoder (DAE) based LDA prediction approach. The proposed model uses DAE to learn lncRNA-disease representations from multiple biological networks such as lncRNA-miRNA, miRNA-disease, and disease-lncRNA interactions. The experiments show that the model outperforms other state-of-the-art LDA models concerning the area under the ROC curve (AUC-ROC, 0.94) and the area under precision-recall (AUPR, 0.9592).
De novo protein sequencing is a valuable task in proteomics, yet it is not a fully solved problem. Many state-of-the-art approaches use top-down and bottom-up tandem mass spectrometry (MS/MS) to sequence proteins. How...
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ISBN:
(纸本)9789819970735;9789819970742
De novo protein sequencing is a valuable task in proteomics, yet it is not a fully solved problem. Many state-of-the-art approaches use top-down and bottom-up tandem mass spectrometry (MS/MS) to sequence proteins. However, these approaches often produce protein scaffolds, which are incomplete protein sequences with gaps to fill between contiguous regions. In this paper, we propose a novel convolutional denoising autoencoder (CDA) model to perform the task of filling gaps in protein scaffolds to complete the final step of protein sequencing. We demonstrate our results both on a real dataset and eleven randomly generated datasets based on the MabCampath antibody. Our results show that the proposed CDA outperforms recently published hybrid convolutional neural network and long short-term memory (CNN-LSTM) based sequence model. We achieve 100% gap filling accuracy and 95.32% full sequence accuracy on the MabCampth protein scaffold.
Wind turbine condition monitoring has been extensively studied to reduce maintenance costs. Although there exist a vast amount of literature on anomaly detection for wind turbine, anomaly root cause analysis has not b...
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ISBN:
(纸本)9798350321050
Wind turbine condition monitoring has been extensively studied to reduce maintenance costs. Although there exist a vast amount of literature on anomaly detection for wind turbine, anomaly root cause analysis has not been fully addressed so far. To cope with this problem, we propose a denoising autoencoder (DAE) based anomaly detector and performs anomaly root cause analysis using sparse estimation. For anomaly detection, deep denoising autoencoder is learned with normal history data, with enhanced robustness compared to the conventional autoencoder. The reconstruction error from the DAE is further evaluated by the exponentially weighted moving average control chart (EWMA) to reduce the false positive rate. After anomaly detection, root cause analysis performs sparse fault estimation, with the assumption that a small number of observed variables are affected under the abnormal condition. The fault estimates are then leveraged to identify the variables most relevant to the underlying anomaly root causes. Real cases on a public dataset demonstrate the effectiveness of the proposed method.
Recent e-commerce and location-based services provide personalized recommendations based on machine-learning models that take into account purchase and visiting histories. Because machine-learning models assume the sa...
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ISBN:
(纸本)9783030948221;9783030948214
Recent e-commerce and location-based services provide personalized recommendations based on machine-learning models that take into account purchase and visiting histories. Because machine-learning models assume the same distributions between training and test data, they cannot catch up with concept drifts, i.e., changes of behavioral patterns over time. To keep recommendation accurate, it is important to detect concept drifts. Generally, to achieve this, we need complete data (i.e., data without missing values). In real-world datasets, however, there are many incomplete data, and existing concept drift detection techniques do not deal with incomplete data. To address this issue, we investigate how a deep learning technique (denoising autoencoder), which complements missing values, contributes to detecting concept drifts in incomplete data. We conduct experiments on synthetic and real datasets to evaluate the robustness of this technique, and our results show its advantages.
Text style transfer task is transferring sentences to other styles while preserving the semantics as much as possible. In this work, we study a two-step text style transfer method on non-parallel datasets. In the firs...
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ISBN:
(纸本)9783030863654;9783030863647
Text style transfer task is transferring sentences to other styles while preserving the semantics as much as possible. In this work, we study a two-step text style transfer method on non-parallel datasets. In the first step, the style-relevant words are detected and deleted from the sentences in the source style corpus. In the second step, the remaining style-devoid contents are fed into a Natural Language Generation model to produce sentences in the target style. The model consists of a style encoder and a pre-trained denoisingautoencoder. The former extracts style features of each style corpus and the latter reconstructs source sentences during training and generates sentences in the target style during inference from given contents. We conduct experiments on two text sentiment transfer datasets and comprehensive comparisons with other relevant methods in terms of several evaluation aspects. Evaluation results show that our method outperforms others in terms of sentence fluency and achieves a decent tradeoff between content preservation and style transfer intensity. The superior performance on the Caption dataset illustrates our method's potential advantage on occasions of limited data.
The BeiDou Navigation Satellite System (BDS) provides real-time absolute location services to users around the world and plays a key role in the rapidly evolving field of autonomous driving. In complex urban environme...
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The BeiDou Navigation Satellite System (BDS) provides real-time absolute location services to users around the world and plays a key role in the rapidly evolving field of autonomous driving. In complex urban environments, the positioning accuracy of BDS often suffers from large deviations due to non-line-of-sight (NLOS) signals. Deep learning (DL) methods have shown strong capabilities in detecting complex and variable NLOS signals. However, these methods still suffer from the following limitations. On the one hand, supervised learning methods require labeled samples for learning, which inevitably encounters the bottleneck of difficulty in constructing databases with a large number of labels. On the other hand, the collected data tend to have varying degrees of noise, leading to low accuracy and poor generalization performance of the detection model, especially when the environment around the receiver changes. In this article, we propose a novel deep neural architecture named convolutional denoising autoencoder network (CDAENet) to detect NLOS in urban forest environments. Specifically, we first design a denoising autoencoder based on unsupervised DL to reduce the long time series signal dimension and extract the deep features of the data. Meanwhile, denoising autoencoders improve the model's robustness in identifying noisy data by introducing a certain amount of noise into the input data. Then, an MLP algorithm is used to identify the non-linearity of the BDS signal. Finally, the performance of the proposed CDAENet model is validated on a real urban forest dataset. The experimental results show that the satellite detection accuracy of our proposed algorithm is more than 95%, which is about an 8% improvement over existing machine-learning-based methods and about 3% improvement over deep-learning-based approaches.
Random telegraph signals (RTSs) are specific time -fluctuating signal patterns marked by a series of distinctive switching events between well-defined signal levels. These signals are ubiquitous in many electronic, ch...
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Random telegraph signals (RTSs) are specific time -fluctuating signal patterns marked by a series of distinctive switching events between well-defined signal levels. These signals are ubiquitous in many electronic, chemical, and biological devices and systems. Analyzing RTSs unveils associated system structures and internal operation mechanisms, offering valuable insights into performance sensitivity. Therefore, accurate parameter quantification of RTSs is essential for understanding their origin and significance. While two -level RTS analysis is straightforward, complications arise at multiple levels, especially with unwanted background fluctuations. To address this challenge, we developed a novel denoising autoencoder model with U -Net and bidirectional long short-term memory (DAE UBL) for denoising multi -level RTSs degraded by Gaussian white and pink noise. DAE UBL extracts lower -dimensional latent features with its encoder and reconstructs denoised RTS with its decoder. Trained and validated with large datasets of noisy multi -level RTSs, our DAE UBL demonstrates superior and stable denoising performance compared to four classic models with lower average median root mean squared errors by over 78% and 63% for all RTS data accompanying various strengths of white noise and pink noise. Average median signal-to-noise ratios in the DAE UBL analysis are increased by over 65% and 56% for the white noise and pink noise datasets. In the time domain, DAE UBL effectively suppresses both local and global fluctuations, thereby successfully removing background noise. Our model exhibits robust performance in denoising multi -level RTSs with strong pink noise. We envision that our DAE UBL will be an attractive denoising methodology for the complex multi -level RTS analysis.
The knowledge graph convolution network (KGCN) is a recommendation model that provides a set of top recommendations based on knowledge graph developed between users, items, and their attributes. In this study, we inte...
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The knowledge graph convolution network (KGCN) is a recommendation model that provides a set of top recommendations based on knowledge graph developed between users, items, and their attributes. In this study, we integrate the KGCN model with denoising autoencoder (DAE) to improve its recommendation performance. A trained DAE is used to sample K-dimensional latent representation for each user, which then transforms that representation to generate a probability distribution over items. The relationship between acquired latent representation and the meta features is modelled using multivariate multiple regression (MMR) kernel. As a result, without the need for new configuration assessments, performance estimation of new data is pursued directly through MMR and the decoder of DAE. Empirically, we demonstrate that on real-world datasets, the proposed method substantially outperforms other state-of-the-art baselines. Movie-Lens 100K (ML-100K) and Movie-Lens 1M (ML-1M), two common MovieLens datasets, are used to verify the accuracy of the proposed approach. The results from experiments show significant improvement of 41.17% when the proposed method is applied on KGCN model. The proposed framework outperforms other state-of-the-art frameworks on Recall@K and normalized discounted cumulative gain (NDCG@K) metrics by achieving higher scores for Recall@5, Recall@10, NDCG@1, and NDCG@10.
Electric load data are essential for data-driven approaches (including deep learning) in smart grid, and advanced smart meter technologies provide fine-grained data with reliable communications. Despite the recent dev...
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Electric load data are essential for data-driven approaches (including deep learning) in smart grid, and advanced smart meter technologies provide fine-grained data with reliable communications. Despite the recent development of smart metering devices, however, missing data still arise due to unexpected device power off, communication failure, measuring error, or other unknown reasons. In this paper, we investigate a deep learning framework for missing imputation of smart meter data by leveraging a denoising autoencoder (DAE). Then, we compare the performance of the proposed DAE with traditional methods as well as other recently developed generative models, e.g., variational autoencoder and Wasserstein autoencoder. The proposed DAE based imputation shows significantly better results compared to other methods in terms of root mean square error (RMSE) by up to 28.9% for point-wise error, and by up to 56% for daily-accumulated error.
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