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Remaining useful life estimation using a bidirectional recurrent neural network based autoencoder scheme

MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2019年第0期129卷 764-780页

作者： Yu, Wennian Kim, Il Yong Mechefske, Chris Queens Univ Dept Mech & Mat Engn Kingston ON K7L 3N6 Canada

System remaining useful life (RUL) estimation is one of the major prognostic activities in industrial applications. In this paper, we propose a sensor-based data-driven scheme using a deep learning tool and the similarity-based curve matching technique to estimate the RUL of a system. The whole procedure consists of two steps: in the first step, a bidirectional recurrent neural network based autoencoder is trained in an unsupervised way to convert the multi-sensor (high-dimensional) readings collected from historical run-to-failure instances (i.e. multiple units of the same system) to low-dimensional embeddings, which are used to construct the one-dimensional health index (HI) values to reflect various health degradation patterns of the instances. In the second step, the test HI curve obtained from sensor readings collected from an on-line instance is compared with the degradation patterns built in the offline phase using the similarity-based curve matching technique, from which the RUL of the test unit can be estimated at an early stage. The proposed scheme was tested on two publicly available run-to-failure datasets: the turbofan engine datasets (simulation datasets) and the milling datasets (experimental datasets). The prognostic performance of the proposed procedure was directly compared with the existing state-of-art prognostic models in terms of various prognostic metrics on the two datasets respectively. The comparison results demonstrate the competitiveness of the proposed method used for RUL estimation of systems. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Remaining useful life Bidirectional recurrent neural network autoencoder Health index

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Adaptation of autoencoder for Sparsity Reduction From Clinical Notes Representation Learning

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IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2023年 11卷 469-478页

作者： Le, Thanh-Dung Noumeir, Rita Rambaud, Jerome Sans, Guillaume Jouvet, Philippe Univ Quebec Ecole Technol Super Biomed Informat Proc Lab Montreal PQ H3C 1K3 Canada Univ Montreal CHU St Justine Res Ctr Montreal PQ H3T 1J4 Canada

When dealing with clinical text classification on a small dataset, recent studies have confirmed that a well-tuned multilayer perceptron outperforms other generative classifiers, including deep learning ones. To increase the performance of the neural network classifier, feature selection for the learning representation can effectively be used. However, most feature selection methods only estimate the degree of linear dependency between variables and select the best features based on univariate statistical tests. Furthermore, the sparsity of the feature space involved in the learning representation is ignored. Goal: Our aim is, therefore, to access an alternative approach to tackle the sparsity by compressing the clinical representation feature space, where limited French clinical notes can also be dealt with effectively. Methods: This study proposed an autoencoder learning algorithm to take advantage of sparsity reduction in clinical note representation. The motivation was to determine how to compress sparse, high-dimensional data by reducing the dimension of the clinical note representation feature space. The classification performance of the classifiers was then evaluated in the trained and compressed feature space. Results: The proposed approach provided overall performance gains of up to 3% for each test set evaluation. Finally, the classifier achieved 92% accuracy, 91% recall, 91% precision, and 91% f1-score in detecting the patient's condition. Furthermore, the compression working mechanism and the autoencoder prediction process were demonstrated by applying the theoretic information bottleneck framework. Clinical and Translational Impact Statement- An autoencoder learning algorithm effectively tackles the problem of sparsity in the representation feature space from a small clinical narrative dataset. Significantly, it can learn the best representation of the training data because of its lossless compression capacity compared to other approaches. Consequently,

关键词： Clinical natural language processing cardiac failure autoencoder sparsity

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Residual Codean autoencoder for Facial Attribute Analysis

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PATTERN RECOGNITION LETTERS 2019年 119卷 157-165页

作者： Sethi, Akshay Singh, Maneet Singh, Richa Vatsa, Mayank IIIT Delhi New Delhi India

Facial attributes can provide rich ancillary information which can be utilized for different applications such as targeted marketing, human computer interaction, and law enforcement. This research focuses on facial attribute prediction using a novel deep learning formulation, termed as R-Codean autoencoder. The paper first presents Cosine similarity based loss function in an autoencoder which is then incorporated into the Euclidean distance based autoencoder to formulate R-Codean. The proposed loss function thus aims to incorporate both magnitude and direction of image vectors during feature learning. Inspired by the utility of shortcut connections in deep models to facilitate learning of optimal parameters, without incurring the problem of vanishing gradient, the proposed formulation is extended to incorporate shortcut connections in the architecture. The proposed R-Codean autoencoder is utilized in facial attribute prediction framework which incorporates patch-based weighting mechanism for assigning higher weights to relevant patches for each attribute. The experimental results on publicly available CelebA and LFWA datasets demonstrate the efficacy of the proposed approach in addressing this challenging problem. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Attribute prediction Cosine similarity Residual learning Deep learning autoencoder

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Subspace clustering using a low-rank constrained autoencoder

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INFORMATION SCIENCES 2018年 424卷 27-38页

作者： Chen, Yuanyuan Zhang, Lei Yi, Zhang Sichuan Univ Coll Comp Sci Machine Intelligence Lab Chengdu 610065 Sichuan Peoples R China

The performance of subspace clustering is affected by data representation. Data representation for subspace clustering maps data from the original space into another space with the property of better separability. Many data representation methods have been developed in recent years. Typical among them are low-rank representation (LRR) and an autoencoder. LRR is a linear representation method that captures the global structure of data with low rank constraint. Alternatively, an autoencoder nonlinearly maps data into a latent space using a neural network by minimizing the difference between the reconstruction and input. To combine the advantages of an LRR (globality) and autoencoder (self-supervision based locality), we propose a novel data representation method for subspace clustering. The proposed method, called low-rank constrained autoencoder (LRAE), forces the latent representation of the neural network to be of low rank, and the low-rank constraint is computed as a prior from the input space. One major advantage of the LRAE is that the learned data representation not only maintains the local features of the data, but also preserves the underlying low-rank global structure. Extensive experiments on several datasets for subspace clustering were conducted. They demonstrated that the proposed LRAE substantially outperformed state-of-the-art subspace clustering methods. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Deep neural networks Subspace clustering autoencoder Low-rank representation

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Denoising-Contractive autoencoder for Robust Device-Free Occupancy Detection

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IEEE INTERNET OF THINGS JOURNAL 2019年第6期6卷 9572-9582页

作者： Ng, Pai Chet She, James Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China

Device-free occupancy detection is very important for certain Internet of Things applications that do not require the user to carry a receiver. This paper achieves the device-free occupancy detection with RF fingerprinting, which labels each zone with a 2M-dimensional fingerprint vector. Specifically, the fingerprint vector consists of received signal strength (RSS) values measured from M Bluetooth low energy (BLE) beacons and also their corresponding temporal RSS variations. However, the unreliable RSS values caused two common issues with the fingerprint vector: 1) noise and 2) sparsity. To this end, we propose denoising-contractive autoencoder (DCAE) to jointly deal with these two issues, by learning a robust fingerprint prior to device-free occupancy detection. We validate the performance of our proposed DCAE with large-scale real-world datasets. The experimental results indicate the substantial performance gain of our proposed DCAE in comparison with state-of-the-art autoencoders. In particular, the classifier trained using the fingerprints learned by our proposed DCAE is able to maintain at least 90% accuracy when the noise factor or sparsity ratio increases to 0.6 and 0.5, respectively.

关键词： autoencoder Bluetooth low energy (BLE) beacon occupancy detection

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Predicting circRNA-disease associations based on autoencoder and graph embedding

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INFORMATION SCIENCES 2021年 571卷 323-336页

作者： Yang, Jing Lei, Xiujuan Shaanxi Normal Univ Coll Comp Sci Xian 710119 Peoples R China

Circular RNAs (circRNAs) are a special kind of non-coding RNA. They play important regulatory role in diseases through interactions of miRNAs associated with the diseases. Due to their insensitivity to nucleases, they are more stable than linear RNAs. It is thus imperative to integrate available information for predicting circRNA-disease associations in humans. Here, we propose a computational model to predict circRNA-disease associations based on accelerated attributed network embedding (AANE) algorithm and autoencoder(AE). First, we use AANE algorithm to extract low-dimensional features of circRNAs and diseases and then stacked autoencoder (SAE) to automatically extract in-depth features. The features obtained by AANE and the SAE are integrated and XGBoost is used as a binary classifier to get the predicted results. The proposed model has an average area under the receiver operating characteristic curve value of 0.8800 in 5-fold cross validation and 0.8988 in 10-fold cross validation. The factors that can affect the performance of the model are discussed and some common diseases are used as case studies. Results indicated that the model has great performance in predicting circRNA-disease associations. (c) 2021 Elsevier Inc. All rights reserved.

关键词： circRNA Disease Graph embedding autoencoder XGBoost

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A hierarchical autoencoder learning model for path prediction and abnormality detection

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PATTERN RECOGNITION LETTERS 2020年 130卷 216-224页

作者： Dotti, Dario Popa, Mirela Asteriadis, Stylianos Maastricht Univ Dept Data Sci & Knowledge Engn Maastricht Netherlands

In this paper, we introduce an unsupervised hierarchical framework for modeling trajectories in surveillance scenarios. Inspired by the object recognition field, a novel feature representation optimized for a neural network learning architecture is proposed. Low levels of the hierarchy capture local spatio-temporal motion attributes such as spatial orientation and speed, while higher levels contribute to obtaining richer semantic information. The bottom-up construction of the hierarchical framework exploits the inherent statistical correlations between neighboring elements using an increasing spatio-temporal grid. Cross-entropy based optimization in combination with autoencoders is used to learn weights for subsequent hierarchical layers. Finally, the Bayesian probabilistic framework built on top of the hierarchical model is proposed for applications such as long-term path prediction and abnormality detection. We demonstrate the efficiency of the proposed model on both indoor and outdoor datasets, achieving results comparable with state-of-the-art methods. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Motion features autoencoder Hierarchical learning Behavior understanding Abnormality detection Path prediction

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DAS-GNN: Denoising autoencoder integrated with self-supervised learning in graph neural network-based recommendations

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APPLIED INTELLIGENCE 2023年第14期53卷 17292-17309页

作者： Dai, Jiuqian Yuan, Weihua Bao, Chen Zhang, Zhijun Shandong Jianzhu Univ Sch Comp Sci & Technol Jinan 250001 Shandong Peoples R China

To enhance the recommendation performance, session-based recommendations typically model based on graph neural networks (GNN). These models use the most recently clicked item as the user's short-term interest, as well as the query vector in the attention mechanism. Based on it, the attention score is calculated with the remaining items to obtain the user's long-term interest. However, the obtained representation of long-term interest is one-sided. Furthermore, unlike other recommendation technology, such as collaborative filtering that includes the user's entire history information, the session-based recommendation is more vulnerable to data sparsity. Existing models primarily make predictions based on observable user-item interactions and ignore items not interacted with by users. To address the aforementioned issues, we propose the denoising autoencoder integrated with self-supervised learning (SSL) in graph neural networks (DAS-GNN). In DAS-GNN, the query extraction module based on denoising autoencoder can mine multiple user interests and assist long-term interest to express user needs more comprehensively. We propose an effective way of dividing positive and negative samples in the SSL module and use adaptive thresholds to mine negative hard samples, thereby improving training efficiency and alleviating data sparsity. Extensive experiments demonstrate that the proposed DAS-GNN outperforms state-of-the-art models on four benchmarks. The source code is available at: https://***/daijiuqian/DAS-GNN.

关键词： Session-based recommendation autoencoder Graph neural networks Attention Self-supervised learning

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Recurrent autonomous autoencoder for intelligent DDoS attack mitigation within the ISP domain

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2021年第11期12卷 3145-3167页

作者： Ko, Ili Chambers, Desmond Barrett, Enda Natl Univ Ireland Galway Galway Ireland

The continuous advancement of DDoS attack technology and an increasing number of IoT devices connected on 5G networks escalate the level of difficulty for DDoS mitigation. A growing number of researchers have started to utilise Deep Learning algorithms to improve the performance of DDoS mitigation systems. Real DDoS attack data has no labels, and hence, we present an intelligent attack mitigation (IAM) system, which takes an ensemble approach by employing Recurrent Autonomous autoencoders (RAA) as basic learners with a majority voting scheme. The RAA is a target-driven, distributionenabled, and imbalanced clustering algorithm, which is designed to work with the ISP's blackholing mechanism for DDoS flood attack mitigation. It can dynamically select features, decide a reference target (RT), and determine an optimal threshold to classify network traffic. A novel Comparison-Max Random Walk algorithm is used to determine the RT, which is used as an instrument to direct the model to classify the data so that the predicted positives are close or equal to the RT. We also propose Estimated Evaluation Metrics (EEM) to evaluate the performance of unsupervised models. The IAM system is tested with UDP flood, TCP flood, ICMP flood, multi-vector and a real UDP flood attack data. Additionally, to check the scalability of the IAM system, we tested it on every subdivided data set for distributed computing. The average Recall on all data sets was above 98%.

关键词： Deep learning autoencoder Machine learning Unsupervised learning DDoS mitigation Random walk Evaluation metrics for unsupervised learning Cyber security Network security

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Improving visibility of rear surface cracks during inductive thermography of metal plates using autoencoder

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INFRARED PHYSICS & TECHNOLOGY 2018年 91卷 233-242页

作者： Xie, Jing Xu, Changhang Chen, Guoming Huang, Weiping Ocean Univ China Coll Engn Qingdao 266071 Peoples R China China Univ Petr East China Coll Mech & Elect Engn Changjiang West Rd 66 Qingdao 266580 Shandong Peoples R China

Inductive thermography is one kind of infrared thermography (IRT) technique, which is effective in detection of front surface cracks in metal plates. However, rear surface cracks are usually missed due to their weak indications during inductive thermography. Here we propose a novel approach (AET: AE Thermography) to improve the visibility of rear surface cracks during inductive thermography by employing the autoencoder (AE) algorithm, which is an important block to construct deep learning architectures. We construct an integrated framework for processing the raw inspection data of inductive thermography using the AE algorithm. Through this framework, underlying features of rear surface cracks are efficiently extracted and new clearer images are constructed. Experiments of inductive thermography were conducted on steel specimens to verify the efficacy of the proposed approach. We visually compare the raw thermograms, the empirical orthogonal functions (EOFs) of the prominent component thermography (PCT) technique and the results of AET. We further quantitatively evaluated AET by calculating crack contrast and signal-to-noise ratio (SNR). The results demonstrate that the proposed AET approach can remarkably improve the visibility of rear surface cracks and then improve the capability of inductive thermography in detecting rear surface cracks in metal plates (C) 2018 Elsevier B.V. All rights reserved.

关键词： Infrared thermography Inductive thermography Real surface cracks autoencoder

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