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Rare Failure Prediction Using an Integrated auto-encoder and Bidirectional Gated Recurrent Unit Network 4

Rare Failure Prediction Using an Integrated Auto-encoder and...

引用

4th International-Federation-of-automatic-Control (IFAC) Workshop on Advanced Maintenance Engineering, Services and Technologies (AMEST)

作者： Dangut, Maren David Skaf, Zakwan Jennions, Ian K. Cranfield Univ Bedford Integrated Vehicle Hlth Management IVHM Ctr Cranfield MK43 0AL Beds England Cranfield Univ Integrated Vehicle Hlth Management IVHM Ctr Bedford MK43 0AL England Higher Coll Technol Mech Engn Dept Abu Dhabi U Arab Emirates

Aircraft fault detection and prediction is a critical element of preventing failures, reducing maintenance costs, and increasing fleet availability. This paper considers a problem of rare failure prediction in the context of aircraft predictive maintenance. It presents a novel approach of predicting extremely rare failures, based on combining two deep learning techniques, auto-encoder (AE) and Bidirectional Gated Recurrent Unit (BGRU) network. AE is modified and trained to detect rare failure, and the result from AE is fed into the BGRU to predict the next occurrence of failure. The applicability of the proposed approach is evaluated using real-world test cases of log-based warning and failure messages obtained from the aircraft central maintenance system fleet database and the records of maintenance history. The proposed AE-BGRU model is compared with other similar deep learning methods, the proposed approach is 25% better in precision, 14% in the recall, and 3% in G-mean. The result also shows robustness in predicting failure within a defined useful period. Copyright (C) 2020 The Authors.

关键词： predictive maintenance machine learning extreme rare failure auto-encoder GRU network aircraft

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Variational auto-encoder Based Variability Encoding for Dysarthric Speech Recognition 22

Variational Auto-Encoder Based Variability Encoding for Dysa...

引用

Interspeech Conference

作者： Xie, Xurong Ruzi, Rukiye Liu, Xunying Wang, Lan Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781713836902

Dysarthric speech recognition is a challenging task due to acoustic variability and limited amount of available data. Diverse conditions of dysarthric speakers account for the acoustic variability, which make the variability difficult to be modeled precisely. This paper presents a variational auto-encoder based variability encoder (VAEVE) to explicitly encode such variability for dysarthric speech. The VAEVE makes use of both phoneme information and low-dimensional latent variable to reconstruct the input acoustic features, thereby the latent variable is forced to encode the phoneme-independent variability. Stochastic gradient variational Bayes algorithm is applied to model the distribution for generating variability encodings, which are further used as auxiliary features for DNN acoustic modeling. Experiment results conducted on the UASpeech corpus show that the VAEVE based variability encodings have complementary effect to the learning hidden unit contributions (LHUC) speaker adaptation. The systems using variability encodings consistently outperform the comparable baseline systems without using them, and obtain absolute word error rate (WER) reduction by up to 2.2% on dysarthric speech with "Very low" intelligibility level, and up to 2% on the "Mixed" type of dysarthric speech with diverse or uncertain conditions.

关键词： dysarthric speech recognition acoustic variability variational auto-encoder

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3D Shape and Texture Features Fusion using auto-encoder for Efficient Face Recognition 26

3D Shape and Texture Features Fusion using Auto-Encoder for ...

引用

26th International Conference on Pattern Recognition / 8th International Workshop on Image Mining - Theory and Applications (IMTA)

作者： Bahroun, Sahbi Abed, Rahma Zagrouba, Ezzeddine Univ Tunis ElManar Inst Super Informat Lab Limtic 2 Rue Abou Rayhane Bayrouni Ariana Tunisia

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Face recognition is one of the most widely used biometrics for identifying people. However, face images suffer from several issues that could affect the achieved results, especially in a crowded environment. Such as facial expression, occlusion, low resolution, noise, illumination and pose variation. In this paper, we propose a robust image representation system for face recognition. First, 3D face data reconstructed from 2D images are used instead of 3D capture. This is accomplished by modeling the difference in the texture map of the 3D aligned input and reference images. Then, fusing shape and texture local binary patterns (LBP) on a mesh for face recognition using the Mesh-LBP. Finally, we used a deep auto-encoder to create a compact data representation based on the obtained face images descriptors from the Mesh-LBP. Through experiments conducted on the Multi-PIE and Bosphorus databases, we show that our method is very competitive against state-of-the-art methods.

关键词： 3D morphable model Face recognition Mesh-LBP feature fusion auto-encoder

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PATCH-WISE auto-encoder FOR VISUAL ANOMALY DETECTION 30

PATCH-WISE AUTO-ENCODER FOR VISUAL ANOMALY DETECTION

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30th IEEE International Conference on Image Processing (ICIP)

作者： Cui, Yajie Liu, Zhaoxiang Lian, Shiguo China Unicom Unicom Digital Technol Beijing 100013 Peoples R China

ISBN: (纸本)9781728198354

Anomaly detection without priors of the anomalies is challenging. In the field of unsupervised anomaly detection, traditional auto-encoder (AE) tends to fail based on the assumption that by training only on normal images, the model will not be able to reconstruct abnormal images correctly. On the contrary, we propose a novel patch-wise auto-encoder (Patch AE) framework, which aims at enhancing the reconstruction ability of AE to anomalies instead of weakening it. Each patch of image is reconstructed by corresponding spatially distributed feature vector of the learned feature representation, i.e., patch-wise reconstruction, which ensures anomaly-sensitivity of AE. Our method is simple and efficient. It advances the state-of-the-art performances on Mvtec AD benchmark, which proves the effectiveness of our model. It shows great potential in practical industrial application scenarios.

关键词： Anomaly detection auto-encoder Patchwise Unsupervised learning

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Improvement of Texture Clustering Performance in Complex-Valued SOM by Using Complex-Valued auto-encoder for Millimeter-Wave Coherent Imaging 24th

Improvement of Texture Clustering Performance in Complex-Val...

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24th International Conference on Neural Information Processing (ICONIP)

作者： Arima, Yuya Hirose, Akira Univ Tokyo Dept Elect Engn & Informat Syst Bunkyo Ku 7-3-1 Hongo Tokyo 1138656 Japan

ISBN: (纸本)9783319701363;9783319701356

Interference in millimeter-wave active radar imaging causes harmful effects such as amplitude fluctuation and phase distortion, resulting in deterioration in visualization quality in a radar system employing complex-valued self-organizing map. We show that a complex-valued auto-encoder is capable of extracting features properly even under these influences, resulting in improvement of clustering performance effectively.

关键词： Complex-valued neural network Self-organizing map auto-encoder Millimeter-wave imaging Security

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An auto-encoder for Learning Conversation Representation Using LSTM 22nd

An Auto-Encoder for Learning Conversation Representation Usi...

引用

22nd International Conference on Neural Information Processing (ICONIP)

作者： Zhou, Xiaoqiang Hu, Baotian Chen, Qingcai Wang, Xiaolong Harbin Inst Technol Shenzhen Grad Sch Intelligent Comp Res Ctr Shenzhen Peoples R China

ISBN: (纸本)9783319265322;9783319265315

In this paper, an auto-encoder is proposed to learn conversation representation. First, the long short term memory (LSTM) neural network is used to encode the sequence of sentences in a conversation. The interactive context is encoded into a fixed-length vector. Then, through the LSTM-decoder, the learnt representation is used to reconstruct the sentence vectors of a conversation. To train our model, we construct one corpus with 32,881 conversations from the online shopping platform. Finally, experiments on topic recognition task demonstrate the effectiveness of the proposed auto-encoder on learning conversation representation, especially when training data of topic recognition is relatively small.

关键词： auto-encoder LSTM Conversation representation

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Noise-Adaptive auto-encoder for Modulation Recognition of RF Signal

Noise-Adaptive Auto-Encoder for Modulation Recognition of RF...

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IEEE/MTT-S International Microwave Symposium (IMS)

作者： Woo, Jongseok Jung, Kuchul Mukhopadhyay, Saibal Georgia Inst Technol Atlanta GA 30332 USA

ISBN: (纸本)9798350375053;9798350375046

This paper presents an efficient Deep Neural Network (DNN) design optimized for the modulation classification of the received Radio Frequency (RF) signal. Considering that the transmitted signals are exposed to various noise sources through the transmission channel, we propose an adaptive auto-encoder mechanism to suppress the noise efficiently. The proposed auto-encoder enables the adaptive characteristics by adopting the additional parameters to make a balance between the skip connection and the compression/decompression process. The results show that the proposed adaptive auto-encoder can improve the classification accuracy especially in low signal-to-noise ratio (SNR) area. The impact due to the additional network required to generate the balancing parameters on the hardware design is minimized by sharing the data compression process that already exists in the auto-encoder.

关键词： modulation recognition machine learning DNN auto-encoder

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Regularized Stacked auto-encoder Based Pre-training for Generalization of Multi-layer Perceptron 1

引用

6th International Conference on the Theory and Practice of Natural Computing (TPNC)

作者： Dey, Prasenjit Ghosh, Abhijit Pal, Tandra Natl Inst Technol Dept Comp Sci & Engn Durgapur 713209 W Bengal India

ISBN: (数字)9783319710693

ISBN: (纸本)9783319710693;9783319710686

Generalization capability of multi-layer perceptron (MLP) depends on the initialization of its weights. If the weights of an MLP are not initialized properly, it may fail to achieve good generalization. In this article, we propose a weight initialization technique for MLP to improve its generalization. This is achieved by a regularized stacked auto-encoder based pre-trainingmethod. During pre-training, the weights between each adjacent layers of an MLP, upto the penultimate layer, are trained layer wise by an auto-encoder. To train the auto-encoder, we use weighted sum of two terms: (i) mean squared error (MSE) and (ii) sum of squares of the first order derivatives of the outputs with respect to inputs. Here, the second term acts as a regularizer. It is used to penalize the training of autoencoder during pre-training to generate better initial values of the weights for each successive layers of MLP. To compare the proposed initialization technique with random weight initialization, we have considered ten standard classification data sets. Empirical results show that the proposed initialization technique improves the generalization of MLP.

关键词： auto-encoder Initialization Multi-layer perceptron Pre-training Regularization

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Attention-Based auto-encoder Framework for Abnormal Driving Detection

Attention-Based Auto-Encoder Framework for Abnormal Driving ...

引用

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Liu, Jing Liu, Yang Wei, Donglai Ni, Wei Zeng, Xinhua Song, Liang Fudan Univ Acad Engn & Technol Shanghai Peoples R China Shanghai East Bund Res Inst NSAI Shanghai Peoples R China

ISBN: (纸本)9781665484855

With the popularity of smartphones, abnormal driving detection via smartphone sensors has been proposed in recent years. However, existing methods are insufficient in exploring feature extraction, so the practical value is limited due to the low accuracy. To address this problem, we propose an attentionbased auto-encoder framework for abnormal driving detection that combines the advantages of bi-directional long short-term memory and self-attention. Specifically, these two modules are embedded in the auto-encoder for modeling latent vector and exploring the internal correlations of spatial-temporal features, respectively, so as to improve the capability of reconstructing driving time series using small and representative features. We conduct experiments on the real-world datasets, and the results show that the proposed framework achieves significant performance with recall and F1-score of 96.2% and 95.0%, superior to the other baselines.

关键词： Abnormal driving detection bi-directional long short-term memory auto-encoder time series self-attention

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Pulmonary Nodules Segmentation Method Based on auto-encoder 10

Pulmonary Nodules Segmentation Method Based on Auto-encoder

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10th International Conference on Digital Image Processing (ICDIP)

作者： Zhang, Guodong Guo, Mao Gong, Zhaoxuan Bi, Jing Kim, Yoohwan Guo, Wei Shenyang Aerosp Univ Sch Comp Sci Shenyang 110136 Liaoning Peoples R China Chinese Acad Sci Shenyang Inst Comp Technol Shenyang 110168 Liaoning Peoples R China Univ Nevada Dept Comp Sci Las Vegas NV 89154 USA

ISBN: (数字)9781510622005

ISBN: (纸本)9781510622005

In this paper, we proposed a semi-automatic pulmonary nodule segmentation algorithm, which is operated within a region of interest for each nodule. It mainly includes two parts: the unsupervised training of auto-encoder and the supervised training of segmentation network. Applying an auto-encoder's unsupervised learning, we obtain a feature extractor that consists of its encoded part. Through adding some new neural network layers behind the feature extractor and do supervised learning on it, we get the final segmentation neural network. Compared with the traditional maximum two-dimensional entropy threshold segmentation algorithm, the dice correlation coefficient of this algorithm is 1% - 9% higher in 36 regions of interest segmentation experiments.

关键词： Pulmonary nodule segmentation auto-encoder feature extraction medical image process

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