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A deep domain adaption model with multi-task networks for planetary gearbox fault diagnosis

NEUROCOMPUTING 2020年 409卷 173-190页

作者： Cao, Xincheng Chen, Binqiang Zeng, Nianyin Xiamen Univ Sch Aerosp Engn Xiamen 361005 Peoples R China

The planetary gearbox plays an important role in many advanced electromechanical mechanisms. The mechanical fault is a major factor that threatens the service performance of a planetary gearbox. Deep learning (DL) algorithms have been widely used to identify faults and health status of industrial equipment. However, owing to diversified equipment structures, variable working conditions and disparate data acquisition, the service performance of DL-based methods may degrade significantly when applied to different industrial sites. Domain adaptation emerges as a promising idea that aims to transfer knowledge from a source domain to a different but related target domain. In this paper, we introduce a novel deep domain-adaptive multi-task learning model Y-Net, which is exploited to enable domain-adaptive diagnosis of faults in planetary gearboxes. The SE-Res modules are utilized to reduce the redundancy of the model and improving the separability of deep features. Furthermore, a soft joint maximum mean discrepancy (SJMMD) is introduced to link the two pipeline in order to reduce both the marginal and conditional distribution discrepancy of the learned features, with the enhancement of auxiliary soft labels. The domain adaption between different planetary gearbox under variant operating condition is realized by the Y-Net. Experiments demonstrate the superiority of the proposed SJMMD over conventional maximum mean discrepancy, especially when the datasets of different domains suffer different imbalances. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Domain adaption Intelligent fault diagnosis Planetary gearbox convolutional neural network convolutional autoencoder

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Comparison of Dynamic Mode Decomposition and Deep Learning Techniques for Two-Phase Flows Analysis

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FLOW TURBULENCE AND COMBUSTION 2020年第4期105卷 1345-1379页

作者： Ramos, Eliaquim M. Darze, Gabriella M. do Nascimento, Francisco R. T. Faccini, Jose Luiz H. Giraldi, Gilson A. Natl Lab Sci Comp BR-25651075 Petropolis RJ Brazil Nucl Engn Inst BR-21941906 Rio De Janeiro Brazil

Dynamic mode decomposition (DMD) and deep learning are data-driven approaches that allow a description of the target phenomena in new representation spaces. This fact motivates their comparison in the analysis of flow data, generated through experimental setups and numerical simulations. The focused application is the processing of high-speed videos of horizontal two-phase stratified and slug flows regimes. Henceforth, in this work, we consider the traditional DMD, the sparsity-promoting DMD (SPDMD) and, in the deep learning context, we select an unsupervised convolutional autoencoder (CAE). In this avenue, it becomes imperative to compare DMD and deep learning with respect to: computational complexity of target techniques;reduced order modeling versus data representation;data set necessary to compute the dynamic modes and deep learning training;the preservation of the phase interface in the DMD and CAE space;data synthesis. In general, the results favor DMD in the considered applications.

关键词： Dynamic mode decomposition Deep learning convolutional autoencoder Two-phase flows

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DeepFuseOSV: online signature verification using hybrid feature fusion and depthwise separable convolution neural network architecture

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IET BIOMETRICS 2020年第6期9卷 259-268页

作者： Vorugunti, Chandra Sekhar Pulabaigari, Viswanath Mukherjee, Prerana Sharma, Abhishek Indian Inst Informat Technol SriCity Comp Sci & Engn Sri City Andhra Pradesh India Indian Inst Informat Technol Naya Raipur Elect & Commun Chattisgarh India

Online signature verification (OSV) is a widely utilised technique in the medical, e-commerce and m-commerce applications to lawfully bind the user. These high-speed systems demand faster writer verification with a limited amount of information along with restrictions on training and storage cost. This study makes two major contributions: (i) A competent feature fusion technique in which traditional statistical-based features are fused with deep representations from a convolutional auto-encoder;and (ii) a hybrid architecture combining depth-wise separable convolution neural network (DWSCNN) and long short term memory (LSTM) network delivering state-of-the-art performance for OSV is proposed. DWSCNN is utilised for extracting deep feature representations and LSTM is competent in learning long term dependencies of stroke points of a signature. This hybrid combination accomplishes better classification accuracy (lower error rates) even with one-shot learning, i.e. achieving higher classification accuracies with only one training signature sample per user. The authors have extensively evaluated their model using three widely used datasets MCYT-100, SVC and SUSIG. These exhaustive experimental studies confirm that the DeepFuseOSV framework results in the state-of-the-art outcome by achieving an equal error rate (EER) of 13.26, 2.58, 0.07% in Skilled 1, Skilled 10 and Random 10 categories of MCYT-100, respectively, 7.71% in Skilled 1 category of SVC, 1.70% in Random 1 category of SUSIG.

关键词： learning (artificial intelligence) feature extraction image classification handwriting recognition convolutional neural nets recurrent neural nets neural net architecture image representation statistical analysis image fusion online signature verification hybrid feature fusion depthwise separable convolution neural network architecture writer verification statistical based features convolutional autoencoder deep feature representation extraction DeepFuseOSV long short term memory network

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Semi-Supervised Deep Learning Approach for Transportation Mode Identification Using GPS Trajectory Data

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2020年第5期32卷 1010-1023页

作者： Dabiri, Sina Lu, Chang-Tien Heaslip, Kevin Reddy, Chandan K. Virginia Tech Dept Civil Engn Blacksburg VA 24061 USA Virginia Tech Dept Comp Sci Blacksburg VA 24061 USA

Identification of travelers' transportation modes is a fundamental step for various problems that arise in the domain of transportation such as travel demand analysis, transport planning, and traffic management. In this paper, we aim to identify travelers' transportation modes purely based on their GPS trajectories. First, a segmentation process is developed to partition a user's trip into GPS segments with only one transportation mode. A majority of studies have proposed mode inference models based on hand-crafted features, which might be vulnerable to traffic and environmental conditions. Furthermore, the classification task in almost all models have been performed in a supervised fashion while a large amount of unlabeled GPS trajectories has remained unused. Accordingly, we propose a deep SEmi-Supervised convolutional autoencoder (SECA) architecture that can not only automatically extract relevant features from GPS segments but also exploit useful information in unlabeled data. The SECA integrates a convolutional-deconvolutional autoencoder and a convolutional neural network into a unified framework to concurrently perform supervised and unsupervised learning. The two components are simultaneously trained using both labeled and unlabeled GPS segments, which have already been converted into an efficient representation for the convolutional operation. An optimum schedule for varying the balancing parameters between reconstruction and classification errors are also implemented. The performance of the proposed SECA model, trip segmentation, the method for converting a raw trajectory into a new representation, the hyperparameter schedule, and the model configuration are evaluated by comparing to several baselines and alternatives for various amounts of labeled and unlabeled data. Our experimental results demonstrate the superiority of the proposed model over the state-of-the-art semi-supervised and supervised methods with respect to metrics such as accuracy and F-meas

关键词： Deep learning semi-supervised learning convolutional neural network convolutional autoencoder GPS trajectory data trip segmentation transportation mode identification

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Soft autoencoder and Its Wavelet Adaptation Interpretation

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IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING 2020年 6卷 1245-1257页

作者： Fan, Fenglei Li, Mengzhou Teng, Yueyang Wang, Ge Rensselaer Polytech Inst Dept Biomed Engn Troy NY 12180 USA Northeastern Univ Sino Dutch Biomed & Informat Engn Sch Shenyang 110169 Peoples R China

Recently, deep learning becomes the main focus of machine learning research and has greatly impacted many important fields. However, deep learning is criticized for lack of interpretability. As a successful unsupervised model in deep learning, the autoencoder embraces a wide spectrum of applications, yet it suffers from the model opaqueness as well. In this article, we propose a new type of convolutional autoencoders, termed as Soft autoencoder (Soft-AE), in which the activation functions of encoding layers are implemented with adaptable soft-thresholding units while decoding layers are realized with linear units. Consequently, Soft-AE can be naturally interpreted as a learned cascaded wavelet shrinkage system. Our denoising experiments demonstrate that Soft-AE not only is interpretable but also offers a competitive performance relative to its counterparts. Furthermore, we propose a generalized linear unit (GenLU) to make an autoencoder more adaptive in nonlinearly filtering images and data, such as denoising and deblurring.

关键词： Convolution Machine learning Thresholding (Imaging) Noise reduction Wavelet coefficients Deep learning interpretability convolutional autoencoder activation functions

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Deep Learning Analysis of Coronary Arteries in Cardiac CT Angiography for Detection of Patients Requiring Invasive Coronary Angiography

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2020年第5期39卷 1545-1557页

作者： Zreik, Majd van Hamersvelt, Robbert W. Khalili, Nadieh Wolterink, Jelmer M. Voskuil, Michiel Viergever, Max A. Leiner, Tim Isgum, Ivana Univ Med Ctr Utrecht Image Sci Inst NL-3584 CS Utrecht Netherlands Univ Med Ctr Utrecht Dept Radiol NL-3584 CS Utrecht Netherlands Univ Amsterdam Dept Biomed Engn & Phys Med Ctr NL-1105 AZ Amsterdam Netherlands Univ Med Ctr Utrecht Dept Cardiol NL-3584 CS Utrecht Netherlands Univ Utrecht NL-3512 JE Utrecht Netherlands Univ Amsterdam Dept Radiol & Nucl Med Med Ctr NL-1105 AZ Amsterdam Netherlands

In patients with obstructive coronary artery disease, the functional significance of a coronary artery stenosis needs to be determined to guide treatment. This is typically established through fractional flow reserve (FFR) measurement, performed during invasive coronary angiography (ICA). We present a method for automatic and non-invasive detection of patients requiring ICA, employing deep unsupervised analysis of complete coronary arteries in cardiac CT angiography (CCTA) images. We retrospectively collected CCTA scans of 187 patients, 137 of them underwent invasive FFR measurement in 192 different coronary arteries. These FFR measurements served as a reference standard for the functional significance of the coronary stenosis. The centerlines of the coronary arteries were extracted and used to reconstruct straightened multi-planar reformatted (MPR) volumes. To automatically identify arteries with functionally significant stenosis that require ICA, each MPR volume was encoded into a fixed number of encodings using two disjoint 3D and 1D convolutional autoencoders performing spatial and sequential encodings, respectively. Thereafter, these encodings were employed to classify arteries using a support vector machine classifier. The detection of coronary arteries requiring invasive evaluation, evaluated using repeated cross-validation experiments, resulted in an area under the receiver operating characteristic curve of 0.81 +/- 0.02 on the artery-level, and 0.87 +/- 0.02 on the patient-level. The results demonstrate the feasibility of automatic non-invasive detection of patients that require ICA and possibly subsequent coronary artery intervention. This could potentially reduce the number of patients that unnecessarily undergo ICA.

关键词： Functionally significant coronary artery stenosis convolutional autoencoder convolutional neural network fractional flow reserve coronary CT angiography deep learning

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An Intellectual Detection System for Intrusions based on Collaborative Machine Learning

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2020年第2期11卷 446-452页

作者： Dhikhi, T. Saravanan, M. S. Saveetha Inst Med & Tech Sci Saveetha Sch Engn Chennai Tamil Nadu India SRM Inst Sci & Technol Chennai Tamil Nadu India

The necessity for safety of information in a network has inflated due to the impressive growth of web applications. Several methods of intrusion detection are used to detect irregularities which depend on precision, detection frequency, other parameters and are anticipated to familiarize to vigorously varying risk scenes. To accomplish consistent abnormalities detection in a network many machine learning algorithms have been formulated by researchers. A technique based on unsupervised machine learning that use two separate machine learning algorithms to identify anomalies in a network viz convolutional autoencoder and softmax classifier is proposed. These profound models were skilled as well as evaluated on NSLKDD test data sets on the NSLKDD training dataset. Using well-known classification metrics such as accuracy, precision and recall, these machine learning models were assessed. The developed intrusion detection system model experimental findings showed promising outcomes in anomaly detection systems for real-world implementation and is compared with the prevailing definitive machine learning techniques. This strategy increases the detection of network intrusion and offers a renewed intrusion detection study method.

关键词： Intrusion detection machine learning deep learning convolutional autoencoder softmax classifier NSL-KDD dataset

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An Automatic Classification of he Early Osteonecrosis of Femoral Head with Deep Learning

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CURRENT MEDICAL IMAGING 2020年第10期16卷 1323-1331页

作者： Zhu, Liyang Han, Jungang Guo, Renwen Wu, Dong Wei, Qiang Chai, Wei Tang, Shaojie Xian Univ Posts & Telecommun Sch Comp Sci Xian 710121 Peoples R China Chinese Peoples Liberat Army Gen Hosp Dept Orthopaed Beijing 100039 Peoples R China Xian Univ Posts & Telecommun Sch Automat Xian 710121 Peoples R China Shaanxi Key Lab Network Data Anal & Intelligence Xian 710121 Peoples R China

Background: Osteonecrosis of Femoral Head (ONFH) is a common complication in orthopaedics, wherein femoral structures are usually damaged due to the impairment or interruption of femoral bead blood supply. Aim: In this study, an automatic approach for the classification of the early ONFH with deep learning has been proposed. Methods: All femoral CT slices according to their spatial locations with the convolutional Neural Network (CNN) are first classified. Therefore, all CT slices are divided into upper, middle or lower segments of femur head. Then the femur head areas can be segmented with the Conditional Generative Adversarial Network (CGAN) for each part. The convolutional autoencoder is employed to reduce dimensions and extract features of femur head, and finally K-means clustering is used for an unsupervised classification of the early ONFH. Results: To invalidate the effectiveness of the proposed approach, the experiments on the dataset with 120 patients are carried out. The experimental results show that the segmentation accuracy is higher than 95%. The convolutional autoencoder can reduce the dimension of data, the Peak Signal-to-Noise Ratios (PSNRs) are better than 34dB for inputs and outputs. Meanwhile, there is a great intra-category similarity, and a significant inter-category difference. Conclusion: The research on the classification of the early ONFH has a valuable clinical merit, and hopefully it can assist physicians to apply more individualized treatment for patient.

关键词： Osteonecrosis of femoral head convolutional neural network conditional generative adversarial network convolutional autoencoder K-means clustering peak signal-to-noise ratios

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Sketch-guided Deep Portrait Generation

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ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 2020年第3期16卷 1-18页

作者： Ho, Trang-Thi Virtusio, John Jethro Chen, Yung-Yao Hsu, Chih-Ming Hua, Kai-Lung Natl Taiwan Univ Sci & Technol Dept Comp Sci & Informat Engn Taipei Taiwan Natl Taiwan Univ Sci & Technol Dept Elect & Comp Engn Taipei Taiwan Natl Taipei Univ Technol Dept Mech Engn Taipei Taiwan Natl Taiwan Univ Sci & Technol Dept CSIE Taipei Taiwan Natl Taiwan Univ Sci & Technol Ctr Cyber Phys Syst Innovat Taipei Taiwan

Generating a realistic human class image from a sketch is a unique and challenging problem considering that the human body has a complex structure that must be preserved. Additionally, input sketches often lack important details that are crucial in the generation process, hence making the problem more complicated. In this article, we present an effective method for synthesizing realistic images from human sketches. Our framework incorporates human poses corresponding to locations of key semantic components (e.g., arm, eyes, nose), seeing that its a strong prior for generating human class images. Our sketch-image synthesis framework consists of three stages: semantic keypoint extraction, coarse image generation, and image refinement. First, we extract the semantic keypoints using Part Affinity Fields (PAFs) and a convolutional autoencoder. Then, we integrate the sketch with semantic keypoints to generate a coarse image of a human. Finally, in the image refinement stage, the coarse image is enhanced by a Generative Adversarial Network (GAN) that adopts an architecture carefully designed to avoid checkerboard artifacts and to generate photo-realistic results. We evaluate our method on 6,300 sketch-image pairs and show that our proposed method generates realistic images and compares favorably against state-of-the-art image synthesis methods.

关键词： Image synthesis generative adversarial networks semantic keypoints perceptual loss convolutional autoencoder

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Development of ensemble smoother-neural network and its application to history matching of channelized reservoirs

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2020年 191卷 107159-107159页

作者： Kim, Sungil Lee, Kyungbook Lim, Jungtek Jeong, Hoonyoung Min, Baehyun Korea Inst Geosci & Mineral Resources Petr & Marine Res Div 124 Gwahak Ro Daejeon 34132 South Korea SmartMind Inc 1006379 Yeouidaebang Ro Seoul 07333 South Korea Seoul Natl Univ Dept Energy Resources Engn Gwanak Ro 1 Seoul 08826 South Korea Ewha Womans Univ Dept Climate & Energy Syst Engn 52 Ewhayeodae Gil Seoul 03760 South Korea

This study develops ensemble smoother-neural network (ES-NN) that combines an ensemble smoother (ES) with a convolutional autoencoder (CAE) to yield comparable performance at a lower computational cost to that of an ensemble smoother-multiple data assimilation (ES-MDA). The ES-NN updates reservoir facies models using CAE trained by importing initial and updated ensembles of ES as input and output of the CAE, respectively, which aims to learn the principle of assimilation of the ES. The trained CAE is recurrently applied in reservoir model calibration without additional forward simulation. The ES-NN yields satisfactory history matching results in terms of production profiles and facies distributions compared to ES and ES-MDA in two case studies. This comparison highlights the efficacy of ES-NN as a prospective data assimilation tool for history matching.

关键词： Ensemble smoother-neural network convolutional autoencoder Ensemble smoother-multiple data assimilation History matching

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