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SeqOAE: Deep sequence-to-sequence orthogonal auto-encoder for time-series forecasting under variable population sizes

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2024年 247卷

作者： Chehade, Abdallah Hassanieh, Wael Krivtsov, Vasiliy Univ Michigan Ind & Mfg Syst Engn Dearborn MI 48128 USA Ford Motor Co Reliabil Analyt Dearborn MI 48126 USA Univ Maryland Dept Mech Engn College Pk MD 20742 USA

This paper presents a novel approach to modeling non-linear time-series data in which the population size changes over observation time. This is a common phenomenon when forecasting the time-series of an ongoing study/operation. The goal is to make predictions about the mature time-series at the end of the study/operation given the immature time-series at an early stage of the study/operation. Existing time-series models often ignore this phenomenon by focusing on a fixed population size or a single unit of interest. This paper proposes a sequence-to-sequence deep learning model (SeqOAE) that captures the change in the time-series as a function of observation time. It learns an orthogonal latent feature space for robust long-term temporal forecasting. SeqOAE is non-parametric in time, which allows it to capture non-trivial and non-linear temporal patterns. SeqOAE enables forecasting performance behavior, which is paramount for reliability engineering applications. The model performance is validated on a reliability engineering case study using an extensive automotive warranty dataset with over 12,500 components from millions of vehicles.

关键词： auto-encoder Deep learning Forecasting Time-series Orthogonality Machine learning Reliability Warranty

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End-to-End Self-Driving Approach Independent of Irrelevant Roadside Objects With auto-encoder

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第1期23卷 641-650页

作者： Wang, Tinghan Luo, Yugong Liu, Jinxin Chen, Rui Li, Keqiang Tsinghua Univ Sch Vehicle & Mobil Beijing 100084 Peoples R China

On a highway, the frequency of occurrence of irrelevant features, such as trees, varies a lot in different scenes. A limitation of the deep conventional neural networks used in end-to-end self-driving systems is that if the incoming images contain too much information, it makes it difficult for the network to extract only the subset of features required for decision making. Consequently, while existing end-to-end approaches may perform well in training scenes, they may not work correctly in other scenes. In this study, we developed a novel training method for an auto-encoder that equips it to ignore irrelevant features in input images while simultaneously retaining relevant features. Compared with feature extraction methods in existing end-to-end approaches, the proposed method reduces the labeling costs by only requiring image-level tags. The method was validated by training a convolutional neural network model to process the output of the encoder and produce a steering angle to control the vehicle. The entire end-to-end self-driving approach can ignore the influence of irrelevant features even though there are no such features when training the convolutional neural network.

关键词： Feature extraction Training Task analysis Roads Decision making autonomous vehicles Neural networks End-to-end self-driving auto-encoder irrelevant features

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A non-linear non-intrusive reduced order model of fluid flow by auto-encoder and self-attention deep learning methods

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INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING 2023年第13期124卷 3087-3111页

作者： Fu, R. Xiao, D. Navon, I. M. Fang, F. Yang, L. Wang, C. Cheng, S. Swansea Univ Fac Sci & Engn ZCCE Bay Campus Swansea SA1 8EN Wales Tongji Univ Sch Math Sci Shanghai 200092 Peoples R China Florida State Univ Dept Sci Comp Tallahassee FL 32306 USA Imperial Coll London Dept Earth Sci & Engn AMCG London SW7 2BP England Cranfield Univ Div Energy & Sustainabil Bedford MK43 0AL England Imperial Coll London Data Sci Inst London SW7 2AZ England Tongji Univ Sch Math Sci Shanghai 200092 Peoples R China

This paper presents a new nonlinear non-intrusive reduced-order model (NL-NIROM) that outperforms traditional proper orthogonal decomposition (POD)-based reduced order model (ROM). This improvement is achieved through the use of auto-encoder (AE) and self-attention based deep learning methods. The novelty of this work is that it uses stacked auto-encoder (SAE) network to project the original high-dimensional dynamical systems onto a low dimensional nonlinear subspace and predict fluid dynamics using an self-attention based deep learning method. This paper introduces a new model reduction neural network architecture for fluid flow problem, as well as, a linear non-intrusive reduced order model (L-NIROM) based on POD and self-attention mechanism. In the NL-NIROM, the SAE network compresses high-dimensional physical information into several much smaller sized representations in a reduced latent space. These representations are expressed by a number of codes in the middle layer of SAE neural network. Then, those codes at different time levels are trained to construct a set of hyper-surfaces using self-attention based deep learning methods. The inputs of the self-attention based network are previous time levels' codes and the outputs of the network are current time levels' codes. The codes at current time level are then projected back to the original full space by the decoder layers in the SAE network. The capability of the new model, NL-NIROM, is demonstrated through two test cases: flow past a cylinder, and a lock exchange. The results show that the NL-NIROM is more accurate than the popular model reduction method namely POD based L-NIROM.

关键词： auto-encoder deep learning non-intrusive reduced order model self-attention

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3D Tensor auto-encoder with Application to Video Compression

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ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 2021年第2期17卷 1–18页

作者： Li, Yang Liu, Guangcan Sun, Yubao Liu, Qingshan Chen, Shengyong Jiangsu Vocat Coll Informat Technol Sch Informat Secur Sch IoT Engn 1 Qianou Rd Wuxi 214153 Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol 219 Ningliu Rd Nanjing 210044 Peoples R China Tianjin Univ Technol Minist Educ Key Lab Comp Vis & Syst 319 Binshui West Rd Tianjin 300384 Peoples R China

auto-encoder has been widely used to compress high-dimensional data such as the images and videos. However, the traditional auto-encoder network needs to store a large number of parameters. Namely, when the input data is of dimension n, the number of parameters in an auto-encoder is in generalO(n). In this article, we introduce a network structure called 3D Tensor auto-encoder (3DTAE). Unlike the traditional auto-encoder, in which a video is represented as a vector, our 3DTAE considers videos as 3D tensors to directly pass tensor objects through the network. The weights of each layer are represented by three small matrices, and thus the number of parameters in 3DTAE is just O(n(1/3)). The compact nature of 3DTAE fits well the needs of video compression. Given an ensemble of high-dimensional videos, we represent them as 3DTAE networks plus some small core tensors, and we further quantize the network parameters and the core tensors to get the final compressed data. Experimental results verify the efficiency of 3DTAE.

关键词： Video compression tensor auto-encoder ADAM

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Learning a good representation with unsymmetrical auto-encoder

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NEURAL COMPUTING & APPLICATIONS 2016年第5期27卷 1361-1367页

作者： Sun, Yanan Mao, Hua Guo, Quan Yi, Zhang Sichuan Univ Coll Comp Sci Machine Intelligence Lab Chengdu 610065 Peoples R China

auto-encoders play a fundamental role in unsupervised feature learning and learning initial parameters of deep architectures for supervised tasks. For given input samples, robust features are used to generate robust representations from two perspectives: (1) invariant to small variation of samples and (2) reconstruction by decoders with minimal error. Traditional auto-encoders with different regularization terms have symmetrical numbers of encoder and decoder layers, and sometimes parameters. We investigate the relation between the number of layers and propose an unsymmetrical structure, i.e., an unsymmetrical auto-encoder (UAE), to learn more effective features. We present empirical results of feature learning using the UAE and state-of-the-art auto-encoders for classification tasks with a range of datasets. We also analyze the gradient vanishing problem mathematically and provide suggestions for the appropriate number of layers to use in UAEs with a logistic activation function. In our experiments, UAEs demonstrated superior performance with the same configuration compared to other autoencoders.

关键词： auto-encoder Neural networks Feature learning Deep learning Unsupervised learning

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Robust heart rate estimation from mobile face videos using an auto-encoder

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ELECTRONICS LETTERS 2020年第22期56卷 1169-1171页

作者： Lomaliza, J. -P. Park, H. Pukyong Natl Univ Busan South Korea

Providing an easy-to-use and non-invasive heart rate estimation system for home healthcare is imperative for detecting health deterioration in the early stages and for providing preventive treatments. To this end, we propose a ballistocardiographic method that estimates heart rate by analysing face videos and exploiting cyclic head motions caused by blood pumping through the carotid arteries during heartbeats. The proposed method addresses hand motion artefacts that occur while tracking head motions on a mobile platform. This is achieved by training an auto-encoder that retrieves a heart rate-related signal by learning consistent and statistically dominant relationships between facial landmarks. Moreover, unlike conventional methods, where a feature point tracker was used to track the facial landmarks, we propose using region-based tracking to increase tracking robustness in challenging lighting conditions. Experimental results demonstrate that the proposed method performs best in challenging light conditions and can robustly manage hand motion artefacts on a mobile platform.

关键词： medical signal detection medical signal processing image sequences blood vessels electrocardiography image motion analysis diseases medical image processing video signal processing feature extraction tracking face recognition patient monitoring biomechanics cardiology learning (artificial intelligence) facial landmarks tracking robustness challenging lighting conditions challenging light conditions hand motion artefacts mobile platform robust heart rate estimation mobile face videos auto-encoder noninvasive heart rate estimation system home healthcare health deterioration preventive treatments ballistocardiographic method exploiting cyclic head motions carotid arteries heart rate-related signal consistent relationships statistically dominant relationships

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Deep Multiple auto-encoder-Based Multi-view Clustering

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DATA SCIENCE AND ENGINEERING 2021年第3期6卷 323-338页

作者： Du, Guowang Zhou, Lihua Yang, Yudi Lu, Kevin Wang, Lizhen Yunnan Univ Sch Informat Sci & Engineer Kunming 650091 Yunnan Peoples R China Brunel Univ Uxbridge UB8 3PH Middx England

Multi-view clustering (MVC), which aims to explore the underlying structure of data by leveraging heterogeneous information of different views, has brought along a growth of attention. Multi-view clustering algorithms based on different theories have been proposed and extended in various applications. However, most existing MVC algorithms are shallow models, which learn structure information of multi-view data by mapping multi-view data to low-dimensional representation space directly, ignoring the nonlinear structure information hidden in each view, and thus, the performance of multi-view clustering is weakened to a certain extent. In this paper, we propose a deep multi-view clustering algorithm based on multiple auto-encoder, termed MVC-MAE, to cluster multi-view data. MVC-MAE adopts auto-encoder to capture the nonlinear structure information of each view in a layer-wise manner and incorporate the local invariance within each view and consistent as well as complementary information between any two views together. Besides, we integrate the representation learning and clustering into a unified framework, such that two tasks can be jointly optimized. Extensive experiments on six real-world datasets demonstrate the promising performance of our algorithm compared with 15 baseline algorithms in terms of two evaluation metrics.

关键词： Multi-view Clustering auto-encoder Complementary information Consistent information Local geometrical information

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New Golay decoding method using auto-encoder and OSD

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ICT EXPRESS 2024年第2期10卷 393-399页

作者： Cho, Hyun Woo Song, Young Joon Kumoh Natl Inst Technol Dept Elect Engn Gumi South Korea

In this study, we explore the potential of leveraging machine learning techniques, specifically auto-encoders (AE), for the decoding of linear block codes. Our findings suggest that this approach can outperform the conventional ordered statistics decoding (OSD) method, especially in a Rayleigh fading channel environment. We have rigorously trained the AE under both additive white Gaussian noise and Rayleigh fading channel conditions to ensure robustness in its performance. The output of the AE is combined with the received vector in a suitable manner to perform OSD. Through our experiments, we demonstrate that this proposed decoding approach yields better results than the conventional OSD method in Rayleigh fading channel when we used (23,12) Golay code. (c) 2024 The Author(s). Published by Elsevier B.V. on behalf of The Korean Institute of Communications and Information Sciences. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Machine learning auto-encoder Ordered statistics decoding Golay code

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A deep learning method for lincRNA detection using auto-encoder algorithm

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BMC BIOINFORMATICS 2017年第15-Sup期18卷 511-511页

作者： Yu, Ning Yu, Zeng Pan, Yi SUNY Coll Brockport Dept Comp Sci 350 New Campus Dr Brockport NY 14420 USA Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 610031 Sichuan Peoples R China Georgia State Univ Dept Comp Sci 25 Pk Pl Atlanta GA 30303 USA

Background: RNA sequencing technique (RNA-seq) enables scientists to develop novel data-driven methods for discovering more unidentified lincRNAs. Meantime, knowledge-based technologies are experiencing a potential revolution ignited by the new deep learning methods. By scanning the newly found data set from RNA-seq, scientists have found that: (1) the expression of lincRNAs appears to be regulated, that is, the relevance exists along the DNA sequences;(2) lincRNAs contain some conversed patterns/motifs tethered together by non-conserved regions. The two evidences give the reasoning for adopting knowledge-based deep learning methods in lincRNA detection. Similar to coding region transcription, non-coding regions are split at transcriptional sites. However, regulatory RNAs rather than message RNAs are generated. That is, the transcribed RNAs participate the biological process as regulatory units instead of generating proteins. Identifying these transcriptional regions from non-coding regions is the first step towards lincRNA recognition. Results: The auto-encoder method achieves 100% and 92.4% prediction accuracy on transcription sites over the putative data sets. The experimental results also show the excellent performance of predictive deep neural network on the lincRNA data sets compared with support vector machine and traditional neural network. In addition, it is validated through the newly discovered lincRNA data set and one unreported transcription site is found by feeding the whole annotated sequences through the deep learning machine, which indicates that deep learning method has the extensive ability for lincRNA prediction. Conclusions: The transcriptional sequences of lincRNAs are collected from the annotated human DNA genome data. Subsequently, a two-layer deep neural network is developed for the lincRNA detection, which adopts the auto-encoder algorithm and utilizes different encoding schemes to obtain the best performance over intergenic DNA sequence da

关键词： Deep learning Long intergenic non-coding RNA (lincRNA) auto-encoder Transcription sites RNA-seq Knowledge-based discovery

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Towards reliable hepatocytic anatomy segmentation in laparoscopic cholecystectomy using U-Net with auto-encoder

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SURGICAL ENDOSCOPY AND OTHER INTERVENTIONAL TECHNIQUES 2023年第9期37卷 7358-7369页

作者： Alkhamaiseh, Koloud N. Grantner, Janos L. Shebrain, Saad Abdel-Qader, Ikhlas Western Michigan Univ Dept Elect & Comp Engn Kalamazoo MI 49008 USA Western Michigan Univ Homer Stryker MD Sch Med Kalamazoo MI USA

BackgroundMost bile duct (BDI) injuries during laparoscopic cholecystectomy (LC) occur due to visual misperception leading to the misinterpretation of anatomy. Deep learning (DL) models for surgical video analysis could, therefore, support visual tasks such as identifying critical view of safety (CVS). This study aims to develop a prediction model of CVS during LC. This aim is accomplished using a deep neural network integrated with a segmentation model that is capable of highlighting hepatocytic *** images from LC videos were annotated with four hepatocystic landmarks of anatomy segmentation. A deep autoencoder neural network with U-Net to investigate accurate medical image segmentation was trained and tested using fivefold cross-validation. Accuracy, Loss, Intersection over Union (IoU), Precision, Recall, and Hausdorff Distance were computed to evaluate the model performance versus the annotated ground *** total of 1550 images from 200 LC videos were annotated. Mean IoU for segmentation was 74.65%. The proposed approach performed well for automatic hepatocytic landmarks identification with 92% accuracy and 93.9% precision and can segment challenging ***, can potentially provide an intraoperative model for surgical video analysis and can be trained to guide surgeons toward reliable hepatocytic anatomy segmentation and produce selective video documentation of this safety step of LC.

关键词： Critical view of safety Laparoscopic cholecystectomy Deep learning Anatomy segmentation auto-encoder U-Net Challenging cases Surgical safety

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