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Age invariant face recognition and retrieval by coupled auto-encoder networks

NEUROCOMPUTING 2017年 222卷 62-71页

作者： Xu, Chenfei Liu, Qihe Ye, Mao Univ Elect Sci & Technol China Sch Comp Sci & Engn Ctr Robot Chengdu 611731 Peoples R China

Recently many promising results have been shown on face recognition related problems. However, age invariant face recognition and retrieval remains a challenge. Inspired by the observation that age variation is a nonlinear but smooth transform and the ability of auto-encoder network to learn latent representations from inputs, in this paper, we propose a new neural network model called coupled auto-encoder networks (CAN) to handle age-invariant face recognition and retrieval problem. CAN is a couple of two auto-encoders which bridged by two shallow neural networks used to fit complex nonlinear aging and de-aging process. We further propose a nonlinear factor analysis method to nonlinearly decompose one given face image into three components which are identity feature, age feature and noise, where identity feature is age-invariant and can be used for face recognition and retrieval. Experiments on three public available face aging datasets: FGNET, CACD and CACD-VS show the, effectiveness of the proposed approach.

关键词： Face recognition Age invariant auto-encoder

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Representation learning by hierarchical ELM auto-encoder with double random hidden layers

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IET COMPUTER VISION 2019年第4期13卷 411-419页

作者： Li, Rui Wang, Xiaodan Lei, Lei Wu, Chongming Air Force Engn Univ Coll Air & Missile Def Xian 710051 Shanxi Peoples R China Xijing Univ Coll Business Xian 710123 Shanxi Peoples R China

Recent research developments of extreme learning machine (ELM) with multilayer network architecture lead to a promising high performance with extremely fast training speed for representation learning. In this work, the authors are dedicated to develop an efficient and expressive representation learning method with hierarchical ELM, and proposing a novel architectural unit named as double random hidden layers ELM auto-encoder (DELM-AE). The novel DELM-AE consists of one input layer, two random hidden mapping layers for encoding feature, and one output layer for decoding feature. When stacking DELM-AE in the hierarchical structure, they can construct an H-DELM model, where the input of the current AE is the feature representation learned by the previous one, but the output is identical to the original input information and is not the input. Hence, the H-DELM can reproduce the original input data as much as possible to learn more expressive and compact feature. They validate their method on various widely public datasets, and the results demonstrate that H-DELM can bring significant performance improvements in terms of classification accuracy and robustness compared with existing relevant multilayer ELM and other deep learning algorithms at a slight computational cost.

关键词： learning (artificial intelligence) pattern classification random hidden mapping layers output layer decoding feature stacking DELM-AE hierarchical structure H-DELM model feature representation original input information original input data expressive feature compact feature deep learning algorithms extreme learning machine multilayer network architecture lead extremely fast training speed expressive representation learning method hierarchical ELM H-ELM novel architectural unit double random hidden layers ELM auto-encoder novel DELM-AE research developments relevant multilayer ELM

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A feature extraction and classification algorithm based on improved sparse auto-encoder for round steel surface defects

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2020年第5期17卷 5369-5394页

作者： Yan, Xuguo Gao, Liang Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

Traditional feature dimensionality reduction (FDR) algorithms can extract features by reducing feature dimensions. However, it may lose some useful information and affect the accuracy of classification. Normally, in traditional defect feature extraction, it first obtain the defect area of the defect image by image preprocessing and defect segmentation, select the original feature set of defects by prior knowledge, and extract the optimal features by traditional FDR algorithms to solve the problem of "curse of dimensionality". In this paper, a feature extraction and classification algorithm based on improved sparse auto-encoder (AE) is proposed. We adopt three traditional FDR algorithms at the same time, combine the defect features obtained in pairs, take the merged defect features as the input of sparse AE, then use the "bottleneck" of sparse AE to conduct the defects classification by Softmax classifier. The experimental results show that the proposed algorithm can extract the optimal features of round steel surface defects with less network training time than individual sparse AE, finally get higher classification accuracy than individual FDR algorithm in the actual production line.

关键词： feature extraction feature dimensionality reduction auto-encoder defect detection round steel

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Surrogate-based model using auto-encoder for optimising multi-band antennas

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IET MICROWAVES ANTENNAS & PROPAGATION 2022年第11期16卷 725-732页

作者： Um, Kwi Seob Kim, Nam Jik Heo, Seo Weon Hongik Univ Sch Elect & Elect Engn Seoul 04066 South Korea

This paper suggests an optimisation method to design multi-band antenna using artificial neural networks. The proposed network, surrogate-based model using auto-encoder (SBM-AE), is composed of two parts, ordinary neural network and auto-encoder. First, the front neural network predicts the encoded antenna characteristics and then decodes the predicted data to obtain the antenna characteristics. After training the encoder to obtain a characteristic signature vector, the front neural network regresses only the characteristic signature vectors, reducing the complexity and number of parameters of the neural network. This not only reduces the training time but also significantly reduces the number of training data required. We confirm the effectiveness of the design method by designing a multi-band antenna where the proposed SBM-AE required 270 training data while the conventional neural network without auto-encoder needed 1350 training data to achieve comparatively the same error rate.

关键词： auto-encoder broadband antennas neural nets optimisation surrogate based model

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automatic detection of arrhythmias from an ECG signal using an auto-encoder and SVM classifier

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PHYSICAL AND ENGINEERING SCIENCES IN MEDICINE 2022年第2期45卷 665-674页

作者： Ojha, Manoj Kumar Wadhwani, Sulochna Wadhwani, Arun Kumar Shukla, Anupam Madhav Inst Sci & Technol Gwalior Madhya Pradesh India Indian Inst Informat Technol Pune Maharashtra India

Millions of people around the world are affected by arrhythmias, which are abnormal activities of the functioning of the heart. Most arrhythmias are harmful to the heart and can suddenly become life-threatening. The electrocardiogram (ECG) is an important non-invasive tool in cardiology for the diagnosis of arrhythmias. This work proposes a computer-aided diagnosis (CAD) system to automatically classify different types of arrhythmias from ECG signals. First, the auto-encoder convolutional network (ACN) model is used, which is based on a one-dimensional convolutional neural network (1D-CNN) that automatically learns the best features from the raw ECG signals. After that, the support vector machine (SVM) classifier is applied to the features learned by the ACN model to improve the detection of arrhythmic beats. This classifier detects four different types of arrhythmias, namely the left bundle branch block (LBBB), right bundle branch block (RBBB), paced beat (PB), and premature ventricular contractions (PVC), along with the normal sinus rhythms (NSR). Among these arrhythmias, PVC is particularly a dangerous type of heartbeat in ECG signals. The performance of the model is measured in terms of accuracy, sensitivity, and precision using a tenfold cross-validation strategy on the MIT-BIH arrhythmia database. The obtained overall accuracy of the SVM classifier was 98.84%. The result of this model is portrayed as a better performance than in other literary works. Thus, this approach may also help in further clinical studies of cardiac cases.

关键词： ECG Arrhythmia auto-encoder SVM classifier

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Unsupervised medical image feature learning by using de-melting reduction auto-encoder

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NEUROCOMPUTING 2023年第1期523卷 145-156页

作者： Sun, Yu Cong, Jinyu Zhang, Kuixing Jian, Muwei Wei, Benzheng Shandong Univ Tradit Chinese Med Ctr Med Artificial Intelligence Qingdao 266112 Peoples R China Shandong Univ Tradit Chinese Med Qingdao Acad Chinese Med Sci Qingdao 266112 Peoples R China Shandong Univ Tradit Chinese Med Coll Intelligence & Informat Engn Jinan 250355 Shandong Peoples R China Shandong Univ Finance & Econ Jinan 250355 Shandong Peoples R China

Unsupervised feature learning is a fundamental and highly prioritized problem in medical image analysis. Although it has shown considerable improvements, it remains challenging because of its weak feature expression ability, low model-learning efficiency, and weak robustness. To address these limitations, a novel unsupervised feature learning method in the medical image classification task, named de-melting reduction auto-encoder (DMRAE), is proposed in this study. A joint fusion network structure is constructed;it not only improves the expression of target features but also reduces the loss of feature decoding and parameters. To obtain a robust solution, a newly designed decomposed-reconstructed loss function is used to strengthen the semantic context between adjacent feature extractor layers, success-fully avoiding the insufficient model-learning ability from the single optimization objective and improv-ing the quality of the extracted features. Finally, extensive experiments on datasets consisting of 400 breast ultrasonographic images and 6000 lung computed tomography images are conducted to demon-strate the effectiveness of the proposed method. Experimental results reveal that the DMRAE significantly reduces the annotation effort and outperforms existing methods by a significant margin.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Unsupervised feature learning auto-encoder Medical image analysis Joint fusion network structure Decomposed-reconstructed loss function

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A network intrusion detection framework on sparse deep denoising auto-encoder for dimensionality reduction

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SOFT COMPUTING 2024年第5期28卷 4503-4517页

作者： Manjunatha, B. A. Shastry, K. Aditya Naresh, E. Pareek, Piyush Kumar Reddy, Kadiri Thirupal Nitte Meenakshi Inst Technol Bengaluru India Manipal Acad Higher Educ Manipal Inst Technol Bengaluru Dept Informat Technol Manipal India Bharat Inst Engn & Technol Hyderabad India

In today's internet-driven world, a multitude of attacks occurs daily, propelled by a vast user base. The effective detection of these numerous attacks is a growing area of research, primarily accomplished through intrusion detection systems (IDS). IDS are vital for monitoring network traffic to identify malicious activities, such as Denial of Service, Probe, Remote-to-Local, and User-to-Root attacks. Our research focused on evaluating different auto-encoders for enhancing network intrusion detection. The proposed method sparse deep denoising auto-encoder approach produces the dimensionality reduction used to predict and classify attacks in datasets. With the most records among the datasets by training the auto-encoder on normal network data, this utilized reconstruction error as an indicator of anomalies. We tested our approach using standard datasets like KDDCup99, NSL-KDD, UNSW-NB15, and NMITIDS. Remarkably, our sparse deep denoising auto-encoder achieved an accuracy of over 96% based solely on reconstruction error. The primary aim of this work is to improve intrusion detection by achieving higher detection accuracy compared to existing methods.

关键词： Intrusion detection auto-encoder Dimensionality reduction Network security NMITIDS Support vector machine

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A novel fusion approach of deep convolution neural network with auto-encoder and its application in planetary gearbox fault diagnosis

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART O-JOURNAL OF RISK AND RELIABILITY 2021年第1期235卷 3-16页

作者： Chen, Fafa Liu, Lili Tang, Baoping Chen, Baojia Xiao, Wenrong Zhang, Fajun China Three Gorges Univ Hubei Key Lab Hydroelect Machinery Design & Maint Yichang 443002 Peoples R China Chongqing Univ State Key Lab Mech Transmiss Chongqing Peoples R China Nanyang Technol Univ Sch Mech & Aerosp Engn Singapore Singapore

The fault features of gearbox are often influenced and interwoven with each other under the non-stationary condition. The traditional shallow intelligent diagnosis models are difficult to detect and identify gearbox faults with selected features according to prior knowledge. To solve this problem, a novel deep convolutional auto-encoding neural network is designed based on the fusion of the convolutional neural network with the automatic encoder in this research. The vibration signals of gearbox are transformed into Hilbert envelope spectrum by using Hilbert transform and Fourier transform, and the different characteristics of spectral spatial data are automatically learned by convolutional auto-encoding neural network with multiple convolution kernels. The parameters of the convolutional neural network are fine-tuned through a fully connected neural network with a small number of labeled samples. Through the analysis for gearbox fault experiments, the effectiveness and practicability of the proposed method in equipment fault diagnosis are verified. The deep convolutional neural network embedded in the auto-encoder has stronger learning ability, and the diagnosis performance is more stable and reliable in practical engineering application.

关键词： Deep learning convolutional neural network auto-encoder gearbox fault diagnosis

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Discriminative Representation Learning with Supervised auto-encoder

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NEURAL PROCESSING LETTERS 2019年第2期49卷 507-520页

作者： Du, Fang Zhang, Jiangshe Ji, Nannan Hu, Junying Zhang, Chunxia Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Shaanxi Peoples R China Changan Univ Dept Mathmat & Informat Sci Xian 710046 Shaanxi Peoples R China

auto-encoders have been proved to be powerful unsupervised learning methods that able to extract useful features from input data or construct deep artificial neural networks by recent studies. In such settings, the extracted features or the initialized networks only learn the data structure while contain no class information which is a disadvantage in classification tasks. In this paper, we aim to leverage the class information of input to learn a reconstructive and discriminative auto-encoder. More specifically, we introduce a supervised auto-encoder that combines the reconstruction error and the classification error to form a unified objective function while taking the noisy concatenate data and label as input. The noisy concatenate input is constructed in such a method that one third has only original data and zero labels, one third has only label and zero data, the last one third has both original data and label. We show that the representations learned by the proposed supervised auto-encoder are more discriminative and more suitable for classification tasks. Experimental results demonstrate that our model outperforms many existing learning algorithms.

关键词： Supervised learning auto-encoder De-noising

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Joint optimization of degradation assessment and remaining useful life for with convolutional auto-encoder

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ISA TRANSACTIONS 2024年 146卷 451-462页

作者： Ding, Yifei Jia, Minping Zhao, Xiaoli Yan, Xiaoan Lee, Chi-Guhn Southeast Univ Sch Mech Engn Nanjing 211189 Peoples R China Univ Toronto Ctr Maintenance Optimizat & Reliabil Engn Toronto ON M5S 3G8 Canada Nanjing Univ Sci & Technol Sch Mech Engn Nanjing 210014 Peoples R China Nanjing Forestry Univ Sch Mechatron Engn Nanjing 210037 Peoples R China

Remaining useful life (RUL) prediction and degradation assessment are pivotal components of prognostic and health management (PHM) and represent vital tasks in the implementation of predictive maintenance for bearings. In recent years, data -driven PHM techniques for bearings have made substantial progress through the integration of deep learning methods. However, modeling the temporal dependencies inherent in raw vibration signals for both degradation assessment and RUL prediction remains a significant challenge. Hence, we propose a joint optimization architecture that uses a temporal convolutional auto -encoder (TCAE) for the degradation assessment and RUL prediction of bearings. Specifically, the architecture includes a sequence -tosequence model to extract degradation -sensitive features from the raw signal and utilizes temporal distribution characterization (TDC) and a nonlinear regressor to determine the degradation stages and predict RUL, respectively. Our framework integrates the tasks of degradation assessment and RUL prediction in a unified, end -to -end manner, using raw signals as input, resulting in high RUL prediction accuracy (RMSE = 0.0832) on publicly available and self -built datasets. Our approach outperforms state-of-the-art methods, indicating its potential to significantly advance the field of PHM for bearings.

关键词： Temporal convolution auto-encoder Remaining useful life Degradation assessment Joint optimization

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