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Gabor filter bank with deep autoencoder based face recognition system

EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期197卷 116743-116743页

作者： Hammouche, Rabah Attia, Abdelouahab Akhrouf, Samir Akhtar, Zahid Mohamed El Bachir El Ibrahimi Univ Dept Comp Sci Bordj Bouarreridj 34000 Algeria Mohamed Boudiaf Univ Comp Sci & Its Applicat Lab Msila 28000 Algeria Mohamed El Bachir El Ibrahimi Univ LMSE Lab Bordj Bouarreridj 34000 Algeria Mohamed Boudiaf Univ Dept Comp Sci Msila 28000 Algeria State Univ New York Polytech Inst Dept Network & Comp Secur Utica NY 13502 USA

These days, face recognition systems are widely being employed in various daily applications such as smart phone unlocking, tracking school attendance, and secure online bank transactions, smarter border control, to name a few. In spite of the remarkable progress, face recognition systems still suffer from occlusions, light variations, camera types and their resolutions. Face recognition is still a dynamic research field. In this paper, we propose an efficient face recognition system based on Gabor filter bank and a deep learning method known as sparse autoencoder (SAE). The main aim of the proposed system is to improve the features extracted by Gabor filter bank using SAE method. Then, these enhanced features are subjected to features reduction using principal component analysis and linear discriminant analysis (PCA + LDA) technique. Finally, the matching stage is accomplished via cosine Mahalanobis distance. Experiments on seven publicly available databases (i.e., JAFFE, AT & T, Yale, Georgia Tech, CASIA, Extended Yale, Essex) show that the proposed system can achieve promising results with the combination of Gabor and SAE, as well as outperform previously proposed methods.

关键词： sparse autoencoder Gabor filter bank Face recognition PCA plus LDA

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Baby cry recognition based on SLGAN model data generation and deep feature fusion

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 242卷

作者： Zhang, Ke Ting, Hua-Nong Choo, Yao-Mun Univ Malaya Fac Engn Dept Biomed Engn Jalan Pantai Baharu Kuala Lumpur 50603 Malaysia Jining Med Univ Fac Med Engn Univ PkNatl Hightech Zone Jining 272067 Shandong Peoples R China Univ Malaya Fac Med Dept Paediat Jalan Pantai Baharu Kuala Lumpur 50603 Malaysia

Deep learning models have been applied in baby cry recognition to enhance the recognition accuracy. However, the current research still suffers from data imbalance problem, which leads to bias in model learning. sparse autoencoder Long Short-Term Memory based Generative Adversarial Network (SLGAN) is proposed to solve the data imbalance problem. The proposed SLGAN model generates new baby cry data to ensure the number of samples for every cry class is equal. Speech features are extracted using Mel-spectrograms and Short-Time Fourier Transform (STFT). Two deep learning models, i.e. VGG16 and VGG19 are used to extract the deep features. The deep features are then dimensionally reduced by using Principal Component Analysis (PCA). A sparse autoencoder model is used to fuse the deep features. Finally, the fused features are trained and classified using the Deep Neural Network. The experimental results show that the proposed method outperforms the existing methods.

关键词： Baby cry Data generation Generative adversarial networks (GANs) sparse autoencoder Feature fusion

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Unsupervised rail vehicle running instability detection algorithm for passenger trains (iVRIDA)

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MEASUREMENT 2023年 216卷

作者： Kulkarni, Rohan Qazizadeh, Alireza Berg, Mats KTH Royal Inst Technol Engn Mech Dept S-10044 Stockholm Sweden

Intelligently identifying rail vehicle faults instigating running instability from carbody floor acceleration is essential to ensure operational safety and reduce maintenance costs. However, the vehicle-track interaction's nonlinearities and scarcity of running instability occurrences complicate the task. The running instability is an anomaly in the vehicle-track interaction. Thus, we propose unsupervised anomaly detection and clustering al-gorithms based iVRIDA framework to detect and identify running instability and corresponding root cause. We deploy and compare the performance of the PCA-AD (baseline), sparse autoencoder (SAE-AD), and LSTM-Encoder-Decoder (LSTMEncDec-AD) model to detect the running instability ***, we deploy a k-means algorithm on latent space to identify clusters associated with root causes instigating instability. We deployed the iVRIDA framework on simulated and measured accelerations of European high-speed rail vehicles where SAE-AD and LSTMEncDec-AD models showed 97% accuracy. The proposed method contributes to smart maintenance by intelligently identifying anomalous vehicle-track interaction events.

关键词： Vehicle hunting Unsupervised machine learning sparse autoencoder LSTM encoder decoder Worn wheel Failed yaw damper

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A hybrid neural network based on KF-SA-Transformer for SOC prediction of lithium-ion battery energy storage systems

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FRONTIERS IN ENERGY RESEARCH 2024年 12卷

作者： Xiong, Yifei Shi, Qinglian Shen, Lingxu Chen, Chen Lu, Wu Xu, Cong Shanghai Univ Elect Power Coll Elect Engn Shanghai Peoples R China State Grid Xiongan New Area Elect Power Supply Co Baoding Hebei Peoples R China Shanghai Univ Intellectual Property Acad Shanghai Peoples R China

With the widespread application of energy storage stations, BMS has become an important subsystem in modern power systems, leading to an increasing demand for improving the accuracy of SOC prediction in lithium-ion battery energy storage systems. Currently, common methods for predicting battery SOC include the Ampere-hour integration method, open circuit voltage method, and model-based prediction techniques. However, these methods often have limitations such as single-variable research, complex model construction, and inability to capture real-time changes in SOC. In this paper, a novel prediction method based on the KF-SA-Transformer model is proposed by combining model-based prediction techniques with data-driven methods. By using temperature, voltage, and current as inputs, the limitations of single-variable studies in the Ampere-hour integration method and open circuit voltage method are overcome. The Transformer model can overcome the complex modeling process in model-based prediction techniques by implementing a non-linear mapping between inputs and SOC. The presence of the Kalman filter can eliminate noise and improve data accuracy. Additionally, a sparse autoencoder mechanism is integrated to optimize the position encoding embedding of input vectors, further improving the prediction process. To verify the effectiveness of the algorithm in predicting battery SOC, an open-source lithium-ion battery dataset was used as a case study in this paper. The results show that the proposed KF-SA-Transformer model has superiority in improving the accuracy and reliability of battery SOC prediction, playing an important role in the stability of the grid and efficient energy allocation.

关键词： state-of-charge Transformer Kalman filter sparse autoencoder lithium-ion battery

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A fault diagnosis method for electric vehicle power lithium battery based on wavelet packet decomposition

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JOURNAL OF ENERGY STORAGE 2022年 56卷

作者： Jiang, Jiuchun Zhang, Ruhang Wu, Yutong Chang, Chun Jiang, Yan Hubei Univ Technol Hubei Key Lab High efficiency Utilizat Solar Energ Wuhan 430068 Peoples R China Sunwoda Elect Co Ltd Shenzhen 518108 Peoples R China

This paper proposes a fault diagnosis method for electric vehicle power lithium battery based on wavelet packet decomposition. Firstly, the original voltage signal is decomposed into the low-frequency part and high-frequency part based on wavelet packet decomposition. For the high-frequency part, after filtering the noise using wavelet packet energy noise reduction method, the time domain voltage signal is obtained by wavelet packet recon-struction, and the characteristic parameters reflecting the battery fault are extracted by using sparse autoen-coder;for the low-frequency part, the characteristic parameters reflecting the battery inconsistency are obtained by using singular value decomposition. The similarity between each individual cell and the average cell is then measured using the discrete Fre ' chet distance algorithm. Finally, the outlier detection method based on the Chauvenet criterion is used to detect the faulty cells using the obtained curve similarity. The effectiveness of the proposed method is verified by the data of two thermal runaway vehicles.

关键词： Battery fault diagnosis Wavelet packet decomposition and reconstruction sparse autoencoder Singular value decomposition Discrete Fre ?chet distance Chauvenet criterion

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Broad fuzzy cognitive map systems for time series classification

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APPLIED SOFT COMPUTING 2022年第0期128卷

作者： Wu, Kai Yuan, Kaixin Teng, Yingzhi Liu, Jing Jiao, Licheng Xidian Univ Sch Artificial Intelligence Xian Peoples R China Xidian Univ Guangzhou Inst Technol Guangzhou Peoples R China

Time series classification (TSC) is a crucial and challenging problem in sequential analysis. However, most of the existing best-performing methods are time-consuming, even if coping with small-scale datasets. Broad learning systems (BLS) have shown low time complexity and high accuracy in handling various tasks and have been applied to many fields. However, none of the BLS-based methods is suitable to tackle TSC due to (1) unsuitable structure in capturing temporal information of time series;(2) low interpretability in representing the evolving relations among states along time. Thus, this paper develops a broad fuzzy cognitive map system (BFCMS) to address time series classification efficiently, which consists of the sparse autoencoder (SAE) based feature extraction block, the highorder fuzzy cognitive map (HFCM) based spatiotemporal information aggregation block, and one multilayer perceptron (MLP) based prediction layer. The feature extraction block is designed to capture the underlying core evolving patterns, and the spatiotemporal information aggregation block is developed to model the underlying causal relationships and contextual dependencies. These two blocks are designed to overcome the limitations of BLS. MLP is applied to map the feature representation to the label of time series based on the aggregated feature representations from these two blocks. In addition, BFCMS develops three incremental learning strategies for fast updating in broad expansion without a retraining procedure if the model deems to be expanded. We compared BFCMS with other state-of-the-art baselines on 26 datasets. The experimental results demonstrate the superiority of BFCMS. Concretely, BFCMS achieves a lower training cost with on-par classification accuracy. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Broad learning systems Fuzzy cognitive maps Time series classification Incremental learning sparse autoencoder

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Diabetes detection using deep learning techniques with oversampling and feature augmentation

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021年 202卷 105968-105968页

作者： Garcia-Ordas, Maria Teresa Benavides, Carmen Benitez-Andrades, Jose Alberto Alaiz-Moreton, Hector Garcia-Rodriguez, Isaias Univ Leon Escuela Ingenierias Ind & Informat SECOMUCI Res Grp Campus Vegazana S-N Leon 24071 Spain Univ Leon SALBIS Res Grp Dept Elect Syst & Automat Engn Campus Vegazana S-N Leon 24071 Spain

Background and objective : Diabetes is a chronic pathology which is affecting more and more people over the years. It gives rise to a large number of deaths each year. Furthermore, many people living with the disease do not realize the seriousness of their health status early enough. Late diagnosis brings about numerous health problems and a large number of deaths each year so the development of methods for the early diagnosis of this pathology is essential.& nbsp;& nbsp;Methods: In this paper, a pipeline based on deep learning techniques is proposed to predict diabetic peo-ple. It includes data augmentation using a variational autoencoder (VAE), feature augmentation using an sparse autoencoder (SAE) and a convolutional neural network for classification. Pima Indians Diabetes Database, which takes into account information on the patients such as the number of pregnancies, glu-cose or insulin level, blood pressure or age, has been evaluated.& nbsp;Results: A 92 . 31% of accuracy was obtained when CNN classifier is trained jointly the SAE for featuring augmentation over a well balanced dataset. This means an increment of 3.17% of accuracy with respect the state-of-the-art.& nbsp;Conclusions : Using a full deep learning pipeline for data preprocessing and classification has demonstrate to be very promising in the diabetes detection field outperforming the state-of-the-art proposals.& nbsp;(c) 2021 Elsevier B.V. All rights reserved.

关键词： Diabetes Detection Deep learning sparse autoencoder Variational autoencoder Oversampling

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Fine-grained representation learning in convolutional autoencoders

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JOURNAL OF ELECTRONIC IMAGING 2016年第2期25卷 023018-023018页

作者： Luo, Chang Wang, Jie Air Force Engn Univ Air & Missile Def Coll 1 East Changle Rd Xian 710051 CN Peoples R China

Convolutional autoencoders (CAEs) have been widely used as unsupervised feature extractors for high-resolution images. As a key component in CAEs, pooling is a biologically inspired operation to achieve scale and shift invariances, and the pooled representation directly affects the CAEs' performance. Fine-grained pooling, which uses small and dense pooling regions, encodes fine-grained visual cues and enhances local characteristics. However, it tends to be sensitive to spatial rearrangements. In most previous works, pooled features were obtained by empirically modulating parameters in CAEs. We see the CAE as a whole and propose a fine-grained representation learning law to extract better fine-grained features. This representation learning law suggests two directions for improvement. First, we probabilistically evaluate the discrimination-invariance tradeoff with fine-grained granularity in the pooled feature maps, and suggest the proper filter scale in the convolutional layer and appropriate whitening parameters in preprocessing step. Second, pooling approaches are combined with the sparsity degree in pooling regions, and we propose the preferable pooling approach. Experimental results on two independent benchmark datasets demonstrate that our representation learning law could guide CAEs to extract better fine-grained features and performs better in multiclass classification task. This paper also provides guidance for selecting appropriate parameters to obtain better fine-grained representation in other convolutional neural networks. (C) 2016 SPIE and IS&T

关键词： Pooling fine-grained representation convolutional neural networks sparse autoencoder discrimination-invariance tradeoff unsupervised learning deep learning

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Simultaneous fault detection and isolation based on multi-task long short-term memory neural networks

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2023年第1期240卷

作者： Qin, Ken Sinkou Du, Yegang Dalian Maritime Univ Informat Sci & Technol Coll Dalian 116026 Peoples R China Waseda Univ Future Robot Org Tokyo 1620041 Japan

Fault detection and isolation are two significant problems in process monitoring. However, due to the complicated relationships between the process variables, it becomes a challenging problem to build a model to capture the complicated relationship between process variables and perform fault detection and isolation based on the model. This paper proposes an advanced model, called multi-task long short-term memory (MTLSTM) neural networks, that can capture the most complicated relationships between process variables and a simultaneous fault detection and isolation approach based on MTLSTM neural networks. The proposed simultaneous fault detection and isolation approach takes a sparse autoencoder (SAE) to extract features from the measurements of process variables. Then, a long short-term memory neural network is trained on the extracted features with a multi-task learning strategy. Compared with the traditional multivariate statistical models (MVSM) such as principal component analysis and independent component analysis models, the MTLSTM neural networks can capture the nonlinear autocorrelations of process variables and correlations between process variables and perform fault detection and isolation for an industrial process simultaneously. To demonstrate the advantages and superiority of the proposed simultaneous fault detection and isolation approach, a case study on fault detection and isolation for the penicillin fermentation process is carried out. Case study results show that the proposed approach significantly outperforms existing fault detection and isolation approaches.

关键词： Fault detection and isolation sparse autoencoder Long short-term memory Penicillin fermentation process

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Lifted Bregman training of neural networks

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 10909-10959页

作者： Xiaoyu Wang Martin Benning Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge UK School of Mathematical Sciences Queen Mary University of London London UK

We introduce a novel mathematical formulation for the training of feed-forward neural networks with (potentially non-smooth) proximal maps as activation functions. This formulation is based on Bregman distances and a key advantage is that its partial derivatives with respect to the network's parameters do not require the computation of derivatives of the network's activation functions. Instead of estimating the parameters with a combination of first-order optimisation method and back-propagation (as is the state-of-the-art), we propose the use of non-smooth first-order optimisation methods that exploit the specific structure of the novel formulation. We present several numerical results that demonstrate that these training approaches can be equally well or even better suited for the training of neural network-based classifiers and (denoising) autoencoders with sparse coding compared to more conventional training frameworks.

关键词： lifted network training distributed optimisation Bregman distances sparse autoencoder denoising autoencoder classification compressed sensing

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