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Cable Incipient Fault Identification with a sparse autoencoder and a Deep Belief Network

ENERGIES 2019年第18期12卷 3424-3424页

作者： Liu, Ning Fan, Bo Xiao, Xianyong Yang, Xiaomei Sichuan Univ Coll Elect Engn Chengdu 610065 Sichuan Peoples R China State Grid Ningxia Power Co Power Res Inst Yinchuan 750000 Peoples R China

Incipient faults in power cables are a serious threat to power safety and are difficult to accurately identify. The traditional pattern recognition method based on feature extraction and feature selection has strong subjectivity. If the key feature information cannot be extracted accurately, the recognition accuracy will directly decrease. To accurately identify incipient faults in power cables, this paper combines a sparse autoencoder and a deep belief network to form a deep neural network, which relies on the powerful learning ability of the neural network to classify and identify various cable fault signals, without requiring preprocessing operations for the fault signals. The experimental results demonstrate that the proposed approach can effectively identify cable incipient faults from other disturbances with a similar overcurrent phenomenon and has a higher recognition accuracy and reliability than the traditional pattern recognition method.

关键词： incipient fault sparse autoencoder deep belief network identification

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Investigation of a sparse autoencoder-Based Feature Transfer Learning Framework for Hydrogen Monitoring Using Microfluidic Olfaction Detectors

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SENSORS 2022年第20期22卷 7696-7696页

作者： Mirzaei, Hamed Ramezankhani, Milad Earl, Emily Tasnim, Nishat Milani, Abbas S. Hoorfar, Mina Univ British Columbia Sch Engn Okanagan Campus Kelowna BC V1V 1V7 Canada Univ Victoria Dept Mech Engn Victoria BC V8P 5C2 Canada

Alternative fuel sources, such as hydrogen-enriched natural gas (HENG), are highly sought after by governments globally for lowering carbon emissions. Consequently, the recognition of hydrogen as a valuable zero-emission energy carrier has increased, resulting in many countries attempting to enrich natural gas with hydrogen;however, there are rising concerns over the safe use, storage, and transport of H2 due to its characteristics such as flammability, combustion, and explosivity at low concentrations (4 vol%), requiring highly sensitive and selective sensors for safety monitoring. Microfluidic-based metal-oxide-semiconducting (MOS) gas sensors are strong tools for detecting lower levels of natural gas elements;however, their working mechanism results in a lack of real-time analysis techniques to identify the exact concentration of the present gases. Current advanced machine learning models, such as deep learning, require large datasets for training. Moreover, such models perform poorly in data distribution shifts such as instrumental variation. To address this problem, we proposed a sparse autoencoder-based Transfer Learning (SAE-TL) framework for estimating the hydrogen gas concentration in HENG mixtures using limited datasets from a 3D printed microfluidic detector coupled with two commercial MOS sensors. Our framework detects concentrations of simulated HENG based on time-series data collected from a cost-effective microfluidic-based detector. This modular gas detector houses metal-oxide-semiconducting (MOS) gas sensors in a microchannel with coated walls, which provides selectivity based on the diffusion pace of different gases. We achieve a dominant performance with the SAE-TL framework compared to typical ML models (94% R-squared). The framework is implementable in real-world applications for fast adaptation of the predictive models to new types of MOS sensor responses.

关键词： hydrogen detection HENG transfer learning sparse autoencoder microfluidic gas sensor

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Application of deep canonically correlated sparse autoencoder for the classification of schizophrenia

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020年 183卷 105073-000页

作者： Li, Gang Han, Depeng Wang, Chao Hu, Wenxing Calhoun, Vince D. Wang, Yu-Ping Changan Univ Sch Elect & Control Engn Xian 710064 Shaanxi Peoples R China Changan Univ Key Lab Rd Construct Technol & Equipment MOE Xian Shaanxi Peoples R China Tulane Univ Biomed Engn Dept New Orleans LA 70118 USA Univ New Mexico Mind Res Network Albuquerque NM 87106 USA Univ New Mexico Dept ECE Albuquerque NM 87106 USA

Background and objective: Imaging genetics has been widely used to help diagnose and treat mental illness, e.g., schizophrenia, by combining magnetic resonance imaging of the brain and genomic information for comprehensive and systematic analysis. As a result, utilizing the correlation between magnetic resonance imaging of the brain and genomic information is becoming an important challenge. Methods: In this paper, the joint analysis of single nucleotide polymorphisms and functional magnetic resonance imaging is conducted for comprehensive study of schizophrenia. We developed a deep canonically correlated sparse autoencoder to classify schizophrenia patients from healthy controls, which can address the limitation of many existing methods such as canonical correlation analysis, deep canonical correlation analysis and sparse autoencoder. Results: The proposed deep canonically correlated sparse autoencoder can not only use complex nonlinear transformation and dimension reduction, but also achieve more accurate classifications. Our experiments showed the proposed method achieved an accuracy of 95.65% for SNP data sets and an accuracy of 80.53% for fMRI data sets. Conclusions: Experiments demonstrated higher accuracy of using the proposed method over other conventional models when classifying schizophrenia patients and healthy controls. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Imaging-genetic associations Canonical correlation analysis sparse autoencoder Deep canonically correlated sparse autoencoder Schizophrenia classification

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DDSA: A Defense Against Adversarial Attacks Using Deep Denoising sparse autoencoder

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IEEE ACCESS 2019年 7卷 160397-160407页

作者： Bakhti, Yassine Fezza, Sid Ahmed Hamidouche, Wassim Deforges, Olivier Natl Inst Telecommun & ICT Oran 31000 Algeria Univ Rennes CNRS INSA Rennes IETR UMR CNRS 6164 F-35700 Rennes France IRT B Com F-35510 Cesson Sevigne France

Given their outstanding performance, the Deep Neural Networks (DNNs) models have been deployed in many real-world applications. However, recent studies have demonstrated that they are vulnerable to small carefully crafted perturbations, i.e., adversarial examples, which considerably decrease their performance and can lead to devastating consequences, especially for safety-critical applications, such as autonomous vehicles, healthcare and face recognition. Therefore, it is of paramount importance to offer defense solutions that increase the robustness of DNNs against adversarial attacks. In this paper, we propose a novel defense solution based on a Deep Denoising sparse autoencoder (DDSA). The proposed method is performed as a pre-processing step, where the adversarial noise of the input samples is removed before feeding the classifier. The pre-processing defense block can be associated with any classifier, without any change to their architecture or training procedure. In addition, the proposed method is a universal defense, since it does not require any knowledge about the attack, making it usable against any type of attack. The experimental results on MNIST and CIFAR-10 datasets have shown that the proposed DDSA defense provides a high robustness against a set of prominent attacks under white-, gray- and black-box settings, and outperforms state-of-the-art defense methods.

关键词： Perturbation methods Noise reduction Training Neural networks Robustness Licenses Training data Deep neural network security adversarial attacks defense sparse autoencoder denoising

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Alz-SAENet: A Deep sparse autoencoder based Model for Alzheimer's Classification

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第10期13卷 340-348页

作者： Reddy, G. Nagarjuna Reddy, K. Nagi JNT Univ Ananthapuramu India NBKRIST Dept ECE Vijayanagar India

Precise identification of Alzheimer's Disease (AD) is vital in health care, especially at an early stage, since recognizing the likelihood of incidence and progression allows patients to adopt preventive measures before irreparable brain damage occurs. Magnetic Resonance Imaging is an effective and common clinical strategy to diagnose AD due to its structural details. we built an advanced deep sparse autoencoder-based architecture, named Alz-SAENet for the identification of diseased from typical control subjects using MRI volumes. We focused on a novel optimal feature extraction procedure using the combination of a 3D Convolutional Neural Network (CNN) and deep sparse autoencoder (SAE). Optimal features derived from the bottleneck layer of the hyper-tuned SAE network are subsequently passed via a deep neural network (DNN). This approach results in the improved four-way categorization of AD-prone 3D MRI brain images that prove the capability of this network in AD prognosis to adopt preventive measures. This model is further evaluated using ADNI and Kaggle data and achieved 98.9% and 98.215% accuracy and showed a tremendous response in distinguishing the MRI volumes that are in a transitional of AD.

关键词： Alzheimer&rsquo s disease MRI CNN sparse autoencoder DNN mild cognitive impairment

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Credit Card Fraud Detection Using sparse autoencoder and Generative Adversarial Network 9

Credit Card Fraud Detection Using Sparse Autoencoder and Gen...

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9th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)

作者： Chen, Jian Shen, Yao Ali, Riaz Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China

ISBN: (纸本)9781538672662

Current credit card detection methods usually utilize the idea of classification, requiring a balanced training dataset which should contain both positive and negative samples. However, we often get highly skewed datasets with very few frauds. In this paper, we want to apply deep learning techniques to help handle this situation. We firstly use sparse autoencoder (SAE) to obtain representations of normal transactions and then train a generative adversarial network (GAN) with these representations. Finally, we combine the SAE and the discriminator of GAN and apply them to detect whether a transaction is genuine or fraud. The experimental results show that our solution outperforms the other state-of-the-art one-class methods.

关键词： credit fraud detection deep learning sparse autoencoder generative adversarial network

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Self-Taught Learning Based on sparse autoencoder for E-Nose in Wound Infection Detection

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SENSORS 2017年第10期17卷 2279页

作者： He, Peilin Jia, Pengfei Qiao, Siqi Duan, Shukai Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China

For an electronic nose (E-nose) in wound infection distinguishing, traditional learning methods have always needed large quantities of labeled wound infection samples, which are both limited and expensive;thus, we introduce self-taught learning combined with sparse autoencoder and radial basis function (RBF) into the field. Self-taught learning is a kind of transfer learning that can transfer knowledge from other fields to target fields, can solve such problems that labeled data (target fields) and unlabeled data (other fields) do not share the same class labels, even if they are from entirely different distribution. In our paper, we obtain numerous cheap unlabeled pollutant gas samples (benzene, formaldehyde, acetone and ethylalcohol);however, labeled wound infection samples are hard to gain. Thus, we pose self-taught learning to utilize these gas samples, obtaining a basis vector theta. Then, using the basis vector theta, we reconstruct the new representation of wound infection samples under sparsity constraint, which is the input of classifiers. We compare RBF with partial least squares discriminant analysis (PLSDA), and reach a conclusion that the performance of RBF is superior to others. We also change the dimension of our data set and the quantity of unlabeled data to search the input matrix that produces the highest accuracy.

关键词： electronic nose self-taught learning sparse autoencoder wound infection

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Image Super-Resolution Algorithm Based on an Improved sparse autoencoder

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INFORMATION 2018年第1期9卷 11页

作者： Huang, Detian Huang, Weiqin Yuan, Zhenguo Lin, Yanming Zhang, Jian Zheng, Lixin Huaqiao Univ Coll Engn 269 Chenghuabei Rd Quanzhou 362021 Peoples R China Shantou Univ Dept Elect Engn 243 Daxue Rd Shantou 515063 Peoples R China

Due to the limitations of the resolution of the imaging system and the influence of scene changes and other factors, sometimes only low-resolution images can be acquired, which cannot satisfy the practical application's requirements. To improve the quality of low-resolution images, a novel super-resolution algorithm based on an improved sparse autoencoder is proposed. Firstly, in the training set preprocessing stage, the high-and low-resolution image training sets are constructed, respectively, by using high-frequency information of the training samples as the characterization, and then the zero-phase component analysis whitening technique is utilized to decorrelate the formed joint training set to reduce its redundancy. Secondly, a constructed sparse regularization term is added to the cost function of the traditional sparse autoencoder to further strengthen the sparseness constraint on the hidden layer. Finally, in the dictionary learning stage, the improved sparse autoencoder is adopted to achieve unsupervised dictionary learning to improve the accuracy and stability of the dictionary. Experimental results validate that the proposed algorithm outperforms the existing algorithms both in terms of the subjective visual perception and the objective evaluation indices, including the peak signal-to-noise ratio and the structural similarity measure.

关键词： super-resolution sparse autoencoder dictionary learning ZCA whitening

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Evaluation of Aging Suppression of LiBr-coated Lithium-Air Batteries Using Time-of-Flight Secondary Ion Mass Spectrometry and sparse autoencoder

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E-JOURNAL OF SURFACE SCIENCE AND NANOTECHNOLOGY 2023年第1期21卷 9-16页

作者： Aoyagi, Satoka Hayashi, Daisuke Nagataki, Atsuko Horiba, Tatsuo Saito, Morihiro Seikei Univ Fac Sci & Technol Musashino Tokyo 1808633 Japan Natl Inst Mat Sci 1-2-1 Sengen Tsukuba Ibaraki 3050047 Japan

For understanding complex reactions in the electrodes of lith-ium-air batteries (LABs), time-of-flight secondary ion mass spectrometry (ToF-SIMS) is one of the most powerful methods because ToF-SIMS provides molecular information including organic and organic-inorganic complex materials. Although ToF-SIMS has been used to characterize various lithium-ion bat-tery electrodes, low-mass peaks, less than 100 Da, were mainly focused for obtaining the distribution images and depth profiles. Nonetheless, high-mass peaks are important for identifying the products and reactions that cause battery aging. The selection of the mass peaks specific to a particular sample among similar samples, such as a particular electrode and control electrodes, is generally very difficult by manual analysis because the ToF-SIMS spectra contain more than hundreds of mass peaks. This renders the detailed evaluation by TOF-SIMS for improved LABs challenging. Herein, we demonstrate that by applying a sparse autoencoder to the ToF-SIMS data of air electrodes con-taining a redox mediator that suppressed aging, it was possible to distinguish specific mass peaks corresponding to the elec-trodes with the redox mediator, initial electrodes before aging, and aged electrodes. The results indicate that the intensity of the mass peaks originating directly from the electrolyte material increases when the residual products on the air electrode are re-moved by the redox mediator. The analysis presented herein can be utilized to monitor the aging and degradation of these next-generation batteries.

关键词： ToF-SIMS sparse autoencoder Lithium-air battery Redox mediator LiBr

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A sparse autoencoder and Softmax Regression Based Diagnosis Method for the Attachment on the Blades of Marine Current Turbine

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SENSORS 2019年第4期19卷 826-826页

作者： Zheng, Yilai Wang, Tianzhen Xin, Bin Xie, Tao Wang, Yide Shanghai Maritime Univ Dept Elect Automat Shanghai 201306 Peoples R China Univ Nantes IETR F-44306 Nantes France

The development and application of marine current energy are attracting more and more attention around the world. Due to the hardness of its working environment, it is important and difficult to study the fault diagnosis of a marine current generation system. In this paper, an underwater image is chosen as the fault-diagnosing signal, after different sensors are compared. This paper proposes a diagnosis method based on the sparse autoencoder (SA) and softmax regression (SR). The SA is used to extract the features and SR is used to classify them. Images are used to monitor whether the blade is attached by benthos and to determine its corresponding degree of attachment. Compared with other methods, the experiment results show that the proposed method can diagnose the blade attachment with higher accuracy.

关键词： marine current turbine blade attachment sparse autoencoder softmax regression

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