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A method for fault detection in multi-component systems based on sparse autoencoder-based deep neural networks

RELIABILITY ENGINEERING & SYSTEM SAFETY 2022年第0期220卷 108278-108278页

作者： Yang, Zhe Baraldi, Piero Zio, Enrico Politecn Milan Energy Dept Via La Masa 34 I-20156 Milan Italy PSL Res Univ CRC MINES ParisTech Sophia Antipolis France

In multi-component systems, degradation, maintenance, renewal and operational mode change continuously the operating conditions. The identification of the onset of abnormal conditions from signal measurements taken in such evolving environments can be quite challenging, due to the difficulty of distinguishing the real cause of the signal variations. In this work, we present a method for fault detection in evolving environments that uses a sparse autoencoder-based Deep Neural Network (SAE-DNN) and a novel procedure that remarkably reduces the computational burden for setting the values of the hyperparameters. The method is applied to a synthetic case study and to a bearing vibration dataset. The results show that it is able to accurately detect faults in multi-component systems, outperforming other state-of-the-art methods.

关键词： Deep learning sparse autoencoder Deep neural network Fault detection Evolving environment Multi-component system

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Fault detection and calibration for building energy system using Bayesian inference and sparse autoencoder: A case study in photovoltaic thermal heat pump system

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ENERGY AND BUILDINGS 2023年第1期290卷

作者： Wang, Peng Li, Congwei Liang, Ruobing Yoon, Sungmin Mu, Song Liu, Yuchuan Dalian Univ Technol Fac Infrastructure Engn Dalian Peoples R China Chongqing Univ Key Lab Low grade Energy Utilizat Technol & Syst Minist Educ China Chongqing Peoples R China Sungkyunkwan Univ Dept Global Smart City Suwon 16419 South Korea Sungkyunkwan Univ Sch Civil Architectural Eng & Landscape Architectu Suwon 16419 South Korea Guangdong Airport Baiyun Informat Technol Co Ltd Guangzhou 510000 Peoples R China Sino Ocean Grp Holding Ltd Beijing 100025 Peoples R China

The rise of clean energy such as solar energy provides a new idea to optimize the energy structure, and Photovoltaic thermal (PVT) heat pump system is one of the mainstream development at present. The wrong sensor data will have a great impact on the control and efficiency of the whole PVT heat pump system. In order to deal with this situation, Virtual in-situ calibration (VIC) based on Bayesian inference and Markov chain Monte Carlo (MCMC) is applied to PVT system. This paper presents a generic sensor fault diagnosis and calibration method for building energy systems using a PVT heat pump system as an example. Firstly, the data generated based on the mathematical model of PVT system is used to test the feasibility of VIC in this system. The results show that VIC can well reduce the systematic error and random error of the sensor. However, with the further study, it is found that in the actual system, it is impossible to establish a mathematical model which can reflect the relationship between the sensors with high accuracy. The VIC may fail or even have worse calibration results. Therefore, in order to solve the above problems, this study suggests using sparse autoencoder model instead of mathematical model. The model proved to be more accurate and to reflect the interconnectedness of the sensors, which helped the implementation of the VIC. The calibration method based on sparse autoencoder (SAE) can calibrate not only a single sensor in the PVT heat pump system, but also multiple sensors in a local area. After sparse autoencoder Virtual in-situ calibration (SAE-VIC) calibration, systematic and random errors of all sensors can be effectively reduced, and the accuracy of sensor calibration can reach more than 90 %.

关键词： Sensor fault detection and calibration sparse autoencoder Bayesian inference Virtual in-situ calibration Photovoltaic thermal heat pump system

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Learning Feature Representations with a Cost-Relevant sparse autoencoder

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INTERNATIONAL JOURNAL OF NEURAL SYSTEMS 2015年第1期25卷 1450034-1450034页

作者： Langkvist, Martin Loutfi, Amy Univ Orebro Sch Sci & Technol Appl Autonomous Sensor Syst SE-70182 Orebro Sweden

There is an increasing interest in the machine learning community to automatically learn feature representations directly from the (unlabeled) data instead of using hand-designed features. The autoencoder is one method that can be used for this purpose. However, for data sets with a high degree of noise, a large amount of the representational capacity in the autoencoder is used to minimize the reconstruction error for these noisy inputs. This paper proposes a method that improves the feature learning process by focusing on the task relevant information in the data. This selective attention is achieved by weighting the reconstruction error and reducing the influence of noisy inputs during the learning process. The proposed model is trained on a number of publicly available image data sets and the test error rate is compared to a standard sparse autoencoder and other methods, such as the denoising autoencoder and contractive autoencoder.

关键词： sparse autoencoder unsupervised feature learning weighted cost function

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A detection method of oil content for maize kernels based on CARS feature selection and deep sparse autoencoder feature extraction

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INDUSTRIAL CROPS AND PRODUCTS 2024年第Part1期222卷

作者： Yang, Dongfeng Hu, Jun Heilongjiang Bayi Agr Univ Coll Informat & Elect Engn Daqing 163319 Peoples R China Heilongjiang Bayi Agr Univ Coll Engn Daqing 163319 Peoples R China

To achieve accurate, rapid, and non-destructive oil content determination in maize, a neural fitting model that combines feature selection and feature extraction is proposed. Competitive adaptive re-weighted sampling (CARS) is used to choose features, while the deep sparse autoencoder (DSAE) network is utilized to extract features. The 160 spectral data from the near-infrared spectra public datasets of maize served as experimental samples. Several preprocessing approaches were tested, and the first-order derivatives fared the best. To capture the various degrees of abstract features in spectral data, SSAE networks with two or three hidden layers are developed. The number of nodes in the network's hidden layer (30, 50, 70, 100, 200, 300, respectively) and the network's sparsity parameter (0.001, 0.004, 0.007, 0.01, 0.04, 0.07, 0.1, 0.2, ..., 1) are tuned to generate partial least squares regression (PLSR) models for testing the feature learning effect. The highest performance comes from a three-layer network with 200 hidden layer nodes and a sparsity parameter of 0.04. This network extracts three levels of features from the preprocessed spectral data: F1, F2, and F3. Full_cars, F1_cars, F2_cars, and F3_cars are obtained using the CARS algorithm for variable selection on the preprocessed spectral data, F1, F2, and F3, respectively. They are then integrated into a final feature set to build a neural-fitting network (FNN). After training and testing, the test set has a correlation coefficient of 0.964205 and an mean square error (MSE) of 0.00636809. Comparing this model to support vector machine regression (SVR), PLSR, and gaussian process regression (GPR), the findings demonstrate that the model described in this study outperforms the others in terms of fitting accuracy and generalization performance. To summarize, this paper introduces a new regression analysis method based on near-infrared spectra.

关键词： Maize Oil content Competitive adaptive re-weighting algorithm sparse autoencoder Feature selection Feature extraction Deep learning Near infrared spectroscopy

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GSAMDA: a computational model for predicting potential microbe-drug associations based on graph attention network and sparse autoencoder

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BMC BIOINFORMATICS 2022年第1期23卷 492-492页

作者： Tan, Yaqin Zou, Juan Kuang, Linai Wang, Xiangyi Zeng, Bin Zhang, Zhen Wang, Lei Xiangtan Univ Key Lab Hunan Prov Internet Things & Informat Sec Xiangtan 411105 Peoples R China Changsha Univ Inst Bioinformat Complex Network Big Data Changsha 410022 Peoples R China Changsha Univ Big Data Innovat & Entrepreneurship Educ Ctr Huna Changsha 410022 Peoples R China

Background Clinical studies show that microorganisms are closely related to human health, and the discovery of potential associations between microbes and drugs will facilitate drug research and development. However, at present, few computational methods for predicting microbe-drug associations have been proposed. Results In this work, we proposed a novel computational model named GSAMDA based on the graph attention network and sparse autoencoder to infer latent microbe-drug associations. In GSAMDA, we first built a heterogeneous network through integrating known microbe-drug associations, microbe similarities and drug similarities. And then, we adopted a GAT-based autoencoder and a sparse autoencoder module respectively to learn topological representations and attribute representations for nodes in the newly constructed heterogeneous network. Finally, based on these two kinds of node representations, we constructed two kinds of feature matrices for microbes and drugs separately, and then, utilized them to calculate possible association scores for microbe-drug pairs. Conclusion A novel computational model is proposed for predicting potential microbe-drug associations based on graph attention network and sparse autoencoder. Compared with other five state-of-the-art competitive methods, the experimental results illustrated that our model can achieve better performance. Moreover, case studies on two categories of representative drugs and microbes further demonstrated the effectiveness of our model as well.

关键词： Microbe-drug associations Graph attention network-based autoencoder sparse autoencoder

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Enhancing performance of end-to-end communication system using Attention Mechanism-based sparse autoencoder over Rayleigh fading channel

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PHYSICAL COMMUNICATION 2024年 67卷

作者： Sindal, Safalata S. Trivedi, Y. N. Nirma Univ Inst Technol Dept Elect & Commun Engn Ahmadabad 382481 Gujarat India

Deep learning has revolutionized communication systems by introducing innovative approaches to address channel impairments through end-to-end models. autoencoders, a type of deep learning architecture, are adept at learning compact data representations. However, conventional autoencoders in end-to-end models can suffer from overfitting, which limits their effectiveness in noisy communication environments. To address this issue, we propose a sparse autoencoder-based (SAE) model that enforces sparsity and promotes the extraction of robust features. Despite its effectiveness, the SAE model may still lack the ability to focus on the most relevant features of the input data. To overcome this limitation, we further introduce an Attention Mechanism-based sparse autoencoder (ASA) model. This model integrates the feature extraction capabilities of a sparse autoencoder with an attention mechanism that selectively highlights informative features of the signal. Through simulations, we demonstrate that both proposed models significantly improve M-PSK and M-QAM communication system performance. When trained at 7 dB, both proposed models exhibit significant performance improvements at higher testing average SNRs. Our results show that the SAE model outperforms the conventional Maximum Likelihood Detection (MLD) model and baseline autoencoder systems but suffers from error floor issues. The SAE model suffers from an error floor at average SNRs beyond 16 dB for BPSK and 14 dB for higher-order modulation schemes. As the value of M increases, the performance gap between the MLD and the proposed SAE model narrows. The ASA model, however, effectively mitigates the error floor observed in the SAE model for all values of M and across all modulation schemes. This research highlights the benefits of integrating an attention mechanism with SAE, resulting in enhanced robustness and reliability in communication systems characterized by improved accuracy and reduced error rates.

关键词： Deep learning End-to-end communication sparse autoencoder Overfitting Attention mechanism

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Deep sparse autoencoder prediction model based on adversarial learning for cross-domain recommendations

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KNOWLEDGE-BASED SYSTEMS 2021年 220卷 106948-106948页

作者： Li, Yakun Ren, Jiadong Liu, Jiaomin Chang, Yixin Yanshan Univ Coll Informat Sci & Engn Qinhuangdao Hebei Peoples R China Key Lab Comp Virtual Technol & Syst Integrat Hebe Qinhuangdao Hebei Peoples R China

Online recommender systems generally suffer from severe data sparsity problems, and this are particularly prevalent in newly launched systems that do not have sufficient amounts of data. Cross-domain recommendations can provide us with some new ideas for assisting with user recommendations in sparse target domains by transferring knowledge from a source domain with rich data. In this paper, a deep sparse autoencoder prediction model based on adversarial learning for cross-domain recommendations (DSAP-AL) is proposed to improve the accuracy of rating predictions in similar cross domain recommender systems. Specifically, joint matrix factorization and adversarial network learning models are adopted to integrate and align user and item latent factor spaces in a unified pattern. Then, a deep sparse autoencoder is represented and modeled by transferring the latent factors and interlayer weights. Furthermore, a domain factor adaptation algorithm is proposed to capture robust user and item factors, and the learned regularization constraints are added to the objective function, thereby alleviating the data sparsity issue. Experimental results on four real-world datasets demonstrate that, even without overlapping entities (users or items) in the source and target domains, the proposed DSAP-AL method achieves competitive performance relative to other state-of-the-art individual and cross domain approaches. Moreover, the DSAP-AL model is not only effective for scenarios with sparse data but also robust for noise-containing recommendations. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Data sparsity sparse autoencoder Adversarial learning Recommender systems Matrix factorization

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A computer-aided diagnosis system for the classification of COVID-19 and non-COVID-19 pneumonia on chest X-ray images by integrating CNN with sparse autoencoder and feed forward neural network

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COMPUTERS IN BIOLOGY AND MEDICINE 2022年 141卷 105134-105134页

作者： Gayathri, J. L. Abraham, Bejoy Sujarani, M. S. Nair, Madhu S. Coll Engn Perumon Dept Comp Sci & Engn Kollam 691601 Kerala India Cochin Univ Sci & Technol Dept Comp Sci Artificial Intelligence & Comp Vis Lab Kochi 682022 Kerala India

Several infectious diseases have affected the lives of many people and have caused great dilemmas all over the world. COVID-19 was declared a pandemic caused by a newly discovered virus named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) by the World Health Organisation in 2019. RT-PCR is considered the golden standard for COVID-19 detection. Due to the limited RT-PCR resources, early diagnosis of the disease has become a challenge. Radiographic images such as Ultrasound, CT scans, X-rays can be used for the detection of the deathly disease. Developing deep learning models using radiographic images for detecting COVID-19 can assist in countering the outbreak of the virus. This paper presents a computer-aided detection model utilizing chest X-ray images for combating the pandemic. Several pre-trained networks and their combinations have been used for developing the model. The method uses features extracted from pre-trained networks along with sparse autoencoder for dimensionality reduction and a Feed Forward Neural Network (FFNN) for the detection of COVID-19. Two publicly available chest X-ray image datasets, consisting of 504 COVID-19 images and 542 non-COVID-19 images, have been combined to train the model. The method was able to achieve an accuracy of 0.9578 and an AUC of 0.9821, using the combination of InceptionResnetV2 and Xception. Experiments have proved that the accuracy of the model improves with the usage of sparse autoencoder as the dimensionality reduction technique.

关键词： Computer-aided detection COVID-19 CNN sparse autoencoder Feed forward neural network

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State-of-Charge Prediction of Lithium-Ion Batteries Based on sparse autoencoder and Gated Recurrent Unit Neural Network

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ENERGY TECHNOLOGY 2023年第6期11卷 2201510-2201510页

作者： Zhang, Huahua Bai, Yun Yang, Shuai Li, Chuan Chongqing Technol & Business Univ Sch Management Sci & Engn Chongqing 400067 Peoples R China Chongqing Technol & Business Univ Res Ctr Syst Hlth Maintenance Chongqing 400067 Peoples R China

Lithium-ion batteries are widely used in daily life because of their fast charging and high energy density. The accurate prediction of state-of-charge (SOC), critical for the quality evaluation and long-term planning of lithium-ion batteries, has still been challenging owing to sophisticated battery dynamics and ever-changing ambient conditions. Herein, a joint sparse autoencoder (SAE) and gated recurrent unit (GRU) model are developed to improve SOC prediction performance. The sliding time window method is first utilized for data reconstruction to explore the continuous time trend and discrete state characteristics in multi-cycle data of the lithium-ion batteries. The SAE is then used to extract features from the reconstruction data. The GRU is finally applied to realize an accurate SOC prediction. The performance of proposed method is tested on a benchmark lithium-ion battery degradation dataset and actual battery degradation experiments. Experimental tests show that the present model has the lowest root mean square error of 0.176 and 0.271, the lowest mean absolute error of 0.1396 and 0.2412, and the lowest mean square error of 0.03098 and 0.07355 on two datasets, respectively, demonstrating that the proposed method outperforms the state-of-the-art methods in terms of accuracy and robustness.

关键词： gated recurrent unit lithium-ion batteries SOC prediction sparse autoencoder

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Sequential sparse autoencoder for dynamic heading representation in ventral intraparietal area

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COMPUTERS IN BIOLOGY AND MEDICINE 2023年第1期163卷 107114-107114页

作者： Gao, Wei Shen, Jiangrong Lin, Yipeng Wang, Kejun Lin, Zheng Tang, Huajin Chen, Xiaodong Zhejiang Univ Affiliated Hosp 2 Interdisciplinary Inst Neurosci & Technol Dept Neurol & PsychiatColl Biomed Engn & Instrume 268 Kaixuan Rd Hangzhou 310029 Peoples R China Zhejiang Univ Coll Comp Sci & Technol 38 Zheda Rd Hangzhou 310027 Peoples R China Zhejiang Univ Sch Software Technol 38 Zheda Rd Hangzhou 310027 Peoples R China Zhejiang Univ Affiliated Hosp 2 Sch Med Dept Psychiat 88 Jiefang Rd Hangzhou 310009 Peoples R China

To navigate in space, it is important to predict headings in real-time from neural responses in the brain to vestibular and visual signals, and the ventral intraparietal area (VIP) is one of the critical brain areas. However, it remains unexplored in the population level how the heading perception is represented in VIP. And there are no commonly used methods suitable for decoding the headings from the population responses in VIP, given the large spatiotemporal dynamics and heterogeneity in the neural responses. Here, responses were recorded from 210 VIP neurons in three rhesus monkeys when they were performing a heading perception task. And by specifically and separately modelling the both dynamics with sparse representation, we built a sequential sparse autoencoder (SSAE) to do the population decoding on the recorded dataset and tried to maximize the decoding performance. The SSAE relies on a three-layer sparse autoencoder to extract temporal and spatial heading features in the dataset via unsupervised learning, and a softmax classifier to decode the headings. Compared with other pop-ulation decoding methods, the SSAE achieves a leading accuracy of 96.8% & PLUSMN;2.1%, and shows the advantages of robustness, low storage and computing burden for real-time prediction. Therefore, our SSAE model performs well in learning neurobiologically plausible features comprising dynamic navigational information.

关键词： Heading Neural decoding Neural dynamics sparse autoencoder Ventral intraparietal area Vestibular

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