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GRU-based stacked sparse autoencoder with attention mechanism for process monitoring

CANADIAN JOURNAL OF CHEMICAL ENGINEERING 2025年第0期

作者： Miao, Zengdi Wu, Ping Wu, Zhenquan Jia, Xiangjun Xu, Hui Jiang, Jian Zhejiang Sci Tech Univ Sch Informat Sci & Engn 5 Second Ave Hangzhou 310018 Peoples R China Zhejiang Sanhua Automot Components Co Ltd Hangzhou Peoples R China Hangzhou Dahe Thermo Magnet Co Ltd 777 Binkang Rd Hangzhou 310053 Peoples R China Bluetron Digital Technol Co LTD Hangzhou Peoples R China Zhejiang Deli Machine Manufacturer Co Ltd Lishui Peoples R China

To ensure the operation safety of industrial processes, process monitoring techniques have been receiving considerably increasing attention from both academia and industry. The advent of deep learning has revolutionized data-driven process monitoring by effectively dealing with the inherent nonlinearity of industrial processes. In this work, a gated recurrent unit-based stacked sparse autoencoder with attention mechanism (GRU-SSAE-AM) model is developed for process monitoring. This model leverages the power of recurrent neural networks in conjunction with the sparse stacked autoencoder to tackle the temporal and nonlinear features present in process data. To enhance the model's ability to distill critical information indicative of process status, an attention mechanism is naturally integrated within the encoder-decoder framework. Experiments on an industrial benchmark of Tennessee Eastman process (TEP) and a real blast furnace ironmaking process (BFIP) are carried out to verify the capability and effectiveness of the proposed GRU-SSAE-AM based process monitoring method by comparison with other related methods.

关键词： attention mechanism blast furnace ironmaking process GRU process monitoring stacked sparse autoencoder

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Early identification of out-of-roundness damage wheels in railway freight vehicles using a wayside system and a stacked sparse autoencoder

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VEHICLE SYSTEM DYNAMICS 2025年第2期63卷 232-257页

作者： Jorge, T. Magalhaes, J. Silva, R. Guedes, A. Ribeiro, D. Vale, C. Meixedo, A. Mosleh, A. Montenegro, P. Cury, A. Polytech Porto Sch Engn CONSTRUCT LESE Porto Portugal Univ Porto Fac Engn CONSTRUCT LESE Porto Portugal Univ Fed Juiz de Fora Grad Program Civil Engn Juiz De Fora Brazil

Early identification of wheel defects can prevent serious damage to railways, considerably lowering maintenance costs for both railway administrations and rolling stock operators. Within this context, an unsupervised methodology based on artificial intelligence techniques is presented, which allows the detection and classification of out-of-roundness damage wheels, such as wheel flats and polygonal wheels, based on dynamic responses induced on the track by crossing freight railway vehicles. The methodology involves the following steps: (i) data collection and pre-processing, (ii) feature extraction (iii) data fusion and (iv) feature discrimination. In the first phase, an FFT algorithm is applied to the acceleration track responses. Then, features are extracted after training a stacked sparse autoencoder, in which the main features of the responses are obtained after a compression stage using an encoder network. This lower dimensional layer forces the model to learn a compression of the input data. Then, these extracted features are merged using the Mahalanobis distance, which enhances the sensitivity to the damage recognition. Posteriorly, an outlier analysis is performed to distinguish a healthy wheel from a defective one and a cluster analysis to discriminate the two types of out-of-roundness (OOR) damage and classify the severity of each type of damage.

关键词： OOR wheel damage damage identification freight trains wayside condition monitoring stacked sparse autoencoder

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stacked sparse autoencoder in cavitation noise signal data classification of hydro turbine based on power spectrum

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JOURNAL OF LOW FREQUENCY NOISE VIBRATION AND ACTIVE CONTROL 2020年第2期39卷 233-245页

作者： Kang, Ziyang Feng, Chi Wan, Xiaoqing Liu, Zhiliang Chen, Liwei Harbin Engn Univ Coll Informat & Commun Engn Harbin Heilongjiang Peoples R China Harbin Inst Large Elect Machinery Natl Engn Res Ctr Hydropower Equipment Harbin Peoples R China

Cavitation is a phenomenon that occurs during the continuous operation of a hydro turbine that directly affects the efficiency and working capacity of the unit. This paper proposes an innovative classification paradigm that uses deep learning-based methodologies in order to identify both cavitation noise signal and non-cavitation noise signal that will help prevent the damage or breakage in the earliest possible time to avoid more irreversible and irreparable damage to the hydro turbine. The stacked sparse autoencoder (SSA) nuclear framework is utilized to learn more abstract and invariant high-level features from the multiple feature sets. Then, the method on minimum redundancy maximum relevance (mRMR) selection is used to evaluate and sort out all the characteristics found by the stacked sparse autoencoder. Finally, the random forest (RF) classifier algorithm is employed to perform supervised fine-tuning and classification. The traditional supervised learning models such as support vector machine, logistic regression, and sparse representation classification are chosen to be used as contrast algorithms. SSA-mRMR-RF generally produces a better performance than the support vector machine, logistic regression and sparse representation classification when used in the same set of features. The SSA-mRMR-RF produced the highest overall average accuracy since it reached 93.18%. The SSA-mRMR-RF offers a 3.85% higher overall accuracy than the support vector machine. It also offers a 2.44% higher overall accuracy than logistic regression. In addition, it also offers a 20.30% higher overall accuracy than the sparse representation classification on the average. When the signals are divided into three categories, the highest overall accuracy decreased to 71.88%, and the classification accuracy of incipient noise signals is very low. Therefore, this paper proposes the models of power spectral-SSA-mRMR-RF and fast Fourier transform-SSA-mRMR-RF to be used, and these discoveries ar

关键词： Cavitation noise signal hydrophone stacked sparse autoencoder minimum redundancy maximum relevance random forest power spectrum

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stacked sparse autoencoder and case-based postprocessing method for nucleus detection

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NEUROCOMPUTING 2019年 359卷 494-508页

作者： Li, Siqi Jiang, Huiyan Bai, Jie Liu, Ye Yao, Yu-dong Northeastern Univ Dept Software Coll Shenyang 110819 Liaoning Peoples R China Northeastern Univ Dept Sino Dutch Biomed & Informat Engn Sch Shenyang 110819 Liaoning Peoples R China Sun Yat Sen Univ Affiliated Hosp 5 Dept Pathol 52 Meihua Dong Rd Zhuhai 519000 Guangdong Peoples R China Stevens Inst Technol Dept Elect & Comp Engn Hoboken NJ 07030 USA

Accurate nucleus detection is of great importance in pathological image analyses and diagnoses, which is a critical prerequisite for tasks such as automated grading hepatocellular carcinoma (HCC) nuclei. This paper proposes an automated nucleus detection framework based on a stacked sparse autoencoder (SSAE) and a case-based postprocessing method (CPM) in a coarse-to-fine manner. SSAE, an unsupervised learning model, is first trained using image patches of breast cancer. Then, the transfer learning and sliding window techniques are applied to other cancers' pathological images (HCC and colon cancer) to extract the high-level features of image patches via the trained SSAE. Subsequently, these high-level features are fed to a logistic regression classifier (LRC) to classify whether each image patch contains a complete nucleus in a coarse detection process. Finally, CPM is developed for refining the coarse detection results which removes false positive nuclei and locates adhesive or overlapped nuclei effectively. SSAE-CPM achieves an average nucleus detection accuracy of 0.8748 on HCC pathological images, which can accurately locate almost all nuclei on the pathological images with serious differentiation. In addition, our proposed detection framework is also evaluated on a public dataset of colon cancer, with a mean F-1 score of 0.8355. Experimental results demonstrate the performance advantages of our proposed SSAE-CPM detection framework as compared with related work. While our detection framework is trained on the pathological images of breast cancer, it can be easily and effectively applied to nucleus detection tasks on other cancers without re-training. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Automated nucleus detection stacked sparse autoencoder Case-based postprocessing method Transfer learning Coarse-to-fine manner

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stacked sparse autoencoder based Automatic Detection of Ripples and Fast Ripples in Epilepsy 39

Stacked Sparse Autoencoder based Automatic Detection of Ripp...

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39th Chinese Control Conference (CCC)

作者： Qin, Hongzhen Wu, Min Wan, Xiongbo Du, Yuxiao China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China

ISBN: (纸本)9789881563903

High frequency oscillations (HFOs) have been acknowledged as a putative biomarker of epileptic seizure onset zones (SOZs). Accurate detection of HFOs is significant for the preoperative localization of epileptic SOZs. In this paper, a new method is proposed to automatically detect ripples (Rs) and fast ripples (FRs) from intracranial electroencephalography (iEEG) in epilepsy. A moving-window technique is utilized to segment the filtered signals which are obtained by filtering the raw iEEG signals using two Chebyshev band-pass filters. Two stacked sparse autoencoder (SSAE) models are proposed to automatically detect Rs and FRs, respectively. By optimizing the parameters of the two SSAE models, our method yields higher sensitivity (88.9+/-2.4% for Rs and 83.2+/-2.5% for FRs) and higher specificity (92.3+/-2.8% for Rs and 86.1+/-2.8% for ERs) than other three methods do.

关键词： Epilepsy Intracranial electroencephalography Ripples Fast ripples stacked sparse autoencoder

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Novel Pelican Optimization Algorithm (POA) With stacked sparse autoencoder (SSAE) Based IDS for Network Security

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Transactions on Emerging Telecommunications Technologies 2025年第5期36卷

作者： R. Kanimozhi A. Neela Madheswari Department of Computer Science and Engineering Muthayammal Engineering College Rasipuram Tamil Nadu India Faculty of Computer Science and Engineering Mahendra Engineering College Namakkal Tamil Nadu India

Security is a crucial factor for information systems and other vital infrastructures. Ensuring robust security measures is imperative due to the substantial volume of network traffic. On the other hand, many network components are susceptible to cyber threats and attacks due to their inherent properties. The increasing use of networks paves the way for widespread security vulnerabilities. In this context, the implementation of intrusion detection systems (IDS) plays a key role in safeguarding information systems and their network architectures. This research introduces an optimized deep learning model aimed at improving network security by accurately detecting intrusions. The proposed IDS, also termed as the POA-SSAE IDS model (pelican optimization model-stacked sparse autoencoder), integrates a POA for optimal feature selection and an SSAE for feature classification. The effectiveness of this IDS was tested using benchmark datasets, namely CICIDS2018 and KDDCUP'99. The results exhibited the proposed model's superior performance, achieving an accuracy of 97.45% on the CICIDS2018 dataset and 98.7% accuracy on the KDDCUP'99 dataset.

关键词： data privacy deep learning network security optimization pelican optimization algorithm stacked sparse autoencoder

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Fault Diagnosis for Power Batteries Based on a stacked sparse autoencoder and a Convolutional Block Attention Capsule Network

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PROCESSES 2024年第4期12卷 816页

作者： Zhou, Juan Zhang, Shun Wang, Peng China Jiliang Univ Coll Qual & Safety Engn Hangzhou 310018 Peoples R China China Automot Engn Res Inst Co Ltd Chongqing 401120 Peoples R China

The power battery constitutes the fundamental component of new energy vehicles. Rapid and accurate fault diagnosis of power batteries can effectively improve the safety and power performance of the vehicle. In response to the issues of limited generalization ability and suboptimal diagnostic accuracy observed in traditional power battery fault diagnosis models, this study proposes a fault diagnosis method utilizing a Convolutional Block Attention Capsule Network (CBAM-CapsNet) based on a stacked sparse autoencoder (SSAE). The reconstructed dataset is initially input into the SSAE model. Layer-by-layer greedy learning using unsupervised learning is employed, combining unsupervised learning methods with parameter updating and local fine-tuning to enhance visualization capabilities. The CBAM is then integrated into the CapsNet, which not only mitigates the effect of noise on the SSAE but also improves the model's ability to characterize power cell features, completing the fault diagnosis process. The experimental comparison results show that the proposed method can diagnose power battery failure modes with an accuracy of 96.86%, and various evaluation indexes are superior to CNN, CapsNet, CBAM-CapsNet, and other neural networks at accurately identifying fault types with higher diagnostic accuracy and robustness.

关键词： power battery stacked sparse autoencoder convolutional block attention capsule network fault diagnosis

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Combustion stability monitoring through flame imaging and stacked sparse autoencoder based deep neural network

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APPLIED ENERGY 2020年第0期259卷 114159-000页

作者： Han, Zhezhe Hossain, Md Moinul Wang, Yuwei Li, Jian Xu, Chuanlong Southeast Univ Sch Energy & Environm Key Lab Energy Thermal Convers & Control Minist Educ Nanjing 210096 Peoples R China Univ Kent Sch Engn & Digital Arts Canterbury CT2 7NT Kent England China Energy Jianbi Power Plant Zhenjiang 212006 Jiangsu Peoples R China

Combustion instability is a well-known problem in the combustion processes and closely linked to lower combustion efficiency and higher pollutant emissions. Therefore, it is important to monitor combustion stability for optimizing efficiency and maintaining furnace safety. However, it is difficult to establish a robust monitoring model with high precision through traditional data-driven methods, where prior knowledge of labeled data is required. This study proposes a novel approach for combustion stability monitoring through stacked sparse autoencoder based deep neural network. The proposed stacked sparse autoencoder is firstly utilized to extract flame representative features from the unlabeled images, and an improved loss function is used to enhance the training efficiency. The extracted features are then used to identify the classification label and stability index through clustering and statistical analysis. Classification and regression models incorporating the stacked sparse autoencoder are established for the qualitative and quantitative characterization of combustion stability. Experiments were carried out on a gas combustor to establish and evaluate the proposed models. It has been found that the classification model provides an F-1-score of 0.99, whilst the R-squared of 0.98 is achieved through the regression model. Results obtained from the experiments demonstrated that the stacked sparse autoencoder model is capable of extracting flame representative features automatically without having manual interference. The results also show that the proposed model provides a higher prediction accuracy in comparison to the traditional data-driven methods and also demonstrates as a promising tool for monitoring the combustion stability accurately.

关键词： Combustion stability Flame imaging stacked sparse autoencoder Innovative loss function Gaussian mixture model

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Deep Neural Network Hardware Implementation Based on stacked sparse autoencoder

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IEEE ACCESS 2019年 7卷 40674-40694页

作者： Coutinho, Maria G. F. Torquato, Matheus F. Fernandes, Marcelo A. C. Fed Univ Rio Grande do Norte UFRN Dept Comp Engn & Automat BR-59078970 Natal RN Brazil Swansea Univ Coll Engn Swansea SA2 8PP W Glam Wales

Deep learning techniques have been gaining prominence in the research world in the past years;however, the deep learning algorithms have high computational cost, making them hard to be used to several commercial applications. On the other hand, new alternatives have been studied and some methodologies focusing on accelerating complex algorithms including those based on reconfigurable hardware has been showing significant results. Therefore, the objective of this paper is to propose a neural network hardware implementation to be used in deep learning applications. The implementation was developed on a field-programmable gate array (FPGA) and supports deep neural network (DNN) trained with the stacked sparse autoencoder (SSAE) technique. In order to allow DNNs with several inputs and layers on the FPGA, the systolic array technique was used in the entire architecture. Details regarding the designed implementation were evidenced, as well as the hardware area occupation and the processing time for two different implementations. The results showed that both implementations achieved high throughput enabling deep learning techniques to be applied for problems with large data amounts.

关键词： Deep learning stacked sparse autoencoder FPGA systolic array

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Computer-aided classification of prostate cancer grade groups from MRI images using texture features and stacked sparse autoencoder

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COMPUTERIZED MEDICAL IMAGING AND GRAPHICS 2018年 69卷 60-68页

作者： Abraham, Bejoy Nair, Madhu S. Univ Kerala Dept Comp Sci Thiruvananthapuram 695581 Kerala India Cochin Univ Sci & Technol Dept Comp Sci Kochi 682022 Kerala India

A novel method to determine the Grade Group (GG) in prostate cancer (PCa) using multi-parametric magnetic resonance imaging (mpMRI) biomarkers is investigated in this paper. In this method, highlevel features are extracted from hand-crafted texture features using a deep network of stacked sparse autoencoders (SSAE) and classified them using a softmax classifier (SMC). Transaxial T2 Weighted (T2W), Apparent Diffusion Coefficient (ADC) and high B-Value Diffusion-Weighted (BVAL) images obtained from PROSTATEx-2 2017 challenge dataset are used in this technique. The method was evaluated on the challenge dataset composed of a training set of 112 lesions and a test set of 70 lesions. It achieved a quadratic-weighted Kappa score of 0.2772 on evaluation using test dataset of the challenge. It also reached a Positive Predictive Value (PPV) of 80% in predicting PCa with GC > 1. The method achieved first place in the challenge, winning over 43 methods submitted by 21 groups. A 3-fold cross-validation using training data of the challenge was further performed and the method achieved a quadratic-weighted kappa score of 0.2326 and Positive Predictive Value (PPV) of 80.26% in predicting PCa with GG > 1. Even though the training dataset is a highly imbalanced one, the method was able to achieve a fair kappa score. Being one of the pioneer methods which attempted to classify prostate cancer into 5 grade groups from MRI images, it could serve as a base method for further investigations and improvements. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Deep learning stacked sparse autoencoder Gleason grade Texture features Prostate cancer Multiparametric MRI

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