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FA-SconvAE-LSTM: Feature-Aligned stacked convolutional autoencoder with Long Short-Term Memory Network for Soft Sensor Modeling

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 150卷

作者： Wu, Ping Miao, Zengdi Wang, Ke Gao, Jinfeng Zhang, Xujie Lou, Siwei Yang, Chunjie Zhejiang Sci Tech Univ Sch Informat Sci & Engn 5 Second Ave Hangzhou 310018 Peoples R China Zhejiang Univ Coll Control Sci & Engn Hangzhou Peoples R China

The advancement of soft sensor technology has enabled the real-time estimation of critical parameters in complex industrial processes, where direct measurement through hardware sensors is often infeasible. Industrial process data typically exhibit both spatial correlations and temporal dependencies, necessitating sophisticated modeling approaches to capture these characteristics effectively. In this study, a spatio-temporal model, termed the feature-aligned stacked convolutional autoencoder with long short-term memory, is proposed to develop soft sensors for nonlinear dynamic industrial processes. The proposed model begins with the systematic training of a stacked convolutional autoencoder using a layer-by-layer pre-training technique. This approach facilitates the extraction of high-level spatial feature representations from the process variables. To address the issue of feature misalignment in the spatial features extracted by the stacked convolutional autoencoder, a feature alignment strategy is implemented, ensuring that the extracted spatial features are properly aligned. Subsequently, the aligned spatial features are fed into a long short-term memory network to capture temporal dependencies, with quality variables serving as the output for soft sensor development. The effectiveness and superiority of the proposed method are demonstrated through experiments conducted on two industrial processes: the sulfur recovery unit and the multiphase flow process. Comparative analyses with other state-ofthe-art methods reveal that the proposed model achieves the highest performance, with R2 values of 0.86222 for the sulfur recovery unit and 0.94307 for the multiphase flow process, outperforming all compared methods.

关键词： Spatiotemporal feature extraction Feature alignment stacked convolutional autoencoder Soft sensor

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SCAE-stacked convolutional autoencoder for Fault Diagnosis of a Hydraulic Piston Pump with Limited Data Samples

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SENSORS 2024年第14期24卷 4661页

作者： Eraliev, Oybek Lee, Kwang-Hee Lee, Chul-Hee Inha Univ Dept Future Vehicle Engn 100 Inharo Incheon 22212 South Korea Inha Univ Dept Mech Engn 100 Inharo Incheon 22212 South Korea

Deep learning (DL) models require enormous amounts of data to produce reliable diagnosis results. The superiority of DL models over traditional machine learning (ML) methods in terms of feature extraction, feature dimension reduction, and diagnosis performance has been shown in various studies of fault diagnosis systems. However, data acquisition can sometimes be compromised by sensor issues, resulting in limited data samples. In this study, we propose a novel DL model based on a stacked convolutional autoencoder (SCAE) to address the challenge of limited data. The innovation of the SCAE model lies in its ability to enhance gradient information flow and extract richer hierarchical features, leading to superior diagnostic performance even with limited and noisy data samples. This article describes the development of a fault diagnosis method for a hydraulic piston pump using time-frequency visual pattern recognition. The proposed SCAE model has been evaluated on limited data samples of a hydraulic piston pump. The findings of the experiment demonstrate that the suggested approach can achieve excellent diagnostic performance with over 99.5% accuracy. Additionally, the SCAE model has outperformed traditional DL models such as deep neural networks (DNN), standard stacked sparse autoencoders (SSAE), and convolutional neural networks (CNN) in terms of diagnosis performance. Furthermore, the proposed model demonstrates robust performance under noisy data conditions, further highlighting its effectiveness and reliability.

关键词： fault diagnosis hydraulic piston pump stacked convolutional autoencoder classification limited data samples

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stacked convolutional autoencoder for Detecting Animal Images in Cluttered Scenes with a Novel Feature Extraction Framework 8th

Stacked Convolutional Autoencoder for Detecting Animal Image...

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8th International Conference on Soft Computing for Problem Solving (SocProS)

作者： Meena, S. Divya Agilandeeswari, L. VIT Univ Sch Informat Technol & Engn Vellore Tamil Nadu India

ISBN: (纸本)9789811501845;9789811501838

Detection of animals from a cluttered scene is not a trivial task. So far, convolutional neural network (CNN) architectures have served this purpose. We introduce stacked convolutional autoencoders (SCAE) for this purpose. It is an unsupervised stratified feature extractor that could be used for high-dimensional input images. We also introduce a hybrid feature extraction technique based on Fisher Vectors (FV) and stacked autoencoders (SAE). SCAE learns significant features utilizing plain stochastic gradient descent and finds a good initialization for CNNs so as to eliminate the various unique local minima of exceptionally non-convex target functions emerging in virtually all deep learning problems. We have proposed a parallel pipeline for both detecting animals in both visible and infrared images. The framework model has achieved 97% accuracy.

关键词： convolutional neural network stacked convolutional autoencoder Animal detection Fisher Vector Infrared images Fuzzy

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Face Recognition Based on stacked convolutional autoencoder and Sparse Representation 23

Face Recognition Based on Stacked Convolutional Autoencoder ...

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23rd IEEE International Conference on Digital Signal Processing (DSP)

作者： Chang, Liping Yang, Jianjun Li, Sheng Xu, Hong Liu, Kai Huang, Chaogeng Zhejiang Univ Technol Coll Informat Engn Hangzhou 310023 Zhejiang Peoples R China Zhejiang Univ Finance & Econ Sch Informat Hangzhou 310018 Zhejiang Peoples R China

ISBN: (纸本)9781538668122;9781538668115

Face recognition is one of the most challenging topics in the field of machine vision and pattern recognition, and has a wide range of applications. The face features play an important role in the classification, while the features extracted by traditional methods are simple and elementary. To solve this problem, a stacked convolutional autoencoder (SCAE) based on deep learning theory is used to extract deeper features. The output of the encoder can be taken to design a feature dictionary. Meanwhile sparse representation is a general classification algorithm which has shown the good performance in the field of object recognition. In this paper a framework based on stacked convolutional autoencoder and sparse representation is proposed. Experiments, carried out with the LFW face database, have shown that the proposed framework can extract more deep and abstract features by multi-level cascade, and has high recognition speed and high accuracy.

关键词： Face recognition feature extraction stacked convolutional autoencoder sparse representation

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Improving diversity and quality of adversarial examples in adversarial transformation network

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SOFT COMPUTING 2023年第7期27卷 3689-3706页

作者： Nguyen, Duc-Anh Minh, Kha Do Le, Khoi Nguyen Nguyen, Le-Minh Hung, Pham Ngoc VNU Univ Engn & Technol VNU UET Bldg E3144 Xuan Thuy Rd Hanoi Vietnam Japan Adv Inst Sci & Technol JAIST Sch Informat Sci ASAHIDAI 1 1 Nomi 9231211 Japan

This paper proposes PatternAttack to mitigate two major issues of Adversarial Transformation Network (ATN) including the low diversity and the low quality of adversarial examples. In order to deal with the first issue, this research proposes a stacked convolutional autoencoder based on patterns to generalize ATN. This proposed autoencoder could support different patterns such as all-pixel pattern, object boundary pattern, and class model map pattern. In order to deal with the second issue, this paper presents an algorithm to improve the quality of adversarial examples in terms of L-0-norm and L-2-norm. This algorithm employs adversarial pixel ranking heuristics such as JSMA and COI to prioritize adversarial pixels. To demonstrate the advantages of the proposed method, comprehensive experiments have been conducted on the MNIST dataset and the CIFAR-10 dataset. For the first issue, the proposed autoencoder generates diverse adversarial examples. For the second issue, the proposed algorithm significantly improves the quality of adversarial examples. In terms of L-0-norm, the proposed algorithm decreases from hundreds of adversarial pixels to one adversarial pixel. In terms of L-2-norm, the proposed algorithm reduces the average distance considerably. These results show that the proposed method can generate high-quality and diverse adversarial examples in practice.

关键词： Adversarial example Robustness stacked convolutional autoencoder Targeted attack

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MULTI-SCALE FUSION MAXIMUM ENTROPY SUBSPACE CLUSTERING FOR HYPERSPECTRAL BAND SELECTION

MULTI-SCALE FUSION MAXIMUM ENTROPY SUBSPACE CLUSTERING FOR H...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Ma, Haipeng Wang, Yulei Jiang, Liru Song, Meiping Yu, Chunyan Zhao, Enyu Dalian Maritime Univ Informat Sci & Technol Coll Ctr Hyperspectral Imaging Remote Sensing CHIRS Dalian 116026 Peoples R China Air Force Commun Officer Sch Dept Ground Air Nav Dalian Peoples R China

ISBN: (数字)9781665427920

ISBN: (纸本)9781665427920

A novel multi-scale fusion maximum entropy subspace clustering (MFMESC) for hyperspectral image (HSI) band selection is proposed in this paper. Subspace clustering is combined as a self-expression layer with stacked convolutional autoencoder, so that subspace clustering working in linear subspaces can deal with complicated HSI data with nonlinear characteristics. Multiple fully-connected linear layers are inserted between the encoder layers and their corresponding decoder layers to promote learning more favorable representations for subspace clustering. A multi-scale fusion module is designed to guide the fusion of multi-scale information extracted from different layers to learn a more discriminative self-expression coefficient matrix. Furthermore, the maximum entropy regularization is introduced in the subspace clustering to promote the connectivity within each subspace. Experimental results demonstrate the superiority of the proposed model against state of-the-art methods.

关键词： hyperspectral band selection maximum entropy regularization subspace clustering multi-scale fusion stacked convolutional autoencoder

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Development of transparent high-frequency soft sensor of total nitrogen and total phosphorus concentrations in rivers using stacked convolutional auto-encoder and explainable AI

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JOURNAL OF WATER PROCESS ENGINEERING 2023年 53卷

作者： Ba-Alawi, Abdulrahman H. Heo, SungKu Aamer, Hanaa Chang, Roberto Woo, TaeYong Kim, MinHan Yoo, ChangKyoo Kyung Hee Univ Dept Environm Sci & Engn Integrated Engn Yongin 17104 Gyeonggi Do South Korea

Monitoring total nitrogen (TN) and total phosphorus (TP) concentrations in rivers is essential to assess the water quality and control the eutrophication risk. However, the online measurements of TN and TP are difficult due to the high costs, uncertainties, and the time lag associated with real-time monitoring. This study aims to develop a new transparent high-frequency soft sensing model for the online estimation of TN and TP concentrations, based on explainable artificial intelligence (XAI) and convolutional autoencoder (CAE) integrated with deep fully connected layers (DFC) and robust wrapper feature selection. The explainable stacked CAE-DFC-based soft sensor model can effectively capture complex nonlinear relationships among water quality parameters and provide superior prediction and interpretability using the explainable SHapley Additive exPlanations (SHAP). The explainable CAE-DFC model-based soft sensor showed a superior online prediction of TN and TP concentrations with R2 = 0.9607 and 0.9137, respectively. SHAP analysis provided a clear explanation of the proposed CAE-DFC model outcomes in the prediction of TN and TP, enhancing the reliability and transparency of the model. The developed transparent soft sensor can offer a trustworthy tool for the decision-makers to monitor TN and TP in waterbodies, avoiding the expensive acquisition cost and controlling the eutrophication risk.

关键词： Transparent soft sensor Total nitrogen Total phosphorus Water quality monitoring stacked convolutional autoencoder Explainable artificial intelligence

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Improved Prediction on Heart Transplant Rejection Using convolutional autoencoder and Multiple Instance Learning on Whole-Slide Imaging

Improved Prediction on Heart Transplant Rejection Using Conv...

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IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)

作者： Zhu, Yuanda Tong, Li Deshpande, Shriprasad R. Wang, May D. Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA Georgia Inst Technol Dept Biomed Engn Atlanta GA 30332 USA Emory Univ Atlanta GA USA Childrens Natl Hlth Syst Pediat Cardiol Washington DC USA

ISBN: (纸本)9781728108483

Heart transplant rejection is one major threat for the survival of patients with a heart transplant. Endomyocardial biopsies are effective in showing signs of heart transplant rejection even before patients have any symptoms. Manually examining the tissue samples is costly, time-consuming and error-prone. With recent advances in deep learning (DL) based image processing methods, automatic training and prediction on heart transplant rejection using whole-slide images expect to be promising. This paper develops an advanced pipeline for quality control, feature extraction, clustering and classification. We first implement a stacked convolutional autoencoder to extract feature maps for each tile;we then incorporate multiple instance learning (MIL) with dimensionality reduction and unsupervised clustering prior to classification. Our results show that utilizing unsupervised clustering after feature extraction can achieve higher classification results while preserving the capability for multi-class classification.

关键词： heart transplant rejection pathological whole-slide imaging stacked convolutional autoencoder multiple instance learning weakly-supervised learning

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RESIDUAL UNET FOR URBAN BUILDING CHANGE DETECTION WITH SENTINEL-1 SAR DATA 39

RESIDUAL UNET FOR URBAN BUILDING CHANGE DETECTION WITH SENTI...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Li, Lu Wang, Chao Zhang, Hong Zhang, Bo Chinese Acad Sci Key Lab Digital Earth Sci Inst Remote Sensing & Digital Earth Beijing 100094 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (纸本)9781538691540

Urban building change detection is one of the most important parts of remote sensing applications. Researching change detection method based on deep learning is an effective solution to monitor the urban expansion and recognize the specific change classes. In this paper, we propose a novel urban building change detection method based on the revised residual Unet with Sentinel-1 SAR intensity images. Firstly, we present a new difference image by combing both the original intensity image and the enhanced log-ratio difference image using a non-linear function. Then, the combined difference image is sent to a revised residual Unet network to detect the building changes. By the proposed combined difference image and the revised network, our method is able to focus on the building's change while ignoring other land type changes in a large area. A pair of real bitemporal SAR images is used to test the proposed approach and the obtained experimental results confirm its effectiveness.

关键词： urban building change detection residual Unet feature combination stacked convolutional autoencoder

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