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Decentralized adaptively weighted stacked autoencoder-based incipient fault detection for nonlinear industrial processes

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ISA TRANSACTIONS 2023年第1期139卷 216-228页

作者： Gao, Huihui Huang, Wenjie Gao, Xuejin Han, Honggui Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Univ Technol Key Lab Computat Intelligence & Intelligent Syst Beijing 100124 Peoples R China Minist Educ Engn Res Ctr Digital Community Beijing 100124 Peoples R China Beijing Artificial Intelligence Inst Beijing Peoples R China

Modern industrial processes often exhibit large-scale and nonlinear characteristics. Incipient fault detection for industrial processes is a big challenge because of the faint fault signature. To improve the performance of incipient fault detection for large-scale nonlinear industrial processes, a decentralized adaptively weighted stacked autoencoder (DAWSAE) -based fault detection method is proposed. First, the industrial process is divided into several sub-blocks and local adaptively weighted stacked autoencoder (AWSAE) is established for each sub-block to mine local information and obtain local adaptively weighted feature vectors and residual vectors. Second, the global AWSAE is established for the whole process to mine global information and obtain global adaptively weighted feature vectors and residual vectors. Finally, local statistics and global statistics are constructed based on local and global adaptively weighted feature vectors and residual vectors to detect the sub-blocks and the whole process, respectively. The advantages of proposed method are verified by a numerical example and Tennessee Eastman process (TEP).

关键词： Incipient fault Industrial processes stacked autoencoder Adaptively weighting Local fault detection Global fault detection

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Hourly PM2.5 concentration forecast using stacked autoencoder model with emphasis on seasonality

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JOURNAL OF CLEANER PRODUCTION 2019年 224卷 739-750页

作者： Bai, Yun Li, Yong Zeng, Bo Li, Chuan Zhang, Jin Chongqing Technol & Business Univ Natl Res Base Intelligent Mfg Serv Chongqing 400067 Peoples R China Lanzhou Univ Coll Earth Environm Sci Lanzhou 730000 Peoples R China Jinan Univ Inst Groundwater & Earth Sci Guangzhou 510632 Guangdong Peoples R China

Accurate PM2.5 forecasting provides a possibility for establishing an early warning system to notify the public and take precautionary measures to prevent negative effects on ambient air quality and public health. Considering strong seasonal variation in meteorological conditions, in this paper, a seasonal stacked autoencoder model combining seasonal analysis and deep feature learning is proposed for forecasting the hourly PM2.5 concentration, named DL-SSAE model. The original data are firstly decomposed into four seasonal subseries according to the Chinese calendar, and then the Kendall correlation coefficient method is employed to search inherent relationships between PM2.5 concentrations and meteorological parameters within 1-h ahead for each seasonal time series. The inherent relationships of each seasonal subseries are finally extracted, learned, and modeled by different deep neural networks (stacked autoencoders for regression), and the hourly PM2.5 forecasts are yielded. The addressed model is tested by the dataset collected from three environmental monitoring stations in Beijing, China. The results demonstrate that the proposed model outperforms all other considered models with/without seasonality consideration in this paper. (C) 2019 Published by Elsevier Ltd.

关键词： stacked autoencoder PM2.5 Meteorological data Seasonality Forecasting

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User Behavior Analysis Based on stacked autoencoder and Clustering in Complex Power Grid Environment

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第12期23卷 25521-25535页

作者： Deng, Song Cai, Qingyuan Zhang, Zi Wu, Xindong Nanjing Univ Posts & Telecommun Inst Adv Technol Nanjing 210003 Peoples R China Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210003 Peoples R China Guilin Univ Elect Technol Sch Comp Sci & Informat Secur Guilin 541004 Peoples R China Hefei Univ Technol Key Lab Knowledge Engn Big Data Minist Educ Hefei 230009 Peoples R China Mininglamp Technol Mininglamp Acad Sci Shanghai 200232 Peoples R China

Analyzing user behavior characteristics in a complex power grid environment is essential for user behavior planning and resource coordination optimization. Traditional user behavior analysis methods based on model-driven and causal analysis have the disadvantages of strong subjectivity and physical models that are difficult to deal with the randomness and uncertainty of user behavior in complex grid environments. In this paper, we use unsupervised learning methods to analyze user behavior in complex power grid environments, and propose user behavior analysis methods based on stacked autoencoder and clustering. We first reduce the complexity of user behavior data by proposing adaptive feature selection algorithm of user behavior based on stacked autoencoder and unsupervised learning (AFS-SAEUL). Finally, we build a user behavior analysis model based on adaptive feature selection and improved clustering (UBA-AFSIC). The model improved the performance of unsupervised classification of user behavior by fusing the adaptive generation strategy of the initial cluster centers. The simulation experiment results on two real electricity datasets and one public electric vehicle charging dataset show that compared with the existing feature selection algorithm and clustering algorithm, the algorithms proposed in this paper have higher feature selection rate and better clustering performance.

关键词： Feature extraction Clustering algorithms Analytical models Power demand Correlation Power grids Load modeling Feature selection stacked autoencoder unsupervised learning power consumption behavior analysis electric vehicle charging behavior analysis

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Classification and diagnosis of cervical cancer with softmax classification with stacked autoencoder

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EXPERT SYSTEMS WITH APPLICATIONS 2019年 115卷 557-564页

作者： Adem, Kemal Kilicarslan, Serhat Comert, Onur Gaziosmanpasa Univ Dept Informat TR-60250 Tokat Turkey Gaziosmanpasa Univ Tech Sci Vocat Sch TR-60250 Tokat Turkey

Cervical cancer is one of the most common cancer types in the world, which causes many people to lose their lives. Cancer research is of importance since early diagnosis of cancer facilitates clinical applications for patients. The aim of the study is the automatic diagnosis of cervical cancer. For this purpose, a data set containing 668 samples, 30 attributes and 4 target variables (Schiller, Citology, Biopsy and Hinselmann) from the UCI database was used in the training and testing phases. Softmax classification with stacked autoencoder, one of the deep learning methods, has been used to classify data sets. At first, by applying stacked autoencoder to the raw data set, a reduced dimension dataset is obtained. This data set has been trained for classification by applying the softmax layer. In this phase, 70% (468) of the data set was allocated for training and the remaining 30% (200) for testing. In order to compare the classification performance of the softmax classification with stacked autoencoder, decision tree, kNN, SVM, Feed Forward NN, Rotation Forest models, which are machine learning methods, are used. In the study, proposed models were applied separately to 4 target variables of the dataset, and their classification successes were compared. Finally, softmax classification with stacked autoencoder model, which was applied for the first time in the cervical cancer dataset, performed better than the other machine learning methods with a correct classification rate of 97.8%. Given the interest in machine learning methods in cancer research, new methods of diagnosis are presented in this study in terms of patient diagnostic support systems. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Cervical cancer Machine learning Deep learning stacked autoencoder

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Multi-layer features ensemble soft sensor regression model based on stacked autoencoder and vine copula

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CANADIAN JOURNAL OF CHEMICAL ENGINEERING 2023年第8期101卷 4606-4619页

作者： Chen, Hongmin Li, Shaojun East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai Peoples R China East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China

It is crucial in industrial processes to consider key variables to ensure safe operation and high product quality. Moreover, these variables are difficult to obtain using traditional measurement methods;hence, it makes sense to develop soft sensor regression models to process the variable prediction. However, there are numerous variables integrating noisy and redundant information in complex industrial processes. Using such variables in traditional regression models may result in reducing the model's efficiency and performance. Thus, this paper proposes a multi-layer feature ensemble soft sensor regression method using a stacked auto-encoder (SAE) and vine copula (ESAE-VCR) to address these problems. To do so, the number of neurons in the hidden layer of the SAE is determined by the principal component analysis (PCA). The multi-layer features of the process variables are extracted using a stacked AE, and the regression models are established for each feature layer. A linear regression ensemble method is used to combine the regression models with the multi-layer features to obtain the final predictive model that will estimate the values of the key variables. The effectiveness and practicality of the ESAE-VCR are validated by comparing them with several common soft measurement methods in two examples. In the numerical example, the ESAE-VCR yields an accuracy of prediction (R-2) of 0.9898 and a root mean square error (RMSE) of 0.1804. In the industrial example, the ESAE-VCR yields an R-2 of 0.9908 and an RMSE of 0.1205.

关键词： feature extraction principal component analysis sensors measurement stacked autoencoder vine copula

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Two-stage multi-dimensional convolutional stacked autoencoder network model for hyperspectral images classification

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第8期83卷 23489-23508页

作者： Bai, Yang Sun, Xiyan Ji, Yuanfa Fu, Wentao Zhang, Jinli Guilin Univ Elect Technol Sch Informat & Commun Guilin Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Precis Nav Technol & Applicat Guilin Peoples R China Guilin Univ Elect Technol Natl & Local Joint Engn Res Ctr Satellite Nav & Locat Serv Guilin Peoples R China

Deep learning models have been widely used in hyperspectral images classification. However, the classification results are not satisfactory when the number of training samples is small. Focused on above-mentioned problem, a novel Two-stage Multi-dimensional Convolutional stacked autoencoder (TMC-SAE) model is proposed for hyperspectral images classification. The proposed model is composed of two sub-models SAE-1 and SAE-2. The SAE-1 is a 1D autoencoder with asymmetric structre based on full connection layers and 1D convolution layers to reduce spectral dimensionality. The SAE-2 is a hybrid autoencoder composed of 2D and 3D convolution operations to extract spectral-spatial features from the reduced dimensionality data by SAE-1. The SAE-1 is trained with raw data by unsupervised learning and the encoder of SAE-1 is employed to reduce spectral dimensionality of raw data. The data after dimension reduction is used to train the SAE-2 by unsupervised learning. The fine-tuning of SAE-2 encoder and the training of classifier are implemented simultaneously with small number of samples by supervised learning. Comparative experiments are performed on three widely used hyperspectral remote sensing data. The extensive comparative experiments demonstrate that the proposed architecture can effectively extract deep features and maintain high classification accuracy with small number of training samples.

关键词： Hyperspectral image classification Deep learning stacked autoencoder Multi-dimensional convolutional neural networks

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Learning view-invariant features using stacked autoencoder for skeleton-based gait recognition

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IET COMPUTER VISION 2021年第7期15卷 527-545页

作者： Hasan, Md Mahedi Mustafa, Hossen Asiful Bangladesh Univ Engn & Technol BUET Inst Informat & Commun Technol Dhaka Bangladesh

Human gait recognition in a multicamera environment is a challenging task in biometrics because of the presence of the large pose and variations in illumination among different views. In this work, to address the problem of variations in view, we present a novel stacked autoencoder for learning discriminant view-invariant gait representations. Our autoencoder can efficiently and progressively translate skeleton joint coordinates from any arbitrary view to a common canonical view without requiring the prior estimation of the view angle or covariate type and without losing temporal information. Then, we construct a discriminative gait feature vector by fusing the encoded features with two other spatiotemporal gait features to feed into the main recurrent neural network. Experimental evaluations of the challenging CASIA A and CASIA B gait datasets demonstrate that the proposed approach outperformed other state-of-the-art methods on single-view gait recognition. In particular, the proposed method achieved 46.31% and 33.86% average correct class recognition on probe set ProbeBG and ProbeCL, respectively, of the CASIA B dataset while considering the view variation;this is 0.3% and 30.68% higher than previous best-performing methods. Furthermore, in cross-view recognition, our method shows better results over other state-of-the-art methods when the view-angle variation is large than 36 degrees.

关键词： multicamera environment discriminant view-invariant gait representations skeleton-based gait recognition learning (artificial intelligence) image motion analysis gait analysis recurrent neural nets human gait recognition feature extraction CASIA A image representation CASIA B gait datasets stacked autoencoder learning view-invariant features encoded features biometrics (access control) recurrent neural network image recognition spatiotemporal phenomena

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Regularized label relaxation-based stacked autoencoder for zero-shot learning

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APPLIED INTELLIGENCE 2023年第19期53卷 22348-22362页

作者： Song, Jianqiang Zhao, Heng Wei, Xing Zhang, Xiutai Yao, Haiyan Anyang Inst Technol Coll Elect Informat & Elect Engn Anyang 455000 Peoples R China Xidian Univ Sch Life Sci & Technol Xian 710071 Peoples R China Yangtze Univ Elect & Informat Sch Jingzhou 434023 Peoples R China

Recently, Zero-Shot Learning (ZSL) has gained great attention due to its significant classification performance for novel unobserved classes. As seen and unseen classes are completely disjoint, the current ZSL methods inevitably suffer from the domain shift problem when transferring the knowledge between the observed and unseen classes. Additionally, most ZSL methods especially those targeting the semantic space may cause the hubness problem due to their use of nearest-neighbor classifiers in high-dimensional space. To tackle these issues, we propose a novel pathway termed Regularized Label Relaxation-based stacked autoencoder (RLRSA) to diminish the domain difference between seen and unseen classes by exploiting an effective label space, which has some notable advantages. First, the proposed method establishes the tight relations among the visual representation, semantic information and label space using via the stacked autoencoder, which is beneficial for avoiding the projection domain shift. Second, by incorporating a slack variable matrix into the label space, our RLRSA method has more freedom to fit the test samples whether they come from the observed or unseen classes, resulting in a very robust and discriminative projection. Third, we construct a manifold regularization based on a class compactness graph to further reduce the domain gap between the seen and unseen classes. Finally, the learned projection is utilized to predict the class label of the target sample, thus the hubness issue can be prevented. Extensive experiments conducted on benchmark datasets clearly show that our RLRSA method produces new state-of-the-art results under two standard ZSL settings. For example, the RLRSA obtains the highest average accuracy of 67.82% on five benchmark datasets under the pure ZSL setting. For the generalized ZSL task, the proposed RLRSA is still highly effective, e.g., it achieves the best H result of 58.9% on the AwA2 dataset.

关键词： Label relaxation stacked autoencoder Zero-shot learning

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Mechanistic block-based attention mechanism stacked autoencoder for describing typical unit connection industrial processes and their monitoring

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CANADIAN JOURNAL OF CHEMICAL ENGINEERING 2024年第1期102卷 291-306页

作者： Wang, Chenhao Tang, Xujia Yu, Jianbo Yang, Xiaofeng Yan, Xuefeng East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai Peoples R China Fudan Univ Sch Microelect Shanghai Peoples R China East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ POB 293MeiLong Rd 130 Shanghai 200237 Peoples R China

The overall information of a process can be obtained through global modelling, and the local information is easily ignored in the research of the industrial process monitoring of unit connection. Thus, finding the global type of faults is easy, but occurs at the expense of drowning out the local faults. The use of block modelling can highlight local information, thereby improving local fault detection capability. However, the connection information between blocks is usually ignored in block modelling, which makes finding fault classes that only affect the connection relationship between blocks difficult. A mechanistic block-based attention mechanism stacked autoencoder (MB-AMSAE) monitoring method is proposed in this paper. The industrial process is divided into several parts in accordance with its mechanistic relationships, and each part represents an independent block. Self-attention is used to focus on the information of each block itself. Cross-attention is adopted to focus on the information between blocks, and this information is fused to form new blocks. The new block is used as the feature of the original block, and the original block is reconstructed by using a stacked autoencoder. The corresponding control limit is obtained in accordance with the reconstruction ability of normal samples, and whether the working conditions are normal is judged according to the control limit. The proposed algorithm is used in numerical simulation, Tennessee-Eastman processes, and is compared with other advanced algorithms based on its fault detection capability. Results show the effectiveness of the MB-AMSAE algorithm in process monitoring.

关键词： attention mechanism block process monitoring stacked autoencoder

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Hybrid Image Compression-Encryption Scheme Based on Multilayer stacked autoencoder and Logistic Map

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China Communications 2022年第1期19卷 238-252页

作者： Neetu Gupta Ritu Vijay Computer Science&Engineering Department Banasthali VidyapithBanasthaliIndia Department of Electronics Engineering Banasthali VidyapithBanasthaliIndia.

Secure transmission of images over a communication channel, with limited data transfer capacity, possesses compression and encryption schemes. A deep learning based hybrid image compression-encryption scheme is proposed by combining stacked auto-encoder with the logistic map. The proposed structure of stacked autoencoder has seven multiple layers, and back propagation algorithm is intended to extend vector portrayal of information into lower vector space. The randomly generated key is used to set initial conditions and control parameters of logistic map. Subsequently, compressed image is encrypted by substituting and scrambling of pixel sequences using key stream sequences generated from logistic *** proposed algorithms are experimentally tested over five standard grayscale images. Compression and encryption efficiency of proposed algorithms are evaluated and analyzed based on peak signal to noise ratio(PSNR), mean square error(MSE), structural similarity index metrics(SSIM) and statistical,differential, entropy analysis respectively. Simulation results show that proposed algorithms provide high quality reconstructed images with excellent levels of security during transmission..

关键词： compression-encryption stacked autoencoder chaotic system back propagation algorithm logistic map

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