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Deep stacked autoencoder-Based Long-Term Spectrum Prediction Using Real-World Data

IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2023年第3期9卷 534-548页

作者： Pan, Guangliang Wu, Qihui Ding, Guoru Wang, Wei Li, Jie Xu, Fuyuan Zhou, Bo Nanjing Univ Aeronaut & Astronaut Key Lab Dynam Cognit Syst Electromagnet Spectrum S Minist Ind & Informat Technol Nanjing 211106 Peoples R China Army Engn Univ Coll Commun Engn Nanjing 210007 Peoples R China Nanjing Elect Equipment Res Inst Nanjing 210000 Peoples R China

Spectrum prediction is challenging due to its multi-dimension, complex inherent dependency, and heterogeneity among the spectrum data. In this paper, we first propose a stacked autoencoder (SAE) and bi-directional long short-term memory (Bi-LSTM) based spectrum prediction method (SAEL-SP). Specifically, a SAE is designed to extract the hidden features (semantic coding) of spectrum data in an unsupervised manner. Then, the output of SAE is connected to a predictor (Bi-LSTM), which is used for long-term prediction by learning hidden features. The main advantage of SAEL-SP is that the underlying features of spectrum data can be retained automatically, layer by layer, rather than designing them manually. To further improve the prediction accuracy of SAEL-SP and achieve a wider bandwidth prediction, we propose a SAE-based spectrum prediction method using temporal-spectral-spatial features of data (SAE-TSS). Different from SAEL-SP, the input of SAE-TSS is the image format. SAE-TSS achieves higher prediction accuracy than SAEL-SP using the features extracted from time, frequency, and space dimensions. We use a real-world spectrum dataset to validate the effectiveness of two prediction frameworks. Experiment results show that both SAEL-SP and SAE-TSS outperform existing spectrum prediction approaches.

关键词： Predictive models Feature extraction Hidden Markov models Time-frequency analysis Frequency measurement Autoregressive processes Data models Spectrum prediction temporal-spectral-spatial correlations deep learning stacked autoencoder

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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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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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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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A Noise Estimation Method for Hyperspectral Image Based on stacked autoencoder

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IEEE ACCESS 2023年 11卷 89835-89843页

作者： Deng, Lei Zhou, Bing Ying, Jiaju Zhao, Runze Army Engn Univ PLA Elect & Opt Engn Dept Shijiazhuang 050003 Peoples R China Army Engn Univ PLA Equipment Command & Management Dept Shijiazhuang 050003 Peoples R China

The imaging spectrometer is limited by a short response time and narrow channel, resulting in a low signal-to-noise ratio of hyperspectral images. The accurate estimation of noise has a significant impact on some preprocessing and downstream tasks. The existing noise estimation methods for hyperspectral images are all focused on satellite and aviation data, and there is little research on hyperspectral images with high spatial resolution. For this type of image, This article proposes a noise estimation method based on a stacked autoencoder. Firstly, the image is divided into multiple uniform regions using the K-means algorithm, and then a stacked automatic encoder is set for each uniform region. Reconstruct the spectral signal on each pixel through the corresponding stacked automatic encoder. Calculate the residual between the reconstructed image and the original image to achieve signal-to-noise separation. Finally, the image is divided into a large number of subblocks, and the subblocks containing edges are removed. The remaining subblocks are used for noise estimation in this band. The applicability of some classic noise estimation methods was experimentally tested, and the effectiveness and stability of the proposed method were verified through simulation and real data experiments.

关键词： Hyperspectral imaging Estimation Image reconstruction Image edge detection Training Spatial resolution Noise measurement Encoding Hyperspectral images noise estimation stacked autoencoder K-means algorithm

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Fault classification based on variable-weighted dynamic sparse stacked autoencoder for industrial processes

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CANADIAN JOURNAL OF CHEMICAL ENGINEERING 2023年第1期101卷 420-430页

作者： Dong, Jie Tu, Yanan Peng, Kaixiang Univ Sci & Technol Beijing Sch Automat & Elect Engn Minist Educ Key Lab Knowledge Automat Ind Proc Beijing Peoples R China Univ Sci & Technol Beijing Natl Engn Res Ctr Adv Rolling Technol Beijing Peoples R China

Effective process monitoring and fault diagnosis are of great significance to the safe operation of industrial processes as production scale increases and production systems become more and more complex. Identifying fault types in complex industries by fault classification can help workers to determine the source of the fault as soon as possible, which is crucial to timely recover work. In this paper, a fault classification method based on a variable-weighted dynamic sparse stacked autoencoder is proposed. First, considering the dynamic characteristics of process data, the input data are processed dynamically by a sliding window. Then, in the pre-training stage, the weight of each variable is calculated by Fisher discriminant analysis for the reconstruction of the loss function. The sparse term is added to the loss function so that it can learn the effective representation of data in a harsh environment. Finally, the proposed method is applied to the Tennessee-Eastman benchmark to evaluate the classification performance. The result shows the superiority of the proposed method.

关键词： deep learning fault classification Fisher discriminant analysis stacked autoencoder

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Feature learning based on entropy estimation density peak clustering and stacked autoencoder for industrial process monitoring

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CANADIAN JOURNAL OF CHEMICAL ENGINEERING 2023年第7期101卷 3998-4015页

作者： Yu, Feng Liu, Jianchang Liu, Dongming Wang, Honghai Shang, Liangliang Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China Northeastern Univ State Key Lab Synthet Automation Proc Ind Shenyang Peoples R China Nantong Univ Sch Elect Engn Nantong Peoples R China

Recently, stacked autoencoder (SAE)-based deep learning has been widely employed for industrial process fault detection, which can extract representative low-dimensional features. However, SAE fails to take the distribution structure of raw data into consideration in the unsupervised self-reconstruction learning, and this may lead to limited monitoring performance. Focusing on this issue, this paper proposes a new fault detection method based on entropy estimation density peak clustering and stacked autoencoder (EEDPC-SAE) for industrial process monitoring. First, an improved automatic clustering algorithm, namely, entropy estimation density peak clustering (EEDPC), is presented to analyze the distribution structure of process data. Then, the data distribution information is integrated into the learning procedure of SAE to capture the data intrinsic structure. EEDPC-SAE can describe the distribution characteristics of process data, and more informative high-level features can be learned for fault detection. Finally, two statistics, that is, Hotelling's T-squared (T-2) and squared prediction error (SPE), are established based on the encoder features and residual features generated by EEDPC-SAE. The effectiveness of the proposed method is evaluated by the Tennessee Eastman (TE) process and fed-batch fermentation penicillin (FBFP) process.

关键词： deep learning density peak clustering fault detection stacked autoencoder

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Speech emotion recognition in Persian based on stacked autoencoder by comparing local and global features

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第23期82卷 36413-36430页

作者： Bastanfard, Azam Abbasian, Alireza Islamic Azad Univ Dept Comp Engn Karaj Branch Karaj Iran Iran Broadcasting Univ Fac Media Engn Tehran Iran

Among the barriers to establishing effective human-machine interactions is the machines' inability to properly distinguish emotions from the human voice. The Speech Emotion Recognition (SER) systems have emerged to tackle this limitation. The accuracy of these systems depends on different factors such as the quantity and the types of emotions included in the database, feature extraction process including local and global features, feature selection method, and the type of classifier. This study presents a methodology for speech emotion recognition using an autoencoder neural network. It is shown that using a digit-level stacked autoencoder can be suitable for digit classification. The speech emotion recognition is done using the Persian emotional speech database (Persian ESD), which includes six emotional states: Happiness, Sadness, Fear, Disgust, Anger, and Neutral. Moreover, the popular, widely-used Berlin Emotional database (EMO-DB) is used to evaluate the effectiveness of the proposed approach. The experimental results show that the proposed method has significantly improved recognition accuracy.

关键词： Speech emotion recognition stacked autoencoder Persian language Deep learning

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stacked autoencoder based diagnosis applied on a Solar Photovoltaic System

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IFAC-PapersOnLine 2024年第4期58卷 384-389页

作者： Mouaad Bougoffa Samir Benmoussa Mohand Djeziri Thierry Contaret Badji Mokhtar-Annaba University LASA Laboratory Annaba Algeria Aix-Marseille Univ Univ de Toulon CNRS IM2NP Marseille France

This paper deals with the modeling of a photovoltaic system connected to a grid for the simulation of normal and faulty operations and the generation of a data-set for learning a fault detection algorithm based on a stacked autoencoder. To evaluate the effectiveness of the proposed approach, a Mean Squared Error is used. This method enables early fault detection, enhancing system relability and efficiency while addressing the need for proactive fault management in the system under normal conditions. Obtained results under different radiation and temperature conditions highlight the relevance of the proposed model and the effectiveness of the fault detection algorithm.

关键词： Photovoltaic fault detection Mean Squared Error stacked autoencoder

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