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32nd Chinese Control And Decision Conference (CCDC)

作者： Chen, Jin Pu, Wang Kai, Wang Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China

ISBN: (纸本)9781728158556

Fault monitoring can find out-of-control conditions of equipment operation in a timely manner, which is essential for eliminating faults and for stable operation of industrial systems in batch processes. Many conventional data-driven fault detection methods focus less on the non-Gaussian and Multi-stage characteristics of batch process data, which may result in degradation of monitoring performance. In this paper, a Multi-stage Fourth Order Moment Staked autoencoder (M-FOM-SAE) is designed to solve the above problems. The proposed method firstly automatically determines the number of clusters and divides the batch process into multiple stages. After that, the FOM-SAE model is established in each sub-stage, which can not only effectively learn the nonlinear features of process data, but also extract the non-Gaussian information. The proposed strategy is applied to real-world industrial processes. Experimental results indicate that it can better capture the non-Gaussian and Multi-stage characteristics of process data, and improve the ability to monitor abnormalities.

关键词： Batch process Fault monitoring non-Gaussian Multi-stage stacked autoencoder

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Enhanced Fault Detection Using Deviation Degree Penalty with stacked autoencoder in Industry Process 9

Enhanced Fault Detection Using Deviation Degree Penalty with...

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9th IEEE Data Driven Control and Learning Systems Conference (DDCLS)

作者： Wang, Kai Zhou, Le Wang, Yalin Cent South Univ Sch Automat Changsha 410083 Peoples R China Zhejiang Univ Sci & Technol Sch Automat & Elect Engn Hangzhou 310023 Peoples R China

ISBN: (纸本)9781728159225

In a data-driven fault detection strategy, not only the data model, but the detection index also influences the detection performance a lot. Deep learning has a flexible and powerful ability for modeling strongly nonlinear processes with good generalization performance. However, lack of statistical interpretations, the detection indices have to resort to nonparametric density estimation methods in most of deep models. Thus, the control limits are severely influenced by a specific set of training data, causing a poor robustness. Simultaneously, the squashed activations in neural networks make the detection margin limited. To solve these problems, a novel sample deviation degree penalty strategy with stacked autoencoder is proposed to regularize the neurons and the reconstruction errors, which makes the neurons in each layer and the reconstruction error of different samples tend to be converged. Thus, the robustness and the detection margin are improved. Experiment data in a multiphase flow facility are used to verify the efficacy of the proposed algorithm.

关键词： Fault detection stacked autoencoder degrees of outliers regularization

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A combination method of stacked autoencoder and 3D deep residual network for hyperspectral image classification

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2021年 102卷

作者： Zhao, Jinling Hu, Lei Dong, Yingying Huang, Linsheng Weng, Shizhuang Zhang, Dongyan Anhui Univ Natl Engn Res Ctr Agroecol Big Data Anal & Applic 111 Jiulong Rd Hefei 230601 Peoples R China Anhui Univ Sch Elect & Informat Engn Hefei 230601 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci Beijing 100094 Peoples R China

In comparison with conventional machine learning algorithms, deep learning can effectively express the deep features of remote sensing images. Considering the rich spectral and spatial information contained in hyperspectral images (HSIs), a combination method was proposed for HSI classification based on stacked autoencoder (SAE) and 3D deep residual network (3DDRN). Specifically, a SAE neural network was first built to reduce the dimensions of original HSIs. A 3D convolutional neural network (3DCNN) was then designed and the residual network module was introduced to build a 3DDRN. The dimension-reduced 3D HSI cubes were input into the 3DDRN to extract identifiable joint spectral-spatial features. Finally, the deep features continuously identified by the 3DDRN were input to Softmax classification layer to realize the classification. In addition, Batch Normalization (BN) and Dropout were used during the learning process to avoid overfitting on training data. The training and test sets of Indian Pines (IP), Pavia University (PU) and Salinas (SA) hyperspectral data sets were selected as the modeling and verification data sources. Six classical classification algorithms were adopted for comparing our proposed method, specifically including conventional machine learning algorithms of Radial Basis FunctionSupport Vector Machine (RBF-SVM), Kernel Simultaneous Orthogonal Matching Pursuit (KSOMP) and Local Binary Pattern-K-Nearest Neighbor (LBP-KNN), and mainstream deep learning algorithms of Variational autoencoder (VAE), Convolutional Neural Network (CNN) and Spectral-Spatial Residual Network (SSRN). The results showed that the overall accuracy (OA) reached 98.97%, 99.69% and 99.24%, respectively, only based on 10%, 5% and 1% of training samples for IP, PU and SA. Consequently, the proposed method shows a better classification performance, even in the case of limited samples.

关键词： Deep learning Hyperspectral remote sensing Residual network 3D convolutional neural network Spectral-spatial features stacked autoencoder

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A Soft-Sensor Modeling Method Based on Gated stacked Isomorphic autoencoder 41

A Soft-Sensor Modeling Method Based on Gated Stacked Isomorp...

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第41届中国控制会议

作者： Jianjun Yang Xiaolu Xu Xiaohui Yin Wenxing Liu Yi Yuan Jian Peng School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Huaxin Cement Co. Ltd

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Soft sensing mainly studies the real-time prediction of some key performance indicators or quality variables in the actual production process,which has the role of guiding production in the actual production *** stacked autoencoder is a multi-layer autoencoder *** input variables will be encoded and decoded through each layer of autoencoders,and the obtained hidden features will be retained as the input of the next *** this way,high-level data features can be successfully learned from the input layer to the intermediate *** isomorphic autoencoder reconstruct an identical data input layer,and the reconstruction is performed by minimizing the error of the decoded data from the original input ***,for a soft measurement model,some data information may also reduce the accuracy or generalization of the *** order to overcome the above shortcomings,this paper proposes a gated stacked isomorphic autoencoder,which evaluates the contribution of each hidden layer feature through the gating unit,and then integrates the information of different hidden layers to complete the prediction and estimation of related main ***,the effectiveness and feasibility of the method are verified in practical industrial cases.

关键词： Deep learning soft sensor stacked autoencoder gating unit

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stacked-autoencoder-based model for COVID-19 diagnosis on CT images

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APPLIED INTELLIGENCE 2021年第5期51卷 2805-2817页

作者： Li, Daqiu Fu, Zhangjie Xu, Jun Nanjing Univ Informat Sci Technol Sch Comp & Software Nanjing 210044 Peoples R China Peng Cheng Lab Shenzhen 518000 Peoples R China Nanjing Univ Informat Sci & Technol Sch Automat Nanjing 210044 Peoples R China

With the outbreak of COVID-19, medical imaging such as computed tomography (CT) based diagnosis is proved to be an effective way to fight against the rapid spread of the virus. Therefore, it is important to study computerized models for infectious detection based on CT imaging. New deep learning-based approaches are developed for CT assisted diagnosis of COVID-19. However, most of the current studies are based on a small size dataset of COVID-19 CT images as there are less publicly available datasets for patient privacy reasons. As a result, the performance of deep learning-based detection models needs to be improved based on a small size dataset. In this paper, a stacked autoencoder detector model is proposed to greatly improve the performance of the detection models such as precision rate and recall rate. Firstly, four autoencoders are constructed as the first four layers of the whole stacked autoencoder detector model being developed to extract better features of CT images. Secondly, the four autoencoders are cascaded together and connected to the dense layer and the softmax classifier to constitute the model. Finally, a new classification loss function is constructed by superimposing reconstruction loss to enhance the detection accuracy of the model. The experiment results show that our model is performed well on a small size COVID-2019 CT image dataset. Our model achieves the average accuracy, precision, recall, and F1-score rate of 94.7%, 96.54%, 94.1%, and 94.8%, respectively. The results reflect the ability of our model in discriminating COVID-19 images which might help radiologists in the diagnosis of suspected COVID-19 patients.

关键词： COVID-19 diagnosis Computed tomography Deep learning stacked autoencoder

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Deep Spatio-Temporal Representation for Detection of Road Accidents Using stacked autoencoder

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2019年第3期20卷 879-887页

作者： Singh, Dinesh Mohan, Chalavadi Krishna IIT Hyderabad Dept Comp Sci & Engn Visual Learning & Intelligence Grp Hyderabad 502205 India

Vision-based detection of road accidents using traffic surveillance video is a highly desirable but challenging task. In this paper, we propose a novel framework for automatic detection of road accidents in surveillance videos. The proposed framework automatically learns feature representation from the spatiotemporal volumes of raw pixel intensity instead of traditional hand-crafted features. We consider the accident of the vehicles as an unusual incident. The proposed framework extracts deep representation using denoising autoencoders trained over the normal traffic videos. The possibility of an accident is determined based on the reconstruction error and the likelihood of the deep representation. For the likelihood of the deep representation, an unsupervised model is trained using one class support vector machine. Also, the intersection points of the vehicle's trajectories are used to reduce the false alarm rate and increase the reliability of the overall system. We evaluated out proposed approach on real accident videos collected from the CCTV surveillance network of Hyderabad City in India. The experiments on these real accident videos demonstrate the efficacy of the proposed approach.

关键词： Accident detection anomaly detection deep learning stacked autoencoder

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Research on CTR prediction based on stacked autoencoder

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APPLIED INTELLIGENCE 2019年第8期49卷 2970-2981页

作者： Wang, Qianqian Liu, Fang'ai Xing, Shuning Zhao, Xiaohui Shandong Normal Univ Sch Informat Sci & Engn Jinan Shandong Peoples R China Shandong Normal Univ Sch Math Sci Jinan Shandong Peoples R China

Click-through rate prediction is critical in internet advertising and affects web publisher's profits and advertiser's payment. In the CTR prediction, mining the interaction between features and extracting user interest are key factors affecting the prediction rate. The traditional method of obtaining features using feature extraction did not consider the sparseness of advertising data and the highly nonlinear association between features. To reduce the sparseness of data and to mine the hidden features and user interest in advertising data, a method that learns the sparse features is proposed. Our method exploits dimension reduction based on decomposition, and uses the bidirectional gated recurrent unit (Bi-GRU) to extract user interest. We utilize stacked autoencoder to portray the nonlinear associated relationship of data. The experiment shows that our method improves the effect of CTR prediction and produces economic benefits in internet advertising.

关键词： Click through rate stacked autoencoder Bidirectional gated recurrent unit Tensor decomposition

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A novel soft sensor method based on stacked fusion autoencoder with feature enhancement for industrial application

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MEASUREMENT 2023年 221卷

作者： Wang, Wenhua Wang, Hengqian Chen, Lei Hao, Kuangrong Donghua Univ Engn Res Ctr Digitized Text & Apparel Technol Minist Educ Shanghai 201620 Peoples R China Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China

Soft sensor is pivotal in contemporary industrial processes. However, extracting effective feature representa-tions from intricate process data remains a challenging task. To this end, a feature enhancement stacked fusion autoencoder (FE-SFAE) is proposed to leverage spatiotemporal characteristics and linear residual fusion for modeling. Firstly, the feature enhancement (FE) module that incorporates multidirectional delayed transform (MDT) and bilateral smoothing constraint canonical polyadic (BS-CP) decomposition is developed to alleviate the issues of time-lag and spatial dependence. Based on the FE module, complementary data can be generated for the subsequent stage. Secondly, to address the problem of variables being overly non-linearized, we propose a linear residual fusion gate mechanism. On this basis, we design the model named FE-SFAE that not only captures spatiotemporal features in complementary data but also balances the degree of network nonlinearization. Finally, the superiority of FE-SFAE is demonstrated through an industrial case involving the esterification process of polyester polymerization.

关键词： Deep learning Soft sensor CP decomposition stacked autoencoder Polyester polymerization process

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A stacked autoencoder-Based miRNA Regulatory Module Detection Framework

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2019年第2期12卷 822-832页

作者： Yang, Yi Song, Yan Hunan Womens Univ Sch Informat Sci & Engn Zhongyi Rd 160 Changsha 410004 Hunan Peoples R China

MicroRNA regulatory module (MRM) plays an important role in the study of microRNA synergism. To detect MRMs, researchers have developed a number of related methods in the preceding decades. However, some existing methods are stochastic or specific to a certain situation. In this paper, we presented a novel deep ensemble framework called DeMosa to identify MRM for different cancers. In the proposed framework, we integrated stacked autoencoders and K-means method to detect MRMs in high-dimensional complex biological networks. We tested our method on synthetic data and three types of cancer data sets. In the synthetic data, we found DeMosa is superior to existing three methods SNMNMF, Mirsynergy, and bi-cliques merging (BCM) on clustering accuracy, stability, and module quality, while in the cancer datasets, DeMosa is more adaptable in different situations than the counterparts. In addition, we applied Kaplan-Meier survival analysis to predict several MRMs as potential prognostic biomarkers in cancers. (c) 2019 The Authors. Published by Atlantis Press SARL.

关键词： Module detection Intimacy K-means stacked autoencoder

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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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