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Cointegration stacked autoencoder model based on stationary features reconstruction for non-stationary process monitoring

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2025年 193卷 1287-1299页

作者： Huang, Jian Liu, Yupeng Yang, Xu Lv, Zhaomin Peng, Kaixiang Univ Sci & Technol Beijing Sch Automat & Elect Engn Key Lab Knowledge Automat Ind Proc Minist Educ Beijing Peoples R China Univ Sci & Technol Beijing Shunde Innovat Sch Foshan Peoples R China Shanghai Univ Engn Sci Sch Urban Railway Transportat Shanghai Peoples R China

In industrial process monitoring, the long-term stationary features play an important role in representing essential statistical information. However, the autoencoder-based methods extract the deep features by achieving the numerical approximation of the original data, which may lead to the destruction of the hidden stationary information. To solve this problem, a cointegration stacked autoencoder model based on stationary features reconstruction is proposed in this paper to maintain long-term equilibrium relationships during model training. First, a cointegration analysis model is constructed to extract the stationary features hidden in the non-stationary data. Based on this, a cointegration stacked autoencoder is designed to reconstruct the extracted stationary features and the original data simultaneously. In addition, the monitoring statistics for both deep and stationary features are integrated by Bayesian inference criterion. By reconstructing the stationary features, the proposed network is able to retain the beneficial relationship among the non-stationary variables. Finally, the fault detection performance of the proposed method is verified in two cases.

关键词： Non-stationary process monitoring Stationary features reconstruction Bayesian inference criterion stacked autoencoder

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stacked autoencoder Based Weak Supervision for Social Image Understanding

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IEEE ACCESS 2019年 7卷 21777-21786页

作者： Xu, Chaoyang Dai, Yuanfei Lin, Renjie Wang, Sniping Putian Univ Sch Informat Engn Putian 351100 Peoples R China Fuzhou Univ Coll Math & Comp Sci Fuzhou 350116 Fujian Peoples R China

Many studies in recent years have focused on social image understanding due to the increasing number of shared images from social networks and online communities. However, previous work in social image understanding fails to learn an effective feature representation because of a large amount of missing and irrelevant tags, though matrix completion techniques are frequently utilized for this purpose. autoencoder models have been validated to be effective in learning latent low-dimensional representations in unsupervised learning. In this paper, we propose a new social image understanding model based on deep autoencoders, which can learn the shared latent codes of social images and tags as supervision information in the deep autoencoders. First, social images are extracted with multi-modal features, which provide a comprehensive characterization to image semantic understanding. And, the social image understanding problems are transformed into the problem of minimizing an optimization objective. Second, multi-layered autoencoders with weak supervision integration are employed to learn an efficient low-dimensional representation from the multi-view feature sources that can make up the semantic gap between image features and tags through minimizing the problem formulation. Finally, we design a new balanced loss function based on binary cross entropy, in which we address highly sparse inputs for a better optimization performance. The extensive experiments on several real-world social image datasets confirm the effectiveness and robustness of the proposed model compared with the state-of-the-art methods.

关键词： Computer vision social image understanding stacked autoencoder image tag refinement tag-based retrieval

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stacked autoencoder-Based Deep Reinforcement Learning for Online Resource Scheduling in Large-Scale MEC Networks

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IEEE INTERNET OF THINGS JOURNAL 2020年第10期7卷 9278-9290页

作者： Jiang, Feibo Wang, Kezhi Dong, Li Pan, Cunhua Yang, Kun Hunan Normal Univ Hunan Prov Key Lab Intelligent Comp & Language In Changsha 410081 Hunan Peoples R China Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne NE1 8ST Tyne & Wear England Hunan Univ Technol & Business Key Lab Hunan Prov New Retail Virtual Real Techno Changsha 410205 Hunan Peoples R China Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England Changchun Inst Technol Sch Comp Technol & Engn Changchun 130000 Jilin Peoples R China Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ Essex England

An online resource scheduling framework is proposed for minimizing the sum of weighted task latency for all the Internet-of-Things (IoT) users, by optimizing offloading decision, transmission power, and resource allocation in the largescale mobile-edge computing (MEC) system. Toward this end, a deep reinforcement learning (DRL)-based solution is proposed, which includes the following components. First, a related and regularized stacked autoencoder (2r-SAE) with unsupervised learning is applied to perform data compression and representation for high-dimensional channel quality information (CQI) data, which can reduce the state space for DRL. Second, we present an adaptive simulated annealing approach (ASA) as the action search method of DRL, in which an adaptive h-mutation is used to guide the search direction and an adaptive iteration is proposed to enhance the search efficiency during the DRL process. Third, a preserved and prioritized experience replay (2p-ER) is introduced to assist the DRL to train the policy network and find the optimal offloading policy. The numerical results are provided to demonstrate that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computational time compared with existing benchmarks.

关键词： Adaptive simulated annealing deep reinforcement learning (DRL) large-scale mobile-edge computing (MEC) stacked autoencoder

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stacked autoencoder for operation prediction of coke dry quenching process

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CONTROL ENGINEERING PRACTICE 2019年 88卷 110-118页

作者： Wang, Jian-Guo Wang, Yu Yao, Yuan Yang, Bang-Hua Ma, Shi-Wei Shanghai Univ Sch Mechatron Engn & Automat Shanghai Key Lab Power Stn Automat Technol Shanghai 200072 Peoples R China Natl Tsing Hua Univ Dept Chem Engn Hsinchu 30013 Taiwan

Coke dry quenching (CDQ) is widely adopted for waste heat recovery in iron and steel plants. In this work, an economic benefit index was introduced to evaluate the performance of the CDQ system and stacked autoencoder (SAE) based deep neural networks are adopted for CDQ operation prediction. Based on the prediction results, a guidance is provided for online adjustment of the supplementary air flow rate, hence the efficiency and safety of the CDQ system can be improved. The case study on a real plant shows that the proposed method increases the economic efficiency of the CDQ process by 4.39%.

关键词： Coke dry quenching stacked autoencoder Deep learning Economic benefit index Process modeling

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stacked autoencoder with echo-state regression for tourism demand forecasting using search query data

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APPLIED SOFT COMPUTING 2018年 73卷 119-133页

作者： Lv, Sheng-Xiang Peng, Lu Wang, Lin Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Hubei Peoples R China

Accurate tourism demand forecasting is fundamental in the tourism industry, while effective tourism demand forecasting using search query data (SQD) has become popular in the tourism management field. SQD is a type of statistical time series provided by search engines that can reflect netizens' attention on certain events. Scholars attempt to establish a reasonable relationship between tourism demand and SQD for its timeliness and comprehensiveness. The current study proposes an effective deep learning technique called stacked autoencoder with echo-state regression (SAEN) to accurately forecast tourist flow based on search query data. In the proposed SAEN approach, stacked autoencoder is adopted to hierarchically learn high-level predictive indicators from substantial SQD and connected by an echo-state regression layer to model the nonlinear time series relationship between tourism flow and the learned indicators. Four realistic applications (i.e., one comparative case and three extended cases in the US and China with different SQD sources) are used to verify the forecasting performance of SAEN. Numerical results indicate that SAEN is better than the current literature findings, including time series approach, econometric model, common machine learning algorithms, and state-of-the-art deep learning techniques. The structure parameters of SAEN are further analyzed empirically and theoretically. Moreover, this study determined a different impact of network depth and echo-state reservoir scale on the performance of SAEN. The proposed SAEN can be an appropriate alternative for tourism demand forecasting in complex data situations. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Tourism demand forecasting Search query data Deep learning stacked autoencoder Echo-state network

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stacked autoencoder with novel integrated activation functions for the diagnosis of autism spectrum disorder

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NEURAL COMPUTING & APPLICATIONS 2023年第23期35卷 17043-17075页

作者： Elakkiya, M. Kaviya Dejey Anna Univ Dept Comp Sci & Engn Reg Campus Tirunelveli Tirunelveli 627007 Tamil Nadu India

Autism screening is crucial for the early diagnosis of developmental disorder. The combination of machine learning (ML) and deep learning (DL) approaches are applied to produce memory efficient and less complex deep learning models for the computer aided diagnosis (CAD) of autism screening. In the proposed work, two novel integrated activation functions such as Li-ReLU and S-RReLU are developed to aid in the classification of autistic subjects and typical controls (TC) with maximum accuracy. As functional magnetic resonance imaging (fMRI) data is noisy, it undergoes temporal and spatial pre-processing. The artifact free high dimensional fMRI data is exercised for the process of feature extraction and dimensionality reduction employing group principal component analysis (Group PCA) and group independent component analysis (Group ICA). The selected features are normalized using 0-1 normalization and converted to tensors. stacked autoencoder (SAE) utilizes the fMRI tensor data for the classification of autism spectrum disorder (ASD) subjects and typical controls. The proposed work is implemented and tested on all datasets of ABIDE I database. The validation accuracy of CMU_a, KKI, UCLA_2, OLIN, Yale and NYU datasets are obtained as 100, 80, 71.43, 100, 85.71 and 93.33% using novel Li-ReLU activation function in the proposed system. With the help of new activation function called S-RReLU, the proposed system achieves validation accuracy of about 10, 100, 57.14, 100, 78.57 and 93.33% for CMU_a, KKI, UCLA_2, OLIN, Yale and NYU datasets. Thus, the proposed method outperforms all other existing state-of-the-art works in terms of accuracy.

关键词： Autism spectrum disorder Functional MRI Group PCA Group ICA Activation function stacked autoencoder

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stacked autoencoder-based community detection method via an ensemble clustering framework

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INFORMATION SCIENCES 2020年 526卷 151-165页

作者： Xu, Rongbin Che, Yan Wang, Xinmei Hu, Jianxiong Xie, Ying Putian Univ Sch Informat Engn Fujian Peoples R China Putian Unv Engn Res Ctr Big Data Applicat Private Hlth Med Fujian Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei Peoples R China Putian Univ Intens Care Unit Affiliated Hosp Fujian Peoples R China

Community detection is a challenging issue because most existing methods are not well suited for complex social networks with ambiguous structures. In this paper, we propose a novel community detection method named stacked autoencoder-Based Community Detection Method via Ensemble Clustering (CDMEC). This is the first time that we have attempted to apply four different complex network similarity representations to the community detection problem. This work makes up for the insufficiency of the single similarity matrix to describe the similarity relationship between nodes. These similarity representations can fully describe and consider the sufficient local information between nodes in a network topology. Our CDMEC framework combines transfer learning and a stacked autoencoder to obtain an efficient low-dimensional feature representation of complex networks and aggregates multiple inputs through a novel ensemble clustering framework. This novel framework first uses the basic clustering results to construct a consistent matrix, and then it employs the nonnegative matrix factorization (NMF)-based clustering method to detect reliable clustering results from the consistent matrix. The results of various extensive experiments on artificial benchmark networks and real-world networks showed that the proposed CDMEC framework is superior to the existing state-of-the-art community detection methods and has great potential in solving the community detection problems. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Community detection Complex networks stacked autoencoder Ensemble clustering

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An attention graph stacked autoencoder for anomaly detection of electro-mechanical actuator using spatio-temporal multivariate signals

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Chinese Journal of Aeronautics 2024年第9期37卷 506-520页

作者： Jianyu WANG Heng ZHANG Qiang MIAO College of Electrical Engineering Sichuan UniversityChengdu 610065China

Health monitoring of electro-mechanical actuator(EMA)is critical to ensure the security of *** is difficult or even impossible to collect enough labeled failure or degradation data from actual *** autoencoder based on reconstruction loss is a popular model that can carry out anomaly detection with only consideration of normal training data,while it fails to capture spatio-temporal information from multivariate time series signals of multiple monitoring *** mine the spatio-temporal information from multivariate time series signals,this paper proposes an attention graph stacked autoencoder for EMA anomaly ***,attention graph con-volution is introduced into autoencoder to convolve temporal information from neighbor features to current features based on different weight ***,stacked autoencoder is applied to mine spatial information from those new aggregated temporal ***,based on the bench-mark reconstruction loss of normal training data,different health thresholds calculated by several statistic indicators can carry out anomaly detection for new testing *** comparison with tra-ditional stacked autoencoder,the proposed model could obtain higher fault detection rate and lower false alarm rate in EMA anomaly detection experiment.

关键词： Anomaly detection Spatio-temporal informa-tion Multivariate time series signals Attention graph convolution stacked autoencoder

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stacked autoencoder based Intrusion Detection System using One-Class Classification 11

Stacked Autoencoder based Intrusion Detection System using O...

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11th International Conference on Cloud Computing, Data Science and Engineering (Confluence)

作者： Gupta, Prabhav Ghatole, Yash Reddy, Nihal Mu Sigma Inc Bangalore Karnataka India BITS Pilani Dept Comp Sci Hyderabad India

ISBN: (纸本)9781665414517

The paper presents a study of deep learning based approach for Intrusion Detection System. Already existing models for classification were based on supervised learning methods which fails to classify instances of unknown attacks. Effective prediction of network packets as normal or attack, known and unknown to the model, is imperative requiring detection with minimal false alarm rate. Even for the attacks not known to (he model, stacked autoencoder turns out to be one such deep learning architecture which identifies complex pattern leading to generation of the best latent representation of inputs. The proposed model was (rained on single labeled instances from KDD Cup 99 dataset along with standardizing the inputs using batch normalization to minimize the problem of internal covariance shift and vanishing gradient to some extent. Experimental results obtained show that the proposed method outperforms all the other algorithms giving accuracy of 98.17% and false alarm rate of 0.38%.

关键词： IDS Anomaly detection One-Class classification stacked autoencoder Batch Normalization

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stacked autoencoder based diagnosis applied on a Solar Photovoltaic System 12

Stacked AutoEncoder based diagnosis applied on a Solar Photo...

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12th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS)

作者： Bougoffa, Mouaad Benmoussa, Samir Djeziri, Mohand Contaret, Thierry Badji Mokhtar Annaba Univ LASA Lab Annaba Algeria Aix Marseille Univ Univ 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. Copyright (c) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Photovoltaic fault detection Mean Squared Error stacked autoencoder

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