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

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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Breast cancer diagnosis model using stacked autoencoder with particle swarm optimization

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AIN SHAMS ENGINEERING JOURNAL 2024年第6期15卷

作者： Manimurugan, S. Karthikeyan, P. Aborokbah, Majed Narmatha, C. Ganesan, Subramaniam Univ Tabuk Fac Comp & Informat Technol Tabuk Saudi Arabia RV Univ Sch Comp Sci & Engn Bengaluru India Oakland Univ Sch Engn & Comp Sci Dept Elect & Comp Engn Rochester MI USA

Breast cancer (BrC) stands as the most prevalent cancer affecting women globally, comprising 24.5% of all female cancer diagnoses and contributing to 15.0% of total cancer-related fatalities. The timely detection and precise categorization of breast cancer play pivotal roles in enhancing patient prognosis and treatment outcomes. The main goal is to enhance the precision of classifying mammogram images, thus offering vital support to radiology experts in diagnosing BrCs. The proposed model encompasses several pivotal stages, including preprocessing, feature extraction, segmentation, and classification. To assess the model's efficacy, we employed the INBreast dataset. During pre-processing, mammogram images were enhanced through a customized contrastlimited adaptive histogram equalization (mCLAHE) technique coupled with data augmentation. Segmentation was executed utilizing the Res-SegNet model, and feature extraction employing the VGG-19 model. The classification was conducted via a stacked autoencoder (SAE) with particle swarm optimization (PSO). Our proposed model exhibited notably high performance compared to alternative models such as CNN, Yolo-v4, and Inceptionv3. The results unveiled an accuracy of 98.33%, precision of 99.39%, recall of 98.78%, specificity of 93.75%, an F1-score of 99.08%, and an MCC score of 90.04%.

关键词： Breast cancer CAD INBreast Res-SegNet VGG-19 stacked autoencoder

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Multifractal analysis and stacked autoencoder-based feature learning method for multivariate processes monitoring 41

Multifractal analysis and stacked autoencoder-based feature ...

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第41届中国控制会议

作者： Feng Yu Jianchang Liu Liangliang Shang Dongming Liu Department of Information Science and Engineering Northeastern University The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University School of Electrical Engineering Nantong University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Deep learning has become a popular tool for fault detection in industrial processes to learn complex nonlinear ***,the features extracted from most traditional deep networks usually ignore the geometric characters and singularities of the process *** representative features cannot be extracted effectively,which may lead to the inaccurate modeling and is not beneficial for the process ***,this paper proposes a feature learning method based on multifractal analysis and stacked autoencoder(MF-SAE) for fault detection in complex multivariate ***-SAE can learn high-level multifractal features from the raw data in an unsupervised *** analysis is frrst introduced to extract the multi-scale self-similar characteristics from industrial process data,in which sigmoid function is added for preprocessing the process *** to the redundant information existing in the multi-scale feature,SAE is then utilized to learn key feature from the extracted multifractal *** learned hidden feature and residual feature are provided to construct the monitoring *** fault detection performance of MF-SAE is tested on the Tennessee Eastman(TE) process.

关键词： Deep learning Industrial process Multifractal analysis stacked autoencoder

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Cell Classification Based on stacked autoencoder for Single-Cell RNA Sequencing 18th

Cell Classification Based on Stacked Autoencoder for Single-...

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18th International Conference on Intelligent Computing (ICIC)

作者： Qi, Rong Zheng, Chun-Hou Ji, Cun-Mei Yu, Ning Ni, Jian-Cheng Wang, Yu-Tian Qufu Normal Univ Sch Cyber Sci & Engn Qufu Shandong Peoples R China Anhui Univ Sch Artifial Intelligence Hefei Peoples R China Qufu Normal Univ Network Informat Ctr Qufu Shandong Peoples R China

ISBN: (纸本)9783031138294;9783031138287

Single-cell transcriptome sequencing (scRNA-seq) allows researchers to investigate cellular heterogeneity in gene expression profiles, identify cell types and predict cell fate at the single-cell level. Analysis of scRNA-seq data can effectively extract unknown heterogeneity and functional diversity of cell populations. Cell classification is one of the most important tasks in scRNA-seq data analysis, which contains cell clustering and classification of cell subtypes. Before assigning a cell type to each cluster, the unsupervised clustering methods look for marker genes for each cluster. These approaches are susceptible to a number of drawbacks in terms of sources of variation, technology, etc. Meanwhile, as more cell subtypes are gradually discovered, cluster-based cell type identification methods have been gradually leaning towards classification-based cell type identification. In this paper, we proposed a new cell classification method based on stacked autoencoder for representation learning (scSAERLs), which enhances the accuracy of classification by learning the feature representation of the data through deep network models. The stacked autoencoder-based classification model employed an unsupervised greedy pre-training learning procedure, which was followed by supervised label-based fine-tuning of the entire classification model. We tested the model in the intra and inter datasets, evaluated its performance with a standard classification metric. Experimental results showed that scSAERLs outperformed other commonly used classification methods in terms of classification accuracy and F1-score.

关键词： scRNA-seq Representation learning stacked autoencoder Classification

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A new deep model based on the stacked autoencoder with intensified iterative learning style for industrial fault detection

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2021年 153卷 47-59页

作者： Yu, Jianbo Yan, Xuefeng East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China

Deep learning-based process monitoring methods utilize the features extracted from deep neural net-works to perform fault detection and diagnosis. Traditional deep learning models are constructed in a fully connected manner and stacked by a sequential connection style. However, the feature information of the observed data would be discarded as the information is compressed and propagated layer-by-layer in hidden layers. Motivated by this, an intensified iterative learning (IIL) model which is developed from the stacked autoencoder is proposed in this study. In the process of feature extraction of IIL model, the traditional constraints of the hidden layer connection in deep neural networks are disregarded and the feature information of the current hidden layer comes from the information of all previous hidden layers to avoid the loss of information. In the process of real-time process monitoring, the features which are advantageous to accomplish fault detection would be intensified to obtain the most favorable features for monitoring. Finally, Euclidean distance and reconstruction error are employed to indicate and visualize the process status. The monitoring performance of the proposed IIL model is evaluated on three process tasks, and the results show it outperforms other deep learning methods on fault detection . (c) 2021 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： Process monitoring stacked autoencoder Intensified iterative learning model Information loss Feature information intensify

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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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Research on Intelligent Diagnosis of Wear Faults of Centrifugal Pumps Based on stacked autoencoder 3

Research on Intelligent Diagnosis of Wear Faults of Centrifu...

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3rd International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2022

作者： Xiang, Mingsheng Li, Yingli Feng, Kun Beijing University of Chemical Technology Key Laboratory of Engine Health Monitoring-Control and Networking Ministry of Education Beijing China China Petroleum Safety and Environmental Protection Technology Research Institute Beijing China Beijing University of Chemical Technology Beijing Key Laboratory of High-End Mechanical Equipment Health Monitoring and Self-Recovery Beijing China

ISBN: (纸本)9781665492812

Mechanical fault diagnosis is very important in industry because early detection can avoid some dangerous situations, and not much research has been done on the diagnosis of wear faults in centrifugal pumps. With the rapid development of data analysis techniques, data-driven diagnosis methods are becoming increasingly popular. In this study, stacked autoencoder based method is proposed to solve the centrifugal pump seal wear fault diagnosis problem. The method extracts power spectral density features directly from the vibration signal and chunks the features, greatly reducing the training difficulty and improving the accuracy of the model. The effectiveness of the method is verified using a centrifugal pump dataset, and the results show that the method can diagnose not only the fault site but also the degree of wear. © 2022 IEEE.

关键词： power spectral density stacked autoencoder wear failure

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Remaining useful life prediction of bearing based on stacked autoencoder and recurrent neural network

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JOURNAL OF MANUFACTURING SYSTEMS 2021年 61卷 576-591页

作者： Han, Tian Pang, Jiachen Tan, Andy C. C. Univ Sci & Technol Beijing Sch Mech Engn 30 Xueyuan Rd Beijing 100083 Peoples R China Univ Tunku Abdul Rahman LKC Fac Engn Sungai Long Campus Kajang 43000 Selangor Malaysia

Remaining Useful Life (RUL) prediction play a crucial part in bearing maintenance, which directly affects the production efficiency and safety of equipment. Moreover, the accuracy of the prediction model is constrained by the feature extraction process and full life data of bearings. In this paper, the life prediction method of faulty rolling bearing with limited data is presented including degradation state model and RUL prediction model. In order to obtain health indication (HI) without human interference in the degradation state modeling stage, the bottleneck structure of stacked autoencoder (SAE) is utilized to fuse the four selected features into one HI using Intelligent Maintenance Systems (IMS) bearing dataset as training sample. In RUL prediction model, the Long Short-Term Memory (LSTM) neural network is carried out to establish the model with Standard deviation (Std) input and HI training label. In order to solve the problem of large training error caused by insufficient data in the failure stage of bearing acceleration test, the third-order spline curve interpolation is utilized to enhance the data points. Through parameter analysis, the RMSE and MAE of the test set on the prediction model are 0.032582 and 0.024038, respectively. Furthermore, the effectiveness of the proposed method is further validated by dataset from Case Western Reserve University (CWRU) with different bearing fault degrees. The analysis indicates that the RUL prediction of bearing fault data is consistent with the size of artificial added faults, that is,the more severe the fault the shorter the time of remaining life. The results validate that the proposed method can effectively extract the bearing health state by incorporating feature fusion and establish accurately prediction model for bearing remaining life.

关键词： Remaining useful life prediction stacked autoencoder Recurrent neural network Bearing

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SMALF: miRNA-disease associations prediction based on stacked autoencoder and XGBoost

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BMC BIOINFORMATICS 2021年第1期22卷 1-18页

作者： Liu, Dayun Huang, Yibiao Nie, Wenjuan Zhang, Jiaxuan Deng, Lei Cent South Univ Sch Comp Sci & Engn Changsha 410083 Hunan Peoples R China Univ Calif San Diego Dept Cognit Sci La Jolla CA 92093 USA

Background Identifying miRNA and disease associations helps us understand disease mechanisms of action from the molecular level. However, it is usually blind, time-consuming, and small-scale based on biological experiments. Hence, developing computational methods to predict unknown miRNA and disease associations is becoming increasingly important. Results In this work, we develop a computational framework called SMALF to predict unknown miRNA-disease associations. SMALF first utilizes a stacked autoencoder to learn miRNA latent feature and disease latent feature from the original miRNA-disease association matrix. Then, SMALF obtains the feature vector of representing miRNA-disease by integrating miRNA functional similarity, miRNA latent feature, disease semantic similarity, and disease latent feature. Finally, XGBoost is utilized to predict unknown miRNA-disease associations. We implement cross-validation experiments. Compared with other state-of-the-art methods, SAMLF achieved the best AUC value. We also construct three case studies, including hepatocellular carcinoma, colon cancer, and breast cancer. The results show that 10, 10, and 9 out of the top ten predicted miRNAs are verified in MNDR v3.0 or miRCancer, respectively. Conclusion The comprehensive experimental results demonstrate that SMALF is effective in identifying unknown miRNA-disease associations.

关键词： miRNA-disease associations stacked autoencoder Latent feature XGBoost

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Anomaly Detection for HTS Cable Using stacked autoencoder and Reflectometry

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IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY 2023年第5期33卷 1页

作者： Bang, Su Sik Kwon, Gu-Young Tech Univ Korea Dept Elect Engn Shihung Gyeonggi Do South Korea Dongguk Univ Dept Smart Safety Engn Gyeongju South Korea

Since a high-temperature superconducting (HTS) cable has a cooling process and the possibility of a quench, a real-time monitoring technique is essential than other conventional power cable. Conventional reflectometry-based monitoring parameters have inherent disadvantages in that the boundary between normal and abnormal cannot be determined and multiple baselines cannot be considered during the monitoring process. In this paper, a stacked autoencoder-based anomaly detection technique for HTS cable is newly proposed. The autoencoder monitors the condition of HTS cable in real-time by calculating an anomaly score from the input data. The proposed method can automatically set the boundary between normal and abnormal and has the advantage of using multiple baselines. The performance of the proposed method is verified by emulating a local quench in a testbed containing a commercial 22.9 kV HTS cable and compared with that of the conventional monitoring parameter. Based on the advantage of being able to set multiple baselines, it is expected that the proposed technique can be used for real-time monitoring of HTS cable to enhance the reliability of the systems.

关键词： Superconducting cables High-temperature superconductors Monitoring Power cables Training Reflectometry Anomaly detection high-temperature superconducting (HTS) cable quench reflectometry stacked autoencoder

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