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stacked Denoising Extreme Learning Machine autoencoder Based on Graph Embedding for Feature Representation

IEEE ACCESS 2019年 7卷 13433-13444页

作者： Ge, Hongwei Sun, Weiting Zhao, Mingde Yao, Yao Dalian Univ Technol Coll Comp Sci & Technol Dalian 116023 Peoples R China McGill Univ Sch Comp Sci Montreal PQ H3A 0E9 Canada

Extreme learning machine is characterized by less training parameters, fast training speed, and strong generalization ability. It has been applied to obtain feature representations from the complex data in the tasks of data clustering or classification. In this paper, a graph embedding-based denoising extreme learning machine autoencoder (GDELM-AE) is proposed for capturing the structure of the inputs. Specifically, in GDELM-AE, a graph embedding framework that contains an intrinsic graph and a penalty graph constructed by local Fisher discrimination analysis is integrated into the autoencoder. So, it can exploit both local structure and global structure information in extreme learning machine (ELM) spaces. Further, we propose a stacked graph embedded denoising (SGD)-ELM by stacking several GDELM-AEs. The experimental results on several benchmarks validate that GDELM-AE can obtain efficient and robust feature representation of original data;moreover, the stacked GDELM-AE can obtain high-level and noise-robust representations. The comparative results with the state-of-the-art algorithms indicate that the proposed algorithm can obtain better accuracy as well as faster training speed.

关键词： Extreme learning machine stacked autoencoder denoising graph embedding

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LaMeta: An efficient locality aware metadata management technique for an ultra-large distributed storage system

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2022年第10期34卷 8323-8335页

作者： Singh, Harcharan Jit Bawa, Seema Thapar Inst Engn & Technol Deemed Univ Dept Comp Sci & Engn Patiala 147004 Punjab India

The locality of metadata is a critical parameter of data storage and retrieval performance for an ultra -large distributed storage system. The data of an ultra-large distributed storage system is accessed through its distributed metadata servers (MDSs). The distributed MDSs store and cache partial metadata for scalability and performance. The MDSs are connected through varied latencies multipath network connections of using various metadata distribution techniques. The locality of MDS in the metadata dis-tribution techniques improves the metadata query routing and enhances the performance of the dis-tributed storage system. Motivated by this insight, this work introduces an intelligent locality-aware metadata management technique based on recommendation system that exploits MDS path ratings in a globally distributed ultra-large storage system and proposes a novel locality-aware metadata query routing algorithm. The proposed metadata server locality aware employs collaborative filtering with stacked autoencoder to address the sparsity of the path rating matrix and its overfitting issue. The exten-sive experiments on real datasets show that the proposed metadata server locality aware technique enhances the locality of metadata operations and improves aggregate operations throughput by 12% to 21% of contemporary distributed metadata management techniques in ultra-large distributed storage systems. (c) 2022 The Authors. Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Deep learning stacked autoencoder Machine learning High performance computing Distributed metadata management Locality Storage system

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Analysis of Extremely Obese Individuals Using Deep Learning stacked autoencoders and Genome-Wide Genetic Data 15th

Analysis of Extremely Obese Individuals Using Deep Learning ...

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15th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB)

作者： Montanez, Casimiro A. Curbelo Fergus, Paul Chalmers, Carl Hind, Jade Liverpool John Moores Univ Dept Comp Sci Liverpool L3 3AF Merseyside England

ISBN: (纸本)9783030345853;9783030345846

Genetic predisposition has been identified as one of the components contributing to the obesity epidemic in modern societies. The aetiology of polygenic obesity is multifactorial, which indicates that lifestyle and environmental factors may influence multiples genes to aggravate this disorder. Several low-risk single nucleotide polymorphisms (SNPs) have been associated with BMI. However, identified loci only explain a small proportion of the variation observed for this phenotype. The linear nature of genome wide association studies (GWAS) used to identify associations between genetic variants and the phenotype have had limited success in explaining the heritability variation of BMI and shown low predictive capacity in classification studies. GWAS ignores the epistatic interactions that less significant variants have on the phenotypic outcome. In this paper we utilise a novel deep learning-based methodology to reduce the high dimensional space in GWAS and find epistatic interactions between SNPs for classification purposes. SNPs were filtered based on the effects associations have with BMI. Since Bonferroni adjustment for multiple testing is highly conservative, an important proportion of SNPs involved in SNP-SNP interactions are ignored. Therefore, only SNPs with p-values <1 x 10(-2) were considered for subsequent epistasis analysis using stacked autoencoders (SAE). This allows the nonlinearity present in SNP-SNP interactions to be discovered through progressively smaller hidden layer units and to initialise a multi-layer feedforward artificial neural network (ANN) classifier. The classifier is fine-tuned to classify extremely obese and non-obese individuals. The performance of classifications using progressively smaller compressed layers was compared and the results reported. The best results were obtained with 2,000 compressed units (SE = 0.949153, SP = 0.933014, Gini = 0.949936, Logloss = 0.1956, AUC = 0.97497 and MSE = 0.054057). Using 50 compressed units it was

关键词： Classification Deep learning Dimensionality reduction Epistasis GWAS Polygenic obesity SNPs stacked autoencoder

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Aeroengine Remaining Life Prediction Based on Advanced Health Index Construction and Gray Similarity Multiscale Matching

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1页

作者： Sun, Shilong Huang, Haodong Ding, Jian Wang, Dong Xu, Wenfu Harbin Inst Technol Sch Mech Engn & Automat Shenzhen 518055 Peoples R China Shanghai Jiao Tong Univ State Key Lab Mech Syst & Vibrat Shanghai 200240 Peoples R China Key Univ Lab Mech & Machine Theory & Intelligent U Shenzhen 518055 Guangdong Peoples R China

In this article, we address the challenge of predicting the remaining useful life (RUL) of aeroengines, which are critical components of aircraft that operate under increasingly extreme conditions as engine performance enhances. Ensuring the safety and reliability of these engines is paramount. To this end, we introduce a novel RUL prediction methodology that leverages gray similarity multiscale matching. This approach employs the robust capabilities of long short-term memory (LSTM) networks for processing time-series data. First, an LSTM stacked autoencoder (L-SAE) is designed to extract pivotal operational features of the engine, thereby delineating its degradation trajectory. Furthermore, the gray correlation analysis is utilized to assess the similarity between these degradation trajectories, which are complemented by a multitime scale sliding window technique for enhanced similarity matching. Subsequently, kernel density estimation (KDE) is applied to gauge the uncertainty associated with the prediction outcomes. The efficacy and superiority of our proposed method are demonstrated through the validation of the experiment study. Comparative analysis reveals that our method outperforms existing techniques in key evaluation metrics, underscoring its potential applicability to large-scale datasets. This validation confirms not only the method's effectiveness but also its advantage in predicting the RUL of aeroengines with greater accuracy and reliability.

关键词： Sensors Degradation Time series analysis Trajectory Predictive models Information entropy Engines Sensor phenomena and characterization Indexes Aircraft propulsion Gray correlation analysis remaining useful life (RUL) similarity matching stacked autoencoder

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Unsupervised domain adversarial network for few-sample fault detection in industrial processes

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ADVANCED ENGINEERING INFORMATICS 2024年 61卷

作者： Fang, Ruiyi Wang, Kai Li, Jing Yuan, Xiaofeng Wang, Yalin Cent South Univ Sch Automat Changsha 410083 Peoples R China Agcy Sci Technol & Res Inst High Performance Comp Singapore Singapore Agcy Sci Technol & Res Ctr Frontier AI Res Singapore Singapore

Industrial processes are evolving to become larger and more integrated, resulting in frequent transitions between different operating conditions. When performing fault detection for processes under new operation conditions, the scarcity of samples poses a significant challenge in constructing effective monitoring models. To address the issue of fault detection with few samples, we propose a method called DASAE. This method is based on a stacked autoencoder (SAE) and incorporates domain adversarial (DA) techniques to model the data-poor target domain (new working mode) by transferring valuable information from a data-rich source domain (historical working mode). DASAE involves a new unsupervised knowledge transfer paradigm that relies on domain similarity without label guidance. In tackling the challenge of data imbalance during adversarial training, we introduce a domain-imbalance-aware-margin Loss (DIAM), which alleviates the problem by encouraging a larger margin for the minority domain. The proposed method is evaluated using both a numerical case and a real-world industry case, the continuous stirred tank reactor (CSTR). The results demonstrate that in few-sample scenarios, the proposed method generally exhibits the best performance compared to other state-of-the-art approaches, showing enhanced detection effectiveness in both T2 and SPE metrics.

关键词： Domain adversarial training Imbalanced data stacked autoencoder Fault detection Few samples

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Explainable and interpretable bearing fault classification and diagnosis under limited data

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ADVANCED ENGINEERING INFORMATICS 2024年 62卷

作者： Magadan, L. Ruiz-Carcel, C. Granda, J. C. Suarez, F. J. Starr, A. Univ Oviedo Dept Comp Sci & Engn Gijon 33204 Spain Univ Cranfield Sch Aerosp Transport & Mfg Bedford MK43 0AL England

Rotating machinery plays an essential role in various industrial processes such as manufacturing, power generation, and transportation. These machines, which include turbines, pumps, motors, compressors, and many others, are the heartbeats of numerous industries. The seamless operation of these machines is critical for the efficiency and productivity of these sectors. However, over time, these machines degrade and can suffer faults. One of the most critical components are bearings, which can suffer different types of faults. This paper presents a novel approach for bearing fault classification and diagnosis under limited data. A Monotonic Smoothed stacked autoencoder (MS2AE) is used to infer a smoothed monotonic health index from raw bearing acceleration data. The MS2AE is trained using only healthy data, so this approach can also be used with recently comisioned equipment that has not failed yet. Then, using the evolution of the health index, a first faulty point is computed, so two stages are identified in the lifespan of the rotating machinery: healthy and faulty. Correlation matrices are computed to show the relationship of the health index with time-domain and frequency-domain features in order to provide explainability and validate the health index construction process. When the health index is classified as faulty, Dynamic Time Warping is applied between healthy samples and faulty samples to extract differences. Finally, based on a 1/3-binary tree 3 level kurtogram, these differences are filtered using a bandpass filter and converted to the frequency domain, where characteristic harmonics are used to identify the type of bearing fault. The explainability provided in the health index construction process makes the system useful in certain industries where black-box AI models cannot be trusted due to strict regulations. The classification and diagnosis system achieves robustness in fault classification under different working conditions by utilizing multiple be

关键词： Industry 4.0 Fault diagnosis Fault classification Rotating machinery Dynamic time warping stacked autoencoder Explainable AI

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Research on Deep Knowledge Tracking Incorporating Rich Features and Forgetting Behaviors

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Journal of Harbin Institute of Technology(New Series) 2022年第4期29卷 1-6页

作者： Lasheng Yu Xiaopeng Zheng School of Computer Science and Engineering Central South UniversityChangsha 410083China

The individualization of education and teaching through the computer⁃aided education system provides students with personalized learning,so that each student can obtain the knowledge they *** this stage,there are a lot of intelligent tutoring *** these systems,students􀆳learning actions are tracked in real⁃time,and there are a lot of available *** these data,personalized education that suits each student can be *** improve the quality of education,some models for predicting students􀆳next practice have been produced,such as Bayesian Knowledge Tracing(BKT),Performance Factor Analysis(PFA),and Deep Knowledge Tracing(DKT)with the development of deep ***,the model only considers the knowledge component and correctness of the problem,ignoring the breadth of other characteristics of the information collected by the intelligent tutoring system,the lag time of the previous interaction,the number of past attempts to a problem,and situations that students have forgotten the *** some studies consider forgetting and rich information when modeling student knowledge,they often ignore student learning *** main contribution of this paper is in two *** is to transform the input into a position feature vector by introducing an auto⁃encoding network layer and to carry out multiple sets of bad political *** other is to consider repeated time intervals,sequence time intervals,and the number of attempts to simulate forgetting *** paper proposes an adaptive algorithm for the original DKT *** using the stacked auto⁃encoder network,the input dimension is reduced to half of the original and the original features are retained and consider the forgetting memory behavior according to the time sequence of students􀆳*** model proposed in this paper has been experimented on two public data sets to improve the original accuracy.

关键词： LSTM knowledge of tracking DKT stacked autoencoder forgetting behavior feature information

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Tourism demand forecasting: An ensemble deep learning approach

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TOURISM ECONOMICS 2022年第8期28卷 2021-2049页

作者： Sun, Shaolong Li, Yanzhao Guo, Ju-e Wang, Shouyang Xi An Jiao Tong Univ Sch Management 28 Xianning West Rd Xian 710049 Peoples R China Chinese Acad Sci Acad Math & Syst Sci Management Sci Beijing Peoples R China Univ Chinese Acad Sci Management Sci & Engn Beijing Peoples R China Chinese Acad Sci Ctr Forecasting Sci Beijing Peoples R China

The availability of tourism-related big data increases the potential to improve the accuracy of tourism demand forecasting but presents significant challenges for forecasting, including curse of dimensionality and high model complexity. A novel bagging-based multivariate ensemble deep learning approach integrating stacked autoencoder and kernel-based extreme learning machine (B-SAKE) is proposed to address these challenges in this study. By using historical tourist arrival data, economic variable data, and search intensity index (SII) data, we forecast tourist arrivals in Beijing from four countries. The consistent results of multiple schemes suggest that our proposed B-SAKE approach outperforms the benchmark models in terms of level accuracy, directional accuracy, and even statistical significance. Both bagging and stacked autoencoder can effectively alleviate the challenges brought by tourism big data and improve the forecasting performance of the models. The ensemble deep learning model we propose contributes to tourism demand forecasting literature and benefits relevant government officials and tourism practitioners.

关键词： bagging economic variables ensemble deep learning search intensity index stacked autoencoder tourism demand forecasting

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An Abnormal Data Processing Method Based on An Ensemble Algorithm for Early Warning of Wind Turbine Failure

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IEEE ACCESS 2021年 9卷 69044-69060页

作者： Zhao, Qiang Bao, Kunkun Wei, Zhenfan Han, Yinghua Wang, Jinkuan Northeastern Univ Qinhuangdao Sch Control Engn Qinhuangdao 066004 Hebei Peoples R China Northeastern Univ Qinhuangdao Sch Comp & Commun Engn Qinhuangdao 066004 Hebei Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

In the modeling process of fault warning models, modeling data plays an important role, the quality of which affects the performance of the model. The manual selection of modeling data according to fault records is time-consuming and makes it difficult to guarantee the high quality of the data because of inconsistencies, errors, and losses of records in the fault log file. For this reason, the present study proposes a framework of abnormal data processing based on an unsupervised serialization ensemble algorithm, which considers the high dimensional characteristics of operational data and the relationship between known low dimensional variables. Meanwhile, the influence of modeling data using different data processing methods on fault prediction performance is studied. The improved stacked autoencoder (I-SAE) based on the idea of partial data reconstruction is proposed to learn the high dimensional characteristics of operational data, which can enhance the separability of normal and abnormal operational data. The improved density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm based on the density ratio is developed to handle sparse abnormal data with data imbalance characteristics. Finally, the case analysis results demonstrate that the abnormal data processing model proposed in this article has better performance than other methods, and the performance of the fault prediction model can be effectively enhanced by improving the quality of modeling data.

关键词： Data models Wind turbines Predictive models Data processing Mathematical model Analytical models Training Density ratio early warning serialization ensemble stacked autoencoder

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Predicting soluble solids content in "Fuji" apples of different ripening stages based on multiple information fusion

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PATTERN RECOGNITION LETTERS 2021年 151卷 76-84页

作者： Huang, Yirui Wang, Juan Li, Na Yang, Jing Ren, Zhenhui Hebei Agr Univ Coll Mech & Elect Engn Baoding Peoples R China

Soluble solids content (SSC) is one of the important components for the assessment of fruit quality. Near infrared (NIR) spectroscopy, multispectral and hyperspectral imaging techniques are widely used methods for the SSC estimation. However, the high prices are unaffordable f or small commercial orchards. The objective of this work is to present a computer vision system for the SSC estimation of "Fuji" apples in different ripening stages. Multiple information of the color channels (channel a * and channel b *) in L*a*b* color space translated from RGB images were applied to extract color features. stacked autoencoder (SAE) algorithm was used to extract color features in pixel-level, and the trained parameters were used as the initial parameters of the prediction model by using back propagation neural network (BPNN) algorithm. The results show that the prediction accuracy of the SAE-BPNN model based on color features in pixel level is higher than that of the BPNN model based on pure color features in feature-level. Moreover, the model with fusion information of both color channels as inputs can get better prediction results than that of single channel. The SAE-BPNN model with SAE structure of 270-120-30 yielded the best result with R-p(2) = 0.5953 and RMSEP = 0.8856%. This research indicates that RGB imaging technology based on multiple information has potential for the SSC prediction, which provides reference values for fruit harvesting and non-destructive evaluation of fruit quality. (C) 2021 Elsevier B.V. All rights reserved.

关键词： RGB imaging Soluble solids content L*a*b* color space stacked autoencoder Back propagation neural network

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