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A Novel Prediction Method for Blast Furnace Gas Utilization Rate Based on Dynamic Weighted stacked Output-Relevant autoencoder

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STEEL RESEARCH INTERNATIONAL 2023年第5期94卷

作者： Jiang, Zhaohui Zhu, Jicheng Pan, Dong Yu, Haoyang Zhou, Ke Gui, Weihua Cent South Univ Sch Automat Changsha 410083 Peoples R China Peng Cheng Lab Shenzhen 518000 Peoples R China

In the blast furnace (BF) ironmaking process, the gas utilization rate (GUR) is a crucial indicator for reflecting the energy consumption and operating status of BF. However, due to the complex and harsh environment in the BF top, accurately obtaining GUR online is not an effortless task. Although many studies have been carried out to predict GUR through data-driven methods, some challenges still exist: 1) limited feature extraction capability for complex data patterns;2) prediction accuracy is sensitive to the fluctuation of BF working conditions. Therefore, a novel deep learning method is proposed based on dynamic weighted stacked output-relevant autoencoder (DW-SOAE) for GUR online prediction. First, the input layer variables for each AE are weighted according to their importance, which will reduce output-unrelated features. Then, the output variable is also reconstructed at the output layer of each AE, which ensures extracted features can largely predict GUR. Next, considering that the fluctuation of BF working conditions may affect prediction accuracy, the density peak clustering algorithm is used to cluster the process variables, and several DW-SOAE-based submodels are built for GUR prediction. Finally, the effectiveness and superiority of the proposed GUR prediction method are verified in industrial experiments.

关键词： blast furnace density peak clustering gas utilization rate prediction stacked autoencoder

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Adaptive parameter tuning stacked autoencoders for process monitoring

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SOFT COMPUTING 2020年第17期24卷 12937-12951页

作者： Kong, Diehao Yan, Xuefeng East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China Tongji Univ Shanghai Inst Intelligent Sci & Technol Shanghai 200237 Peoples R China

In process monitoring based on stacked autoencoders (SAEs), the performance of monitoring models is directly decided by the validity of the structure and parameters, which are primarily determined by time-consuming manual adjustments. This paper presents a novel method that can adaptively select parameters rather than tuning them manually. The proposed method is called adaptive parameter tuning SAE (APT-SAE). Basic SAEs aim to compress the original input data and extract simple and abstract features. Thus, the redundant information of each hidden layer output should be as small as possible. The next layer of nodes can be remarkably reduced if the amount of redundant information is large. During the pre-training stage of APT-SAE, an adaptive parameter tuning strategy is used for rapidly determining the number of layers and nodes in the paper. The cross-covariance of each AE's input data is used to determine the node number of succeeding AE. The pre-training stage ends when the correlation is weak, which is decided by the average value of cross-variance matrix. The proposed method is applied to a benchmark problem, and it outperforms several state-of-the-art methods.

关键词： Process monitoring autoencoder stacked autoencoder Adaptive parameter tuning

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A study of combination of autoencoders and boosted Big-Bang crunch theory architectures for Land-Use classification using remotely sensed imagery

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Scientific Reports 2025年第1期15卷 1-18页

作者： Xiong, Qiongbing Wu, Xuecheng Yu, Cizhen Hosseinzadeh, Hasan College of Tourism Management Guizhou University of Commerce Guiyang Guizhou 550014 China Ardabil Branch Islamic Azad University Ardabil Iran College of Technical Engineering The Islamic University Najaf Iraq

The research introduced a new method for land-use classification by merging deep convolutional neural networks with a modified variant of a metaheuristic optimization technique. The methodology involved utilizing the VGG-19 model for feature extraction, dimensionality reduction, and a stacked autoencoder optimized with a boosted version of the Big Bang Crunch Theory. Through testing on the Aerial Image Dataset the and UC Merced Land Use Dataset and comparing it with other published works, the approach showed higher classification accuracy compared to current state-of-the-art methods. The study revealed that incorporating boosted Big-Bang Crunch significantly enhances the performance of stacked autoencoder in land-use classification tasks. Moreover, comparisons with other techniques, including convolutional neural networks, Cascaded Residual Dilated Networks, hierarchical convolutional recurrent neural networks, Fusion Region Proposal Networks, and multi-level context-guided classification techniques using Object-Based Convolutional Neural Networks, emphasized the benefits of using Convolutional Neural Network models over traditional methods. The proposed model achieved an accuracy of 92.49% on the AID dataset and 95.93% on the UC Merced dataset, with precision scores of 98.64% and 98.93%, respectively. These results emphasize the importance of integrating deep learning architectures with sophisticated optimization techniques, contributing to enhanced land-use classification accuracy. © The Author(s) 2025.

关键词： Boosted Big-Bang crunch theory Deep learning Land-use classification Metaheuristic stacked autoencoder VGG-19

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Identification of multiple power quality disturbances in hybrid microgrid using deep stacked auto-encoder based bi-directional LSTM classifier

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2025年 11卷

作者： Jalli, Ravi Kumar Priyadarshini, Lipsa Dash, P.K. Bisoi, Ranjeeta Siksha 'O' Anusandhan Deemed to be University Odisha Bhubaneswar India Department of EEE GMR Institute of Technology Andhra Pradesh Rajam India

In recent years microgrid technology has created widespread interest for the integration of renewable energy sources into main utility grid to supply clean energy to the end users. However, the use of power electronic equipments, electronic controllers, and uncertain nature of the renewable energy sources, in the microgrid network power quality disturbances (PQD) are becoming quite complex and challenging task. Thus to design an effective PQD recognition system, this paper proposes a novel time-frequency analysis method based on adaptively fast complementary ensemble local mean decomposition (AFCELMD) technique that decomposes the multicomponent PQD signal into a series of demodulated product functions (PFs). Out of the several PFs the most sensitive one is selected adaptively and used for feature extraction and classification through a deep stacked auto-encoder (dSAE) hybridized with a time-recursive bi-directional long short term memory (BiLSTM) network classifier. The proposed BILSTM classifier captures the temporal features and their long term dependencies from the processed PF data samples and detects single and simultaneously occurring twenty complex power quality disturbances in the grid connected mode and five PQDs during uncertain PV insolence variation and load and capacitor switching during islanded mode of microgrid operation with significant accuracy of 99.90%. © 2025 The Author(s)

关键词： Bidirectional LSTM Microgrid Multiple power quality events Signal decomposition stacked autoencoder

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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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Feature Learning Using stacked autoencoders to Predict the Activity of Antimicrobial Peptides 13th

Feature Learning Using Stacked Autoencoders to Predict the A...

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13th International Conference on Computational Methods in Systems Biology (CMSB)

作者： Camacho, Francy Torres, Rodrigo Ramos-Pollan, Raul UIS Sch Comp Sci Bucaramanga Colombia UIS Sch Chem Grp Invest Bioquim & Microbiol GIBIM Bucaramanga Colombia UIS Sch Comp Sci Ctr High Performance & Sci Comp Bucaramanga Colombia

ISBN: (纸本)9783319234014;9783319234007

In recent years, pattern recognition methods have been applied to determine the activity of biological molecules, including the prediction of antimicrobial activity of synthetic and natural peptides where Quantitative Structure-Activity Relationship methodologies are widely used. Traditionally, works focused on designing descriptors for sequences to yield better correlations with the biological activity and improve predictors performance. Albeit there have been remarkable results, the small size of available datasets leave large room for improvement. In this work, rather than hand-crafting new descriptors, our approach consists in automatically learning them from existing ones. We use stacked autoencoders (a class of unsupervised neural networks), and the descriptors learnt are fed to a support vector regression task to predict biological activity. This method improves results in existing literature by roughly 12% simultaneously in different metrics, providing interesting insights into the nature of descriptors learnt and suggesting its applicability in other areas in protein properties prediction.

关键词： autoencoder stacked autoencoder Antimicrobial peptides Support vector regression

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Enhanced Deep autoencoder Based Feature Representation Learning for Intelligent Intrusion Detection System

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Computers, Materials & Continua 2021年第9期68卷 3271-3288页

作者： Thavavel Vaiyapuri Adel Binbusayyis College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

In the era of Big data,learning discriminant feature representation from network traffic is identified has as an invariably essential task for improving the detection ability of an intrusion detection system(IDS).Owing to the lack of accurately labeled network traffic data,many unsupervised feature representation learning models have been proposed with state-of-theart ***,these models fail to consider the classification error while learning the feature ***,the learnt feature representation may degrade the performance of the classification *** the first time in the field of intrusion detection,this paper proposes an unsupervised IDS model leveraging the benefits of deep autoencoder(DAE)for learning the robust feature representation and one-class support vector machine(OCSVM)for finding the more compact decision hyperplane for intrusion ***,the proposed model defines a new unified objective function to minimize the reconstruction and classification error *** unique contribution not only enables the model to support joint learning for feature representation and classifier training but also guides to learn the robust feature representation which can improve the discrimination ability of the classifier for intrusion *** set of evaluation experiments are conducted to demonstrate the potential of the proposed ***,the ablation evaluation on benchmark dataset,NSL-KDD validates the design decision of the proposed ***,the performance evaluation on recent intrusion dataset,UNSW-NB15 signifies the stable performance of the proposed ***,the comparative evaluation verifies the efficacy of the proposed model against recently published state-of-the-art methods.

关键词： Cybersecurity network intrusion detection deep learning autoencoder stacked autoencoder feature representational learning joint learning one-class classifier OCSVM

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Data Analytics of Network Intrusion Based on Deep Neural Networks with Weights Initialized by stacked autoencoders and Deep Belief Networks 13

Data Analytics of Network Intrusion Based on Deep Neural Net...

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IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC)

作者： Wang, Lidong Mosher, Reed L. Duett, Patti Falls, Terril C. Mississippi State Univ Inst Syst Engn Res Mississippi State MS 39762 USA

ISBN: (纸本)9798350332865

Data analytics is performed based on the deep neural network (DNN) with weights initialized by the stacked autoencoder (SA), DNN with weights initialized by the deep belief network (DBN), respectively in this paper. False positive rate (FPR), false negative rate (FNR), and accuracy are used as evaluation metrics to assess the performance of the methods. The data analytics mainly includes: 1) the effect of the hidden layer structures on the FPR, FNR, and accuracy of a DNN with weights initialized by the SA;2) the effect of the visible dropout on the accuracy of a DNN with weights initialized by the SA;3) the effect of the combination of the visible dropout and the hidden dropout on the accuracy of a DNN with weights initialized by the SA;4) the effect of hidden layer structures on the FPR, FNR, and accuracy of a DNN with weights initialized by the DBN;and 5) the effect of the batch size on the accuracy of a DNN with weights initialized by the DBN.

关键词： data analytics network intrusion cybersecurity deep neural network stacked autoencoder deep belief network dropout

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An autoencoder-based spectral clustering algorithm

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SOFT COMPUTING 2020年第3期24卷 1661-1671页

作者： Li, Xinning Zhao, Xiaoxiao Chu, Derun Zhou, Zhiping Jiangnan Univ Sch Internet Things Engn Wuxi 214122 Jiangsu Peoples R China Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China

Spectral clustering algorithm suffers from high computational complexity due to the eigen decomposition of Laplacian matrix and large similarity matrix for large-scale datasets. Some researches explore the possibility of deep learning in spectral clustering and propose to replace the eigen decomposition with autoencoder. K-means clustering is generally used to obtain clustering results on the embedding representation, which can improve efficiency but further increase memory consumption. An efficient spectral algorithm based on stacked autoencoder is proposed to solve this issue. In this paper, we select the representative data points as landmarks and use the similarity of landmarks with all data points as the input of autoencoder instead of similarity matrix of the whole datasets. To further refine clustering result, we combine learning the embedding representation and performing clustering. Clustering loss is used to update the parameters of autoencoder and cluster centers simultaneously. The reconstruction loss is also included to prevent the distortion of embedding space and preserve the local structure of data. Experiments on several large-scale datasets validate the effectiveness of the proposed method.

关键词： Spectral clustering stacked autoencoder Sparse representation KL divergence

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Electroencephalogram Emotion Recognition Based on 3D Feature Fusion and Convolutional autoencoder

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2021年 15卷 743426页

作者： An, Yanling Hu, Shaohai Duan, Xiaoying Zhao, Ling Xie, Caiyun Zhao, Yingying Beijing Jiaotong Univ Inst Informat Sci Beijing Peoples R China Northwest Univ Sch Econom & Management Xian Shaanxi Peoples R China Hebei Univ Coll Qual & Syst Supervis Baoding Peoples R China Hebei Univ Coll Elect & Informat Engn Baoding Peoples R China Machine Vis Technol Creat Ctr Hebei Prov Baoding Peoples R China

As one of the key technologies of emotion computing, emotion recognition has received great attention. Electroencephalogram (EEG) signals are spontaneous and difficult to camouflage, so they are used for emotion recognition in academic and industrial circles. In order to overcome the disadvantage that traditional machine learning based emotion recognition technology relies too much on a manual feature extraction, we propose an EEG emotion recognition algorithm based on 3D feature fusion and convolutional autoencoder (CAE). First, the differential entropy (DE) features of different frequency bands of EEG signals are fused to construct the 3D features of EEG signals, which retain the spatial information between channels. Then, the constructed 3D features are input into the CAE constructed in this paper for emotion recognition. In this paper, many experiments are carried out on the open DEAP dataset, and the recognition accuracy of valence and arousal dimensions are 89.49 and 90.76%, respectively. Therefore, the proposed method is suitable for emotion recognition tasks.

关键词： emotion recognition differential entropy feature fusion convolution neural network stacked autoencoder

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