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A Multi-Branch Deep Feature Fusion Network with SAE for Rare Earth Extraction Process Simulation

PROCESSES 2024年第12期12卷 2861页

作者： Xu, Fangping Zhu, Jianyong Wang, Wei East China Jiaotong Univ Sch Elect & Automat Engn Nanchang 330013 Peoples R China East China Jiaotong Univ State Key Lab Performance Monitoring & Protecting Nanchang 330013 Peoples R China

The Rare Earth Extraction Process (REEP) model is difficult to accurately establish via the extraction mechanism method due to its high complexity. This paper proposes a multi-branch deep feature fusion network with SAE (SAE-MBDFFN) for modeling REEP. We first design a neural network with a multi-branch output structure to simulate the cascade REEP by introducing a multiscale feature fusion mechanism, which can simultaneously concatenate hidden features, original features, and inter-branch coupling features. In order to deal with insufficient labeled data during model training, we then adopt a stacked sparse auto-encoder (SAE) technology to extract the hidden information of mass unlabeled data based on unsupervised learning. This technology can determine the initial parameters of SAE-MBDFFN by unsupervised pretraining. The design methodology of the network is well-founded. Experiments on industrial data indicate that the proposed method has the lowest initial loss value and a faster convergence rate in the fine-tuning stage than other comparison methods, while the prediction accuracy is better well. These results show the effectiveness of the proposed method.

关键词： multi-branch network feature fusion unsupervised learning sparse auto-encoder rare earth extraction process

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Novelty detection on a laboratory benchmark slender structure using an unsupervised deep learning algorithm

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LATIN AMERICAN JOURNAL OF SOLIDS AND STRUCTURES 2023年第9期20卷

作者： Finotti, Rafaelle Piazzaroli da Silva, Clayton Felicio Oliveira, Pedro Henrique Eveling Barbosa, Flavio de Souza Cury, Alexandre Abrahao Silva, Rafael Cerqueira Univ Fed Juiz De Fora Programa Posgrad Engn Civil Juiz De Fora MG Brazil Univ Brasilia Programa Posgrad Geotecn Brasilia DF Brazil Univ Fed Juiz De Fora Programa Posgrad Modelagem Computac Juiz De Fora MG Brazil

The process that involves the continuous monitoring and analysis of a structure's behavior and performance is known as Structural Health Monitoring (SHM). SHM typically involves the use of sensors and other monitoring devices to collect data, such as displacements, strains, accelerations, among others. These data are analyzed using advanced algorithms and machine learning techniques to identify any signs of abnormal behavior or deterioration. This paper presents a numerical and experimental study of a slender frame subjected to five levels of structural changes under impact loading. The dynamic responses were recorded by four accelerometers and used to build models based on sparse auto -encoders (SAE). Such models can identify each of the five structural states in an unsupervised way. A new strategy to define the hyperparameters of the SAE is presented, which proved to be adequate in the experiments conducted. Finally, the experimental data set is made available to the scientific community to serve as a benchmark for validating SHM methodologies to identify structural changes from dynamic measurements.

关键词： Structural Health Monitoring Damage Detection Vibration Signals Unsupervised Deep Learning sparse auto-encoder

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Transfer fault diagnosis based on local maximum mean difference and K-means

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COMPUTERS & INDUSTRIAL ENGINEERING 2022年第PartA期172卷

作者： Zhang, Xue-yang He, Lang Wang, Xiao-kang Wang, Jian-qiang Cheng, Peng-fei Shenzhen Univ Inst Big Data Intelligent Management & Decis Makin Coll Management Shenzhen 518060 Peoples R China Cent South Univ Sch Business Changsha 410083 Peoples R China Hunan Univ Sci & Technol Hunan Engn Res Ctr Intelligent Decis Making & Big Xiangtan 411201 Peoples R China

Existing feature-based transfer learning methods have achieved great performance in the transfer fault diagnosis with unlabeled data. While most of them are global alignment methods based on maximum mean difference (MMD), which ignore the differences between different faults and pay little attention to the structural infor-mation in the unlabeled target samples. This paper proposes a transfer sparse auto-encoder (SAE) based on local maximum mean difference (LMMD) and K-means to solve the above problems. Firstly, we build a deep network based on SAE and LMMD for learning a common latent feature space where source and target subdomains are aligned. Subsequently, to fully explore the target domain information, we put forward the K-means-based method which can obtain final diagnosis results by synthesizing the source and target domain information in the latent feature space. Lastly, a case study is conducted to verify the robustness and effectiveness of the proposed methods. The experimental result demonstrates that the proposed methods outperform the MMD-based methods in the transfer fault diagnosis problem.

关键词： Fault diagnosis K-means Local maximum mean difference sparse auto-encoder Transfer learning

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Fault diagnosis for rotor based on multi-sensor information and progressive strategies

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MEASUREMENT SCIENCE AND TECHNOLOGY 2023年第6期34卷 065111-065111页

作者： Hu, Xiao Wei, Daohong Liu, Dong Xiao, Zhihuai Xia, Xiangchen Malik, O. P. North China Univ Water Resources & Elect Power Coll Energy & Power Engn Zhengzhou 450045 Peoples R China Wuhan Univ Sch Power & Mech Engn Wuhan 430072 Peoples R China China Natl Water Resources& Elect Power Mat & Equi Beijing 100040 Peoples R China Univ Calgary Dept Elect & Comp Engn Calgary AB T2N 1N4 Canada

Fault diagnosis is an effective tool to ensure safe operation of machinery and avoid serious accidents. As most currently used fault diagnosis methods usually employ mapping relationship established by training samples and their labels to achieve classification of testing samples, it is difficult for them to achieve fault diagnosis under the condition of incomplete training sample types. In addition, previous studies usually focus on feature extraction of single-channel vibration signal, which cannot get complete fault feature information. To solve the above problems, a progressive fault diagnosis method is investigated in this paper. First, the preliminary fault detection for the rotor is performed by studying reconstruction error of a sparse auto-encoder. Second, if a fault exists in the rotor, the outlier detection is implemented by the support vector data description method. Finally, if there are no outlier samples, the well -trained support vector machine is used to confirm the type of fault samples and complete the diagnosis. The performance of the proposed method was verified using the data obtained from a rotor laboratory bench. The types of rotor states investigated include normal, contact-rubbing, unbalance and misalignment. The experimental results verify the effectiveness and superiority of the proposed method in reducing the incidents of fault omission and fault misunderstanding.

关键词： rotor fault diagnosis sparse auto-encoder support vector data description progressive strategies

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A Novel Network Intrusion Detection Method Based on DSAE-PSOCNN Model

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ENGINEERING LETTERS 2022年第4期30卷 1306页

作者： Sun, Qiaochu Dai, Hong Xu, Yao Shi, Tianwei Univ Sci & Technol Liaoning Sch Comp Sci & Software Engn Anshan Peoples R China

Network intrusion detection plays a vital role in information network security protection. To solve the deficiency of the feature dimension reduction and the detection performance, we propose a novel intrusion detection model, referred to as DSAE-PSOCNN. The proposed model is a deep learning model which fuses with the improved sparse auto-encoder (SAE) and optimization of Convolutional Neural Network (CNN) based on Particle Swarm Optimization (PSO). The intrusion data has problems such as high feature dimension and noise data, which may cause overfitting. The model of DSAE designs SAE based on Directed Acyclic Graph (DAG) structure to solve the above problems. It extracts differentially the characteristics of each attack type by analytic hierarchy process and gets the high correlation features. The hyper-parameters of CNN are optimized by using PSO algorithm in PSOCNN model to select independently the best CNN structure without the guidance of experience. Finally, the excellence of the proposed model is verified on the CIC-IDS2017 dataset. DSAE-PSOCNN model achieves an accuracy of 98.6% and it compares with the other three models. We conclude that DSAE-PSOCNN model outperforms the comparative models in the precision and recall rate. The suggested model provides an effective solution to the feature dimension reduction.

关键词： intrusion detection sparse auto-encoder convolutional neural network particle swarm optimization directed acyclic graph

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Lithium batteries State of Health estimation based on optimized SAE-Transformer network 23

Lithium batteries State of Health estimation based on optimi...

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Proceedings of the 4th International Conference on Artificial Intelligence and Computer Engineering

作者： Liefa Liao Haoqiang Ding E Tang Jiangxi University of Science and Technology China

ISBN: (纸本)9798400708831

Accurate prediction of the State of Health (SOH) of lithium batteries is of paramount importance in mitigating the risks associated with battery usage. The emergence of artificial intelligence and deep learning has introduced innovative data-driven methodologies for forecasting the SOH of lithium-ion batteries. In this paper, we present a deep learning framework that leverages sparse autoencoders (SAE) and Transformer networks for SOH prediction in lithium-ion batteries. Prior to model training, we employ the Particle Swarm Optimization (PSO) algorithm to fine-tune the model parameters. Subsequently, the sparse autoencoder is utilized to extract and integrate degradation features from lithium-ion batteries. These amalgamated features serve as inputs, while battery capacity serves as the output, thereby establishing a comprehensive predictive model. To validate our methodology, we conducted experiments using a battery capacity dataset provided by the Center for Advanced Life Cycle Engineering (CALCE) at the University of Maryland. We benchmarked the performance of our method against existing algorithms, such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The results demonstrate the superior predictive accuracy of our proposed approach, with a Mean Absolute Error (MAE) of 0.0186 and a Root Mean Square Error (RMSE) of 0.0268, affirming the effectiveness of our research approach.

关键词： Lithium-ion battery sparse auto-encoder State of health Transformer

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Feature Selection using Multiple auto-encoders

Feature Selection using Multiple Auto-Encoders

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International Joint Conference on Neural Networks

作者： Xinyu Guo Ali A. Minai Long J. Lu Department of Electrical Engineering and Computing Systems University of Cincinnati Division of Biomedical Informatics Cincinnati Children's Hospital Research Foundation

ISBN: (纸本)9781509061839

Real-world data such as medical images and sensor measurements is usually high-dimensional and limited. Using such datasets directly in machine learning tasks can lead to poor generalization. Feature learning is a general approach for transforming high-dimensional data points to a representational space with lower dimensionality. Machine learning models can be trained efficiently with such representations. In this paper, a novel feature selection method based on multiple trained sparse auto-encoders (SAEs) is described. It works by selecting diverse, non-redundant features from multiple pinched SAEs with very narrow hidden layers, and then using these features in a more appropriately sized classifier without further feature tuning. The feature learning ability of the method is evaluated in a handwritten digits recognition task. Results show that this type of feature selection provides improved representations for a softmax classifier, and that using pinched SAEs produces results equal to or better than regular SAEs.

关键词： Machine learning Feature learning Handwritten digits recognition sparse auto-encoder Feature diversity Feature selection Feature extraction Artificial Intelligence Machine Learning representations

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An Offline Text-independent Writer Identification System with SAE Feature Extraction

An Offline Text-independent Writer Identification System wit...

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2016 IEEE International Conference on Progress in Informatics and Computing

作者： Yu Zhu Yining Wang School of Information Science and Engineering East China University of Science and Technology

This paper presents an offline text-independent writer identification system in a multi-language environment using sparse auto-encoder(SAE) based codebook(SAEC) and non-text-segmentation feature extraction methods. The proposed codebook is designed by SAE structure and clustering. The superior of the designed frame work is that features can be effectively extracted and without the pre-step of text segmentation. The novel codebook can deal with mixed-languages at the same time. Hand-written texts in Chinese and English are considered in this study. We use both HIT Chinese database and IAM offline English database. The classification rate achieves the challenging accuracy of 95.56% concerning top 1 and 99.17% concerning top 10 on the two mixed databases. Another interesting aspect of our study is the evaluation of the factors such as patch sizes, patch numbers and the amount of text that influence the identification results.

关键词： offline text-independent writer identification sparse auto-encoder Codebook

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Knowledge discovery in databases based on deep neural networks

Knowledge discovery in databases based on deep neural networ...

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IEEE Conference on Industrial Electronics and Applications

作者： Yuanhua Tan Chaolin Zhang Yonglin Ma Yici Mao Karamay Hongyou Software Co. Karamay China

ISBN: (纸本)9781479984671

Knowledge discovery in databases (KDD) has received great progress in recent years for the need of mining useful knowledge in the ever growing information. The advances in machine learning technologies effectively promote KDD in the procedures of feature extraction and data categorization. This paper introduces a framework that combines feature extraction and categorization of the collected data in order to recognize useful structured patterns that underlies the raw data. This frame work consists of three modules: data pre-processing module, feature extraction module, and feature classification module. We propose a four-layered deep neural network as the feature extraction architecture. Each layer is trained in an unsupervised way as one auto-encoder with sparsity constraint. We employ a softmax classifier to assign a label to the extracted feature. The supervised and unsupervised training strategies are discussed at the end of this paper to disambiguate the training procedure of the entire model.

关键词： Knowledge discovery deep neural network sparse auto-encoder softmax classification

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