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

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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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 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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Groundwater Pollution Source and Aquifer Parameter Estimation Based on a stacked autoencoder Substitute

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WATER 2024年第18期16卷 2564页

作者： Wang, Han Zhang, Jinping Li, Hang Li, Guanghua Guo, Jiayuan Lu, Wenxi Minist Water Resources Song Liao River Water Resources Commiss Changchun 130000 Peoples R China Song Liao River Water Resources Commiss River Basin Planning & Policy Res Ctr Changchun 130000 Peoples R China Jilin Univ Coll New Energy & Environm Changchun 130000 Peoples R China

A concurrent heuristic search iterative process (CHSIP) is used for estimating groundwater pollution sources and aquifer parameters in this work. Frequent calls to carry out a numerical simulation of groundwater pollution have generated a huge calculated load during the CHSIP. Therefore, a valid means to mitigate this is building a substitute to emulate the numerical simulation at a low calculated load. However, there is a complicated nonlinear correlativity between the import and export of the numerical simulation on account of the large quantity of variables. This leads to a poor approach accuracy of the substitute compared to the simulation when using shallow learning methods. Therefore, we first built a stacked autoencoder substitute, using the deep learning method, to boost the approach accuracy of the substitute compared to the numerical simulation. In total, 400 training samples and 100 testing samples for the substitute were collected by employing the Latin hypercube sampling method and running the numerical simulator. The CHSIP was then employed for estimating the groundwater pollution sources and aquifer parameters, and the estimated outcome was obtained when the CHSIP was terminated. The data analysis, including interval estimation and point estimation, was implemented on the MATLAB platform. A relevant hypothetical case is set to verify our approaches, which shows that the CHSIP is helpful for estimating the groundwater pollution source and aquifer parameters and that the stacked autoencoder method can effectively boost the approach precision of the substitute for the simulator.

关键词： groundwater pollution source estimation concurrent heuristic search iterative process deep learning method substitute stacked autoencoder

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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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Mechanistic block-based attention mechanism stacked autoencoder for describing typical unit connection industrial processes and their monitoring

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CANADIAN JOURNAL OF CHEMICAL ENGINEERING 2024年第1期102卷 291-306页

作者： Wang, Chenhao Tang, Xujia Yu, Jianbo Yang, Xiaofeng Yan, Xuefeng East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai Peoples R China Fudan Univ Sch Microelect Shanghai Peoples R China East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ POB 293MeiLong Rd 130 Shanghai 200237 Peoples R China

The overall information of a process can be obtained through global modelling, and the local information is easily ignored in the research of the industrial process monitoring of unit connection. Thus, finding the global type of faults is easy, but occurs at the expense of drowning out the local faults. The use of block modelling can highlight local information, thereby improving local fault detection capability. However, the connection information between blocks is usually ignored in block modelling, which makes finding fault classes that only affect the connection relationship between blocks difficult. A mechanistic block-based attention mechanism stacked autoencoder (MB-AMSAE) monitoring method is proposed in this paper. The industrial process is divided into several parts in accordance with its mechanistic relationships, and each part represents an independent block. Self-attention is used to focus on the information of each block itself. Cross-attention is adopted to focus on the information between blocks, and this information is fused to form new blocks. The new block is used as the feature of the original block, and the original block is reconstructed by using a stacked autoencoder. The corresponding control limit is obtained in accordance with the reconstruction ability of normal samples, and whether the working conditions are normal is judged according to the control limit. The proposed algorithm is used in numerical simulation, Tennessee-Eastman processes, and is compared with other advanced algorithms based on its fault detection capability. Results show the effectiveness of the MB-AMSAE algorithm in process monitoring.

关键词： attention mechanism block process monitoring stacked autoencoder

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Mutual stacked autoencoder for unsupervised fault detection under complex multi-residual correlations

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ADVANCED ENGINEERING INFORMATICS 2024年 62卷

作者： Yu, Jianbo Lv, Zhaomin Ruan, Hang Hu, Shijie Jiang, Qingchao Yan, Xuefeng Liu, Yuping Yang, Xiaofeng Fudan Univ Sch Microelect Shanghai 200433 Peoples R China Shanghai Univ Engn Sci Sch Urban Railway Transportat Shanghai 201620 Peoples R China Fudan Univ Acad Engn & Technol Shanghai 200433 Peoples R China East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China

Due to the increasing complexity of variable relationships, fault detection has garnered significant attention, as it is crucial for ensuring industrial safety and engineering reliability. Traditional detection methods can be classified as twofold: global-based and local-based strategies, which respectively focus on mining macro- and micro-level information. However, our theoretical derivation and experiment results reveal that some spurious assumptions, such as local groups and their provided information are mutually independent are implicitly adhered to but are hardly satisfied in unsupervised fault detection under real industrial scenarios. Hence, this study introduces a novel mutual stacked autoencoder (M-SAE) which can be divided into three subnetworks: L-Net, R-Net, and M-Net. L-Net enriches local information learning through multiple local backbones by incorporating the unsupervised clustering algorithm. R-Net, employing a multi-scale attention mechanism, leverages complete local information for residual strength calculation and utilizes local features to capture residual information within the latent feature space. M-Net fuses the multi-scale local feature information to perform a reconstruction for each local. A multitask entropy-aided loss function is introduced to enrich local details, the global structure, and the residual associations. Finally, results on eleven datasets validate the high-performance of the proposed M-SAE and the ablation experiments demonstrate the efficacy of each component in M-SAE, confirming that this research effectively and accurately addresses multivariable industrial fault detection tasks, thereby enabling timely interventions that are crucial for maintaining operational safety in real-world scenarios.

关键词： Fault detection Global-based strategy Local-based strategy stacked autoencoder

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Enhanced particle swarm optimization-based hyperparameter optimized stacked autoencoder for credit card fraud detection

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INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS 2024年 1-15页

作者： Abadlia, Houda Smairi, Nadia Univ Manouba ENSI LARIA UR22ES01 Campus Univ Manouba Manouba 2010 Tunisia

In recent years, several fraud attempts have been made in various sectors including finance, banking and insurance. In fact, credit card fraud refers to the unauthorized use of a credit card account to obtain money, products or services. It involves the manipulation of card details for fraudulent purchases or withdrawals. These fraudulent incidents result in substantial financial losses of different divisions of businesses. The present manuscript presents an innovative system used to detect credit card fraud employing unsupervised deep learning. However, the effectiveness of deep learning models relies on the configuration of hyperparameter values and the avoiding of overffiting issue, tasks that provide time-consuming and require significant trial and error. The proposed model utilizes an improved particle swarm optimization (PSO) to optimize the training hyperparameters such as global initial connection weights and thresholds, while leveraging stacked autoencoder for classification purposes. Unlike the existing model, that introduced in this work takes into account transactional data and enables the classifier to accurately identify the most crucial transactions within the input sequence, which allows predicting fraudulent transactions more accurately. This approach combines the strengths of three methods: SMOTE-Tomek for handling imbalanced data, local search PSO for hyperparameter optimization, and an enhanced stacked autoencoder used to detect anomaly in credit card fraud transactions. The comparative study reveals that the developed model is the most efficient, in comparison with the other models.

关键词： Fraud detection stacked autoencoder PSO Hyperparameter optimization Imbalanced Data

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Quantum stacked autoencoder fault diagnosis model for bearing faults

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INSIGHT 2023年第11期65卷 631-638页

作者： Yu, Tianyi Li, Shunming Lu, Jiantao Nanjing Univ Aeronaut & Astronaut NUAA Coll Energy & Power Engn Nanjing 210016 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Energy & Power Engn Nanjing Peoples R China Nantong Inst Technol Sch Automot Engn Nantong 226002 Peoples R China

The use of neural network models to monitor bearing vibration signals can easily be affected by noise, which leads to a decrease in the model test accuracy. Therefore, the existence of noise problems increases the requirements for non-linear mapping capability and robustness of deep neural network (DNN) models. In order to deal with the noise problem, the concept of qubit neurons is introduced into a deep learning stacked autoencoder (SAE) model, and a quantum stacked autoencoder (QSAE) model based on qubits and quantum gates is proposed. The properties of SAE layer-by-layer coding and the arithmetic of qubit neurons are combined in the QSAE. The quantum state signal is taken as the input signal to the encoder and the coding activation function and coding weight matrix are redefined by quantum-controlled non-gates and quantum revolving gates, so that the quantum state signal can be coded layer by layer. Experimental results show that the QSAE can train and diagnose noisy experimental data and maintain high test accuracy in an anti-attack test. This shows that the QSAE has non-linear mapping capability and robustness.

关键词： stacked autoencoder quantum neural network non-linear mapping robustness bearing fault

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Classification of data on stacked autoencoder using modified sigmoid activation function

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第1期44卷 1-18页

作者： Kumar, Arvind Sodhi, Sartaj Singh Comp Sci & Engn USICT GGSIPU Delhi India

A Neural Network is one of the techniques by which we classify data. In this paper, we have proposed an effectively stacked autoencoder with the help of a modified sigmoid activation function. We have made a two-layer stacked autoencoder with a modified sigmoid activation function. We have compared our autoencoder to the existing autoencoder technique. In the existing autoencoder technique, we generally use the logsigmoid activation function. But in multiple cases using this technique, we cannot achieve better results. In that case, we may use our technique for achieving better results. Our proposed autoencoder may achieve better results compared to this existing autoencoder technique. The reason behind this is that our modified sigmoid activation function gives more variations for different input values. We have tested our proposed autoencoder on the iris, glass, wine, ovarian, and digit image datasets for comparison propose. The existing autoencoder technique has achieved 96% accuracy on the iris, 91% accuracy on wine, 95.4% accuracy on ovarian, 96.3% accuracy on glass, and 98.7% accuracy on digit (image) dataset. Our proposed autoencoder has achieved 100% accuracy on the iris, wine, ovarian, and glass, and 99.4% accuracy on digit (image) datasets. For more verification of the effeteness of our proposed autoencoder, we have taken three more datasets. They are abalone, thyroid, and chemical datasets. Our proposed autoencoder has achieved 100% accuracy on the abalone and chemical, and 96% accuracy on thyroid datasets.

关键词： autoencoder sigmoid activation function logsigmoid neural network classification stacked autoencoder

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