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An Evaluation of Mine Water Inrush Based on Data Expansion and Machine Learning

APPLIED SCIENCES-BASEL 2025年第8期15卷 4229-4229页

作者： Zhang, Ye Tang, Shoufeng China Univ Min & Technol Sch Informat & Elect Engn Xuzhou 221000 Peoples R China

The accuracy of coal mine water inrush prediction models is affected mainly by the small number of samples and difficulty in feature extraction. A new data augmentation water inrush prediction method is proposed. This method uses the natural neighbor theory and mutual information sparse autoencoder-improved SMOTE to augment and predict the risk of water inrush. By learning features through the autoencoder, we can achieve better separation between classes and weaken the influence of data overlap between classes in the original sample. Then, the natural neighbor search algorithm is used to determine the intrinsic neighbor relationships between samples, remove outliers and noise samples, and use different oversampling methods for borderline samples and center samples in the minority class. Synthetic samples are generated in the feature space, mapped back to the original space, and merged with the original samples to form an expanded water inrush dataset. Finally, the experiment demonstrates that the enhanced SMOTE oversampling algorithm suggested in this paper broadens the dataset. With a Gmean value of 0.9025 from training with the standard dataset, it outperforms the contrast algorithm, SMOTE average of 0.8581, B-SMOTE average of 0.873, and ADASYN average of 0.8909. Additionally, it performs well in the coal mine floor water inrush dataset, increasing the water inrush prediction algorithm's accuracy.

关键词： mine water inrush feature extraction natural neighbor theory improved SMOTE mutual information dropout sparse autoencoder

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Detection of human movement by combining supervised machine learning and an embroidered textile capacitance sensor

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TEXTILE RESEARCH JOURNAL 2025年第3-4期95卷 305-322页

作者： Kim, Ji-seon Kim, Jooyong Soongsil Univ 369 Sangdo Ro Seoul 06978 South Korea

This study contributes to respiratory pattern detection by introducing a fabric sensor utilizing capacitance measurement and a semi-supervised machine learning algorithm known as an AI-based autoencoder. The sensor, consisting of two embroidered electrodes composed of silver-coated conductive nylon filaments, leverages the body as a dielectric material. In the research, a garment-type respiratory sensor was employed to continuously monitor respiratory data during both static (standing) and dynamic (walking, brisk walking, running) actions. The sparse autoencoder algorithm was particularly employed for individual static and dynamic actions, effectively distinguishing respiratory patterns corresponding to various movements. In addition, the sparse autoencoder helps prevent overfitting, fundamentally minimizing errors between the compression and reconstruction of signals. The maximum number of epochs was set to 2000, and the target error was set at 0.005. All data were compared against the static walking as the training baseline. Ultimately, the root mean squared error (RMSE) between static postures averaged 0.1, while the RMSEs between dynamic actions of walking, brisk walking, and running were 0.61, 0.91, and 2.78, respectively. These results suggest that movement detection through error detection is practically feasible and possesses discernible capabilities.

关键词： Respiratory data sparse autoencoder embroidered sensor capacitance detection of human movement fabrication

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A hybrid model for detecting intrusions using stacked autoencoders and extreme gradient boosting

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COMPUTERS & SECURITY 2025年 150卷

作者： Vinayak, M. V. Hari Jarin, T. APJ Abdul Kalam Technol Univ Jyothi Engn Coll Dept Elect & Commun Engn Thiruvananthapuram Kerala India APJ Abdul Kalam Technol Univ Jyothi Engn Coll Dept Elect & Elect Engn Thiruvananthapuram Kerala India

In the contemporary digital landscape dominated by the internet, a wide array of attacks occurs daily, driven by a large and diverse user base. The field of identifying these cyberattacks is rapidly growing and is mainly accomplished through the utilization of intrusion detection systems (IDS). The IDS is designed to continuously observe data flow and identify any potentially harmful or suspicious acts that could signal a cyberattack. Traditional machine learning (ML) techniques encounter challenges ineffectively detecting unknown attacks and dealing with imbalanced data distributions, resulting in reduced detection performance. This paper presents a hybrid IDS model that integrates an ML classifier like XGBoost with a stacked sparse autoencoder (SSAE). The low-dimensional features obtained from the SSAE are utilized for training the classifier. The experimental outcomes indicate that the model surpasses the formerly recommended approaches regarding intrusion detection and decreases the ML classifier's training and testing times. We have also evaluated our model's performance by comparing it with other advanced techniques documented in the existing literature.

关键词： Deep learning sparse autoencoder Intrusion detection systems Extreme gradient boosting (XGBoost) Network security

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Research on the Fault Diagnosis Method of Automotive Charging Pile Based on the Improved MLP with SAE

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2025年第3期20卷 1333-1345页

作者： Han, Yuanxun Ren, Mingzhu Ma, Qingliang Zhong, Chaochun Chen, Jing Liang, Xuming Zhou, Shaoyuan Zhejiang Inst Qual Sci Hangzhou 310007 Zhejiang Peoples R China Guangzhou Inst Measurement & Testing Technol Guangzhou 510000 Guangdong Peoples R China

In order to improve the situation that the fault data set of electric vehicle charging pile has unbalanced data distribution under each fault and the small amount of data leads to the inconspicuous data features, this paper proposes a method of SAE-MLP model for fault diagnosis of charging pile fault data. This paper firstly utilizes AE to realize the augmentation of the original data, which solves the problem of inconspicuous features, and utilizes the MLP neural network which is good at handling small sample data to classify the faults on the AE processed data. Combining the features of both SAE and MLP two models can effectively improve the accuracy of fault diagnosis of the data in this paper compared with the previous methods of small sample data processing, and minimizes computational costs.

关键词： Charging pile Faults diagnosis Multi-layer perceptions sparse autoencoder

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Reconstructing Waddington Landscape from Cell Migration and Proliferation

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INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES 2025年 1-14页

作者： Han, Yourui Chen, Bolin Bi, Zhongwen Zhang, Jianjun Hu, Youpeng Bian, Jun Kang, Ruiming Shang, Xuequn Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China Northwestern Polytech Univ Key Lab Big Data Storage & Management Minist Ind & Informat Technol Xian 710072 Peoples R China Northwestern Polytech Univ Sch Math & Stat Xian 710072 Peoples R China Xi An Jiao Tong Univ Xian Childrens Hosp Affiliated Childrens Hosp Dept Gen Surg Xian 710003 Peoples R China Rewise Hangzhou Informat Technol Co Ltd Hangzhou 310000 Peoples R China

The Waddington landscape was initially proposed to depict cell differentiation, and has been extended to explain phenomena such as reprogramming. The landscape serves as a concrete representation of cellular differentiation potential, yet the precise representation of this potential remains an unsolved problem, posing significant challenges to reconstructing the Waddington landscape. The characterization of cellular differentiation potential relies on transcriptomic signatures of known markers typically. Numerous computational models based on various energy indicators, such as Shannon entropy, have been proposed. While these models can effectively characterize cellular differentiation potential, most of them lack corresponding dynamical interpretations, which are crucial for enhancing our understanding of cell fate transitions. Therefore, from the perspective of cell migration and proliferation, a feasible framework was developed for calculating the dynamically interpretable energy indicator to reconstruct Waddington landscape based on sparse autoencoders and the reaction diffusion advection equation. Within this framework, typical cellular developmental processes, such as hematopoiesis and reprogramming processes, were dynamically simulated and their corresponding Waddington landscapes were reconstructed. Furthermore, dynamic simulation and reconstruction were also conducted for special developmental processes, such as embryogenesis and Epithelial-Mesenchymal Transition process. Ultimately, these diverse cell fate transitions were amalgamated into a unified Waddington landscape. [GRAPHICS] .

关键词： Waddington landscape Energy indicator Cell migration and proliferation Reaction diffusion advection equation sparse autoencoder

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Out-of-Roundness Wheel Damage Identification in Railway Vehicles Using autoencoder Models

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APPLIED SCIENCES-BASEL 2025年第5期15卷 2662-2662页

作者： Melo, Renato Finotti, Rafaelle Guedes, Antonio Goncalves, Vitor Meixedo, Andreia Ribeiro, Diogo Barbosa, Flavio Cury, Alexandre Univ Fed Juiz de Fora Grad Program Civil Engn BR-36036900 Juiz De Fora Brazil Univ Porto Fac Engn CONSTRUCT LESE P-4200465 Porto Portugal Polytech Porto Sch Engn CONSTRUCT LESE P-4200465 Porto Portugal

This study presents a comparative analysis of three autoencoder (AE) models-Variational autoencoder (VAE), sparse autoencoder (SAE), and Convolutional autoencoder (CAE)-to detect and quantify structural anomalies in railway vehicle wheels, such as polygonization. Vertical acceleration data from a virtual wayside monitoring system serve as input for training the AE models, which are coupled with Hotelling's T2 Control Charts to differentiate normal and abnormal railway component behaviors. The results indicate that the SAE-T2 model outperforms its counterparts, achieving 16.67% higher accuracy than the CAE-T2 model in identifying distinct structural conditions, although with a 35.78% higher computational cost. Conversely, the VAE-T2 model is outperformed in 100% of the analyzed scenarios when compared to SAE-T2 in identifying distinct structural conditions while also exhibiting a 21.97% higher average computational cost. Across all scenarios, the SAE-T2 methodology consistently provided better classifications of wheel damage, showing its capability to extract relevant features from dynamic signals for Structural Health Monitoring (SHM) applications. These findings highlight SAE's potential as an interesting tool for predictive maintenance, offering improved efficiency and safety in railway operations.

关键词： structural health monitoring railways damage detection out of roundness sparse autoencoder convolutional autoencoder variational autoencoder

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Stacked sparse autoencoders monitoring model based on fault-related variable selection

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SOFT COMPUTING 2021年第5期25卷 3531-3543页

作者： Yin, Jie Yan, Xuefeng East China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc MeiLong Rd 130POB 293 Shanghai 200237 Peoples R China

In the modern chemical industry, the process data collected are high-dimensional and complex. All measured variables are usually incorporated in statistical process monitoring models because these models generally perform dimension reduction. However, if modeling involves variables that do not contain useful information about faults, that is, variables that are not relevant to faults, monitoring performance may be degraded. In typical process monitoring methods, offline modeling only uses normal data without any fault information, making monitoring performance unlikely to be optimal. Hence, a novel stacked sparse autoencoder (SSAE) monitoring model based on fault-related variable selection was proposed. From the point of view that correlation characteristics between measured variables will change when faults occur, strongly fault-related variables are selected. Mutual information was used to calculate correlations between measured variables, including normal and fault data. Euclidean distance was adopted as a similarity index to measure the similarity between each correlation vector of measured variables in a normal state and that in a fault state. Only variables strongly related to fault effects were retained, and other uninformative variables were excluded from model development. Then, SSAEs were used to construct a monitoring model for selected data. The proposed method can utilize historical fault data to select strongly fault-related variables, making the model contain useful process information and features extracted by SSAE have high interpretability. A case study on the Tennessee-Eastman process demonstrated its availability.

关键词： Fault-related variable selection Mutual information Fault detection sparse autoencoder

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SSAE-MLP: Stacked sparse autoencoders-based multi-layer perceptron for main bearing temperature prediction of large-scale wind turbines

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2021年第17期33卷 e6315-e6315页

作者： Xiao, Xiaocong Liu, Jianxun Liu, Deshun Tang, Yufei Dai, Juchuan Zhang, Fan Hunan Univ Sci & Technol Sch Mech Engn Xiangtan Peoples R China Hunan Univ Sci & Technol Sch Comp Sci & Engn Xiangtan 411201 Peoples R China Florida Atlantic Univ Dept Comp & Elect Engn & Comp Sci Boca Raton FL 33431 USA

Condition monitoring and fault diagnosis of main bearings of large-scale wind turbines is critical for improving its reliability and reducing operating and maintenance costs, especially in the early stages. To achieve the goal, this paper proposes a novel deep learning approach named stacked sparse autoencoder multi-layer perceptron (SSAE-MLP) with a new framework by utilizing supervisory control and data acquisition (SCADA) data for wind turbine main bearing temperature prediction. After the SCADA parameter variables related to the temperature change of the main bearing are extracted, the input characteristic vector is constructed. Then, the multiple sparse autoencoders are stacked to learn the deep features inside the input data by applying the greedy layerwise unsupervised learning algorithm. Finally, a regression predictor is added to the top layer of the stacked sparse autoencoder model for supervised learning to fine-tune the overall network. Comparative experiments show that the proposed approach has superior performance for wind turbine main bearing temperature prediction.

关键词： deep learning model main bearing SCADA sparse autoencoder temperature prediction wind turbines

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Structural novelty detection based on sparse autoencoders and control charts

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STRUCTURAL ENGINEERING AND MECHANICS 2022年第5期81卷 647-664页

作者： Finotti, Rafaelle P. Gentile, Carmelo Barbosa, Flavio Cury, Alexandre Univ Fed Juiz de Fora Grad Program Computat Modeling Juiz De Fora Brazil Politecn Milan Dept Architecture Built Environm & Construct Engn Milan Italy Univ Fed Juiz de Fora Grad Program Civil Engn Juiz De Fora Brazil

The powerful data mapping capability of computational deep learning methods has been recently explored in academic works to develop strategies for structural health monitoring through appropriate characterization of dynamic responses. In many cases, these studies concern laboratory prototypes and finite element models to validate the proposed methodologies. Therefore, the present work aims to investigate the capability of a deep learning algorithm called sparse autoencoder (SAE) specifically focused on detecting structural alterations in real-case studies. The idea is to characterize the dynamic responses via SAE models and, subsequently, to detect the onset of abnormal behavior through the Shewhart T control chart, calculated with SAE extracted features. The anomaly detection approach is exemplified using data from the Z24 bridge, a classical benchmark, and data from the continuous monitoring of the San Vittore bell-tower, Italy. In both cases, the influence of temperature is also evaluated. The proposed approach achieved good performance, detecting structural changes even under temperature variations.

关键词： damage detection deep learning machine learning sparse autoencoder structural health monitoring

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A strategy for out-of-roundness damage wheels identification in railway vehicles based on sparse autoencoders

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Railway Engineering Science 2024年第4期32卷 421-443页

作者： Jorge Magalhães Tomás Jorge Rúben Silva António Guedes Diogo Ribeiro Andreia Meixedo Araliya Mosleh Cecília Vale Pedro Montenegro Alexandre Cury CONSTRUCT-LESE School of EngineeringPolytechnic of PortoPortoPortugal CONSTRUCT-LESE Faculty of EngineeringUniversity of PortoPortoPortugal Graduate Program in Civil Engineering Federal University of Juiz de ForaJuiz de ForaBrazil

Wayside monitoring is a promising cost-effective alternative to predict damage in the rolling stock. The main goal of this work is to present an unsupervised methodology to identify out-of-roundness(OOR) damage wheels, such as wheel flats and polygonal wheels. This automatic damage identification algorithm is based on the vertical acceleration evaluated on the rails using a virtual wayside monitoring system and involves the application of a two-step procedure. The first step aims to define a confidence boundary by using(healthy) measurements evaluated on the rail constituting a baseline. The second step of the procedure involves classifying damage of predefined scenarios with different levels of severities. The proposed procedure is based on a machine learning methodology and includes the following stages:(1) data collection,(2) damage-sensitive feature extraction from the acquired responses using a neural network model, i.e., the sparse autoencoder(SAE),(3) data fusion based on the Mahalanobis distance, and(4) unsupervised feature classification by implementing outlier and cluster analysis. This procedure considers baseline responses at different speeds and rail irregularities to train the SAE model. Then, the trained SAE is capable to reconstruct test responses(not trained) allowing to compute the accumulative difference between original and reconstructed signals. The results prove the efficiency of the proposed approach in identifying the two most common types of OOR in railway wheels.

关键词： OOR wheel damage Damage identification sparse autoencoder Passenger trains Wayside condition monitoring

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