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State estimation and visualization of lithium-ion battery using transformer autoencoder model

JOURNAL OF POWER ELECTRONICS 2025年第3期25卷 500-509页

作者： Woo, Tae-Geol Kim, Beom-Jun Cho, In-Ho Park, Kang-Moon Korea Natl Univ Transportat Dept Elect & Elect Engn Chungju South Korea

This study developed and evaluated artificial neural network models for estimating and visualizing the State of Health (SOH) of lithium-ion batteries (LIBs) used in railway vehicles. The models, which include autoencoder, Long Short-Term Memory (LSTM)-autoencoder, Attention LSTM-autoencoder, and Transformer autoencoder, were trained on large-scale time-series data of LIB voltage, current, and temperature. The Transformer autoencoder model demonstrated substantial performance improvements, achieving over a 99% enhancement compared to the basic autoencoder model and up to 82% improvement over the Attention LSTM-autoencoder model. Unlike classical Transformer models, which typically focus on compressing data into high-dimensional spaces, the autoencoder approach is applied to the Transformer model, facilitating low-dimensional compression and clustering of the data. This clustering technique was further employed to visualize battery health by converting the clustered data into RGB values, offering an intuitive representation of the SOH. By overcoming the limitations of traditional methods, this novel approach provides an effective means of assessing battery condition. These findings indicate that the proposed method could notably enhance battery management systems, leading to a safe and reliable operation of electric mobility systems.

关键词： Transformer autoencoder Deep learning Battery state State of health (SOH)

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Design and use of a Denoising Convolutional autoencoder for reconstructing electrocardiogram signals at super resolution

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2025年 160卷 103058-103058页

作者： Lomoio, Ugo Veltri, Pierangelo Guzzi, Pietro Hiram Lio, Pietro Magna Graecia Univ Catanzaro Dept Surg & Med Sci Catanzaro Italy Univ Calabria DIMES Arcavacata Di Rende Italy Univ Cambridge Dept Comp Sci & Technol Cambridge England

Electrocardiogram signals play a pivotal role in cardiovascular diagnostics, providing essential information on electrical hearth activity. However, inherent noise and limited resolution can hinder an accurate interpretation of the recordings. In this paper an advanced Denoising Convolutional autoencoder designed to process electrocardiogram signals, generating super-resolution reconstructions is proposed;this is followed by in-depth analysis of the enhanced signals. The autoencoder receives a signal window (of 5 s) sampled at 50 Hz (low resolution) as input and reconstructs a denoised super-resolution signal at 500 Hz. The proposed autoencoder is applied to publicly available datasets, demonstrating optimal performance in reconstructing high-resolution signals from very low-resolution inputs sampled at 50 Hz. The results were then compared with current stateof-the-art for electrocardiogram super-resolution, demonstrating the effectiveness of the proposed method. The method achieves a signal-to-noise ratio of 12.20 dB, a mean squared error of 0.0044, and a root mean squared error of 4.86%, which significantly outperforms current state-of-the-art alternatives. This framework can effectively enhance hidden information within signals, aiding in the detection of heart-related diseases.

关键词： ECG Super-resolution Signals Denoising autoencoder

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Predicting process quality in multi-stage manufacturing using AE-BilA: an autoencoder-BiLSTM with attention mechanism

ENGINEERING RESEARCH EXPRESS

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ENGINEERING RESEARCH EXPRESS 2025年第1期7卷

作者： Hady, Haider N. Hadi, Russul H. Hassoon, Omar Hashim Hasan, Ahmed M. Humaidi, Amjad J. Univ Technol Baghdad Dept Prod Engn & Met Baghdad Iraq Univ Technol Baghdad Control & Syst Engn Dept Baghdad Iraq

As industrial processes are becoming increasingly complex and data-driven, the need for accurate quality predictions in manufacturing systems is regarded as critical. To address this challenge, AE-BiLA (autoencoder-Bidirectional Long Short-Term Memory with Attention mechanism) has been proposed as a framework in which a stacked Long Short-Term Memory (LSTM) autoencoder is combined with a bidirectional LSTM enhanced by an Attention mechanism for predicting quality in multi-stage manufacturing processes (MMP). First, high-dimensional, noisy data are reduced by employing the stacked LSTM autoencoder, with essential information being preserved. Next, the compressed features are fed into the bidirectional LSTM, where significant temporal patterns are highlighted by the Attention mechanism. The method was validated on a real-world MMP dataset. An R2 (coefficient of determination) of 0.9452 was obtained in Stage 1, demonstrating that upstream process dynamics were effectively captured. In contrast, an R2 of 0.7329 was produced in Stage 2, reflecting increased complexity and variability in downstream operations. Moreover, the Symmetric Mean Absolute Percentage Error (SMAPE) in Stage 2 was reduced to 1.9319 from 19.3583 in Stage 1, thereby underscoring that outliers and noise were successfully managed. Overall, the AE-BiLA framework outperforms existing methods by effectively integrating denoising with a bidirectional recurrent structure. Despite the increased computational overhead, it is expected that the framework will yield substantial gains in productivity, lower waste levels, and reduce operational costs.

关键词： smart manufacturing quality control internet of things deep learning autoencoder bidirectional LSTM multivariate time series

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Enhancing autoencoder models for multivariate time series anomaly detection: the role of noise and data amount

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JOURNAL OF SUPERCOMPUTING 2025年第4期81卷 1-27页

作者： Sefati, Seyedeh Tina Razavi, Seyed Naser Salehpour, Pedram Univ Tabriz Comp Engn Tabriz Iran

Unsupervised anomaly detection in multivariate time series is important in many applications including cyber intrusion detection and medical diagnostics. Both traditional and supervised techniques had limitations due to data scale, labeling complexity, and cluster imbalance. Also, deep learning methods have drawbacks such as sensitivity to noise and difficulty in capturing spatial-temporal correlations. To address these challenges, we propose MTSAD, a new AE-based anomaly detection model for multivariate time series data that uses ConvLSTM and transposed convolution to effectively learn spatio-temporal features. Furthermore, in this paper, we explore the effect of noise injection and data amount utilization that improves the model performance and prevents overfitting. It increases the robustness to real sensor noise and improves the robustness of anomaly detection in industrial environments. On SWaT and WADI datasets, MTSAD achieves higher F1 scores than the competing models. The results of the study also show that data amount and noise injection are very important factors that can be used to improve the performance of AE-based anomaly detection. This work offers new understandings of the optimization of reconstruction-based architectures for unsupervised multivariate time series anomaly detection.

关键词： Anomaly detection Unsupervised learning Multivariate time series autoencoder Reconstruction-based methods

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Research on anomaly detection of steam power system based on the coupling of thermoeconomics and autoencoder

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ENERGY 2025年 318卷

作者： Li, Guolong Li, Yanjun Li, Site Sun, Shengdi Wang, Haotong Su, Jian Shi, Jianxin Zhou, Xin Harbin Engn Univ Coll Power & Energy Engn Harbin 150001 Peoples R China Harbin Marine Boiler & Turbine Res Inst Res Inst CSSC 703 35 Honghu Rd Harbin 150078 Peoples R China

Discovering the anomalies of the steam power system in time can optimize the operating efficiency and avoid major losses. The existing single anomaly detection method is not effective outside its assumptions. Aiming at this problem, a new anomaly detection method based on the coupling of thermoeconomics and autoencoder is proposed. This method uses the autoencoder to reconstruct the normal values of the thermoeconomic calculation benchmark and other parameters. Then the endogenous irreversible loss of each component is calculated according to the benchmark. Finally, it is detected together with the reconstruction error of the parameters, and the deviation exceeding the threshold is abnormal. The experimental results show that under the premise of ensuring the precision, the traditional thermoeconomic anomaly detection method, the autoencoder anomaly detection method and the proposed coupling anomaly detection method can detect 58.7 %, 88.9 % and 94 % abnormal samples, respectively. In terms of the accuracy and F1-score, the coupling method is also the highest, reaching 93.9 % and 96.8 % respectively. It is proved that the coupling method is superior to the single thermoeconomic method or the autoencoder method, which is of great significance to ensure the safe and stable operation of the steam power system.

关键词： Steam power system Anomaly detection autoencoder Thermoeconomics Coupling method

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Analysis of time-of-flight secondary ion mass spectrometry data of human skin treated with diclofenac using sparse autoencoder

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ANALYTICAL AND BIOANALYTICAL CHEMISTRY 2025年第6期417卷 1049-1054页

作者： Shinozaki, Atsumi Matsuda, Kazuhiro Aoyagi, Satoka Seikei Univ Fac Sci & Technol 3-3-1 Kichijoji Kitamachi Musashino Tokyo 1808633 Japan Toray Res Ctr Ltd Surface Sci Labs 3-2-11 Sonoyama Otsu Shiga 5208567 Japan

Methods that facilitate molecular identification and imaging are required to evaluate drug penetration into tissues. Time-of-flight secondary ion mass spectrometry (ToF-SIMS), which has high spatial resolution and allows 3D distribution imaging of organic materials, is suitable for this purpose. However, the complexity of ToF-SIMS data, which includes nonlinear factors, makes interpretation challenging. Therefore, in this study, ToF-SIMS data of a stratum corneum treated with diclofenac were analyzed using machine learning to enable the evaluation of drug distribution. Diclofenac-related mass peaks were identified using autoencoder results, and the degree of penetration was evaluated across 2-20th stripped tapes. In addition, the permeation pathway was clarified by comparing the secondary ion images of phosphatidylethanolamine (PhEA;a marker of the inside of the cell);cholesterol, which is abundant in cell membranes;and diclofenac. Based on the biomolecule-related ion images showing the penetration pathway of diclofenac applied to the skin, diclofenac penetrates both the extracellular space and inside cells.

关键词： ToF-SIMS Mass imaging autoencoder Machine learning Stratum corneum Diclofenac

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LLM-AE-MP: Web Attack Detection Using a Large Language Model with autoencoder and Multilayer Perceptron

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 274卷

作者： Yang, Jing Wu, Yuangui Yuan, Yuping Xue, Haozhong Bourouis, Sami Abdel-Salam, Mahmoud Prajapat, Sunil Por, Lip Yee Univ Malaya Fac Comp Sci & Informat Technol Dept Comp Syst & Technol Kuala Lumpur 50603 Malaysia Jilin Univ Sch Software Changchun 130012 Jilin Province Peoples R China Radio Film & Televis Design & Res Inst Co Ltd Informat & Network Inst Beijing 100045 Peoples R China NYU Tandon Sch Engn New York NY 11101 USA Taif Univ Coll Comp & Informat Technol Dept Informat Technol POB 11099 Taif 21944 Saudi Arabia Mansoura Univ Fac Comp & Informat Sci Mansoura 35516 Egypt Cent Univ Himachal Pradesh Srinivasa Ramanujan Dept Math Dharamshala 176215 India

Web applications store sensitive data, making them prime targets for cybercriminals and posing national security risks. This study introduces a new approach to distinguishing legitimate and malicious hypertext transfer protocol (HTTP) requests using an autoencoder (AE). The integration of AE allows for efficient feature distillation, enhancing the sensitivity of the model to anomalies in HTTP traffic. The AE framework is combined with a transductive long short-term memory (TLSTM) network, which is trained with an advanced generative adversarial network (GAN). Using GAN promotes an adaptive learning environment, significantly boosting the robustness and generalizability of our method against evolving web attack vectors. TLSTM uses transductive learning to focus on data points near the test set, improving the adaptability of the model to outperform traditional LSTM models. In our GAN, the generator purposely excludes gradients from the most influential batch elements, improving the ability of the model to generate diverse and generalized outputs. After training the AE, its latent representations are passed to a multilayer perceptron (MLP) for detection tasks. To address the imbalanced classification in MLP, we use a reinforcement learning (RL) strategy. The RL approach strategically adjusts incentives, enhancing the performance of the model in identifying less frequent but critical malicious instances, thereby supporting a balanced security assessment. Our evaluations using the CSIC 2010 (Spanish National Research Council 2010), FWAF (web application firewall), and HttpParams datasets show that our method outperforms existing techniques, achieving (Accuracy, F-measure, geometric mean (G-means), and area under the curve (AUC)) reaching (90.937%, 89.755%, 88.446%, 0.838), (89.055, 90.663%, 88.334%, 0.847) and (92.242%, 93.774%, 91.356%, 0.897), respectively. Moreover, our model achieves efficient runtime and memory usage across the datasets, providing a practical soluti

关键词： Web attack Anomalous request autoencoder Long short-term memory Generative adversarial network Reinforcement learning

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Application of latent Dirichlet allocation and autoencoder to real estate datasets

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JOURNAL OF SUPERCOMPUTING 2025年第1期81卷 1-24页

作者： Gu, Runhe Lin, Luchun Changzhou Univ Sch Comp Sci & Artificial Intelligence Aliyun Sch Big Data Sch Software 2468 Yanzheng West Ave Changzhou 213159 Jiangsu Peoples R China

At present, there are too many types and numbers of real estate features in the real estate market, and it is difficult to effectively recommend real estate to customers with more complicated needs. The datasets of real estate often encompass a wide array of features, including categories, numerical values, and textual descriptions, which complicates the process of delivering precise and satisfactory recommendations. To address this issue, a cluster-based hybrid method combining latent Dirichlet allocation and autoencoder is proposed in this paper. It can effectively improve the early input of clustering by the two-stage feature extraction of the data through the topic model and the autoencoder. Experimental findings demonstrate that our hybrid method surpasses standalone LDA and autoencoder, as well as other conventional clustering algorithms. The combined results of Silhouette coefficient, DB index, and CH index are significantly better than the results of traditional clustering. Moreover, we conduct a comprehensive analysis of the results, emphasizing the benefits of our approach in managing multi-type features, encompassing categorical, numerical, and string data. It is anticipated that this research will contribute valuable insights into the field of recommendation systems.

关键词： Latent Dirichlet allocation autoencoder Hybrid method Real estate recommendation Deeping clustering

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Data-driven strategy: A robust battery anomaly detection method for short circuit fault based on mixed features and autoencoder

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APPLIED ENERGY 2025年 382卷

作者： Zhao, Hongyu Zhang, Chengzhong Liao, Chenglin Wang, Liye Liu, Weilong Wang, Lifang Chinese Acad Sci Inst Elect Engn Key Lab High Dens Electromagnet Power & Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Beijing Sifang Automat Co Ltd 9 Shangdi Fourth St Beijing 100085 Peoples R China

The anomaly detection of lithium-ion batteries for short circuit (SC) faults is crucial to ensure the safety of the energy storage system. Compared to the diagnosis fault of packs, individual cell fault diagnosis lacks a reference target, leading to difficulties in effectively detecting whether an abnormality exists. In this paper, a data-driven detection method based on the autoencoder strategy is proposed for early detection of battery faults without pack information. Within, the autoencoder strategy is used to reconstruct the voltage and detect potential faults. Using the generative adversarial network (GAN) framework for model training reduces its overfitting and improves efficiency. In addition, during anomaly detection, due to the lack of battery pack reference, some abnormal voltage changes due to current variations can lead to misdiagnosis. To address this concern, the mixed features input is proposed to reduce the misdiagnosis rate, which incorporates the equivalent circuit model parameters. Experiments demonstrate that the proposed method can accurately detect SC faults, in particular, it can detect some moderate or weak faults within 1.6 h. Compared to other methods, this method has better effectiveness and robustness. The method proposed in this paper is in line with the development trend for big data and opens up new perspectives for the development of energy storage safety technology.

关键词： Anomaly detection lithium-ion batteries Short circuit faults Mixed features autoencoder Generative adversarial network

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An improved autoencoder for denoising acoustic emission signals in rock fracturing

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NONDESTRUCTIVE TESTING AND EVALUATION 2025年第6期40卷 2572-2597页

作者： Wang, Tingting Qin, Yifan Zhao, Wanchun Gamage, Ranjith Pathegama Jiang, Jingyi Du, Xuetong Northeast Petr Univ Sch Elect Engn & Informat Daqing Peoples R China Northeast Petr Univ Heilongjiang Prov Key Lab Networking & Intelligent Daqing Peoples R China Northeast Petr Univ Inst Petr Engn Daqing Peoples R China Minist Educ Key Lab Continental Shale Hydrocarbon Accumulat & Daqing Peoples R China Monash Univ Dept Civil Engn Deep Earth Energy Lab Melbourne Vic Australia

Rock fracture acoustic emission (AE) signals are commonly used non-destructive testing data in geological exploration, resource exploitation, and engineering fields. However, these signals are often accompanied by noise interference caused by environmental factors. In this study, we propose an enhanced model for denoising rock fracture AE signals, called simplified fully convolutional denoising autoencoder (SFCDAE). This model is based on the denoising autoencoder principle in the field of deep learning neural networks. The SFCDAE model consists of only seven layers, with minimal preprocessing of data input. By comparing denoising performance evaluation indicators, higher peak signal-to-noise ratio (PSNR) and lower root mean square error (RMSE) were achieved. On average, PSNR increased by 5.575% and RMSE decreased by 22.225%. Using simulated environmental noise to validate the model, it was found that the model has good robustness and can remove artefacts from sudden noise. The practical application value of the LSTM classification model was validated using data containing real experimental noise, resulting in a higher classification accuracy of 80.083%. These results indicate that the proposed model has better denoising performance compared to existing intelligent models and has certain practical value.

关键词： Acoustic emission denoising rock fracture autoencoder deep learning

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