IF 2.2 4区计算机科学

IET Image Processing Pub Date : 2025-09-27 DOI: 10.1049/ipr2.70219

Zipeng Fu, Xiaoling Ge, Weixian Qian, Xuelian Yu

{"title":"Quaternion-Based Image Restoration via Saturation-Value Total Variation and Pseudo-Norm Regularization","authors":"Zipeng Fu, Xiaoling Ge, Weixian Qian, Xuelian Yu","doi":"10.1049/ipr2.70219","DOIUrl":"https://doi.org/10.1049/ipr2.70219","url":null,"abstract":"<p>Color image restoration is a fundamental task in computer vision and image processing, with extensive real-world applications. In practice, color images often suffer from degradations caused by sensor noise, optical blur, compression artifacts, and data loss during the acquisition, transmission, or storage. Unlike grayscale images, color images exhibit high correlations among their RGB channels. Directly extending grayscale restoration methods to color images often leads to issues such as color distortion and structural artifacts. To address these challenges, this paper proposes a novel quaternion-based color image restoration framework. The method integrates low-rank pseudo-norm constraints with saturation-value total variation (SVTV) regularization, effectively enhancing restoration quality in tasks including denoising, deblurring, and inpainting of degraded color images. The proposed algorithm is efficiently solved using the alternating direction method of multipliers (ADMM), and restoration performance is rigorously evaluated through quantitative metrics including peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and S-CIELAB error. Extensive experimental results demonstrate the superior performance of our method compared to existing approaches.</p>","PeriodicalId":56303,"journal":{"name":"IET Image Processing","volume":"19 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/ipr2.70219","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Systematic Mapping of AI-Based Approaches for Requirements Prioritization 基于人工智能的需求优先排序方法的系统映射

IF 1.3 4区计算机科学

IET Software Pub Date : 2025-09-27 DOI: 10.1049/sfw2/8953863

María-Isabel Limaylla-Lunarejo, Nelly Condori-Fernandez, Miguel Rodríguez Luaces

{"title":"Systematic Mapping of AI-Based Approaches for Requirements Prioritization","authors":"María-Isabel Limaylla-Lunarejo, Nelly Condori-Fernandez, Miguel Rodríguez Luaces","doi":"10.1049/sfw2/8953863","DOIUrl":"https://doi.org/10.1049/sfw2/8953863","url":null,"abstract":"<p><b>Context and Motivation:</b> Requirements prioritization (RP) is a main concern of requirements engineering (RE). Traditional prioritization techniques, while effective, often involve manual effort and are time-consuming. In recent years, thanks to the advances in AI-based techniques and algorithms, several promising alternatives have emerged to optimize this process.</p><p><b>Question:</b> The main goal of this work is to review the current state of requirement prioritization, focusing on AI-based techniques and a classification scheme to provide a comprehensive overview. Additionally, we examine the criteria utilized by these AI-based techniques, as well as the datasets and evaluation metrics employed. For this purpose, we conducted a systematic mapping study (SMS) of studies published between 2011 and 2023.</p><p><b>Results:</b> Our analysis reveals a diverse range of AI-based techniques in use, with fuzzy logic being the most commonly applied. Moreover, most studies continue to depend on stakeholder input as a key criterion, limiting the potential for full automation of the prioritization process. Finally, there appears to be no standardized evaluation metric or dataset across the reviewed papers, focusing on the need for standardized approaches across studies.</p><p><b>Contribution:</b> This work provides a systematic categorization of current AI-based techniques used for automating RP. Additionally, it updates and expands existing reviews, offering a valuable resource for practitioners and nonspecialists.</p>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/8953863","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Security Monitoring of Railway Vehicle Communication Protocol Based on Fuzzy Test 基于模糊测试的轨道车辆通信协议安全监控

IF 2.5 4区计算机科学

Transactions on Emerging Telecommunications Technologies Pub Date : 2025-09-27 DOI: 10.1002/ett.70270

Hui Li

{"title":"Security Monitoring of Railway Vehicle Communication Protocol Based on Fuzzy Test","authors":"Hui Li","doi":"10.1002/ett.70270","DOIUrl":"https://doi.org/10.1002/ett.70270","url":null,"abstract":"<div>\u0000 \u0000 <p>With the rapid development of rail transit, the requirements of Internet of Things technology and communication capability continue to increase, and the security problems involved are gradually exposed. How to enhance the stability and security of rail vehicle communication is a key problem. Therefore, a security vulnerability technology of vehicle communication protocol in rail transit industry based on fuzzy testing technology is proposed. By adding a Seq2Seq model to the security vulnerability detection system, the technology makes it learn twice to improve the detection ability. At the same time, the new system also improves the limited number of possibilities and supports custom frame types. By using the TRDP protocol for analysis and testing on the vehicle device, the new system has obvious advantages in acceptance coverage and anomaly rate compared with the traditional fuzzy test method. It is proved that this method can effectively improve the detection efficiency, discover vulnerabilities, and solve the problem that traditional testing cannot effectively detect protocol vulnerabilities in rail transit vehicle communication system.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 10","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Web-Based Early Dementia Detection Using Deep Learning, Ensemble Machine Learning, and Model Explainability Through LIME and SHAP 基于网络的早期痴呆检测使用深度学习，集成机器学习，并通过LIME和SHAP模型的可解释性

IF 1.3 4区计算机科学

IET Software Pub Date : 2025-09-27 DOI: 10.1049/sfw2/5455082

Khandaker Mohammad Mohi Uddin, Abir Chowdhury, Md Mahbubur Rahman Druvo, Md. Shariful Islam, Md Ashraf Uddin

{"title":"Web-Based Early Dementia Detection Using Deep Learning, Ensemble Machine Learning, and Model Explainability Through LIME and SHAP","authors":"Khandaker Mohammad Mohi Uddin, Abir Chowdhury, Md Mahbubur Rahman Druvo, Md. Shariful Islam, Md Ashraf Uddin","doi":"10.1049/sfw2/5455082","DOIUrl":"https://doi.org/10.1049/sfw2/5455082","url":null,"abstract":"<p>Dementia is a gradual and incapacitating illness that impairs cognitive abilities and causes memory loss, disorientation, and challenges with daily tasks. Treatment of the disease and better patient outcomes depend on early identification of dementia. In this paper, the study uses a publicly available dataset to develop a comprehensive ensemble model of machine learning (ML) and deep learning (DL) framework for classifying the dementia stages. Before using SMOTE to balance the data, the procedure starts with data preprocessing which includes handling missing values, normalization and encoding. <i>F</i>-value and <i>p</i>-value help to select the best seven features, and the dataset is divided into training (70%) and testing (30%) portions. In addition, four DL models like long short-term memory (LSTM), convolutional neural networks (CNNs), multilayer perceptron (MLP), artificial neural networks (ANNs), and 12 ML models are trained such as logistic regression (LR), random forest (RF) and support vector machine (SVM). Hyperparameter tuning was utilized to further enhance each model’s performance and an ensemble voting technique was applied to aggregate predictions from several ML and DL algorithms, providing more reliable and accurate outcomes. For ensuring model transparency, interpretability strategies like as shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME) are applied in ANN and LR. The suggested model’s ANN shows a promising accuracy of 97.32% demonstrating its efficacy in the early diagnosis and categorization of dementia which can support clinical decisions. Furthermore, the proposed work, created a web-based solution for diagnosing dementia in real-time.</p>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/5455082","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Carbon trading price prediction with spikes: A novel hybrid model framework using heuristic multi-head attention convolutional bidirectional recurrent neural network 碳交易价格尖峰预测：基于启发式多头注意卷积双向递归神经网络的新型混合模型框架

IF 8 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-09-26 DOI: 10.1016/j.engappai.2025.112438

Rongquan Zhang , Siqi Bu , Gangqiang Li , Min Zhou

{"title":"Carbon trading price prediction with spikes: A novel hybrid model framework using heuristic multi-head attention convolutional bidirectional recurrent neural network","authors":"Rongquan Zhang , Siqi Bu , Gangqiang Li , Min Zhou","doi":"10.1016/j.engappai.2025.112438","DOIUrl":"10.1016/j.engappai.2025.112438","url":null,"abstract":"<div><div>Accurate forecasting of carbon trading prices (CTPs) with spikes is crucial for developing carbon emission reduction policies and planning corporate investments. However, most existing CTP approaches usually focus on designing a cutting-edge model without considering spike prediction. Therefore, this paper presents a novel heuristic optimization-based hybrid model framework for CTP prediction with spikes. First, random forest is exploited to identify the relevant features of spikes and non-spikes for CTPs, and categorical boosting is employed to predict the spike occurrences of CTPs. Then, a novel hybrid model based on multiple linear regression, categorical boosting, and two dimensions convolutional neural network and bidirectional gated recurrent unit with multi-head regularized attention mechanism (2DCNN-BiGRU-MRA) is proposed to predict spikes and non-spikes for CTPs. In this model, multiple linear regression and categorical boosting are respectively applied to capture the linear and complex nonlinear features of the CTPs, in which their prediction results and deviations are integrated into the 2DCNN-BiGRU-MRA model as relevant features. The proposed 2DCNN-BiGRU-MRA can learn the spatiotemporal features and enhance representation capabilities by introducing 2DCNN, BiGRU, and MRA, thereby improving the accuracy of CTP prediction. In addition, to construct appropriate model hyperparameters of 2DCNN-BiGRU-MRA, the strength honey badger algorithm based on the adaptive momentum estimation is proposed to optimize the hyperparameters of 2DCNN-BiGRU-MRA. Finally, the proposed framework is tested on the actual data of European Union emissions trading and the carbon market in Hubei, China, and case studies have confirmed the superiority and achievable local interpretability of the proposed hybrid model framework.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"162 ","pages":"Article 112438"},"PeriodicalIF":8.0,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145134846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Virtual Target-Oriented Neural Learning for Robust Optimal Tracking Control of Discrete Strict-Feedback Systems. 离散严格反馈系统鲁棒最优跟踪控制的虚拟目标导向神经学习。

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-09-26 DOI: 10.1109/tnnls.2025.3604566

Ying Yan,Huaguang Zhang,Jiayue Sun,Zhongyang Ming

{"title":"Virtual Target-Oriented Neural Learning for Robust Optimal Tracking Control of Discrete Strict-Feedback Systems.","authors":"Ying Yan,Huaguang Zhang,Jiayue Sun,Zhongyang Ming","doi":"10.1109/tnnls.2025.3604566","DOIUrl":"https://doi.org/10.1109/tnnls.2025.3604566","url":null,"abstract":"This article proposes a hierarchical neural learning (HNL) algorithm for optimal tracking control (OTC) of nonlinear strict-feedback systems (SFSs) with unmatched disturbances (uMDs) and unknown dynamics. Leveraging the recursive structure of SFSs, we introduce the virtual target (VT) construction scheme in which each VT is a nonlinear mapping of the current state and desired output, thereby eliminating the noncausal that typically plagues discrete-time SFS control. The VTs serve as auxiliary inputs for low-order subsystems, while a time-varying affine Hamilton-Jacobi-Isaacs (HJI) formulation establishes an explicit relationship between the auxiliary control and the disturbance. The controller is synthesized directly from input-output data, removing the need for an accurate plant model. Within an adaptive dynamic programming (ADP) framework, we further enhance the neural architecture by replacing the conventional action network with a tracking network (T-network) whose energy function merges gradient information with future tracking errors, ensuring that each policy update simultaneously reduces control effort and improves tracking accuracy. Simulations confirm that the proposed HNL scheme achieves outstanding performance in both (optimal) tracking modes, exhibiting strong robustness to uMDs and significant model uncertainties.","PeriodicalId":13303,"journal":{"name":"IEEE transactions on neural networks and learning systems","volume":"5 1","pages":""},"PeriodicalIF":10.4,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145153462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel federated deep learning for intrusion detection in smart grid cyber-physical systems 一种新的用于智能电网网络物理系统入侵检测的联合深度学习方法

IF 8 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-09-26 DOI: 10.1016/j.engappai.2025.112404

Rong Xie , Bin Wang , Xin Xu

{"title":"A novel federated deep learning for intrusion detection in smart grid cyber-physical systems","authors":"Rong Xie , Bin Wang , Xin Xu","doi":"10.1016/j.engappai.2025.112404","DOIUrl":"10.1016/j.engappai.2025.112404","url":null,"abstract":"<div><div>The fusion of sophisticated computational, communicative, and physical elements in Smart Grid Cyber-Physical Systems (SGCPS) has greatly improved the efficiency and reliability of power grids. However, this complexity introduces enhanced cybersecurity risks, evidenced by significant cyberattacks on the Ukrainian power grid during 2015 and 2016. Despite progress in Artificial Intelligence (AI)-driven security solutions for SGCPS, practical deployment of these technologies is often limited due to a lack of high-quality attack data and owners’ hesitance to distribute sensitive details. This paper introduces an innovative strategy to fortify SGCPS against diverse network threats via a comprehensive intrusion detection system. We present a deep learning model leveraging a temporal convolutional network with multi-feature integration, aimed at robust threat identification. We also propose a federated learning framework enabling various SGCPS to jointly develop an extensive intrusion detection model, ensuring data privacy. Moreover, we incorporate a gradient compression technique utilizing the Long Short Term Memory-<span><math><mi>β</mi></math></span>-Total Correlation Variational Autoencoder (LSTM-<span><math><mi>β</mi></math></span>-TCVAE) model to enhance and secure model parameters throughout the training phase. Thorough experimental validations confirm the efficacy of our method in recognizing multiple cyber threat types to SGCPS and its advantages over current methods.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"162 ","pages":"Article 112404"},"PeriodicalIF":8.0,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145134851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mgs-Stereo: Multi-scale Geometric-Structure-Enhanced Stereo Matching for Complex Real-World Scenes. Mgs-Stereo：复杂现实场景的多尺度几何结构增强立体匹配。

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2025-09-26 DOI: 10.1109/tip.2025.3612754

Zhien Dai,Zhaohui Tang,Hu Zhang,Yongfang Xie

{"title":"Mgs-Stereo: Multi-scale Geometric-Structure-Enhanced Stereo Matching for Complex Real-World Scenes.","authors":"Zhien Dai,Zhaohui Tang,Hu Zhang,Yongfang Xie","doi":"10.1109/tip.2025.3612754","DOIUrl":"https://doi.org/10.1109/tip.2025.3612754","url":null,"abstract":"Complex imaging environments and conditions in real-world scenes pose significant challenges for stereo matching tasks. Models are susceptible to underperformance in non-Lambertian surfaces, weakly textured regions, and occluded regions, due to the difficulty in establishing accurate matching relationships between pixels. To alleviate these problems, we propose a multi-scale geometrically enhanced stereo matching model that exploits the geometric structural relationships of the objects in the scene to mitigate these problems. Firstly, a geometric structure perception module is designed to extract geometric information from the reference view. Secondly, a geometric structure-adaptive embedding module is proposed to integrate geometric information with matching similarity information. This module integrates multi-source features dynamically to predict disparity residuals in different regions. Third, a geometric-based normalized disparity correction module is proposed to improve matching robustness for pathological regions in realistic complex scenes. Extensive evaluations on popular benchmarks demonstrate that our method achieves competitive performance against leading approaches. Notably, our model provides robust and accurate predictions in challenging regions containing edges, occlusions, reflective, and non-Lambertian surfaces. Our source code will be publicly available.","PeriodicalId":13217,"journal":{"name":"IEEE Transactions on Image Processing","volume":"42 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145153460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A multi-scale feature extraction and fusion framework based on wavelet Kolmogorov–Arnold networks and parallel Bi-directional gated recurrent units for electric load forecasting 基于小波Kolmogorov-Arnold网络和并行双向门控循环单元的电力负荷预测多尺度特征提取与融合框架

IF 8 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-09-26 DOI: 10.1016/j.engappai.2025.112517

Chunliang Mai , Lixin Zhang , Xuewei Chao , Xue Hu , Omar Behar

{"title":"A multi-scale feature extraction and fusion framework based on wavelet Kolmogorov–Arnold networks and parallel Bi-directional gated recurrent units for electric load forecasting","authors":"Chunliang Mai , Lixin Zhang , Xuewei Chao , Xue Hu , Omar Behar","doi":"10.1016/j.engappai.2025.112517","DOIUrl":"10.1016/j.engappai.2025.112517","url":null,"abstract":"<div><div>Short-term electric load forecasting remains challenged by the dual requirements of accuracy and robustness due to the combined effects of strong seasonality, multi-scale spikes, and stochastic disturbances. To address this, we propose a novel multi-scale forecasting framework, NP-WavKAN-Fusion, which integrates Neural Prophet for data decomposition and a Wavelet-based Kolmogorov–Arnold Network (WavKAN) with learnable wavelet kernels for multi-scale encoding. This fusion model utilizes a Bi-directional Gated Recurrent Unit (BiGRU) to capture long-term temporal dependencies and an adaptive feature fusion gate (AFF) to dynamically re-weight static and dynamic features for final load predictions. Extensive experiments on two public datasets from Australia and Morocco show that NP-WavKAN-Fusion consistently outperforms traditional models, reducing the mean absolute error by at least 30 %. For multi-step forecasting tasks, NP-WavKAN-Fusion maintains error inflation within 15 %, demonstrating superior performance compared to state-of-the-art long-sequence models such as Informer and PatchTST. The Diebold–Mariano test confirms that NP-WavKAN-Fusion yields statistically significant improvements, with 19 out of 20 comparisons showing lower errors. Ablation studies show that removing either the Neural Prophet component or the AFF significantly increases the forecasting error, validating the necessity of our layered denoising and fusion strategies. The proposed NP-WavKAN-Fusion framework demonstrates strong potential for real-world applications in electric load forecasting, offering robust performance under various temporal and non-stationary conditions.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"162 ","pages":"Article 112517"},"PeriodicalIF":8.0,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145134845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A reliable degradation prediction method for proton exchange membrane fuel cells based on uncertainty Bayesian self-attention 基于不确定性贝叶斯自关注的质子交换膜燃料电池降解预测方法

IF 8 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-09-26 DOI: 10.1016/j.engappai.2025.112444

Mengyu Liu , Zhe Cheng , Yu Yang , Niaoqing Hu , Guoji Shen , Yi Yang

{"title":"A reliable degradation prediction method for proton exchange membrane fuel cells based on uncertainty Bayesian self-attention","authors":"Mengyu Liu , Zhe Cheng , Yu Yang , Niaoqing Hu , Guoji Shen , Yi Yang","doi":"10.1016/j.engappai.2025.112444","DOIUrl":"10.1016/j.engappai.2025.112444","url":null,"abstract":"<div><div>Health state prediction of Proton Exchange Membrane Fuel Cells (PEMFCs) is a critical technology to ensure their long-term reliable operation. Prediction accuracy directly influences the effectiveness of maintenance strategies and risk management. However, existing PEMFC degradation prediction methods based on Recurrent Neural Networks (RNNs) or Transformer architectures mostly focus on point estimation while neglecting uncertainty quantification. This limitation makes it difficult to assess the confidence level of predictions in practical engineering applications, reducing the models' reliability in decision support. To address this issue, this paper proposes a novel Bayesian Patch Time Series Transformer (B-PatchTST) method. By deeply integrating Bayesian variational inference with time series patch modeling, the method enables probabilistic prediction of PEMFC degradation trajectories and disentangled analysis of uncertainty sources. Unlike traditional Bayesian Neural Networks (BNNs) that primarily apply Bayesian modeling to fully connected layers, B-PatchTST introduces a Bayesian Self-Attention Mechanism, which models epistemic uncertainty in three stages: patch embedding, uncertainty-aware self-attention computation, and adaptive regularization. This design significantly enhances the credibility of the model. Extensive experiments on the fuel cell datasets demonstrate the proposed method's outstanding performance. It achieves an average reduction of 36.31 % in root mean square error and an average compression of 83.39 % in the 95 % confidence interval, significantly outperforming existing methods. This approach offers a trustworthy basis for predictive maintenance in PEMFC systems, promoting a shift from “experience-based maintenance” to “reliable prognostics” in hydrogen energy applications.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"162 ","pages":"Article 112444"},"PeriodicalIF":8.0,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145134847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

计算机科学最新文献