Expert Systems with Applications最新文献_第2页

Aligning sequence and structure representations leveraging protein domains for function prediction

IF 7.5 1区计算机科学

Expert Systems with Applications Pub Date : 2025-03-25 DOI: 10.1016/j.eswa.2025.127246

Mingqing Wang , Zhiwei Nie , Yonghong He , Athanasios V. Vasilakos , Zhixiang Ren

引用次数: 0

An intelligent design methodology for multi-stage loading paths of variable parameters during large-scale electric upsetting process

IF 7.5 1区计算机科学

Expert Systems with Applications Pub Date : 2025-03-25 DOI: 10.1016/j.eswa.2025.127316

Yan-ze Yu , Guo-zheng Quan , Yu-qing Zhang , Ying-ying Liu , Li-he Jiang , Wei Xiong , Jiang Zhao

{"title":"An intelligent design methodology for multi-stage loading paths of variable parameters during large-scale electric upsetting process","authors":"Yan-ze Yu , Guo-zheng Quan , Yu-qing Zhang , Ying-ying Liu , Li-he Jiang , Wei Xiong , Jiang Zhao","doi":"10.1016/j.eswa.2025.127316","DOIUrl":"10.1016/j.eswa.2025.127316","url":null,"abstract":"<div><div>It is a great challenge to design the multi-stage loading path of variable parameters to obtain the component with smooth shape and fine-grained microstructures during the electric upsetting process of large-scale valves. To achieve this, an intelligent design methodology was developed and applied in an electric upsetting process of Ni80A alloy. The methodology integrates backpropagation neural network (BP neural network), case-based reasoning (CBR), and parameter self-feedback adjustment coupling with finite element (FE). Firstly, a BP neural network model was developed based on the basic database to predict the initial processing parameters of components (upsetting force, current, and pre-heating time). Secondly, utilizing the CBR method, the suitable design schemes were identified by retrieving similar components, and then the multi-stages loading paths for upsetting force and current were devised. Thirdly, a subroutine of self-feedback adjustment to fine-tune the loading paths was developed and implanted into the multi-field and multi-scale coupling FE model. Finally, the optimal loading paths was obtained using the FE model until the deformed component meets the requirements of shape and grain size. The results indicated that the surface contour of component was smoother and without macroscopic defects under the optimal loading paths, with the maximum grain size refined to 103.9 μm. To further improve the automation level of the parameters design process, an expert system was developed based on the designed methodology. This work contributes to the intelligent design of processing parameters for the electric upsetting process, which provides a design framework of processing parameter in other manufacturing technologies.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"278 ","pages":"Article 127316"},"PeriodicalIF":7.5,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724082","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 new difference feature extraction method of slewing bearings in wind turbines via optimization bispectrum domain model

IF 7.5 1区计算机科学

Expert Systems with Applications Pub Date : 2025-03-25 DOI: 10.1016/j.eswa.2025.127325

Miaorui Yang , Kun Zhang , Yanping Zhu , Long Zhang , Yonggang Xu

{"title":"A new difference feature extraction method of slewing bearings in wind turbines via optimization bispectrum domain model","authors":"Miaorui Yang , Kun Zhang , Yanping Zhu , Long Zhang , Yonggang Xu","doi":"10.1016/j.eswa.2025.127325","DOIUrl":"10.1016/j.eswa.2025.127325","url":null,"abstract":"<div><div>The slewing bearing is a critical component in large equipment like shield machines and wind turbines. Because slewing bearings operate in complex situations with fluctuating speed and load on a regular basis, the vibration signal they produce contains several interferences, making fault features difficult to identify. The specific objective of this study is to provide a new fault diagnosis method, named difference optimization bispectrum, for slewing bearing signals under strong noise interference. The method designs a convex optimization bispectrum model by the convex optimization theory, covering the shortage of traditional decomposition by differentiating features. Based on the model, a two-dimensional weight coefficient is constructed to calculate the difference optimization bispectrum, which reduces the noise and enhances the features in positive and negative bispectrum-domain. This study offers a fresh perspective on extraction of fault information from the signal under strong noise interference, making an original contribution for the fault diagnosis of the slewing bearing. The experiment work presented here provides the practical effect of the method for the slewing bearing signals.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"278 ","pages":"Article 127325"},"PeriodicalIF":7.5,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724565","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

Uncertainty-informed dynamic threshold for time series anomaly detection

IF 7.5 1区计算机科学

Expert Systems with Applications Pub Date : 2025-03-25 DOI: 10.1016/j.eswa.2025.127379

Jungmin Lee , Jiyoon Lee , Seoung Bum Kim

{"title":"Uncertainty-informed dynamic threshold for time series anomaly detection","authors":"Jungmin Lee , Jiyoon Lee , Seoung Bum Kim","doi":"10.1016/j.eswa.2025.127379","DOIUrl":"10.1016/j.eswa.2025.127379","url":null,"abstract":"<div><div>As time series data continues to be collected across various fields, the importance of automated anomaly detection systems is steadily increasing. A key challenge in anomaly detection lies in setting an optimal threshold for anomaly scores to distinguish anomalies from normal data. Most existing studies use a fixed threshold, often resulting in misclassification of ambiguous data. Therefore, defining a dynamic and optimal threshold is crucial for improving detection performance. We aim to quantify uncertainty as a metric that determines the degree of ambiguity in the data. Because our models are trained only on normal data, anomalies exhibiting patterns divergent from the normal data entail higher uncertainty. Accordingly, in this study, we propose a dynamic thresholding method that better aligns with the nature of the data through uncertainty quantification. Through experimentation with synthetic datasets and five benchmark datasets for time series anomaly detection, we demonstrate the efficacy of our proposed method. Our proposed method outperforms both the fixed threshold and existing dynamic thresholding methods, achieving an average F1-score improvement of over 0.06 across benchmark datasets. In particular, the performance improvement is more significant when the distributions of normal data and anomalies are more similar. The source code can be accessed at https://github.com/jungminkr9195/UDT.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"278 ","pages":"Article 127379"},"PeriodicalIF":7.5,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724624","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

Joint spatial feature adaption and confident pseudo-label selection for cross-subject motor imagery EEG signals classification

IF 7.5 1区计算机科学

Expert Systems with Applications Pub Date : 2025-03-25 DOI: 10.1016/j.eswa.2025.127312

Siqi Yang , Zhihua Huang , Tian-jian Luo

{"title":"Joint spatial feature adaption and confident pseudo-label selection for cross-subject motor imagery EEG signals classification","authors":"Siqi Yang , Zhihua Huang , Tian-jian Luo","doi":"10.1016/j.eswa.2025.127312","DOIUrl":"10.1016/j.eswa.2025.127312","url":null,"abstract":"<div><div>Motor imagery electroencephalograph (MI-EEG) classification plays an important role in noninvasive brain-computer interfaces (BCIs). However, the distribution shifts among different subjects make a major challenge to build classification models. Due to temporally-varying and spatially-coupling characteristics of MI-EEG data, recent methods have suffered from incomplete feature representations and the accumulation of incorrect pseudo-labels, even lower efficiency. To address these issues, the paper proposes a novel method for cross-subject MI-EEG classification, namely <strong>J</strong>oint spatial <strong>F</strong>eature <strong>A</strong>daptation and <strong>C</strong>onfident <strong>P</strong>seudo-label <strong>S</strong>election (JFACPS). JFACPS extracts joint spatial feature representations from two perspectives upon the aligned MI-EEG samples, where the spatio-temporal filtering features are extracted upon Euclidean space and the tangent space mapping features are extracted upon Riemannian space. Then, the joint spatial features are incorporated into a discriminative pseudo-labeling framework for feature adaptation. Among them, the samples with large differences in confidence between the highest and second-highest predictions are selected for adaptation. Meanwhile, a novel classifier is introduced to initialize more accurate pseudo-labels with high confidence during the first iteration of feature adaptation. We systematically conducted the experiments on two benchmark MI-EEG datasets, and the classification performance of JFACPS surpasses several state-of-the-art methods. Moreover, ablation studies also demonstrated the significance for both joint spatial feature and confident pseudo-label selection. Based on the parameter insensitivity experiments, our JFACPS method provides a novel calibration option for new subjects participating in MI-BCIs.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"278 ","pages":"Article 127312"},"PeriodicalIF":7.5,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724873","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

PULSE: A personalized physiological signal analysis framework via unsupervised domain adaptation and self-adaptive learning

IF 7.5 1区计算机科学

Expert Systems with Applications Pub Date : 2025-03-25 DOI: 10.1016/j.eswa.2025.127317

Yanan Wang , Shuaicong Hu , Jian Liu , Aiguo Wang , Guohui Zhou , Cuiwei Yang

{"title":"PULSE: A personalized physiological signal analysis framework via unsupervised domain adaptation and self-adaptive learning","authors":"Yanan Wang , Shuaicong Hu , Jian Liu , Aiguo Wang , Guohui Zhou , Cuiwei Yang","doi":"10.1016/j.eswa.2025.127317","DOIUrl":"10.1016/j.eswa.2025.127317","url":null,"abstract":"<div><div>Despite the remarkable success of artificial intelligence (AI) in physiological signal analysis, the inherent variability between individuals poses significant challenges to model generalization. Existing personalization approaches typically rely on supervised fine-tuning of pre-trained general models (GMs) using labeled data from unseen subjects, which limits their practical deployment due to labeling costs and scalability issues. To address this challenge, we propose PULSE, a personalized unsupervised domain adaptation framework that enhances model generalization through self-adaptive learning. Our approach incorporates three key components: (1) an Adaptive Channel Selection and Embedding (ACSE) module that optimizes multi-channel signal processing through learnable attention mechanisms, (2) an Embedding-guided Representation Learning (ERL) strategy that enhances intra-class feature consistency during GM pre-training, and (3) a Self-adaptive Pseudo-label Enhancement (SPE) method that generates high-quality pseudo-labels to facilitate alignment between inter-domain data distributions during GM fine-tuning. Extensive experiments on large-scale physiological datasets, including cross-database validation, demonstrate that PULSE achieves 2.8%-6.5% improvements in F1 score (from 91.5% to 95.2 to 98.0%) and 2.6%-6.4% improvements in accuracy (from 90.8% to 94.6% to 97.2%). The framework’s effectiveness is validated through dynamic electrocardiogram analysis, showcasing its potential for broader applications in physiological signal processing. The code is publicly available at <span><span>https://github.com/fdu-harry/PULSE</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"278 ","pages":"Article 127317"},"PeriodicalIF":7.5,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143734944","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

Mathematical modeling and optimization of multi-period fourth-party logistics network design problems with customer satisfaction-sensitive demand

IF 7.5 1区计算机科学

Expert Systems with Applications Pub Date : 2025-03-24 DOI: 10.1016/j.eswa.2025.127219

Yuxin Zhang , Min Huang , Yaping Fu , Songchen Jiang , Xingwei Wang , Shu-Cherng Fang

{"title":"Mathematical modeling and optimization of multi-period fourth-party logistics network design problems with customer satisfaction-sensitive demand","authors":"Yuxin Zhang , Min Huang , Yaping Fu , Songchen Jiang , Xingwei Wang , Shu-Cherng Fang","doi":"10.1016/j.eswa.2025.127219","DOIUrl":"10.1016/j.eswa.2025.127219","url":null,"abstract":"<div><div>In the current customer-driven logistics environment, customer satisfaction has become a critical factor influencing demand. When services differ from expectations, customers often exhibit bounded rational behavior. However, existing research on fourth-party logistics (4PL) network design commonly ignores the impact of customer satisfaction and psychological behaviors on demand, creating a significant gap between current models and customer-centric demands. To address this gap, this work proposes a multi-period 4PL network design problem with demand sensitive to customer satisfaction considering bounded rational behavior. First, a novel mixed integer non-linear programming model is developed to maximize profit under investment budget and service level constraints. Second, due to the NP-hardness and non-convexity, an integration-driven Q-learning based hyper-heuristic algorithm framework is proposed. To prevent reduced diversity and premature convergence resulting from over-exploitation of the global optimum, this algorithm efficiently selects suitable low-level heuristics by integrating both population and individual states with a corresponding adaptive reward function. Finally, the proposed algorithm is compared with eight commonly used algorithms and the exact solver CPLEX using different scale instances. The effectiveness and efficiency are demonstrated by numerical results. Furthermore, managerial insights are provided for investors. Company profit depends not only on investment, costs, and income, but also on customer satisfaction. Increasing the investment budget is more beneficial when the cost-income ratio is around the required service level. Customers with higher required service levels will bring greater profits when the budget is insufficient. Ignoring the impact of customer satisfaction on demand may result in the failure to achieve expected profits.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"278 ","pages":"Article 127219"},"PeriodicalIF":7.5,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724623","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 data trading scheme based on blockchain and game theory in federated learning

IF 7.5 1区计算机科学

Expert Systems with Applications Pub Date : 2025-03-24 DOI: 10.1016/j.eswa.2025.127158

Jiqun Zhang , Shengli Zhang , Gaojun Zhang , Guofu Liao

{"title":"A data trading scheme based on blockchain and game theory in federated learning","authors":"Jiqun Zhang , Shengli Zhang , Gaojun Zhang , Guofu Liao","doi":"10.1016/j.eswa.2025.127158","DOIUrl":"10.1016/j.eswa.2025.127158","url":null,"abstract":"<div><div>This paper proposes a federated learning scheme based on blockchain and game theory to address the main challenges in traditional federated learning models, including the risk of malicious user intrusion, imperfect data aggregation algorithms, and the problem of untraceable data. To solve the problems of malicious user intrusion and imperfect data aggregation algorithms, we introduce a verification game model and a weighted federated aggregation algorithm. By applying game theory principles to analyze users’ data verification behaviors, we generate a credit function and then integrate it into the weighted federated aggregation algorithm. This method significantly improves the data aggregation quality. In addition, our scheme takes advantage of the decentralized, transparent, and tamper-proof characteristics of blockchain to construct a verification body, realizing the functions of data flow recording and tracking. Experimental results show that when the participating nodes are only 75% and the training epoch is 7, the model accuracy reaches 98%, which is comparable to that of the full data model. In terms of data traceability, all data can be effectively tracked.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"278 ","pages":"Article 127158"},"PeriodicalIF":7.5,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724626","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

Predatory-imminence-continuum-inspired graph reinforcement learning for interactive motion planning in dense traffic

IF 7.5 1区计算机科学

Expert Systems with Applications Pub Date : 2025-03-24 DOI: 10.1016/j.eswa.2025.127205

Xiaohui Hou , Minggang Gan , Wei Wu , Tiantong Zhao , Jie Chen

{"title":"Predatory-imminence-continuum-inspired graph reinforcement learning for interactive motion planning in dense traffic","authors":"Xiaohui Hou , Minggang Gan , Wei Wu , Tiantong Zhao , Jie Chen","doi":"10.1016/j.eswa.2025.127205","DOIUrl":"10.1016/j.eswa.2025.127205","url":null,"abstract":"<div><div>This study introduces the Predatory-Imminence-Continuum-Inspired Graph Reinforcement Learning (PICI-GRL) algorithm, tailored for navigating unprotected interactive left turns in dense traffic scenarios—one of the most daunting challenges in autonomous driving. It unveils an innovative Reinforcement Learning (RL) framework that merges the Knowledge-Based Graph Attention Network (KBGAT) module with the Predatory-Imminence-Continuum-Inspired Auxiliary Loss Function (PICI-ALF), thereby creating connections between AI, neuroscience, and psychology. The KBGAT module integrates domain expert knowledge and a novel metric of vehicle relative aggression to improve the understanding of inter-vehicular interactions and risk evaluation. Leveraging the Predatory Imminence Continuum (PIC) theory from neuroscience, the PICI-ALF smartly divides the motion-planning process into three linked phases: pre-encounter, post-encounter, and circa-strike, utilizing an auxiliary loss function in the RL actor network with adaptive weighting coefficients to dynamically fine-tune interaction strategies and objectives, ensuring fluid transitions between phases. Simulated tests in dense traffic with environmental uncertainty and diverse interactions have shown this method’s superiority over two baseline approaches, significantly increasing the success rate of unprotected left turns while decreasing collision rates and time-to-goal, striking an optimal balance between safety and efficiency.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"277 ","pages":"Article 127205"},"PeriodicalIF":7.5,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681827","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

Majorization ordering of dependent aggregate claims clustered by statistical machine learning

IF 7.5 1区计算机科学

Expert Systems with Applications Pub Date : 2025-03-24 DOI: 10.1016/j.eswa.2025.127279

Ezgi Nevruz , Kasirga Yildirak , Ashis SenGupta

{"title":"Majorization ordering of dependent aggregate claims clustered by statistical machine learning","authors":"Ezgi Nevruz , Kasirga Yildirak , Ashis SenGupta","doi":"10.1016/j.eswa.2025.127279","DOIUrl":"10.1016/j.eswa.2025.127279","url":null,"abstract":"<div><div>The primary driver of decision-making is prioritization or ordering of risks, which plays a vital role in optimizing risk management strategies. This paper focuses on ordering aggregate claim vectors across various risk clusters utilizing agricultural insurance data. The data was sourced from the Turkish Agricultural Insurance Pool (TARSİM), the sole entity responsible for compiling agricultural insurance claim datasets. We consider the spatial and temporal features of claims, supposing that individual claims subject to similar environmental risks are dependent. We cluster risks based on meteorological values related to the location and time of the reported crop-hail insurance claims, estimated using an extended spatiotemporal interpolation method that we proposed. Bayesian regularization enhanced the performance of the statistical machine learning approach. Having clustered the risk regions, we order the aggregate claim vectors by using majorization relation and Schur-convex risk measures, which are more flexible for multivariate actuarial risks. Moreover, as a contribution to the literature, we modify the definition of majorization to fulfill the criteria for continuous random variables. The findings of this study indicate that the risk clusters, when ordered according to both the modified majorization conditions and the Schur-convex risk measure, exhibit consistency. These results further demonstrate the compatibility of the climate-based, probabilistic clustering method with the modified majorization relation.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"277 ","pages":"Article 127279"},"PeriodicalIF":7.5,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143696205","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