Advanced Engineering Informatics最新文献

A large language model-enabled machining process knowledge graph construction method for intelligent process planning

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-03-08 DOI: 10.1016/j.aei.2025.103244

Qingfeng Xu , Fei Qiu , Guanghui Zhou , Chao Zhang , Kai Ding , Fengtian Chang , Fengyi Lu , Yongrui Yu , Dongxu Ma , Jiancong Liu

{"title":"A large language model-enabled machining process knowledge graph construction method for intelligent process planning","authors":"Qingfeng Xu , Fei Qiu , Guanghui Zhou , Chao Zhang , Kai Ding , Fengtian Chang , Fengyi Lu , Yongrui Yu , Dongxu Ma , Jiancong Liu","doi":"10.1016/j.aei.2025.103244","DOIUrl":"10.1016/j.aei.2025.103244","url":null,"abstract":"<div><div>As a pivotal step in translating design into production, process planning significantly influences product quality, cost, production efficiency, and market competitiveness. The process knowledge base, a fundamental element of process planning, determines the intelligence level of product manufacturing. Methods that construct process knowledge bases using Knowledge Graphs (KGs) have increasingly become critical technologies for supporting intelligent process planning. However, traditional deep learning-based named entity recognition methods for constructing KGs require extensive manual effort in domain-specific data annotation, resulting in inefficiencies, prolonged construction cycles, and high costs. To address these challenges, this paper introduces a Large Language Model-enabled method for constructing Machining Process KGs (LLM-MPKG). Initially, Large Language Models (LLMs) are employed to pre-annotate machining process text datasets. A verifier is then developed to assess and filter the pre-annotated datasets, with domain experts re-annotating deficient data to create a high-quality annotated machining process dataset. Subsequently, using this dataset and a fine-tuned LLM, a machining process knowledge extraction model, MPKE-GPT, is constructed. MPKE-GPT is then applied to extract knowledge from process planning case data for 50 parts within an enterprise, leading to the creation of the MPKG. A prototype system was also developed to support intelligent process planning. Compared to traditional deep learning methods, the proposed method reduces construction time by 48.58%, lowers costs by 46.44%, and enhances performance by 1.96%.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103244"},"PeriodicalIF":8.0,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143577474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Scaffolding worker IMU time-series dataset for deep learning-based construction site behavior recognition

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-03-08 DOI: 10.1016/j.aei.2025.103232

Minsoo Park , Seongwoo Son , Yuntae Jeon , Dongyoung Ko , Mingeon Cho , Seunghee Park

{"title":"Scaffolding worker IMU time-series dataset for deep learning-based construction site behavior recognition","authors":"Minsoo Park , Seongwoo Son , Yuntae Jeon , Dongyoung Ko , Mingeon Cho , Seunghee Park","doi":"10.1016/j.aei.2025.103232","DOIUrl":"10.1016/j.aei.2025.103232","url":null,"abstract":"<div><div>The construction industry is one of the most dangerous industries worldwide, and scaffold-related accidents are a significant concern. Despite the widespread use of scaffolds, the safety regulations pertaining to scaffolding remain among the most frequently violated, leading to a high frequency of accidents. Advancements in deep learning offer promising avenues for automating safety monitoring. However, the field is hindered by the lack of accessible datasets for training models in worker-behavior recognition. This study introduces the scaffolding worker inertial measurement unit (IMU) time-series (SWIT) dataset, which is designed to enrich the development of deep learning models for the automated recognition of construction worker behaviors. The SWIT dataset addresses the limitations of existing datasets by incorporating a wide range of hazardous behaviors, regulatory violations, and emergency situations specific to scaffolding. The dataset was developed through a rigorous process involving the analysis of sensor positions from previous studies, studies on abnormal behavior recognition, and scaffolding-safety regulations. It comprises ten categories of behaviors, including hazardous actions, near-miss incidents, and activities that may lead to musculoskeletal disorders. By providing a comprehensive collection of annotated time-series data from IMU sensors, this dataset aims to facilitate the development of robust deep learning models for automated worker-behavior recognition.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103232"},"PeriodicalIF":8.0,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143577473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A regional domestic energy consumption model based on LoD1 to assess energy-saving potential

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-03-08 DOI: 10.1016/j.aei.2025.103247

Minghao Liu, Zhonghua Gou

{"title":"A regional domestic energy consumption model based on LoD1 to assess energy-saving potential","authors":"Minghao Liu, Zhonghua Gou","doi":"10.1016/j.aei.2025.103247","DOIUrl":"10.1016/j.aei.2025.103247","url":null,"abstract":"<div><div>The residential sector accounts for a significant share of global carbon emissions, and energy efficiency retrofitting of buildings is crucial for achieving carbon neutrality. However, assessing energy demand and determining retrofit priorities within large building stocks presents numerous challenges. This study proposes an innovative and simplified approach that reduces the complexity of evaluating large-scale residential building stocks by focusing on building prototypes, thereby effectively assessing regional energy consumption. The innovation of this method lies in the combination of Shapley values with clustering techniques to ensure that building prototypes are representative in terms of energy efficiency. This not only enhances the interpretability of clustering results but also improves their practical application in energy efficiency analysis. Taking England as an example, this study identifies six residential building prototypes and constructs an energy consumption model based on Level of Detail 1 (LoD1), using calibration to capture regional heterogeneity. The research also finds that factors such as climate, demographics, and income significantly influence EUI, and there are notable variations across different regions and building types. Moreover, if all homes in the UK were to achieve a C-grade in Energy Performance Certificate (EPC), it is estimated that approximately 60,922.85 GWh of energy could be saved, representing 17.4% of the total residential sector energy consumption in the UK in 2021. This study provides a framework for the effective allocation of retrofit resources and identification of high-potential energy-saving opportunities.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103247"},"PeriodicalIF":8.0,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143577475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mitigating potential risk via counterfactual explanation generation in blast-based tunnel construction

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-03-06 DOI: 10.1016/j.aei.2025.103227

Fenghua Liu , Wenli Liu , Jiajing Liu , Botao Zhong , Jun Sun

{"title":"Mitigating potential risk via counterfactual explanation generation in blast-based tunnel construction","authors":"Fenghua Liu , Wenli Liu , Jiajing Liu , Botao Zhong , Jun Sun","doi":"10.1016/j.aei.2025.103227","DOIUrl":"10.1016/j.aei.2025.103227","url":null,"abstract":"<div><div>Machine learning and deep learning have significantly enhanced the ability to mitigate risks in blast-based tunnel construction. However, most studies fall short in model constraints, data quality, and explainability, making non-robust risk mitigation strategies. Therefore, this study aims to investigate the following questions: <em>how to accurately assess risk for blast-based tunnel construction using limited data, and develop effective risk mitigation strategies?</em> This research leverages counterfactual explanation generation, a key technique of explainable artificial intelligence, along with data augmentation to develop a framework for guiding risk mitigation, which includes:</div><div>(1) a two-stage data augmentation technique to address data shortage and imbalance; (2) a novel counterfactual explanation generation algorithm to optimize blasting parameters and reduce risk; and (3) a post-hoc explainable approach to provide insights on feature importance. A railway tunnel in Hubei is conducted as a case study to test the validity of the proposed method. The results show that the proposed method accurately predict overbreak, achieving the highest <em>R</em><sup>2</sup> (0.883) and the lowest RMSE (1.335) compared to baseline models. Additionally, it effectively optimizes the blasting parameters to mitigate risk, reducing the average overbreak in six scenarios. The explainable analytic identifies key factors (e.g., periphery hole spacing) influencing construction risk, thereby enhancing personnel’s understanding of complex construction systems.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103227"},"PeriodicalIF":8.0,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fast detection of short circuits in copper electrolytic refining with PCA and a branching perceptron

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-03-06 DOI: 10.1016/j.aei.2025.103239

Yusi Dai , Chunhua Yang , Hongqiu Zhu , Can Zhou , Xi Wang

{"title":"Fast detection of short circuits in copper electrolytic refining with PCA and a branching perceptron","authors":"Yusi Dai , Chunhua Yang , Hongqiu Zhu , Can Zhou , Xi Wang","doi":"10.1016/j.aei.2025.103239","DOIUrl":"10.1016/j.aei.2025.103239","url":null,"abstract":"<div><div>Short circuit faults greatly affect the current efficiency and product quality of copper electrolytic refining. Irregular cloth covers and uneven heating make it difficult to detect short-circuit faults of copper electrolytic refining with infrared images, so complex object detection models believed to do well in feature digs were previously used. Yet, such methods have high computation costs, which limits the detection efficiency and hinders the function expansion to portable devices. We find that with proper feature extraction, the model can be succinct, and the checkbox output of object detection models is not the most applicable form. Therefore, this paper proposes a fast multilabel classification method to detect the short circuit faults of copper electrolytic refining with principal components analysis (PCA) and a branching perceptron. In the method, PCA reduces data dimensions in an unsupervised and reversible way according to the maximum projection variance principle since faults appear as discrepancy signals in images. Then, a branching perceptron is presented for fault identification. Each branch corresponds with a pair of anode and cathode electrodes and the output of the branch predicts the state of the pair. Reversible low-dimensional features obtained with PCA can reduce the pressure on data transfer and storage, and support a more succinct detection model to fasten the training and detection. The binary sequence form of the designed output is more convenient for on-site fault removal and other purposes. The proposed PCA-Perceptron method is verified on real-world data of electrolytic refining of recycled copper.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103239"},"PeriodicalIF":8.0,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A multiple-criteria sensor selection framework based on qualitative physical models

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-03-05 DOI: 10.1016/j.aei.2025.103228

Xiaoxu Diao, Md Ragib Rownak, Samuel Olatubosun, Pavan Kumar Vaddi, Carol Smidts

{"title":"A multiple-criteria sensor selection framework based on qualitative physical models","authors":"Xiaoxu Diao, Md Ragib Rownak, Samuel Olatubosun, Pavan Kumar Vaddi, Carol Smidts","doi":"10.1016/j.aei.2025.103228","DOIUrl":"10.1016/j.aei.2025.103228","url":null,"abstract":"<div><div>Sensor selection is critical for designing effective online monitoring systems for safety–critical applications. This paper proposes a novel sensor selection framework that utilizes qualitative system models to evaluate various sensor configurations based on multiple criteria. The criteria assess capabilities like fault diagnostics, risk reduction, observability, functionality, integrability, and cost. The framework uses the Integrated System Failure Analysis to generate signal features from qualitative system models. These features are used to evaluate sensor configurations against the selection criteria. The criteria can be applied as constraints or objectives for optimization. The Non-dominated Sorting Genetic Algorithm handles the multi-objective optimization to find Pareto optimal sensor deployment solutions. The framework is demonstrated on a reactor cavity cooling system case study, generating optimal configurations considering temperature, flow, pressure, density, and radiation sensors. The framework aids online monitoring system design by recommending sensor deployment configurations that balance critical capabilities. Qualitative models provide effective analysis despite the lack of operational data. The flexible criteria and multi-objective optimization enable extensive exploration of configurations in early development stages.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103228"},"PeriodicalIF":8.0,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Crafting user-centric prompts for UI generations based on Kansei engineering and knowledge graph

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-03-05 DOI: 10.1016/j.aei.2025.103217

Xuejing Feng , Huifang Du , Jun Ma , Haofen Wang , Lijuan Zhou , Meng Wang

{"title":"Crafting user-centric prompts for UI generations based on Kansei engineering and knowledge graph","authors":"Xuejing Feng , Huifang Du , Jun Ma , Haofen Wang , Lijuan Zhou , Meng Wang","doi":"10.1016/j.aei.2025.103217","DOIUrl":"10.1016/j.aei.2025.103217","url":null,"abstract":"<div><div>Text-to-image (T2I) models are emerging as a powerful tool for designers to create user interface (UI) prototypes from natural language inputs (i.e., prompts). However, the discrepancy between designer inputs and model-preferred prompts makes it challenging for designers to consistently deliver effective results to end users. To bridge this gap, we introduce a novel hybrid method that assists designers in crafting user-centric prompts for T2I models, ensuring that the generated UIs align with end-user expectations. First, this method merges text mining and Kansei Engineering (KE) to analyze online user reviews and construct a Knowledge Graph (KG), mapping the intricate relationships between diverse affective requirements of users, design features, and corresponding text prompts for UI generation. Then, our approach automatically transforms designer inputs into model-preferred prompts through entity mention recognition and entity linking during the human-AI collaborative design process. Finally, we validate the proposed approach with a case study on automotive human–machine interface design. Experimental results demonstrate that our approach achieves high performance in perceived efficiency, satisfaction, and expectation disconfirmation. Overall, this study represents a step forward in integrating human and AI contributions in design and innovation within engineering disciplines, enabling AI to inspire, develop, and reinforce human creativity from a human factors perspective.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103217"},"PeriodicalIF":8.0,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting the performance status of aero-engines using a spatio-temporal decoupled digital twin modeling method

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-03-04 DOI: 10.1016/j.aei.2025.103218

Dasheng Xiao, Shuo Song, Hong Xiao, Zhanxue Wang

{"title":"Predicting the performance status of aero-engines using a spatio-temporal decoupled digital twin modeling method","authors":"Dasheng Xiao, Shuo Song, Hong Xiao, Zhanxue Wang","doi":"10.1016/j.aei.2025.103218","DOIUrl":"10.1016/j.aei.2025.103218","url":null,"abstract":"<div><div>The digital twin model for predicting engine performance enhances engine health management. Key indicators such as exhaust gas temperature (EGT) and thrust are essential for evaluating engine performance. This study focuses on extracting and integrating complex spatio-temporal features from multiple sensors to construct an effective prediction model. A data-driven modeling method that combines the physical structure of an engine while achieving spatio-temporal feature decoupled was proposed. This method is based on Long Short-Term Memory (LSTM) and a self-attention mechanism, and incorporates time-variant parameter derivatives into the model’s input using first-order backward differences. Case studies were conducted on the EGT and thrust predictions. The mean absolute relative error (<span><math><mrow><mi>M</mi><mi>A</mi><mi>R</mi><mi>E</mi></mrow></math></span>) was used to evaluate the accuracy of each test, whereas the average <span><math><mrow><mi>M</mi><mi>A</mi><mi>R</mi><mi>E</mi></mrow></math></span> (<span><math><msub><mrow><mi>μ</mi></mrow><mrow><mi>M</mi><mi>A</mi><mi>R</mi><mi>E</mi></mrow></msub></math></span>) across ten tests was used to assess the accuracy of each model. The results show that the spatio-temporal decoupled modeling method improves prediction accuracy and stability, achieving a minimum <span><math><msub><mrow><mi>μ</mi></mrow><mrow><mi>M</mi><mi>A</mi><mi>R</mi><mi>E</mi></mrow></msub></math></span> of 0.64% for the EGT and 0.277% for the normalized thrust. Furthermore, to test the method’s robustness against varying sampling frequencies during deployment, the sampling intervals of the test data were adjusted to simulate changes in sampling frequency. The results demonstrate that the proposed method exhibits excellent stability.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103218"},"PeriodicalIF":8.0,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Production-logistics collaborative scheduling in dynamic flexible job shops using nested-hierarchical deep reinforcement learning

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-03-04 DOI: 10.1016/j.aei.2025.103195

Jiaxuan Shi , Fei Qiao , Juan Liu , Yumin Ma , Dongyuan Wang , Chen Ding

{"title":"Production-logistics collaborative scheduling in dynamic flexible job shops using nested-hierarchical deep reinforcement learning","authors":"Jiaxuan Shi , Fei Qiao , Juan Liu , Yumin Ma , Dongyuan Wang , Chen Ding","doi":"10.1016/j.aei.2025.103195","DOIUrl":"10.1016/j.aei.2025.103195","url":null,"abstract":"<div><div>Effective manufacturing in flexible job shops often requires collaboratively organizing production and logistics activities. This necessitates a thorough exploration of corresponding collaborative scheduling problem. However, extant studies remain relatively preliminary, not only neglecting the inevitable disturbances in real-world but also failing to satisfy the essential need for collaboration, that is, to simultaneously optimize both activities’ objectives. Therefore, this study proposes a novel production-logistics collaborative scheduling problem for dynamic flexible job shops, in which the common yet underappreciated disturbance of logistics equipment breakdowns is meticulously considered, and two typical objectives individually pursued by two activities are optimized simultaneously. To solve the proposed problem, a nested-hierarchical deep reinforcement learning method is developed. In this method, a new nested-hierarchical framework that rationally deploys multiple agents is designed to facilitate the required multi-objective optimization while ensuring the practicality of decision-making process. Based on this framework, appropriate state features, actions, and reward functions are devised for each agent, and a training mechanism based on multi-agent proximal policy optimization is proposed to train agents effectively. Experiments in an aviation component production shop are conducted to confirm the effectiveness of proposed method and problem.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103195"},"PeriodicalIF":8.0,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-scale 4D localized spatio-temporal graph convolutional networks for spatio-temporal sequences forecasting in aluminum electrolysis

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-03-03 DOI: 10.1016/j.aei.2025.103222

Yubo Sun , Xiaofang Chen , Weihua Gui , Lihui Cen , Yongfang Xie , Zhong Zou

{"title":"Multi-scale 4D localized spatio-temporal graph convolutional networks for spatio-temporal sequences forecasting in aluminum electrolysis","authors":"Yubo Sun , Xiaofang Chen , Weihua Gui , Lihui Cen , Yongfang Xie , Zhong Zou","doi":"10.1016/j.aei.2025.103222","DOIUrl":"10.1016/j.aei.2025.103222","url":null,"abstract":"<div><div>Spatio-temporal sequences forecasting fulfills a vital role in the intelligent advancement of aluminum electrolysis production process. The localized spatio-temporal correlations contained in spatio-temporal sequences, caused by the dynamicity of regional working conditions, have complex and diverse (multi-scale) characteristics. The existing deep learning-based prediction methods are difficult to capture the multi-scale localized spatio-temporal correlations, and the adverse effects of industrial noise on spatio-temporal correlation acquisition have not been considered. In this article, we propose the multi-scale 4D localized spatio-temporal graph convolutional networks (Ms-4D-LStGCN) to address the above issues. Concretely, we propose a data-driven accurate similarity search method and fuse the prior knowledge to construct the spatio-temporal graph. Then,a novel 4D localized spatio-temporal graph convolution module is proposed to capture the complex localized spatio-temporal correlations. Finally, the multi-scale 4D localized spatio-temporal graph convolution framework is designed to obtain the multi-scale and multi-depth localized spatio-temporal correlation features. Illustrative examples on 16 real-world industrial aluminum electrolysis datasets attest that our method has superior prediction performance compared with state-of-the-art methods.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103222"},"PeriodicalIF":8.0,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0