Advanced Engineering Informatics最新文献

Revealing the hidden correlations of elements in intelligent transportation systems with a novel knowledge graph-based path calculation approach

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-04-02 DOI: 10.1016/j.aei.2025.103299

Ke Huang, Ming Cai, Yao Xiao

{"title":"Revealing the hidden correlations of elements in intelligent transportation systems with a novel knowledge graph-based path calculation approach","authors":"Ke Huang, Ming Cai, Yao Xiao","doi":"10.1016/j.aei.2025.103299","DOIUrl":"10.1016/j.aei.2025.103299","url":null,"abstract":"<div><div>Intelligence has emerged as an integral trend within Intelligent Transportation Systems (ITS), making the comprehension of interrelations among its key elements critical for unveiling potential influence mechanisms. To foster research in this domain, we present an innovative method aimed at unearthing explainable correlations among these pivotal ITS elements. Our approach is underpinned by two primary stages: the construction of a knowledge graph drawn from ITS-related patents, followed by the application of an enhanced breadth-first path calculation algorithm. This novel algorithm carefully balances consideration between element correlations and the structural nuances of the knowledge graph. To verify the robustness of our algorithm, we engage in meticulous node similarity calculations and undertake an assessment of its effectiveness using an array of performance indicators. Furthermore, to provide practical insight, we offer two case studies exploring the correlation among elements within the realms of Vehicle-to-Everything (V2X) communication system and smart logistics center. These case studies not only validate the method’s effectiveness but also illustrate its broad applicability. Our method’s utility extends beyond merely unraveling evolution mechanisms and forecasting development trends within transportation systems, and it has the potential to significantly contribute to correlation research across a broad spectrum of fields.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103299"},"PeriodicalIF":8.0,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748048","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

Denoising autoencoder multilayer perceptron spiking neural network for isonicotinic acid yield prediction on real industrial dataset

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-04-01 DOI: 10.1016/j.aei.2025.103273

Pinze Ren , Yitian Wang , Zisheng Wang , Dandan Peng , Chenyu Liu , Te Han

{"title":"Denoising autoencoder multilayer perceptron spiking neural network for isonicotinic acid yield prediction on real industrial dataset","authors":"Pinze Ren , Yitian Wang , Zisheng Wang , Dandan Peng , Chenyu Liu , Te Han","doi":"10.1016/j.aei.2025.103273","DOIUrl":"10.1016/j.aei.2025.103273","url":null,"abstract":"<div><div>Isonicotinic acid (INA) has attracted considerable interest as a crucial pharmaceutical intermediate, especially for the production of the anti-tuberculosis drug isoniazid. Nonetheless, industrial production of INA encompasses intricate procedures that are highly sensitive to process parameters, leading to yield variability. Hence, an efficient prediction model for forecasting INA yield is essential for enhancing production yields and ensuring the consistency of INA in pharmaceutical manufacturing processes. To address this challenge, the present study developed a brain-inspired spiking neural network (SNN) tailored to the prediction of INA yield. Specifically, we propose a novel denoising autoencoder multilayer perceptron based spiking neural network (DAEMLP-SNN) for this purpose. The SNN is designed to accurately emulate the dynamic behavior of biological neurons while maintaining low power consumption, thereby ensuring high biological plausibility. Drawing upon the principles of autoencoders, our research constructs a denoising autoencoder SNN capable of extracting meaningful latent features and compressing high-dimensional industrial data. Moreover, we concatenatedthe extracted features with the original data, thereby creating a more comprehensive representation of the input. This enriched input was then fed into the multilayer perceptron SNN, which markedly enhances the robustness and precision of INA yield predictions. Experimental findings demonstrated the superior performance of DAEMLP-SNN, as it consistently achieved accurate predictions across diverse process parameters.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103273"},"PeriodicalIF":8.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739429","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

Critical challenges and advances in vibration signal processing for non-stationary condition monitoring

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-04-01 DOI: 10.1016/j.aei.2025.103290

Anil Kumar , Agnieszka Wyłomańska , Radosław Zimroz , Jiawei Xiang , Jérôme Antoni

{"title":"Critical challenges and advances in vibration signal processing for non-stationary condition monitoring","authors":"Anil Kumar , Agnieszka Wyłomańska , Radosław Zimroz , Jiawei Xiang , Jérôme Antoni","doi":"10.1016/j.aei.2025.103290","DOIUrl":"10.1016/j.aei.2025.103290","url":null,"abstract":"<div><div>This study provides a comprehensive overview of challenges and advancements in vibration analysis for machinery operations under non-stationary and non-linear conditions. Non-stationary operation in machinery occurs when operating conditions such as speed, load, and environmental factors change over time. This results in dynamic behaviours that cause fluctuating vibration signals, making fault detection challenging with traditional methods that assume stationary conditions. The paper provides foundational insights and clear concepts on essential topics, including non-stationary operations in rotary machinery, vibration signals in non-stationary operations, cycle-stationary analysis, and the quantification of non-stationary operations. Further advancing, this paper explores the challenges and methodologies in condition-based monitoring for non-stationary machinery operations, focusing on the analysis of vibrational signals. It examines the complexities of working with non-stationary and <em>cyclo</em>-stationary signals and the limitations of traditional signal processing techniques. The study reviews classical time–frequency and advanced signal-processing methods, highlighting their advantages, drawbacks, and applicability in real-world scenarios. Additionally, it addresses the identification of defects across varying operational speeds, identifying gaps in current methodologies and suggesting potential avenues for future research. The paper also emphasizes the importance of transfer learning in non-stationary environments, analyzing various approaches and their effectiveness in improving monitoring performance. Lastly, it discusses the development of expertise and adoption pathways for AI-based predictive maintenance, offering insights into the practical integration of advanced technologies in industrial settings.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103290"},"PeriodicalIF":8.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748049","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

Decentralized coordination of intelligent system of systems under partial observability

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-04-01 DOI: 10.1016/j.aei.2025.103286

Hao Yuan, Bangbang Ren, Tao Chen, Xueshan Luo

{"title":"Decentralized coordination of intelligent system of systems under partial observability","authors":"Hao Yuan, Bangbang Ren, Tao Chen, Xueshan Luo","doi":"10.1016/j.aei.2025.103286","DOIUrl":"10.1016/j.aei.2025.103286","url":null,"abstract":"<div><div>Limited by the physical constraints of the weapon platform equipment, such as cameras and sensors, it is only capable of observing local information in its immediate vicinity, particularly within high-confrontation and high-interference battlefield environments. Consequently, this hinders the effective realization of decentralized coordination between platforms within the combat system of systems (SoS), thereby impeding efficient execution of combat tasks. To enhance the efficient utilization of combat resources for the construction of task communities, enabling platforms to decentralized coordination in executing combat tasks based solely on local information, this study proposes an approach utilizing the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm that leverages partial information for the construction of task communities. By engaging in continuous interaction with the environment, the platform can enhance its decision-making capabilities and independently generate optimal solutions based on local information. Furthermore, we propose an information sharing mechanism to enable the platform to obtain a wider observation area, thereby enhancing the accuracy of its task resource allocation. The evaluation results demonstrate that the proposed method significantly enhances platform coordination efficiency and resource utilization, even when operating with limited information. In comparison to other baseline methods, the task satisfaction degree can be increased by approximately <span><math><mrow><mn>15</mn><mtext>%</mtext><mo>∼</mo><mn>20</mn><mtext>%</mtext></mrow></math></span> with only partial information.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103286"},"PeriodicalIF":8.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739428","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

Tackling dual-resource flexible job shop scheduling problem in the production line reconfiguration scenario: An efficient meta-heuristic with critical path-based neighborhood search

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-04-01 DOI: 10.1016/j.aei.2025.103282

Ziyu Zhang, Xinyu Li, Liang Gao, Qihao Liu, Jin Huang

{"title":"Tackling dual-resource flexible job shop scheduling problem in the production line reconfiguration scenario: An efficient meta-heuristic with critical path-based neighborhood search","authors":"Ziyu Zhang, Xinyu Li, Liang Gao, Qihao Liu, Jin Huang","doi":"10.1016/j.aei.2025.103282","DOIUrl":"10.1016/j.aei.2025.103282","url":null,"abstract":"<div><div>Addressing diverse production demands, companies must frequently reconfigure the production line to manufacture various customized products. However, production line reconfiguration requires reasonable scheduling of workers and auxiliary resources to ensure the debugging of different machines. Therefore, this paper defines the dual-resource flexible job shop scheduling problem in the production line reconfiguration (DRFJSP-PLR) scenario to minimize makespan. While traditional single-resource scheduling methods inadequately tackle the dual-resource cooperative constraints, struggle to guarantee solution quality. Hence, a mixed integer linear programming (MILP) model is developed, addressing the lack of rigorous mathematical characterization in prior methods. Based on this, a rule-guided exemplar learning genetic algorithm with neighborhood search (RgELGA_NS) is proposed. The main innovations include: (a) a rule-guided initialization approach is designed to enhance the initial population quality. (b) an exemplar learning strategy is adopted to select crossover individuals to reduce the destruction of inferior solutions to superior ones. (c) a neighborhood search operator considering resource cooperation based on critical path is presented, which significantly augments the population local exploitation ability. Experimental results on 60 instances demonstrate that the MILP model can effectively solve small- and medium-sized problems, and RgELGA_NS can obtain near-optimal solutions for different scale problems. Compared to other meta-heuristics, our algorithm exhibits superior convergence and stability, achieving the best scheduling schemes on 93.33% instances.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103282"},"PeriodicalIF":8.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739430","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

Neuromorphic computing-enabled generalized machine fault diagnosis with dynamic vision

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-04-01 DOI: 10.1016/j.aei.2025.103300

Changhao Liu , Xiang Li , Xinrui Chen , Samir Khan

{"title":"Neuromorphic computing-enabled generalized machine fault diagnosis with dynamic vision","authors":"Changhao Liu , Xiang Li , Xinrui Chen , Samir Khan","doi":"10.1016/j.aei.2025.103300","DOIUrl":"10.1016/j.aei.2025.103300","url":null,"abstract":"<div><div>Rotating machinery plays a critical role in modern industry, and accurate fault diagnosis is essential for ensuring reliable operations. Event-based cameras have recently emerged as a promising tool for non-contact vibration measurement and fault diagnosis of rotating machines. However, the camera factors such as monitoring angles, lighting conditions, etc., have a noticeable influence on the diagnostic performance. A simply trained model cannot be well deployed in new testing scenarios with factor variations. To address this issue, this paper proposes a neuromorphic computing-enabled method for generalizing non-contact fault diagnosis. Dynamic vision data is captured using event-based cameras under varying operational conditions. A dynamic vision data representation method is developed to transform event streams into features that are well-suited for processing by neuromorphic spiking neural networks. Furthermore, a specially designed neuromorphic domain generalization approach is proposed to improve generalization ability across different working conditions. Extensive experiments are conducted to validate the domain generalization performance of the proposed method, along with comparisons with popular domain generalization techniques. The results demonstrate that the proposed approach achieves robust diagnostic performance under different conditions, validating its effectiveness for potential industrial applications.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103300"},"PeriodicalIF":8.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748046","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

AutoTRIZ: Automating engineering innovation with TRIZ and large language models

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-04-01 DOI: 10.1016/j.aei.2025.103312

Shuo Jiang , Weifeng Li , Yuping Qian , Yangjun Zhang , Jianxi Luo

{"title":"AutoTRIZ: Automating engineering innovation with TRIZ and large language models","authors":"Shuo Jiang , Weifeng Li , Yuping Qian , Yangjun Zhang , Jianxi Luo","doi":"10.1016/j.aei.2025.103312","DOIUrl":"10.1016/j.aei.2025.103312","url":null,"abstract":"<div><div>Various ideation methods, such as morphological analysis and design-by-analogy, have been developed to aid creative problem-solving and innovation. Among them, the Theory of Inventive Problem Solving (TRIZ) stands out as one of the best-known methods. However, the complexity of TRIZ and its reliance on users’ knowledge, experience, and reasoning capabilities limit its practicality. To address this, we introduce AutoTRIZ, an artificial ideation system that integrates Large Language Models (LLMs) to automate and enhance the TRIZ methodology. By leveraging LLMs’ vast pre-trained knowledge and advanced reasoning capabilities, AutoTRIZ offers a novel, generative, and interpretable approach to engineering innovation. AutoTRIZ takes a problem statement from the user as its initial input, automatically conduct the TRIZ reasoning process and generates a structured solution report. We demonstrate and evaluate the effectiveness of AutoTRIZ through comparative experiments with textbook cases and a real-world application in the design of a Battery Thermal Management System (BTMS). Moreover, the proposed LLM-based framework holds the potential for extension to automate other knowledge-based ideation methods, such as SCAMPER, Design Heuristics, and Design-by-Analogy, paving the way for a new era of AI-driven innovation tools.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103312"},"PeriodicalIF":8.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748047","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

A skill vector-based multi-task optimization algorithm for achieving objectives of multiple users in cloud manufacturing

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-03-31 DOI: 10.1016/j.aei.2025.103295

Yixiao Jiang, Dunbing Tang, Haihua Zhu, Changchun Liu, Kai Chen, Zequn Zhang, Jie Chen

{"title":"A skill vector-based multi-task optimization algorithm for achieving objectives of multiple users in cloud manufacturing","authors":"Yixiao Jiang, Dunbing Tang, Haihua Zhu, Changchun Liu, Kai Chen, Zequn Zhang, Jie Chen","doi":"10.1016/j.aei.2025.103295","DOIUrl":"10.1016/j.aei.2025.103295","url":null,"abstract":"<div><div>Cloud Manufacturing (CMfg) is a new manufacturing mode that provides efficient manufacturing services to customers by centrally scheduling manufacturing resources distributed across various regions. In CMfg, each participant is an independent economic entity with distinct objectives and effectively achieving the objectives of customers, suppliers, and the CMfg platform under limited resources is a significant challenge. To solve this problem, this study first proposed a three-level multi-task optimization (TMTO) model. The upper-level and lower-level of the TMTO model respectively optimize the personalized objectives of customers and suppliers, as well as the objectives of the CMfg platform are optimized at the middle-level. Subsequently, a skill vector-guided multi-task optimization algorithm (SMTOA) is proposed to collaboratively optimize the objectives of all participants, with the skill vector designed to evaluate the ability of scheduling schemes to meet the objectives of all customers and suppliers. Finally, experimental cases based on an aerospace manufacturing enterprise confirm the effectiveness of the TMTO model and the advantages of SMTOA in solving the TMTO model.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103295"},"PeriodicalIF":8.0,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739426","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

3D-MMFN: Multi-level multimodal fusion network for 3D industrial image anomaly detection

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-03-31 DOI: 10.1016/j.aei.2025.103284

Mujtaba Asad , Waqar Azeem , Aftab Ahmad Malik , He Jiang , Ahmad Ali , Jie Yang , Wei Liu

{"title":"3D-MMFN: Multi-level multimodal fusion network for 3D industrial image anomaly detection","authors":"Mujtaba Asad , Waqar Azeem , Aftab Ahmad Malik , He Jiang , Ahmad Ali , Jie Yang , Wei Liu","doi":"10.1016/j.aei.2025.103284","DOIUrl":"10.1016/j.aei.2025.103284","url":null,"abstract":"<div><div>3D-based image anomaly detection (AD) is a crucial computer vision task in industrial manufacturing. Most existing methods only focus on 2D shape-based detections. However, there is still limited research for detecting anomalies in 3D shapes using multimodal features. Some existing techniques developed for this task are mostly unsuitable for industrial defect detection for several reasons. Firstly, they rely mostly on memory banks, resulting in high storage overheads, making them difficult to deploy on production lines. Secondly, the multimodal features, in the existing 3D industrial AD algorithms, are concatenated directly which cause a significant disruption between the features and degrades the detection efficiency. Thirdly, their inference speed is not fast enough to achieve real-time detection. To address these challenges, we propose a deployment-friendly network named 3D-MMFN. Our model comprises of the following components: (1) The pre-trained feature extractors that generate local features from multi-stream inputs of RGB images, surface normal maps, and point clouds. (2) A novel point-to-pixel based fusion module that efficiently fuses multi-level multimodal features to mitigate disruption during the fusion operation. (3) An anomaly generator module that generates anomalous features from normal multimodal fused features, enabling self-supervised training of 3D-MMFN while eliminating the need for extensive memory banks. Experimental results on the MVTec3D-AD and Eyecandies dataset demonstrate the effectiveness of our proposed model, showcasing significant performance improvements over state-of-the-art methods.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103284"},"PeriodicalIF":8.0,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739427","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 water demand for spraying operations in dry bulk ports: A hybrid approach based on data decomposition and deep learning

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-03-31 DOI: 10.1016/j.aei.2025.103313

Jiaqi Guo , Wenyuan Wang , Chi Wai Kwong , Yun Peng , Zicheng Xia , Xin Li

{"title":"Predicting water demand for spraying operations in dry bulk ports: A hybrid approach based on data decomposition and deep learning","authors":"Jiaqi Guo , Wenyuan Wang , Chi Wai Kwong , Yun Peng , Zicheng Xia , Xin Li","doi":"10.1016/j.aei.2025.103313","DOIUrl":"10.1016/j.aei.2025.103313","url":null,"abstract":"<div><div>Dust pollution from materials in dry bulk ports (DBPs) significantly impacts air quality and public health in coastal cities. Spraying operations are the primary dust control measures in ports and accurately predicting water demand for these operations helps optimize water scheduling and conserve resources. However, challenges remain in addressing non-stationary time series and improving prediction accuracy. Additionally, existing studies rarely consider the impacts of port operations on water demand for spraying. Therefore, this study proposes a hybrid approach based on data decomposition and deep learning to predict water demand for spraying operations in DBPs. Port operational data is specifically integrated into the input features. An optimal variational mode decomposition (OVMD) method is introduced to reduce data non-stationarity. Compared to other methods, OVMD adaptively selects the optimal modes and effectively mitigates mode mixing issues. The 1-D Convolutional Neural Network integrated with an Attention BILSTM model, combined with OVMD, an Artificial Neural Network, and Error Correction, is employed to capture long-term temporal dependencies. Moreover, the relationship between material surface moisture content and water consumption for spraying operations is uniquely incorporated into the prediction process. This approach is compared with benchmark models using a dataset from a DBP in northern China. The results demonstrate that the proposed method achieves superior predictive performance, with a MAE of 0.47, a RMSE of 0.71, and an R2 of 0.95. The proposed approach enables port operators to accurately determine water consumption for spraying operations, thereby promoting the intelligent and sustainable development of dust control in DBPs.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103313"},"PeriodicalIF":8.0,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738838","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