Advanced Engineering Informatics最新文献

{"title":"EEG-based floor vibration serviceability evaluation using machine learning","authors":"Jiang Li ,&nbsp;Weizhao Tang ,&nbsp;Jiepeng Liu ,&nbsp;Yunfei Zhao ,&nbsp;Y.Frank Chen","doi":"10.1016/j.aei.2024.103089","DOIUrl":"10.1016/j.aei.2024.103089","url":null,"abstract":"<div><div>The emergence of novel floor systems has made the vibration serviceability evaluation, conducted through testing and finite element simulation, a time-consuming process that struggles to accurately reflect the user experience. Even when occupant feelings are taken into account, the evaluations may be subjective. This study introduced electroencephalogram (EEG) to grade the ranges of peak accelerations (ACCs) and maximum transient vibration values (MTVVs). To achieve this goal, a supervised learning algorithm, the eXtreme Gradient Boosting (XGBoost), were adopted to conduct binary classification to recognize the threshold of peak ACCs and MTVVs. Accelerometers and an EEG acquisition instrument were utilized to simultaneously capture the ACC dataset and the EEG of volunteers. Characteristic EEG time-domain curves were then obtained by averaging the preprocessed curves corresponding to specific ranges of peak ACCs. The vibration-induced event-related potential components, P50 (the positive potential component with 50 ms latency) and N200 (the negative potential component with 200 ms latency), were identified based on the averaged EEG curves. Additionally, the peak amplitude and peak latency were calculated using cognitive neuroscience methods. The study results suggest that the potential for floor vibration-induced components is around 10 μV. Lastly, XGBoost was utilized to identify the thresholds of different ranges of peak ACCs and MTVVs using EEG time-domain and frequency-domain features. The classification accuracy according to MTVV using XGBoost can reach up to 99 %. This study quantified human perception of floor vibrations based on EEG and optimized peak ACC and MTVV threshold for the cold-formed steel floor vibration serviceability evaluation.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"64 ","pages":"Article 103089"},"PeriodicalIF":8.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143129699","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}

{"title":"A fuzzy dematel-based delegated Proof-of-Stake consensus mechanism for medical model fusion on blockchain","authors":"Zhi Li ,&nbsp;Fuhe Liang ,&nbsp;Ming Li","doi":"10.1016/j.aei.2024.103095","DOIUrl":"10.1016/j.aei.2024.103095","url":null,"abstract":"<div><div>To ensure consensus regarding the contribution of distributed medical institutions to data models and the transformation of their application value, this paper proposes a fuzzy DEMATEL-based delegated proof-of-stake consensus mechanism for medical model fusion on blockchain. By utilizing transparent, verifiable consensus methods and monitorable on-chain distributed service logic, this framework determines the value-added performance and value-added application of distributed models. Considering that traditional consensus mechanisms are designed primarily for static, deterministic numerical data, they fall short in terms of accommodating consensus for dynamic, interval-based models. To address this limitation, we propose an enhancement to the DPOS consensus mechanism by using fuzzy DEMATEL. This approach enables contribution measurement and consensus for distributed models on the basis of interval-based model characteristics, thereby improving the interpretability of contribution assessments in medical institutions. Since the current lack of application paradigms for data models in distributed environments limits the value conversion of models at the application layer, we propose the construction of a distributed application logic using blockchain and smart contracts. By leveraging smart contracts to protect data privacy and model ownership, this approach enables the standardized and service-oriented transformation of application values. Finally, we conducted an experimental case study using a real medical image diagnostic model to verify and evaluate the feasibility and efficiency of the proposed framework, and a prototype system is established to demonstrate the distributed model consensus and service requirements when collaborating with companies in real-life scenarios. Four sets of experiments were conducted to ensure the feasibility and efficiency of both the distributed consensus and the distributed service process. The results indicate that the proposed consensus mechanism achieves distributed consensus with a latency of approximately 0.2853 s. While the proposed distributed service framework has disadvantages in terms of the throughput and average latency, the differences are minimal—only 0.3937 requests per second and 0.4060 s, respectively, compared with on-chain business creation. Additionally, compared with on-chain business creation, the framework increases CPU and memory utilization by just 15.8902% and 2.4697%, respectively.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"64 ","pages":"Article 103095"},"PeriodicalIF":8.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143130279","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}

{"title":"Quantitative recommendation of fault diagnosis algorithms based on multi-order random graph convolution under case-learning paradigm","authors":"Chen Lu ,&nbsp;Xinyu Zou ,&nbsp;Lulu Sun ,&nbsp;Zhengduo Zhao ,&nbsp;Laifa Tao ,&nbsp;Yu Ding ,&nbsp;Jian Ma","doi":"10.1016/j.aei.2025.103108","DOIUrl":"10.1016/j.aei.2025.103108","url":null,"abstract":"<div><div>The rapid development of intelligent algorithms has significantly expanded the range of algorithms available for Prognostics and Health Management (PHM). Selecting the appropriate algorithm for a specific task is crucial for effective PHM applications. Learning from past PHM cases is an effective way to automate algorithm recommendations, reducing reliance on expert experience. Human-AI collaboration provides new ideas for achieving this capability. However, in emerging fields or early-stage research, the limited number of cases—coupled with volatility and noise—often results in low recommendation accuracy and weak stability. To address this issue, we propose a multi-order random graph convolution network (MOR-GCN) within a case-learning paradigm. This method uses graphs to model and optimize case correlations, helping engineers narrow down algorithm choices to suitable candidates. We first develop a correlation modeling and optimization method based on a graph network, enhancing information aggregation between similar cases and reducing the impact of case noise on the recommendation model. Next, we design an ensemble recommender using MOR-GCN, which aggregates features of adjacent case nodes through a case correlation network graph (CCNG), further improving recommendation accuracy and stability through ensemble learning. Experimental results from a gearbox fault diagnosis case set demonstrate that the MOR-GCN model can automatically recommend fault diagnosis algorithms based on task attributes, achieving an average accuracy of 77.20 % for single recommendations and 89.90 % for fuzzy recommendations. This framework showcases the potential of artificial intelligence (AI) to assist human decision-making in PHM, minimizing the dependency on expert knowledge during the PHM design stage.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"64 ","pages":"Article 103108"},"PeriodicalIF":8.0,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143129629","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}

{"title":"Automatic identification of integrated construction elements using open-set object detection based on image and text modality fusion","authors":"Ruying Cai ,&nbsp;Zhigang Guo ,&nbsp;Xiangsheng Chen ,&nbsp;Jingru Li ,&nbsp;Yi Tan ,&nbsp;Jingyuan Tang","doi":"10.1016/j.aei.2024.103075","DOIUrl":"10.1016/j.aei.2024.103075","url":null,"abstract":"<div><div>The application of object detection technology in the field of construction safety contributes significantly to on-site safety management and has already shown considerable progress. However, current research primarily focuses on detecting pre-defined classes annotated within single datasets. In-depth research in construction safety requires the detection of all influencing factors related to construction safety. The emergence of large language models offers new possibilities, and multimodal models that combine these with computer vision technology could break through the existing limitations. Therefore, this paper proposes the Grounding DINO multimodal model for the automatic detection of integrated construction elements, enhancing construction safety. First, this study reviews the literature to collect relevant datasets, summarizes their characteristics, and processes the data, including the processing of annotation files and the integration of classes. Subsequently, the Grounding DINO model is constructed, encompassing image and text feature extraction and enhancement, and a cross-modal decoder that fuses image and text features. Multiple dataset experimental strategies are designed to validate Grounding DINO’s capabilities in continuous learning, with a unified class system created based on integrated classes for model detection input text prompts. Finally, experiments involving zero-shot and fine-tuning evaluations, continuous learning validation, and effectiveness testing are conducted. The experimental results demonstrate the generalization capability and potential for continuous learning of the multimodal model.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"64 ","pages":"Article 103075"},"PeriodicalIF":8.0,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143129695","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}

{"title":"You can be more trustworthy: A feature fusion reinforcement network for credible anti-noise fault diagnosis","authors":"Yuan Wei ,&nbsp;Hongchong Peng ,&nbsp;Mansong Rong ,&nbsp;Xiaohui Gu ,&nbsp;Xiangyan Chen","doi":"10.1016/j.aei.2024.103056","DOIUrl":"10.1016/j.aei.2024.103056","url":null,"abstract":"<div><div>Significant research progress has been made in intelligent fault diagnosis algorithms. However, these methods face challenges such as noise interference and untrustworthy diagnostic results in industrial practice, which limit their performance in practical applications. This paper proposes a new feature fusion and reinforcement network combined with swin-transformer (Swin-FFRN) for noise-resistant and trustworthy diagnosis, which combines a global feature extraction network and a staged convolutional fusion operation for fine-grained fault feature extraction and noise suppression in 2D time–frequency maps. The Swin-FFRN is used to analyze 2D time–frequency map data of different mechanical faults in a low signal-to-noise ratio environment by introducing a channel attention mechanism and a spatial attention mechanism to strengthen the critical fault features that are strongly correlated with the classification of the faults so that the model focuses on the crucial features. Moreover, the noise immunity performance is evaluated using the latest methods on two different datasets, and intuitive visual interpretability is provided to show model credibility. The results show that the noise-resistant diagnostic accuracy of the proposed method is improved by 5.43% on average with respect to the SOTA method. By enhancing the key input features, the proposed method can give diagnostic results with a reasonable decision basis.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"64 ","pages":"Article 103056"},"PeriodicalIF":8.0,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143129697","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}

{"title":"Target-driven dynamic coverage planning method for marsupial cluster system","authors":"Zhiyao Lu,&nbsp;Chongyu Liang,&nbsp;Chen Bai,&nbsp;Weichao Wu,&nbsp;Aigang Pan","doi":"10.1016/j.aei.2024.103071","DOIUrl":"10.1016/j.aei.2024.103071","url":null,"abstract":"<div><div>Using marsupial unmanned cluster systems can significantly improve underwater unexploded ordnance (UXO) clearance through strategic planning. This study examines the planning method for these systems. Current geographic information databases provide limited insights on UXO targets, and neither coverage path planning (CPP) nor multi-robot task allocation (MRTA) alone can effectively tackle UXO clearance complexities. A target-driven planning approach is proposed to enhance the system’s performance by utilizing known target information while ensuring adequate area coverage. A multi-agent decision rule is proposed, focusing on pre-planning and agent empathy to assign new targets in scenarios with limited communication effectively. These two aspects form a target-driven dynamic coverage planning method, with simulation experiments designed to compare the time required for UXO clearance across various planning methods. The most important new thing that this study adds is a new planning method tailored to the marsupial cluster system. This method increases the effectiveness of removing underwater UXO by 0.86% to 8.96% when the target known rate is above 30.3%. In addition, the simulation results indicate a direct correlation between the utilization of known information and system efficiency improvements. The article can also further support that the more information is known, the more intelligent planning methods make sense.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"64 ","pages":"Article 103071"},"PeriodicalIF":8.0,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143129696","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}

{"title":"A supervised contrastive learning method based on online complement strategy for long-tailed fine-grained fault diagnosis","authors":"Zhiqian Zhao ,&nbsp;Yinghou Jiao ,&nbsp;Yeyin Xu ,&nbsp;Runchao Zhao","doi":"10.1016/j.aei.2024.103079","DOIUrl":"10.1016/j.aei.2024.103079","url":null,"abstract":"<div><div>As industrial automation and intelligence advance, equipment complexity rises, leading to diverse fault patterns. In fine-grained fault diagnosis, sample scarcity causes a significant long-tail effect, where main fault categories dominate. High intra-class variance and inter-class similarity in fine-grained categories impede the performance of traditional supervised contrastive learning, particularly for underrepresented tail categories in feature space. To address the above problems, a novel supervised contrast learning method for long-tailed fine-grained fault diagnosis, OC-SupCon, is proposed to improve the feature representations through the online complement strategy. Supervised contrastive learning is used as the model framework to ensure that each batch contains the inherent features of all fine-grained categories by introducing a class-centered prototype. Then, data augmentation is dynamically complemented by assessing the neighborhood sparsity of the samples to reduce the unfavorable influence on the features of the tail categories. Finally, the dominance of the head category is mitigated by balancing the gradient contributions of different fine-grained categories. In addition, Logit compensation technique is used in the classifier branch to adjust the category boundaries, and the class center prototypes are dynamically updated during the training process. The experimental results show that the proposed method exhibits significant performance in long-tailed fine-grained fault diagnosis tasks compared to existing state-of-the-art methods. The code is available from <span><span>https://github.com/zhiqan/OC-Supcon</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"64 ","pages":"Article 103079"},"PeriodicalIF":8.0,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143129693","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}

{"title":"Multi-scale dynamic graph mutual information network for planet bearing health monitoring under imbalanced data","authors":"Wenbin Cai ,&nbsp;Dezun Zhao ,&nbsp;Tianyang Wang","doi":"10.1016/j.aei.2024.103096","DOIUrl":"10.1016/j.aei.2024.103096","url":null,"abstract":"<div><div>In engineering, imbalanced data collected from planet bearings causes most intelligent models to shrink the decision boundary of minor classes and degrade diagnostic accuracy. Different from these models under the assumption of data balance, graph-based methods focus on the relationship between data to alleviate the issue of data imbalance, but they have restrictions on single-feature propagation and only rely on the feature extraction capability of convolutional operations. As such, a multi-scale dynamic graph mutual information network (MDGMIN) is proposed for the health monitoring of planet bearings with imbalanced data. First, a dual spatial–temporal graph generation algorithm is designed to construct dynamic and distance graphs via the gated convolution in the temporal dimension and the cosine similarity and Top-k sorting mechanism in the spatial dimension. Second, multi-scale dynamic edge graph convolutional layers are constructed to extract specific and similar features, and they are weighted fused via an attention mechanism. Finally, mutual information learning is developed to foster the model in capturing graph features in-depth through commonality and discrepancy constraints, and a new loss-driven function based on two constraints is proposed to update the training objective. Experimental analysis on an imbalanced planet bearing dataset verifies that the developed MDGMIN reaches the diagnostic accuracy of 92.80%, exceeding that of state-of-the-art methods on the dataset with an imbalanced ratio of 20:1. In addition, the generalizability of the MDGMIN is validated in another bearing dataset from the planetary gearbox.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"64 ","pages":"Article 103096"},"PeriodicalIF":8.0,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143129627","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}

{"title":"Neural ODE powered model for bearing remaining useful life predictions with intra- and inter-domain shifts","authors":"Tao Hu,&nbsp;Zhenling Mo,&nbsp;Zijun Zhang","doi":"10.1016/j.aei.2024.103077","DOIUrl":"10.1016/j.aei.2024.103077","url":null,"abstract":"<div><div>In bearing remaining useful life (RUL) predictions, current domain adaptation (DA) and domain generalization (DG) methods are typically concerned with mitigating inter-domain shifts (DSs)—a type of DSs existing across the bearing degradation data sequences. Yet, intra-DSs along the bearing degradation data sequences, which are another type of DSs governing inter-DSs, have not attracted sufficient attention, thus hindering the applicability of existing methods. Moreover, many existing DG methods are developed based on multi-source domains, while bearing RUL predictions in reality often expect models of single-source DG capability. This study investigates the potential of the neural ordinary differential equation (ODE) for filling the aforementioned research gaps, leading to a novel neural ODE powered modeling (NOMI) scheme. First, the ODE characteristic of time invariance is utilized to address intra-DSs for learning time-invariant latent features from a single source bearing degradation data domain. Then, the gained time consistency could reduce heterogeneous intra-DS patterns, thereby decreasing inter-DSs and promoting model generalizability. The designed ODE module can be conveniently employed under DA and DG scenarios. Additionally, with a further gradient manipulation technique, the proposed model can be trained efficiently. Theoretical analyses demonstrate the benefits of intra-domain minimization for solving the data distribution problem. The experimental results based on multiple bearing datasets also verify the superiority of our proposed method compared with state-of-the-art approaches.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"64 ","pages":"Article 103077"},"PeriodicalIF":8.0,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143129694","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}

{"title":"LLMOA: A novel large language model assisted hyper-heuristic optimization algorithm","authors":"Rui Zhong ,&nbsp;Abdelazim G. Hussien ,&nbsp;Jun Yu ,&nbsp;Masaharu Munetomo","doi":"10.1016/j.aei.2024.103042","DOIUrl":"10.1016/j.aei.2024.103042","url":null,"abstract":"<div><div>This work presents a novel approach, the large language model assisted hyper-heuristic optimization algorithm (LLMOA), tailored to address complex optimization challenges. Comprising two essential components – the high-level component and the low-level component – LLMOA leverages the LLM (i.e., Gemini) with prompt engineering in its high-level component to construct optimization sequences automatically and intelligently. Furthermore, we propose novel elite-based local search operators as low-level heuristics (LLHs), which draw inspiration from the proximate optimality principle (POP). These local search operators cooperated with well-known mutation and crossover operators from differential evolution (DE), at a total of ten efficient and versatile search operators, forming the whole LLHs. To assess the competitiveness of LLMOA, we conducted comprehensive numerical experiments across CEC2014, CEC2020, CEC2022, and ten engineering optimization problems, benchmarking against eleven state-of-the-art optimizers. Our experimental findings and statistical analyses underscore the powerfulness and effectiveness of LLMOA. Moreover, ablation experiments reveal the pivotal role of integrating the LLM Gemini and prompt engineering as the high-level component. Conclusively, this study provides a feasible avenue to introduce LLM to the evolutionary computation (EC) community. The research’s source code is available for download at <span><span>https://github.com/RuiZhong961230/LLMOA</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"64 ","pages":"Article 103042"},"PeriodicalIF":8.0,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143129691","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}