Advanced Engineering Informatics最新文献

{"title":"Adaptive exit choices of pedestrians during emergency evacuation: A study combining virtual experiments, survey and modelling","authors":"Zhichao Zhang ,&nbsp;Wenke Zhang ,&nbsp;Tingting Nong ,&nbsp;Tao Wang ,&nbsp;Jingyu Tan ,&nbsp;Eric Wai Ming Lee ,&nbsp;Meng Shi","doi":"10.1016/j.aei.2025.103302","DOIUrl":"10.1016/j.aei.2025.103302","url":null,"abstract":"<div><div>Understanding the adaptive exit choice behaviours during pedestrian evacuation, along with the associated complex phenomena, is essential for developing effective evacuation models and ensuring pedestrian safety. In this study, we conducted a series of virtual evacuation experiments involving multiple simultaneous participants under normal and fire conditions. The first-person perspective videos, trajectories, and rotation data of visual centre points were collected to analyse pedestrians’ microscopic behaviours and exit choices over time. The results showed that pedestrians exhibited patterns of multi-attribute conjoint decision-making, with obstacles and fires influencing movement and exit choices. Additionally, initial decision points of pedestrians were mainly located near the entrance, and decision changes were observed. However, such changes were infrequent. The more hazardous the condition, the less likely pedestrians were to alter their initial exit choices. A post-experiment survey was designed to assess participants’ perceptions and exit choice strategies during evacuation. The results indicated that the virtual environment closely resembled reality and fire emergencies significantly impacted exit choice strategies. Pedestrians primarily considered factors including distance to exits, crowd density near exits, and risks of exits in their decision-making process, and the distance was the most influential factor in all scenarios. Finally, a multinomial logit model was developed and calibrated for exit choice prediction using these three factors. This model was integrated into an extended multi-grid cellular automata model for evacuation simulation, effectively replicating pedestrian movement and adaptive exit choice behaviours with minor differences. These findings provide valuable theoretical insights and simulation support for future building safety assessments.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103302"},"PeriodicalIF":8.0,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143767893","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 dynamically balanced wavelet coefficient matching transient energy operator for state identification of rotating machinery","authors":"Ruijun Wang ,&nbsp;Zhixia Fan ,&nbsp;Yuan Liu ,&nbsp;Xiaogang Xu ,&nbsp;Huijie Wang","doi":"10.1016/j.aei.2025.103276","DOIUrl":"10.1016/j.aei.2025.103276","url":null,"abstract":"<div><div>Currently, the state recognition of rotating machinery mostly relies on vibration signals as data sources. However, the actual operating environment of the equipment has an impact on the readings of the sensors, therefore, the diagnostic results are greatly affected by noise and interference. Until now, effective measures against noise and interference have not been found. We are looking for a new paradigm of neural network encoding traditional signal processing methods to attempt to solve diagnostic problems in noisy environments. We propose a method of dynamically balancing wavelet coefficients to match transient energy operators to enhance noise resistance. The first step is to design a self-learning wavelet threshold denoising mode for multi-step signal encoding and reconstruction to remove interference components. The second step is to embed the Teager energy operator into the model to enhance high-frequency components such as transient shocks and pulse excitations. The third step is to construct a joint attention fusion function of scale and channel dimensions to select discriminative elements. We validated the effectiveness of the proposed method using different rotating mechanical equipment in operating environments with varying levels of noise intensity, and the results showed that the model has strong noise robustness.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103276"},"PeriodicalIF":8.0,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143767891","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":"Revealing the hidden correlations of elements in intelligent transportation systems with a novel knowledge graph-based path calculation approach","authors":"Ke Huang,&nbsp;Ming Cai,&nbsp;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}

{"title":"Compound fault diagnosis method of rotating machinery using multi-view multi-label feature selection based on label compression and local label correlation","authors":"Wei Zhang ,&nbsp;Jialong He ,&nbsp;Chi Ma ,&nbsp;Wanfu Gao ,&nbsp;Guofa Li","doi":"10.1016/j.aei.2025.103310","DOIUrl":"10.1016/j.aei.2025.103310","url":null,"abstract":"<div><div>The missing fault labels and the complexity of inter-fault correlations pose a great challenge for compound fault diagnosis of rotating machinery. Therefore, this paper proposes a compound fault diagnosis method using multi-view multi-label feature selection based on label compression and local label correlation (MVML-LCLLC). Firstly, the method develops an adaptive view weight assignment mechanism that dynamically assign weights according to the importance of each view in the fault information representation. Secondly, it achieves effective compression and recovery of labels through low-rank decomposition of sparse label matrix, while local label correlation is introduced to compensate for the lack of global information. Furthermore, to solve the optimization problem in the model, an alternating optimization algorithm is designed to generate sparse feature weight matrix for feature selection. Finally, the top-ranked features from the MVML-LCLLC method are selected and fed into a multi-label k-nearest neighbor (MLKNN) classifier to complete the diagnosis task. By comparing six multi-label classification evaluation metrics and fault classification confusion matrices for three rotating machinery cases, the results show that the proposed method possesses high accuracy and stability.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103310"},"PeriodicalIF":8.0,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143758998","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":"BFA-YOLO: A balanced multiscale object detection network for building façade elements detection","authors":"Yangguang Chen ,&nbsp;Tong Wang ,&nbsp;Guanzhou Chen ,&nbsp;Kun Zhu ,&nbsp;Xiaoliang Tan ,&nbsp;Jiaqi Wang ,&nbsp;Wenchao Guo ,&nbsp;Qing Wang ,&nbsp;Xiaolong Luo ,&nbsp;Xiaodong Zhang","doi":"10.1016/j.aei.2025.103289","DOIUrl":"10.1016/j.aei.2025.103289","url":null,"abstract":"<div><div>The detection of façade elements on buildings, such as doors, windows, balconies, air conditioning units, billboards, and glass curtain walls, is a critical step in automating the creation of Building Information Modeling (BIM). However, this field faces significant challenges, including the uneven distribution of façade elements, the presence of small objects, and substantial background noise, which hamper detection accuracy. To address these issues, we developed the BFA-YOLO model and the BFA-3D dataset in this study. The BFA-YOLO model is an advanced architecture designed specifically for analyzing multi-view images of façade elements. It integrates three novel components: the Feature Balanced Spindle Module (FBSM) that tackles the issue of uneven object distribution; the Target Dynamic Alignment Task Detection Head (TDATH) that enhances the detection of small objects; and the Position Memory Enhanced Self-Attention Mechanism (PMESA), aimed at reducing the impact of background noise. These elements collectively enable BFA-YOLO to effectively address each challenge, thereby improving model robustness and detection precision. The BFA-3D dataset offers multi-view images with precise annotations across a wide range of façade element categories. This dataset is developed to address the limitations present in existing façade detection datasets, which often feature a single perspective and insufficient category coverage. Through comparative analysis, BFA-YOLO demonstrated improvements of 1.8% and 2.9% in mAP₅₀ on the BFA-3D dataset and the public Façade-WHU dataset, respectively, when compared to the baseline YOLOv8 model. These results highlight the superior performance of BFA-YOLO in façade element detection and the advancement of intelligent BIM technologies. The dataset and code are available at <span><span>https://github.com/CVEO/BFA-YOLO</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103289"},"PeriodicalIF":8.0,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759100","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 diffusion-based feature enhancement approach for driving behavior classification with EEG data","authors":"Tianqi Liu ,&nbsp;Yanjun Qin ,&nbsp;Shanghang Zhang ,&nbsp;Xiaoming Tao","doi":"10.1016/j.aei.2025.103279","DOIUrl":"10.1016/j.aei.2025.103279","url":null,"abstract":"<div><div>The recognition and prediction of driving behaviors play a significant role in addressing the substantial human factors involved in traffic safety. Electroencephalogram (EEG), as a sensitive physiological indicator, has unique advantages in detecting driving behavior compared to vehicle data. However, most existing studies only focus on a few specific driving behaviors, such as only considering braking, with a small amount of data. In this paper, we utilized an event-related simulated driving experiment to test five types of driving behaviors, and collected EEG signals from 35 subjects during the experiment. We proposed an encoder–decoder model structure containing a DDPM module for EEG signal classification. DDPM is able to enhance EEG features and solve the problem of insufficient sample size by generating new samples and learning reconstruction errors. We also analyzed the EEG response to event-induced behavior from the perspective of power spectrum. The topographical map of the power spectrum indicates a significant response to event-induced driving behavior within specific brain regions. In the classification experiment, our model achieved a classification accuracy of 82.12% on the partial dataset, and an accuracy of 83.65% across all participants, representing an improvement of 10.01%, 7.11% over comparison model EEG-Inception and EEG-Conformer. The results indicate that EEG physiological signals can be utilized for decoding driving behavior, thereby laying the groundwork for further in-depth investigations into real-world road traffic safety.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103279"},"PeriodicalIF":8.0,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759097","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":"Denoising autoencoder multilayer perceptron spiking neural network for isonicotinic acid yield prediction on real industrial dataset","authors":"Pinze Ren ,&nbsp;Yitian Wang ,&nbsp;Zisheng Wang ,&nbsp;Dandan Peng ,&nbsp;Chenyu Liu ,&nbsp;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 concatenated<!--> <!-->the 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}

{"title":"Critical challenges and advances in vibration signal processing for non-stationary condition monitoring","authors":"Anil Kumar ,&nbsp;Agnieszka Wyłomańska ,&nbsp;Radosław Zimroz ,&nbsp;Jiawei Xiang ,&nbsp;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}

{"title":"Decentralized coordination of intelligent system of systems under partial observability","authors":"Hao Yuan,&nbsp;Bangbang Ren,&nbsp;Tao Chen,&nbsp;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}

{"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,&nbsp;Xinyu Li,&nbsp;Liang Gao,&nbsp;Qihao Liu,&nbsp;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}