Advanced Engineering Informatics最新文献

{"title":"A hybrid CNN-LSTM model for involuntary fall detection using wrist-worn sensors","authors":"Xinyao Hu,&nbsp;Shiling Yu,&nbsp;Jihan Zheng,&nbsp;Zhimeng Fang,&nbsp;Zhong Zhao,&nbsp;Xingda Qu","doi":"10.1016/j.aei.2025.103178","DOIUrl":"10.1016/j.aei.2025.103178","url":null,"abstract":"<div><div>Falls have become a significant global safety and health concern, which may lead to physical injuries as well as declined mental health, reduced mobility, and deteriorated quality of life. Wearable sensor-based fall detection has emerged as a promising solution for preventing fall-related injuries. However, existing solutions are hindered by user compliance related to sensor placement locations, overall model accuracy, and dependence on simulated voluntary falls. To overcome these limitations, this study aimed to propose a novel involuntary fall detection solution by using wearable sensors and deep learning algorithms. Forty-nine participants were involved in an experimental study, in which activities of daily living and involuntary falls were simulated and kinematic data from these activities were collected using wrist-worn sensors. A novel hybrid model which integrates a convolutional neural network (CNN) and a long short-term memory (LSTM) model was proposed and its performance was compared with the CNN-alone model and LSTM-alone model. The results showed that the proposed hybrid CNN-LSTM model could effectively detect involuntary falls with 96.94% detection accuracy, 98.33% sensitivity, and 96.67% specificity, superior to the CNN-alone model and LSTM-alone model. These results highlight the effectiveness of our proposed approach in significantly improving fall detection accuracy, providing a more reliable and less intrusive solution for preventing fall-related injuries.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103178"},"PeriodicalIF":8.0,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143350700","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":"Robust correlation measures for informative frequency band selection in heavy-tailed signals","authors":"Justyna Hebda-Sobkowicz ,&nbsp;Radosław Zimroz ,&nbsp;Anil Kumar ,&nbsp;Agnieszka Wyłomańska","doi":"10.1016/j.aei.2025.103174","DOIUrl":"10.1016/j.aei.2025.103174","url":null,"abstract":"<div><div>Vibration signals are commonly used to detect local damage in rotating machinery. However, raw signals are often noisy, particularly in crusher machines, where the technological process (falling pieces of rock) generates random impulses that complicate detection. To address this, signal pre-filtering (extracting the informative frequency band from noise-affected signals) is necessary. This paper proposes an algorithm for processing vibration signals from a bearing used in an ore crusher. Selecting informative frequency bands (IFBs) in the presence of impulsive noise is notably challenging. The approach employs correlation maps to detect cyclic behavior within specific frequency bands in the time–frequency domain (spectrogram), enabling the identification of IFBs. Robust correlation measures and median filtering are applied to enhance the correlation maps and improve the final IFB selection. Signal segmentation and the use of averaged results for IFB selection are also highlighted. The proposed trimmed and quadrant correlations are compared with the Pearson and Kendall correlations using simulated signal, real vibration signal from crusher in mining industry and acoustic signal measured on the test rig. Furthermore, the results of real vibration analyses are compared with established IFB selectors, including the spectral kurtosis, the alpha selector and the conditional variance-based selector.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103174"},"PeriodicalIF":8.0,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143350699","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":"Retrieval augmented generation-driven information retrieval and question answering in construction management","authors":"Chengke Wu ,&nbsp;Wenjun Ding ,&nbsp;Qisen Jin ,&nbsp;Junjie Jiang ,&nbsp;Rui Jiang ,&nbsp;Qinge Xiao ,&nbsp;Longhui Liao ,&nbsp;Xiao Li","doi":"10.1016/j.aei.2025.103158","DOIUrl":"10.1016/j.aei.2025.103158","url":null,"abstract":"<div><div>Construction management is a communication-intensive field, requiring prompt responses to queries from various stakeholders to ensure project continuity. However, retrieving accurate information from project documents is hampered by the mismatch in granularity between queries and vast contents and by inherent ambiguities in information. Large language models (LLMs) and retrieval-augmented generation (RAG) offer new opportunities to address the challenges. However, their effectiveness is limited by the segmentation of documents and insufficient consideration of engineers’ preferences. Therefore, we propose a novel paradigm: RAG for Construction Management (RAG4CM). It includes three components: 1) a pipeline that parses project documents into hierarchical structures to establish a knowledge pool; 2) novel RAG search algorithms; and 3) a user preference learning mechanism. The first two components enhance granularity alignment and RAG results by integrating document-level hierarchical features with raw contents. The preference learning realizes continuously improved responses along with user-system interactions. We developed a prototype system and conducted extensive experiments, demonstrating that the knowledge pool efficiently accommodates texts, tables, and images. RAG4CM realized a 0.924 Top-3 and 0.898 answer accuracy, surpassing both open-source frameworks and commercial products. In addition, preference learning further increases answer accuracy by 1.3 % to 9.5 %. Consequently, RAG4CM enables multi-source information retrieval in a user-friendly manner, improving communication efficiency and facilitating construction management activities.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103158"},"PeriodicalIF":8.0,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143368297","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":"Create or revise? A comparative study on CAD rework after team-based engineering design review in virtual reality and desktop interface","authors":"Nikola Horvat ,&nbsp;Jelena Šklebar ,&nbsp;Mario Štorga ,&nbsp;Stanko Škec","doi":"10.1016/j.aei.2025.103177","DOIUrl":"10.1016/j.aei.2025.103177","url":null,"abstract":"<div><div>While numerous studies have delved into the effect of virtual reality (VR) on design reviews (DR), this research study explores how the use of VR in team-based DRs influences the design work after the review. Thus, it explores the effects of VR on broader design contexts. The study employed an experimental case study involving 14 design teams working in CAD over several weeks, engaging in a design review with external reviewers, and subsequently revising the design based on the feedback. The experimental aspect involved randomly allocating teams to one of two conditions: low-immersion (desktop interface) or high-immersion (VR). Furthermore, the results indicate that teams that had DR in VR executed slightly more CAD actions compared to those that underwent DR in low-immersion. Furthermore, the VR group exhibited a significantly higher proportion of creation actions and assembly actions compared to the low-immersion group. These findings suggest that incorporating VR into DRs has the potential to change the course of the design process, making it a valuable tool for early design phases or agile methodologies, primarily due to an increased focus on creation during the rework phases. The findings also highlight the distinct focus of designers before and after the DR in terms of creation and revision, emphasizing the need for CAD tools to be more adaptable and responsive to the evolving needs of designers, considering both the phase of design and the broader ecosystem of design support tools. In summary, this study serves as an initial step for implementing VR in the industry, demonstrating that its use can indeed change the course of the design.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103177"},"PeriodicalIF":8.0,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143368498","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 novel user scenario and behavior sequence recognition approach based on vision-context fusion architecture","authors":"Wenyu Yuan,&nbsp;Danni Chang,&nbsp;Chenlu Mao,&nbsp;Luyao Wang,&nbsp;Ke Ren,&nbsp;Ting Han","doi":"10.1016/j.aei.2025.103161","DOIUrl":"10.1016/j.aei.2025.103161","url":null,"abstract":"<div><div>Understanding user scenario and behavior is essential for the development of human-centered intelligent service systems. However, the presence of cluttered objects, uncertain human behaviors, and overlapping timelines in daily life scenarios complicates the problem of scenario understanding. This paper aims to address the challenges of identifying and predicting user scenario and behavior sequences through a multimodal data fusion approach, focusing on the integration of visual and environmental data to capture subtle scenario and behavioral features.</div><div>For the purpose, a novel Vision-Context Fusion Scenario Recognition (VCFSR) approach was proposed, encompassing three stages. First, four categories of context data related to home scenarios were acquired: physical context, time context, user context, and inferred context. Second, scenarios were represented as multidimensional data relationships through modeling technologies. Third, a scenario recognition model was developed, comprising context feature processing, visual feature handling, and multimodal feature fusion. For illustration, a smart home environment was built, and twenty-six participants were recruited to perform various home activities. Integral sensors were used to collect environmental context data, and video data was captured simultaneously, both of which jointly form a multimodal dataset. Results demonstrated that the VCFSR model achieved an average accuracy of 98.1 %, outperforming traditional machine learning models such as decision trees and support vector machines. This method was then employed for fine-grained human behavior sequence prediction tasks, showing good performance in predicting behavior sequences across all scenarios constructed in this study. Furthermore, the results of ablation experiments revealed that the multimodal feature fusion method increased the average accuracy by at least 1.8 % compared to single-modality data-driven methods.</div><div>This novel approach to user behavior modeling simultaneously handles the relationship threads across scenarios and the rich details provided by visual data, paving the way for advanced intelligent services in complex interactive environments such as smart homes and hospitals.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103161"},"PeriodicalIF":8.0,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143368501","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":"The human-centric framework integrating knowledge distillation architecture with fine-tuning mechanism for equipment health monitoring","authors":"Jr-Fong Dang ,&nbsp;Tzu-Li Chen ,&nbsp;Hung-Yi Huang","doi":"10.1016/j.aei.2025.103167","DOIUrl":"10.1016/j.aei.2025.103167","url":null,"abstract":"<div><div>Human-centricity serves as the cornerstone of the evolution of manufacturing into Industry 5.0. Accordingly, modern manufacturing prioritizes both the well-being of human workers and their collaboration with production systems. Successful systems would be user-friendly and market-appropriate, effectively identifying and analyzing user needs. This study aims to integrate user requirements into the framework for equipment health monitoring (EHM). The proposed framework addresses issues related to insufficient training samples and variable-length data by combining an encoder-decoder architecture with an attention mechanism and a conditional generative adversarial network (EDA-CGAN). Furthermore, the authors utilize a teacher-student network to reduce model complexity through knowledge distillation (KD). To prevent negative knowledge distillation, this study incorporates user requirements using Kullback-Leibler divergence (KLD) to determine whether the teacher model would be fine-tuned. Consequently, we employ the explainable AI (XAI) to provide a clear and understandable explanation for the prediction results. Thus, the proposed human-centric EHM consisting of four modules: (i) the data augmentation (ii) the fine-tuning mechanism (ii) the equipment health prediction model (iv) the explainable AI (XAI). The authors employ these methods to uncover new research insights that are vital for advancing the methodological innovation within the proposed framework. To evaluate model performance, this study conducts an empirical investigation to illustrate the capability and practicality of the proposed framework. The results indicate that our algorithm outperforms existing machine learning models, enabling the implementation of the proposed framework in the real-world manufacturing environment to maintain equipment health.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103167"},"PeriodicalIF":8.0,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143368497","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":"Vehicle spatiotemporal distribution identification in low-light environment based on image enhancement and object detection","authors":"Jie Zhang ,&nbsp;Jiaqiang Peng ,&nbsp;Xuan Kong ,&nbsp;Shuo Wang ,&nbsp;Jiexuan Hu","doi":"10.1016/j.aei.2025.103165","DOIUrl":"10.1016/j.aei.2025.103165","url":null,"abstract":"<div><div>The spatiotemporal distribution of vehicles on roads and bridges is important for the operation and maintenance of transportation systems. The accuracy of vehicle identification is affected by the lighting conditions, especially low-light environments. This study proposes a vehicle spatiotemporal distribution identification method using image enhancement and object detection. First, the FP-ZeroDCE algorithm is used to enhance low-light images, which improves the brightness and contrast of images. Next, the enhanced images are input into the AFF-YOLO model to identify the spatiotemporal distribution of vehicles. Finally, the proposed method is validated using public datasets and tested in the field. The results indicate that the proposed method can enhance the quality of low-light images, with an increase in the Peak Signal-to-Noise Ratio by 8.257 dB, and improve the accuracy of vehicle detection, with an accuracy of 92.7 %. The proposed method is an effective means for identifying vehicle spatiotemporal distribution under low-light conditions.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103165"},"PeriodicalIF":8.0,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143368502","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":"Knowledge augmented generalizer specializer: A framework for early stage design exploration","authors":"Vijayalaxmi Sahadevan ,&nbsp;Rohin Joshi ,&nbsp;Kane Borg ,&nbsp;Vishal Singh ,&nbsp;Abhishek Raj Singh ,&nbsp;Bilal Muhammed ,&nbsp;Soban Babu Beemaraj ,&nbsp;Amol Joshi","doi":"10.1016/j.aei.2025.103141","DOIUrl":"10.1016/j.aei.2025.103141","url":null,"abstract":"<div><div>In non-routine engineering design projects, the design outcome is determined by how the problem is formulated and represented in the early conceptual stage. The problem representation comprises schemas, ontologies, variables, and parameters relevant to the given problem class. Despite the critical role of early conceptual decisions in shaping the eventual design outcome, most of the computational support and automation are focused on the latter stages of parametric modelling, problem-solving, and optimization. There is inadequate support for aiding and automating problem formulation, variable and parameter identification and representation, and early-stage conceptual decisions. Therefore, this paper presents an innovative, transparent, and explainable method employing semantic reasoning to automate the step-by-step conceptual design generation process, including problem formulation, identification and representation of the variables and parameters and their dependencies. The method is realized through a novel framework called Knowledge Augmented Generalizer Specializer (KAGS). KAGS employs the Function-Behavior-Structure (FBS) ontology and the Graph-of-Thought (GoT) mechanism to enable automated reasoning with a Large Language Model (LLM). The workflow comprises various stages: problem breakdown, design prototype creation, assessment, and prototype merging. The framework is implemented and tested on a Subsea Layout (SSL) planning problem, a special class of infrastructure planning projects in deep-sea oil and gas production systems. The experimentations with KAGS demonstrate its capacity to support problem formulation, hierarchical decomposition, and solution generation. The research also provides new insights into the FBS framework and <em>meta</em>-level reasoning in early design stages.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103141"},"PeriodicalIF":8.0,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136971","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":"Dual adversarial and contrastive network for single-source domain generalization in fault diagnosis","authors":"Guangqiang Li ,&nbsp;M. Amine Atoui ,&nbsp;Xiangshun Li","doi":"10.1016/j.aei.2025.103140","DOIUrl":"10.1016/j.aei.2025.103140","url":null,"abstract":"<div><div>Domain generalization achieves fault diagnosis on unseen modes. In process industrial systems, fault samples are limited, and it is quite common that the available fault data are from a single mode. Extracting domain-invariant features from single-mode data for unseen mode fault diagnosis poses challenges. Existing methods utilize a generator module to simulate samples of unseen modes. However, multi-mode samples contain complex spatiotemporal information, which brings significant difficulties to accurate sample generation. To solve this problem, this paper proposed a dual adversarial and contrastive network (DACN) for single-source domain generalization in fault diagnosis. The main idea of DACN is to generate diverse sample features and extract domain-invariant feature representations. An adversarial pseudo-sample feature generation strategy is developed to create fake unseen mode sample features with sufficient semantic information and diversity, leveraging adversarial learning between the feature transformer and domain-invariant feature extractor. An enhanced domain-invariant feature extraction strategy is designed to capture common feature representations across multi-modes, utilizing contrastive learning and adversarial learning between the domain-invariant feature extractor and the discriminator. Experiments on the Tennessee Eastman process and continuous stirred-tank reactor demonstrate that DACN achieves high classification accuracy on unseen modes while maintaining a small model size.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103140"},"PeriodicalIF":8.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143137038","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":"Collaborative scheduling of handling equipment in automated container terminals with limited AGV-mates considering energy consumption","authors":"Xurui Yang ,&nbsp;Hongtao Hu ,&nbsp;Chen Cheng","doi":"10.1016/j.aei.2025.103133","DOIUrl":"10.1016/j.aei.2025.103133","url":null,"abstract":"<div><div>AGV-mates (automated guided vehicle, AGV) are a type of buffering equipment installed in the seaside area of the yard block, which can decouple AGV and yard crane operations. In recent years, an AGV charging function has been integrated in AGV-mates, providing AGVs with an alternative charging method besides battery recovery at the battery swapping station. This has resulted in time constraints and additional energy replenishment decisions in collaborative scheduling optimization, complicating the terminal equipment scheduling problem. Therefore, this paper investigates the collaborative scheduling problem of yard equipment in each operation stage of an automated container terminal, proposes charging-swapping mode for AGV energy replenishment, and develops a mixed integer programming model to minimize equipment no-load energy consumption and operational delay costs. In order to address the difficulty of solving large-scale cases, a solution method based on the variable neighborhood search algorithm is developed. Considering the decoupling and charging characteristics of AGV-mates, local search operators for the AGVs’ task sequence, the yard crane’s task sequence, and the AGV battery swapping task nodes are designed. Finally, the efficiency and effectiveness of proposed solution and operators are verified through a series of numerical experiments. This paper presents practical equipment scheduling solutions and management strategies, compared to a single charging or swapping mode, the charging-swapping mode proposed in this paper has a significant improvement in the no-load cost and the delay cost.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103133"},"PeriodicalIF":8.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143137074","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}