Advanced Engineering Informatics最新文献_第10页

Using gesture and speech communication modalities for safe human-drone interaction in construction 利用手势和语音通信模式实现建筑施工中安全的人机交互

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2024-09-18 DOI: 10.1016/j.aei.2024.102827

{"title":"Using gesture and speech communication modalities for safe human-drone interaction in construction","authors":"","doi":"10.1016/j.aei.2024.102827","DOIUrl":"10.1016/j.aei.2024.102827","url":null,"abstract":"<div><p>Drones are increasingly being used in the construction industry for numerous applications. However, their presence poses safety risks to construction workers who work around them but have limited control and information about these drones. To ensure safety, general construction workers who are not part of the pilot teams should also be able to communicate their concerns with drones effectively and naturally. Despite its importance, research on human-drone communication within construction for non-operator workers is scarce. This study developed and evaluated communication protocols using gesture and speech modalities to ensure safe human-drone interactions for non-operator workers in construction environments. An immersive VR environment replicating construction site dynamics was developed, enabling workers to utilize gesture or speech communication protocols while working with drones. A total of 100 participants were recruited for the user-centered study analysis on an immersive VR construction site, and the safety implications and cognitive loads of both protocols were assessed both quantitatively and qualitatively. The findings suggest that gesture-based communication is more effective than speech-based communication in mitigating risks and alleviating the negative impacts of drones without imposing additional cognitive strain on users on construction sites.</p></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241883","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

Exploring product rendering generation design catering to multi-emotional needs through the Superiority Chart-Entropy Weight method and Stable Diffusion model 通过优越性图表-熵权法和稳定扩散模型探索满足多情感需求的产品渲染生成设计

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2024-09-18 DOI: 10.1016/j.aei.2024.102809

{"title":"Exploring product rendering generation design catering to multi-emotional needs through the Superiority Chart-Entropy Weight method and Stable Diffusion model","authors":"","doi":"10.1016/j.aei.2024.102809","DOIUrl":"10.1016/j.aei.2024.102809","url":null,"abstract":"<div><p>The experience economy has shifted user demands towards emotionalization, emphasizing multi-emotional considerations as pivotal in design. This study addresses challenges in accurately determining emotional needs and the inadequacy of current intelligent design approaches. It proposes a method for designing multi-emotional product renderings by integrating the Superiority Chart-Entropy Weight method with the Stable Diffusion model within a big data framework. Initially, online user comments, hand-drawn sketches, and renderings of target products are collected. The Superiority Chart-Entropy Weight is then adopted to establish weights for multi-emotional needs, creating an allocation mechanism of these weights. Incorporating these multi-emotional weights, a Stable Diffusion model embedded with LoRa is trained to generate diverse rendering schemes. Finally, the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method is employed to select the optimal rendering scheme for 3D display. An experimental case study focusing on new energy vehicle renderings demonstrates the efficiency of this approach in precisely meeting users’ multi-emotional needs, thereby enhancing design efficiency and quality. Comparative experiments indicate that the method proposed in this study offers advantages in creating multi-emotional renderings. This study innovatively introduces a finer-grained multi-emotional needs confirmation method for users, overcoming the ambiguity and uncertainty of traditional recognition approaches, and develops a Stable Diffusion generation method tailored for product renderings, providing practical value in streamlining the conventional product design representation cycle and enhancing design efficiency, quality and user satisfaction.</p></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241955","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

Unsupervised-ensemble-based method for automatic running-in information extraction in reciprocating compressors 基于无监督集合的往复式压缩机运行信息自动提取方法

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2024-09-18 DOI: 10.1016/j.aei.2024.102841

{"title":"Unsupervised-ensemble-based method for automatic running-in information extraction in reciprocating compressors","authors":"","doi":"10.1016/j.aei.2024.102841","DOIUrl":"10.1016/j.aei.2024.102841","url":null,"abstract":"<div><p>This work presents a fully automatic method for extracting running-in information from data of hermetic reciprocating compressors by analyzing clusters of subsequenced time series data. We used the <span><math><mi>k</mi></math></span>-means, kernel <span><math><mi>k</mi></math></span>-means, employing both a radial basis function and a novel application of the Mahalanobis radial basis function kernel, and agglomerative hierarchical clustering algorithms for clustering the data. The method is based on an ensemble of single occurrence transition detection models trained considering several parameter combinations and clustering algorithms. We developed a pruning method to identify the most meaningful transitions, discarding models whose results did not relate to the running-in process and allowing for feature interpretation based on the parameters of the remaining models. Experimental evaluation of the proposed method revealed that the electric current of the compressor is the most significant feature for tribological steady state detection and that the Mahalanobis-RBF kernel provides the best results. As a result, the proposed method offers an automated analysis of the running-in duration in hermetic compressors, potentially improving the reliability of compressor tests and saving resources in the preparation process.</p></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241882","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

Autonomous vehicle extreme control for emergency collision avoidance via Reachability-Guided reinforcement learning 通过可达性引导强化学习实现紧急避撞的自主车辆极端控制

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2024-09-16 DOI: 10.1016/j.aei.2024.102801

{"title":"Autonomous vehicle extreme control for emergency collision avoidance via Reachability-Guided reinforcement learning","authors":"","doi":"10.1016/j.aei.2024.102801","DOIUrl":"10.1016/j.aei.2024.102801","url":null,"abstract":"<div><p>The emergency collision avoidance capabilities of autonomous vehicles (AVs) are crucial for enhancing their active safety performance, particularly in extreme scenarios where standard methods fall short. This study introduces an Extreme Maneuver Controller (EMC) for AVs, utilizing reachability-guided reinforcement learning (RL) to address these challenging situations. By applying pseudospectral methods, we solve the minimum backward reachable tube (Min-BRT) to identify regions where conventional avoidance maneuvers are infeasible, establishing a theoretical basis for triggering extreme maneuvers. A novel controller, employing reachability-guided RL, enables vehicles to execute extreme maneuvers to escape these critical regions. During training, the value function derived from the Min-BRT solution informs the initialization of the Critic networks, enhancing training efficiency. Real-world scenario-based experimental results with actual vehicles validate that the proposed policy, effectively executes beyond-the-limit maneuvers, mitigating collision risks under emergency condition. Furthermore, these extreme maneuvers are executed with minimal deviation from the original driving objectives, ensuring a smooth and stable transition upon completion of extreme maneuvers.</p></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241735","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

The 30th international conference on intelligent computing in engineering (EG-ICE): Sustainable, smart and resilient buildings, infrastructures and cities 第 30 届国际工程智能计算大会（EG-ICE）：可持续、智能和弹性建筑、基础设施和城市

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2024-09-16 DOI: 10.1016/j.aei.2024.102828

引用次数: 0

Sam-based instance segmentation models for the automation of structural damage detection 基于 Sam 的实例分割模型，用于结构损伤的自动化检测

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2024-09-15 DOI: 10.1016/j.aei.2024.102826

{"title":"Sam-based instance segmentation models for the automation of structural damage detection","authors":"","doi":"10.1016/j.aei.2024.102826","DOIUrl":"10.1016/j.aei.2024.102826","url":null,"abstract":"<div><p>In infrastructure asset management, monitoring structural condition is vital for safety and cost-efficiency. Traditional visual inspections are subjective, inconsistent, and time-consuming. Advanced automating visual inspections using digital technologies and artificial intelligence can effectively address these issues. Previous studies mainly focused on concrete structures and pavements, neglecting masonry defects and lacking publicly available datasets. In this paper, we address these gaps by introducing the “MCrack1300” dataset, annotated for bricks, broken bricks, and cracks, targeting instance segmentation. We propose two novel, automatically executable methods based on the latest visual large-scale model, the prompt-based Segment Anything Model (SAM). We fine-tune SAM’s encoder using Low-Rank Adaptation (LoRA). The first method connects SAM’s encoder to other decoders directly, while the second uses a learnable self-generating prompter. We modify the feature extractor for seamless integration of these methods with SAM’s encoder. Both methods outperform the state-of-the-art models, improving benchmark results approximately 3 % across all classes and around 6 % specifically for cracks. Building on successful detection, we then propose a monocular-based method to automatically convert images into orthographic projection maps via Hough Line Transform. By incorporating known real sizes of brick units and employing Euclidean Distance Transform, we accurately estimate crack dimensions, with the error less than 10 %. Overall, we offer reliable automated solutions for masonry crack detection and size estimation, which effectively enhances the management and maintenance efficiency of masonry structural asset.</p></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142233611","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

DAUP: Enhancing point cloud homogeneity for 3D industrial anomaly detection via density-aware point cloud upsampling DAUP：通过密度感知点云上采样增强三维工业异常检测的点云均匀性

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2024-09-14 DOI: 10.1016/j.aei.2024.102823

{"title":"DAUP: Enhancing point cloud homogeneity for 3D industrial anomaly detection via density-aware point cloud upsampling","authors":"","doi":"10.1016/j.aei.2024.102823","DOIUrl":"10.1016/j.aei.2024.102823","url":null,"abstract":"<div><p>The use of 3D information in industrial anomaly detection tasks has been shown to enhance performance by uncovering unseen abnormal patterns in the RGB modality. Despite the focus on detection pipeline design and multimodal fusion schemes in previous approaches, explorations of dataset characteristics were often overlooked. In contrast to RGB images where pixels form regular grids, point clouds intrinsically lack order and exhibit inhomogeneous densities across regions, thereby adversely affecting the feature extraction process. In this work, we propose a learning-based density-aware point cloud upsampling module (DAUP) to address the inhomogeneous problem. A learning-based neural shape function is developed to generate a local representation of the surface for point upsampling purposes. Utilizing the points generated by the neural shape function, we devise a density-aware resampling mechanism aimed at selecting a diverse number of points from varied regions to facilitate adaptive upsampling within regions of varying densities. DAUP can substantially reducing the misclassification rate for off-the-shelf anomaly detection pipelines. Extensive experiments confirm the effectiveness of our upsampling method on the benchmark dataset MVTec 3D-AD. Notably, our method surpasses previous state-of-the-art methods in terms of image-level AUROC based on the feature bank-based anomaly detection pipeline.</p></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142232002","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

Study on penetration depth in laser welding: A process information database-based control strategy and OCT measuring verification 激光焊接熔深研究：基于过程信息数据库的控制策略和 OCT 测量验证

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2024-09-14 DOI: 10.1016/j.aei.2024.102825

{"title":"Study on penetration depth in laser welding: A process information database-based control strategy and OCT measuring verification","authors":"","doi":"10.1016/j.aei.2024.102825","DOIUrl":"10.1016/j.aei.2024.102825","url":null,"abstract":"<div><p>Penetration depth acts as a crucial indicator reflecting laser welding quality, thus the control of its stability and the perception of its fluctuation state are increasingly garnering attention. This paper proposes a process information database-based control strategy for penetration depth, and the control validity is verified through penetration depth detection utilizing optical coherence tomography (OCT). The process information database stores diverse expected penetration depth knowledge formed by a substantial quantity of varying welding speeds with fixed other process parameters under undisturbed welding conditions. In the database, the stable average values inside the standard penetration depth information and the corresponding heat input (HI) values are connected and mapped via an artificial neural network (ANN). In response to abnormal variations in the penetration depth curve caused by interferences during welding, according to the HI gap predicted by the trained ANN from the penetration depth gap arising from the curve deviation, the control unit can calculate the new welding speed required to feed the penetration depth curve back to within the steady fluctuation range. Based on OCT, the keyhole depth signal is acquired, and a deep belief network is built to predict the penetration depth curve via the correlation between the reconstructed keyhole depth obtained by ensemble empirical mode decomposition and the penetration depth. This detection method demonstrates that the penetration depth curve can be controlled accurately. Finally, a closed-loop real-time feedback control system for penetration depth is established.</p></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142232091","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

Connecting humans and machines: Deep integration of advanced HCI in intelligent engineering 连接人类与机器：在智能工程中深度融合先进的人机交互技术

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2024-09-12 DOI: 10.1016/j.aei.2024.102824

引用次数: 0

Physics-informed probabilistic deep network with interpretable mechanism for trustworthy mechanical fault diagnosis 具有可解释机制的物理信息概率深度网络，用于可信的机械故障诊断

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2024-09-12 DOI: 10.1016/j.aei.2024.102806

{"title":"Physics-informed probabilistic deep network with interpretable mechanism for trustworthy mechanical fault diagnosis","authors":"","doi":"10.1016/j.aei.2024.102806","DOIUrl":"10.1016/j.aei.2024.102806","url":null,"abstract":"<div><p>The application of data-driven models based on the neural network is pivotal to developing an intelligent fault diagnostic flowchart. However, the reliability and interpretability of these models for fault prediction have presented challenges in neural network-based diagnostic approach. Another challenge is data availability, which has also been a limiting factor in using artificial Intelligence for condition monitoring and fault assessment in industrial mechanical systems. Consequently, this study proposes a Physics Informed Probabilistic Deep Network (PIPDN) framework to overcome these challenges. The PIPDN comprises two main components: the physical labelling module, designed to enhance physical labels with mechanical failure information, and the main body of PIPDN, responsible for learning fault representative features and generating smart data guided by conditional and physical labels. Furthermore, a multi-scale PIPDN model is developed to integrate the proposed uncertainty quantification (UQ) with decision-fusion module for accurate interpretation and enhanced fault diagnosis. The applicability, effectiveness, and superiority of the proposed framework and approach are validated using an experimental bearing dataset. The results indicate that integrating physical labels significantly assists the PIPDN model in capturing more accurate fault characteristics. This increases the importance of latent space features for subsequent fault diagnosis and also enhances the diagnostic interpretability. Furthermore, the addition of UQ-based decision-making module improves the reliability of the MS-PIPDN model by reducing epistemic uncertainty in the predictions.</p></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1474034624004543/pdfft?md5=c660d489ebf866bd1766b5c218695efb&pid=1-s2.0-S1474034624004543-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142171582","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