Advanced Engineering Informatics最新文献

Distributed design optimisation in collaborative product development by integrating analytical target cascading with kriging 基于分析目标级联与克里格法的协同产品开发中的分布式设计优化

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-07-28 DOI: 10.1016/j.aei.2025.103708

Kai-Wen Tien , Chih-Hsing Chu

{"title":"Distributed design optimisation in collaborative product development by integrating analytical target cascading with kriging","authors":"Kai-Wen Tien , Chih-Hsing Chu","doi":"10.1016/j.aei.2025.103708","DOIUrl":"10.1016/j.aei.2025.103708","url":null,"abstract":"<div><div>In the current era of economic globalization, small and medium-sized enterprises have increasingly recognized the imperative of inter-company collaboration across the supply chain to enhance competitiveness. The effective utilization of distributed design resources has become crucial to address product complexity and shorter life cycles. Collaborative product development (CPD) has thus emerged as a common practice to achieve this goal, in which design teams, possibly dispersed across different companies, negotiate engineering solutions that not only fulfil the overall product development goal but also align with their own interests. This research proposes a novel approach by integrating Analytical Target Cascading (ATC) with Kriging to solve distributed optimal design problems in the context of CPD. The focus is to improve the iterative process of the ATC coordination strategy by incorporating the Kriging model to address the situation of limited information disclosure. Case studies of real-world engineering problems validate the effectiveness of the proposed approach. Test results show that it contributes not only to increasing the efficiency of the design process but also to improving the overall design quality in CPD.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"68 ","pages":"Article 103708"},"PeriodicalIF":8.0,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714504","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

Process mechanisms fusion enhanced spatially scalable convolution network for multi-indicator prediction in process industries 过程机制融合增强空间可扩展卷积网络用于过程工业多指标预测

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-07-28 DOI: 10.1016/j.aei.2025.103684

Ming Wang , Jie Zhang , Youlong Lyu , Peng Zhang , Cheng Li

{"title":"Process mechanisms fusion enhanced spatially scalable convolution network for multi-indicator prediction in process industries","authors":"Ming Wang , Jie Zhang , Youlong Lyu , Peng Zhang , Cheng Li","doi":"10.1016/j.aei.2025.103684","DOIUrl":"10.1016/j.aei.2025.103684","url":null,"abstract":"<div><div>In process industries, the detection of interrelated production indicators, including throughput, quality, etc., is often delay due to the inherent continuity, causing production disruptions and quality issues. Accurate prediction of multi-indicator is crucial, but intricate nonlinear correlations among parameters and indicators pose significant challenges. Data-driven prediction can overcome the challenge of accurately constructing process mechanism models covering the entire production process and all elements, but struggle to infer cross-space migration patterns of parameters’ impacts on indicators. To address this issue, this study proposes a process mechanism fusion enhanced intelligent multi-indicator prediction method for process industries, using the polyester fiber polymerization process as an illustrative case. Firstly, a process mechanism model is established to generate mechanism data encapsulating process mechanisms like coupled relationships and spatial correlations, and these mechanisms are extracted as mechanism features, which are fused with data features to enhance the model’s performance. Secondly, a spatially scalable convolutional neural network is raised, which extracts the implicit deep data features and mechanism features between parameters and indicators from both within-process and cross-process dimensions, utilizing both real and mechanism data. Furthermore, a multi-head self-attention mechanism is employed to adaptively adjust the self-attention weights of the fused features, guiding the model to learn the complex relationships between fused features and enhancing the ability to learn complex coupled relationships and spatial correlations. Finally, the proposed prediction method is validated using polymerization process data and demonstrated superior performance in achieving accurate multi-indicator prediction compared to both efficient machine learning and advanced deep learning methods.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"68 ","pages":"Article 103684"},"PeriodicalIF":8.0,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714505","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

A safety risk formation model for intelligent human–machine interaction based on computational neuroscience 基于计算神经科学的智能人机交互安全风险形成模型

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-07-28 DOI: 10.1016/j.aei.2025.103668

Yining Zeng , Youchao Sun , Xia Zhang , Yuanyuan Guo , Heming Wu

{"title":"A safety risk formation model for intelligent human–machine interaction based on computational neuroscience","authors":"Yining Zeng , Youchao Sun , Xia Zhang , Yuanyuan Guo , Heming Wu","doi":"10.1016/j.aei.2025.103668","DOIUrl":"10.1016/j.aei.2025.103668","url":null,"abstract":"<div><div>The widespread application of intelligent technologies in cockpits enhances the efficiency of human–machine systems, introduces new challenges for safety risk analysis. Safety risk analysis focuses on explaining the safety risk formation process from a macro perspective. However, the neurocognitive mechanisms of pilots during this process are still unclear. This paper proposes a safety risk formation model for intelligent human–machine interaction based on computational neuroscience. Specifically, the safety risk factors affecting pilot behavior during intelligent human–machine interaction are identified. The regulatory mechanisms involving dopamine, acetylcholine, and the thalamus corresponding to different safety risk factors are elucidated. The structure and function of the Cortico-Basal Ganglia-Thalamus-Cortical (CBTC) neural circuit are introduced. To quantitatively describe the dynamic characteristics of neurons, a computational model of CBTC is established. Experimental validation of the proposed model is conducted using a cockpit intelligent interaction platform. The results indicate that the established computational model of CBTC effectively simulates the impact of varying levels of safety risk factors on pilots.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"68 ","pages":"Article 103668"},"PeriodicalIF":8.0,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714506","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

Controllable and flexible residential floor plan layout design based on multi-agent deep reinforcement learning with layout prior size and similar experience abandon 基于布局先验尺寸和相似经验抛弃的多智能体深度强化学习的可控柔性住宅平面布局设计

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-07-28 DOI: 10.1016/j.aei.2025.103702

Gan Luo , Xuhong Zhou , Liang Feng , Jiepeng Liu , Pengkun Liu , Yunzhu Liao , Wenchen Shan , Hongtuo Qi

{"title":"Controllable and flexible residential floor plan layout design based on multi-agent deep reinforcement learning with layout prior size and similar experience abandon","authors":"Gan Luo , Xuhong Zhou , Liang Feng , Jiepeng Liu , Pengkun Liu , Yunzhu Liao , Wenchen Shan , Hongtuo Qi","doi":"10.1016/j.aei.2025.103702","DOIUrl":"10.1016/j.aei.2025.103702","url":null,"abstract":"<div><div>Automated floor plan generation can significantly enhance the efficiency of designers and reduce associated design costs. Nonetheless, ensuring the model’s controllability and flexibility is crucial for its practical applications, presenting distinct challenges for current methods. This paper presents an automated residential layout design method utilizing multi-agent deep reinforcement learning (MADRL) to address the challenges of spatial variability and customization requirements in architectural layouts. By simulating the collaborative design process through multiple agents, the proposed method effectively accommodates diverse layout environments while ensuring valid and personalized designs. A refined reward system was developed to guide agents in generating rational room arrangements and meeting different custom constraints. Additionally, layout prior size (LPS) was proposed to address the size selection challenge, effectively reducing the action space and enhancing layout quality. To further improve diversity, a similar experience abandon (SEA) mechanism was proposed, allowing efficient experience interaction among agents and eliminating redundant exploration of similar layouts. Experimental results demonstrate the proposed method’s capability to generate valid floor plans and provide diversified layout options under various design inputs and custom tasks. Meanwhile, the agent achieves a design consistent with the real layout in 1187 episodes, demonstrating the method’s compatibility. This paper highlights the potential of MADRL in advancing the automation and efficiency of architectural layout design, offering a novel solution for the integration of flexibility and controllability in residential planning.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"68 ","pages":"Article 103702"},"PeriodicalIF":8.0,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714507","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

Operation twins-driven human-centric replenishment-kitting synchronization for smart customized production logistics 操作孪生驱动的以人为中心的补货配套同步，实现智能定制生产物流

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-07-28 DOI: 10.1016/j.aei.2025.103687

Mingxing Li , Fen Liu , Ming Li , Qu Zhou , Shiquan Ling , Ting Qu , Zhen He

{"title":"Operation twins-driven human-centric replenishment-kitting synchronization for smart customized production logistics","authors":"Mingxing Li , Fen Liu , Ming Li , Qu Zhou , Shiquan Ling , Ting Qu , Zhen He","doi":"10.1016/j.aei.2025.103687","DOIUrl":"10.1016/j.aei.2025.103687","url":null,"abstract":"<div><div>Customized manufacturing mode is characterized by a wide variety of products and materials, mixed production volume, and order-driven operations. This study focuses on replenishment and kitting operations in a mass customization workshop under assemble-to-order (ATO) mode. Field investigation reveals that the lack of coordination between replenishment and kitting operations can increase the operation time and ergonomic risks of operators, thereby leading to low overall efficiency, increased operational costs, and reduced health levels. To address this issue, this paper proposes a novel operation twin driven human-centric replenishment-kitting synchronization framework (HCRK-Sync) for coordinated operations. The proposed operation twin framework consists of vertical twinning and horizontal twining, in which vertical twinning leverages Industry 5.0 technologies and human digital twin for real-time operational status perception and horizontal twining focuses on HCRK-Sync decision mechanism. The HCRK-Sync mechanism aims to minimize order operation time and human ergonomic risks, thereby achieving a harmonious balance between replenishment tasks and picking efficiency. Experiment results indicate that compared to traditional methods, HCRK-Sync shows significant advantages in improving picking efficiency and reducing ergonomic risks, with an average reduction of 9%-35% in total order operation time and 8%-50% in ergonomic risks, demonstrating stability and adaptability across different production scenarios.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"68 ","pages":"Article 103687"},"PeriodicalIF":8.0,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714516","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

Learning globally ordered and locally consistent degradation representations for remaining useful life prediction 学习全局有序和局部一致的退化表示，用于剩余使用寿命预测

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-07-28 DOI: 10.1016/j.aei.2025.103692

Yasong Li , Chenye Hu , Zheng Zhou , Chuang Sun , Jun Peng , Ruqiang Yan

{"title":"Learning globally ordered and locally consistent degradation representations for remaining useful life prediction","authors":"Yasong Li , Chenye Hu , Zheng Zhou , Chuang Sun , Jun Peng , Ruqiang Yan","doi":"10.1016/j.aei.2025.103692","DOIUrl":"10.1016/j.aei.2025.103692","url":null,"abstract":"<div><div>Recent studies on remaining useful life (RUL) estimation have shown that deep neural networks can effectively extract informative features from sensor data, thereby improving the prediction performance. However, most existing methods rely solely on direct mapping between labels and data to construct the feature space, while ignoring the exploration of feature relationships. This study believes that strongly generalized degradation features should have two properties: global orderliness and local consistency. The former stems from the irreversibility of the degradation process, while the latter reflect the stability of the system state in a short period of time. In this work, a <strong>g</strong>lobally <strong>o</strong>rdered and <strong>l</strong>ocally <strong>c</strong>onsistent <strong>r</strong>epresentation <strong>l</strong>earning (GOLCRL) method is proposed for RUL prediction. GOLCRL extracts degradation representations using stacked convolutional neural networks, integrating multi-scale convolution and channel attention mechanism to facilitate the information interaction across spatial and temporal dimensions. To refine the ordered relationships, GOLCRL regularizes the geometric structure of the feature space through supervised group contrastive learning and correlation-aware distribution alignment. Moreover, GOLCRL guides the label smoothing of neighboring samples in the feature space through a pseudo-labeling strategy, mapping them to a more coherent label region, thereby enhancing local consistency. Two case studies demonstrate that GOLCRL outperforms existing methods in terms of generalization capabilities, achieving more accurate RUL prediction results.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"68 ","pages":"Article 103692"},"PeriodicalIF":8.0,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714501","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

Automated underwater concrete crack width measurement system based on dual lasers and DeepCrack network 基于双激光和DeepCrack网络的水下混凝土裂缝宽度自动测量系统

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-07-28 DOI: 10.1016/j.aei.2025.103713

Fei Kang , Dong Chen , Junjie Li , Gang Wan , Zhe Li

{"title":"Automated underwater concrete crack width measurement system based on dual lasers and DeepCrack network","authors":"Fei Kang , Dong Chen , Junjie Li , Gang Wan , Zhe Li","doi":"10.1016/j.aei.2025.103713","DOIUrl":"10.1016/j.aei.2025.103713","url":null,"abstract":"<div><div>To address the challenges of low automation in underwater dam crack measurement and errors caused by multi-interface refraction distortion, this study proposes an automated measurement method based on “imaging calibration - intelligent segmentation - precise measurement.” First, an underwater light propagation model was developed using Snell’s law, which enables the transformation between pixel coordinates and real-world coordinates. By integrating underwater camera calibration and refraction compensation, refraction-induced distortion is effectively minimized. Next, a high-precision crack segmentation model, combined with dual laser technology, enables automated crack width measurement. Comparative experiments on real datasets validate the superior performance of DeepCrack in underwater crack segmentation, and underwater optical darkroom measurement experiments show that the proposed method achieves millimeter-level accuracy within a measurement range of 0.5 to 2.0 m. Additionally, the developed hardware and software for underwater crack measurement were successfully applied in real scenarios, providing reliable technical support for visualizing underwater crack measurement.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"68 ","pages":"Article 103713"},"PeriodicalIF":8.0,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714509","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

Knowledge-driven 3D damage mapping and decision support for fire-damaged reinforced concrete structures using enhanced deep learning and multi-modal sensing 基于深度学习和多模态感知的火灾钢筋混凝土结构的知识驱动三维损伤映射和决策支持

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-07-28 DOI: 10.1016/j.aei.2025.103715

Caiwei Liu , Libin Tian , Pengfei Wang , Qian-Qian Yu , Xiangyi Zhong , Jijun Miao

{"title":"Knowledge-driven 3D damage mapping and decision support for fire-damaged reinforced concrete structures using enhanced deep learning and multi-modal sensing","authors":"Caiwei Liu , Libin Tian , Pengfei Wang , Qian-Qian Yu , Xiangyi Zhong , Jijun Miao","doi":"10.1016/j.aei.2025.103715","DOIUrl":"10.1016/j.aei.2025.103715","url":null,"abstract":"<div><div>Rapid and precise damage assessment of fire-damaged reinforced concrete (RC) structures is critical for structural safety decisions. To overcome limitations of existing 2D methods in spatial localization and real-time deployment, an integrated knowledge-driven framework is proposed. Multi-modal sensing is combined with an enhanced FastSAM-P deep learning network for automated 3D damage mapping. Three core innovations are introduced: (1) Deformable Spatial-Channel Reconstruction Convolution (DSCConv) dynamically adjusts receptive fields to capture fine-grained damage features; (2) Receptive Field Block (RFB) module optimizes multi-scale feature extraction; (3) Pyramid Pooling Shuffle Attention (PPSM) enhances robustness in noisy environments through contextual fusion. The framework achieves 92.0 % mean Intersection-over-Union (mIoU) for segmenting concrete spalling and rebar exposure, with inference at 65.11 FPS on GPU. Validation across five public datasets (roads, bridges, buildings) confirms generalization capability. Deployment on Jetson TX1 edge devices demonstrates operational feasibility (123.4 ms latency). Integration with photogrammetric 3D reconstruction enables damage localization within ± 15 mm accuracy. This approach establishes a scientifically rigorous pipeline from data acquisition to decision support, significantly advancing automated post-fire assessment for knowledge-intensive engineering tasks.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"68 ","pages":"Article 103715"},"PeriodicalIF":8.0,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714508","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

A graph-based systems-of-systems architecture enabling multi-scale Digital Twins for maintaining road infrastructure 基于图形的系统的系统架构，支持维护道路基础设施的多尺度数字孪生

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-07-28 DOI: 10.1016/j.aei.2025.103649

Ina Heise, Sebastian Esser, André Borrmann

{"title":"A graph-based systems-of-systems architecture enabling multi-scale Digital Twins for maintaining road infrastructure","authors":"Ina Heise, Sebastian Esser, André Borrmann","doi":"10.1016/j.aei.2025.103649","DOIUrl":"10.1016/j.aei.2025.103649","url":null,"abstract":"<div><div>Road infrastructure constitutes a complex system characterized by multiple interacting subsystems. A comprehensive understanding of the correlations and dependencies among various road infrastructure elements is essential for enhancing infrastructure management and maintenance by providing a robust foundation for decision support. Digital Twins (DT) are recognized as effective tools for facilitating such decision-making. However, the development of comprehensive DTs considering road infrastructure in its entirety is still in its early stages. Hence, this paper focuses on the conceptualization of a digital representation of road infrastructure that enables the evaluation of relationships between the various heterogeneous subsystems. To accomplish this, we apply the systems-of-systems principle to road infrastructure. At its core, Labeled Property Graphs (LPG) are employed to capture intra-subsystem relationships and inter-system linkages, facilitating a holistic representation of interactions. Furthermore, we acknowledge the current organizational status of distributed responsibilities resulting in distributed data storage and maintenance by using the concept of federated databases. The presented approach enables multi-scale evaluations of relations among road infrastructure elements while preserving the system’s scalability and the distributed management of infrastructure data. Thus, previously separate data sets can be evaluated in relation to each other on a big scale. Doing so, the presented concept provides a foundation for extensive correlation studies between different heterogeneous infrastructure datasets. The concept is validated by applying it to a large-scale real-world data set stemming from multiple Bavarian road authorities, transferring into the proposed graph structure, and demonstrating the gained capabilities through cross-domain queries and analysis.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"68 ","pages":"Article 103649"},"PeriodicalIF":8.0,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714511","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

A multi-view contrastive embedding framework for filtering fuzzy requirements of complex products 一种过滤复杂产品模糊需求的多视图对比嵌入框架

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-07-28 DOI: 10.1016/j.aei.2025.103659

Yufeng Ma , Xiang Zhao , Yajie Dou , Anastasia Dimou , Xuemin Duan , Yuejin Tan

{"title":"A multi-view contrastive embedding framework for filtering fuzzy requirements of complex products","authors":"Yufeng Ma , Xiang Zhao , Yajie Dou , Anastasia Dimou , Xuemin Duan , Yuejin Tan","doi":"10.1016/j.aei.2025.103659","DOIUrl":"10.1016/j.aei.2025.103659","url":null,"abstract":"<div><div>In complex product development, requirement teams must filter large volumes of user input to identify valid and representative requirements. Compared to professional users, broad user requirements come from diverse sources such as feedback, surveys, and social media, but are often subjective, unstructured, and fuzzy—posing challenges for effective filtering. Existing methods typically overlook this fuzziness. To address this, we propose a multi-view contrastive embedding framework for filtering fuzzy requirements. Requirement triples are modeled as nodes in a knowledge graph and extended into multiple hyper-views for fuzziness-aware representation learning. We integrate knowledge graph embedding with a contrastive learning mechanism. By leveraging multi-view modeling and a fuzziness-aware scoring function, the proposed framework effectively captures and models the degree of fuzziness in user requirements, thereby enabling robust filtering of ambiguous requirements. Experiments on real-world datasets show that our method outperforms existing approaches in filtering and ranking tasks, offering a robust solution for large-scale fuzzy requirement analysis.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"68 ","pages":"Article 103659"},"PeriodicalIF":8.0,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714515","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