Computers in Industry最新文献_第4页

A learning model for perceived visual complexity assessment of automotive 3D shapes based on visual perception elements 基于视觉感知元素的汽车三维形状感知视觉复杂性评估学习模型

IF 9.1 1区计算机科学

Computers in Industry Pub Date : 2025-09-10 DOI: 10.1016/j.compind.2025.104358

Chengyi Shen, Changao Liu, Shijian Luo, Deyin Zhang, Yao Wang

{"title":"A learning model for perceived visual complexity assessment of automotive 3D shapes based on visual perception elements","authors":"Chengyi Shen, Changao Liu, Shijian Luo, Deyin Zhang, Yao Wang","doi":"10.1016/j.compind.2025.104358","DOIUrl":"10.1016/j.compind.2025.104358","url":null,"abstract":"<div><div>As the automotive industry matures, automotive exterior design has become a key factor affecting market performance and user purchasing decisions. But the current assessment methods mainly rely on expert experience and lack systematic use of user perception knowledge. To remedy this issue, this study introduces a learning model for perceived visual complexity (PVC) assessment of automotive 3D shapes, grounded in user cognition. It aims to connect user perception with shape attributes. PVC offers key advantages, including quantifiability and relevance to both aesthetics and functionality, among others. To develop and validate this model, we first conducted paired comparison experiments to measure PVC of automotive 3D shapes, thereby establishing a dataset correlating user assessments with shape attributes influencing such evaluations. These attributes were then translated into computable features informed by human visual perception, followed by correlation analysis for feature selection. Finally, a variety of regression models and feature combinations were employed to construct learning models for assessment, from which the best-performing representative model was identified. The evaluation results demonstrated that the representative learning model underscored its efficacy in predicting the PVC of automotive 3D shapes. Its average Spearman correlation with human subjective evaluations was 0.7991 based on K-fold cross-validation. Notably, comparative analysis revealed that the representative model outperformed previous models of 3D complexity within the test set.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"173 ","pages":"Article 104358"},"PeriodicalIF":9.1,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A semi-automated compliance checking framework for shield tunnel design integrating ontology and natural language processing 结合本体和自然语言处理的盾构隧道设计遵从性半自动检测框架

IF 9.1 1区计算机科学

Computers in Industry Pub Date : 2025-09-09 DOI: 10.1016/j.compind.2025.104362

Yuxian Zhang, Xuhua Ren, Jixun Zhang

{"title":"A semi-automated compliance checking framework for shield tunnel design integrating ontology and natural language processing","authors":"Yuxian Zhang, Xuhua Ren, Jixun Zhang","doi":"10.1016/j.compind.2025.104362","DOIUrl":"10.1016/j.compind.2025.104362","url":null,"abstract":"<div><div>Conventional compliance checking for shield tunnel design models relies on two-dimensional drawings and the designer's subjective interpretation of specifications, which limits the efficiency and introduces potential errors. This study developed a semi-automated framework for shield tunnel design compliance checking using ontology and natural language processing. The adopted methodology establishes a shield tunnel design ontology (STDO) model, which includes six classes of information and relationships that need to be considered in the design phase. A novel method for converting text into a computer-readable format was proposed for design specification content. The design specification text is converted into a word sequence format, including STDO semantics, through word segmentation and semantic alignment. The pattern-matching method converts semantically enriched specification text into a Prolog rule format by extracting grammatical structure elements and transforming logical checking elements. The established design compliance checking framework generates facts through interaction with the building information model and performs compliance reasoning tasks using Prolog rules derived from the specification text. To demonstrate the effectiveness of the conversion method proposed in this study and the designed compliance checking framework, a shield tunnel project was selected for experimental verification. The results showed the following: (1) The proposed method of converting specification text into predicate logic achieved an <span><math><mrow><mi>F</mi><mn>1</mn></mrow></math></span> of 86.25 %, providing a convenient approach for transforming it into a computer-readable format. (2) The established semi-automated framework could provide a convenient solution to assist in conducting model compliance checking tasks according to both quantitative and non-quantitative clauses. The results of this study provide significant guidance for the intelligent design of shield tunnels.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"173 ","pages":"Article 104362"},"PeriodicalIF":9.1,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145020157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An individual generalization framework based on independent samples towards a more reasonable fault diagnosis benchmark 提出了一种基于独立样本的个体泛化框架，以获得更合理的故障诊断基准

IF 9.1 1区计算机科学

Computers in Industry Pub Date : 2025-09-09 DOI: 10.1016/j.compind.2025.104359

Yiming He, Weiming Shen

引用次数: 0

Leakage detection of oil and gas pipelines based on a multi-channel and multi-branch one-dimensional convolutional neural network with imbalanced samples 基于非平衡样本的多通道多分支一维卷积神经网络油气管道泄漏检测

IF 9.1 1区计算机科学

Computers in Industry Pub Date : 2025-09-08 DOI: 10.1016/j.compind.2025.104356

Dandi Yang , Peng Wang , Jingyi Lu , Chuang Guan , Hongli Dong

{"title":"Leakage detection of oil and gas pipelines based on a multi-channel and multi-branch one-dimensional convolutional neural network with imbalanced samples","authors":"Dandi Yang , Peng Wang , Jingyi Lu , Chuang Guan , Hongli Dong","doi":"10.1016/j.compind.2025.104356","DOIUrl":"10.1016/j.compind.2025.104356","url":null,"abstract":"<div><div>In recent years, intelligent pipeline leakage detection technology has played a crucial role in ensuring pipeline safety and energy security. However, most existing methods assume balanced datasets, overlooking the inherent imbalance between normal and abnormal data in real-world scenarios. This limitation hampers effective feature extraction for anomaly detection. To address this challenge, we propose a novel multi-channel and multi-branch one-dimensional convolutional neural network (MCB1DCNN). The model integrates a multi-channel convolution module and a multi-branch network structure to extract both global and local signal features. To mitigate the impact of data imbalance, we propose an adaptive weighted cross-entropy loss function. This function dynamically adjusts the loss weight of minority class samples based on the imbalance ratio. Furthermore, we construct a multi-channel acoustic signal dataset for oil and gas pipelines using the overlapping sample segmentation method. Variational mode decomposition (VMD) is applied to decompose acoustic signals into different frequency components, enabling comprehensive feature extraction. Ablation experiments analyze the impact of key model parameters. Experimental results show that MCB1DCNN outperforms several state-of-the-art methods in terms of accuracy, F1 score, false alarm rate, and missing alarm rate. These findings demonstrate its superior performance and practical applicability in real-world pipeline leakage detection.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"173 ","pages":"Article 104356"},"PeriodicalIF":9.1,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145020142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data issues in industrial AI systems: A meta-review and research strategy 工业人工智能系统中的数据问题：元综述和研究策略

IF 9.1 1区计算机科学

Computers in Industry Pub Date : 2025-09-08 DOI: 10.1016/j.compind.2025.104361

Xuejiao Li , Yang Cheng , Charles Møller , Jay Lee

{"title":"Data issues in industrial AI systems: A meta-review and research strategy","authors":"Xuejiao Li , Yang Cheng , Charles Møller , Jay Lee","doi":"10.1016/j.compind.2025.104361","DOIUrl":"10.1016/j.compind.2025.104361","url":null,"abstract":"<div><div>In the era of Industry 4.0, artificial intelligence (AI) is assumed to play an increasingly pivotal role within industrial systems. Despite the recent trend within various industries to adopt AI, the actual adoption of AI is not as developed as perceived. A significant factor contributing to this lag is the data issues in AI implementation. How to address these data issues stands as a significant concern confronting both industry and academia. Thus, this study conducts a comprehensive meta-review of data issues and corresponding methods in industrial AI. Eighty-two data issues are identified and categorized into seven stages of the data lifecycle. To supplement the existing research that focuses more on data issues arising in historical data, this study subsequently discusses the management of real-time sensor data and expert domain knowledge. Meanwhile, it proposes a model-aware data preparation approach, which integrates the data characteristics with specific AI model requirements to enhance data usability and algorithm alignment. This approach is further integrated into a conceptual framework that combines managerial and technical perspectives for systematically resolving data issues. The framework provides actionable insights and a systematic method for AI practitioners and industrial system developers to anticipate and address data-related challenges. Finally, the study highlights future research directions. This study advances the existing body of knowledge, supports a seamless transition from traditional model-centric AI to data-centric AI, and offers practical guidelines for professionals navigating the complexities of achieving data excellence in industrial AI applications.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"173 ","pages":"Article 104361"},"PeriodicalIF":9.1,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145020141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel attention-free FCformer network with dynamic subdomain adaptation for open-set fault diagnosis of motor bearing 基于动态子域自适应的电机轴承开集故障诊断网络

IF 9.1 1区计算机科学

Computers in Industry Pub Date : 2025-09-04 DOI: 10.1016/j.compind.2025.104357

Chaoyang Weng, Baochun Lu, Longmiao Chen, Xiaoli Zhao, Wenbo Huang

{"title":"A novel attention-free FCformer network with dynamic subdomain adaptation for open-set fault diagnosis of motor bearing","authors":"Chaoyang Weng, Baochun Lu, Longmiao Chen, Xiaoli Zhao, Wenbo Huang","doi":"10.1016/j.compind.2025.104357","DOIUrl":"10.1016/j.compind.2025.104357","url":null,"abstract":"<div><div>Domain adaptation has emerged as an effective technique for addressing domain shift in motor bearing fault diagnosis. However, motor bearings often operate under harsh and uncertain conditions in actual industrial applications, where new fault modes may arise. This scenario gives rise to the open-set domain adaptation (OSDA) problem, which challenges the domain adaptation assumption that the source and target domains share the same label space. Therefore, this paper proposes a novel Attention-free FCformer network (AFCNet) with dynamic subdomain adaptation to address the OSDA fault diagnosis of motor bearing. Specifically, an improved Transformer based on Fourier and Convolutional embedding is first introduced to construct long-distance dependence in the frequency domain and extract more representative local domain-invariant fault features. Thereafter, an open-set subdomain adaptation module based on dynamic local maximum mean discrepancy is designed to align the conditional feature distribution of known classes by masking potential unknown classes. Furthermore, to reduce the impact of the empirical threshold setting on unknown class detection, an adaptive threshold learning (ATL) strategy is proposed to establish a reliable decision boundary between known and unknown classes. Finally, two fault diagnosis cases of motor bearing are carried out to validate the effectiveness and superiority of AFCNet. Experimental results demonstrate that AFCNet outperforms five benchmark models in terms of both accuracy and generalization across OSDA tasks with different source label spaces. These findings suggest that AFCNet offers a robust and reliable method for detecting new fault modes in motor bearings of rotating machinery.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"173 ","pages":"Article 104357"},"PeriodicalIF":9.1,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144989985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

RLGBS: Reinforcement Learning-Guided Beam Search for process optimization in a paper machine dryer section 基于强化学习引导的束搜索的纸机干燥段工艺优化

IF 9.1 1区计算机科学

Computers in Industry Pub Date : 2025-08-28 DOI: 10.1016/j.compind.2025.104351

Siyuan Chen , Munevver Elif Asar , Jamal Yagoobi , Chenhui Shao

{"title":"RLGBS: Reinforcement Learning-Guided Beam Search for process optimization in a paper machine dryer section","authors":"Siyuan Chen , Munevver Elif Asar , Jamal Yagoobi , Chenhui Shao","doi":"10.1016/j.compind.2025.104351","DOIUrl":"10.1016/j.compind.2025.104351","url":null,"abstract":"<div><div>Paper drying is responsible for over two-thirds of energy consumption in the U.S. pulp and paper industry, presenting significant potential for energy savings through optimization of process parameters. Current approaches often assume fixed operating conditions, neglecting dynamic ambient and process variations that limit achievable savings and real-world applicability. To this end, we develop a physics-based simulation environment for a paper machine dryer section and propose a reinforcement learning (RL) framework to minimize overall energy consumption by optimizing drying process parameters under diverse operating conditions. To mitigate overdrying and numerical instabilities caused by suboptimal local RL actions, we introduce Reinforcement Learning-Guided Beam Search (RLGBS), which explores multiple action sequences in parallel using beam search. Instead of making step-by-step decisions, RLGBS prioritizes solutions based on cumulative probability, reducing the impact of individual suboptimal actions. Experiments demonstrate that RLGBS achieves consistent energy savings under unseen operating conditions not encountered during training, outperforming conventional RL methods. While validated in drying optimization, this framework is broadly applicable to other RL-based industrial process control problems.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"172 ","pages":"Article 104351"},"PeriodicalIF":9.1,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Knowledge graph construction with meta-learning for continuously accumulated manufacturing knowledge 利用元学习构建知识图谱，实现制造知识的持续积累

IF 9.1 1区计算机科学

Computers in Industry Pub Date : 2025-08-28 DOI: 10.1016/j.compind.2025.104353

Yanzhen Jing , Guanghui Zhou , Chao Zhang , Fengtian Chang , Jiacheng Li

{"title":"Knowledge graph construction with meta-learning for continuously accumulated manufacturing knowledge","authors":"Yanzhen Jing , Guanghui Zhou , Chao Zhang , Fengtian Chang , Jiacheng Li","doi":"10.1016/j.compind.2025.104353","DOIUrl":"10.1016/j.compind.2025.104353","url":null,"abstract":"<div><div>The construction of manufacturing knowledge graph (MKG) has been regarded as an important technical roadmap to support designer-oriented manufacturing knowledge reuse. It can improve product manufacturability and reduce design iterations. However, manufacturing knowledge is lesson-learned texts of enterprises. Traditional deep learning-driven MKG construction requires sufficient training samples, which heavily rely on manual labeling. It is both time-consuming and labor-intensive. Meanwhile, due to the new manufacturing knowledge accumulation, an MKG also needs to be continuously updated. To bridge the gap, this paper proposes an efficient MKG construction approach with meta-learning. Based on the manufacturing knowledge ontology, a novel two-stage knowledge extraction model (TKEM) is presented to achieve low-resource entity recognition. Then, considering the newly accumulated manufacturing knowledge, a continuous knowledge fusion strategy is illustrated to complete the MKG construction and update. Finally, the experimental results show that the TKEM outperforms state-of-the-art baselines on both the manufacturing knowledge dataset and a public dataset. In addition, a prototype system provides the application of MKG-based manufacturing knowledge reuse, which can perceive explicit and implicit knowledge requirements of designers by MKG embedding learning.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"172 ","pages":"Article 104353"},"PeriodicalIF":9.1,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144912916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Carbon footprint optimization of a LoRa-based multi-hop Industrial Internet of Things network deployment 基于lora的多跳工业物联网网络部署的碳足迹优化

IF 9.1 1区计算机科学

Computers in Industry Pub Date : 2025-08-27 DOI: 10.1016/j.compind.2025.104348

Francisco-Jose Alvarado-Alcon, Rafael Asorey-Cacheda, Antonio-Javier Garcia-Sanchez, Joan Garcia-Haro

{"title":"Carbon footprint optimization of a LoRa-based multi-hop Industrial Internet of Things network deployment","authors":"Francisco-Jose Alvarado-Alcon, Rafael Asorey-Cacheda, Antonio-Javier Garcia-Sanchez, Joan Garcia-Haro","doi":"10.1016/j.compind.2025.104348","DOIUrl":"10.1016/j.compind.2025.104348","url":null,"abstract":"<div><div>The seamless integration of the Internet of Things (IoT) into various societal and economic domains is unfolding before us. In the industrial sector, this digital transformation propelled by Industrial Internet of Things (IIoT) networks, among other technologies is referred to as Industry 4.0, where wireless technologies are transforming the industry as currently conceived. While considerable efforts have been devoted to optimizing performance and energy consumption in these networks, relatively little attention has been directed towards comprehensively studying and optimizing the carbon footprint (CF) associated with these network deployments. Additionally, scarce literature has analyzed the use of multi-hop topologies with well-known standards like LoRaWAN. This research delves into the CF of a generic multi-hop IIoT network using renewable energy sources and communicating through the LoRa physical layer while also proposing an optimization framework. The findings indicate that up to a 85% reduction in carbon emissions can be achieved by enabling packet forwarding through end devices, offering greater scalability. Interestingly, an optimal end device density emerges, implying that decreasing the number of end devices may actually lead to higher CFs. These results underscore the necessity for a fresh perspective on optimizing IIoT networks, urging the inclusion of environmental sustainability criteria that have hitherto been overlooked.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"172 ","pages":"Article 104348"},"PeriodicalIF":9.1,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Semantic middleware for demand response systems: Enhancing data interoperability in green electricity management for manufacturing 需求响应系统的语义中间件：增强制造业绿色电力管理中的数据互操作性

IF 9.1 1区计算机科学

Computers in Industry Pub Date : 2025-08-27 DOI: 10.1016/j.compind.2025.104354

Tina Boroukhian , Kritkorn Supyen , Christopher William Mclaughlan , Atit Bashyal , Tuan Pham , Hendro Wicaksono

{"title":"Semantic middleware for demand response systems: Enhancing data interoperability in green electricity management for manufacturing","authors":"Tina Boroukhian , Kritkorn Supyen , Christopher William Mclaughlan , Atit Bashyal , Tuan Pham , Hendro Wicaksono","doi":"10.1016/j.compind.2025.104354","DOIUrl":"10.1016/j.compind.2025.104354","url":null,"abstract":"<div><div>Optimizing the consumption of green electricity across sectors, including manufacturing, is a critical strategy for achieving net-zero emissions and advancing clean production in Europe by 2050. Demand Response (DR) represents a promising approach to engaging power consumers from all sectors in the transition toward increased utilization of renewable energy sources. A functional DR system for manufacturing power consumers requires seamless data integration and communication between information systems across multiple domains, including both power consumption and generation. This paper introduces a semantic middleware specifically designed for DR systems in the manufacturing sector, using an ontology as the central information model. To develop this ontology, we adopted a strategy that reuses and unifies existing ontologies from multiple domains, ensuring comprehensive coverage of the data requirements for DR applications in manufacturing. To operationalize this strategy, we designed novel methods for effective ontology unification and implemented them within a dedicated unification tool. This process was followed by data-to-ontology mapping to construct a knowledge graph, and was further extended through the development of a querying system equipped with a natural language interface. Additionally, this paper offers insights into the deployment environment of the semantic middleware, encompassing multiple data sources and the applications that utilize this data. The proposed approach is implemented in multiple German manufacturing small and medium-sized enterprises connected to a utility company, demonstrating consistent data interpretation and seamless information integration. Consequently, the method offers practical potential for optimizing green electricity usage in the manufacturing sector and supporting the transition toward a more sustainable and cleaner future.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"172 ","pages":"Article 104354"},"PeriodicalIF":9.1,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0