Computers in Industry最新文献

{"title":"Integration of industry 4.0 technologies for agri-food supply chain resilience","authors":"Rohit Sharma ,&nbsp;Balan Sundarakani ,&nbsp;Ioannis Manikas","doi":"10.1016/j.compind.2024.104225","DOIUrl":"10.1016/j.compind.2024.104225","url":null,"abstract":"<div><div>The agri-food supply chain (AFSC) operations are becoming challenging due to globalization, constantly shifting consumer demands, and intensive disruptions leading to inefficient production and distribution of safe and high-quality food. Technological advancements are the most promising ways to ensure firms’ survival and supply chains. To enhance the resilience of AFSCs, the present study aims to identify and model the challenges associated with AFSC operations in the context of the United Arab Emirates (UAE) food processing industry. An integrated methodology using the Grey Influence Analysis (GINA) and Fuzzy Linguistic Quantifier Ordered Weighted Aggregation (FLQOWA) methodology is applied to analyze resilience enablers and assess industry 4.0 technologies (I4Ts) that can enhance resilience in AFSCs. The GINA technique helps identify the most influential resilience enablers, and the FLQOWA helps assess and prioritize I4Ts to enhance resilient enablers. The findings reveal that out of thirteen sub-enablers, four are the most influential resilient enablers, viz., real-time information sharing, enhanced product traceability, improved risk management, and planning and network design; and out of ten I4Ts, three are the most influential technologies viz., big data analytics, Internet of things, and cloud computing can further enhance resilience enablers. The findings from the study can help AFSC organizations and the government formulate appropriate strategies based on the integrated matrix developed by selecting the best combination of technologies for strengthening the required resilient enablers among the AFSC stakeholders.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"165 ","pages":"Article 104225"},"PeriodicalIF":8.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142841247","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}

{"title":"BlurRes-UNet: A novel neural network for automated surface characterisation in metrology","authors":"Weixin Cui ,&nbsp;Shan Lou ,&nbsp;Wenhan Zeng ,&nbsp;Visakan Kadirkamanathan ,&nbsp;Yuchu Qin ,&nbsp;Paul J. Scott ,&nbsp;Xiangqian Jiang","doi":"10.1016/j.compind.2024.104228","DOIUrl":"10.1016/j.compind.2024.104228","url":null,"abstract":"<div><div>Surface characterisation is essential in metrology for precise measurement and analysis of surface features, ensuring product quality and compliance with industry standards. Form removal is the primary step in surface characterisation, isolating features of interest by eliminating the primary shape from measurements. Traditional least-squares methods, as specified in ISO standards, are effective but offer limited adaptability for diverse surfaces and often require manual parameter tuning. With this limitation in mind, this paper proposes BlurRes-UNet, a deep learning-based model designed for fully automatic form removal. Built on an encoder–decoder architecture with residual learning, skip connections, and a tailored loss function, the model incorporates domain knowledge, feature engineering, and regularisation techniques to optimise performance with limited training data. The model is evaluated against traditional least squares methods and assessed using various strategies to demonstrate its performance and robustness. It processes surfaces of 256 × 256 resolution in 7.32 ms per sample on a T4 GPU, achieving superior accuracy in recognising reference forms across diverse surfaces compared to traditional methods. The results suggest that the model is capable of accurately recognising different order reference forms from diverse surfaces, facilitating an autonomous surface characterisation system without the need for manual intervention.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"165 ","pages":"Article 104228"},"PeriodicalIF":8.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142911787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A robotic skill transfer learning framework of dynamic manipulation for fabric placement","authors":"Tianyu Fu ,&nbsp;Cheng Li ,&nbsp;Yunfeng Bai ,&nbsp;Fengming Li ,&nbsp;Jiang Wu ,&nbsp;Chaoqun Wang ,&nbsp;Rui Song","doi":"10.1016/j.compind.2024.104216","DOIUrl":"10.1016/j.compind.2024.104216","url":null,"abstract":"<div><div>Placing fabric poses a challenge to robots since fabric with high dimensional configuration space can deform during manipulation. Existing methods for placing fabric mostly rely on static operations, which are inefficient and require a large workspace. Therefore, this study applies dynamic manipulation (manipulating uncontrollable parts of the fabric by swinging) to fabric placement, proposing a novel learning framework for robotic dynamic fabric placement skill learning and generalization. The proposed framework integrates reinforcement learning with imitation learning, leveraging expert demonstration data to guide and accelerate skill acquisition. Additionally, fabric characteristics are combined with imitation learning to enable the transfer and generalization of the learned policy to real-world environments The experiments suggest that the proposed framework is capable of achieving the placement tasks for a range of positions and fabrics. For success rate, the policy of the proposed framework ultimately achieves a flatness of exceeding 95% and a placement distance error of less than 2 mm. Moreover, the proposed approach is similar in operation time to the fastest method, while it can reduce the space required for manipulating the fabric by over 15%. Compared with other placement policies, it is promising because of its high accuracy, flexibility, efficiency, as well as adaptability.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"165 ","pages":"Article 104216"},"PeriodicalIF":8.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142763009","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}

{"title":"A context-aware decision support system for selecting explainable artificial intelligence methods in business organizations","authors":"Marcelo I. Reis ,&nbsp;João N.C. Gonçalves ,&nbsp;Paulo Cortez ,&nbsp;M. Sameiro Carvalho ,&nbsp;João M. Fernandes","doi":"10.1016/j.compind.2024.104233","DOIUrl":"10.1016/j.compind.2024.104233","url":null,"abstract":"<div><div>Explainable Artificial Intelligence (XAI) methods are valuable tools for promoting understanding, trust, and efficient use of Artificial Intelligence (AI) systems in business organizations. However, the question of how organizations should select suitable XAI methods for a given task and business context remains a challenge, particularly when the number of methods available in the literature continues to increase. Here, we propose a context-aware decision support system (DSS) to select, from a given set of XAI methods, those with higher suitability to the needs of stakeholders operating in a given AI-based business problem. By including the human-in-the-loop, our DSS comprises an application-grounded analytical metric designed to facilitate the selection of XAI methods that align with the business stakeholders’ desiderata and promote a deeper understanding of the results generated by a given machine learning model. The proposed system was tested on a real supply chain demand problem, using real data and real users. The results provide evidence on the usefulness of our metric in selecting XAI methods based on the feedback and analytical maturity of stakeholders from the deployment context. We believe that our DSS is sufficiently flexible and understandable to be applied in a variety of business contexts, with stakeholders with varying degrees of AI literacy.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"165 ","pages":"Article 104233"},"PeriodicalIF":8.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142911788","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}

{"title":"YOLOv10-pose and YOLOv9-pose: Real-time strawberry stalk pose detection models","authors":"Zhichao Meng ,&nbsp;Xiaoqiang Du ,&nbsp;Ranjan Sapkota ,&nbsp;Zenghong Ma ,&nbsp;Hongchao Cheng","doi":"10.1016/j.compind.2024.104231","DOIUrl":"10.1016/j.compind.2024.104231","url":null,"abstract":"<div><div>In the computer-aided industry, particularly within the domain of agricultural automation, fruit pose detection is critical for optimizing efficiency across various applications such as robotic harvesting, aerial crop surveillance, precision pruning, and automated sorting. These technologies enhance productivity and precision, addressing challenges posed by an aging labor force and the increasing demand for sophisticated robotic applications in agriculture. This is particularly crucial for strawberries, which are globally recognized for their high nutritional value. The strawberry pickting robots generally cut the stems, so knowing the pose of the strawberry stalks before cutting can effectively adjust the pose of the end effector, thereby improving the success rate of picking. This paper referred to the keypoint detection branch and loss function of the YOLOv8-pose model, and combined the latest YOLOv9 and YOLOv10 object detection models to propose YOLOv9-pose and YOLOv10-pose. The experimental results showed that YOLOv9-base-pose had the best comprehensive performance, reaching 0.962 in Box_mAP50 and 0.914 in Pose_mAP50, and the speed met the real-time requirement of FPS 51. The entire YOLOv10-pose series did not achieve satisfactory accuracy, but not using non-maximum suppression did indeed speed up the post-processing. In the YOLOv10-pose series, YOLOv10m-pose achieved the best comprehensive performance with Box_mAP50 of 0.954, Pose_ mAP50 of 0.903, and a speed of 61 FPS. Comparing YOLOv9-base-pose with the entire series of YOLOv8-pose and YOLOv5-pose also demonstrated the superior performance of YOLOv9-base-pose. YOLOv9-pose and YOLOv10-pose can provide a theoretical basis for pose detection and a reference for other similar fruit pose detection.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"165 ","pages":"Article 104231"},"PeriodicalIF":8.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142884318","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}

{"title":"Streamlining Assembly Instruction Design (S-AID): A comprehensive systematic framework","authors":"Mirco Bartolomei ,&nbsp;Federico Barravecchia ,&nbsp;Luca Mastrogiacomo ,&nbsp;Davide Maria Gatta ,&nbsp;Fiorenzo Franceschini","doi":"10.1016/j.compind.2024.104232","DOIUrl":"10.1016/j.compind.2024.104232","url":null,"abstract":"<div><div>Assembly instructions are detailed directives used to guide the assembly of products across various manufacturing sectors. As production processes evolve to become more flexible, the significance of assembly instructions in meeting rigorous efficiency and quality standards becomes increasingly pronounced. Nevertheless, the development of assembly instructions often remains unstructured and predominantly dependent on the experience or personal skills of the designer. This paper aims to address these issues by pursuing three main goals: (i) deciphering the assembly process and the information that characterizes it, thereby providing a taxonomy of instruction constituents; (ii) presenting a framework to assess the various formats in which such information can be communicated; and (iii) introducing a step-by-step method, named <em>S-AID</em>, which offers a consistent methodology for designers during the instruction design phase. Overall, this research provides a rigorous taxonomy of the building blocks of assembly instructions and defines their relationships with various instruction formats. Furthermore, by proposing a systematic design method, this works aims to address the redundancy and inconsistency commonly encountered in traditional instruction design processes. The proposed methodology is illustrated using a real-world case study involving the assembly of a mechanical equipment. Finally, the effectiveness of the <em>S-AID</em> method was evaluated quantitatively through comparative analysis with other instruction sets, focusing on metrics such as process failures, assembly completion time, and perceived cognitive load.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"165 ","pages":"Article 104232"},"PeriodicalIF":8.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142884277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Non-contact rPPG-based human status assessment via a spatial–temporal attention feature fusion network with anti-aliasing","authors":"Qiwei Xue ,&nbsp;Xi Zhang ,&nbsp;Yuchong Zhang ,&nbsp;Amin Hekmatmanesh ,&nbsp;Huapeng Wu ,&nbsp;Yuntao Song ,&nbsp;Yong Cheng","doi":"10.1016/j.compind.2024.104227","DOIUrl":"10.1016/j.compind.2024.104227","url":null,"abstract":"<div><div>Remote Photoplethysmography (rPPG) is a cost-effective and non-contact technology that enables real-time monitoring of physiological status by extracting vital information such as heart rate (HR). This capability enables the assessment of fatigue and stress, helping to prevent accidents by identifying risky conditions early. Continuous monitoring with rPPG reduces operational risks, contributing to safer industrial and medical environments. However, the performance of rPPG is challenged by complex backgrounds and facial motions in industrial environments, which complicates feature extraction. To address these challenges, this paper proposes a spatial–temporal attention feature fusion network with anti-aliasing (ST-ASENet) for human status assessment. The ST-ASENet encodes spatial–temporal facial signals from multiple regions of interest (ROI) and enhances feature extraction through the attention mechanism. The network integrates anti-aliasing by low-pass filtering during the downsampling process to improve the accuracy of rPPG signals in complex environments. It calculates HR, respiratory rate (RR), and heart rate variability (HRV) for status evaluation. Additionally, the Robotics Operator Factors Assessment (ROFA) dataset is introduced, featuring diverse individuals and environments to improve the robustness of ST-ASENet. Experimental results demonstrate that ST-ASENet outperforms state-of-the-art methods in HR estimation and shows effectiveness across various industrial scenarios. The proposed method fosters operational efficiency and a data-driven approach to human-centric safety, making rPPG invaluable in modern, health-focused workplaces.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"165 ","pages":"Article 104227"},"PeriodicalIF":8.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142884278","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}

{"title":"Deep hierarchical sorting networks for fault diagnosis of aero-engines","authors":"Jinlei Wu,&nbsp;Lin Lin,&nbsp;Dan Liu,&nbsp;Song Fu,&nbsp;Shiwei Suo,&nbsp;Sihao Zhang","doi":"10.1016/j.compind.2024.104229","DOIUrl":"10.1016/j.compind.2024.104229","url":null,"abstract":"<div><div>In modern industry, timely health assessments of aero-engines are crucial for ensuring their proper functionality and the safety of aviation operations. However, during the collection of operating data for aero-engines, influential fault features may exhibit hysteresis or even overwhelmed due to transmission delays in some sensors. Furthermore, these features in the data at interval points are difficult to extract using traditional deep neural networks. Moreover, in aero-engine fault diagnosis, the number of normal samples is significantly higher than that of fault samples. As a result, traditional deep neural networks tend to focus on normal samples while fault samples are neglected, increasing the risk of missed diagnoses or misdiagnoses. To address these problems, this paper proposes a parallel convolutional neural network based on hierarchical sorting of state points (FSHSM-PCNN), to improve the synergistic effect between state point data at different hierarchical levels via the hierarchical sorting module, and to efficiently extract fault information via the parallel convolutional neural network. First, the state point data in the original samples is internally sorted along the time dimension by the fault significance-based hierarchical sorting module (FSHSM), and the different levels of state point data obtained after sorting reveal a reinforced synergistic effect. Second, a parallel convolutional neural network is developed to extract temporal status features and reinforced synergistic features, and the fused information is used for fault diagnosis. Finally, the performance of the proposed FSHSM-PCNN is evaluated using actual monitoring data from aero-engines. The experimental results show that the proposed method is effective in extracting fault features from the monitoring data. Compared to other methods in the ablation study, the proposed method improves average performance in aero-engine fault diagnosis by 12.46 %, 7.07 %, and 12.62 %, respectively. In diagnosis tasks with imbalanced datasets, its accuracy exceeds that of other methods by at least 5.01 %.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"165 ","pages":"Article 104229"},"PeriodicalIF":8.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142884280","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}

{"title":"An integrated approach for enhanced early-phase space system design and optimization","authors":"Yutong Zhang ,&nbsp;Dong Ye ,&nbsp;Cheng Wei ,&nbsp;Zhaowei Sun","doi":"10.1016/j.compind.2025.104258","DOIUrl":"10.1016/j.compind.2025.104258","url":null,"abstract":"<div><div>The integration of Model-Based Systems Engineering (MBSE) and Multidisciplinary Design Analysis and Optimization (MDAO) presents a powerful opportunity to enhance early-stage system design, particularly for complex space systems. However, the lack of efficient integration between these methods results in limitations such as unclear boundary between domain models, reduced automation, and challenges in maintaining traceability of optimization results. Overcoming these barriers is essential for conducting high-quality trade studies in systems engineering. In this work, we propose a novel framework that integrates MDAO with MBSE to streamline system modeling, optimization, and verification. This approach enables the seamless exchange of knowledge between design and optimization models, while performing optimizations and managing results directly within the MBSE environment. By using MBSE as a central knowledge repository, the framework minimizes errors and improves the traceability of optimization processes. Case studies demonstrate that this framework enhances both efficiency and accuracy during the early design phases of space mission development. Our findings indicate that integrating MDAO with MBSE allows for comprehensive system evaluation and more informed decision-making, ultimately improving the quality and efficiency of the design process. This integrated framework offers a flexible, scalable solution for multidisciplinary optimization, making it a valuable tool for the design of future complex systems.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"166 ","pages":"Article 104258"},"PeriodicalIF":8.2,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143125025","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}

{"title":"A triple semantic-aware knowledge distillation network for industrial defect detection","authors":"Zhitao Wen,&nbsp;Jinhai Liu,&nbsp;He Zhao,&nbsp;Qiannan Wang","doi":"10.1016/j.compind.2025.104252","DOIUrl":"10.1016/j.compind.2025.104252","url":null,"abstract":"<div><div>Knowledge distillation (KD) is a powerful model compression technique that aims to transfer knowledge from heavy teacher networks to compact student networks via distillation. However, effectively transferring semantic knowledge in industrial settings poses significant challenges. On one hand, the appearance of defects (e.g., size and shape) may vary considerably due to the influence of the industrial site, which potentially weakens the semantic associations between class-specific features. On the other hand, agnostic background interference (e.g., spike anomalies and low light) may foster semantic ambiguity of class-specific features. As such, the weakened semantic associations and fostered semantic ambiguities hinder the efficacy and adequacy of knowledge transfer in KD. To mitigate these limitations, we propose a triple semantic-aware knowledge distillation (TSKD) network for industrial defect detection. TSKD contains three refinements, i.e., dual-relation distillation (DRD), decoupled expert distillation (DED), and cross-response distillation (CRD). Specifically, DRD employs graph reasoning networks to strengthen semantic associations at both the instance and pixel levels, DED enhances semantic explicitness by decoupling foreground and background features while injecting expert priors, and CRD further captures task-specific semantic response knowledge. By integrating these components, TSKD can effectively perceive triple semantic knowledge of relations, features, and responses, ensuring more robust and comprehensive knowledge transfer. Experimental evaluations on two challenging industrial datasets show that TSKD can significantly improve detector performance (MFL-DET: 98.9% mAP; NEU-DET: 81.0% mAP) and compress computation (MFL-DET: 19.7M Params and 105 FPS; NEU-DET: 19.7M Params and 116 FPS).</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"166 ","pages":"Article 104252"},"PeriodicalIF":8.2,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143125033","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}