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An immersive spatially consistent multi-modal augmented virtuality human-machine interface for telerobotic systems Comput. Ind. (IF 8.2) Pub Date : 2025-02-05 Rebecca Schwenk, Shana Smith
This study presents an immersive augmented virtuality (AV)-based human-machine interface (HMI) designed to enhance telepresence and operator performance in telerobotic systems. Traditional telerobotic systems often face limitations such as 2D representations of 3D environments, restricted fields of view, and reduced depth perception, all of which hinder operator effectiveness. Although extended reality
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Measure2Shape: A novel footwear customisation framework utilising 3D shape estimation from anthropometric measurements with an orthosis case study Comput. Ind. (IF 8.2) Pub Date : 2025-02-05 Zhaohua Zhu, Wenxuan Ji, Yadie Yang, Sio-Kei Im, Jie Zhang
To address the limitations of relying on expensive 3D scanners for obtaining foot data in footwear customisation, this paper introduces a novel framework, Measure2Shape, which estimates 3D foot shapes using anthropometric measurement data. To achieve this, we established a large-scale 3D foot dataset with measurement data and created statistical shape models (SSMs) to represent the range of foot variations
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Evaluating unsignalized crosswalk safety in the age of autonomous vehicles Comput. Ind. (IF 8.2) Pub Date : 2025-02-03 Andrea Avignone, Marco Bassani, Beatrice Borgogno, Brunella Caroleo, Silvia Chiusano, Federico Princiotto
As autonomous vehicles are poised to enter public roadways, a major concern is their interaction with pedestrians. It requires attention and ability for pedestrians to interact correctly and for autonomous vehicles to detect pedestrians hence avoiding collisions. We propose a complete pipeline to collect, process and elaborate video data to quantitatively assess the possible occurrence of conflicts
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An integrated approach for enhanced early-phase space system design and optimization Comput. Ind. (IF 8.2) Pub Date : 2025-01-31 Yutong Zhang, Dong Ye, Cheng Wei, Zhaowei Sun
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
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Collaborative fault tolerance for cyber–physical systems: The detection stage Comput. Ind. (IF 8.2) Pub Date : 2025-01-30 Luis Piardi, André Schneider de Oliveira, Pedro Costa, Paulo Leitão
In the era of Industry 4.0, fault tolerance is essential for maintaining the robustness and resilience of industrial systems facing unforeseen or undesirable disturbances. Current methodologies for fault tolerance stages namely, detection, diagnosis, and recovery, do not correspond with the accelerated technological evolution pace over the past two decades. Driven by the advent of digital technologies
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A triple semantic-aware knowledge distillation network for industrial defect detection Comput. Ind. (IF 8.2) Pub Date : 2025-01-30 Zhitao Wen, Jinhai Liu, He Zhao, Qiannan Wang
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
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Automated construction contract analysis for risk and responsibility assessment using natural language processing and machine learning Comput. Ind. (IF 8.2) Pub Date : 2025-01-25 Irem Dikmen, Gorkem Eken, Huseyin Erol, M. Talat Birgonul
Construction contracts contain critical risk-related information that requires in-depth examination, yet tight schedules for bidding limit the possibility of comprehensive review of extensive documents manually. This research aims to develop models for automating the review of construction contracts to extract information on risk and responsibility that will provide inputs for risk management plans
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Domain ontology to integrate building-integrated photovoltaic, battery energy storage, and building energy flexibility information for explicable operation and maintenance Comput. Ind. (IF 8.2) Pub Date : 2025-01-23 Xiaoyue Yi, Llewellyn Tang, Reynold Cheng, Mengtian Yin, Yu Zheng
Building-integrated photovoltaics (BIPV) incorporated with battery energy storage (BES) and building energy flexibility (BEF) system is nowadays increasingly prevalent. During the operation and maintenance (O&M) of BIPV, BES, and BEF, various knowledge is contained and generated. This highlights information interaction among systems and the demand for incorporating diverse domain knowledge. However
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Acoustic signal-based wear monitoring for belt grinding tools with pyramid-structured abrasives using BO-KELM Comput. Ind. (IF 8.2) Pub Date : 2025-01-03 Yingjie Liu, Wenxi Wang, Xiaoyu Zhao, Shudong Zhao, Lai Zou, Chao Wang
Pyramid-structured abrasive belts have been widely used in the field of precision machining of complex surfaces over recent years. However, continuous wear directly affects their machining performance and quality. The lack of effective engineering monitoring methods limits the further application of such abrasive belts. To address this issue, this study presents an acoustic signal monitoring method
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Predictive analysis-based sustainable waste management in smart cities using IoT edge computing and blockchain technology Comput. Ind. (IF 8.2) Pub Date : 2025-01-03 C. Anna Palagan, S. Sebastin Antony Joe, S.J. Jereesha Mary, E. Edwin Jijo
Effective waste management has become the key challenge in developing smart cities with the increase in population. Traditional waste management systems are often inefficient, which leads to unnecessary trips, high operational costs, difficulties in tracking waste, and the inefficient use of resources. The proposed work aims to integrate real-time predictive analysis-based waste collection and disposal
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Human Digital Twins: A systematic literature review and concept disambiguation for industry 5.0 Comput. Ind. (IF 8.2) Pub Date : 2025-01-02 Ben Gaffinet, Jana Al Haj Ali, Yannick Naudet, Hervé Panetto
Human Digital Twins (HDTs) are an emerging concept with the potential to create human-centric systems for Industry 5.0. The concept has rapidly spread to new application domains, most notably Healthcare, leading to diverging conceptual interpretations. This Systematic Literature Review analyses the conceptual understanding of HDTs across all application domains to clarify the conceptual foundation
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BlurRes-UNet: A novel neural network for automated surface characterisation in metrology Comput. Ind. (IF 8.2) Pub Date : 2024-12-30 Weixin Cui, Shan Lou, Wenhan Zeng, Visakan Kadirkamanathan, Yuchu Qin, Paul J. Scott, Xiangqian Jiang
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
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A context-aware decision support system for selecting explainable artificial intelligence methods in business organizations Comput. Ind. (IF 8.2) Pub Date : 2024-12-27 Marcelo I. Reis, João N.C. Gonçalves, Paulo Cortez, M. Sameiro Carvalho, João M. Fernandes
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
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Streamlining Assembly Instruction Design (S-AID): A comprehensive systematic framework Comput. Ind. (IF 8.2) Pub Date : 2024-12-24 Mirco Bartolomei, Federico Barravecchia, Luca Mastrogiacomo, Davide Maria Gatta, Fiorenzo Franceschini
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
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Non-contact rPPG-based human status assessment via a spatial–temporal attention feature fusion network with anti-aliasing Comput. Ind. (IF 8.2) Pub Date : 2024-12-21 Qiwei Xue, Xi Zhang, Yuchong Zhang, Amin Hekmatmanesh, Huapeng Wu, Yuntao Song, Yong Cheng
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
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Meta-task interpolation-based data augmentation for imbalanced health status recognition of complex equipment Comput. Ind. (IF 8.2) Pub Date : 2024-12-21 Jinyuan Li, Wenqing Wan, Yong Feng, Jinglong Chen
In the research of health status detection technology for complex equipment such as liquid rocket engines, the extreme working environment hinders the widespread conduct of fault experimental simulations, leading to data scarcity and imbalance. Consequently, the performance of intelligent models deteriorates rapidly with direct training. To address this issue, this paper proposes a meta-task feature
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Deep hierarchical sorting networks for fault diagnosis of aero-engines Comput. Ind. (IF 8.2) Pub Date : 2024-12-20 Jinlei Wu, Lin Lin, Dan Liu, Song Fu, Shiwei Suo, Sihao Zhang
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
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YOLOv10-pose and YOLOv9-pose: Real-time strawberry stalk pose detection models Comput. Ind. (IF 8.2) Pub Date : 2024-12-19 Zhichao Meng, Xiaoqiang Du, Ranjan Sapkota, Zenghong Ma, Hongchao Cheng
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
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Integration of industry 4.0 technologies for agri-food supply chain resilience Comput. Ind. (IF 8.2) Pub Date : 2024-12-14 Rohit Sharma, Balan Sundarakani, Ioannis Manikas
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
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Intelligent prediction and soft-sensing of comprehensive production indicators for iron ore sintering: A review Comput. Ind. (IF 8.2) Pub Date : 2024-12-11 Sheng Du, Xian Ma, Haipeng Fan, Jie Hu, Weihua Cao, Min Wu, Witold Pedrycz
Iron ore sintering is a critical process in iron and steel production, with a substantial impact on overall energy consumption and the emission of various environmental pollutants. Enhancing the efficiency of this process is crucial for achieving sustainability in the iron and steel industry. Accurate prediction and real-time monitoring of comprehensive production indicators are essential for optimizing
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A robotic skill transfer learning framework of dynamic manipulation for fabric placement Comput. Ind. (IF 8.2) Pub Date : 2024-12-03 Tianyu Fu, Cheng Li, Yunfeng Bai, Fengming Li, Jiang Wu, Chaoqun Wang, Rui Song
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
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An approach for adaptive filtering with reinforcement learning for multi-sensor fusion in condition monitoring of gearboxes Comput. Ind. (IF 8.2) Pub Date : 2024-11-27 Shahis Hashim, Sitesh Kumar Mishra, Piyush Shakya
Condition monitoring of gearboxes is integral to maintaining floor safety, system stability, and inventory management. Capturing vibration response using sensors and subsequent response analysis is the standard procedure for gearbox fault detection. However, the sensors are susceptible to non-constant reliability due to the convolution of vibration responses from multiple sources, background noise
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Wasserstein distributionally robust learning for predicting the cycle time of printed circuit board production Comput. Ind. (IF 8.2) Pub Date : 2024-11-19 Feng Liu, Yingjie Lu, Debiao Li, Raymond Chiong
This paper proposes a Wasserstein distributionally robust learning (WDRL) model to predict the production cycle time of highly mixed printed circuit board (PCB) orders on multiple production lines. The PCB production cycle time is essential for optimizing production plans. However, the design of the PCB, production line configuration, order combinations, and personnel factors make the prediction of
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BRepQL: Query language for searching topological elements in B-rep models Comput. Ind. (IF 8.2) Pub Date : 2024-11-18 Seungeun Lim, Changmo Yeo, Byung Chul Kim, Kyung Cheol Bae, Duhwan Mun
Topological elements form the basis for tasks such as geometric calculations, feature analysis, and direct modeling in 3D CAD systems. Handling these elements is also essential in various automated systems. This study proposes a method to search for topological elements within a boundary representation (B-rep) model by employing topological queries. To address complex scenarios that are difficult to
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A Comparative Study of Handheld Augmented Reality Interaction Techniques for Developing AR Instructions using AR Authoring Tools Comput. Ind. (IF 8.2) Pub Date : 2024-11-17 Kay Hönemann, Björn Konopka, Michael Prilla, Manuel Wiesche
Augmented Reality (AR) instructions offer companies tremendous savings potential. However, developing these AR instructions has traditionally been challenging due to the need for programming skills and spatial knowledge. To address this complexity, industry and academia are working to simplify AR development. A crucial aspect of this process is the accurate positioning of AR content within the physical
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Discovering data spaces: A classification of design options Comput. Ind. (IF 8.2) Pub Date : 2024-11-15 Anna Gieß, Thorsten Schoormann, Frederik Möller, Inan Gür
Technical coordination between organizations and security concerns are among the major barriers to data sharing. Data spaces are an emerging digital infrastructure that helps address these challenges by sovereignly sharing data across institutional boundaries. The data space concept is at the core of many high-profile research initiatives in the European Union and receives great adoption in practice
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Evaluating the noise tolerance of Cloud NLP services across Amazon, Microsoft, and Google Comput. Ind. (IF 8.2) Pub Date : 2024-11-14 Juliano Barbosa, Baldoino Fonseca, Márcio Ribeiro, João Correia, Leandro Dias da Silva, Rohit Gheyi, Davy Baia
Natural Language Processing (NLP) has revolutionized industries, streamlining customer service through applications in healthcare, finance, legal, and human resources domains, and simplifying tasks like medical research, financial analysis, and sentiment analysis. To avoid the high costs of building and maintaining NLP infrastructure, companies turn to Cloud NLP services offered by major cloud providers
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Enhanced detection of unknown defect patterns on wafer bin maps based on an open-set recognition approach Comput. Ind. (IF 8.2) Pub Date : 2024-11-14 Jin-Su Shin, Min-Joo Kim, Beom-Seok Kim, Dong-Hee Lee
It is crucial to detect and classify defect patterns on wafers in semiconductor-manufacturing processes for wafer-quality management and prompt analysis of defect causes. In recent years, continuous technological innovation and advancements in semiconductor-industry processes have led to an increase in unknown defect patterns, which must be detected and classified. However, detection of unknown defect
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Improving device access efficiency using a device protocol matching model Comput. Ind. (IF 8.2) Pub Date : 2024-11-12 Zheng Gao, Danfeng Sun, Kai Wang, Huifeng Wu
The connectivity of devices and systems in the Industrial Internet of Things (IIoT) enables interoperability and collaboration between industrial systems. Device access is the pathway to achieve connectivity, while protocol matching is the basis for device access. Protocol matching is a complex task due to the diverse range of device types, numerous protocols, the issues related to protocol privatization
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D3 framework: An evidence-based data-driven design framework for new product service development Comput. Ind. (IF 8.2) Pub Date : 2024-11-12 Boyeun Lee, Saeema Ahmed-Kristensen
Despite growing interest in the use of data for product and service development, a comprehensive understanding of how data is employed in the context of new product, service and product–service system development is lacking. With the aim of deepening understanding of data as a critical resource for generating value through new products and services, we conducted a systematic literature review, conceptualised
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MBGB-detector: A multi-branch gradient backhaul lightweight model for mini-LED surface defect detection Comput. Ind. (IF 8.2) Pub Date : 2024-11-11 Yuanda Lin, Shuwan Pan, Jie Yu, Yade Hong, Fuming Wang, Jianeng Tang, Lixin Zheng, Songyan Chen
To meet the growing demand for lightweight models and rapid defect detection in mini-light emitting diode (LED) chip manufacturing, we developed a highly efficient and lightweight multi-branch gradient backhaul (MBGB) block. Based on the MBGB block, a mini-LED surface defect detector was designed, which included an MBGB network (MBGB-net) for the backbone and an MBGB feature pyramid network (MBGB-FPN)
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Advancing photovoltaic system design: An enhanced social learning swarm optimizer with guaranteed stability Comput. Ind. (IF 8.2) Pub Date : 2024-11-08 Lingyun Deng, Sanyang Liu
Parameter estimation of photovoltaic (PV) models, mathematically, is a typical complicated nonlinear multimodal optimization problem with box constraints. Although various methodologies have been explored in the literature, their performance tends to be unstable owing to inadequate adaptability. In this paper, an enhanced social learning swarm optimizer (ESLPSO) is developed to achieve more reliable
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Battery testing ontology: An EMMO-based semantic framework for representing knowledge in battery testing and battery quality control Comput. Ind. (IF 8.2) Pub Date : 2024-11-08 Pierluigi Del Nostro, Gerhard Goldbeck, Ferry Kienberger, Manuel Moertelmaier, Andrea Pozzi, Nawfal Al-Zubaidi-R-Smith, Daniele Toti
The demand for advanced battery management systems (BMSs) and battery test procedures is growing due to the rising importance of electric vehicles (EVs) and energy storage systems. The diversity of battery types, chemistries and application scenarios presents challenges in designing and optimizing BMSs and determining optimal battery test strategies. To address these challenges, semantic web technologies
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A deep reinforcement learning approach for online and concurrent 3D bin packing optimisation with bin replacement strategies Comput. Ind. (IF 8.2) Pub Date : 2024-11-04 Y.P. Tsang, D.Y. Mo, K.T. Chung, C.K.M. Lee
In the realm of robotic palletisation, the quest for optimal space utilization remains vital but also presents a critical challenge, particularly due to the constraints of decision complexity and the need for real-time decision-making without complete prior information. The widely adopted rule-based heuristics approaches were ease to use, but failed to adapt dynamically to the complex and changing
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Estimation of coal dust parameters via an effective image-based deep learning model Comput. Ind. (IF 8.2) Pub Date : 2024-11-01 Zheng Wang, Shukai Yang, Jiaxing Zhang, Zhaoxiang Ji
In high-pressure transportation, characterizing the leakage status of coal dust is an effective means to reduce potential safety hazards in the optimization of energy structures, and it is also conducive to disaster prevention and safety management. Given the existing methods, manual inspection of leakage points requires high measurement skills, entails significant maintenance costs, and is time-consuming
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Developing a BIM-enabled robotic manufacturing framework to facilitate mass customization of prefabricated buildings Comput. Ind. (IF 8.2) Pub Date : 2024-10-30 Saeid Metvaei, Kamyab Aghajamali, Qian Chen, Zhen Lei
Industrialized construction has been accepted as an effective production method for building project stakeholders to improve installation quality. Recent advancements in industrialized construction have focused on parametric designs for manufacturing and assembly to ensure accurate information flows and workflows across different project stages, however, they have not adequately addressed the challenges
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Leveraging asymmetric price limits for financial stability in industrial applications: An agent-based model Comput. Ind. (IF 8.2) Pub Date : 2024-10-30 Xinhui Yang, Jie Zhang, Qing Ye, Victor Chang
How to upgrade business processes to improve production efficiency is an ongoing concern in industrial research. While previous studies have extensively examined various prioritization schemes at each stage of the business process, there has been a lack of investigation into the financial resources required for their implementation. The attainment of sufficient and stable financial support necessitates
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A deep transfer learning model for online monitoring of surface roughness in milling with variable parameters Comput. Ind. (IF 8.2) Pub Date : 2024-10-24 Kai Zhou, Pingfa Feng, Feng Feng, Haowen Ma, Nengsheng Kang, Jianjian Wang
Surface roughness is crucial for the functional and aesthetic properties of mechanical components and must be carefully controlled during machining. However, predicting it under varying machining parameters is challenging due to limited experimental data and fluctuating factors like tool wear and vibration. This study develops a deep transfer learning model that incorporates the correlation alignment
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A novel FuseDecode Autoencoder for industrial visual inspection: Incremental anomaly detection improvement with gradual transition from unsupervised to mixed-supervision learning with reduced human effort Comput. Ind. (IF 8.2) Pub Date : 2024-10-23 Nejc Kozamernik, Drago Bračun
The industrial implementation of automated visual inspection leveraging deep learning is limited due to the labor-intensive labeling of datasets and the lack of datasets containing images of defects, which is especially the case in high-volume manufacturing with zero defect constraints. In this study, we present the FuseDecode Autoencoder (FuseDecode AE), a novel reconstruction-based anomaly detection
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Rapid quality control for recycled coarse aggregates (RCA) streams: Multi-sensor integration for advanced contaminant detection Comput. Ind. (IF 8.2) Pub Date : 2024-10-09 Cheng Chang, Francesco Di Maio, Rajeev Bheemireddy, Perry Posthoorn, Abraham T. Gebremariam, Peter Rem
Recycling coarse aggregates from construction and demolition waste is essential for sustainable construction practices. However, the quality of recycled coarse aggregates (RCA) often fluctuates significantly, in contrast to the more stable quality of natural aggregates. Contaminants in RCA notably compromise its quality and usability. Therefore, automating the quality control of RCA is necessary for
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Apple varieties and growth prediction with time series classification based on deep learning to impact the harvesting decisions Comput. Ind. (IF 8.2) Pub Date : 2024-09-28 Mustafa Mhamed, Zhao Zhang, Wanjia Hua, Liling Yang, Mengning Huang, Xu Li, Tiecheng Bai, Han Li, Man Zhang
Apples are among the most popular fruits globally due to their health and nutritional benefits for humans. Artificial intelligence in agriculture has advanced, but vision, which improves machine efficiency, speed, and production, still needs to be improved. Managing apple development from planting to harvest affects productivity, quality, and economics. In this study, by establishing a vision system
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Maximum subspace transferability discriminant analysis: A new cross-domain similarity measure for wind-turbine fault transfer diagnosis Comput. Ind. (IF 8.2) Pub Date : 2024-09-27 Quan Qian, Fei Wu, Yi Wang, Yi Qin
In the field of fault transfer diagnosis, many approaches only focus on the distribution alignment and knowledge transfer between the source domain and target domain. However, most of these approaches ignore the precondition of whether this transfer task is transferable. Current mainstream transferability discrimination methods heavily depend on expert knowledge and are extremely vulnerable to the
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Video-based automatic people counting for public transport: On-bus versus off-bus deployment Comput. Ind. (IF 8.2) Pub Date : 2024-09-26 Chris McCarthy, Hadi Ghaderi, Felip Martí, Prem Jayaraman, Hussein Dia
Interest in Automatic People Counting (APC) for crowd detection and management is rapidly growing. While a range of Internet of Things (IoT) sensors and systems exist, video analytics is emerging as a particularly attractive option — especially for applications where more traditional methods of people counting are not available, unreliable or expensive. In this paper we focus on automatic people counting
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Dual channel visible graph convolutional neural network for microleakage monitoring of pipeline weld homalographic cracks Comput. Ind. (IF 8.2) Pub Date : 2024-09-26 Jing Huang, Zhifen Zhang, Rui Qin, Yanlong Yu, Yongjie Li, Quanning Xu, Ji Xing, Guangrui Wen, Wei Cheng, Xuefeng Chen
When using a single sensor to monitor early microleakage of nuclear power pressure pipeline leakage, there are problems such as low monitoring accuracy and poor early warning reliability due to the limitations of the monitoring range and weak difference between the leakage signals. To address these challenges, this paper proposes a dual channel visible graph convolutional neural network (DCV-GCN).
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TDAD: Self-supervised industrial anomaly detection with a two-stage diffusion model Comput. Ind. (IF 8.2) Pub Date : 2024-09-26 Changyun Wei, Hui Han, Yu Xia, Ze Ji
Visual anomaly detection has emerged as a highly applicable solution in practical industrial manufacturing, owing to its notable effectiveness and efficiency. However, it also presents several challenges and uncertainties. To address the complexity of anomaly types and the high cost associated with data annotation, this paper introduces a self-supervised learning framework called TDAD, based on a two-stage
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A novel anomaly detection method for magnetic flux leakage signals via a feature-based unsupervised detection network Comput. Ind. (IF 8.2) Pub Date : 2024-09-25 He Zhao, Jinhai Liu, Qiannan Wang, Xiangkai Shen, Lin Jiang
High-precision anomaly detection, as the key technology of magnetic flux leakage (MFL) signal detection, is a challenging task. It is difficult to detect anomalies in MFL signals due to the variety of anomalies and the characteristics of the anomalies are easily submerged in the variation of the natural signals. To address the above issues, a feature-based unsupervised detection network (FUDet) is
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Extended realities and discrete events simulations: A systematic review to define design trade-offs and directions Comput. Ind. (IF 8.2) Pub Date : 2024-09-24 Giulia Wally Scurati, Francesco Ferrise, Marco Bertoni
Extended Reality (XR) technologies are increasingly popular to support the engagement of different audiences and stakeholders with Discrete Event Simulations (DES) due to their capability to deliver more accessible visual and immersive experiences. XR applications can be developed either using modules integrated into DES software or game engines, providing different sets of opportunities in the environment
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Development of immersive bridge digital twin platform to facilitate bridge damage assessment and asset model updates Comput. Ind. (IF 8.2) Pub Date : 2024-09-23 Muhammad Fawad, Marek Salamak, Qian Chen, Mateusz Uscilowski, Kalman Koris, Marcin Jasinski, Piotr Lazinski, Dawid Piotrowski
Conventional infrastructure asset management practices have heavily relied on static data collection and suffered from decision lags. Though advanced Structural Health Monitoring (SHM) systems were extensively explored based on multi-functional sensor deployment, asset model updating has not been achieved to facilitate timely and effective decision-making of infrastructure managers due to a lack of
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Product digital twins: An umbrella review and research agenda for understanding their value Comput. Ind. (IF 8.2) Pub Date : 2024-09-21 Francisco Gomez Medina, Veronica Martinez Hernandez
Product Digital Twins (DTs) are digital representations of a physical asset that update synchronously throughout its lifecycle. Over the past decade, a rich and varied literature on the development of new technologies and approaches to implementing product DTs has emerged. This literature has been reviewed multiple times, but the variety in focus and scope of DT reviews has become so extensive that
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Automated corner grading of trading cards: Defect identification and confidence calibration through deep learning Comput. Ind. (IF 8.2) Pub Date : 2024-09-19 Lutfun Nahar, Md. Saiful Islam, Mohammad Awrangjeb, Rob Verhoeve
This research focuses on trading card quality inspection, where defects have a significant effect on both the quality inspection and grading. The present inspection procedure is subjective which means the grading is sensitive to mistakes made by individuals. To address this, a deep neural network based on transfer learning for automated defect detection is proposed with a particular emphasis on corner
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Decomposing maintenance actions into sub-tasks using natural language processing: A case study in an Italian automotive company Comput. Ind. (IF 8.2) Pub Date : 2024-09-17 Vito Giordano, Gualtiero Fantoni
Industry 4.0 has led to a huge increase in data coming from machine maintenance. At the same time, advances in Natural Language Processing (NLP) and Large Language Models provide new ways to analyse this data. In our research, we use NLP to analyse maintenance work orders, and specifically the descriptions of failures and the corresponding repair actions. Many NLP studies have focused on failure descriptions
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Multi-granularity spatiotemporal object modelling of waterborne traffic elements Comput. Ind. (IF 8.2) Pub Date : 2024-09-17 Xiaodong Cheng, Yuanqiao Wen, Zhongyi Sui, Liang Huang, He Lin
The electronic navigational charts are crucial carriers for representing the multi-source heterogeneous data of Waterborne Traffic Elements (WTEs). However, their layer-based modelling method has some shortcomings in expressing the multi-granularity features, complex relationships, and dynamic evolution of elements. This paper proposes an objectification modelling method for WTEs based on the concept
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FedCPG: A class prototype guided personalized lightweight federated learning framework for cross-factory fault detection Comput. Ind. (IF 8.2) Pub Date : 2024-09-16 Haodong Li, Xingwei Wang, Peng Cao, Ying Li, Bo Yi, Min Huang
Industrial equipment condition monitoring and fault detection are crucial to ensure the reliability of industrial production. Recently, data-driven fault detection methods have achieved significant success, but they all face challenges due to data fragmentation and limited fault detection capabilities. Although centralized data collection can improve detection accuracy, the conflicting interests brought
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A technical patent map construction method and system based on multi-dimensional technical feature extraction Comput. Ind. (IF 8.2) Pub Date : 2024-09-12 Chuanxiao Li, Wenqiang Li, Hai Xiang, Yida Hong
A patent map is widely used in the field of technical information mining, which can support tasks such as detecting patent vacuums and predicting technical trends. However, existing patent map construction methods have the limitations of insufficient intelligence and accuracy in mining patent technical features, which leads to failure to effectively complete the above tasks. To address these limitations
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Virtual warehousing through digitalized inventory and on-demand manufacturing: A case study Comput. Ind. (IF 8.2) Pub Date : 2024-09-11 Elham Sharifi, Atanu Chaudhuri, Saeed D. Farahani, Lasse G. Staal, Brian Vejrum Waehrens
Novel digital on-demand manufacturing technologies provide a significant opportunity to support development of virtual warehousing and in turn improve supply chain performance. However, the implementation of virtual warehouse comes with a set of challenges, especially where the objective is to virtually warehouse standard or legacy parts that have been developed and verified initially for conventional
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Remaining useful life prediction model of cross-domain rolling bearing via dynamic hybrid domain adaptation and attention contrastive learning Comput. Ind. (IF 8.2) Pub Date : 2024-09-10 Xingchi Lu, Xuejian Yao, Quansheng Jiang, Yehu Shen, Fengyu Xu, Qixin Zhu
Performance degradation and remaining useful life (RUL) prediction are of great significance in improving the reliability of mechanical equipment. Existing cross-domain RUL prediction methods usually reduce data distribution discrepancy by domain adaptation, to overcome domain shift under cross-domain conditions. However, the fine-grained information between cross-domain degradation features and the
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Learning 3D human–object interaction graphs from transferable context knowledge for construction monitoring Comput. Ind. (IF 8.2) Pub Date : 2024-09-10 Liuyue Xie, Shreyas Misra, Nischal Suresh, Justin Soza-Soto, Tomotake Furuhata, Kenji Shimada
We propose a novel framework for detecting 3D human–object interactions (HOI) in construction sites and a toolkit for generating construction-related human–object interaction graphs. Computer vision methods have been adopted for construction site safety surveillance in recent years. The current computer vision methods rely on videos and images, with which safety verification is performed on common-sense
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Operational process monitoring: An object-centric approach Comput. Ind. (IF 8.2) Pub Date : 2024-09-10 Gyunam Park, Wil M.P. van der Aalst
In business processes, an operational problem refers to a deviation and an inefficiency that prohibits an organization from reaching its goals, e.g., a delay in approving a purchase order in a Procure-To-Pay (P2P) process. Operational process monitoring aims to assess the occurrence of such operational problems by analyzing event data that record the execution of business processes. Once the problems
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Detecting coagulation time in cheese making by means of computer vision and machine learning techniques Comput. Ind. (IF 8.2) Pub Date : 2024-09-09 Andrea Loddo, Cecilia Di Ruberto, Giuliano Armano, Andrea Manconi
Cheese production, a globally cherished culinary tradition, faces challenges in ensuring consistent product quality and production efficiency. The critical phase of determining cutting time during curd formation significantly influences cheese quality and yield. Traditional methods often struggle to address variability in coagulation conditions, particularly in small-scale factories. In this paper