Pub Date : 2025-11-09DOI: 10.1016/j.jii.2025.101009
Hongyi Qu , Luo Fang , Jinbiao Tan
In the process of multi-cavity hot runner injection molding, the issue of mold filling imbalance caused by uneven temperature distribution significantly affects the quality of precision products such as optical lenses. Traditional methods primarily rely on mold thermal structure design and lack dynamic optimization strategies aimed at product quality. This paper proposes an embodied intelligent online optimization method integrated with digital twin technology, which fundamentally overcomes the limitations of traditional fixed-temperature control and offline optimization by enabling dynamic, data-driven adjustment of process parameters. By utilizing real-time process information from sensor readings within a batch, along with product quality data obtained through machine vision inspection after each batch, and employing a ‘mutual feedback’ sharing mechanism for multi-cavity process information, a ‘time-batch’ dual-scale real-time iterative learning and updating framework is established for the digital twin model. This approach enables closed-loop adaptive optimization of the mold filling state. Experimental results show that this method significantly outperforms traditional fixed temperature setting controls in terms of profile accuracy, offering an innovative solution for high-precision injection molding.
{"title":"An embodied intelligence-based online optimization methodology for injection molding process using multi-cavity hot-runner","authors":"Hongyi Qu , Luo Fang , Jinbiao Tan","doi":"10.1016/j.jii.2025.101009","DOIUrl":"10.1016/j.jii.2025.101009","url":null,"abstract":"<div><div>In the process of multi-cavity hot runner injection molding, the issue of mold filling imbalance caused by uneven temperature distribution significantly affects the quality of precision products such as optical lenses. Traditional methods primarily rely on mold thermal structure design and lack dynamic optimization strategies aimed at product quality. <em>This paper proposes an embodied intelligent online optimization method integrated with digital twin technology, which fundamentally overcomes the limitations of traditional fixed-temperature control and offline optimization by enabling dynamic, data-driven adjustment of process parameters</em>. By utilizing real-time process information from sensor readings within a batch, along with product quality data obtained through machine vision inspection after each batch, and employing a ‘mutual feedback’ sharing mechanism for multi-cavity process information, a ‘time-batch’ dual-scale real-time iterative learning and updating framework is established for the digital twin model. This approach enables closed-loop adaptive optimization of the mold filling state. Experimental results show that this method significantly outperforms traditional fixed temperature setting controls in terms of profile accuracy, offering an innovative solution for high-precision injection molding.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"49 ","pages":"Article 101009"},"PeriodicalIF":10.4,"publicationDate":"2025-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145473276","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}
Pub Date : 2025-11-05DOI: 10.1016/j.jii.2025.101007
R. Gowthamani , S. Oswalt Manoj
Internet of Things (IoT) health systems have severe issues distinguishing malicious from legitimate traffic and ensuring secure and efficient data transmission for real-time patient care. Existing solutions have high complexity, low dynamic attack adaptability, and low encryption strength. For the purpose of solving these problems, this study suggests a security-enhanced intelligent edge computing system that involves Normalized Distance-Based Encoding (NDBE) for effective feature extraction, Adaptive Layout Decomposition with Graph Embedding Neural Networks (ADGENN) for malicious data identification, Musical Chairs Optimization Algorithm (MCOA) for adaptive hyperparameter tuning, and a novel Light-weight Dynamic Elliptic Curve Cryptography with Schoof's Algorithm (LDECCSA) for data encryption protection. Together, these modules enhance classification efficiency, reduce computational costs, and facilitate low-latency, safe communication. Evaluated on the ToN-IoT and CICIoMT2024 dataset, the system achieves up to 99.87 % accuracy, 97 % throughput, and a low latency of 1.2 s, which performs better than current cutting-edge solutions by a large margin. The significance of this work is that it has the capacity to handle some of the most significant issues in healthcare. Systems are currently confronting, wherein IoT devices and edge computing have taken patient tracking to a new height, but also created gargantuan challenges such as cyberattacks, data breaches, and performance congestion. The major novelties are the application of NDBE for pre-processing network traffic, dynamic graph-based classification through ADGENN, resource-aware optimization through MCOA, and light-weighted, secure ECC with dynamic curve generation. While the model shows better efficiency and resilience, its dependence on pre-labeled datasets might restrict flexibility towards unknown real-world threats, and resource-limited IoT devices might struggle with heavy computation. In summary, the framework offers a real-world, scalable solution for real-time threat identification, secure data transfer, and effective healthcare surveillance in an IoT-based, cutting-edge healthcare environment.
{"title":"A Novel graph-embedded musical chairs optimization with secure elliptic encryption framework for intelligent edge computing in healthcare iot networks","authors":"R. Gowthamani , S. Oswalt Manoj","doi":"10.1016/j.jii.2025.101007","DOIUrl":"10.1016/j.jii.2025.101007","url":null,"abstract":"<div><div>Internet of Things (IoT) health systems have severe issues distinguishing malicious from legitimate traffic and ensuring secure and efficient data transmission for real-time patient care. Existing solutions have high complexity, low dynamic attack adaptability, and low encryption strength. For the purpose of solving these problems, this study suggests a security-enhanced intelligent edge computing system that involves Normalized Distance-Based Encoding (NDBE) for effective feature extraction, Adaptive Layout Decomposition with Graph Embedding Neural Networks (ADGENN) for malicious data identification, Musical Chairs Optimization Algorithm (MCOA) for adaptive hyperparameter tuning, and a novel Light-weight Dynamic Elliptic Curve Cryptography with Schoof's Algorithm (LDECCSA) for data encryption protection. Together, these modules enhance classification efficiency, reduce computational costs, and facilitate low-latency, safe communication. Evaluated on the ToN-IoT and CICIoMT2024 dataset, the system achieves up to 99.87 % accuracy, 97 % throughput, and a low latency of 1.2 s, which performs better than current cutting-edge solutions by a large margin. The significance of this work is that it has the capacity to handle some of the most significant issues in healthcare. Systems are currently confronting, wherein IoT devices and edge computing have taken patient tracking to a new height, but also created gargantuan challenges such as cyberattacks, data breaches, and performance congestion. The major novelties are the application of NDBE for pre-processing network traffic, dynamic graph-based classification through ADGENN, resource-aware optimization through MCOA, and light-weighted, secure ECC with dynamic curve generation. While the model shows better efficiency and resilience, its dependence on pre-labeled datasets might restrict flexibility towards unknown real-world threats, and resource-limited IoT devices might struggle with heavy computation. In summary, the framework offers a real-world, scalable solution for real-time threat identification, secure data transfer, and effective healthcare surveillance in an IoT-based, cutting-edge healthcare environment.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"49 ","pages":"Article 101007"},"PeriodicalIF":10.4,"publicationDate":"2025-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145447594","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}
Pub Date : 2025-11-01DOI: 10.1016/j.jii.2025.100988
Da Long, Sheng Yang
Fatigue assessment based on human motion plays a critical role in human-centric intelligent manufacturing, intelligent monitoring, and ergonomics. This growing demand underscores the need for low-cost, high-precision pose estimation techniques with broad application adaptability. To meet these requirements, we propose LightPose, a lightweight human pose estimation framework guided by bone segment principles. LightPose is designed to balance spatial accuracy with computational efficiency, delivering pose quality comparable to recent sequence-based baselines while remaining lightweight enough for real-time, fatigue-aware analysis. The framework incorporates a dual-stream supervision mechanism that enforces local geometric consistency through mutual prediction between joint pairs on the same bone segment. Additionally, kinematic constraints and fatigue-relevant metric regulations are embedded within the training objective, promoting biomechanical plausibility and alignment with fatigue-related motion patterns. Experimental results on standard 3D pose estimation benchmarks demonstrate that LightPose delivers competitive accuracy with reduced computational cost. Further evaluations confirm its effectiveness in estimating fatigue-related kinematic indicators, establishing its suitability for fatigue detection tasks. By effectively bridging efficiency and biomechanical relevance, LightPose presents a promising front-end solution for fatigue-aware motion analysis in manufacturing settings.
{"title":"LightPose: A lightweight fatigue-aware pose estimation framework","authors":"Da Long, Sheng Yang","doi":"10.1016/j.jii.2025.100988","DOIUrl":"10.1016/j.jii.2025.100988","url":null,"abstract":"<div><div>Fatigue assessment based on human motion plays a critical role in human-centric intelligent manufacturing, intelligent monitoring, and ergonomics. This growing demand underscores the need for low-cost, high-precision pose estimation techniques with broad application adaptability. To meet these requirements, we propose <em>LightPose</em>, a lightweight human pose estimation framework guided by bone segment principles. <em>LightPose</em> is designed to balance spatial accuracy with computational efficiency, delivering pose quality comparable to recent sequence-based baselines while remaining lightweight enough for real-time, fatigue-aware analysis. The framework incorporates a dual-stream supervision mechanism that enforces local geometric consistency through mutual prediction between joint pairs on the same bone segment. Additionally, kinematic constraints and fatigue-relevant metric regulations are embedded within the training objective, promoting biomechanical plausibility and alignment with fatigue-related motion patterns. Experimental results on standard 3D pose estimation benchmarks demonstrate that <em>LightPose</em> delivers competitive accuracy with reduced computational cost. Further evaluations confirm its effectiveness in estimating fatigue-related kinematic indicators, establishing its suitability for fatigue detection tasks. By effectively bridging efficiency and biomechanical relevance, <em>LightPose</em> presents a promising front-end solution for fatigue-aware motion analysis in manufacturing settings.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"48 ","pages":"Article 100988"},"PeriodicalIF":10.4,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145383881","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}
Pub Date : 2025-11-01DOI: 10.1016/j.jii.2025.100985
Wenyan Zhao, Yaguang Yuan, Cong Cheng, Wenheng Liu
The rapid advancement of e-commerce has driven unprecedented expansion in urban logistics networks, where their sustainability is constrained by multifaceted factors including strict time-bound service requirements, employee’s satisfaction, traffic congestion, and carbon emission regulations. Among these critical elements, employee’s satisfaction reflected by the workload balance not only influences task execution quality but also affects long-term operational sustainability for logistics enterprises, rendering its enhancement an urgent priority in contemporary urban logistics practices. This paper thus investigates a sustainable urban logistics vehicle routing problem mainly focusing on this perspective. Initially, a bi-objective mixed-integer programming model is formulated to simultaneously minimize total delivery cost and workload balance. Subsequently, a hybrid metaheuristic algorithm combining path relinking (PR) with multi-directional local search framework is developed. The adaptive large neighborhood search is adopted to facilitate the intensive local exploration, while PR techniques enhance global search capabilities through systematic solution space diversification. The algorithm's validity is rigorously verified through comparative analyses with state of art multi-objective optimization algorithms using adapted benchmark instances. Computational results demonstrate the algorithmic effectiveness and efficiency, accompanied by detailed analyses of approximate Pareto front and model’s sensitivity. These findings advance the field of urban delivery and provide practical insights for implementing efficient and sustainable urban logistic systems.
{"title":"Bi-objective sustainable urban logistics vehicle routing problem with workload balance","authors":"Wenyan Zhao, Yaguang Yuan, Cong Cheng, Wenheng Liu","doi":"10.1016/j.jii.2025.100985","DOIUrl":"10.1016/j.jii.2025.100985","url":null,"abstract":"<div><div>The rapid advancement of e-commerce has driven unprecedented expansion in urban logistics networks, where their sustainability is constrained by multifaceted factors including strict time-bound service requirements, employee’s satisfaction, traffic congestion, and carbon emission regulations. Among these critical elements, employee’s satisfaction reflected by the workload balance not only influences task execution quality but also affects long-term operational sustainability for logistics enterprises, rendering its enhancement an urgent priority in contemporary urban logistics practices. This paper thus investigates a sustainable urban logistics vehicle routing problem mainly focusing on this perspective. Initially, a bi-objective mixed-integer programming model is formulated to simultaneously minimize total delivery cost and workload balance. Subsequently, a hybrid metaheuristic algorithm combining path relinking (PR) with multi-directional local search framework is developed. The adaptive large neighborhood search is adopted to facilitate the intensive local exploration, while PR techniques enhance global search capabilities through systematic solution space diversification. The algorithm's validity is rigorously verified through comparative analyses with state of art multi-objective optimization algorithms using adapted benchmark instances. Computational results demonstrate the algorithmic effectiveness and efficiency, accompanied by detailed analyses of approximate Pareto front and model’s sensitivity. These findings advance the field of urban delivery and provide practical insights for implementing efficient and sustainable urban logistic systems.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"48 ","pages":"Article 100985"},"PeriodicalIF":10.4,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416550","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}
Pub Date : 2025-11-01DOI: 10.1016/j.jii.2025.101004
Yao Shan, Jindong Zhao, Yongchao Song, Haojun Teng, Wenhan Hou, Zhaowei Liu
Modern information and communication technologies have propelled transformative modernization of Industrial Control Systems (ICSs) while exacerbating cybersecurity risks. Federated Learning (FL) offers a privacy-preserving framework for collaborative development of intrusion detection models among distributed participants. However, its effectiveness is significantly limited by inherent model divergence caused by non-independent and identically distributed (Non-IID) data characteristics. Moreover, direct implementation of FL in ICS environments faces critical challenges due to insufficient capabilities in network traffic feature representation and device concealment. To address these challenges, we propose CoperFed, a covert personalized FL framework that generates unique intrusion detection models for individual participants. First, we developed Gicsmeter, a multi-dimensional ICS traffic representation tool for all participants, to enhance model performance at the data level. Second, we designed a personalized update algorithm based on key model parameters to improve collaboration among similar participants. By integrating global knowledge during model aggregation, this algorithm equips the model with local and global scenario detection capabilities. Finally, we designed a covert federated communication scheme for ICS that can effectively conceal the federated training process within regular ICS traffic and reduce the exposure risk of FL participants. Experiments show that CoperFed outperforms baseline methods in intrusion detection and robustness and can effectively divert attackers’ attention from FL participants.
{"title":"CoperFed: A covert personalized federated learning framework for Industrial Control Systems intrusion detection","authors":"Yao Shan, Jindong Zhao, Yongchao Song, Haojun Teng, Wenhan Hou, Zhaowei Liu","doi":"10.1016/j.jii.2025.101004","DOIUrl":"10.1016/j.jii.2025.101004","url":null,"abstract":"<div><div>Modern information and communication technologies have propelled transformative modernization of Industrial Control Systems (ICSs) while exacerbating cybersecurity risks. Federated Learning (FL) offers a privacy-preserving framework for collaborative development of intrusion detection models among distributed participants. However, its effectiveness is significantly limited by inherent model divergence caused by non-independent and identically distributed (Non-IID) data characteristics. Moreover, direct implementation of FL in ICS environments faces critical challenges due to insufficient capabilities in network traffic feature representation and device concealment. To address these challenges, we propose CoperFed, a covert personalized FL framework that generates unique intrusion detection models for individual participants. First, we developed Gicsmeter, a multi-dimensional ICS traffic representation tool for all participants, to enhance model performance at the data level. Second, we designed a personalized update algorithm based on key model parameters to improve collaboration among similar participants. By integrating global knowledge during model aggregation, this algorithm equips the model with local and global scenario detection capabilities. Finally, we designed a covert federated communication scheme for ICS that can effectively conceal the federated training process within regular ICS traffic and reduce the exposure risk of FL participants. Experiments show that CoperFed outperforms baseline methods in intrusion detection and robustness and can effectively divert attackers’ attention from FL participants.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"48 ","pages":"Article 101004"},"PeriodicalIF":10.4,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145461741","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}
The safety of helicopter operations is paramount, yet early signs of potential failures often go undetected, highlighting the need for robust signal alert systems during flights. Detecting anomalies in helicopter engine behavior through vibration analysis is critically important due to the long-sequence nature and complexity of the data, which present significant challenges for real-time assessment and are not adequately addressed by traditional methods such as preset thresholds or basic statistical models, as these approaches struggle to capture intricate spatiotemporal dependencies and overlapping fault patterns in real-world scenarios. To address these challenges, we introduce a novel hybrid model that leverages Empirical Mode Decomposition (EMD) for signal decomposition and analysis, effectively overcoming the limitations of traditional approaches. EMD is particularly advantageous as it decomposes complex signals into Intrinsic Mode Functions (IMFs), enabling more accurate anomaly detection in long sequences. Following EMD, the Gaussian Mixture Model (GMM) is employed to precisely recognize various fault patterns, ensuring a robust foundation for anomaly detection. Bidirectional Long Short-Term Memory (BiLSTM) networks further enhance the model by capturing temporal dependencies in both directions, integrating critical spatiotemporal information and improving predictive accuracy. Experimental results demonstrate that this integrated EMD-GMM-BiLSTM approach is not only highly sensitive and accurate in detecting anomalies but also significantly simpler and more efficient than more complex frameworks such as encoder-decoder models or Transformers. This method ensures the operational safety of helicopters and supports the broader adoption of low-altitude economic activities by providing essential safety guarantees.
{"title":"An EMD-based forecasting framework integrating GMM and BiLSTM for helicopter engine anomaly detection","authors":"Qi Shen , Jingwei Guo , Yihui Tian , Zhen-Song Chen","doi":"10.1016/j.jii.2025.101003","DOIUrl":"10.1016/j.jii.2025.101003","url":null,"abstract":"<div><div>The safety of helicopter operations is paramount, yet early signs of potential failures often go undetected, highlighting the need for robust signal alert systems during flights. Detecting anomalies in helicopter engine behavior through vibration analysis is critically important due to the long-sequence nature and complexity of the data, which present significant challenges for real-time assessment and are not adequately addressed by traditional methods such as preset thresholds or basic statistical models, as these approaches struggle to capture intricate spatiotemporal dependencies and overlapping fault patterns in real-world scenarios. To address these challenges, we introduce a novel hybrid model that leverages Empirical Mode Decomposition (EMD) for signal decomposition and analysis, effectively overcoming the limitations of traditional approaches. EMD is particularly advantageous as it decomposes complex signals into Intrinsic Mode Functions (IMFs), enabling more accurate anomaly detection in long sequences. Following EMD, the Gaussian Mixture Model (GMM) is employed to precisely recognize various fault patterns, ensuring a robust foundation for anomaly detection. Bidirectional Long Short-Term Memory (BiLSTM) networks further enhance the model by capturing temporal dependencies in both directions, integrating critical spatiotemporal information and improving predictive accuracy. Experimental results demonstrate that this integrated EMD-GMM-BiLSTM approach is not only highly sensitive and accurate in detecting anomalies but also significantly simpler and more efficient than more complex frameworks such as encoder-decoder models or Transformers. This method ensures the operational safety of helicopters and supports the broader adoption of low-altitude economic activities by providing essential safety guarantees.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"48 ","pages":"Article 101003"},"PeriodicalIF":10.4,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145383754","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}
Pub Date : 2025-11-01DOI: 10.1016/j.jii.2025.100991
Songshou Dong , Yanqing Yao , Huaxiong Wang
Smart grids (SGs) can greatly improve the efficiency, reliability, and sustainability of traditional grids. In an industrial SG, real-time user-side metering data may be frequently collected for monitoring and controlling electricity consumption. To reduce the burden on SGs, most existing privacy-preserving schemes use aggregated signatures to ensure the integrity of the message and improve communication efficiency. In CRYPTO ’24, Marius et al. proposed an aggregating Falcon signature scheme LaBRADOR, which is a trapdoor-based lattice signature scheme. Currently, there are two types of lattice-based signature schemes: one is a trapdoor-based signature scheme, and the other is a Fiat-Shamir-based signature scheme. There is currently no particularly efficient Fiat-Shamir-based lattice-based aggregate signature scheme. Therefore, we construct an aggregate signature scheme with constant signature size without rejection sampling under the Fiat-Shamir style based on the G+G lattice signature (ASIACRYPT ’23) and the intersection method (EUROCRYPT ’11). In addition, we make our scheme certificateless to resist malicious key generation centers and the key escrow problem, and apply our scheme to SGs. Compared with other schemes, our signature scheme has a smaller aggregated signature size (any number of signatures), less signature time, and is more secure. Finally, we demonstrate that our scheme is existentially unforgeable in the context of adaptive chosen message attacks against type I and type II adversaries in the random oracle model.
{"title":"A Certificateless Aggregate G+G Signature Scheme with Intersection Method for Efficiency Improvement in Smart Grids","authors":"Songshou Dong , Yanqing Yao , Huaxiong Wang","doi":"10.1016/j.jii.2025.100991","DOIUrl":"10.1016/j.jii.2025.100991","url":null,"abstract":"<div><div>Smart grids (SGs) can greatly improve the efficiency, reliability, and sustainability of traditional grids. In an industrial SG, real-time user-side metering data may be frequently collected for monitoring and controlling electricity consumption. To reduce the burden on SGs, most existing privacy-preserving schemes use aggregated signatures to ensure the integrity of the message and improve communication efficiency. In CRYPTO ’24, Marius et al. proposed an aggregating Falcon signature scheme LaBRADOR, which is a trapdoor-based lattice signature scheme. Currently, there are two types of lattice-based signature schemes: one is a trapdoor-based signature scheme, and the other is a Fiat-Shamir-based signature scheme. There is currently no particularly efficient Fiat-Shamir-based lattice-based aggregate signature scheme. Therefore, we construct an aggregate signature scheme with constant signature size without rejection sampling under the Fiat-Shamir style based on the G+G lattice signature (ASIACRYPT ’23) and the intersection method (EUROCRYPT ’11). In addition, we make our scheme certificateless to resist malicious key generation centers and the key escrow problem, and apply our scheme to SGs. Compared with other schemes, our signature scheme has a smaller aggregated signature size (any number of signatures), less signature time, and is more secure. Finally, we demonstrate that our scheme is existentially unforgeable in the context of adaptive chosen message attacks against type I and type II adversaries in the random oracle model.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"48 ","pages":"Article 100991"},"PeriodicalIF":10.4,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145383758","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}
Pub Date : 2025-11-01DOI: 10.1016/j.jii.2025.100997
Le Chen , Ligang Wu , Qichao Ren
To address the challenges of low detection precision for lump coal on underground coal mine conveyor belts, this study proposes an intelligent detection method based on multimodal data fusion. The method is named YOLO DKH (YOLO Dynamic Snake Attention-KANA2-High-level Screening Feature Pyramid Network). This approach specifically targets the insufficient robustness of single-modal data under dust interference and varying lighting conditions in complex underground environments. First, a Deformable Spatial Attention (DSA) mechanism is designed, utilizing strip-shaped deformable convolution kernels along the x- and y-axes for feature extraction, which achieves adaptive geometric learning and reduces computational complexity simultaneously. Second, the KANA2 dual-attention mechanism is proposed by combining regional attention with the KAN Conv module. Through B-spline smoothing and dual-branch processing, computational complexity is reduced, enhancing the fusion effect of RGB-infrared multimodal features. Then, a High-frequency Spatial Feature Pyramid Network (HSFPN) was constructed by integrating high-frequency perception modules and spatial dependency perception mechanisms to enhance multi-scale feature fusion by filtering out low-frequency background interference and capturing pixel-level spatial relationships. Finally, a comprehensive multi-modal RGB-infrared dataset comprising 9250 annotated images and 14,840 bounding boxes was constructed to provide a standardized benchmark for the development and validation of lump coal detection algorithms. The experimental results showed that the YOLO DKH model achieved 79.1 %, 74.3 %, and 77.2 % precision, recall, and [email protected], respectively, representing improvements of 6.03 %, 7.06 %, and 5.18 % compared to the baseline YOLOv11n model, while reducing the number of parameters by 2.71 %. and a 25.9 % reduction in single-image processing time to 6.1 milliseconds, providing an efficient and reliable technical solution for lump coal monitoring on underground conveyor belts in intelligent manufacturing.
{"title":"A multimodal data fusion-based intelligent detection method for lump coal on underground conveyor belts in smart manufacturing","authors":"Le Chen , Ligang Wu , Qichao Ren","doi":"10.1016/j.jii.2025.100997","DOIUrl":"10.1016/j.jii.2025.100997","url":null,"abstract":"<div><div>To address the challenges of low detection precision for lump coal on underground coal mine conveyor belts, this study proposes an intelligent detection method based on multimodal data fusion. The method is named YOLO DKH (YOLO Dynamic Snake Attention-KANA<sup>2</sup>-High-level Screening Feature Pyramid Network). This approach specifically targets the insufficient robustness of single-modal data under dust interference and varying lighting conditions in complex underground environments. First, a Deformable Spatial Attention (DSA) mechanism is designed, utilizing strip-shaped deformable convolution kernels along the x- and y-axes for feature extraction, which achieves adaptive geometric learning and reduces computational complexity simultaneously. Second, the KANA<sup>2</sup> dual-attention mechanism is proposed by combining regional attention with the KAN Conv module. Through B-spline smoothing and dual-branch processing, computational complexity is reduced, enhancing the fusion effect of RGB-infrared multimodal features. Then, a High-frequency Spatial Feature Pyramid Network (HSFPN) was constructed by integrating high-frequency perception modules and spatial dependency perception mechanisms to enhance multi-scale feature fusion by filtering out low-frequency background interference and capturing pixel-level spatial relationships. Finally, a comprehensive multi-modal RGB-infrared dataset comprising 9250 annotated images and 14,840 bounding boxes was constructed to provide a standardized benchmark for the development and validation of lump coal detection algorithms. The experimental results showed that the YOLO DKH model achieved 79.1 %, 74.3 %, and 77.2 % precision, recall, and [email protected], respectively, representing improvements of 6.03 %, 7.06 %, and 5.18 % compared to the baseline YOLOv11n model, while reducing the number of parameters by 2.71 %. and a 25.9 % reduction in single-image processing time to 6.1 milliseconds, providing an efficient and reliable technical solution for lump coal monitoring on underground conveyor belts in intelligent manufacturing.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"48 ","pages":"Article 100997"},"PeriodicalIF":10.4,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145383455","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}
Pub Date : 2025-11-01DOI: 10.1016/j.jii.2025.100957
Yuk Ming Tang , Andrew W.H. Ip , Kai Leung Yung , Zhuming Bi , Zhili Sun
{"title":"Special issue on “Industrial information integration in space informatics”","authors":"Yuk Ming Tang , Andrew W.H. Ip , Kai Leung Yung , Zhuming Bi , Zhili Sun","doi":"10.1016/j.jii.2025.100957","DOIUrl":"10.1016/j.jii.2025.100957","url":null,"abstract":"","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"48 ","pages":"Article 100957"},"PeriodicalIF":10.4,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145094142","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}
Pub Date : 2025-11-01DOI: 10.1016/j.jii.2025.100993
Anderson Rogério Faia Pinto , Esra Boz , Rafael Henrique Faia Pinto , Marcelo Seido Nagano
This manuscript presents a literature review with a bibliometric analysis on the Order Batching Problem (OBP). The research analyzed Literature Reviews (30) and Picking Optimization Methods (138). Most approaches focus on hypothetical warehouses with static (offline) orders and are configured as the classical OBP. These warehouses feature rectangular layouts (single-block and parallel aisles) with low-level picker-to-parts systems and one Pick-up and Drop-off. Most effective solutions have emerged from metaheuristics in conjunction with constructive heuristics, and the most frequently utilized techniques are the Genetic Algorithm and Variable Neighborhood Search. The main performance indicators are the Total Picking Time, the Total Routing Distance, and the Computational Processing Time. The bibliometric analyses encompassed Journals (77), Universities (169), and Researchers (331). Most publications originate from journals in Europe and North America. The countries with the highest concentration of universities and researchers are the United States and China. Nevertheless, authorship analysis shows that China and Germany outperform the United States. The continents with the largest number of researchers are Asia and Europe. However, a ranking by authorship reveals that the researchers with the most publications are from Europe and South America. This manuscript presents the state of the art, demonstrates advancements in the field, identifies research interests, examines customer service level requirements and warehouse efficiency, and addresses the gap for more comprehensive bibliometric analyses on OBP. Formulating Picking Optimization Methods better adapted and capable of addressing real-world trade-offs constitutes the primary challenge and the most promising future approaches for the OBP.
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