Pub Date : 2025-11-18DOI: 10.1016/j.jii.2025.101014
Zongyuan Wang , Xin Zhou , Jianliang Mao , Chuanlin Zhang , Chenggang Cui , Jun Yang
Manually designing robotic task sequences is labor intensive and inefficient, especially in power inspection tasks that involve academic background knowledge and complex operation rules. To overcome this limitation, this paper presents a large language model-based multi-agent task and motion planning framework, LLM-MTMP, to enable autonomous human–robot interaction and task execution of robot in power inspection scenarios. It combines enhanced resource generation technology with a specific knowledge base in the field of power inspection, converting and decomposing natural language into a set of operation sequences that are readable by robots, thereby enabling autonomous inspection operations that meet specific industrial requirements. Experimental results from physical deployments on robotic platforms demonstrate that LLM-MTMP significantly improves task generation success rates and expands operational adaptability compared to baseline methods, highlighting its practical value for industrial applications.
{"title":"LLM-MTMP: A large language model-based multi-agent task and motion planning framework for power inspection robots","authors":"Zongyuan Wang , Xin Zhou , Jianliang Mao , Chuanlin Zhang , Chenggang Cui , Jun Yang","doi":"10.1016/j.jii.2025.101014","DOIUrl":"10.1016/j.jii.2025.101014","url":null,"abstract":"<div><div>Manually designing robotic task sequences is labor intensive and inefficient, especially in power inspection tasks that involve academic background knowledge and complex operation rules. To overcome this limitation, this paper presents a large language model-based multi-agent task and motion planning framework, LLM-MTMP, to enable autonomous human–robot interaction and task execution of robot in power inspection scenarios. It combines enhanced resource generation technology with a specific knowledge base in the field of power inspection, converting and decomposing natural language into a set of operation sequences that are readable by robots, thereby enabling autonomous inspection operations that meet specific industrial requirements. Experimental results from physical deployments on robotic platforms demonstrate that LLM-MTMP significantly improves task generation success rates and expands operational adaptability compared to baseline methods, highlighting its practical value for industrial applications.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"49 ","pages":"Article 101014"},"PeriodicalIF":10.4,"publicationDate":"2025-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145554068","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 : 2025-11-18DOI: 10.1016/j.jii.2025.101017
Zhengzhong Zheng , Shijiang Li , Liang Hou , Haojing Lin , Jiancheng Chen
Accurate bus mass estimation is crucial for safety and efficiency. However, existing approaches often exhibit limited accuracy when applied to Internet of Vehicles (IoV) data, primarily due to the low sampling rates in practical scenarios. To address this challenge, a framework is proposed for bus mass estimation using IoV data. It focuses on data imputation and information fusion. A Triple Dependency Network (TDN) is developed to impute missing data. TDN captures both temporal dependencies and variable correlations in low-sampling-rate sequences. Then, a Multi-Source Information Fusion Network (MSIFN) is introduced to integrate both original and imputed data. MSIFN enhances the accuracy and robustness of bus mass estimation. Experimental results on both real-world IoV and simulation datasets demonstrate that the proposed approach significantly improves mass estimation accuracy compared to existing methods, while effectively utilizing low-sampling-rate data and reducing data acquisition burdens. These results highlight the method's effectiveness and practical value for industrial applications.
{"title":"An IoV data imputation-fusion bus mass estimation framework based on triple dependency and multi-source information fusion networks","authors":"Zhengzhong Zheng , Shijiang Li , Liang Hou , Haojing Lin , Jiancheng Chen","doi":"10.1016/j.jii.2025.101017","DOIUrl":"10.1016/j.jii.2025.101017","url":null,"abstract":"<div><div>Accurate bus mass estimation is crucial for safety and efficiency. However, existing approaches often exhibit limited accuracy when applied to Internet of Vehicles (IoV) data, primarily due to the low sampling rates in practical scenarios. To address this challenge, a framework is proposed for bus mass estimation using IoV data. It focuses on data imputation and information fusion. A Triple Dependency Network (TDN) is developed to impute missing data. TDN captures both temporal dependencies and variable correlations in low-sampling-rate sequences. Then, a Multi-Source Information Fusion Network (MSIFN) is introduced to integrate both original and imputed data. MSIFN enhances the accuracy and robustness of bus mass estimation. Experimental results on both real-world IoV and simulation datasets demonstrate that the proposed approach significantly improves mass estimation accuracy compared to existing methods, while effectively utilizing low-sampling-rate data and reducing data acquisition burdens. These results highlight the method's effectiveness and practical value for industrial applications.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"49 ","pages":"Article 101017"},"PeriodicalIF":10.4,"publicationDate":"2025-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145554207","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 : 2025-11-16DOI: 10.1016/j.jii.2025.101015
Wei Li , Linbing Wang , Maogui Sun , Dengcai Yin , Yajian Wang , Xiang Zhou , Yongming Wang , Zhoujing Ye
As the structural carrier of mineral resources, underground mine is a typical artificial large layered underground infrastructure. The safety of mining systems remains a critical concern for nations worldwide. Based on the environmental characteristics of underground mines, the accompanying safety issues are evident. Conventional personnel evacuation drills for mine disasters often fail to create effective disaster evolution memories for people. When a real accident occurs, people cannot escape efficiently in a panic state, which reduces survival probability. To solve this problem, an escape space connection algorithm is developed based on the physical information and management rules in this study, and it is used to drive the extended reality escape system by the game engine. Firstly, this study takes the water-inrush accidents of underground layered mines as the engineering research object and background, the characteristics of water-inrush accidents evolution and personnel evacuation are systematically analyzed based on the scenario construction theory. Secondly, this study develops an escape space connection algorithm by integrating the two-dimensional A* algorithm and the connection weights of escape spaces based on the spatial geometric information and escape strategy of layered mines. Thirdly, a distributed extended reality (XR) human-computer interaction system is developed for escape path guidance in real environments based on the spatial structure characteristics of layered mines and the escape space connection algorithm. Finally, application testing is conducted in the experimental mine to analyze the system performance and future application potential. This study provides a comprehensive technical framework for personnel evacuation in layered underground infrastructure during evolutionary accidents, and the theories and systems involved are universal. In addition, this method can be used as a new, low-cost and efficient digital reference system for personnel safety emergency drills in underground infrastructure.
{"title":"A distributed extended reality escape method for layered underground infrastructure based on AI game engine","authors":"Wei Li , Linbing Wang , Maogui Sun , Dengcai Yin , Yajian Wang , Xiang Zhou , Yongming Wang , Zhoujing Ye","doi":"10.1016/j.jii.2025.101015","DOIUrl":"10.1016/j.jii.2025.101015","url":null,"abstract":"<div><div>As the structural carrier of mineral resources, underground mine is a typical artificial large layered underground infrastructure. The safety of mining systems remains a critical concern for nations worldwide. Based on the environmental characteristics of underground mines, the accompanying safety issues are evident. Conventional personnel evacuation drills for mine disasters often fail to create effective disaster evolution memories for people. When a real accident occurs, people cannot escape efficiently in a panic state, which reduces survival probability. To solve this problem, an escape space connection algorithm is developed based on the physical information and management rules in this study, and it is used to drive the extended reality escape system by the game engine. Firstly, this study takes the water-inrush accidents of underground layered mines as the engineering research object and background, the characteristics of water-inrush accidents evolution and personnel evacuation are systematically analyzed based on the scenario construction theory. Secondly, this study develops an escape space connection algorithm by integrating the two-dimensional A* algorithm and the connection weights of escape spaces based on the spatial geometric information and escape strategy of layered mines. Thirdly, a distributed extended reality (XR) human-computer interaction system is developed for escape path guidance in real environments based on the spatial structure characteristics of layered mines and the escape space connection algorithm. Finally, application testing is conducted in the experimental mine to analyze the system performance and future application potential. This study provides a comprehensive technical framework for personnel evacuation in layered underground infrastructure during evolutionary accidents, and the theories and systems involved are universal. In addition, this method can be used as a new, low-cost and efficient digital reference system for personnel safety emergency drills in underground infrastructure.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"49 ","pages":"Article 101015"},"PeriodicalIF":10.4,"publicationDate":"2025-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145531166","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 : 2025-11-15DOI: 10.1016/j.jii.2025.101016
Ting Li , Di Li , Chunhua Zhang , Peng Chi , Ziren Luo
Embodied Intelligence (EI) integrates perception, cognition, and action within manufacturing systems, enabling on-device learning and human-machine collaboration. For surface defect inspection, this requires real-time reasoning over subtle 3D geometries and continuous self-improvement using high-quality training data. However, current point cloud generation methods fall short in synthesizing 3D defects due to inefficient single-class generation, lack of pixel-level annotations, and poor diversity. We propose Category-Controllable and High-Fidelity Generative Adversarial Network (CFGAN) to address these issues. CFGAN generates paired RGB and depth defect images with controllable categories and pure backgrounds, enabling multi-class synthesis and facilitating pixel-level annotation. A gradient-adaptive Poisson fusion method ensures seamless blending of generated RGB and depth defects into normal backgrounds, while domain transfer and depth mapping modules are further applied to preserve the consistency and reliability of the generated depth. Moreover, by sampling random latent codes, CFGAN produces diverse defect samples. Finally, spatial alignment of defect images maps 2D features into 3D space, resulting in realistic defect point clouds. The effectiveness of the proposed method is validated through experiments on fruit, metal, and plastic objects. In addition, our framework enables zero-shot inspection by transferring defects across datasets with different backgrounds but similar defects, achieving an Overall Accuracy of 0.9736. Our work provides diverse, well-annotated point cloud defects, enhancing the adaptability and autonomy of EI inspection systems.
{"title":"Category-controllable and high-fidelity 3D defect synthesis for Embodied Intelligence-based industrial inspection","authors":"Ting Li , Di Li , Chunhua Zhang , Peng Chi , Ziren Luo","doi":"10.1016/j.jii.2025.101016","DOIUrl":"10.1016/j.jii.2025.101016","url":null,"abstract":"<div><div>Embodied Intelligence (EI) integrates perception, cognition, and action within manufacturing systems, enabling on-device learning and human-machine collaboration. For surface defect inspection, this requires real-time reasoning over subtle 3D geometries and continuous self-improvement using high-quality training data. However, current point cloud generation methods fall short in synthesizing 3D defects due to inefficient single-class generation, lack of pixel-level annotations, and poor diversity. We propose Category-Controllable and High-Fidelity Generative Adversarial Network (CFGAN) to address these issues. CFGAN generates paired RGB and depth defect images with controllable categories and pure backgrounds, enabling multi-class synthesis and facilitating pixel-level annotation. A gradient-adaptive Poisson fusion method ensures seamless blending of generated RGB and depth defects into normal backgrounds, while domain transfer and depth mapping modules are further applied to preserve the consistency and reliability of the generated depth. Moreover, by sampling random latent codes, CFGAN produces diverse defect samples. Finally, spatial alignment of defect images maps 2D features into 3D space, resulting in realistic defect point clouds. The effectiveness of the proposed method is validated through experiments on fruit, metal, and plastic objects. In addition, our framework enables zero-shot inspection by transferring defects across datasets with different backgrounds but similar defects, achieving an Overall Accuracy of 0.9736. Our work provides diverse, well-annotated point cloud defects, enhancing the adaptability and autonomy of EI inspection systems.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"49 ","pages":"Article 101016"},"PeriodicalIF":10.4,"publicationDate":"2025-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145531167","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 : 2025-11-11DOI: 10.1016/j.jii.2025.101008
Jin-Qiang Wang , Lirong Song , Jun Shen , Binbin Yong , Xiaoteng Han , Yuanbo Jiang , Mona Raoufi , Qingguo Zhou
Deep reinforcement learning (DRL) plays a crucial role in complex sequential decision-making tasks. However, existing data-driven DRL methods primarily rely on an empirical risk minimization (ERM) strategy to fit optimal value function models. This approach often neglects the environment’s dynamical system properties, which in turn leads to an inadequate consideration of the structural risk minimization (SRM) strategy. To address this limitation, this paper proposes a physics-informed continuous-time reinforcement learning (PICRL) to validate model effectiveness from both ERM and SRM perspectives. Specifically, we begin by theoretically analyzing the mechanism of SRM in reinforcement learning models. Then, physics information is integrated into both discrete and continuous reinforcement learning algorithms for comparative experiments. Finally, we systematically examine the effects of various physics-informed and boundary constraints on these two learning frameworks. Experimental results on the PandaGym demonstrate that the proposed method achieves comparable or superior performance in both discrete and continuous-time reinforcement learning frameworks. This provides strong evidence for its significant advantages in learning control policies for dynamical systems with small time intervals.
{"title":"Physics-informed continuous-time reinforcement learning with data-driven approach for robotic arm manipulation","authors":"Jin-Qiang Wang , Lirong Song , Jun Shen , Binbin Yong , Xiaoteng Han , Yuanbo Jiang , Mona Raoufi , Qingguo Zhou","doi":"10.1016/j.jii.2025.101008","DOIUrl":"10.1016/j.jii.2025.101008","url":null,"abstract":"<div><div>Deep reinforcement learning (DRL) plays a crucial role in complex sequential decision-making tasks. However, existing data-driven DRL methods primarily rely on an empirical risk minimization (ERM) strategy to fit optimal value function models. This approach often neglects the environment’s dynamical system properties, which in turn leads to an inadequate consideration of the structural risk minimization (SRM) strategy. To address this limitation, this paper proposes a physics-informed continuous-time reinforcement learning (PICRL) to validate model effectiveness from both ERM and SRM perspectives. Specifically, we begin by theoretically analyzing the mechanism of SRM in reinforcement learning models. Then, physics information is integrated into both discrete and continuous reinforcement learning algorithms for comparative experiments. Finally, we systematically examine the effects of various physics-informed and boundary constraints on these two learning frameworks. Experimental results on the PandaGym demonstrate that the proposed method achieves comparable or superior performance in both discrete and continuous-time reinforcement learning frameworks. This provides strong evidence for its significant advantages in learning control policies for dynamical systems with small time intervals.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"49 ","pages":"Article 101008"},"PeriodicalIF":10.4,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145492241","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 : 2025-11-10DOI: 10.1016/j.jii.2025.101010
Cheng Wang , Shiyong Wang , Wujie Zhang , Min Xia , Zhenfeng Shi
The perception-control-execution layered architecture, commonly used in current embodied intelligent robot control systems, suffers from inherent latency caused by its serial processing mechanism, which limits a robot's ability to respond to sudden disturbances, such as falls and collisions. To overcome this bottleneck, this study proposes a biomimetic emergency response control architecture for embodied intelligent robots. This architecture is inspired by the collaborative control principles of higher-level central control and spinal reflex mechanisms in the human nervous system. In addition, this architecture decouples the process of conventional decision-making and planning from emergency response mechanisms, thus constructing a four-layer heterogeneous control framework containing a perception-planning layer, a motion control layer, an emergency response layer, and a physical execution layer. The perception-planning layer is responsible for scene understanding and long-term planning. The motion control layer performs precise control of the entire body's posture and motion trajectory. The emergency response layer transmits upper-layer control commands under normal conditions, achieving fine motion control. In the event of sudden disturbances, the emergency response layer receives sensor signals directly, without waiting for the perception and decision results of the perception-planning layer. A lightweight, online-learnable reflex rule base, such as a balance compensation mechanism based on contact force mutation thresholds, enables rapid response to sudden disturbances. The emergency response layer is used as an independent module in the embodied intelligent control architecture, addressing the serial delay problem and offering an innovative solution for improving motion robustness and operational safety of robots in highly dynamic and uncertain environments.
{"title":"From brain to reflex: An emergency response control architecture for embodied intelligent robots","authors":"Cheng Wang , Shiyong Wang , Wujie Zhang , Min Xia , Zhenfeng Shi","doi":"10.1016/j.jii.2025.101010","DOIUrl":"10.1016/j.jii.2025.101010","url":null,"abstract":"<div><div>The perception-control-execution layered architecture, commonly used in current embodied intelligent robot control systems, suffers from inherent latency caused by its serial processing mechanism, which limits a robot's ability to respond to sudden disturbances, such as falls and collisions. To overcome this bottleneck, this study proposes a biomimetic emergency response control architecture for embodied intelligent robots. This architecture is inspired by the collaborative control principles of higher-level central control and spinal reflex mechanisms in the human nervous system. In addition, this architecture decouples the process of conventional decision-making and planning from emergency response mechanisms, thus constructing a four-layer heterogeneous control framework containing a perception-planning layer, a motion control layer, an emergency response layer, and a physical execution layer. The perception-planning layer is responsible for scene understanding and long-term planning. The motion control layer performs precise control of the entire body's posture and motion trajectory. The emergency response layer transmits upper-layer control commands under normal conditions, achieving fine motion control. In the event of sudden disturbances, the emergency response layer receives sensor signals directly, without waiting for the perception and decision results of the perception-planning layer. A lightweight, online-learnable reflex rule base, such as a balance compensation mechanism based on contact force mutation thresholds, enables rapid response to sudden disturbances. The emergency response layer is used as an independent module in the embodied intelligent control architecture, addressing the serial delay problem and offering an innovative solution for improving motion robustness and operational safety of robots in highly dynamic and uncertain environments.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"49 ","pages":"Article 101010"},"PeriodicalIF":10.4,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145485560","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 : 2025-11-09DOI: 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.
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