Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone t...Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone to errors and lacks consistency,emphasizing the need for a reliable and automated inspection system.Leveraging both object detection and image segmentation approaches,this research proposes a vision-based solution for the detection of various kinds of tools in the toolkit using deep learning(DL)models.Two Intel RealSense D455f depth cameras were arranged in a top down configuration to capture both RGB and depth images of the toolkits.After applying multiple constraints and enhancing them through preprocessing and augmentation,a dataset consisting of 3300 annotated RGB-D photos was generated.Several DL models were selected through a comprehensive assessment of mean Average Precision(mAP),precision-recall equilibrium,inference latency(target≥30 FPS),and computational burden,resulting in a preference for YOLO and Region-based Convolutional Neural Networks(R-CNN)variants over ViT-based models due to the latter’s increased latency and resource requirements.YOLOV5,YOLOV8,YOLOV11,Faster R-CNN,and Mask R-CNN were trained on the annotated dataset and evaluated using key performance metrics(Recall,Accuracy,F1-score,and Precision).YOLOV11 demonstrated balanced excellence with 93.0%precision,89.9%recall,and a 90.6%F1-score in object detection,as well as 96.9%precision,95.3%recall,and a 96.5%F1-score in instance segmentation with an average inference time of 25 ms per frame(≈40 FPS),demonstrating real-time performance.Leveraging these results,a YOLOV11-based windows application was successfully deployed in a real-time assembly line environment,where it accurately processed live video streams to detect and segment tools within toolkits,demonstrating its practical effectiveness in industrial automation.The application is capable of precisely measuring socket dimensions by utilising edge detection techniques on YOLOv11 segmentation masks,in addition to detection and segmentation.This makes it possible to do specification-level quality control right on the assembly line,which improves the ability to examine things in real time.The implementation is a big step forward for intelligent manufacturing in the Industry 4.0 paradigm.It provides a scalable,efficient,and accurate way to do automated inspection and dimensional verification activities.展开更多
In recent years,the country has spent significant workforce and material resources to prevent traffic accidents,particularly those caused by fatigued driving.The current studies mainly concentrate on driver physiologi...In recent years,the country has spent significant workforce and material resources to prevent traffic accidents,particularly those caused by fatigued driving.The current studies mainly concentrate on driver physiological signals,driving behavior,and vehicle information.However,most of the approaches are computationally intensive and inconvenient for real-time detection.Therefore,this paper designs a network that combines precision,speed and lightweight and proposes an algorithm for facial fatigue detection based on multi-feature fusion.Specifically,the face detection model takes YOLOv8(You Only Look Once version 8)as the basic framework,and replaces its backbone network with MobileNetv3.To focus on the significant regions in the image,CPCA(Channel Prior Convolution Attention)is adopted to enhance the network’s capacity for feature extraction.Meanwhile,the network training phase employs the Focal-EIOU(Focal and Efficient Intersection Over Union)loss function,which makes the network lightweight and increases the accuracy of target detection.Ultimately,the Dlib toolkit was employed to annotate 68 facial feature points.This study established an evaluation metric for facial fatigue and developed a novel fatigue detection algorithm to assess the driver’s condition.A series of comparative experiments were carried out on the self-built dataset.The suggested method’s mAP(mean Average Precision)values for object detection and fatigue detection are 96.71%and 95.75%,respectively,as well as the detection speed is 47 FPS(Frames Per Second).This method can balance the contradiction between computational complexity and model accuracy.Furthermore,it can be transplanted to NVIDIA Jetson Orin NX and quickly detect the driver’s state while maintaining a high degree of accuracy.It contributes to the development of automobile safety systems and reduces the occurrence of traffic accidents.展开更多
Previous studies aiming to accelerate data processing have focused on enhancement algorithms,using the graphics processing unit(GPU)to speed up programs,and thread-level parallelism.These methods overlook maximizing t...Previous studies aiming to accelerate data processing have focused on enhancement algorithms,using the graphics processing unit(GPU)to speed up programs,and thread-level parallelism.These methods overlook maximizing the utilization of existing central processing unit(CPU)resources and reducing human and computational time costs via process automation.Accordingly,this paper proposes a scheme,called SSM,that combines“Srun job submission mode”,“Sbatch job submission mode”,and“Monitor function”.The SSM scheme includes three main modules:data management,command management,and resource management.Its core innovations are command splitting and parallel execution.The results show that this method effectively improves CPU utilization and reduces the time required for data processing.In terms of CPU utilization,the average value of this scheme is 89%.In contrast,the average CPU utilizations of“Srun job submission mode”and“Sbatch job submission mode”are significantly lower,at 43%and 52%,respectively.In terms of the data-processing time,SSM testing on the Five-hundred-meter Aperture Spherical radio Telescope(FAST)data requires only 5.5 h,compared with 8 h in the“Srun job submission mode”and 14 h in the“Sbatch job submission mode”.In addition,tests on the FAST and Parkes datasets demonstrate the universality of the SSM scheme,which can process data from different telescopes.The compatibility of the SSM scheme for pulsar searches is verified using 2 days of observational data from the globular cluster M2,with the scheme successfully discovering all published pulsars in M2.展开更多
The Multipurpose Enhanced Cognitive Architecture(MECA)is a cognitive framework designed to model complex,human-like processes across multiple domains.Originally focusing on implementing a Dual Process Theory approach ...The Multipurpose Enhanced Cognitive Architecture(MECA)is a cognitive framework designed to model complex,human-like processes across multiple domains.Originally focusing on implementing a Dual Process Theory approach and integrating a machine consciousness mechanism based on Global Workspace Theory,MECA has been updated to integrate a dual-layer subsumption mechanism,enabling both reactive and deliberative behaviors,dynamic goal setting and a visual-spatial memory subsystem,enhancing MECA’s capacity for real-world interaction and adaptive behavior.Also,with the introduction of the new computational ideas’knowledge representation scheme,MECA proposes to organize knowledge dynamically to handle context-sensitive reasoning and flexible categorization.MECA’s implementation relies on the Cognitive Systems Toolkit(CST),facilitating its integration with cutting-edge technologies.MECA and CST are being continuously developed and updated,aligned,and open to incorporate the latest AI artifacts and methodologies.This approach ensures the delivery of organized,monitorable,auditable,and controllable AI solutions,significantly reducing reliance on“black box”cognitive processes while enhancing transparency and accountability in AI-driven systems.These updates reinforce MECA’s potential as a robust architecture for developing autonomous,adaptable,and context-aware AI systems capable of real-world interaction and adaptive learning.展开更多
针对目前三维零件工艺审查过程中存在的问题,进行MBD设计模型的工艺性审查研究。利用Pro/E为支撑软件,在Visual Studio 2005软件的编译环境中,通过编程调用Pro/Toolkit工具中的函数,对Pro/E软件进行二次开发,实现MBD设计模型工艺信息的...针对目前三维零件工艺审查过程中存在的问题,进行MBD设计模型的工艺性审查研究。利用Pro/E为支撑软件,在Visual Studio 2005软件的编译环境中,通过编程调用Pro/Toolkit工具中的函数,对Pro/E软件进行二次开发,实现MBD设计模型工艺信息的识别、提取、修改和存储等功能。对于工艺审查的研究能极大地降低工艺人员的工作量,提高MBD设计模型的工艺审查效率。展开更多
基金National Science and Technology Council,the Republic of China,under grants NSTC 113-2221-E-194-011-MY3 and Research Center on Artificial Intelligence and Sustainability,National Chung Cheng University under the research project grant titled“Generative Digital Twin System Design for Sustainable Smart City Development in Taiwan.
文摘Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone to errors and lacks consistency,emphasizing the need for a reliable and automated inspection system.Leveraging both object detection and image segmentation approaches,this research proposes a vision-based solution for the detection of various kinds of tools in the toolkit using deep learning(DL)models.Two Intel RealSense D455f depth cameras were arranged in a top down configuration to capture both RGB and depth images of the toolkits.After applying multiple constraints and enhancing them through preprocessing and augmentation,a dataset consisting of 3300 annotated RGB-D photos was generated.Several DL models were selected through a comprehensive assessment of mean Average Precision(mAP),precision-recall equilibrium,inference latency(target≥30 FPS),and computational burden,resulting in a preference for YOLO and Region-based Convolutional Neural Networks(R-CNN)variants over ViT-based models due to the latter’s increased latency and resource requirements.YOLOV5,YOLOV8,YOLOV11,Faster R-CNN,and Mask R-CNN were trained on the annotated dataset and evaluated using key performance metrics(Recall,Accuracy,F1-score,and Precision).YOLOV11 demonstrated balanced excellence with 93.0%precision,89.9%recall,and a 90.6%F1-score in object detection,as well as 96.9%precision,95.3%recall,and a 96.5%F1-score in instance segmentation with an average inference time of 25 ms per frame(≈40 FPS),demonstrating real-time performance.Leveraging these results,a YOLOV11-based windows application was successfully deployed in a real-time assembly line environment,where it accurately processed live video streams to detect and segment tools within toolkits,demonstrating its practical effectiveness in industrial automation.The application is capable of precisely measuring socket dimensions by utilising edge detection techniques on YOLOv11 segmentation masks,in addition to detection and segmentation.This makes it possible to do specification-level quality control right on the assembly line,which improves the ability to examine things in real time.The implementation is a big step forward for intelligent manufacturing in the Industry 4.0 paradigm.It provides a scalable,efficient,and accurate way to do automated inspection and dimensional verification activities.
基金supported by the Science and Technology Bureau of Xi’an project(24KGDW0049)the Key Research and Development Programof Shaanxi(2023-YBGY-264)the Key Research and Development Program of Guangxi(GK-AB20159032).
文摘In recent years,the country has spent significant workforce and material resources to prevent traffic accidents,particularly those caused by fatigued driving.The current studies mainly concentrate on driver physiological signals,driving behavior,and vehicle information.However,most of the approaches are computationally intensive and inconvenient for real-time detection.Therefore,this paper designs a network that combines precision,speed and lightweight and proposes an algorithm for facial fatigue detection based on multi-feature fusion.Specifically,the face detection model takes YOLOv8(You Only Look Once version 8)as the basic framework,and replaces its backbone network with MobileNetv3.To focus on the significant regions in the image,CPCA(Channel Prior Convolution Attention)is adopted to enhance the network’s capacity for feature extraction.Meanwhile,the network training phase employs the Focal-EIOU(Focal and Efficient Intersection Over Union)loss function,which makes the network lightweight and increases the accuracy of target detection.Ultimately,the Dlib toolkit was employed to annotate 68 facial feature points.This study established an evaluation metric for facial fatigue and developed a novel fatigue detection algorithm to assess the driver’s condition.A series of comparative experiments were carried out on the self-built dataset.The suggested method’s mAP(mean Average Precision)values for object detection and fatigue detection are 96.71%and 95.75%,respectively,as well as the detection speed is 47 FPS(Frames Per Second).This method can balance the contradiction between computational complexity and model accuracy.Furthermore,it can be transplanted to NVIDIA Jetson Orin NX and quickly detect the driver’s state while maintaining a high degree of accuracy.It contributes to the development of automobile safety systems and reduces the occurrence of traffic accidents.
基金supported by the National Nature Science Foundation of China(12363010)supported by the Guizhou Provincial Basic Research Program(Natural Science)(ZK[2023]039)the Key Technology R&D Program([2023]352).
文摘Previous studies aiming to accelerate data processing have focused on enhancement algorithms,using the graphics processing unit(GPU)to speed up programs,and thread-level parallelism.These methods overlook maximizing the utilization of existing central processing unit(CPU)resources and reducing human and computational time costs via process automation.Accordingly,this paper proposes a scheme,called SSM,that combines“Srun job submission mode”,“Sbatch job submission mode”,and“Monitor function”.The SSM scheme includes three main modules:data management,command management,and resource management.Its core innovations are command splitting and parallel execution.The results show that this method effectively improves CPU utilization and reduces the time required for data processing.In terms of CPU utilization,the average value of this scheme is 89%.In contrast,the average CPU utilizations of“Srun job submission mode”and“Sbatch job submission mode”are significantly lower,at 43%and 52%,respectively.In terms of the data-processing time,SSM testing on the Five-hundred-meter Aperture Spherical radio Telescope(FAST)data requires only 5.5 h,compared with 8 h in the“Srun job submission mode”and 14 h in the“Sbatch job submission mode”.In addition,tests on the FAST and Parkes datasets demonstrate the universality of the SSM scheme,which can process data from different telescopes.The compatibility of the SSM scheme for pulsar searches is verified using 2 days of observational data from the globular cluster M2,with the scheme successfully discovering all published pulsars in M2.
基金Supported by the Sao Paulo Research Foundation(FAPESP),CPE SMARTNESS(2021/00199-8)and CEPID/BRAINN(2013/07559-3).
文摘The Multipurpose Enhanced Cognitive Architecture(MECA)is a cognitive framework designed to model complex,human-like processes across multiple domains.Originally focusing on implementing a Dual Process Theory approach and integrating a machine consciousness mechanism based on Global Workspace Theory,MECA has been updated to integrate a dual-layer subsumption mechanism,enabling both reactive and deliberative behaviors,dynamic goal setting and a visual-spatial memory subsystem,enhancing MECA’s capacity for real-world interaction and adaptive behavior.Also,with the introduction of the new computational ideas’knowledge representation scheme,MECA proposes to organize knowledge dynamically to handle context-sensitive reasoning and flexible categorization.MECA’s implementation relies on the Cognitive Systems Toolkit(CST),facilitating its integration with cutting-edge technologies.MECA and CST are being continuously developed and updated,aligned,and open to incorporate the latest AI artifacts and methodologies.This approach ensures the delivery of organized,monitorable,auditable,and controllable AI solutions,significantly reducing reliance on“black box”cognitive processes while enhancing transparency and accountability in AI-driven systems.These updates reinforce MECA’s potential as a robust architecture for developing autonomous,adaptable,and context-aware AI systems capable of real-world interaction and adaptive learning.
文摘针对目前三维零件工艺审查过程中存在的问题,进行MBD设计模型的工艺性审查研究。利用Pro/E为支撑软件,在Visual Studio 2005软件的编译环境中,通过编程调用Pro/Toolkit工具中的函数,对Pro/E软件进行二次开发,实现MBD设计模型工艺信息的识别、提取、修改和存储等功能。对于工艺审查的研究能极大地降低工艺人员的工作量,提高MBD设计模型的工艺审查效率。
