Generalized morphological operator can generate less statistical bias in the output than classical morphological operator. Comprehensive utilization of spectral and spatial information of pixels, an endmember extracti...Generalized morphological operator can generate less statistical bias in the output than classical morphological operator. Comprehensive utilization of spectral and spatial information of pixels, an endmember extraction algorithm based on generalized morphology is proposed. For the limitations of morphological operator in the pixel arrangement rule and replacement criteria, the reference pixel is introduced. In order to avoid the cross substitution phenomenon at the boundary of different object categories in the image, an endmember is extracted by calculating the generalized opening-closing(GOC) operator which uses the modified energy function as a distance measure. The algorithm is verified by using simulated data and real data. Experimental results show that the proposed algorithm can extract endmember automatically without prior knowledge and achieve relatively high extraction accuracy.展开更多
This article describes a biologically inspired node generator for the path planning of serially connected hyper-redundant manipulators using probabilistic roadmap planners. The generator searches the configuration spa...This article describes a biologically inspired node generator for the path planning of serially connected hyper-redundant manipulators using probabilistic roadmap planners. The generator searches the configuration space surrounding existing nodes in the roadmap and uses a combination of random and deterministic search methods that emulate the behaviour of octopus limbs. The strategy consists of randomly mutating the states of the links near the end-effector, and mutating the states of the links near the base of the robot toward the states of the goal configuration. When combined with the small tree probabilistic roadmap planner, the method was successfully used to solve the narrow passage motion planning problem of a 17 degree-of-freedom manipulator.展开更多
系统阐述了AI for Engineering(AI4E)驱动数字生态系统网络发展范式的转型动因、机理与实践路径,指出传统数字生态系统网络发展范式面临“刚性架构与场景多样化”的根本矛盾,亟需以“超融合、高可信、一体化”为目标进行重构。介绍了AI4...系统阐述了AI for Engineering(AI4E)驱动数字生态系统网络发展范式的转型动因、机理与实践路径,指出传统数字生态系统网络发展范式面临“刚性架构与场景多样化”的根本矛盾,亟需以“超融合、高可信、一体化”为目标进行重构。介绍了AI4E驱动数字生态系统网络发展范式转型的重要基础、技术支撑、运作方式,从思维视角、方法论、实践规范、发展路径等方面阐述了新范式的主要特征;同时,介绍了AI4E赋能转型的实践探索,提出基于生成式AI的多模态网络环境(PINE),开辟网络技术体制“第二曲线”;提出晶上生成式变结构计算,打造智能算力“芯物种”;推动内生安全赋能数字系统网络弹性工程,提升人工智能应用系统内生安全能力;呼吁建设"超融合网络与智能计算实验床"大科学装置,验证“结构决定效能/安全/多样性”的科学猜想,为构建自主知识体系、推动科技自主创新、深化人才自主培养改革提供支撑。展开更多
针对军事智能博弈对抗面临的超自动化需求,研究了基于机器人流程自动化(Robotic Process Automation,RPA)/认知机器人流程自动化(Cognitive Robotic Process Automation,CRPA)的技术途径与应用方法。分析了RPA/CRPA技术的起源与发展,指...针对军事智能博弈对抗面临的超自动化需求,研究了基于机器人流程自动化(Robotic Process Automation,RPA)/认知机器人流程自动化(Cognitive Robotic Process Automation,CRPA)的技术途径与应用方法。分析了RPA/CRPA技术的起源与发展,指出了其在智能博弈对抗中的重要地位;梳理了RPA/CRPA技术应用现状,分析其发展趋势;提出了RPA/CRPA的技术体系,建立了基于RPA/CRPA的无人系统智慧控制体系框架;以巨型星座、地外天体机器人生态圈、复杂指挥自动化系统、无人作战系统、赛博空间软件机器人集群等典型博弈对抗场景为例,指出了RPA/CRPA技术的应用模式,为实现军事智能博弈对抗的超自动化提供了技术途径。展开更多
Fire detection has held stringent importance in computer vision for over half a century.The development of early fire detection strategies is pivotal to the realization of safe and smart cities,inhabitable in the futu...Fire detection has held stringent importance in computer vision for over half a century.The development of early fire detection strategies is pivotal to the realization of safe and smart cities,inhabitable in the future.However,the development of optimal fire and smoke detection models is hindered by limitations like publicly available datasets,lack of diversity,and class imbalance.In this work,we explore the possible ways forward to overcome these challenges posed by available datasets.We study the impact of a class-balanced dataset to improve the fire detection capability of state-of-the-art(SOTA)vision-based models and propose the use of generative models for data augmentation,as a future work direction.First,a comparative analysis of two prominent object detection architectures,You Only Look Once version 7(YOLOv7)and YOLOv8 has been carried out using a balanced dataset,where both models have been evaluated across various evaluation metrics including precision,recall,and mean Average Precision(mAP).The results are compared to other recent fire detection models,highlighting the superior performance and efficiency of the proposed YOLOv8 architecture as trained on our balanced dataset.Next,a fractal dimension analysis gives a deeper insight into the repetition of patterns in fire,and the effectiveness of the results has been demonstrated by a windowing-based inference approach.The proposed Slicing-Aided Hyper Inference(SAHI)improves the fire and smoke detection capability of YOLOv8 for real-life applications with a significantly improved mAP performance over a strict confidence threshold.YOLOv8 with SAHI inference gives a mAP:50-95 improvement of more than 25%compared to the base YOLOv8 model.The study also provides insights into future work direction by exploring the potential of generative models like deep convolutional generative adversarial network(DCGAN)and diffusion models like stable diffusion,for data augmentation.展开更多
基金supported by the National Natural Science Foundation of China(No.61275010)the PhD Programs Foundation of Ministry of Education of China(No.20132304110007)
文摘Generalized morphological operator can generate less statistical bias in the output than classical morphological operator. Comprehensive utilization of spectral and spatial information of pixels, an endmember extraction algorithm based on generalized morphology is proposed. For the limitations of morphological operator in the pixel arrangement rule and replacement criteria, the reference pixel is introduced. In order to avoid the cross substitution phenomenon at the boundary of different object categories in the image, an endmember is extracted by calculating the generalized opening-closing(GOC) operator which uses the modified energy function as a distance measure. The algorithm is verified by using simulated data and real data. Experimental results show that the proposed algorithm can extract endmember automatically without prior knowledge and achieve relatively high extraction accuracy.
文摘This article describes a biologically inspired node generator for the path planning of serially connected hyper-redundant manipulators using probabilistic roadmap planners. The generator searches the configuration space surrounding existing nodes in the roadmap and uses a combination of random and deterministic search methods that emulate the behaviour of octopus limbs. The strategy consists of randomly mutating the states of the links near the end-effector, and mutating the states of the links near the base of the robot toward the states of the goal configuration. When combined with the small tree probabilistic roadmap planner, the method was successfully used to solve the narrow passage motion planning problem of a 17 degree-of-freedom manipulator.
文摘系统阐述了AI for Engineering(AI4E)驱动数字生态系统网络发展范式的转型动因、机理与实践路径,指出传统数字生态系统网络发展范式面临“刚性架构与场景多样化”的根本矛盾,亟需以“超融合、高可信、一体化”为目标进行重构。介绍了AI4E驱动数字生态系统网络发展范式转型的重要基础、技术支撑、运作方式,从思维视角、方法论、实践规范、发展路径等方面阐述了新范式的主要特征;同时,介绍了AI4E赋能转型的实践探索,提出基于生成式AI的多模态网络环境(PINE),开辟网络技术体制“第二曲线”;提出晶上生成式变结构计算,打造智能算力“芯物种”;推动内生安全赋能数字系统网络弹性工程,提升人工智能应用系统内生安全能力;呼吁建设"超融合网络与智能计算实验床"大科学装置,验证“结构决定效能/安全/多样性”的科学猜想,为构建自主知识体系、推动科技自主创新、深化人才自主培养改革提供支撑。
文摘针对军事智能博弈对抗面临的超自动化需求,研究了基于机器人流程自动化(Robotic Process Automation,RPA)/认知机器人流程自动化(Cognitive Robotic Process Automation,CRPA)的技术途径与应用方法。分析了RPA/CRPA技术的起源与发展,指出了其在智能博弈对抗中的重要地位;梳理了RPA/CRPA技术应用现状,分析其发展趋势;提出了RPA/CRPA的技术体系,建立了基于RPA/CRPA的无人系统智慧控制体系框架;以巨型星座、地外天体机器人生态圈、复杂指挥自动化系统、无人作战系统、赛博空间软件机器人集群等典型博弈对抗场景为例,指出了RPA/CRPA技术的应用模式,为实现军事智能博弈对抗的超自动化提供了技术途径。
基金supported by a grant from R&D Program Development of Rail-Specific Digital Resource Technology Based on an AI-Enabled Rail Support Platform,grant number PK2401C1,of the Korea Railroad Research Institute.
文摘Fire detection has held stringent importance in computer vision for over half a century.The development of early fire detection strategies is pivotal to the realization of safe and smart cities,inhabitable in the future.However,the development of optimal fire and smoke detection models is hindered by limitations like publicly available datasets,lack of diversity,and class imbalance.In this work,we explore the possible ways forward to overcome these challenges posed by available datasets.We study the impact of a class-balanced dataset to improve the fire detection capability of state-of-the-art(SOTA)vision-based models and propose the use of generative models for data augmentation,as a future work direction.First,a comparative analysis of two prominent object detection architectures,You Only Look Once version 7(YOLOv7)and YOLOv8 has been carried out using a balanced dataset,where both models have been evaluated across various evaluation metrics including precision,recall,and mean Average Precision(mAP).The results are compared to other recent fire detection models,highlighting the superior performance and efficiency of the proposed YOLOv8 architecture as trained on our balanced dataset.Next,a fractal dimension analysis gives a deeper insight into the repetition of patterns in fire,and the effectiveness of the results has been demonstrated by a windowing-based inference approach.The proposed Slicing-Aided Hyper Inference(SAHI)improves the fire and smoke detection capability of YOLOv8 for real-life applications with a significantly improved mAP performance over a strict confidence threshold.YOLOv8 with SAHI inference gives a mAP:50-95 improvement of more than 25%compared to the base YOLOv8 model.The study also provides insights into future work direction by exploring the potential of generative models like deep convolutional generative adversarial network(DCGAN)and diffusion models like stable diffusion,for data augmentation.
