针对车载边缘计算(vehicular edge computing,VEC)中路侧单元(road side unit,RSU)资源受限和高负载的难题,以及现有的任务卸载优化方案局限于降低时延或能耗,忽视了边缘节点所面临的安全问题,提出一种基于信任感知和近端策略优化算法(P...针对车载边缘计算(vehicular edge computing,VEC)中路侧单元(road side unit,RSU)资源受限和高负载的难题,以及现有的任务卸载优化方案局限于降低时延或能耗,忽视了边缘节点所面临的安全问题,提出一种基于信任感知和近端策略优化算法(PPO)的任务卸载方案.首先,构建VEC网络架构,利用周围空闲车辆的计算资源,将任务在本地执行或卸载至RSU、空闲服务车辆进行计算处理,以降低系统整体时延与能耗.其次,构建一种基于多源赋权和奖惩机制的动态反馈信任评估模型,实现对边缘节点可信度的量化评估.最后,利用基于深度强化学习的PPO算法对任务卸载策略进行优化.实验结果表明,相较于DQN、D3QN和TASACO算法,所提方案具有更好的收敛性和稳定性,而且在任务执行时延和能耗等方面优于现有方案.展开更多
Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combinin...Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combining double deep Q-networks(DDQNs)and graph neural networks(GNNs)for joint routing and resource allocation.The framework uses GNNs to model the network topology and DDQNs to adaptively control routing and resource allocation,addressing interference and improving network performance.Simulation results show that the proposed approach outperforms traditional methods such as Closest-to-Destination(c2Dst),Max-SINR(mSINR),and Multi-Layer Perceptron(MLP)-based models,achieving approximately 23.5% improvement in throughput,50% increase in connection probability,and 17.6% reduction in number of hops,demonstrating its effectiveness in dynamic UAV networks.展开更多
Underwater images frequently suffer from chromatic distortion,blurred details,and low contrast,posing significant challenges for enhancement.This paper introduces AquaTree,a novel underwater image enhancement(UIE)meth...Underwater images frequently suffer from chromatic distortion,blurred details,and low contrast,posing significant challenges for enhancement.This paper introduces AquaTree,a novel underwater image enhancement(UIE)method that reformulates the task as a Markov Decision Process(MDP)through the integration of Monte Carlo Tree Search(MCTS)and deep reinforcement learning(DRL).The framework employs an action space of 25 enhancement operators,strategically grouped for basic attribute adjustment,color component balance,correction,and deblurring.Exploration within MCTS is guided by a dual-branch convolutional network,enabling intelligent sequential operator selection.Our core contributions include:(1)a multimodal state representation combining CIELab color histograms with deep perceptual features,(2)a dual-objective reward mechanism optimizing chromatic fidelity and perceptual consistency,and(3)an alternating training strategy co-optimizing enhancement sequences and network parameters.We further propose two inference schemes:an MCTS-based approach prioritizing accuracy at higher computational cost,and an efficient network policy enabling real-time processing with minimal quality loss.Comprehensive evaluations on the UIEB Dataset and Color correction and haze removal comparisons on the U45 Dataset demonstrate AquaTree’s superiority,significantly outperforming nine state-of-the-art methods across five established underwater image quality metrics.展开更多
The purpose of this research was to evaluate radiological safety in pediatric radiology in hospitals in the Kongo Central province of the DRC. To this end, we surveyed a convenience sample of 50 health professionals, ...The purpose of this research was to evaluate radiological safety in pediatric radiology in hospitals in the Kongo Central province of the DRC. To this end, we surveyed a convenience sample of 50 health professionals, including 10 radiologists working in the hospitals covered by the survey, to assess the practice of pediatric radiology and the degree of compliance with radiation protection principles for the safety of children and the environment. We collected radiophysical parameters to calculate entrance doses in pediatric radiology in radiology departments to determine the dosimetric level by comparison with the diagnostic reference levels of the International Commission on Radiological Protection (ICRP). All in all, we found that in Kongo Central in the DRC, many health personnel surveyed reported that more than 30% of requested radiological examinations are not justified. Also, after comparing the entrance doses produced in the surveyed departments with those of the International Commission on Radiological Protection (ICRP), a statistically significant difference was found in pediatric radiology between the average doses in five out of six surveyed departments and those of the ICRP. Therefore, almost all of the surveyed departments were found to be highly irradiating in children, while excessive X-ray irradiation in children can have significant effects due to their increased sensitivity to radiation. Among the risks are: increased cancer risks, damage to developing cells, potential genetic effects, and neurological effects. This is why support for implementing radiation protection principles is a necessity to promote the safety of patients and the environment against the harmful effects of X-rays in conventional radiology.展开更多
Embodied visual exploration is critical for building intelligent visual agents. This paper presents the neural exploration with feature-based visual odometry and tracking-failure-reduction policy(Ne OR), a framework f...Embodied visual exploration is critical for building intelligent visual agents. This paper presents the neural exploration with feature-based visual odometry and tracking-failure-reduction policy(Ne OR), a framework for embodied visual exploration that possesses the efficient exploration capabilities of deep reinforcement learning(DRL)-based exploration policies and leverages feature-based visual odometry(VO) for more accurate mapping and positioning results. An improved local policy is also proposed to reduce tracking failures of feature-based VO in weakly textured scenes through a refined multi-discrete action space, keyframe fusion, and an auxiliary task. The experimental results demonstrate that Ne OR has better mapping and positioning accuracy compared to other entirely learning-based exploration frameworks and improves the robustness of feature-based VO by significantly reducing tracking failures in weakly textured scenes.展开更多
文摘针对车载边缘计算(vehicular edge computing,VEC)中路侧单元(road side unit,RSU)资源受限和高负载的难题,以及现有的任务卸载优化方案局限于降低时延或能耗,忽视了边缘节点所面临的安全问题,提出一种基于信任感知和近端策略优化算法(PPO)的任务卸载方案.首先,构建VEC网络架构,利用周围空闲车辆的计算资源,将任务在本地执行或卸载至RSU、空闲服务车辆进行计算处理,以降低系统整体时延与能耗.其次,构建一种基于多源赋权和奖惩机制的动态反馈信任评估模型,实现对边缘节点可信度的量化评估.最后,利用基于深度强化学习的PPO算法对任务卸载策略进行优化.实验结果表明,相较于DQN、D3QN和TASACO算法,所提方案具有更好的收敛性和稳定性,而且在任务执行时延和能耗等方面优于现有方案.
文摘Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combining double deep Q-networks(DDQNs)and graph neural networks(GNNs)for joint routing and resource allocation.The framework uses GNNs to model the network topology and DDQNs to adaptively control routing and resource allocation,addressing interference and improving network performance.Simulation results show that the proposed approach outperforms traditional methods such as Closest-to-Destination(c2Dst),Max-SINR(mSINR),and Multi-Layer Perceptron(MLP)-based models,achieving approximately 23.5% improvement in throughput,50% increase in connection probability,and 17.6% reduction in number of hops,demonstrating its effectiveness in dynamic UAV networks.
基金supported by theHubei Provincial Technology Innovation Special Project and the Natural Science Foundation of Hubei Province under Grants 2023BEB024,2024AFC066,respectively.
文摘Underwater images frequently suffer from chromatic distortion,blurred details,and low contrast,posing significant challenges for enhancement.This paper introduces AquaTree,a novel underwater image enhancement(UIE)method that reformulates the task as a Markov Decision Process(MDP)through the integration of Monte Carlo Tree Search(MCTS)and deep reinforcement learning(DRL).The framework employs an action space of 25 enhancement operators,strategically grouped for basic attribute adjustment,color component balance,correction,and deblurring.Exploration within MCTS is guided by a dual-branch convolutional network,enabling intelligent sequential operator selection.Our core contributions include:(1)a multimodal state representation combining CIELab color histograms with deep perceptual features,(2)a dual-objective reward mechanism optimizing chromatic fidelity and perceptual consistency,and(3)an alternating training strategy co-optimizing enhancement sequences and network parameters.We further propose two inference schemes:an MCTS-based approach prioritizing accuracy at higher computational cost,and an efficient network policy enabling real-time processing with minimal quality loss.Comprehensive evaluations on the UIEB Dataset and Color correction and haze removal comparisons on the U45 Dataset demonstrate AquaTree’s superiority,significantly outperforming nine state-of-the-art methods across five established underwater image quality metrics.
文摘The purpose of this research was to evaluate radiological safety in pediatric radiology in hospitals in the Kongo Central province of the DRC. To this end, we surveyed a convenience sample of 50 health professionals, including 10 radiologists working in the hospitals covered by the survey, to assess the practice of pediatric radiology and the degree of compliance with radiation protection principles for the safety of children and the environment. We collected radiophysical parameters to calculate entrance doses in pediatric radiology in radiology departments to determine the dosimetric level by comparison with the diagnostic reference levels of the International Commission on Radiological Protection (ICRP). All in all, we found that in Kongo Central in the DRC, many health personnel surveyed reported that more than 30% of requested radiological examinations are not justified. Also, after comparing the entrance doses produced in the surveyed departments with those of the International Commission on Radiological Protection (ICRP), a statistically significant difference was found in pediatric radiology between the average doses in five out of six surveyed departments and those of the ICRP. Therefore, almost all of the surveyed departments were found to be highly irradiating in children, while excessive X-ray irradiation in children can have significant effects due to their increased sensitivity to radiation. Among the risks are: increased cancer risks, damage to developing cells, potential genetic effects, and neurological effects. This is why support for implementing radiation protection principles is a necessity to promote the safety of patients and the environment against the harmful effects of X-rays in conventional radiology.
基金supported by the National Natural Science Foundation of China (No.62202137)the China Postdoctoral Science Foundation (No.2023M730599)the Zhejiang Provincial Natural Science Foundation of China (No.LMS25F020009)。
文摘Embodied visual exploration is critical for building intelligent visual agents. This paper presents the neural exploration with feature-based visual odometry and tracking-failure-reduction policy(Ne OR), a framework for embodied visual exploration that possesses the efficient exploration capabilities of deep reinforcement learning(DRL)-based exploration policies and leverages feature-based visual odometry(VO) for more accurate mapping and positioning results. An improved local policy is also proposed to reduce tracking failures of feature-based VO in weakly textured scenes through a refined multi-discrete action space, keyframe fusion, and an auxiliary task. The experimental results demonstrate that Ne OR has better mapping and positioning accuracy compared to other entirely learning-based exploration frameworks and improves the robustness of feature-based VO by significantly reducing tracking failures in weakly textured scenes.
