Great strides have been made to realistically deploy multiple Unmanned Aerial Vehicles(UAVs)within the commercial domain,which demands a proper coordination and reliable communication among the UAVs.UAVs suffer from l...Great strides have been made to realistically deploy multiple Unmanned Aerial Vehicles(UAVs)within the commercial domain,which demands a proper coordination and reliable communication among the UAVs.UAVs suffer from limited time of flight.Conventional techniques suffer from high delay,low throughput,and early node death due to aerial topology of UAV networks.To deal with these issues,this paper proposes a UAV parameter vector which considers node energy,channel state information and mobility of UAVs.By intelligently estimating the proposed parameter,the state of UAV can be predicted closely.Accordingly,efficient clustering may be achieved by using suitable metaheuristic techniques.In the current work,Elbow method has been used to determine optimal cluster count in the deployed FANET.The proposed UAV parameter vector is then integrated into two popular hybrid metaheuristic algorithms,namely,water cycle-moth flame optimization(WCMFO)and Grey Wolf-Particle Swarm optimization(GWPSO),thereby enhancing the lifespan of the system.A methodology based on the holistic approach of parameter and signal formulation,estimation model for intelligent clustering,and statistical parameters for performance analysis is carried out by the energy consumption of the network and the alive node analysis.Rigorous simulations are run to demonstrate node density variations to validate the theoretical developments for various proportions of network system sizes.The proposed method presents significant improvement over conventional stateof-the-art methods.展开更多
因无人机机载激光雷达(Light detection and ranging,LiDAR)数据具有离散性,在生成数字高程模型(Digital elevation model,DEM)时需选择有效插值方法。以荒漠植被区为研究背景,使用零均值标准化方法归一化点云回波强度,利用肘方法确定...因无人机机载激光雷达(Light detection and ranging,LiDAR)数据具有离散性,在生成数字高程模型(Digital elevation model,DEM)时需选择有效插值方法。以荒漠植被区为研究背景,使用零均值标准化方法归一化点云回波强度,利用肘方法确定最佳聚类数目,采用K-means方法对点云强度值聚类得到地面点云。在此基础上,采用克里金(Kriging)方法插值抽稀率为20%和80%的地面点云数据,且将点云高程作为变量,建立RBF神经网络预测模型,并通过线性回归检验方法对模型进行精度分析,采用Delaunay三角网内插生成高精度DEM。结果表明:采用K-means方法实现最佳聚类数目为4的聚类,得到地面点云48722个,在点云较优抽稀率20%的情况下,径向基函数神经网络(Radical basis function neural network,RBFNN)训练时间为56s,点云高程预测的决定系数R2为0.887,均方根误差RMSE为0.168m。说明使用RBFNN对K-means聚类滤波得到的地面点云进行高程预测效果较好,可为基于点云构建高精度DEM提供参考。展开更多
目的探讨囊外法关节镜手术治疗顽固性网球肘的手术治疗技术规范,分析治疗效果及其影响因素。方法回顾性分析2012年3月~2016年11月因顽固性网球肘由同一位医生实施的囊外法关节镜手术连续50例的中长期随访资料,总结手术技术要点,包括刨...目的探讨囊外法关节镜手术治疗顽固性网球肘的手术治疗技术规范,分析治疗效果及其影响因素。方法回顾性分析2012年3月~2016年11月因顽固性网球肘由同一位医生实施的囊外法关节镜手术连续50例的中长期随访资料,总结手术技术要点,包括刨刀清理、去皮质化和微骨折、裂口缝合和术后石膏固定等。采用疼痛视觉模拟评分(Visual Analogue Scale,VAS)、Mayo肘功能评分和臂肩功能障碍评分(Disability of Arm,Shoulder and Hand,DASH)等评价术后效果,采用多元logistic回归分析各技术要点对手术效果的影响。结果50例随访13~60个月,平均36.7月。未发生血管神经损伤、感染等严重并发症。术后VAS、Mayo、DASH评分均较术前明显改善(P=0.000)。单因素及多因素分析显示术后石膏固定与术后VAS评分预后良好相关(OR=6.525,95%CI:1.005~42.364,P=0.049),术前Mayo评分与术后Mayo评分预后良好相关(OR=1.059,95%CI:1.003~1.119,P=0.040)。结论囊外法关节镜治疗顽固性网球肘的疗效满意,操作安全性高。主要技术要点有刨刀清理、去皮质化和微骨折、镜下裂口缝合和术后石膏固定。其中术后采用石膏固定与长期预后良好相关。展开更多
文摘Great strides have been made to realistically deploy multiple Unmanned Aerial Vehicles(UAVs)within the commercial domain,which demands a proper coordination and reliable communication among the UAVs.UAVs suffer from limited time of flight.Conventional techniques suffer from high delay,low throughput,and early node death due to aerial topology of UAV networks.To deal with these issues,this paper proposes a UAV parameter vector which considers node energy,channel state information and mobility of UAVs.By intelligently estimating the proposed parameter,the state of UAV can be predicted closely.Accordingly,efficient clustering may be achieved by using suitable metaheuristic techniques.In the current work,Elbow method has been used to determine optimal cluster count in the deployed FANET.The proposed UAV parameter vector is then integrated into two popular hybrid metaheuristic algorithms,namely,water cycle-moth flame optimization(WCMFO)and Grey Wolf-Particle Swarm optimization(GWPSO),thereby enhancing the lifespan of the system.A methodology based on the holistic approach of parameter and signal formulation,estimation model for intelligent clustering,and statistical parameters for performance analysis is carried out by the energy consumption of the network and the alive node analysis.Rigorous simulations are run to demonstrate node density variations to validate the theoretical developments for various proportions of network system sizes.The proposed method presents significant improvement over conventional stateof-the-art methods.
文摘因无人机机载激光雷达(Light detection and ranging,LiDAR)数据具有离散性,在生成数字高程模型(Digital elevation model,DEM)时需选择有效插值方法。以荒漠植被区为研究背景,使用零均值标准化方法归一化点云回波强度,利用肘方法确定最佳聚类数目,采用K-means方法对点云强度值聚类得到地面点云。在此基础上,采用克里金(Kriging)方法插值抽稀率为20%和80%的地面点云数据,且将点云高程作为变量,建立RBF神经网络预测模型,并通过线性回归检验方法对模型进行精度分析,采用Delaunay三角网内插生成高精度DEM。结果表明:采用K-means方法实现最佳聚类数目为4的聚类,得到地面点云48722个,在点云较优抽稀率20%的情况下,径向基函数神经网络(Radical basis function neural network,RBFNN)训练时间为56s,点云高程预测的决定系数R2为0.887,均方根误差RMSE为0.168m。说明使用RBFNN对K-means聚类滤波得到的地面点云进行高程预测效果较好,可为基于点云构建高精度DEM提供参考。
文摘目的探讨囊外法关节镜手术治疗顽固性网球肘的手术治疗技术规范,分析治疗效果及其影响因素。方法回顾性分析2012年3月~2016年11月因顽固性网球肘由同一位医生实施的囊外法关节镜手术连续50例的中长期随访资料,总结手术技术要点,包括刨刀清理、去皮质化和微骨折、裂口缝合和术后石膏固定等。采用疼痛视觉模拟评分(Visual Analogue Scale,VAS)、Mayo肘功能评分和臂肩功能障碍评分(Disability of Arm,Shoulder and Hand,DASH)等评价术后效果,采用多元logistic回归分析各技术要点对手术效果的影响。结果50例随访13~60个月,平均36.7月。未发生血管神经损伤、感染等严重并发症。术后VAS、Mayo、DASH评分均较术前明显改善(P=0.000)。单因素及多因素分析显示术后石膏固定与术后VAS评分预后良好相关(OR=6.525,95%CI:1.005~42.364,P=0.049),术前Mayo评分与术后Mayo评分预后良好相关(OR=1.059,95%CI:1.003~1.119,P=0.040)。结论囊外法关节镜治疗顽固性网球肘的疗效满意,操作安全性高。主要技术要点有刨刀清理、去皮质化和微骨折、镜下裂口缝合和术后石膏固定。其中术后采用石膏固定与长期预后良好相关。
