In this study,a 3D virtual reality and visualization engine for rendering the ocean,named VV-Ocean,is designed for marine applications.The design goals of VV-Ocean aim at high fidelity simulation of ocean environment,...In this study,a 3D virtual reality and visualization engine for rendering the ocean,named VV-Ocean,is designed for marine applications.The design goals of VV-Ocean aim at high fidelity simulation of ocean environment,visualization of massive and multidimensional marine data,and imitation of marine lives.VV-Ocean is composed of five modules,i.e.memory management module,resources management module,scene management module,rendering process management module and interaction management module.There are three core functions in VV-Ocean:reconstructing vivid virtual ocean scenes,visualizing real data dynamically in real time,imitating and simulating marine lives intuitively.Based on VV-Ocean,we establish a sea-land integration platform which can reproduce drifting and diffusion processes of oil spilling from sea bottom to surface.Environment factors such as ocean current and wind field have been considered in this simulation.On this platform oil spilling process can be abstracted as movements of abundant oil particles.The result shows that oil particles blend with water well and the platform meets the requirement for real-time and interactive rendering.VV-Ocean can be widely used in ocean applications such as demonstrating marine operations,facilitating maritime communications,developing ocean games,reducing marine hazards,forecasting the weather over oceans,serving marine tourism,and so on.Finally,further technological improvements of VV-Ocean are discussed.展开更多
The study of marine data visualization is of great value. Marine data, due to its large scale, random variation and multiresolution in nature, are hard to be visualized and analyzed. Nowadays, constructing an ocean mo...The study of marine data visualization is of great value. Marine data, due to its large scale, random variation and multiresolution in nature, are hard to be visualized and analyzed. Nowadays, constructing an ocean model and visualizing model results have become some of the most important research topics of ‘Digital Ocean'. In this paper, a spherical ray casting method is developed to improve the traditional ray-casting algorithm and to make efficient use of GPUs. Aiming at the ocean current data, a 3D view-dependent line integral convolution method is used, in which the spatial frequency is adapted according to the distance from a camera. The study is based on a 3D virtual reality and visualization engine, namely the VV-Ocean. Some interactive operations are also provided to highlight the interesting structures and the characteristics of volumetric data. Finally, the marine data gathered in the East China Sea are displayed and analyzed. The results show that the method meets the requirements of real-time and interactive rendering.展开更多
目的探讨镜像视觉反馈结合运动再学习在卒中后上肢功能障碍康复中的疗效。方法前瞻性连续纳入2016年8月至2018年8月期间在南阳市第九人民医院接受卒中康复治疗的上肢功能障碍患者。依据随机数字表法将患者分为镜像视觉反馈结合运动再学...目的探讨镜像视觉反馈结合运动再学习在卒中后上肢功能障碍康复中的疗效。方法前瞻性连续纳入2016年8月至2018年8月期间在南阳市第九人民医院接受卒中康复治疗的上肢功能障碍患者。依据随机数字表法将患者分为镜像视觉反馈结合运动再学习组(镜像疗法组)和单独运动再学习组(对照组)。每天康复治疗1~2次,每周5 d,1周为1个疗程,共4个疗程。治疗前后分别采用Fugl-Meyer评定量表(Fugl-Meyer Assessment,FMA)上肢部分评定上肢功能,采用Carroll上肢功能测试量表(Upper Extremity Function Test,UEFT)评定手功能,采用上肢动作研究量表(Action Research Arm Test,ARAT)评定上肢操作性和灵活性,采用运动功能评估量表(Motor Assessment Scale,MAS)评定手、手指和上肢运动功能,采用视觉模拟评分(Visual Analogue Scale,VAS)评定上肢疼痛程度,采用改良巴塞尔指数(modified Barthel Index,MBI)评定日常生活自理能力,采用改良Ashworth痉挛量表评定上肢肩关节、腕关节和肘关节痉挛程度。依据FMA评分对两组患者的临床疗效进行评定,>31分定义为疗效优良。结果共纳入60例伴有上肢功能障碍的卒中患者,镜像疗法组和对照组各30例。两组年龄、性别、病程、卒中类型和卒中部位以及各项基线评分均无统计学差异。两组治疗后FMA评分、UEFT评分、ARAT评分、MAS评分和MBI评分以及改良Ashworth分级Ⅰ级和Ⅰ^+级的比例均显著高于治疗前(P均<0.05),而VAS评分以及改良Ashworth分级Ⅲ级和Ⅳ级的比例显著低于治疗前(P均<0.05)。治疗后镜像疗法组FMA评分、UEFT评分、ARAT评分、MAS评分和MBI评分以及Ashworth分级Ⅰ级和Ⅰ^+级的比例均显著高于对照组(P均<0.05),VAS评分以及改良Ashworth分级Ⅲ级的比例均显著低于对照组(P均<0.05)。根据FMA评分,镜像疗法组患者治疗的优良率显著高于对照组(93.3%对70.0%;χ^2=5.455,P=0.020)。结论镜像视觉反馈结合运动再学习在卒中后上肢功能障碍康复治疗中的疗效优于单独运动再学习。展开更多
基金supported by the Global Change Research Program of China under Project 2012CB955603the Natural Science Foundation of China under Project 41076115+2 种基金the National Basic Research Program of China under Project 2009CB723903the Public Science and Technology Research Funds of the Ocean under Project 201005019the National High-Tech Research and Development Program of China under Project 2008AA121701
文摘In this study,a 3D virtual reality and visualization engine for rendering the ocean,named VV-Ocean,is designed for marine applications.The design goals of VV-Ocean aim at high fidelity simulation of ocean environment,visualization of massive and multidimensional marine data,and imitation of marine lives.VV-Ocean is composed of five modules,i.e.memory management module,resources management module,scene management module,rendering process management module and interaction management module.There are three core functions in VV-Ocean:reconstructing vivid virtual ocean scenes,visualizing real data dynamically in real time,imitating and simulating marine lives intuitively.Based on VV-Ocean,we establish a sea-land integration platform which can reproduce drifting and diffusion processes of oil spilling from sea bottom to surface.Environment factors such as ocean current and wind field have been considered in this simulation.On this platform oil spilling process can be abstracted as movements of abundant oil particles.The result shows that oil particles blend with water well and the platform meets the requirement for real-time and interactive rendering.VV-Ocean can be widely used in ocean applications such as demonstrating marine operations,facilitating maritime communications,developing ocean games,reducing marine hazards,forecasting the weather over oceans,serving marine tourism,and so on.Finally,further technological improvements of VV-Ocean are discussed.
基金supported by the Natural Science Foundation of China under Project 41076115the Global Change Research Program of China under project 2012CB955603the Public Science and Technology Research Funds of the Ocean under project 201005019
文摘The study of marine data visualization is of great value. Marine data, due to its large scale, random variation and multiresolution in nature, are hard to be visualized and analyzed. Nowadays, constructing an ocean model and visualizing model results have become some of the most important research topics of ‘Digital Ocean'. In this paper, a spherical ray casting method is developed to improve the traditional ray-casting algorithm and to make efficient use of GPUs. Aiming at the ocean current data, a 3D view-dependent line integral convolution method is used, in which the spatial frequency is adapted according to the distance from a camera. The study is based on a 3D virtual reality and visualization engine, namely the VV-Ocean. Some interactive operations are also provided to highlight the interesting structures and the characteristics of volumetric data. Finally, the marine data gathered in the East China Sea are displayed and analyzed. The results show that the method meets the requirements of real-time and interactive rendering.
文摘目的探讨镜像视觉反馈结合运动再学习在卒中后上肢功能障碍康复中的疗效。方法前瞻性连续纳入2016年8月至2018年8月期间在南阳市第九人民医院接受卒中康复治疗的上肢功能障碍患者。依据随机数字表法将患者分为镜像视觉反馈结合运动再学习组(镜像疗法组)和单独运动再学习组(对照组)。每天康复治疗1~2次,每周5 d,1周为1个疗程,共4个疗程。治疗前后分别采用Fugl-Meyer评定量表(Fugl-Meyer Assessment,FMA)上肢部分评定上肢功能,采用Carroll上肢功能测试量表(Upper Extremity Function Test,UEFT)评定手功能,采用上肢动作研究量表(Action Research Arm Test,ARAT)评定上肢操作性和灵活性,采用运动功能评估量表(Motor Assessment Scale,MAS)评定手、手指和上肢运动功能,采用视觉模拟评分(Visual Analogue Scale,VAS)评定上肢疼痛程度,采用改良巴塞尔指数(modified Barthel Index,MBI)评定日常生活自理能力,采用改良Ashworth痉挛量表评定上肢肩关节、腕关节和肘关节痉挛程度。依据FMA评分对两组患者的临床疗效进行评定,>31分定义为疗效优良。结果共纳入60例伴有上肢功能障碍的卒中患者,镜像疗法组和对照组各30例。两组年龄、性别、病程、卒中类型和卒中部位以及各项基线评分均无统计学差异。两组治疗后FMA评分、UEFT评分、ARAT评分、MAS评分和MBI评分以及改良Ashworth分级Ⅰ级和Ⅰ^+级的比例均显著高于治疗前(P均<0.05),而VAS评分以及改良Ashworth分级Ⅲ级和Ⅳ级的比例显著低于治疗前(P均<0.05)。治疗后镜像疗法组FMA评分、UEFT评分、ARAT评分、MAS评分和MBI评分以及Ashworth分级Ⅰ级和Ⅰ^+级的比例均显著高于对照组(P均<0.05),VAS评分以及改良Ashworth分级Ⅲ级的比例均显著低于对照组(P均<0.05)。根据FMA评分,镜像疗法组患者治疗的优良率显著高于对照组(93.3%对70.0%;χ^2=5.455,P=0.020)。结论镜像视觉反馈结合运动再学习在卒中后上肢功能障碍康复治疗中的疗效优于单独运动再学习。