全国矿产资源潜力评价(2006—2013)工作历时8年,是中华人民共和国成立以来规模最大的矿情调查工作之一,形成了海量(TB级)的成果数据。如何高效地管理该数据集,实现数据的广泛应用,成为数据共享服务的关键和难点。文章以全国矿产潜力评...全国矿产资源潜力评价(2006—2013)工作历时8年,是中华人民共和国成立以来规模最大的矿情调查工作之一,形成了海量(TB级)的成果数据。如何高效地管理该数据集,实现数据的广泛应用,成为数据共享服务的关键和难点。文章以全国矿产潜力评价成果数据为基础,运用GIS技术,研究了地质大数据存储管理、基于元数据的查询检索、空间数据可视化等关键技术,提出了一种针对海量、多源、异构的地质数据的统一管理模型。首先,构建元数据库作为存储不同类型数据的索引,完成数据的统一集成管理,同时实现数据的快速查询访问;其次,借助强大成熟的Mapgis K9功能模块和开源的NASA World Wind三维数字地球引擎,进行二次开发,搭建适合于矿产资源潜力评价成果数据信息管理系统平台,为矿产资源潜力评价成果数据推广应用提供信息技术支撑,提高潜力评价数据的信息化服务能力。展开更多
Background A medical content-based image retrieval(CBIR)system is designed to retrieve images from large imaging repositories that are visually similar to a user′s query image.CBIR is widely used in evidence-based di...Background A medical content-based image retrieval(CBIR)system is designed to retrieve images from large imaging repositories that are visually similar to a user′s query image.CBIR is widely used in evidence-based diagnosis,teaching,and research.Although the retrieval accuracy has largely improved,there has been limited development toward visualizing important image features that indicate the similarity of retrieved images.Despite the prevalence of 3D volumetric data in medical imaging such as computed tomography(CT),current CBIR systems still rely on 2D cross-sectional views for the visualization of retrieved images.Such 2D visualization requires users to browse through the image stacks to confirm the similarity of the retrieved images and often involves mental reconstruction of 3D information,including the size,shape,and spatial relations of multiple structures.This process is time-consuming and reliant on users'experience.Methods In this study,we proposed an importance-aware 3D volume visualization method.The rendering parameters were automatically optimized to maximize the visibility of important structures that were detected and prioritized in the retrieval process.We then integrated the proposed visualization into a CBIR system,thereby complementing the 2D cross-sectional views for relevance feedback and further analyses.Results Our preliminary results demonstrate that 3D visualization can provide additional information using multimodal positron emission tomography and computed tomography(PETCT)images of a non-small cell lung cancer dataset.展开更多
To improve fishing gear efficiency, it is important to understand the interactions among sea current, fishing vessel, line hauler, and catches during pelagic longline gear retrieval. In this study, fishing gear config...To improve fishing gear efficiency, it is important to understand the interactions among sea current, fishing vessel, line hauler, and catches during pelagic longline gear retrieval. In this study, fishing gear configuration parameters, operational parameters, and 3 D ocean current data were collected from Indian Ocean. Dynamic models of pelagic longline gear retrieval were built using the lumped mass method and solved using the Euler-Trapezoidal method. From the results, the pulling force of line hauler exerted on the gear was 2800–3600 N. There were no significant differences(P > 0.05) between the time of the hook retrieval measured at sea and that obtained from the simulation. The absolute values of the movement velocity at representative nodes along the X, Y, and Z axes were 0.01–25.5 m s^(-1). These results suggest that the dynamic model of longline fishing gear retrieval can be used to analyze the interactions among sea current, fishing vessel, line hauler, longline gear, and catches, and to acquire the basic data for optimizing the design of the line hauler. Moreover, the model can serve as a reference to study the hydrodynamic performance of other fishing gears during the hauling process.展开更多
作为非线性降维的有效算法,局部线性嵌入(Locally linear embedding)(LLE)和深度自编码网络,被广泛应用于数据挖掘、故障诊断、模式识别等多种领域。本文采用定性与定量相结合的方法,对两种算法进行了对比研究。对LLE算法的基本原理进...作为非线性降维的有效算法,局部线性嵌入(Locally linear embedding)(LLE)和深度自编码网络,被广泛应用于数据挖掘、故障诊断、模式识别等多种领域。本文采用定性与定量相结合的方法,对两种算法进行了对比研究。对LLE算法的基本原理进行了简单介绍。描述了深度自编码网络的理论与模型。提供了数值实验分析,在可视化,人脸识别以及文本检索方面,对两种降维方法进行比较,得到各自适用的优缺点。展开更多
文摘全国矿产资源潜力评价(2006—2013)工作历时8年,是中华人民共和国成立以来规模最大的矿情调查工作之一,形成了海量(TB级)的成果数据。如何高效地管理该数据集,实现数据的广泛应用,成为数据共享服务的关键和难点。文章以全国矿产潜力评价成果数据为基础,运用GIS技术,研究了地质大数据存储管理、基于元数据的查询检索、空间数据可视化等关键技术,提出了一种针对海量、多源、异构的地质数据的统一管理模型。首先,构建元数据库作为存储不同类型数据的索引,完成数据的统一集成管理,同时实现数据的快速查询访问;其次,借助强大成熟的Mapgis K9功能模块和开源的NASA World Wind三维数字地球引擎,进行二次开发,搭建适合于矿产资源潜力评价成果数据信息管理系统平台,为矿产资源潜力评价成果数据推广应用提供信息技术支撑,提高潜力评价数据的信息化服务能力。
文摘Background A medical content-based image retrieval(CBIR)system is designed to retrieve images from large imaging repositories that are visually similar to a user′s query image.CBIR is widely used in evidence-based diagnosis,teaching,and research.Although the retrieval accuracy has largely improved,there has been limited development toward visualizing important image features that indicate the similarity of retrieved images.Despite the prevalence of 3D volumetric data in medical imaging such as computed tomography(CT),current CBIR systems still rely on 2D cross-sectional views for the visualization of retrieved images.Such 2D visualization requires users to browse through the image stacks to confirm the similarity of the retrieved images and often involves mental reconstruction of 3D information,including the size,shape,and spatial relations of multiple structures.This process is time-consuming and reliant on users'experience.Methods In this study,we proposed an importance-aware 3D volume visualization method.The rendering parameters were automatically optimized to maximize the visibility of important structures that were detected and prioritized in the retrieval process.We then integrated the proposed visualization into a CBIR system,thereby complementing the 2D cross-sectional views for relevance feedback and further analyses.Results Our preliminary results demonstrate that 3D visualization can provide additional information using multimodal positron emission tomography and computed tomography(PETCT)images of a non-small cell lung cancer dataset.
基金funded by the National High Technology Research and Development Program of China (No. 2012 AA092302)the Specialized Research Fund for the Doctoral Program of Higher Education (No. 20113104110004)Shanghai Municipal Education Commission Innovation Project (No. 12ZZ168)
文摘To improve fishing gear efficiency, it is important to understand the interactions among sea current, fishing vessel, line hauler, and catches during pelagic longline gear retrieval. In this study, fishing gear configuration parameters, operational parameters, and 3 D ocean current data were collected from Indian Ocean. Dynamic models of pelagic longline gear retrieval were built using the lumped mass method and solved using the Euler-Trapezoidal method. From the results, the pulling force of line hauler exerted on the gear was 2800–3600 N. There were no significant differences(P > 0.05) between the time of the hook retrieval measured at sea and that obtained from the simulation. The absolute values of the movement velocity at representative nodes along the X, Y, and Z axes were 0.01–25.5 m s^(-1). These results suggest that the dynamic model of longline fishing gear retrieval can be used to analyze the interactions among sea current, fishing vessel, line hauler, longline gear, and catches, and to acquire the basic data for optimizing the design of the line hauler. Moreover, the model can serve as a reference to study the hydrodynamic performance of other fishing gears during the hauling process.
文摘作为非线性降维的有效算法,局部线性嵌入(Locally linear embedding)(LLE)和深度自编码网络,被广泛应用于数据挖掘、故障诊断、模式识别等多种领域。本文采用定性与定量相结合的方法,对两种算法进行了对比研究。对LLE算法的基本原理进行了简单介绍。描述了深度自编码网络的理论与模型。提供了数值实验分析,在可视化,人脸识别以及文本检索方面,对两种降维方法进行比较,得到各自适用的优缺点。
