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71.
利用运动学模型 ,研究了弯曲急流控制参数为定常和发生周期扰动时的流体质点运动轨迹以及混沌现象的产生机制。发现弯曲急流控制参数为定常时 ,流体质点的运动为规则的周期运动 ;而控制参数发生扰动时 ,可以发生流体质点在不同特征区的穿行 ,流体质点的运动具有混沌特性。弯曲急流的相速、振幅发生周期扰动均可使流体质点的运动出现混沌特性。作者从运动学上对弯曲急流区的漂流浮标混沌特性给出了解释。  相似文献   
72.
复杂网络视角下时空行为轨迹模式挖掘研究   总被引:3,自引:0,他引:3  
张文佳  季纯涵  谢森锴 《地理科学》2021,41(9):1505-1514
针对时空行为轨迹大数据的序列性、时空交互性、多维度性等复杂特性,构建结合时间地理学与复杂网络的分析框架,建立时空行为路径与时空行为网络之间的转换关系,利用复杂网络社群发现算法对时空行为轨迹进行社群聚类、模式挖掘与可视化。基于北京郊区居民一周内活动出行GPS轨迹数据的案例分析发现:① 复杂网络分析方法可以有效挖掘具有相似行为的群体特征和识别出典型的行为模式。② 可以灵活处理多元异构与多维度的行为轨迹大数据以及满足不同叙事、不同空间相互作用、不同时序的应用需求。③ 北京郊区被调查居民的行为模式存在日间差异与空间分异。  相似文献   
73.
黄海绿潮分布年际变化特征分析   总被引:2,自引:1,他引:1  
郭伟  赵亮  李秀梅 《海洋学报》2016,38(12):36-45
2008-2015年,连续8年在黄海海域暴发大规模绿潮,但因暴发时间、规模及漂移路径的不同,对沿海地区造成的环境影响和经济损失大不相同。本文利用EOS/MODIS卫星的多通道资料,采用NDVI算法获取绿潮信息,给出了2007年以来5-8月所有无云或少云晴朗天气下黄海海面绿潮的分布情况。黄海绿潮覆盖面积变化每年呈现单峰值分布,MODIS卫星在5月中旬至6月初首次发现绿潮,随后30~40 d内达到覆盖面积峰值,7月快速消退,8月上旬完全消失。2007年绿潮出现首年覆盖面积极低,发现绿潮时面积均未超过50 km2;2008年和2009年绿潮覆盖面积峰值分别为3 110 km2和4 075 km2,自此绿潮灾害成为新的海洋环境事件;2010-2012年绿潮暴发规模异常低值,各年覆盖面积峰值均未超过1 800 km2,但从2013年开始绿潮规模逐渐攀升,到2015年绿潮覆盖面积峰值达5 629 km2,持续可达98 d,覆盖面积和持续时间为历年之最。绿潮漂移路径可归纳分为3类:2008年和2011年绿潮主体先北向漂移越过34°30'N后,西北向垂直于岸线漂移,主要对连云港、日照和青岛造成较大影响;2009年和2012年绿潮主体先北向漂移越过35°N后,东北向平行于岸线漂移,故只有少许绿潮上岸;2010年、2013-2015年绿潮主体北向漂移至近岸后东北向沿岸漂移,对日照、青岛和荣成沿海造成大面积影响。所有年份绿潮影响范围均限于南黄海内,东侧边界最远未越过124.2°E。  相似文献   
74.
针对现有路网轨迹数据模型与时空索引结构自适应调节能力低的问题,提出了一种面向路网轨迹的自适应数据模型与时空索引结构,以支持路网时空轨迹的高效存储与查询。所提出的自适应时空数据模型为多层CLR数据模型的扩展,该模型以从时空轨迹群中挖掘的高频路网路径为主要网络线性元素建立自适应线性基准,并根据自适应线性基准对路网时空轨迹进行转换,转换后的时空轨迹其时空子实体数量变少,可以通过更高的效率进行存储;所提出的自适应时空索引结构为基于LRS的时空索引结构的扩展,该索引结构根据自适应线性基准构建自适应线性参考系统,基于自适应线性参考系统的索引结构其保存的时空子实体数量变少,可以通过更高的效率进行时空查询。为了验证所提出方法的有效性,本文最后采用真实开源T-Drive出租车轨迹数据集与人工合成轨迹数据集进行了充足的实验。实验以2种常见的时空相交查询类型为例,将所提出的方法与原始数据模型以及时空索引结构进行了存储效率和查询效率的对比。对比分析结果表明,所提出的自适应数据模型与索引结构最高能够提升40%的存储效率以及50%的查询效率,为路网轨迹数据的管理提供了新的解决方案。  相似文献   
75.
In this paper,we focus on trajectories at intersections regulated by various regulation types such as traffic lights,priority/yield signs,and right-of-way rules.We test some methods to detect and recognize movement patterns from GPS trajectories,in terms of their geometrical and spatio-temporal components.In particular,we first find out the main paths that vehicles follow at such locations.We then investigate the way that vehicles follow these geometric paths(how do they move along them).For these scopes,machine learning methods are used and the performance of some known methods for trajectory similarity measurement(DTW,Hausdorff,and Fréchet distance)and clustering(Affinity propagation and Agglomerative clustering)are compared based on clustering accuracy.Afterward,the movement behavior observed at six different intersections is analyzed by identifying certain movement patterns in the speed-and time-profiles of trajectories.We show that depending on the regulation type,different movement patterns are observed at intersections.This finding can be useful for intersection categorization according to traffic regulations.The practicality of automatically identifying traffic rules from GPS tracks is the enrichment of modern maps with additional navigation-related information(traffic signs,traffic lights,etc.).  相似文献   
76.
Prograde P–T–t paths of eclogites are often ambiguous owing to high variance of mineral assemblages, large uncertainty in isotopic age determinations and/or variable degree of retrograde equilibration. We investigated these issues using the barroisite eclogites from the Lanterman Range, northern Victoria Land, Antarctica, which are relatively uncommon but free of retrogression. These eclogites revealed three stages of prograde metamorphism, defining two distinctive P–T trajectories, M1–2 and M3. Inclusion minerals in garnet porphyroblasts suggest that initial prograde assemblages (M1) consist of garnet+omphacite+barroisite/Mg‐pargasite+epidote+phengite+paragonite+rutile/titanite+quartz, and subsequent M2 assemblages of garnet+omphacite+barroisite+phengite+rutile±quartz. The inclusion‐rich inner part of garnet porphyroblasts preserves a bell‐shaped Mn profile of the M1, whereas the inclusion‐poor outer part (M2) is typified by the outward decrease in Ca/Mg and XFe (=Fe2+/(Fe2++Mg)) values. A pseudosection modelling employing fractionated bulk‐rock composition suggests that the eclogites have initially evolved from ~15 to 20 kbar and 520–570°C (M1) to ~22–25 kbar and 630–650°C (M2). The latter is in accordance with P–T conditions estimated from two independent geothermobarometers: the garnet–clinopyroxene–phengite (~25 ± 3 kbar and 660 ± 100°C) and Zr‐in‐rutile (~650–700°C at 2227 kbar). The second segment (M3A–B) of prograde P–T path is recorded in the grossular‐rich overgrowth rim of garnet. Apart from disequilibrium growth of the M3A garnet, ubiquitous overgrowth of the M3B garnet permits us to estimate the P–T conditions at ~26 ± 3 kbar and 720 ± 80°C. The cathodoluminescence (CL) imaging of zircon grains separated from a barroisite eclogite revealed three distinct zones with bright rim, dark mantle and moderately dark core. Eclogitic phases such as garnet, omphacite, epidote and rutile are present as fine‐grained inclusions in the mantle and rim of zircon, in contrast to their absence in the core. The sensitive high‐resolution ion microprobe U–Pb dating on metamorphic mantle domains and neoblasts yielded a weighted mean 206Pb/238U age of 515 ± 4 Ma (), representing the time of the M2 stage. On the other hand, overgrowth rims as well as bright‐CL neoblasts of zircon were dated at 498 ± 11 Ma (), corresponding to the M3. Average burial rates estimated from the M2 and M3 ages are too low (<2 mm/year) for cold subduction regime (~5–10°C/km), suggesting that an exhumation stage intervened between two prograde segments of P–T path. Thus, the P–T–t evolution of barroisite eclogites is typified by two discrete episodes with an c. 15 Ma gap during the middle Cambrian subduction of the Antarctic Ross Orogeny.  相似文献   
77.
ABSTRACT

Trajectory data mining is a lively research field in the domain of spatio-temporal data mining. Trajectory pattern mining comprises a set of specific pattern mining methods, which are applied as consecutive steps on a trajectory with the goal to extract and classify re-occurring spatio-temporal patterns. Despite the common nature and frequent usage of such methods by the GIScience community, a methodological approach is missing so far, especially when it comes to the use of machine learning-based classification methods. The current work closes this gap by proposing and evaluating a machine learning-based 3-steps trajectory data mining methodology using the detection and classification of stop points in vehicle trajectories as example. The work describes in detail the applied methodologies with respect to the three mining steps ‘stop detection’, ‘feature extraction’ and ‘classification in traffic-relevant and non-traffic-relevant stops’ and evaluates six machine learning-based classification algorithms using a real-world dataset of 15,498 vehicle trajectories with 5,899 detected stops (thereof 2,032 manually classified). Due to its exemplary nature, the presented methodology is suited to act as blueprint for similar trajectory data mining problems.  相似文献   
78.
This study compared the longitudinal size distribution of bluegill bullies (Gobiomorphus hubbsi) from Hutt River with existing data on the size distributions of bluegill bullies and torrentfish (Cheimarrichthys fosteri) from Rakaia River, New Zealand. All populations had longitudinal trajectories that showed some increase in size with distance upstream. This increase in size appeared to primarily reflect the influence of amphidromous life styles, where juveniles diffuse upstream from the sea. However, a quantile regression analysis revealed differences in growth and migration rate between the two bluegill bully populations; bluegill bullies from Rakaia River grew at a slower rate and showed variation in migration rate within their population that was not evident in the Hutt River population. Null models were also generated for each population to test for the presence of all size classes of fish in the lower reaches of each river. Both bluegill bully populations showed a significant absence of the largest size classes in the lower reaches and the size trajectories differed significantly from the null models. This difference suggests that all individual bluegill bullies continuously move upstream throughout their lives. In comparison, the size distribution of torrentfish closely resembled the null model, indicating that some individuals did not migrate upstream to the same extent as others.  相似文献   
79.
Current tracking technologies enable collection of data, describing movements of various kinds of objects, including people, animals, icebergs, vehicles, containers with goods and so on. Analysis of movement data is now a hot research topic. However, most of the suggested analysis methods deal with movement data alone. Little has been done to support the analysis of movement in its spatio-temporal context, which includes various spatial and temporal objects as well as diverse properties associated with spatial locations and time moments. Comprehensive analysis of movement requires detection and analysis of relations that occur between moving objects and elements of the context in the process of the movement. We suggest a conceptual model in which movement is considered as a combination of spatial events of diverse types and extents in space and time. Spatial and temporal relations occur between movement events and elements of the spatial and temporal contexts. The model gives a ground to a generic approach based on extraction of interesting events from trajectories and treating the events as independent objects. By means of a prototype implementation, we tested the approach on complex real data about movement of wild animals. The testing showed the validity of the approach.  相似文献   
80.
With a huge volume of trajectories being collected and stored in databases, more and more researchers try to discover outlying trajectories from trajectory databases. In this article, we propose a novel framework called relative distance-based trajectory outliers detection (RTOD). In RTOD, we first employed relative distances to measure the dissimilarity between trajectory segments, and then formally defined the outlying trajectories based on distance measures. In order to improve the time performance, we proposed an optimization method that employs R-tree and local feature correlation matrix to eliminate unrelated trajectory segments. Finally, we conducted extensive experiments to estimate the advantages of the proposed approach. The experimental results show that our proposed approach is more efficient and effective at identifying outlying trajectories than existing algorithms. Particularly, we analyzed the effect of each parameter in theory.  相似文献   
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