全部文献期刊学位论文会议报纸专利标准年鉴图书|学者科研项目
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作者:Yongqi Chen , Zhilin Li
来源:[J].Chinese Science Bulletin(IF 1.319), 2001, Vol.46 (21), pp.1778-1782Springer
摘要:Abstract(#br)Satellite synthetic aperture radar (SAR) interferometry is used to investigate the slowly accumulating ground settlement at the new Chek Lap Kok Airport in Hong Kong. Most of the land occupied by the airport was reclaimed from the sea and therefore certain ground set...
作者:Yongqi Chen , Zhilin Li
来源:[J].Chinese Science Bulletin(IF 1.319), 2000, Vol.45 (24), pp.2231-2236Springer
摘要:Abstract(#br)Data sets of the changes of the length of day, the sea surface temperature in the eastern Pacific and of the sea level in Hong Long from tide gauge observations are used to analyze and reveal the reflections in the observations of the length of day and the sea l...
作者:Dewen Du , Yongqi Chen
来源:[J].Geo-Marine Letters(IF 1.852), 2007, Vol.27 (5), pp.339-344Springer
摘要:Abstract(#br)In recent years, increasing attention is being given to investigating the relationships between seafloor echo strength and sediment type. This aspect was assessed in a course of a marine survey conducted in Jiaozhou Bay near the city of Qingdao, China, where various ...
作者:... Youlin Xu , Kenny Kwok , Yongqi Chen
来源:[J].Journal of Geodesy(IF 2.808), 2010, Vol.84 (2), pp.145-158Springer
摘要:Abstract(#br)Carrier-phase multipath effects are one of the most significant error sources in precise Global Positioning System (GPS) positioning applications. A new sidereal filtering algorithm based on single differences is developed to mitigate multipath effects for short-base...
作者:Yongqi Chen , Chunmei Zhao
来源:[J].Journal of Geodesy(IF 2.808), 2010, Vol.84 (10), pp.597-604Springer
摘要:Abstract(#br)To ensure reliable ambiguity resolution, ambiguity validation is an indispensable step. It has been a challenge for many years and is far from being resolved. Over the past years, various ambiguity validation methods have been proposed, such as F -ratio test, R -rati...
作者:... Han Hu , Zhilin Li , Yongqi Chen
来源:[J].Earth and Planetary Science Letters(IF 4.349), 2013, Vol.364, pp.68-84Elsevier
摘要:Abstract(#br)Various lunar digital topographic models (DTMs) have been generated from the data collected from earlier and recent lunar missions. There are usually inconsistencies among them due to differences in sensor configurations, data acquisition periods, and production...
作者:... Zhilin Li , Meng Yu , Yongqi Chen
来源:[J].Journal of Intelligent Transportation Systems(IF 0.806), 2008, Vol.12 (4), pp.168-175Taylor & Francis
摘要:Global Positioning System (GPS)-based vehicle navigation systems have been rapidly developed over recent years. In urban areas, due to the problems of satellite signal blockage and severe multipath effects, GPS may not be available for a long period of time. Integrated GPS/Dead R...
作者:Xinghua Zhou , Yongqi Chen
来源:[J].Marine Geodesy(IF 1.407), 2005, Vol.28 (2), pp.201-206Taylor & Francis
摘要:In this study, the self-organizing map (SOM), which is an unsupervised clustering algorithm, and a supervised proportional learning vector quantization (PLVQ), are employed to develop a combined method of seafloor classification using multibeam sonar backscatter data. The<font color="red">...</font></td></tr></tbody></table><div class="document_down clearfix"><div class="fl mr15"><span><a title="全文获取" href="http://d.scholar.cnki.net/link/doi/STJDTEMP_U/STJD2CB139261B5FEFAE33907AE72FDDD266" target="_blank"><img src="images/springer-s.gif"/>全文获取</a></span></div><div class="fl mr15 position-re none zzgcWth"><span><a title="纸质馆藏位置信息" href="Javascript:void(0)">纸质馆藏</a><ul class="zzgcDiv"></ul></span><span class="caret-dw"></span></div></div></div></div><div class="record_document"><div class="document_frame"><div class="document_title"><div class="text"><input type="checkbox" name="FileNameM" onclick="ChangeDigitalDisplayName();" value="STJDTEMP_U!STJD8E87DB4D1B75208286C2662D2956841E!!" id="r_id"><span class="title"><span class="gotodetaillink"><a href="http://d.scholar.cnki.net/detail/STJDTEMP_U/STJD8E87DB4D1B75208286C2662D2956841E" target="_blank">High Precision Geoid Models HKGEOID-2000 for Hong Kong and SZGEOID-2000 for Shenzhen, China</a></span></span></div></div><table class="tableResult" cellspacing="0" cellpadding="0"><tbody><tr><td class="item">作者:... <a href="result.aspx?q=%e4%bd%9c%e8%80%85%3a(Jinsheng+Ning)" target="_self">Jinsheng Ning</a> , <a href="result.aspx?q=%e4%bd%9c%e8%80%85%3a(Yongqi+Chen)" target="_self"><font color="red">Yongqi Chen</font></a> , <a href="result.aspx?q=%e4%bd%9c%e8%80%85%3a(Zhanji+Yang)" target="_self">Zhanji Yang</a></tr><tr><td class="item">来源:<a title="期刊" href="javascript:void(0);" style="text-decoration:none;">[J]</a>.<span class="first"><a href="result.aspx?q=%e6%9d%a5%e6%ba%90%3a(Marine+Geodesy)" target="_self">Marine Geodesy</a><span class="if">(IF 1.407)</span>, 2005, Vol.28 (2), pp.191-200</span><span class="tab">Taylor & Francis</span></td></tr><tr><td class="item">摘要:<title/>High precision geoid models HKGEOID-2000 for Hong Kong and SZGEOID-2000 for Shenzhen, China, have been developed with a hybrid approach of so-called sequential processing, using high precision GPS/leveling data, land and sea gravity anomalies, and digital terrain mod<font color="red">...</font></td></tr></tbody></table><div class="document_down clearfix"><div class="fl mr15"><span><a title="全文获取" href="http://d.scholar.cnki.net/link/doi/STJDTEMP_U/STJD8E87DB4D1B75208286C2662D2956841E" target="_blank"><img src="images/springer-s.gif"/>全文获取</a></span></div><div class="fl mr15 position-re none zzgcWth"><span><a title="纸质馆藏位置信息" href="Javascript:void(0)">纸质馆藏</a><ul class="zzgcDiv"></ul></span><span class="caret-dw"></span></div></div></div></div></div><div class="div_dataset_turnpage"><div class="div_dataset_result_pagination"><span class="nowPage">1</span><span class="page"><a href="javascript:void(0);" onclick="turnPage(2);">2</a></span><span class="page"><a href="javascript:void(0);" onclick="turnPage(3);">3</a></span><span class="page"><a href="javascript:void(0);" onclick="turnPage(4);">4</a></span><span class="page"><a href="javascript:void(0);" 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" 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