Three uranium provinces are recognized in China, the Southeast China uranium province, the Northeast China-Inner Mongolia uranium province and the Northwest China (Xinjiang) uranium province. The latter two promise go...Three uranium provinces are recognized in China, the Southeast China uranium province, the Northeast China-Inner Mongolia uranium province and the Northwest China (Xinjiang) uranium province. The latter two promise good potential for uranium resources and are major exploration target areas in recent years. There are two major types of uranium deposits: the Phanerozoic hydrothermal type (vein type) and the Meso-Cenozoic sandstone type in different proportions in the three uranium provinces. The most important reason or prerequisite for the formation of these uranium provinces is that Precambrian uranium-enriched old basement or its broken parts (median massifs) exists or once existed in these regions, and underwent strong tectonomagmatic activation during Phanerozoic time. Uranium was mobilized from the old basement and migrated upwards to the upper structural level together with the acidic magma originating from anatexis and the primary fluids, which were then mixed with meteoric water and resulted in the formation of Phanerozoic hydrothermal uranium deposits under extensional tectonic environments. Erosion of uraniferous rocks and pre-existing uranium deposits during the Meso-Cenozoic brought about the removal of uranium into young sedimentary basins. When those basins were uplifted and slightly deformed by later tectonic activity, roll-type uranium deposits were formed as a result of redox in permeable sandstone strata.展开更多
Accurately identifying network traffics at the early stage is very important for the application of traffic identification.Recent years,more and more research works have tried to build effective machine learning model...Accurately identifying network traffics at the early stage is very important for the application of traffic identification.Recent years,more and more research works have tried to build effective machine learning models to identify traffics with the few packets at the early stage.However,a basic and important problem is still unresolved,that is how many packets are most effective in early stage traffic identification.In this paper,we try to resolve this problem using experimental methods.We firstly extract the packet size of the first 2-10 packets of 3 traffic data sets.And then execute crossover identification experiments with different numbers of packets using 11 well-known machine learning classifiers.Finally,statistical tests are applied to find out which number is the best performed one.Our experimental results show that 5-7are the best packet numbers for early stage traffic identification.展开更多
Traffic classification research has been suffering from a trouble of collecting accurate samples with ground truth.A model named Traffic Labeller(TL) is proposed to solve this problem.TL system captures all user socke...Traffic classification research has been suffering from a trouble of collecting accurate samples with ground truth.A model named Traffic Labeller(TL) is proposed to solve this problem.TL system captures all user socket calls and their corresponding application process information in the user mode on a Windows host.Once a sending data call has been captured,its 5-tuple {source IP,destination IP,source port,destination port and transport layer protocol},associated with its application information,is sent to an intermediate NDIS driver in the kernel mode.Then the intermediate driver writes application type information on TOS field of the IP packets which match the 5-tuple.In this way,each IP packet sent from the Windows host carries their application information.Therefore,traffic samples collected on the network have been labelled with the accurate application information and can be used for training effective traffic classification models.展开更多
文摘Three uranium provinces are recognized in China, the Southeast China uranium province, the Northeast China-Inner Mongolia uranium province and the Northwest China (Xinjiang) uranium province. The latter two promise good potential for uranium resources and are major exploration target areas in recent years. There are two major types of uranium deposits: the Phanerozoic hydrothermal type (vein type) and the Meso-Cenozoic sandstone type in different proportions in the three uranium provinces. The most important reason or prerequisite for the formation of these uranium provinces is that Precambrian uranium-enriched old basement or its broken parts (median massifs) exists or once existed in these regions, and underwent strong tectonomagmatic activation during Phanerozoic time. Uranium was mobilized from the old basement and migrated upwards to the upper structural level together with the acidic magma originating from anatexis and the primary fluids, which were then mixed with meteoric water and resulted in the formation of Phanerozoic hydrothermal uranium deposits under extensional tectonic environments. Erosion of uraniferous rocks and pre-existing uranium deposits during the Meso-Cenozoic brought about the removal of uranium into young sedimentary basins. When those basins were uplifted and slightly deformed by later tectonic activity, roll-type uranium deposits were formed as a result of redox in permeable sandstone strata.
基金This research was partially supported by National Natural Science Foundation of China under grant No.61472164,No.61402475,No.61173078,No.61203105,No.61173079,No.61070130,and No.60903176,the Provincial Natural Science Foundation of Shandong under grant No.ZR2012FM010,No.ZR2011FZ001,No.ZR2010FM047,No.ZR2010FQ028 and No.ZR2012FQ016
文摘Accurately identifying network traffics at the early stage is very important for the application of traffic identification.Recent years,more and more research works have tried to build effective machine learning models to identify traffics with the few packets at the early stage.However,a basic and important problem is still unresolved,that is how many packets are most effective in early stage traffic identification.In this paper,we try to resolve this problem using experimental methods.We firstly extract the packet size of the first 2-10 packets of 3 traffic data sets.And then execute crossover identification experiments with different numbers of packets using 11 well-known machine learning classifiers.Finally,statistical tests are applied to find out which number is the best performed one.Our experimental results show that 5-7are the best packet numbers for early stage traffic identification.
基金ACKNOWLEDGEMENT This research was partially supported by the National Basic Research Program of China （973 Program） under Grant No. 2011CB30- 2605 the National High Technology Research and Development Program of China （863 Pro- gram） under Grant No. 2012AA012502+3 种基金 the National Key Technology Research and Dev- elopment Program of China under Grant No. 2012BAH37B00 the Program for New Cen- tury Excellent Talents in University under Gr- ant No. NCET-10-0863 the National Natural Science Foundation of China under Grants No 61173078, No. 61203105, No. 61173079, No. 61070130, No. 60903176 and the Provincial Natural Science Foundation of Shandong under Grants No. ZR2012FM010, No. ZR2011FZ001, No. ZR2010FM047, No. ZR2010FQ028, No. ZR2012FQ016.
文摘Traffic classification research has been suffering from a trouble of collecting accurate samples with ground truth.A model named Traffic Labeller(TL) is proposed to solve this problem.TL system captures all user socket calls and their corresponding application process information in the user mode on a Windows host.Once a sending data call has been captured,its 5-tuple {source IP,destination IP,source port,destination port and transport layer protocol},associated with its application information,is sent to an intermediate NDIS driver in the kernel mode.Then the intermediate driver writes application type information on TOS field of the IP packets which match the 5-tuple.In this way,each IP packet sent from the Windows host carries their application information.Therefore,traffic samples collected on the network have been labelled with the accurate application information and can be used for training effective traffic classification models.
