[Objective] This study aimed to improve classification accuracy of RS images using rough set theory in the growth of crops. [Method] Technique methods of data mining and knowledge discovery have been used. The develop...[Objective] This study aimed to improve classification accuracy of RS images using rough set theory in the growth of crops. [Method] Technique methods of data mining and knowledge discovery have been used. The development status of spatial data mining and knowledge discovery （SDMKD） is presented and data mining techniques in remote sensing were deeply analyzed. Then, SDMKD of TM image are researched using method of rough set, mainly including four methods （rough set, apriori algorithms, inductive learning, clustering）. [Result] The proposed method raises efficiency of land use and land reclaim. Based on the SDMKD, the characteristics of TM showed that the information after using rough set is more intensive than that of none. Especially, much better results can be gained while kinds of corps are less than five. [Conclusion] This study laid significant basis for further research on data mining in the growth of crops.展开更多
Recent researches show that inter-session network coding could decrease the number of packets transmission and achieve higher throughput in wireless network compared with traditional forwarding mechanism. In most exis...Recent researches show that inter-session network coding could decrease the number of packets transmission and achieve higher throughput in wireless network compared with traditional forwarding mechanism. In most existing relay mechanisms based on inter-session network such as COPE, relay node demands to collect the messages from its neighbor nodes to get notice of which packets already overheard by them so as to determine whether there exists coding opportunity between or among forwarding packets. However, transmission overhead of this message collection and computing cost of opportunity determination will degrade the performance of these mechanisms. It is observed that coding opportunity at relay node is much more related with the local topology, and the opportunity of encoding three or more packets together is far less than that of encoding two packets together in wireless network with general density. Based on this, a new coding-aware routing mechanism, named TCAR, is proposed. TCAR ignores the oppommity of encoding three or more than three packets together. Each relay node maintains an encoding mapping table being established according to the result of its local topology detection, which can be used to calculate the path cost during routing setup phase, and determine that which two packets can be encoded together during the packets forwarding phase. In TCAR, instead of periodic messages collection, each relay nodes just need once local topology detection, and the encoding determination is much simpler than that of the former mechanisms. Simulation results show that compared with typical inter-session network coding mechanisms COPE and COPE-based routing, TCAR achieves 12% and 7% throughput gains, and keeps the minimum end to end delay.展开更多
Social computing, as the technical foundation of future computational smart societies, has the potential to improve the effectiveness of opensource big data usage, systematically integrate a variety of elements includ...Social computing, as the technical foundation of future computational smart societies, has the potential to improve the effectiveness of opensource big data usage, systematically integrate a variety of elements including time, human, resources, scenarios, and organizations in the current cyber-physical-social world, and establish a novel social structure with fair information, equal rights, and a flat configuration. Meanwhile, considering the big modeling gap between the model world and the physical world, the concept of parallel intelligence is introduced. With the help of software-defined everything, parallel intelligence bridges the big modeling gap by means of constructing artificial systems where computational experiments can be implemented to verify social policies, economic strategies, and even military operations. Artificial systems play the role of ＂social laboratories＂ in which decisions are computed before they are executed in our physical society. Afterwards, decisions with the expected outputs are executed in parallel in both the artificial and physical systems to interactively sense, compute, evaluate and adjust system behaviors in real-time, leading system behaviors in the physical system converging to those proven to be optimal in the artificial ones. Thus, the smart guidance and management for our society can be achieved.展开更多
基金Supported by the by Research Fund for the Doctoral Program of Higher Education of China(20096121120001)Science Research Program of Educational Commission of Shaanxi Province of China(12JK0781)~~
文摘[Objective] This study aimed to improve classification accuracy of RS images using rough set theory in the growth of crops. [Method] Technique methods of data mining and knowledge discovery have been used. The development status of spatial data mining and knowledge discovery （SDMKD） is presented and data mining techniques in remote sensing were deeply analyzed. Then, SDMKD of TM image are researched using method of rough set, mainly including four methods （rough set, apriori algorithms, inductive learning, clustering）. [Result] The proposed method raises efficiency of land use and land reclaim. Based on the SDMKD, the characteristics of TM showed that the information after using rough set is more intensive than that of none. Especially, much better results can be gained while kinds of corps are less than five. [Conclusion] This study laid significant basis for further research on data mining in the growth of crops.
基金Projects(61173169,61106036)supported by the National Natural Science Foundation of ChinaProject(NCET-10-0798)Program for New Century Excellent Talents in University,China
文摘Recent researches show that inter-session network coding could decrease the number of packets transmission and achieve higher throughput in wireless network compared with traditional forwarding mechanism. In most existing relay mechanisms based on inter-session network such as COPE, relay node demands to collect the messages from its neighbor nodes to get notice of which packets already overheard by them so as to determine whether there exists coding opportunity between or among forwarding packets. However, transmission overhead of this message collection and computing cost of opportunity determination will degrade the performance of these mechanisms. It is observed that coding opportunity at relay node is much more related with the local topology, and the opportunity of encoding three or more packets together is far less than that of encoding two packets together in wireless network with general density. Based on this, a new coding-aware routing mechanism, named TCAR, is proposed. TCAR ignores the oppommity of encoding three or more than three packets together. Each relay node maintains an encoding mapping table being established according to the result of its local topology detection, which can be used to calculate the path cost during routing setup phase, and determine that which two packets can be encoded together during the packets forwarding phase. In TCAR, instead of periodic messages collection, each relay nodes just need once local topology detection, and the encoding determination is much simpler than that of the former mechanisms. Simulation results show that compared with typical inter-session network coding mechanisms COPE and COPE-based routing, TCAR achieves 12% and 7% throughput gains, and keeps the minimum end to end delay.
文摘Social computing, as the technical foundation of future computational smart societies, has the potential to improve the effectiveness of opensource big data usage, systematically integrate a variety of elements including time, human, resources, scenarios, and organizations in the current cyber-physical-social world, and establish a novel social structure with fair information, equal rights, and a flat configuration. Meanwhile, considering the big modeling gap between the model world and the physical world, the concept of parallel intelligence is introduced. With the help of software-defined everything, parallel intelligence bridges the big modeling gap by means of constructing artificial systems where computational experiments can be implemented to verify social policies, economic strategies, and even military operations. Artificial systems play the role of ＂social laboratories＂ in which decisions are computed before they are executed in our physical society. Afterwards, decisions with the expected outputs are executed in parallel in both the artificial and physical systems to interactively sense, compute, evaluate and adjust system behaviors in real-time, leading system behaviors in the physical system converging to those proven to be optimal in the artificial ones. Thus, the smart guidance and management for our society can be achieved.
