In May 2008, ScienceWatch.com named Advances in Atmospheric Sciences a Rising Star among Geosciences journals. According to Essential Science IndicatorsSM from Thomson Reuters, the journal＇s cur-rent citation record ...In May 2008, ScienceWatch.com named Advances in Atmospheric Sciences a Rising Star among Geosciences journals. According to Essential Science IndicatorsSM from Thomson Reuters, the journal＇s cur-rent citation record includes 764 papers cited a total of 1,658 times between January 1, 1998 and February 29 2008.展开更多
In 2008, the Brazilian Department of Science and Technology created the INCTs (Brazilian Science and Technology Institutes). One of them was the Cancer Control INCT. Due to its importance and considering that there ar...In 2008, the Brazilian Department of Science and Technology created the INCTs (Brazilian Science and Technology Institutes). One of them was the Cancer Control INCT. Due to its importance and considering that there are different groups working together in the same area, it is important that they collaborate intensely. Envisioning an empowerment of scientific collaboration, the BRINCA project was created to support a set of analyses of the social networks from this particular INCT. These analyses were created by mining curricular and publications bases, and identifying different types of scientific relationships and areas. We were able to observe, for instance, how the interaction is amongst researchers from related areas, which researchers were more collaborative and which ones were isolated from the network. These analyzes were used by the INCT coordination to understand and act to improve scientific collaboration.展开更多
Wireless sensor networks are increasingly used in sensitive event monitoring.However,various abnormal data generated by sensors greatly decrease the accuracy of the event detection.Although many methods have been prop...Wireless sensor networks are increasingly used in sensitive event monitoring.However,various abnormal data generated by sensors greatly decrease the accuracy of the event detection.Although many methods have been proposed to deal with the abnormal data,they generally detect and/or repair all abnormal data without further differentiate.Actually,besides the abnormal data caused by events,it is well known that sensor nodes prone to generate abnormal data due to factors such as sensor hardware drawbacks and random effects of external sources.Dealing with all abnormal data without differentiate will result in false detection or missed detection of the events.In this paper,we propose a data cleaning approach based on Stacked Denoising Autoencoders(SDAE)and multi-sensor collaborations.We detect all abnormal data by SDAE,then differentiate the abnormal data by multi-sensor collaborations.The abnormal data caused by events are unchanged,while the abnormal data caused by other factors are repaired.Real data based simulations show the efficiency of the proposed approach.展开更多
We are involved in an embarrassing situation that the limited capability of automated feature extraction in digital photogrammetric systems cannot satisfy the increasing needs for rapid acquisition of semantic informa...We are involved in an embarrassing situation that the limited capability of automated feature extraction in digital photogrammetric systems cannot satisfy the increasing needs for rapid acquisition of semantic information for applications. Facing this challenge, a new tactic, Human-Computer Collaborative (HCC) tactic, and a corresponding new method, Operator-Object Directed (OOD) method, are proposed for the design of a system for feature extraction from large scale aerial images. We hold that in almost all technical complex systems, full automation will be neither technically feasible nor socially acceptable. The system should be designed to optimize through the cooperative operation with two agents in the system: the hurtan and the computer.展开更多
文摘In May 2008, ScienceWatch.com named Advances in Atmospheric Sciences a Rising Star among Geosciences journals. According to Essential Science IndicatorsSM from Thomson Reuters, the journal＇s cur-rent citation record includes 764 papers cited a total of 1,658 times between January 1, 1998 and February 29 2008.
文摘In 2008, the Brazilian Department of Science and Technology created the INCTs (Brazilian Science and Technology Institutes). One of them was the Cancer Control INCT. Due to its importance and considering that there are different groups working together in the same area, it is important that they collaborate intensely. Envisioning an empowerment of scientific collaboration, the BRINCA project was created to support a set of analyses of the social networks from this particular INCT. These analyses were created by mining curricular and publications bases, and identifying different types of scientific relationships and areas. We were able to observe, for instance, how the interaction is amongst researchers from related areas, which researchers were more collaborative and which ones were isolated from the network. These analyzes were used by the INCT coordination to understand and act to improve scientific collaboration.
基金This work is supported by the National Natural Science Foundation of China(Grant No.61672282)the Basic Research Program of Jiangsu Province(Grant No.BK20161491).
文摘Wireless sensor networks are increasingly used in sensitive event monitoring.However,various abnormal data generated by sensors greatly decrease the accuracy of the event detection.Although many methods have been proposed to deal with the abnormal data,they generally detect and/or repair all abnormal data without further differentiate.Actually,besides the abnormal data caused by events,it is well known that sensor nodes prone to generate abnormal data due to factors such as sensor hardware drawbacks and random effects of external sources.Dealing with all abnormal data without differentiate will result in false detection or missed detection of the events.In this paper,we propose a data cleaning approach based on Stacked Denoising Autoencoders(SDAE)and multi-sensor collaborations.We detect all abnormal data by SDAE,then differentiate the abnormal data by multi-sensor collaborations.The abnormal data caused by events are unchanged,while the abnormal data caused by other factors are repaired.Real data based simulations show the efficiency of the proposed approach.
文摘We are involved in an embarrassing situation that the limited capability of automated feature extraction in digital photogrammetric systems cannot satisfy the increasing needs for rapid acquisition of semantic information for applications. Facing this challenge, a new tactic, Human-Computer Collaborative (HCC) tactic, and a corresponding new method, Operator-Object Directed (OOD) method, are proposed for the design of a system for feature extraction from large scale aerial images. We hold that in almost all technical complex systems, full automation will be neither technically feasible nor socially acceptable. The system should be designed to optimize through the cooperative operation with two agents in the system: the hurtan and the computer.
