This study discusses the fusion of chlorophyll-a (Chl.a) estimates around Tachibana Bay (Nagasaki Prefecture, Japan) obtained from MODIS and GOCI satellite data. First, the equation of GOCI LCI was theoretically calcu...This study discusses the fusion of chlorophyll-a (Chl.a) estimates around Tachibana Bay (Nagasaki Prefecture, Japan) obtained from MODIS and GOCI satellite data. First, the equation of GOCI LCI was theoretically calculated on the basis of the linear combination index (LCI) method proposed by Frouin et al. (2006). Next, assuming a linear relationship between them, the MODIS LCI and GOCI LCI methods were compared by using the Rayleigh reflectance product dataset of GOCI and MODIS, collected on July 8, July 25, and July 31, 2012. The results were found to be correlated significantly. GOCI Chl.a estimates of the finally proposed method favorably agreed with the in-situ Chl.a data in Tachibana Bay.展开更多
A hierarchical peer-to-peer（P2P）model and a data fusion method for network security situation awareness system are proposed to improve the efficiency of distributed security behavior monitoring network.The single po...A hierarchical peer-to-peer（P2P）model and a data fusion method for network security situation awareness system are proposed to improve the efficiency of distributed security behavior monitoring network.The single point failure of data analysis nodes is avoided by this P2P model,in which a greedy data forwarding method based on node priority and link delay is devised to promote the efficiency of data analysis nodes.And the data fusion method based on repulsive theory-Dumpster/Shafer（PSORT-DS）is used to deal with the challenge of multi-source alarm information.This data fusion method debases the false alarm rate.Compared with improved Dumpster/Shafer（DS）theoretical method based on particle swarm optimization（PSO）and classical DS evidence theoretical method,the proposed model reduces false alarm rate by 3%and 7%,respectively,whereas their detection rate increases by 4%and 16%,respectively.展开更多
We present a novel paradigm of sensor placement concerning data precision and estimation.Multiple abstract sensors are used to measure a quantity of a moving target in the scenario of a wireless sensor network.These s...We present a novel paradigm of sensor placement concerning data precision and estimation.Multiple abstract sensors are used to measure a quantity of a moving target in the scenario of a wireless sensor network.These sensors can cooperate with each other to obtain a precise estimate of the quantity in a real-time manner.We consider a problem on planning a minimum-cost scheme of sensor placement with desired data precision and resource consumption.Measured data is modeled as a Gaussian random variable with a changeable variance.A gird model is used to approximate the problem.We solve the problem with a heuristic algorithm using branch-and-bound method and tabu search.Our experiments demonstrate that the algorithm is correct in a certain tolerance,and it is also efficient and scalable.展开更多
Track association of multi-target has been recognized as one of the key technologies in distributed multiple-sensor data fusion system,and its accuracy directly impacts on the performance of the whole tracking system....Track association of multi-target has been recognized as one of the key technologies in distributed multiple-sensor data fusion system,and its accuracy directly impacts on the performance of the whole tracking system.A multi-sensor data association is proposed based on aftinity propagation(AP)algorithm.The proposed method needs an initial similarity,a distance between any two points,as a parameter,therefore,the similarity matrix is calculated by track position,velocity and azimuth of track data.The approach can automatically obtain the optimal classification of uncertain target based on clustering validity index.Furthermore,the same kind of data are fused based on the variance of measured data and the fusion result can be taken as a new measured data of the target.Finally,the measured data are classified to a certain target based on the nearest neighbor ideas and its characteristics,then filtering and target tracking are conducted.The experimental results show that the proposed method can effectively achieve multi-sensor and multi-target track association.展开更多
文摘This study discusses the fusion of chlorophyll-a (Chl.a) estimates around Tachibana Bay (Nagasaki Prefecture, Japan) obtained from MODIS and GOCI satellite data. First, the equation of GOCI LCI was theoretically calculated on the basis of the linear combination index (LCI) method proposed by Frouin et al. (2006). Next, assuming a linear relationship between them, the MODIS LCI and GOCI LCI methods were compared by using the Rayleigh reflectance product dataset of GOCI and MODIS, collected on July 8, July 25, and July 31, 2012. The results were found to be correlated significantly. GOCI Chl.a estimates of the finally proposed method favorably agreed with the in-situ Chl.a data in Tachibana Bay.
基金Supported by the National Natural Science Foundation of China(61370212)the Research Fund for the Doctoral Program of Higher Education of China(20122304130002)+1 种基金the Natural Science Foundation of Heilongjiang Province(ZD 201102)the Fundamental Research Fund for the Central Universities(HEUCFZ1213,HEUCF100601)
文摘A hierarchical peer-to-peer（P2P）model and a data fusion method for network security situation awareness system are proposed to improve the efficiency of distributed security behavior monitoring network.The single point failure of data analysis nodes is avoided by this P2P model,in which a greedy data forwarding method based on node priority and link delay is devised to promote the efficiency of data analysis nodes.And the data fusion method based on repulsive theory-Dumpster/Shafer（PSORT-DS）is used to deal with the challenge of multi-source alarm information.This data fusion method debases the false alarm rate.Compared with improved Dumpster/Shafer（DS）theoretical method based on particle swarm optimization（PSO）and classical DS evidence theoretical method,the proposed model reduces false alarm rate by 3%and 7%,respectively,whereas their detection rate increases by 4%and 16%,respectively.
基金Supported of Project of Fok Ying Tong Education Foundation(No.104030)Supported of Key Project of National Natural Science of Foundation of China(No.70531020)+2 种基金Supported of Project of New Century Excellent Talent(No.NCET-06-0382)Supported of Key Project of Education Ministry of China(No.306023)Supported of Project of Doctoral Education(20070247075)
文摘We present a novel paradigm of sensor placement concerning data precision and estimation.Multiple abstract sensors are used to measure a quantity of a moving target in the scenario of a wireless sensor network.These sensors can cooperate with each other to obtain a precise estimate of the quantity in a real-time manner.We consider a problem on planning a minimum-cost scheme of sensor placement with desired data precision and resource consumption.Measured data is modeled as a Gaussian random variable with a changeable variance.A gird model is used to approximate the problem.We solve the problem with a heuristic algorithm using branch-and-bound method and tabu search.Our experiments demonstrate that the algorithm is correct in a certain tolerance,and it is also efficient and scalable.
基金supported by National Natural Science Foundation of China(61364017,60804066)The Scientific and Technological Project of Education Department of Jiangxi Province(KJLD12068)Natural Science Foundation of Jiangxi Province(20132BAB201039)
基金Supported by the National Natural Science Foundation of China(11078001)
文摘Track association of multi-target has been recognized as one of the key technologies in distributed multiple-sensor data fusion system,and its accuracy directly impacts on the performance of the whole tracking system.A multi-sensor data association is proposed based on aftinity propagation(AP)algorithm.The proposed method needs an initial similarity,a distance between any two points,as a parameter,therefore,the similarity matrix is calculated by track position,velocity and azimuth of track data.The approach can automatically obtain the optimal classification of uncertain target based on clustering validity index.Furthermore,the same kind of data are fused based on the variance of measured data and the fusion result can be taken as a new measured data of the target.Finally,the measured data are classified to a certain target based on the nearest neighbor ideas and its characteristics,then filtering and target tracking are conducted.The experimental results show that the proposed method can effectively achieve multi-sensor and multi-target track association.
