无线传感网络(WSN,Wireless Sensor Network)中节点触发与数据传输往往会呈现出某种活动模式,基于活动模式特性提出了基于活动的节点分簇算法(AACP,ActivityAware Clustering Protocol),将网络中的传感器节点分成多个活动簇,并通过对节...无线传感网络(WSN,Wireless Sensor Network)中节点触发与数据传输往往会呈现出某种活动模式,基于活动模式特性提出了基于活动的节点分簇算法(AACP,ActivityAware Clustering Protocol),将网络中的传感器节点分成多个活动簇,并通过对节点的历史触发数据进行分析,结合分簇结果对当前发生的活动进行预测.基于活动预测结果,综合能耗均衡、节点剩余能量、传输能耗等影响因素,提出了基于活动预测和能耗均衡的WSN路由算法(AEBRP,Activity-aware and Energy Balanced Routing Protocol).仿真实验中与低功耗自适应集簇分层型协议(LEACH,Low Energy Adaptive Clustering Hierarchy)、基于跟踪的动态节点分簇算法(HCMTT,Hybrid Clustering for Multitarget Tracking in wireless sensor networks)和传感器信息系统中的高能效采集算法(PEGASIS,Power Efficient Gathering in Sensor Information System)进行比较,验证了AEBRP算法在维持网络能耗均衡、延长网络生命周期方面具有明显优势.展开更多
研究了利用已发现的频繁序列模式对序列数据库进行再聚类再发现的问题,针对已有的K-均值聚类算法随机选取初始中心点而导致聚类结果不稳定性的缺点,提出了一种基于Huffman思想的初始中心点选取算法——K-SPAM(K-means algorithm of sequ...研究了利用已发现的频繁序列模式对序列数据库进行再聚类再发现的问题,针对已有的K-均值聚类算法随机选取初始中心点而导致聚类结果不稳定性的缺点,提出了一种基于Huffman思想的初始中心点选取算法——K-SPAM(K-means algorithm of sequence pattern mining based on the Huffman Method)算法.该算法能够在一定程度上减少陷入局部最优的可能,而且对序列间相似度的计算采用一种高效的"与"、"或"运算,可极大提高算法的执行效率.展开更多
The FIFA World Cup^(TM) is the most profitable worldwide event.The FIFA publishes global statistics of this competition which provide global data about the players and teams during the competition.This work is focused...The FIFA World Cup^(TM) is the most profitable worldwide event.The FIFA publishes global statistics of this competition which provide global data about the players and teams during the competition.This work is focused on the extraction of behavioural patterns for both,players and teams strategies,through the automated analysis of this dataset.The knowledge and models extracted in this work could be applied to soccer leagues or even it could be oriented to sport betting.However,the main contribution is related to the study on several automatic knowledge extraction techniques,such as clustering methods,and how these techniques can be used to obtain useful behavioural models from a global statistics dataset.The information provided by the clustering algorithms shows similar properties which have been combined to define the models,making the human interpretation of these statistics easier.Finally,the most successful teams strategies have been analysed and compared.展开更多
This study represents the first comprehensive analysis of the residency patterns of a coastal population of bottlenose dolphin off the coast of Aragua,Venezuela,over a multi-year period.Using photo-identification,the ...This study represents the first comprehensive analysis of the residency patterns of a coastal population of bottlenose dolphin off the coast of Aragua,Venezuela,over a multi-year period.Using photo-identification,the most recent study(2019-2020)identified 56 individuals with the time between encounters from one to 344 days between the first and last sighting.Site Fidelity(SF)and Residence(RES)indices were calculated and Agglomerative Hierarchical Clustering(AHC)modeling was performed,with three patterns of residence obtained:resident(25%),semi-resident(17.86%)and transient(57.14%).These results were contrasted with remodeled data from a previous study(2006-2007),showing similar patterns:resident(24.44%),semi-resident(28.89%)and transient(46.67%).Importantly,two individuals were found to have been resident over the extended period.A breeding female sighted for the first time in 2004 and again in 2020(16 years)and the other from 2005 to 2020(15 years).This region is an important area for marine mammals,known to support a resident reproductive population over many years,as well seabirds,sea turtles,whale sharks and fishermen.We recommend that consideration be given to designating the waters as a Marine Protected Area to safeguard the existing population and provide benefit to the surrounding marine environment.展开更多
文摘无线传感网络(WSN,Wireless Sensor Network)中节点触发与数据传输往往会呈现出某种活动模式,基于活动模式特性提出了基于活动的节点分簇算法(AACP,ActivityAware Clustering Protocol),将网络中的传感器节点分成多个活动簇,并通过对节点的历史触发数据进行分析,结合分簇结果对当前发生的活动进行预测.基于活动预测结果,综合能耗均衡、节点剩余能量、传输能耗等影响因素,提出了基于活动预测和能耗均衡的WSN路由算法(AEBRP,Activity-aware and Energy Balanced Routing Protocol).仿真实验中与低功耗自适应集簇分层型协议(LEACH,Low Energy Adaptive Clustering Hierarchy)、基于跟踪的动态节点分簇算法(HCMTT,Hybrid Clustering for Multitarget Tracking in wireless sensor networks)和传感器信息系统中的高能效采集算法(PEGASIS,Power Efficient Gathering in Sensor Information System)进行比较,验证了AEBRP算法在维持网络能耗均衡、延长网络生命周期方面具有明显优势.
文摘研究了利用已发现的频繁序列模式对序列数据库进行再聚类再发现的问题,针对已有的K-均值聚类算法随机选取初始中心点而导致聚类结果不稳定性的缺点,提出了一种基于Huffman思想的初始中心点选取算法——K-SPAM(K-means algorithm of sequence pattern mining based on the Huffman Method)算法.该算法能够在一定程度上减少陷入局部最优的可能,而且对序列间相似度的计算采用一种高效的"与"、"或"运算,可极大提高算法的执行效率.
基金partly supported by:Spanish Ministry of Science and Education under project TIN201019872the grant BES-2011-049875 from the same MinistryJobssy.com company under project FUAM076913
文摘The FIFA World Cup^(TM) is the most profitable worldwide event.The FIFA publishes global statistics of this competition which provide global data about the players and teams during the competition.This work is focused on the extraction of behavioural patterns for both,players and teams strategies,through the automated analysis of this dataset.The knowledge and models extracted in this work could be applied to soccer leagues or even it could be oriented to sport betting.However,the main contribution is related to the study on several automatic knowledge extraction techniques,such as clustering methods,and how these techniques can be used to obtain useful behavioural models from a global statistics dataset.The information provided by the clustering algorithms shows similar properties which have been combined to define the models,making the human interpretation of these statistics easier.Finally,the most successful teams strategies have been analysed and compared.
基金We thank the fisherman José“Cata”,Grisel Velásquez(UNISIG-IVIC),Laboratory of Ecosystems and Global Change,Venezuelan Institute of Scientific Research,PADI Foundation(N°40470)the Cetacean Society International and the Society of Marine Mammalogy for their funding which enabled this study.
文摘This study represents the first comprehensive analysis of the residency patterns of a coastal population of bottlenose dolphin off the coast of Aragua,Venezuela,over a multi-year period.Using photo-identification,the most recent study(2019-2020)identified 56 individuals with the time between encounters from one to 344 days between the first and last sighting.Site Fidelity(SF)and Residence(RES)indices were calculated and Agglomerative Hierarchical Clustering(AHC)modeling was performed,with three patterns of residence obtained:resident(25%),semi-resident(17.86%)and transient(57.14%).These results were contrasted with remodeled data from a previous study(2006-2007),showing similar patterns:resident(24.44%),semi-resident(28.89%)and transient(46.67%).Importantly,two individuals were found to have been resident over the extended period.A breeding female sighted for the first time in 2004 and again in 2020(16 years)and the other from 2005 to 2020(15 years).This region is an important area for marine mammals,known to support a resident reproductive population over many years,as well seabirds,sea turtles,whale sharks and fishermen.We recommend that consideration be given to designating the waters as a Marine Protected Area to safeguard the existing population and provide benefit to the surrounding marine environment.
