明确不同作业类型渔船最少调查样本量对收集高质量的渔获量数据至关重要。本研究根据2008―2018年南海北部渔港抽样调查获得的36499个生产航次数据,基于蓝圆鲹(Decapterus maruadsi)单位捕捞努力量渔获量(catch per unit effort,CPUE),...明确不同作业类型渔船最少调查样本量对收集高质量的渔获量数据至关重要。本研究根据2008―2018年南海北部渔港抽样调查获得的36499个生产航次数据,基于蓝圆鲹(Decapterus maruadsi)单位捕捞努力量渔获量(catch per unit effort,CPUE),采用计算机模拟重抽样方法,对5种作业类型(单拖网、双拖网、光诱围网、刺网和光诱罩网)的调查样本量进行优化,使用相对估计误差(REE)和相对偏差(RB)作为评价指标,分析调查样本量的变化对CPUE估值的影响。结果显示,CPUE在不同作业类型间差异明显,同种作业类型在不同季节亦存在差异,其中,光诱围网四季CPUE同比高于其他作业类型,CPUE变化范围为(1.714~4.984)kg/(kW·d)。单拖网、双拖网和光诱罩网宜以REE≤10%确定最少样本量,而刺网和光诱围网(除冬季外)则宜以REE≤5%确定最少样本量,各作业类型最少样本量四季不同,其中,单拖网平均为76航次,双拖网平均为54航次,刺网平均为218航次,光诱围网平均为101航次,光诱罩网为72航次。当样本量达到特定值时,REE和RB的变化趋于稳定,冗余样本量减少也能够在一定程度上保证估计精度。本研究可为渔获量调查捕捞信息船样本量优化提供科学参考。展开更多
As to the fact that it is difficult to obtain analytical form of optimal sampling density and tracking performance of standard particle probability hypothesis density(P-PHD) filter would decline when clustering algori...As to the fact that it is difficult to obtain analytical form of optimal sampling density and tracking performance of standard particle probability hypothesis density(P-PHD) filter would decline when clustering algorithm is used to extract target states,a free clustering optimal P-PHD(FCO-P-PHD) filter is proposed.This method can lead to obtainment of analytical form of optimal sampling density of P-PHD filter and realization of optimal P-PHD filter without use of clustering algorithms in extraction target states.Besides,as sate extraction method in FCO-P-PHD filter is coupled with the process of obtaining analytical form for optimal sampling density,through decoupling process,a new single-sensor free clustering state extraction method is proposed.By combining this method with standard P-PHD filter,FC-P-PHD filter can be obtained,which significantly improves the tracking performance of P-PHD filter.In the end,the effectiveness of proposed algorithms and their advantages over other algorithms are validated through several simulation experiments.展开更多
文摘明确不同作业类型渔船最少调查样本量对收集高质量的渔获量数据至关重要。本研究根据2008―2018年南海北部渔港抽样调查获得的36499个生产航次数据,基于蓝圆鲹(Decapterus maruadsi)单位捕捞努力量渔获量(catch per unit effort,CPUE),采用计算机模拟重抽样方法,对5种作业类型(单拖网、双拖网、光诱围网、刺网和光诱罩网)的调查样本量进行优化,使用相对估计误差(REE)和相对偏差(RB)作为评价指标,分析调查样本量的变化对CPUE估值的影响。结果显示,CPUE在不同作业类型间差异明显,同种作业类型在不同季节亦存在差异,其中,光诱围网四季CPUE同比高于其他作业类型,CPUE变化范围为(1.714~4.984)kg/(kW·d)。单拖网、双拖网和光诱罩网宜以REE≤10%确定最少样本量,而刺网和光诱围网(除冬季外)则宜以REE≤5%确定最少样本量,各作业类型最少样本量四季不同,其中,单拖网平均为76航次,双拖网平均为54航次,刺网平均为218航次,光诱围网平均为101航次,光诱罩网为72航次。当样本量达到特定值时,REE和RB的变化趋于稳定,冗余样本量减少也能够在一定程度上保证估计精度。本研究可为渔获量调查捕捞信息船样本量优化提供科学参考。
文摘As to the fact that it is difficult to obtain analytical form of optimal sampling density and tracking performance of standard particle probability hypothesis density(P-PHD) filter would decline when clustering algorithm is used to extract target states,a free clustering optimal P-PHD(FCO-P-PHD) filter is proposed.This method can lead to obtainment of analytical form of optimal sampling density of P-PHD filter and realization of optimal P-PHD filter without use of clustering algorithms in extraction target states.Besides,as sate extraction method in FCO-P-PHD filter is coupled with the process of obtaining analytical form for optimal sampling density,through decoupling process,a new single-sensor free clustering state extraction method is proposed.By combining this method with standard P-PHD filter,FC-P-PHD filter can be obtained,which significantly improves the tracking performance of P-PHD filter.In the end,the effectiveness of proposed algorithms and their advantages over other algorithms are validated through several simulation experiments.
