为了提高评估结果的可靠性并支持可持续管理,在进行种群资源评估前,评估单位捕捞努力量渔获量(Catch per unit effort,CPUE)标准化方法的稳健性是必要的。渔业资源评估需要长期且连续的工作,其精确性对于管理决策和物种保护至关重要。基...为了提高评估结果的可靠性并支持可持续管理,在进行种群资源评估前,评估单位捕捞努力量渔获量(Catch per unit effort,CPUE)标准化方法的稳健性是必要的。渔业资源评估需要长期且连续的工作,其精确性对于管理决策和物种保护至关重要。基于1995—2022年间大西洋西班牙延绳钓船队的数据进行分析,利用sdmTMB方法对大西洋剑鱼(Xiphias gladius)渔业的CPUE进行标准化,同时利用Mohn'sρ方法进行回溯性分析,验证13个不同时间序列数据(A~M)的稳健性。结果显示,不同时间序列的标准化CPUE的结果差异较小,回溯性分析进一步确认了其一致性。研究表明,sdmTMB方法对大西洋剑鱼进行CPUE标准化具有较高的稳健性,能够准确反映该种群的相对资源丰度。本研究可为可靠的资源评估及科学的渔业管理决策提供有力的支持。展开更多
With the increasing demand for indoor localization,indoor location based on Wi-Fi has gained wide attention due to its convenience of access.In this paper,we propose a new multi-feature fusion convolutional neural net...With the increasing demand for indoor localization,indoor location based on Wi-Fi has gained wide attention due to its convenience of access.In this paper,we propose a new multi-feature fusion convolutional neural network(CNN)based on channel state information(CSI)images,which contains more feature information by constituting a new CSI image with amplitude and angle of arrival information of CSI information collected at known points.Moreover,the global mean filtering(GMC)algorithm with median filtering proposed in this paper is used to filter and reduce the noise of CSI images to obtain clearer images for network training.To extract more features from the CSI images,the traditional single-channel network is extended,and a two-channel design is introduced to extract feature information between adjacent subcarriers.Experimental evaluation is performed in a typical indoor environment,and the proposed method is experimentally proven to have good localization performance.展开更多
文摘为了提高评估结果的可靠性并支持可持续管理,在进行种群资源评估前,评估单位捕捞努力量渔获量(Catch per unit effort,CPUE)标准化方法的稳健性是必要的。渔业资源评估需要长期且连续的工作,其精确性对于管理决策和物种保护至关重要。基于1995—2022年间大西洋西班牙延绳钓船队的数据进行分析,利用sdmTMB方法对大西洋剑鱼(Xiphias gladius)渔业的CPUE进行标准化,同时利用Mohn'sρ方法进行回溯性分析,验证13个不同时间序列数据(A~M)的稳健性。结果显示,不同时间序列的标准化CPUE的结果差异较小,回溯性分析进一步确认了其一致性。研究表明,sdmTMB方法对大西洋剑鱼进行CPUE标准化具有较高的稳健性,能够准确反映该种群的相对资源丰度。本研究可为可靠的资源评估及科学的渔业管理决策提供有力的支持。
基金supported by Natural Science Foundation of Hunan Province under Grant(NO:2021JJ31142).
文摘With the increasing demand for indoor localization,indoor location based on Wi-Fi has gained wide attention due to its convenience of access.In this paper,we propose a new multi-feature fusion convolutional neural network(CNN)based on channel state information(CSI)images,which contains more feature information by constituting a new CSI image with amplitude and angle of arrival information of CSI information collected at known points.Moreover,the global mean filtering(GMC)algorithm with median filtering proposed in this paper is used to filter and reduce the noise of CSI images to obtain clearer images for network training.To extract more features from the CSI images,the traditional single-channel network is extended,and a two-channel design is introduced to extract feature information between adjacent subcarriers.Experimental evaluation is performed in a typical indoor environment,and the proposed method is experimentally proven to have good localization performance.
