近岸岛屿毗邻海域作为陆架边缘海中最具代表性的区域之一,是重要的海-陆过渡区域。在自然环境变动与人类活动的双重影响下,其生态系统具有多样性和复杂性。长岛毗邻海域具有典型的海岛生态环境特征,为渤黄海渔业种类的洄游通道和关键栖...近岸岛屿毗邻海域作为陆架边缘海中最具代表性的区域之一,是重要的海-陆过渡区域。在自然环境变动与人类活动的双重影响下,其生态系统具有多样性和复杂性。长岛毗邻海域具有典型的海岛生态环境特征,为渤黄海渔业种类的洄游通道和关键栖息地,对该海域生态系统食物网结构和能流过程具有重要意义。2021年3月至12月,山东长岛近海渔业资源国家野外科学观测研究站在长岛毗邻海域开展10航次,每航次10站的底层渔业生物逐月调查与样品测定。通过对渔获物的生物学测定数据进行计算,获得相对重要性指数(index of relative importance,IRI)、物种更替率、单位捕捞努力量渔获量(catch per unit effort,CPUE)以及包含Margalef丰富度指数(D)、Shannon-Wiener多样性指数(H′)和Pielou均匀度指数(J′)在内的生物多样性指数,构成了本数据集。基于多人全样本交叉复核完成审查与校对过程,确保数据集的规范性与准确性。本数据集可为渤黄海底层渔业生物时空格局和海岛生态系统研究提供数据支撑。展开更多
为了解大亚湾西南海域食物网的营养结构特征,本研究于2020年1月份使用底拖网采集了该海域的渔业生物,并分析了35种主要渔业生物的碳氮稳定同位素值。根据δ^(13)C和δ^(15)N值,计算出该海域食物网6种营养结构的生态指标和主要渔业生物...为了解大亚湾西南海域食物网的营养结构特征,本研究于2020年1月份使用底拖网采集了该海域的渔业生物,并分析了35种主要渔业生物的碳氮稳定同位素值。根据δ^(13)C和δ^(15)N值,计算出该海域食物网6种营养结构的生态指标和主要渔业生物的营养级,并绘制了连续营养谱。本次调查渔业生物主要为鱼类和虾蟹类,鱼类的δ^(13)C和δ^(15)N值范围分别为–17.63‰~–14.85‰和12.92‰~15.46‰,平均值分别为–16.47‰和13.80‰;虾蟹类的δ^(13)C和δ^(15)N值范围分别为–17.67‰~–15.51‰和11.05‰~12.62‰,平均值分别为–16.30‰和11.85‰。根据δ^(15)N值,用相加模型(trophic position by the additive model,TPA)和缩比模型(trophic position by the scaled model,TPS)分别计算了主要渔业生物的营养级,结果显示两个模型计算的结果无显著性差异(P>0.1),呈现鱼类平均营养级>虾蟹类的趋势。本研究发现大亚湾西南海域食物网初始食物来源较为单一,存在食物链营养层级较少和长度不足,食物网营养级多样性较低和营养结构冗余程度高的现象。与30多年前相比,大亚湾近年高营养级生物量减少,食物网结构由复杂趋向简单化,生态系统稳定性较差。本研究结果不仅为了解大亚湾食物网结构组成提供了基础资料,也为保护大亚湾渔业资源,维持生态系统结构的稳定性提供参考依据。展开更多
Unmanned aerial vehicles(UAV)are applied widely and profoundly in various fields.Moreover,high-precision positioning and tracking in multiple scenarios are the core requirements for UAV usage.To ensure stable communic...Unmanned aerial vehicles(UAV)are applied widely and profoundly in various fields.Moreover,high-precision positioning and tracking in multiple scenarios are the core requirements for UAV usage.To ensure stable communication of UAVs in denial environments with substantial electromagnetic interference,a systematic solution is proposed based on a deep learning algorithm for target detection and visible light for UAV tracking.Considering the cost and computational power limitations on the hardware,the you only look once(YOLO)v4-Tiny model is used for static target detection of the UAV model.For UAV tracking,and a light tracker that can adjust the angle of emitted light and focus it on the target is used for dynamic tracking processing.Thus,achieving the primary conditions of UAV optical communication with good secrecy is also suitable for dynamic situations.The UAV tracker positions the UAV model by returning the coordinates and calculating the time delay,and then controls the spotlight to target the UAV.In order to facilitate the deployment of deep learning models on hardware devices,the lighter and more efficient model is selected after comparison.The trained model can achieve 99.25%accuracy on the test set.The dynamic target detection can reach 20 frames per second(FPS)on a computer with an MX520 graphics processing unit(GPU)and 6 GB of random access memory(RAM).Dynamic target detection on a Jetson Nano can reach 5.4 FPS.展开更多
文摘近岸岛屿毗邻海域作为陆架边缘海中最具代表性的区域之一,是重要的海-陆过渡区域。在自然环境变动与人类活动的双重影响下,其生态系统具有多样性和复杂性。长岛毗邻海域具有典型的海岛生态环境特征,为渤黄海渔业种类的洄游通道和关键栖息地,对该海域生态系统食物网结构和能流过程具有重要意义。2021年3月至12月,山东长岛近海渔业资源国家野外科学观测研究站在长岛毗邻海域开展10航次,每航次10站的底层渔业生物逐月调查与样品测定。通过对渔获物的生物学测定数据进行计算,获得相对重要性指数(index of relative importance,IRI)、物种更替率、单位捕捞努力量渔获量(catch per unit effort,CPUE)以及包含Margalef丰富度指数(D)、Shannon-Wiener多样性指数(H′)和Pielou均匀度指数(J′)在内的生物多样性指数,构成了本数据集。基于多人全样本交叉复核完成审查与校对过程,确保数据集的规范性与准确性。本数据集可为渤黄海底层渔业生物时空格局和海岛生态系统研究提供数据支撑。
文摘为了解大亚湾西南海域食物网的营养结构特征,本研究于2020年1月份使用底拖网采集了该海域的渔业生物,并分析了35种主要渔业生物的碳氮稳定同位素值。根据δ^(13)C和δ^(15)N值,计算出该海域食物网6种营养结构的生态指标和主要渔业生物的营养级,并绘制了连续营养谱。本次调查渔业生物主要为鱼类和虾蟹类,鱼类的δ^(13)C和δ^(15)N值范围分别为–17.63‰~–14.85‰和12.92‰~15.46‰,平均值分别为–16.47‰和13.80‰;虾蟹类的δ^(13)C和δ^(15)N值范围分别为–17.67‰~–15.51‰和11.05‰~12.62‰,平均值分别为–16.30‰和11.85‰。根据δ^(15)N值,用相加模型(trophic position by the additive model,TPA)和缩比模型(trophic position by the scaled model,TPS)分别计算了主要渔业生物的营养级,结果显示两个模型计算的结果无显著性差异(P>0.1),呈现鱼类平均营养级>虾蟹类的趋势。本研究发现大亚湾西南海域食物网初始食物来源较为单一,存在食物链营养层级较少和长度不足,食物网营养级多样性较低和营养结构冗余程度高的现象。与30多年前相比,大亚湾近年高营养级生物量减少,食物网结构由复杂趋向简单化,生态系统稳定性较差。本研究结果不仅为了解大亚湾食物网结构组成提供了基础资料,也为保护大亚湾渔业资源,维持生态系统结构的稳定性提供参考依据。
文摘Unmanned aerial vehicles(UAV)are applied widely and profoundly in various fields.Moreover,high-precision positioning and tracking in multiple scenarios are the core requirements for UAV usage.To ensure stable communication of UAVs in denial environments with substantial electromagnetic interference,a systematic solution is proposed based on a deep learning algorithm for target detection and visible light for UAV tracking.Considering the cost and computational power limitations on the hardware,the you only look once(YOLO)v4-Tiny model is used for static target detection of the UAV model.For UAV tracking,and a light tracker that can adjust the angle of emitted light and focus it on the target is used for dynamic tracking processing.Thus,achieving the primary conditions of UAV optical communication with good secrecy is also suitable for dynamic situations.The UAV tracker positions the UAV model by returning the coordinates and calculating the time delay,and then controls the spotlight to target the UAV.In order to facilitate the deployment of deep learning models on hardware devices,the lighter and more efficient model is selected after comparison.The trained model can achieve 99.25%accuracy on the test set.The dynamic target detection can reach 20 frames per second(FPS)on a computer with an MX520 graphics processing unit(GPU)and 6 GB of random access memory(RAM).Dynamic target detection on a Jetson Nano can reach 5.4 FPS.
