检索结果-南通市图书馆

Modelling height-diameter relationships in complex tropical rain forest ecosystems using deep learning algorithm

Journal of Forestry Research 2022年第3期33卷 883-898页

作者： Friday Nwabueze Ogana Ilker Ercanli Department of Social and Environmental Forestry Faculty of Renewable Natural ResourcesUniversity of IbadanIbadanOyo200284Nigeria Department of Forest Engineering Faculty of ForestryÇankiri Karatekin UniversityÇankiri18200Turkey

Modelling tree height-diameter relationships in complex tropical rain forest ecosystems remains a challenge because of characteristics of multi-species, multi-layers, and indeterminate age composition. Effective modelling of such complex systems required innovative techniques to improve prediction of tree heights for use for aboveground biomass estimations. Therefore, in this study, deep learning algorithm (DLA) models based on artificial intelligence were trained for predicting tree heights in a tropical rain forest of Nigeria. The data consisted of 1736 individual trees representing 116 species, and measured from 52 0.25 ha sample plots. A K-means clustering was used to classify the species into three groups based on height-diameter ratios. The DLA models were trained for each species-group in which diameter at beast height, quadratic mean diameter and number of trees per ha were used as input variables. Predictions by the DLA models were compared with those developed by nonlinear least squares (NLS) and nonlinear mixed-effects (NLME) using different evaluation statistics and equivalence test. In addition, the predicted heights by the models were used to estimate aboveground biomass. The results showed that the DLA models with 100 neurons in 6 hidden layers, 100 neurons in 9 hidden layers and 100 neurons in 7 hidden layers for groups 1, 2, and 3, respectively, outperformed the NLS and NLME models. The root mean square error for the DLA models ranged from 1.939 to 3.887 m. The results also showed that using height predicted by the DLA models for aboveground biomass estimation brought about more than 30% reduction in error relative to NLS and NLME. Consequently, minimal errors were created in aboveground biomass estimation compared to those of the classical methods.

关键词： Artificial intelligence Height-diameter model Mixed-effects nonlinear least squares Tropical mixed forest

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Method for Visual Localization of Oil and Gas Wellhead Based on Distance Function of Projected Features

引用

International Journal of Automation and computing 2017年第2期14卷 147-158页

作者： Ying Xie Xiang-Dong Yang Zhi Liu Shu-Nan Ren Ken Chen Department of Mechanical Engineering Tsinghua University Beijing 100084 China Beijing Key Lab of Precision/Ultra-precision Manufacturing Equipments and Control Beijing 100084 China State Key Laboratory of Tribology Beijing 100084 China

A localization method based on distance function of projected features is presented to solve the accuracy reduction or failure problem due to occlusion and blurring caused by smog, when dealing with vision based localization for target oil and gas wellhead （OGWH）. Firstly, the target OGWH is modeled as a cylinder with marker, and a vector with redundant parameter is used to describe its pose. Secondly, the explicit mapping relationship between the pose vector with redundant parameter and projected features is derived. Then, a 2D-point-to-feature distance function is proposed, as well as its derivative. Finally, based on this distance function and its derivative, an algorithm is proposed to estimate the pose of target OGWH directly according to the 2D image information, and the validity of the method is verified by both synthetic data and real image experiments. The results show that this method is able to accomplish the localization in the case of occlusion and blurring, and its anti-noise ability is good especially with noise ratio of less than 70%.

关键词： Robot vision visual localization 3D object localization model based pose estimation distance function of projectedfeatures nonlinear least squares random sample consensus （RANSAC）.

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Elimination of Algorithm's Initial Value Error by Optimizing the Maneuver Direction in Airborne Passive Location

Elimination of Algorithm's Initial Value Error by Optimizing...

引用

2010 3rd International Conference on Computer and Electrical Engineering(ICCEE 2010)

作者： LIANG Jie CAO Jian XIE Xiao-fang HU Xun-Qiang Graduate Students' Brigade Naval Aeronautical and Astronautical University Department of Ordnance Science and Technology Naval Aeronautical and Astronautical University

Single airborne passive bearing-only multi-point cross location is studied and the nonlinear least squares estimate model is built up in this paper whose applying background is locating remote slow offing targets from fast airborne observer. The paper studies the relationship of the location error and the target position, in the same time this paper also analyses the influence of the initial guess on accuracy. Through mathematical calculation an expression of the optimal maneuver direction and a figure depicted the optimal maneuver direction under different distance are given. The simulation results show that the approach obviously improves the precision and stabilization of the system of single airborne passive bearing-only cross location.

关键词： Passive location cross location nonlinear least squares location error

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

Approximate maximum likelihood approaches for multivariate nonlinear mixed-effects models

Approximate maximum likelihood approaches for multivariate n...

引用

第十届海峡两岸统计与概率研讨会

作者：王婉倫 Department of Statistics Feng Chia University

Multivariate nonlinear mixed-effects models(MNLMM) have seen an increasing use due to its flexibility for analyzing multi-outcome longitudinal data following nonlinear profiles. In this work, I present and compare five different numerical algorithms for maximum likelihood estimation of the MNLMM. These algorithmic approaches include the penalized nonlinear least squares coupled with multivariate linear mixed-effects(PNLS-MLME) approximation, Laplacian approximation, pseudo-data ECM algorithm, Monte Carlo EM algorithm, and importance sampling EM algorithm. When estimating the MNLMM, it is rather difficult to exactly evaluate the observed log-likelihood function in a closed-form expression because it involves evaluating a multiple integral. Therefore, the corresponding approximations of the observed log-likelihood function under the five algorithms are presented. A comparison of their computational performances is investigated through simulation and real data from an AIDS clinical study.

关键词： Importance sampling Laplacian approximation Monte Carlo EM penalized nonlinear least squares pseudo ECM algorithm

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：