Assessment of the value of rivers for Otters (Lutra lutra)

The British otter Lutra lutra L. requires good water quality, an adequate supply of fish and certain types of vegetation or cavities (holts) adjacent to water bodies for resting and breeding sites. In the U.K. otters and their holts are legally protected. Trained surveyors are needed to confirm the presence of otters on a river as the animals are rarely seen, but their tracks and droppings (spraints) may be detected. To evaluate a river for otters, information is required on water quality, fish species and biomass, as well as available habitat. Pioneer evaluation work based on the River Corridor Survey techniques has been done on Welsh rivers with good otter populations. Riparian vegetation, especially trees, is shown to be particularly important. This work is now being extended to areas on the margins of otter distribution such as the lower river Wye in England and Wales. Such surveys provide important management information for water authorities and give opportunities for enhancement work to benefit this animal.     

基于小波分析与随机森林算法的电力电子电路故障诊断

提出一种基于Db6小波函数和随机森林算法相结合的电力电子故障诊断新方法.介绍应用Db6小波进行波形分解和提取故障特征量的方法与步骤,阐述随机森林算法决策树的生长和投票过程,应用随机森林算法设计出故障分类器,并将其应用于12脉波可控整流电路的故障识别.诊断结果表明,所提方法具有较高的正确诊断率和较强的抗噪声能力.在加入5...     

长江大保护与流域生态学

长江经济带发展的基本点是"共抓大保护、不搞大开发"。在梳理了长江流域主要生态环境问题基础上,从长江大保护的核心是统筹山水林田湖草、山水林田湖草生命共同体即是流域生态系统、流域生态系统是典型的集合生态系统的角度,论述了长江大保护与流域生态学的关系,并从科学研究、规划管理及教育宣传等方面展开思考提出建议,为实现长江大保护的国家需求提供服务。     

基于集成学习的离子通道药物靶点预测

新药研制成功的关键在于药物靶点的发现和准确定位.在已知的药物靶点中,离子通道蛋白是一类广受欢迎的靶点,它与免疫系统、心血管等疾病密切相关.对于靶点的发现,传统生物方法成本高、耗时久.因此,探讨了基于机器学习的离子通道蛋白药物靶点的挖掘,以加快药物靶点发现过程,节约经费.由于药物靶点相关序列的长度不一致,考虑了蛋白质序列编码的13种特征,它们能将不等长的蛋白质序列转化成等长序列.通过数值实验筛选能够较好地区分靶点和非靶点的特征子集,并采用集成学习的方法整合特征得到预测模型.通过与已有工作的比较表明,提出的集成模型能得到较高的准确率,具有很好的应用前景.     

面向对象的黑河下游河岸林植被覆盖信息分类!

地表植被覆盖是描述区域生态系统的基础数据,也是全球及区域陆面过程、生态与水文众多模型中所需的重要地表参数。对于黑河下游额济纳绿洲,以Landsat 30m分辨率为主的遥感影像难以真实提取下游绿洲河岸林植被覆盖信息,而高分辨率影像目标地物轮廓清晰、空间细节信息丰富,有利于干旱背景下景观破碎、异质性强的植被覆盖信息分类。基于黑河下游额济纳绿洲QuickBird影像,通过面向对象的分类方法提取耕地、胡杨、柽柳、草地和裸地等主要植被覆盖类型,分类总体精度和Kappa系数分别为84.71%和0.7986。结果表明:利用面向对象分类方法对高分辨率影像进行植被覆盖信息分类,分类结果较好,能够满足精度要求。     

随机森林理论浅析

随机森林是一种著名的集成学习方法,被广泛应用于数据分类和非参数回归。本文对随机森林算法的主要理论进行阐述,包括随机森林收敛定理、泛化误差界以和袋外估计三个部分。最后介绍一种属性加权子空间抽样的随机森林改进算法,用于解决超高维数据的分类问题。     

一种基于降维密度聚类的船舶异常轨迹识别方法

目的 有效分析和探索海洋船舶时空轨迹行为模式,提高船舶轨迹聚类的效率与质量,更好地检测真实船舶的异常行为。方法 针对当前船舶轨迹数据研究中存在的对多维特征信息利用不足、检测效率不高、检测精度较差等问题,提出一种精确度高、能自主识别分析多维特征的船舶异常轨迹识别方法。首先利用随机森林分类器评估多维特征重要性,构建轨迹特征的最优组合;然后提出一种降维密度聚类方法,将T–分布随机邻域嵌入(T–SNE)和自适应密度聚类(DBSCAN)模型结合,通过构建特征选择层和无监督聚类层实现对数据元素非线性关系的高效提取以及对聚类参数的智能选择;最后根据聚类结果构建类簇特征向量,计算距离阈值判别轨迹相似度,实现轨迹异常检测模型的构建。结果 以UCI数据集为例,降维密度聚类方法对4、13、30、64维特征数据集的F1分数能达到0.9 048、0.9 534、0.8 218、0.6 627,多个聚类指标均优于DBSCAN、K–Means等常见聚类算法的。结论 研究结果表明,降维密度聚类方法能有效提取数据多维特征结构,实现聚类参数自适应,弥补密度聚类中参数难以确定的问题,有效实现对多种类型船舶轨迹异常的识别。     

Feasibility of multi-temporal interferometric SAR data for stand-level estimation of boreal forest stem volume

The feasibility of interferometric SAR (INSAR) coherence observations for stem volume (biomass) retrieval is investigated by applying coherence data determined from 14 ERS-1 and ERS-2 C-band SAR image pairs. The image set covers a single forested test area in Finland, and both summer (snow-free) and winter conditions are represented. The data set enabled (a) the study of stem volume retrieval performance under varying conditions, (b) the analysis of the seasonal behavior of interferometric coherence, and (c) the determination of the accuracy characteristics of empirical (nonlinear) coherence modeling. Additionally, a new technique to estimate forest stem volume from INSAR data was developed based on constrained iterative inversion of the applied empirical model. The results indicate that the usability of winter images with snow-covered terrain is superior to that of images obtained under summer conditions. The applied empirical model appears to be adequate for describing the stand-wise coherence of boreal forest. Hence, a practical stem volume estimation method can be established based on it. The highest correlation coefficient between the estimated stem volume and the ground truth stem volume showed values as high as r=0.9 and a relative RMSE level of 48% was obtained, respectively.     

长江中游防浪林消能效益分析与研究

对不同类型林带结构进行了风浪观测消浪试验，测出了可靠数据，进行了质量评估，并加以分析．为选择防浪林优化模式、提高消能质量提供了理论依据．     

A machine-vision inspection system for conveying attitudes of columnar objects in packing processes

This paper is a new study on developing a machine vision system for inspecting the conveying attitudes of columnar objects. The presented system consists of image pre-processing, feature extraction, and attitude diagnosis. First of all, in order to segment the objects from the background (namely image pre-processing), an improved maximum between-class variance method is proposed for searching a histogram peak and calculating a threshold value based on the statistics and probability, to solve the problems caused by the non-uniform brightness in a realistic conveyor belt. Then, an open morphological operation is used to eliminate the noise from the binary images produced in the pre-processing step. In the second step (feature extraction), the features of columnar objects are determined by four methods, edge line detecting method, intercepting method, rectangle locating method and feature statistic method. Finally, the diagnosis for the conveying attitudes of columnar objects is based on a hybrid classifier using random forests, and a fuzzy logic. The proposed system is applied to a realistic process for packing industrial explosives. The results of experiments show that the proposed system allows efficient and accurate 100% inspection for the conveying attitude, which ensures the high speed and steady operation of a packing line.