The paper proposes a new deep structure model,called Densely Connected Cascade Forest-Weighted K Nearest Neighbors(DCCF-WKNNs),to implement the corrosion data modelling and corrosion knowledgemining.Firstly,we collect...The paper proposes a new deep structure model,called Densely Connected Cascade Forest-Weighted K Nearest Neighbors(DCCF-WKNNs),to implement the corrosion data modelling and corrosion knowledgemining.Firstly,we collect 409 outdoor atmospheric corrosion samples of low-alloy steels as experiment datasets.Then,we give the proposed methods process,including random forests-K nearest neighbors(RF-WKNNs)and DCCF-WKNNs.Finally,we use the collected datasets to verify the performance of the proposed method.The results show that compared with commonly used and advanced machine-learning algorithms such as artificial neural network(ANN),support vector regression(SVR),random forests(RF),and cascade forests(cForest),the proposed method can obtain the best prediction results.In addition,the method can predict the corrosion rates with variations of any one single environmental variable,like pH,temperature,relative humidity,SO2,rainfall or Cl-.By this way,the threshold of each variable,upon which the corrosion rate may have a large change,can be further obtained.展开更多
激光雷达LiDAR(Light Detection and Ranging)能够精准地还原被测物体的3D结构,是遥感领域最具革新性的技术之一。近几十年来,LiDAR技术取得了快速的发展,并极大地推动了生态与地学领域的相关研究。本文系统回顾并展望了LiDAR硬件和算...激光雷达LiDAR(Light Detection and Ranging)能够精准地还原被测物体的3D结构,是遥感领域最具革新性的技术之一。近几十年来,LiDAR技术取得了快速的发展,并极大地推动了生态与地学领域的相关研究。本文系统回顾并展望了LiDAR硬件和算法的最新发展及其在生态与地学领域的应用。首先,LiDAR的硬件呈现出多样化、高精度的发展态势,特别是近些年无人驾驶技术的成熟极大丰富了近地面LiDAR平台的类型;其次,深度学习、同步定位与地图构建SLAM(Simultaneous Localization And Mapping)、大模型等人工智能技术的发展极大推动了LiDAR算法的进步,使得点云配准、点云分割与分类、点云与多源数据融合等算法不断推陈出新;最后,本文详述了LiDAR在内陆地形测绘、海洋测绘、地质灾害监测、森林结构测量、树木枝干结构网络、3D辐射传输及场景重建、森林微气候模拟、智慧农业、生物多样性、城市与建筑,以及行星测量11个生态与地学分支领域的应用。未来,随着硬件、算法、及LiDAR大数据的进一步发展,LiDAR将继续推动生态与地学的研究,并有望在更多领域发挥重要作用。展开更多
基金financially supported by the National Key R&D Program of China(No.2017YFB0702100)the National Natural Science Foundation of China(No.51871024)。
文摘The paper proposes a new deep structure model,called Densely Connected Cascade Forest-Weighted K Nearest Neighbors(DCCF-WKNNs),to implement the corrosion data modelling and corrosion knowledgemining.Firstly,we collect 409 outdoor atmospheric corrosion samples of low-alloy steels as experiment datasets.Then,we give the proposed methods process,including random forests-K nearest neighbors(RF-WKNNs)and DCCF-WKNNs.Finally,we use the collected datasets to verify the performance of the proposed method.The results show that compared with commonly used and advanced machine-learning algorithms such as artificial neural network(ANN),support vector regression(SVR),random forests(RF),and cascade forests(cForest),the proposed method can obtain the best prediction results.In addition,the method can predict the corrosion rates with variations of any one single environmental variable,like pH,temperature,relative humidity,SO2,rainfall or Cl-.By this way,the threshold of each variable,upon which the corrosion rate may have a large change,can be further obtained.
文摘激光雷达LiDAR(Light Detection and Ranging)能够精准地还原被测物体的3D结构,是遥感领域最具革新性的技术之一。近几十年来,LiDAR技术取得了快速的发展,并极大地推动了生态与地学领域的相关研究。本文系统回顾并展望了LiDAR硬件和算法的最新发展及其在生态与地学领域的应用。首先,LiDAR的硬件呈现出多样化、高精度的发展态势,特别是近些年无人驾驶技术的成熟极大丰富了近地面LiDAR平台的类型;其次,深度学习、同步定位与地图构建SLAM(Simultaneous Localization And Mapping)、大模型等人工智能技术的发展极大推动了LiDAR算法的进步,使得点云配准、点云分割与分类、点云与多源数据融合等算法不断推陈出新;最后,本文详述了LiDAR在内陆地形测绘、海洋测绘、地质灾害监测、森林结构测量、树木枝干结构网络、3D辐射传输及场景重建、森林微气候模拟、智慧农业、生物多样性、城市与建筑,以及行星测量11个生态与地学分支领域的应用。未来,随着硬件、算法、及LiDAR大数据的进一步发展,LiDAR将继续推动生态与地学的研究,并有望在更多领域发挥重要作用。
文摘目的比较深度森林联合模型、深度森林以及随机森林在医学影像数据分类中的预测性能。方法本研究提出深度森林联合模型,通过Sobol-MDA(Sobol-mean decrease accuracy)结合深度森林级联结构和随机森林的特征提取能力,对模拟实验和真实医学影像数据进行分析。模拟实验涵盖结局变量不均衡、变量间非线性关系、噪声变量、多重共线性及交互作用等场景。实例分析基于腮腺MRI数据,比较各模型在曲线下面积(area under curve,AUC)值等指标上的表现。结果在模拟实验以及实例分析中,深度森林联合模型表现优越,特别是在复杂交互作用场景下,其预测性能显著优于深度森林或随机森林模型。结论深度森林联合模型在应对复杂医学影像数据分类任务中具有显著优势,尤其在处理变量间存在高阶交互作用时,其预测性能优于深度森林。
