Landslide susceptibility prediction using slope unit-based machine learning models considering the heterogeneity of conditioning factors

To perform landslide susceptibility prediction(LSP),it is important to select appropriate mapping unit and landslide-related conditioning factors.The efficient and automatic multi-scale segmentation(MSS)method proposed by the authors promotes the application of slope units.However,LSP modeling based on these slope units has not been performed.Moreover,the heterogeneity of conditioning factors in slope units is neglected,leading to incomplete input variables of LSP modeling.In this study,the slope units extracted by the MSS method are used to construct LSP modeling,and the heterogeneity of conditioning factors is represented by the internal variations of conditioning factors within slope unit using the descriptive statistics features of mean,standard deviation and range.Thus,slope units-based machine learning models considering internal variations of conditioning factors(variant slope-machine learning)are proposed.The Chongyi County is selected as the case study and is divided into 53,055 slope units.Fifteen original slope unit-based conditioning factors are expanded to 38 slope unit-based conditioning factors through considering their internal variations.Random forest(RF)and multi-layer perceptron(MLP)machine learning models are used to construct variant Slope-RF and Slope-MLP models.Meanwhile,the Slope-RF and Slope-MLP models without considering the internal variations of conditioning factors,and conventional grid units-based machine learning(Grid-RF and MLP)models are built for comparisons through the LSP performance assessments.Results show that the variant Slopemachine learning models have higher LSP performances than Slope-machine learning models;LSP results of variant Slope-machine learning models have stronger directivity and practical application than Grid-machine learning models.It is concluded that slope units extracted by MSS method can be appropriate for LSP modeling,and the heterogeneity of conditioning factors within slope units can more comprehensively reflect the relationships between conditioning factors and landslides.The research results have important reference significance for land use and landslide prevention.

移动模架制梁技术在我国桥梁施工中的应用历程

我国移动模架制梁技术经历了从国外引进到自主研制的发展历程。国内企业最早于20世纪80年代在建造伊拉克摩苏尔4号桥时从瑞士引进了2套由德国PZ公司设计的移动模架,并于1990年将其改造后用于厦门高集海峡大桥,开启了移动模架在国内的应用历程。文章分析了我国移动模架制梁技术发展过程中的2个不同阶段,归纳了移动模架应用和发展初期的主要工程案例;介绍了移动模架技术在我国高速铁路桥梁建设中发挥的重要作用、专利技术及文献的研究趋势、发展机遇等;针对一些文献中容易将移动模架法和移动支架法相互混淆的情况,阐明了2种工法在技术原理上的重要区别:前者属原位现浇技术,后者属预制架设技术,结合两者在高速铁路桥梁中的应用情况,对其技术特点和各自优势进行了比较,可为相关技术推广研究提供一定的参考。

双栈环境下IP城域网MTU研究

通过研究城域网双栈环境下各类MTU处理机制的特点,对已有技术存在的不足和适用场景进行了分析,提出了现网部署时需关注的问题和现阶段建议方案,并对理想方案进行了探讨。

基于自适应方向性滤波和非局部均值修补的CT图像金属伪影消除

CT图像金属伪影的校正长期以来都是一个重要课题。首先采用自适应方向性前置滤波器对含有金属伪影的图像进行处理,在一定程度上消除噪声,抑制条状伪影;其后采用均值漂移分割出原图像中的金属成分,最大互信息熵分割出伪影成分;使用平行束投影获取原始图像和金属成分的弦空间数据,从原图弦空间数据中减除伪影成分对应的弦图;继而采用非局部均值修补结合线性插值方法对弦空间数据进行补全;最后采样滤波反投影得到校正后的图像。实验表明,本算法对于含有金属伪影的水模和真实临床图像的校正,获得更好的匀质区域一致性和更好的伪影抑制性能。

一种自适应改进无线TCP性能的算法

对采用TCP协议传输数据的实现过程及其在无线网络中可能遇到的问题进行了描述,针对这些问题提出了一种算法对无线链路下一时刻的误码率进行了估计,根据这个估计对TCP进行了修改以自适应选取合适的TCP报文段尺寸来传输数据。在网络仿真器(NS2)中仿真一个误码率时变的无线信道,把修改后的TCP置于NS仿真环境中进行仿真,实验结果表明了该改进方法有效。