Improved Transferability of Data‐Driven Damage Models Through Sample Selection Bias Correction

Damage models for natural hazards are used for decision making on reducing and transferring risk. The damage estimates from these models depend on many variables and their complex sometimes nonlinear relationships with the damage. In recent years, data‐driven modeling techniques have been used to capture those relationships. The available data to build such models are often limited. Therefore, in practice it is usually necessary to transfer models to a different context. In this article, we show that this implies the samples used to build the model are often not fully representative for the situation where they need to be applied on, which leads to a “sample selection bias.” In this article, we enhance data‐driven damage models by applying methods, not previously applied to damage modeling, to correct for this bias before the machine learning (ML) models are trained. We demonstrate this with case studies on flooding in Europe, and typhoon wind damage in the Philippines. Two sample selection bias correction methods from the ML literature are applied and one of these methods is also adjusted to our problem. These three methods are combined with stochastic generation of synthetic damage data. We demonstrate that for both case studies, the sample selection bias correction techniques reduce model errors, especially for the mean bias error this reduction can be larger than 30%. The novel combination with stochastic data generation seems to enhance these techniques. This shows that sample selection bias correction methods are beneficial for damage model transfer.     

A new financial stress index model based on support vector regression and control chart

Financial stress index (FSI) is considered to be an important risk management tool to quantify financial vulnerabilities. This paper proposes a new framework based on a hybrid classifier model that integrates rough set theory (RST), FSI, support vector regression (SVR) and a control chart to identify stressed periods. First, the RST method is applied to select variables. The outputs are used as input data for FSI–SVR computation. Empirical analysis is conducted based on monthly FSI of the Federal Reserve Bank of Saint Louis from January 1992 to June 2011. A comparison study is performed between FSI based on the principal component analysis and FSI–SVR. A control chart based on FSI–SVR and extreme value theory is proposed to identify the extremely stressed periods. Our approach identified different stressed periods including internet bubble, subprime crisis and actual financial stress episodes, along with the calmest periods, agreeing with those given by Federal Reserve System reports.     

社会主义精神富裕论

马克思主义经典作家的相关阐述 ,社会主义本质的实现和比较优势的获取 ,精神富裕和物质富裕特殊的关系均表明 ,精神富裕是建设社会主义的应有之义。而精神富裕的导向、认识、实践和凝聚作用 ,又反过来在促进社会主义事业快速、健康与持续发展中扮演着重要角色。同时 ,社会主义精神富裕还具有向度的特性 ,是一个动态开放的发展过程     

一个汉英机器翻译系统中的语义处理模型

简略阐述了三种语义分析理论 -配价语法 ,格语法 ,论旨理论后 ,重点介绍了机器翻译系统所采用的语义模型     

开放式数控切割机神经网络误差补偿研究

分析了影响数控火焰切割机加工精度的主要因素,利用开放式数控系统的软件开放性,提出了采用IGCAQBP学习算法的神经网络方法来对包括金属热变形、机械传动误差等非线性因素在内的多种因素造成的加工误差进行误差补偿,设计了嵌入开放式数控系统中的神经网络误差补偿器,给出了实用的补偿器使用方法,并对误差补偿功能进行了扩展,仿真结果和实际应用表明该方法稳定有效。     

高速SDRAM控制器设计的FPGA实现

同步动态存储器(SDRAM)控制器通常用有限状态机实现,对于一般的设计方法,由于状态数量多,状态转换通常伴随大的组合逻辑而影响运行速度,因此,SDRAM控制器的速度限制了SDRAM存储器的访问速度。该文从结构优化入手来优化方法,利用状态机分解的思想将大型SDRAM控制状态机用若干小的子状态机实现,达到简化逻辑的目的,不仅提高了速度还节省了资源,对该类大型SDRAM控制器的实现有一定参考意义。     

数控机床功能的扩展

本文主要分析了HEIDENHAIN TNC 310数控铣床系统中圆弧铣削指令及螺纹铣削指令,对螺纹铣削功能进行延伸,扩展到圆弧三轴联动,并对螺纹铣削指令格式进行了修改,从而具有圆弧三轴联动铣削功能。     

陶土坩埚玻璃拉丝机用红外测控仪的研制

介绍了对陶土坩埚玻璃拉丝机加装红外测控仪的原理、方法及试用效果。     

There be句型在机器翻译软件中的对比研究

机器翻译虽已取得了很大进展,但仍主要停留在查词功能上,其译文可读性不强,准确性不够。以英语特殊句型Therebe为切入点,着重探讨英汉机器翻译中特殊句型的对应与不对应的调整问题,并以五种英汉全文机器翻译软件作为测试对象,就可以分析发现机器翻译软件在Therebe句型英汉翻译过程中的两点不足:一方面,将there译成地点状语“那里”;另一方面,翻译时不能对地点状语或时间状语进行位置调整,只能机械地逐词顺译。     

三维Minkowski空间中有相等主曲率函数的一类特殊曲面的位置向量场

讨论三维Minkowski空间R2 .1中一类特殊曲面在主曲率函数相等时的情形 ,给出了这类曲面在这一情形下的位置向量场