Human error(HE) is the most important factor influencing on structural safety because its effect often exceeds the random deviation.Large numbers of facts have shown that structural failures may be caused by the gross...Human error(HE) is the most important factor influencing on structural safety because its effect often exceeds the random deviation.Large numbers of facts have shown that structural failures may be caused by the gross error due to HE.So it is essential to analyze HE in construction.The crucial work of human error analysis(HEA) is the estimation of human error probability(HEP) in construction.The method for estimating HEP,analytic hierarchy process and failure likelihood index method(AHP-FLIM),is introduced in this paper.The method also uses the process of expert judgment within the failure likelihood index method(FLIM).A numerical example shows the effectiveness of the methods proposed.展开更多
A new and convenient method is presented to calculate the total sensitivity indices defined by variance-based sensitivity analysis. By decomposing the output variance using error propagation equations, this method can...A new and convenient method is presented to calculate the total sensitivity indices defined by variance-based sensitivity analysis. By decomposing the output variance using error propagation equations, this method can transform the ＂double-loop＂ sampling procedure into ＂single-loop＂ one and obviously reduce the computation cost of analysis. In contrast with Sobors and Fourier amplitude sensitivity test （FAST） method, which is limited in non-correlated variables, the new approach is suitable for correlated input variables. An application in semiconductor assembling and test manufacturing （ATM） factory indicates that this approach has a good performance in additive model and simple non-additive model.展开更多
【目的】解决现有地形复杂度误差评价方法主观性强、缺少误差解析式的问题。【方法】利用误差传播定律推导了局部高差、局部标准差、局部褶皱度和局部全曲率的中误差表达式,依据复合地形因子误差传递规律构建了复合地形复杂度指标(compo...【目的】解决现有地形复杂度误差评价方法主观性强、缺少误差解析式的问题。【方法】利用误差传播定律推导了局部高差、局部标准差、局部褶皱度和局部全曲率的中误差表达式,依据复合地形因子误差传递规律构建了复合地形复杂度指标(compound terrain complexity index,CTCI)提取的误差估计模型(error estimation model for CTCI,CEEM)。通过模拟数字高程模型(digital elevation model,DEM)试验验证了该模型的有效性,并采用三个不同地貌区域的实体DEM进行CEEM泛化试验。【结果】在不同噪声影响下,CEEM平均误差、均方差、残差和平均绝对百分比误差最大值分别为-2.1×10^(-3)、7.99×10^(-6)、8.4×10^(-3)和22.8%,决定系数均在0.961以上,CEEM整体误差微小;不同地貌类型的地形复杂度提取误差存在差异,试验统计结果表明误差由大到小依次为高山、中山、丘陵。【结论】CEEM能定量化描述地形复杂度的提取误差,可为不同复杂地形地貌区域提取复合地形复杂度指标提供参考。展开更多
在林业研究中,胸径-树高二元联合分布多由相同边缘分布构造,而林分的胸径与树高的实际分布状况可能有所差异。为降低这种差异带来的影响,依据佳木斯市孟家岗林场的115块长白落叶松人工林数据,选择适用条件低、适应范围广的Copula函数方...在林业研究中,胸径-树高二元联合分布多由相同边缘分布构造,而林分的胸径与树高的实际分布状况可能有所差异。为降低这种差异带来的影响,依据佳木斯市孟家岗林场的115块长白落叶松人工林数据,选择适用条件低、适应范围广的Copula函数方法拟合落叶松胸径-树高二元联合分布模型。首先选择威布尔(Weibull)、广义威布尔(G-Weibull)、逻辑斯蒂(Logistic)、轻量逻辑斯蒂(Logit-Logistic)、伽马(Gamma)、对数正态(Log-Normal)6个分布函数作为备选基础模型,根据K-S(kolmogorov smirnov test)检验与半参数估计结果筛选并构建Copula胸径-树高二元联合分布模型,再通过负对数似然(negative log-likelihood,NLL)、Sn拟合优度统计量和似然比检验(likelihood ratio test,LRT)与二元对数logistic分布函数和二元Weibull分布函数进行比较,最后使用雷诺误差指数(error index of Reynolds,EI)对模型预测能力进行评估。结果表明,基于Copula函数的二元分拟合结果与模型(EI=0.3184)预估能力皆优于二元Weibull分布(EI=0.6381)和二元对数Logistic分布(EI=0.9490),说明此方法构建胸径-树高二元联合Copula分布模型能够很好地描述落叶松人工林胸径树高联合分布,以Copula方法构建树高-胸径联合分布是可行的。展开更多
电力线信道是一种非常严苛的有线信道,不仅具有时变性和频率选择性,还存在大量的背景噪声和脉冲噪声,严重影响信号的传输质量。为构建智能电网,抵抗电力线信道的影响,文章将具有优良误码性能的正交频分复用索引调制(orthogonal frequenc...电力线信道是一种非常严苛的有线信道,不仅具有时变性和频率选择性,还存在大量的背景噪声和脉冲噪声,严重影响信号的传输质量。为构建智能电网,抵抗电力线信道的影响,文章将具有优良误码性能的正交频分复用索引调制(orthogonal frequency division multiplexing with index modulation,OFDM-IM)应用至电力线通信,并选择信道增益较大的载波进行索引调制,以提高电力线信道下信号的传输质量。首先对电力线传输信道进行多径建模,对脉冲噪声进行Bernoulli-Gaussian过程建模,然后详细介绍了OFDM-IM的调制解调方案,并研究了该方案在电力线信道下的误码性能,最终与传统的OFDM方案在电力线信道下进行误码性能比较。实验结果表明,在电力线信道下OFDM-IM比OFDM具有更优的误码性能。展开更多
文摘Human error(HE) is the most important factor influencing on structural safety because its effect often exceeds the random deviation.Large numbers of facts have shown that structural failures may be caused by the gross error due to HE.So it is essential to analyze HE in construction.The crucial work of human error analysis(HEA) is the estimation of human error probability(HEP) in construction.The method for estimating HEP,analytic hierarchy process and failure likelihood index method(AHP-FLIM),is introduced in this paper.The method also uses the process of expert judgment within the failure likelihood index method(FLIM).A numerical example shows the effectiveness of the methods proposed.
文摘A new and convenient method is presented to calculate the total sensitivity indices defined by variance-based sensitivity analysis. By decomposing the output variance using error propagation equations, this method can transform the ＂double-loop＂ sampling procedure into ＂single-loop＂ one and obviously reduce the computation cost of analysis. In contrast with Sobors and Fourier amplitude sensitivity test （FAST） method, which is limited in non-correlated variables, the new approach is suitable for correlated input variables. An application in semiconductor assembling and test manufacturing （ATM） factory indicates that this approach has a good performance in additive model and simple non-additive model.
文摘【目的】解决现有地形复杂度误差评价方法主观性强、缺少误差解析式的问题。【方法】利用误差传播定律推导了局部高差、局部标准差、局部褶皱度和局部全曲率的中误差表达式,依据复合地形因子误差传递规律构建了复合地形复杂度指标(compound terrain complexity index,CTCI)提取的误差估计模型(error estimation model for CTCI,CEEM)。通过模拟数字高程模型(digital elevation model,DEM)试验验证了该模型的有效性,并采用三个不同地貌区域的实体DEM进行CEEM泛化试验。【结果】在不同噪声影响下,CEEM平均误差、均方差、残差和平均绝对百分比误差最大值分别为-2.1×10^(-3)、7.99×10^(-6)、8.4×10^(-3)和22.8%,决定系数均在0.961以上,CEEM整体误差微小;不同地貌类型的地形复杂度提取误差存在差异,试验统计结果表明误差由大到小依次为高山、中山、丘陵。【结论】CEEM能定量化描述地形复杂度的提取误差,可为不同复杂地形地貌区域提取复合地形复杂度指标提供参考。
文摘在林业研究中,胸径-树高二元联合分布多由相同边缘分布构造,而林分的胸径与树高的实际分布状况可能有所差异。为降低这种差异带来的影响,依据佳木斯市孟家岗林场的115块长白落叶松人工林数据,选择适用条件低、适应范围广的Copula函数方法拟合落叶松胸径-树高二元联合分布模型。首先选择威布尔(Weibull)、广义威布尔(G-Weibull)、逻辑斯蒂(Logistic)、轻量逻辑斯蒂(Logit-Logistic)、伽马(Gamma)、对数正态(Log-Normal)6个分布函数作为备选基础模型,根据K-S(kolmogorov smirnov test)检验与半参数估计结果筛选并构建Copula胸径-树高二元联合分布模型,再通过负对数似然(negative log-likelihood,NLL)、Sn拟合优度统计量和似然比检验(likelihood ratio test,LRT)与二元对数logistic分布函数和二元Weibull分布函数进行比较,最后使用雷诺误差指数(error index of Reynolds,EI)对模型预测能力进行评估。结果表明,基于Copula函数的二元分拟合结果与模型(EI=0.3184)预估能力皆优于二元Weibull分布(EI=0.6381)和二元对数Logistic分布(EI=0.9490),说明此方法构建胸径-树高二元联合Copula分布模型能够很好地描述落叶松人工林胸径树高联合分布,以Copula方法构建树高-胸径联合分布是可行的。
文摘电力线信道是一种非常严苛的有线信道,不仅具有时变性和频率选择性,还存在大量的背景噪声和脉冲噪声,严重影响信号的传输质量。为构建智能电网,抵抗电力线信道的影响,文章将具有优良误码性能的正交频分复用索引调制(orthogonal frequency division multiplexing with index modulation,OFDM-IM)应用至电力线通信,并选择信道增益较大的载波进行索引调制,以提高电力线信道下信号的传输质量。首先对电力线传输信道进行多径建模,对脉冲噪声进行Bernoulli-Gaussian过程建模,然后详细介绍了OFDM-IM的调制解调方案,并研究了该方案在电力线信道下的误码性能,最终与传统的OFDM方案在电力线信道下进行误码性能比较。实验结果表明,在电力线信道下OFDM-IM比OFDM具有更优的误码性能。
