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舰艇目标航行时往往向水中辐射大量线状谱结构的声波信号,线谱信号检测对舰艇目标的被动探测至关重要.实际水下环境噪声常表现为时域非平稳噪声,常规离散傅里叶变换类线谱检测方法时域积分时,不可避免会引入非平稳噪声背景中出现的短时强干扰噪声,使积分结果中线谱信噪比大幅降低,线谱检测性能急剧恶化.为解决上述问题,对分段离散傅里叶变... 相似文献
974.
收缩情境下城市用地结构时空格局演变及动力机制——以中国东北地区为例 总被引:3,自引:1,他引:2
构建城市用地结构时空格局演变分析框架,引入信息熵和偏移份额模型定量剖析2000-2017年中国东北地区城市用地结构时空格局演变特征,通过计量经济模型分阶段对比分析收缩情境下东北地区城市用地结构演变的动力机制.结果表明:东北地区城市用地总面积持续增长,用地结构信息熵整体呈现先上升、后下降的趋势.2011年以后,城市用地结... 相似文献
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Cheng-Wu CHEN Hsien-Chueh Peter YANG Chen-Yuan CHEN Alex Kung-Hsiung CHANG Tsung-Hao CHEN 《中国海洋工程》2008,22(1):43-56
Internal solitary wave propagation over a submarine ridge results in energy dissipation, in which the hydrodynamic interaction between a wave and ridge affects marine environment. This study analyzes the effects of ridge height and potential energy during wave-ridge interaction with a binary and cumulative logistic regression model. In testing the Global Null Hypothesis, all values are p 〈0.001, with three statistical methods, such as Likelihood Ratio, Score, and Wald. While comparing with two kinds of models, tests values obtained by cumulative logistic regression models are better than those by binary logistic regression models. Although this study employed cumulative logistic regression model, three probability functions p^1, p^2 and p^3, are utilized for investigating the weighted influence of factors on wave reflection. Deviance and Pearson tests are applied to cheek the goodness-of-fit of the proposed model. The analytical results demonstrated that both ridge height (X1 ) and potential energy (X2 ) significantly impact (p 〈 0. 0001 ) the amplitude-based refleeted rate; the P-values for the deviance and Pearson are all 〉 0.05 (0.2839, 0.3438, respectively). That is, the goodness-of-fit between ridge height ( X1 ) and potential energy (X2) can further predict parameters under the scenario of the best parsimonious model. Investigation of 6 predictive powers ( R2, Max-rescaled R^2, Sorners' D, Gamma, Tau-a, and c, respectively) indicate that these predictive estimates of the proposed model have better predictive ability than ridge height alone, and are very similar to the interaction of ridge height and potential energy. It can be concluded that the goodness-of-fit and prediction ability of the cumulative logistic regression model are better than that of the binary logistic regression model. 相似文献
979.
Evaluation of Inference Adequacy in Cumulative Logistic Regression Models: An Empirical Validation of ISW-Ridge Relationships 总被引:3,自引:0,他引:3
Internal solitary wave propagation over a submarine ridge results in energy dissipation, in which the hydrodynamic interaction between a wave and ridge affects marine environment. This study analyzes the effects of ridge height and potential energy during wave-ridge interaction with a binary and cumulative logistic regression model. In testing the Global Null Hypothesis, all values are p<0.001, with three statistical methods, such as Likelihood Ratio, Score, and Wald. While comparing with two kinds of models, tests values obtained by cumulative logistic regression models are better than those by binary logistic regression models. Although this study employed cumulative logistic regression model, three probability functions p^1, p^2 and p^3, are utilized for investigating the weighted influence of factors on wave reflection. Deviance and Pearson tests are applied to check the goodness-of-fit of the proposed model. The analytical results demonstrated that both ridge height (X1) and potential energy (X2) significantly impact (p<0.0001) the amplitude-based reflected rate; the P-values for the deviance and Pearson are all >0.05 (0.2839, 0.3438, respectively). That is, the goodness-of-fit between ridge height (X1) and potential energy (X2) can further predict parameters under the scenario of the best parsimonious model.Investigation of 6 predictive powers (R2, Max-rescaled R2, Somers'D, Gamma, Tau-a, and c, respectively) indicate that these predictive estimates of the proposed model have better predictive ability than ridge height alone, and are very similar to the interaction of ridge height and potential energy. It can be concluded that the goodness-of-fit and prediction ability of the cumulative logistic regression model are better than that of the binary logistic regression model. 相似文献
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