电力电缆载流量模型及其应用研究

研究了电力电缆载流量算法模型的原理、分类方法和特性,并分析了多个实用的模型及其应用;特别介绍了SCR和DCR电缆模型、综合分析方法在电缆系统的设计、在线监测和运行优化方面的应用,并提供了一个应用案例。     

近似空间上粗糙路径的研究及应用

以Pawlak粗糙集理论中近似空间M=(U,R)为基础展开讨论,不采用粗糙集理论通常以上、下近似开始的做法,而是从M上二元关系S粗糙化后所得到的粗糙关系S觹出发,给出关于S粗糙路径的概念。同时,通过对跨度的定义与讨论,又建立起粗糙路径与上近似之间的联系,这是对粗糙路径研究所得到的重要性质。为了应用目的,构建了判定粗糙路径是否存在的路径矩阵。这为其系统化的应用奠定了基础。     

分片线性逼近及其在预测控制中的应用

介绍了分片线性逼近的相关理论并将其应用于预测控制。自适应链接超平面模型(AHH)是一种具有应用潜力的分片线性模型。采用AHH模型对被控制系统进行建模,由于AHH模型的辨识算法是自适应的,整个过程简单易实现。随后,在线解一个开环优化问题得到最优控制序列并应用滚动优化控制策略对系统进行控制。并且证明此开环优化问题实质上可以看成一系列子问题,每个子问题都是二次规划问题,因此,全局最优解的存在性得以保证。对于实际问题,提出了一个下降算法用以搜索局部最优解,仿真结果表明,基于AHH模型的预测控制具有一定的应用前景。     

Generalized Degrees of Freedom and Adaptive Model Selection in Linear Mixed-Effects Models

Linear mixed-effects models involve fixed effects, random effects and covariance structures, which require model selection to simplify a model and to enhance its interpretability and predictability. In this article, we develop, in the context of linear mixed-effects models, the generalized degrees of freedom and an adaptive model selection procedure defined by a data-driven model complexity penalty. Numerically, the procedure performs well against its competitors not only in selecting fixed effects but in selecting random effects and covariance structure as well. Theoretically, asymptotic optimality of the proposed methodology is established over a class of information criteria. The proposed methodology is applied to the BioCycle Study, to determine predictors of hormone levels among premenopausal women and to assess variation in hormone levels both between and within women across the menstrual cycle.     

Simulation program for power and sample size determination in logistic analysis of single nucleotide polymorphisms when the response variable is subject to misclassification

The main objective of this study was to develop a simulation program to determine the sample size for a clinical study to confirm a genetic-disease association observed in a retrospective exploratory study. The effect of misclassification of a binary response variable on the power is also investigated. A general expression for the magnitude of the decrease in statistical power due to misclassification is obtained based on the Pitman asymptotic relative efficiency. The simulation program presents an estimate of the exact power when misclassification exists. Running the program several times under different settings of parameters, it revealed that the effect of even low misclassification rates is serious. Response misclassification should be taken into consideration when determining the sample size. The program can be used on the Internet.     

两类动态信息规律模型及其在信息伪装、风险识别中的应用

函数P-集合是P-集合的函数形式,是通过改进P-集合得到的一个具有动态特征、规律(函数)特征的信息规律模型。在函数P-集合中,函数的属性满足数理逻辑中的合取范式。函数逆P-集合是函数P-集合的对偶模型,在函数逆P-集合中,函数的属性满足数理逻辑中的析取范式。这里定义函数P-集合是一类动态信息规律模型,定义函数逆P-集合是另一类动态信息规律模型;在函数P-集合与函数逆P-集合的结构、动态特征与它们的属性范式特征的基础上,给出函数P-集合在信息图像拼接与伪装中的简单应用,以及函数逆P-集合在商品利润的风险估计-识别中的简单应用。函数P-集合、函数逆P-集合是关于动态信息规律应用研究的新理论、新模型。     

Use of freely available datasets and machine learning methods in predicting deforestation

The range and quality of freely available geo-referenced datasets is increasing. We evaluate the usefulness of free datasets for deforestation prediction by comparing generalised linear models and generalised linear mixed models (GLMMs) with a variety of machine learning models (Bayesian networks, artificial neural networks and Gaussian processes) across two study regions. Freely available datasets were able to generate plausible risk maps of deforestation using all techniques for study zones in both Mexico and Madagascar. Artificial neural networks outperformed GLMMs in the Madagascan (average AUC 0.83 vs 0.80), but not the Mexican study zone (average AUC 0.81 vs 0.89). In Mexico and Madagascar, Gaussian processes (average AUC 0.89, 0.85) and structured Bayesian networks (average AUC 0.88, 0.82) performed at least as well as GLMMs (average AUC 0.89, 0.80). Bayesian networks produced more stable results across different sampling methods. Gaussian processes performed well (average AUC 0.85) with fewer predictor variables.