It is shown that the average [O] in the steel can be decreased to 12.86 ppm and there is an optimal [%Si] at the end of melting as 0.11%, at which [O] after vacuum carbon deoxidation （VCD） is minimum. A kinetical model has been deduced to simulate the VCD process. The results show that adding Si before Al is superior to the adding Al before Si with respect to the final [O] in the steel. It is suggested that the melting point of covering slag should be lower than 1550℃, and （FeO+MnO） should be less than 1%, so as to effectively refine GCr15 steel

提出了一种面向网络长文本的话题检测方法.针对文本表示的高维稀疏性和忽略潜在语义的问题，提出了Word2vec&LDA（latent dirichlet allocation）的文本表示方法.将LDA提取的文本特征词隐含主题和Word2vec映射的特征词向量进行加权融合既能够进行降维的作用又可以较为完整的表示出文本信息.针对传统话题发现方法对长文本输入顺序敏感问题，提出了基于文本聚类的Single-Pass&HAC（hierarchical agglomerative clustering）的话题发现方法，在引入时间窗口和凝聚式层次聚类的基础上对于文本的输入顺序具有了更强的鲁棒性，同时提高了聚类的精度和效率.为了评估所提出方法的有效性，本文从某大学社交平台收集了来自真实世界的多源数据集，并基于此进行了大量的实验.实验结果证明，本文提出的方法相对于现有的方法，如VSM（state vector space model）、Single-Pass等拥有更好的效果，话题检测的精度提高了10%~20%

目前钢铁行业已成为大气污染防治的重点，为解决现有钢铁行业对于 PM2.5 细颗粒难以捕集的难题，实现粉尘的超低排放. 基于 CFD-DPM（computational fluid dynamics-discrete phase model）方法对磁性纤维产生的磁场以及高梯度磁场等不同磁场形式下单纤维对钢铁行业捕集 PM2.5 性能的影响进行研究，通过 X 射线衍射图谱分析可知钢铁行业生产过程产生的粉尘因含有 Fe3O4 以及单质 Fe 而具有磁特性，进而提出了利用磁场来增强单纤维捕集 PM2.5 性能的方法

SiC作为一种综合性能优异宽禁带半导体，在金属氧化物半导体场效应晶体管中具有广泛的应用。然而SiC热氧化生成SiO2的过程具有各向异性，导致不同晶面上的氧化速率差异较大，这会对半导体器件的性能产生不利影响，因而研究SiC各个晶面上SiO2的生长规律尤其重要。建立有效合理的动力学模型是认识上述规律的有效手段。本文从反应机理和拟合准确度两方面对目前具有代表性的改进的Deal-Grove模型（Song模型和Massoud经验关系式）以及硅碳排放模型（Si?C emission model）进行系统研究和比较。在此基础上，分析已有模型的优缺点，提出本课题组建立的真实物理动力学模型应用的可能性，为SiC不同晶面氧化动力学的准确描述提供进一步优化和修正思路

1 Introduction 2 Deterministic Dynamic Programming and Viscosity Solutions 3 Stochastic Control 3.1 Some Probability Theory 3.2 Controlled State Space Models 3.3 Filtering 3.4 Dynamic Programming - Case I : Complete State Information 3.5 Dynamic Programming - Case II : Partial State Information 3.6 Two Continuous Time Problems 4 Robust Control 4.1 Introduction and Background 4.2 The Standard Problem of H∞ Control 4.3 The Solution for Linear Systems 4.4 Risk-Sensitive Stochastic Control and Robustness 5 Optimal Feedback Control of Quantum Systems 5.1 Preliminaries 5.2 The Feedback Control Problem 5.3 Conditional Dynamics 5.4 Optimal Control 5.5 Appendix: Formulas for the Two-State System with Feedback Example 6 Optimal Risk-Sensitive Feedback Control of Quantum Systems 6.1 System Model 6.2 Risk-Neutral Optimal Control 6.3 Risk-Sensitive Optimal Control 6.4 Control of a Two Level Atom 6.5 Control of a Trapped Atom

1 Introduction 2 Deterministic Dynamic Programming and Viscosity Solutions 2.1 Introduction 2.2 Value Functions are Viscosity Solutions 2.3 Comparison and Uniqueness 3 Stochastic Control 3.1 Some Probability Theory 3.2 Controlled State Space Models 3.3 Filtering 3.4 Dynamic Programming - Case I : Complete State Information 3.5 Dynamic Programming - Case II : Partial State Information 3.6 Two Continuous Time Problems 4 Robust Control 4.1 Introduction and Background 4.2 The Standard Problem of H∞ Control 4.3 The Solution for Linear Systems 4.4 Risk-Sensitive Stochastic Control and Robustness 5 Optimal Feedback Control of Quantum Systems 5.1 Preliminaries 5.2 The Feedback Control Problem 5.3 Conditional Dynamics 5.4 Optimal Control 5.5 Appendix: Formulas for the Two-State System with Feedback Example 6 Optimal Risk-Sensitive Feedback Control of Quantum Systems 6.1 System Model

《企业战略管理》课程教学资源（课外学习）竞争战略三角模型（Triangle Model）

《企业战略管理》课程教学资源（课外学习）波特五力分析模型（Michael Porter's Five Forces Model）

《企业战略管理》课程教学资源（课外学习）SCP分析模型（Structure-Conduct-Performance Model）

▪ How flowering is induced ▪ How flowering is controlled (ABC model) ▪ The structure of flowers and their reproductive components ▪ Pollination