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diff --git a/macros/latex/contrib/hitszthesis/back/acknowledgements.tex b/macros/latex/contrib/hitszthesis/back/acknowledgements.tex new file mode 100644 index 0000000000..8442f867f6 --- /dev/null +++ b/macros/latex/contrib/hitszthesis/back/acknowledgements.tex @@ -0,0 +1,11 @@ +% !TEX root = ../main.tex + +% 致谢 +\begin{acknowledgements} +衷心感谢导师~XXX~教授对本人的精心指导。他的言传身教将使我终生受益。 + +…… + +感谢哈深\LaTeX{}论文模板\hitszthesis\ ! + +\end{acknowledgements} diff --git a/macros/latex/contrib/hitszthesis/back/appendix01.tex b/macros/latex/contrib/hitszthesis/back/appendix01.tex new file mode 100644 index 0000000000..faae623114 --- /dev/null +++ b/macros/latex/contrib/hitszthesis/back/appendix01.tex @@ -0,0 +1,175 @@ +% !TEX root = ../main.tex + +% 附录1 +\chapter{外文资料原文} +\label{cha:engorg} + +\title{The title of the English paper} + +\textbf{Abstract:} As one of the most widely used techniques in operations +research, \emph{ mathematical programming} is defined as a means of maximizing a +quantity known as \emph{bjective function}, subject to a set of constraints +represented by equations and inequalities. Some known subtopics of mathematical +programming are linear programming, nonlinear programming, multiobjective +programming, goal programming, dynamic programming, and multilevel +programming$^{[1]}$. + +It is impossible to cover in a single chapter every concept of mathematical +programming. This chapter introduces only the basic concepts and techniques of +mathematical programming such that readers gain an understanding of them +throughout the book$^{[2,3]}$. + + +\section{Single-Objective Programming} +The general form of single-objective programming (SOP) is written +as follows, +\begin{equation}\tag*{(123)} % 如果附录中的公式不想让它出现在公式索引中，那就请 + % 用 \tag*{xxxx} +\left\{\begin{array}{l} +\max \,\,f(x)\\[0.1 cm] +\mbox{subject to:} \\ [0.1 cm] +\qquad g_j(x)\le 0,\quad j=1,2,\cdots,p +\end{array}\right. +\end{equation} +which maximizes a real-valued function $f$ of +$x=(x_1,x_2,\cdots,x_n)$ subject to a set of constraints. + +\newtheorem{mpdef}{Definition}[chapter] +\begin{mpdef} +In SOP, we call $x$ a decision vector, and +$x_1,x_2,\cdots,x_n$ decision variables. The function +$f$ is called the objective function. The set +\begin{equation}\tag*{(456)} % 这里同理，其它不再一一指定。 +S=\left\{x\in\Re^n\bigm|g_j(x)\le 0,\,j=1,2,\cdots,p\right\} +\end{equation} +is called the feasible set. An element $x$ in $S$ is called a +feasible solution. +\end{mpdef} + +\newtheorem{mpdefop}[mpdef]{Definition} +\begin{mpdefop} +A feasible solution $x^*$ is called the optimal +solution of SOP if and only if +\begin{equation} +f(x^*)\ge f(x) +\end{equation} +for any feasible solution $x$. +\end{mpdefop} + +One of the outstanding contributions to mathematical programming was known as +the Kuhn-Tucker conditions\ref{eq:ktc}. In order to introduce them, let us give +some definitions. An inequality constraint $g_j(x)\le 0$ is said to be active at +a point $x^*$ if $g_j(x^*)=0$. A point $x^*$ satisfying $g_j(x^*)\le 0$ is said +to be regular if the gradient vectors $\nabla g_j(x)$ of all active constraints +are linearly independent. + +Let $x^*$ be a regular point of the constraints of SOP and assume that all the +functions $f(x)$ and $g_j(x),j=1,2,\cdots,p$ are differentiable. If $x^*$ is a +local optimal solution, then there exist Lagrange multipliers +$\lambda_j,j=1,2,\cdots,p$ such that the following Kuhn-Tucker conditions hold, +\begin{equation} +\label{eq:ktc} +\left\{\begin{array}{l} + \nabla f(x^*)-\sum\limits_{j=1}^p\lambda_j\nabla g_j(x^*)=0\\[0.3cm] + \lambda_jg_j(x^*)=0,\quad j=1,2,\cdots,p\\[0.2cm] + \lambda_j\ge 0,\quad j=1,2,\cdots,p. +\end{array}\right. +\end{equation} +If all the functions $f(x)$ and $g_j(x),j=1,2,\cdots,p$ are convex and +differentiable, and the point $x^*$ satisfies the Kuhn-Tucker conditions +(\ref{eq:ktc}), then it has been proved that the point $x^*$ is a global optimal +solution of SOP. + +\subsection{Linear Programming} +\label{sec:lp} + +If the functions $f(x),g_j(x),j=1,2,\cdots,p$ are all linear, then SOP is called +a {\em linear programming}. + +The feasible set of linear is always convex. A point $x$ is called an extreme +point of convex set $S$ if $x\in S$ and $x$ cannot be expressed as a convex +combination of two points in $S$. It has been shown that the optimal solution to +linear programming corresponds to an extreme point of its feasible set provided +that the feasible set $S$ is bounded. This fact is the basis of the {\em simplex + algorithm} which was developed by Dantzig as a very efficient method for +solving linear programming. +\begin{table}[ht] +\centering + \centering + \caption*{Table~1\hskip1em This is an example for manually numbered table, which + would not appear in the list of tables} + \label{tab:badtabular2} + \begin{tabular}[c]{|m{1.5cm}|c|c|c|c|c|c|}\hline + \multicolumn{2}{|c|}{Network Topology} & \# of nodes & + \multicolumn{3}{c|}{\# of clients} & Server \\\hline + GT-ITM & Waxman Transit-Stub & 600 & + \multirow{2}{2em}{2\%}& + \multirow{2}{2em}{10\%}& + \multirow{2}{2em}{50\%}& + \multirow{2}{1.2in}{Max. Connectivity}\\\cline{1-3} + \multicolumn{2}{|c|}{Inet-2.1} & 6000 & & & &\\\hline + & \multicolumn{2}{c|}{ABCDEF} &\multicolumn{4}{c|}{} \\\hline +\end{tabular} +\end{table} + +Roughly speaking, the simplex algorithm examines only the extreme points of the +feasible set, rather than all feasible points. At first, the simplex algorithm +selects an extreme point as the initial point. The successive extreme point is +selected so as to improve the objective function value. The procedure is +repeated until no improvement in objective function value can be made. The last +extreme point is the optimal solution. + +\subsection{Nonlinear Programming} + +If at least one of the functions $f(x),g_j(x),j=1,2,\cdots,p$ is nonlinear, then +SOP is called a {\em nonlinear programming}. + +A large number of classical optimization methods have been developed to treat +special-structural nonlinear programming based on the mathematical theory +concerned with analyzing the structure of problems. + +Now we consider a nonlinear programming which is confronted solely with +maximizing a real-valued function with domain $\Re^n$. Whether derivatives are +available or not, the usual strategy is first to select a point in $\Re^n$ which +is thought to be the most likely place where the maximum exists. If there is no +information available on which to base such a selection, a point is chosen at +random. From this first point an attempt is made to construct a sequence of +points, each of which yields an improved objective function value over its +predecessor. The next point to be added to the sequence is chosen by analyzing +the behavior of the function at the previous points. This construction continues +until some termination criterion is met. Methods based upon this strategy are +called {\em ascent methods}, which can be classified as {\em direct methods}, +{\em gradient methods}, and {\em Hessian methods} according to the information +about the behavior of objective function $f$. Direct methods require only that +the function can be evaluated at each point. Gradient methods require the +evaluation of first derivatives of $f$. Hessian methods require the evaluation +of second derivatives. In fact, there is no superior method for all +problems. The efficiency of a method is very much dependent upon the objective +function. + +\subsection{Integer Programming} + +{\em Integer programming} is a special mathematical programming in which all of +the variables are assumed to be only integer values. When there are not only +integer variables but also conventional continuous variables, we call it {\em + mixed integer programming}. If all the variables are assumed either 0 or 1, +then the problem is termed a {\em zero-one programming}. Although integer +programming can be solved by an {\em exhaustive enumeration} theoretically, it +is impractical to solve realistically sized integer programming problems. The +most successful algorithm so far found to solve integer programming is called +the {\em branch-and-bound enumeration} developed by Balas (1965) and Dakin +(1965). The other technique to integer programming is the {\em cutting plane + method} developed by Gomory (1959). + +\hfill\textit{Uncertain Programming\/}\quad(\textsl{BaoDing Liu, 2006.2}) + +\section*{References} +\noindent{\itshape NOTE: These references are only for demonstration. They are + not real citations in the original text.} + +\begin{translationbib} +\item Donald E. Knuth. The \TeX book. Addison-Wesley, 1984. ISBN: 0-201-13448-9 +\item Paul W. Abrahams, Karl Berry and Kathryn A. Hargreaves. \TeX\ for the + Impatient. Addison-Wesley, 1990. ISBN: 0-201-51375-7 +\item David Salomon. The advanced \TeX book. New York : Springer, 1995. ISBN:0-387-94556-3 +\end{translationbib} diff --git a/macros/latex/contrib/hitszthesis/back/appendix02.tex b/macros/latex/contrib/hitszthesis/back/appendix02.tex new file mode 100644 index 0000000000..15e3919847 --- /dev/null +++ b/macros/latex/contrib/hitszthesis/back/appendix02.tex @@ -0,0 +1,72 @@ +% !TEX root = ../main.tex + +% 附录2 +\chapter{外文资料的调研阅读报告或书面翻译} + +\title{英文资料的中文标题} + +{\heiti 摘要：} 本章为外文资料翻译内容。如果有摘要可以直接写上来，这部分好像没有 +明确的规定。 + +\section{单目标规划} +北冥有鱼，其名为鲲。鲲之大，不知其几千里也。化而为鸟，其名为鹏。鹏之背，不知其几 +千里也。怒而飞，其翼若垂天之云。是鸟也，海运则将徙于南冥。南冥者，天池也。 +\begin{equation}\tag*{(123)} + p(y|\mathbf{x}) = \frac{p(\mathbf{x},y)}{p(\mathbf{x})}= +\frac{p(\mathbf{x}|y)p(y)}{p(\mathbf{x})} +\end{equation} + +吾生也有涯，而知也无涯。以有涯随无涯，殆已！已而为知者，殆而已矣！为善无近名，为 +恶无近刑，缘督以为经，可以保身，可以全生，可以养亲，可以尽年。 + +\subsection{线性规划} +庖丁为文惠君解牛，手之所触，肩之所倚，足之所履，膝之所倚，砉然响然，奏刀騞然，莫 +不中音，合于桑林之舞，乃中经首之会。 +\begin{table}[ht] +\centering + \centering + \caption*{表~1\hskip1em 这是手动编号但不出现在索引中的一个表格例子} + \label{tab:badtabular3} + \begin{tabular}[c]{|m{1.5cm}|c|c|c|c|c|c|}\hline + \multicolumn{2}{|c|}{Network Topology} & \# of nodes & + \multicolumn{3}{c|}{\# of clients} & Server \\\hline + GT-ITM & Waxman Transit-Stub & 600 & + \multirow{2}{2em}{2\%}& + \multirow{2}{2em}{10\%}& + \multirow{2}{2em}{50\%}& + \multirow{2}{1.2in}{Max. Connectivity}\\\cline{1-3} + \multicolumn{2}{|c|}{Inet-2.1} & 6000 & & & &\\\hline + & \multicolumn{2}{c|}{ABCDEF} &\multicolumn{4}{c|}{} \\\hline +\end{tabular} +\end{table} + +文惠君曰：“嘻，善哉！技盖至此乎？”庖丁释刀对曰：“臣之所好者道也，进乎技矣。始臣之 +解牛之时，所见无非全牛者；三年之后，未尝见全牛也；方今之时，臣以神遇而不以目视， +官知止而神欲行。依乎天理，批大郤，导大窾，因其固然。技经肯綮之未尝，而况大坬乎！ +良庖岁更刀，割也；族庖月更刀，折也；今臣之刀十九年矣，所解数千牛矣，而刀刃若新发 +于硎。彼节者有间而刀刃者无厚，以无厚入有间，恢恢乎其于游刃必有余地矣。是以十九年 +而刀刃若新发于硎。虽然，每至于族，吾见其难为，怵然为戒，视为止，行为迟，动刀甚微， +謋然已解，如土委地。提刀而立，为之而四顾，为之踌躇满志，善刀而藏之。” + +文惠君曰：“善哉！吾闻庖丁之言，得养生焉。” + + +\subsection{非线性规划} +孔子与柳下季为友，柳下季之弟名曰盗跖。盗跖从卒九千人，横行天下，侵暴诸侯。穴室枢 +户，驱人牛马，取人妇女。贪得忘亲，不顾父母兄弟，不祭先祖。所过之邑，大国守城，小 +国入保，万民苦之。孔子谓柳下季曰：“夫为人父者，必能诏其子；为人兄者，必能教其弟。 +若父不能诏其子，兄不能教其弟，则无贵父子兄弟之亲矣。今先生，世之才士也，弟为盗 +跖，为天下害，而弗能教也，丘窃为先生羞之。丘请为先生往说之。” + +柳下季曰：“先生言为人父者必能诏其子，为人兄者必能教其弟，若子不听父之诏，弟不受 +兄之教，虽今先生之辩，将奈之何哉？且跖之为人也，心如涌泉，意如飘风，强足以距敌， +辩足以饰非。顺其心则喜，逆其心则怒，易辱人以言。先生必无往。” + +孔子不听，颜回为驭，子贡为右，往见盗跖。 + +\subsection{整数规划} +盗跖乃方休卒徒大山之阳，脍人肝而餔之。孔子下车而前，见谒者曰：“鲁人孔丘，闻将军 +高义，敬再拜谒者。”谒者入通。盗跖闻之大怒，目如明星，发上指冠，曰：“此夫鲁国之 +巧伪人孔丘非邪？为我告之：尔作言造语，妄称文、武，冠枝木之冠，带死牛之胁，多辞缪 +说，不耕而食，不织而衣，摇唇鼓舌，擅生是非，以迷天下之主，使天下学士不反其本，妄 +作孝弟，而侥幸于封侯富贵者也。子之罪大极重，疾走归！不然，我将以子肝益昼餔之膳。” diff --git a/macros/latex/contrib/hitszthesis/back/appendix03.tex b/macros/latex/contrib/hitszthesis/back/appendix03.tex new file mode 100644 index 0000000000..2b3cca8247 --- /dev/null +++ b/macros/latex/contrib/hitszthesis/back/appendix03.tex @@ -0,0 +1,6 @@ +% !TEX root = ../main.tex + +% 附录3 +\chapter{其它附录} + +其他的附录如数据、代码等，可以放在这里。 diff --git a/macros/latex/contrib/hitszthesis/back/appendixA.tex b/macros/latex/contrib/hitszthesis/back/appendixA.tex new file mode 100644 index 0000000000..e549eebc93 --- /dev/null +++ b/macros/latex/contrib/hitszthesis/back/appendixA.tex @@ -0,0 +1,23 @@ +% !TEX root = ../main.tex + +% 附录A +\chapter{带章节的附录}[Full Appendix]% +完整的附录内容，包含章节，公式，图表等 + +\section{附录节的内容}[Section in Appendix] +这是附录的节的内容 + +附录中图的示例： +\begin{figure}[htbp] +\centering +\includegraphics[width = 0.4\textwidth]{golfer} +%\bicaption[golfer5]{}{\xiaosi[0]打高尔夫球的人}{Fig.$\!$}{The person playing golf}\vspace{-1em} +\caption{\xiaosi[0]打高尔夫球的人} +\end{figure} + +附录中公式的示例： +\begin{align} +a & = b \times c \\ +E & = m c^2 +\label{eq} +\end{align} diff --git a/macros/latex/contrib/hitszthesis/back/appendixB.tex b/macros/latex/contrib/hitszthesis/back/appendixB.tex new file mode 100644 index 0000000000..ec2a47bc2a --- /dev/null +++ b/macros/latex/contrib/hitszthesis/back/appendixB.tex @@ -0,0 +1,38 @@ +% !TEX root = ../main.tex + +% 附录B +\chapter{这个星球上最好的免费Linux软件列表}[List of the Best Linux Software in our Planet] +\section{系统} + +\href{http://fvwm.org/}{FVWM 自从上世纪诞生以来，此星球最强大的窗口管理器。} +推荐基于FVWM的桌面设计hifvwm：\href{https://github.com/dustincys/hifvwm}{https://github.com/dustincys/hifvwm}。 + +\subsection{hifvwm的优点} + +\begin{enumerate} + \item 即使打开上百个窗口也不会“蒙圈”。计算机性能越来越强大，窗口任务的管理必须要升级到打怪兽级别。 + \item 自动同步Bing搜索主页的壁纸。每次电脑开机，午夜零点自动更新，用户 + 也可以手动更新，从此审美再也不疲劳。 + \item 切换窗口自动聚焦到最上面的窗口。使用键盘快捷键切换窗口时候，减少 + 操作过程，自动聚焦到目标窗口。这一特性是虚拟窗口必须的人性化设 + 计。 + \item 类似window右下角的功能的最小化窗口来显示桌面的功能此处类似 + win7/win10，实现在一个桌面之内操作多个任务。 + \item 任务栏结合标题栏。采用任务栏和标题栏结合，节省空间。 + \item 同类窗口切换。可以在同类窗口之内类似alt-tab的方式切换。 + \item …… +\end{enumerate} + +\section{其他} + +\href{https://orgmode.org/}{orgmode，最强大的笔记系统，从来没有之一。} + +\href{https://www.jianguoyun.com/}{坚果云，国内一款支持WebDav的云盘系统，国内真正的云盘没有之一。} + +\section{vim} +实现中英文每一句一行，以及实现每一句折叠断行的简单正则式，tex源码更加乖乖。 +\begin{lstlisting} +vnoremap <leader>fae J:s/[.!?]\zs\s\+/\="\r".matchstr(getline('.'), '^\s*')/g<CR> +vnoremap <leader>fac J:s/[。！？]/\=submatch(0)."\n".matchstr(getline('.'), '^\s*')/g<CR> +vnoremap <leader>fle :!fmt -80 -s<CR> +\end{lstlisting} diff --git a/macros/latex/contrib/hitszthesis/back/ceindex.tex b/macros/latex/contrib/hitszthesis/back/ceindex.tex new file mode 100644 index 0000000000..7d2b284dd7 --- /dev/null +++ b/macros/latex/contrib/hitszthesis/back/ceindex.tex @@ -0,0 +1,6 @@ +% !TEX root = ../main.tex + +% 中英文索引 +\begin{ceindex} + \printsubindex* +\end{ceindex} diff --git a/macros/latex/contrib/hitszthesis/back/conclusion.tex b/macros/latex/contrib/hitszthesis/back/conclusion.tex new file mode 100644 index 0000000000..bebd69932e --- /dev/null +++ b/macros/latex/contrib/hitszthesis/back/conclusion.tex @@ -0,0 +1,10 @@ +% !TEX root = ../main.tex + +% 结论 +\begin{conclusions} + +学位论文的结论作为论文正文的最后一章单独排写，但不加章标题序号。 + +结论应是作者在学位论文研究过程中所取得的创新性成果的概要总结，不能与摘要混为一谈。博士学位论文结论应包括论文的主要结果、创新点、展望三部分，在结论中应概括论文的核心观点，明确、客观地指出本研究内容的创新性成果（含新见解、新观点、方法创新、技术创新、理论创新），并指出今后进一步在本研究方向进行研究工作的展望与设想。对所取得的创新性成果应注意从定性和定量两方面给出科学、准确的评价，分（1）、（2）、（3）…条列出，宜用“提出了”、“建立了”等词叙述。 + +\end{conclusions} diff --git a/macros/latex/contrib/hitszthesis/back/publications.tex b/macros/latex/contrib/hitszthesis/back/publications.tex new file mode 100644 index 0000000000..04013c303c --- /dev/null +++ b/macros/latex/contrib/hitszthesis/back/publications.tex @@ -0,0 +1,28 @@ +% !TEX root = ../main.tex + +% 发表论文、专利、获奖情况 +\begin{publication} + \noindent\textbf{（一）发表的学术论文} + \begin{publist} + \item XXX，XXX. Static Oxidation Model of Al-Mg/C Dissipation Thermal Protection Materials[J]. Rare Metal Materials and Engineering, 2010, 39(Suppl. 1): 520-524.（SCI~收录，IDS号为~669JS，IF=0.16） + \item XXX，XXX. 精密超声振动切削单晶铜的计算机仿真研究[J]. 系统仿真学报，2007，19（4）：738-741，753.（EI~收录号：20071310514841） + \item XXX，XXX. 局部多孔质气体静压轴向轴承静态特性的数值求解[J]. 摩擦学学报，2007（1）：68-72.（EI~收录号：20071510544816） + \item XXX，XXX. 硬脆光学晶体材料超精密切削理论研究综述[J]. 机械工程学报，2003，39（8）：15-22.（EI~收录号：2004088028875） + \item XXX，XXX. 基于遗传算法的超精密切削加工表面粗糙度预测模型的参数辨识以及切削参数优化[J]. 机械工程学报，2005，41（11）：158-162.（EI~收录号：2006039650087） + \item XXX，XXX. Discrete Sliding Mode Cintrok with Fuzzy Adaptive Reaching Law on 6-PEES Parallel Robot[C]. Intelligent System Design and Applications, Jinan, 2006: 649-652.（EI~收录号：20073210746529） + \end{publist} + + \noindent\textbf{（二）申请及已获得的专利（无专利时此项不必列出）} + \begin{publist} + \item XXX，XXX. 一种温热外敷药制备方案：中国，88105607.3[P]. 1989-07-26. + \end{publist} + + \noindent\textbf{（三）参与的科研项目及获奖情况} + \begin{publist} + \item XXX，XXX. XX~气体静压轴承技术研究, XX~省自然科学基金项目.课题编号：XXXX. + \item XXX，XXX. XX~静载下预应力混凝土房屋结构设计统一理论. 黑江省科学技术二等奖, 2007. + \end{publist} + %\vfill + %\hangafter=1\hangindent=2em\noindent + %\setlength{\parindent}{2em} +\end{publication} diff --git a/macros/latex/contrib/hitszthesis/back/resume.tex b/macros/latex/contrib/hitszthesis/back/resume.tex new file mode 100644 index 0000000000..75fb26677b --- /dev/null +++ b/macros/latex/contrib/hitszthesis/back/resume.tex @@ -0,0 +1,20 @@ +% !TEX root = ../main.tex + +% 个人简历 +\begin{resume} + + XXXX~年~XX~月~XX~日出生于~XXXX。 + + XXXX~年~XX~月考入~XX~大学~XX~院（系）XX~专业，XXXX~年~XX~月本科毕业并获得~XX~学学士学位。 + + XXXX~年~XX~月------XXXX~年~XX~月在~XX~大学~XX~院（系）XX~学科学习并获得~XX~学硕士学位。 + + XXXX~年~XX~月------XXXX~年~XX~月在~XX~大学~XX~院（系）XX~学科学习并获得~XX~学博士学位。 + + 获奖情况：如获三好学生、优秀团干部、X~奖学金等（不含科研学术获奖）。 + + 工作经历： + + \textbf{（除全日制硕士生以外，其余学生均应增列此项。个人简历一般应包含教育经历和工作经历。）} + +\end{resume} |