☰

迁移期间表结构被破坏

tag-icon • tag-icon tables sidewaystable
迁移期间表结构被破坏

现在这个受到了影响

\documentclass[listof=totoc,a4paper,11pt,oneside,chapterprefix=true,sfdefaults=false]{scrbook}
\usepackage[margin=1in]{geometry} 
%Table packages
\usepackage[figuresright]{rotating}
%\setlength{\rotFPtop}{0pt plus 1fil}
\usepackage{booktabs, makecell, tabularx, ragged2e}
\renewcommand{\theadfont}{\normalsize}
\renewcommand\theadgape{}
\newcolumntype{L}{>{\RaggedRight}X}
\newcolumntype{P}[1]{>{\RaggedRight}p{#1}}

\providecommand\textcite[1]{} % ??

\begin{document}

%%%BIG TABLE BEGIN
\begin{sidewaystable}
\footnotesize
\setlength\tabcolsep{4pt}
\centering
\caption{Comparative study on some Fall Detection Systems~[TB-Threshold and ML-Machine Learning based]}
\label{tab:1}       % Give a unique label
\begin{tabular}{@{} P{0.40in}P{0.8in}P{0.85in}
                    c
                    P{0.65in}
                    c c
                    P{1.2in} P{1.2in}
                @{}}
    \toprule
\thead[b]{Ref.}
    & \thead[b]{Sensor\\type}
        & \thead[b]{Sensor\\ location}
            & \thead[b]{No. of\\ sensors}
                & \thead[b]{Processing\\location}
                    & \thead[b]{Method}
                         & \thead[b]{Accuracy\\in \%}
                            & \thead[b]{Features}
                                & \thead[b]{Limitations}       \\
    \midrule
\cite{Gonzalez2020}
    & Accelerometer, Gyroscope
    & External & $>1$ & On board & TB & 77.5
    & Fine grained fall detection with good accuracy.
    & No text based location, Fall and break of device aspect not considered.\\
    \addlinespace

\cite{Abdulaziz2021}
    & Triaxial Accelerometer
    & External & 1 & On board and Remote
    & TB+ML & 99.45
    &Killer heuristic optimized AlexNet convolution neural network(KHANCN). Sensor information is initially collected by placing 6 sensors on 14 subjects.
    &Fall location and time not available. No real life implementation case study. Fall and break aspect not considered.\\
    \addlinespace

\cite{Tsinganos2017}
    & Triaxial Accelerometer
    & External & $>1$ & On board and remote
    & ML & 91.83
    &Fall detection and ADL based on KNN classifier\%
    &Store \& analyse, no live data, device fall is not considered.\\
    \addlinespace

\cite{Boudouane2019}
    &Camera
    & External & $>1$ & Remote
    & ML & 68.33
    & Image information is used for fall classification.
    &Slow, multiple image capturing device may be required, privacy issues.\\
    \addlinespace

\cite{Junior2018}
    & Triaxial Accelerometer
    & External & $>1$ & Remote
    & TB + ML & Unknown
    & Threshold analysis, reminder analysis and decision tree algorithm .
    & The non-functional aspect of the device after a fall is not considered.\\
\addlinespace

\cite{Silva2018}
    & Pressure Sensor
    &Integrated in the operator's shoe & $>1$ & remote
    & TB & 86
    & Good result accuracy and can be implemented in IoT platform.
    & The nature of walking surface has a direct impact on the accuracy.\\
    \addlinespace

\cite{Lee2019}
    & Accelerometer, Gyroscope
    &Smartphone in chest pocket & $>1$ & On board and remote
    & TB & 92.5
    & Smartphone Google API (location), Good accuracy.
    & Device location is not suitable for heart patient, Google API is not accurate in remote locations.\\
    \addlinespace

\cite{Tong2013}
    & MEMS tri-axis accelerometer
    &Upper trunk of the body & 1 & Remote
    & ML & 100
    & Fall detection and prediction using hidden Markov chain.
    & Location information as well as fall alike cases are not considered.\\
    \addlinespace

\cite{Ruan2015}
    & UHF-RFID
    &Different locations inside the room & $>1$ & Remote
    &TB + ML & 92.45
    & Device and location independent fine grained fall detection.
    & Not suitable for outdoor monitoring.\\
    \addlinespace

Proposed system
    & Smartphone accelerometer
    &Gender and garment independent, easy to wear phone holder & 1 & Remote
    &TB & 94.45
    & \multicolumn{2}{p{\dimexpr2.4in+2\tabcolsep+\arrayrulewidth}}{
        Text based location + SMS, Indoor and outdoor monitoring, Ineffectual device consideration, Non ambulatory, Non self-recovery warning only so number of warnings are less, In real life, the system could reduce the FoF in the PD patients upto 10\%.}  \\
    \bottomrule
\end{tabular}
\end{sidewaystable}
%%%BIG TABLE END
\end{document}

原始输出是原始输出

答案1

由于某种原因，你的代码使用了tabular环境而不是tabularx环境，最终没有充分利用可用空间。为了解决这个问题，我建议你

  • 切换到tabularx环境。将 (a) 更改\begin{tabular}为\begin{tabularx}{\textwidth}，将 (b)\end{tabular}更改为\end{tabularx}，并将 (c)（P{1.2in} P{1.2in}最后两列的定义）更改为L L。

  • 在表格的最后一行数据中，更改

    \multicolumn{2}{p{\dimexpr2.4in+2\tabcolsep+\arrayrulewidth}}

    到

    \multicolumn{2}{>{\hsize=\dimexpr2\hsize+2\tabcolsep\relax}L}

  • 为了便于管理，请将第 1 列和第 5 列的定义分别从P{0.40in}和更改为和P{0.65in}P{0.50in}P{0.75in}

另一个建议：我会将所有实例更改为\thead[b]-\thead[t]或者完全省略垂直位置说明符。

最后一条评论：由于某些或您的P-type 列非常窄，我建议将列类型的定义从

\newcolumntype{P}[1]{>{\RaggedRight}p{#1}}

到

\newcolumntype{P}[1]{>{\RaggedRight\hspace{0pt}}p{#1}}

允许对给定单元格中的第一个单词进行连字。

在此处输入图片描述

\documentclass[listof=totoc,a4paper,11pt,oneside,%
   chapterprefix=true,sfdefaults=false]{scrbook}
\usepackage[margin=1in]{geometry} 
%Table packages
\usepackage[figuresright]{rotating}
%\setlength{\rotFPtop}{0pt plus 1fil}
\usepackage{booktabs, makecell, tabularx, ragged2e}
\renewcommand{\theadfont}{\normalsize}
\renewcommand\theadgape{}
\newcolumntype{L}{>{\RaggedRight}X}
\newcolumntype{P}[1]{>{\RaggedRight\hspace{0pt}}p{#1}}

%\providecommand\textcite[1]{} % ??

\begin{document}

%%%BIG TABLE BEGIN
\begin{sidewaystable}
\footnotesize
\setlength\tabcolsep{4pt} % default: 6pt
%%\centering
\caption{Comparative study on some Fall Detection Systems [TB-Threshold and ML-Machine Learning based]}
\label{tab:1}       % Give a unique label
\begin{tabularx}{\textwidth}{@{} 
     P{0.5in} P{0.8in} P{0.85in}
     c
     P{0.75in}
     c c
     L L @{}}
\toprule
      \thead[t]{Ref.}
    & \thead[t]{Sensor\\type}
    & \thead[t]{Sensor\\ location}
    & \thead[t]{No.\ of\\ sensors}
    & \thead[t]{Processing\\location}
    & \thead[t]{Method}
    & \thead[t]{Accuracy\\in \%}
    & \thead[t]{Features}
    & \thead[t]{Limitations} \\
\midrule

\cite{Gonzalez2020}
    & Accelerometer, Gyroscope
    & External & $>1$ & On board & TB & 77.5
    & Fine grained fall detection with good accuracy.
    & No text based location, Fall and break of device aspect not considered.\\
    \addlinespace

\cite{Abdulaziz2021}
    & Triaxial Accelerometer
    & External & 1 & On board and Remote
    & TB+ML & 99.45
    &Killer heuristic optimized AlexNet convolution neural network(KHANCN). Sensor information is initially collected by placing 6 sensors on 14 subjects.
    &Fall location and time not available. No real life implementation case study. Fall and break aspect not considered.\\
    \addlinespace

\cite{Tsinganos2017}
    & Triaxial Accelerometer
    & External & $>1$ & On board and remote
    & ML & 91.83
    &Fall detection and ADL based on KNN classifier\%
    &Store \& analyse, no live data, device fall is not considered.\\
    \addlinespace

\cite{Boudouane2019}
    &Camera
    & External & $>1$ & Remote
    & ML & 68.33
    & Image information is used for fall classification.
    &Slow, multiple image capturing device may be required, privacy issues.\\
    \addlinespace

\cite{Junior2018}
    & Triaxial Accelerometer
    & External & $>1$ & Remote
    & TB + ML & Unknown
    & Threshold analysis, reminder analysis and decision tree algorithm .
    & The non-functional aspect of the device after a fall is not considered.\\
\addlinespace

\cite{Silva2018}
    & Pressure Sensor
    &Integrated in the operator's shoe & $>1$ & remote
    & TB & 86
    & Good result accuracy and can be implemented in IoT platform.
    & The nature of walking surface has a direct impact on the accuracy.\\
    \addlinespace

\cite{Lee2019}
    & Accelerometer, Gyroscope
    &Smartphone in chest pocket & $>1$ & On board and remote
    & TB & 92.5
    & Smartphone Google API (location), Good accuracy.
    & Device location is not suitable for heart patient, Google API is not accurate in remote locations.\\
    \addlinespace

\cite{Tong2013}
    & MEMS tri-axis accelerometer
    &Upper trunk of the body & 1 & Remote
    & ML & 100
    & Fall detection and prediction using hidden Markov chain.
    & Location information as well as fall alike cases are not considered.\\
    \addlinespace

\cite{Ruan2015}
    & UHF-RFID
    &Different locations inside the room & $>1$ & Remote
    &TB + ML & 92.45
    & Device and location independent fine grained fall detection.
    & Not suitable for outdoor monitoring.\\
    \addlinespace

Proposed system
    & Smartphone accelerometer
    &Gender and garment independent, easy to wear phone holder & 1 & Remote
    &TB & 94.45
    & \multicolumn{2}{>{\hsize=\dimexpr2\hsize+2\tabcolsep\relax}L}{
        Text based location + SMS, Indoor and outdoor monitoring, Ineffectual device consideration, Non ambulatory, Non self-recovery warning only so number of warnings are less, In real life, the system could reduce the FoF in the PD patients upto 10\%.}  \\
    \bottomrule
\end{tabularx}
\end{sidewaystable}
%%%BIG TABLE END
\end{document}

相关内容