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MBulli
2018-02-26 19:38:03 +01:00
parent 6f9d191622
commit 9d4927a365
3 changed files with 8 additions and 13 deletions
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5
tex/chapters/experiments.tex
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@@ -42,7 +42,7 @@ However, both cases do not give a deeper insight of the error behavior of our me
\begin{figure}[t]
%\includegraphics[width=\textwidth,height=6cm]{gfx/tmpPerformance.png}
\input{gfx/perf.tex}
\caption{Logarithmic plot of the runtime performance with increasing grid size $G$ and bivariate data. The weighted average estimate (blue) performs fastest followed by the boxKDE (orange) approximation. Both the BKDE (red), and the fastKDE (green) are magnitudes slow, especially for $G<10^4$.}\label{fig:performance}
\caption{Logarithmic plot of the runtime performance with increasing grid size $G$ and bivariate data. The weighted average estimate (blue) performs fastest followed by the boxKDE (orange) approximation. Both the BKDE (red), and the fastKDE (green) are magnitudes slower, especially for $G<10^4$.}\label{fig:performance}
\end{figure}
% kde, box filter, exbox in abhänigkeit von h (bild)
@@ -94,8 +94,5 @@ In addition, modern CPUs do benefit from the recursive computation scheme of the
Furthermore, the computation is easily parallelized, as there is no data dependency between the one-dimensional filter passes in algorithm~\ref{alg:boxKDE}.
Hence, the inner loops can be parallelized using threads or SIMD instructions, but the overall speedup depends on the particular architecture and the size of the input.
\commentByFrank{Fig4 (error over time) checken ob die beiden farbigen linien jetzt richtig rum sind. NIEMALS GENERIERTE TEX GRAFIKEN DIREKT EDITIEREN}
\input{chapters/realworld}

3
tex/chapters/realworld.tex
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@@ -45,8 +45,7 @@ Additionally, in most real world scenarios many particles share the same weight
\label{fig:realWorldTime}
\end{figure}
Further investigating fig. \ref{fig:realWorldTime}, the boxKDE performs slightly better then the weighted-average, however after deploying \SI{100} MC runs, the difference becomes insignificant.
\commentByMarkus{Was sind MC Runs? Die Abkürzung kommt das erste mal vor.}
Further investigating fig. \ref{fig:realWorldTime}, the boxKDE performs slightly better then the weighted-average, however after deploying \SI{100} Monte Carlo runs, the difference becomes insignificant.
The main reason for this are again multimodalities caused by faulty or delayed measurements, especially when entering or leaving rooms.
Within our experiments the problem occurred due to slow and attenuated Wi-Fi signals inside thick-walled rooms.
While the system's dynamics are moving the particles outside, the faulty Wi-Fi readings are holding back a majority by assigning corresponding weights.