ME310 NUMERICAL METHODS PROGRAMMING PROJECT 1 TABLE OF CONTENTS 1. TABLE OF CONTENTS -------------------------------------------------------------------------------------------------1 2. INTRODUCTION --------------------------------------------------------------------------------------------------------2 3.NECESSARY HAND CALCULATION-----------------------------------------------------------------------------------2 4.FORMULATIONS USED IN THE CALCULATION--------------------------------------------------------------------3 5.NUMERICAL RESULTS --------------------------------------------------------------------------------------------------3 6.PLOTTED GRAGHICS----------------------------------------------------------------------------------------------------4 …show more content…
Both Methods reaches the result demanded fast. On Figure 3, approximate errors decreases suddenly and reaches the with 5 for Secant Method, the graph shows the 7 iteration because of 2 initial guesses, that is, the code count the 2 initial guesses as first 2 iteration.. On the other hand, On Figure 5, approximate errors decreases like Figure 3 andreaches to the value which is defined by user with in 6 iterations, because, our function count y1 as a first iteration. This results shows us the Secant Method is more succesful than the Quadratic Method as expressed at the end of the code. That is, the code finally decide which method achieve the result faster. There is also an important points for both formula. The secant method has on its denominator and the quadratic method has square root as and denominator as . The user is warned by the code before the initial guesses about the no result or complex result could be obtained due to zero denominator or negative value in the square
Prelab week 1 Calculations Preparation of 1.5μmol/L mixed low-level standard dilution 150μmol/L × V1=1.5μmol/L × 10ml V1=(1.5μmol/L×10ml)/(150μmol/L)=0.1ml Conversion of milliliters to microliters (0.1ml×1000)μL= 100μL Preparation of 3μmol/L mixed low-level standard dilution 150μmol/L × V1=3μmol/L × 10ml V1=(3μmol/L×10ml)/(150μmol/L)=0.2ml Conversion of milliliters to microliters (0.2ml×1000)μL= 200μL Preparation of 3μmol/L mixed low-level standard dilution 150μmol/L × V1=7.5μmol/L × 10ml V1=(7.5μmol/L×10ml)/(150μmol/L)=0.5ml Conversion of milliliters to microliters (0.5ml×1000)μL= 500μL Preparation of the blank samples The volumetric flask will be filled to the mark with 150μmole/L of stock solution to act as blank (reference). Additional two blanks will
& { 2872(25$\%$)} & { 2499(22$\%$)} & { 5795(26$\%$)} & { 5100(23$\%$)}\\ $N_{\omega\to\pi^0\gamma}^{\circ}$ & { 4487(15$\%$)} & { 3590(12$\%$)} & { 1978(6$\%$)} & { 1721(5$\%$)} & { 5846(9$\%$)} & { 5145(8$\%$)} \\ \hline $BR^{measured}_{\omega\to\pi^0\gamma}$ & \textcolor{red}{ 1.07} & \textcolor{red}{ 0.78} & \textcolor{red}{ 0.52} & \textcolor{red}{ 0.43} & \textcolor{red}{ 0.73} & \textcolor{red}{ 0.61} \\ ($\%$) & \textcolor{red}{ (15$\%$)} & \textcolor{red}{ (11$\%$)} & \textcolor{red}{ (6$\%$)} & \textcolor{red}{ (5$\%$)} & \textcolor{red}{ (9$\%$)} & \textcolor{red}{ (8$\%$)} \\ \hline & \multicolumn{6}{c|} {\bf $\sigma_{dedp-sys}=\sigma^{av}_{rms}\times(1-\sigma_{fit-sys}^{rel})$ } \\ \hline \end{tabular} \caption[The standard deviation $\sigma^{av}_{rms}$ in ${N_{\omega\to\pi^0\gamma}}^{rec}$, ${N_{\omega\to\pi^0\gamma}}^{\circ}$ and $BR^{measured}_{\omega\to\pi^0\gamma}$ for the different energy-momentum conservation constraint are presented] { The standard deviation $\sigma^{av}_{rms}$ in ${N_{\omega\to\pi^0\gamma}}^{rec}$ and $BR^{measured}_{\omega\to\pi^0\gamma}$ for the different energy-momentum conservation
double atan2 (doubly y, double x) It will return the arc tangent in terms of radians of y/x based on the signs of both values to determine the correct quadrant. double cos (double x) It will return the cosine of a radian angle x. double cosh (double x) It will return the hyperbolic cosine of
onvergence of Adaptive Noise Canceller '); legend( 'Measured Signal ', 'Error Signal '); subplot(3,3,6); plot(t,e, 'r '); hold on; plot(t,fhb, 'b '); axis([Time-4 Time -0.5 0.5]); grid on; xlabel( 'Time [sec] '); ylabel( 'Voltage [mV] '); title( 'Steady-State Error Signal '); legend( 'Calc Fetus ', 'Ref Fetus ECG '); filt_e = filter(Hd,e); subplot(3,3,7); plot(t,fhb, 'r '); hold on; plot(t,filt_e, 'b '); axis([Time-4 Time -0.5 0.5]); grid on; xlabel( 'Time [sec] '); ylabel( 'Voltage [mV] '); title( 'Filtered signal '); legend( 'Ref Fetus ', 'Filtered Fetus '); thresh = 4*mean(abs(filt_e))*ones(size(filt_e)); peak_e = (filt_e >= thresh); edge_e = (diff([0; peak_e]) >0); subplot(3,3,8); plot(t,filt_e, 'c '); hold on; plot(t,thresh, 'r '); plot(t,peak_e, 'b '); xlabel( 'Time [sec] '); ylabel( 'Voltage [mV] '); title( 'Peak detection '); legend( 'Filtered fetus ', 'Dyna thresh ', 'Peak marker ', 'Location ', 'SouthEast '); axis([Time-4 Time -0.5 0.5]); subplot(3,3,9); plot(t,filt_e, 'r '); hold on; plot(t,edge_e, 'b '); plot(0,0, 'w '); fetus_calc = round((60/length(edge_e(16001:end))*Fs) * sum(edge_e(16001:end))); fetus_bpm = [ 'Fetus Heart Rate = ' mat2str(fetus_calc)]; xlabel( 'Time [sec] '); ylabel( 'Voltage [mV] '); title( 'Reconstructed fetus
X$ then the resulting error would have been precisely what really wanted to
In Figs. (\ref{fig:NC3Mont.eps}-\ref{fig:NC6Mont.eps}), the results of the Monte Carlo analysis is presented along with the result obtained for the central values of the parameter. It is observed that for the form factors $C_3^{N \Delta}(Q^2)$, $C_4^{N \Delta}(Q^2)$ and $C_6^{N \Delta}(Q^2)$, Monte Carlo analysis and the prediction for the central values agree at large values of $Q^2$, but deviate from each other for small values of
line from x = y to y = x. Mathematically, x = y and y = x mean the same thing. How- ever, this is not so in programming. Run the second program.
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Due to both the implementation
The equation is f(x+h)-f(x)/h. This formula finds the slope of the sectant line that goes through two points that are on a graph of f. These are the points with x- coordinates x and x+h. It also allows you to find the slope of any curve or line at any single point. The difference between this and the slope formula is y is used as the y-axis, but in the difference quotient, the change in the y-axis is described by f(x).
However Franklin commonly used the Socratic method saying "I found this method safest for myself and very embarrassing to those against whom I used it; therefore I took a delight
This method a person questions every definition he or she gives until the real definition is found or one closest enough. Socratic Method can be confusion and a person might not ever get a definition, but it does make a person think more about things. To break the Socratic
(Hammer Br. (De Sole Dkt. No. 214) at 16-17; Hammer Reply Br. (De Sole Dkt. No 240) at 29-31; [*39] Hammer Br.
Web. 2 May 2014. Document URL http://go.galegroup.com/ps/i.do?id=GALE%7CH1420002699&v=2 1&u= cclc_reed&it= r&p=LitRC&sw
Introduction: Our earth is the most precious gift of the universe. It is the sustenance of ‘nature’ that is the key to the development of the future of mankind. It is the duty and responsibility of each one of us to protect nature. It is here that the understanding of the ‘environment’ comes into the picture. The degradation of our environment is linked with the development process and the ignorance of people about retaining the ecological balance.