In (7) and (8) the CACM-RL and CACM-RL* evaluation per action are shown. In (8) it is considered

To create a mousetrap car and measure its performance. We will also see where force and energy is impacting on the performance, for example friction will impact on the cars performance as it generates heat and slows to car down thus meaning that the car may not travel as far as it should. Another force that is demonstrated in testing of the car kinetic energy, without kinetic energy the car would not travel at all.

The objective of the lab was to program a vehicle to continuously drive in a one meter square. The square was to be completed in less than 20 seconds, and only the wheel encoder could be used to navigate the vehicle. All of this was done by using the Arduino software tool and the Redbot library to create a program that would satisfy the given lab requirements.

If they do not, but are within the acceptable margin of error of ±20√4=±40’’, each angle is corrected to create a sum of 360˚. For this project, the angles added to 360˚ 00’ 10’’. After each angle is corrected, angle 412 is 78˚31’03’’, angle 123 is 99˚01’23’’, angle 234 is 92˚32’21’’, and angle 341 is 89˚53’13’’. After using the Total Station, the Auto level is used to measure the elevations of each point. The known elevation outside of Powell is 380’. The Auto level is placed between each point to measure the backsight and foresight. Once all the points are measured, all the backsights and foresights can be measured with the difference taken to find the error. With this project, the error was -0.128’. Next, the pacing between each point must be taken to find the correction for each elevation with the equation, (distance from known elevation/total level network distance) X total error. The total pacing for the project is 235 steps with a pace of 2.31 feet per step. After the corrections, point 4 is 379.384’, point 3 is 378.182’, point 2 is 379.093’, and point 1 is 379.645’. A transit is used to find the true north from the magnetic

The programmed algorithm is shown in Figure 6.The program was developed using LabVIEW System design software. The entire experimental set-up is shown in Figure 7.

Given that P_0=0.35 (the starting value for Pn) conducting 30 iterations on the first family of curves when 0.5≤K≤1 I’ve noticed that the system values increases from its initial value until it reaches a steady state on 1 smoothly without oscillating around it.

The third simulation is loss location (-9, -9). First f all, this test has error in one location. It is showing 28dB, this value is impossible. The reason is we haven't set program can detect value more than 27dB. This mistake may program error or test error. Compare with last two tests, this test is inaccuracy because many abnormal values are showing in SITA results. The normal values are higher frequency implements. The lowest has showing very abnormal value that is 14dB. This value should not possible shows in this test. The reason cause those errors may program error or test error. The reason is program using the computer vision technology, but the problem is image noise. The technology cannot guarantee all of flashlight can be detected.

Attending to the average accuracy results, the highest accuracy obtained by the Hassanat distance which achieved the first rank in all noise levels, followed by Lorentzian distance. The Lorentzian distance achieved the second rank only at level noise 10 %, 30%, 40%, 50%, 80% and 90%, but at level noise 20%, 60%, and 80% outperformed by Canberra distance and at level noise 40 %, it outperformed by Manhattan distance, where these distances, Canberra and Manhattan achieved the second rank in theses level of noise. The Canberra distance achieved the third rank at a level of noise 80% and 50%, 40%, 30% and 10 %. This distance at level noise 10 % was equivalent to the Square chi-square distance in the result. At a level of noise 90%, the

We were given groups to design and make a mousetrap powered car that will roll as far as possible. This will be measured and be put into a graph. We will make three modifications to our mousetrap car over the course of the experiment. We have a variety of different materials, including plastic, wooden wheels and a dowel, screws, mousetrap, blue tack and a piece of string. Forces were acting in a negative way and a positive way on the car. Gravity was pulling the car down to the ground. Uplift was pushing up upon the car against gravity. Drag was also known as friction, holding back the car while it was moving. Thrust was in the cars favour, pushing forward against the force drag. There were also many forms of energy being used and being wasted like heat and sound energy. Potential energy was stored in the mousetrap, propelling itself forward. Kinetic energy was also demonstrated when the car started to roll.

Using the propagation of error formula, theoretical uncertainty of the unknown resistor RX can be calculated as,

We made the correct prediction that the shape of our target was a triangle. The characteristics of the target that were

Figure 5.9 Basic execution of TurtleMouse over time [l-r, t-b], using above initialization weights. All options are set to initialization states. “Learning” is characterized by periods of oscillatory states (long, straight lines) separated by “interesting behavior” where the turtle responds to the position and presence of the

The first thing I wanted to do was to estimate the approximate distance of which the

intervals to 125 ms. The in-plane spatial resolution of the MSCCT scanner is nine line

From the results we got, SRT and CRT groups showed significant differences all four phases and the total initiation time with CRT group costs 225.4ms longer than SRT. This was consistent with Sun et al study[14] which found RT, Release, Unloading and DSP phase were quite sensitive to additional processing in stimulus evaluation and response selection. Among the four phases, RT and Release phase had pretty large time consuming differences between CRT and SRT with CRT always required longer time to complete than SRT. This can be explained as the very first two parts of initiation phase worked together to figure out what kind of stepping task you will perform. However, unlike RT and Release phase, we found SRT group had longer time duration than CRT group during transition and DSP phases even though the delayed time between SRT and CRT was relatively small and so was the effect size. This indicated that at the finishing part of step initiation, stimulus evaluation of stepping task (simple or choice) might not play a major role

The time for one oscillation was found by dividing the amount of oscillations (20) done by the time taken for them= 31.4720 = 1.57s