Experimental Results. To compare the actual performance, we implemented the four protocols and compared their costs in this section. We simulated the total computation delay from the time when the membership event happens to the time when group key agreement finishes. Average delay has been measured, since all members do not finish group key agreement simultaneously.
Experimental Results. This section describes the results of evaluating the agreement among the outputs of multiple LVCSR models as an estimate of confi- dence for each hypothesized word. Xxxxxx-SPOJUS Average Precision 98.0 Average Precision 96.8 Average Precision 96.1 Xxxxxx-Xxxxxx SPOJUS-SPOJUS with/without short pause states (Xxxxxx) units in HMMs (Xxxxxx) with/without short pause states (SPOJUS) frame shift lengths (SPOJUS) sampling frequencies (SPOJUS) feature parameters (SPOJUS) duration control / self loop (SPOJUS) 99 98 98 97 Precision (%) Precision (%) 97 96 95 95 94 94 93 0 20 40 60 80 100 Order sorted by Precision Order sorted by Precision
Experimental Results. To ensure an accurate comparison between the simulated results and the experimental results, the same state feedback gain K and integral gain Ki were used for experimental testing. The results can be seen in Figure 6 and Figure 7. The system parameters are slightly different from the simulation in that the initial angular position is the stable equilibrium point at 180°. Addi- tionally, the swing up control is used to erect the pendulum in the upright position. The system gain A is shown to be greater than the ±5 Volt saturation cutoff limit of the simulation. The software has been programmed with two terminal voltage saturation points. The first is set to ±7.5 V during swing up, and the second is set to ±5 V once the stabilization controller initiates. This is necessary to achieve a cart driving force great enough to erect the pendulum rod. At the ±5 V limit, the motor is not able to produce enough energy to swing the rod upright. However, using the ±7.5 V limit during stabilization can cause the system to destabilize. Once the swing up controller erected the pendulum, the stabilization controller initiated at roughly 5.5 seconds. After stabilization was achieved a disturbance was introduced to the pendulum rod at about 10 seconds. This disturbance was introduced by ‘tapping’ the pendulum rod in one direction with a force from the hand. Also at roughly 17 sec- onds the linear set point was changed from 0 cm to 25 cm. When viewing the experimental results, there are a number of observa- tions to be made. It is important to note the angular position response of the physical system during swing up. It appears that the angle is cross- ing the 0° threshold on each pass of the cart. This would imply a full revolution of the pendulum rod. However, this is not the case. When viewing the GUI main form, one can see the angular position measure- ment has been set up with 0° at the top, and 180° and -180° both meeting at the bottom of the circle. Due to this configuration, as the pendulum swings through the bottom of the measurement circle the sys- tem interprets this as a full revolution due to the digital filter on the angular position. The calculated motor voltage seen in Figure 7 shows a noisy response. Even with the digital filter added to the output of this calculation, there is a substantial amount of calculation noise present. However, it is important to note the peak values at different times during this test. When a disturbance was introduced at 10 seconds,...
Experimental Results. 17 The Decay Chain Nd140 - Pr140 - Ce140 ... 17 Americium 241 ... 24 Lead 210 ... 35 Plutonium 239 ... 43 Uranium 237 ... 45 Protactinium 232 ... 49 Protactinium 233 ... 54 DISCUSSIONS AND CONCLUSIONS ... 66 ACKNOWLEDGMENTS ... 68 REFERENCES ... 69 LIST OF TABLES Table Page I. Extrapolated values of the Κ and L x-ray energies of the transuranium elements ... 15
Experimental Results. First experiments showed a very high concentration of the larger particles measured by APS at the beginning of the generation, which were above the APS upper concentration limit (>> 103 particles/cm3). Thus, the APS was implemented a dilution line by a factor of 25, as drawn in Figure 4, to decrease aiborne particle concentration. Figure 5 present the evolution of the normalized number concentration of airborne particles measured by SMPS (a) and by APS (b) with a bypass flow rate of 8 L/min with the different agitation speeds (1500 and 2500 rpm). The results for SMPS measurement presented were obtained with only the diffusion corrections. Normalized Number Concentration (C / Ct=0) 6.5 6 5.5 5 4.5 4 3.5 3 2.5 2 1.5 1 0.5 0 a) Agitation speed = 1500 rpm Agitation speed = 2500 rpm 5 15 25 Time of generation-measurement (minute) Normalized Number Concentration (C / Ct=0) 7 6.5 6 5.5 5 4.5 4 3.5 3 2.5 2 1.5 1 0.5 0 b) Agitation speed =1500 rpm Agitation speed = 2500 rpm 5 15 25 Time of generation-measurement (minute) Figure 5. Evolution of the normalized number concentration (C / C t=0) of airborne particles measured by SMPS
Experimental Results. To verify the effectiveness of the BlendCAC approach against unauthorized access requests, a service access experiment was carried out on a real network environment. In the test scenario, one Raspberry Pi 3 device worked as the client and another worked as the service provider. Given the access authorization process shown in Figure 9a,b, when any of the steps in the authorization procedure failed, the running process immediately aborts instead of continuing to step through all the authorization stages. As shown by Figure 9b, the server stopped the authorization process due to the failure to verify the granted actions or the conditional constraints specified in the access right list. Consequently, the client node received a deny access notification from the server and could not read the requested data. In contrast, Figure 9a presents a successful data request example, in which the whole authorization process was accomplished at the server side without any error, allowing the client to successfully retrieve the data from the service provider. The delegate authorization and revocation results are shown in Figure 9c,f. Figure 9c shows that the delegator ‘0xaa09c6d65908e54bf695748812c51d8f2ceea0f5’ successfully delegated a subset of its delegated permissions to the delegatee ‘0xfa4c5d320d638cbdff557c4c1f3110d3143f40c3’ whose parent was empty. Figure 9d shows a failed delegation scenario caused by assigning permissions to a delegated entity. In the revocation process, only the supervisor or ancestor of the delegatee is allowed to call back the delegated permissions. Figure 9e shows that the delegatee’s parent ‘0xaa09c6d65908e54bf695748812c51d8f2ceea0f5’ was able to successfully revoke the delegation relationship. Otherwise, the delegation revocation request which was from neither the delegatee’s parent ‘0x3d40fad73c91aed74ffbc1f09f4cde7cce533671’ nor any ancestor in delegate path was denied and a failed result is shown in Figure 9f.
Experimental Results. After the completion of each blast, a thorough inspection of each column was performed. Crack patterns were observed, and the damage to each column and its respective strengthening system was assessed. Crack sensor measurements were taken both before and after each blast, along with dynamic measurements on Column 1. It should be noted that on the two columns that were strengthened, cracks cannot be observed visually. For these columns, the coaxial cable crack sensors were used to locate cracks after each blast.
Experimental Results. In the proposed approach a new key management system is used for secure communication. A new member can join and also existing member can get deleted from the group. The keys are updated automatically by using the group member. The keys are distributed before starting the transmissions. Group is created to join the node, sender will encrypt the message and the session key is placed in the header. The receiver will decrypt the key and also the encrypted message. In the receiver side the sender node, transmission times are displayed.
Experimental Results. RQ 1. For the first research question, we need to show that we can construct interactive verifiers from automatic verifiers, and that they can be useful in terms of effectiveness. By “interactive verifier”, we mean a verifier that can verify more programs correct if we feed it with invariants, for example, by annotating the input program with ACSL annotations. Using our building blocks from Sect. 3, an interactive verifier can be composed as illustrated in Fig. 4e (that is, configurations of the form ACSL2Witness|Witness2Assert|Verifier). For a meaningful evaluation we need a large number of annotated programs, which we would be able to get if we converted the witnesses from SV-COMP using Witness2ACSL in advance. But since the first component ACSL2Witness in Fig. 4e essentially does the inverse operation, we can generalize and directly consider witnesses as input, as illustrated in Fig. 4b (that is, configurations of the form Witness2Assert|Verifier). Now we look at the results in Table 1: The first row reports that cooperation improves the verifier 2ls in 179 cases, that is, there are 179 witnesses that contain information that helps 2ls to prove a program that it could not prove without the information. In other words, for 179 witnesses, we ran Witness2Assert to transform the original program to one in which the invariants from the witness were written as assertions, and 2ls was then able to verify the program. Since there are often several witnesses for the same program, 2ls verified in total 111 unique programs that it was not able to verify without the annotated invariants as assertion.
Experimental Results
