### Table 2: Steady state image plane position error for the given visual servo controller proportional and integral gains.

2000

"... In PAGE 41: ...EXPERIMENTAL TESTS AND RESULTS 34 same location in the image. Table2 shows mean and standard deviation of the visual error signals (both u and v directions) for three different settings of the proportional and integral gains. Note that with the integral term disabled (K i =0), the resulting u axis error was much greater than the v axis error (note that 1 pixel corresponds to approximately 7 #16m on the image plane and 10 #16m in 3D camera coordinates through perspective projection).... In PAGE 41: ... Setting K i =0:005 significantly reduced the error during motion, as shown in Fig. 32 and Table2 , at the cost of slightly slower transient performance.... ..."

Cited by 4

### Table 5 Setup costs: proportion due to near eld integrals

1997

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### Table 7 The benefit of fully integrated approach (h = 0.4) b Proportion of the

2005

"... In PAGE 17: ... For instance, Table 9 shows the benefit of integration when a genetic algorithm is applied to calculate the routing distance instead of (7). It is easy to see that the data in Table 9 reveal the same managerial insights as the data in Table7 do. 6.... ..."

### Table 8 The benefit of fully integrated approach (b = 0.0012) h Proportion of the

2005

### Table 9 The benefit of fully integrated approach using genetic algorithm (h = 0.4) b Proportion of the

2005

"... In PAGE 17: ... Similar observations can also be obtained if we apply other methods to calculate the routing distance. For instance, Table9 shows the benefit of integration when a genetic algorithm is applied to calculate the routing distance instead of (7). It is easy to see that the data in Table 9 reveal the same managerial insights as the data in Table 7 do.... In PAGE 17: ... For instance, Table 9 shows the benefit of integration when a genetic algorithm is applied to calculate the routing distance instead of (7). It is easy to see that the data in Table9 reveal the same managerial insights as the data in Table 7 do. 6.... ..."

### Table 7.1 the values for the proportional and integrating constants Kp and Ki used in the -controller is shown. Di erent values is used depending what kind of -sensor we use. Also shown in table 7.1 is the value used for the gain K in the observer of ^ from (4.16). The gain K in the observer of pman was never decided since the observer used exactly the same model as it was simulated against and therefore ^ pman = pman for every value of K.

### Table 7. Actual and expected proportion of exercises completed correctly across students in Tests 2 and 3 when declarative knowledge is integrated into the knowledge tracing model.

### TABLE 1 Proportion of Subjects in Each Condition Whose Data Were Best Fit by Rule-Based Versus Information-Integration Models

### Table 11: The Ontology Integration Cost Driver OI

2005

"... In PAGE 28: ... type of mappings between ontological primitives: 1 to 1 mappings are more easily discovered than multiple one (1 to n or n to m) integration quality, in terms of precision (rate of correct mappings) and recall (rate of mappings discovered): higher quality requirements imply automatically increased efforts to perform the integration task. number of ontologies: it is clear that the integration effort is directly proportional to the number of sources to be integrated According to these considerations the ratings for the OI cost drivers were de ned as depicted in Table11 below.... ..."

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### Table 2: shows the dependency of spurious features on the signi cance number . 2) The dependency of the length of the spurious edges on the smoothing scale s is shown in Fig. 5 . The integration scales are chosen to depend on s by tl = p2 s and tp = 2 s . The length of the spurious edges obviously increases nearly proportional to s.

1994

Cited by 5