Furthermore, the analytical framework serves as a basis for developing an adaptive, robust, and near-optimal prototype termed ARSoS, along with a reinforcement learning algorithm for approximately inferring the optimal behavior of a reputation-based ADRS in a specific autonomic network variant, mobile ad-hoc network.
This paper presents the concept, design procedure, simulation and experimental evaluation results of a prototype optimal shock-damping caster in detail.
The design approach is implemented to optimize a real design mold and the final optimal design is prototyped in SLA and the manufacturability in PBF has been discussed.
To verify the validity of the optimal design, an experimental prototype of inductive angle sensor has been fabricated and tested.
The optimal choice of the prototype consists of a full-scale injector, a full-modelled radiofrequency quadrupole, and a short drift tube linear accelerator associated with a beam diagnostics/dump.
The optimal settings for the prototype operation are: (i) a corona current of 1μA and an ion driving voltage of 40 V for the 0.3 lpm flow rate, and (ii) 2μA and 120 V for the 1.5 lpm flowrate.
The prototype with optimal spring stiffness generates a maximum 61.3 μW average power at ±25° rotational amplitude and 1 Hz frequency which is about 6-times higher than its unsprung counterpart under same excitation condition.
As per the optimal design, seventeen SNEDDS prototypes were prepared.
Finally, we prototyped the optimal FDBs with high load capacity and verified the load capacity and friction torque of the FDBs via experiments.
(An example of this is in [24].) The quality of SOM learning (including topology preservation, completion of ordering, optimal placement of quantization prototypes (the SOM weights) in the data space, and convergence) is important for ensuring good results.
