Analysis of dynamic acoustic source positioning : an autonomous swarm approach

The pseudo-code of the ADAP-PSO implemented is described in Algorithm 1 and its flowchart is represented in Figure 1, where M is the number of particles in the swarm, L

[ 10 ], who analyzed cycling Chinese hamster ovary (CHO) cells using flow cytom- etry. They reported variability in G1 phase caused mainly by unequal division of

PEG in the “PVP-L-PEG” polymer systems after spray drying. Values were calculated using Eq. Figure 5: Micromeritic and aerodynamic properties of PVP/L-PEG particles having

Thus, we propose to improve the source selection stage using long-term learning: within a search session, paths leading to relevant images are learned using relevance feedback..

In this paper we have described a new framework called Thunderbird that can be used to learn how to pilot a drone, to train when preparing for a field operation by repeating the

However we limit the scales for the social and individual cognitive factors to dif- ferent values since it has shown a statistically significant improvement in mono-objective PSO

In this report we present the theoretical analysis of the electron swarm in a relatively high E/N, considering an anisotropy of the distribution function.. The rest of where v

Yet such research has been limited to optimizing only the control strategy [15], or optimizing the control and machine geometry (excluding the converter) [16], or lastly optimizing