A particle swarm optimization approach using adaptive entropy-based fitness quantification of expert knowledge for high-level, real-time cognitive robotic control

Jager, Deon De; Zweiri, Yahya; Makris, Dimitrios

doi:10.1007/s42452-019-1697-4

Cited by 2 publications

(2 citation statements)

References 26 publications

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“…It is used to solve both continuous and discrete optimization problems. In PSO, it maintains a number of potential solutions which are termed as the particles and the whole population of solutions called swarm optimization [45][46][47]. It was originated by British Military in 1940s.…”

Section: Based On Psomentioning

confidence: 99%

Study of fuzzy logic and particle swarm methods in map matching algorithm

Gupta

Shanker

2020

SN Appl. Sci.

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Navigation system is used in deploying the real-time position of vehicles on the map of route with the help of Global Positioning System (GPS). However, the imprecision is included in the map of route due to erroneous GPS signal (i.e., inclusion of errors related to receiver and/or propagation). Also, the current navigation system involves dense road network areas having higher probability of imprecise inputs. The map matching (MM) method requires capability of inherent tolerant to these imprecise inputs and is capable of suggesting certain vehicles more likely to run on one specific link than other links in navigation system. The fuzzy inference system (FIS)-based technique converting the qualitative terms into quantitative values is very effective to deal with human intervention and linguistic vagueness in MM algorithms. In case of highly unreliable positioning errors included in MM, the particle swarm optimization (PSO) is used as an option for continuously updating sensor information to find vehicle motion and respective route link on map. This has directed us to do the comparative study of both the above two methods with the other methods. Simulation results show that FIS-and PSO-based MM algorithm significantly outperforms the previous algorithms in terms of running time, accuracy and recall.

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Section: Based On Psomentioning

confidence: 99%

Study of fuzzy logic and particle swarm methods in map matching algorithm

Gupta

Shanker

2020

SN Appl. Sci.

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“…In regard to the signal processing, AE method requires effective calibration and analysis to discriminate between all sorts of damage modes and failure mechanisms [20][21][22]. Meanwhile, the quantification of irreversible damage is a challenge and crucial aspect to understand the properties of materials, so it is necessary to find ways of quantifying the dynamic behaviors and micro-damage mechanisms [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Statistical analysis of acoustic emission signals and tensile deformation measurement for three dimensional carbon fiber braided composites

et al. 2020

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Accurate assessment for mechanical and damage behaviors of three dimensional carbon fiber braided composites by acoustic emission technique is difficult in practical application. In this study, a statistical analysis approach with calculated amplitude spectrum, probability distribution and entropy is proposed to process acoustic emission signals. Moreover, the tensile behaviors and damage characteristics of three dimensional four directional and five directional braided composites are investigated systematically by combining digital image correlation and acoustic emission methods. The results indicate that three dimensional five directional braided composites possess excellent load-bearing and deformation resistibility capacity, owing to more compact preform structure with additional axial yarns. The acoustic emission statistical analysis method can be used as criteria to distinguish and quantify various irreversible damage and failure mechanisms in three dimensional braided composites. Furthermore, the deformation, damage and failure criterion for two types of composites are fully described based on deformation fields and acoustic emission signals.

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A particle swarm optimization approach using adaptive entropy-based fitness quantification of expert knowledge for high-level, real-time cognitive robotic control

Cited by 2 publications

References 26 publications

Study of fuzzy logic and particle swarm methods in map matching algorithm

Study of fuzzy logic and particle swarm methods in map matching algorithm

Statistical analysis of acoustic emission signals and tensile deformation measurement for three dimensional carbon fiber braided composites

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