Self-Optimizing Mode-Locked Laser using a Genetic Algorithm

Woodward, Robert I.; Kelleher, E. J. R.

doi:10.1364/cleo_si.2016.stu3p.6

Cited by 2 publications

(5 citation statements)

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“…Crossover operator-which merges with each individual which is best. Mutation operator-a randomly modified process is shown (Barros et al, 2011;Woodward and Kelleher, 2016).…”

Section: Genetic Algorithmmentioning

confidence: 99%

Detecting spams in social networks using ML algorithms - a review

Murugan

Devi

2018

IJEWM

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The social network, by the name which has popularised in today's world and growing rapidly at all times and controlling over mankind. The social networks like Twitter, Facebook, and LinkedIn, etc., have become a regular and daily usage of many people. It becomes a good mediator for the people who would like to share some posts, are some of their own videos, or some messages. But there has been major issues that the particular user of the social networks like Twitter and Facebook have the problem of indiscipline actions which we call as spam, by the third person who is knowingly doing this to spoil their intention and good opinion upon each other. Also, these spams help to steal information about the people who using social networks. In this paper, we study and analyse about the spam in social networks and machine learning algorithms to detect such kind of spams. This paper also focuses on the ML algorithms detection rate and false positive rate over different datasets.

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“…Crossover operator-which merges with each individual which is best. Mutation operator-a randomly modified process is shown (Barros et al, 2011;Woodward and Kelleher, 2016).…”

Section: Genetic Algorithmmentioning

confidence: 99%

Detecting spams in social networks using ML algorithms - a review

Murugan

Devi

2018

IJEWM

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Section: Self-optimising Laser Designmentioning

confidence: 99%

“…Therefore, we propose that a compound fitness function, including optical and electrical spectral, and temporal measurements, is required for reliable self-optimisation. 25 While this approach could be applied for a wide range of target output properties, here, we focus on ultrafast single-pulse CW mode-locking. Such pulse sources are an enabling tool in a wide range of applications including manufacturing, research and medical imaging, where pulse to pulse stability, and reliability of the system are of critical importance.…”

Section: Choice Of Fitness Functionmentioning

confidence: 99%

“…The application of GAs for extremum seeking in laser mode-locking was recently proposed theoretically2122, and a basic implementation utilizing a singular fitness function to locate self-starting regimes in an NPE-mode-locked fibre laser was demonstrated2324. It was observed, however, that coherent single-pulse mode-locking was not repeatable, and optimisation of the fitness function led to a tendency towards the emission of noise-like pulses, highlighting the need to implement a more sophisticated fitness function25.…”

mentioning

confidence: 99%

“…23,24 It was observed, however, that coherent single-pulse mode-locking was not repeatable, and optimisation of the fitness function led to a tendency towards the emission of noise-like pulses, highlighting the need to implement a more sophisticated fitness function. 25 Here, we experimentally demonstrate the first photonic application of a GA based on a compound fitness function to achieve optimised, reliable self-starting operation of an all-fibre ultrafast pulse source, paving the way towards fully automated 'smart lasers'. We show that the self-optimising scheme can rapidly explore a large multi-parameter space, locating and maintaining a global optimum in the presence of external disturbances.…”

mentioning

confidence: 99%

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Towards ‘smart lasers’: self-optimisation of an ultrafast pulse source using a genetic algorithm

Woodward

Kelleher

2016

Sci Rep

Self Cite

117

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Short-pulse fibre lasers are a complex dynamical system possessing a broad space of operating states that can be accessed through control of cavity parameters. Determination of target regimes is a multi-parameter global optimisation problem. Here, we report the implementation of a genetic algorithm to intelligently locate optimum parameters for stable single-pulse mode- locking in a Figure-8 fibre laser, and fully automate the system turn-on procedure. Stable ultrashort pulses are repeatably achieved by employing a compound fitness function that monitors both temporal and spectral output properties of the laser. Our method of encoding photonics expertise into an algorithm and applying machine-learning principles paves the way to self-optimising ‘smart’ optical technologies.

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Self-Optimizing Mode-Locked Laser using a Genetic Algorithm

Abstract: Abstract:We apply the concept of natural selection to intelligently find optimum operating regimes in a fiber laser -a multi-parameter space. Using a compound fitness function we reliably achieve self-optimizing mode-locked operation.

Cited by 2 publications

References 5 publications

Detecting spams in social networks using ML algorithms - a review

Detecting spams in social networks using ML algorithms - a review

Towards ‘smart lasers’: self-optimisation of an ultrafast pulse source using a genetic algorithm

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