University of Groningen
Performance of Neural Networks in Source Localization using Artificial Lateral Line Sensor
Configurations
van der Meulen, Pim; Wolf, Berend; Pirih, Primoz; van Netten, Sietse
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Publication date:
2018
Link to publication in University of Groningen/UMCG research database
Citation for published version (APA):
van der Meulen, P., Wolf, B., Pirih, P., & van Netten, S. (2018). Performance of Neural Networks in Source
Localization using Artificial Lateral Line Sensor Configurations. Poster session presented at ICT OPEN
2018: The Interface for Dutch ICT-Research, Amersfoort, Netherlands.
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Theoptimalconfiguration improved performanceforboth sources, compared to other configurations. Therefore, the main research question can be answered positively in that using an optimal configuration can improvesourcelocalization performanceusing CNNs.
With regard to the secondary research question, both neural networksarecapableofdetecting two sourcesin a 3D environment, ifsourcesarean equaldistanceremoved from theALL.Ifnot,only theclosestsourceto thearray isaccurately reconstructed.
Theoptimalconfiguration also improved ELM resultsforallsource generation conditions; the use of an ELM leads to a higher performance of the worst estimated source, for the majority of conditions,compared to using a CNN.
Artificiallaterallines(ALLs)areused to detectthemovementand locationsofsourcesunderwater,and arebased on theneuromasts (fig.1) located in the lateral line organ found in fish and amphibians.ALLsconsistsofa setofbiaxialsensors(fig.2)
Görner, P. (1963). Untersuchungen zur morphologie und elektrophysiologie des seitenlinienorgansvom krallenfrosch (xenopuslaevisdaudin).JournalofComparative Physiology A:Neuroethology,Sensory,Neural,and BehavioralPhysiology,47 (3),316– 338.
Wolf,B.J.,Morton,J.A.,MacPherson,W.B.N.,& van Netten,S.M.(2018).Bioinspired all -opticalartificialneuromastfor2d flow sensing.Bioinspiration & biomimetics.
Data generation: source locations: sensorlocations:
teacherobject: 3D matrix containing 1331 density probability pointsforsourcelocations Neuralnetworks:
convolutionalneuralnetwork extreme learning machine sensorreadings 3D matrix Sourceprediction process:
3D matrix source predictions
Can theplacementofartificiallaterallinesensorsbebeneficialfor improving theaccuracy ofsourcelocalization through theuseof convolutionalneuralnetworks?
Areconvolutionalneuralnetworksand extremelearning machines capableofpredicting thelocationsofmultiplesourcesin three -dimensionalenvironments?
Fig.1:Superficialneuromastsofa clawed frog.From Görner(1963).
Fig.2:BiaxialALL sensor.From Wolfetal.(2018).
EXPERIMENT 1
A Cramér-Rao lowerbound analysiswasperformed on a subsetof sensor configurations (16 sensors, 1m3 basin) to estimate their
likely performancesand indicatethebestand worstconfigurations.
EXPERIMENT 2
Thebestand worstconfigurationswereused to generatesimulated datasetsto train and testextremelearning machines(ELMs)and convolutionalneuralnetworks(CNNs)on theirlocation accuracy. Simulated datasetsconsisted of2 sourcesin a 3D basin (1m3)and
thesensorreadingsof16 ALL sensors. S o u rc e l o c a li z a ti o n p ip e li n e Calculatesensorreadings EXPERIMENT 1: EXPERIMENT 2:
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REFERENCES k-means (b) (a) (a) (b)Fig.4:Barplotsforthenormalized worstpredicted sourcelocation and prediction distributions versus the distance between both source locations(horizontalALL,using 30:HorFours).(a):CNN;(b):ELM.Colors indicatedistributions.With CNNs,thesecondary sourceperformance showsa linearrelationship with thedistancebetween sources,which is notthecasewith ELMs.
Fig.5:Estimated probability curvesforthedistribution oftotalsource -prediction distancesforthebest(a)and worst(b)predicted sources. ALLs were placed horizontally at z:0. Curves were averaged over 5 repetitionsand 4 datasetconditions.
Fig.3:2D sensorconfigurationsfor16 sensors.Configurationswere applied horizontally (at z:0) and vertically (at y:0).The Cramér-Rao lower bound analysis indicates that30:HorFours (green) and
22:VertLineMid (blue) performed best horizontally and vertically, respectively.5:HorLineLow(red)isindicated astheworstperforming configuration.
