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Pregled bibliografske jedinice broj: 1076279

Estimation of moving agents density in 2D space based on LSTM neural network


Polic, Marsela; Salem, Ziad; Griparic, Karlo; Bogdan, Stjepan; Schmickl, Thomas
Estimation of moving agents density in 2D space based on LSTM neural network // 2017 Evolving and Adaptive Intelligent Systems (EAIS)
Ljubljana, Slovenija: IEEE, 2017. str. 1-8 doi:10.1109/eais.2017.7954842 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


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Naslov
Estimation of moving agents density in 2D space based on LSTM neural network

Autori
Polic, Marsela ; Salem, Ziad ; Griparic, Karlo ; Bogdan, Stjepan ; Schmickl, Thomas

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Skup
2017 Evolving and Adaptive Intelligent Systems (EAIS)

Mjesto i datum
Ljubljana, Slovenija, 31.5.-2.6.2017

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Robot sensing systems ; Neural networks ; Estimation ; Animals ; Machine learning algorithms

Sažetak
As a part of ASSISIbf project, with a final goal of forming a collective adaptive bio-hybrid society of animals and robots, an artificial neural network based on LSTM architecture was designed and trained for bee density estimation. During experiments, the bees are placed inside a plastic arena covered with wax, where they interact with and adapt to specialized static robotic units, CASUs, designed specially for this project. In order to interact with honeybees, the CASUs require the capability i) to produce and perceive the stimuli, i.e., environmental cues, that are relevant to honeybee behaviour, and ii) to sense the honeybees presence. The second requirement is implemented through 6 proximity sensors mounted on the upper part of CASU. In this paper we present estimation of honeybees (moving agents) density in 2D space (experimental arena) that is based on LSTM neural network. When compared to previous work done in this field, experiments demonstrate satisfactory results in estimating sizes of bee groups placed in the arena within a larger scope of outputs. Two different approaches were tested: regression and classification, with classification yielding higher accuracy.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo



POVEZANOST RADA


Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb

Profili:

Avatar Url Stjepan Bogdan (autor)

Avatar Url Karlo Griparić (autor)

Avatar Url Marsela Polić (autor)

Poveznice na cjeloviti tekst rada:

doi

Citiraj ovu publikaciju:

Polic, Marsela; Salem, Ziad; Griparic, Karlo; Bogdan, Stjepan; Schmickl, Thomas
Estimation of moving agents density in 2D space based on LSTM neural network // 2017 Evolving and Adaptive Intelligent Systems (EAIS)
Ljubljana, Slovenija: IEEE, 2017. str. 1-8 doi:10.1109/eais.2017.7954842 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Polic, M., Salem, Z., Griparic, K., Bogdan, S. & Schmickl, T. (2017) Estimation of moving agents density in 2D space based on LSTM neural network. U: 2017 Evolving and Adaptive Intelligent Systems (EAIS) doi:10.1109/eais.2017.7954842.
@article{article, author = {Polic, Marsela and Salem, Ziad and Griparic, Karlo and Bogdan, Stjepan and Schmickl, Thomas}, year = {2017}, pages = {1-8}, DOI = {10.1109/eais.2017.7954842}, keywords = {Robot sensing systems, Neural networks, Estimation, Animals, Machine learning algorithms}, doi = {10.1109/eais.2017.7954842}, title = {Estimation of moving agents density in 2D space based on LSTM neural network}, keyword = {Robot sensing systems, Neural networks, Estimation, Animals, Machine learning algorithms}, publisher = {IEEE}, publisherplace = {Ljubljana, Slovenija} }
@article{article, author = {Polic, Marsela and Salem, Ziad and Griparic, Karlo and Bogdan, Stjepan and Schmickl, Thomas}, year = {2017}, pages = {1-8}, DOI = {10.1109/eais.2017.7954842}, keywords = {Robot sensing systems, Neural networks, Estimation, Animals, Machine learning algorithms}, doi = {10.1109/eais.2017.7954842}, title = {Estimation of moving agents density in 2D space based on LSTM neural network}, keyword = {Robot sensing systems, Neural networks, Estimation, Animals, Machine learning algorithms}, publisher = {IEEE}, publisherplace = {Ljubljana, Slovenija} }

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