Zobrazeno 1 - 10
of 45
pro vyhledávání: '"Nir Lipovetzky"'
Autor:
Sigfredo Fuentes, Claudia Gonzalez Viejo, Eden Tongson, Frank R. Dunshea, Hai Ho Dac, Nir Lipovetzky
Publikováno v:
Journal of Agriculture and Food Research, Vol 10, Iss , Pp 100388- (2022)
Digitally extracted biometrics from visible videos of farm animals could be used to automatically assess animal welfare, contributing to the future of automated veterinary support systems. This study proposed using non-invasive video acquisition and
Externí odkaz:
https://doaj.org/article/a3d4781f8ce14887991d8abfa4c0f72a
Publikováno v:
Frontiers in Artificial Intelligence, Vol 4 (2021)
We investigate the application of state-of-the-art goal recognition techniques for behaviour recognition over complex continuous domains using model predictive control (MPC) for trajectory generation. We formally define the problem of kinodynamic beh
Externí odkaz:
https://doaj.org/article/0985be7fe47a42adba2f2e212d6d285e
Autor:
Hai Ho Dac, Claudia Gonzalez Viejo, Nir Lipovetzky, Eden Tongson, Frank R. Dunshea, Sigfredo Fuentes
Publikováno v:
Sensors, Vol 22, Iss 21, p 8256 (2022)
Farm livestock identification and welfare assessment using non-invasive digital technology have gained interest in agriculture in the last decade, especially for accurate traceability. This study aimed to develop a face recognition system for dairy f
Externí odkaz:
https://doaj.org/article/def6f787aa3046368e140a4b4d9fecd8
Publikováno v:
Sensors, Vol 21, Iss 20, p 6844 (2021)
New and emerging technologies, especially those based on non-invasive video and thermal infrared cameras, can be readily tested on robotic milking facilities. In this research, implemented non-invasive computer vision methods to estimate cow’s hear
Externí odkaz:
https://doaj.org/article/7c2d48b2def747ce90b0a3e4df679c68
Autor:
Sigfredo Fuentes, Vasiliki Summerson, Claudia Gonzalez Viejo, Eden Tongson, Nir Lipovetzky, Kerry L. Wilkinson, Colleen Szeto, Ranjith R. Unnithan
Publikováno v:
Sensors, Vol 20, Iss 18, p 5108 (2020)
Bushfires are increasing in number and intensity due to climate change. A newly developed low-cost electronic nose (e-nose) was tested on wines made from grapevines exposed to smoke in field trials. E-nose readings were obtained from wines from five
Externí odkaz:
https://doaj.org/article/aa210bf6770c423487aa464e1e82a9a6
Publikováno v:
IEEE Robotics and Automation Letters. :1-8
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::857a231425feeab8057fe5107b1beb83
https://doi.org/10.1109/lra.2023.3279619
https://doi.org/10.1109/lra.2023.3279619
Publikováno v:
Journal of Artificial Intelligence Research. 75:489-539
In this paper, we present a novel approach to epistemic planning called planning with perspectives (PWP) that is both more expressive and computationally more efficient than existing state-of-the-art epistemic planning tools. Epistemic planning — p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::73254157d1906415f5a42d4496c54b2c
https://doi.org/10.1613/jair.1.13446
https://doi.org/10.1613/jair.1.13446
Publikováno v:
Proceedings of the International Symposium on Combinatorial Search. 15:276-278
The aim of decentralised multi-agent (DMA) planning is to coordinate a set of agents to jointly achieve a goal while preserving their privacy. Blind search algorithms, such as width-based search, have recently proved to be very effective in the conte
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::473e5ddd3de41d65651719c302e71c31
https://doi.org/10.1609/socs.v15i1.21784
https://doi.org/10.1609/socs.v15i1.21784
Publikováno v:
Proceedings of the International Symposium on Combinatorial Search. 15:308-310
We introduce a new algorithm, Regression based Supervised Learning (RSL), for learning per instance Neural Network (NN) defined heuristic functions for classical planning problems. RSL uses regression to select relevant sets of states at a range of d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f736c5d7da543186820ee55eb22fc0cf
https://doi.org/10.1609/socs.v15i1.21795
https://doi.org/10.1609/socs.v15i1.21795