Zobrazeno 1 - 10
of 34
pro vyhledávání: '"Qixin Sha"'
Publikováno v:
IEEE Access, Vol 8, Pp 24258-24268 (2020)
Autonomous underwater vehicle (AUV) plays an increasingly important role in ocean exploration. Existing AUVs are usually not fully autonomous and generally limited to pre-planning or pre-programming tasks. Reinforcement learning (RL) and deep reinfor
Externí odkaz:
https://doaj.org/article/fc08c4ef4e924de4b63b996e99b352d2
Publikováno v:
Ocean Engineering. 262:112182
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::99e17ffe362bd67c4189e967dc99f0e0
https://doi.org/10.1016/j.oceaneng.2022.112182
https://doi.org/10.1016/j.oceaneng.2022.112182
Shaping Progressive Net of Reinforcement Learning for Policy Transfer with Human Evaluative Feedback
Publikováno v:
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::19e0e5fce4c63ec7c1d7b8255de7e703
https://doi.org/10.1109/iros51168.2021.9636061
https://doi.org/10.1109/iros51168.2021.9636061
Autor:
Yan Song, Guangliang Li, Rui Nian, Ying Zhao, Tianhong Yan, Amaury Lendasse, Bo He, Qixin Sha, Yue Shen
Publikováno v:
IEEE Journal of Oceanic Engineering. 44:502-513
As a widely used segmentation scheme, Markov random field (MRF) utilizes $k$ -means clustering to calculate the initial model for sidescan sonar image segmentation. However, for the noise and intensity inhomogeneity nature of the sidescan sonar image
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::41743800c9c605f5ea3e7211ba5c405d
https://doi.org/10.1109/joe.2018.2819278
https://doi.org/10.1109/joe.2018.2819278
Publikováno v:
Ocean Engineering. 260:111971
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b3167553054933fd6a956c1c963928bc
https://doi.org/10.1016/j.oceaneng.2022.111971
https://doi.org/10.1016/j.oceaneng.2022.111971
Publikováno v:
Applied Ocean Research. 118:102960
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3a329068d3eba18ddb0801089b1ff732
https://doi.org/10.1016/j.apor.2021.102960
https://doi.org/10.1016/j.apor.2021.102960
Publikováno v:
Multidimensional Systems and Signal Processing. 30:1149-1169
In this paper, an innovative method called extreme learning machine with hybrid local receptive fields (ELM-HLRF) is presented for image classification. In this method, filters generated by Gabor functions and the randomly generated convolution filte
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::509440c1d346392dc8ff2dd4813a9bf4
https://doi.org/10.1007/s11045-018-0598-9
https://doi.org/10.1007/s11045-018-0598-9
Publikováno v:
Neurocomputing. 277:53-64
In this paper, we propose an innovative classification method which combines texture features of images filtered by Gaussian derivative models with extreme learning machine (ELM). In the texture image classification, feature extraction is a very cruc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::404fabce533b3fc94ad3a9c250208a61
https://doi.org/10.1016/j.neucom.2017.01.113
https://doi.org/10.1016/j.neucom.2017.01.113
Publikováno v:
Ocean Engineering. 148:386-400
This paper focuses on the application of AUV in shallow-sea, which environment is more complicated than deep-sea. Owing to independence of external signals, inertial navigation system (INS) has become the most suitable navigation and positioning syst
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0f1e3863a0a70096a6b976fb756f91bc
https://doi.org/10.1016/j.oceaneng.2017.11.015
https://doi.org/10.1016/j.oceaneng.2017.11.015
Publikováno v:
Ocean Engineering. 235:109355
Autonomous underwater vehicle plays a more and more important role in the exploration of marine resources. Path planning and obstacle avoidance is the core technology to realize the autonomy of AUV, which will determine the application prospect of AU
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::35577fb257bcbb0544d8221d3ce14b87
https://doi.org/10.1016/j.oceaneng.2021.109355
https://doi.org/10.1016/j.oceaneng.2021.109355