Zobrazeno 1 - 10
of 20
pro vyhledávání: '"YiNa Jeong"'
Publikováno v:
Sensors, Vol 22, Iss 12, p 4414 (2022)
A fully autonomous vehicle must ensure not only fully autonomous driving but also the safety and comfort of its passengers. However, the self-driving technology that is currently completed focuses only on perfect driving and does not guarantee the sa
Externí odkaz:
https://doaj.org/article/386a7f6d5bd94327aaf0b173e0fcd017
Publikováno v:
Sensors, Vol 19, Iss 22, p 5035 (2019)
When blind and deaf people are passengers in fully autonomous vehicles, an intuitive and accurate visualization screen should be provided for the deaf, and an audification system with speech-to-text (STT) and text-to-speech (TTS) functions should be
Externí odkaz:
https://doaj.org/article/db1a0db14c714b4f84c88107f7c625c5
Publikováno v:
Sensors, Vol 19, Iss 11, p 2534 (2019)
This paper proposes the lightweight autonomous vehicle self-diagnosis (LAVS) using machine learning based on sensors and the internet of things (IoT) gateway. It collects sensor data from in-vehicle sensors and changes the sensor data to sensor messa
Externí odkaz:
https://doaj.org/article/bccee005387f48fdac519a0d9a36ebf0
Publikováno v:
Applied Sciences, Vol 8, Iss 10, p 1992 (2018)
This paper proposes a total crop-diagnosis platform (TCP) based on deep learning models in a natural nutrient environment, which collects the weather information based on a farm’s location information, diagnoses the collected weather information an
Externí odkaz:
https://doaj.org/article/c39289f544ff49ddbbb25babc690c5c6
Publikováno v:
Applied Sciences, Vol 8, Iss 9, p 1594 (2018)
This paper proposes a Lightweight In-Vehicle Edge Gateway (LI-VEG) for the self-diagnosis of an autonomous vehicle, which supports a rapid and accurate communication between in-vehicle sensors and a self-diagnosis module and between in-vehicle protoc
Externí odkaz:
https://doaj.org/article/dd805644671644e18b1f3f84d517d5c7
Publikováno v:
Applied Sciences, Vol 8, Iss 7, p 1164 (2018)
This paper proposes “An Integrated Self-diagnosis System (ISS) for an Autonomous Vehicle based on an Internet of Things (IoT) Gateway and Deep Learning” that collects information from the sensors of an autonomous vehicle, diagnoses itself, and th
Externí odkaz:
https://doaj.org/article/93e04af351df4526aa90e8c28750168b
Publikováno v:
ICOIN
A current autonomous vehicle determines its driving strategy by considering only external factors (Pedestrians, road conditions, etc.) without considering the interior condition of the vehicle. To solve the problem, this paper proposes "A Driving Dec
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::830d0c9f2d49d213ec5d01c7639c9e7d
https://doi.org/10.1109/icoin48656.2020.9016493
https://doi.org/10.1109/icoin48656.2020.9016493
Publikováno v:
Sustainability
Volume 11
Issue 13
Sustainability, Vol 11, Iss 13, p 3637 (2019)
Volume 11
Issue 13
Sustainability, Vol 11, Iss 13, p 3637 (2019)
This paper proposes a self-predictable crop yield platform (SCYP) based on crop diseases using deep learning that collects weather information (temperature, humidity, sunshine, precipitation, etc.) and farm status information (harvest date, disease i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::60d1d10e2806db3e90678d4bc9a5d205
Publikováno v:
Applied Sciences
Volume 8
Issue 9
Applied Sciences, Vol 8, Iss 9, p 1594 (2018)
Volume 8
Issue 9
Applied Sciences, Vol 8, Iss 9, p 1594 (2018)
This paper proposes a Lightweight In-Vehicle Edge Gateway (LI-VEG) for the self-diagnosis of an autonomous vehicle, which supports a rapid and accurate communication between in-vehicle sensors and a self-diagnosis module and between in-vehicle protoc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::996f781c0e63496a50551d09c9984400
Publikováno v:
Journal of Internet Computing and Services. 16:57-67
This paper proposes an Fuzzy-based Risk Reasoning Driving Strategy on VANET. Its first reasoning phase consists of a WC_risk reasoning that reasons the risk by using limited road factors such as current weather, density, accident, and construction, a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a4770b45efb9cf434ef6f8da6afd390c
https://doi.org/10.7472/jksii.2015.16.6.57
https://doi.org/10.7472/jksii.2015.16.6.57