| dc.contributor | 應數系 | |
| dc.creator (作者) | 曾正男 | |
| dc.creator (作者) | Tzeng, Jengnan | |
| dc.creator (作者) | Lai, Yen-Ru | |
| dc.creator (作者) | Lin, Ming-Lai | |
| dc.creator (作者) | Lin, Yu-Han | |
| dc.creator (作者) | Shih, Yu-Cheng | |
| dc.date (日期) | 2020-07 | |
| dc.date.accessioned | 10-Feb-2022 15:00:25 (UTC+8) | - |
| dc.date.available | 10-Feb-2022 15:00:25 (UTC+8) | - |
| dc.date.issued (上傳時間) | 10-Feb-2022 15:00:25 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/139050 | - |
| dc.description.abstract (摘要) | Regarding collision prediction technology, the most common are car reversing radar and infrared rays, which provide warnings by sensing the distance between objects and cars. Although radar detection is accurate, radar systems that can provide instant 360-degree feedback are very expensive. The cost of infrared is much lower, but they cannot be applied to high-temperature environments. As the result, the technology of preventing collisions using only images from cameras is an important artificial intelligence topic in recent years. If a low-resolution CCD and artificial intelligence technology can be used to achieve a certain degree of accuracy in collision prediction, then low-cost anti-collision technology is worth looking forward to. Furthermore, we hope to provide anti-collision warnings on motorcycles and bicycles using this technology. In order to achieve this goal, computational simplification is a technical threshold. It`s only when simple calculations achieve high-precision prediction can we meet the low power consumption requirements for image AI to be applied small vehicles. Therefore, we hope to find out a better image representation basis and combine it with AI technology to fulfill the requirements of less calculation and high accuracy. In addition, we also hope to create models with sufficient accuracy with small training data. This experiment will reduce development costs and get better efficiency in the early stage of developing ADAS. | |
| dc.format.extent | 887690 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.relation (關聯) | 2020 International Joint Conference on Neural Networks (IJCNN), IEEE | |
| dc.subject (關鍵詞) | Wavelet transforms ; Logic gates ; Artificial intelligence ; Training ; Training data ; Cameras ; alarm systems ; artificial intelligence ; automobiles ; CCD image sensors ; collision avoidance ; image representation ; image resolution ; motorcycles ; radar detection ; recurrent neural nets ; road safety ; traffic engineering computing ; wavelet transforms ; infrared rays ; car reversing radar ; GRU model ; LSTM ; AI technology ; image representation ; image AI ; low power consumption requirements ; high-precision prediction ; anti-collision warnings ; low-cost anti-collision technology ; artificial intelligence technology ; low-resolution CCD ; high-temperature environments ; 360-degree feedback ; radar systems ; radar detection ; collision prediction technology ; wavelet transformation ; small training data ; development costs ; wavelet ; Haar basis ; AI ; ADAS ; small training data | |
| dc.title (題名) | Improve the LSTM and GRU model for small training data by wavelet transformation | |
| dc.type (資料類型) | conference | |
| dc.identifier.doi (DOI) | 10.1109/IJCNN48605.2020.9206840 | |
| dc.doi.uri (DOI) | https://doi.org/10.1109/IJCNN48605.2020.9206840 | |
