Zobrazeno 1 - 10
of 45
pro vyhledávání: '"Fukai ZHANG"'
Publikováno v:
Complex & Intelligent Systems, Vol 10, Iss 4, Pp 5107-5126 (2024)
Abstract Currently, many real-time semantic segmentation networks aim for heightened accuracy, inevitably leading to increased computational complexity and reduced inference speed. Therefore, striking a balance between accuracy and speed has emerged
Externí odkaz:
https://doaj.org/article/ff1a76ea65a1463cba554ef023f127aa
Autor:
Dengke WANG, Liyuan ZHANG, Jianping WEI, Hailong DU, Zhen LI, Zhiming WANG, Bowen DONG, Fukai ZHANG, Yanbo YIN, Hongtu ZHANG, Yanzhao WEI
Publikováno v:
Meitan xuebao, Vol 49, Iss 3, Pp 1432-1446 (2024)
Using an impact damage-percolation experimental system for gas-bearing coal or rock, the authors carry out various gas-bearing coal impact experiments with in-situ seepage tests to reveal the law of dynamic mechanical characteristics, fracture expans
Externí odkaz:
https://doaj.org/article/beb962e4e0434eb090543efab45b51a7
Publikováno v:
Sensors, Vol 24, Iss 16, p 5145 (2024)
Most real-time semantic segmentation networks use shallow architectures to achieve fast inference speeds. This approach, however, limits a network’s receptive field. Concurrently, feature information extraction is restricted to a single scale, whic
Externí odkaz:
https://doaj.org/article/3eb759b6139e48739851b50d06343973
Publikováno v:
Sensors, Vol 24, Iss 16, p 5305 (2024)
Deep learning has recently made significant progress in semantic segmentation. However, the current methods face critical challenges. The segmentation process often lacks sufficient contextual information and attention mechanisms, low-level features
Externí odkaz:
https://doaj.org/article/6e238a39e5e94eadb2790f42a42c7e04
Publikováno v:
Actuators, Vol 13, Iss 3, p 105 (2024)
The Bouc–Wen model has been widely adopted to describe hysteresis nonlinearity in many smart material-actuated systems, such as piezoelectric actuators, shape memory alloy actuators, and magnetorheological dampers. For effective control design, it
Externí odkaz:
https://doaj.org/article/9935553fef0d4161ae22bdb0179d2754
Publikováno v:
Sensors, Vol 24, Iss 3, p 989 (2024)
In recent years, significant progress has been witnessed in the field of deep learning-based object detection. As a subtask in the field of object detection, traffic sign detection has great potential for development. However, the existing object det
Externí odkaz:
https://doaj.org/article/2bec008d62214dd2bff1a4845895ed06
Publikováno v:
IEEE Access, Vol 7, Pp 72660-72671 (2019)
A wide variety of vehicle detection approaches using the deep convolutional neural network (CNN) have achieved great success in recent years. However, the existing CNN-based feature extraction algorithms, especially residual network, cannot obtain po
Externí odkaz:
https://doaj.org/article/23feba0f2376423e825f856355c39748
Publikováno v:
International Journal of Distributed Sensor Networks, Vol 15 (2019)
Currently, the boosting of location acquisition devices makes it possible to track all kinds of moving objects, and collect and store their trajectories in database. Therefore, how to find knowledge from huge amount of trajectory data has become an a
Externí odkaz:
https://doaj.org/article/7769b60f8f1f4496bfc0a2311d7efe08
Publikováno v:
Sensors, Vol 19, Iss 3, p 594 (2019)
Vehicle detection with category inference on video sequence data is an important but challenging task for urban traffic surveillance. The difficulty of this task lies in the fact that it requires accurate localization of relatively small vehicles in
Externí odkaz:
https://doaj.org/article/45bbdf9b85d749c89bd1aae00dd370a8
Publikováno v:
IEEE Transactions on Industrial Electronics. 70:9379-9389
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fd6cb4138942e3e17012f42cf163dae9
https://doi.org/10.1109/tie.2022.3213916
https://doi.org/10.1109/tie.2022.3213916