|
|
|
|
|
Tao Sun, Longfei Cui, Lixuan Zong, Songchao Zhang, Yuxuan Jiao, Xinyu Xue and Yongkui Jin
The high cost of manual weed control and the overuse of herbicides restrict the yield and quality of soybean. Intelligent mechanical weeding and precise application of pesticides can be used as effective alternatives for weed control in the field, and th...
ver más
|
|
|
|
|
|
|
|
|
|
|
Varsha S. Lalapura, Veerender Reddy Bhimavarapu, J. Amudha and Hariram Selvamurugan Satheesh
The Recurrent Neural Networks (RNNs) are an essential class of supervised learning algorithms. Complex tasks like speech recognition, machine translation, sentiment classification, weather prediction, etc., are now performed by well-trained RNNs. Local o...
ver más
|
|
|
|
|
|
|
|
|
|
|
Haojie Wang, Pingqing Fan, Xipei Ma and Yansong Wang
The intelligent identification of coal gangue on industrial conveyor belts is a crucial technology for the precise sorting of coal gangue. To address the issues in coal gangue detection algorithms, such as high false negative rates, complex network struc...
ver más
|
|
|
|
|
|
|
|
|
|
|
Jinhui Guo, Xiaoli Zhang, Kun Liang and Guoqiang Zhang
In recent years, the emergence of large-scale language models, such as ChatGPT, has presented significant challenges to research on knowledge graphs and knowledge-based reasoning. As a result, the direction of research on knowledge reasoning has shifted....
ver más
|
|
|
|
|
|
|
|
|
|
|
Diego Renza and Dora Ballesteros
CNN models can have millions of parameters, which makes them unattractive for some applications that require fast inference times or small memory footprints. To overcome this problem, one alternative is to identify and remove weights that have a small im...
ver más
|
|
|
|
|
|
|
|
|
|
|
Zhuo Li, Hengyi Li and Lin Meng
Currently, with the rapid development of deep learning, deep neural networks (DNNs) have been widely applied in various computer vision tasks. However, in the pursuit of performance, advanced DNN models have become more complex, which has led to a large ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Wenxin Yang, Xiaoli Zhi and Weiqin Tong
Current edge devices for neural networks such as FPGA, CPLD, and ASIC can support low bit-width computing to improve the execution latency and energy efficiency, but traditional linear quantization can only maintain the inference accuracy of neural netwo...
ver más
|
|
|
|
|
|
|
|
|
|
|
Sichao Zhuo, Xiaoming Zhang, Ziyi Chen, Wei Wei, Fang Wang, Quanlong Li and Yufan Guan
With the development of Industry 4.0, although some smart meters have appeared on the market, traditional mechanical meters are still widely used due to their long-standing presence and the difficulty of modifying or replacing them in large quantities. M...
ver más
|
|
|
|
|
|
|
|
|
|
|
Jiarun Wu and Qingliang Chen
Massively pre-trained transformer models such as BERT have gained great success in many downstream NLP tasks. However, they are computationally expensive to fine-tune, slow for inference, and have large storage requirements. So, transfer learning with ad...
ver más
|
|
|
|
|
|
|
|
|
|
|
Zhichen Wang, Hengyi Li, Xuebin Yue and Lin Meng
As the proportion of the working population decreases worldwide, robots with artificial intelligence have been a good choice to help humans. At the same time, field programmable gate array (FPGA) is generally used on edge devices including robots, and it...
ver más
|
|
|
|
|
|