No abstract

No abstract

Manufacturing enterprises are facing how to utilise industrial knowledge and continuously accumulating massive unlabelled data to achieve human-cyber-physical collaborative and autonomous intelligence. Recently, artificial intelligence-generative content has achieved great performance in several domains and scenarios. A new concept of industrial generative pre-trained Transformer (Industrial-GPT) for intelligent manufacturing systems is introduced to solve various scenario tasks. It refers to pre-training with industrial datasets, fine-tuning with industrial scenarios, and reinforcement learning with domain knowledge. To enable Industrial-GPT to better empower the manufacturing industry, Model as a Service is introduced to cloud computing as a new service mode, which provides a more efficient and flexible service approach by directly invoking the general model of the upper layer and customising it for specific businesses. Then, the operation mechanism of the Industrial-GPT driven intelligent manufacturing system is described. Finally, the challenges and prospects of applying the Industrial-GPT in the manufacturing industry are discussed.

No abstract

A nasal pattern recognition method based on multi-feature fusion is proposed to address the problems of imperfect feature information extraction, low recognition accuracy and interference of redundant information in nasal pattern images by a single method. An improved two-channel attention mechanism (I_CBAM) is introduced in the residual network to reduce the interference of redundant information; the output feature information of Layer2, Layer3 and Layer4 in the fusion depth residual network structure is used to enrich the extracted global features of the image by using the complementarity between feature maps of different scales; meanwhile, extract the underlying local features with better matching in the nasal pattern image using the improved SURF algorithm, and fuse the extracted global features with the local features; the training of the model is supervised using the improved fusion loss function. The experimental results on the nasal pattern dataset show that the recognition accuracy is improved compared with other mainstream recognition methods.