While deep learning has significantly improved the accuracy of visual detection systems, the integration of process semantics with visual perception for industrial assembly tasks remains largely unexplored. This study aims to develop a high-accuracy cable connector detection framework that incorporates process knowledg...
Ziang Wang, Xi-Tian Tian, Y. Bolea et al.· Electronics· 0 citations
Data preparation, including source profiling, quality diagnosis, and cleaning, remains a labor-intensive bottleneck in data-driven applications. Traditional approaches require manual rule design for each data source and lack adaptability to heterogeneous formats. This paper proposes an LLM-driven intelligent agent for...
The proposed Diagnostic Evidence Network (DENet) is an encoder-agnostic multi-task framework that extends the output to a structured evidence record: the classification, a predicted characteristic frequency comparable against the theoretical value determined by bearing geometry and shaft speed, and a temporal localizat...
Yun-Tong Chen, Jian-Yu Liu, Ying-Qi Li et al.· 1 citation
By connecting the heterogeneous stages of computational materials discovery, the LLM-based agents of MAESTRO can operate across application domains and uncover high-performance materials that conventional screening approaches would be unlikely to consider.
Yun-Tong Chen, Ju Huang, Yu Liu et al.· 0 citations
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