gru 썸네일형 리스트형 2nd K-AI Manufacturing Competition (2) Analysis Model DevelopmentAI Analysis Model Selection: GRURecurrent Neural Networks (RNNs) and LSTMsRecurrent Neural Networks (RNNs) are highly effective for sequential data processing due to their ability to capture temporal dependencies. However, traditional RNNs face limitations in retaining long-term dependencies, often suffering from the vanishing gradient problem.To address this, Long Shor.. 더보기 2nd K-AI Manufacturing Competition (1) Manufacturing Data Definition and ProcessingOverview of Manufacturing Data CollectionThe dataset analyzed in this study was collected from the melting and mixing process during powdered cream production. This data was obtained via PLCs and a Database Management System (DBMS) with a collection cycle set at 6-second intervals. The data collection period spans approximately two months, from March 4.. 더보기 2nd K-AI Manufacturing Competition (0) OverviewKAMP, an AI manufacturing platform managed by the Ministry of SMEs and Startups in the Republic of Korea, held a competition. The goal was to define and solve a problem based on the anonymized random dataset, and we received a melting tank dataset from the food manufacturing industry.Analysis BackgroundOverview of the Process and EquipmentThe dataset analyzed in this study originates fro.. 더보기 이전 1 다음
