The Fourth Industrial Revolution and sensor technology have led to increased utilization of sensor data. In our modern society, data complexity is rising, and the extraction of valuable information has become crucial with the rapid changes in information technology (IT). Recurrent neural networks (RNN) and long short-term memory (LSTM) models have shown remarkable performance in natural language processing (NLP) and time series prediction. Consequently, there is a strong expectation that models excelling in NLP will also excel in time series prediction. However, current research on Transformer models for time series prediction remains limited. Traditional RNN and LSTM models have demonstrated superior performance compared to Transformers in big data analysis. Nevertheless, with continuous advancements in Transformer models, such as GPT-2 (Generative Pre-trained Transformer 2) and ProphetNet, they have gained attention in the field of time series prediction. This study aims to evaluate the classification performance and interval prediction of remaining useful life (RUL) using an advanced Transformer model. The performance of each model will be utilized to establish a health index (HI) for cutting blades, enabling real-time monitoring of machine health. The results are expected to provide valuable insights for machine monitoring, evaluation, and management, confirming the effectiveness of advanced Transformer models in time series analysis when applied in industrial settings.

Two pesticides commonly used in spinach were subjected to a field residue trial to ensure safety of terminal residues in the harvest. The residual patterns of two pesticides, which were Azoxystrobin and chlorpyrifos were examined after applying with the recommended dose and their DT50 were calculated. Also degradation patterns of pesticides at storage 4℃ were compared to those at 20℃, and removal rates of pesticides by washing spinach with water were measured. Biological half-lives of azoxystrobin and chlorpyrifos were 3.2~3.8 and 3.8~4.7 days, respectively. During the storage period, the degradation patterns were appeared more obviously at 20℃ than 4℃. Removal rates of azoxystrobin and chlorpyrifos were 9.6~90.0% and 17.7~85.8% by various washing methods.

Three benzimidazole pesticides commonly used in korean lettuce were subjected to a field residue trial to ensure safety of terminal residue in the harvest. The residual patterns of three benzimidazole pesticides, which were carbendazim, benomyl and thiophanate-methyl were examined after applying with the recommended dose in two types of korean lettuce (Chima and Chuckmeon) and their DT50 were calculated. In Chima lettuce, biological half-lives of carbendazim, benomyl and thiophanate-methyl were 2.56, 1.37 and 2.54 days, respectively and their required time under MRL(5.0 mg/kg as carbendazim) were 4.5, 2.2 and 1.0days. In Chuckmeon lettuce, biological half-lives of them were 3.41, 1.70 and 4.20 days, respectively and their required time under MRL were 5.4, 1.9 and 0.5days.