- Semiconductor
- Anomaly detection
Auto-tuning parameters through the use of a data-based simulator and reinforcement learning is a highly effective approach to minimize the discrepancy between software-based motion control solutions' control values and their corresponding actual values.
READ MOREReinforcement learning is used to reduce the time required for SMT procedures by making electronic components more efficiently installed on PCBs.
READ MOREUsing an unsupervised learning algorithm, it detects those anomalies before they occur in the equipment.
READ MOREA machine learning algorithm is used to automate the distribution of tasks among multiple robots on an assembly line.
READ MOREIn place of an HVAC simulator, the trained dynamics model is used to predict changes in the HVAC's internal state, and it serves as the basis for development of an efficient air conditioning control model.
READ MOREDeveloping chip simulation environments and placement algorithms to train RL models that optimize element placement.
READ MORETime-series analysis based on historical climate data forecasts the power generation of each photovoltaic power plant during 24 hours.
READ MOREData-based anomaly detection models can help prevent emergency shutdown in petrochemical plants.
READ MOREBattery management system data and EV operation patterns can be analyzed to predict battery life remaining and monitor their lifecycles.
READ MOREESS anomaly detection models are designed to predict and monitor anomalies before a fire occurs and provide safety indicators.
READ MOREAI algorithms learn the pattern of sensor data from semiconductor manufacturing equipment and identify possible anomalies during the manufacturing process.
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