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Use Cases

We are creating AI application cases in manufacturing and industrial fields with core ML capabilities and Enterprise MLOps products.

Core ML

Machine Learning & Deep Learning
Reinforcement Learning

Enterprise MLOps

Link™
Runway™

AI Solutions

Anomaly Detection
Control & Optimization
Predictive Analysis
  • Control and optimization
  • Manufacturing

Multi-robot task & Motion planning

A machine learning algorithm is used to automate the distribution of tasks among multiple robots on an assembly line.

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  • Control and optimization
  • Automobile

RL-based EMS control

In 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.

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  • Automobile
  • Anomaly detection

Robot Arm Anomaly Detection

Using an unsupervised learning algorithm, it detects those anomalies before they occur in the equipment.

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  • Control and optimization
  • Semiconductor

Chip Placement with RL

Developing chip simulation environments and placement algorithms to train RL models that optimize element placement.

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  • Predictive analysis
  • Energy

Solar power generation prediction

Time-series analysis based on historical climate data forecasts the power generation of each photovoltaic power plant during 24 hours.

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  • Chemical
  • Anomaly detection

Polymer Reactor shutdown analysis ​

Data-based anomaly detection models can help prevent emergency shutdown in petrochemical plants.

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  • Predictive analysis
  • Battery

EV Li+ Battery​ RUL Prediction

Battery management system data and EV operation patterns can be analyzed to predict battery life remaining and monitor their lifecycles.

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  • Energy
  • Anomaly detection

Li+ Battery ESS​ Anomaly Detection ​

ESS anomaly detection models are designed to predict and monitor anomalies before a fire occurs and provide safety indicators.

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  • Semiconductor
  • Anomaly detection

Semicon equipment time-to-failure

AI algorithms learn the pattern of sensor data from semiconductor manufacturing equipment and identify possible anomalies during the manufacturing process.

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