"Visualizing invisible changes." It is a high-precision change detection technology using teacher-assisted data synthesis.
We would like to introduce our "High-Precision Change Detection Technology Using Synthetic Labeled Data." This method allows for the easy synthesis of labeled data to detect subtle changes hidden in time series data. It enables the detection of change points that were previously difficult to identify by synthesizing labeled data from existing datasets. This technology can be applied across a wide range of fields, from manufacturing to infrastructure and health management. 【Features】 ■ Easily prepare training data by synthesizing labeled training data from existing datasets. ■ Capable of accurately detecting minute changes compared to conventional technologies. *For more details, please download the PDF or feel free to contact us.
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[Demonstration Example] Detection of Weather Observation Station Relocation Using 26 years of temperature data from Kyushu, training data with teacher signals was synthesized only within the same prefecture, and supervised learning was conducted using a recurrent neural network (NN). Using the recurrent NN trained with training data that simulated movements of about 10 km, actual weather observation data was validated. ⇒ The recurrent NN detected a relocation of approximately 900 m for the Tokyo observation station and about 200 m for the Kiire observation station. ⇒ It was able to detect minute changes, more subtle than the 10 km changes used in the training data. [Expected Applications] Manufacturing and Machinery: "Utilizing data on power consumption, vibrations, and sounds" ⇒ Anomaly detection for machine failures, predictive maintenance Infrastructure: "Data on vibrations, sounds, and traffic volume" ⇒ Monitoring of bridges, tunnels, and roads Healthcare: "Vital data such as blood pressure and body temperature" ⇒ Monitoring of vital data, health management
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