Anomaly detection AI utilizing technology invented

by The National Institute of Advanced

Industrial Science and Technology (AIST)

AIST, one of Japan’s largest research institutes, invented the feature extraction method combining

“High-order Local Auto-Correlation (HLAC)” and multivariate data analysis.

Adacotech has implemented this method into its original anomaly detection system and is applying it to various fields.

3 Characteristics

Small amount of Anomaly Data

Models can be trained with much less anomaly data compared to deep-learning methods using autoencoder.

No need of GPU

High speed processing is available on a normal PC due to simplified calculation.

Not “Black Box”

Features are extracted in multiple steps focusing on geometric aspects making it is easy to explain.

What is High-order Local Auto-Correlation (HLAC) feature extraction method

(AIST patent technology)?

HLAC is a feature extraction method used for the analysis and recognition of images, etc. It is versatile, high-speed, and has excellent recognition accuracy.
Since it can be calculated by product-sum operations, the object can be recognized instantly using a normal PC.
In addition, since it is position invariant (does not depend on the position of the object), segmentation is not required (segmentation free).

Furthermore, it has characteristics preferable for image recognition such as additivity

(if there are two objects in an image, the sum of the respective features result in the feature of the whole image).

What is judgement using HLAC?

After extracting the HLAC features of the data to be analyzed (image, video, sound, sensor data, etc.),

the range (subspace) of data that is considered “normal” is determined by principal component analysis,

and the distance of deviation from the subspace is determined as abnormal value.

Flow of HLAC judgement

*25 types in case of binary HLAC

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