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Artificial intelligence acoustic inspection system

AI Square

State-of-the-art acoustic inspection algorithm developed in-house with domestic technology



Innovate the manufacturing process

In-line based intelligent acoustic tester.


Tfoi & Tfos

Failure diagnosis and monitoring

​At once

Unmanned aerial vehicle, driving robot based

Industrial equipment failure diagnosis and monitoring system.


Drone detection, 

​A safe future

Acoustic basis using multi-channel microphone sensor
Urban unmanned aerial vehicle (UAV) detection system.


AI² is an advanced acoustic inspection algorithm developed independently with domestic technology and is an acoustic detection, tracking and analysis system and solution using a multi-channel microphone sensor.
Intelligent acoustic inspection system Smart, industrial equipment failure diagnosis and monitoring system Tfoi&Tfos, urban unmanned aerial vehicle (UAV) detection system AirScope 
It is divided into three categories.

Acoustic Inspector ×Artificial Iintelligence

AI² is a state-of-the-art acoustic inspection developed independently with domestic technology.
Algorithm using multi-channel microphone sensor
Acoustic detection, tracking and analysis systems and solutions.

Intelligent acoustic tester, industrial equipment failure diagnosis and monitoring system, 
Urban Unmanned Aerial Vehicle (UAV) Detection System 
It is divided into three categories.


AI²’s learning data construction process

Check out AI²’s five-step learning data construction process.

Source data collection
Step 1

from the measurement object through a multi-channel microphone sensor.
Abnormal noise generated is collected by band, and 
From the camera within the multi-channel microphone sensor 
With an image of the measured objectSecured with original data

data design
Step 2

Analyze abnormal noise for each secured band and 
To analyze the location and size of the acquired sound

design classification criteria

data labeling
Step 3

To analyze the location and size of the acquired sound
Using classification criteriaLabeling by dataproceed

Data set construction
Step 4

Using labeled data, abnormal noise coordinates and  
By integrating image information for each measurement object,
To detect accurate location informationData set construction

A.I. modeling
Step 5

Build an artificial intelligence (A.I) modelTo 
Different types such as linear or logistic regression
Use it as learning data using algorithms

AI Square

A.I-based acoustic data learning & definition, 
Enhancing detection power through deep learning

Based on machine learning normal acoustic dataWow Abnormal acoustic dataare learned and defined respectively, and 
Accumulated data can be used to enhance detection power through deep learning.
Immediately upon detection of abnormal dataReal-time visualization algorithmThrough the location of the abnormal noise source


provided to users.


Acoustic data collected through a multi-channel microphone sensor isDecision TreeIt is classified through  abnormal noise, environmental noise, and irregular noise based on normal sound data.

A complex node is created, and the resulting class is collected and judged.
machine visionIt is a cutting-edge detection algorithm that determines the operating state of the product and the physical characteristics of the inspection object, and allows complex judgment by combining it with acoustic data.

capable of making complex judgments

Advanced detection algorithm

Decision tree

able to judge
Advanced detection algorithm


Via AI²in 3D spaceLocation of abnormal noise occurring based on videostandard colorNot only can it be implemented through a  discrimination algorithm, it can be used as a targeting technique by removing environmental noise and combining possible acoustic data for each space with a learning algorithm. Auto Gainsecond

You can use it to enhance detection power.

  using auto gain
Advancement of detection power

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