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Machine Learning
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Garbage In, Garbage Out

아무리 완벽한 알고리즘 모델을 구현하더라도, 좋은 품질의 데이터를 갖추지 못하면 머신러닝의 수행 결과는 좋을 수 없습니다.

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We are

에이치큐브는 알고리즘 모델 구현은 물론이고, 각종 센서를 통해 데이터를 수집하고 분석하여 시스템을 학습시키기 위한 최적의 데이터로 가공합니다.

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최적의 데이터로 학습된 시스템을 통해 부품의 수명, 고장 등을 미리 판단하여 고객님의 시간 절약과 불필요한 지출을 피할 수 있게 도와드립니다.

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Machine Learning Data Acq System

Customer : BAT


Software: LabVIEW, Python

Description 
- BAT is a world-renowned tobacco producer, building a machine learning-based product management and product line maintenance system.
- They wanted to solve the problem of production stoppage due to frequent breakdowns in the production line.
- Hcubed provides DAQ and Machine Learning System for Condition Based Maintenance.

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Wire Bonder Predictive Maintenance System

Customer : Semiconductor Test House Company


Software: 

Description 
- Wire bonder, an essential equipment in the semiconductor field, currently uses more than 90% of foreign equipment, and periodic parts replacement is also costly.
- After accumulating learning data through the data measurement system, we are developing a system that aims to reduce the maintenance cost of the system by developing the most suitable machine learning model through analysis to identify the performance degradation outcome of the equipment.

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Tele Haptic Auto Recognition Model

Customer : ETRI


Software: Machine Learning, Sensor Data Measurements & Actuator Control.

Description 
- Hcube developed a Tele Haptic system upon request of ETRI.
- By measuring the sensor data developed by ETRI, classifying the touch through the machine learning model, and operating a small actuator according to the classified results to make users feel different vibrations.
- The technology will continue to be studied in the future.

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Solenoid Valve Controller Diagnosys

Customer : KIMM, Automotive Valve Company Joint Task


Software: Machine Learning, Auto Test Program

Description 
- The vehicle solenoid valve is the most important part because it is used for driving and stopping the vehicle.
- Hcube has developed a system that not only determines a simple defect in the solenoid valve controller but also infers and diagnoses the type of product defect.

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