• Title/Summary/Keyword: 고주파 수분센서

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Low Cost Signal Generator with Frequency High-Resolution for SS-OCT (SS-OCT용 고 주파수분해능 저비용 정현파 발생기)

  • Lee, Juchan;Eom, Jinseob
    • Journal of Sensor Science and Technology
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    • v.22 no.1
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    • pp.84-88
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    • 2013
  • In this paper, the low price signal generator with capability of frequency high-resolution and variable sync pulse has implemented. It fulfils well the requirements for SS-OCT of the frequency resolution less than 1Hz, frequency stability of ${\leq}{\pm}0.5Hz$/10 min and variable sync pulse output timing. Through its performance test applied to wavelength swept laser, 120 nm sweeping range and 10 mW average optical power were obtained. This shows that the realized sine-wave generator can replace the commercial high cost and high performance signal generators employed by current SS-OCT systems.

Comparative Experimental Study on the Evaluation of the Unit-water Content of Mortar According to the Structure of the Deep Learning Model (딥러닝 모델 구조에 따른 모르타르의 단위수량 평가에 대한 비교 실험 연구)

  • Cho, Yang-Je;Yu, Seung-Hwan;Yang, Hyun-Min;Yoon, Jong-Wan;Park, Tae-Joon;Lee, Han-Seung
    • Proceedings of the Korean Institute of Building Construction Conference
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    • 2021.11a
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    • pp.8-9
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    • 2021
  • The unit-water content of concrete is one of the important factors in determining the quality of concrete and is directly related to the durability of the construction structure, and the current method of measuring the unit-water content of concrete is applied by the Air Meta Act and the Electrostatic Capacity Act. However, there are complex and time-consuming problems with measurement methods. Therefore, high frequency moisture sensor was used for quick and high measurement, and unit-water content of mortar was evaluated through machine running and deep running based on measurement big data. The multi-input deep learning model is as accurate as 24.25% higher than the OLS linear regression model, which shows that deep learning can more effectively identify the nonlinear relationship between high-frequency moisture sensor data and unit quantity than linear regression.

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