• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.2
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• pp.22-27
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• 2011

Rain sensor detecting the presence of rain outside windshield glass of automobile by receiving scattered light from rain drops is proposed. Rain sensor using windshield glass as light waveguide required precision optical apparatus to inject light signal into windshield glass, and it was susceptible to outside shock and vibration, resulting malfunction, which altered optical coupling ratio. Proposed rain sensor, which detected scattered light from rain drops outside optical waveguide, did not require optical components because it did not need to inject light signal into light waveguide. This was advantageous because the sensor was less effected by shock and vibration. Fabricated rain sensor using scattered light outside waveguide responded not only to rain drops but also mist particles under simulated rain conditions using spraying nozzle, thus it showed prospects as rain sensor for automobile application.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.24 no.9
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• pp.944-950
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• 2013

This paper is a study of a rain sensor using an open-ended coaxial cavity resonator which senses the amount of rain drops linearly. It shows that it will be used as a sensor to sense the amount of rain dropped on the windshield of an automobile based on the principle of varied resonant frequency and the loss according to the amount and characteristics of an dielectric lied on the open side of a resonator. The input and output ports are built in the both sides of the resonator and the input and output coupling probes are formed like 'ㄱ' shape. The response of rain drops were simulated by the radius of inner conductor of 2 mm, 5 mm, and 10 mm respectively and it showed that the raindrop was sensed most linearly and sensitively when the radius of inner conductor is 5 mm, We have measured that the resonant frequency have varied from 3.55 GHz to 3 GHz and the Q value have varied from 42.38 to 24.3 according to the variation of rain drop amount on the fabricated resonator. Therefore, it shows that the designed resonator can be applied as a rain sensor that measures the amount of rain drops linearly by using the resonant frequency as a measurement parameter.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.5
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• pp.223-228
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• 2018

In this paper the water sensor using a coaxial cavity resonator is designed and manufactured. The water sensor which can sense water drop linearly has been constructed with voltage controlled oscillator(VCO), coaxial cavity resonator, RF switch, RF detector, A/D converter, DAC and micro controller. The operating frequency range of the designed water sensor is from 2.5GHz to 3.2GHz and the input voltage and current source are 24[V/DC] and 1[A]. The designed sensor circuit includes VCO, RF switch, RF detector which varies the frequency characteristics of the devices in the high frequency of 3GHz. And so we should correct the error of the frequency characteristics of those devices in the sensor circuit. To do this, we make the reference path which switches the signals to the RF detector directly without sending it to the resonator. According to the result of simulation and measurement, we can see that there is 0-50MHz difference between simulated resonator frequency and manufactured resonator frequency.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.78-78
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• 2021

영상유속분석법은 비접촉식으로 유속을 측정하는 방법으로 특히 홍수시 하천의 표면유속을 안전하게 계측할 수 있어서 경제적이고 안전한 하천유속 측정 방법 중 하나이다. STIV는 영상의 휘도 정보를 시간 방향으로 나열하여 작성된 STI(Space-Time Image)에 나타나는 패턴의 기울기를 이용하여 유속을 산정하는 방법이다. 특히 STIV(Space-Time Image Velocimetry)는 기존 입자군의 상호상관법에 기초한 입자영상유속계와 달리 표식자의 유무와 상관없이 유속을 측정할 수 있어 적용성과 안정성이 확보된다. 하지만 영상의 상태가 불량한 경우 정확한 유속 측정이 난해하며 야간에는 별도의 조명 추가 및 태풍과 같은 악기상에서는 빗방울이 카메라에 맺히거나 수면의 진동, 구조물의 진동에 의한 영상의 상태가 불량하게 되어 측정 정도가 떨어진다. 이처럼 영상을 이용한 유속 계측에 있어 다양한 연구 및 기술개발이 요구되는 시점이다. 따라서 본 연구에서는 영상을 이용한 정확한 유속측정을 위해 STIV와 인공지능을 융합하여 정확한 유속 평가를 목적으로 한다. 우선 기존 STI에 의한 기울기 추정방법을 확장하여 딥러닝(CNN)에 의한 기울기 추정방법을 도입하였다. CNN은 일반적으로 이미지의 특성을 추출하는데 유용한 방법으로서 STI의 2차원 Fourier변환 이미지를 사용하여 패턴의 기울기를 감지하도록 학습하였고 적용 결과 기울기에 대한 인식율은 매우 양호하였으며 이를 이용한 실제 관측 영상에 적용한 결과 유속에 대한 정밀도도 매우 양호하게 나타났다. 또한 딥러닝을 적용한 STIV는 노이즈(진동, 화면 불량 등)가 있는 영상에서도 안정적으로 유속을 산정할 수 있으며 전파유속계를 이용한 실제 하천의 표면유속 관측치와 비교 검토 결과 매우 양호하게 유속을 평가하고 있는 것으로 나타났다.