• Smart Structures and Systems
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• 제32권2호
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• pp.123-133
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• 2023

This paper proposes a thermography-based coating thickness estimation method for steel structures using model-agnostic meta-learning. In the proposed method, a halogen lamp generates heat energy on the coating surface of a steel structure, and the resulting heat responses are measured using an infrared (IR) camera. The measured heat responses are then analyzed using model-agnostic meta-learning to estimate the coating thickness, which is visualized throughout the inspection surface of the steel structure. Current coating thickness estimation methods rely on point measurement and their inspection area is limited to a single point, whereas the proposed method can inspect a larger area with higher accuracy. In contrast to previous ANN-based methods, which require a large amount of data for training and validation, the proposed method can estimate the coating thickness using only 10- pixel points for each material. In addition, the proposed model has broader applicability than previous methods, allowing it to be applied to various materials after meta-training. The performance of the proposed method was validated using laboratory-scale and field tests with different coating materials; the results demonstrated that the error of the proposed method was less than 5% when estimating coating thicknesses ranging from 40 to 500 ㎛.

• 한국통신학회논문지
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• 제40권8호
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• pp.1606-1612
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• 2015

본 논문에서는 스마트폰에 장착 가능하고 휴대가 가능한 고효율 NDIR $CO_2$ 센서 모듈을 개발하였다. 저전력 회로 설계를 위하여 텅스텐램프 대신에 적외선 LED를 사용하였으며, 센서 모듈에 최적화된 광도파로를 설계 및 제작하였다. 스마트폰과 인터페이스가 가능한 회로를 통하여 스마트폰의 전원으로 센서 모듈이 구동되도록 설계하였다. $CO_2$ 농도, 온도 및 습도 등 측정된 센서의 데이터는 스마트폰 앱을 통하여 화면에 표시하였다. 측정 결과, 개발된 센서 모듈은 온도$-10^{\circ}C{\sim}50^{\circ}C$ 구간에서 0 ~ 3,000ppm 범위의 $CO_2$ 농도를 측정할 수 있었으며 측정 오차는 ${\pm}60ppm$이내였다.

• 한국신재생에너지학회:학술대회논문집
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• 한국신재생에너지학회 2010년도 춘계학술대회 초록집
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• pp.58.1-58.1
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• 2010

In solar cell module manufacturing, single solar cells has to be joined electrically to strings. Copper stripes coated with tin-silver-copper alloy are joined on screen printed silver of solar cells which is called busbar. The bus bar collects the electrons generated in solar cell and it is connected to the next cell in the conventional module manufacturing by a metal stringer using conventional hot air or infrared lamp soldering systems. For thin solar cells, both soldering methods have disadvantages, which heats up the whole cell to high temperatures. Because of the different thermal expansion coefficient, mechanical stresses are induced in the solar cell. Recently, the trend of solar cell is toward thinner thickness below 180um and thus the risk of breakage of solar cells is increasing. This has led to the demand for new joining processes with high productivity and reduced error rates. In our project, we have developed a new method to solder solar cells with a laser heating source. The soldering process using diode laser with wavelength of 980nm was examined. The diode laser used has a maximum power of 60W and a scanner system is used to solder dimension of 6" solar cell and the beam travel speed is optimized. For clamping copper stripe to solar cell, zirconia(ZrO)coated iron pin-spring system is used to clamp both joining parts during a scanner system is traveled. The hot plate temperature that solar cell is positioned during lasersoldering process is optimized. Also, conventional solder joints after $180^{\circ}C$ peel tests are compared to the laser soldering methods. Microstructures in welded zone shows that the diffusion zone between solar cell and metal stripes is better formed than inIR soldering method. It is analyzed that the laser solder joints show no damages to the silicon wafer and no cracks beneath the contact. Peel strength between 4N and 5N are measured, with much shorter joining time than IR solder joints and it is shown that the use of laser soldering reduced the degree of bending of solar cell much less than IR soldering.

• 대한환경공학회지
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• 제27권10호
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• pp.1129-1135
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• 2005

한강에서 추출한 휴믹물질의 산화를 위해 회전 반응기를 도입하였다. $TiO_2$ 광촉매의 혼합을 위해 사용되는 공기는 UV 램프와 광촉매 사이에서 UV 조사를 방해할 수 있으므로, 더 나은 UV 조사율을 위해 반응기 내부에 배플이 설치된 회전 반응기를 고안하였다. FT-IR, $^{13}C$-NMR의 분석 결과, 한강 휴믹물질은 다른 상용화된 휴믹물질과는 다른 특성을 보여주었다. XAD-7HP 수지로 분리된 한강 휴믹물질을, 반응 후 광촉매의 분리 및 회수문제를 해결하기 위해, $TiO_2$를 hollow bead에 고정화한 광촉매와 UV-A, UV-C 램프를 사용하여 광촉매산화시켰다. 초기 휴믹물질의 TOC 농도가 5 mg/L일 때, 초기 pH 3, $TiO_2$ 주입률 2.0 g/L을 최적 조건으로 결정하였다. 또한 UV-C와 UV-A 램프의 비교실험을 수행한 결과, 비슷한 TOC 제거율을 보였다. 하지만, 분자량 분포 실험 결과, UV-A 램프보다 UV-C 램프로 광촉매산화시킨 것이 상대적으로 저분자량 부분이 증가하였다.

• Current Photovoltaic Research
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• 제11권2호
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• pp.44-48
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• 2023

In heterojunction technology (HJT) solar cells, low-temperature curing paste is used because the passivation layer deteriorates at high temperatures of 200℃ or higher. However, manufacturing HJT photovoltaic (PV) modules is challenging due to the weak peel strength between busbar electrodes and cells after soldering process. For this issue, the electrode thicknesses of the busbars of the HJT solar cell were analyzed, and the peel strengths between electrodes and wires were measured after soldering using an infrared (IR) lamp. As a result, the electrodes printed by the screen printing method had a difference in thickness due to screen mask. Also, as the thickness of the electrode increased, the peel strength of the wire increased.

• Smart Structures and Systems
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• 제33권1호
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• pp.55-64
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• 2024

This paper proposes a fusion imaging-based coating-defect classification method for steel structures that uses zero-shot learning. In the proposed method, a halogen lamp generates heat energy on the coating surface of a steel structure, and the resulting heat responses are measured by an infrared (IR) camera, while photos of the coating surface are captured by a charge-coupled device (CCD) camera. The measured heat responses and visual images are then analyzed using zero-shot learning to classify the coating defects, and the estimated coating defects are visualized throughout the inspection surface of the steel structure. In contrast to older approaches to coating-defect classification that relied on visual inspection and were limited to surface defects, and older artificial neural network (ANN)-based methods that required large amounts of data for training and validation, the proposed method accurately classifies both internal and external defects and can classify coating defects for unobserved classes that are not included in the training. Additionally, the proposed model easily learns about additional classifying conditions, making it simple to add classes for problems of interest and field application. Based on the results of validation via field testing, the defect-type classification performance is improved 22.7% of accuracy by fusing visual and thermal imaging compared to using only a visual dataset. Furthermore, the classification accuracy of the proposed method on a test dataset with only trained classes is validated to be 100%. With word-embedding vectors for the labels of untrained classes, the classification accuracy of the proposed method is 86.4%.