• Title/Summary/Keyword: Non-Peeling Detection

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Determination of PCB film of Un-peeling Defect Using Deep Learning (딥러닝을 이용한 PCB 필름 미박리 양품 판정)

  • Jeong-Gu, Lee;Young-Chul, Bae
    • The Journal of the Korea institute of electronic communication sciences
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    • v.17 no.6
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    • pp.1075-1080
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    • 2022
  • Recently, the effort is continuously applied in machine learning and deep learning algorithm which is represented as artificial intelligence algorithm in the varies field such as prediction, classification and clustering. In this paper, we propose detection algorithm for un-peeling status of PCB protection film by using Dectron2. We use 42 images of data as training and 19 images of data as testing based on 61 images which was taken under the condition of a critical reflection angel of 42.8°. As a result, we get 16 images that was detected and 3 images that was not detected among 19 images of testing data.

Joint Space-time Coding and Power Domain Non-orthogonal Multiple Access for Future Wireless System

  • Xu, Jin;Ding, Hanqing;Yu, Zeqi;Zhang, Zhe;Liu, Weihua;Chen, Xueyan
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.14 no.1
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    • pp.93-113
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    • 2020
  • According to information theory, non-orthogonal transmission can achieve the multiple-user channel capacity with an onion-peeling like successive interference cancellation (SIC) based detection followed by a capacity approaching channel code. However, in multiple antenna system, due to the unideal characteristic of the SIC detector, the residual interference propagated to the next detection stage will significantly degrade the detection performance of spatial data layers. To overcome this problem, we proposed a modified power-domain non-orthogonal multiple access (P-NOMA) scheme joint designed with space-time coding for multiple input multiple output (MIMO) NOMA system. First, with proper power allocation for each user, inter-user signals can be separated from each other for NOMA detection. Second, a well-designed quasi-orthogonal space-time block code (QO-STBC) was employed to facilitate the SIC-based MIMO detection of spatial data layers within each user. Last, we proposed an optimization algorithm to assign channel coding rates to balance the bit error rate (BER) performance of those spatial data layers for each user. Link-level performance simulation results demonstrate that the proposed time-space-power domain joint transmission scheme performs better than the traditional P-NOMA scheme. Furthermore, the proposed algorithm is of low complexity and easy to implement.

Concrete Crack Detection Inside Finishing Materials Using Lock-in Thermography (위상 잠금 열화상 기법을 이용한 콘크리트 마감재 내부 균열 검출)

  • Myung-Hun Lee;Ukyong Woo;Hajin Choi;Jong-Chan Kim
    • Journal of the Korea institute for structural maintenance and inspection
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    • v.27 no.6
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    • pp.30-38
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    • 2023
  • As the number of old buildings subject to safety inspection increases, the burden on designated institutions and management entities that are responsible for safety management is increasing. Accordingly, when selecting buildings subject to safety inspection, appropriate safety inspection standards and appropriate technology are essential. The current safety inspection standards for old buildings give low scores when it is difficult to confirm damage such as cracks in structural members due to finishing materials. This causes the evaluation results to be underestimated regardless of the actual safety status of the structure, resulting in an increase in the number of aging buildings subject to safety inspection. Accordingly, this study proposed a thermal imaging technique, a non-destructive and non-contact inspection, to detect cracks inside finishing materials. A concrete specimen was produced to observe cracks inside the finishing material using a thermal imaging camera, and thermal image data was measured by exciting a heat source on the concrete surface and cracked area. As a result of the measurement, it was confirmed that it was possible to observe cracks inside the finishing material with a width of 0.3mm, 0.5mm, and 0.7mm, but it was difficult to determine the cracks due to uneven temperature distribution due to surface peeling and peeling of the wallpaper. Accordingly, as a result of performing data analysis by deriving the amplitude and phase difference of the thermal image data, clear crack measurement was possible for 0.5mm and 0.7mm cracks. Based on this study, we hope to increase the efficiency of field application and analysis through the development of technology using big data-based deep learning in the diagnosis of internal crack damage in finishing materials.