• 반도체디스플레이기술학회지
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• 제19권2호
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• pp.11-15
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• 2020

In a typical initial setup of a PCB component inspection system, operators should manually input various information such as category, position, and inspection area for each component to be inspected, thus causing much inconvenience and longer setup time. Although there are many deep learning based object detectors, RetinaNet is regarded as one of best object detectors currently available. In this paper, a method using an extended RetinaNet is proposed that automatically detects its component category and position for each component mounted on PCBs from a high-resolution color input image. We extended the basic RetinaNet feature pyramid network by adding a feature pyramid layer having higher spatial resolution to the basic feature pyramid. It was demonstrated by experiments that the extended RetinaNet can detect successfully very small components that could be missed by the basic RetinaNet. Using the proposed method could enable automatic generation of inspection areas, thus considerably reducing the setup time of PCB component inspection systems.

• ETRI Journal
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• 제45권4호
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• pp.666-677
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• 2023

In the last few years, convolutional neural networks (CNNs) have demonstrated good performance while solving various computer vision problems. However, since CNNs exhibit high computational complexity, signal processing is performed on the server side. To reduce the computational complexity of CNNs for edge computing, a lightweight algorithm, such as a MobileNet, is proposed. Although MobileNet is lighter than other CNN models, it commonly achieves lower classification accuracy. Hence, to find a balance between complexity and accuracy, additional hyperparameters for adjusting the size of the model have recently been proposed. However, significantly increasing the number of parameters makes models dense and unsuitable for devices with limited computational resources. In this study, we propose a novel MobileNet architecture, in which the number of parameters is adaptively increased according to the importance of feature maps. We show that our proposed network achieves better classification accuracy with fewer parameters than the conventional MobileNet.

• Communications for Statistical Applications and Methods
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• 제21권4호
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• pp.349-361
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• 2014

This paper compares of lasso type estimators in various high-dimensional data situations with sparse parameters. Lasso, adaptive lasso, fused lasso and elastic net as lasso type estimators and ridge estimator are compared via simulation in linear models with correlated and uncorrelated covariates and binary regression models with correlated covariates and discrete covariates. Each method is shown to have advantages with different penalty conditions according to sparsity patterns of regression parameters. We applied the lasso type methods to Arabidopsis microarray gene expression data to find the strongly significant genes to distinguish two groups.

• 한국조경학회지
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• 제28권1호
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• pp.54-61
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• 2000

Covering rate, visual rating and sod development were evaluated under three natural fiber materials with Phragmites spp. when over a plastic sheet. The results were as follows. (1) The last covering rate was high on jute net, coir mat and on Miscanthus sacchariflorus, respectively while the early covering rate was high on coir mat and on Miscanthus sinensis+perennial ryegrass. (2) The early growth was good on perennial ryegrass but the covering rate gradually turned poor because of summer drought. (3) Sod was highly developed on Phragmites japonica, Miscanthus sacchariflorus and Miscanthus sinensis compared with other species and mixtures. (4) The covering rate and visual rating were high on natural fiber materials such as coir mat and jute net when compared with on natural fiber materials such as none treatment plots. (5) The natural fibers materials on Phragmites spp. and Miscanthus spp. were effect on sod establishment. Sod coir mat was highly established. (6) The carpet-type sod was best developed on the coir mat.

• 한국유체기계학회 논문집
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• 제13권6호
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• pp.19-24
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• 2010

The demands for small, high performance and high loaded aircraft compressor are increased in the world. But the design requirements become increasingly complex to design these high technical engines, the requirement of the design optimization become increased. The optimal design result of several disciplines show different tendencies and nonlinear characteristics of the compressor design, the multidisciplinary design optimization method must be considered in compressor design. Therefore, the artificial Neural Net method is adapted to make the approximation model of 3-stage axial compressor design optimization for considering the nonlinear characteristic. At last, the optimal result of this study is compared to that of previous study.

• 한국안전학회지
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• 제27권5호
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• pp.95-98
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• 2012

There are many high-rise apartment building construction in Korea. There was an accident to pass through worker head by rebar dropped from height place. Therefore, low cost-high effectiveness method to prevent this type of accident should be revised and applied into the construction site. This study is to find out which method could be effectively applied to a site with low cost. Practical field test at 4th floor, 10th floor of apartment building site using re-bar diameter D10, D13, D16, D19, D22 with a length of 1 m, 1.5 m, 2 m, 2.5 m, 3 m which are common by used in a site. The test has also been done with a cover of styrofoam thickness 4.5 cm and thickness 9cm on field drop preventing net. One sheet of styrofoam thickness 45 mm has approximately two times stronger than only prevention net, It is found. Also, Two sheets have approximately two times stronger than one sheet of it.

• 한국산학기술학회논문지
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• 제11권2호
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• pp.584-589
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• 2010

본 논문에서는 만족스런 서비스 품질을 제공하는 페트리 네트 기반의 멀티미디어 동기화 모델을 제안한다. 제안한 모델은 실시간 특징을 나타내는 데이터의 서비스 품질을 보장할 수 있는 가변적 버퍼를 적용하였다. 본 논문에서는 페트리네트를 확장하여 새로운 멀티미디어 동기화 규격 모델을 표현할 수 있는 PBMSM을 제안하고, 제안한 모델의 검증을 위하여 페트리 네트의 두가지 분석방법에 의해 증명을 하였다. 그리고 기존의 모델과 비교하여 성능이 우수함을 보였다. 제안한 모델은 양질의 서비스 보장을 요구하는 시스템에 적합하다.

• Nuclear Engineering and Technology
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• 제54권12호
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• pp.4751-4758
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• 2022

Two-phase flow may almost exist in every branch of the energy industry. For the corresponding engineering design, it is very essential and crucial to monitor flow patterns and their transitions accurately. With the high-speed development and success of deep learning based on convolutional neural network (CNN), the study of flow pattern identification recently almost focused on this methodology. Additionally, the photographing technique has attractive implementation features as well, since it is normally considerably less expensive than other techniques. The development of such a two-phase flow pattern online monitoring system is the objective of this work, which seldom studied before. The ongoing preliminary engineering design (including hardware and software) of the system are introduced. The flow pattern identification method based on CNNs and transfer learning was discussed in detail. Several potential CNN candidates such as ALexNet, VggNet16 and ResNets were introduced and compared with each other based on a flow pattern dataset. According to the results, ResNet50 is the most promising CNN network for the system owing to its high precision, fast classification and strong robustness. This work can be a reference for the online monitoring system design in the energy system.

• 한국농림기상학회지
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• 제16권2호
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• pp.146-154
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• 2014

월동기간 동안의 차나무에 대한 차광망 색상과 차광율에 따른 동해경감에 미치는 영향을 조사한 결과, 차광율 변화에 대한 효과는 차광율이 높아질수록 차나무생육이 불량하고 동해 피해율이 높았으며, 무처리구 대비 55% 차광율에서 신초 및 생엽수확량이 좋게 나타났다. 차광망 색상별로 보면, 투명망(차광망 55% 수준) 처리구와 녹색 차광망 처리구가 무처리구와 검정 차광망 처리구에 비해 생엽수확량과 피해면적이 감소하였다. 투명망 처리구가 무처리구에 비해 동해 피해율이 50%이상 감소하고, 생엽수확량은 무처리구보다 투명망 처리구에서 단위면적당(10a) 68kg 더 많았다. 차광망의 색상별 동해경감은 검정<녹색<투명 색상 순으로 동해 경감률이 다소 높게 나타났다. 처리구간의 미기상변화를 보면 처리구가 무처리구에 비해 평균기온은 $0.7^{\circ}C$ 낮았으며, 평균상대습도는 14.9% 높게 관측되었고, 지중온도는 $0.6^{\circ}C$ 낮은 반면에 토양수분은 4.6% 높게 관측 되었다. 또한 차광망 피복물 설치에 의한 평균풍속은 무처리구 대비 0.7m/s 감소하여, 바람에 의한 과잉 증발산과 토양의 건조를 줄여주는 효과가 있었으며, 동해경감의 방안으로 활용이 가능한 것으로 판단되었다.

• 한국지진공학회논문집
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• 제28권4호
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• pp.183-191
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• 2024

Accurate seismic vulnerability assessment requires high quality and large amounts of ground motion data. Ground motion data generated from time series contains not only the seismic waves but also the background noise. Therefore, it is crucial to determine the high-pass cut-off frequency to reduce the background noise. Traditional methods for determining the high-pass filter frequency are based on human inspection, such as comparing the noise and the signal Fourier Amplitude Spectrum (FAS), f² trend line fitting, and inspection of the displacement curve after filtering. However, these methods are subject to human error and unsuitable for automating the process. This study used a deep learning approach to determine the high-pass filter frequency. We used the Mel-spectrogram for feature extraction and mixup technique to overcome the lack of data. We selected convolutional neural network (CNN) models such as ResNet, DenseNet, and EfficientNet for transfer learning. Additionally, we chose ViT and DeiT for transformer-based models. The results showed that ResNet had the highest performance with R² (the coefficient of determination) at 0.977 and the lowest mean absolute error (MAE) and RMSE (root mean square error) at 0.006 and 0.074, respectively. When applied to a seismic event and compared to the traditional methods, the determination of the high-pass filter frequency through the deep learning method showed a difference of 0.1 Hz, which demonstrates that it can be used as a replacement for traditional methods. We anticipate that this study will pave the way for automating ground motion processing, which could be applied to the system to handle large amounts of data efficiently.