• Journal of Korean Society for Atmospheric Environment
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• 제17권E2호
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• pp.61-70
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• 2001

In recent years there have been growing interests in the potential environmental effects of global climate change. Of specific interests is the role that climate change may play in altering natural volatile organic compound.(NVOC) emissions from trees and the subsequent impact of this perturbation on air quality and ozone formation. A novel vegetation enclosure chamber method was designed and constructed of Tedlar in order to estimate more accurate and precise NVOC emission rates of either small whole plants or the branches of large trees. The enclosure chamber was initially tested in the laboratory and also successfully evaluated in the field. Overall precision for this enclosure was estimated as RSD<10%(n=9). The overall errors associated with the enclosure method in a laboratory system might be relatively small (say<$\pm$15%); however, they might be rather large(say$\pm$40%) in a field-based system. Two consecutive samples were collected on each sampling day from the two pine species during the test period. Slash pine studies showed that the absolute percentage difference between the first and second samples varied from 0.33 to 29%. The percent differences between consecutive emission for loblolly pines varied from 0.74 to 24.2%.

• Smart Structures and Systems
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• 제29권1호
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• pp.53-62
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• 2022

The deployment of advanced structural health monitoring (SHM) systems in large-scale civil structures collects large amounts of data. Note that these data may contain multiple types of anomalies (e.g., missing, minor, outlier, etc.) caused by harsh environment, sensor faults, transfer omission and other factors. These anomalies seriously affect the evaluation of structural performance. Therefore, the effective analysis and mining of SHM data is an extremely important task. Inspired by the deep learning paradigm, this study develops a novel generative adversarial network (GAN) and convolutional neural network (CNN)-based data anomaly detection approach for SHM. The framework of the proposed approach includes three modules : (a) A three-channel input is established based on fast Fourier transform (FFT) and Gramian angular field (GAF) method; (b) A GANomaly is introduced and trained to extract features from normal samples alone for class-imbalanced problems; (c) Based on the output of GANomaly, a CNN is employed to distinguish the types of anomalies. In addition, a dataset-oriented method (i.e., multistage sampling) is adopted to obtain the optimal sampling ratios between all different samples. The proposed approach is tested with acceleration data from an SHM system of a long-span bridge. The results show that the proposed approach has a higher accuracy in detecting the multi-pattern anomalies of SHM data.

• Current Optics and Photonics
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• 제8권3호
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• pp.246-258
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• 2024

In aerospace and energy engineering, the reconstruction of three-dimensional (3D) temperature distributions is crucial. Traditional methods like algebraic iterative reconstruction and filtered back-projection depend on voxel division for resolution. Our algorithm, blending deep learning with computer graphics rendering, converts 2D projections into light rays for uniform sampling, using a fully connected neural network to depict the 3D temperature field. Although effective in capturing internal details, it demands multiple cameras for varied angle projections, increasing cost and computational needs. We assess the impact of camera number on reconstruction accuracy and efficiency, conducting butane-flame simulations with different camera setups (6 to 18 cameras). The results show improved accuracy with more cameras, with 12 cameras achieving optimal computational efficiency (1.263) and low error rates. Verification experiments with 9, 12, and 15 cameras, using thermocouples, confirm that the 12-camera setup as the best, balancing efficiency and accuracy. This offers a feasible, cost-effective solution for real-world applications like engine testing and environmental monitoring, improving accuracy and resource management in temperature measurement.

• 전력전자학회논문지
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• 제4권5호
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• pp.462-473
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• 1999

디지털 제어 방식으로 구동되는 모든 교류 전동기 벡터제어시스템의 근간을 이루는 전류 측정에 관한 문제를 다룬다. 일반적인 펄스폭 변조 방식 교류 전동기 구동 시스템에서 전동기 전류에 포함된 인버터 스위칭 노이즈를 없애기 위하여 저역 통과 필터를 사용하는데 이러한 필터는 필연적으로 측정된 신호의 시간 지연을 유발하게 된다. 따라서 샘플링한 전류값에는 기본파 성분 뿐만아니라 고조파 리플 성분이 포함된다. 본 논문에서는 3상 대칭 펄스폭 변조시 기준 전압 벡터의 위치에 다른 전류 샘플링 오차를 해석적으로 구하고 이러한 샘플링 오차를 최소화하기 위한 기법을 제안한다. 제안된 지연 보상 샘플링 기법을 사용하면 정상 상태 전류 측정 오차를 최소화할 수 있고 보다 정확한 토오크 제어가 가능하게 됨을 시뮬레이션과 실험을 통해 보였다.

• International Journal of Safety
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• 제3권1호
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• pp.27-31
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• 2004

Some of the petroleum products contain benzene which is well known as a confirmed human carcinogen. For example, gasoline products contain benzene ranging up to several percents by weight. High exposures to the benzene and other organic solvents would be likely to occur during intermittent tasks and or processes rather than continuous jobs such as sampling, repair, inspection, and loading/unloading jobs. The work time for these jobs is various. However, most of work time is very short and the representative time interval is 15 minutes. Thus, it is preferable to do exposure assessment for 15 minute time weighted average which is known as a short time exposure level(STEL) by ACGIH rather than for 8-hours TWA. It is particularly significant to the exposure monitoring for benzene since it has been known that the exposure rate plays an important role to provoke the leukemia. Due to the large variations, a number of processes/tasks, the traditional sampling technique for organic solvents with the use of the charcoal and sampling pumps is not appropriate. Limited number of samples can be obtained due to the shortage of sampling pumps. Passive samplers can eliminate these limitations. However, low sampling rates resulted in collection of small amount of the target analysts in the passive samplers. This is originated the nature of passive samplers. Field applications were made with use of passive samplers to compare with the charcoal tube methods for 15 minutes. Gasoline loading processes to the tank lorry trucks at the loading stations in the petroleum products storage area. Good agreements between the results of passive samplers and those of the charcoal tubes were achieved. However, it was found that special cautions were necessary during the analysis at very low concentration levels.

• 한국융합학회논문지
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• 제11권11호
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• pp.89-94
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• 2020

자기공명영상 고주파를 차단할 수 있는 금속물질 중 자장에 영향을 받지 않으며 가격이 저렴하고 주위에서 쉽게 구할 수 있는 알루미늄 호일을 이용하여 차폐체를 제작하였다. Phantom 실험 결과 over-sampling 기법을 적용한 경우 적용 전보다 aliasing artifact가 약 94% 감소하였고, 알루미늄 차폐체를 적용한 경우도 적용 전 보다 약 92% 감소하였다. 그리고 scan time도 over-sampling 기법의 경우 적용 전보다 약 3배이상 증가한 반면, 알루미늄 차폐체의 경우는 적용 전과 변화가 없는 것으로 나타났다. 따라서 알루미늄 호일을 이용한 차폐체도 기존의 흡수재 및 차폐체들과 마찬가지로 scan time의 증가 없이 aliasing artifact를 효율적으로 제거할 수 있는 것을 확인하였다.

• Journal of Korean Society for Atmospheric Environment
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• 제24권E1호
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• pp.1-11
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• 2008

Mercury (Hg), which is mainly emitted from coal-fired power plants, remains one of the most toxic compounds to both humans and ecosystems. Hg pollution is not a local or regional issue, but a global issue. Hg compounds emitted from anthropogenic sources such as coal-fired power plants, incinerators, and boilers, can be transported over long distances. Since the last decade, many European countries, Canada, and especially the United States, have focused on technology to control Hg emissions. Korea has also recently showed an interest in managing Hg pollution from various combustion sources. Previous studies indicate that coal-fired power plants are one of the major sources of Hg in Korea. However, lack of Hg emission data and feasible emission controls have been major obstacles in Hg study. In order to achieve effective Hg control, understanding the characteristics of current Hg sampling methods and control technologies is essential. There is no one proven technology that fits all Hg emission sources, because Hg emission and control efficiency depend on fuel type, configuration of air pollution control devices, flue gas composition, among others. Therefore, a broad knowledge of Hg sampling and control technologies is necessary to select the most suitable method for each Hg-emitting source. In this paper, various Hg sampling methods, including wet chemistry, dry sorbents trap, field, and laboratory demonstrated control technologies, and international regulations, are introduced, with a focus on coal-fired power plants.

• 한국전자파학회논문지
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• 제26권1호
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• pp.88-93
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• 2015

대규모 물체의 RCS(Radar Cross Section)값을 임의의 각도에서 예측하기 위해, 미리 계산된 샘플링 지점 외 각도에서는 보간법을 사용한다. 대규모 물체의 경우, RCS 데이터베이스를 구성하기 위해 많은 입사각도에서 RCS값을 계산해야 한다. 이는 수치적으로 시간이 많이 걸려, 실질적으로 필요한 모든 입사각에서 RCS값을 미리 계산하기는 어렵다. 그러므로 가능한 적은 샘플을 이용하여 데이터베이스를 구축하고, 이를 보간하여 RCS값을 예측하는 방법이 필요하다. 본 논문에서는 계산된 RCS를 임의의 각도에서 예측하기 위한 보간법으로 Sinc 함수 및 VSH(Vector Spherical Harmonics) 함수를 이용한 방법을 고려하고, 그 정확성을 시뮬레이션을 통하여 검증한다.

• 융합보안논문지
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• 제21권3호
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• pp.57-66
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• 2021

침입탐지 분야에서 딥러닝과 머신러닝을 이용한 탐지성능이 검증되면서 이를 활용한 사례가 나날이 증가하고 있다. 하지만, 학습에 필요한 데이터 수집이 어렵고, 수집된 데이터의 불균형으로 인해 머신러닝 성능이 현실에 적용되는데 어려움이 있다. 본 논문에서는 이에 대한 해결책으로 불균형 데이터 처리를 위해 t-SNE 시각화를 이용한 혼합샘플링 기법을 제안한다. 이를 위해 먼저, 페이로드를 포함한 침입탐지 이벤트에 대해서 특성에 맞게 필드를 분리한다. 분리된 필드에 대해 TF-IDF 기반의 피처를 추출한다. 추출된 피처를 기반으로 혼합샘플링 기법을 적용 후 t-SNE를 이용한 데이터 시각화를 통해 불균형 데이터가 처리된 침입탐지에 최적화된 데이터셋을 얻게 된다. 공개 침입탐지 데이터셋 CSIC2012를 통해 9가지 샘플링 기법을 적용하였으며, 제안한 샘플링 기법이 F-score, G-mean 평가 지표를 통해 탐지성능이 향상됨을 검증하였다.

• Journal of Biosystems Engineering
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• 제33권4호
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• pp.260-268
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• 2008

Optical diffuse reflectance sensing has potential for rapid and reliable on-site estimation of soil properties. For good results, proper calibration to measured soil properties is required. One issue is whether it is necessary to develop calibrations using samples from the specific area or areas (e.g., field, soil series) in which the sensor will be applied, or whether a general "factory" calibration is sufficient. A further question is if specific calibration is required, how many sample points are needed. In this study, these issues were addressed using data from 42 paddy fields representing 14 distinct soil series accounting for 74% of the total Korean paddy field area. Partial least squares (PLS) regression was used to develop calibrations between soil properties and reflectance spectra. Model evaluation was based on coefficient of determination ($R^2$) root mean square error of prediction (RMSEP), and RPD, the ratio of standard deviation to RMSEP. When sample data from a soil series were included in the calibration stage (full information calibration), RPD values of prediction models were increased by 0.03 to 3.32, compared with results from calibration models not including data from the test soil series (calibration without site-specific information). Higher $R^2$ values were also obtained in most cases. Including some samples from the test soil series (hybrid calibration) generally increased RPD rapidly up to a certain number of sample points. A large portion of the potential improvement could be obtained by adding about 8 to 22 points, depending on the soil properties to be estimated, where the numbers were 10 to 18 for pH, 18-22 for EC, and 8 to 22 for total C. These results provide guidance on sampling and calibration requirements for NIR soil property estimation.