본 연구에서는 실내 실험의 효율성을 확보하기 위해 실제 콘크리트 슬래브를 1/6 크기로 축소한 실험체를 제작하고 구조 거동 분석을 위한 계측 및 해석 방법을 수립하였다. 축소 슬래브에 스트레인 게이지와 LVDT를 설치하여 변형률과 처짐을 계측하고 3차원 유한요소해석 결과와 비교 분석하였다. 분석 결과 변형률의 경우 60mm 스트레인 게이지의 계측값이 해석값 과 가장 유사한 경향을 보였으며 처짐은 모든 LVDT에서 해석 결과와 유사하게 나타났다. 이를 통해 축소 슬래브의 처짐 거동을 정밀하게 계측 가능함을 확인하였으며 변형률 계측 시에는 슬래브 축소 비율에 따른 단순 비례 크기의 센서 선정이 아닌 해석 기반 검증이 필요함을 확인하였다. 본 연구에서 제안한 축소 슬래브 거동 분석 방법은 다양한 축소 비율의 콘크 리트 슬래브 실내 실험에 효과적으로 활용될 수 있을 것이다.

In order to study the noise propagation characteristics and noise evaluation of military helicopters, this study conducted continuous noise monitoring of 20 noise-sensitive areas within 3 km around a military heliport in South Korea over a period of 5 days, and the results showed that the noise values at the 20 measurement points around the military heliport were lower than the limit of WECPNL 75 stipulated in the standard for civil airports, and it was found that the maximum noise level had the greatest influence on the evaluation indexes. The study found that the maximum noise level has the greatest influence on the evaluation index. At the same time, the analysis of the noise source spectrum shows that the helicopter noise is dominated by low and medium frequencies, and the low-frequency noise is more irritating in a short period of time.

Tire noise is one of major causes of vehicle noise, and urban traffic noise pollution is becoming more and more serious as the number of vehicles continues to increase. Especially in the age of electric vehicles, improvement of tire noise is getting more important. Especially during highway driving, the noise from the tire became the main noise source of the vehicle. Tire noise is generated from the mutual friction between the tire and the road surface when driving. In this paper, we analyze various factors affecting tire noise generation, reduce environmental noise pollution, and increase ride comfort. The EU carried out the EC 1222/2009 tire labeling system in 2012, which was a severe blow to the tire exporting countries of all countries due to the stringent demand for tire noise. In this paper, 5 tires from 3 countries were selected and selected as test subjects. The purpose of this study is to analyze the noise of road / tire noise according to road condition of each tire.

Exposure to extremely low frequency (ELF) electromagnetic fields (EMFs) from power transmission and distribution facilities has gained increasing attention with rising power demand driven by artificial intelligence (AI). This study proposes a practical EMF measurement method suitable for domestic power facility environments in Korea. Field measurements were conducted on 345 kV and 154 kV overhead transmission lines and 22.9 kV distribution lines based on ICNIRP guidelines and international standards IEC 62110 and IEEE Std 644. Measurements were performed at the maximum sag point at different lateral distances and representative heights. The results show that EMF levels were highest directly beneath the conductors and decreased rapidly with distance, while all measured values remained well below domestic exposure limits.

우리나라의 선박부문 대기오염물질 배출량 산정에 활용되는 배출계수는 유럽환경청의 가이드라인에서 제시하는 수치를 적용 하고 있다. 유럽환경청 가이드라인에서 제시하고 있는 수치는 2000년대 초에 발간된 문헌자료 기반의 배출계수로 선박부문의 다양한 규 제가 시행중인 현재의 배출상황과는 차이가 존재한다. 이에, 본 연구에서는 항만 입출항 선박현황을 조사하여, 여객선, 벌크선, 컨테이너 선, 탱커선 등 주요 선종을 대상으로 배기가스 실측 분석연구를 수행하였다. 배기가스 내 5종의 물질(CO2, CO, NOx, SOx, BC)을 대상으로 선박 운항형태별(운항모드,접안모드), 유종별(경유, 벙커유) 배출계수를 제시하였다. 실측기반의 배출계수를 활용하여 대기오염물질을 산 정하는 경우, 배출량 통계 정합성 확보에 기여할 수 있으며, 정확한 배출량 산출을 토대로 국가 대기환경개선 목표 달성에 이바지할 것으 로 기대된다.

목적: 본 연구의 목적은 고화질로 인쇄된 사진 형태의 모형안을 이용하여 실시간 영상 기반 안구운동 측정 장비를 개발하고, 반복 측정 실험을 통해 동공 중심 검출 알고리즘의 안정성과 신뢰도를 정량적으로 평가하는 것이다. 기존 상용 eye-tracking 시스템에 비해 저비용 하드웨어와 오픈소스 소프트웨어만으로 구축 가능한 장비의 초기 성능을 검증하고자 하였다. 방법 : XIMEA 고속 카메라를 기반으로 적외선 조명 및 실시간 영상 처리 알고리즘을 구성하여 동공 영역을 검 출하고 중심 좌표(x, y)를 추적하였다. 모형안을 고정된 거리에서 촬영한 후, 동일한 환경에서 10회 반복 측정을 수행하였다. 각 반복 측정은 900프레임으로 구성되었으며, 총 9,000프레임의 동공 영상 데이터를 수집하였다. 동 공 중심 검출 성공률을 산출하였으며, 반복 측정 간 중심 좌표의 변동성을 표준편차로 정량화하여 알고리즘의 안정 성을 평가하였다. 결과 : 총 9,000프레임 중 동공 중심 검출 성공률은 평균 97.3%를 나타냈다. 반복 측정 간 중심 좌표의 변동성 은 x축 표준편차 0.46±0.05 pixel, y축 표준편차 0.52±0.04 pixel로 측정되었으며, 모든 조건에서 중심 좌표의 표준편차가 1 pixel 미만을 유지하였다. 시간 분포 시계열 분석 결과, 중심 좌표는 특정 방향으로의 점진적인 위치 편향이 거의 관찰되지 않았으며, 중심 주변에 밀집된 분포를 보였다. 결론 : 본 연구에서 개발한 실시간 안구운동 측정 장비는 모형안 기반 반복 측정 실험에서 높은 동공 검출 성공 률과 우수한 반복 측정 안정성을 보여주었다. 저비용 장비 구성과 자유로운 알고리즘 수정 가능성은 연구 단계의 eye-tracking 시스템 개발에 유리한 장점을 제공하며, 향후 사람 대상 연구 이전의 초기 장비 검증 모델로 활용 가능하다. 또한 동공 중심뿐만 아니라 동공 지름 변화, 홱보기 검출 등 다양한 시기능 분석 지표로 확장할 수 있는 기술적 기반을 마련하였다.

Organic carbon (OC) and elemental carbon (EC) in PM2.5 influence regional climate change by scattering and absorbing solar radiation. Recent attention has focused on the long-range transport of OC and EC to high-altitude regions due to their potential role in accelerating spring snowmelt. Although subalpine and alpine areas account for only about 1% of South Korea, these high-elevation zones are highly vulnerable to climate change and provide important insights into how ecosystems may respond and adapt in the future. We collected 29 PM2.5 samples near Nogodan Peak (1,440 m a.s.l.) in Jirisan National Park and 10 samples at Seoul National University (91 m a.s.l.) between March 2022 and April 2024 to quantify OC and EC concentrations. The mean concentrations and standard deviations of OC and EC were 2.0±1.4 and 0.2±0.1 μg m-3 in Jirisan, and 3.6±0.9 and 0.3±0.2 μg m-3 in Seoul, respectively. These concentrations are lower than previously reported values across ~20 sites in South Korea, likely due to the national reduction in PM2.5 during the study period. Given these lower concentrations, the effect of EC on snowmelt might have been small in Jirisan. High OC/EC ratios (Jirisan: 22.1; Seoul: 12.5) may reflect biomass burning or the formation of secondary organic aerosols. As biomass burning is projected to increase under future climate scenarios and may alter the source and composition of carbonaceous aerosols, long-term research is essential to better understand their potential impacts on high-altitude ecosystems.

This study proposes a novel diagnostic methodology combining mobile measurement using selected ion flow tube mass spectrometry (SIFT-MS) and explainable artificial intelligence (XAI) to effectively monitor and diagnose localized highozone (O3) events in industrial complexes. The methodology was applied to a highconcentration ozone episode (maximum 94.0 ppb) observed in the Hwaseong Bio Valley, an industrial complex. A nonlinear regression model based on the Random Forest algorithm was developed to quantify the contribution of precursor species. Specifically, to precisely diagnose the individual contributions of volatile organic compounds (VOCs), which are critical determinants of ozone formation, a modeling approach centered on VOCs was employed by excluding inorganic precursors (NOx). Contrary to traditional ozone formation potential (OFP) analysis, which prioritized high-reactivity alkenes such as propene, the AI model identified cyclohexane and butanone (MEK) as the key drivers positively correlated with ozone concentration fluctuations. This discrepancy is attributed to the “abundance effect,” where atmospheric partial pressures of organic solvents, extensively emitted from pharmaceutical and bio-industrial processes, overwhelm the differences in chemical reactivity of individual species. The findings suggest that AI techniques can interpret the nonlinearity of complex photochemical reactions based on observational data, serving as a complementary site-specific diagnostic tool to existing property-based assessments (e.g., MIR). Consequently, future air quality policies should shift from uniform regulations to a more targeted approach, utilizing the proposed methodology to establish precise emission tracking and management systems.

This study presents an approach to estimate the measurement uncertainty in food moisture and vitamin C analyses by applying the top-down factor to the bottom-up method, following the GUM (Guide to the expression of uncertainty in measurement) and the EURACHEM guide. Uncertainty sources were identified as measurement repeatability, weight of dish, and pre- and post-drying weight for moisture analysis; and measurement repeatability, weight of sample, final volume of sample, standard solution preparation, and calibration curve for vitamin C analysis. Each source was evaluated as type A or type B, and the combined uncertainty was calculated. The applicability across diverse food matrices was confirmed by assessing the measurement uncertainty using three representative samples, each from a different food group. For the moisture content, the measurement results and expanded uncertainty were 11.76±0.17% for whole wheat, 63.57±0.45% for peeled chestnut, and 91.44±0.15% for green onion. The vitamin C content was 8.56±0.35 mg/100 g for peach, 10.51±1.65 mg/100 g for seaweed fusiforme, and 104.72±3.31 mg/100 g for bell pepper. The proposed approach simplifies the calculations and is applicable across diverse food matrices, facilitating a cost-effective and efficient estimation of the measurement uncertainty in food nutrient analyses and enhancing the reliability of the analytical results.

With the growing interest in generative AI (GenAI) for language assessment, its potential as a rater has been discussed. This study compares trained human raters’ scores with GenAI ratings in assessing L2 pragmatic speaking performance across different task types. Fifty L2 English learners of varying proficiency levels completed pragmatic speaking test items, which were scored by five trained raters and ChatGPT-5. To examine the comparability, many-facet Rasch measurement was employed, focusing on examinees’ abilities, raters’ severity, item difficulty, and rating criteria functioning. Findings indicated a moderate correlation between GenAI and human ratings in terms of examinee ability. Compared to human raters, ChatGPT exhibited higher internal consistency and produced a narrower examinee ability distribution. ChatGPT ratings tended to focus on explicit features, such as specific conditions in real-life pragmatic tasks and formulaic expressions, while showing inconsistency in scoring off-task performances and implicit sociopragmatic dimensions. These findings are discussed in light of the potential of GenAI for low-stakes classroom assessment.

This study aims to validate the feasibility of using LiDAR reflectivity data to quantitatively estimate the retroreflectivity of road lane markings. The goal is to establish the optimal scanning conditions considering the channel position, angle of incidence, and vehicle speed for an accurate and consistent retroreflectivity assessment in mobile environments. Fifteen standard lane marking samples with known retroreflectivity values were scanned using an OS1-128 LiDAR sensor under controlled field conditions. A two-phase experiment was conducted: (1) a speed-based test to assess the influence of vehicle velocity (20-80 km/h) on LiDAR reflectivity measurements, and (2) a channel–angle–distance test using a static testbed to analyze the relationship between retroreflectivity, LiDAR channel position (that is, the angle of incidence), and measurement distance. Ground truth retroreflectivity values were obtained using a high-precision handheld retroreflectometer. Reflectivity measurements showed a strong correlation with standard retroreflectivity values, particularly at scanning angle between 100-115° and distances of 4.9-5.6 m. The coefficient of determination (R2) exceeded 0.97 across optimal conditions. Speedrelated tests confirmed that the LiDAR-based reflectivity remained stable with a minimal RMSE (< 5), even under high-speed driving scenarios. LiDAR sensors provided reliable and contactless estimates of pavement marking retroreflectivity when the channel angle and scanning distance were appropriately selected. The findings demonstrated that channel-specific calibration and incidence angle correction significantly improved the measurement accuracy. This suggests a practical path forward for automated large-scale retroreflectivity monitoring in road asset management systems.

Hydrogen has a wide flammability range and rapidly diffuses in air, making precision detection technology essential to prevent explosion risks and ensure system safety as the adoption of hydrogen infrastructure expands. Polymer materials are employed in such infrastructure to seal high-pressure hydrogen, and reliable measurement techniques capable of quantifying trace amounts of hydrogen permeating or leaking through these materials is necessary. In this study, a hydrogen quantification system combining volumetric analysis with image analysis was utilized to evaluate the hydrogen uptake and diffusivity of HDPE (high-density polyethylene), NBR (nitrile butadiene rubber), and EPDM (ethylene propylene diene monomer) under high-pressure conditions. The results indicated that HDPE and NBR samples containing silica filler exhibited hydrogen uptake behavior consistent with Henry’s law, while EPDM samples with carbon black filler demonstrated additional hydrogen adsorption on the carbon black surface. These research results provide a foundation for more precisely evaluating the permeation and leakage behavior of polymers in high-pressure hydrogen environments, and are expected to contribute to the safe and efficient development of hydrogen infrastructure.

본 연구는 지역 고용 회복력에 대한 이론적 개념과 정책 적용 가능성 을 통합적으로 고찰하였다. 이를 위해 회복력 개념의 유형과 진화를 정 리하고, 고용 수준의 회복 속도뿐 아니라 구조적 전환과 경로 변화까지 포착할 수 있는 측정 방식(민감도 분석, 국가벤치마크 비교, 동태적 변이 할당 분석)을 비교·정리하였다. 특히 산업 구조의 다각화, 정책 개입의 시의성, 지역 주체의 제도적 역량을 핵심 요인으로 포함하는 다층적 분 석 틀을 제시하였다. 이 틀을 바탕으로, 유럽연합(EU)의 Cohesion 정책 사례를 분석하여 회복력 중심 정책 설계의 세 가지 원칙-① 사전 예방적 대응, ②다층적 거버넌스, ③ 장기 전략 수립-을 도출하고, 이를 한국의 고용위기지역 정책과 비교하여 제도적 한계와 개선 방향을 진단하였다. 특히, 한국 정책의 중앙집중성과 단기 대응 구조가 지역의 회복 역량 형 성에 제약이 됨을 비판적으로 검토하고, 지역 단위 전략 수립, 과정성과 구조적 조건을 반영한 복합 지표 체계, 산업전환을 위한 지역 경로 개척 전략의 실증 적용 가능성을 제안하였다.