River discharge is a crucial indicator of climate change and requires accurate and continuous estimation for effective water resource management and environmental monitoring. This study used satellite gravimetry data to estimate river discharge in major basins with high discharge volumes, specifically the Congo and Orinoco basins. By enhancing the spatial resolution of gravity data through advanced post-processing techniques, including forward modeling and river routing schemes, we effectively detected changes in the water mass stored within river channels. Additionally, signals from surrounding regions were statistically removed using the Empirical Orthogonal Function (EOF) analysis to isolate river-specific discharge signals. These refined signals were then converted into river discharge data through seasonal calibration using the modeled discharge data. Our results demonstrate that this method yields accurate and reliable discharge estimates comparable to in-situ measurements from gauge stations, even without ground-based surveys such as an Acoustic Doppler Current Profiler (ADCP) field campaigns. This research highlights the significant potential of satellite-based gravity data as an alternative to traditional ground surveys, providing practical information on the hydrological status of regions associated with large-scale river systems.

본 연구에서는 호흡동조화기법의 대안으로 딥러닝 자유호흡기법에서 b-value 별 겉보기확산계수 값을 평가하고 확 산강조영상과 겉보기확산계수 지도의 해부학적 일치성을 분석하여 적절한 여기횟수 값을 알아보고자 하였다. 연구 방법은 2023년 7월부터 2024년 1월까지 간 자기공명영상 검사가 의뢰된 성인 남녀 35명을 대상으로 하였고 사용 장비는 Magnetom Skyra 3.0T(Siemens, Germany)를 이용하였다. 자유호흡기법의 비교를 위해 b-value 50, 400, 800(s/mm2)의 여기횟수를 각각 딥러닝 호흡동조화기법에서 2,3,4으로 딥러닝을 이용하지 않은 일반 자유호 흡기법에서 4,6,8으로 검사하였다. 딥러닝을 추가한 일반 자유호흡기법에서는 1,2,3 여기횟수, 2,3,4 여기횟수, 3,5,6 여기횟수, 4,6,8 여기횟수로 변화하였다. 연구 결과 딥러닝 자유호흡기법에서 간의 좌엽과 우엽, 담낭의 평균 겉보기확산계수 값은 딥러닝 호흡동조화기법과 비교하여 모두 통계적 유의성을 확인하였다. 한편 정성적 평가의 해 부학적 일치성을 분석한 결과 딥러닝 자유호흡기법의 3,5,6 여기횟수와 4,6,8 여기횟수에서 가장 높은 점수를 얻었 으며 검사 시간에서는 딥러닝 호흡동조화기법과 비교하여 약 51%, 40% 감소하였다. 따라서 간 진단에 있어 딥러닝 자유호흡기법에서 b-value 별 적절한 여기횟수 값을 이용한다면 겉보기확산계수 지도의 정확도 유지와 함께 검사 시간을 감소시킬 수 있어 임상적으로 유용한 검사가 될 것으로 사료된다.

PURPOSES : In this study, a preliminary study on the optimal clustering techniques for the preprocessing of pavement management system (PMS) data was conducted using K-means and mean-shift techniques to improve the correlation between the dependent and independent variables of the pavement performance model. METHODS : The PMS data of Jeju Island was preprocessed using the K-means and mean-shift algorithms. In the case of the K-means method, the elbow method and silhouette score were used to determine the optimal number of clusters (K). Moreover, in the case of the mean-shift method, Scott’s rule of thumb and Silverman’s rule of thumb were used to determine the optimal cluster bandwidth. RESULTS : The optimal cluster sets were selected for the rut depth (RD), annual average daily traffic (AADT), and annual maximum temperature (AMT) for each clustering technique, and their similarities with the original data were investigated. Additionally, the correlation improvement between the dependent and independent variables were investigated by calculating the clustering score (CS). Consequently, the K-means method was selected as the optimal clustering technique for the preprocessing of PMS data. The K-means method improved the correlations of more variables with the dependent variable compared to the mean-shift method. The correlations of the variables related to high temperature—such as the annual temperature change, summer days, and heat wave days—were improved in the case wherein the AMT, a climate factor, was used as an independent variable in the K-means clustering method. CONCLUSIONS : The applicability of the clustering methods to preprocessing of PMS data was identified in this study. Improvements in the pavement performance prediction model developed using traditional statistical methods may be identified by developing a model using clustering techniques in a future study.

In order to maximize the function and increase the compatibility of silicone hydrogel lens, this study compared and analyzed the properties of Amino modified silicone oil using mini and microemulsion technique, respectively. Optical and physical properties were evaluated by spectral transmittance, refractive index, water content, oxygen transmittance and contact angle measurements to evaluate the performance of the manufactured hydrogel lens. The spectral transmittance results revealed the copolymerization method lens showed 31 % of the visible light area, which did not satisfy the basic optical properties. However, the lens using the mini and microemulsion materials showed more than 90 % of the visible light area, satisfying the optical characteristics. In addition, all physical properties were superior to a basic hydrogel lens. The mini and microemulsion techniques effectively improved the stability and function of the ophthalmic hydrogel lens and are considered a promising ways of manufacturing an ophthalmic hydrogel contact lens with increased compatibility and stability.

This study pioneers a transformative approach of discarded orange peels (Citrus sinensis) into highly porous carbon, demonstrating its potential application in energy storage devices. The porous carbon structure offers a substantial surface area, making it conducive for effective ion adsorption and storage, thereby enhancing capacitance. The comprehensive characterization, including X-ray diffraction, Fourier transform infrared, Raman spectroscopy, field emission scanning electron microscopy, and XPS verifies the material’s suitability for energy storage applications by confirming its nature, functional groups, graphitic structure, porous morphology and surface elemental compositions. Moreover, the introduced plasma treatment not only improves the material’s intensity, bending vibrations, and morphology but also increases capacitance, as evidenced by galvanostatic charge–discharge tests. The air plasma-treated carbon exhibits a noteworthy capacitance of 1916F/g at 0.05A/g in 2 M KOH electrolyte. long term cyclic stability has been conducted up to 10,000 cycles, the calculated capacitance retention and columbic efficiency is 92.7% and 97.6%. These advancements underscore the potential of utilizing activated carbon from agricultural waste in capacitors and supercapatteries, offering a sustainable solution for energy storage with enhanced performance characteristics.

본 논문에서는 신뢰성 기반 최적설계(RBDO)에서 성능함수의 비선형성을 고려한 효율적인 차원감소법(DRM)을 제안한다. 차원감 소법은 적분직교점과 가중치를 사용하여 1차 신뢰도법(FORM) 보다 더 정확하게 신뢰도를 평가하는 반면 성능함수를 추가로 해석해 야하기 때문에 적분직교점의 개수가 증가하면 효율성이 저해된다. 본 논문에서는 신뢰성 기반 최적설계에서 성능함수의 비선형도를 평가하고, 비선형도에 따라 적분직교점의 수를 결정하는 기준을 제안한다. 이를 통해 신뢰성 기반 최적설계가 진행될 때 반복마다 적 분직교점의 수를 조절하여 차원감소법의 정확도는 유지하면서 계산의 효율성은 개선하는 방안을 제안한다. 성능함수의 비선형도 평 가는 최대가능목표점(MPTP) 탐색에 사용한 벡터 사이의 각도를 통해 이루어지며, 수치 테스트를 통해 비선형도에 따른 적절한 적분 직교점의 수를 도출하였다. 2차원 수치예제를 통해 개발된 방법이 차원감소법이나 몬테카를로 시뮬레이션(MCS)의 정확도는 유지하 면서 효율성이 향상된다는 것을 확인하였다.

This study aimed to provide an accurate estimate of sodium intake from jangajji by examining the changes in sodium content according to the type of jangajji and the length of storage period, specifically differentiating between the solid ingredients and the seasoning liquid. It focused on six types of jangajji: chili pepper, perilla leaf, onion, radish, garlic scape, and cucumber. The sodium content in the solid ingredients and the seasoning was measured using a salinometer and ICP-AES. The results indicated that across all types of jangajji, the seasoning liquid consistently contained significantly higher levels of sodium than the solid ingredients. When comparing the sodium content measured by ICP-AES with that from a salinometer, the salinometer readings were significantly lower for both the solid ingredients and the seasoning liquid in all types of jangajji. Additionally, when comparing the sodium content of the solid ingredients with that listed in the nation’s representative nutritional databases, a substantial discrepancy was noted, with some cases potentially overstating the actual sodium intake from jangajji. Overall, this study suggests that an urgent review should be conducted to identify and resolve the causes of such discrepancies and accurately estimate the actual sodium intake from jangajji.

In this study, we present a sewer pipe inspection technique through a combination of active sonar technology and deep learning algorithms. It is difficult to inspect pipes containing water using conventional CCTV inspection methods, and there are various limitations, so a new approach is needed. In this paper, we introduce a inspection method using active sonar, and apply an auto encoder deep learning model to process sonar data to distinguish between normal and abnormal pipelines. This model underwent training on sonar data from a controlled environment under the assumption of normal pipeline conditions and utilized anomaly detection techniques to identify deviations from established standards. This approach presents a new perspective in pipeline inspection, promising to reduce the time and resources required for sewer system management and to enhance the reliability of pipeline inspections.

In this study, an evaluation system that can be used to evaluate the feasibility of developing and supplying hydrothermal energy for the operation of large-scale complex facilities was developed. To this end, this study derived factors to be considered when selecting a location for the use of hydrothermal energy using raw water from multi-purpose dams and regional water supply systems through literature survey and expert interviews. The evaluation indicators derived from this study are divided into four sectors: hydrothermal energy utilization factors, location factors, planning factors, and disaster safety factors, and are composed of 10 mid-level indicators and 34 detailed planning indicators. The relative importance of all factors was derived using the Analytic Hierarchy Process (AHP) technique, and the developed evaluation indicators and relative importance were applied to four multi-purpose dam regions in the country. As a result, it was found that in the development and use of hydrothermal energy utilizing regional raw water supply line the urban planning conditions of the supply site can have a greater impact on the location selection results than the hydrothermal energy development itself. Due to the characteristics of the evaluation indicators developed in this study and their nature as comprehensive indicators, it is believed that the results should be applied to determine the overall adequacy of site selection in the early stages of hydrothermal energy development. In the future, it is believed that it will be necessary to analyze the problems in supplying and operating hydrothermal energy using raw water from multi-purpose dams and regional water resources. Based on the analysis the evaluation system developed in this study is expected to be improved and supplemented.

최근 광주 화정아이파크, 인천 검단 신도시 아파트 사고 등 국내에서 건축물 안전사고가 잇따라 발 생하고 있다. 시공 중 발생한 구조물 붕괴로 인해 인명·재산 피해가 수반된 대형 건설사고가 다수 발 생하였다. 건축물 안전사고의 발생 원인으로 무단 구조변경, 설계 및 시공 시 철근 누락 등이 제시되 면서 부실 감리에 대한 우려가 증가하고 있다. 하지만 현실적으로 건설 현장의 모든 장소에서 감리직 원이 상주하며 확인하는 것은 불가능하며 시공 정확도 검사 역시 감리자의 경험에 근거하여 육안 판 독 및 일부 수작업 계측으로 진행되고 있다. 감리 작업의 효율성을 높이기 위해 최근에는 3D 스캐너, Depth Camera 등을 구조 감리 기법 연구가 진행되고 있다. 철근 길이와 철근 배근 간격에 대한 연구 는 많이 진행되었지만 철근 직경의 검출 정확도는 아직 미흡한 상황이며, 특히 직경이 작은 D10과 D13의 구별에서는 한계를 나타내고 있다. 따라서 본 연구에서는 접근성이 용이한 스마트폰을 사용하 여 영상을 획득하고 이를 기반으로 3D 포인트 클라우드를 제가한 다음 철근 직경, 철근 길이, 철근 배근 간격 등의 자동 검측 기술을 개발하고 건설현장에서의 적용 가능성을 확인하고자 한다. 검증을 위한 실험체는 길이 2100mm, 폭 195mm, 높이 395mm의 철근 조립 상태의 보이다. 포인트 클라우드 제작을 위한 영상 촬영은 iPhone SE (3rd generation)을 사용하였다. 이후 MATLAB과 METASHAPE 를 사용하여 포인트 클라우드를 생성하고 Computer Vision과 Image Processing 기술을 활용하여 구 하고자 하는 철근 정보를 자동 검출하였다. 이후 실제 측정한 값과 자동 검출한 값을 비교하여 개발한 기법에 대한 적합성을 확인하였다.

Engineering design primarily focuses on product improvement through enhancing existing functionalities, integrating features, or adding new capabilities. In other words, it can be said that more design(adaptive design) changes to existing products based on benchmarking with competing products, differentiation strategies, changes in customer needs, etc. are actually performed rather than developing new products that did not exist before. Especially in the case of custom production, such as ships or buildings, a significant portion of actual design work involves modifying and adjusting past performance data according to the current customer's requirements. Therefore, design methods should be developed in a way that effectively supports these processes. Therefore, in this study, as QFD (Quality Function Deployment) ‘analysis of existing products’ and ‘creation of new alternatives’ is supported in Marine Concept Design with AHP (Analytic Hierarchy Process) techniques such as ‘Value Evaluation in Analysis Work’ and ‘Design Alternative Evaluation’, as a result, basic research was conducted on whether it could be used as a tool to effectively support the flow of the design process.

스마트팜형 시설 딸기에 예찰 없이 작물 정식 초기에 천적을 먼저 적용하는 생태공학적 Natural Enemy in First (NEF) 기법이 총채벌레류 와 진딧물류의 밀도에 미치는 영향을 확인하였다. 대조구는 약제를 처리하여 비교하였다. NEF 처리구에서 총채벌레류와 진딧물류의 천적과 서식 처로 참멋애꽃노린재와 Portulaca sp.를 적용하여 작기 종료시점까지 해충의 밀도를 대조구와 유사하게 효과적으로 관리할 수 있었다.