• Nuclear Engineering and Technology
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• v.55 no.1
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• pp.339-352
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• 2023

Prediction of the time-related traits of pebble flow inside pebble-bed HTGRs is of great significance for reactor operation and design. In this work, an image-driven approach with the aid of a convolutional neural network (CNN) is proposed to predict the remaining time of initially loaded pebbles and the time interval of paired flow images of the pebble bed. Two types of strategies are put forward: one is adding FC layers to the classic classification CNN models and using regression training, and the other is CNN-based deep expectation (DEX) by regarding the time prediction as a deep classification task followed by softmax expected value refinements. The current dataset is obtained from the discrete element method (DEM) simulations. Results show that the CNN-aided models generally make satisfactory predictions on the remaining time with the determination coefficient larger than 0.99. Among these models, the VGG19+DEX performs the best and its CumScore (proportion of test set with prediction error within 0.5s) can reach 0.939. Besides, the remaining time of additional test sets and new cases can also be well predicted, indicating good generalization ability of the model. In the task of predicting the time interval of image pairs, the VGG19+DEX model has also generated satisfactory results. Particularly, the trained model, with promising generalization ability, has demonstrated great potential in accurately and instantaneously predicting the traits of interest, without the need for additional computational intensive DEM simulations. Nevertheless, the issues of data diversity and model optimization need to be improved to achieve the full potential of the CNN-aided prediction tool.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.1
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• pp.51-66
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• 2022

SST (Sea Surface Temperature) is based on the atmosphere-ocean interaction, one of the most important mechanisms for the Earth system. Because it is a crucial oceanic and meteorological factor for understanding climate change, gap-free grid data at a specific spatial and temporal resolution is beneficial in SST studies. This paper examined the production of daily SST grid maps from 137 stations in 2020 through the ordinary kriging with variogram optimization and their accuracy assessment. The variogram optimization was achieved by WLS (Weighted Least Squares) method, and the blind tests for the interpolation accuracy assessment were conducted by an objective and spatially unbiased sampling scheme. The four-round blind tests showed a pretty high accuracy: a root mean square error between 0.995 and 1.035℃ and a correlation coefficient between 0.981 and 0.982. In terms of season, the accuracy in summer was a bit lower, presumably because of the abrupt change in SST affected by the typhoon. The accuracy was better in the far seas than in the near seas. West Sea showed better accuracy than East or South Sea. It is because the semi-enclosed sea in the near seas can have different physical characteristics. The seasonal and regional factors should be considered for accuracy improvement in future work, and the improved SST can be a member of the SST ensemble around South Korea.

• Journal of Internet of Things and Convergence
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• v.9 no.3
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• pp.93-98
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• 2023

In this paper, an artificial neural network based on simulated annealing was constructed. The mapping relationship between the parameters of micro-scaled flow channels electrochemical machining and the channel shape was established by training the samples. The depth and width of micro-scaled flow channels electrochemical machining on stainless steel surface were predicted, and the flow channels experiment was carried out with pulse power supply in NaNO3 solution to verify the established network model. The results show that the depth and width of the channel predicted by the simulated annealing artificial neural network with "4-7-2" structure are very close to the experimental values, and the error is less than 5.3%. The predicted and experimental data show that the etching degree in the process of channels electrochemical machining is closely related to voltage and current density. When the voltage is less than 5V, a "small island" is formed in the channel; When the voltage is greater than 40V, the lateral etching of the channel is relatively large, and the "dam" between the channels disappears. When the voltage is 25V, the machining morphology of the channel is the best.

• Journal of Aerospace System Engineering
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• v.16 no.1
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• pp.97-103
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• 2022

An aircraft component can only be mounted on an aircraft if it has been certified to have a structural robustness under flight load conditions. Among the major components of the aircraft, a pylon is a structure that connects external equipment such as an engine, and external attachments with the main wing of an aircraft and transmits the loads acting on it to the main structure of the aircraft. In civil aircraft, when there is an incident of fire in the engine area, the pylon prevents the fire from spreading to the wings. This study presents the results of structural static tests performed to verify the structural robustness of a fuel pylon used to mount external fuel tank in an aircraft. In the main text, we present the test set-up diagram consisting of test fixture, hydraulic pressure unit, load control system, and data acquisition equipment used in the structure static test of the fuel pylon. In addition, we introduce the software that controls the load actuator, and provide a test profile for each test load condition. As a result of the structural static test, it was found that the load actuator was properly controlled within the allowable error range in each test, and the reliability of the numerical analysis was verified by comparing the numerical analysis results and the strain obtained from the structural test at the main positions of the test specimen. In conclusion, it was proved that the fuel pylon covered in this study has sufficient structural strength for the required load conditions through structural static tests.

• Journal of Appropriate Technology
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• v.5 no.2
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• pp.70-81
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• 2019

The garment industry is one of the most labor-intensive manufacturing industries, with its sewing process relying almost entirely on manual labor. Its costs highly depend on the efficiency of this production line and thus is crucial to determine the production rate in real-time for line balancing. However, current production tracking methods are costly and make it difficult for many Small and Medium-sized Enterprises (SMEs) to implement them. As a result, their reliance on manual counting of finished products is both time consuming and prone to error, leading to high manufacturing costs and inefficiencies. In this paper, a production tracking system that uses the sewing machines' energy consumption data to track and count the total number of sewing tasks completed through Convolutional Neural Network (CNN) classifiers is proposed. This system was tested on two target sewing tasks, with a resulting maximum classification accuracy of 98.6%; all sewing tasks were detected. In the developing countries, the garment sewing industry is a very important industry, but the use of a lot of capital is very limited, such as applying expensive high technology to solve the above problem. Applied with the appropriate technology, this system is expected to be of great help to the garment industry in developing countries.

• Journal of agriculture & life science
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• v.50 no.1
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• pp.95-104
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• 2016

The objective of this paper is to develop volume equation of street tree and its carbon stock for urban forest in Seoul. To develop the volume equation by major species in Seoul, data for street trees were obtained from four-species (e.g. Gingko biloba, Platanus occidentalis, Zelkova serrata, and Metasequoia glyptostroboides), which accounted for 79% all street trees in Seoul. This study used a variable based on diameter on breast height and four equation for calculating volume. The coefficient of determination, bias, and root mean square error were used to evaluate the precision of four equations. From these methods, the most suitable equations for Platanus occidentalis was aDb, the other was aD+bD2; coefficient of determination upper on 0.873. From the volume equation developed in this research, the estimated carbon stock were derived as about 33,760tC for four-species of urban forest in Seoul. The results of this paper offered volume equation and carbon stock that present growth information for street trees in urban forestry and these can be made available for evaluating the management for carbon in settlement.

• Journal of Chest Surgery
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• v.56 no.4
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• pp.244-251
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• 2023

Background: Extracorporeal membrane oxygenation (ECMO) has been widely used in patients with cardiorespiratory failure. The serum albumin level is an important prognostic marker in critically ill patients. We evaluated the efficacy of using pre-ECMO serum albumin levels to predict 30-day mortality in patients with cardiogenic shock (CS) who underwent venoarterial (VA) ECMO. Methods: We reviewed the medical records of 114 adult patients who underwent VA-ECMO between March 2021 and September 2022. The patients were divided into survivors and non-survivors. Clinical data before and during ECMO were compared. Results: Patients' mean age was 67.8±13.6 years, and 36 (31.6%) were female. The proportion of survival to discharge was 48.6% (n=56). Cox regression analysis showed that the pre-ECMO albumin level independently predicted 30-day mortality (hazard ratio, 0.25; 95% confidence interval [CI], 0.11-0.59; p=0.002). The area under the receiver operating characteristic curve of albumin levels (pre-ECMO) was 0.73 (standard error [SE], 0.05; 95% CI, 0.63-0.81; p<0.001; cut-off value=3.4 g/dL). Kaplan-Meier survival analysis showed that the cumulative 30-day mortality was significantly higher in patients with a pre-ECMO albumin level ≤3.4 g/dL than in those with a level >3.4 g/dL (68.9% vs. 23.8%, p<0.001). As the adjusted amount of albumin infused increased, the possibility of 30-day mortality also increased (coefficient=0.140; SE, 0.037; p<0.001). Conclusion: Hypoalbuminemia during ECMO was associated with higher mortality, even with higher amounts of albumin replacement, in patients with CS who underwent VA-ECMO. Further studies are needed to predict the timing of albumin replacement during ECMO.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.261-261
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• 2020

물관리의 기본이 되는 연속적인 유량 자료 확보를 위해서는 정확도 높은 수위-유량 관계 곡선식 개발이 필수적이다. 수위-유량 관계곡선식은 모든 수문시설 설계의 기초가 되며 홍수, 가뭄 등 물재해 대응을 위해서도 중요한 의미를 가지고 있다. 그러나 일반적으로 유량 측정은 많은 비용과 시간이 들고, 식생성장, 단면변화 등의 통제특성(control)이 변함에 따라 구간분리, 기간분리와 같은 비선형적인 양상이 나타나 자료 해석에 어려움이 존재한다. 특히, 국내 하천의 경우 자연적 및 인위적인 환경 변화가 다양하여 지점 및 기간에 따라 세밀한 분석이 요구된다. 머신러닝(Machine Learning)이란 데이터를 통해 컴퓨터가 스스로 학습하여 모델을 구축하고 성능을 향상시키는 일련의 과정을 뜻한다. 기존의 수위-유량 관계곡선식은 개발자의 판단에 의해 데이터의 종류와 기간 등을 설정하여 회귀식의 파라미터를 산출한다면, 머신러닝은 유효한 전체 데이터를 이용해 스스로 학습하여 자료 간 상관성을 찾아내 모델을 구축하고 성능을 지속적으로 향상 시킬 수 있다. 머신러닝은 충분한 수문자료가 확보되었다는 전제 하에 복잡하고 가변적인 수자원 환경을 반영하여 유량 추정의 정확도를 지속적으로 향상시킬 수 있다는 이점을 가지고 있다. 본 연구는 머신러닝의 대표적인 알고리즘들을 활용하여 유량을 추정하는 모델을 구축하고 성능을 비교·분석하였다. 대상지역은 안정적인 수량을 확보하고 있는 한강수계의 거운교 지점이며, 사용자료는 2010~2018년의 시간, 수위, 유량, 수면폭 등 이다. 프로그램은 파이썬을 기반으로 한 머신러닝 라이브러리인 사이킷런(sklearn)을 사용하였고 알고리즘은 랜덤포레스트 회귀, 의사결정트리, KNN(K-Nearest Neighbor), rgboost을 적용하였다. 학습(train) 데이터는 입력자료 종류별로 조합하여 6개의 세트로 구분하여 모델을 구축하였고, 이를 적용해 검증(test) 데이터를 RMSE(Roog Mean Square Error)로 평가하였다. 그 결과 모델 및 입력 자료의 조합에 따라 3.67~171.46로 다소 넓은 범위의 값이 도출되었다. 그 중 가장 우수한 유형은 수위, 연도, 수면폭 3개의 입력자료를 조합하여 랜덤포레스트 회귀 모델에 적용한 경우이다. 비교를 위해 동일한 검증 데이터를 한국수문조사연보(2018년) 내거운교 지점의 수위별 수위-유량 곡선식을 이용해 유량을 추정한 결과 RMSE가 3.76이 산출되어, 머신러닝이 세분화된 수위-유량 곡선식과 비슷한 수준까지 성능을 내는 것으로 확인되었다. 본 연구는 양질의 유량자료 생산을 위해 기 구축된 수문자료를 기반으로 머신러닝 기법의 적용 가능성을 검토한 기초 연구로써, 국내 효율적인 수문자료 측정 및 수위-유량 곡선 산출에 도움이 될 수 있을 것으로 판단된다. 향후 수자원 환경 및 통제특성에 영향을 미치는 다양한 영향변수를 파악하기 위해 기상자료, 취수량 등의 입력 자료를 적용할 필요가 있으며, 머신러닝 내 비지도학습인 딥러닝과 같은 보다 정교한 모델에 대한 추가적인 연구도 수행되어야 할 것이다.

• Journal of Korean Academy of Nursing
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• v.53 no.3
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• pp.295-308
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• 2023

Purpose: This study evaluated the validity and reliability of Shively and colleagues' self-efficacy for HIV disease management skills (HIV-SE) among Korean participants. Methods: The original HIV-SE questionnaire, comprising 34 items, was translated into Korean using a translation and back-translation process. To enhance clarity and eliminate redundancy, the author and expert committee engaged in multiple discussions and integrated two items with similar meanings into a single item. Further, four HIV nurse experts tested content validity. Survey data were collected from 227 individuals diagnosed with HIV from five Korean hospitals. Construct validity was verified through confirmatory factor analysis. Criterion validity was evaluated using Pearson's correlation coefficients with the new general self-efficacy scale. Internal consistency reliability and test-retest were examined for reliability. Results: The Korean version of HIV-SE (K-HIV-SE) comprises 33 items across six domains: "managing depression/mood," "managing medications," "managing symptoms," "communicating with a healthcare provider," "getting support/help," and "managing fatigue." The fitness of the modified model was acceptable (minimum value of the discrepancy function/degree of freedom = 2.49, root mean square error of approximation = .08, goodness-of-fit index = .76, adjusted goodness-of-fit index = .71, Tucker-Lewis index = .84, and comparative fit index = .86). The internal consistency reliability (Cronbach's α = .91) and test-retest reliability (intraclass correlation coefficient = .73) were good. The criterion validity of the K-HIV-SE was .59 (p < .001). Conclusion: This study suggests that the K-HIV-SE is useful for efficiently assessing self-efficacy for HIV disease management.

• Journal of the Society of Naval Architects of Korea
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• v.60 no.4
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• pp.222-230
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• 2023

In the present work, the scale effect on the Boil-Off Rate (BOR) was investigated based on an analytical method to systematically evaluate the thermal performance of a Liquefied Natural Gas (LNG) Cargo Containment System (CCS). A two-dimensional thermal resistance network model was developed to accurately estimate the heat ingress into the CCS from the outside. The analysis was performed for the KC-1 LNG membrane tank under the IGC and USCG design conditions. The ballast compartment of both the LNG tank and cofferdam was divided into six sections and a thermal resistance network model was made for each section. To check the validity of the developed model, the analysis results were compared with those from existing literature. It was shown that the BOR values under the IGC and USCG design conditions were agreed well with previous numerical results with a maximum error of 1.03% and 0.60%, respectively. A SDR, the scale factor of the LNG CCS was introduced and the BOR, air temperature of the ballast compartment, and the surface temperature of the inner hull were obtained to examine the influence of the SDR on the thermal performance. Finally, a correlation for the BOR was proposed, which could be expressed as a simple formula inversely proportional to the SDR. The proposed correlation could be utilized for predicting the BOR of a full-scale LNG tank based on the BOR measurement data of lab-scale model tanks.