• Korean Journal of Community Nutrition
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• v.18 no.2
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• pp.154-164
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• 2013

The objectives of this study were to assign reasonability to importance of weight selection issue in key performance indicator for performance evaluation of Centers for Child-care Foodservice Management (CCFSM) developed by using Balanced Scorecard (BSC), to draw key performance indicator (KPI) by perspective and to analyze differences in recognition on importance. From September 25 to October 9, 2012, we conducted a questionnaire-based study via e-mail, targeting chiefs and team leaders of nationwide 21 CCFSMs (43 persons), officials of local governments where CCFSM was established (21 persons), officials of Korea Food and Drug Administration (2 persons) and foodservice management experts (27 persons) in order to estimate the relative importance on 4 perspectives and 14 KPIs and analyzed its results by using 61 collected data. The results showed that relative importance of perspectives was estimated in order of importance as follows: business performance (0.3519), customer (0.3393), resource (0.1557), learning and growth (0.1531). Relative importance of KPIs was in order of importance as follows: Evaluation of sanitary management level in child-care foodservice facilities (0.1327), Level of customer recognition and behavior improvement (0.1153), performances of round visiting inspection on foodservice, sanitary, safety management, and foodservice consulting (0.0913). Our results showed that the recognition differences exist on the relative importance of perspectives and KPIs between officials of CCFSM, KFDA, local government and foodservice management experts. These observations will form the basis for developing evaluation systems, and it is considered that performance indicators developed on this basis will suggest direction of operation which CCFSM will have to perform.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.508-518
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• 2023

This paper presents a new approach for the automatic mapping of discontinuities in a tunnel face based on its 3D digital model reconstructed by LiDAR scan or photogrammetry techniques. The main idea revolves around the identification of discontinuity areas in the 3D digital model of a tunnel face by segmenting its 2D projected images using a deep-learning semantic segmentation model called U-Net. The proposed deep learning model integrates various features including the projected RGB image, depth map image, and local surface properties-based images i.e., normal vector and curvature images to effectively segment areas of discontinuity in the images. Subsequently, the segmentation results are projected back onto the 3D model using depth maps and projection matrices to obtain an accurate representation of the location and extent of discontinuities within the 3D space. The performance of the segmentation model is evaluated by comparing the segmented results with their corresponding ground truths, which demonstrates the high accuracy of segmentation results with the intersection-over-union metric of approximately 0.8. Despite still being limited in training data, this method exhibits promising potential to address the limitations of conventional approaches, which only rely on normal vectors and unsupervised machine learning algorithms for grouping points in the 3D model into distinct sets of discontinuities.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.156-156
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• 2022

Explicitly spatially distributed and reliable data on industrial water demand is very much important for both policy makers and researchers in order to carry a region-specific analysis of water resources management. However, such type of data remains scarce particularly in underdeveloped and developing countries. Current research is limited in using different spatially available socio-economic, climate data and geographical data from different sources in accordance to predict industrial water demand at finer resolution. This study proposes a random forest regression (RFR) model to predict the industrial water demand at 0.50× 0.50 spatial resolution by combining various features extracted from multiple data sources. The dataset used here include National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) night-time light (NTL), Global Power Plant database, AQUASTAT country-wise industrial water use data, Elevation data, Gross Domestic Product (GDP), Road density, Crop land, Population, Precipitation, Temperature, and Aridity. Compared with traditional regression algorithms, RF shows the advantages of high prediction accuracy, not requiring assumptions of a prior probability distribution, and the capacity to analyses variable importance. The final RF model was fitted using the parameter settings of ntree = 300 and mtry = 2. As a result, determinate coefficients value of 0.547 is achieved. The variable importance of the independent variables e.g. night light data, elevation data, GDP and population data used in the training purpose of RF model plays the major role in predicting the industrial water demand.

• Nuclear Engineering and Technology
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• v.54 no.11
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• pp.4209-4214
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• 2022

Precise modelling of the interaction of ions with materials is important for many applications including material characterization, ion implantation in devices, thermonuclear fusion, hadron therapy, secondary particle production (e.g. neutron), etc. In this study, a new approach using the Geant4 toolkit in combination with the Bayesian regularization (BR) learning algorithm of the feed-forward neural network (FFNN) is developed to estimate the range of ions in materials accurately and quickly. The different incident ions at different energies are interacted with the target materials. The Geant4 is utilized to model the interactions and to calculate the range of the ions. Afterward, the appropriate architecture of the FFNN-BR with the relevant input features is utilized to learn the modelled ranges and to estimate the new ranges for the new cases. The notable achievements of the proposed approach are: 1- The range of ions in different materials is given as quickly as possible and the time required for estimating the ranges can be neglected (i.e. less than 0.01 s by a typical personal computer). 2- The proposed approach can generalize its ability for estimating the new untrained cases. 3- There is no need for a pre-made lookup table for the estimation of the range values.

• Proceedings of the Korean Society of Computer Information Conference
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• 2011.06a
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• pp.249-252
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• 2011

이 연구는 중학교 과학 수업의 온 오프라인 혼합 협동학습에 대한 효과를 검증해 보고자 하였다. 연구의 목적을 달성하기 위해 먼저 온 오프라인 혼합 협동학습과 관련된 문헌 고찰을 통해 연구의 수행에 필요한 이론적 기반을 마련하였다. 중학교 1학년 과학 내용 중에서 연구 단원을 선정하여 온 오프라인 혼합 협동학습 모형을 제시하였다. 연구대상은 경기도 김포시에 위치한 'K'중학교 1학년 학생들 중에서 사전 학업성취도 검사와 학습태도 검사에 의해 동질집단으로 확인된 2개 학습 79명이다. 연구대상 중 1개 학습 40명을 실험대상으로 선정하여 온 오프라인 혼합 협동학습의 실험을 실시하고 통제집단에는 기존의 면대면 협동학습을 실시하였으며 실험이 끝난 후 두 집단의 학업성취도 및 학습태도 변화 차이를 비교 분석하였다. 결과 분석은 SPSS Ver.12.0을 이용하였으며 학업성취도는 다변량 분산분석(MANOVA)을 하였고, 학습태도는 독립표본 t검정을 통해 분석하였다. 분석한 연구의 결과 첫째, 중학교 과학 수업에서 온 오프라인 혼합 협동학습은 면대면 협동학습과 학업성취도에서 유의미한 차이가 나타났다. 또한 온 오프라인 혼합 협동학습 실험집단이 면대면 협동학습 통제집단보다 학업성취도의 하위 영역 중 기억 영역에 그 효과성이 두드러짐을 확인하였다. 둘째, 중학교 과학 수업에서 온 오프라인 혼합 협동학습은 면대면 협동학습과 학습태도에서 유의미한 차이가 나타나지 않았다. 연구 결과를 토대로 온 오프라인 혼합 협동학습은 첫째, 학습자들로 하여금 자료 수집, 분석, 정리 단계에서 정보의 공유를 통해 적극적으로 학습을 유도하였다고 예측할 수 있다. 이는 온 오프라인 혼합 협동학습이 면대면 협동학습보다 학업성취도 향상에 효과적인 교수학습 방안으로 제시될 수 있음을 의미한다. 둘째, 중학교 과학수업에서 온 오프라인 혼합 협동학습은 학습자의 학습태도에 효과적이라고 확신할 수 없다. 따라서 학습자의 교과에 대한 학습태도의 향상을 위해서는 교수 학습방법을 다각화하고 교과와 학습목표에 맞는 적절한 학습방법의 지속적 활용이 중요하다고 판단된다.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.463-481
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• 2020

Cutter suction dredgers (CSDs) are widely used in various dredging constructions such as channel excavation, wharf construction, and reef construction. During a CSD construction, the main operation is to control the swing speed of cutter to keep the slurry concentration in a proper range. However, the slurry concentration cannot be monitored in real-time, i.e., there is a "time-lag effect" in the log of slurry concentration, making it difficult for operators to make the optimal decision on controlling. Concerning this issue, a solution scheme that using real-time monitored indicators to predict current slurry concentration is proposed in this research. The characteristics of the CSD monitoring data are first studied, and a set of preprocessing methods are presented. Then we put forward the concept of "index class" to select the important indices. Finally, an ensemble learning algorithm is set up to fit the relationship between the slurry concentration and the indices of the index classes. In the experiment, log data over seven days of a practical dredging construction is collected. For comparison, the Deep Neural Network (DNN), Long Short Time Memory (LSTM), Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and the Bayesian Ridge algorithm are tried. The results show that our method has the best performance with an R² of 0.886 and a mean square error (MSE) of 5.538. This research provides an effective way for real-time predicting the slurry concentration of CSDs and can help to improve the stationarity and production efficiency of dredging construction.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1077-1093
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• 2020

Sea Surface Temperature (SST) is an important environmental indicator that affects climate coupling systems around the world. In particular, coastal regions suffer from abnormal SST resulting in huge socio-economic damage. This study used Long Short Term Memory (LSTM) and Convolutional Long Short Term Memory (ConvLSTM) to predict SST up to 7 days in the south sea region in South Korea. The results showed that the ConvLSTM model outperformed the LSTM model, resulting in a root mean square error (RMSE) of 0.33℃ and a mean difference of -0.0098℃. Seasonal comparison also showed the superiority of ConvLSTM to LSTM for all seasons. However, in summer, the prediction accuracy for both models with all lead times dramatically decreased, resulting in RMSEs of 0.48℃ and 0.27℃ for LSTM and ConvLSTM, respectively. This study also examined the prediction of abnormally high SST based on three ocean heatwave categories (i.e., warning, caution, and attention) with the lead time from one to seven days for an ocean heatwave case in summer 2017. ConvLSTM was able to successfully predict ocean heatwave five days in advance.

• Journal of Korean Home Economics Education Association
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• v.33 no.1
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• pp.169-185
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• 2021

The purpose of this study is to apply the clothing teaching-learning plan from a critical science perspective developed to improve teenager's empowerment, and to examine it's effects. A total of 12 plans of 5 modules(Module A to E) developed from critical science perspective were implemented for four weeks. Second-year students (N 42) of K Middle School located in Y-si, Gyeonggi-do participated in the study in the study, and the survey results were analyzed quantitatively using t-tests. For the quality analysis, The student interview data, action reports and etc. were collected, and qualitative analysis was conducted using empowerment model as the analysis framework. The findings of study are follows. First, two hours each for modules A to D, and four hours for module E were assigned, because module E included an action project. In the action projects by for groups, students were expected to take the lead in conducting the activities such as developing promotional posters, posting opinions online, promoting videos, informing how to make recyclables, and donating to the community. Second, as a result of analyzing the pre-implementation vs post-implementation empowerment scores, a significant difference was found in social-political empowerment (t=-2.06, p<0.05). According to the analysis of student interviews and students project's reports, students were found to become aware of empowerment through the instruction. On the intrapersonal level, positive self-awareness and self-efficacy, and on the interpersonal level, smooth communication and democratic decision-making were confirmed. This study is meaningful in that regular a home economics instruction class from a critical science perspective have made a quantitative and qualitative impact on teenagers' improvement empowerment, providing opportunities to find their roles in the soceity, cooperate with others, and behave responsibly as members of society.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.5
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• pp.431-449
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• 2022

As many tunnels generally have been constructed, various experiences and techniques have been accumulated for tunnel design as well as tunnel construction. Hence, there are not a few cases that, for some usual tunnel design works, it is sufficient to perform the design by only modifying or supplementing previous similar design cases unless a tunnel has a unique structure or in geological conditions. In particular, for a tunnel blast design, it is reasonable to refer to previous similar design cases because the blast design in the stage of design is a preliminary design, considering that it is general to perform additional blast design through test blasts prior to the start of tunnel excavation. Meanwhile, entering the industry 4.0 era, artificial intelligence (AI) of which availability is surging across whole industry sector is broadly utilized to tunnel and blasting. For a drill and blast tunnel, AI is mainly applied for the estimation of blast vibration and rock mass classification, etc. however, there are few cases where it is applied to blast pattern design. Thus, this study attempts to automate tunnel blast design by means of machine learning, a branch of artificial intelligence. For this, the data related to a blast design was collected from 25 tunnel design reports for learning as well as 2 additional reports for the test, and from which 4 design parameters, i.e., rock mass class, road type and cross sectional area of upper section as well as bench section as input data as well as16 design elements, i.e., blast cut type, specific charge, the number of drill holes, and spacing and burden for each blast hole group, etc. as output. Based on this design data, three machine learning models, i.e., XGBoost, ANN, SVM, were tested and XGBoost was chosen as the best model and the results show a generally similar trend to an actual design when assumed design parameters were input. It is not enough yet to perform the whole blast design using the results from this study, however, it is planned that additional studies will be carried out to make it possible to put it to practical use after collecting more sufficient blast design data and supplementing detailed machine learning processes.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1739-1756
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• 2021

Atmospheric nitrogen dioxide (NO₂) is mainly caused by anthropogenic emissions. It contributes to the formation of secondary pollutants and ozone through chemical reactions, and adversely affects human health. Although ground stations to monitor NO₂ concentrations in real time are operated in Korea, they have a limitation that it is difficult to analyze the spatial distribution of NO₂ concentrations, especially over the areas with no stations. Therefore, this study conducted a comparative experiment of spatial interpolation of NO₂ concentrations based on two linear-regression methods(i.e., multi linear regression (MLR), and regression kriging (RK)), and two machine learning approaches (i.e., random forest (RF), and support vector regression (SVR)) for the year of 2020. Four approaches were compared using leave-one-out-cross validation (LOOCV). The daily LOOCV results showed that MLR, RK, and SVR produced the average daily index of agreement (IOA) of 0.57, which was higher than that of RF (0.50). The average daily normalized root mean square error of RK was 0.9483%, which was slightly lower than those of the other models. MLR, RK and SVR showed similar seasonal distribution patterns, and the dynamic range of the resultant NO₂ concentrations from these three models was similar while that from RF was relatively small. The multivariate linear regression approaches are expected to be a promising method for spatial interpolation of ground-level NO₂ concentrations and other parameters in urban areas.