• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.45 no.4
• /
• pp.210-216
• /
• 2022

The army is concerned about the decrease in enlistment resources due to the low birth rate and the weakening of military combat power due to the shortening of the military service period. Now, the military's quantitative growth is no longer limited and it is a time for qualitative growth. To this end, the Army has been applying the Israeli learning method Havruta to recruit training to improve the quality of training since 2019. After applying Havruta, several scholars have studied the effect of recruit training applying Havruta. As a result, it was verified that recruit training applying Havruta improves the inner motive, creativity, and military service value of trainees. This study investigated how trainees' inner motive, creativity, and military service value affect their satisfaction and achievement. In addition, it was studied whether the effect of recruit training applied with Havruta on achievement differs according to the educational background (high school graduate or higher) and military family (professional soldiers within 4th degree) of the trainees. To this end, a survey was conducted on 472 recruits, and the structural relationship between each variable and the moderating effect were analyzed using the structural equation model. As a result of the study, military service value did not affect training satisfaction. Also, there was a difference in the effect of creativity on training satisfaction according to the educational background of new recruits, and there was a difference in the effect of military service value on training satisfaction and training achievement according to military family members. The purpose of this study is to contribute to the improvement of the army's recruit training development plan and effective training system.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.517-517
• /
• 2023

현재 우리나라에서는 4대강 및 주요 호소 29지점을 대상으로 조류경보제가 시행되고 있으며 조류 경보 단계는 실시간 모니터링지점에서 측정되는 유해 조류의 셀농도를 기반으로 발령 단계가 결정된다. 상수원 구간은 관심, 경계, 조류 대발생, 해제 또는 미발생 총 4구간으로 구성되며, 친수 활동 구간의 경우 조류 대발생을 제외한 3구간으로 구성된다. 현재 시행되는 조류 경보제의 목적은 유해 조류 발생 시 사후 대응 방안 마련에 보다 초점이 맞춰져 있으며 특히, 모니터링 주기 확대 여부, 오염원 관리 방안 마련, 조류 제거 여부 등의 의사 결정 수단으로 사용되고 있다. 하지만 조류 경보 단계에 대한 사전 예측이 가능한 경우 유해 조류의 성장을 억제할 수 있으며 이를 통해 안전하고 깨끗한 수자원을 확보할 수 있다. 본 연구에서는 조류 경보 단계의 사전적 예측을 위해 국가 실시간 측정망에서 제공하는 전국 보 모니터링 종합 정보 자료, 기상측정망 자료, 실시간 보 현황 자료를 활용하여 예측 모델을 구축하였다. 또한, 단계 예측의 정확도를 개선하기 위해 변수 선택 기법을 활용하여 조류 경보 단계에 영향을 미치는 환경변수를 선정하였으며 자료의 불균형으로 인해 모델 학습 과정에서 발생하는 예측 오류를 최소화하기 위해 다양한 샘플링 기법을 적용하여 모델의 성능을 평가하였다. 변수 선택 및 샘플링 기법을 고려하지 않은 원자료를 사용하여 예측 모델을 구축한 결과 관심 단계(Level-1) 및 경보 단계(Level-2)에 대해 각각 50%, 62.5%의 예측 정확도를 보인 반면 비선형 변수 선택 기법 및 Synthetic Minority Over-sampling Technique-Edited Nearrest Neighbor(SMOTE-ENN) 샘플링 기법을 적용하여 구축한 모델에서는 Level-1은 85.7%, Level-2는 75.0%의 예측 정확도를 보였다.

• Journal of Forest and Environmental Science
• /
• v.39 no.2
• /
• pp.105-117
• /
• 2023

Climate change and invasive alien plant species (IAPs) are having a significant impact on mountain ecosystems. The combination of climate change and socio-economic development is exacerbating the invasion of IAPs, which are a major threat to biodiversity loss and ecosystem functioning. Species distribution modelling has become an important tool in predicting the invasion or suitability probability under climate change based on occurrence data and environmental variables. MaxEnt modelling was applied to predict the current suitable distribution of most noxious weed A. adenophora (Spreng) R. King and H. Robinson and analysed the changes in distribution with the use of current (year 2000) environmental variables and future (year 2050) climatic scenarios consisting of 3 representative concentration pathways (RCP 2.6, RCP 4.5 and RCP 8.5) in Bhutan. Species occurrence data was collected from the region of interest along the road side using GPS handset. The model performance of both current and future climatic scenario was moderate in performance with mean temperature of wettest quarter being the most important variable that contributed in model fit. The study shows that current climatic condition favours the A. adenophora for its invasion and RCP 2.6 climatic scenario would promote aggression of invasion as compared to RCP 4.5 and RCP 8.5 climatic scenarios. This can lead to characterization of the species as preferring moderate change in climatic conditions to be invasive, while extreme conditions can inhibit its invasiveness. This study can serve as reference point for the conservation and management strategies in control of this species and further research.

• Journal of Korean Society on Water Environment
• /
• v.39 no.1
• /
• pp.46-60
• /
• 2023

Climate change causes fluctuations in water quality in the aquatic environment, which can cause changes in water circulation patterns and severe adverse effects on aquatic ecosystems in the future. Therefore, research is needed to predict and respond to water quality changes caused by climate change in advance. In this study, we tried to predict the dissolved oxygen (DO), chlorophyll-a, and turbidity of the Paldang reservoir for about two weeks using long short-term memory (LSTM) and gated recurrent units (GRU), which are deep learning algorithms based on recurrent neural networks. The model was built based on real-time water quality data and meteorological data. The observation period was set from July to September in the summer of 2021 (Period 1) and from March to May in the spring of 2022 (Period 2). We tried to select an algorithm with optimal predictive power for each water quality parameter. In addition, to improve the predictive power of the model, an important variable extraction technique using random forest was used to select only the important variables as input variables. In both Periods 1 and 2, the predictive power after extracting important variables was further improved. Except for DO in Period 2, GRU was selected as the best model in all water quality parameters. This methodology can be useful for preventive water quality management by identifying the variability of water quality in advance and predicting water quality in a short period.

• Journal of Engineering Education Research
• /
• v.25 no.6
• /
• pp.93-102
• /
• 2022

Due to COVID-19, all activities in society are emphasized non-face-to-face, and the educational environment is changing without exception. Looking at the results of the survey after conducting non-face-to-face education, there was a lot of rejection of non-face-to-face practical education. The biggest reason was that instructors were not familiar with the non-face-to-face education method, and feedback was not smooth during or after education. In particular, software practice education was not easy to share the software development environment, but communication and feedback on class contents and tasks were important. In particular, if face-to-face and non-face-to-face are alternately variable, it is not easy for practical education to be consistently connected. Even if non-face-to-face hands-on education is changed to face-to-face hands-on education, we will present a plan to use a data sharing system such as question-and-answer, assignment, practice content, and board content so that it can proceed smoothly. This study presents an efficient software education process that can provide learners with a software integrated practice environment based on a shared server, question-and-answer between instructors and learners, and share feedback on tasks. For the verification of the presented process, the effectiveness was confirmed through the survey results by applying the face-to-face/non-face-to-face education process to 220 trainees for 30 months in software education classes such as A university hands-on education, B company new employees, and ICT education courses.

• Journal of Korean Society of Rural Planning
• /
• v.28 no.1
• /
• pp.57-69
• /
• 2022

Crop classification is very important for estimating crop yield and figuring out accurate cultivation area. The purpose of this study is to classify crops harvested in fall in Idam-ri, Goesan-gun, Chungcheongbuk-do by using unmanned aerial vehicle (UAV) images and support vector machine (SVM) model. The study proceeded in the order of image acquisition, variable extraction, model building, and evaluation. First, RGB and multispectral image were acquired on September 13, 2021. Independent variables which were applied to Farm-Map, consisted gray level co-occurrence matrix (GLCM)-based texture characteristics by using RGB images, and multispectral reflectance data. The crop classification model was built using texture characteristics and reflectance data, and finally, accuracy evaluation was performed using the error matrix. As a result of the study, the classification model consisted of four types to compare the classification accuracy according to the combination of independent variables. The result of four types of model analysis, recursive feature elimination (RFE) model showed the highest accuracy with an overall accuracy (OA) of 88.64%, Kappa coefficient of 0.84. UAV-based RGB and multispectral images effectively classified cabbage, rice and soybean when the SVM model was applied. The results of this study provided capacity usefully in classifying crops using single-period images. These technologies are expected to improve the accuracy and efficiency of crop cultivation area surveys by supplementing additional data learning, and to provide basic data for estimating crop yields.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.10a
• /
• pp.297-299
• /
• 2021

Predicting future productivity of tight oil is an important task for analyzing residual oil recovery and reservoir behavior. In general, productivity prediction is made using the decline curve analysis(DCA). In this study, we intend to propose an effective model for predicting future production using deep learning-based recurrent neural networks(RNN), LSTM, and GRU algorithms. As input variables, the main parameters are oil, gas, water, which are calculated during the production of tight oil, and the type curve calculated through various cluster analyzes. the output variable is the monthly oil production. Existing empirical models, the DCA and RNN models, were compared, and an optimal model was derived through hyperparameter tuning to improve the predictive performance of the model.

• Journal of the Korean Child Neurology Society
• /
• v.25 no.3
• /
• pp.204-207
• /
• 2017

Hyperornithinemia-hyperammonemia-homocitrullinuria syndrome (HHH syndrome) is a neurometabolic disorder with highly variable clinical severity ranging from mild learning disability to severe encephalopathy. Diagnosis of HHH syndrome can easily be delayed or misdiagnosed due to insidious symptoms and incomplete biochemical findings, in that case, genetic testing should be considered to confirm the diagnosis. HHH syndrome is caused by biallelic mutations of SLC25A15, which is involved in the urea cycle and the ornithine transport into mitochondria. Here we report a boy with spastic paraplegia and asymptomatic younger sister who have compound heterozygous mutations of c.535C>T (p.R179^*) and c.116C>A (p.T39K) in the SLC25A15 gene. We identified that p.T39K mutation is a novel pathogenic mutation causing HHH syndrome and that p.R179^*, which is prevalent in Japanese and Middle Eastern heritage, is also found in the Korean population.

• The Korean Journal of Applied Statistics
• /
• v.37 no.1
• /
• pp.13-38
• /
• 2024

As economic uncertainties have increased recently due to COVID-19, there is a growing need to quickly grasp private consumption trends that directly reflect the economic situation of private economic entities. This study proposes a method of estimating private consumption in real-time by comprehensively utilizing big data as well as existing macroeconomic indicators. In particular, it is intended to improve the accuracy of private consumption estimation by comparing and analyzing various machine learning methods that are capable of fitting ultra-high-dimensional big data. As a result of the empirical analysis, it has been demonstrated that when the number of covariates including big data is large, variables can be selected in advance and used for model fit to improve private consumption prediction performance. In addition, as the inclusion of big data greatly improves the predictive performance of private consumption after COVID-19, the benefit of big data that reflects new information in a timely manner has been shown to increase when economic uncertainty is high.

• Journal of The Korean Association For Science Education
• /
• v.28 no.8
• /
• pp.848-859
• /
• 2008

The purposes of this study were to investigate the relationship between science achievement and student-related variables in the 2006 National Assessment of Educational Achievement (NAEA), the subjects of which included 3% of students within the entire population of the grades 6, 9 and 10. The results showed that the more they talk with parents, study by themselves, and read the books, the higher the students achieved in science. The science achievement was also significantly and positively related to self-regulated learning, adaptation to school life and attitude toward science. It is implied that the approach of stimulating internal motive such as interest, attitude toward science and human relations is more effective in resulting in the students' higher science achievement than focusing on external attitudes such as forcing good study habits.