본 연구에서는 대학 교수학습센터에서 제공하는 학습지원 프로그램의 성과를 종합적으로 평가하기 위한 BSC(Balanced Score Card) 기반의 성과평가 모형을 개발하고 적용하는 데 있다. 문헌 연구를 통해 성과평 가의 이론적 배경을 조사하고, BSC 모형을 교육 분야에 맞게 수정하여 학습지원 프로그램에 적용 가능한 평가 체계를 설계하였다. 재무, 수요 자, 운영, 프로그램의 네 가지 관점에서 성과평가 지표를 설정하고, 이를 기반으로 대학의 다양한 학습지원 프로그램의 성과를 분석하였다. 분석 결과, 특정 프로그램들이 높은 성과를 보임을 확인하였으며, 동시에 개선 이 필요한 영역을 확인하였다. 개발된 BSC 기반 성과평가 모형은 대학 학습지원 프로그램의 다각도에서의 성과를 평가하는 데 유용하였으며, 프로그램의 강점과 개선점을 명확하게 확인할 수 있었다. 이 연구를 통 하여 대학 교수학습센터가 학습지원 프로그램의 질을 개선하고, 대학 교 육의 질적 향상에 기여하길 기대한다.

작물의 스트레스 조기 진단은 농업에 있어 빠른 대응을 가능하게 해 피해를 경감시킬 수 있어 중요한 기술이다. 기존의 스트레스 진단이 가진 파괴적인 형식의 시료 채집과 양분 분석에 많은 노동력을 필요로 한다는 단점 극복을 위해 새로운 기술 개발이 필요하다. 미래에는 대단위 영상을 이용한 생육 진단 기술에 대한 수요가 높아질 것으로 예상되어 이를 이용한 연구를 진행하였다. 본 연구는 2023년 경상남도 밀양시에 위치한 국립식량과학원 실험 포장에서 수행되었으며, 무인항공기(UAV)를 이용하여 양분 결핍 처리(관행시비, 질소 결핍, 인 결핍, 칼륨 결핍, 무비)에 따른 벼의 생육을 조사했다. UAV를 이용해 생육 기간 중 총 6회에 걸쳐 포장을 촬영하였고, 영상을 기반으로 11개의 식생 지수를 산출하여 기계학습을 통해 양분 결핍을 진단하는 모델을 구축하여 평가했다. 연구 결과에 따르면, 엽록소 함량과 관련된 지수인 NDRE (Normalized Difference Red Edge)가 가장 높은 중요도를 나타내어 벼의 양분 상태를 효과적으로 진단하는 데 유용하다는 것을 확인하였다. 6개의 각 단계별로 모델을 평가하였을 때 모든 단계에서 accuracy가 0.7 이상으로 나타났다. 조기 진단을 위해 첫 촬영 날짜인 7월 5일의 자료로 모델을 만들어 다른 회차에 적용하여 모델의 성능을 평가한 결과, 5개의 모든 단계에서 0.9 이상의 accuracy를 얻었다. 종합적으로, UAV 영상 기반의 식생 지수를 활용한 양분 결핍 진단은 벼의 생육을 조기에 예측하는 데 효과적이며, 이는 정밀 농업 분야에서 시간과 노동을 절감하고 양분 관리를 개선하는 데 도움이 될 것으로 기대된다.

PURPOSES : To enhance the accuracy of predicting the compressive strength of practical concrete mixtures, this study aimed to develop a machine learning model by utilizing the most commonly employed curing age, specifically, the 28-day curing period. The training dataset consisted of concrete mixture sample data at this curing age, along with samples subjected to a total load not exceeding 2,350 kg. The objective was to train a machine learning model to create a more practical predictive model suitable for real-world applications. METHODS : Three machine learning models—random forest, gradient boosting, and AdaBoost—were selected. Subsequently, the prepared dataset was used to train the selected models. Model 1 was trained using concrete sample data from the 28th curing day, followed by a comprehensive analysis of the results. For Model 2, training was conducted using data from the 28th day of curing, focusing specifically on instances where the total load was 2,350 kg or less. The results were systematically analyzed to determine the most suitable machine learning model for predicting the compressive strength of concrete. RESULTS : The machine learning model trained on concrete sample data from the 28th day of curing with a total weight of 2,350 kg or less exhibited higher accuracy than the model trained on weight-unrestricted data from the 28th day of curing. The models were evaluated in terms of accuracy, with the gradient boosting, AdaBoost, and random forest models demonstrating high accuracy, in that order. CONCLUSIONS : Machine learning models trained using concrete mix data based on practical and real-world scenarios demonstrated a higher accuracy than models trained on impractical concrete mix data. This case illustrates the significance of not only the quantity but also the quality of the data during the machine learning training process. Excluding outliers from the data appears to result in better accuracy for machine learning models. This underscores the importance of using high-quality and practical mixed concrete data for reliable and accurate model training.

PURPOSES : In this study, an optimal model for compressive strength prediction was derived by learning and directly comparing several machine learning models based on the same data. METHODS : Approximately 478 pieces of concrete compressive strength data were obtained to compare the performance of the machine learning models. In addition, five machine learning models were trained based on the obtained data. The performance of the learned model was compared using three performance indicators. Finally, the performance of the model trained using additional data was reviewed. RESULTS : As a result of comparing the performance of machine learning models, the XGB(eXtra Gradient Boost) model showed the best performance. In addition, as a result of the verification based on additional data, highly reliable results can be obtained if the XGB model is used to predict the compressive strength of concrete. CONCLUSIONS : If a concrete strength prediction model is derived based on a machine learning model, a highly reliable model can be derived.

PURPOSES : Construction cost estimates are important information for business feasibility analysis in the planning stage of road construction projects. The quality of current construction cost estimates are highly dependent on the expert's personal experience and skills to estimate the arithmetic average construction cost based on past cases, which makes construction cost estimates subjective and unreliable. An objective approach in construction cost estimation shall be developed with the use of machine learning. In this study, past cases of road projects were analyzed and a machine learning model was developed to produce a more accurate and time-efficient construction cost estimate in teh planning stage. METHODS : After conducting case analysis of 100 road construction, a database was constructed including the road construction's details, drawings, and completion reports. To improve the construction cost estimation, Mallow's Cp. BIC, Adjusted R methodology was applied to find the optimal variables. Consequently, a plannigs-stage road construction cost estimation model was developed by applying multiple regression analysis, regression tree, case-based inference model, and artificial neural network (ANN, DNN). RESULTS : The construction cost estimation model showed excellent prediction performance despite an insufficient amount of learning data. Ten cases were randomly selected from the data base and each developed machine learning model was applied to the selected cases to calculate for the error rate, which should be less than 30% to be considered as acceptable according to American Estimating Association. As a result of the analysis, the error rates of all developed machine learning models were found to be acceptable with values rangine from 17.3% to 26.0%. Among the developed models, the ANN model yielded the least error rate. CONCLUSIONS : The results of this study can help raise awareness of the importance of building a systematic database in the construction industry, which is disadvantageous in machine learning and artificial intelligence development. In addition, it is believed that it can provide basic data for research to determine the feasibility of construction projects that require a large budget, such as road projects.

Numerous factors contribute to the deterioration of reinforced concrete structures. Elevated temperatures significantly alter the composition of the concrete ingredients, consequently diminishing the concrete's strength properties. With the escalation of global CO2 levels, the carbonation of concrete structures has emerged as a critical challenge, substantially affecting concrete durability research. Assessing and predicting concrete degradation due to thermal effects and carbonation are crucial yet intricate tasks. To address this, multiple prediction models for concrete carbonation and compressive strength under thermal impact have been developed. This study employs seven machine learning algorithms—specifically, multiple linear regression, decision trees, random forest, support vector machines, k-nearest neighbors, artificial neural networks, and extreme gradient boosting algorithms—to formulate predictive models for concrete carbonation and thermal impact. Two distinct datasets, derived from reported experimental studies, were utilized for training these predictive models. Performance evaluation relied on metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analytical outcomes demonstrate that neural networks and extreme gradient boosting algorithms outshine the remaining five machine learning approaches, showcasing outstanding predictive performance for concrete carbonation and thermal effect modeling.

기후변화 영향으로 이상고수온, 태풍, 홍수, 가뭄 등 재난 및 안전 관리기술은 지속적으로 고도화를 요구받고 있으며, 특히 해 수면 온도는 한반도 주변에서 발생되는 여름철 적조 발생과 동해안 냉수대 출현, 소멸 등에 영향을 신속하게 분석할 수 있는 중요한 인자 이다. 따라서, 본 연구에서는 해수면 온도 자료를 해양 이상현상 및 연구에 적극 활용되기 위해 통계적 방법과 딥러닝 알고리즘을 적용하 여 예측성능을 평가하였다. 예측에 사용된 해수면 수온자료는 흑산도 조위관측소의 2018년부터 2022년까지 자료이며, 기존 통계적 ARIMA 방법과 Long Short-Term Memory(LSTM), Gated Recurrent Unit(GRU)을 사용하였고, LSTM의 성능을 더욱 향상할 수 있는 Sequence-to-Sequence(s2s) 구조에 Attention 기법을 추가한 Attention Long Short-Term Memory (LSTM)기법을 사용하여 예측 성능 평가를 진행하 였다. 평가 결과 Attention LSTM 모델이 타 모델과 비교하여 더 좋은 성능을 보였으며, Hyper parameter 튜닝을 통해 해수면 수온 성능을 개 선할 수 있었다.

모기는 감염병을 매개하는 종으로 전염병 확산 억제를 위해서는 개체수의 감시와 정확한 예측이 필요하다. 본 연구에서는 모기 개체수 및 기상 및 현장 자료를 활용해 모기 개체수 머신러닝 모델을 개발하였다. 모기 개체수는 디지털 모기 측정기(Digital Mosquito Monitoring System, DMS)의 2015 년~2022년의 5월~10월의 자료를 활용하였다. 기상 자료는 기온, 강수량, 풍속, 습도를 사용하였으며, 현장 조사 자료는 현장을 명목척도와 서열척도로 나누어 기록하여, 명목 척도의 경우 원핫 인코딩으 로 변환해 수치화하여 사용하였다. 분석에 사용된 머신러닝 모델은 Artificial Neural Network, Random Forest, Gradient Boosting Machine, Support Vector Machine이며 성능지표로 R2, RMSE를 사용하였다. 연구 결과, Gradient Boosting 모델이 R2 0.4, RMSE 22.45로 가장 좋은 성능을 나타냈다. 현장 조사 자료 를 분석에 활용하였을 때 R2는 증가하였고, RMSE는 감소하였다. 본 연구 결과 모기 개체수에 현장 조사 자료가 예측 정확도를 향상시킬 수 있음을 확인하였다.

Chloride is one of the most common threats to reinforced concrete (RC) durability. Alkaline environment of concrete makes a passive layer on the surface of reinforcement bars that prevents the bar from corrosion. However, when the chloride concentration amount at the reinforcement bar reaches a certain level, deterioration of the passive protection layer occurs, causing corrosion and ultimately reducing the structure’s safety and durability. Therefore, understanding the chloride diffusion and its prediction are important to evaluate the safety and durability of RC structure. In this study, the chloride diffusion coefficient is predicted by machine learning techniques. Various machine learning techniques such as multiple linear regression, decision tree, random forest, support vector machine, artificial neural networks, extreme gradient boosting annd k-nearest neighbor were used and accuracy of there models were compared. In order to evaluate the accuracy, root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2) were used as prediction performance indices. The k-fold cross-validation procedure was used to estimate the performance of machine learning models when making predictions on data not used during training. Grid search was applied to hyperparameter optimization. It has been shown from numerical simulation that ensemble learning methods such as random forest and extreme gradient boosting successfully predicted the chloride diffusion coefficient and artificial neural networks also provided accurate result.

Consumers' online reviews have become more powerful in the Internet market. Consumers share reviews, post comments and constantly evaluate products online. In previous studies, the analysis of online reviews mainly focused on purchasing products based on consumers' own use experience, but in innovative products, it was difficult to find an analysis of product acceptor's response to product user reviews. In particular, there is no online review study of VR covered in this study. This study not only quantitatively analyzed online reviews of consumers who purchased VR products on Amazon, an online distribution site, but also qualitatively analyzed them through crawling. This study used Amazon's VR product user review, where purchases were confirmed, to select algorithms that are more likely to be matched by predicting a helpful review and presenting a predictive model. In addition, the online review extracted deep text associated with Helpful and conducted topical modeling. As a result, topics related to 1) experience in use, 2) post-product evaluation, 3) product composition and peripherals, 4) immersion, and 5) comfort were highly acceptable to potential inmates. To enhance the acceptability of innovative products through online reviews, it is not just highlighting the product advantages of VR, but also suggests that the link between smartphones and applications can bring in more potential users. Also, interworking with other peripheral devices (speakers or screens) can be predicted as a way to increase the acceptability of VR products. From a marketing perspective, this study has found targeted topics that help consumers in pioneering the VR market, which will help potential customers create the services they want.

With about 80% of the global economy expected to shift to the global market by 2030, exports of reverse direct purchase products, in which foreign consumers purchase products from online shopping malls in Korea, are growing 55% annually. As of 2021, sales of reverse direct purchases in South Korea increased 50.6% from the previous year, surpassing 40 million. In order for domestic SMEs(Small and medium sized enterprises) to enter overseas markets, it is important to come up with export strategies based on various market analysis information, but for domestic small and medium-sized sellers, entry barriers are high, such as lack of information on overseas markets and difficulty in selecting local preferred products and determining competitive sales prices. This study develops an AI-based product recommendation and sales price estimation model to collect and analyze global shopping malls and product trends to provide marketing information that presents promising and appropriate product sales prices to small and medium-sized sellers who have difficulty collecting global market information. The product recommendation model is based on the LTR (Learning To Rank) methodology. As a result of comparing performance with nDCG, the Pair-wise-based XGBoost-LambdaMART Model was measured to be excellent. The sales price estimation model uses a regression algorithm. According to the R-Squared value, the Light Gradient Boosting Machine performs best in this model.

Radioactive wastes, including used nuclear fuel and decommissioning wastes, have been treated using molten salts. Electrochemical sensors are one of the options for in-situ process monitoring using molten salts. However, in order to use electrochemical sensors in molten salt, the surface area must be known. This is because the surface area affects the current of the electrode. Previous studies have used a variety of methods to determine the electrode surface area in molten salts. One method of calculating the electrode surface area is to use the reduction current peak difference between electrodes with known length differences. The method is based on the reduction peak and has the benefit of providing long-term in-situ monitoring of surfaces immersed in molten salt. A number of assumptions have been made regarding this method, including that there is no mass transport by migration or convection; the reaction is reversible and limited by diffusion; the chemical activity of the deposit should be unity; and species should follow linear diffusion. For the purpose of overcoming these limitations, a variety of machine learning algorithms were applied to different voltammogram datasets in order to calculate the surface area. Voltammogram datasets were collected from multiarray electrodes, comprising a multiarray holder, two tungsten rods (1 mm diameter) working electrodes, a quasi-reference electrode, and a counter electrode. The multiarray electrode holder was connected to the auto vertical translator, which uses a servo motor, for changing the height of the rod in the molten salts. To make big and diverse data for training machine learning models, various concentrations of corrosion products (Cr, Fe) and fission products (Eu, Sm) in NaCl-MgCl2 eutectic salts were used as electrolyte; electrolyte temperatures were 500, 525, 550, 575, and 600°C. This study will demonstrate the potential of utilizing machine learning based electrochemical in situ monitoring in molten salt processing.

Surveillance plays a crucial role in safeguards. Reviewing surveillance data requires a significant number of inspection manpower. As the number of surveillance cameras increases, the demand for such manpower is expected to grow even more. Recently, in the field of security, there has been a development of deep learning models that automatically detect abnormal events from video images, and their usage is expanding. In this study, we used an AutoEncoder-based semi-supervised learning model, which can detect unexpected abnormal events, to detect anomalies in the UCSDped2 dataset and in simulating safeguards-related event videos taken at Dry Mockup facility of KAERI. To improve the model performance, we transformed the video images into two parts: the appearance part, which are sequences of video image frames, and the motion part, which are the pixel value differences of consecutive video frames. In addition, we added memory module to the bottle neck of the AutoEncoder model, and skip connection to enhance the model performance. To evaluate the model performance, we proposed a new evaluation index, which is adequate to the video images of safeguards surveillance in addition to the widely used AUC (Area Under the ROC Curve).

본 연구에서는 2 016년부터 2 02 0년까지 내륙 관측소 중 안개 최다발 지역인 안동을 대상으로 XGBoost-DART 머신러닝 알고리즘을 이용하여 1 시간 후 안개 유무를 예측하였다. 기상자료, 농업관측자료, 추가 파생자료와 각 자료 를 오버 샘플링한 확장자료, 총 6개의 데이터 세트를 사용하였다. 목측으로 획득한 기상현상번호와 시정계 관측으로 측 정된 시정거리 자료를 각각 안개 유[1]무[0]로 이진 범주화하였다. 총 12개의 머신러닝 모델링 실험을 설계하였고, 안개 가 사회와 지역사회에 미치는 유해성을 고려하여 모델의 성능은 재현율과 AUC-ROC를 중심으로 평가하였다. 전체적으 로, 오버샘플링한 기상자료와 기상현상번호 기반의 예측 목표를 조합한 실험이 최고 성능을 보였다. 이 연구 결과는 머 신러닝 알고리즘을 활용한 안개 예측에 있어서, 목측으로 획득한 기상현상번호의 중요성을 암시한다.

본 연구는 교수자가 학습자를 위해 긍정적 가치탐색을 효과적으로 적용할 수 있도록 4D 프로세스 기반 학습모형을 개발하고 학습유형을 분류하여 연구하는 것을 목적으로 하였다. 긍정적 가치탐색 교육 방법은 학습자의 사고방식과 행동 변화에 효과적이다. 또한, 의미와 가치발견에 중점을 둔 강점 기반 접근을 통해 학습 참여를 증진하고 지속 가능한 학습 환경과 배움을 실현할 수 있다. 이러한 교육적 효과는 긍정적 가 치 탐색의 4D 프로세스를 토대로 한 활동으로 이루어진다. 교육 현장에서 긍정적 가치탐색 4D 프로세스 를 보다 유용하게 활용하기 위해서는 교육목표와 지향하는 역량개발에 따라 4D 프로세스에 적합한 학습 유형 분류와 체계적이고 구조화된 학습모형 개발이 필요하다. 본 연구는 4D 프로세스 기반 4가지 학습유 형을 구조화하여 학습모형을 개발하고 모형타당화를 진행하였다. 4D 프로세스 기반 학습모형 구성요소 도 출은 선행 문헌의 검토와 분석을 통해 이루어졌고, 구성요소의 구조화는 사례연구를 통해 진행하였다. 그 리고 해당 분야 전문가 검토를 통한 타당성 평가를 3차에 걸쳐 실시하였다. Discover, Dream, Design, Destiny 4D 프로세스는 탐색과 발견, 사고와 상상, 공유와 구성, 발표와 실천으로 개선되어 적용되었다. 학습에 적합하도록 보완된 4D 프로세스는 도달할 학습 목표와 개발할 학습자의 역량에 따라 탐구형, 창의 형, 과제해결형, 실천형으로 세분화하여 개발되었다. 개발된 학습모형에서의 학습유형은 다양한 교육 환경 에 맞게 긍정적 가치탐색 활동이 선택적으로 운영될 수 있다는 이점이 있다.

본 연구의 주요 목적은 회귀기반의 다양한 머신러닝 알고리즘을 개발하고 다양한 농업 분야에서 사용되는 트랙터의 연료 소비량을 예측하는 것이다. 비포장 도로주행 농업 기계중에서도 사용 비중이 가장 높은 트랙터를 선정하였다. 실제 농가에 방문하여 현업 전문가 조언을 바탕으로 연구하여 설문지를 작성하였으며, 설문 대상은 경남 사천시에 있는 농가 10곳, 진주시에 있는 농가 62곳 등, 총 72곳의 농가이다. 농작업으로는 벼농사, 보리농사, 밭농사 등이 있으며, 작업내용으로는 쟁기, 로터리, 비료살포, 베토, 모내기작업 등이 있다. 다중 회귀분석을 통해 연료 소비량 예측에 영향을 미치는 변수(마력, 기계사용연수, 경작면적, 작업 시간)를 추출하였고. 머신러닝 회귀 학습기 모형으로 학습하여 예측 모형의 성능을 검증하였다. 연료 소비량을 예측하는 모델의 성능은 결정 계수(R), RMSE (제곱 평균 제곱근 오차), MSE (평균 제곱 오차) 및 MAE (평균 절대 오차)를 포함한 4가지 통계적 품질 매개변수를 사용하여 결정되었다. 연구 결과 4가지 모델(다중회귀, 랜덤포레스트, 아다부스트, K-최근접 이웃) 중 K-최근접 이웃의 성능이 제일 높은 것으로 나타났다. 결론적으로 본 연구의 결과는 실제 농가의 연료 소비량을 예측하여 면세유 유통의 투명성을 확보하고 추후 개발 모델의 의사결정에 활용될 수 있을 것으로 기대된다.