• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.94-107
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• 2022

Accidents prevention and mitigation is the highest priority of nuclear power plant (NPP) operation, particularly in the aftermath of the Fukushima Daiichi accident, which has reignited public anxieties and skepticism regarding nuclear energy usage. To deal with accident scenarios more effectively, operators must have ample and precise information about key safety parameters as well as their future trajectories. This work investigates the potential of machine learning in forecasting NPP response in real-time to provide an additional validation method and help reduce human error, especially in accident situations where operators are under a lot of stress. First, a base-case SGTR simulation is carried out by the best-estimate code RELAP5/MOD3.4 to confirm the validity of the model against results reported in the APR1400 Design Control Document (DCD). Then, uncertainty quantification is performed by coupling RELAP5/MOD3.4 and the statistical tool DAKOTA to generate a large enough dataset for the construction and training of neural-based machine learning (ML) models, namely LSTM, GRU, and hybrid CNN-LSTM. Finally, the accuracy and reliability of these models in forecasting system response are tested by their performance on fresh data. To facilitate and oversee the process of developing the ML models, a Systems Engineering (SE) methodology is used to ensure that the work is consistently in line with the originating mission statement and that the findings obtained at each subsequent phase are valid.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3423-3440
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• 2023

In this work, a multivariate time-series machine learning meta-model is developed to predict the transient response of a typical nuclear power plant (NPP) undergoing a steam generator tube rupture (SGTR). The model employs Recurrent Neural Networks (RNNs), including the Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a hybrid CNN-LSTM model. To address the uncertainty inherent in such predictions, a Bayesian Neural Network (BNN) was implemented. The models were trained using a database generated by the Best Estimate Plus Uncertainty (BEPU) methodology; coupling the thermal hydraulics code, RELAP5/SCDAP/MOD3.4 to the statistical tool, DAKOTA, to predict the variation in system response under various operational and phenomenological uncertainties. The RNN models successfully captures the underlying characteristics of the data with reasonable accuracy, and the BNN-LSTM approach offers an additional layer of insight into the level of uncertainty associated with the predictions. The results demonstrate that LSTM outperforms GRU, while the hybrid CNN-LSTM model is computationally the most efficient. This study aims to gain a better understanding of the capabilities and limitations of machine learning models in the context of nuclear safety. By expanding the application of ML models to more severe accident scenarios, where operators are under extreme stress and prone to errors, ML models can provide valuable support and act as expert systems to assist in decision-making while minimizing the chances of human error.

• Safety and Health at Work
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• v.13 no.4
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• pp.379-386
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• 2022

Emergency medical services (EMS) personnel are at high risk for adverse mental health outcomes during disease outbreaks. To support the development of evidence-informed mitigation strategies, we conducted a scoping review to identify the extent of research pertaining to EMS personnel's mental health during disease outbreaks and summarized key factors associated with mental health outcomes. We systematically searched three databases for articles containing keywords within three concepts: EMS personnel, disease outbreaks, and mental health. We screened and retained original peer-reviewed articles that discussed, in English, EMS personnel's mental health during disease outbreaks. Where inferential statistics were reported, the associations between individual and work-related factors and mental health outcomes were synthesized. Twenty-five articles were eligible for data extraction. Our findings suggest that many of the contributing factors for adverse mental health outcomes are related to inadequacies in fulfilling EMS personnel's basic safety and informational needs. In preparation for future disease outbreaks, resources should be prioritized toward ensuring adequate provisions of personal protective equipment and infection prevention and control training. This scoping review serves as a launching pad for further research and intervention development.

• Korean Journal of Culture and Social Issue
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• v.14 no.1_spc
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• pp.47-62
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• 2008

The 'education panic' is one of the most phenomenal social issue in the current Korean society. The explanations of it until now, however, are rather superficial in a way that they only describe apparent facts and its seriousness, rendering further examination of the psychological motivation of parents who are the protagonist of education panic necessary. With 548 elementary, middle, and highschool students and their parents, the present study has investigated the impact of parents' past experience of 'han', regret, and learning effect regarding education on their parenting style as well as on their children's academic experience. The result revealed that parents' learning effect was related with more affective/autonomous parenting style and reasonable expectation for their children's educational career. On the contrary, parents' 'han' and regret indicated relationship with hostile and controlling parenting style and also with blind intention toward their children's educational career. The negative emotions also seemed to increase their children's academic stress, and lower academic self-efficacy. Such results suggest that the extraordinary education panic in Korea is more than a simple quantitative matter of intensity. The psychological basis and motivation of the people included, a much more quantitative information, should be taken into account.

• Journal of Animal Science and Technology
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• v.66 no.1
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• pp.31-56
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• 2024

Pig farming, a vital industry, necessitates proactive measures for early disease detection and crush symptom monitoring to ensure optimum pig health and safety. This review explores advanced thermal sensing technologies and computer vision-based thermal imaging techniques employed for pig disease and piglet crush symptom monitoring on pig farms. Infrared thermography (IRT) is a non-invasive and efficient technology for measuring pig body temperature, providing advantages such as non-destructive, long-distance, and high-sensitivity measurements. Unlike traditional methods, IRT offers a quick and labor-saving approach to acquiring physiological data impacted by environmental temperature, crucial for understanding pig body physiology and metabolism. IRT aids in early disease detection, respiratory health monitoring, and evaluating vaccination effectiveness. Challenges include body surface emissivity variations affecting measurement accuracy. Thermal imaging and deep learning algorithms are used for pig behavior recognition, with the dorsal plane effective for stress detection. Remote health monitoring through thermal imaging, deep learning, and wearable devices facilitates non-invasive assessment of pig health, minimizing medication use. Integration of advanced sensors, thermal imaging, and deep learning shows potential for disease detection and improvement in pig farming, but challenges and ethical considerations must be addressed for successful implementation. This review summarizes the state-of-the-art technologies used in the pig farming industry, including computer vision algorithms such as object detection, image segmentation, and deep learning techniques. It also discusses the benefits and limitations of IRT technology, providing an overview of the current research field. This study provides valuable insights for researchers and farmers regarding IRT application in pig production, highlighting notable approaches and the latest research findings in this field.

• Journal of the Korean Geotechnical Society
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• v.16 no.1
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• pp.75-81
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• 2000

The anisotropy of soils has an important effect on stress-strain behavior. In this study, an attempt has been made to implement artificial neural network model for modeling the stress-strain relationship and predicting the undrained shear strength of normally consolidated clay with varying consolidation pressure ratios. The multi-layer neural network model, adopted in this study, utilizes the error back-propagation loaming algorithm. The artificial neural networks use the results of undrained triaxial test with various consolidation pressure ratios and different effective vertical consolidation pressure fur learning and testing data. After learning from a set of actual laboratory testing data, the neural network model predictions of the undrained shear strength of the normally consolidated clay are found to agree well with actual measurements. The predicted values by the artificial neural network model have a determination coefficient$(r^2)$ above 0.973 compared with the measured data. Therefore, this results show a positive potential for the applications of well-trained neural network model in predicting the undrained shear strength of cohesive soils.

• Journal of the Korean Institute of Educational Facilities
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• v.26 no.3
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• pp.15-23
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• 2019

This study aims to suggest a Biophilic classroom design of high school to obtain attention restoration for students. The learning space for youth in the modern society is composed of dry artificial structures. This space is considered to be a space that can not relieve stress caused by learning. "The Attention Restoration Theory" is divided into "Directed Attention" of humans, which is the cause of fatigue and stress, and "Involuntary Attention" as a solution to it. "Involuntary Attention" takes place in a rest state and helps the brain recover when exposed to nature. And the core of "Biophilic-Design Theory" is that humans can recover physical and mental conditions when exposed to nature. The purpose of this study is to apply "The Biophilic-Design Theory" that emphasizes the importance of exposure to nature to the educational space and plan the space where the 'Attention Restoration' can be achieved. The research method is as follows. First, we review previous studies related to "The Biophilic-Design Theory" and "The Attention Restoration Theory". Second, we analyze the application examples of "The Biophilic-Design Theory" and "The Attention Restoration Theory" in domestic and foreign educational spaces. Third, the concept of educational space is set up based on the elements derived from previous studies. Finally, we propose the planning direction of classroom design based on Biophilic-Design. The following conclusions were drawn. First, The creation of the education space to restore the learner's attention requires a visual space plan that utilizes natural elements such as natural light, artificial light, plants, and natural materials that can directly experience nature. Second, the direction in which students in the classroom can be "The Attention Restoration Theory" should consider the use of indirect natural elements that bring the surrounding natural landscape into the interior. This study will be used as the baseline data for the spatial design and planning of education facilities based on Biophilic-Design.

• Geomechanics and Engineering
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• v.30 no.3
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• pp.259-272
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• 2022

Rockburst is a dynamic, multivariate, and non-linear phenomenon that occurs in underground mining and civil engineering structures. Predicting rockburst is challenging since conventional models are not standardized. Hence, machine learning techniques would improve the prediction accuracies. This study describes decision based uncertainty models to predict rockburst in underground engineering structures using gradient boosting algorithms (GBM). The model input variables were uniaxial compressive strength (UCS), uniaxial tensile strength (UTS), maximum tangential stress (MTS), excavation depth (D), stress ratio (SR), and brittleness coefficient (BC). Several models were trained using different combinations of the input variables and a 3-fold cross-validation resampling procedure. The hyperparameters comprising learning rate, number of boosting iterations, tree depth, and number of minimum observations were tuned to attain the optimum models. The performance of the models was tested using classification accuracy, Cohen's kappa coefficient (k), sensitivity and specificity. The best-performing model showed a classification accuracy, k, sensitivity and specificity values of 98%, 93%, 1.00 and 0.957 respectively by optimizing model ROC metrics. The most and least influential input variables were MTS and BC, respectively. The partial dependence plots revealed the relationship between the changes in the input variables and model predictions. The findings reveal that GBM can be used to anticipate rockburst and guide decisions about support requirements before mining development.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.88-88
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• 2022

Detection of stress responses in crops is important to diagnose crop growth and evaluate yield. Also, the multi-spectral sensor is effectively known to evaluate stress caused by nutrient and moisture in crops or biological agents such as weeds or diseases. Therefore, in this experiment, multispectral images were taken by an unmanned aerial vehicle(UAV) under field condition. The experiment was conducted in the long-term fertilizer field in the National Institute of Crop Science, and experiment area was divided into different status of NPK(Control, N-deficiency, P-deficiency, K-deficiency, Non-fertilizer). Total 11 vegetation indices were created with RGB and NIR reflectance values using python. Variations in nutrient content in plants affect the amount of light reflected or absorbed for each wavelength band. Therefore, the objective of this experiment was to evaluate vegetation indices derived from multispectral reflectance data as input into machine learning algorithm for the classification of nutritional deficiency in rice. RandomForest model was used as a representative ensemble model, and parameters were adjusted through hyperparameter tuning such as RandomSearchCV. As a result, training accuracy was 0.95 and test accuracy was 0.80, and IPCA, NDRE, and EVI were included in the top three indices for feature importance. Also, precision, recall, and f1-score, which are indicators for evaluating the performance of the classification model, showed a distribution of 0.7-0.9 for each class.

• Journal of vocational education research
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• v.30 no.4
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• pp.281-300
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• 2011

This study tried to look into what are happening in the 'class for the talented in invention' using COS-R developed by VanTassel-Baska. Teaching and learning activities within the classroom were observed and analyzed in terms of teacher's observation and teacher's observation, respectively. Based on results of this study, conclusions are as follows. First, it was founded that there are some commonalities between teacher observations and student observations. Based on teacher observations, differentiated teaching activities considering individual characteristics are rarely observed, and for students, it was true. Therefore, supplying a special training program for teachers are needed in order to make teachers and students engage in changing their teaching and learning behaviors. Second, on the side of teachers, they usually emphasize the importance of curriculum planning and implementation, problem solving, creative thinking et al. However, they barely stress the characteristics of research methods, critical thinking, and considering individual characteristics and the level of intellectual ability. Third, on the side of students, they frequently respond to solving problems and critical thinking at the same degree. On the other hand, systemic efforts of considering individual differences and adapting to them have been less regarded in both teaching and learning. In sum, for the successful 'Invention gifted classroom', establishing an educational environment to consider individually guided instruction and taking a balance among various factors embedded in teaching and learning situation should be required.