• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.2
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• pp.278-286
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• 2019

Conventional methods for selecting jamming techniques in electronic warfare are based on libraries in which a list of jamming techniques for radar signals is recorded. However, the choice of jamming techniques by the library is limited when modified signals are received. In this paper, we propose a method to predict the jamming technique for radar signals by using deep learning methods. Long short-term memory(LSTM) is a deep running method which is effective for learning the time dependent relationship in sequential data. In order to determine the optimal LSTM model structure for jamming technique prediction, we test the learning parameter values that should be selected, such as the number of LSTM layers, the number of fully-connected layers, optimization methods, the size of the mini batch, and dropout ratio. Experimental results demonstrate the competent performance of the LSTM model in predicting the jamming technique for radar signals.

• Environmental Engineering Research
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• v.24 no.3
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• pp.404-411
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• 2019

In this study, we designed a data-driven model to predict chlorophyll-a using M5P model tree and extreme learning machine (ELM). The Juksan weir in the Youngsan River has high chlorophyll-a, which is the primary indicator of algal bloom every year. Short-term algal bloom prediction is important for environmental management and ecological assessment. Two models were developed and evaluated for short-term algal bloom prediction. M5P is a classification and regression-analysis-based method, and ELM is a feed-forward neural network with fast learning using the least square estimate for regression. The dataset used in this study includes water temperature, rainfall, solar radiation, total nitrogen, total phosphorus, N/P ratio, and chlorophyll-a, which were collected on a daily basis from January 2013 to December 2016. The M5P model showed that the prediction model after one day had the highest performance power and dropped off rapidly starting with predictions after three days. Comparing the performance power of the ELM model with the M5P model, it was found that the performance power of the 1-7 d chlorophyll-a prediction model was higher. Moreover, in a period of rapidly increasing algal blooms, the ELM model showed higher accuracy than the M5P model.

• Computers and Concrete
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• v.30 no.1
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• pp.43-74
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• 2022

Machine learning technique is recently opening new opportunities to identify the complex shear transfer mechanisms of reinforced concrete (RC) beam members. This study employed 1224 shear test specimens to train decision tree-based machine learning (ML) programs, by which strong correlations between shear capacity of RC beams and key input parameters were affirmed. In addition, shear contributions of concrete and shear reinforcement (the so-called V_c and V_s) were identified by establishing three independent ML models trained under different strategies with various combinations of datasets. Detailed parametric studies were then conducted by utilizing the well-trained ML models. It appeared that the presence of shear reinforcement can make the predicted shear contribution from concrete in RC beams larger than the pure shear contribution of concrete due to the intervention effect between shear reinforcement and concrete. On the other hand, the size effect also brought a significant impact on the shear contribution of concrete (V_c), whereas, the addition of shear reinforcements can effectively mitigate the size effect. It was also found that concrete tends to be the primary source of shear resistance when shear span-depth ratio a/d<1.0 while shear reinforcements become the primary source of shear resistance when a/d>2.0.

• Journal of Korean Academy of Nursing
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• v.26 no.1
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• pp.73-93
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• 1996

This study aimed to analyse the test contents of the national examination for the registered nurses (NERN) over 3 years from 1991 to 1993 in Korea. In recent years in Korea, the MCQ(multiple choice question) has been showing to be a highly recognized method for assessing the qualification of registered nurses. Unfortunately, nursing faculties have found NERN had some bad MCQs through having evaluation workshop for Some MCQs often provide so many unwriting clues which become a bias of the results, and some items fell into the category of the lower level of educational taxonomy such as isolated recall a fact or data. Frequently the stems of the questions are ambigous, unclear, disputable, esoterical or trivial. Considering those fallacies of the national examination, it is very critical to review the test items to see whether it is of high quality, is more fair, reliable and objective in depth. Therefore, this study was done to provide data for the improvement of the test contents as well as the teachers's assessment skill. For this study, the ad hoc committee was composed of 16 members, including 5 education board members of Korean Academic Nurses Association and 11 nursing faculty members. This committee had one day panel discussion and filled the checklist for this study. The process of analysing data was held over 10 times during 1992-1994. The analysis focussed on educational taxonomy such as cognitive domain(knowledge), psychmotor domain (skill), affective domain(attitude) and the level of learning such as recall, understanding, problems solving, and learning area of theory and practice, and the learning content categorised by nursing process and disease process. The test analysed using difficulty index and the structure of the test items was analysed. The conclusions and suggestion as follows : 1. In learning area, the average ratio of the theory and practice was 1 : 1.1 which was less than 1 : 2 suggested by Korean National Health Institute, and the ratio was different by the 8 leaning subjects of nursing. 2. In category of the educational taxonomy, the knowledge domain was emphasized mostly（79. 7％）, the skill domain was 14.9％, and the attitude domain was 5.4％ only. 3. In the level of learning, generally, the test items of the level of recall（45.5％） and the understanding（46.3％） were covered almost and the problem solving was 8.1％. 4. In the learning contents, generally, the test items related to nursing process was 67.2％ and that of disease process was 32.8％. However, this proportion was different by the 8 leaning subjects. Even though the nursing diagnosis has been emphasized in nursing curricula recently, the test items of this was identified very few. 5. In the structure of the test item, some were not clear, incorrect grammar, unclear description and some have clues to answer. 6. In the item analysis, the non-acceptable level of the difficulty index (means too easy) was 65.7％, and the acceptable level was 33.9％. Considering the results we would like to suggest the followings, 1. Since the test items of knowledge domain was dominant, the test items of the practice domain and attitude domain should be emphasized more. 2. The regular review and analysis of NERN should be arranged in order to improve the quality of the test items which will give influence to the nursing education positively.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.9
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• pp.375-384
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• 2021

Deep learning techniques have been proven to have high performance in image processing and are applied in various fields. The most widely used methods for validating a deep learning model include a holdout verification method, a k-fold cross verification method, and a bootstrap method. These legacy methods consider the balance of the ratio between classes in the process of dividing the data set, but do not consider the ratio of various features that exist within the same class. If these features are not considered, verification results may be biased toward some features. Therefore, we propose a deep learning model validation method based on data feature coverage for image classification by improving the legacy methods. The proposed technique proposes a data feature coverage that can be measured numerically how much the training data set for training and validation of the deep learning model and the evaluation data set reflects the features of the entire data set. In this method, the data set can be divided by ensuring coverage to include all features of the entire data set, and the evaluation result of the model can be analyzed in units of feature clusters. As a result, by providing feature cluster information for the evaluation result of the trained model, feature information of data that affects the trained model can be provided.

• Journal of The Korean Association For Science Education
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• v.36 no.2
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• pp.347-360
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• 2016

This study focused on the use of student assessment activities to investigate the impact on reflective thinking in Argument-based Inquiry. The participants of the study were 166 10th grade students (six classes). Over one semester, students participated in five ABI programs that we developed. The experimental group (84 students) was taught Argument-Based Inquiry with students' self and peer assessment activities. The comparative group (82 students) was taught without the activities. We analyzed students' reflective writing to investigate how the student assessment activities influenced the students' reflective thinking. We also used the interviews and surveys to examine the validity of student assessment activities. According to analysis of the reflective writing, the experimental group had a significantly higher mean score than the comparative group in the 3rd and 5th writing. The ratio of students who showed a metacognitive level of reflection with regard to analysis of inquiry process, understanding of learning, and change of thinking increased in both groups, but the experimental group's ratio was higher than the comparative group's. The result of analysis of the reflective practice showed that the ratio of the experimental group's students who reached the metacognitive level of reflection in their writing increased, while the comparative group's decreased. Therefore, we conclude that student assessment activities can create a learning environment that facilitates student participation, increases the students' engagement in the learning process, and can be used as a tool to scaffold learning.

• Nuclear Engineering and Technology
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• v.49 no.7
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• pp.1423-1430
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• 2017

Detonation cell width is an important parameter in hydrogen explosion assessments. The experimental data on gas detonation are statistically analyzed to establish a universal method to numerically predict detonation cell widths. It is commonly understood that detonation cell width, ${\lambda}$, is highly correlated with the characteristic reaction zone width, ${\delta}$. Classical parametric regression methods were widely applied in earlier research to build an explicit semiempirical correlation for the ratio of ${\lambda}/{\delta}$. The obtained correlations formulate the dependency of the ratio ${\lambda}/{\delta}$ on a dimensionless effective chemical activation energy and a dimensionless temperature of the gas mixture. In this paper, support vector regression (SVR), which is based on nonparametric machine learning, is applied to achieve functions with better fitness to experimental data and more accurate predictions. Furthermore, a third parameter, dimensionless pressure, is considered as an additional independent variable. It is found that three-parameter SVR can significantly improve the performance of the fitting function. Meanwhile, SVR also provides better adaptability and the model functions can be easily renewed when experimental database is updated or new regression parameters are considered.

• Journal of Korean Library and Information Science Society
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• v.39 no.4
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• pp.283-304
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• 2008

This is to develop Intelligent Retrieval System which can automatically present early query's category terms(association terms connected with knowledge structure of relevant terminology) through learning function and it changes searching form automatically and runs it with association terms. For the reason, this theoretical study of Intelligent Automatic Indexing System abstracts expert's index term through learning and clustering algorism about automatic classification, text mining(categorization), and document category representation. It also demonstrates a good capacity in the aspects of expense, time, recall ratio, and precision ratio.

• Nuclear Engineering and Technology
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• v.55 no.11
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• pp.4057-4065
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• 2023

Organic scintillators are widely used for neutron/gamma detection. Pulse shape discrimination algorithms have been commonly used to discriminate the detected radiations. These algorithms have several limits, in particular with plastic scintillator which has lower discrimination ability, compared to liquid scintillator. Recently, machine learning (ML) models have been explored to enhance discrimination performance. Nevertheless, obtaining an accurate ML model or evaluating any discrimination approach requires a reference neutron dataset. The preparation of this is challenging because neutron sources are also gamma-ray emitters. Therefore, this paper proposes a pipeline to prepare clean labeled neutron/gamma datasets acquired by an organic scintillator. The method is mainly based on a Time of Flight setup and Tail-to-Total integral ratio (TTT_ratio) discrimination algorithm. In the presented case, EJ276 plastic scintillator and ²⁵²Cf source were used to implement the acquisition chain. The results showed that this process can identify and remove mislabeled samples in the entire ToF spectrum, including those that contribute to peak values. Furthermore, the process cleans ToF dataset from pile-up events, which can significantly impact experimental results and the conclusions extracted from them.

• Journal of The Korean Association For Science Education
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• v.23 no.6
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• pp.627-633
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• 2003

This study is to investigate the level and connection of learning concepts related to metabolism presented in life science textbooks developed according to the national common basic curriculum. One kind of elementary school, and three kinds of middle school and high school science textbooks were analysed. The gross number of concepts related to metabolism was 42 in elementary, 149 in middle and 126 in high school science textbooks. The number of concepts was much more different by school than by publisher. Ratio of the number of concrete versus formal concepts decreased gradationally by grade, but the number of learning concepts increased radically by grade. Thus, it is implied that science learning concepts are presented considering the number of concepts as well as cognitive level of learner, and unit and content are constructed on the connection among them in developing science curriculum and textbooks.