• Proceedings of the Korean Information Science Society Conference
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• 2002.10c
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• pp.739-742
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• 2002

This paper addresses a new statistical method, IFSAcycle, which is an unsupervised learning method of analyzing cell cycle-related gene expression data. The IFSAcycle is based on the independent feature subspace analysis (IFAS) [3], which generalizes the independent component analysis (ICA). Experimental results show the usefulness of IFAS: (1) the ability of assigning genes to multiple coexpression pattern groups; (2) the capability of clustering key genes that determine each critical point of cell cycle.

• Journal of Information Technology Applications and Management
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• v.29 no.5
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• pp.27-37
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• 2022

This study intends to link agricultural machine history data with related organizations or collect them through IoT sensors, receive input from agricultural machine users and managers, and analyze them through AI algorithms. Through this, the goal is to track and manage the history data throughout all stages of production, purchase, operation, and disposal of agricultural machinery. First, LSTM (Long Short-Term Memory) is used to estimate oil consumption and recommend maintenance from historical data of agricultural machines such as tractors and combines, and C-LSTM (Convolution Long Short-Term Memory) is used to diagnose and determine failures. Memory) to build a deep learning algorithm. Second, in order to collect historical data of agricultural machinery, IoT sensors including GPS module, gyro sensor, acceleration sensor, and temperature and humidity sensor are attached to agricultural machinery to automatically collect data. Third, event-type data such as agricultural machine production, purchase, and disposal are automatically collected from related organizations to design an interface that can integrate the entire life cycle history data and collect data through this.

• Journal of Korean Home Economics Education Association
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• v.31 no.2
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• pp.113-135
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• 2019

The purpose of this study was to analyze the characteristics reflected in the 2015 revised home economics curriculum for the key competencies presented in the OECD education 2030 project. The results indicate that first, in general, about 46.5% of the competencies could be classified into the skill, attitude and value category; 17% into the learning concept framework category; 24.2% into the competency development cycle category; and 12.5% into the complex competency category. Overall, the competencies of the OECD learning framework are found to be reflected primarily in the achievement standards(59%), followed by characteristics(16.1%), teaching-learning and assessments orientation(9.4%), content system(8%), and goals(7.6%). Second, the key competencies were reflected in the middle school curriculum, more often in the descending order of action, problem-solving, communication, respect, creative thinking, conflict resolution, empathy, critical thinking, self-regulation, and student agency. In the high school curriculum, the competencies were reflected more often in the descending order of action, empathy, problem-solving, anticipation, global competence, self-regulation, student agency, literacy for sustainable development, reflection, and critical thinking. Third, the heat map shows that the competencies corresponding to the third and fourth levels are most frequently reflected in the curriculum. Therefore, it is advisable to develop effective plans to execute and support the reflection of key competencies in the curriculum. Through this study, home economics educators are expected to understand the inter-connectivity between the key competencies emphasized by the OECD learning framework and the competencies of home economics as a practical subject, and to scrutinize how to help individual students develop their overall competencies and be prepared for the future.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.945-953
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• 2006

The researchers in the field of cognitive science and learning science suggest that the teaching activity induces the elaborative and meaningful learning. Actually, lots of research findings have shown the beneficial effect of learning by teaching such as peer tutoring. But peer tutoring has some limitations in the practical learning context. To overcome some limitations, the new concept of "learning by teaching" through the agent called Teachable Agent. The teachable agent is a modified version of traditional intelligent tutoring system that assigns a role of tutor to teach the agent. The teachable agent monitors individual difference and provides a student with a chance for deep learning and motivation to learn by allowing them to play an active role in the process of learning. That is, The teaching activity induces the elaborative and meaningful learning. This study compared the effects of our teachable agent, KORI, and peer tutoring on the cognition and motivation. The field experiment was conducted to examine whether learning by teaching the teachable agent would be more effective than peer tutoring and reading condition. In the experiment, all participants took 30 minutes lesson on rock and rock cycle together to acquire the base knowledge in the domain. After the lesson, participants were randomly assigned to one of the three experimental conditions; reading condition, peer tutoring condition, and teachable agent condition. Next, participants of each condition moved into separated place and performed their own learning activity. After finishing all of the learning activities in each condition, all participants were instructed to rate the interestingness using a 5-point scale on their own learning activity and leaning material, and were given the comprehension test. The results indicated that the teachable agent condition and the peer tutoring condition showed more interests in the learning than the reading condition. It is suggested that teachable agent has more advantages in overcoming the several practical limitations of peer tutoring such as restrictions in time and place, tutor's cognitive burden, unnecessary interaction during peer tutoring. The applicability and prospects of the teachable agent as an efficient substitute for peer tutoring and traditional intelligent tutoring system were also discussed.

• Korean Journal of Child Education & Care
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• v.17 no.4
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• pp.91-119
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• 2017

The purpose of this study is to determine whether using a dietary education program based on learning cycle model has any significant effect on enhancing their nutrition knowledge, dietary behavior, science-process skill and scientific attitude. The subjects of this study were children in H and G daycare center in G City. The experiment group of this study was 16 children in the class of five-year-olds and 7 children in the class of four-year-olds who passed their birthday and became five-year-olds in H daycare center. The Analysis of Covariance(ANCOVA) and Pared t-test was conducted using SPSS WINDOWS 20.0 program. The results of applying dietary education program were as follows. Experimental group indicated enhancements between pre and post test of Nutrition Achievement Test, Nutrition Quotient for Preschooler, Science Process Skill and Scientific Attitude Assessment compare to comparative group. Therefore, we can conclude that the dietary education program does have effects on enhancing of nutrition knowledge, dietary behavior, science process skill and scientific attitude. The result of this study can be used as basic data to study dietary related factors that present importance of health dietary life of young children and need to provide educational experience of healthy diet for young children.

• Geophysics and Geophysical Exploration
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• v.26 no.2
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• pp.37-51
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• 2023

The ground roll is the most common coherent noise in land seismic data and has an amplitude much larger than the reflection event we usually want to obtain. Therefore, ground roll suppression is a crucial step in seismic data processing. Several techniques, such as f-k filtering and curvelet transform, have been developed to suppress the ground roll. However, the existing methods still require improvements in suppression performance and efficiency. Various studies on the suppression of ground roll in seismic data have recently been conducted using deep learning methods developed for image processing. In this paper, we introduce three models (DnCNN (De-noiseCNN), pix2pix, and CycleGAN), based on convolutional neural network (CNN) or conditional generative adversarial network (cGAN), for ground roll suppression and explain them in detail through numerical examples. Common shot gathers from the same field were divided into training and test datasets to compare the algorithms. We trained the models using the training data and evaluated their performances using the test data. When training these models with field data, ground roll removed data are required; therefore, the ground roll is suppressed by f-k filtering and used as the ground-truth data. To evaluate the performance of the deep learning models and compare the training results, we utilized quantitative indicators such as the correlation coefficient and structural similarity index measure (SSIM) based on the similarity to the ground-truth data. The DnCNN model exhibited the best performance, and we confirmed that other models could also be applied to suppress the ground roll.

• Journal of the Korea Society of Computer and Information
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• v.24 no.10
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• pp.233-242
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• 2019

At present, our society is reducing the birth rate, and the school population is decreasing, but multicultural students are facing the increasing social phenomenon. We all need to make sure that bilingual teaching and learning is effective for children of multicultural families who need to live in the days of Phono Sapiens so that they can live confidently as members of our society. To this end, there is a great need for a bilingual teaching and learning method that enables children from multicultural families to be free from language and cultural prejudice and to actively communicate and interact. In this paper, we propose a customized bilingual education method that applies various teaching and learning methods according to the development cycle, school age, and Korean language ability of children of multicultural families. The proposed bilingual teaching method for children of multicultural families is a teaching and learning method that applies the Waldorf teaching principle.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.3
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• pp.98-105
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• 2021

The sensory stimulation of a cosmetic product has been deemed to be an ancillary aspect until a decade ago. That point of view has drastically changed on different levels in just a decade. Nowadays cosmetic formulators should unavoidably meet the needs of consumers who want sensory satisfaction, although they do not have much time for new product development. The selection of new products from candidate products largely depend on the panel of human sensory experts. As new product development cycle time decreases, the formulators wanted to find systematic tools that are required to filter candidate products into a short list. Traditional statistical analysis on most physical property tests for the products including tribology tests and rheology tests, do not give any sound foundation for filtering candidate products. In this paper, we suggest a deep learning-based analysis method to identify hand cream products by raw electric signals from tribological sliding test. We compare the result of the deep learning-based method using raw data as input with the results of several machine learning-based analysis methods using manually extracted features as input. Among them, ResNet that is a deep learning model proved to be the best method to identify hand cream used in the test. According to our search in the scientific reported papers, this is the first attempt for predicting test cosmetic product with only raw time-series friction data without any manual feature extraction. Automatic product identification capability without manually extracted features can be used to narrow down the list of the newly developed candidate products.

• Journal of The Korean Association For Science Education
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• v.15 no.2
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• pp.201-212
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• 1995

The purpose of this study was to analyze science teaching models: Cognitive Conflict Teaching Model(CCTM), Generative Learning Model(GLM), Learning Cycle Model(LCM), Hypothesis-Testing Model(HTM), and Discovery Teaching Model(DTM). Using literature review, the models were analyzed and compared in several aspects; philosophical and psychological bases, primary goals and assumptions, syntax, implementation environments, and probable effects. The major finding were as follows; 1. Science teaching models had been diverse features. In the comparisons of science teaching models, some differences and similarities were founded. These were different in the degree of similarity and emphasis. 2. CCTM and GLM resemble each other in philosophical and psychological bases, primary goals and main assumptions, implementation environments, and probable effects. 3. LCM and HTM showed similarities in philosophical bases, syntax, and implementation environments. But differences were founded in other aspects These results showed that the diverse features of science teaching models should be considered in choosing a model for science teaching.

• Journal of the Korean Society of Systems Engineering
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• v.17 no.2
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• pp.91-97
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• 2021

User interface for cancer classification system is a software application with clinician's friendly tools and functions to diagnose cancer from pathology images. Pathology evolved from manual diagnosis to computer-aided diagnosis with the help of Artificial Intelligence tools and algorithms. In this paper, we explained each block of the project life cycle for the implementation of automated breast cancer classification software using AI and machine learning algorithms to classify normal and invasive breast histology images. The system was designed to help the pathologists in an automatic and efficient diagnosis of breast cancer. To design the classification model, Hematoxylin and Eosin (H&E) stained breast histology images were obtained from the ICIAR Breast Cancer challenge. These images are stain normalized to minimize the error that can occur during model training due to pathological stains. The normalized dataset was fed into the ResNet-34 for the classification of normal and invasive breast cancer images. ResNet-34 gave 94% accuracy, 93% F Score, 95% of model Recall, and 91% precision.