• Electronics and Telecommunications Trends
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• v.34 no.4
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• pp.55-64
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• 2019

In this study, we introduce trends in neural-network-based deep learning research applied to dialogue systems. Recently, end-to-end trainable goal-oriented dialogue systems using long short-term memory, sequence-to-sequence models, among others, have been studied to overcome the difficulties of domain adaptation and error recognition and recovery in traditional pipeline goal-oriented dialogue systems. In addition, some research has been conducted on applying reinforcement learning to end-to-end trainable goal-oriented dialogue systems to learn dialogue strategies that do not appear in training corpora. Recent neural network models for end-to-end trainable chit-chat systems have been improved using dialogue context as well as personal and topic information to produce a more natural human conversation. Unlike previous studies that have applied different approaches to goal-oriented dialogue systems and chit-chat systems respectively, recent studies have attempted to apply end-to-end trainable approaches based on deep neural networks in common to them. Acquiring dialogue corpora for training is now necessary. Therefore, future research will focus on easily and cheaply acquiring dialogue corpora and training with small annotated dialogue corpora and/or large raw dialogues.

• Wind and Structures
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• v.36 no.6
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• pp.367-377
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• 2023

This study proposes a few-shot learning model for extrapolating the wind pressure of scaled experiments to full-scale measurements. The proposed ML model can use scaled experimental data and a few full-scale tests to accurately predict the remaining full-scale data points (for new specimens). This model focuses on extrapolating the prediction to different scales while existing approaches are not capable of accurately extrapolating from scaled data to full-scale data in the wind engineering domain. Also, the scaling issue observed in wind tunnel tests can be partially resolved via the proposed approach. The proposed model obtained a low mean-squared error and a high coefficient of determination for the mean and standard deviation wind pressure coefficients of the full-scale dataset. A parametric study is carried out to investigate the influence of the number of selected shots. This technique is the first of its kind as it is the first time an ML model has been used in the wind engineering field to deal with extrapolation in wind performance prediction. With the advantages of the few-shot learning model, physical wind tunnel experiments can be reduced to a great extent. The few-shot learning model yields a robust, efficient, and accurate alternative to extrapolating the prediction performance of structures from various model scales to full-scale.

• 대한공업교육학회지
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• v.30 no.1
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• pp.1-18
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• 2005

The purpose of this study was to development a model of problem solving instruction for improving practical skill-competence in technical high school. For the study, various literature researches were reviewed intensively about problem solving process, laboratory instruction's approaches and learning principals. The problem solving instruction process was composed with identifying problems, generating alternative solutions, investigation and research, choosing a solution, acting on a plan, modeling of problem solving, testing and evaluating, redesigning and improving. The skills schema combines a four domain of skilled activity, that is, cognitive skills, psychomotor skills, reactive skills and interactive skills. The problem solving instruction was composed with five major learning systems-emotional, social, cognitive, physical, and reflective-that can be used extensively as generic lesson plashing. The teacher serves as a coach or guide for student learning. As a facilitator, the teacher challenges, questions, and stimulates the students in their thinking, problem solving and self-directed study. In this process, students represent problem with think aloud, assume responsibility for their learning and move from teacher-centered to student-centered education.

• International Journal of Contents
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• v.8 no.1
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• pp.1-9
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• 2012

The present study explores how the stress of online learners is related to Korean cultural norms and social expectation and presents the criteria online education should aim at when designing instructional approaches. A sample of 176 students from a Korean online university participated in a study investigating the patterns in the academic and personal stressors they face. This study also examines stressor types in relation to sample characteristics, analyzed with a categorization method developed by extant researchers on the stress faced by U.S. college students. Unlike the findings of previous studies on college student stress, this study's results reveal that nontraditional Korean online students were faced with (1) taking on the multiple roles at work and home prescribed by cultural and social norms, and (2) challenges in regulating study habits and the learning environment as adult learners. The relevant implications for the design of online learning are discussed.

• The Mathematical Education
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• v.43 no.3
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• pp.257-273
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• 2004

In this paper, we analyze what the components of mathematics teacher` knowledge are, and find that mathematics teacher need knowledge of three areas: subject matter knowledge, pedagogical knowledge, and pedagogical content knowledge. Studies of practicing teachers suggest that When teachers lack understanding in their respective disciplines, it inhibits them from providing students the best learning opportunities, but that a teacher possessing pedagogical content knowledge provides learners with multiple approaches into learning. Some teachers having sound knowledge of mathematics and students were able to respond appropriately to students' questions, design appropriate learning activities involving a variety of mathematical representations, and orchestrate mathematical discourse in the classroom. Thus, it appears that mathematics teachers' knowledge positively affect teaching and student learning..

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.177-181
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• 2021

With the recent advances in information and communication technology, online management of students' learning data has become the norm. Research on learning analysis that predicts the near future (in a few years) of students' careers using machine learning methods and state transition models has been widely conducted. It is important for educators to evaluate the comprehension stability of students to prevent a decrease in their comprehension rate and dropouts in the class. In this study, we measured the comprehension process of university students in different types of lectures. Herein, we report on the results of data analysis using time series and data statistics, and consider several educational approaches.

• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.343-351
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• 2022

A brain tumor forms when some tissue becomes old or damaged but does not die when it must, preventing new tissue from being born. Manually finding such masses in the brain by analyzing MRI images is challenging and time-consuming for experts. In this study, our main objective is to detect the brain's tumorous part, allowing rapid diagnosis to treat the primary disease instantly. With image processing techniques and deep learning prediction algorithms, our research makes a system capable of finding a tumor in MRI images of a brain automatically and accurately. Our tumor segmentation adopts the U-Net deep learning segmentation on the standard MICCAI BRATS 2018 dataset, which has MRI images with different modalities. The proposed approach was evaluated and achieved Dice Coefficients of 0.9795, 0.9855, 0.9793, and 0.9950 across several test datasets. These results show that the proposed system achieves excellent segmentation of tumors in MRIs using deep learning techniques such as the U-Net algorithm.

• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.57-65
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• 2020

At present, the existing virus recognition systems usually use signature approach to detect malicious executable files, but these methods often fail to detect new and invisible malware. At the same time, some methods try to use more general features to detect malware, and achieve some success. Moreover, machine learning-based approaches are applied to detect malware, which depend on features extracted from malicious codes. However, the different distribution of features oftraining and testing datasets also impacts the effectiveness of the detection models. And the generation oflabeled datasets need to spend a significant amount time, which degrades the performance of the learning method. In this paper, we use transfer learning to detect new and previously unseen malware. We first extract the features of Portable Executable (PE) files, then combine transfer learning training model with KNN approachto detect the new and unseen malware. We also evaluate the detection performance of a classifier in terms of precision, recall, F1, and so on. The experimental results demonstrate that proposed method with high detection rates andcan be anticipated to carry out as well in the real-world environment.

• Korean Journal of Agricultural Science
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• v.47 no.2
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• pp.205-217
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• 2020

In this study, a deep learning-based tillage boundary detection method for autonomous tillage by a tractor was developed, which consisted of image cropping, object classification, area segmentation, and boundary detection methods. Full HD (1920 × 1080) images were obtained using a RGB camera installed on the hood of a tractor and were cropped to 112 × 112 size images to generate a dataset for training the classification model. The classification model was constructed based on convolutional neural networks, and the path boundary was detected using a probability map, which was generated by the integration of softmax outputs. The results show that the F1-score of the classification was approximately 0.91, and it had a similar performance as the deep learning-based classification task in the agriculture field. The path boundary was determined with edge detection and the Hough transform, and it was compared to the actual path boundary. The average lateral error was approximately 11.4 cm, and the average angle error was approximately 8.9°. The proposed technique can perform as well as other approaches; however, it only needs low cost memory to execute the process unlike other deep learning-based approaches. It is possible that an autonomous farm robot can be easily developed with this proposed technique using a simple hardware configuration.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.3
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• pp.869-885
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• 2012

The purpose of this study is to present innovative approaches for u-Learning environment in public education with Digital Textbook. The Korean Government has been making efforts to introduce the u-Learning environment to maximize the learning effect in public education with Digital Textbook. However, there are only a few studies that analyze the effectiveness of u-Learning environment and Digital Textbook. This paper reviews the current status of u-Learning environment in Korea and analyzes the satisfaction level with Digital Textbooks. The first survey regarding technological factors was collected from 197 students. The results of the survey revealed that the level of satisfaction has declined over a year. The weakness of the study is that the sample frame is insufficient and survey questions did not reflect diverse factors of learning effectiveness. To supplement these shortcomings, 2,226 students were asked about learning performance. The results of the survey showed that the satisfaction with Digital Textbooks is much higher than that of paper textbooks. However, this paper is limited to u-Learning environments in public education. Therefore, research needs to be improved by reflecting both public and private sectors of education in following studies. This paper suggests useful guidelines to educators in improving their u-Learning environment.