• Journal of the Earthquake Engineering Society of Korea
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• v.28 no.2
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• pp.113-119
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• 2024

Existing reinforced concrete buildings with seismically deficient column details affect the overall behavior depending on the failure type of column. This study aims to develop and validate a machine learning-based prediction model for the column failure modes (shear, flexure-shear, and flexure failure modes). For this purpose, artificial neural network (ANN), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) models were used, considering previously collected experimental data. Using four machine learning methodologies, we developed a classification learning model that can predict the column failure modes in terms of the input variables using concrete compressive strength, steel yield strength, axial load ratio, height-to-dept aspect ratio, longitudinal reinforcement ratio, and transverse reinforcement ratio. The performance of each machine learning model was compared and verified by calculating accuracy, precision, recall, F1-Score, and ROC. Based on the performance measurements of the classification model, the RF model represents the highest average value of the classification model performance measurements among the considered learning methods, and it can conservatively predict the shear failure mode. Thus, the RF model can rapidly predict the column failure modes with simple column details.

• Journal of Digital Contents Society
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• v.9 no.2
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• pp.225-233
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• 2008

Rapid advancement information and communication technologies has introduced various dimension of e-Learning environment such as u-learning(ubiquitous learning), m-learning(mobile learning) and t-learning(television learning). These technologies enabled learners to access learning contents through variety of devices with more flexibility and consistency. In order to implement learning through these multiple environments, basically it is necessary to acquire and process the platform information that contains properties and status of the web-accessing devices. In this study, we introduce the design and implementation of a Platform Analyzer Model which is essential for learning systems that support multi-platform environment. We also present a Interactive DTV-Centered multi-platform learning environment framework using PC, PDA or Mobile phone. Finally, we will discuss the possibility of the multi-platform learning environment with sample scenario and contents.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.2
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• pp.245-250
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• 2005

With rapid development of Internet, new paradigm creation requirement about the education environment and method is increasing and also the e-learning to apply traditional education industry was introduced in many field of education. The research about a learner's satisfaction of the e-learning, aided by effort to spread this e-learning, have been processed much but most of these researches were intended for the enterprise and there are few for the high school. Therefore, in this study we proposed a model for evaluating the education satisfaction of the e-learning and analyzed the consciousness structure about the e-learning education satisfaction of the high school students using Fuzzy Structural Modeling method. Also, constructing an evaluation model considered the results of consciousness structure analysis, we evaluated the e-learning education satisfaction and showed a method which improved it by the sensitivity analysis.

• Journal of Korea Trade
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• v.24 no.6
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• pp.135-156
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• 2020

Purpose - This study investigates whether the internationalization process of traditional industry firms can be categorized as born global, early internationalization, or gradual internationalization, and examines what factors promote internationalization in traditional industries using a case study of two firms, one each in China and Korea. Design/methodology - This study elects to use case study methodology to determine the "how" and "why" of internationalization process of traditional industry firms. Taking into consideration that factors that impact the internationalization process of firms are diverse and unclear in terms of causality, this study utilizes exploratory case study methodology. This research performs a comparative two-case study of two firms in traditional industries, one each in China and Korea, to examine similarities and differences of study subjects in order to improve the validity and suitability of research results. Findings - The findings of this research are as follows: First, traditional industries are more likely go through early and rapid internationalization rather than being born global; born globals are far more likely to appear in high tech industries. Second, the internationalization process of companies that go through early and rapid internationalization differs from what is indicated by traditional internationalization theories, and are not limited by factors like psychological distance and lack of experiential knowledge. Third, international entrepreneurship, international market orientation, and imitation and learning are important internal driving factors for early and rapid internationalization. Fourth, conditions within the domestic market, policy support from the government, and pilot effect from industry leaders are external driving factors for early and rapid internationalization. Originality/value - This study shows that the internationalization process of traditional industry firms is more likely to be early and rapid internationalization rather than being born global and suggests answers to why this may be the case. In addition, through an examination of case studies, it reveals that the internationalization process of traditional industry firms that undergo early and rapid internationalization is different from traditional internationalization theory, in that they are not limited by the lack of psychological proximity and empirical knowledge, and are driven by international entrepreneurship, international market orientation, imitation and learning, competitive pressure within the domestic market, government's policy support, and the pilot effect of industry leaders. Therefore, this study contributes to literature by expanding the scope of application of born global theory to traditional industries, making born global theory more generalizable and identifying driving factors to internationalization of traditional industry firms.

• Journal of Internet Computing and Services
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• v.20 no.6
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• pp.73-84
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• 2019

Machine learning as a service, the so-called MLaaS, has recently attracted much attention in almost all industries and research groups. The main reason for this is that you do not need network servers, storage, or even data scientists, except for the data itself, to build a productive service model. However, machine learning is often very difficult for most developers, especially in traditional science due to the lack of well-structured big data for scientific data. For experiment or application researchers, the results of an experiment are rarely shared with other researchers, so creating big data in specific research areas is also a big challenge. In this paper, we introduce the KISTI-ML platform, a community-based rapid AI model development for scientific data. It is a place where machine learning beginners use their own data to automatically generate code by providing a user-friendly online development environment. Users can share datasets and their Jupyter interactive notebooks among authorized community members, including know-how such as data preprocessing to extract features, hidden network design, and other engineering techniques.

• Journal of Society of Preventive Korean Medicine
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• v.12 no.2
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• pp.73-83
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• 2008

The rapid change of the health and medical environment and the globalization of medicine has driven doctors to converge and analyse of new and up-to-date medical information and decide to what to make decision for diagnosis and treatments in clinical practice. Medical environment goes with the changes with social environment such as rapid increase of aging population, changes of disease pattern, formation of new area of experts except doctors, government intervention for the medical system, medical insurance of the charges of medical treatment, a increased desire for human rights. These trends should be adopted rapidly to the education system for the students of medical school. The learning objectives of the preventive medicine was developed in 1995 and underwent necessary revision of the contents to create the first revision in 2006. However, the required educational contents of health promotion and disease prevention have been changed by the new trends of medical education such as PBL and integrated curriculum and the 2006 revision does not satisfy these needs. We formed a task force which surveyed all the Western and Traditional Korean medical colleges to describe the state of preventive medicine education in Korea, analyzed the changing education demand according to the change of health environment and quantitatively measured the validity and usefulness of each learning objective in the previous curriculum. With these results, for the good education for preventive medicine, each Traditional Korean medicine schools need more preventive medicine faculties and teaching assistants and opening of some required subjects such as Yangsaeng and Qigong. And future studies of the learning process and ongoing development of teaching materials according to the new learning objectives should be undertaken with persistence in order to ensure the progress of preventive medicine education.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.238-246
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• 2021

The rapid rise of the Internet and social media has resulted in a large number of text-based reviews being placed on sites such as social media. In the age of social media, utilizing machine learning technologies to analyze the emotional context of comments aids in the understanding of QoS for any product or service. The classification and analysis of user reviews aids in the improvement of QoS. (Quality of Services). Machine Learning algorithms have evolved into a powerful tool for analyzing user sentiment. Unlike traditional categorization models, which are based on a set of rules. In sentiment categorization, Bidirectional Long Short-Term Memory (BiLSTM) has shown significant results, and Convolution Neural Network (CNN) has shown promising results. Using convolutions and pooling layers, CNN can successfully extract local information. BiLSTM uses dual LSTM orientations to increase the amount of background knowledge available to deep learning models. The suggested hybrid model combines the benefits of these two deep learning-based algorithms. The data source for analysis and classification was user reviews of Indian Railway Services on Twitter. The suggested hybrid model uses the Keras Embedding technique as an input source. The suggested model takes in data and generates lower-dimensional characteristics that result in a categorization result. The suggested hybrid model's performance was compared using Keras and Word2Vec, and the proposed model showed a significant improvement in response with an accuracy of 95.19 percent.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4816-4834
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• 2020

This paper proposes transfer learning and fine-tuning techniques for a deep learning model to detect three distinct brain tumors from Magnetic Resonance Imaging (MRI) scans. In this work, the recent YOLOv4 model trained using a collection of 3064 T1-weighted Contrast-Enhanced (CE)-MRI scans that were pre-processed and labeled for the task. This work trained with the partial 29-layer YOLOv4-Tiny and fine-tuned to work optimally and run efficiently in most platforms with reliable performance. With the help of transfer learning, the model had initial leverage to train faster with pre-trained weights from the COCO dataset, generating a robust set of features required for brain tumor detection. The results yielded the highest mean average precision of 93.14%, a 90.34% precision, 88.58% recall, and 89.45% F1-Score outperforming other previous versions of the YOLO detection models and other studies that used bounding box detections for the same task like Faster R-CNN. As concluded, the YOLOv4-Tiny can work efficiently to detect brain tumors automatically at a rapid phase with the help of proper fine-tuning and transfer learning. This work contributes mainly to assist medical experts in the diagnostic process of brain tumors.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.5
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• pp.1042-1048
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• 2017

Recently rapid changes in the industrial environment require new talents in companies. Flipped learning is drawing attention as an effective teaching-learning method. The existing traditional lecture teaching-learning method have various problems that the dropout rates of the student is high and the creative problem solving ability is hindered. In the case of the IT engineering college, most of the major theoretical courses require prior learning of the prerequisite coursework subjects. Therefore, effective teaching-learning methods must be developed to improve student participation and academic achievement. This paper proposes the flipped learning model consisting of five sets that combine the flipped learning and practice to improve student motivation and self - directed learning. Also, this paper analyzes the learning effect by applying it to the algorithm lecture of computer engineering and presents problem and utilization plan according to the result.

• Physical Therapy Korea
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• v.10 no.3
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• pp.17-27
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• 2003

Repetitive transcranial magnetic stimulation (rTMS) modulates cortical excitability beyond the duration of the rTMS trains themselves. Depending on rTMS parameters, a lasting inhibition or facilitation of cortical excitability can be induced. Therefore, rTMS of high or low frequency over motor cortex may change certain aspects of motor learning performance and cortical activation. This study investigated the effect of high and low frequency subthreshold rTMS applied to the motor cortex on motor learning of sequential finger movements and brain activation using functional MRI (fMRI). Three healthy right-handed subjects (mean age 23.3) were enrolled. All subjects were trained with sequences of seven-digit rapid sequential finger movements, 30 minutes per day for 5 consecutive days using their left hand. 10 Hz (high frequency) and 1 Hz (low frequency) trains of rTMS with 80% of resting motor threshold and sham stimulation were applied for each subject during the period of motor learning. rTMS was delivered on the scalp over the right primary motor cortex using a figure-eight shaped coil and a Rapid(R) stimulator with two Booster Modules (Magstim Co. Ltd, UK). Functional MRI (fMRI) was performed on a 3T ISOL Forte scanner before and after training in all subjects (35 slices per one brain volume TR/TE = 3000/30 ms, Flip angle $60^{\circ}$, FOV 220 mm, $64{\times}64$ matrix, slice thickness 4 mm). Response time (RT) and target scores (TS) of sequential finger movements were monitored during the training period and fMRl scanning. All subjects showed decreased RT and increased TS which reflecting learning effects over the training session. The subject who received high frequency rTMS showed better performance in TS and RT than those of the subjects with low frequency or sham stimulation of rTMS. In fMRI, the subject who received high frequency rTMS showed increased activation of primary motor cortex, premotor, and medial cerebellar areas after the motor sequence learning after the training, but the subject with low frequency rTMS showed decreased activation in above areas. High frequency subthreshold rTMS on the motor cortex may facilitate the excitability of motor cortex and improve the performance of motor sequence learning in normal subject.