• 한국지진공학회논문집
• /
• 제28권2호
• /
• pp.113-119
• /
• 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.

• 디지털콘텐츠학회 논문지
• /
• 제9권2호
• /
• pp.225-233
• /
• 2008

정보통신 기술의 급격한 발달은 u-러닝, m-러닝, t-러닝과 같은 다양한 형태의 e-러닝을 유도 하였다. 각종 기술을 통하여 학습자는 고정된 학습 공간이 아니라 다양한 환경에서 연속성을 가지고 학습할 수 있게 되었다. 이러한 다중 환경에서의 학습을 위해서는 기본적으로 웹 콘텐츠에 접속하는 장치들의 성능과 상태를 나타내는 플랫폼 정보를 획득하고 처리할 수 있는 기능이 필수적이다. 본 논문에서는 다중 환경을 지원하는 학습 시스템의 필수요소인 플랫폼 분석기의 모델을 설계하고 구현하였다. 또한 DTV를 중심으로 PC, PDA 및 휴대폰을 이용하는 다중 환경 학습 프레임워크를 제안하고, 샘플 학습 시나리오와 콘텐츠를 통하여 다중 환경 학습의 가능성을 살펴보았다.

• 한국지능시스템학회논문지
• /
• 제15권2호
• /
• pp.245-250
• /
• 2005

인터넷의 급격한 발전으로 교육환경$\cdot$방법에 대한 새로운 패러다임 창출요구가 증가하고 있으며 전통적인 교육산업도 교육의 전 분야에서 이론 활용한 e-teaming이 많은 분야에서 도입되었고, 빠른 속도로 그 영역이 확장되고 있다. 이러한 e-learning 확산 노력에 힘입어 그동안 e-learning의 학습자 만족도에 대한 연구도 많이 진행되어 왔지만 기업체를 대상으로 한 연구가 거의 대부분이었고 고등학교를 대상으로 한 연구는 거의 없는 실정이다. 따라서, 본 연구에서는 이러한 배경을 바탕으로 고등학생을 대상으로 한 e-learning 교육만족도 평가를 위한 모델을 제안하고, 제안한 모델을 대상으로 퍼지구조 모델링법을 이용하여 고등학생의 e-learning 교육 만족도에 관한 의식구조를 분석하였다. 또한, 의식구조분석의 결과가 고려된 평가모델을 구축하여 e-learning 교육 만족도를 평가하고, 민감도분석을 통하여 e-learning 교육만족도 향상 방안을 제시 하였다.

• Journal of Korea Trade
• /
• 제24권6호
• /
• pp.135-156
• /
• 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.

• 인터넷정보학회논문지
• /
• 제20권6호
• /
• pp.73-84
• /
• 2019

최근 서비스로서의 머신러닝(MLaaS) 개념은 데이터 자체를 제외하고 네트워크 서버, 스토리지 또는 데이터 과학자 없이도 생산적인 서비스 모델을 구축할 수 있다는 점에서 기계학습을 다루는 대부분의 산업 분야와 연구 그룹들의 많은 관심을 받고 있다. 그러나 과학 분야에서는 양질의 빅데이터를 확보하는 가정 자체가 커다란 도전이 된다. 즉, 연구자 간 연구 결과물의 공유가 쉽지 않을 뿐 아니라 과학기술 데이터의 비정형성 문제를 해결해야하는 문제가 선행된다. 본 논문에서 제안된 KISTI-ML 플랫폼은 과학기술 데이터를 위한 AI 모델 고속 개발 도구로서, 머신러닝에 익숙하지 않은 연구자들을 위해 웹 기반 GUI 인터페이스를 제공하고 연구자는 자신의 데이터를 이용하여 머신러닝 코드를 손쉽게 생성하고 구동할 수 있다. 또한 승인된 커뮤니티 멤버들을 중심으로 데이터셋 및 특징 추출에 사용되는 데이터전처리, 학습 네트워크 설계 등이 포함되는 프로그래밍 코드를 공유할 수 있는 환경을 제공한다.

• 대한예방한의학회지
• /
• 제12권2호
• /
• pp.73-83
• /
• 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
• /
• 제21권8호
• /
• pp.238-246
• /
• 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)
• /
• 제14권12호
• /
• pp.4816-4834
• /
• 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.

• 한국정보통신학회논문지
• /
• 제21권5호
• /
• pp.1042-1048
• /
• 2017

최근 새로운 산업 환경의 변화에 필요한 맞춤형 기업 인재양성을 위한 효과적인 교수학습방법으로 플립러닝이 주목 받고 있다. 기존 강의식 수업방식의 경우 중도탈락률이 높고 창의적 문제 해결력을 저해하는 등의 다양한 문제점이 있다. IT 공과대학의 경우 선수 교과목의 선행이 필요한 전공 이론과목이 대부분이므로 학생들의 학습 참여도와 학업 성취도를 높일 수 있는 효과적인 교수학습방법의 개발이 필요하다. 본 논문에서는 학생들의 학습 동기를 유발하고 자기 주도적 학습을 통한 학습 효과를 높이기 위해 플립러닝과 실습수업을 병행한 5단계 플립러닝 수업모형을 제안하였다. 또한 컴퓨터공학과의 알고리즘 수업에 적용하여 학습 효과를 분석하고 그 결과를 바탕으로 문제점 및 활용방안을 제시하고자 한다.

• 한국전문물리치료학회지
• /
• 제10권3호
• /
• pp.17-27
• /
• 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.