• Steel and Composite Structures
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• 제45권4호
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• pp.563-577
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• 2022

This paper presents experimental and numerical investigations of a new seismic enhancement method for existing reinforced concrete (RC) frames by using an external sub-structure, the hybrid seismic retrofit method (HSRM) system. This retrofit system is an H-shaped frame bolt-connected to an existing RC frame with an infilled-concrete layer between their gaps. Two RC frames were built, one with and one without HSRM, and tested under cyclic loading. The experimental findings showed that the retrofitted RC frame was superior to the non-retrofitted specimen in terms of initial stiffness, peak load, and energy dissipation capacity. A numerical simulation using a commercial program was employed for verification with the experiments. The results obtained from the simulations were consistent with those from the experiments, indicating the finite element (FE) models can simulate the seismic behaviors of bare RC frame and retrofitted RC frame using HSRM.

• 대한산업공학회지
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• 제20권4호
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• pp.113-134
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• 1994

A radial basis hybrid neural network (RHNN) is presented for an on-line detection of machine condition change. Two-phase modeling by RHNN is designed for describing a machine condition process and for predicting future signal. A moving block procedure is also designed for detecting a process change. A fast on-line learning algorithm, the recursive least square estimation, is introduced. Experimental results showed the RHNN could be utilized efficiently for on-line machine condition monitoring.

• Journal of East Asia Management
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• 제4권2호
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• pp.39-62
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• 2023

Online and offline mixed teaching mode has become an important way to promote the connotative development of higher education. Under the background that offline teaching has become mature, in order to further promote the development of online education, and promote the implementation of the mixed teaching mode, to mix and to provide basis for the construction of the mixed teaching mode, this study takes the online learning effect as the evaluation basis, adopts the online questionnaire survey to conduct statistical analysis of the online learning behavior of 2213 college students, and discusses the differentiation phenomenon of online learning groups from the micro, meso and macro perspectives. It is found that there are significant differences in the online learning effect of college students in terms of the type of learning platform, whether the school implements the online offline mixed teaching mode, education background, grade (bachelor's degree), and region. Colleges and universities should strengthen the promotion of online and offline mixed teaching mode; The online learning platform should improve the platform function and strengthen the functional differentiation design of learning resources for students. Education departments pay attention to the learning effect of online learners in different regions, and bridge the gap in regional education.

• 한국문헌정보학회지
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• 제39권3호
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• pp.75-91
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• 2005

인터넷의 발전과 성장은 웹상에서의 정보의 량에 있어서 폭발적인 성장을 가져 왔다. 이러한 웹상에서의 정보량의 증가는 정보이용자의 요구와 필요에 맞는 정보 제공을 위한 서비스로서 웹기반의 개인화서비스에 대한 요구를 더욱 더 강조하게 되었다. 개인화서비스는 정보이용자의 요구와 필요에 의해 현실화 될 수 있으며 이러한 정보이용자의 관심사와 정보요구는 지속적으로 또한 급격하게 변화되고 있다. 웹상의 수많은 정보로부터 정보이용자의 요구와 필요를 만족 시킬 수 있기 위하여 본 논고에서는 이용자의 관심과 요구를 표현하기 위하여 이용자 프로파일 정보를 이용하였으며 이러한 이용자의 프로파일 정보는 이용자의 요구와 흥미에 대한 변화를 반영하기 위하여 지속적으로 갱신하였다. 본고에서는 정보이용자의 정보요구와 흥미의 변화를 지속적으로 이용자프로파일에 반영하기 위한 방안으로서 학습알고리즘을 제안하였다. 정보이용자의 정보에 대한 피드백을 기반으로 이용자의 정보에 대한 흥미와 요구는 본 고에서 제안한 학습알고리즘을 통하여 지속적으로 갱신 되므로서 정보이용자에게 보다 정확한 정보를 제공할 수 있다고 할 수 있다. 이러한 학습알고리즘은 보다 개선된 하이브리드 정보추천시스템에 적용하였다.

• 컴퓨터교육학회논문지
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• 제6권1호
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• pp.109-120
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• 2003

오늘날 웹 활용이 본격적인 대중화 시대로 접어들게 되면서 교육현장에서도 다양한 교육방법이 연구, 개발되어 WBI 학습, 웹기반 CAI, 원격강의 등 웹을 기반으로 하는 가상교육이 활발히 진행되고 있다. 웹기반 교육이 교실수업에 비해 가지는 장점을 최대한 살려서 현실성 있고 효율적인 교육의 효과를 얻으려면 학습자의 능력과 관심, 흥미, 적성 등을 고려한 웹기반 수준별 수업 모형 개발이 요구된다. 본 논문에서는 제7차 교육과정에서 실시되는 단계형 수준별 교육과정에서 진급형 수업모형과 심화 보충형 수업모형을 웹기반 수업을 위해 혼합한 혼합형 수준별 수업모형으로 제시하고 구현한다. 또한, 모형의 비교를 위하여 이를 실업계고등학교의 전산회계 교과에 적용한다. 웹기반 수업을 하기 위해서 인터넷이 가능한 학교 전산실을 이용하며 100분 동안 학생들이 자율적으로 웹기반 학습을 하도록 한다. 수업 적용을 하고 설문 조사를 실시하여 웹기반 수업에 대한 효과와 수업모형의 학업성취도에 미치는 영향을 분석 후 결과를 제시한다.

• 한국실천공학교육학회논문지
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• 제4권2호
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• pp.53-59
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• 2012

본고는 학생주도 창의융합 프로젝트식 교육 모델의 설계와 시행을 통해 21세기에 요구되는 창의적 공학인재 양성을 위한 교육의 새로운 방법론을 제시하고 그 성과에 대해 고찰하는 것을 목적으로 한다. 이를 위해 먼저, 공학적 창의성에 대한 개념과 R&D 3.0 및 교육3.0의 구체적 내용을 검토하였다. R&D 3.0과 교육 3.0의 내용검토를 통해 기술과 인문을 융합할 수 있는 융합 역량과 국제 협력 역량 강화를 지향하는 학생 주도의 프로젝트식 교육의 필요성을 확인하였고, 공학적 창의성에 대한 논의를 통해 기술의 사회인문학적 맥락을 읽을 수 있는 인문사회적 소양 교육과 경험 중심의 현장 교육 및 집단적 창의성을 함양할 수 있는 팀 기반 교육의 필요성을 도출하였다. 이를 바탕으로, 학생주도 창의융합 프로젝트인 공학도를 위한 Global Engineering Project(이하 GEP) 프로그램을 설계하고 이를 3차에 걸쳐 시행한 후, 시행 결과를 바탕으로 프로그램의 성과와 기대 효과를 정리하였다.

• 한국지진공학회논문집
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• 제25권5호
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• pp.213-221
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• 2021

Many Korean domestic masonry structures constructed since 1970 have been found to be vulnerable to earthquakes because they lack efficient lateral force resistance. Many studies have shown that the brick and mortar suddenly experience brittle fracture and out-of-plane collapse when they reach the inelastic range. This study evaluated the seismic retrofitting of non-reinforced masonry with Hybrid Super Coating (HSC) and Cast, manufactured using glass fiber. Four types of specimen original specimen (BR-OR), one layered HSC (BR-HS-O), two-layered HSC (BR-HS-B), one layered HSC, and Cast (BR-CT-HS-O) were constructed and analyzed using compression, flexural tensile, diagonal compression, and triplet tests. The specimen responses were presented and discussed in load-displacement curves, maximum strength, and crack propagation. The compressive strength of the retrofit specimens slightly increased, while the flexural tensile strength of the retrofit specimens increased significantly. In addition, the HSC and Cast also produced a considerable increase in the ductile response of specimens before failure. Diagonal compression test results showed that HSC delayed brittle cracks between the mortar and bricks and resulted in larger displacement before failure than the original brick. The triplet test results confirmed that the bonding strength of the retrofit specimens also increased. The application of HSC and Cast was found to restrain the occurrence of brittle failure effectively and delayed the collapse of masonry wall structures.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2021년도 제64차 하계학술대회논문집 29권2호
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• pp.305-307
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• 2021

Alzheimer's Disease (AD) is a cognitive disorder characterized by memory impairment that can be assessed at early stages based on administering clinical tests. However, the AD pathophysiological mechanism is still poorly understood due to the difficulty of distinguishing different levels of AD severity, even using a variety of brain modalities. Therefore, in this study, we present a hybrid EEG-fNIRS modalities to compensate for each other's weaknesses with the help of Machine Learning (ML) techniques for classifying four subject groups, including healthy controls (HC) and three distinguishable groups of AD levels. A concurrent EEF-fNIRS setup was used to record the data from 41 subjects during Oddball and 1-back tasks. We employed both a traditional neural network (NN) and a CNN-LSTM hybrid model for fNIRS and EEG, respectively. The final prediction was then obtained by using majority voting of those models. Classification results indicated that the hybrid EEG-fNIRS feature set achieved a higher accuracy (71.4%) by combining their complementary properties, compared to using EEG (67.9%) or fNIRS alone (68.9%). These findings demonstrate the potential of an EEG-fNIRS hybridization technique coupled with ML-based approaches for further AD studies.

• 한국멀티미디어학회논문지
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• 제23권2호
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• pp.343-350
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• 2020

In the era of the Fourth Industrial Revolution in the digital revolution is taking place, various attempts have been made to provide various contents in a digital environment. In this paper, agent-recruitment service system based on collaborative deep learning is proposed for the intelligent head hunting service. The service system is improved from previous research [7] using collaborative deep learning for more reliable recommendation results. The Collaborative deep learning is a hybrid recommendation algorithm using "Recurrent Neural Network(RNN)" specialized for exponential calculation, "collaborative filtering" which is traditional recommendation filtering methods, and "KNN-Clustering" for similar user analysis. The proposed service system can expect more reliable recommendation results than previous research and showed high satisfaction in user survey for verification.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제5권10호
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• pp.1799-1813
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• 2011

Many researches indicate that programming learning could help improve problem solving skills through algorithmic thinking. But in general, programming learning has been focused on programming language features and it also gave a heavy cognitive load to learners. Therefore, this paper proposes a programming activity process to improve novice programming learners' algorithmic thinking efficiently. An experiment was performed to measure the effectiveness of the proposed programming activity process. After the experiment, the learners' perception on programming was shown to be changed, to effective activity in improving problem solving.