• 콘크리트학회지
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• 제8권2호
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• pp.139-150
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• 1996

본 연구의 목적은 프리캐스트 콘크리트(PC) 대형판 구조물의 축소모델 제작 및 실험기법에 관한 정보을 제공하는 것이다. 적용된 축소율은 1/5이며, 4가지의 실험이 수행되었다. : 모델 콘크리트와 모델 철근의 재료실험, 수평접합부의 압축실험, 수직접합부의 전단실험, 2층 부분구조물의 정적 주기 실험, 이들 실험결과를 기초로 다음의 결론을 도출하였다. : (1)모델 콘크리트는 일반적으로 예상보다 압축강도가 크게 나타났다. (2)모델 철근은 진공관을 사용하지 않고 열처리를 할 경우 연성의 저하를 보인다. (3)수평접합부와 수직접합부의 파괴모드는 실물크기와 모델이 거의 유사하였으나. 모델의 강도는 상사법칙에 의한 요구강도보다 크게 나타났다. (4) 실물크기와 유사하게 모델철근의 연성과 모델 콘크리트의 압축강도를 확보할 수 있다면, 1/5모델 부분구조물의 이력거동은 실물크리와 매우 근접되게 나타낼 수 있다.

• 한국HCI학회논문지
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• 제9권2호
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• pp.23-31
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• 2014

작가의 내러티브 창작을 지원하는 다양한 소프트웨어들은 일반적으로 작가가 생산하고 필요로 하는 정보의 관리와 상업적 성공을 거둔 내러티브 텍스트에 대한 분석에 주목한다. 이러한 관점에서는 내러티브 창작 과정에서의 독자의 적극적 역할이 간과된다. 작가는 독자의 반응이나 기대를 예상하여, 이를 충족시키거나 배반하면서 내러티브를 구성한다. 사건 전개에 따른 독자의 이해 상황을 파악하고 이를 적절히 조절하는 작가의 활동은 내러티브 전체의 미학적 완성도와 연관되어 있다. 본 논문에서는 서사학의 내러티브 구조 모델과 인지과학의 '사건 색인 상황모델'을 이론적 근거로, 독자의 이해와 관련된 내러티브의 다차원적 특성을 시각적으로 확인하고 조절할 수 있는 창작 지원 시스템 설계를 제안한다. 먼저 사건을 기본 단위로 하여 그 속성을 설정하고 내러티브의 두 시간축에 유기적으로 배열할 수 있는 프레임워크를 설계하고, 이를 실제 영화의 내러티브에 적용하여 전체 구조를 분석하였다. 다음으로, 독자의 이해에 영향을 미치는 상황 모델 차원들의 연속성을 시각화하는 방안과 정보처리 요구량으로써 인지적 복잡도를 분석하는 방안을 제시하고, 사례 영화에 대해 시각화한 결과를 내러티브의 특성과 작가 지원 관점에서 논의하였다.

• 만화애니메이션 연구
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• 통권51호
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• pp.83-105
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• 2018

그레마스(Algirdas Julius Greimas)의 역할소 모델(Actantial model)에서 스토리는 6개의 역할소로 각각 3가지의 관계를 만드는데, 그 안에서 주체와 객체 사이에 관계되는 욕망이 운용된다. 또한 주체가 욕망을 행할 때 원조자의 도움과 반대자의 방해가 공존하게 되며, 주체가 원하는 욕망의 대상(객체)은 발령자와 수령자가 제공한다. 이러한 역할소 모델을 기본으로, 본 연구는 <달빛궁궐>의 서사 구조를 분석한 후 역량의 주체를 재배치하여 커뮤니케이션 전반의 흐름을 살펴보았다. 한국의 애니메이션은 다양한 시각에서 한국의 문화원형을 모티브로 활용한 접근을 시도하고 있다. 그러나 여전히 문화원형을 모티브로 각색 윤색된 외국의 애니메이션보다 서사 구조의 수준이 다소 미흡한 것이 사실이다. 이에 본 연구는 한국문화원형을 모티브로 가장 최근에 제작된 <달빛궁궐>의 서사 구조를 분석해 보고, 해당 애니메이션 안에서 연출된 역량의 주체를 재배치하여 전체적인 커뮤니케이션의 흐름을 살펴보았다. 이를 통해 좀 더 발전된 형태의 서사 구조를 지닌 애니메이션의 제작 방법을 찾고자 하였다. 물론, 그레마스를 통한 서사 구조 연구는 지금까지 다양한 측면에서 논의돼 왔다. 하지만 이 이론을 통해 문화원형성의 활용법을 강구한 애니메이션 연구는 아직 부족하다고 사료된다. 이러한 점을 고려해, 본 연구는 그레마스의 설화문 분석을 바탕으로 문화원형성의 활용 방안을 모색하는 데 중점을 두었다.

• 한국공간구조학회논문집
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• 제20권2호
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• pp.51-58
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• 2020

Recently, deep learning that is the most popular and effective class of machine learning algorithms is widely applied to various industrial areas. A number of research on various topics about structural engineering was performed by using artificial neural networks, such as structural design optimization, vibration control and system identification etc. When nonlinear semi-active structural control devices are applied to building structure, a lot of computational effort is required to predict dynamic structural responses of finite element method (FEM) model for development of control algorithm. To solve this problem, an artificial neural network model was developed in this study. Among various deep learning algorithms, a recurrent neural network (RNN) was used to make the time history response prediction model. An RNN can retain state from one iteration to the next by using its own output as input for the next step. An eleven-story building structure with semi-active tuned mass damper (TMD) was used as an example structure. The semi-active TMD was composed of magnetorheological damper. Five historical earthquakes and five artificial ground motions were used as ground excitations for training of an RNN model. Another artificial ground motion that was not used for training was used for verification of the developed RNN model. Parametric studies on various hyper-parameters including number of hidden layers, sequence length, number of LSTM cells, etc. After appropriate training iteration of the RNN model with proper hyper-parameters, the RNN model for prediction of seismic responses of the building structure with semi-active TMD was developed. The developed RNN model can effectively provide very accurate seismic responses compared to the FEM model.

• 수산해양교육연구
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• 제25권1호
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• pp.12-28
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• 2013

The purpose of this study was to develop storytelling Instructional model for promote problem-solving in a Blended learning Environment. To achieve the purpose, the study was performed by dividing into two stages. First, the draft of storytelling Instructional model was proposed by performing a literature survey and a case study. Second, the draft model was applied to the actual work. And the draft was modified and developed to the final model on the basis of the draft model's strength and implemented to 28 students who were the sophomore of child care education department and enrolled the profession class of at S University for 6 weeks. From the implementation result of the model, it was obtained that there was the positive reaction on applying storytelling technique to the beginning stage of learning. Instructional model storytelling consists phases Preparing to perform Storytelling, Building the team and role sharing team, Problem providing, Planning for problem solving, Brend Story structuralization, Cooperative Learning and Problem solving, announcement of the results and evaluating and reflection of general. And then learning supporting components for a facilitator and a learner were prepared for each process. Established in a Blended learning Environment was created based on all-line, how to teach and learning supporting organization. Final Model was suggested as a blueprint for stages actual learning which was consisted of a introductory storytelling part, an main storytelling part and a post storytelling part.

• Smart Structures and Systems
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• 제21권4호
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• pp.521-535
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• 2018

The main purpose of this paper is to predict missing absolute out-of-plane displacements and failure limits of infill walls by artificial neural network (ANN) models. For this purpose, two shake table experiments are performed. These experiments are conducted on a 1:1 scale one-bay one-story reinforced concrete frame (RCF) with an infill wall. One of the experimental models is composed of unreinforced brick model (URB) enclosures with an RCF and other is composed of an infill wall with bed joint reinforcement (BJR) enclosures with an RCF. An artificial earthquake load is applied with four acceleration levels to the URB model and with five acceleration levels to the BJR model. After a certain acceleration level, the accelerometers are detached from the wall to prevent damage to them. The removal of these instruments results in missing data. The missing absolute maximum out-of-plane displacements are predicted with ANN models. Failure of the infill wall in the out-of-plane direction is also predicted at the 0.79 g acceleration level. An accuracy of 99% is obtained for the available data. In addition, a benchmark analysis with multiple regression is performed. This study validates that the ANN-based procedure estimates missing experimental data more accurately than multiple regression models.

• Steel and Composite Structures
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• 제23권5호
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• pp.501-516
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• 2017

Numerical simulations are prevalently used to evaluate the seismic behaviour of structures. The accuracy of the simulation results depends directly on the accuracy of the modelling techniques employed to simulate the behaviour of individual structural members. An empirical modelling technique is employed in this paper to simulate the behaviour of column members under cyclic and seismic loading. Despite the common modelling techniques, this technique is capable of simulating two important aspects of the cyclic and seismic behaviour of columns simultaneously. The proposed fiber-based modelling technique captures explicitly the interaction between the bending moment and the axial force in columns, and the cyclic deterioration of the hysteretic behaviour of these members is implicitly taken into account. The fiber-based model is calibrated based on the cyclic behaviour of square hollow steel sections. The behaviour of several column archetypes is investigated under a dual cyclic loading protocol to develop a benchmark database before the calibration procedure. The dual loading protocol used in this study consists of both axial and lateral loading cycles with varying amplitudes. After the calibration procedure, a regression analysis is conducted to derive an equation for predicting a varying calibrated modelling parameter. Finally, several nonlinear time-history analyses are conducted on a 6-story steel special moment frame in order to investigate how the results of numerical simulations can be affected by employing the intended modelling technique for columns instead of other common modelling techniques.

• Smart Structures and Systems
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• 제15권2호
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• pp.409-424
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• 2015

Structural nonlinearity is a common phenomenon encountered in engineering structures under severe dynamic loading. It is necessary to localize and identify structural nonlinearities using structural dynamic measurements for damage detection and performance evaluation of structures. However, identification of nonlinear structural systems is a difficult task, especially when proper mathematical models for structural nonlinear behaviors are not available. In prior studies on nonparametric identification of nonlinear structures, the locations of structural nonlinearities are usually assumed known and all structural responses are measured. In this paper, an identification algorithm is proposed for locating and identifying model-free structural nonlinearities and systems using incomplete measurements of structural responses. First, equivalent linear structural systems are established and identified by the extended Kalman filter (EKF). The locations of structural nonlinearities are identified. Then, the model-free structural nonlinear restoring forces are approximated by power series polynomial models. The unscented Kalman filter (UKF) is utilized to identify structural nonlinear restoring forces and structural systems. Both numerical simulation examples and experimental test of a multi-story shear building with a MR damper are used to validate the proposed algorithm.

• Structural Engineering and Mechanics
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• 제47권3호
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• pp.401-419
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• 2013

In this study, the efficiency of adaptive neuro-fuzzy inference system (ANFIS) and genetic expression programming (GEP) in predicting the effects of infill walls on base reactions and roof drift of reinforced concrete frames were investigated. Current standards generally consider weight and fundamental period of structures in predicting base reactions and roof drift of structures by neglecting numbers of floors, bays, shear walls and infilled bays. Number of stories, number of bays in x and y directions, ratio of shear wall areas to the floor area, ratio of bays with infilled walls to total number bays and existence of open story were selected as parameters in GEP and ANFIS modeling. GEP and ANFIS have been widely used as alternative approaches to model complex systems. The effects of these parameters on base reactions and roof drift of RC frames were studied using 3D finite element method on 216 building models. Results obtained from 3D FEM models were used to in training and testing ANFIS and GEP models. In ANFIS and GEP models, number of floors, number of bays, ratio of shear walls and ratio of infilled bays were selected as input parameters, and base reactions and roof drifts were selected as output parameters. Results showed that the ANFIS and GEP models are capable of accurately predicting the base reactions and roof drifts of RC frames used in the training and testing phase of the study. The GEP model results better prediction compared to ANFIS model.

• Earthquakes and Structures
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• 제12권6호
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• pp.699-712
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• 2017

This study evaluates the effectiveness of a newly developed retrofitting scheme for masonry-infilled non-ductile RC frames experimentally and by numerical simulation. The technique focuses on modifying the load path and yield mechanism of the infilled frame to enhance the ductility. A vertical gap between the column and the infill panel was strategically introduced so that no shear force is directly transferred to the column. Steel brackets and small vertical steel members were then provided to transfer the interactive forces between the RC frame and the masonry panel. Wire meshes and high-strength mortar were provided in areas with high stress concentration and in the panel to further reduce damage. Cyclic load tests on a large-scale specimen of a single-bay, single-story, masonry-infilled RC frame were carried out. Based on those tests, the retrofitting scheme provided significant improvement, especially in terms of ductility enhancement. All retrofitted specimens clearly exhibited much better performances than those stipulated in building standards for masonry-infilled structures. A macro-scale computer model based on a diagonal-strut concept was also developed for predicting the global behavior of the retrofitted masonry-infilled frames. This proposed model was effectively used to evaluate the global responses of the test specimens with acceptable accuracy, especially in terms of strength, stiffness and damage condition.