• Title/Summary/Keyword: 비정형구조

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Modal Combination Method for Prediction of Story Earthquake Load Profiles (층지진하중분포 예측을 위한 모드조합법)

  • Eom, Tae-Sung;Lee, Hye-Lin;Park, Hong-Gun
    • Journal of the Earthquake Engineering Society of Korea
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    • v.10 no.3 s.49
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    • pp.65-75
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    • 2006
  • Nonlinear pushover analysis is used to evaluate the earthquake response of building structures. To accurately predict the inelastic response of a structure, the prescribed story load profile should be able to describe the earthquake force profile which actually occurs during the time-history response of the structure. In the present study, a new modal combination method was developed to predict the earthquake load profiles of building structures. In the proposed method, multiple story load profiles are predicted by combining the modal spectrum responses multiplied by the modal combination factors. Parametric studies were performed far moment-resisting frames and walls. Based on the results. the modal combination factors were determined according to the hierarchy of each mode affecting the dynamic responses of structures. The proposed modal combination method was applied to prototype buildings with and without vertical irregularity. The results showed that the proposed method predicts the actual story load profiles which occur during the time-history responses of the structures.

Customer Behavior Prediction of Binary Classification Model Using Unstructured Information and Convolution Neural Network: The Case of Online Storefront (비정형 정보와 CNN 기법을 활용한 이진 분류 모델의 고객 행태 예측: 전자상거래 사례를 중심으로)

  • Kim, Seungsoo;Kim, Jongwoo
    • Journal of Intelligence and Information Systems
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    • v.24 no.2
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    • pp.221-241
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    • 2018
  • Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

Seismic Performance Evaluation of Complex-Shaped Tall Buildings by Lateral Resisting Systems (횡력저항시스템에 따른 비정형 초고층건물 내진성능평가)

  • Youn, Wu-Seok;Lee, Dong-Hun;Cho, Chang-Hee;Kim, Eun-Seong;Lee, Dong-Chul;Kim, Jong-Ho
    • Journal of the Computational Structural Engineering Institute of Korea
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    • v.25 no.6
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    • pp.513-523
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    • 2012
  • The objective of this research is to examine how the lateral resisting system of selected prototypes are affected by seismic zone effect and shape irregularity on its seismic performance. The lateral resisting systems are divided into the three types, diagrid, braced tube, and outrigger system. The prototype models were assumed to be located in LA, a high-seismicity region, and in Boston, a low-seismicity region. The shape irregularity was classified with rotated angle of plane, $0^{\circ}$, $1^{\circ}$, $2^{\circ}$. This study performed two parts of analyses, Linear Response and Non-Linear Response History(NLRH) analysis. The Linear Response analysis was used to check the displacement at the top and natural period of models. NLRH analysis was conducted to invest base shear and story drift ratio of buildings. As results, the displacement of roof and natural period of three structural systems increase as the building stiffness reduces due to the changes in rotation angle of the plane. Also, the base shear is diminished by the same reason. The result of NLRH, the story drift ratio, that was subject to Maximum Considered Earthquake(MCE) satisfied 0.045, a recommended limit according to Tall Building Initiative(TBI).

Analytical Study on the Seismic Retrofit Method of Irregular Piloti Building Using Knee-Brace (Knee - Brace를 활용한 비정형 필로티 건물의 내진보강방안에 대한 해석적 연구)

  • Yoo, Suk-Hyung;Kim, Dal-Gee
    • Journal of the Korea institute for structural maintenance and inspection
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    • v.24 no.1
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    • pp.35-42
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    • 2020
  • Torsional behavior due to the plane irregularities of the piloti building can cause excessive story drift in the torsionally outermost column, which can lead to shear failure of the column. As a seismic retrofit method that can control the torsional behavior of the piloti building, the expansion of RC wall, steel frame or steel brace may be used, but such methods may hinder the openness of the piloti floor. Therefore, in this study, linear dynamic analysis and nonlinear static analysis for piloti buildings retrofitted by knee brace were performed, and seismic performance evaluation and torsion control effect of knee brace were analyzed. The results showed that the shear force of the column increased when the piloti building retrofitted by knee brace, but it was effective in controlling the torsional deformation. In case of retrofit between knee brace and column by 30°, the shear force of the column increased less than that of 60°, and the lateral displacement of column was decreased in the order of □, ◯ and H in cross-section.

Mesh Refinement for Isogeometric Analysis and Post-Processing (등기하 해석을 위한 요소망 정제와 후처리 방법)

  • Kim, Jee-In;Luu, Tuan Anh;Lee, Jae-Hong;Kang, Joo-Won
    • Journal of Korean Association for Spatial Structures
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    • v.12 no.2
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    • pp.45-53
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    • 2012
  • This paper derives Isogeometric analysis and post-processing method of surface that are generated by NURBS basis function for accurate geometric modeling and structure analysis of free-form. By deforming these parameters that are consisted of control points, knots, polynomial, variable geometric models are derived. The basis function that is used to Isogeometric analysis is same to the basis function of NURBS that is used to generate geometric models. For performing isogeometric analysis, h-p-k refinement is performed without changing of original geometry. To visualize the results of isogeometric analysis that control points' displacements, post-processing method that is the interface method between IGES format and Rhinoceros is derived.

A Vibration Problem and Countermeasures for the Deck House and Stern of a Ro/Ro Ship (차량운반선의 거주구와 선미의 연성진동문제 및 방진대책)

  • Man-Cheol Han;Sang-Heon Oh;Il-Cook Baik
    • Journal of the Society of Naval Architects of Korea
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    • v.31 no.3
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    • pp.135-144
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    • 1994
  • The coupled vibration of the deck house and stern structure, which was experienced on a 12,900 TDW Ro/Ro ship, has been studied. It was a large-scale vibration problem where the structure resonates with the propeller excitation at the first blade passing frequency. After discussing the structural characteristics of the ship, the vibration characteristics measured ducting the sea-trial are presented and compared with the analysis results which are based on a 3 dimensional finite element(FE) model. The FE model is also used to verify various reinforcement options and to predict their effectiveness. A substantial reduction or the vibration was confirmed during the sea-trial after installing a few selected reinforcement. The forced vibration response, which is computed using the FE model, is compared with the measured data. The change of the vibration characteristics according to loading conditions is also studied.

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Efficient Deep Neural Network Architecture based on Semantic Segmentation for Paved Road Detection (효율적인 비정형 도로영역 인식을 위한 Semantic segmentation 기반 심층 신경망 구조)

  • Park, Sejin;Han, Jeong Hoon;Moon, Young Shik
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.24 no.11
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    • pp.1437-1444
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    • 2020
  • With the development of computer vision systems, many advances have been made in the fields of surveillance, biometrics, medical imaging, and autonomous driving. In the field of autonomous driving, in particular, the object detection technique using deep learning are widely used, and the paved road detection is a particularly crucial problem. Unlike the ROI detection algorithm used in general object detection, the structure of paved road in the image is heterogeneous, so the ROI-based object recognition architecture is not available. In this paper, we propose a deep neural network architecture for atypical paved road detection using Semantic segmentation network. In addition, we introduce the multi-scale semantic segmentation network, which is a network architecture specialized to the paved road detection. We demonstrate that the performance is significantly improved by the proposed method.

Development of Efficient Analytical Model for a Diagrid Mega-Frame Super Tall Building (다이어그리드 메가프레임 초고층 건물을 위한 효율적인 해석모델의 개발)

  • Kim, Hyun-Su;Kang, Joo-Won
    • Journal of Korean Association for Spatial Structures
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    • v.11 no.3
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    • pp.95-103
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    • 2011
  • Among structural systems for complex-shaped tall buildings, diagrid system is widely used because of its structural efficiency and beauty of form. Recently, mega frame is favorably employed as a suitable structural system for skyscrapers because this structural system has sufficient stiffness against the lateral forces by combination of mega members which consist of many columns and girders. Diagrid mega frame system is expected to be promising structural system for future super tall buildings. However, it takes tremendous analysis times and engineer's efforts to predict the structural behavior of tall buildings applied with diagrid mega frame system because the diagrid mega frame structure has significant numbers of elements and nodes. Therefore, efficient analytical method for all buildings applied with diagrid mega frame system has been proposed in this study to reduce the efforts and time required for the analysis and design of diagrid mega frame structure. To this end, an efficient modelling technique using the characteristics of diagrid mega frame structures and an efficient analytical model using minimal DOFs by the matrix condensation method were proposed in this study. Based on the analysis of an example structure, the effectiveness and accuracy of the proposed method have been verified by the comparison between the results of the proposed method and the conventional method.

A Study on the Relationship between Class Similarity and the Performance of Hierarchical Classification Method in a Text Document Classification Problem (텍스트 문서 분류에서 범주간 유사도와 계층적 분류 방법의 성과 관계 연구)

  • Jang, Soojung;Min, Daiki
    • The Journal of Society for e-Business Studies
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    • v.25 no.3
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    • pp.77-93
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    • 2020
  • The literature has reported that hierarchical classification methods generally outperform the flat classification methods for a multi-class document classification problem. Unlike the literature that has constructed a class hierarchy, this paper evaluates the performance of hierarchical and flat classification methods under a situation where the class hierarchy is predefined. We conducted numerical evaluations for two data sets; research papers on climate change adaptation technologies in water sector and 20NewsGroup open data set. The evaluation results show that the hierarchical classification method outperforms the flat classification methods under a certain condition, which differs from the literature. The performance of hierarchical classification method over flat classification method depends on class similarities at levels in the class structure. More importantly, the hierarchical classification method works better when the upper level similarity is less that the lower level similarity.

3D modeling of Korean Traditional House based on BIM for Uploading to Spatial Information Open Platform (공간정보 오픈플랫폼 탑재를 위한 한옥의 BIM 기반 3차원 모델링 연구)

  • Kim, Kyeong-Min;Kim, Chan-Yong;Choi, Yun-Woong;Cho, Gi-Sung
    • Journal of Cadastre & Land InformatiX
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    • v.44 no.2
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    • pp.91-101
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    • 2014
  • This study tried to create 3D object with LOD3 level for Korean traditional house which is atypical structure, upload to spatial information open platform and confirm the possibility for creating 3D-map. And this study tried to create 3D model for Korean traditional house based on BIM, performed 3D modeling for interior spatial information of Korean traditional house and confirm the development possibility of 3D modeling and visualization method of Korean traditional house. Also this study present the possibility of LOD4 level visualization for spatial information of Korean traditional house which is atypical structure, but 3D object with LOD4 level can't be uploaded to Spatial Information Open Platform currently, cause by data volume limitation of spatial information open platform.