• 한국공간구조학회논문집
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• 제20권2호
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• pp.87-94
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• 2020

The present study is aimed to calculate the optimal damping according to the seismic load on the structure with a non-seismic design to perform structure analysis considering the deformation of structural joint connection and panel zone; to develop design program equipped with structural stability of the steel frame structures reinforced with the panel zone and viscous dampers, using the results of the analysis, in order to systematically integrate the seismic reinforcement of the non-seismic structures and the analysis and design of steel frame structures. The study results are as follows: When considering the deformation of the panel zone, the deformation has been reduced up to thickness of the panel double plate below twice the flange thickness, which indicates the effect of the double plate thickness on the panel zone, but the deformation showed uniform convergence when the ration is more than twice. The SMRPF system that was applied to this study determines the damping force and displacement by considering the panel zone to the joint connection and calculating the shear each floor for the seismic load at the same time. The result indicates that the competence of the damper is predictable that can secure seismic performance for the structures with non-seismic design without changing the cross-section of the members.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2007년도 한국컴퓨터종합학술대회논문집 Vol.34 No.1 (A)
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• pp.5-6
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• 2007

• International Journal of Computer Science & Network Security
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• 제24권9호
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• pp.30-40
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• 2024

Building an accurate 3-D spatial road network model has become an active area of research now-a-days that profess to be a new paradigm in developing Smart roads and intelligent transportation system (ITS) which will help the public and private road impresario for better road mobility and eco-routing so that better road traffic, less carbon emission and road safety may be ensured. Dealing with such a large scale 3-D road network data poses challenges in getting accurate elevation information of a road network to better estimate the CO₂ emission and accurate routing for the vehicles in Internet of Vehicle (IoV) scenario. Clustering and regression techniques are found suitable in discovering the missing elevation information in 3-D spatial road network dataset for some points in the road network which is envisaged of helping the public a better eco-routing experience. Further, recently Explainable Artificial Intelligence (xAI) draws attention of the researchers to better interprete, transparent and comprehensible, thus enabling to design efficient choice based models choices depending upon users requirements. The 3-D road network dataset, comprising of spatial attributes (longitude, latitude, altitude) of North Jutland, Denmark, collected from publicly available UCI repositories is preprocessed through feature engineering and scaling to ensure optimal accuracy for clustering and regression tasks. K-Means clustering and regression using Support Vector Machine (SVM) with radial basis function (RBF) kernel are employed for 3-D road network analysis. Silhouette scores and number of clusters are chosen for measuring cluster quality whereas error metric such as MAE ( Mean Absolute Error) and RMSE (Root Mean Square Error) are considered for evaluating the regression method. To have better interpretability of the Clustering and regression models, SHAP (Shapley Additive Explanations), a powerful xAI technique is employed in this research. From extensive experiments , it is observed that SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions with an accuracy of 97.22% and strong performance metrics across all classes having MAE of 0.0346, and MSE of 0.0018. On the other hand, the ten-cluster setup, while faster in SHAP analysis, presented challenges in interpretability due to increased clustering complexity. Hence, K-Means clustering with K=4 and SVM hybrid models demonstrated superior performance and interpretability, highlighting the importance of careful cluster selection to balance model complexity and predictive accuracy.

• 한국공간정보시스템학회 논문지
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• 제9권2호
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• pp.35-53
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• 2007

GML은 OGC(OpenGIS Consortium)에서 공간지리정보의 저장 및 전송을 위한 인코딩 표준으로 제안한 마크업 언어이다. 일반적인 공간 네트워크 데이터베이스에서 GML 지원을 위한 연구는 GML 문서의 파싱, GML 문서의 저장, 그리고 GML 문서의 질의어로 분류된다. GML 문서 저장에 관한 연구는 효율적인 GML 문서 검색을 위해 필수적인 연구이다. 그러나 GML 문서의 저장 스키마에 관한 연구는 거의 전무한 형편이다. 또한 기존 XML 문서 저장 스키마는 공간지리정보 저장에 적합하지 않다. 따라서 본 논문에서는 공간지리정보를 포함한 GML 문서를 효율적으로 저장하기 위한 저장 스키마를 제안한다. 아울러 제안하는 저장 스키마의 성능평가를 실시한다.

• 한국실내디자인학회논문집
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• 제19권5호
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• pp.234-242
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• 2010

In order to make an effective use of spatial resources in the city, detailed environmental plan strategies based on 'culture' are needed. An integral part of city spaces-'underground space' serves as one of the spatial resources with much effective usage potentials. Hence, 'underground space' needs cultural vitality. The purpose of this study is to propose an thorough indoor environmental plan guideline for the cultural vitality of 'underground space'. The methods used to conduct research include precedent study reviews and survey. Through this research, following conclusions are drawn. First, to promote cultural vitality at the 'underground space', it must be transformed into the cultural public place to be utilized for cultural activities by residents. Second, the 'underground space' requires 'concept of eco-friendly space for clean environment'. Third, 'safe environment' concept must be installed in the 'underground space' to promote the cultural vitality. Fourth, the 'underground space' requires 'complex/block level network plans between cultural spaces in addition to horizontal/vertical walking network between cultural environments in ground level and underground spaces. Fifth, the 'underground space' requires underground public cultural space plan through 'reasonable underground development with considerations of the facilities related to education, culture and history'. Sixth, 'public cultural space plan for various cultural spaces' and 'supply of space for cultural activities for residents and design plan for mutual culture exchange' are necessary.

• 한국공간구조학회논문집
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• 제19권1호
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• pp.93-100
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• 2019

There has been considerable recent interest in deep learning techniques for structural analysis and design. However, despite newer algorithms and more precise methods have been developed in the field of computer science, the recent effective deep learning techniques have not been applied to the damage detection topics. In this study, we have explored the structural damage detection method of truss structures using the state-of-the-art deep learning techniques. The deep neural networks are used to train knowledge of the patterns in the response of the undamaged and the damaged structures. A 31-bar planar truss are considered to show the capabilities of the deep learning techniques for identifying the single or multiple-structural damage. The frequency responses and the elasticity moduli of individual elements are used as input and output datasets, respectively. In all considered cases, the neural network can assess damage conditions with very good accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권10호
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• pp.3668-3684
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• 2021

Video action recognition is widely used in video surveillance, behavior detection, human-computer interaction, medically assisted diagnosis and motion analysis. However, video action recognition can be disturbed by many factors, such as background, illumination and so on. Two-stream convolutional neural network uses the video spatial and temporal models to train separately, and performs fusion at the output end. The multi segment Two-Stream convolutional neural network model trains temporal and spatial information from the video to extract their feature and fuse them, then determine the category of video action. Google Xception model and the transfer learning is adopted in this paper, and the Xception model which trained on ImageNet is used as the initial weight. It greatly overcomes the problem of model underfitting caused by insufficient video behavior dataset, and it can effectively reduce the influence of various factors in the video. This way also greatly improves the accuracy and reduces the training time. What's more, to make up for the shortage of dataset, the kinetics400 dataset was used for pre-training, which greatly improved the accuracy of the model. In this applied research, through continuous efforts, the expected goal is basically achieved, and according to the study and research, the design of the original dual-flow model is improved.

• 한국통신학회논문지
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• 제17권3호
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• pp.307-322
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• 1992

For designing the radar integrated data network, we construct the network structure with a spatial hieratchy decomposition scheme. The RIDN can be decomposed into several subent classes, those of which are composed of the several group classes of radar sites, In a group class. The communication nodes of a radar site are modeled by the software modules formulated with the statistical attributes of discrete events. And we get the analysis over the network through the separately constructed infra group level models which were coded with the C language.From the result of the simulation. We could findthe fact that the data integration system;s performance approaches to the theordtically calculated value after being stable. And also we could get the packet processing status of a communication module’s inner processor which is difficult to oberve through the mathematical calculation tin the subnet model of the integrated data network.

• 한국실내디자인학회논문집
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• 제21권5호
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• pp.399-410
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• 2012

Due to development of modern medical services and economics, people raised expectation and demand about medical services from previous disease treatment to comprehensive health care covering prevention and health care. Responses of each medical facility to these social needs and the evolution of concept of medicine rapidly occur. The health examination centers are being operated with the purpose of health examination and this trend is reflected on several aspects such as the size of the facilities, function and configuration of space in health examination centers. Thus, health examination centers consisting of various space systems appear, but this trend and interpretations are lacking. Therefore, the purpose of this study is to draw trends of system through analysis of types and its evolved space systematic analysis and establish it. Analysis targets were classified into small, medium and large groups by sizes based on number of space and a total of 12 health examination centers in four for each category were selected. As research methods, functional relationship of space was examined through analysis of type in which segmentalized type tools were applied in local units. The flow diagram was established based on direction turning point and was classified into sub-flow and main-flow in local units and the systems between local units were derived. Finally, the results of this study can be summarized as the following three results. 1) The space connection system of health examination center showed four systems such as circulation, independence, continuation, and network. 2) Local type indicators and global type indicators which were evolved more from limitation of type analysis tools in existing research were derived so that more systematic analysis could be made. 3) Network system is distributed approach system and space for each function is formed around public space.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2007년도 추계종합학술대회
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• pp.457-461
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• 2007

In many applications, received signal strength indicator is used for location tracking and sensor nodes localization. For location finding, the distances between sensor nodes can be estimated by converting received signal's power into distance using path loss prediction model. Many researches have done the analysis of power-distance relationship for radio channel characterization. In indoor environment, the general conclusion is the non-linear variation of RSSI values as distance varied linearly. This has been one of the difficulties for indoor localization. This paper presents works on indoor RSSI characterization based on statistical methods to find the overall trend of RSSI variation at different places and times within the same room From experiments, it has been shown that the variation of RSSI values can be determined by both spatial and temporal factors. This two factors are directly indicated by the two main parameters of path loss prediction model. The results show that all sensor nodes which are located at different places share the same characterization value for the temporal parameter whereas different values for the spatial parameters. Using this relationship, the characterization for location estimation can be more efficient and accurate.