• Journal of the Society of Naval Architects of Korea
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• v.56 no.3
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• pp.251-262
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• 2019

This paper is about design concept and simplified analysis method against dropped object events. The ships and offshore structures are exposed to various types of dropped object accidents such as laydown area struck by drill collar and topside deck hit by food container during their lifetime. Mitigation can be accomplished by proper facility layout and designing structures to safely absorb energy from accidental loads. It shall be designed to avoid loss of life, environmental pollution and loss of assets. Impact loads can lead to structural global collapse of the main structure or punching of a local barrier type structure with potential to escalate directly or indirectly to a global collapse of the structure. This study provides the background information on the issue of dropped object of the shipyard and also focuses on structural assessment of the local individual component such as deck plate, stiffener and web/girder by using simplified analysis method. The results of the simplified analysis method were compared with numerical results using non-linear finite element simulation.

• Journal of the Korean Geographical Society
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• v.48 no.6
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• pp.897-913
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• 2013

The main purpose of the paper is to examine the spatial characteristics of school choice through residential mobility by conducting a correlation analysis on the relationships between the middle schools' entrance rates to special high schools and the elementary schools' net transfer rates. Analyses are done at both the individual school level and the school catchment area level. Prior to the calculation, the two variables involved in the correlation analysis are transformed via a standardization equation, and the standardized scores are mapped and explored. Both the global and local correlation analyses are done for the standardized variables. Main findings are twofold. First, the global correlation analysis reports that there exists a statistically significant correlation between the two variables at both the analytical levels. Second, albeit the prominent positive correlation at the global level, the local analysis reveals the existence of a considerable level of spatial heterogeneity in terms of bivariate association. While several school catchment areas with very high local correlation coefficients (the HH association type) are popped up, other areas with various types of bivariate association including ones even opposite to the global trend are also observed.

• Communications of the Korean Mathematical Society
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• v.35 no.3
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• pp.825-841
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• 2020

We characterize bicomplex holomorphic functions from several estimates. Originally, Riley [5] studied such problems in local case. In our study, we treat global estimates on various unbounded domains. In many cases, we can determine the explicit form of a function.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.04a
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• pp.693-694
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• 2012

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.1
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• pp.29-40
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• 2021

Risk factors that threaten public safety such as crime, fire, and traffic accidents have spatial characteristics. Since each region has different dangerous environments, it is necessary to analyze the spatial pattern of risk factors for each sector such as traffic accident, fire, crime, and living safety. The purpose of this study is to analyze the spatial distribution pattern of local safety level index, which act as an index that rates the safety level of each sector (traffic accident, fire, crime, living safety, suicide, and infectious disease) for basic local governments across the nation. The following analysis tools were used to analyze the spatial autocorrelation of local safety level index : Global Moran's I, Local Moran's I, and Getis-Ord's G⁎i. The result of the analysis shows that the distribution of safety level on traffic accidents, fire, and suicide tends to be more clustered spatially compared to the safety level on crime, living safety, and infectious disease. As a result of analyzing significant spatial correlations between different regions, it was found that the Seoul metropolitan areas are relatively safe compared to other cities based on the integrated index of local safety. In addition, hot spot analysis using statistical values from Getis-Ord's G⁎i derived three hot spots(Samchuck, Cheongsong-gun, and Gimje) in which safety-vulnerable areas are clustered and 15 cold spots which are clusters of areas with high safety levels. These research findings can be used as basic data when the government is making policies to improve the safety level by identifying the spatial distribution and the spatial pattern in areas with vulnerable safety levels.

• Journal of Korean Association for Spatial Structures
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• v.15 no.3
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• pp.43-49
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• 2015

A single-layerd steel lattice roof, which has 50m span, was constructed. In order to figure out the realistic buckling load level, the structural analysis of this roof structure was performed especially by local snow load. Due to the characteristics of application of snow load, the load combinations of snow should be considered not only global area but also local part so that the critical buckling load could be observed as easy as possible. Geometrical imperfection was simulated to consider inaccurate shape of structure. And then nonlinear analysis were performed. Finally, this paper could investigate that the local snow load with geometrical imperfection decreased the level of buckling load significantly.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.7
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• pp.1014-1020
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• 2004

The present work complies the elastic stress concentration factor for a pipe with local wall thinning, based on detailed three-dimensional elastic FE analysis. To cover practically interesting cases, a wide range of pipe and defect geometries are considered, and both internal pressure and global bending are considered. Resulting values of stress concentration factors are tabulated for practical use, and the effect of relevant parameters such as pipe and defect geometries on stress concentration factors are discussed. The present results would provide valuable information to estimate fatigue damage of the pipe with local wall thinning under high cycle fatigue.

• Journal of Ocean Engineering and Technology
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• v.26 no.6
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• pp.74-79
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• 2012

The present study aims at suggesting the systematic approach to analyze the local drag components as the resistance performance characterized by the flow of the ship. In order to identify the local areas, the hull surface is decomposed into SVM (Station-Vertical Section Map) which consists of 20 stations along the longitudinal direction and 20 sections along the vertical direction (from the bottom to the waterline). Successively, on the SVM, the friction and pressure drag coefficients as the components of total drag coefficient have been analyzed for two different hull forms of Wigley and KVLCC by using CFD.

• Journal of KIISE:Software and Applications
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• v.29 no.5
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• pp.289-294
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• 2002

It is known that restructuring feed-forward neural network affects generalization capability and efficiency of the network. In this paper, we introduce a new approach to restructure a neural network using abstraction of the hidden knowledge that the network has teamed. This method involves extracting local rules from non-input nodes and aggregation of the rules into global rule base. The extracted local rules are used for pruning unnecessary connections of local nodes and the aggregation eliminates any possible redundancies arid inconsistencies among local rule-based structures. Final network is generated by the global rule-based structure. Complexity of the final network is much reduced, compared to a fully-connected neural network and generalization capability is improved. Empirical results are also shown.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.528-529
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• 2023

Due to vast points and irregular structure, labeling full points in large-scale point clouds is highly tedious and time-consuming. To resolve this issue, we propose a novel point-based transformer network in weakly-supervised semantic segmentation, which only needs 0.1% point annotations. Our network introduces general local features, representing global factors from different neighborhoods based on their order positions. Then, we share query point weights to local features through point attention to reinforce impacts, which are essential in determining sparse point labels. Geometric encoding is introduced to balance query point impact and remind point position during training. As a result, one point in specific local areas can obtain global features from corresponding ones in other neighborhoods and reinforce from its query points. Experimental results on benchmark large-scale point clouds demonstrate our proposed network's state-of-the-art performance.