• 한국전산유체공학회:학술대회논문집
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• 2003.10a
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• pp.218-219
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• 2003

• Communications for Statistical Applications and Methods
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• v.2 no.1
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• pp.216-228
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• 1995

Under the certain assumptions, we derive the recursive formula for the predictive probabilities that a new patient will survive up to the time, conditional on the data. The formula for a new patient is extended to obtain the computational algorithms for the predictive probabilties for several new patients. We correct Genest and Kalbfleisch's approach for several new patients, since we find that their approach is incorrect.

• Journal of KIISE:Software and Applications
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• v.32 no.4
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• pp.277-289
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• 2005

To overcome the long design time/high computational effort/low computational performance of phylogenetic learning featuring selection and reproduction, this paper proposes a genetic representation based on XML. Since genetic programs (GP) and genetic operations of this representation are maintained by the invocation of the built-in off-the-shelf XML parser's API, the proposed approach features significant reduced time consumption of GP design process. Handling only semantically correct GPs with standard XML schema can reduce search space and computational effort. Furthermore, computational performance can be improved by the parallelism of GP caused by the utilization of XML, which is a feasible system and wire format for migration of genetic programs in heterogeneous distributed computer environments. To verify the proposed approach, it is applied to the evolution of social behaviors of multiple agents modeling the predator-prey pursuit problem. The results show that the approach can be applied for fast development and time efficiency of GPs.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.102-114
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• 2021

Biofouling represents an important problem in the shipping industry since it causes the increase in surface roughness. The most of ships in the current world fleet do not have good coating condition which represents an important problem due to strict rules regarding ship energy efficiency. Therefore, the importance of the control and management of the hull and propeller fouling is highlighted by the International Maritime Organization and the maintenance schedule optimization became valuable energy saving measure. For adequate implementation of this measure, the accurate prediction of the effects of biofouling on the hydrodynamic characteristics is required. Although computational fluid dynamics approach, based on the modified wall function approach, has imposed itself as one of the most promising tools for this prediction, it requires significant computational time. However, during the maintenance schedule optimization, it is important to rapidly predict the effect of biofouling on the ship hydrodynamic performance. In this paper, the effect of biofilm on the ship hydrodynamic performance is studied using the proposed performance prediction method for three merchant ships. The applicability of this method in the assessment of the effect of biofilm on the ship hydrodynamic performance is demonstrated by comparison of the obtained results using the proposed performance prediction method and computational fluid dynamics approach. The comparison has shown that the highest relative deviation is lower than 4.2% for all propulsion characteristics, lower than 1.5% for propeller rotation rate and lower than 5.2% for delivered power. Thus, a practical tool for the estimation of the effect of biofouling with lower fouling severity on the ship hydrodynamic performance is developed.

• Composites Research
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• v.36 no.5
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• pp.329-334
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• 2023

The classical multiscale finite element (FE² ) method involves iterative calculations of micro-boundary value problems for representative volume elements at every integration point in macro scale, making it a computationally time and data storage space. To overcome this, we developed the data-driven multiscale analysis method based on the mean-field homogenization (MFH). Data-driven computational mechanics (DDCM) analysis is a model-free approach that directly utilizes strain-stress datasets. For performing multiscale analysis, we efficiently construct a strain-stress database for the microstructure of composite materials using mean-field homogenization and conduct data-driven computational mechanics simulations based on this database. In this paper, we apply the developed multiscale analysis framework to an example, confirming the results of data-driven computational mechanics simulations considering the microstructure of a hyperelastic composite material. Therefore, the application of data-driven computational mechanics approach in multiscale analysis can be applied to various materials and structures, opening up new possibilities for multiscale analysis research and applications.

• Proceedings of the Korean Society of Computational and Applied Mathematics Conference
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• 2003.09a
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• pp.15-15
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• 2003

We provide some recent results of approximation algorithms for solving Markov Games and discuss their applications to problems that arise in Computer Science. We consider a receding horizon approach as an approximate solution to two-person zero-sum Markov games with an infinite horizon discounted cost criterion. We present error bounds from the optimal equilibrium value of the game when both players take “correlated” receding horizon policies that are based on exact or approximate solutions of receding finite horizon subgames. Motivated by the worst-case optimal control of queueing systems by Altman, we then analyze error bounds when the minimizer plays the (approximate) receding horizon control and the maximizer plays the worst case policy. We give two heuristic examples of the approximate receding horizon control. We extend “parallel rollout” and “hindsight optimization” into the Markov game setting within the framework of the approximate receding horizon approach and analyze their performances. From the parallel rollout approach, the minimizing player seeks to combine dynamically multiple heuristic policies in a set to improve the performances of all of the heuristic policies simultaneously under the guess that the maximizing player has chosen a fixed worst-case policy. Given $\varepsilon$>0, we give the value of the receding horizon which guarantees that the parallel rollout policy with the horizon played by the minimizer “dominates” any heuristic policy in the set by $\varepsilon$, From the hindsight optimization approach, the minimizing player makes a decision based on his expected optimal hindsight performance over a finite horizon. We finally discuss practical implementations of the receding horizon approaches via simulation and applications.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1993.10a
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• pp.240-247
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• 1993

The precise prediction of reserved carrying capacity of bridge as a system is extremely difficult especially when the bridges are highly redundant and significantly deteriorated or damaged. This paper is intended to propose a new approach for the evaluation of reserved system carrying capacity of bridges in terms of equivalent system-strength, which may be defined as a bridge system-strength corresponding to the system reliability of the bridge. This can be derived from an inverse process based on the concept of FOSM form of system reliability index. It may be emphasized that this approach is very useful for the evaluation of the deterministic system redundancy and reserve strength which are measured in terms of either probabilistic system redundancy factor and reserve factor or deterministic system redundancy factor and reserve factor. The system reliability of bridges is formulated as a parallel-series model obtained from the FAM(Failure Mode Approach) based on the major failure mechanisms. AFOSM and IST methods are used for the reliability analysis of the proposed models. The proposed approach and method for the system redundancy and reserve safety/strength are applied to the safety assessment of actual RC and steel box-girder bridges. The results of the evaluation of reserved system safety or bridge system-strength in terms of the system redundancy and the system safety/strength are significantly different from those of element reliability-based or conventional methods.

• 한국전산유체공학회:학술대회논문집
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• 2007.04a
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• pp.103-107
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• 2007

Computational Fluid Dynamics (CFD in short) approach is now playing an important role in the engineering process recently. Generating proper grid system for the region of interest in time is prerequisite for the efficient numerical calculation of flow physics using CFD approach. Grid generation is, however, usually considered as a major obstacle for a routine and successful application of numerical approaches in the engineering process. CFD approach based on the unstructured grid system is gaining popularity due to its simplicity and efficiency for generating grid system compared to the structured grid approaches. In this paper an automated triangular surface grid generation using CAD surface data is proposed According to the present method, the CAD surface data imported in the STL format is processed to identify feature edges defining the topology and geometry of the surface shape first. When the feature edges are identified, node points along the edges are distributed. The initial fronts which connect those feature edge nodes are constructed and then they are advanced along the CAD surface data inward until the surface is fully covered by triangular surface grid cells using Advancing Front Method. It is found that this approach can be implemented in an automated way successfully saving man-hours and reducing human-errors in generating triangular surface grid system.

• Smart Structures and Systems
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• v.17 no.4
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• pp.611-629
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• 2016

This study presents a new approach of surrogate modeling for time-consuming finite element analysis. A surrogate model is widely used to reduce the computational cost under an iterative computational analysis. Although a variety of the methods have been widely investigated, there are still difficulties in surrogate modeling from a practical point of view: (1) How to derive optimal design of experiments (i.e., the number of training samples and their locations); and (2) diagnostics of the surrogate model. To overcome these difficulties, we propose a sequential surrogate modeling based on Gaussian process model (GPM) with self-adaptive sampling. The proposed approach not only enables further sampling to make GPM more accurate, but also evaluates the model adequacy within a sequential framework. The applicability of the proposed approach is first demonstrated by using mathematical test functions. Then, it is applied as a substitute of the iterative finite element analysis to Monte Carlo simulation for a response uncertainty analysis under correlated input uncertainties. In all numerical studies, it is successful to build GPM automatically with the minimal user intervention. The proposed approach can be customized for the various response surfaces and help a less experienced user save his/her efforts.

• Journal of Computational Design and Engineering
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• v.2 no.2
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• pp.105-112
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• 2015

Product Lifecycle Management (PLM) ranges from design concepts of products to disposal. In this paper, we focus on the production planning phase in PLM, which is related to process planning and production scheduling and so on. In this study, key decisions for the creation of production plans are defined as production-planning attributes. Production-planning attributes correlate complexly in production-planning problems. Traditionally, the production-planning problem splits sub-problems based on experiences, because of the complexity. In addition, the orders in which to solve each sub-problem are determined by priorities between sub-problems. However, such approaches make solution space over-restricted and make it difficult to find a better solution. We have proposed a representation of combinations of alternatives in production-planning attributes by using Zero-Suppressed Binary Decision Diagrams. The ZDD represents only feasible combinations of alternatives that satisfy constraints in the production planning. Moreover, we have developed a solution search method that solves production-planning problems with ZDDs. In this paper, we propose an approach for managing solution candidates by ZDDs' network for addressing larger production-planning problems. The network can be created by linkages of ZDDs that express constraints in individual sub-problems and between sub-problems. The benefit of this approach is that it represents solution space, satisfying whole constraints in the production planning. This case study shows that the validity of the proposed approach.