• Journal of the Korea Society for Simulation
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• v.14 no.3
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• pp.91-100
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• 2005

Grid computing is widely applicable to various fields of industry including process control and manufacturing, military command and control, transportation management, and so on. In a viewpoint of application area, grid computing can be classified to three aspects that are computational grid, data grid and access grid. This paper focuses on computational grid which handles complex and large-scale computing problems. Computational grid is characterized by system dynamics which handles a variety of processors and jobs on continuous time. To solve problems of system complexity and reliability due to complex system dynamics, computational grid needs scheduling policies that allocate various jobs to proper processors and decide processing orders of allocated jobs. This paper proposes a service prediction-based job scheduling model and present its scheduling algorithm that is applicable for computational grid. The service prediction-based job scheduling model can minimize overall system execution time since the model predicts the next processing time of each processing component and distributes a job to a processing component with minimum processing time. This paper implements the job scheduling model on the DEVS modeling and simulation environment and evaluates its efficiency and reliability. Empirical results, which are compared to conventional scheduling policies, show the usefulness of service prediction-based job scheduling.

• Proceedings of the Korea Society for Simulation Conference
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• 2005.05a
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• pp.29-33
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• 2005

Grid computing is widely applicable to various fields of industry including process control and manufacturing, military command and control, transportation management, and so on. In a viewpoint of application area, grid computing can be classified to three aspects that are computational grid, data grid and access grid. This paper focuses on computational grid which handles complex and large-scale computing problems. Computational grid is characterized by system dynamics which handles a variety of processors and jobs on continuous time. To solve problems of system complexity and reliability due to complex system dynamics, computational grid needs scheduling policies that allocate various jobs to proper processors and decide processing orders of allocated jobs. This paper proposes the service prediction-based job scheduling model and present its algorithm that is applicable for computational grid. The service prediction-based job scheduling model can minimize overall system execution time since the model predicts a processing time of each processing component and distributes a job to processing component with minimum processing time. This paper implements the job scheduling model on the DEVSJAVA modeling and simulation environment and simulates with a case study to evaluate its efficiency and reliability Empirical results, which are compared to the conventional scheduling policies such as the random scheduling and the round-robin scheduling, show the usefulness of service prediction-based job scheduling.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.2
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• pp.197-205
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• 2017

We investigate the slat noise generation mechanism by using large-eddy simulation (LES) and simple source modeling based on linearized Euler equations. An incompressible LES of an MD 30P30N three-element airfoil in the high-lift configuration is conducted at $Re_c=1.7{\times}10^6$. Using the total derivative of the hydrodynamic pressure (DP/Dt) acquired from the incompressible LES, representative noise sources in the slat cove region are characterized in terms of simple sources such as frequency-specific monopoles and dipoles. Acoustic radiation around the 30P30N multi-element airfoil is effectively computed using the Brinkman penalization method incorporated with the linearized Euler equation. The directivity pattern of $p^{\prime}_{rms}$ at $r=20c_{slat}$ in the multiple sources is closely compared to that obtained by the application of the LES/Ffowcs-Williams and Hawking's methods to the entire flow field. The power spectrum of p' at ${\theta}=290^{\circ}$ is in good agreement with the data reported in BANC-III, especially the broadband part of the spectrum with a decaying slope ${\propto}f^{-3}$.

• Journal of computational fluids engineering
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• v.19 no.3
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• pp.14-19
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• 2014

The flow characteristics of inertance pulse tube cryocooler(IPTC) was investigated with a computational thermal fluid dynamics for the reciprocating flow in IPTC including the piston movement of linear compressor. Two dimensional axisymmetric modeling was applied for the flow in an IPTC with a clearance between the piston and cylinder wall of linear compressor. The pressure, velocity, and temperature distribution were examined for the steady state. These were compared with previous results to confirm the validity in the modeling and computational results. The leakage between piston and cylinder wall affect the cooling capacity seriously. The dependence on mesh numbers were also examined to obtain a proper mesh numbers to improve the accuracy of calculation, which showed significant effect on the results. The user-defined function was used for the process of compression and expansion of piston.

• Journal of Pharmacopuncture
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• v.18 no.3
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• pp.11-18
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• 2015

Objectives: Systems biology is a novel subject in the field of life science that aims at a systems' level understanding of biological systems. Because of the significant progress in high-throughput technologies and molecular biology, systems biology occupies an important place in research during the post-genome era. Methods: The characteristics of systems biology and its applicability to traditional medicine research have been discussed from three points of view: data and databases, network analysis and inference, and modeling and systems prediction. Results: The existing databases are mostly associated with medicinal herbs and their activities, but new databases reflecting clinical situations and platforms to extract, visualize and analyze data easily need to be constructed. Network pharmacology is a key element of systems biology, so addressing the multi-component, multi-target aspect of pharmacology is important. Studies of network pharmacology highlight the drug target network and network target. Mathematical modeling and simulation are just in their infancy, but mathematical modeling of dynamic biological processes is a central aspect of systems biology. Computational simulations allow structured systems and their functional properties to be understood and the effects of herbal medicines in clinical situations to be predicted. Conclusion: Systems biology based on a holistic approach is a pivotal research methodology for understanding the mechanisms of traditional medicine. If systems biology is to be incorporated into traditional medicine, computational technologies and holistic insights need to be integrated.

• Smart Structures and Systems
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• v.15 no.3
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• pp.897-911
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• 2015

This study presents a novel approach based on advancements in Evolutionary Computation for data-driven modeling of complex multi-dimensional memory-dependent systems. The investigated example is a benchmark coupled three-dimensional system that incorporates 6 Bouc-Wen elements, and is subjected to external excitations at three points. The proposed technique of this research adapts Genetic Programming for discovering the optimum structure of the differential equation of an auxiliary variable associated with every specific degree-of-freedom of this system that integrates the imposed effect of vibrations at all other degrees-of-freedom. After the termination of the first phase of the optimization process, a system of differential equations is formed that represent the multi-dimensional hysteretic system. Then, the parameters of this system of differential equations are optimized in the second phase using Genetic Algorithms to yield accurate response estimates globally, because the separately obtained differential equations are coupled essentially, and their true performance can be assessed only when the entire system of coupled differential equations is solved. The resultant model after the second phase of optimization is a low-order low-complexity surrogate computational model that represents the investigated three-dimensional memory-dependent system. Hence, this research presents a promising data-driven modeling technique for obtaining optimized representative models for multi-dimensional hysteretic systems that yield reasonably accurate results, and can be generalized to many problems, in various fields, ranging from engineering to economics as well as biology.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2004.03a
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• pp.142-147
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• 2004

The flow in the LOX manifold of liquid rocket has been investigated using a CAE technique with an objective of economical modeling of injection holes in order to reduce the overall computational cost of flow analysis during the optimal rocket design procedure. The computational geometry is very close to that of the actual rocket design and the flow condition through the injection holes resembles that in the actual manifold of the liquid rocket. The result shows that the flow in the plane just above the injection holes is not uniformly distributed in terms of pressure and mass flow rate and this is attributed to the large-scale flow patterns present the LOX manifold. Thus, the flow physics should be understood correctly before making any attempt to model the injection holes. In the present study, several boundary conditions which were designed to effectively replace the presence of injection holes have been tested and it was found that a simple modeling can be possible by mimicking the actual geometry of the injection holes. By using this simple injection hole modeling, it was able to obtain about 30% reduction in computational cost but it was still able to reproduce the flow patterns correctly. Also the flow has been analyzed after incorporating a couple of different types of pre-distributors in LOX manifold and the effect of those will be discussed.

• Clinical and Experimental Pediatrics
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• v.65 no.5
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• pp.239-249
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• 2022

Cells survive and proliferate through complex interactions among diverse molecules across multiomics layers. Conventional experimental approaches for identifying these interactions have built a firm foundation for molecular biology, but their scalability is gradually becoming inadequate compared to the rapid accumulation of multiomics data measured by high-throughput technologies. Therefore, the need for data-driven computational modeling of interactions within cells has been highlighted in recent years. The complexity of multiomics interactions is primarily due to their nonlinearity. That is, their accurate modeling requires intricate conditional dependencies, synergies, or antagonisms between considered genes or proteins, which retard experimental validations. Artificial intelligence (AI) technologies, including deep learning models, are optimal choices for handling complex nonlinear relationships between features that are scalable and produce large amounts of data. Thus, they have great potential for modeling multiomics interactions. Although there exist many AI-driven models for computational biology applications, relatively few explicitly incorporate the prior knowledge within model architectures or training procedures. Such guidance of models by domain knowledge will greatly reduce the amount of data needed to train models and constrain their vast expressive powers to focus on the biologically relevant space. Therefore, it can enhance a model's interpretability, reduce spurious interactions, and prove its validity and utility. Thus, to facilitate further development of knowledge-guided AI technologies for the modeling of multiomics interactions, here we review representative bioinformatics applications of deep learning models for multiomics interactions developed to date by categorizing them by guidance mode.

• Textile Coloration and Finishing
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• v.20 no.5
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• pp.41-46
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• 2008

Recently, computational calculation of molecular energy potentials and electrochemical reduction/oxidation behaviors are of very importance in view point of prediction of dye's properties such as energy levels and bandgaps of absorption. This can be influenced by their different constituents or substituents in chromogen molecules. Structural conformations and properties with computational modeling calculation are numerically simulated, which are fully or partly based on fundamental laws of physics. In addition, cyclic voltammetric measurement was used to obtain the experimental redox potential values, which were compared to the computed simulation values.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2017.05a
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• pp.416-417
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• 2017

In this study, Three-Dimensional igniter modeling and computational Analysis for PMD internal flow analysis have been conducted. The igniter modeling used the lumped parameter method and the computational analysis has been performed in conjunction with the commercial program STAR-CCM+. The result of computational analysis has been compared with those of CBT and PMD experiments.