• Journal of Communications and Networks
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• v.11 no.3
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• pp.261-270
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• 2009

We propose a tree-based integration of infrastructure networks and MANETs (TUM) to efficiently extend the scope of mobile Internet protocol to mobile ad hoc networks and devise a tree-based routing protocol (TBRP) that is suitable for the TUM architecture. Infrastructure networks have a number of fixed Internet Gateways that connect two heterogeneous networks. Mobile nodes form a number of small trees named pMANETs, each of them growing from anchor node which can communicate directly with an Internet Gateway. A new node registers with foreign agent and home agent along the tree path without resorting to an inefficient flooding. Furthermore, the TBRP sets up a routing path efficiently by exploiting the tree information without relying on flooding. We show by resorting to simulation that our approach is competitive against the conventional AODV based approach.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.432-439
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• 2022

This paper introduces deliberative behavior tree using utility theory. The proposed approach combine the strengths of behavior trees and utility theory to implement complex behavior of AI agents in an easier and more concise way. To achieve this goal, we devised and implemented three types of additional behavior tree nodes, which evaluate utility values of its own node or its subtree while traversing and selecting its child nodes based on the evaluated values. In order to validate our approach, we implemented a sample scenario using conventional behavior tree and our proposed deliberative tree respectively. And then we compared and analyzed the simulation results.

• Journal of the Korea Society for Simulation
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• v.5 no.1
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• pp.1-12
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• 1996

We propose a new approach to classifying a time series data into one of the autoregressive moving-average (ARMA) models. It is bases on two pattern recognition concepts for solving time series identification. The one is an extended sample autocorrelation function (ESACF). The other is a neural network-driven decision tree classifier(NNDTC) in which two pattern recognition techniques are tightly coupled : neural network and decision tree classfier. NNDTc consists of a set of nodes at which neural network-driven decision making is made whether the connecting subtrees should be pruned or not. Therefore, time series identification problem can be stated as solving a set of local decisions at nodes. The decision values of the nodes are provided by neural network functions attached to the corresponding nodes. Experimental results with a set of test data and real time series data show that the proposed approach can efficiently identify the time seires patterns with high precision compared to the previous approaches.

• Publications of The Korean Astronomical Society
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• v.30 no.2
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• pp.473-474
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• 2015

Halo merger trees are the essential backbone of semi-analytic models for galaxy formation and evolution. Srisawat et al. (2013) show that different tree building algorithms can build different halo merger histories from a numerical simulation for structure formation. In order to understand the differences induced by various tree building algorithms, we investigate the impact of halo merger trees on a semi-analytic model. We find that galaxy properties in our models show differences between trees when using a common parameter set. The models independently calibrated for each tree can reduce the discrepancies between global galaxy properties at z=0. Conversely, with regard to the evolutionary features of galaxies, the calibration slightly increases the differences between trees. Therefore, halo merger trees extracted from a common numerical simulation using different, but reliable, algorithms can result in different galaxy properties in the semi-analytic model. Considering the uncertainties in baryonic physics governing galaxy formation and evolution, however, these differences may not necessarily be significant.

• The KIPS Transactions:PartC
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• v.16C no.6
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• pp.731-736
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• 2009

In wireless sensor network, many sensor nodes are distributed in the field. They communicate the sensing data each other and forward it to sink. Routing protocols, which define the delivery methods of sending data, affect to the lifetime of sensor network. This paper proposes RTAF that is a routing-tree construction algorithm of sensor nodes by a single flooding process in wireless sensor network. A routing tree is constructed by selecting a parent node using the forward-direction flooding packet and gathering children nodes using the reverse-direction flooding packet. In this process, a node with much energy becomes the parent node. And the routing tree is periodically reconstructed in order to distribute the loads of parent nodes. The proposed algorithm compared performance with Modified-LEACH using NS2 network simulation tool. The simulation result shows that the proposed algorithm constructs a routing-tree faster and reduced 40-80% in routing-tree construction packet.

• Asian Journal of Atmospheric Environment
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• v.2 no.2
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• pp.81-89
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• 2008

The numerical simulation of wind and ozone ($O_3$) transport in mountainous regions was performed with a computational fluid dynamics technique. A dry deposition model for $O_3$ was designed to estimate $O_3$ deposition in complex terrain, and the qualitative validity of the predicted $O_3$ concentration field was confirmed by comparison with observed data collected with passive samplers. The simulation revealed that wind velocity increases around ridge lines and peaks of mountains. The areas with strong wind corresponded well with the sites of tree decline at high altitudes, suggesting that it is an important factor in the localization of tree/forest decline. On the other hand, there is no direct relationship between forest decline and $O_3$ concentration. The $O_3$ concentration, however, tends to increase as wind velocity becomes higher, thus the $O_3$ concentration itself may be a potential secondary factor in the localized decline phenomena. While the diffusion flux of $O_3$ is not related to localized tree decline, the pattern of advection flux is related to those of high wind velocity and localized tree decline. These results suggest that strong wind with large advection flux of $O_3$ may play a key role in the promotion of tree/forest decline at high mountain ridges and peaks.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10b
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• pp.896-900
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• 1988

Tree search can be applied to a control problem if the system is a discrete-time one and if the control input takes only discrete values. This paper considers the application of heuristic tree search to a simple control problem. The results of simulation studies show the good possibility of this approach to a control problem.

• The Korean Journal of Ecology
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• v.6 no.4
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• pp.1-9
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• 1983

The observation of branching pattern on Magnolia denudata Desr. was performed from July 1982 to September 1983 and then computer simulation was carried out. Tree crown pattern depends on not genetic factors but also environmental factors and the determination of branchin pattern which characterized it appears to properly explain the relationships such as branching pattern and allocation of materials through the analysis of influence branches under several assumptions. Now that computer simulated simulated pattern was considered as the accumulation of two factors which controled the growth, it was represented as the stimulated tree which differs in branching rate that described allocation of material necessary for the growth of each branch. There was a tendency of allocation ratio of nutrients, i.e. subbranch to main branch to decrease by the passage of year. Under assumption that branch was branched when accumulated material reached 1, it was possible to represent the allocation of nutrients are residual $nutrient{\times}\frac{1}{1+F};in main; branch, ; residual; nutrient{\times}\frac{F}{1+F}$ in subbranch, A(iA, iC)+$F^(iA-1)$ in current twig. Like this, the basic minute difference of the allocation of nutrients according to the branch resulted in complicated patterns in the tree crown.

• The Journal of Information Systems
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• v.17 no.3
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• pp.25-37
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• 2008

In a fault tree analysis, an uncertainty importance measure is often used to assess how much uncertainty of the top event probability (Q) is attributable to the uncertainty of a basic event probability ($q_i$), and thus, to identify those basic events whose uncertainties need to be reduced to effectively reduce the uncertainty of Q. For evaluating the measures suggested by many authors which assess a percentage change in the variance V of Q with respect to unit percentage change in the variance $v_i$ of $q_i$, V and ${\partial}V/{\partial}v_i$ need to be estimated analytically or by Monte Carlo simulation. However, it is very complicated to analytically compute V and ${\partial}V/{\partial}v_i$ for large-sized fault trees, and difficult to estimate them in a robust manner by Monte Carlo simulation. In this paper, we propose a method for evaluating the measure using discretization technique and Monte Carlo simulation. The proposed method provides a stable uncertainty importance of each basic event.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.580-586
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• 2021

The CAT methodology is a numerical analysis technique using CAE. Recently, a methodology of applying artificial intelligence techniques to a simulation has been studied. A previous study compared the deformation results according to the injection molding process using a machine learning technique. Although MLP has excellent prediction performance, it lacks an explanation of the decision process and is like a black box. In this study, data was generated using Autodesk Moldflow 2018, an injection molding analysis software. Several Machine Learning Algorithms models were developed using RapidMiner version 9.5, a machine learning platform software, and the root mean square error was compared. The decision-tree showed better prediction performance than other machine learning techniques with the RMSE values. The classification criterion can be increased according to the Maximal Depth that determines the size of the Decision-tree, but the complexity also increases. The simulation showed that by selecting an intermediate value that satisfies the constraint based on the changed position, there was 7.7% improvement compared to the previous simulation.