• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.561-562
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• 2008

In this paper, we propose a establishment of system level environment to exploit PC system with SSD (Solid State Disk) by using TLM (Transaction Level Modeling) method with SystemC language. The reason why we choose this modeling method is that it eases RTL (Register Transfer Level) modeling burdens and we can accurately estimate the performance about different architectural changes. Also, it provides simulation speed which is relatively faster than RTL modeling method. The baseline architectural platform we implemented showed that SSD's internal transfer time is a dominant factor, so we need to improve that part and it is expected to be a good simulator to measure the system's overall performance by exploiting SSD's internal architectures.

• International journal of advanced smart convergence
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• v.12 no.2
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• pp.117-126
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• 2023

To improve the overall performance and realism of your game, it is important to calculate the trajectory of a projectile accurately and quickly. One way to increase realism is to use a ballistic model that takes into account factors such as air resistance, density, and wind when calculating a projectile's trajectory. However, the more these factors are taken into account, the more computationally time-consuming and expensive it becomes, creating a trade-off between overall performance and efficiency. Therefore, we present an optimal solution to find a balance between ballistic model accuracy and computation time. We perform ballistic calculations using numerical methods such as Euler, Velocity Verlet, RK2, RK4, and Akima interpolation, and measure and compare the computation time, memory usage (RSS, Resident Set Size), and accuracy of each method. We show developers how to implement more accurate and efficient ballistic models and help them choose the right computational method for their numerical applications.

• Korean Journal of Computational Design and Engineering
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• v.5 no.4
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• pp.301-311
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• 2000

For effective part modifications which is necessary in the design process frequently, variational geometric modeling with constraint management being used in a wide. Most variational geometric modeling methods, however, manage just the constraints about sketch elements used for generation of primitives. Thus, not only constraint propagation but also re-build of various modeling operations stored in the modeling history is necessary iota part geometry modifications. Especially, re-build of high-cost Boolean operations is apt to deteriorate overall modeling efficiency abruptly. Therefore, in this paper we proposed an algorithm that can handle all geometric entities of the part directly. For this purpose, we introduced eight type geometric constraints to the various geometric calculations about all geometric entities in sweepings and Boolean operations as well as the existing constraints of the sketch elements. The algorithm has a merit of rapid part geometric modifications through only constraint propagation without rebuild of modeling operations which are necessary in the existing variational geometric modeling method.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.3
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• pp.107-118
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• 1998

Two-phase neuro-system identification method is presented. The 1$^{st}$-phase identification uses conventional neural network mapping for modeling an input-output system. The 2$^{nd}$ -phase modeling is also performed sequentially using the 1$^{st}$-phase modeling errors. In the 2$^{nd}$ a phase modeling, newly generated input signals, which are obtained by summing the 1st-phase modeling error and artificially generated uniform series, are utilized as system's I-O mapping elements. The 1$^{st}$-phase identification is interpreted as a “Real Model” system identification because it uses system's real data(i.e., observations and control inputs) while the 2$^{nd}$ -phase identification as a “Artificial Model” identification because of using artificial data. Experimental results are given to verify that the two-phase neuro-system identification could reduce the overall modeling errors.rrors.

• The KSFM Journal of Fluid Machinery
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• v.18 no.3
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• pp.38-45
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• 2015

In this study, two computational methodologies were compared to consider an effective conjugate heat transfer analysis technique for the cooled vane with thermal barrier coating. The first one is the physical modeling method of the TBC layer on the vane surface, which means solid volume of the TBC on the vane surface. The second one is the numerical modeling method of the TBC layer by putting the heat resistance interface condition on the surface between the fluid and solid domains, which means no physical layer on the vane surface. For those two methodologies, conjugate heat transfer analyses were conducted for the cooled vane with TBC layer having various thickness from 0.1 mm to 0.3 mm. Static pressure distributions for two cases show quite similar patterns in the overall region while the physical modeling shows quite a little difference around the throat area. Thermal analyses indicated that the metal temperature distributions are quite similar for both methods. The results show that the numerical modeling method can reduce the computational resources significantly and is quite suitable method to evaluate the overall performance of TBC even though it does not reflect the exact geometry and flow field characteristics on the vane surface.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.11
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• pp.1988-1993
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• 2008

Photovoltaic (PV) modules operate over a large range of conditions but manufacturer's information is not sufficient to determine their overall performance. Designers need a reliable tool to predict energy production from a photovoltaic module under all conditions in order to make a sound decision. The modeling method of photovoltaic (PV) module are useful to perform detailed analysis of PV system performance for changing meteorological conditions, verify actual rated power of PV system sizing and determine the optimal design of PV system and components. This paper indicates a modeling approach of PV module performance in terms of meteorological conditions and identifies validity of PV modules modeling by comparing measured with simulated value.

• The Transactions of the Korean Institute of Power Electronics
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• v.17 no.1
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• pp.67-76
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• 2012

This paper proposes the aggregated modeling and control of integated boost-flyback converter (IBFC) for understanding of dynamics characteristic and designing of relevant controller. The basic concept of the aggregated modeling is to substitute the boost or the flyback converter with an equivalent current source. Since each converter with equivalent current source corresponds to the basic boost and flyback converters, the overall mathematical process is significantly simplified for the modeling. Afterwards each result is combined to construct the complete model of the IBFC, and the relevant controller is designed through the achieved small-signal model. Simulation and experimental results show excellent agreement with the theoretical expectations.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1254-1259
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• 2005

In this paper, we proposed a learning algorithm for the neuro-fuzzy modeling using a learning rule to adapt clustering. The proposed algorithm includes the data partition, assigning the rule into the process of partition, and optimizing the parameters using predetermined threshold value in self-constructing algorithm. In order to improve the clustering, the learning method of neuro-fuzzy model is extended and the learning scheme has been modified such that the learning of overall model is extended based on the error-derivative learning. The effect of the proposed method is presented using simulation compare with previous ones.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.2
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• pp.95-101
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• 2005

In this paper, we proposed a learning algorithm for the neuro-fuzzy modeling using a learning rule to adapt clustering. The proposed algorithm includes the data partition, assigning the rule into the process of partition, and optimizing the parameters using predetermined threshold value in self-constructing algorithm. In order to improve the clustering, the learning method of neuro-fuzzy model is extended and the learning scheme has been modified such that the learning of overall model is extended based on the error-derivative learning. The effect of the proposed method is presented using simulation compare with previous ones.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.9
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• pp.2232-2245
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• 2013

The present study proposes efficient procedural modeling methods for enabling the growth and creation of various trees with minimal user control. Growth volume algorithms are utilized in order to easily and effectively calculate many parameters that determine tree growth, including branch propagation. Procedural methods are designed so that users' interactive control structures can be applied to these algorithms to create unique tree models efficiently. First, through a two-line-based interactive growth volume control method, the growth information that determines the overall shape of the tree is intuitively adjusted. Thereafter, independent branch control methods designed to control individual branches are added to the growth deformation in order to enable the growth of unique trees. Whether the growth processes of desired trees can be easily and intuitively controlled by the proposed method is verified through experiments. Methods that can apply the proposed methods are also verified.