• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.122-122
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• 2020

Urban flood simulation plays a vital role in national flood early warning, prevention and mitigation. In recent studies on 2-dimensional flood modeling, the integrated run-off inundation model is gaining grounds due to its ability to perform in greater computational efficiency. The adaptive quadtree shallow water numerical technique used in this model implements the adaptive mesh refinement (AMR) in this simulation, a procedure in which the grid resolution is refined automatically following the flood flow. The method discounts the necessity to create a whole domain mesh over a complex catchment area, which is one of the most time-consuming steps in flood simulation. This research applies the dynamic grid refinement method in simulating the recent extreme flood events in Metro Manila, Philippines. The rainfall events utilized were during Typhoon Ketsana 2009, and Southwest monsoon surges in 2012 and 2013. In order to much more visualize the urban flooding that incorporates the flow within buildings and high-elevation areas, Digital Surface Model (DSM) resolution of 5m was used in representing the ground elevation. Results were calibrated through the flood point validation data and compared to the present flood hazard maps used for policy making by the national government agency. The accuracy and efficiency of the method provides a strong front in making it commendable to use for early warning and flood inundation analysis for future similar flood events.

• Journal of the Korea Society for Simulation
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• v.21 no.4
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• pp.47-56
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• 2012

In this study, we use a parallel simulator PASENS(Parallel SEnsor Network Simulator) to predict power consumption and data reception rate of the hierarchical routing protocols for sensor network - LEACH (Low-Energy Adaptive Clustering Hierarchy), TL-LEACH (Two Level Low-Energy Adaptive Clustering Hierarchy), M-LEACH (Multi hop Low-Energy Adaptive Clustering Hierarchy) and LEACH-C (LEACH-Centralized). According to simulation results, M-LEACH routing protocol shows the highest data reception rate for the wider area, since more sensor nodes are involved in the data transmission. And LEACH-C routing protocol, where the sink node considers the entire node's residual energy and location to determine the cluster head, results in the most efficient energy consumption and in the narrow area needed long life of sensor network.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.2
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• pp.501-506
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• 2010

The proposal set out in this paper, is the Adaptive-MCM(Modulation, Coding and MIMO) system, which results from the combination of adaptive modulation and coding (AMC) and multiple input multiple output (MIMO) schemes. The performance of this system is analyzed through computer simulation. By using the MIMO scheme adaptively as well, the proposed Adaptive-MCM system, presents a better improvement of data rate and error performance compared to the AMC system. The throughput performance of the Adaptive-MCM system is analyzed and compared with the throughput performance of Non-Adaptive-MCM Systems. As a result of the simulation, we can infer that, at a fixed MCM level, there is an improvement of the trade-off between secure Signal to Noise Ratio (SNR) and a high data rate. Consequently, this trade-off improvement results in a better average data rate.

• Journal of the Korea Society for Simulation
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• v.23 no.4
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• pp.111-119
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• 2014

The cloud environment need resource management method that to enable the big data issue and data analysis technology. Existing resource management uses the limited calculation method, therefore concentrated the resource bias problem. To solve this problem, the resource management requires the learning-based scheduling using resource history information. In this paper, we proposes the ART (Adaptive Resonance Theory)-based adaptive resource management. Our proposed method assigns the job to the suitable method with the resource monitoring and history management in cloud computing environment. The proposed method utilizes the unsupervised learning method. Our goal is to improve the data processing and service stability with the adaptive resource management. The propose method allow the systematic management, and utilize the available resource efficiently.

• Transactions of the Korean Society of Automotive Engineers
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• v.2 no.6
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• pp.29-37
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• 1994

Active noise control of random noise which propatate in the vehicle cabin as a form of spherical wave is the target of this study. In the previous study, the adaptive algorithm for adaptive controller is presented for the application in active noise control system. And for the preliminary study of adaptive active noise control in vehicle cabin as a real system, a computer simulation is performed on the effectiveness of the adaptive algorithm in the amplitude of the pressure fluctuation. This work studies the implementation of multi-channel feedforward adaptive algorithm for the reduction of the noise inside a vehicle cabin using a number of secondary sources derived by adaptive filtering of reference noise source. Multi-channel adaptive feedforward algorithm are verified in numerical simulation and semi-experimental justification of developed system is made on a domestic passenger car. In the results of semi-experimental study, the noise of specific region in the interior of automobile are reduced for the appreciabe sound pressure level in the operating engine rpm and finally this study suggests the capabilities of the real time active noise control in 3 dimensional acoustic fields.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.875-878
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• 1998

In this paper, an adaptive event detection algorithm is proposed, for which we use the statistics of subblock image and adaptive threshold levels. The adaptive threshold level for a parameter binarization is taken by averaging the corresponding paramerter obtained from several input images. As simulation results, it is shown that the proposed algorithm is much more adaptive to the input images and effective in event detection rate than the conventional difference based algorithms.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.314-317
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• 1996

In this paper, we present a nonlinear adaptive controller for position tracking of induction motors. In constructing the adaptive controller, a backstepping approach is used under the condition of full state information, while a nonlinear observer is adopted for rotor flux estimation. The adaptive controller is shown to drive the state variables of system to the desired ones asymptotically and whose effectiveness is also shown via computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.260-265
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• 1992

This paper presents an adaptive control scheme for flexible joint robot manipulators. This control scheme is based on the Lyapunov direct method with the arm energy-based Lyapunov function. The proposed adaptive control scheme uses only the position and velocity feedback of link and motor shaft. The adaptive control system of flexible joint robots is asymptotically stable regardless of the joint flexibility value. Therefore, the assumption of weak joint ealsticity is not needed. Also, joint flexibility value is unknown. Simulation results are presented to show the feasibility of the proposed adaptive control scheme.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.313-316
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• 1990

This paper proposes the design of the decentralized adaptive controllers which are an arbitrary interconnection of sub-systems with unknown parameters, nonlinear ities and bounded disturbances. In order to exponentially converge the state and parameter errors, robust decentralized adaptive controllers are developed for stabilization and tracking the parameters. In the simulation studies of the decentralized adaptive control of a two-area interconnected power system, the effectiveness of the proposed adaptive schemes is demonstrated.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.136-138
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• 1997

This paper proposes a design method to improve the performance of Indirect Adaptive Controllers using a CP net. This hybrid control architecture consists of Indirect Adaptive Controllers and CP net Controller. The performance of a single Adaptive Controller, multi Adaptive Controllers and the proposed model is compared by control problems. The simulation results show that the proposed model is superior to the others in most cases, in regard of not only learning speed but also control problems.