• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.3
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• pp.220-225
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• 1999

This paper presents an UPFC (Unified Power Flow Controller) model for stability study using PSS/E. The proposed UPEC model was implemented by IPLAN which is a high level language. As a control strategy for damping electromechanical oscillations, energy function method was adopted. By the adopted control law, the damping effect is robust with respect to loading condition, fault location and network structure. Furthermore, the control imputs are based on local signals. The effect of control of the UPFC model was demonstrated on an one machine infinite bus system and a two area system.

• Journal of Korean Society of Transportation
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• v.17 no.5
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• pp.111-122
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• 1999

A service network design model for LTL freight transportation is formulated as a mixed integer Programming Problem with two heuristic solution a1gorithms. The Proposed model derives the transportation Path for each origination-destination pair, taking into account transportation cost over the links and handling costs over the nodes. The first algorithm searches for a local minimum solution from a given initial solution by improving the quality of solution repeatedly while the second a1gorithm searches for a better solution using Simulated Annealing Method. For both solution algorithms, the initial solution is derived by a modified reverse Diikstras shortest Path a1gorithm. An illustrative example, Presented in the last part of the Paper, shows that the proposed algorithms find solutions which reduce the cost by 12% and 15% respectively, compared to the initial solution.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.8
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• pp.731-738
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• 2011

In this paper, we introduce an advanced architecture of K-Means clustering-based polynomial Radial Basis Function Neural Networks (p-RBFNNs) designed with the aid of SSOA (Space Search Optimization Algorithm) and develop a comprehensive design methodology supporting their construction. In order to design the optimized p-RBFNNs, a center value of each receptive field is determined by running the K-Means clustering algorithm and then the center value and the width of the corresponding receptive field are optimized through SSOA. The connections (weights) of the proposed p-RBFNNs are of functional character and are realized by considering three types of polynomials. In addition, a WLSE (Weighted Least Square Estimation) is used to estimate the coefficients of polynomials (serving as functional connections of the network) of each node from output node. Therefore, a local learning capability and an interpretability of the proposed model are improved. The proposed model is illustrated with the use of nonlinear function, NOx called Machine Learning dataset. A comparative analysis reveals that the proposed model exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5269-5286
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• 2018

Task scheduling is one of the key issues in improving system performance and optimizing resource management in cloud computing environment. In order to provide appropriate services for heterogeneous users, we propose a novel task scheduling strategy with service differentiation, in which the delay sensitive tasks are assigned to the rapid cloud with high-speed processing, whereas the fault sensitive tasks are assigned to the reliable cloud with service restoration. Considering that a user can receive service from either local SaaS (Software as a Service) servers or public IaaS (Infrastructure as a Service) cloud, we establish a hybrid queueing network based system model. With the assumption of Poisson arriving process, we analyze the system model in steady state. Moreover, we derive the performance measures in terms of average response time of the delay sensitive tasks and utilization of VMs (Virtual Machines) in reliable cloud. We provide experimental results to validate the proposed strategy and the system model. Furthermore, we investigate the Nash equilibrium behavior and the social optimization behavior of the delay sensitive tasks. Finally, we carry out an improved intelligent searching algorithm to obtain the optimal arrival rate of total tasks and present a pricing policy for the delay sensitive tasks.

• Journal of Electrical Engineering and Technology
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• v.7 no.4
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• pp.636-645
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• 2012

This study introduces an information granular-based fuzzy radial basis function neural networks (FRBFNN) based on multiobjective optimization and weighted least square (WLS). An improved multiobjective space search algorithm (IMSSA) is proposed to optimize the FRBFNN. In the design of FRBFNN, the premise part of the rules is constructed with the aid of Fuzzy C-Means (FCM) clustering while the consequent part of the fuzzy rules is developed by using four types of polynomials, namely constant, linear, quadratic, and modified quadratic. Information granulation realized with C-Means clustering helps determine the initial values of the apex parameters of the membership function of the fuzzy neural network. To enhance the flexibility of neural network, we use the WLS learning to estimate the coefficients of the polynomials. In comparison with ordinary least square commonly used in the design of fuzzy radial basis function neural networks, WLS could come with a different type of the local model in each rule when dealing with the FRBFNN. Since the performance of the FRBFNN model is directly affected by some parameters such as e.g., the fuzzification coefficient used in the FCM, the number of rules and the orders of the polynomials present in the consequent parts of the rules, we carry out both structural as well as parametric optimization of the network. The proposed IMSSA that aims at the simultaneous minimization of complexity and the maximization of accuracy is exploited here to optimize the parameters of the model. Experimental results illustrate that the proposed neural network leads to better performance in comparison with some existing neurofuzzy models encountered in the literature.

• Journal of Korea Water Resources Association
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• v.47 no.2
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• pp.207-221
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• 2014

Recently, due to global warming, climate change has affected short time concentrated local rain and unexpected heavy rain which is increasingly causing life and property damage. Therefore, this paper studies the characteristic of localized heavy rain and flash flood in Nakdong basin study area by applying Data Mining method to predict flood and constructing water level predicting model. For the verification neural network from Data Mining method and hydraulic flood routing was used for flood from July 1989 to September 1999 in Nakdong point and Iseon point was used to compare flood level change between observed water level and SAM (Slope Area Method). In this research, the study area was divided into three cases in which each point's flood discharge, water level was considered to construct the model for hydraulic flood routing and neural network based on artificial intelligence which can be made from simple input data used for comparison analysis and comparison evaluation according to actual water level and from the model.

• Journal of Communications and Networks
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• v.12 no.5
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• pp.480-491
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• 2010

Along with the wide acceptance of IEEE 802.11 wireless local area network (WLAN), new applications such as Internet protocol (IP) telephony over WLAN are fast emerging today. For battery-powered IP phone devices, the life time extension is a key concern for the market acceptance while today's 802.11 is not optimized for snch an operation. In this paper, we propose a novel idle mode operation, which comprises paging, idle handoff, and delayed handoff. Under the idle mode operation, a mobile host(MH) without any active session does not need to perform handoff within a predefined paging area (PA). Only when it enters a new PA, an idle handoff is performed. The proposed idle mode allows an MH without traffic to extend its life time. We develop a new analytical model in order to comparatively evaluate our proposed scheme. The numerical resnlts demonstrate that the proposed scheme outperforms the existing schemes with respect to power consumption.

• Proceedings of the KIEE Conference
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• 1994.11a
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• pp.6-8
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• 1994

In this paper an attempt is made to understand the voltage stability when the power system networks are represented by the differential-algebraic equations (DAEs) form. The problem is analyzed by interpreting the shape of constraint manifold, based on the singular perturbation model. The global picture or constraint manifold is given to show how the local shape or constraint manifold can be used to guess for the system behavior. The gradient analysis is used systematically to obtain a local shape or the constraint manifold.

• Proceedings of the KIEE Conference
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• 2004.11b
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• pp.258-260
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• 2004

This paper analyse a operating characteristics when the Community Energy System (CES) is operated islanding mode. In the near future, CES might be one of major energy supply structures. The basic concept of CES is that it supplies electrical and thermal energy to the local customer loads through the islanded power network separated from the grid. The CES must be supplying local load with stable energy on the islanding mode, analysing necessary to thoroughly the operation feature. In order to show them, in this paper, we model the CES with 2.34 MVA DG and simulate the operating feature on the islanding mode of CES. The simulation results show that, in order to stability operate, the CES need the efficient load management and generation control schemes during the transition periods.

• Journal of the Korean Society for Geothermal and Hydrothermal Energy
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• v.18 no.3
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• pp.1-6
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• 2022

Ground source heat pump (GSHP) system is highly efficient and environment-friendly and supplies heating, cooling and hot water to buildings. For an optimal design of the GSHP system, the ground thermal properties should be determined to estimate the heat exchange rate between ground and borehole heat exchangers (BHE) and the system performance during long-term operating periods. However, the process increases the initial cost and construction period, which causes the system to be hindered in distribution. On the other hand, much research has been applied to the artificial neural network (ANN) to solve problems based on data efficiently and stably. This research proposes the predictive performance model utilizing ANN considering local characteristics and weather data for the predictive performance model. The ANN model predicts the entering water temperature (EWT) from the GHEs to the heat pump for the modular GHEs, which were developed to reduce the cost and spatial disadvantages of the vertical-type GHEs. As a result, the temperature error between the data and predicted results was 3.52%. The proposed approach was validated to predict the system performance and EWT of the GSHP system.