• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.3
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• pp.296-303
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• 2014

In this paper, we propose an algorithm to visualize radio wave environment based on the measured Received Signal Strength Indication( RSSI) and 3D geographic information. We estimate the source position using the circumcenter of the triangle and visualize the radio wave environment using the empirical propagation models. A mode seeking algorithm(mean-shift clustering) is used to seek the peak points and the center of gravity is utilized to reduce the estimation errors. Our approach finds its applications in the radio wave monitoring systems for the efficient utilization of radio resources.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.10
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• pp.671-677
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• 2004

This paper presents the design of a now controller for the read/write head of a hard disk drive. The general controller for seeking is the time-optimal control. However if we use only the time optimal control law, this could be vulnerable to chattering effect. To solve this problem, we propose a modified controller design algorithm in this paper. The proposed controller consists of bang-bang control for seeking and sliding-mode control for tracking. Moreover, to test the robustness and stability of control system, a bounded disturbance is selected to maximize a severity index. Simulation results show the superiority of the proposed controller through comparison with time optimal VSC(variable structure control).

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.5
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• pp.763-770
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• 2017

A radome mounted on the front of an aircraft can cause refraction errors for various reasons that occur during maneuver in seeking and tracking a target. This refraction error means that the microwave seeker is detecting apparent target. An Interactive Multiple Model (IMM) algorithm is applied to estimate radome slope mounted on an aircraft in 3D space. However, even though the parameter of uncertain system model such as radome slope can be estimated, the estimated performance can not be guaranteed when it exceeds the range of the predicted value. In this paper, we propose a method to update the predicted value by using the radome slope as the mode parameter of the IMM algorithm, and confirm the radome slope estimation performance of the proposed method.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.804-811
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• 2015

The presence of responsive loads in the promising active distribution networks (ADNs) would definitely affect the power system problems such as distributed generations (DGs) studies. Hence, an optimal procedure is proposed herein which takes into account the simultaneous placement of DGs and smart meters (SMs) in ADNs. SMs are taken into consideration for the sake of successful implementing of demand response programs (DRPs) such as direct load control (DLC) with end-side consumers. Seeking to power loss minimization, the optimization procedure is tackled with genetic algorithm (GA) and tested thoroughly on 69-bus distribution test system. Different scenarios including variations in the number of DG units, adaptive power factor (APF) mode for DGs to support reactive power, and individual or simultaneous placing of DGs and SMs have been established and interrogated in depth. The obtained results certify the considerable effect of DRPs and APF mode in determining the optimal size and site of DGs to be connected in ADN resulting to the lowest value of power losses as well.

• Journal of KIISE:Software and Applications
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• v.35 no.9
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• pp.572-587
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• 2008

The Self-organizing Feature Map(SOFM) that is one of unsupervised neural networks is a very powerful tool for data clustering and visualization in high-dimensional data sets. Although the SOFM has been applied in many engineering problems, it needs to cluster similar weights into one class on the trained SOFM as a post-processing, which is manually performed in many cases. The traditional clustering algorithms, such as t-means, on the trained SOFM however do not yield satisfactory results, especially when clusters have arbitrary shapes. This paper proposes automatic clustering on trained SOFM, which can deal with arbitrary cluster shapes and be globally optimized by graph cuts. When using the graph cuts, the graph must have two additional vertices, called terminals, and weights between the terminals and vertices of the graph are generally set based on data manually obtained by users. The Proposed method automatically sets the weights based on mode-seeking on a distance matrix. Experimental results demonstrated the effectiveness of the proposed method in texture segmentation. In the experimental results, the proposed method improved precision rates compared with previous traditional clustering algorithm, as the method can deal with arbitrary cluster shapes based on the graph-theoretic clustering.