• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.1
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• pp.225-231
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• 2013

In this study, we propose the design of optimized pRBFNNs-based night vision face recognition system using PCA algorithm. It is difficalt to obtain images using CCD camera due to low brightness under surround condition without lighting. The quality of the images distorted by low illuminance is improved by using night vision camera and histogram equalization. Ada-Boost algorithm also is used for the detection of face image between face and non-face image area. The dimension of the obtained image data is reduced to low dimension using PCA method. Also we introduce the pRBFNNs as recognition module. The proposed pRBFNNs consists of three functional modules such as the condition part, the conclusion part, and the inference part. In the condition part of fuzzy rules, input space is partitioned by using Fuzzy C-Means clustering. In the conclusion part of rules, the connection weights of pRBFNNs is represented as three kinds of polynomials such as linear, quadratic, and modified quadratic. The essential design parameters of the networks are optimized by means of Differential Evolution.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.6
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• pp.608-616
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• 2001

This paper proposes a face recognition technique that effectively combines fixed graph matching (FGM) and Fisherface algorithm. EGM as one of dynamic link architecture uses not only face-shape but also the gray information of image, and Fisherface algorithm as a class specific method is robust about variations such as lighting direction and facial expression. In the proposed face recognition adopting the above two methods, linear projection per node of an image graph reduces dimensionality of labeled graph vector and provides a feature space to be used effectively for the classification. In comparison with a conventional EGM, the proposed approach could obtain satisfactory results in the perspectives of recognition speeds. Especially, we could get higher average recognition rate of 90.1% than the conventional methods by hold-out method for the experiments with the Yale Face Databases and Olivetti Research Laboratory (ORL) Databases.

• Journal of Digital Convergence
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• v.14 no.1
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• pp.203-209
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• 2016

In this paper, we introduce a fuzzy inference systems for nonlinear inference using fuzzy cluster. Typically, the generation of fuzzy rules for nonlinear inference causes the problem that the number of fuzzy rules increases exponentially if the input vectors increase. To handle this problem, the fuzzy rules of fuzzy model are designed by dividing the input vector space in the scatter form using fuzzy clustering algorithm which expresses fuzzy cluster. From this method, complex nonlinear process can be modeled. The premise part of the fuzzy rules is determined by means of FCM clustering algorithm with fuzzy clusters. The consequence part of the fuzzy rules have four kinds of polynomial functions and the coefficient parameters of each rule are estimated by using the standard least-squares method. And we use the data widely used in nonlinear process for the performance and the nonlinear characteristics of the nonlinear process. Experimental results show that the non-linear inference is possible.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.6
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• pp.78-87
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• 2008

Digital image find own level core media in multimedia as image restoration technology fields, which remove degradation factor for image enhancement, have been growing. Linear space-invariant image restoration algorithm often introduce ringing artifacts near sharp intensity transition areas. This paper presents a new adaptive post-filtering algorithm for reducing ringing artifact. The proposed method extracts an edge map of the image using wavelet transform Based on the edge information, ringing artifacts are detected, and removed by an adaptive bilateral filter. Experimental results show that the proposed algorithm can efficiently remove ringing artifacts with edge preservation.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.29 no.11
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• pp.6-17
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• 2015

In this paper, colors of broadcast lightings composed of Red, Green, and Blue LED(Light Emitted Diode) can be linearly and quantitatively controlled in low illuminance. Because LED cannot emit uniform illuminance in low illuminance, the colors of RGB LED are unmixable. Furthermore, the illuminances are nonlinear with the dimming values of the RGB LED due to the nonlinearity of the output illuminance with the current through the LED. This nonlinearity generated errors of the target colors and illuminances. The proposed algorithm set up the target colors, which is expressed by the color coordinates in CIE 1931 color space, and the target illuminances. Then the illuminances of RGB LED were calculated using color mixing theory. The calculated illuminances determined the dimming values of the RGB LED for transmission via DMX512 communication. After the broadcasting lighting received the dimming values of the RGB LED via DMX512 communication,.RGB LED can emit target color and illuminance, and be controlled by calculating the PWM(Pulse Width Modulation) duty ratio of the hybrid LED driver which be considered the nonlinearity for the illuminances of the LED. As a result, the proposed algorithm can linearly and quantitatively control the colors and illuminances in full range of illuminance. Then we verify experimentally that the errors of the emitted color coordination x, y and illuminance are 2.27%, 3.6% and 1.5%, respectively.

• Smart Structures and Systems
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• v.31 no.4
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• pp.421-436
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• 2023

This paper presents a vibration displacement measurement and damage identification method for a space truss structure from its vibration videos. Features from Accelerated Segment Test (FAST) algorithm is combined with adaptive threshold strategy to detect the feature points of high quality within the Region of Interest (ROI), around each node of the truss structure. Then these points are tracked by Kanade-Lucas-Tomasi (KLT) algorithm along the video frame sequences to obtain the vibration displacement time histories. For some cases with the image plane not parallel to the truss structural plane, the scale factors cannot be applied directly. Therefore, these videos are processed with homography transformation. After scale factor adaptation, tracking results are expressed in physical units and compared with ground truth data. The main operational frequencies and the corresponding mode shapes are identified by using Subspace Stochastic Identification (SSI) from the obtained vibration displacement responses and compared with ground truth data. Structural damages are quantified by elemental stiffness reductions. A Bayesian inference-based objective function is constructed based on natural frequencies to identify the damage by model updating. The Success-History based Adaptive Differential Evolution with Linear Population Size Reduction (L-SHADE) is applied to minimise the objective function by tuning the damage parameter of each element. The locations and severities of damage in each case are then identified. The accuracy and effectiveness are verified by comparison of the identified results with the ground truth data.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.236-242
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• 2008

In this paper, we propose an optimized fuzzy controller based on Hierarchical Fair Competition-based Genetic Algorithms (HFCGA) for rotary inverted pendulum system. We adopt fuzzy controller to control the rotary inverted pendulum and the fuzzy rules of the fuzzy controller are designed based on the design methodology of Linear Quadratic Regulator (LQR) controller. Simple Genetic Algorithms (SGAs) is well known as optimization algorithms supporting search of a global character. There is a long list of successful usages of GAs reported in different application domains. It should be stressed, however, that GAs could still get trapped in a sub-optimal regions of the search space due to premature convergence. Accordingly the parallel genetic algorithm was developed to eliminate an effect of premature convergence. In particular, as one of diverse types of the PGA, HFCGA has emerged as an effective optimization mechanism for dealing with very large search space. We use HFCGA to optimize the parameter of the fuzzy controller. A comparative analysis between the simulation and the practical experiment demonstrates that the proposed HFCGA based fuzzy controller leads to superb performance in comparison with the conventional LQR controller as well as SGAs based fuzzy controller.

• Geophysics and Geophysical Exploration
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• v.11 no.1
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• pp.68-77
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• 2008

We have developed an inversion algorithm for loop-loop electromagnetic (EM) data, based on the localised non-linear or extended Born approximation to the solution of the 2.5D integral equation describing an EM scattering problem. Source and receiver configuration may be horizontal co-planar (HCP) or vertical co-planar (VCP). Both multi-frequency and multi-separation data can be incorporated. Our inversion code runs on a PC platform without heavy computational load. For the sake of stable and high-resolution performance of the inversion, we implemented an algorithm determining an optimum spatially varying Lagrangian multiplier as a function of sensitivity distribution, through parameter resolution matrix and Backus-Gilbert spread function analysis. Considering that the different source-receiver orientation characteristics cause inconsistent sensitivities to the resistivity structure in simultaneous inversion of HCP and VCP data, which affects the stability and resolution of the inversion result, we adapted a weighting scheme based on the variances of misfits between the measured and calculated datasets. The accuracy of the modelling code that we have developed has been proven over the frequency, conductivity, and geometric ranges typically used in a loop-loop EM system through comparison with 2.5D finite-element modelling results. We first applied the inversion to synthetic data, from a model with resistive as well as conductive inhomogeneities embedded in a homogeneous half-space, to validate its performance. Applying the inversion to field data and comparing the result with that of dc resistivity data, we conclude that the newly developed algorithm provides a reasonable image of the subsurface.

• Journal of the Korean Society for Precision Engineering
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• v.34 no.1
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• pp.41-46
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• 2017

Magnetically levitated (Maglev) vehicles maintain a constant air gap between guideway and car bogie, and thereby achieves non-contact riding. Since the straightness and the flatness of the guideway directly affect the stability of levitation as well as the ride comfort, it is necessary to monitor the status of the guideway and to alert the train operators to any abnormal conditions. In order to develop a signal processing algorithm that extracts guideway irregularities from sensor data, virtual testing using a simulation model would be convenient for analyzing the exact effects of any input as long as the model describes the actual system accurately. Simulation model can also be used as an estimation model. In this paper, we develop a state-space dynamic model of a maglev vehicle system, running on the guideway that contains jumps. This model contains not only the dynamics of the vehicle, but also the descriptions of the power amplifier, the anti-aliasing filter and the sampling delay. A test rig is built for the validation of the model. The test rig consists of a small-scale maglev vehicle, tracks with artificial jumps, and various sensors measuring displacements, accelerations, and coil currents. The experimental data matches well with those from the simulation model, indicating the validity of the model.

• Proceedings of the KSR Conference
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• 2003.10c
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• pp.236-246
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• 2003

This paper is implemented a control algorithm in order to be stable and minimized to entire train traffic system at delayed case. Signal ing system is described wi th algebraic equations given for train headway, Discrete-event simulation principles are reviewed and a demonstration block signaling model using the technique is implemented. Train congestion at station entrance for short headway operation is demonstrated and the propagation of delays along a platform of trains from any imposed delay to the leading train is also shown. A rail way signaling system is by nature a distributed operation with event triggered at discrete intervals. Although the train kinematic variables of position, velocity, and acceleration are continually changing, the changes are triggered when the trains pass over section boundaries and arrive at signals and route switches. This paper deals with linear-mode1ing, stability and optimal control for the traffic on such metro line of the model is reconstructed in order to adapt the circuits. This paper propose optimal control laws wi th state feedback ensuring the stability of the modeled system for circuits. Simulation results show the benefit to be expected from an efficient traffic control. The main results are summarized as follows: 1. In this paper we develop a linear model describing the traffic for both loop lines, two state space equations have been analyzed. The first one is adapted to the situation where a complete nominal time schedule is available while second one is adapted when only the nominal time interval between trains is known, in both cases we show the unstability of the traffic when the proceeding train is delayed following properties, - They are easily implemented at law cost on existing lines. - They ensure the exponetial stability of loop system. 2. These control laws have been tested on a traffic simulation software taking into the non-linearites and the physical constraints on a metro line. By means of simulation, the efficiency of the proposed optimal control laws are shown.