• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1304-1307
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• 1996

In this paper, we present a robust Two Degree of Freedom (TDF) $H_{\infty}$ controllers for a missile system. The feedback controller is designed to meet robust stability and disturbance rejection specifications while the prefilter is used to improve the robust model matching properties of the closed loop system. As the perturbed model, we use the normalized coprim factor perturbations. These controllers are designed using $H_{\infty}$ optimization procedures, and applied to a missile model via simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.3
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• pp.363-369
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• 1999

In this paper, we design the robust $H_{\infty}$ controller for water level control of steam generator using a mixed $H_{\infty}$ optimization with model-matching method. Firstly we choose the desired model which has good disturbance rejection performance. Secondly we design a stabilizing controller to keep the model-matching error small and also provide sufficiently large stability margin against additive perturbations of the nominal plant. Simulation results show that proposed robust $H_{\infty}$ controller at specific power operation has satisfactory performances against the variations of load power, steam flow rate, primary circuit coolant temperature, and feedwater temperature. It can be also observed that the proposed robust $H_{\infty}$ controller exhibits better robust stability than conventional PI controller.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.580-583
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• 2007

Various matching methods have been introduced by investigators to improve digital elevation model (DEM) accuracy of satellite imagery. This study proposed an area-based matching method according to land cover property using correlation coefficient of pixel brightness value between the two images for DEM generation from IKONOS stereo imagery. For this, matching line (where "matching line" implies straight line that is approximated to complex nonlinear epipolar geometry) is established by exterior orientation parameters to minimize search area. The matching is carried out based on this line. Land cover classes are divided off into water, urban land, forest and agricultural land. Matching size is selected using a correlation-coefficient image in the four areas. The selected sizes are $81{\times}81$ pixels window, $21{\times}21$ pixels window, $119{\times}119$ pixels window and $51{\times}51$ pixels window in the water area, urban land, forest land and agricultural land, respectively. And hence, DEM is generated from IKONOS stereo imagery using the selected matching sizes and land cover map on the four types.

• Animal Bioscience
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• v.36 no.6
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• pp.980-989
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• 2023

Objective: Iris pattern recognition system is well developed and practiced in human, however, there is a scarcity of information on application of iris recognition system in animals at the field conditions where the major challenge is to capture a high-quality iris image from a constantly moving non-cooperative animal even when restrained properly. The aim of the study was to validate and identify Black Bengal goat biometrically to improve animal management in its traceability system. Methods: Forty-nine healthy, disease free, 3 months±6 days old female Black Bengal goats were randomly selected at the farmer's field. Eye images were captured from the left eye of an individual goat at 3, 6, 9, and 12 months of age using a specialized camera made for human iris scanning. iGoat software was used for matching the same individual goats at 3, 6, 9, and 12 months of ages. Resnet152V2 deep learning algorithm was further applied on same image sets to predict matching percentages using only captured eye images without extracting their iris features. Results: The matching threshold computed within and between goats was 55%. The accuracies of template matching of goats at 3, 6, 9, and 12 months of ages were recorded as 81.63%, 90.24%, 44.44%, and 16.66%, respectively. As the accuracies of matching the goats at 9 and 12 months of ages were low and below the minimum threshold matching percentage, this process of iris pattern matching was not acceptable. The validation accuracies of resnet152V2 deep learning model were found 82.49%, 92.68%, 77.17%, and 87.76% for identification of goat at 3, 6, 9, and 12 months of ages, respectively after training the model. Conclusion: This study strongly supported that deep learning method using eye images could be used as a signature for biometric identification of an individual goat.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.04a
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• pp.209-214
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• 2004

This paper presents a RFM-based image matching algorithm which put constraints on the search space through the object-space approach. Also, the detail procedure of generating 3-D surface models from the RFM is introduced as an end-user point of view. The proposed algorithm provides the PML (Piecewise Matching Line) for image matching and reduces the search space to within the confined line-shape area.

• Communications for Statistical Applications and Methods
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• v.18 no.4
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• pp.467-475
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• 2011

This paper develops the noninformative priors for the stress-strength reliability from one parameter generalized exponential distributions. When this reliability is a parameter of interest, we develop the first, second order matching priors, reference priors in its order of importance in parameters and Jeffreys' prior. We reveal that these probability matching priors are not the alternative coverage probability matching prior or a highest posterior density matching prior, a cumulative distribution function matching prior. In addition, we reveal that the one-at-a-time reference prior and Jeffreys' prior are actually a second order matching prior. We show that the proposed reference prior matches the target coverage probabilities in a frequentist sense through a simulation study and a provided example.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.3
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• pp.79-87
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• 1996

A new method for recognizing and locating partially occluded or overlapped two-dimensional objects regardless of their size, translation, and rotation, is presented. Dominant points approximating occuluding contoures of objects are generated by finding local maxima of smoothed k-cosine function, and then used to guide the contour segment matching procedure. Primitives between the dominant points are produced by projecting the local contours onto the line between the dominant points. Robust classification of primitives. Which is crucial for reliable partial shape matching, is performed using adaptive resonance theory (ART2). The matched primitives having similar scale factors and rotation angles are detected in the hough space to identify the presence of the given model in the object scene. Finally the translation vector is estimated by minimizing the mean squred error of the matched contur segment pairs. This model-based matching algorithm may be used in diveerse factory automation applications since models can be added or changed simply by training ART2 adaptively without modifying the matching algorithm.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.54 no.5
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• pp.218-223
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• 2005

Impedance matching characteristics of planar type inductively coupled plasma sources are investigated utilizing the previously reported two-dimensional theory[1] of the anomalous skin effect. Two types of matching networks are considered, and the values of the circuit elements are expressed as functions of various reactor parameter. Also, two cases of perfect and imperfect matching conditions are considered and the functional dependence of the values of matching capacitance and reflection coefficient on the various reactor parameters are investigated using the present circuit model.

• International Journal of CAD/CAM
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• v.6 no.1
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• pp.125-137
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• 2006

Nowadays, many commercial CAD systems support history-based, constraint-based and feature-based modeling. Unfortunately, most systems fail during the re-evaluation phase when various kind of topological changes occur. This issue is known as "persistent naming" which refers to the problem of identifying entities in an initial parametric model and matching them in the re-evaluated model. Most works in this domain focus on the persistent naming of atomic entities such as vertices, edges or faces. But very few of them consider the persistent naming of aggregates like shells (any set of faces). We propose in this paper a complete framework for identifying and matching any kind of entities based on their underlying topology, and particularly shells. The identifying method is based on the invariant structure of each class of form features (a hierarchical structure of shells) and on its topological evolution (an historical structure of faces). The matching method compares the initial and the re-evaluated topological histories, and computes two measures of topological similarity between any couple of entities occurring in both models. The naming and matching method has been implemented and integrated in a prototype of commercial CAD Software (Topsolid).

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.304-306
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• 2007

Methods of Object recognition from camera image are to compare features of color. edge or pattern with model in a general way. SIFT(scale-invariant feature transform) has good performance but that has high complexity of computation. Using simple color histogram has low complexity. but low performance. In this paper we represent a model as a color cooccurrence histogram. and we improve performance using pyramid matching. The color cooccurrence histogram keeps track of the number of pairs of certain colored pixels that occur at certain separation distances in image space. The color cooccurrence histogram adds geometric information to the normal color histogram. We suggest object recognition by pyramid matching of color cooccurrence histogram.