• Journal of Korea Multimedia Society
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• v.15 no.2
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• pp.195-203
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• 2012

This paper proposes ground target classification algorithm based on decision fusion and feature extraction method using multi-sensor images. The decisions obtained from the individual classifiers are fused by applying a weighted voting method to improve target recognition rate. For classifying the targets belong to the individual sensors images, features robust to scale and rotation are extracted using the difference of brightness of CM images obtained from CCD image and the boundary similarity and the width ratio between the vehicle body and turret of target in FLIR image. Finally, we verity the performance of proposed ground target classification algorithm and feature extraction method by the experimentation.

• Journal of the Korean Institute of Telematics and Electronics D
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• v.34D no.7
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• pp.88-96
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• 1997

As the need of automatized system has been increased recently together with the development of industrial and military technologies, the adaptive real-time target detection technologies that can be embedded on vehicles, planes, ships, robots and so on, are hgihly demanded. Accordingly, this paper proposes a novel approach to detect and segment the moving targets using the binary phase extraction joint transform correlator (BPEJTC), the advanced image subtraction filter and convex hull processing. The BPEJTC which was used as a target detection unit mainly for target tracking compensating the camera movement. The target region has been detected by processing the successful three frames using the advanced image subtraction filter, and has become more accurate by applying the developed convex hull filter. As shown by some experimental results, it is expected that the proposed approaches for compensation of the camera movement and segmentationof of target region, can be used for th emissile guiddance, aero surveillance, automatic inspectin system as well as the target detection unit of automatic target recognition system that request adaptive real-time processing.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.18 no.9
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• pp.1090-1097
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• 2007

The multibeam surveillance radar is a state-of art of 3D radar technology. It applies the stacked beam-on-received realized by a digital beamformer. In this paper, a design concept of beamformer and a method of target altitude extraction for multibeam surveillance radar in the normal environmental condition considering no multipath situations are proposed and investigated. The extraction algorithm based on antenna sine space coordinated system in a FFT digital beamformer is described. The proposed algorithm is simulated by 1 look-up table data and confirmed to have consistent results in accordance with a variety of target altitudes and a full radar frequency range.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1987.04a
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• pp.42-45
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• 1987

In this paper a target tracking algorithm of the centroid extraction from moving edges is proposed, It aims to avoid the difficulty of imahe segmentation in case of the centroid extraction from one frame. The performance of the proposed algorithmfor noisy and occluded images is discussed Finally it is also applied to a real time target tracker.

• Proceedings of the Optical Society of Korea Conference
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• 1995.06a
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• pp.105-116
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• 1995

In this paper, we propose a new a new EOST by using the optical JTC(joint transform correlator) as the feature extraction park, because the JTC can adaptively detect the relative displacements of moving targest. Firstly, we derive the BPEJCT(binary phase extraction JTC) which is a phase type JTC and can remove the intracless correlation peaks of the conventional JTC. Then, especially we hardware construction for driving the BPEJTC in real, and with the Kalman target estimation alogorithm, we carried out a target tracking experiment only to show the possibility of real-time implementation of the EOST.

• Carbon letters
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• v.26
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• pp.43-50
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• 2018

In this research, a novel and efficient quinoline thioacetamide functionalized magnetic graphene oxide composite ($GO@Fe_3O_4@QTA$) was synthesized and utilized for dispersive magnetic solid phase preconcentration of Cd(II) and Ni(II) ions in urine and various food samples. A number of diverse methods were employed for characterization of the new nanosorbent. The design of experiments approach and response surface methodology were applied to monitor and find the parameters that affect the extraction performance. After sorption and elution steps, the concentrations of target analytes were measured by employing FAAS. The highest extraction performance was achieved under the following experimental conditions: pH, 5.8; sorption time, 6.0 min; $GO@Fe_3O_4@QTA$ amount, 17 mg; 2.4 mL $1.1mol\;L^{-l}$ $HNO_3$ solution as the eluent and elution time, 13.0 min. The detection limit is 0.02 and $0.2ng\;mL^{-1}$ for Cd(II), and Ni(II) ions, respectively. The accuracy of the new method was investigated by analyzing two certified reference materials (sea food mix, Seronorm LOT NO 2525 urine powder). The interfering study revealed that there are no interferences from commonly occurring ions on the extractability of target ions. Finally, the new method was satisfactorily employed for rapid extraction and determination of target ions in urine and various food samples.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.629-634
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• 2009

In real-time system application, the feature extraction and scoring algorithm for classification of the passive sonar target has the following problems: it requires an accurate and efficient feature extraction method because it is very difficult to distinguish the features of the propeller shaft rate (PSR) and the blade rate (BR) from the frequency spectrum in real-time, it requires a robust and effective feature scoring method because the classification database (DB) composed of extracted features is noised and incomplete, and further, it requires an easy design procedure in terms of structures and parameters. To solve these problems, an intelligent feature extraction and scoring algorithm using the evolution strategy (ES) and the fuzzy theory is proposed here. To verify the performance of the proposed algorithm, a passive sonar target classification is performed in real-time. Simulation results show that the proposed algorithm effectively solves sonar classification problems in real-time.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.3
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• pp.978-983
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• 2010

Acoustic target classification in wireless sensor network is important research at environmental surveillance, invasion surveillance, multiple target separation. General sensor node signal processing methods concentrated on received signal energy based target detection and received raw signal compression. The former is not suited to target classification because of almost every target information are lost except target energy. The latter bring down life-time of sensor node owing to high computational complexity and transmission energy. In this paper, we introduce an feature extraction algorithm for acoustic target classification in wireless sensor network which has time and frequency information. The proposed method extracts time information and de-noised target classification information using wavelet decomposition step. This method reduces communication energy by 28% of original signal and computational complexity.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.5
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• pp.632-637
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• 2008

In real system application, the propeller shaft rate (PSR) estimation algorithm for the feature extraction of the sonar target operates with the following problems: it requires both accurate and efficient the fundamental finding method because it is essential and difficult to distinguish harmonic family composed of the fundamental and its harmonics from the multiple spectral lines in the frequency spectrum-based sonar target classification, and further, it requires an easy design procedure in terms of its structures and parameters. To solve these problems, an evolutionary PSR estimation algorithm using an expert knowledge and the evolution strategy, is proposed. To verify the performance of the proposed algorithm, a sonar target PSR estimation is performed. Simulation results show that the proposed algorithm effectively solves the problems in the realtime system application.

• Proceedings of the Korea Electromagnetic Engineering Society Conference
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• 2003.11a
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• pp.193-197
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• 2003

Radar target identification can be achieved by using various radar signatures, such as one-dimensional(1-D) range profile, 2-D radar images, and 1-D or 2-D scattering centers on a target. In this letter, five 1-D scattering center extraction methods are discussed - TLS(Total Least Square)-Prony, Fast Root-MUSIC (Multiple Signal Classification), Matrix-Pencil, GEESE(GEneralized Eigenvalues utilizing Signal-subspace Eigenvalues), TLS-ESPRIT(Total Least Squares - Estimation of Signal Parameters via Rotational Invariance Technique), These methods are compared in the context of estimation accuracy as well as a computational efficiency using a noisy data. Finally these methods are applied to the target classification experiment with the measured data in the POSTECH compact range facility.