• Journal of the Korea Society of Computer and Information
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• v.22 no.12
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• pp.55-61
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• 2017

In this paper, we propose a novel tracking method using target separation and detection that are based on discriminative correlation filter (DCF), which is studied a lot recently. 'Retainability' is one of the most important factor of tracking. There are some factors making retainability of tracking worse. Especially, fast movement and occlusion of a target frequently occur in image data, and when it happens, it would make target lost. As a result, the tracking cannot be retained. For maintaining a robust tracking, in this paper, separation of a target is used so that normal tracking is maintained even though some part of a target is occluded. The detection algorithm is executed and find new location of the target when the target gets out of tracking range due to occlusion of whole part of a target or fast movement speed of a target. A variety of experiments with various image data sets are conducted. The algorithm proposed in this paper showed better performance than other conventional algorithms when fast movement and occlusion of a target occur.

• International Journal of Contents
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• v.3 no.4
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• pp.22-25
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• 2007

This paper presents a technology of information data fusion between radar and ELINT electronic intelligence system. adar get the information of the range, direction and velocity of targets, and ELINT system get the information of the direction and angular velocity of the same targets at the same place and at the same time. Since we have some common information data of targets from radar and ELINT system, we can find the target on radar is same or not on ELINT system using the information data fusions. If the target on the radar is verified with the same target on ELINT system, we get more information of the target. e can analysis and identify the target exactly and reduce an ambiguity error of unknown targets.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.243-246
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• 2003

Data mining is a process of discovering useful patterns for decision making from an amount of data. It has recently received much attention in a wide range of business and engineering fields Classifying a group into subgroups is one of the most important subjects in data mining Tree-based methods, known as decision trees, provide an efficient way to finding classification models. The primary concern in tree learning is to minimize a node impurity, which is evaluated using a target variable in the data set. However, there are situations where multiple target variables should be taken into account, for example, such as manufacturing process monitoring, marketing science, and clinical and health analysis. The purpose of this article is to present several methods for measuring the node impurity, which are applicable to data sets with multiple target variables. For illustrations, numerical examples are given with discussion.

• Journal of the Korean Vacuum Society
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• v.17 no.3
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• pp.247-252
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• 2008

By using the transmission anode x-ray tube developed to apply to the hand-held XRF equipment, we carried out XRF experiment and evaluated the influences of the x-ray tube on XRF spectra. XRF data, which is measured using the W-target and Rh-target tube, were good agreements with the known results. FWHM of Fe $K_{\alpha}$-line measured by W-target tube with the 35 kV-tube voltage and the $40{\mu}A$-tube current was 180 eV. This result reveals that our XRF equipment using the transmission anode x-ray tube is enough for a qualitative analysis of materials. By comparison XRF data with the integrated intensity of x-ray tube, it was confirmed that Rh-target tube is better than W-target tube for application to the hand-held XRF equipment.

• Journal of the Korea Institute of Military Science and Technology
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• v.12 no.6
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• pp.697-703
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• 2009

Target Motion Analysis(TMA) means to detect target position, velocity and course for using passive sonar system with bearing-only measurement. In this paper, we apply the TMA algorithm for a submarine with Multi-Sensor Data Fusion(MSDF) and we will decide the best TMA algorithm for a submarine by a series of computer simulation runs.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.10
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• pp.1012-1019
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• 2010

In this paper, The flight data processing system was designed for multiple target flight test. For flight data processing, multiple target grouping, data fusion processing, and data slaving processing were performed and, as a data fusion filter, centralized, and federated Kalman filters were designed. A centralized kalman filter was modified in order to improve the vehicle's low altitude measurement using radar's SNR and estimation process. From the testing of multiple target missile, it confirmed flight trajectory measurement was improved in low altitude area and the beginning stage of vehicle.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.943-945
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• 1996

In this paper, we present an algorithm that allows tracking of a target using measurements obtained from a sensor with limited resolution. The Interacting Multiple Model (IMM) algorithm has been shown to be one of the most cost-effective estimation schemes for hybrid systems. The approach consists of IMM algorithm combined with a coupled version of the Joint Probabilistic Data Association Filter for the target that splits into two targets.

• Proceedings of the IEEK Conference
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• 2003.07b
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• pp.807-810
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• 2003

The PCI 2.2 spec introduces Delayed Transaction mechanism to improve system performance for target device with slow local bus. But this mechanism has some restriction since target device doesn't know prefetch data size. So, we propose a new mechanism, which target device prefetch exact data on local bus, to improve data rate on PCI or local interface. The simulation results showed that the proposed mechanism more improves system performance than the Delayed Transaction mechanism.

• Proceedings of the KSME Conference
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• 2004.11a
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• pp.696-701
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• 2004

A vehicle cruise control algorithm using an Interacting Multiple Model (IMM)-based Multi-Target Tracking (MTT) method has been presented in this paper. The vehicle cruise control algorithm consists of three parts; track estimator using IMM-Probabilistic Data Association Filter (PDAF), a primary target vehicle determination algorithm and a single-target adaptive cruise control algorithm. Three motion models; uniform motion, lane-change motion and acceleration motion, have been adopted to distinguish large lateral motions from longitudinal motions. The models have been validated using simulated and experimental data. The improvement in the state estimation performance when using three models is verified in target tracking simulations. The performance and safety benefits of a multi-model-based MTT-ACC system is investigated via simulations using real driving radar sensor data. These simulations show system response that is more realistic and reflective of actual human driving behavior.

• Journal of Korea Multimedia Society
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• v.21 no.9
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• pp.1062-1067
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• 2018

Conventional active sonar technology has relied heavily on the hearing of sonar operator, but recently, many techniques for automatic detection and classification have been studied. In this paper, we extract the image data from the spectrogram of the active sonar signal and classify the extracted data using CNN(convolutional neural networks), which has recently presented excellent performance improvement in the field of pattern recognition. First, we divided entire data set into eight classes depending on the ratio containing the target. Then, experiments were conducted to classify the eight classes data using proposed CNN structure, and the results were analyzed.