• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.23 no.8
• /
• pp.995-1001
• /
• 2012

This paper compares the performance of several tracking filters, namely, alpha-beta filter, Kalman filter and TBM tracking filter for ballistic target tracking problem using multi-function radar. Every of three tracking filters suggested was tested on simulator developed in accordance with TBM trajectory and MFR RSP measurement. The result shows the method using TBM tracking filter gives 75.3 % decreased velocity RMS error than alpha-beta filter. After initialization, the RMS error of range and velocity of the proposed filter is also smaller than the Kalman filter. Finally the proposed filter is suitable for high-speed TBM tracking due to the stable angle tracking accuracy.

• Science of Emotion and Sensibility
• /
• v.13 no.1
• /
• pp.111-120
• /
• 2010

Initial stage of emotion has a great influence on building up product image and impression. Because of its influencing effects, measuring initial stage of emotion has potential to be a key factor for designers and marketers to achieve a distinct product concept. While many researchers have studied this topic with the emotion measurement method in product use stage, there are very few cases specialized in the initial stage of emotion. Even though present emotion measurement methods have difficulties to derive accurate user's initial stage of emotion, most case of initial emotion study applies these defective methods. The purpose of this study is to develop initial stage of emotion measurement method and apply this method to real product context. In the design of the initial stage of emotion measurement method, noticeable characteristics of initial stage of emotion were explored and initial emotion measurement framework was presented. Based on this framework, Initial Emotion Measurement System(IEMS) was suggested. This method collects user's eye movement, behavior and verbal data accurately and objectively.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.13 no.5
• /
• pp.739-747
• /
• 2010

In this paper, we propose new data association method called the Highest Probability Data Association(HPDA) using a Signal Amplitude information ordering method applied to active sonar tracking and track initiation in cluttered environment. The performance of HPDA is tested in a series of Monte Carlo simulations runs and is compared with the existing Probabilistic Data Association with Amplitude Information(PDA-AI) for active sonar tracking in clutter. The proposed HPDA algorithm is also applied to automatic track initiation in clutter and its performance is compared with the existing IPDA-AI algorithm.

• Journal of Broadcast Engineering
• /
• v.16 no.4
• /
• pp.614-623
• /
• 2011

We propose a real-time tracking algorithm for an augmented reality (AR) system for TV broadcasting. The tracking is initialized by detecting the object with the SURF algorithm. A multi-scale approach is used for the stable real-time camera tracking. Normalized cross correlation (NCC) is used to find the patch correspondences, to cope with the unknown and changing lighting condition. Since a zooming camera is used, the focal length should be estimated online. Experimental results show that the focal length of the camera is properly estimated with the proposed online calibration procedure.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.29 no.6
• /
• pp.1375-1382
• /
• 2019

The latest version of commercial protector, Themida, has been updated, it is impossible to apply a normalized unpacking mechanism from previous studies by disable the use of a virtual memory allocation that provides initial data to be tracked. In addition, compared to the previous version, which had many values that determined during execution and easy to track dynamically, it is difficult to track dynamically due to values determined at the time of applying the protector. We will look at how the latest version of Themida make it difficult to normalize the API wrapping process by adopted techniques and examine the possibilities of applying the unpacking techniques to further develop an automated unpacking system.

• KSCI Review
• /
• v.2 no.1
• /
• pp.121-129
• /
• 1995

Fault simulation of logic circuits is an important part of the test-generation process. It is used for the propose of generation fault dictionaries or for the verification of the adequacy of tests. In this paper, a backtrace fault simulation is proposed to test generation. This is consists of 3 part ; initialization phase for given circuit, backtrace fault simulation phase to find fault list and reevaluation phase to list event. The main idea of this algorithm is to retain a minimum fault list by cutting uncontrollable lines of path when a logic event occurs in backward tracing phases. And the simulator is revaluates a fault list associated with the output of an element only if logic event occurs at any of its inputs when a list event occurs at one of its primary inputs. It reguires a O(n) memory space complexity. where n is a number of signal lines for the given circuits. Several examples are given to illustrate the power of this algorithm.

• Journal of Advanced Navigation Technology
• /
• v.23 no.1
• /
• pp.77-83
• /
• 2019

In this paper, we propose an object tracking algorithm based on color histogram and convolutional neural network model. In order to increase the tracking accuracy, we synthesize generic object tracking using regression network algorithm which is one of the convolutional neural network model-based tracking algorithms and a mean-shift tracking algorithm which is a color histogram-based algorithm. Both algorithms are classified through support vector machine and designed to select an algorithm with higher tracking accuracy. The mean-shift tracking algorithm tends to move the bounding box to a large range when the object tracking fails, thus we improve the accuracy by limiting the movement distance of the bounding box. Also, we improve the performance by initializing the tracking start positions of the two algorithms based on the average brightness and the histogram similarity. As a result, the overall accuracy of the proposed algorithm is 1.6% better than the existing generic object tracking using regression network algorithm.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.47 no.5
• /
• pp.358-363
• /
• 2019

If the EOIR equipment mounted on an unmanned aircraft transmits images and receives commands through a data link, there may be delays in data transmission depending on the transmission path of the data and the conditions of the ground equipment or wireless network. This increases the possibility of initial target LOCK-ON failure due to the difference between the time when the received image is viewed and the time when the image is taken. Therefore, this paper proposed a way to use frame indexes to synchronize with images, and to increase the success of target tracking by adding frame indexes to commands from the ground station.

• The KIPS Transactions:PartB
• /
• v.16B no.4
• /
• pp.289-298
• /
• 2009

Active Contour Model, that is, Snake algorithm is effective for detection and tracking the objects. However, this algorithm has some drawbacks; numerous parameters must be designed(weighting factors, iteration steps, etc.), a reasonable initialization must be available and moreover suffers from numerical instability. Therefore we propose a novel Energy Corrected Snake(ECS) algorithm which improved on external energy of Snake algorithm for detection and tracking the moving object more effectively. The proposed algorithm uses the difference image, getting when the object is moving. It copies four direction images from the difference image and performs the accumulating compute to erasing image noise, so that it gets external energy steadily. Then external energy united with contour that is computed by internal energy. Consequently we can detect and track the moving object more speedily and easily. To show the effectiveness of the proposed algorithm, we experiment on 3 situations. The experimental results showed that the proposed algorithm outperformed by 6$\sim$9% of detection rate and 6$\sim$11% of tracker detection rate compared with the Snake algorithm.

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.36S no.8
• /
• pp.81-87
• /
• 1999

In general, feature detection and tracking algorithms is classified by EBGM using Garbor-jet, NNC-R and STK algorithm using pixel eigenvalue. In those algorithms, EBGM and NCC-R detect features with feature model, but STK algorithm has a characteristics of an automatic feature selection. In this paper, to solve the initial problem of NR tracking in STK algorithm, we detected features using STK algorithm in modelled feature region and tracked features with NR method. In tracking, to improve the tracking accuracy for features by NR method, we proposed BMA-NR method. We evaluated that BMA-NR method was superior to NBMA-NR in that feature tracking accuracy, since BMA-NR method was able to solve the local minimum problem due to search window size of NR.