• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.2
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• pp.136-145
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• 2013

It is very important to determine specifications of components included in the drive-train of vehicles at the initial design stage. In this study, component sizing process and performance analysis for Extended-Range Electric Vehicles (E-REV) are discussed based on the foundation of determined system configuration and performance target. This process shows sizing results of an electric driving motor, a final drive gear ratio and a battery capacity for target performance including All Electric Range (AER) limit. For E-REV driving mode, the constant output power of a Gen-set (Engine+Generator) is analyzed in order to sustain State of Charge (SOC) of the battery system.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.12
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• pp.3253-3269
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• 1994

In this paper a new concept, named the Extended Operational Space Formulation, has been proposed for the effective analysis and real-time control of the robot manipulators with kinematic redundancy. The extended operational space consists of operational space and optimal null space. The operational space is used to describe robot end-effector motion; whereas the optimal null space, defined as the target space of the self motion manifold, is used to express the self motion for the secondary tasks. Based upon the proposed formulation, the kinematics, statics, and dynamics of redundant robots have been analyzed, and an efficient control algorithm has been proposed. Using this algorithm, one can optimize a performance measure while tracking a desired end-effector trajectory with a better computational efficiency than the conventional methods. The effective ness of the proposed method has been demonstrated with simulations.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.479-482
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• 2004

In this article we consider a single maneuvering target Tracking algorithm in the presence of missing measurements and high clutter environments for multi-sensor target tracking problem. The tracking algorithm is based on the Particle filtering method to predict and update target states. Proposed is the AMM-PF(Auxiliary Multiple Model Particle Filter)[2] method for maneuvering target tracking to improve performance in track estimate and maintenance with a high level of uncertainty. The algorithm we propose is compared to the Extended Kalman Filter(EKF). A simulation study is included.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.254-258
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• 1996

In this paper, we proposed realtime visual tracking system of moving object for 2D target using extended Kalman Filter Algorithm. A targeting marker are recongnized in each image frame and positions of targer object in each frame from a CCD camera while te targeting marker is attached to the tip of the SCARA robot hand. After the detection of a target coming into any position of the field-of-view, the target is tracked and always made to be located at the center of target window. Then, we can track the moving object which moved in inter-frames. The experimental results show the effectiveness of the Kalman filter algorithm for realtime tracking and estimated state value of filter, predicting the position of moving object to minimize an image processing area, and by reducing the effect by quantization noise of image

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.3
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• pp.110-119
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• 1998

In this paper we discuss an algorithm to discriminate a garget under track against multiple acoustic counter-measure (ACM) sources, based on sequential testings of multiple hypotheses. The ACM sources are separated from the target under track and generate, while drifting, measurements with false range and Doppler information. The purpose of the ACM is to mislead the target tracking and to help the true target evade a pursuer. The proposed algorithm uses as a test statistic a function of the innovation sequences from extended Kalman filters to estimate the target dynamics and the drifting positions of the ACM sources. results of numerical experimenats are presented to show a performance profile of the proposed algorithm.

• Journal of the Ergonomics Society of Korea
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• v.28 no.3
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• pp.55-62
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• 2009

This study is concerned with predicting human search performance using a visual lobe. The most previous studies on human performance in visual search have been limited to a single-target search. This study extended the visual search research to multiple-target search including targets of different types as well as targets of same types. A model for predicting visual search performance was proposed and the model was validated by human search data. Additionally, this study found that human subjects always did not use a constant ratio of the whole visual lobe size for each type of targets in visual search process. The more conspicuous the target is, the more ratio of the whole visual lobe size human subjects use. The model that can predict human performance in multiple-target search may facilitate visual inspection plan in manufacturing.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.562-565
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• 1987

This paper addresses the development of the estimation algorithm to acquire target position, velocity and course using bearing-only measurements in two dimensional environment. System state equations are derived from modified polar coordinates instead of existing Cartesian coordinates system. The Extended Kalman Filter is used to constitute the estimation algorithm because of state equation's nonlinearity. The computer simulation is done to verify the performance of derived algorithm. Simulation result showed that estimated state value of filter was converged to the true value in 10 minutes.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.148-148
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• 2000

In this paper the IMM(Interacting Multiple model) algorithm using the MGEKF(Modified Gain Extended Kalman Filter) which modes are variances of the process noises is proposed to enhance the performance of maneuvering target tracking with bearing and frequency measurements. The state are composed of relative position, relative velocity, relative acceleration and doppler frequency. The mode probability is calculated from the bearing and frequency measurements. The proposed algorithm is tested a series of computer simulation runs.

• Journal of Computing Science and Engineering
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• v.8 no.2
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• pp.65-77
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• 2014

To address the problem of multi-target retrieval (MTR) of remote sensing images, this study proposes a new object-level feature representation model. The model provides an enhanced application image representation that improves the efficiency of MTR. Generating the model in our scheme includes processes, such as object-oriented image segmentation, feature parameter calculation, and symbolic image database construction. The proposed model uses the spatial representation method of the extended nine-direction lower-triangular (9DLT) matrix to combine spatial relationships among objects, and organizes the image features according to MPEG-7 standards. A similarity metric method is proposed that improves the precision of similarity retrieval. Our method provides a trade-off strategy that supports flexible matching on the target features, or the spatial relationship between the query target and the image database. We implement this retrieval framework on a dataset of remote sensing images. Experimental results show that the proposed model achieves competitive and high-retrieval precision.

• ETRI Journal
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• v.34 no.2
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• pp.260-263
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• 2012

In this letter, we propose a moving-target tracking algorithm based on a particle filter that uses the time difference of arrival (TDOA)/frequency difference of arrival (FDOA) measurements acquired by distributed sensors. It is shown that the performance of the proposed algorithm, based on the particle filter, outperforms the one based on the extended Kalman filter. The use of both the TDOA and FDOA measurements is shown to be effective in the moving-target tracking. It is proven that the particle filter deals with the nonlinear nature of the movingtarget tracking problem successfully.