• Journal of International Society for Simulation Surgery
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• v.3 no.1
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• pp.9-11
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• 2016

Purpose In this paper we proposed bi-directional hierarchical regression for accurate human finger landmark detection with only using depth information.Materials and Methods Our algorithm consisted of two different step, initialization and landmark estimation. To detect initial landmark, we used difference of random pixel pair as the feature descriptor. After initialization, 16 landmarks were estimated using cascaded regression methods. To improve accuracy and stability, we proposed bi-directional hierarchical structure.Results In our experiments, the ICVL database were used for evaluation. According to our experimental results, accuracy and stability increased when applying bi-directional hierarchical regression more than typical method on the test set. Especially, errors of each finger tips of hierarchical case significantly decreased more than other methods.Conclusion Our results proved that our proposed method improved accuracy and stability and also could be applied to a large range of applications such as augmented reality and simulation surgery.

• Journal of Electrical Engineering and information Science
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• v.2 no.5
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• pp.79-88
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• 1997

This paper introduces a new energy function, as maximum a posteriori(MAP) estimate of binocular disparity, that can deal with both random dot stereo-gram(RDS) and natural scenes. The energy function uses phase-magnitude as features to detect only the shift for a pair of corrupted conjugate images. Also we adopted Fleet singularity that effectively detects unstable areas of image plant and thus eliminates in advance error-prone stereo mathcing. The multi-scale concept is applied to the multi laser architecture that can search the solutions systematically from coarse to fine details and thereby avoids drastically the local minima. Using mean field approximation, we obtained a compact representation that is suitable for fast computation. In this manner, the energy function satisfies major natural constraints and requirements for implementing parallel relaxation. As an experiment, the proposed algorithm is applied to RDS and natural stereo images. As a result we will see that it reveals good performance in terms of recognition errors, parallel implementation, and noise characteristics.

• Journal of Communications and Networks
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• v.9 no.3
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• pp.236-246
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• 2007

This paper investigates the performance of the transmission control protocol (TCP) transport protocol over IEEE 802.11 infrastructure based wireless networks. A wireless link is generally characterized by high transmission errors, random interference and a varying latency. The erratic packet losses usually lead to a curbing of the flow of segments on the TCP connection and thus limit TCP's performance. This paper examines the impact of the lossy nature of IEEE 802.11 wireless networks on the TCP performance and proposes a scheme to improve the performance of TCP over wireless links. A negative acknowledgment scheme, selective negative acknowledgment (SNACK), is applied on TCP over wireless networks and a series of ns-2 simulations are performed to compare its performance against that of other TCP schemes. The simulation results confirm that SNACK and its proposed enhancement SNACK-S, which incorporates a bandwidth estimation model at the sender, outperform conventional TCP implementations in 802.11 wireless networks.

• Journal of Sensor Science and Technology
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• v.13 no.1
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• pp.27-34
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• 2004

Camera calibration is an important and fundamental procedure for the application of a vision sensor to 3D problems. Recently many camera calibration methods have been proposed particularly in the area of robot vision. However, the reliability of data used in calibration has been seldomly considered in spite of its importance. In addition, a camera model can not guarantee good results consistently in various conditions. This paper proposes methods to overcome such uncertainty problems of data and camera models as we often encounter them in practical camera calibration steps. By the use of the RANSAC (Random Sample Consensus) algorithm, few data having excessive magnitudes of errors are excluded. Artificial neural networks combined in a two-step structure are trained to compensate for the result by a calibration method of a particular model in a given condition. The proposed methods are useful because they can be employed additionally to most existing camera calibration techniques if needed. We applied them to a linear camera calibration method and could get improved results.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.17 no.4
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• pp.1-7
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• 2009

The surge control system in unmanned turbojet engine must be capable of accounting uncertainties from engine transient conditions, random fluctuations of key parameters such as air pressure and fuel flow and engine modeling errors. In this paper, taking into consideration of its effectiveness as well as system stability, a fuzzy PI controller is proposed. The role of the fuzzy PI controller is to stabilize the unmanned aircraft upon occurring unexpected engine surge. The proposed control scheme is proved by computer simulation using a linear engine model. The simulation results on the state space model of a small turbojet engine illustrate the proposed control system achieves the desired performance.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2006.05a
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• pp.86-90
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• 2006

We prosed a distributed fiber-optic sensor system with 10 fiber Bragg gratings, for over temperature protection relay in power systems. We applied Gaussian line-fitting algorithm to compensate the distortion effects in the wavelength-scanned Farby-Perot filter demodulation scheme. Compared with the highest-peak-detection method, the proposed algorithm was proved to minimize the random errors of distorted PD profiles. From experimental results, the overall measurement error was within 1 % compared with the reference thermocouple and the linearity error was less than 0.37 %.

• Communications for Statistical Applications and Methods
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• v.22 no.5
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• pp.463-473
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• 2015

We consider a nonparametric AR(1) model with nonparametric ARCH(1) errors. In order to estimate the unknown function of the ARCH part, we apply the stationary bootstrap procedure, which is characterized by geometrically distributed random length of bootstrap blocks and has the advantage of capturing the dependence structure of the original data. The proposed method is composed of four steps: the first step estimates the AR part by a typical kernel smoothing to calculate AR residuals, the second step estimates the ARCH part via the Nadaraya-Watson kernel from the AR residuals to compute ARCH residuals, the third step applies the stationary bootstrap procedure to the ARCH residuals, and the fourth step defines the stationary bootstrapped Nadaraya-Watson estimator for the ARCH function with the stationary bootstrapped residuals. We prove the asymptotic validity of the stationary bootstrap estimator for the unknown ARCH function by showing the same limiting distribution as the Nadaraya-Watson estimator in the second step.

• 한국가시화정보학회:학술대회논문집
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• 2006.12a
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• pp.25-35
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• 2006

We have evaluated the performances of the following six interpolation schemes used for window deformation in particle image velocimetry (PIV): the linear, quadratic, B-spline, cubic, sinc, Lagrange interpolations. Artificially generated images comprised of particles of diameter in a range $1.1{\leq}d_p\leq10.0$ pixel were investigated. Three particle diameters were selected for detailed evaluation: $d_p$=2.2, 3.3, 4.4 pixel with a constant particle concentration 0.02 $particle/pixel^2$. Two flow patterns were considered: uniform and shear flows. The mean and random errors, and the computation times of the interpolation schemes were determined and compared.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.12
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• pp.1379-1387
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• 2011

A PID-feedforward controller and Robust Internal-loop Compensator (RIC) based on reinforcement learning using random variable sequences are provided to auto-tune parameters for each controller in the high-precision position control of PMLSM (Permanent Magnet Linear Synchronous Motor). Experiments prove the well-tuned controller could be reduced up to one-fifth level of tracking errors before learning by reinforcement learning. The RIC compared to the PID-feedforward controller showed approximately twice the performance in reducing tracking error and disturbance rejection.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.5095-5111
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• 2016

The Probability Hypothesis Density (PHD) filter is a suboptimal approximation and tractable alternative to the multi-target Bayesian filter based on random finite sets. However, the PHD filter fails to track newborn targets when the target birth intensity is unknown prior to tracking. In this paper, a dual detection-guided newborn target intensity PHD algorithm is developed to solve the problem, where two schemes, namely, a newborn target intensity estimation scheme and improved measurement-driven scheme, are proposed. First, the newborn target intensity estimation scheme, consisting of the Dirichlet distribution with the negative exponent parameter and target velocity feature, is used to recursively estimate the target birth intensity. Then, an improved measurement-driven scheme is introduced to reduce the errors of the estimated number of targets and computational load. Simulation results demonstrate that the proposed algorithm can achieve good performance in terms of target states, target number and computational load when the newborn target intensity is not predefined in multi-target tracking systems.