• Journal of KIISE:Software and Applications
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• v.26 no.8
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• pp.1000-1009
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• 1999

본 논문에서는 비디오 데이타를 분석하여 다양한 카메라의 동작을 정량적으로 추출하는 방법을 제안한다. 본 논문에서 제안하는 카메라의 동작 추출 방법은 어파인 모델을 이용한 방법으로 인접 영상으로부터 추출한 동작 벡터를 어파인 모델에 적용하고 회귀분석법을 통해 어파인 모델을 구성하는 파라미터를 구한다. 그런 다음, 파라미터들의 크기를 분석하고 상호 관계를 해석하여 카메라의 동작을 추출한다. 본 논문에서는 잡음이 포함된 동작 벡터를 필터링하여 사용하므로 잡음에 강건한 결과를 얻을 수 있다. 그리고 어파인 모델을 구성하는 파라미터만을 분석함으로써 카메라의 다양한 동작을 간단하면서도 비교적 정확하게 추출한다. 실험 결과는 카메라의 동작을 정확하게 추출하고 있음을 보여준다.Abstract This paper presents an elegant method, an affine-model based approach, that can qualitatively estimate the information of camera motion. We define various types of camera motion by means of parameters of an affine-model. To get those parameters from images, we fit an affine-model to the field of instantaneous velocities, rather than raw images. We correlate consecutive images to get instantaneous velocities. The size filtering of the velocities are applied to remove noisy components, and the regression approach is employed for the fitting procedure. The fitted values of the parameters are examined to get the estimates of camera motion. The experimental results show that the suggested approach can yield the qualitative information of camera motion successfully.

• Journal of Korea Multimedia Society
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• v.17 no.8
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• pp.923-936
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• 2014

Images can be contaminated with different types of noise, for different reasons. The neighborhood averaging and smoothing by image averaging are the classical image processing techniques for noise removal. The classical spatial filtering refers to the aggregate of pixels composing an image and operating directly on these pixels. To reduce or remove effectively noise in image sequences, it usually needs to use noise reduction filter based on space or time domain such as method of spatial or temporal filter. However, the method of spatial filter can generally cause that signals of objects as the target are also blurred. In this paper, we propose temporal filter using the piece-wise quadratic function model and enhancement algorithm of image quality for the performance improvement of target detection and tracking by heterogeneous noise reduction. Image tracking simulation that utilizes real IIR(Imaging Infra-Red) images is employed to evaluate the performance of the proposed image processing algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.8
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• pp.682-687
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• 2013

Navigation is a crucial capability for all types of manned or unmanned vehicles. However, vehicle navigation in underwater environments still remains a challenging problem since GPS signals for position fixes are not available in the water. Terrain-referenced underwater navigation is an alternative navigation technique that utilizes geometric information of the subsea terrain to correct drift errors due to dead-reckoning or inertial navigation. Terrain-referenced navigation requires the description of an undulating terrain surface as a mathematical function or table, which often leads to a highly nonlinear estimation problem. Recently, PFs (Particle Filters), which do not require any restrictive assumptions about the system dynamics and uncertainty distributions, have been widely used for nonlinear filtering applications. However, PF has considerable computational requirements which used to limit its applicability to problems of relatively low state dimensions. This study proposes the use of a Rao-Blackwellized particle filter that is computationally more efficient than the standard PF for terrain-referenced underwater navigation involving a moderate number of states, and its performance is compared with that of the extended Kalman filter algorithm. The validity and feasibility of the proposed algorithm is demonstrated through numerical simulations.

• Journal of Environmental Health Sciences
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• v.31 no.1
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• pp.73-78
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• 2005

A determination method of trace Ni(II) and Zn(II) in aqueous solution was studied and developed by adsorbing on titanium dioxide. For this purpose, several conditions were optimized such as the pH of sample solution, adsorption time, the types and concentration acid, and desorption time. The titanium dioxide was added in sample solution which was pH adjusted. Then, the sample solution was stirred for 5 minutes. This mixture was stored in room temperature for 30 minutes to allow adsorption. After filtering and washing the titanium dioxide, the analytes were dissolved from the titanium dioxide on membrane filter by an ultrasonic vibration for 10 minutes in 1.0 M $HNO_3$ solution. Then, this sample solution was analysed using ICP-AES. The adsorption equilibrium was achieved in 30 minutes. The desorption was the most of effective with 1.0 M(mol/l) nitric acid solution, and desorption time was 10 minutes. This procedure was applied for the analysis of two real samples, i.e., brown seaweed and tangle. The recoveries of Ni(II) and Zn(II) in spiked samples were 89.4${\sim}$98.9% for analytes.

• Journal of Power Electronics
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• v.13 no.5
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• pp.861-876
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• 2013

Instead of extracting every selected harmonic component, the current reference of selective active power filter (APF) can be also obtained by filtering out the fundamental component from distorted load current for computation efficiency. This type of mixed current reference contains kinds of harmonic components and easily involves noises. In this condition, selective harmonic compensation must be realized by the current controller. With regard that selectivity is the most significant feature of controller, this paper presents specific comparison analysis between two types of resonant controllers: proportional-resonant (PR) controller and vector-resonant (VR) controller. The comparison analysis covers the relations, performances, and stability of both controllers. Analysis results conclude that the poorer selectivity of the PR controller could be relatively improved, but limitations from system stability make the improvement hardly realized. By contrast, the VR controller exhibits excellent selectivity and is more suitable for selective APF with mixed current reference. Experimental results from laboratory prototype validate the reasonability of analysis. And the features of each resonant controller are concluded.

• KSBB Journal
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• v.18 no.2
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• pp.140-144
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• 2003

The artificial sewage was treated using a vertical flow zeolite-filled reed bed. The artificial sewage was fec into the reed bed for 10 minutes every 6 hours at the hydraulic load 314 L/$m^2$ \ulcorner day. The filtering height of the reed bed was 100 cm and the zeolite mixture was filled in the reed bed. The mixture consisted of the same volume of two types of zeolites ; 0.5~1 mm and 1~3 mm in diameter. Annual average removal efficiency was CO$D_{Cr}$ 95.1%, T-N 49.5%, $NH{_4^+}$-N 99.4% and T-9 56.4%. T-N removal efficiency decreaced remarkably from 73 to 27% with the operating time. Most of T-P was adsorbed in the uper area of the reed bed. The major portion of adsorbed phosphorus was composed of Fe-, Ca- and reductant selubele Fe-P.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.8
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• pp.65-80
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• 1999

This paper presents an elegant method, an affine-model based approach, that can qualitatively estimate the information of camera motion. We define various types of camera motion by means of parameters of an affine-model. To get those parameters form images, we fit an affine-model to the field of instantaneous velocities, rather than raw images. We correlate consecutive images to get instantaneous velocities. The size filtering of the velocities are applied to remove noisy components, and the regression approach is employed for the fitting procedure. The fitted values of the parameters are examined to get the estimates of camera motion. The experimental results show that the suggested approach can yield the qualitative information of camera motion successfully.

• Journal of Sensor Science and Technology
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• v.20 no.6
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• pp.382-387
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• 2011

Whether a person is feeling sleepy or reasonably awake is important safety information in many areas, such as humans operating in traffic or in heavy industry. The changes of body signals have been mostly researched by looking at electroencephalogram(EEG) signals but more and more other medical signals are being examined. In our study, an electrocardiogram(ECG) signal is measured at a sampling rate of 100 Hz and used to try to distinguish the possible differences in signal between the two states: awake and drowsy. Practical tests are conducted using a wireless sensor node connected to a wearable ECG sensor, and an ECG signal is transmitted wirelessly to a base station connected to a server PC. Through the QRS complex in the ECG analysis it is possible to obtain much information that is helpful for diagnosing different types of cardiovascular disease. A program is made with MATLAB for digital signal filtering and graphing as well as recognizing the parts of the QRS complex within the signal. Drowsiness detection is performed by evaluating the R peaks, R-R interval, interval between R and S peaks and the duration of the QRS complex..

• The Korean Journal of Applied Statistics
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• v.17 no.3
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• pp.535-545
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• 2004

The clustering method suggested in this paper produces clusters based on the 'rules of variables' by merging the 'training' and the identically structured reference data and then by filtering it to obtain the clusters of the 'training data' through the use of the 'tree classification model'. The reference dataset is generated by spatially contrasting it to the 'training data' through the 'reverse arcing' algorithm to effectively identify the clusters. The strength of this method is that it can be applied even to the mixture of continuous and discrete types of 'training data' and the performance of this algorithm is illustrated by applying it to the simulated data as well as to the actual data.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.6
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• pp.1060-1069
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• 2016

In this paper, we introduce the fingerprint recognition system based on Radial Basis Function Neural Network(RBFNN). Fingerprints are classified as four types(Whole, Arch, Right roof, Left roof). The preprocessing methods such as fast fourier transform, normalization, calculation of ridge's direction, filtering with gabor filter, binarization and rotation algorithm, are used in order to extract the features on fingerprint images and then those features are considered as the inputs of the network. RBFNN uses Fuzzy C-Means(FCM) clustering in the hidden layer and polynomial functions such as linear, quadratic, and modified quadratic are defined as connection weights of the network. Particle Swarm Optimization (PSO) algorithm optimizes a number of essential parameters needed to improve the accuracy of RBFNN. Those optimized parameters include the number of clusters and the fuzzification coefficient used in the FCM algorithm, and the orders of polynomial of networks. The performance evaluation of the proposed fingerprint recognition system is illustrated with the use of fingerprint data sets that are collected through Anguli program.