• Journal of the Society of Naval Architects of Korea
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• v.55 no.1
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• pp.37-44
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• 2018

To understand the dynamic characteristics of the vessel with hydroelastic response, it is very important to estimate the dynamic modal parameters such as mode shapes, natural frequency, and damping ratio. These dynamic modal parameters of full scale ship are a priori unknowns, hence to be estimated directly based upon the full scale measurement data. In this paper, dynamic modal parameters were extracted by signal processing of acceleration and strain data measured from a large container ship whose loading capacity is 9400TEU. The mode shapes of the vibrating hull were identified using the proper orthogonal decomposition and the vibration response of hull was decomposed into its modal magnitudes. Natural frequencies of specific modes were derived via Fourier transform of these modal magnitude. Also, the free decay signal of the vibrating hull was obtained through the random decrement technique and the damping ratio was estimated with accuracy.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.5
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• pp.444-450
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• 2015

The accuracy of small and low cost CCD camera is insufficient to provide data for precisely tracking unmanned aerial vehicles(UAVs). This study shows how UAV can hover on a human targeted tracking object by using CCD camera rather than imprecise GPS data. To realize this, UAVs need to recognize their attitude and position in known environment as well as unknown environment. Moreover, it is necessary for their localization to occur naturally. It is desirable for an UAV to estimate of his attitude by environment recognition for UAV hovering, as one of the best important problems. In this paper, we describe a method for the attitude of an UAV using image information of a maker on the floor. This method combines the observed position from GPS sensors and the estimated attitude from the images captured by a fixed camera to estimate an UAV. Using the a priori known path of an UAV in the world coordinates and a perspective camera model, we derive the geometric constraint equations which represent the relation between image frame coordinates for a marker on the floor and the estimated UAV's attitude. Since the equations are based on the estimated position, the measurement error may exist all the time. The proposed method utilizes the error between the observed and estimated image coordinates to localize the UAV. The Kalman filter scheme is applied for this method. its performance is verified by the image processing results and the experiment.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.499-505
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• 2011

In this paper, we proposed an adaptive fuzzy sliding mode control for nonlinear systems without parameter projection method. By modifying the controller structure, the parameters of the estimated input gain function are guaranteed not being identically zero and it is shown that the control scheme will not cause any implementation problem even if the estimated value of input gain function is zero at any moment during on-line operations. Except for the input gain function which an approximate estimate for its lower bound is needed, the proposed control scheme does not assume a priori the exact values of the bounding parameters. Based on Lyapunov synthesis methods, the overall control system guarantees that the tracking error asymptotically converges to zero and that all signals involved in controller are uniformly bounded. This can be illustrated by the simulation results for an inverted pendulum system.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.24 no.1
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• pp.55-63
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• 2013

Digital retrodirective antenna system is easy to modify and upgrade because it can control the phase information of the output signal toward opposite direction to input signal without a priori knowledge of the arrival direction. Due to this advantage, it is possible to do fast beam tracking. Especially, we need to design the digital PLL performance for the digital retrodirective array antenna system. So, in this paper the settling time of phase estimator and BER performance of retrodirective antenna system are investigated according to design of filter in digital PLL. When QAM signal is used for 1 Mbps with $30^{\circ}$ of phase delay, simulation results show that digital phase conjugation technique has better BER performance by about 1 dB than non-phase conjugation system when digital filter is stable. If not, the system can't estimate the exact phase because of oscillation of filter.

• The Journal of Korean Institute of Information Technology
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• v.16 no.11
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• pp.97-103
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• 2018

In this paper, the estimation method for the power signal harmonics is proposed by using the time-varying optimal finite impulse response (FIR) filter. To estimate the magnitude and phase-angle of the harmonic components, the time-varying optimal FIR filter is designed for the state space representation of the noisy power signal which the magnitude and phase is considered as a stochastic process. Since the time-varying optimal FIR filter used in the proposed method does not use any priori information of the initial condition and has FIR structure, the proposed method could overcome the demerits of Kalman filter based method such as poor estimation and divergence problem. Due to the FIR structure, the proposed method is more robust against to the model uncertainty than the Kalman filter. Moreover, the proposed method gives more general solution than the time-invariant optimal FIR filter based harmonic estimation method. To verify the performance and robustness of the proposed method, the proposed method is compared with time-varying Kalman filter based method through simulation.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1131-1131
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• 2001

To develop a nondestructive quality evaluation technique of fruits, a K-mean algorism is applied to near infrared (NIR) spectroscopy of apples. The K-mean algorism is one of neural network partition methods and the goal is to partition the set of objects O into K disjoint clusters, where K is assumed to be known a priori. The algorism introduced by Macqueen draws an initial partition of the objects at random. It then computes the cluster centroids, assigns objects to the closest of them and iterates until a local minimum is obtained. The advantage of using neural network is that the spectra at the wavelengths having absorptions against chemical bonds including C-H and O-H types can be selected directly as input data. In conventional multiple regression approaches, the first wavelength is selected manually around the absorbance wavelengths as showing a high correlation coefficient between the NIR $2^{nd}$ derivative spectrum and Brix value with a single regression. After that, the second and following wavelengths are selected statistically as the calibration equation shows a high correlation. Therefore, the second and following wavelengths are selected not in a NIR spectroscopic way but in a statistical way. In this research, the spectra at the six wavelengths including 900, 904, 914, 990, 1000 and 1016nm are selected as input data for K-mean analysis. 904nm is selected because the wavelength shows the highest correlation coefficients and is regarded as the absorbance wavelength. The others are selected because they show relatively high correlation coefficients and are revealed as the absorbance wavelengths against the chemical structures by B. G. Osborne. The experiment was performed with two phases. In first phase, a reflectance was acquired using fiber optics. The reflectance was calculated by comparing near infrared energy reflected from a Teflon sphere as a standard reference, and the $2^{nd}$ derivative spectra were used for K-mean analysis. Samples are intact 67 apples which are called Fuji and cultivated in Aomori prefecture in Japan. In second phase, the Brix values were measured with a commercially available refractometer in order to estimate the result of K-mean approach. The result shows a partition of the spectral data sets of 67 samples into eight clusters, and the apples are classified into samples having high Brix value and low Brix value. Consequently, the K-mean analysis realized the classification of apples on the basis of the Brix values.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.4
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• pp.438-444
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• 2004

The intelligent robots that will be needed in the near future are human-friendly robots that are able to coexist with humans and support humans effectively. To realize this, robots need to recognize their position and posture in known environment as well as unknown environment. Moreover, it is necessary for their localization to occur naturally. It is desirable for a robot to estimate of his position by solving uncertainty for mobile robot navigation, as one of the best important problems. In this paper, we describe a method for the localization of a mobile robot using image information of a moving object. This method combines the observed position from dead-reckoning sensors and the estimated position from the images captured by a fixed camera to localize a mobile robot. Using the a priori known path of a moving object in the world coordinates and a perspective camera model, we derive the geometric constraint equations which represent the relation between image frame coordinates for a moving object and the estimated robot's position. Since the equations are based or the estimated position, the measurement error may exist all the time. The proposed method utilizes the error between the observed and estimated image coordinates to localize the mobile robot. The Kalman filter scheme is applied for this method. its performance is verified by the computer simulation and the experiment.

• Investigative Magnetic Resonance Imaging
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• v.2 no.1
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• pp.58-66
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• 1998

Purpose : This paper introduces a new three dimensional magnetic Resonance Image classification which is based on Mar kov Random Field-Gibbs Random Field with a line model. Material and Methods : The performance of the Gibbs Classifier over a statistically heterogeneous image can be improved if the local stationary regions in the image are disassociated from each other through the mechanism of the interaction parameters defined at the local neighborhood level. This usually involves the construction of a line model for the image. In this paper we construct a line model for multisignature images based on the differential of the image which can provide an a priori estimate of the unobservable line field, which may lie in regions with significantly different statistics. the line model estimated from the original image data can in turn be used to alter the values of the interaction parameters of the Gibbs Classifier. Results : MRF-Gibbs classifier for volumetric MR images is developed under the condition that the domain of the image classification is $E^{3}$ space rather thatn the conventional $E^{2}$ space. Compared to context free classification, MRF-Gibbs classifier performed better in homogeneous and along boundaries since contextual information is used during the classification. Conclusion : We construct a line model for multisignature, multidimensional image and derive the interaction parameter for determining the energy function of MRF-Gibbs classifier.

• Journal of Korean Society for Atmospheric Environment
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• v.28 no.3
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• pp.233-248
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• 2012

The national emission from energy sector accounted for 84.7% of all domestic emissions in 2007. Of the energy-use emissions, the emission from mobile source as one of key categories accounted for 19.4% and further the road transport emission occupied the most dominant portion in the category. The road transport emissions can be estimated on the basis of either the fuel consumed (Tier 1) or the distance travelled by the vehicle types and road types (higher Tiers). The latter approach must be suitable for simultaneously estimating $CO_2$, $CH_4$, and $N_2O$ emissions in local administrative districts. The objective of this study was to estimate 31 municipal GHG emissions from road transportation in Gyeonggi Province, Korea. In 2008, the municipalities were consisted of 2,014 towns expressed as Dong and Ri, the smallest administrative district unit. Since mobile sources are moving across other city and province borders, the emission estimated by fuel sold is in fact impossible to ensure consistency between neighbouring cities and provinces. On the other hand, the emission estimated by distance travelled is also impossible to acquire key activity data such as traffic volume, vehicle type and model, and road type in small towns. To solve the problem, we applied a hierarchical cluster analysis to separate town-by-town road patterns (clusters) based on a priori activity information including traffic volume, population, area, and branch road length obtained from small 151 towns. After identifying 10 road patterns, a rule building expert system was developed by visual basic application (VBA) to assort various unknown road patterns into one of 10 known patterns. The expert system was self-verified with original reference information and then objects in each homogeneous pattern were used to regress traffic volume based on the variables of population, area, and branch road length. The program was then applied to assign all the unknown towns into a known pattern and to automatically estimate traffic volumes by regression equations for each town. Further VKT (vehicle kilometer travelled) for each vehicle type in each town was calculated to be mapped by GIS (geological information system) and road transport emission on the corresponding road section was estimated by multiplying emission factors for each vehicle type. Finally all emissions from local branch roads in Gyeonggi Province could be estimated by summing up emissions from 1,902 towns where road information was registered. As a result of the study, the GHG average emission rate by the branch road transport was 6,101 kilotons of $CO_2$ equivalent per year (kt-$CO_2$ Eq/yr) and the total emissions from both main and branch roads was 24,152 kt-$CO_2$ Eq/yr in Gyeonggi Province. The ratio of branch roads emission to the total was 0.28 in 2008.

• Journal of Wetlands Research
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• v.22 no.1
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• pp.15-23
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• 2020

The purpose of this study is to investigate how the degree of distribution influences the calibration of snow and runoff in distributed hydrological models using a multi-criteria calibration method. The Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM) developed by NOAA-National Weather Service (NWS) is employed to estimate optimized parameter sets. We have 3 scenarios depended on the model complexity for estimating best parameter sets: Lumped, Semi-Distributed, and Fully-Distributed. For the case study, the Durango River Basin, Colorado is selected as a study basin to consider both snow and water balance components. This study basin is in the mountainous western U.S. area and consists of 108 Hydrologic Rainfall Analysis Project (HRAP) grid cells. 5 and 13 parameters of snow and water balance models are calibrated with the Multi-Objective Shuffled Complex Evolution Metropolis (MOSCEM) algorithm. Model calibration and validation are conducted on 4km HRAP grids with 5 years (2001-2005) meteorological data and observations. Through case study, we show that snow and streamflow simulations are improved with multiple criteria calibrations without considering model complexity. In particular, we confirm that semi- and fully distributed models are better performances than those of lumped model. In case of lumped model, the Root Mean Square Error (RMSE) values improve by 35% on snow average and 42% on runoff from a priori parameter set through multi-criteria calibrations. On the other hand, the RMSE values are improved by 40% and 43% for snow and runoff on semi- and fully-distributed models.