• Journal of Korean Society for Geospatial Information Science
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• v.20 no.1
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• pp.3-12
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• 2012

It is a global trend to give attention to generating precise 3D navigation maps since eco-friendly vehicles have become a critical issue due to environmental protection and depletion of fossil fuels. To date, Mobile Mapping System (MMS) has been a efficient method to acquire the data for generating the 3D navigation maps. To achieve this goal so far in the Mobile Mapping System using the data acquisition method has been proposed to be most effective. For this study the basic RTF filter algorithm was applied to modify to fit MMS quantitative analysis derived floor 99.71%, 99.95% of the highly non-producers to maintain accuracy and high-precision 3D road could create DEM. In addition, the roads that exist within the cars, roadside tree, road cars, such as the median strips have been removed to shields it takes to get results effectively, and effective in practical applications and can be expected to improve operational efficiency is considered.

• KSBB Journal
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• v.22 no.4
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• pp.179-184
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• 2007

This study introduces the characteristics and some applications of repetitive polypeptides, especially to the biomaterial, tissue engineering scaffolds, drug delivery system, and DNA separation systems. Since some fibrous proteins, which consist of repeating peptide monomers, have been reported that their physical properties are changed dramatically by means of temperature alteration or pH shifting. For that reason, fibrous protein-mimetic polypeptides, which are produced by the recombinant technology, can be applied to the diverse biological fields. Repetitive polypeptides can also be used in the bioseparation area such as DNA sequencing, because they make DNA separation possible in free-solution electrophoresis by conjugating DNA fragments to them. Moreover, artificial synthesis of repetitive polypeptides helps to demonstrate the correlations between mechanical properties and structures of natural protein polymer, which have been proven that repetitive domains are affected by the sequence of the repeating domains and the number of repeating subunits. Repetitive polypeptides can be biologically synthesized using some special cloning methods, which are represented here. Recursive directional ligation (RDL) and controlled cloning method (CCM) have been proposed as excellent cloning methods in that we can control the number of repetition in the multimerization of polypeptides and the components of repetitive polypeptides by either method.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.2
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• pp.55-64
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• 1990

This paper concerned about a study on the direct pole placement PID self-tuning controller design for DC servo motor control system. The method of a direct pole placement self-tuning PID control for a DC servo motor of Robot manipulator tracks a reference velocity in spite of the parameters uncertainties in nonminimum phase system. In this scheme, the parameters of classical controller are estimated by the recursive least square (RLS)identification algorithm, the pole placement method and diophantine equation. A series of simulation in which minimum phase system and nonminimum phase system are subjected to a pattern of system parameter changes is presented to show some of the features of the proposed control algorithm. The proposed control algorithm which shown are effective for the practical application, and experiments of DC servo motor speed control for Robot manipulator by a microcomputer IBM-PC/AT are performed and the results are well suited.

• Nuclear Engineering and Technology
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• v.27 no.3
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• pp.336-343
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• 1995

The Self Tuning Regulator(STR) method which is an approach of adaptive control theory, is ap-plied to design the fully automatic power controller of the nonlinear reactor model. The adaptive control represent a proper approach to design the suboptimal controller for nonlinear, time-varying stochastic systems. The control system is based on a third­order linear model with unknown, time-varying parameters. The updating of the parameter estimates is achieved by the recursive extended least square method with a variable forgetting factor. Based on the estimated parameters, the output (average coolant temperature) is predicted one-step ahead. And then, a weighted one-step ahead controller is designed so that the difference between the output and the desired output is minimized and the variation of the control rod position is small. Also, an integral action is added in order to remove the steady­state error. A nonlinear M plant model was used to simulate the proposed controller of reactor power which covers a wide operating range. From the simulation result, the performances of this controller for ramp input (increase or decrease) are proved to be successful. However, for step input this controller leaves something to be desired.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.1
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• pp.157-165
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• 2011

It is challenging to detect foreground objects when background includes an illumination variation, shadow or structural variation due to its motion. Basically pixel-based background models including codebook-based modeling suffer from statistical randomness of each pixel. This paper proposes an algorithm that incorporates Markov random field model into pixel-based background modeling to achieve more accurate foreground detection. Under the assumptions the distance between the pixel on the input imaging and the corresponding background model and the difference between the scene estimates of the spatio-temporally neighboring pixels are exponentially distributed, a recursive approach for estimating the MRF regularizing parameters is proposed. The proposed method alternates between estimating the parameters with the intermediate foreground detection and estimating the foreground detection with the estimated parameters, after computing it with random initial parameters. Extensive experiment is conducted with several videos recorded both indoors and outdoors to compare the proposed method with the standard codebook-based algorithm.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.22 no.4
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• pp.361-379
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• 2011

This paper aims to propose the most appropriate method for calculating the journal PageRank in a domestic citation database. Korean journals show relatively high journal self-citation ratios and have many outgoing citations to external journals which are not included in the domestic citation database. Because the PageRank algorithm requires recursive calculation to converge, those two characteristics of domestic citation databases must be accounted for in order to measure the citation impact of Korean journals. Therefore, two PageRank calculation methods and four formulas for self-citation adjustment have been examined and tested for KSCD journals. The results of the correlation analysis and regression analysis show that the SCImago Journal Rank formula with the cr2 type self-citation adjustment method seems to be a more appropriate way to measure the relative impact of domestic journals in the Korean Science Citation Database.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.18 no.9
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• pp.1023-1029
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• 2007

In this paper, we propose the method to solve the BLT equations using Laguerre polynomials in time domain. The solution of BLT equations is obtained by recursive, differential and integral properties of Laguerre polynomials. The verification of the proposed method is tested by applying it to the two-wired transmission line with resistors and capacitors, which is illuminated by the electromagnetic plane wave pulse. And the result is compared with the corresponding transient responses obtained from inverse fast Fourier transform(IFFT) of the frequency domain solutions of BLT equations.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.12a
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• pp.13-16
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• 2000

In the recent mobile communication systems, the performance of Turbo Code using the error correction coding depends on the interleaver influencing the free distance determination and the recursive decoding algorithms that is executed in the turbo decoder. However, performance depends on the interleaver depth that need great many delay over the reception process. Moreover, Turbo Code has been known as the robust coding methods with the confidence over the fading channel. The International Telecommunication Union(ITU) has recently adopted as the standardization of the channel coding over the third generation mobile communications the same as IMT-2000. Therefore, in this paper, we proposed of that has the better performance than existing Turbo Decoder that has the parallel concatenated four-step structure using MAP algorithm. In the real-time voice and video service over the third generation mobile communications, the performance of the proposed method was analyzed by the reduced decoding delay using the variable decoding method by computer simulation over AWGN and lading channels.

• Geomechanics and Engineering
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• v.25 no.1
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• pp.1-16
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• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.1 s.307
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• pp.53-66
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• 2006

This paper proposes the combined image retrieval system that gives the same relevance as exhaustive search method while its performance can be considerably improved. This system is combined with two different retrieval methods and each gives the same results that full exhaustive search method does. Both of them are two-stage method. One uses condensation of feature vectors, and the other uses binary-tree clustering. These two methods extract the candidate images that always include correct answers at the first stage, and then filter out the incorrect images at the second stage. Inasmuch as these methods use equal algorithm, they can get the same result as full exhaustive search. The first method condenses the dimension of feature vectors, and it uses these condensed feature vectors to compute similarity of query and images in database. It can be found that there is an optimal condensation ratio which minimizes the overall retrieval time. The optimal ratio is applied to first stage of this method. Binary-tree clustering method, searching with recursive 2-means clustering, classifies each cluster dynamically with the same radius. For preserving relevance, its range of query has to be compensated at first stage. After candidate clusters were selected, final results are retrieved by computing similarities again at second stage. The proposed method is combined with above two methods. Because they are not dependent on each other, combined retrieval system can make a remarkable progress in performance.