• Journal of the Optical Society of Korea
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• v.18 no.6
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• pp.650-656
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• 2014

We propose a novel active fiber design for selectively generating cylindrical vector beams (CVBs) or cylindrical vector modes (CVMs) which can be applied to conventional fiber lasers. A fiber is designed to have a ring-shaped core refractive index profile which can lead to the best overlap between the active dopant distribution profile and the lowest-order CVM (LCVM) field profile. Therefore, the overlap factor (OVF) of the LCVM becomes even higher than that of the fundamental mode. We emphasize that this condition cannot be satisfied by a conventional step-index core fiber (SICF) but by the ring-doped core fiber (RDCF). Because the lasing threshold is inversely proportional to the OVF, the LCVM can predominantly be stimulated even without going through special procedures to impose extra loss mechanisms to the fundamental mode. We numerically verify that the OVF of the LCVM with the doped ions can significantly exceed that of the fundamental mode if the proposed fiber design is applied. In addition, an RDCF of the proposed fiber design can also operate in a regime containing no higher-order modes besides the LCVM, so that it can selectively and efficiently generate the LCVM without being disrupted by the parasitic lasing of the higher-order modes. We highlight that an optimized RDCF can lead to a >30 % higher OVF ratio than a SICF having the same doped area. The proposed model is expected to be useful for enhancing the efficiency of generating CVBs in an all-fiber format.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.6
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• pp.527-533
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• 2013

BCI (Brain-Computer Interface) is a system that transforms a subject's brain signal related to their intention into a control signal by classifying EEG (electroencephalograph) signals obtained during the imagination of movement of a subject's limbs. The BCI system allows us to control machines such as robot arms or wheelchairs only by imaging limbs. With the exact same experiment environment, activated brain regions of each subjects are totally different. In that case, a simple approach is to use as many channels as possible when measuring brain signals. However the problem is that using many channels also causes other problems. When applying a CSP (Common Spatial Pattern), which is an EEG extraction method, many channels cause an overfitting problem, and in addition there is difficulty using this technique for medical analysis. To overcome these problems, we suggest an optimal channel selection method using a BPSO (Binary Particle Swarm Optimization), BPSO with channel impact factor, and GA. This paper examined optimal selected channels among all channels using three optimization methods and compared the classification accuracy and the number of selected channels between BPSO, BPSO with channel impact factor, and GA by SVM (Support Vector Machine). The result showed that BPSO with channel impact factor selected 2 fewer channels and even improved accuracy by 10.17~11.34% compared with BPSO and GA.

• KSBB Journal
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• v.7 no.3
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• pp.191-200
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• 1992

Several strains of Agrobacterium tumefaciens were isolated from soil in the Taegu area and characterized to develop some useful vector systems for higher plant genetic engineering. The selected colonies had a unique form, and strains from the colonies were capable of tumor formation on the sunflower leaf surface. They had a large plasmid. The restriction analysis showed that they were another kinds of Ti plasmic compared with C58 and Ach5. The isolated strains were identified as the nopaline type and also as biovar 1 A. tumefaciens, according to their tumor morphology, blophyslcal and biochemical characteristics. One of the isolated strains, AK204 was transformed with binary vector (pGA642), having selectable marker (Kmr, Tcr). Furthermore, maize tissue cells were transformed by cocultivation with AK204/pGA642, and the transformants were selected on the selective medium and identified using PAGE patterns of their soluble proteins.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.5
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• pp.962-969
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• 2006

To address the importance of the process fault detection for productivity, support vector machines (SVMs) is employed to assist the decision to determine process faults in real-time. The reactive ion etching (RIE) tool data acquired from a production line consist of 59 variables, and each of them consists of 10 data points per second. Principal component analysis (PCA) is first performed to accommodate for real-time data processing by reducing the dimensionality or the data. SVMs for eleven steps or etching m are established with data acquired from baseline runs, and they are further verified with the data from controlled (acceptable) and perturbed (unacceptable) runs. Then, each SVM is further utilized for the fault detection purpose utilizing control limits which is well understood in statistical process control chart. Utilizing SVMs, fault detection of reactive ion etching process is demonstrated with zero false alarm rate of the controlled runs on a run to run basis.

• Environmental and Resource Economics Review
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• v.24 no.4
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• pp.709-725
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• 2015

This study is to analyse the relationship between the price and the supply in the farming Olive Flounder's production area market. The data used in this study correspond to daily price and supply quantity covering time period from January 1, 2007 to June 30. 2013. The analysis methods of cointegration and vector error correction model are employed. The empirical results of this study are summarized as follows: First, the price and the supply follow random walks and they are integrated of order 1. Second, the price and the supply are cointegrated. Third, vector error correction model suggests that the relationship between the price change ration and the supply quantity change ratio has negative and feedback effect exists in the long-run, but the disequilibrium between the price and the supply is corrected by the supply quantity. Finally, vector error correction model suggests that the supply quantity leads the price in the short-run. This indicates that the decrease(increase) of the supply quantity results in the increase(decrease) of the price.

• Journal of Microbiology and Biotechnology
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• v.14 no.4
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• pp.789-795
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• 2004

A promoter-probe shuttle vector pSK1Cat was constructed for the isolation of transcriptional signal sequences from Corynebacterium glutamicum. Besides conferring resistance to kanamycin in Escherichia coli and C. glutamicum, the vector carried a promoterless cat gene to confer resistance to chloramphenicol upon insertion of the appropriate transcriptional signals in the multiple cloning site. By utilizing the vector, a series of transcriptionally active fragments were isolated from the genome of C. glutamicum. The clones, ranging from 200 bp to 1 kb in size, were grouped into 3 classes of strong, medium, and weak, based on the chloramphenicol acetyltransferase (CAT) activity and sensitivity to the chloramphenicol of the clone-carrying C. glutamicum cells. C. glutamicum cells carrying the $P_{19}$ clone, a representative in the strong class, were able to grow on minimal agar plates containing over $40 mg/mell$ chloramphenicol, and showed CAT activity of 10 m㏖/mgㆍmin, performing slightly better than the cells carrying $P_{tac}$ , a strong E. coli promoter. Subcloning analysis of the $P_{19}$ clone identified a 180 bp intergenic fragment ($P_{180}$), which was located upstream of a gene encoding a hypothetical membrane protein. The expression conferred by $P_{180}$ was not affected by either the kinds of carbon sources or changes in temperature. These properties make the $P_{180}$ clone useful for the deregulated expression of biosynthetic genes in C. glutamicum during amino acid fermentation.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.61-73
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• 2016

We consider the wild bootstrap Ljung-Box (LB) test for autocorrelation in residuals of fitted multivariate time series models. The asymptotic chi-square distribution under the IID assumption is traditionally used for the LB test; however, size distortion tends to occur in the usage of the LB test, due to the conditional heteroskedasticity of financial time series. In order to overcome such defects, we propose the wild bootstrap LB test for autocorrelation in residuals of fitted vector autoregressive and error correction models. The simulation study and real data analysis are conducted for finite sample performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.1043-1056
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• 2017

A styled document is a document that contains diverse decorating functions such as different font, colors, tables and images generally authored in a word processor (e.g., MS-WORD, Open Office). Compared to a plain-text document, a styled document enables a human to easily recognize a logical structure such as section, subsection and contents of a document. However, it is difficult for a computer to recognize the structure if a writer does not explicitly specify a type of an element by using the styling functions of a word processor. It is one of the obstacles to enhance document version management systems because they currently manage the document with a file as a unit, not the document elements as a management unit. This paper proposes a machine learning based approach to analyzing the logical structure of a styled document composing of sections, subsections and contents. We first suggest a feature vector for characterizing document elements from a styled document, composing of eight features such as font size, indentation and period, each of which is a frequently discovered item in a styled document. Then, we trained machine learning classifiers such as Random Forest and Support Vector Machine using the suggested feature vector. The trained classifiers are used to automatically identify logical structure of a styled document. Our experiment obtained 92.78% of precision and 94.02% of recall for analyzing the logical structure of 50 styled documents.

• The Journal of the Acoustical Society of Korea
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• v.15 no.1
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• pp.87-92
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• 1996

Transient signals in underwater show several characterisrics, that is, short duration, strong nonstationarity, various types of transient sources, which make it difficult to analyze and classify transient signals. In this paper, the feature vector extraction method for transient SOMAR signals is discussed by applying digital signal processing methods to the analysis of transient signals. A feature vector extraction methods using wavelet transform, which enable us to obtain better recognition rate than automatic classification using the classical method, are proposed. It is confirmed by simulation that the proposed method using wavelet transform performs better than the classical method even with smaller number of feature vectors. Especially, the feature vector extraction method using PR-QMF wavelet transform with the Daubechies coefficients is shown to perform well in noisy environment with easy implementation.

• Journal of the Korea Society of Computer and Information
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• v.17 no.9
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• pp.9-16
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• 2012

In this paper, we propose the two dimensional variable-length vector storage format which can be used for efficient storage of sparse matrix in the FEM (finite element method). The proposed storage format is the method storing only actual needed non-zero values of each row on upper triangular matrix with the total rows N, by using two dimensional variable-length vector instead of $N{\times}N$ large sparse matrix of entire equation of finite elements. This method only needs storage spaces of the number of minimum 1 to maximum 5 in 2D grid structure and the number of minimum 1 to maximum 14 in 3D grid structure of analysis target. The number doesn't excess two times although involving index number. From the experimental result, we can find out that the proposed storage format can reduce the memory space more effectively, as the total number of nodes increases, than the existing skyline storage format storing maximum column height.