• 한국양식학회지
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• 제13권1호
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• pp.29-37
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• 2000

In previously study we isolated several fish matrix attachment regions (MARs) capable of replicating the plasmid by itself. In this study we construct a fish expression vector pBaEGFP(+) containing mud loach ${\beta}$-actin promoter EGFP as reporter gene and SV40 signal. To analyze the effects of the fish expression vector respectively. The fish ARS containing constructs pBaEGFP(+)-ARSs were transfected cells with pBaEGFP(+)-ARS101 and pBaEGFP(+)-ARS223 reduced 10 days to 25 days and then was constant to 30 days after transfection while that of the control vector without ARS element was basal level. The intensity of both constructs showed about 30fold of the intensity compared with the control vector on 30days after transfection individually .E. coli back-transformation analysis shows that pBaEGFP(+)-ARS223 and pBaEGFP(+)-ARS905 maintain in episomal state at least 30 days after transfection. The result indicates that both may be able to replicate the vector in BF-2 cell. Therefore the matrix-attached ARSs enhancing expression of the reporter gene might be useful as a component o the expression vector for transgenic studies.

• 지식경영연구
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• 제21권1호
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• pp.99-116
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• 2020

Text is the most widely used means of exchanging or expressing knowledge and information in the real world. Recently, researches on structuring unstructured text data for text analysis have been actively performed. One of the most representative document embedding method (i.e. doc2Vec) generates a single vector for each document using the whole corpus included in the document. This causes a limitation that the document vector is affected by not only core words but also other miscellaneous words. Additionally, the traditional document embedding algorithms map each document into only one vector. Therefore, it is not easy to represent a complex document with interdisciplinary subjects into a single vector properly by the traditional approach. In this paper, we introduce a multi-vector document embedding method to overcome these limitations of the traditional document embedding methods. After introducing the previous study on multi-vector document embedding, we visually analyze the effects of the multi-vector document embedding method. Firstly, the new method vectorizes the document using only predefined keywords instead of the entire words. Secondly, the new method decomposes various subjects included in the document and generates multiple vectors for each document. The experiments for about three thousands of academic papers revealed that the single vector-based traditional approach cannot properly map complex documents because of interference among subjects in each vector. With the multi-vector based method, we ascertained that the information and knowledge in complex documents can be represented more accurately by eliminating the interference among subjects.

• 한국군사과학기술학회지
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• 제16권5호
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• pp.675-682
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• 2013

Reliability analysis and prediction of next failure time is critical to sustain weapon systems, concerning scheduled maintenance, spare parts replacement and maintenance interventions, etc. Since 1981, many methodology derived from various probabilistic and statistical theories has been suggested to do that activity. Nowadays, many A.I. tools have been used to support these predictions. Support Vector Regression(SVR) is a nonlinear regression technique extended from support vector machine. SVR can fit data flexibly and it has a wide variety of applications. This paper utilizes SVM and SVR with combining time series to predict the next failure time based on historical failure data. A numerical case using failure data from the military equipment is presented to demonstrate the performance of the proposed approach. Finally, the proposed approach is proved meaningful to predict next failure point and to estimate instantaneous failure rate and MTBF.

• Journal of the Korean Data and Information Science Society
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• 제18권1호
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• pp.73-80
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• 2007

Cointegration(together with VARMA(vector ARMA)) has been proven to be useful for analyzing multivariate non-stationary data in the field of financial time series. It provides a linear combination (which turns out to be stationary series) of non-stationary component series. This linear combination equation is referred to as long term equilibrium between the component series. We consider two sets of Korean bivariate financial time series and then illustrate cointegration analysis. Specifically estimated VAR(vector AR) and VECM(vector error correction model) are obtained and CV(cointegrating vector) is found for each data sets.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2003년도 추계학술대회논문집
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• pp.383-388
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• 2003

As an effective way of response evaluation in structural vibration analysis, the phase vector sum(PVS) method used in shaft torsional vibration analysis is introduced. Basic relation of PVS applicable to structural problem is derived and applied to Diesel engine structures. Concepts of forced phase vector sum (FPVS) and significance level (SL) are proposed to visualize the correlation between excitation orders and vibration modes in the SL map. The maximum responses and SL are compared and reviewed to confirm the validity of the method. It is regarded FPVS is adequate to newly evaluate the structural vibration based on excitation information.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 B
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• pp.644-646
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• 2000

An induction motor can be controlled like a separately excited do motor by field oriented control(or vector control). In vector control, Because the transformation of the stator's 3-phase current into two orthogonal current is required. the control scheme is complicated. But, Yamamura proposed a field acceleration method(FAM) without the phase transformation. FAM simplify an implementation control scheme for induction motors. In this paper, the analysis of transient phenomena of a squirrel-cage induction motor was achieved by the spiral vector method and the phase segregation method. It simplified control schemes more than those of vector control.

• Journal of electromagnetic engineering and science
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• 제18권3호
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• pp.206-211
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• 2018

In this paper, we proposed a two-level feature vector fusion technique to improve the performance of target classification. The proposed method combines feature vectors of the early-time region and late-time region in the first-level fusion. In the second-level fusion, we combine the monostatic and bistatic features obtained in the first level. The radar cross section (RCS) of the 3D full-scale model is obtained using the electromagnetic analysis tool FEKO, and then, the feature vector of the target is extracted from it. The feature vector based on the waveform structure is used as the feature vector of the early-time region, while the resonance frequency extracted using the evolutionary programming-based CLEAN algorithm is used as the feature vector of the late-time region. The study results show that the two-level fusion method is better than the one-level fusion method.

• 한국멀티미디어학회논문지
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• 제18권7호
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• pp.834-842
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• 2015

We propose a method to construct composite feature vector based on discriminant analysis for face recognition. For this, we first extract the holistic- and local-features from whole face images and local images, which consist of the discriminant pixels, by using a discriminant feature extraction method. In order to utilize both advantages of holistic- and local-features, we evaluate the amount of the discriminative information in each feature and then construct a composite feature vector with only the features that contain a large amount of discriminative information. The experimental results for the FERET, CMU-PIE and Yale B databases show that the proposed composite feature vector has improvement of face recognition performance.

• 말소리와 음성과학
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• 제9권2호
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• pp.85-94
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• 2017

Conventional speaker verification systems using time delay neural network, identity vector and probabilistic linear discriminant analysis (TDNN-Ivector-PLDA) are known to be very effective for verifying long-duration speech utterances. However, when test utterances are of short duration, duration mismatch between enrollment and test utterances significantly degrades the performance of TDNN-Ivector-PLDA systems. To compensate for the I-vector mismatch between long and short utterances, this paper proposes to use probabilistic linear discriminant analysis (PLDA) model adaptation with augmented data. A PLDA model is trained on vast amount of speech data, most of which have long duration. Then, the PLDA model is adapted with the I-vectors obtained from short-utterance data which are augmented by using vocal tract length perturbation (VTLP). In computer experiments using the NIST SRE 2008 database, the proposed method is shown to achieve significantly better performance than the conventional TDNN-Ivector-PLDA systems when there exists duration mismatch between enrollment and test utterances.

• 대한의용생체공학회:의공학회지
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• 제18권3호
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• pp.273-283
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• 1997

위전도는 피부전극을 이용하여 위에서 발생하는 전기신호를 측정하여 위운동 상태를 측정하는 검사방법이다. 여러 연구자들이 위전도를 이용하여 위수축과 관련된 정보를 얻으려고 노력하였지만 아직까지 임상적으로 유용한 정보를 주는 방법을 제시하지 못하고 있다. 본 연구에서는 위전도신호를 주파수영역 분석 및 벡터분석을 이용하여 위수축을 측정할 수 있는 방법을 제시하였다. Running spectrum analysis를 이용한 주파수영역분석에서는 주주파수와 이의 고조파의 크기변화를 위수축신호(스트레스인게이지신호, 위내압신호)와 비교하였다. 벡터분석에서는 피부전극에서 얻은 위전도신호를 이용하여 위전도벡터를 구한 후, 이 위전도벡터의 분포에 대한 분석을 실시하였다. 제시된 방법은 동물실험 및 임상실험을 통하여 그 타당성을 검정하였다. 주파수영역분석에서 위수축이 일어나는 경우 주주파수의 크기에 비해 제1고조파의 크기가 상대적을 증가하는 것을 발견하였다. 위전도 벡터분석에서는 위수축유무에 따라 위전도벡터의 분포가 변함을 알 수 있었으며, 위전도벡터의 분포로부터 계산한 회귀곡선의 기울기 및 회귀곡선에 대한 분산이 위수축유무에 따라 유의한 차이를 가지는 값을 가짐을 알 수 있었다.