• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2010년도 하계학술대회 논문집
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• pp.131-131
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• 2010

The power of semiconductor laser diodes has been limited primarily by the heating effects which occur at high optical intensities. The actual limiting event can take one of a number of forms such as. catastrophic optical damage or filamentation. A general approach to this problem is to design a heterostructure which creates a high powered output while maintaining low internal optical intensities. A graded index separate confinement heterostructure (GRIN-SCH) is one such structure that accomplishes the above task. Here, the active region is sandwiched between graded index layers where the index of refraction increases nearer to the active layer. This structure has been shown to yield a high efficiency due to the confinement of both the optical power and carriers, thereby reducing the optical intensity required to achieve higher powers. The optical confinement also reinforces the optical beam quality against high power effects. Quantum dots have long been a desirable option for laser diodes due to the enhanced optical properties associated with the zeroth dimensionality. In our work, we use PICS3D software created by Crosslight Software Inc. to simulate the performance of In0.67A10.33As/A10.2Ga0.8AsquantumdotsusedwithaGRIN-SCH. The simulation tools are used to optimize the GRIN-SCH structure for high efficiency and optical beam quality.

• 응용통계연구
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• 제36권4호
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• pp.323-335
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• 2023

고차원 데이터의 인과 추론에서 고차원 공변량의 차원을 축소하고 적절히 변형하여 처리와 잠재 결과에 영향을 줄 수 있는 교란을 통제하는 것은 중요한 문제이다. 평균 처리 효과(average treatment effect; ATE) 추정에 있어서, 성향점수와 결과 모형 추정을 이용한 확장된 역확률 가중치 방법이 주로 사용된다. 고차원 데이터의 분석시 모든 공변량을 포함한 모수 모형을 이용하여 성향 점수와 결과 모형 추정을 할 경우, ATE 추정량이 일치성을 갖지 않거나 추정량의 분산이 큰 값을 가질 수 있다. 이런 이유로 고차원 데이터에 대한 적절한 차원 축소 방법과 준모수 모형을 이용한 ATE 방법이 주목 받고 있다. 이와 관련된 연구로는 차원 축소부분에 준모수 모형과 희소 충분 차원 축소 방법을 활용한 연구가 있다. 최근에는 성향점수와 결과 모형을 추정하지 않고, 차원 축소 후 매칭을 활용한 ATE 추정 방법도 제시되었다. 고차원 데이터의 ATE 추정 방법연구 중 최근에 제시된 네 가지 연구에 대해 소개하고, 추정치 해석시 유의할 점에 대하여 논하기로 한다.

• 대한간호학회지
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• 제28권3호
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• pp.751-759
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• 1998

The study of the validity test on the self-monitoring scale for nurses In this study, both the literary survey as well as empirical research has been executed to test the validity of the scales that measure the construct of the self-monitoring scale. The self-monitoring scale could not be classified into five factors as Snyder suggested. Many other scholars (Briggs, Cheek and Buss, 1980) suggested 3 different classifications which was accepted by Snyder and Gangestad (1986). John, Cheek and Klohnen(1996) claimed a two-factor classification. As has been discussed, factor analysis is used to prove convergent validity within the factor and discriminant validity between the factors. However, depending on the researchers, many variations in classification of the factors were found and a lack of content and discriminant validity were found in the previous research findings. It is also important to note that Snyder's self-monitoring scale did not factor-load at over. 30 for all 25 items, regardless of how many factors could be classified. According to findings of this study, the self-monitoring scale neither classified as five, three or two factors nor factor loaded as hypothesized. It is also clear that Snyder's self-monitoring scale lacks convergent validity as the sub-factors of the scale failed to prove its uni-dimensionality. The A self-monit oring scale not only fail to overcome the problems of Snyder's self-monitori ng scale but even lost the attractiveness of the self-monitoring scale. In this study it was also found that the A self-monitoring scale was not classified in either in a two or three-factor classification as hypothesized. It is, of course, not desirable to use any scale that lacks convergent and discriminant validity even though it has been widely used and has held a great deal of influence on the field of social psychology. To overcome the shortcomings of Snyder's self-monitoring scale, Lennox and Wolfe(1984) suggested 13 items. This study was dedicated to test the validity and reliability of the scale, in which we found that the data presented in validity as the two factors were class ified and loaded as expected. Reliability was also proven by checking Cronbach's α for each factor and for the total items. In addition, a confirmatory factor analysis was executed for the 13 items using LISREL 8.12 program to confirm convergent validity in a two-factor classification. The model was fitting and sound : however, the self-monitoring scale was unfitted and not validated. Thus, it is recommended to use not the original nor the abbreviated self-monitoring scale but the 13 items in future studies. It should also be noted that items 7 and 13 should be removed to obtain better uni-dimensionality for the 13 items. These items loaded at over. 30, too high for the two factors in the test results of Factor analysis. In addition, it is necessary to double-check the cause of two-hold loading at over .30 for the two factors. It could be a problem caused by data or by the scale itself. Therefore, additional studies should follow to better clarify this matter.

• 한국지리정보학회지
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• 제22권4호
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• pp.1-11
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• 2019

원격탐사 센서 기술의 발전으로 다양한 분광정보를 지니는 위성영상의 취득이 가능해졌다. 특히, 초분광 영상(hyperspectral image)은 연속적이고 좁은 분광파장대의 영역으로 구성되어 있기 때문에, 토지피복분류, 표적탐지, 환경 모니터링 등 다양한 분야에 효과적으로 활용할 수 있다. 원격탐사자료를 활용한 변화탐지 기법은 일반적으로 동일한 차원을 지닌 자료들의 차분을 통해 수행되기 때문에, 차원이 다른 이종 센서에는 적용하기 어려운 단점을 지니고 있다. 이에 본 연구에서는 다른 차원을 지닌 초분광 영상과 고해상도 위성영상에 적용가능한 변화탐지 기법을 개발하고, 이종 영상 간의 변화탐지기법 적용 가능성을 확인하고자 하였다. 이를 위하여, 변화탐지 기법의 적용을 위해 상관도분석, 주성분분석 등을 활용하여 초분광 영상의 차원을 축소시켜 변화탐지에 사용하였으며, 변화탐지 알고리즘은 CVA(Change Vector Analysis)을 사용하였다. 변화탐지 성능의 평가를 위해 참조자료를 사용하여 ROC(Receiver Operating Characteristics) 곡선과, AUC(Area Under Curve)을 계산하였다. 실험결과, 원 초분광 영상을 활용한 경우보다, 적합한 차원 감소 기법을 통해 제작한 영상을 사용하였을 때의 변화탐지 성능이 더 높은 것으로 나타났다. 이는 차원 감소 기법을 적용하여 초분광 영상이 지니고 있는 잡음을 제거하는 것이 변화탐지 성능에 영향을 미치는 것으로 판단된다. 추후 연구로는 융합기법을 적용한 고해상도 다중분광 영상을 이용하여 공간 해상도의 차이에 따른 변화탐지 성능을 분석할 예정이다.

• 여성건강간호학회지
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• 제4권2호
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• pp.173-186
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• 1998

In this study, both the literary survey as well as empirical research has been executed to test the validity of the scales that measure the construct of self-monitoring scale could not be classified into five factors as Snyder suggested. Many other scholars (Briggs, Cheek and Buss, 1980) suggested 3 different classifications which was accepted by Snyder and Gangestad (1986). John, Cheek and Klohnen (1996) claimed a two-factor classification. As has been discussed, factor analysis is used to prove convergent validity within the factor and discriminant validity between the factors. However, depending on the researchers, many variations in classification of the factors were found and a lack of content and discriminant validity was found in the previous research findings. It is also important to note that Snyder's self-monitoring scale, did not factor-load at over 30 for all 25 items, regardless of how many factors could be classified. According to findings of this study, the self-monitoring scale neither classified as five, three or two factors nor factor loaded as hypothesized. It is also clear that Snyder's self-monitoring scale lack convergent validity as the sub-factors of the scale fail to prove its uni-dimensionality. The A self-monitoring scale not only fail to overcome the problems of Snyder's self-monitoring scale but even lost the attractiveness of the self-monitoring scale. In this study, it was also found that the A self-monitoring scale was not classified as hypothesized in either in a two or three-factor classification. It is, of course, not desirable to use any scale that lacks convergent and discriminant validity even though it has been widely used but also has held a great deal of influence on the field of social psychology. To overcome the shortcomings of Snyder's self-monitoring scale, Lennox and Wolfe(1984) suggested 13 items. This study 1. was dedicated to test the validity and reliability of the scale, in which we found that the data presented in validity as the two factors were classified and loaded as expected. Reliability was also proven by checking Cronbach's alpha for each factor and for the total items. In addition, a confirmatory factor analysis was executed for the 13 items using LISREL 8.12 program to confirm convergent validity in a two-factor classification. The model was fitting and sound ; however, the self-monitoring scale was unfitted and not validated. Thus, it is recommended to use not the original or the abbreviated self-monitoring scale but the 13 items in future studies. It should also be noted that items 7 and 13 should be removed to obtain better uni-dimensionality for the 13 items. These items loaded at over .30, too high for the two factors in the test results of factor analysis. In addition, it is necessary to double-check the cause of two-hold loading at over .30 for the two factors. It could be a problem caused by data or by the scale itself. Therefore, additional studies should follow to better clarify this matter.

• 한국정보통신학회논문지
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• 제9권4호
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• pp.897-904
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• 2005

영상 인식은 영상획득이 용이하다는 것과 실생활에서 광범위하게 사용될 수 있다는 것으로 인해 활발하게 연구되고 있는 분야이다. 그러나 얼굴영상은 높은 차원의 영상공간으로 인해 이미지 처리가 쉽지 않다. 본 논문은 얼굴 영상 데이터의 차원을 특징적인 벡터로 표현하고 이러한 특징벡터를 통해 얼굴 영상을 인식하는 방법은 제안한다. 제안되는 알고리즘은 두 부분으로 나뉜다. 첫째로는 칼라 영상을 그레이 영상으로 변환할 때 RGB 세 개의 플레인의 평균이 아닌 세 플레인의 주성분을 사용하는 PCA(Principal Component Analysis)를 적용한다. PCA는 칼라 영상을 그레이 영상으로 변환하는 과정과 인식률을 높이기 위한 영상 대비 개선 과정이 동시에 수행한다. 두 번째로는 PCA와 LDA(Linear Discriminant Analysis) 방식을 하나의 과정으로 통합하는 개선된 통합 LDA 방법이다. 두 과정을 통합함으로서 간결한 알고리즘 표현이 가능하며 분리된 단계에서 있을 수 있는 정보 손실을 방지할 수 있다. 제안된 알고리즘은 잘 제어된 대용량 얼굴 데이터베이스에서 개인을 확인하는 분야에 적용되어 성능을 향상시키고 있음을 보여주었고, 추후에는 실시간 상황에서 특정 개인을 확인하는 분야의 기초 알고리즘으로 적용될 수 있다.

• The Journal of the Acoustical Society of Korea
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• 제17권2E호
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• pp.31-37
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• 1998

This paper proposes a feature extraction method using wavelet transform for speech recognition. Speech recognition system generally carries out the recognition task based on speech features which are usually obtained via time-frequency representations such as Short-Time Fourier Transform (STFT) and Linear Predictive Coding(LPC). In some respects these methods may not be suitable for representing highly complex speech characteristics. They map the speech features with same may not frequency resolutions at all frequencies. Wavelet transform overcomes some of these limitations. Wavelet transform captures signal with fine time resolutions at high frequencies and fine frequency resolutions at low frequencies, which may present a significant advantage when analyzing highly localized speech events. Based on this motivation, this paper investigates the effectiveness of wavelet transform for feature extraction of wavelet transform for feature extraction focused on enhancing speech recognition. The proposed method is implemented using Sampled Continuous Wavelet Transform (SCWT) and its performance is tested on a speaker-independent isolated word recognizer that discerns 50 Korean words. In particular, the effect of mother wavelet employed and number of voices per octave on the performance of proposed method is investigated. Also the influence on the size of mother wavelet on the performance of proposed method is discussed. Throughout the experiments, the performance of proposed method is discussed. Throughout the experiments, the performance of proposed method is compared with the most prevalent conventional method, MFCC (Mel0frequency Cepstral Coefficient). The experiments show that the recognition performance of the proposed method is better than that of MFCC. But the improvement is marginal while, due to the dimensionality increase, the computational loads of proposed method is substantially greater than that of MFCC.

• Asian-Australasian Journal of Animal Sciences
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• 제30권6호
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• pp.765-772
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• 2017

Objective: This study examines the genetic factors influencing the phenotypes (four economic traits:oleic acid [C18:1], monounsaturated fatty acids, carcass weight, and marbling score) of Hanwoo. Methods: To enhance the accuracy of the genetic analysis, the study proposes a new statistical model that excludes environmental factors. A statistically adjusted, analysis of covariance model of environmental and genetic factors was developed, and estimated environmental effects (covariate effects of age and effects of calving farms) were excluded from the model. Results: The accuracy was compared before and after adjustment. The accuracy of the best single nucleotide polymorphism (SNP) in C18:1 increased from 60.16% to 74.26%, and that of the two-factor interaction increased from 58.69% to 87.19%. Also, superior SNPs and SNP interactions were identified using the multifactor dimensionality reduction method in Table 1 to 4. Finally, high- and low-risk genotypes were compared based on their mean scores for each trait. Conclusion: The proposed method significantly improved the analysis accuracy and identified superior gene-gene interactions and genotypes for each of the four economic traits of Hanwoo.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2009년도 하계학술대회 논문집
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• pp.248-248
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• 2009

Recently, the control of size, morphology and dimensionality in inorganic materials has been rapidly developed into a promising field in materials chemistry. 3D nanostructured flower-like ZnO architecture with different size and shapes have been simply synthesized via a hydrothermal process, using zinc acetate and ammonium hydroxide as reactants.[1] In this study, the Zno thin-films were deposited by RF magnetron sputtering in other to get high adhesion and uniformity of 3D nanostructured flower-like ZnO architecture on a $SiO_2$ substrate. The XRD patterns identified that the obtained the nanocrystallized ZnO architecture exhibited a wurtzite structure. SEM images illustrated that the flower-like ZnO bundles consisted of flower-like or chestnut bur, which were characterized by polycrystalline and [0001] preferential orientation.

• JSTS:Journal of Semiconductor Technology and Science
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• 제15권1호
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• pp.101-113
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• 2015

The Monte-Carlo (MC) technique is very efficient solution for statistical problem. Various MC methods can easily be applied to statistical circuit performance analysis. Recently, as the number of process parameters and their impact, has increasingly affected circuit performance, a sufficient sample size is required in order to consider high dimensionality, profound nonlinearity, and stringent accuracy requirements. Also, it is important to identify the performance of circuit as soon as possible. In this paper, Fast MC method is proposed for efficient analysis of circuit performance. The proposed method analyzes performance using enhanced-precision Latin Hypercube Sampling Monte Carlo (LHSMC). To increase the accuracy of the analysis, we calculate the effective dimension for the low discrepancy value on critical parameters. This will guarantee a robust input vector for the critical parameters. Using a 90nm process parameter and OP-AMP, we verified the accuracy and reliability of the proposed method in comparison with the standard MC, LHS and Quasi Monte Carlo (QMC).