• Communications for Statistical Applications and Methods
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• v.4 no.1
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• pp.91-99
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• 1997

본 연구에서는 신경망이론을 이용하여 시계열자료를 분석할 때 문제가 되고 있는 초기 가중값을 선정하는 방법을 제시하고자 한다. 기존의 연구에서 학습을 위한 초기 가중값의 결정은 난수에 의존하고 있다. 본 연구에서는 신경망학습의 효율적인 초기값을 선택하기 위하여 제어상자를 이용한다. 그리고 학습과정에서 가중값의 변화를 추적하고 적절한 가중값의 범위를 탐색하면서 새로운 초기값을 제어상자를 통하여 실시간으로 재설정하는 방법을 제시한다.

• Korean Journal of Child Education & Care
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• v.18 no.1
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• pp.77-102
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• 2018

The objectives of this study were to analyze the longitudinal mediation effects of maternal parenting stress and psychosocial development of infant on the influence of paternal involvement during infancy to children's peer interactions. For these objectives, latent growth modeling was used and adapted to the data taken from the Panel Study on Korean Children(PSKC), Wave I (0-year olds) to Wave III (2-year olds) and Wave V(4-year olds) to Wave VII(6-year olds). During a total of six years, 1,018 parent responded. The major results of this study were as follows. First, maternal parenting stress(initial status) had a significant mediating effect on the influence of paternal involvement(initial status) to children's play interaction(initial status). Second, maternal parenting stress(initial status/change rate) had a longitudinal mediating eff ect on the influence of paternal involvements(initial status/change rate) to children's play disruption-disconnection interactions(initial status/change rate). Third, psychosocial development of infants(initial status/change rate) had a longitudinal mediating effect on the influence of paternal involvement(initial status/change rate) to children's play interactions(initial status/change rate). Fourth, psychosocial development of infant(initial status) had a significant mediating effect on the influence of paternal involvement(initial status) to children's play disruption-disconnection interactions(initial status). The implications of the findings of the current study and suggestions for future research were also discussed.

• Journal of Korea Multimedia Society
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• v.15 no.6
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• pp.761-769
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• 2012

In this paper, a setting method of initial cluster centers and color image segmentation using superpixels and Fuzzy C-means(FCM) algorithm is proposed. Generally, the FCM can be widely used to segment color images, and an element is assigned to any cluster with each membership values in the FCM. However the algorithm has a problem of local convergence by determining the initial cluster centers. So the selection of initial cluster centers is very important, we proposed an effective method to determine the initial cluster centers using superpixels. The superpixels can be obtained by grouping of some pixels having similar characteristics from original image, and it is projected $La^*b^*$ feature space to obtain the initial cluster centers. The proposed method can be speeded up because number of superpixels are extremely smaller than pixels of original image. To evaluate the proposed method, several color images are used for computer simulation, and we know that the proposed method is superior to the conventional algorithm by the experimental results.

• Proceedings of the Korean Statistical Society Conference
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• 2002.05a
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• pp.25-28
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• 2002

비선형모형분석의 초기 단계에서 초기값(starting value, initial parameter value)를 결정하는 문제는 비선형모형의 모수추정을 위한 반복기법의 수렴속도나 국소값(local minimum)문제에 영향을 주게 된다. 본 논문을 통하여 탐색적 자료분석이 초기값를 결정하는 데 도움을 줄 수 있음을 보이고자 한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.9
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• pp.1957-1964
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• 2010

In Video coding, the first frame is encoded in intra mode which generates a larger number of bits. In addition, the first frame is used for the inter mode encoding of the following frames. Thus the intial QP for the first frame of GOP affects the first frame as well as the following frames. Traditionally, the initial QP of a GOP is determined by the initial QP of the previous GOP and the average QP of the inter mode frames. In case of JM, the initial QP of a GOP is adjusted as the initial QP being less than the average QP of inter mode frames by two. However, this method is not suitable for the low bit rate video coding. In this paper, the linear relationship between the optimal QP and the ratio of the PSNR of the first frame and the average PSNR of the inter mode frames is first investigated and the linear model is proposed based on the results of the investigation. The proposed model calculate the optimal initial QP using the encoding results of the previous GOP. It is shown by experimental results that the new algorithm can predict the optimal initial QP more accurately and generate the PSNR performance better than that of the existing JM algorithm.

• The Korean Journal of Applied Statistics
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• v.12 no.2
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• pp.433-447
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• 1999

검사관련 능력과 문항점수사이의 관계를 모형화하기 위해 사용한 순환모형에서 관측불능인 능력상대변수를 비롯한 모든 변수들이 범주형 변수라 가정하자. 이 범주형 자료를 위한 모수추정문제를 다루기 위해 EM 방법을 이용했는데, EM 방법은 사용하기에 편리하지만 순환모형에 대한 추정값이 적절하지 않는 경우가 발생한다. 그 주된 원인중의 하나로 초기값 선정의 잘못을 들 수 있는데, 본 논문에서는 이 외에 구조상의 결함도 그 원인이 됨을 경험적으로 보았다. 따라서 구조적 결함을 먼저 해결하면 보다 효과적인 초기값을 선정할 수 있으리가 기대한다.

• Korean Journal of Social Welfare Studies
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• v.41 no.2
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• pp.247-278
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• 2010

There are three main purposes of this study: (a) to investigate the developmental trajectories of psychological resilience, social resilience, and school resilience of adolescents in poverty, (b) to identify the longitudinal relationship of three resiliences and (c) to examine the protective factors that help adolescents to develop successfully in the face of poverty. Data were drawn from Korea Youth Panel Survey(KYPS), a longitudinal panel study on Middle school students in the second grade. Sample consists of 648 adolescents in poverty who particiipated in the KYPS. Latent Growth Curve Model(LGM) was used to analyze intraindividual changes in resiliences and interindividual differences in these changes. And AMOS 7.0 and SPSS 15.0 were used. In this study, it is concluded that the resilience of adolescents in poverty is the construct that has uni-dimensionality and multi-dimensionality at the same time. Therefore, in order to improve resilience of adolescents in poverty, protective factors associated with each resilience have to be increased. Those findings have provided various theoretical and practical implications for social workers and professionals helping adolescents in poverty.

• The Korean Journal of Applied Statistics
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• v.32 no.5
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• pp.693-702
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• 2019

Deep learning has gained popularity for the classification and prediction task. Neural network layers become deeper as more data becomes available. Saturation is the phenomenon that the gradient of an activation function gets closer to 0 and can happen when the value of weight is too big. Increased importance has been placed on the issue of saturation which limits the ability of weight to learn. To resolve this problem, Glorot and Bengio (Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 249-256, 2010) claimed that efficient neural network training is possible when data flows variously between layers. They argued that variance over the output of each layer and variance over input of each layer are equal. They proposed a method of initialization that the variance of the output of each layer and the variance of the input should be the same. In this paper, we propose a new method of establishing initialization by adopting truncated normal distribution and truncated cauchy distribution. We decide where to truncate the distribution while adapting the initialization method by Glorot and Bengio (2010). Variances are made over output and input equal that are then accomplished by setting variances equal to the variance of truncated distribution. It manipulates the distribution so that the initial values of weights would not grow so large and with values that simultaneously get close to zero. To compare the performance of our proposed method with existing methods, we conducted experiments on MNIST and CIFAR-10 data using DNN and CNN. Our proposed method outperformed existing methods in terms of accuracy.

• Journal of KIISE:Software and Applications
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• v.31 no.4
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• pp.516-524
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• 2004

In this paper, the initial value problem in clustering using K-means or Fuzzy-c-means is considered to reduce the number of iterations. Conventionally the initial values in clustering using K-means or Fuzzy-c-means are chosen randomly, which sometimes brings the results that the process of clustering converges to undesired center points. The choice of intial value has been one of the well-known subjects to be solved. The system of clustering using K-means or Fuzzy-c-means is sensitive to the choice of intial values. As an approach to the problem, the uniform partitioning method is employed to extract the optimal initial point for each clustering of data. Experimental results are presented to demonstrate the superiority of the proposed method, which reduces the number of iterations for the central points of clustering groups.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.10
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• pp.1451-1456
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• 2013

This paper propose a method for fast convergence technique in frequency domain independent component analysis (FDICA) using eigenanalysis. It important, such as SONAR system, to eliminate the interference sources through fast algorithm. Through eigenanalysing a two-by-two delayed mixture case, information of delay can be used for initial weighting parameters. Simulations show the improved performances in convergence speed and noise rejection rate. The proposed method can present close weights for optimal convergence, noise can be diminished drastically about 3 times epoch, and get the better resultss with 1~3dB than the conventional method.