• The Journal of the Acoustical Society of Korea
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• v.18 no.3
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• pp.62-67
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• 1999

In this paper, we investigates a reliable model of the Predictive Recurrent Neural Network for the speech recognition. Predictive Neural Networks are modeled by syllable units. For the given input syllable, then a model which gives the minimum prediction error is taken as the recognition result. The Predictive Neural Network which has the structure of recurrent network was composed to give the dynamic feature of the speech pattern into the network. We have compared with the recognition ability of the Recurrent Network proposed by Elman and Jordan. ETRI's SAMDORI has been used for the speech DB. In order to find a reliable model of neural networks, the changes of two recognition rates were compared one another in conditions of: (1) changing prediction order and the number of hidden units: and (2) accumulating previous values with self-loop coefficient in its context. The result shows that the optimum prediction order, the number of hidden units, and self-loop coefficient have differently responded according to the structure of neural network used. However, in general, the Jordan's recurrent network shows relatively higher recognition rate than Elman's. The effects of recognition rate on the self-loop coefficient were variable according to the structures of neural network and their values.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.13 no.4
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• pp.405-415
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• 2000

Time series of wind-induced pressure on a structure are modeled using autoregressive moving average (ARMA) model. In an AR process, the current value of the time series is expressed in terms of a finite, linear combination of the previous values and a white noise. In a MA process, the value of the time series is linearly dependent on a finite number of the previous white noises. The ARMA process is a combination of the AR and MA processes. In this paper, the ARMA models with several different combinations of the AR and MA orders are fitted to the wind-induced pressure time series, and the procedure to select the most appropriate ARMA model to represent the data is described. The maximum likelihood method is used to estimate the model parameters, and the AICC model selection criterion is employed in the optimization of the model order, which is assumed to be a measure of the temporal complexity of the pressure time series. The goodness of fit of the model is examined using the LBP test. It is shown that AR processes adequately fit wind pressure time series.

• Journal of the Korean Geographical Society
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• v.39 no.2
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• pp.212-228
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• 2004

This paper is concerned with an important aspect of regional income convergence, ${\beta}$-convergence, which refers to the negative relationship between initial income levels and income growth rates of regions over a period of time. The common research framework on ${\beta}$-convergence which is based on OLS regression models has two drawbacks. First, it ignores spatially autocorrelated residuals. Second, it does not provide any way of exploring spatial heterogeneity across regions in terms of ${\beta}$-convergence. Given that empirical studies on ${\beta}$-convergence need to be edified by spatial data analysis, this paper aims to: (1) provide a critical review of empirical studies on ${\beta}$-convergence from a spatial perspective; (2) investigate spatio-temporal income dynamics across the U.S. labor market areas for the last 30 years (1969-1999) by fitting spatial regression models and applying bivariate ESDA techniques. The major findings are as follows. First, the hypothesis of ${\beta}$-convergence was only partially evidenced, and the trend substantively varied across sub-periods. Second, a SAR model indicated that ${\beta}$-coefficient for the entire period was not significant at the 99％ confidence level, which may lead to a conclusion that there is no statistical evidence of regional income convergence in the US over the last three decades. Third, the results from bivariate ESDA techniques and a GWR model report that there was a substantive level of spatial heterogeneity in the catch-up process, and suggested possible spatial regimes. It was also observed that the sub-periods showed a substantial level of spatio-temporal heterogeneity in ${\beta}$-convergence: the catch-up scenario in a spatial sense was least pronounced during the 1980s.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.370-372
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• 1986

In this paper we propose a Generalized Minimum Variance Self-tuning Control of the system with an autoregressive noise model. To establish a Generalized Minimum Variance Control, the control input is also included in a cost function and a novel identity is introduced. The effectiveness of this algorithm is demonstrated by the computer simulation.

• Journal of Digital Convergence
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• v.16 no.10
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• pp.89-99
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• 2018

The purpose of this study is to analyse the determinants of the change in the welfare expenditure of local governments in 2015. This study analyzed the spatial correlation of welfare expenditure among neighboring local governments and determined the factors affecting the welfare expenditures. According to the results of the study, spatial correlation of welfare expenditure among local governments appears. Determinants, such as socio-economic factors, administrative factors, public financial factors are affecting the amount of the welfare expenditures, but local political factors, and local tax, last year's budgets are not correlated with the amount of local welfare expenditures. In this study, it is significant to found out that the spatial correlation of welfare expenditure among the local governments and to examine the determinants. If possible, it is necessary to analyze the time-series analysis using the multi-year welfare expenditure data, expecially self-welfare expenditures.

• Journal of the Korean Society of Child Welfare
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• no.32
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• pp.7-32
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• 2010

The purpose of this research is to identify the longitudinal reciprocal relationship between family social capital and self-esteem of adolescents. Five waves of panel data from the Korea Youth Panel Survey were employed for this research. Korean Adolescents Policy Research Institute collected the first wave data in 2004 from elementary school children in 4th grade. Autoregressive cross-lagged modeling was performed to analysis the longitudinal reciprocal relationship between family social capital and self-esteem. The major findings were as follows. First, stability coefficient of family social capital and self-esteem showed that both variables were significantly stable over time. Secondly family social capital(t) had statistically significant effect on self-esteem(t+1), whereas self-esteem did not predict family social capital at a statistically significant level. These findings suggest that family social capital and self-esteem is stable overtime, and that obtaining family social capital and establishing positive self-esteem is important during this period in childhood. In addition, the results show that family social capital affects self-esteem, which highlights the importance of family social capital accumulation on the development of adolescent self-esteem.

• Korean Journal of Social Welfare
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• v.62 no.2
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• pp.161-185
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• 2010

The purpose of this study is to verify the causal relationship between depression and aggressiveness among adolescents. The 4-year longitudinal data collected from 2,670 4th grade elementary school students by the Korean Youth Panel study was used in this study. From the analysis result using the Autoregressive Cross-Lagged Model, the depression and aggressiveness in adolescents were continued from elementary school 4th grade to middle school 7th grade in significant stability. In addition, the previous aggressiveness turned out to have significant positive effect on the later period depression. Similarly, the previous depression had significant effect on the later aggressiveness, but the direction was negative. This means that the adolescents's depression increases as their aggressiveness increases, but as the depression increases, the later aggressiveness of the adolescents decreases. There were no differences between girls and boys within the relationship of these two variables. Finally, the implication derived from the results, the limitation of this study, and suggestion for following studies were presented.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.245-251
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• 2002

본 논문에서는 상대적으로 큰 퍼지 엔트로피를 갖는 입력-출력 데이터 집단에 다중 회귀 분석을 적용하여 다차원 평면 클러스터를 생성하고, 이 클러스터를 새로운 퍼지 모델의 규칙으로 추가한 후 퍼지 모델 파라미터의 개략 동조와 정밀 동조를 수행하는 자기구성 퍼지 모델링을 제안한다. Weighted recursive least squared 알고리즘과 fuzzy C-regression model 클러스터링에 의해 퍼지 모델의 파라미터를 개략적으로 동조한 후 gradient descent 알고리즘에 의해 파라미터를 정밀 동조하면서 감수분열 유전 알고리즘을 이용하여 최적의 학습률을 탐색한다. 그리고 자기 구성 퍼지 모델링 기법을 이용하여 Box-Jenkins의 가스로 데이터, 다변수비선형 정적 함수의 데이터와 하수 처리 활성오니 공정의 모델링을 수행하고, 기존의 방법에 의한 모델링 결과와 비교하여 그 성능을 입증한다.

• Korean Journal of Social Welfare Studies
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• v.47 no.1
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• pp.151-178
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• 2016

The primary goal of the current study was to investigate the longitudinal relationship between household income, main caregiver depression, and youth aggression from the interactionist perspective. The data were derived by combining the 2006, 2009 and 2012 survey waves from the Korean Welfare Panel Study. This data set covered the full span of adolescence from elementary to high school. The study utilized 561 families as the final sample and conducted autoregressive cross-lagged analysis. As a result, the early income status, main caregiver depression and youth aggression were likely maintained over time. Second, the results provided support for a reciprocal relationship between income and main caregiver depression. On the other hand, the reciprocal relationship between main caregiver depression and youth aggression was not found in the current study. Finally, the mediating effect of main caregiver depression between income and youth aggression was not found in the present study. In conclusion, the results of this study support the interactionist model in that the association between family income and main caregiver depression involves reciprocity and mutual influence across time. These findings have major implications for policy and interventions in regards to low-income families.

• The Journal of the Acoustical Society of Korea
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• v.31 no.8
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• pp.580-589
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• 2012

In this paper, we studied on Doppler gap-filling algorithms suitable for a portable or low-cost medical ultrasound imaging system, and as a result, found out algorithms based on mirroring or autoregressive model. Moreover, controlling the computational demand in the proper range, we improved the performances of these algorithms by solving their problems. Effectiveness of these modified algorithms is verified by computer simulations and experiments which used artificially generated Doppler signals and Doppler data acquired from human body through an actual ultrasound system.