• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.465-468
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• 2005

MMA(Multi-Modulus Algorithm) is inferior at a initial equalization in high ISI(intersymbol interference), because it is the inaccurate decision. To improve this probel SMMA(Sliced Multi-Modulus Algorithm) is based on using the MCMA(Modified Constant Modulus Algorithm). SMMA is a improved capability than MMA in high SNR but is inaccurate decision in low SNR. In this paper, We propose some multi-modulus blind equalization algorithm scheme. It is a method of operation in some multi-modulus algorithm which does no obstruct a convergence property at the initial equalization in the low SNR. Proposed algorithm improves the steady-state performance. And it uses residual ISI of the equalizer output in order to decide the optimum switching time between the single modulus and the multi-modulus algorithm.

• Journal of Korean Society of Transportation
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• v.22 no.5
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• pp.123-137
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• 2004

The study on traffic assignment is actively being performed which reflect networks status using time. Its background is increasing social needs to use traffic assignment models in not only hardware area of road network plan but also software area of traffic management or control. In addition, multi-class traffic assignment model is receiving study in order to fill a gap between theory and practice of traffic assignment model. This model is made up of two, one of which is multi-driver class and the other multi-vehicle class. The latter is the more realistic because it can be combined with dynamic model. On this background, this study is to build multidynamic model combining the above-mentioned two areas. This has been a theoretic pillar of ITS in which dynamic user equilibrium assignment model is now made an issue, therefore more realistic dynamic model is expected to be built by combining it with multi-class model. In case of multi-vehicle, FIFO would be violated which is necessary to build the dynamic assignment model. This means that it is impossible to build multi-vehicle dynamic model with the existing dynamic assignment modelling method built under the conditions of FIFO. This study builds dynamic network model which could relieve the FIFO conditions. At the same time, simulation method, one of the existing network loading method, is modified to be applied to this study. Also, as a solution(algorithm) area, time dependent shortest path algorithm which has been modified from existing shortest path algorithm and the existing MSA modified algorithm are built. The convergence of the algorithm is examined which is built by calculating dynamic user equilibrium solution adopting the model and algorithm and grid network.

• Environmental and Resource Economics Review
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• v.19 no.3
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• pp.571-595
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• 2010

This research applies an panel data stationarity and stochastic convergence test developed by Carrion-i-Silvestre et al. (2005), which has the advantage of considering multiple structural breaks and the presence of cross-section dependence in order to investigate the hypothesis that relative emission $CO_2$ per capita stochastically converge for 11 Asian countries from 1971~2007. We find that relative emission $CO_2$ per capita is stationary after the structural breaks and cross-section dependence are introduced into the model. We also investigate whether Environmental Kuznets Curve (EKC) hypothesis exists in 11 Asian countries. For EKC test, using the panel cointegration tests of Banerjee and Carrion-i-Silvestre (2006) and Westerlund and Edgerton(2007), we find that relative emission $CO_2$ per capita and relative GDP per capita are cointegrated. However EKC hypothesis in 11 Asian countries is not supported.

• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.839-847
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• 2011

In this study, we derive an estimator based on autocovariance for the regression coefficients vector in the multiple linear regression model. This method is suggested by Park (2009), and although this method does not seem to be intuitively attractive, this estimator is unbiased for the regression coefficients vector. When the vectors of exploratory variables satisfy some regularity conditions, under mild conditions which are satisfied when errors are from autoregressive and moving average models, this estimator has asymptotically the same distribution as the least squares estimator and also converges in probability to the regression coefficients vector. Finally we provide a simulation study that the forementioned theoretical results hold for small sample cases.

• Korean Public Administration Review
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• v.48 no.4
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• pp.109-132
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• 2014

본 연구는 조직관리자의 조직 내부 및 외부관리 노력과 관리경력 등 관리자의 개인적 특성이 조직구성원의 직무만족에 미치는 영향에 관한 실증적 분석을 실시한다. 층화군집추출로 표집된 한국의 150개 중학교에 대해 3년간 학교, 교장 및 교사 조사를 통해 얻어진 다중원천 자료에 근거하여 위계적 선형모형(HLM)분석을 실시한 결과, 조직관리자인 교장의 관리경력 등 개인적 특성과 함께 교장의 내부관리 노력과 상부기관 및 학부모 등 주요 이해관계자에 대한 의견수렴 등 외부관리 노력이 모두 교사들의 직무만족에 긍정적 영향을 미치고 있음을 발견하였다. 이러한 결과는 선행연구에서 제시하는 개인 및 조직수준 직무만족 설명변수들인 교사의 인구통계학적 특성, 직무동기, 자기효능감, 학교유형, 조직규모, 소재지역, 학교자원 등을 모두 통제한 상태에서 나타났다는 점에서 최근 공공관리 문헌에서 주목받고 있는 관리가 중요하다(management matters)는 주장의 한국적 맥락에서의 타당성을 지지하고 있다.

• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.863-874
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• 2021

In this paper, we propose a post-selection inference procedure for the fused lasso signal approximator (FLSA). The FLSA finds underlying sparse piecewise constant mean structure by applying total variation (TV) semi-norm as a penalty term. However, it is widely known that this convex relaxation can cause asymptotic inconsistency in change points detection. As a result, there can remain false change points even though we try to find the best subset of change points via a tuning procedure. To remove these false change points, we propose a post-selection inference for the FLSA. The proposed procedure applies a permutation test based on CUSUM statistic. Our post-selection inference procedure is an extension of the permutation test of Antoch and Hušková (2001) which deals with single change point problems, to multiple change points detection problems in combination with the FLSA. Numerical study results show that the proposed procedure is better than naïve z-tests and tests based on the limiting distribution of CUSUM statistics.

• Korean Journal of Social Welfare
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• v.59 no.4
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• pp.5-34
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• 2007

This study explores the effects of philanthropy education for elementary school students by using a mixed method. To examine the effects of philanthropy education, two different approaches in research methods are conducted with different data collected from different target groups on the same phenomenon: a) experimental designs to analyze students' change(prosocial behaviors) by philanthropy education program performed in a summer camp(43 participants) and elementary schools(162 students); and b) qualitative analysis on students' changes in perceptual, attitudinal, and behavioral aspects by students' diary and memorandum(66 participants) and intensive interviews with teachers(5 teachers) and parents(4 mothers). The analysis of both quantitative and qualitative results shows that philanthropy education has effects on students' changes in diverse aspects including prosocial behavior. First, the results of quantitative analysis show that in every component of the prosocial behavior such as helping, being kind, empathizing, sharing, protecting, and cooperating, students have positive changes after philanthropy education. Such changes are statistically significant as well. Second, the results of qualitative analysis show that students after having philanthropy education display positive changes in diverse aspects. Particularly, the quantitative results are converged with the qualitative results from students, parents, and teachers. On the other hand, unique finding from qualitative analysis is that students after having philanthropy education can have fundamental changes in their personality. Such a change is commonly confirmed by students, parents, and teachers. This study makes it possible to compare results or to validate, confirm, or corroborate quantitative results with qualitative findings on the effects of philanthropy education for students.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.3
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• pp.419-424
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• 2015

The Decision-directed (DD) algorithm is known to be not effective to initialize a blind equalizer in the channel conditions when the eye diagram of received signals is completely closed because it can not open the eye diagram enough. In this paper, we propose a new error to replace the error of the conventional DD algorithm. The new DD error is the conventional DD error scaled by the modulus of symbol decision, new DD algorithm using this error is effective to open the closed eye diagram in early stage of equalization unlike the conventional DD. The new DD algorithm appling the new error is showed excellent convergence characteristics as compared to the CMA widely used in blind initialization, particularly, is useful for equalization of signals having multimodulus. The performance of the new DD algorithm is verified through the simulation for the higher-order QAM signals.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.14 no.1
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• pp.151-160
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• 1994

Recently, terrestrial photogrammetry has been applied effectively to data acquisition in GIS and to monitoring precise machinery for simulation test. Because 3-D coordinates of many object points can be quickly measured with constant accuracy and easy modeling by this method. In this study, the composition concerned with multi-camera system which simultaneously analyzes structure from multi-station using various cameras was developed. The errors of results were analyzed to investigate the accuracy of the system, error of unknown points and control points, convergent and strip adjustment for optimal network design also. As results of this study, the efficiency of multi-camera system developed here was proved through application to monitoring the entire area of the precise model ship. We could also acquire 3-dimensional coordinates with good accuracy by arranging pass points. Therefore, possibility of measurement of instantaneous deformation as well as precision analysis of structures can be suggested.

• Journal of Korea Water Resources Association
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• v.33 no.5
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• pp.537-550
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• 2000

In this study, Neural Networks models were used to forecast daily streamflow at Jindong station of the Nakdong River basin. Neural Networks models consist of CASE 1(5-5-1) and CASE 2(5-5-5-1). The criteria which separates two models is the number of hidden layers. Each model has Fletcher-Reeves Conjugate Gradient BackPropagation(FR-CGBP) and Scaled Conjugate Gradient BackPropagation(SCGBP) algorithms, which are better than original BackPropagation(BP) in convergence of global error and training tolerance. The data which are available for model training and validation were composed of wet, average, dry, wet+average, wet+dry, average+dry and wet+average+dry year respectively. During model training, the optimal connection weights and biases were determined using each data set and the daily streamflow was calculated at the same time. Except for wet+dry year, the results of training were good conditions by statistical analysis of forecast errors. And, model validation was carried out using the connection weights and biases which were calculated from model training. The results of validation were satisfactory like those of training. Daily streamflow forecasting using Neural Networks models were compared with those forecasted by Multiple Regression Analysis Mode(MRAM). Neural Networks models were displayed slightly better results than MRAM in this study. Thus, Neural Networks models have much advantage to provide a more sysmatic approach, reduce model parameters, and shorten the time spent in the model development.