• Journal of Information Technology Applications and Management
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• 제31권1호
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• pp.11-25
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• 2024

The purpose of this study is to investigate the improvement of prediction performance and changes in variable importance in SEM-ANN two-stage analysis. 366 cosmetics repurchase-related survey data were analyzed and the results were presented. The results of this study are summarized as follows. First, in SEM-ANN two-stage analysis, SEM and ANN models were trained with train data and predicted with test data, respectively, and the R² was showed. As a result, the prediction performance was doubled from SEM 0.3364 to ANN 0.6836. Looking at this degree of R² improvement as the effect size f² of Cohen (1988), it corresponds to a very large effect at 110%. Second, as a result of comparing changes in normalized variable importance through SEM-ANN two-stage analysis, variables with high importance in SEM were also found to have high importance in ANN, but variables with little or no importance in SEM became important in ANN. This study is meaningful in that it increased the validity of the comparison by using the same learning and evaluation method in the SEM-ANN two-stage analysis. This study is meaningful in that it compared the degree of improvement in prediction performance and the change in variable importance through SEM-ANN two-stage analysis.

• International Journal of Contents
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• 제5권1호
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• pp.37-45
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• 2009

This paper empirically tests pecking order theory. Korean listed firms are used as the samples. On the whole we find supportive results for pecking order theory. The fixed effect model on the whole period shows that as pecking order theory suggests that debt ratio decreases as cash flow. ROA, physical assets, and firm size increase. Again, it is shown that corporate debt ratio significantly decreases as cash flow or ROA increases in every sub-sample, which coincides with the prediction of pecking order theory. Corporate debt ratio significantly decreases as physical assets or jinn size increases in case of the whole sample, pre-financial crisis period, and the sub-samples by q-ratio, which also supports the prediction of pecking order theory. Statistical significance of the coefficients of physical assets or firm size completely disappears after Korean financial crisis. Perhaps it is because the role of physical assets or firm size as a mitigator of information asymmetry significantly weakens after the financial crisis as Korean financial market becomes more transparent. For small firms only size variable is negatively and significantly related with debt to assets. It seems that size is an important factor for smaller firms in making financing decision.

• Journal of Welding and Joining
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• 제28권3호
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• pp.104-113
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• 2010

In this study, to predict the microstructure in weld HAZ of low alloyed steel, prediction model for the phase transformation considering the influence of prior austenite grain size and cooling rate was developed. For this study, six low alloyed steels were designed and the effect of alloying elements was also investigated. In order to develop the prediction model for ferrite transformation, isothermal ferrite transformation behaviors were analyzed by dilatometer system and 'Avrami equation' which was modified to consider the effect of prior austenite grain size. After that, model for ferrite phase transformation during continuous cooling was proposed based on the isothermal ferrite transformation model through applying the 'Additivity rule'. Also, start temperatures of ferrite transformation were predicted by $A_{r3}$ considering the cooling rate. CCT diagram was calculated through this model, these results were in good agreement with the experimental results. After ferrite transformation, bainite transformation was predicted using Esaka model which corresponded most closely to the experimental results among various models. The start temperatures of bainite transformation were determined using K. J. Lee model. Phase fraction of martensite was obtained according to phase fractions of ferrite and bainite.

• 한국정보과학회논문지:시스템및이론
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• 제27권8호
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• pp.719-729
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• 2000

선인출은 프로세서에 의해 사용될 데이타를 예측하여 미리 프로세서 근처에가져오므로써 메모리 지연 시간을 줄이는 기법이다. 선인출의 효율성은 미래에 사용될 데이타를 얼마나 정확하게 예측하는가(선인출 예측 정확도)에 따라 결정된다. 기존의 명령어 선인출에 관한 연구들은 특정 선인출 기법의 제안 및 성능 평가에 그치고 있어서 명령어 선인출의 특성이 체계적으로 분석 정리되지 못하고 있다. 이에 본 논문에서는 명령어 선인출의 예측 정확도에 대해서 이론적으로 분석하여 이의 한계를 알아보고자 한다. 그 방안으로 명령어 선인출 상한 모델이라는 이론적인 선인출 모델을 제안하고 이 모델을 기반으로 명령어 선인출에 대해 체계화된 분석을 한다. 특히 이러한 연구 결과로써 궁극적으로 시스템 성능을 효 과적으로 향상시킬 수 있는 효율적인 명령어 선인출을 가능하게 하는 데 그 목적이 있으므로 주로 명령어 선인출 효율성 측면에서 분석을 시도하였다. 이러한 선인출 모델을 이용하여 본 논문에서는 SPEC 벤치 마크 프로그램들의 명령어 선인출 예측 정확도의 한계를 이론적으로 분석하였다. 그 결과로 캐쉬가 없는 경우에는 선인출 정확도가 매우 높게 나타남을 보였다. 반면에 캐쉬가 있을 경우에는 캐쉬 크기가 커짐에 따라 선인출의 정확도가 급격히 떨어짐을 관찰하였다. 예를 들어 spice의 경우 플록크기가 16바이트이고 직접사상 캐쉬에서 캐쉬 크기가 2K 바이트와 16K 바이트일 때 이론적으로 가능한 최대 선인출 정확도가 각각 53%，39%로 크게 떨어지는 것을 관찰하였다. 캐쉬의 크기가 커질수록 선인출로 메모리 지연 시간을 줄일 수 있는 명령어 참조의 많은 부분을 캐쉬가 처리하게 되고 또한 캐쉬에서 접근 실패된 명령어 참조는 그 참조 행태가 불규칙하여 예측이 어렵기 때문에 일정 크기 이상의 명령어 캐쉬를 사용하는 경우 명령어 선인출을 사용하는 것은 전체 시스템 성능의 향상에 큰 도움이 되지 않음을 이론적으로 규명하였다.

• Journal of Information Processing Systems
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• 제17권2호
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• pp.213-226
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• 2021

Given that most of the link prediction algorithms for signed social networks can only complete sign prediction, a novel algorithm is proposed aiming to achieve both link prediction and sign prediction in signed networks. Based on the structural balance theory, the local link tightness and global link tightness are defined respectively by using the structural information of paths with the step size of 2 and 3 between the two nodes. Then the total similarity of the node pair can be obtained by combining them. Its absolute value measures the possibility of the two nodes to establish a link, and its sign is the sign prediction result of the predicted link. The effectiveness and correctness of the proposed algorithm are verified on six typical datasets. Comparison and analysis are also carried out with the classical prediction algorithms in signed networks such as CN-Predict, ICN-Predict, and PSNBS (prediction in signed networks based on balance and similarity) using the evaluation indexes like area under the curve (AUC), Precision, improved AUC', improved Accuracy', and so on. Results show that the proposed algorithm achieves good performance in both link prediction and sign prediction, and its accuracy is higher than other algorithms. Moreover, it can achieve a good balance between prediction accuracy and computational complexity.

• 한국멀티미디어학회논문지
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• 제11권7호
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• pp.956-965
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• 2008

H.264는 가장 최근에 제정된 동영상 압축 표준으로 다양한 기법 등을 도입하여 기존의 표준들에 비해 동일한 화질을 유지하면서도 높은 압축 효율을 보여준다. 하지만 이러한 기법들은 처리과정이 복잡해, 계산 과정을 간소화시킨 효율적인 기법들이 요구된다. 따라서 본 논문에서는 새롭게 도입된 기법 중에서 복잡한 처리가 요구되는 인트라 예측의 효율적인 처리를 위한 2단계의 빠른 인트라 예측 방법을 제안한다. 1단계에서는 매크로블록 내 작은 블록들($4{\times}4,\;8{\times}8,\;12{\times}12$ 크기)의 경계 부분의 화소 변화량을 조사하고, 이를 통해서 매크로블록의 평탄 여부를 판단하여 인트라 예측을 위한 블록 크기를 빠르게 선택한다. 2단계에서는 매크로 블록 내부의 대표성을 띄는 화소들을 이용하여 1단계에서 선택된 블록 크기의 여러 모드 중에서 최종 모드를 빠르게 결정한다. 제안한 인트라 예측 기법의 성능측정을 위해 다양한 테스트 동영상으로 화질, 비트율 및 처리시간을 확인한 결과, 관련기법 및 표준과 비교해서 동일한 화질과 비트율을 유지하면서 표준과 비교하여 41.5%, 관련기법과 비교하여 24.7%의 인트라 예측 처리 시간을 감소시켰다.

• 응용통계연구
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• 제34권5호
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• pp.697-710
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• 2021

상대오차를 이용한 예측법은 상대오차(혹은 퍼센트오차)가 중요시되는 분야, 특히 계량경제학이나 소프트웨어 엔지니어링, 또는 정부기관 공식통계 부분에서 기존 예측방법 외에 선호되는 예측방법이다. 그 동안 상대오차를 이용한 예측법은 선형 혹은 비선형 회귀분석 뿐 아니라, 커널회귀를 이용한 비모수 회귀모형, 그리고 정상시계열분석에 이르기까지 그 범위가 확장되어 왔다. 그러나, 지금까지의 분석은 고정효과(fixed effect)만을 고려한 것이어서 임의효과(random effect)에 관한 상대오차 예측법에 대한 확장이 필요하였다. 본 논문의 목적은 상대오차예측법을 일반화선형혼합모형(GLMM)에 속한 감마회귀(gamma regression), 로그정규회귀(lognormal regression), 그리고 역가우스회귀(inverse gaussian regression)의 패널자료(panel data)에 적용시키는데 있다. 이를 위해 실제 자동차 보험회사의 손해액 자료를 사용하였고, 최량예측량과 최량상대오차예측량을 각각 적용-비교해 보았다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제8권1호
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• pp.31-36
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• 2008

This paper presents adaptive learning data of evolvable neural networks (ENNs) for time series prediction of nonlinear dynamic systems. ENNs are a special class of neural networks that adopt the concept of biological evolution as a mechanism of adaptation or learning. ENNs can adapt to an environment as well as changes in the enviromuent. ENNs used in this paper are L-system and DNA coding based ENNs. The ENNs adopt the evolution of simultaneous network architecture and weights using indirect encoding. In general just previous data are used for training the predictor that predicts future data. However the characteristics of data and appropriate size of learning data are usually unknown. Therefore we propose adaptive change of learning data size to predict the future data effectively. In order to verify the effectiveness of our scheme, we apply it to chaotic time series predictions of Mackey-Glass data.

• 대한산업공학회지
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• 제28권2호
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• pp.171-177
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• 2002

In this paper, we propose a new data fusion method to improve the performance of individual prediction models for time series data. Individual models used are ARIMA and neural network and their results are combined based on the weight reflecting the inverse of EWMA of squared prediction error of each individual model. Monte Carlo simulation is used to identify the situation where the proposed approach can take a vintage point over typical fusion methods which utilize MSE for weight. Study results indicate the following: EWMA performs better than MSE fusion when the data size is large with a relatively big amplitude, which is often observed in intra-cranial pressure data. Additionally, EWMA turns out to be a best choice among MSE fusion and the two individual prediction models when the data size is large with relatively small random noises, often appearing in tax revenue data.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.920-924
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• 2005

This paper presents adaptive learning data of evolvable neural networks (ENNs) for time series prediction of nonlinear dynamic systems. ENNs are a special class of neural networks that adopt the concept of biological evolution as a mechanism of adaptation or learning. ENNs can adapt to an environment as well as changes in the environment. ENNs used in this paper are L-system and DNA coding based ENNs. The ENNs adopt the evolution of simultaneous network architecture and weights using indirect encoding. In general just previous data are used for training the predictor that predicts future data. However the characteristics of data and appropriate size of learning data are usually unknown. Therefore we propose adaptive change of learning data size to predict the future data effectively. In order to verify the effectiveness of our scheme, we apply it to chaotic time series predictions of Mackey-Glass data.