• The Journal of the Acoustical Society of Korea
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• 제17권2E호
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• pp.26-30
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• 1998

In this paper, we compose neural network predictive HMM(NNPHMM) to provide the dynamic feature of the speech pattern for the HMM. The NNPHMM is the hybrid network of neura network and the HMM. The NNPHMM trained to predict the future vector, varies each time. It is used instead of the mean vector in the HMM. In the experiment, we compared the recognition abilities of the one hundred Korean syllables according to the variation of hidden layer, state number and prediction orders of the NNPHMM. The hidden layer of NNPHMM increased from 10 dimensions to 30 dimensions, the state number increased from 4 to 6 and the prediction orders increased from 10 dimensions to 30 dimension, the state number increased from 4 to 6 and the prediction orders increased from the second oder to the fourth order. The NNPHMM in the experiment is composed of multi-layer perceptron with one hidden layer and CMHMM. As a result of the experiment, the case of prediction order is the second, the average recognition rate increased 3.5% when the state number is changed from 4 to 5. The case of prediction order is the third, the recognition rate increased 4.0%, and the case of prediction order is fourth, the recognition rate increased 3.2%. But the recognition rate decreased when the state number is changed from 5 to 6.

• Journal of Distribution Science
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• 제13권10호
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• pp.73-82
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• 2015

Purpose - This paper aims to investigate whether information asymmetry could explain capital structures in Korean corporations. According to Myers (1984), firms prefer internal funding to external financing due to the costs associated with information asymmetry. When external financing is necessary, firms prefer to issue debt rather than equity by the same reasoning. Since Shyam-Sunder and Myers (1999), numerous studies continue to debate the validity of the theory. In this paper, we show how the theory depends on assumptions and incorporated variables. We hope our investigation can provide helpful implications regarding capital structure, information asymmetry, and other firm characteristics. Specifically, our empirical results are complementary to the analysis of Son and Lee's (2015), a recent study that examines the pecking order theory prediction for Korean retail firms. Research design, data, and methodology - We test empirical models that are some variants of model used in Shyam-Sunder and Myers (1999). The financial and accounting data are provided by WISEfn for the firms listed on the KOSPI during 1990 to 2013. Bond ratings are supplied by the Korea Investor Service (KIS). We take into account the heterogeneity in debt capacity; a firm's debt capacity is measured by using the method of Lemmon and Zender (2010) based on its bond ratings. Finally, we estimate empirical models suggested by Shyam-Sunder and Myers (1999), Frank and Goyal (2003), and Lemmon and Zender (2010). Results - First, we find that Shyam-Sunder and Myers' (1999) prediction fails to explain total debt changes of Korean firms. Second, we find a non-monotonic relationship between total debt changes and financial deficits with respect to debt capacity. This contradicts the prediction of Lemmon and Zender (2010) that argues the pecking order theory survives with a monotonically increasing relationship. Third, we estimate a negative correlation coefficient between financial deficit and current debt changes. The result is the complete opposite of the prediction of Lemmon and Zender (2010). Finally, we also confirm the non-monotonic relationship between non-current debt changes and financial deficits with respect to debt capacity. Yet, the slope of coefficient is smaller than that of total debt change case. Indeed, the results are, to some extent, consistent with the prediction of pecking order theory, if we exclude the mid-debt capacity firms. Conclusions - Our empirical results complementary to the analysis of Son and Lee (2015), a recent study focusing on capital structure in Korean retail firms; their paper suggests interesting topics regarding capital structure, information asymmetry, and other firm characteristics in Korean corporations. Contrary to Son and Lee (2015), our results show that total debt changes and current debt changes are inconsistent with the prediction of Shyam-Sunder and Myers (1999). However, similar to Son and Lee (2015), non-current debt changes are consistent with the pecking order prediction, in the case of excluding the mid-level debt capacity firms. This contrast allows us to infer that industry characteristics significantly affect the validity of the pecking order prediction. Further studies are needed to analyze the economics behind this phenomenon, which is beyond the scope of our paper. In addition, the estimation bias potentially matters regarding the firm-level debt capacity calculation. We also reserve this topic for future research.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권6호
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• pp.2056-2069
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• 2014

In order to implement interactive and personalized Web services properly, it is necessary to understand the tangible and intangible responses of the users and to recognize their emotional states. Recently, some studies have attempted to build emotional state estimation models based on facial expressions. Most of these studies have applied multiple regression analysis (MRA), artificial neural network (ANN), and support vector regression (SVR) as the prediction algorithm, but the prediction accuracies have been relatively low. In order to improve the prediction performance of the emotion prediction model, we propose a novel SVR model that is optimized using a genetic algorithm (GA). Our proposed algorithm-GASVR-is designed to optimize the kernel parameters and the feature subsets of SVRs in order to predict the levels of two aspects-valence and arousal-of the emotions of the users. In order to validate the usefulness of GASVR, we collected a real-world data set of facial responses and emotional states via a survey. We applied GASVR and other algorithms including MRA, ANN, and conventional SVR to the data set. Finally, we found that GASVR outperformed all of the comparative algorithms in the prediction of the valence and arousal levels.

• Journal of Broadcast Engineering
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• 제16권2호
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• pp.190-206
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• 2011

We propose an adaptive combination scheme of intra and inter prediction modes, where uni-directional intra prediction, bi-directional intra prediction, and inter prediction method are adaptively selected in an EMB (extended macro block). For each EMB, after all inter blocks have been encoded and decoded, the reconstructed blocks are used as reference data for bi-directional intra prediction of other blocks. Whereas conventional intra coding scheme does not use the right and below side pixels of the current block as reference data, the proposed method uses those for bi-directional intra prediction mode. In this paper, we propose three advanced techniques; (a) filter design for bi-directional prediction, (b) adaptive coding order scheme which increases the chance to use the bi-directional intra prediction mode, (c) modification of syntax to represent coding order. The information for the coding order is informed to the decoder by using the modified syntax structure without adding any additional flag. The simulation results show that the proposed scheme reduces the BD-Rate by 0.5%, on average, compared to KTA.

• Journal of Ocean Engineering and Technology
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• 제9권2호
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• pp.112-122
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• 1995

In this work, prediction of fatigue life and fatigue crack growth are studied. 4th order polynominal function is presented to describe the crack growth behaviors from artifical pit of SM45C steel. Crack growth curves obtained from 4th order polyminal growth equations are in good agreement with experimental data The crack growth behaviors at arbitrary stress levels and investigated by the concept of elastic-plastic fracture mechanics using ${\Delta}J$. Fatigue life prediction are carried out by numerical integral method. Prediction lives obtained by proposed method in this study, is in good agreement with the experimental ones. Life prediction results calculated by using of ${\Delta}J$ better than those of ${\Delta}K$.

• Journal of the Korea Safety Management & Science
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• 제26권1호
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• pp.39-44
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• 2024

In order to prevent accidents via defective ammunition, this paper analyzes recent research on ammunition life prediction methodology. This workanalyzes current shelf-life prediction approaches by comparing the pros and cons of physical modeling, accelerated testing, and statistical analysis-based prediction techniques. Physical modeling-based prediction demonstrates its usefulness in understanding the physical properties and interactions of ammunition. Accelerated testing-based prediction is useful in quickly verifying the reliability and safety of ammunition. Additionally, statistical analysis-based prediction is emphasized for its ability to make decisions based on data. This paper aims to contribute to the early detection of defective ammunition by analyzing ammunition life prediction methodology hereby reducing defective ammunition accidents. In order to prepare not only Korean domestic war situation but also the international affairs from Eastern Europe and Mid East countries, it is very important to enhance the stability of organizations using ammunition and reduce costs of potential accidents.

• Computers and Concrete
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• 제29권 6호
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• pp.433-444
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• 2022

Concrete's compressive strength is widely studied in order to understand many qualities and the grade of the concrete mixture. Conventional civil engineering tests involve time and resources consuming laboratory operations which results in the deterioration of concrete samples. Proposing efficient non-destructive models for the prediction of concrete compressive strength will certainly yield advancements in concrete studies. In this study, the efficiency of using radial basis function neural network (RBFNN) which is not common in this field, is studied for the concrete compressive strength prediction. Complementary studies with back propagation neural network (BPNN), which is commonly used in this field, have also been carried out in order to verify the efficiency of RBFNN for compressive strength prediction. A total of 13 input parameters, including novel ones such as cement's and fly ash's compositional information, have been employed in the prediction models with RBFNN and BPNN since all these parameters are known to influence concrete strength. Three different train: test ratios were tested with both models, while different hidden neurons, epochs, and spread values were introduced to determine the optimum parameters for yielding the best prediction results. Prediction results obtained by RBFNN are observed to yield satisfactory high correlation coefficients and satisfactory low mean square error values when compared to the results in the previous studies, indicating the efficiency of the proposed model.

• Journal of the Korean Data and Information Science Society
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• 제10권1호
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• pp.147-154
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• 1999

Using a noninformative prior and an inverted gamma prior, the Bayesian predictive density and the prediction intervals for a future observation or the p - th order statistic of n' future observations from the censord Pareto model have been obtained. In additions, numerical examples are given in order to illustrate the proposed predictive procedure.

• Proceedings of the Korean Geotechical Society Conference
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• 한국지반공학회 2000년도 가을 학술발표회 논문집
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• pp.377-384
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• 2000

The characteristics and prediction model of the shear strength for unsaturated residual soils was studied. In order to investigate the influence of the initial water content on the shear strength, unsaturated triaxial tests were carried out varying the initial water content, and the applicability of existing prediction models for the unsaturated shear strength was testified. It was shown that the soil - water characteristic curve and the shear strength of the unsaturated soil varied with the change of the initial water content. A sample compacted in the lower initial water content needs a higher suction to get the same degree of saturation while the shear strength of a sample with the lower initial water content displays a lower value. In order to apply the existing prediction models of the unsaturated shear strength to granite residual soils, a correction coefficient, α, on the internal friction angle, ø'was added.

• Transactions of Materials Processing
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• 제26권3호
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• pp.162-167
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• 2017

The tensile surface residual stress in the multi-pass drawn wire deteriorates the mechanical properties of the wire. Therefore, the evaluation of the residual stress is very important. Especially, the axial residual stress on the wire surface is the highest. Therefore, the objective of this study was to propose an axial surface residual stress prediction model of the multi-pass drawn steel wire. In order to achieve this objective, an elastoplastic finite element (FE) analysis was carried out to investigate the effect of semi-die angle and reduction ratio of the axial surface residual stress. By using the results of the FE analysis, a surface residual stress prediction model was proposed. In order to verify the effectiveness of the prediction model, the predicted residual stress was compared to that of a wire drawing experiment.