• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.9B
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• pp.1775-1784
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• 1999

This paper proposes a method of removing unnecessary hidden nodes by a new cost function that evaluates the variance and the mean of hidden node outputs during training. The proposed cost function makes necessary hidden nodes be activated and unnecessary hidden nodes be constants. We can remove the constant hidden nodes without performance degradation. Using the CEDAR handwritten digit recognition, we have shown that the proposed method can remove the number of hidden nodes up to 37.2%, with higher recognition rate and shorter learning time.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.630-644
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• 2021

Educational data analysis is attracting increasing attention with the rise of the big data industry. The amounts and types of learning data available are increasing steadily, and the information technology required to analyze these data continues to develop. The early identification of potential dropout students is very important; education is important in terms of social movement and social achievement. Here, we analyze educational data and generate predictive models for student dropout using logistic regression, a decision tree, a naïve Bayes method, and a multilayer perceptron. The multilayer perceptron model using independent variables selected via the variance analysis showed better performance than the other models. In addition, we experimentally found that not only grades but also extracurricular activities were important in terms of preventing student dropout.

• Advances in Computational Design
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• v.8 no.1
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• pp.37-59
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• 2023

Squat reinforced concrete (SRC) shear walls are a critical part of the structure for both office/residential buildings and nuclear structures due to their significant role in withstanding seismic loads. Despite this, empirical formulae in current design standards and published studies demonstrate a considerable disparity in predicting SRC wall shear strength. The goal of this research is to develop and evaluate hybrid and ensemble artificial neural network (ANN) models. State-of-the-art population-based algorithms are used in this research for hybrid intelligence algorithms. Six models are developed, including Honey Badger Algorithm (HBA) with ANN (HBA-ANN), Hunger Games Search with ANN (HGS-ANN), fitness-distance balance coyote optimization algorithm (FDB-COA) with ANN (FDB-COA-ANN), Averaging Ensemble (AE) neural network, Snapshot Ensemble (SE) neural network, and Stacked Generalization (SG) ensemble neural network. A total of 434 test results of SRC walls is utilized to train and assess the models. The results reveal that the SG model not only minimizes prediction variance but also produces predictions (with R²= 0.99) that are superior to other models.

• Journal for History of Mathematics
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• v.35 no.1
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• pp.1-18
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• 2022

This paper presents a statistical machine learning method that generates the implied volatility surface under the rareness of the market data. We apply the practitioner's Black-Scholes model and Gaussian process regression method to construct a Bayesian inference system with observed volatilities as a prior information and estimate the posterior distribution of the unobserved volatilities. The variance instead of the volatility is the target of the estimation, and the radial basis function is applied to the mean and kernel function of the Gaussian process regression. We present two types of Gaussian process regression methods and empirically analyze them.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.11-14
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• 2022

본 논문에서는 회귀 문제에서 예측값들의 분산을 줄이기 위한 딥뉴럴 네트워크 구조를 제안한다. 일반적인 회귀 문제에서 딥뉴럴 네트워크 학습 시, 하나의 입력에 대한 레이블 값을 이용하여 학습한다. 본 눈문에서는 하나의 입력에 대한 레이블 값뿐만 아니라 두 입력에 대한 레이블 값들의 차이를 학습시키는 딥뉴럴 네트워크 구조를 제안한다. 통계학 이론을 통하여 예측값들의 분산이 줄어든다는 것을 증명한다. 또한, 배관 곡관의 감육두께를 예측하는 문제를 통해 제안된 네트워크의 성능을 검증한다. 일반적인 딥뉴럴 네트워크 구조를 이용하였을 때에 비하여 제안한 네트워크 구조를 이용하였을 때, 회귀 문제의 예측값들의 분산이 감소함을 확인한다.

• Annual Conference of KIPS
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• 2022.11a
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• pp.594-596
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• 2022

Over the last decades, deep neural networks have demonstrated significant success in various tasks. To address the special vision task, choosing a hot network as backbone to extract feature is a common way in both research and industry project. However, the choice of backbone usually requires the expert experience and affects the performance of the classification task. In this work, we propose a novel idea to support backbone decision-making by exploring the feature attribution and weights distribution of hidden layers from various backbones. We first analyze the visualization of feature maps on different size object and different depth layers to observe learning ability. Then, we compared the variance of weights and feature in last three layers. Based on analysis of the feature and wights, we summarize the traits and commonalities of existing networks.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.38-45
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• 2011

Studies have shown that infrastructure projects have continued to experience significant delays and cost overrun over an extended period of time and no evidence of learning ever have happened [1] [2]. Various causes contribute to the bias towards overrun [3]. This study contributes to literature by developing and subsequently validating a set of hypothesized relationships between project complexity and project performance. The results show that project complexity is associated with both the magnitude and variance of overrun. Further, the extent and magnitude of the positive bias towards overrun are moderated by project complexity.

• Journal of Korean Society of Disaster and Security
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• v.15 no.2
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• pp.45-56
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• 2022

The countermeasure for the shortage of water during dry season and drought period has not been considered with return flowrate in detail. In this study, the outflow of STP was predicted through a data-based machine learning model, LSTM. As the first step, outflow, inflow, precipitation and water elevation were utilized as input data, and the distribution of variance was additionally considered to improve the accuracy of the prediction. When considering the variability of the outflow data, the residual between the observed value and the distribution was assumed to be in the form of a complex trigonometric function and presented in the form of the optimal distribution of the outflow along with the theoretical probability distribution. It was apparently found that the degree of error was reduced when compared to the case not considering where the variance distribution. Therefore, it is expected that the outflow prediction model constructed in this study can be used as basic data for establishing an efficient river management system as more accurate prediction is possible.

• Journal of dental hygiene science
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• v.23 no.4
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• pp.330-342
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• 2023

Background: As the medical knowledge base grows at an accelerating rate, evidence-based clinical performance becomes increasingly important for providing quality care. Previous studies have highlighted the need to promote job crafting to actualize evidence-based practical skills in the medical field. This study aimed to investigate the degree of evidence-based practice among dental hygienists and assess the impact of job crafting on the evidence-based practical skills of dental hygienists. Methods: Dental hygienists working at dental hospitals and clinics in Seoul and Gyeonggi Province were surveyed between February 28 and April 6, 2023. The sample was comprised of 267 participants. The hypotheses were tested independent t-tests, one-way analysis of variance, Pearson's correlation coefficients, and multiple regression analyses using SPSS 29.0. Results: The degree of job crafting by dental hygienists demonstrated significant differences based on educational attainment, workplace size, and workplace type. Evidence-based practical skills exhibited significant variations based on educational attainment and job position. All job crafting subfactors demonstrated positive correlations with evidence-based practical skills. The job crafting subfactors affecting the evidence-based practical skills of dental hygienists were 'increasing structural job resources' and 'increasing challenging job demands,' which together explained 38.7% of the variance in evidence-based practical skills. Conclusion: This study demonstrates that job crafting was positively and significantly correlated with evidence-based practical skills. To strengthen the job crafting ability of dental hygienists, improving environmental conditions and fostering an organizational culture that motivates continued participation in education is necessary. The development and promotion of programs that enable learning of the latest evidence should be actively pursued. Additionally, regular attendance at workshops and participation in organizational evidence-based practice education programs are necessary.

• Journal of the Korea Convergence Society
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• v.11 no.1
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• pp.349-357
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• 2020

This study is a descriptive study designed to identify the relationships among positive psychological capital, physical health status, depression, interpersonal relationship and learning flow. The subjects were 181 nursing students and the data collection was from May 8 to June 20, 2019. Data analysis methods were descriptive statistics, t-tests, ANOVA, Pearson's correlation coefficients, and stepwise multiple regression, using the SPSS 22.0 program. Positive psychological capital showed statistical differences according to age, grade, motive for major choice, major satisfaction and subjective health status. Positive psychological capital was correlated with depression(r=-.454, p<.001), interpersonal relationship(r=.611, p<.001) and learning flow(r=.452, p<.001). The factors affecting learning flow were positive psychological capital(β=.414, p<.001), major satisfaction(β=.177, p=.014), and grade(β=-.150, p=.026), which explained 24.4% of the variance. Therefore, it is necessary to develop and apply educational programs that can promote positive psychological capital in nursing students.