• Education of Primary School Mathematics
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• v.18 no.1
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• pp.31-44
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• 2015

This study aims to test effect of transfer learning program rather than students' transfer ability. For these purpose, firstly this study design transfer learning program to apply from 'rate concept' in learning math class to 'velocity concept' in science class. Subsequently, this study is to analyze whether this program affect on 'the rate concept understanding' and 'the mathematics learning attitude'. Followings are the findings from this study. First, transfer learning program affect on improving students' rate concept understanding. Moreover, 17 among 35 students' who stay in 'ratio level' move to 'internalized ratio level'. Second, besides transfer learning program is not only cause to change students' learning attitude, this program impact on changing their learning attitude positively. The study has an important implications in that it designed new learning program that students experience transfer and test its effect.

• Journal of Korea Multimedia Society
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• v.23 no.12
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• pp.1586-1593
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• 2020

This study was conducted on the face ratio of mammals. It can also be applied to character automation by checking factors about the difference between animal and human face shapes. This paper used the face and face area data generated for Deep Learning learning. In detail, the proportion factors of the area comprising the faces of 20 species of animals and humans were defined and the average ratio was calculated. Next, the proportion of each animal was analyzed using the Principal Component Analysis (PCA). Through this, we would like to propose the golden ratio of mammals.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.909-914
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• 2024

This study suggests a quantitative evaluation of transfer learning, which is widely used in various AI fields, including image recognition for robot vision. Quantitative and qualitative analyses of results applying transfer learning are presented, but transfer learning itself is not discussed. Therefore, this study proposes a quantitative evaluation of transfer learning itself based on MNIST, a handwritten digit database. For the reference network, the change in recognition accuracy according to the depth of the transfer learning frozen layer and the ratio of transfer learning data and pre-training data is tracked. It is observed that when freezing up to the first layer and the ratio of transfer learning data is more than 3%, the recognition accuracy of more than 90% can be stably maintained. The transfer learning quantitative evaluation method of this study can be used to implement transfer learning optimized according to the network structure and type of data in the future, and will expand the scope of the use of robot vision and image analysis AI in various environments.

• The Journal of the Korea Contents Association
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• v.11 no.4
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• pp.42-50
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• 2011

Using EEG, human physiological signal, as part of research which investigates the state of student learning and provides appropriate feedback to maximize learning efficiency, the relationship of learning attitude and analysis of EEG for female college student is presented. We study the reaction of learner's EEG using the concentration level extracted from the EEG power spectrum when students learn at various learning attitude. The experiment was conducted for the concentrating on learning and, as a control group, erratic attitude and closed eyes state. The attitude of concentrated Learning shows high concentration index and low relaxation index, where as the erratic attitude, such as eye movement and clicking, shows high level of attention index and noisy wave ratio. Especially, the state of closed eyes shows the ratio of alpha and theta wave under 1. This is distinct with open eyes cases.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.1277-1280
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• 1998

We propose a structural learning method of MLP classifiers for a given application using PfSGA (parameter-free species genetic algorithm), which is a combining of species genetic algorithm(SGA) and parameter-free genetic algorithm(PfGA). experimental results show that PfSGA can reduce the learing time of SGA and has no influence of parameter values on structural learning. And we also convince that PfSGA is more efficient than the other methods in the aspect of misclassification ratio, learning rate, and complexity of MLP structure.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.15 no.26
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• pp.77-84
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• 1992

This paper constructs Croup Technology process-based learning curve model adjusted to a Group Technology environment which accounts for shared learning that occurs when multiple products utilize some of the same process steps. Through this constructed model, the estimated times and productivity of labor calculated by the Group Technology process-based learning curve model are compared with those generated by employing product-based 1 earning curve model. For sensitivity analysis of the model, the impact of learning rate and the ordered production quantity on the ratio differences between Group Technology process-based learning curve model and product-based learning curve model are examined. These results indicate the critical importance of employing Group Technology process-based learning curve model when a process spans multiple products.

• The Journal of the Acoustical Society of Korea
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• v.40 no.2
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• pp.121-129
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• 2021

This paper deals with a super-resolution that improves the resolution of side scan sonar images using learning-based compressive sensing. Learning-based compressive sensing combined with deep learning and compressive sensing takes a structure of a feed-forward network and parameters are set automatically through learning. In particular, we propose a method that can effectively extract additional information required in the super-resolution process through various initialization methods. Representative experimental results show that the proposed method provides improved performance in terms of Peak Signal-to-Noise Ratio (PSNR) and Structure Similarity Index Measure (SSIM) than conventional methods.

• Geomechanics and Engineering
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• v.37 no.6
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• pp.539-554
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• 2024

This study explores development of prediction model for seismic site classification through the integration of machine learning techniques with horizontal-to-vertical spectral ratio (HVSR) methodologies. To improve model accuracy, the research employs outlier detection methods and, synthetic minority over-sampling technique (SMOTE) for data balance, and evaluates using seven machine learning models using seismic data from KiK-net. Notably, light gradient boosting method (LGBM), gradient boosting, and decision tree models exhibit improved performance when coupled with SMOTE, while Multiple linear regression (MLR) and Support vector machine (SVM) models show reduced efficacy. Outlier detection techniques significantly enhance accuracy, particularly for LGBM, gradient boosting, and voting boosting. The ensemble of LGBM with the isolation forest and SMOTE achieves the highest accuracy of 0.91, with LGBM and local outlier factor yielding the highest F1-score of 0.79. Consistently outperforming other models, LGBM proves most efficient for seismic site classification when supported by appropriate preprocessing procedures. These findings show the significance of outlier detection and data balancing for precise seismic soil classification prediction, offering insights and highlighting the potential of machine learning in optimizing site classification accuracy.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.2
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• pp.417-426
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• 2024

In general, the implementation of machine learning requires prior knowledge and experience with deep learning models, and substantial computational resources and time are necessary for data processing. As a result, machine learning encounters several limitations when deployed on embedded processors. To address these challenges, this paper introduces a novel approach where a genetic algorithm is applied to the convolution operation within the machine learning process, specifically for performing a selective convolution operation.In the selective convolution operation, the convolution is executed exclusively on pixels identified by a genetic algorithm. This method selects and computes pixels based on a ratio determined by the genetic algorithm, effectively reducing the computational workload by the specified ratio. The paper thoroughly explores the integration of genetic algorithms into machine learning computations, monitoring the fitness of each generation to ascertain if it reaches the target value. This approach is then compared with the computational requirements of existing methods.The learning process involves iteratively training generations to ensure that the fitness adequately converges.

• Journal of the Korea Convergence Society
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• v.11 no.11
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• pp.257-267
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• 2020

In this study, we predicted the employment ratio by the export industry using various machine learning methods and verified whether the prediction performance is improved by applying the constrained forecast combination method to these predicted values. In particular, the constrained forecast combination method is known to improve the prediction accuracy and stability by imposing the sum of predicted values' weights up to one. In addition, this study considered various variables affecting the employment ratio of each industry, and so we adopted recursive feature elimination method that allows efficient use of machine learning methods. As a result, the constrained forecast combination showed more accurate prediction performance than the predicted values of the machine learning methods, and in particular, the stability of the prediction performance of the constrained forecast combination was higher than that of other machine learning methods.