• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.33-34
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• 2018

기계학습이란 인공지능의 한 분야이다. 컴퓨터에 명시적인 프로그램 없이 배울 수 있는 능력을 부여하는 연구 분야이며, 사람이 학습하듯이 컴퓨터에도 데이터들을 줘서 학습하게 함으로써 새로운 지식을 얻어내게 하는 분야이다. 기계학습 종류에는 크게 Supervised Learning, Unsupervised Learning, Reinforcement Learning이 있다. 본 논문에서는 기계학습 종류 및 컴퓨터가 데이터들을 학습하면서 생기는 문제점을 알아보고, 문제점의 종류 및 해결방안을 제시한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.180-184
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• 1997

This paper presents the fuzzy neural network which utilizes a fuzzified Kohonen learning uses a fuzzy membership value, a function of the iteration, and a intra-membership value instead of a learning rate. The IRIS data set if used to test the fuzzy neural network. The test result shows the performance of the fuzzy neural network depends on k and the vigilance parameter T.

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.171-174
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• 2003

In this paper, a Learning module for a reconfigurable neural network processor(ERNIE) was proposed for an On-chip learning. The existing reconfigurable neural network processor(ERNIE) has a much better performance than the software program but it doesn't support On-chip learning function. A learning module which is based on Back Propagation algorithm was designed for a help of this weak point. A pipeline structure let the learning module be able to update the weights rapidly and continuously. It was tested with five types of alphabet font to evaluate learning module. It compared with C programed neural network model on PC in calculation speed and correctness of recognition. As a result of this experiment, it can be found that the neural network processor(ERNIE) with learning module decrease the neural network training time efficiently at the same recognition rate compared with software computing based neural network model. This On-chip learning module showed that the reconfigurable neural network processor(ERNIE) could be a evolvable neural network processor which can fine the optimal configuration of network by itself.

• Korean Journal of Childcare and Education
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• v.20 no.2
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• pp.127-143
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• 2024

Objective: The purpose of this study is to develop a machine learning model to predict the subsequent childbirth intention of married women with one child, aiming to address the low birth rate problem in Korea, This will be achieved by utilizing data from the 2021 Family and Childbirth Survey conducted by the Korea Institute for Health and Social Affairs. Methods: A prediction model was developed using the Random Forest algorithm to predict the subsequent childbirth intention of married women with one child. This algorithm was chosen for its advantages in prediction and generalization, and its performance was evaluated. Results: The significance of variables influencing the Random Forest prediction model was confirmed. With the exception of the presence or absence of leave before and after childbirth, most variables contributed to predicting the intention to have subsequent childbirth. Notably, variables such as the mother's age, number of children planned at the time of marriage, average monthly household income, spouse's share of childcare burden, mother's weekday housework hours, and presence or absence of spouse's maternity leave emerged as relatively important predictors of subsequent childbirth intention.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.1
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• pp.1-8
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• 2020

A derivation method for a quantized gradient for machine learning on an embedded system is proposed, in this paper. The proposed differentiation method induces the quantized gradient vector to an objective function and provides that the validation of the directional derivation. Moreover, mathematical analysis shows that the sequence yielded by the learning equation based on the proposed quantization converges to the optimal point of the quantized objective function when the quantized parameter is sufficiently large. The simulation result shows that the optimization solver based on the proposed quantized method represents sufficient performance in comparison to the conventional method based on the floating-point system.

• The Transactions of the Korea Information Processing Society
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• v.4 no.7
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• pp.1759-1769
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• 1997

The Error BackPropagation (EBP) algorithm for multi-layered neural networks is widely used in various areas such as associative memory, speech recognition, pattern recognition and robotics, etc. Nevertheless, many researchers have continuously published papers about improvements over the original EBP algorithm. The main reason for this research activity is that EBP is exceeding slow when the number of neurons and the size of training set is large. In this study, we developed new learning speed acceleration methods using variable learning rate, variable momentum rate and variable slope for the sigmoid function. During the learning process, these parameters should be adjusted continuously according to the total error of network, and it has been shown that these methods significantly reduced learning time over the original EBP. In order to show the efficiency of the proposed methods, first we have used binary data which are made by random number generator and showed the vast improvements in terms of epoch. Also, we have applied our methods to the binary-valued Monk's data, 4, 5, 6, 7-bit parity checker and real-valued Iris data which are famous benchmark training sets for machine learning.

• Journal of Educational Research in Mathematics
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• v.16 no.1
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• pp.59-78
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• 2006

As a theoretical background for this research, the literatures which focus on the rationale of teaching and learning of connecting with mathematics and science in calculus were investigated. And teaching and learning material of connecting with mathematics and science in calculus was developed. And then, based on the case study using this material, the research questions were analyzed in depth. Students could understand mean-velocity, instant-velocity, and acceleration in the experimenting process of constant acceleration movement. Also Students could understand fundamental ideas that instant-velocity means slope of the tangent line at one point on the time-displacement graph and rate of distance change means rate of area change under a time-velocity graph.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.3
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• pp.201-207
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• 2021

Deep learning, a type of machine learning, performs learning while changing the weights as it progresses through each learning process. Tensor Flow and Keras provide the results of the end of the learning in graph form. Thus, If an error occurs, the result must be discarded. Consequently, existing technologies provide a function to roll back learning results, but the rollback function is limited to results up to five times. Moreover, they applied the concept of MLOps to track the deep learning process, but no rollback capability is provided. In this paper, we construct a system that manages the intermediate value of the learning process by blockchain to record the intermediate learning process and can rollback in the event of an error. To perform the functions of blockchain, the deep learning process and the rollback of learning results are designed to work by writing Smart Contracts. Performance evaluation shows that, when evaluating the rollback function of the existing deep learning method, the proposed method has a 100% recovery rate, compared to the existing technique, which reduces the recovery rate after 6 times, down to 10% when 50 times. In addition, when using Smart Contract in Ethereum blockchain, it is confirmed that 1.57 million won is continuously consumed per block creation.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.8
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• pp.1195-1200
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• 2013

In this paper, we proposed an enhanced Max-Min neural network by auto-tuning of learning rate using fuzzy control method. For the reduction of training time required in the competition stage, the method was proposed that arbitrates dynamically the learning rate by applying the numbers of the accuracy and the inaccuracy to the input of the fuzzy control system. The experiments using real concrete crack images showed that the enhanced Max-Min neural network was effective in the recognition of direction of the extracted cracks.

• The Journal of Bigdata
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• v.4 no.2
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• pp.175-183
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• 2019

Drop-out issue is one of the challenges of cyber university. There are about 130,000 students enrolled in cyber universities, but the dropout rate is also very high. To lower the dropout rate, cyber universities invest heavily in learning analytics. Some cyber universities analyze the possibility of dropout and actively support students who are more likely to drop out. The purpose of this paper is to identify the learning data affecting the dropout prediction index. As a result of the analysis, it is confirmed that number of lessons(progress), credits, achievement and leave of absence have a significant effect on dropout rate. It is necessary to increase the accuracy of the prediction model through post-test on the student dropout prediction index.