• Journal of the Korean Institute of Intelligent Systems
• /
• v.26 no.1
• /
• pp.56-63
• /
• 2016

In this study, we introduce a design of Fuzzy RBFNNs-based digit recognition system using the incremental-PCA in order to recognize the handwritten digits. The Principal Component Analysis (PCA) is a widely-adopted dimensional reduction algorithm, but it needs high computing overhead for feature extraction in case of using high dimensional images or a large amount of training data. To alleviate such problem, the incremental-PCA is proposed for the computationally efficient processing as well as the incremental learning of high dimensional data in the feature extraction stage. The architecture of Fuzzy Radial Basis Function Neural Networks (RBFNN) consists of three functional modules such as condition, conclusion, and inference part. In the condition part, the input space is partitioned with the use of fuzzy clustering realized by means of the Fuzzy C-Means (FCM) algorithm. Also, it is used instead of gaussian function to consider the characteristic of input data. In the conclusion part, connection weights are used as the extended diverse types in polynomial expression such as constant, linear, quadratic and modified quadratic. Experimental results conducted on the benchmarking MNIST handwritten digit database demonstrate the effectiveness and efficiency of the proposed digit recognition system when compared with other studies.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.19 no.5
• /
• pp.29-39
• /
• 2020

Some drivers cannot meet the standards for a full driver's license and many countries allow them to drive a vehicle as long as they can satisfy certain conditions. Korea has mainly issued conditional driver's licenses to the disabled only after supplementary devices are attached either in the vehicle or in their bodies. However, it is becoming important to issue a conditional driver's license to other types of drivers, including the elderly as the population ages in the country. This study aims at improving the current practice of issuing conditional driver's licenses by benchmarking various types of conditional licenses in other countries. In particular, various conditions such as time, space, driving speed, road type, vehicle type, and specific individual conditions are compared. Issuing a conditional driver's license to various types of drivers should be beneficial, not only to elderly drivers but also to drivers who cannot live without a vehicle.

• Science of Emotion and Sensibility
• /
• v.20 no.4
• /
• pp.79-88
• /
• 2017

As the age in which the importance of sensitivity has increased, education for the future generation regarding emotion engineering, affective recognition and cognitive science have taken center stage. We measure human's emotion quantitatively, analyze evaluation and apply them to various services in life, which are based on human technology. Therefore, we need the education which is related to emotion science to cultivate talented people. The goal of this paper is to suggest the possibility of emotion science education and effective methods through development of the STEAM (Science, Technology, Engineering, Arts, Mathematics) program which can teach emotion science to early elementary school students by applying it to pilot classes. For this study, first, we build a program, 'The mind made by figure' for student of early elementary school. The method of STEAM was used in this program, because it is an effective system to educate the emotion science. We recognize the needs and value of this program development through theory and benchmarking of STEAM related to emotion science. And then the contents of class, activities, course book and kit are designed with elementary school textbook of pertinent grade. Secondly, we analyze the result which is applied in two pilot classes of second grade by satisfaction survey and teacher interview. As a result, the average of satisfaction level was very high (4.40/5), Class participation was especially high. Third, we discuss the ability, value and limits of this program based on the result of analysis. The outcome of this research shows that students of early elementary school who have difficulty in understanding science can approach the education program related to emotion science with ease and interest. We hope this education will help students understand emotion science effectively, and to train people to lead the emotion centered era.

• Journal of Intelligence and Information Systems
• /
• v.27 no.4
• /
• pp.73-95
• /
• 2021

This study uses Node2vec graph embedding method and Light GBM link prediction to explore undeveloped export candidate countries in Korea's food and beverage industry. Node2vec is the method that improves the limit of the structural equivalence representation of the network, which is known to be relatively weak compared to the existing link prediction method based on the number of common neighbors of the network. Therefore, the method is known to show excellent performance in both community detection and structural equivalence of the network. The vector value obtained by embedding the network in this way operates under the condition of a constant length from an arbitrarily designated starting point node. Therefore, it has the advantage that it is easy to apply the sequence of nodes as an input value to the model for downstream tasks such as Logistic Regression, Support Vector Machine, and Random Forest. Based on these features of the Node2vec graph embedding method, this study applied the above method to the international trade information of the Korean food and beverage industry. Through this, we intend to contribute to creating the effect of extensive margin diversification in Korea in the global value chain relationship of the industry. The optimal predictive model derived from the results of this study recorded a precision of 0.95 and a recall of 0.79, and an F1 score of 0.86, showing excellent performance. This performance was shown to be superior to that of the binary classifier based on Logistic Regression set as the baseline model. In the baseline model, a precision of 0.95 and a recall of 0.73 were recorded, and an F1 score of 0.83 was recorded. In addition, the light GBM-based optimal prediction model derived from this study showed superior performance than the link prediction model of previous studies, which is set as a benchmarking model in this study. The predictive model of the previous study recorded only a recall rate of 0.75, but the proposed model of this study showed better performance which recall rate is 0.79. The difference in the performance of the prediction results between benchmarking model and this study model is due to the model learning strategy. In this study, groups were classified by the trade value scale, and prediction models were trained differently for these groups. Specific methods are (1) a method of randomly masking and learning a model for all trades without setting specific conditions for trade value, (2) arbitrarily masking a part of the trades with an average trade value or higher and using the model method, and (3) a method of arbitrarily masking some of the trades with the top 25% or higher trade value and learning the model. As a result of the experiment, it was confirmed that the performance of the model trained by randomly masking some of the trades with the above-average trade value in this method was the best and appeared stably. It was found that most of the results of potential export candidates for Korea derived through the above model appeared appropriate through additional investigation. Combining the above, this study could suggest the practical utility of the link prediction method applying Node2vec and Light GBM. In addition, useful implications could be derived for weight update strategies that can perform better link prediction while training the model. On the other hand, this study also has policy utility because it is applied to trade transactions that have not been performed much in the research related to link prediction based on graph embedding. The results of this study support a rapid response to changes in the global value chain such as the recent US-China trade conflict or Japan's export regulations, and I think that it has sufficient usefulness as a tool for policy decision-making.