• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.5
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• pp.600-608
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• 2020

Machine learning constructs an objective function from learning data, and predicts the result of the data generated by checking the objective function through test data. In machine learning, input data is subjected to a normalisation process through a preprocessing. In the case of numerical data, normalization is standardized by using the average and standard deviation of the input data. In the case of nominal data, which is non-numerical data, it is converted into a one-hot code form. However, this preprocessing alone cannot solve the problem. For this reason, we propose a method that uses ontology to normalize input data in this paper. The test data for this uses the received signal strength indicator (RSSI) value of the Wi-Fi device collected from the mobile device. These data are solved through ontology because they includes noise and heterogeneous problems.

• Journal of Korea Multimedia Society
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• v.25 no.10
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• pp.1375-1385
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• 2022

In this paper, we propose a semi-supervised domain adaptation solution to deal with practical face recognition (FR) scenarios where a single face image for each target identity (to be recognized) is only available in the training phase. Main goal of the proposed method is to reduce the discrepancy between the target and the source domain face images, which ultimately improves FR performances. The proposed method is based on the Domain Adatation network (DAN) using an MMD loss function to reduce the discrepancy between domains. In order to train more effectively, we develop a novel loss function learning strategy in which MMD loss and cross-entropy loss functions are adopted by using different weights according to the progress of each epoch during the learning. The proposed weight adoptation focuses on the training of the source domain in the initial learning phase to learn facial feature information such as eyes, nose, and mouth. After the initial learning is completed, the resulting feature information is used to training a deep network using the target domain images. To evaluate the effectiveness of the proposed method, FR performances were evaluated with pretrained model trained only with CASIA-webface (source images) and fine-tuned model trained only with FERET's gallery (target images) under the same FR scenarios. The experimental results showed that the proposed semi-supervised domain adaptation can be improved by 24.78% compared to the pre-trained model and 28.42% compared to the fine-tuned model. In addition, the proposed method outperformed other state-of-the-arts domain adaptation approaches by 9.41%.

• Smart Structures and Systems
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• v.31 no.5
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• pp.485-500
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• 2023

Machine learning-based structural health monitoring (ML-based SHM) methods are researched extensively in the recent decade due to the availability of advanced information and sensing technology. ML methods are well-known for their pattern recognition capability for complex problems. However, the main obstacle of ML-based SHM is that it often requires pre-collected historical data for model training. In most actual scenarios, damage presence can be detected using the unsupervised learning method through anomaly detection, but to further identify the damage types would require prior knowledge or historical events as references. This creates the cold-start problem, especially for new and unobserved structures. Modal-based methods identify damages based on the changes in the structural global properties but often require dense measurements for accurate results. Therefore, a two-stage hybrid modal-machine learning damage detection scheme is proposed. The first stage detects damage presence using Principal Component Analysis-Frequency Response Function (PCA-FRF) in an unsupervised manner, whereas the second stage further identifies the damage. To solve the cold-start problem, mode shape assessment using the first mode is initiated when no trained model is available yet in the second stage. The damage identified by the modal-based method would be stored for future training. This work highlights the performance of the scheme in alleviating the cold-start issue as it transitions through different phases, starting from zero damage sample available. Results showed that single and multiple damages can be identified at an acceptable accuracy level even when training samples are limited.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.611-616
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• 2023

ECG(electrocardiogram) is a test used to measure the rate and regularity of heartbeats, as well as the size and position of the chambers, the presence of any damage to the heart, and the cause of all heart diseases can be found. Because the ECG signal obtained using the ECG-KIT includes noise in the ECG signal, noise must be removed from the ECG signal to apply to the deep learning. In this paper, the noise of the ECG signal was removed using the digital IIR Butterworth low-pass filter. When the performance evaluation of the three activation functions, sigmoid(), ReLU(), and tanh() functions, was compared using the deep learning model of LSTM, it was confirmed that the activation function with the smallest error was the tanh() function. Also, When the performance evaluation and elapsed time were compared for LSTM and GRU models, it was confirmed that the GRU model was superior to the LSTM model.

• The Journal of Korean Association of Computer Education
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• v.7 no.3
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• pp.111-121
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• 2004

One of the cooperative learning models, the cooperative learning of structural approach which is very simple and easily applicable accomplishes teaching-learning through combining learning contents and organic connections of various structures. This work proposes a cooperative teaching-learning model for the structural approach, designs and implements a web-based cooperative system supporting it. The model provides a concrete frame which can apply to a subject learning thus could effectively support cooperative learning. Furthermore, it classifies learning into three types such as knowledge learning, investigation, and function mastering and presents a structure application model for each so as to apply different structures according to learning materials and types. We implemented a system that performs cooperative learning on the basis of these models, applied to a class, and analyzed the effect of it.

• Journal of Korean Biological Nursing Science
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• v.2 no.2
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• pp.38-52
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• 2000

This study was conducted to develop web-based distance learning program of basic nursing physical science which was composed of a multimedia contents such as documents, voices and pictures. This was to advance curriculum of basic nursing physical science based on a proper nursing characteristics related to other major nursing subjects and induce a new and creative method to gain a knowledge. Based on a general learning design model, distance learning program was developed centered on a structure and function of heart physical science in 1999. The stages of development were like this. Firstly, at the analytic stage, specific units and contents for developing multimedia contents were decided and presentation methods for each item were designed and then the evaluation items were developed. Secondly, at the design stage, the most effective design of multimedia construction was selected and interfaces, basic frames, and presentation elements were chosen. Thirdly, at the developmental stage, internet files were designed using Namo web editor 4.0 and this files were modified with Adobe Photoshop.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.2011-2016
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• 2004

Study of human-robot communication is one of the most important research areas. Among various communication media, any useful law we find in voice communication in human-human interactions, is significant in human-robot interactions too. Control strategy of most of such systems available at present is on/off control. These robots activate a function if particular word or phrase associated with that function can be recognized in the user utterance. Recently, there have been some researches on controlling robots using information rich fuzzy commands such as "go little slowly". However, in those works, although the voice command interpretation has been considered, learning from such commands has not been treated. In this paper, learning from such information rich voice commands for controlling a robot is studied. New concepts of the coach-player model and the sub-coach are proposed and such concepts are also demonstrated for a PA-10 redundant manipulator.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.7
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• pp.170-178
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• 1994

The nearest neighbor classification rule is widely used because it is not only simple but the error rate is asymptotically less than twice Bayes theoretical minimum error. But the method basically use the whole training patterns as the reference vectors. so that both storage and classification time increase as the number of training patterns increases. LVQ(Learning Vector Quantization) resolved this problem by training the reference vectors instead of just storing the whole training patterns. But it is a heuristic algorithm which has no theoretic background there is no terminating condition and it requires a lot of iterations to get to meaningful result. This paper is to propose a new training method of the reference vectors. which minimize the given error function. The nearest neighbor network,the network version of the nearest neighbor classification rule is proposed. The network is funtionally identical to the nearest neighbor classification rule is proposed. The network is funtionally identical to the nearest neighbor classification rule and the reference vectors are represented by the weights between the nodes. The network is trained to minimize the error function with respect to the weights by the steepest descent method. The learning algorithm is derived and it is shown that the proposed method can adjust more reference vectors than LVQ in each iteration. Experiment showed that the proposed method requires less iterations and the error rate is smaller than that of LVQ2.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.11
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• pp.151-162
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• 1994

A new neural network structure called Self-organized Distributed Networks (SODN) is proposed for developing the neural network-based multidimensional system models. The learning with the proposed networks is fast and precise. Such properties are caused from the local learning mechanism. The structure of the networks is combination of dual networks such as self-organized networks and multilayered local networks. Each local networks learns only data in a sub-region. Large number of memory requirements and low generalization capability for the untrained region, which are drawbacks of conventional local network learning, are overcomed in the proposed networks. The simulation results of the proposed networks show better performance than the standard multilayer neural networks and the Radial Basis function(RBF) networks.

• Journal of the Korean School Mathematics Society
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• v.6 no.2
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• pp.21-37
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• 2003

In this paper, we improved, compared and analyzed the articulation of school mathematics. We also tried to form the theoretical basis of school mathematics by classifying and considering the articulation into vertical articulation and horizontal articulation depending on the meaning, and give an actual help. The articulation of school mathematics until now has been focused on a study of vertical articulation according to the macroscopic characteristic of mathematics, but now a study of the horizontal articulation as well as the vertical articulation should be done in consideration of the micro characteristics of mathematics and various realities of life by modifying a syllabus of the function unit and by using internet homepage. Thus, we structurally divided the articulation into vertical articulation and horizontal articulation, analyzed mathematical activities and presented actual models of each representative learning activity for smooth teaching in schools through the function unit.