• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.11a
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• pp.30-31
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• 2021

This study aims to build a deep learning model that can predict the value of concrete mixing properties according to a given concrete strength value. A model was created for a total of 1,291 concrete data, including 8 characteristics related to concrete mixing elements and environment, and the compressive strength of concrete. As the deep learning model, DNN-3L-256N, which showed the best performance on the prior study, was used. The average value for each characteristic of the data set was used as the initial input value. In results, in the case of 'curing temperature', which had a narrow range of values in the existing data set, showed the lowest error rate with less than 1% error based on MAE. The highest error rate with an error of 12 to 14% for fly and bfs.

• Korean Linguistics
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• v.63
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• pp.103-132
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• 2014

This study compares various speech rates of Korean learners with those of native Korean. Speech data were collected from 34 native Koreans and 33 Korean learners (19 Chinese and 14 Japanese). Each participant recorded a 9 syllabled Korean sentence at three different speech rate types. A total of 603 speech samples were analyzed by speech rate types (normal, slow, and fast), native languages (Korean, Chinese, Japanese), and learners' proficiency levels (beginner, intermediate, and advanced). We found that learners' L1 background plays a role in categorizing different speech rates in the L2 (Korean), and also that the leaners' proficiency correlates with the increase of speaking rate regardless of speech rate categories. More importantly, faster speech rate values found in the advanced level of learners do not necessarily match to the native speakers' speech rate categories. This means that learning speech rate categories can be more complex than we think of proficiency or fluency. That is, speech rate categories may not be acquired automatically during the course of second language learning, and implicit or explicit exposures to various rate types are necessary for second language learners to acquire a high level of communicative skills including speech rate variation. This paper discusses several pedagogical implications in terms of teaching pronunciation to second language learners.

• Journal of the Ergonomics Society of Korea
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• v.31 no.4
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• pp.525-531
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• 2012

Objective: The aim of this study is to investigate learnability of gestures-commands pair when people use gestures to control a device. Background: In vision-based gesture recognition system, selecting gesture-command pairing is critical for its usability in learning. Subjective preference and its agreement score, used in previous study(Lim et al., 2012) was used to group four gesture-command pairings. To quantify the learnability, two learning models, average time model and marginal time model, were used. Method: Two sets of eight gestures, total sixteen gestures were listed by agreement score and preference data. Fourteen participants divided into two groups, memorized each set of gesture-command pair and performed gesture. For a given command, time to recall the paired gesture was collected. Results: The average recall time for initial trials were differed by preference and agreement score as well as the learning rate R driven by the two learning models. Conclusion: Preference rate agreement score showed influence on learning of gesture-command pairs. Application: This study could be applied to any device considered to adopt gesture interaction system for device control.

• Journal of Korean Association for Spatial Structures
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• v.18 no.4
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• pp.69-80
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• 2018

In this study, an algorithm applying deep learning to the truss structures was proposed. Deep learning is a method of raising the accuracy of machine learning by creating a neural networks in a computer. Neural networks consist of input layers, hidden layers and output layers. Numerous studies have focused on the introduction of neural networks and performed under limited examples and conditions, but this study focused on two- and three-dimensional truss structures to prove the effectiveness of algorithms. and the training phase was divided into training model based on the dataset size and epochs. At these case, a specific data value was selected and the error rate was shown by comparing the actual data value with the predicted value, and the error rate decreases as the data set and the number of hidden layers increases. In consequence, it showed that it is possible to predict the result quickly and accurately without using a numerical analysis program when applying the deep learning technique to the field of structural analysis.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.171-176
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• 2022

With the recent development of deep learning technology, the areas of recommendation systems have also diversified. This paper studied algorithms to improve the learning rate and studied the significance results according to words through comparison with the performance characteristics of the Word2Vec model. The problem recommendation algorithm was implemented with the values expressed through the reflection of meaning and similarity test between texts, which are characteristics of the Word2Vec model. Through Word2Vec's learning results, problem recommendations were conducted using text similarity values, and problems with high similarity can be recommended. In the experimental process, it was seen that the accuracy decreased with the quantitative amount of data, and it was confirmed that the larger the amount of data in the data set, the higher the accuracy.

• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.264-272
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• 2022

Human-robot cooperative tasks are increasingly required in our daily life with the development of robotics and artificial intelligence technology. Interactive reinforcement learning strategies suggest that robots learn task by receiving feedback from an experienced human trainer during a training process. However, most of the previous studies on Interactive reinforcement learning have required an extra feedback input device such as a mouse or keyboard in addition to robot itself, and the scenario where a robot can interactively learn a task with human have been also limited to virtual environment. To solve these limitations, this paper studies training strategies of robot that learn table balancing tasks interactively using deep reinforcement learning with human's facial expression feedback. In the proposed system, the robot learns a cooperative table balancing task using Deep Q-Network (DQN), which is a deep reinforcement learning technique, with human facial emotion expression feedback. As a result of the experiment, the proposed system achieved a high optimal policy convergence rate of up to 83.3% in training and successful assumption rate of up to 91.6% in testing, showing improved performance compared to the model without human facial expression feedback.

• Archives of Plastic Surgery
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• v.50 no.2
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• pp.200-209
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• 2023

Background As the coronavirus disease 2019 virus made its way throughout the world, there was a complete overhaul of our day-to-day personal and professional lives. All aspects of health care were affected including academics. During the pandemic, teaching opportunities for resident training were drastically reduced. Consequently, medical universities in many parts across the globe implemented online learning, in which students are taught remotely and via digital platforms. Given these developments, evaluating the existing mode of teaching via digital platforms as well as incorporation of new models is critical to improve and implement. Methods We reviewed different online learning platforms used to continue regular academic teaching of the plastic surgery residency curriculum. This study compares the four popular Web conferencing platforms used for online learning and evaluated their suitability for providing plastic surgery education. Results In this study with a response rate of 59.9%, we found a 64% agreement rate to online classes being more convenient than normal classroom teaching. Conclusion Zoom was the most user-friendly, with a simple and intuitive interface that was ideal for online instruction. With a better understanding of factors related to online teaching and learning, we will be able to deliver quality education in residency programs in the future.

• Journal of Information Processing Systems
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• v.20 no.3
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• pp.345-359
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• 2024

A regression model represents the relationship between explanatory and response variables. In real life, explanatory variables often affect a response variable with a certain time lag, rather than immediately. For example, the marriage rate affects the birth rate with a time lag of 1 to 2 years. Although deep learning models have been successfully used to model various relationships, most of them do not consider the time lags between explanatory and response variables. Therefore, in this paper, we propose an extension of deep learning models, which automatically finds the time lags between explanatory and response variables. The proposed method finds out which of the past values of the explanatory variables minimize the error of the model, and uses the found values to determine the time lag between each explanatory variable and response variables. After determining the time lags between explanatory and response variables, the proposed method trains the deep learning model again by reflecting these time lags. Through various experiments applying the proposed method to a few deep learning models, we confirm that the proposed method can find a more accurate model whose error is reduced by more than 60% compared to the original model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.249-251
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• 2022

In this paper, we present machine learning to control drone flight using EEG signals. We defined takeoff, forward, backward, left movement and right movement as control targets and measured EEG signals from the frontal lobe for controlling using Fp1. Fp2 Fp2 two-channel dry electrode (NeuroNicle FX2) measuring at 250Hz sampling rate. And the collected data were filtered at 6~20Hz cutoff frequency. We measured the motion image of the action associated with each control target open for 5.19 seconds. Using Matlab's classification learner for the measured EEG signal, the triple layer neural network, logistic regression kernel, nonlinear polynomial Support Vector Machine(SVM) learning was performed, logistic regression kernel was confirmed as the highest accuracy for takeoff and forward, backward, left movement and right movement of the drone in learning by class True Positive Rate(TPR).

• Journal of the Korean Society of Costume
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• v.67 no.4
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• pp.116-130
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• 2017

The aim of this study is to investigate the general status, operating status, and the satisfaction level of participating textile-clothing companies involved in the Work and Learning Dual System in Daegu. The general status and operating status of the participating companies are as follows. As of March 2016, 34 of the 43 companies in Daegu participated in this survey, and they were divided into three areas of textile: weaving, dyeing & finishing, and apparel manufacturing. The breakdown is as follows: 14 dyeing & finishing companies (41.2%), 13 apparel manufacturing companies (38.2%), and 7 textile weaving companies (23.6%). The results of the survey showed that 91.2% of the companies decided to participate in the system to cultivate their employees into experts in the field. The satisfaction rate of the theoretical education and training institutions was 3.88 out of 5 points. In particular, the satisfaction rate of the textile weaving companies was as high as 4.29, and the satisfaction level of the dyeing & finishing companies was higher than the average of 3.71. The overall satisfaction rate for the work-related paradigm was 3.97 out of 5 points. The results of this survey can be used to conclude that the Work and Learning Dual System is operating as it was intended to be by the government.