• Journal of the Korean Society of Industry Convergence
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• v.27 no.4_2
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• pp.1001-1007
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• 2024

Recent deep learning algorithms for object detection in real-time play a crucial role in various applications such as autonomous driving, traffic monitoring, health care, and water quality monitoring. The size of small objects, in particular, significantly impacts the accuracy of detection models. However, data containing small objects can lead to underfitting issues in models. Therefore, this study developed a deep learning model capable of quickly detecting small objects to provide more accurate predictions. The RE-SOD (Residual block based Small Object Detector) developed in this research enhances the detection performance for small objects by using RGB separation preprocessing and residual blocks. The model achieved an accuracy of 1.0 in image classification and an mAP50-95 score of 0.944 in object detection. The performance of this model was validated by comparing it with real-time detection models such as YOLOv5, YOLOv7, and YOLOv8.

• International journal of advanced smart convergence
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• v.9 no.2
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• pp.28-37
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• 2020

We conducted a comprehensive review on the previous research on r-Learning. By reviewing 843 previous studies about r-Learning published from 2004 to 2015, this study investigated 1) the trend of research on r-Learning over time, 2) the characteristics of targeted students in r-Learning, 3) the educational activities implemented for r-Learning, and 4) the types of educational robots used for r-Learning. The study found that the research on r-Learning has rapidly and steadily increased and the types of educational activities and educational robots has been diversified. Relying on the findings of this review, this study suggests 1) ensuring growth in both the quality and the quantity of research on r-Learning, 2) broadening the target student population of r-Learning beyond the age-limited boundaries, 3) enhancing educational activities of r-Learning, and 4) recognizing the necessity for systematic and clear concepts of types of educational robots.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.3
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• pp.98-105
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• 2021

The sensory stimulation of a cosmetic product has been deemed to be an ancillary aspect until a decade ago. That point of view has drastically changed on different levels in just a decade. Nowadays cosmetic formulators should unavoidably meet the needs of consumers who want sensory satisfaction, although they do not have much time for new product development. The selection of new products from candidate products largely depend on the panel of human sensory experts. As new product development cycle time decreases, the formulators wanted to find systematic tools that are required to filter candidate products into a short list. Traditional statistical analysis on most physical property tests for the products including tribology tests and rheology tests, do not give any sound foundation for filtering candidate products. In this paper, we suggest a deep learning-based analysis method to identify hand cream products by raw electric signals from tribological sliding test. We compare the result of the deep learning-based method using raw data as input with the results of several machine learning-based analysis methods using manually extracted features as input. Among them, ResNet that is a deep learning model proved to be the best method to identify hand cream used in the test. According to our search in the scientific reported papers, this is the first attempt for predicting test cosmetic product with only raw time-series friction data without any manual feature extraction. Automatic product identification capability without manually extracted features can be used to narrow down the list of the newly developed candidate products.

• The Journal of the Korea Contents Association
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• v.22 no.6
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• pp.342-353
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• 2022

This study aims to help revitalize the real-time online education platform market by analyzing the instructor characteristics, content characteristics, and platform characteristics of real-time online education platforms for local learners in China. A total of 670 questionnaires were collected through an online survey and an empirical analysis was conducted. As a result of the analysis, first, except for attractiveness, which is the characteristic of the instructor, professionalism and sincerity had a significant positive influence on both learning satisfaction. Second, the usefulness, abundance, and appropriateness of content characteristics had a significant positive influence on learning satisfaction. Third, except for interaction, which is a platform characteristic, technology and convenience confirmed a significant positive influence relationship on both learning satisfaction. Fourth, learning satisfaction had a significant positive effect on the intention to continue using. This study presented practical implications for real-time online education platform and future research directions.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1103-1108
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• 2023

In this paper, we propose a new algorithm for attenuating the background noises in acoustic signal. This algorithm improves the noise attenuation performance by using the FNN(: Full-connected Neural Network) deep learning algorithm instead of the existing adaptive filter after wavelet transform. After wavelet transforming the input signal for each short-time period, noise is removed from a single input audio signal containing noise by using a 1024-1024-512-neuron FNN deep learning model. This transforms the time-domain voice signal into the time-frequency domain so that the noise characteristics are well expressed, and effectively predicts voice in a noisy environment through supervised learning using the conversion parameter of the pure voice signal for the conversion parameter. In order to verify the performance of the noise reduction system proposed in this study, a simulation program using Tensorflow and Keras libraries was written and a simulation was performed. As a result of the experiment, the proposed deep learning algorithm improved Mean Square Error (MSE) by 30% compared to the case of using the existing adaptive filter and by 20% compared to the case of using the STFT(: Short-Time Fourier Transform) transform effect was obtained.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.4
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• pp.685-692
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• 2024

In this paper, we propose a new CNN model to improve the recognition performance of command voice signals. This method obtains a spectrogram image after performing a short-time Fourier transform (STFT) of the input signal and improves command recognition performance through supervised learning using a CNN model. After Fourier transforming the input signal for each short-time section, a spectrogram image is obtained and multi-classification learning is performed using a CNN deep learning model. This effectively classifies commands by converting the time domain voice signal to the frequency domain to express the characteristics well and performing deep learning training using the spectrogram image for the conversion parameters. To verify the performance of the speech recognition system proposed in this study, a simulation program using Tensorflow and Keras libraries was created and a simulation experiment was performed. As a result of the experiment, it was confirmed that an accuracy of 92.5% could be obtained using the proposed deep learning algorithm.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.6
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• pp.287-292
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• 2010

The flow gradually into the Information age to the Information age has changed, leading to the development of computer and communication technology was very important for the value. Of these the most used in computer communication using the Internet, and this proportion accounts for the development of the Internet, the information was established as a means of interaction. In this paper, to improve these problems without the constraints of time and space to allow two-way interactions using web based learning system to enable Computer Architecture were learning. Learn how Computer Architecture using Camtasia the learner, without limitation of time and place of the browser through the Internet to enable real-time learning and assessment appropriate to individual learners and teaching - learning process in conjunction with individual learners can be self-directed learning will play a role in that.

• The Journal of Korean Association of Computer Education
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• v.10 no.1
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• pp.41-53
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• 2007

Recently, there has been much research to improve immersiveness and learning achievement using the edutainment that combines learning with game. However, from the viewpoint of instructors, there has been little research to solve technical difficulties and to reduce the authoring time in tailoring a game-type learning content. Therefore, in this paper, we propose an authoring tool, which enable instructors to tailor game-type learning contents reflecting their own preferences in spite of no backgrounds of technical skills. The authoring tool proposed has key features to hide authoring handicaps and reduce authoring time by providing the overall template for customized game-type learning contents based on template concept. To evaluate the effectiveness of the authoring tool, we applied it to teachers and elementary school students. From the evaluation, the result represented that instructors can make their own learning contents without being aware of technical problems within a short time. Also, the result showed that the learning achievement degree and immersive depth of students have been significantly improved.

• Journal of the Korea Society of Computer and Information
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• v.13 no.3
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• pp.15-25
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• 2008

Recently, with the growing use of e-learning, instructors develop new online courses using a variety of contents and then store the results on their computers. These contents should be updated with new information as time goes on, and a new content also can be produced by reusing these ones. However, a lot of time will be needed for instructors to search, edit, and manage various contents stored from place to place on their computers. Currently, the development of the e-learning content management tool. which performs efficiently these functions on the PC environment, leaves much to be desired. Therefore, in this paper, we proposed an e-learning content creation and management system which can manage efficiently a variety of contents stored from different locations on an instructor's computer and can develop easily new online courses. The proposed system can be used widely to develop contents for instructors based on the PC environment. For performance evaluation, this paper compared the proposed system with the previous system according to the retrieval time of content keyword, and the experiment showed that our system is much better than the previous one.

• Journal of The Korean Association of Information Education
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• v.25 no.3
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• pp.571-578
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• 2021

This study compares Online SW-based maker education in terms of self-efficacy, learning motivation, and interest after applying differently according to blended learning strategies. First, a SW maker program for blended learning was developed and applied as a live seminar-type class including real-time interactive and a support-providing class consisting of online content and Q&A. As a result of comparing the differences between students according to the two strategies divided into pre- and post- survey, in the self-efficacy part, there was a significant difference in the positive efficacy and the overall part, and in the learning motivation part, the live seminar form was significantly higher in the confidence part. In the interest part, the support-providing form showed a significantly higher average in the instrumental interest and nervous part. In order to maintain the effect of maker activities like existing face-to-face situations in Online learning, it is necessary to increase sharing time between students, an integrated learning environment, and sufficient provision of exploration time and learning materials.