• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2348-2360
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• 2023

As science and technology evolve, object detection of moving objects has been widely used in the context of machine learning and artificial intelligence. Traditional moving object detection algorithms, however, are characterized by relatively poor real-time performance and low accuracy in detecting moving objects. To tackle this issue, this manuscript proposes a modified Kalman filter algorithm, which aims to expand the equations of the system with the Taylor series first, ignoring the higher order terms of the second order and above, when the nonlinear system is close to the linear form, then it uses standard Kalman filter algorithms to measure the situation of the system. which can not only detect moving objects accurately but also has better real-time performance and can be employed to predict the trajectory of moving objects. Meanwhile, the accuracy and real-time performance of the algorithm were experimentally verified.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.299-307
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• 2021

As the COVID-19 pandemic spread around the world, more and more universities are conducting real-time distance video lectures using ZOOM, Webex, and MS Teams. This study attempts to identify the factors influencing learner satisfaction of real-time distance video lectures. Based on the existing research, it was composed of five elements (system factor, content quality, interaction, self-direction, and learning motivation) as learner satisfaction elements of real-time distance video lectures. As a result of analyzing the structural equation model of 160 effective questionnaires by conducting a survey of college students in the metropolitan and Chungcheong areas, it was found that three factors (interaction, self-direction, and learning motivation) influence learner satisfaction. Real-time distance video lectures are expected to continue to expand in the future. Therefore, universities should continuously increase learner satisfaction through the development and evaluation of real-time distance video lecture satisfaction models.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.321-328
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• 2023

This study is tried to to identify factors affecting academic achievement depending on the quality of class, learning immersion, level of academic achievement, and class type according to video production classes and real-time video classes in the on-contact learning situation due to the COVID-19 epidemic. The subjects of the study were 122 students enrolled in the nursing department at two universities. As a result of the study, the quality of the class was high in real-time video classes (t=-2.69, P=0.02), learning immersion was high in video production classes (t=1.14, P=0.28), and academic achievement was high in video production classes (t=4.24, P=0.01). Depending on the type of class, the effect on academic achievement is learning immersion in production video classes (β=.37, p<.001) has the most influence, and in real-time video classes, class quality (β=.29, p<.001) had the most influence on academic achievement. Based on the results of this study, it is suggested that it is necessary to develop a strategy for instructional design suitable for class types to improve academic achievement in an on-contact environment.

• Journal of Korean Physical Therapy Science
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• v.28 no.3
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• pp.1-10
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• 2021

Background: The purpose of this study is to investigate the effect of the online team project based learning on problem-solving, cooperative self-efficacy, and cooperative self-regulation of college students. Design: Single group pre-post design. Methods: The online team project based learning was conducted for a total of 92 college students for 8 weeks. A survey was conducted on problem-solving ability, cooperative self-efficacy, and cooperative self-regulation. In the online team project-based class, two projects were performed. It consists of video lectures and real-time video conferencing. Through the real-time video conference, the project was carried out based on discussion among learners and feedback was provided. Results: There was a significant difference in the change in problem-solving ability compared to before learning (p<0.05). As a result of the evaluation of cooperative self-efficacy, there was a significant difference (p<0.05). There was a significant differences in cooperative self-regulation compared to before learning (p<0.05). Conclusion: The online team project-based learning are effective in improving learners' problem-solving ability, cooperative self-efficacy, and cooperative self-regulation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.9
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• pp.1718-1724
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• 2017

Recently, Smart plugs for real time monitoring of household appliances based on IoT(Internet of Things) have been activated. Through this, consumers are able to save energy by monitoring real-time energy consumption at all times, and reduce power consumption through alarm function based on consumer setting. In this paper, we measure the alternating current from a wall power outlet for real-time monitoring. At this time, the current pattern for each household appliance was classified and it was experimented with deep learning to determine which product works. As a result, we used a cross validation method and a bootstrap verification method in order to the classification performance according to the type of appliances. Also, it is confirmed that the cost function and the learning success rate are the same as the train data and test data.

• Smart Media Journal
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• v.9 no.3
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• pp.25-30
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• 2020

The purpose of this paper is to implement a deep learning-based real-time video analysis algorithm that monitors safety of workers in industrial facilities. The worker's clothes were divided into six classes according to whether workers are wearing a helmet, safety vest, and safety belt, and a total of 5,307 images were used as learning data. The experiment was performed by comparing the mAP when weight was applied according to the number of learning iterations for 645 images, using YOLO v4. It was confirmed that the mAP was the highest with 60.13% when the number of learning iterations was 6,000, and the AP with the most test sets was the highest. In the future, we plan to improve accuracy and speed by optimizing datasets and object detection model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.11
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• pp.2127-2134
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• 2007

The electronic learning helped a learner to overcome the time restriction by providing mobility, instantly and flexibility but the restriction in connection with space on cable computer remained unsolved. Accordingly, the electronic learning has tendency to change into mobile learning environment which allows a learner to overcome time and spatial restriction. However, these mobile devices have a limitation to awareness of learning contents provided over the realtime video movie due to its small display size. Therefore, this paper suggests a technique according to the following priority: for a real time learning image, extract region of study for region of interest, rescale the real time image to its proper size suitable for the display device, and then make it displayed on a wireless PDA. As a result of the experiment, we reduced the calculating time by sampling the field centering on learning contents adaptively and computing the field best suited for device size of the user effectively.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.9-17
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• 2021

With the advancement of big data analysis, artificial intelligence, machine learning, etc., data analytics technology has developed to help with optimal decision-making. However, in certain areas, the lack of data restricts the use of these techniques. For example, real estate related data often have a long release cycle because of its recent release or being a non-liquid asset. In order to overcome these limitations, we studied the scalability of the existing time series through the TimeGAN model. A total of 45 time series related to weekly real estate data were collected within the period of 2012 to 2021, and a total of 15 final time series were selected by considering the correlation between the time series. As a result of data expansion through the TimeGAN model for the 15 time series, it was found that the statistical distribution between the real data and the extended data was similar through the PCA and t-SNE visualization algorithms.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.10
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• pp.1038-1046
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• 2008

This paper presents a framework to generate human-like movements of a humanoid in real time using the movement primitive database of a human. The framework consists of two processes: 1) the offline motion imitation learning based on an Evolutionary Algorithm and 2) the online motion generation of a humanoid using the database updated bγ the motion imitation teaming. For the offline process, the initial database contains the kinetic characteristics of a human, since it is full of human's captured motions. The database then develops through the proposed framework of motion teaming based on an Evolutionary Algorithm, having the kinetic characteristics of a humanoid in aspect of minimal torque or joint jerk. The humanoid generates human-like movements far a given purpose in real time by linearly interpolating the primitive motions in the developed database. The movement of catching a ball was examined in simulation.

• Journal of Korea Multimedia Society
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• v.23 no.10
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• pp.1236-1249
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• 2020

In a most recent object tracking research work, applying Convolutional Neural Network and Recurrent Neural Network-based strategies become relevant for resolving the noticeable challenges in it, like, occlusion, motion, object, and camera viewpoint variations, changing several targets, lighting variations. In this paper, the LSTM Network-based Tracking association method has proposed where the technique capable of real-time multi-object tracking by creating one of the useful LSTM networks that associated with tracking, which supports the long term tracking along with solving challenges. The LSTM network is a different neural network defined in Keras as a sequence of layers, where the Sequential classes would be a container for these layers. This purposing network structure builds with the integration of tracking association on Keras neural-network library. The tracking process has been associated with the LSTM Network feature learning output and obtained outstanding real-time detection and tracking performance. In this work, the main focus was learning trackable objects locations, appearance, and motion details, then predicting the feature location of objects on boxes according to their initial position. The performance of the joint object tracking system has shown that the LSTM network is more powerful and capable of working on a real-time multi-object tracking process.