• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.5
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• pp.591-599
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• 2020

Deep learning is widely utilized to classify or recognize objects of real-world. An abundance of data is trained on high-performance computers and a trained model is generated, and then the model is loaded in an inferencer. The inferencer is used in various environments, so that it may cause unrecognized objects or low-accuracy objects. To solve this problem, real-world objects are collected and they are trained periodically. However, not only is it difficult to immediately improve the recognition rate, but is not easy to learn an inferencer on embedded devices. We propose a real-time handwriting recognizer based on partial learning on embedded devices. The recognizer provides a training environment which partially learn on embedded devices at every user request, and its trained model is updated in real time. As this can improve intelligence of the recognizer automatically, recognition rate of unrecognized handwriting increases. We experimentally prove that learning and reasoning are possible for 22 numbers and letters on RK3399 devices.

• 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 Korean Association of Computer Education
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• v.5 no.1
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• pp.45-55
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• 2002

So far, there were a few web-based distance English learning systems for children. The existing systems for children have not appropriately reflected their learning characteristics and interests, since most of those systems regards mainly adults as targets. This thesis is aimed at proposing more effective educational service modules of web-based real-time system in order to encourage children's interests as much as possible in learning English. The web-based real-time distance learning system is a model based on the survey over the current use and satisfaction status of elementary school students who have actually used those kinds of systems. With the great expectation of more effective way of learning English, it also focuses on attracting parents to participate the learning process by providing a service to look over, in real-time, what their children do through the internet-based English learning system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.66-69
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• 2003

This paper considers a reinforcement learning(RL) which deals with real environments. Most reinforcement learning studies have been made by simulations because real-environment learning requires large computational cost and much time. Furthermore, it is more difficult to acquire many rewards efficiently in real environments than in virtual ones. The most important requirement to make real-environment learning successful is the appropriate construction of the state space. In this paper, to begin with, I show the basic overview of the reinforcement learning under real environments. Next, 1 introduce a state-space construction method under real environmental which is State Partition Method. Finally I apply this method to a robot navigation problem and compare it with conventional methods.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.2
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• pp.129-141
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• 2022

The purpose of this study is to reveal the variables that affect learning immersion, in real-time non-face-to-face classes. To this end, a survey was conducted from November 22, 2021 to December 5, 2021 for students with experience in zoom classes. Excluding incorrect questionnaire, 117 copies were analyzed using a structural equation model. The results show that 'interest' and 'interaction level' influenced 'learning immersion', and 'learning immersion' had a positive effect on 'learning outcome'. The contribution of this study is that it empirically analyzed variables affecting learning immersion in real-time non-face-to-face classes. In the follow-up study, it is necessary to verify variables that affect learning immersion in various platforms, including zoom.

• The Journal of Korea Robotics Society
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• v.14 no.1
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• pp.1-7
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• 2019

Recently, smart factories have attracted much attention as a result of the 4th Industrial Revolution. Existing factory automation technologies are generally designed for simple repetition without using vision sensors. Even small object assemblies are still dependent on manual work. To satisfy the needs for replacing the existing system with new technology such as bin picking and visual servoing, precision and real-time application should be core. Therefore in our work we focused on the core elements by using deep learning algorithm to detect and classify the target object for real-time and analyzing the object features. We chose YOLO CNN which is capable of real-time working and combining the two tasks as mentioned above though there are lots of good deep learning algorithms such as Mask R-CNN and Fast R-CNN. Then through the line and inside features extracted from target object, we can obtain final outline and estimate object posture.

• International journal of advanced smart convergence
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• v.7 no.4
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• pp.27-39
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• 2018

Since the global positioning system (GPS) has been included in mobile devices (e.g., for car navigation, in smartphones, and in smart watches), the impact of personal GPS log data on daily life has been unprecedented. For example, such log data have been used to solve public problems, such as mass transit traffic patterns, finding optimum travelers' routes, and determining prospective business zones. However, a real-time analysis technique for GPS log data has been unattainable due to theoretical limitations. We introduced a machine learning model in order to resolve the limitation. In this paper presents a new, three-stage real-time prediction model for a person's daily route activity. In the first stage, a machine learning-based clustering algorithm is adopted for place detection. The training data set was a personal GPS tracking history. In the second stage, prediction of a new person's transient mode is studied. In the third stage, to represent the person's activity on those daily routes, inference rules are applied.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.7
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• pp.584-588
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• 2002

The real-time characteristic of the adaptive leaning control algorithms is validated based on the applied results of the hydraulic servo system that has very strong a non-linearity. The evolutionary strategy automatically adjusts the search regions with natural competition among many individuals. The error that is generated from the dynamic system is applied to the mutation equation. Competitive individuals are reduced with automatic adjustments of the search region in accordance with the error. In this paper, the individual parents and offspring can be reduced in order to apply evolutionary algorithms in real-time. The feasibility of the newly proposed algorithm was demonstrated through the real-time test.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.11
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• pp.1482-1484
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• 2019

We present a method for transmitting 3D object information in real time in a telepresence system. Three-dimensional object information consists of a large amount of point cloud data, which requires high performance computing power and ultra-wideband network transmission environment to process and transmit such a large amount of data in real time. In this paper, multiple users can transmit object motion and facial expression information in real time even in small network bands by using GANs (Generative Adversarial Networks), a non-supervised learning machine learning algorithm, for real-time transmission of 3D point cloud data. In particular, we propose the creation of an object similar to the original using only the feature information of 3D objects using conditional GANs.

• Journal of IKEEE
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• v.26 no.3
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• pp.468-474
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• 2022

In this paper, we propose a method to implement a real-time fire alarm system using deep learning. The deep learning image dataset for fire alarms acquired 1,500 sheets through the Internet. If various images acquired in a daily environment are learned as they are, there is a disadvantage that the learning accuracy is not high. In this paper, we propose a fire image data expansion method to improve learning accuracy. The data augmentation method learned a total of 2,100 sheets by adding 600 pieces of learning data using brightness control, blurring, and flame photo synthesis. The expanded data using the flame image synthesis method had a great influence on the accuracy improvement. A real-time fire detection system is a system that detects fires by applying deep learning to image data and transmits notifications to users. An app was developed to detect fires by analyzing images in real time using a model custom-learned from the YOLO V4 TINY model suitable for the Edge AI system and to inform users of the results. Approximately 10% accuracy improvement can be obtained compared to conventional methods when using the proposed data.