• The Journal of the Korea institute of electronic communication sciences
• /
• v.14 no.2
• /
• pp.433-438
• /
• 2019

This paper proposes a method of improving deep learning based numbers and characters recognition performance on parts of drawing through image preprocessing. The proposed character recognition system consists of image preprocessing and 7 layer deep learning model. Mathematical morphological filtering is used as preprocessing to remove the lines and shapes which causes false recognition of numbers and characters on parts drawing. Further.. Further, the used deep learning model is a 7 layer deep learning model instead of VGG-16 model. As a result of the proposed OCR method, the recognition rate of characters is 92.57% and the precision is 92.82%.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.13 no.2
• /
• pp.391-396
• /
• 2018

A variety of experiments are being conducted with the advent of Learning Model for flipped-learning. In order to apply flipped-learning as a method of teaching, most of them require a pre-prepared learning video. In this case, there is the burden to create the samples of a 13 weeks, except for the mid term and the final exam in college. These systems also make it difficult to change learning content. In this paper, we suggest using blogs to improve the characteristics that existing flippling systems are less adaptable to environmental changes. A blog can be a good thing for learners who are comfortable with the Internet, In this study, we experiment with flipped-learning, which applies blog to one subject. As a result, we would like to evaluate the meaningful learning effects of this study.

• Educational Technology International
• /
• v.10 no.2
• /
• pp.127-143
• /
• 2009

The purpose of this study was to identify the interaction patterns in distance only mode e-Learning. In order to investigate this study, messages shown in the electronic notice board were analyzed to see how interaction occurs between teacher and learner or learner and learner under the e-learning of cyber university. To analyze messages was applied according to the framework by Henri's contents analysis model. As a result of contents analysis on electronic board, the participative dimension was 399 messages. A learner put on 7~8 messages a day. The number of messages was low compared to the number of learners, but the number of inquiries was about 140. That means that each learner contacts and checks messages at least once a day. The meaning dimension was 600 units. The main interaction patterns were Interactive-social-cognitive-metacognitive. This means that e-Learning in distance only mode leads a positive attitude of learners as a self-directed learning, and needs teacher's well-structured instructional strategies for increasing interaction. In conclusion, social dimension and interactive dimension of messages support learners psychologically in the process of learning though they directly guide learning under the circumstances of e-learning lacking face-to-face element. It can be interpreted that the teacher's role is significantly important in order to attract learners' positive participation and cognitive and meta-cognitive dimension of messages and activities

• Journal of Internet Computing and Services
• /
• v.18 no.5
• /
• pp.69-77
• /
• 2017

In this paper, a disaster response scheme based on deep learning and augmented reality technology is proposed and a network resource reservation scheme is presented accordingly. The features of deep learning, augmented reality technology and its relevance to the disaster areas are explained. Deep learning technology can be used to accurately recognize disaster situations and to implement related disaster information as augmented reality, and to enhance disaster response capabilities by providing disaster response On-site disaster response agent, ICS (Incident Command System) and MCS (Multi-agency Coordination Systems). In the case of various disasters, the fire situation is focused on and it is proposed that a plan to strengthen disaster response capability effectively by providing fire situation recognition based on deep learning and augmented reality information. Finally, a scheme to secure network resources to utilize the disaster response method of this paper is proposed.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.9 no.3
• /
• pp.249-256
• /
• 2016

Recently, a number of techniques to ensure the free walking for the visually impaired and transportation vulnerable have been studied. As a device for free walking, there are such as a smart cane and smart glasses to use the computer vision, ultrasonic sensor, acceleration sensor technology. In a typical technique, such as techniques for finds object and detect obstacles and walking area and recognizes the symbol information for notice environment information. In this paper, we studied recognization algorithm of the selected symbols that are required to visually impaired, with the deep learning algorithm. As a results, Use CNN(Convolutional Nueral Network) technique used in the field of deep-learning image processing, and analyzed by comparing through experimentation with various deep learning architectures.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.11
• /
• pp.5425-5448
• /
• 2018

Wireless sensor network has been widely used in Internet of Things (IoT) applications to support large and dense networks. As sensor nodes are usually tiny and provided with limited hardware resources, the existing multiple access methods, which involve high computational complexity to preserve the protocol performance, is not available under such a scenario. In this paper, we propose an intelligent Medium Access Control (MAC) protocol selection scheme based on machine learning in wireless sensor networks. We jointly consider the impact of inherent behavior and external environments to deal with the application limitation problem of the single type MAC protocol. This scheme can benefit from the combination of the competitive protocols and non-competitive protocols, and help the network nodes to select the MAC protocol that best suits the current network condition. Extensive simulation results validate our work, and it also proven that the accuracy of the proposed MAC protocol selection strategy is higher than the existing work.

• Journal of Institute of Control, Robotics and Systems
• /
• v.2 no.2
• /
• pp.108-114
• /
• 1996

We present proofs of the stability and convergence of Self-organizing feature map (SOFM) neural network with time-invarient learning rate and binary reinforcement function. One of the major problems in Self-organizing feature map neural network concerns with learning rate-"Kalman Filter" gain in stochsatic control field which is monotone decreasing function and converges to 0 for satisfying minimum variance property. In this paper, we show that the stability and convergence of Self-organizing feature map neural network with time-invariant learning rate. The analysis of the proposed algorithm shows that the stability and convergence is guranteed with exponentially stable and weak convergence properties as well.s as well.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.8 no.5
• /
• pp.751-756
• /
• 2013

This paper describes the design and implementation of the multi-function learning community system. Recent trends and related researches regarding the learning community system are surveyed and analyzed. The function sections, data flows, database tables and system interface are designed. Manager and user's modes are implemented and we compare the system functions with other learning communities.

• Journal of Biomedical Engineering Research
• /
• v.17 no.3
• /
• pp.395-401
• /
• 1996

In this paper a new neural network model, time delayed counterpropagation neural networks (TDCPN) which have high recognition rate and short total learning time, is proposed for electromyogram(EMG) recognition. Signals the proposed model increases the recognition rates after learned the regional temporal correlation of patterns using time delay properties in input layer, and decreases the learning time by using winner-takes-all learning rule. The ouotar learning rule is put at the output layer so that the input pattern is able to map a desired output. We test the performance of this model with EMG signals collected from a normal subject. Experimental results show that the recognition rates of the suggested model is better and the learning time is shorter than those of TDNN and CPN.

• ETRI Journal
• /
• v.41 no.6
• /
• pp.750-759
• /
• 2019

Presently, the extraction of hand-crafted features is still the dominant method in radar emitter recognition. To solve the complicated problems of selection and updation of empirical features, we present a novel automatic feature extraction structure based on deep learning. In particular, a convolutional neural network (CNN) is adopted to extract high-level abstract representations from the time-frequency images of emitter signals. Thus, the redundant process of designing discriminative features can be avoided. Furthermore, to address the performance degradation of a single platform, we propose the construction of an ensemble learning-based architecture for multi-platform fusion recognition. Experimental results indicate that the proposed algorithms are feasible and effective, and they outperform other typical feature extraction and fusion recognition methods in terms of accuracy. Moreover, the proposed structure could be extended to other prevalent ensemble learning alternatives.