• Journal of Broadcast Engineering
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• v.25 no.2
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• pp.242-251
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• 2020

Recently, many deep convolutional neural networks for image super-resolution have been studied. Existing deep learning-based super-resolution algorithms are architecture that up-samples the resolution at the end of the network. The post-upsampling architecture has an inefficient structure at large scaling factor result of predicting a lot of information for mapping from low-resolution to high-resolution at once. In this paper, we propose a single image super-resolution using Channel Attention Residual Dense Block based on an iterative up-down sampling architecture. The proposed algorithm efficiently predicts the mapping relationship between low-resolution and high-resolution, and shows up to 0.14dB performance improvement and enhanced subjective image quality compared to the existing algorithm at large scaling factor result.

• Journal of Digital Contents Society
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• v.18 no.7
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• pp.1323-1331
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• 2017

Despite the fact that the copyright has grown into a large-scale business, there are many constant problems especially in image copyright. In this study, we propose an automatic object extraction and classification system for the scanned educational book image by combining document image processing and intelligent information technology like deep learning. First, the proposed technology removes noise component and then performs a visual attention assessment-based region separation. Then we carry out grouping operation based on extracted block areas and categorize each block as a picture or a character area. Finally, the caption area is extracted by searching around the classified picture area. As a result of the performance evaluation, it can be seen an average accuracy of 83% in the extraction of the image and caption area. For only image region detection, up-to 97% of accuracy is verified.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.3
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• pp.89-95
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• 2015

Compresses sensing (CS) technique is beneficial for reducing power consumption of biopotential acquisition circuits in wireless healthcare system. This paper investigates the maximum possible compress ratio for various biopotential signal when the CS technique is applied. By using the CS technique, we perform the compression and reconstruction of typical electrocardiogram(ECG), electromyogram(EMG), electroencephalogram(EEG) signals. By comparing the original signal and reconstructed signal, we determines the validity of the CS-based signal compression. Raw-biopotential signal is compressed by using a psuedo-random matrix, and the compressed signal is reconstructed by using the Block Sparse Bayesian Learning(BSBL) algorithm. EMG signal, which is the most sparse biopotential signal, the maximum compress ratio is found to be 10, and the ECG'sl maximum compress ratio is found to be 5. EEG signal, which is the least sparse bioptential signal, the maximum compress ratio is found to be 4. The results of this work is useful and instrumental for the design of wireless biopotential signal monitoring circuits.

• Journal of the Korea Society of Computer and Information
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• v.17 no.3
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• pp.77-83
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• 2012

As web pages provide various services, the distribution of malware via the web pages is being also increased. Malware can make personal information leak, system mal-function and system be zombie. To protect this damages, we should block the malicious web pages. Because the malicious codes embedded in web pages are obfuscated or transformed, it is difficult to detect them using signature-based approaches which are used by current anti-virus software. To overcome this problem, we extracted features to classify malicious web pages and benign ones by analyzing web pages. And we propose a classification method using SVM which is widely used in machine learning. Experimental results show that the proposed method is better than other methods. The proposed method could classify malicious web pages correctly and be helpful to block the distribution of malicious codes.

• Journal of Internet Computing and Services
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• v.20 no.4
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• pp.29-38
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• 2019

Currently, information society is rapidly changing and demands innovation and creativity in various fields. Therefore, the importance of mathematics, which can be the basis of creativity and logic, is emphasized. The purpose of this paper is to develop a math education application that can further expand the logical thinking of mathematics and allow voluntary learning to occur through the use of readily available teaching aid for children to form motivation and interest in learning. This paper provides math education applications using a smartphone and blocks for children. The main function of the application is to shoot with the camera and show the calculated values. When a child uses a block to make a formula and shoots a block using a camera, you can directly see the calculated value of your formula. The preprocessing process, text extraction, and character recognition of the photographed images have been implemented using OpenCV libraries and Tesseract-OCR libraries.

• Journal of The Korean Association of Information Education
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• v.23 no.2
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• pp.189-196
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• 2019

Artificial intelligence has begun to be used in various parts of our lives, and recently its sphere has been expanding. However, students tend to find it difficult to recognize artificial intelligence technology because education on artificial intelligence is not being conducted on elementary school students. This paper examined the teaching programming language and artificial intelligence teaching methods, and looked at the changes in students' attitudes toward artificial intelligence technology by conducting education on artificial intelligence. To this end, education on block-type programming language-based artificial intelligence technology was provided to students' level. And we looked at students' attitudes toward artificial intelligence technology through a single group pre-postmortem. As a result, it brought about significant improvements in interest in artificial intelligence, possible access to artificial intelligence technology and the need for education on artificial intelligence technology in schools.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.2
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• pp.28-36
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• 2022

As port structures are exposed to various extreme external loads such as wind (typhoons), sea waves, or collision with ships; it is important to evaluate the structural safety periodically. To monitor the port structure, especially the rubber fender, a fender segmentation system using a vision sensor and deep learning method has been proposed in this study. For fender segmentation, a new deep learning network that improves the encoder-decoder framework with the receptive field block convolution module inspired by the eccentric function of the human visual system into the DenseNet format has been proposed. In order to train the network, various fender images such as BP, V, cell, cylindrical, and tire-types have been collected, and the images are augmented by applying four augmentation methods such as elastic distortion, horizontal flip, color jitter, and affine transforms. The proposed algorithm has been trained and verified with the collected various types of fender images, and the performance results showed that the system precisely segmented in real time with high IoU rate (84%) and F1 score (90%) in comparison with the conventional segmentation model, VGG16 with U-net. The trained network has been applied to the real images taken at one port in Republic of Korea, and found that the fenders are segmented with high accuracy even with a small dataset.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.6
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• pp.1355-1360
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• 2014

In this paper, we apply the learning of the mobile robot insect augmented reality Digilog Book. In the era of electronic, book written in paper space just have moved to virtual reality space. The virtual reality, constraints spatial and physical, in the real world, it is a technique that enables to experience indirectly situation not experienced directly as user immersive experience type interface. Applied to the learning robot Digilog Book that allows the fusion of paper analog and digital content, using the augmented reality technology, to experience various interactions. Apply critical elements moving, three-dimensional images and animation to enrich the learning, for easier block assembly, designed to grasp more easily rank order between the blocks. Anywhere at any time, is capable of learning of the robot in Digilog Book to be executed by the mobile phone in particular.

• The Journal of Korean Academic Society of Nursing Education
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• v.23 no.1
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• pp.118-130
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• 2017

Purpose: The purpose of this study was to identify the relationship between the satisfaction with clinical practice and clinical performance ability by types of self-directed learning ability of nursing students. Methods: This was a triangular study that was conducted to understand clinical performance ability. The subjects were 260 junior and senior students from a university in P city. The data were collected from April 22 to December 30, 2015. Data were collected by Q-card, Q-block an assessment tool, a structured self-reporting survey and a questionnaire. Results: We classified the self-directed learning abilities into four types: Type 1: a self-reflective person; Type 2: a person who prepares for the future; Type 3: a person with a sense of responsibility and obligation; and Type 4: an enthusiastic learner. We found that clinical performance ability was higher for Type 4 than Type 3. We found that clinical performance satisfaction with clinical practice was also higher for the Type 4 individual than a Type 3 person. Conclusion: To improve students' clinical performance ability, we need plans and support to lead students toward becoming an 'enthusiastic learner' type of person with self-directed learning ability. It is necessary to increase students' satisfaction with clinical practice.

• Journal of the Society of Naval Architects of Korea
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• v.58 no.5
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• pp.303-313
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• 2021

Recently, quality control of the Liquefied Natural Gas Carrier (LNGC) cargo hold and block-erection interference areas using 3D scanners have been performed, focusing on large shipyards and the international association of classification societies. In this study, as a part of the research on LNGC cargo hold quality management advancement, a study on deep-learning-based scaffolding system 3D point cloud object detection and post-processing were conducted using a LNGC cargo hold 3D point cloud. The scaffolding system point cloud object detection is based on the PointNet deep learning architecture that detects objects using point clouds, achieving 70% prediction accuracy. In addition, the possibility of improving the accuracy of object detection through parameter adjustment is confirmed, and the standard of Intersection over Union (IoU), an index for determining whether the object is the same, is achieved. To avoid the manual post-processing work, the object detection architecture allows automatic task performance and can achieve stable prediction accuracy through supplementation and improvement of learning data. In the future, an improved study will be conducted on not only the flat surface of the LNGC cargo hold but also complex systems such as curved surfaces, and the results are expected to be applicable in process progress automation rate monitoring and ship quality control.