• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.111-118
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• 2022

In computer vision research, the two-dimensional human pose is a very extensive research direction, especially in pose tracking and behavior recognition, which has very important research significance. The acquisition of human pose targets, which is essentially the study of how to accurately identify human targets from pictures, is of great research significance and has been a hot research topic of great interest in recent years. Human pose recognition is used in artificial intelligence on the one hand and in daily life on the other. The excellent effect of pose recognition is mainly determined by the success rate and the accuracy of the recognition process, so it reflects the importance of human pose recognition in terms of recognition rate. In this human body gesture recognition, the human body is divided into 17 key points for labeling. Not only that but also the key points are segmented to ensure the accuracy of the labeling information. In the recognition design, use the comprehensive data set MS COCO for deep learning to design a neural network model to train a large number of samples, from simple step-by-step to efficient training, so that a good accuracy rate can be obtained.

• Journal of Internet of Things and Convergence
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• v.8 no.2
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• pp.79-84
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• 2022

This paper presents an enhancement method of document restoration capability which is robust for security, loss, and contamination, It is based on two methods, that is, encryption and DnCNN(DeNoise Convolution Neural Network). In order to implement this encryption method, a mathematical model is applied as a spatial frequency transfer function used in optics of 2D image information. Then a method is proposed with optical interference patterns as encryption using spatial frequency transfer functions and using mathematical variables of spatial frequency transfer functions as ciphers. In addition, by applying the DnCNN method which is bsed on deep learning technique, the restoration capability is enhanced by removing noise. With an experimental evaluation, with 65% information loss, by applying Pre-Training DnCNN Deep Learning, the peak signal-to-noise ratio (PSNR) shows 11% or more superior in compared to that of the spatial frequency transfer function only. In addition, it is confirmed that the characteristic of CC(Correlation Coefficient) is enhanced by 16% or more.

• Journal of Korea Game Society
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• v.21 no.6
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• pp.31-40
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• 2021

Go has a history of more than 2,500 years, and the life-and-death problems in Go is a fundamental problem domain that must be solved when implementing a computer Go. We attempted to determine the numbers of prototypical eye shapes with 3, 4, 5, and 6 eyes that are directly related to the life-and-death problems, and to classify the prototypical eye shapes represented in 4-tuple forms. Experiment was conducted by Monte-Carlo method and point pattern matching. According to the experimental results, the numbers of prototypical eye shapes were 2 for 3-eye, 5 for 4-eye, 12 for 5-eye, and 35 for 6-eye shapes. Further, using a 4-tuple form, we classified prototypical eye shapes into 1 for 3-eye, 3 for 4-eye, 4 for 5-eye, and 8 for 6-eye shapes.

• Journal of Platform Technology
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• v.9 no.4
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• pp.90-96
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• 2021

Most of the vehicles currently under development and in operation are equipped with various IoT sensors, but some of the factors that cause car accidents are relatively difficult to detect. One of the major risk factors among these factors is black ice. Black ice is one of the factors most likely to cause major accidents, as it can affect all vehicles passing through areas covered with black ice. Therefore, black ice detection technique is essential to prevent major accidents. For this purpose, some studies have been carried out in the past, but unrealistic factors have been reflected in some parts, so research to supplement this is needed. In this paper, we tried to detect black ice by analyzing color images using the CNN technique, and we succeeded in detecting black ice to a certain level. However, there were differences from previous studies, and the reason was analyzed.

• Journal of The Korean Association of Information Education
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• v.26 no.2
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• pp.97-104
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• 2022

As the era of digital transformation begins in earnest, the importance of convergent thinking based on software, artificial intelligence, and big data is increasing. In line with these social needs, this study developed a 5th hour SW-STEAM education program using Monte Carlo simulation techniques for Mendelian inheritance in the field of life science. By programming and implementing Mendelian inheritance using Monte carlo simulation, the program was organized so that not only convergent thinking skills but also related knowledge could be understood in depth. In order to verify the validity of the developed education program, 11 experts in related fields were requested to test the content validity, and the validity was verified by meeting the CVR reference value of 0.59 suggested by Lawshe.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1317-1322
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• 2021

Software in the era of the Fourth Industrial Revolution is becoming a key foundation in an intelligent information society. Therefore, it is necessary to study the new direction of manpower training and education that can cope with the times. To this end, the Ministry of Education reorganized the curriculum and is implementing software education based on a logical problem-solving process based on computing thinking skills rather than acquiring general ICT knowledge. However, there is a lack of securing high-quality educational content for software education, and there is also a lack of teaching aids that can be taught in connection with advanced IT technologies. To overcome this, this paper proposes the development of external expansion devices to expand educational content and functions capable of convergent software education such as artificial intelligence using coding robots for software education. Through this, effective software education is possible by improving the curriculum of the existing simple problem-solving method and developing various learning materials.

• Journal of the Microelectronics and Packaging Society
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• v.29 no.1
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• pp.35-41
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• 2022

In the era of Fifth-Generation (5G), technology requirements such as Artificial Intelligence (AI), Cloud computing, automatic vehicles, and smart manufacturing are increasing. For high efficiency of electronic devices, research on high-intensity circuits and packaging for miniaturized electronic components is important. A solder paste which consists of small solder powders is one of common solder for high density packaging, whereas an electroplated solder has limitation of uniformity of bump composition. Researches are underway to improve wettability through the addition of nanoparticles into a solder paste or the surface finish of a substrate, and to suppress the formation of IMC growth at the metal pad interface. This paper describes the principles of improving the wettability of solder paste and suppressing interfacial IMC growth by addition of nanoparticles.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.819-834
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• 2022

Artificial Intelligence (AI) techniques have been effectively used for image classification, object detection, and image segmentation. Along with the recent advancement of computing power, deep learning models can build deeper and thicker networks and achieve better performance by creating more appropriate feature maps based on effective activation functions and optimizer algorithms. This review paper examined technical and academic trends of Convolutional Neural Network (CNN) and Transformer models that are emerging techniques in remote sensing and suggested their utilization strategies and development directions. A timely supply of satellite images and real-time processing for deep learning to cope with disaster monitoring will be required for future work. In addition, a big data platform dedicated to satellite images should be developed and integrated with drone and Closed-circuit Television (CCTV) images.

• Journal of Advanced Navigation Technology
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• v.26 no.5
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• pp.344-349
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• 2022

A compact LPDA antenna consisting of right triangle-shaped dipole elements appended with strips is proposed for UWB applications. First, right triangle-shaped dipole elements are used instead of conventional strip dipole elements to reduce the width of the LPDA antenna. Second, the spacing between the LPDA elements is decreased to reduce the length of the LPDA antenna. Finally, strips are appended at the ends of the right triangle-shaped dipole elements in order to further reduce the width of the antenna. A prototype of the proposed antenna with 16 elements and gain > 4 dBi is fabricated on an FR4 substrate with dimensions of 44 mm×30 mm. Measured frequency band of the fabricated antenna is 2.99-14.76 GHz for a VSWR < 2, which ensures UWB operation, and measured gain range is 4.0-5.5 dBi with a front-to-back ratio larger than 10 dB. The length and width of the proposed compact LPDA antenna are reduced by 40.9% and 20.6%, respectively, compared to the conventional LPDA.

• Journal of the Korea Society of Computer and Information
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• v.28 no.3
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• pp.35-43
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• 2023

Emotion recognition while driving is an essential task to prevent accidents. Furthermore, in the era of autonomous driving, automobiles are the subject of mobility, requiring more emotional communication with drivers, and the emotion recognition market is gradually spreading. Accordingly, in this research plan, the driver's emotions are classified into seven categories using psychological and behavioral data, which are relatively easy to collect. The latent vectors extracted through the auto-encoder model were also used as features in this classification model, confirming that this affected performance improvement. Furthermore, it also confirmed that the performance was improved when using the framework presented in this paper compared to when the existing EEG data were included. Finally, 81% of the driver's emotion classification accuracy and 80% of F1-Score were achieved only through psychological, personal information, and behavioral data.