• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.6
• /
• pp.827-833
• /
• 2022

This study aims to prevent secondary traffic accidents with high severity by overcoming the limitations of existing traffic information collection systems through analysis of traffic information collection detectors and various algorithms used to detect unexpected situations. In other words, this study is meaningful present that analyzing the 'unexpected situation that causes secondary traffic accidents' and 'Existing traffic information collection system' accordingly presenting a solution that can preemptively prevent secondary traffic accidents, intelligent traffic information collection system that enables accurate information collection on all sections of the road. As a result of the experiment, the reliability of data transmission reached 97% based on 95%, the data transmission speed averaged 209ms based on 1000ms, and the network failover time achieved targets of 50sec based on 120sec.

• The Journal of the Convergence on Culture Technology
• /
• v.8 no.4
• /
• pp.367-373
• /
• 2022

VANET (Vehicular Ad hoc NETwork) is one of the special cases of the ad hoc networks in which car nodes communicate with each other and/or with RSUs (Road Side Unit) in order for the drivers to receive nearby road traffic information as well as for the passengers to retrieve nearby gas price or hotel information. In case of constructing VANET over CCN, users do not need to specify a destination server address rather to input a key word such as nearby congestion in order to gather surrounding traffic congestion information. Furthermore, each car node caches its retrieved data for forwarding other nodes when requested. In addition, the data transmission is inherently multicast, which implies fast data propagation to the participating car nodes. This paper measures and evaluates the data transmission performance of the VCCN (VANET over CCN) in which nodes are equipped with diverse wireless communication channels. The simulation result indicates that 802.11a shows the best performance of the data transmission against other wireless channels. Moreover, it indicates that VCCN improves overall data transmission and provides benefit to the nodes that request the same traffic information by exploiting inherent multicast communication.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.17 no.3
• /
• pp.529-536
• /
• 2022

In the past two years, Severe Acute Respiratory Syndrome Coronavirus-2(SARS-CoV-2) has been hitting more and more to people. This paper proposes a novel U-Net Convolutional Neural Network to classify and segment COVID-19 lung CT images, which contains Sub Coding Block (SCB), Atrous Spatial Pyramid Pooling(ASPP) and Attention Gate(AG). Three different models such as FCN, U-Net and U-Net-SCB are designed to compare the proposed model and the best optimizer and atrous rate are chosen for the proposed model. The simulation results show that the proposed U-Net-MMFE has the best Dice segmentation coefficient of 94.79% for the COVID-19 CT scan digital image dataset compared with other segmentation models when atrous rate is 12 and the optimizer is Adam.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.22 no.5
• /
• pp.23-28
• /
• 2022

Today, the online market has grown rapidly due to the development of digital platforms and the pandemic situation. Therefore, unlike the existing offline market, the distinctiveness of the online market has prompted users to check online reviews. It has been established that reviews play a significant part in influencing the user's purchase intention through precedents of several studies. However, the current review writing method makes it difficult for other users to understand the writer's emotions by expressing them through elements like tone and words. If the writer also wanted to emphasize something, it was very cumbersome to thicken the parts or change the colors to reflect their emotions. Therefore, in this paper, we propose a technique to check the user's emotions through facial expression recognition using a camera, to automatically set colors for each emotion using research on existing emotions and colors, and give colors based on the user's intention.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.10
• /
• pp.1525-1530
• /
• 2022

BGP(Border Gateway Protocol) is a routing protocol that is actively used in inter-AS routing on the Internet. However, BGP routing protocol is vulnerable to BGP hijacking attacks that hijack the network by impersonating normal BGP sessions. BGP Hijacking attacks can lead to causing intercept IP traffic or interference with the normal service operation. Recently, BGP hijacking attacks, which have often occurred overseas, have also occurred in Korea. It means threatening the security of the Internet. In this paper, we analyze the overall process of attack through representative attack cases and virtual scenarios of BGP hijacking and based on the results of analyzing the application status of security technology to prevent BGP hijacking attacks by Korea and global major ISPs. It covers the technical proposal of ISPs and autonomous system operators should take to defend against BGP hijacking attacks.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.39 no.6
• /
• pp.381-392
• /
• 2021

In this study, we perform deep learning-based object detection analysis on eight types of buildings defined by the digital map topography standard code, leveraging images taken with UAV (Unmanned Aerial Vehicle). Image labeling was done for 509 images taken by UAVs and the YOLO (You Only Look Once) v5 model was applied to proceed with learning and inference. For experiments and analysis, data were analyzed by applying an open source-based analysis platform and algorithm, and as a result of the analysis, building objects were detected with a prediction probability of 88% to 98%. In addition, the learning method and model construction method necessary for the high accuracy of building object detection in the process of constructing and repetitive learning of training data were analyzed, and a method of applying the learned model to other images was sought. Through this study, a model in which high-efficiency deep neural networks and spatial information data are fused will be proposed, and the fusion of spatial information data and deep learning technology will provide a lot of help in improving the efficiency, analysis and prediction of spatial information data construction in the future.

• Advances in Computational Design
• /
• v.7 no.2
• /
• pp.113-128
• /
• 2022

Water consumption is strongly affected by numerous factors, such as population, climatic, geographic, and socio-economic factors. Therefore, the implementation of a reliable predictive model of water consumption pattern is challenging task. This study investigates the performance of predictive models based on multi-layer perceptron (MLP), multiple linear regression (MLR), and support vector regression (SVR). To understand the significant factors affecting water consumption, the stepwise regression (SW) procedure is used in MLR to obtain suitable variables. Then, this study also implements three predictive models based on these significant variables (e.g., SWMLR, SWMLP, and SWSVR). Annual data of water consumption in Thailand during 2006 - 2015 were compiled and categorized by provinces and distributors. By comparing the predictive performance of models with all variables, the results demonstrate that the MLP models outperformed the MLR and SVR models. As compared to the models with selected variables, the predictive capability of SWMLP was superior to SWMLR and SWSVR. Therefore, the SWMLP still provided satisfactory results with the minimum number of explanatory variables which in turn reduced the computation time and other resources required while performing the predictive task. It can be concluded that the MLP exhibited the best result and can be utilized as a reliable water demand predictive model for both of all variables and selected variables cases. These findings support important implications and serve as a feasible water consumption predictive model and can be used for water resources management to produce sufficient tap water to meet the demand in each province of Thailand.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.12
• /
• pp.111-119
• /
• 2022

The purpose of this paper is to examine the change in perception of 3D virtual fashion before and after COVID-19 using big data analysis. The data collection period is from January 1, 2017, before the outbreak of COVID-19, to October 30, 2022, after the outbreak. Big data was collected for key words related to 3D virtual fashion extracted from social media such as Naver, Daum, Google, and YouTube using Textom. After the collected words were refined, word cloud, word frequency, connection centrality, network visualization, and CONCOR analysis were performed. As a result of extracting and analyzing 32,461 words with 3D virtual fashion as a keyword, the frequency and centrality of fashion, virtual, and technology appeared the highest, and the frequency of appearance of digital, design, clothing, utilization, and manufacturing was also high. Through this, it was found that 3D virtual fashion is being used throughout the industry along with the development of technology. In particular, the key words that stand out the most after COVID-19 are metaverse and 3D education, which are in high demand in the fashion industry.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.15 no.4
• /
• pp.217-222
• /
• 2022

Information plays an important role in modern society. Most of the information is processed and moved in the digital space. In cyberspace, confidential communication based on resistance and security is fundamental. It is essential to protect the information sent and received over the network. However, information may be leaked and forged by unauthorized users. The effectiveness of the existing protection system decreases as an innovative technique is applied to identify the communication contents by a third party. Steganography is a technique for inserting secret information into a specific area of a medium. Stegganography and steganalysis techniques are at odds with each other. A new and sophisticatedly implemented system is needed to cope with the advanced steganalysis. To enhance step-by-step diffusion and irregularity, I propose a hybrid implementation technique of image steganography for Hangul messages based on layered encryption and variable ShiftRows. PSNR was calculated to measure the proposed steganography efficiency and performance. Compared to the basic LSB technique, it was shown that the diffusion and randomness can be increased even though the PSNR decreased by 1.45%.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.12
• /
• pp.1853-1858
• /
• 2021

O-rings fill the gaps between mechanical parts. Until now, the sorting of defective products has been performed visually and manually, so classification errors often occur. Therefore, a camera-based defect classification system without human intervention is required. However, a binarization process is required to separate the required region from the background in the camera input image. In this paper, an adaptive binarization technique that considers the surrounding pixel values is applied to solve the problem that single-threshold binarization is difficult to apply due to factors such as changes in ambient lighting or reflections. In addition, the convex hull technique is also applied to compensate for the missing pixel part. And the learning model to be applied to the separated region applies the residual error-based deep learning neural network model, which is advantageous when the defective characteristic is non-linear. It is suggested that the proposed system through experiments can be applied to the automation of O-ring defect detection.