• Journal of Cadastre & Land InformatiX
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• v.52 no.1
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• pp.81-93
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• 2022

Recently, there have been many studies on object recognition around the vehicle and recognition of traffic signs and traffic lights in autonomous driving. In particular, such the recognition of traffic lights is one of the core technologies in autonomous driving. Therefore, many studies for such the recognition of traffic lights have been performed, the studies based on various deep learning models have increased significantly in recent. In addition, as a high-quality AI training data set for voice, vision, and autonomous driving is released on AIHub, it makes it possible to develop a recognition model for traffic lights suitable for the domestic environment using the data set. In this study, we developed a recognition model for traffic lights that can be used in Korea using the AIHub's training data set. In particular, in order to improve the recognition performance, we used various models of YOLOv4 and YOLOv5, and performed our recognition experiments by defining various classes for the training data. In conclusion, we could see that YOLOv5 shows better performance in the recognition than YOLOv4 and could confirm the reason from the architecture comparison of the two models.

• The Journal of the Korea Contents Association
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• v.22 no.2
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• pp.1-9
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• 2022

To provide real-time traffic conditions, crowd sourcing based traffic information services in which users directly report and share traffic conditions are being developed. However, the existing traffic information service provides limited traffic conditions because it only shares information reported by specific service participants. In this paper, we design and develop a crowd sourcing based traffic information service that provides real-time traffic conditions by collecting direct reports from users and public traffic conditions. The proposed service allows users to directly report traffic conditions by voice and text, and collects and integrates traffic conditions published by external organizations. The collected traffic conditions are provided in real time through a push service, and new traffic conditions are transmitted when the user's location changes. The proposed service can report traffic conditions and share real-time traffic conditions through an Android app.

• Journal of Practical Engineering Education
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• v.14 no.2
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• pp.361-366
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• 2022

Recently, with the development of virtual and augmented reality technology, metaverse is emerging as a new paradigm that will lead the next-generation internet era, and social and economic activities are spreading around the game, entertainment, music, and content industries. Moreover, as non-face-to-face conversion accelerated after the outbreak of COVID-19, lifestyles and industrial sites are becoming untact and further rapidly becoming a metaverse. In particular, the application of metaverse to the education field is attracting attention because realistic classes using real-time voice conversations using avatars, 3D objects, and 360-degree images can increase immersion and overcome the limitations of distance education. This study examines the concept of metaverse and examines that education using metaverse can be an alternative that can increase the efficiency of education in the non-face-to-face era. In particular, it shows that it is effective in language education and suggests an actual metaverse-based Korea language education program.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.193-198
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• 2023

We study a learner-customized lecture recommendation project using deep learning. Recommendation systems can be easily found on the web and apps, and examples using this feature include recommending feature videos by clicking users and advertising items in areas of interest to users on SNS. In this study, the sentence similarity Word2Vec was mainly used to filter twice, and the course was recommended through the Surprise library. With this system, it provides users with the desired classification of course data conveniently and conveniently. Surprise Library is a Python scikit-learn-based library that is conveniently used in recommendation systems. By analyzing the data, the system is implemented at a high speed, and deeper learning is used to implement more precise results through course steps. When a user enters a keyword of interest, similarity between the keyword and the course title is executed, and similarity with the extracted video data and voice text is executed, and the highest ranking video data is recommended through the Surprise Library.

• Journal of Life Science
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• v.31 no.10
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• pp.937-943
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• 2021

In this study, antibacterial activity against pathogenic strains and antioxidant activity were measured using the red pine leaf distilled concentrate. The results of the antibacterial activity measured using an emulsion of the red pine leaf distilled concentrate by the paper disc method showed the antibacterial activities against three Gram negative pathogenic strains, E. coli, S. typhi and Vibrio parahaemolyticus and exhibited growth inhibitions of 12 mm, 10 mm and 9 mm at a 5.0% (v/v) concentration, respectively. In addition, all three strains also showed growth inhibitions even at 0.5% (v/v) concentration. However, no antibacterial activity was exhibited against gram positive bacteria. The results of the antibacterial activity using the red pine leaf distilled concentrate measured by the turbidity method, the same antibacterial activities against three gram negative pathogenic strains, E. coli, S. typhi and V. parahaemolyticus as results of the paper disc method. V. parahaemolyticus showed more than 50% growth inhibition compared to the negative control at a concentration of 5% (v/v), E. coli exhibited 33.5% growth inhibition at 4 hr incubation, and S. typhi showed 65.1% and 44.6% growth inhibitions at 4 and 5 hr incubations, respectively. Antioxidant activities of an emulsion of the red pine leaf distilled concentrate were measured by DPPH and ABTS methods. DPPH method showed the highest activity of 55.81% at a 1.0% (v/v) concentration. ABTS method exhibited the highest activity of 18.44% at a 1.0% (v/v) concentration. Through this study, it is expected that the developments of the food and the cosmetics with enhanced functionality by utilizing the antioxidant and antibacterial activities of the red pine leaf distilled concentrate.

• The KIPS Transactions:PartD
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• v.13D no.3 s.106
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• pp.383-390
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• 2006

Product-line engineering is a dreaming goal in software engineering research. Unfortunately, the current underlying technologies do not seem to be still not much matured enough to make it viable in the industry. Based on our experiences in working on mobile telephony systems over 3 years, now we are in the course of developing an approach to product-line engineering for mobile telephony system software. In this paper, the experiences are shared together with our research motivation and idea. Consequently, we propose an approach to building and maintaining telephony application logics from the perspective of scenes. As a Domain-Specific Language(DSL), Menu Navigation Viewpoint(MNV) DSL is designed to deal with the problem domain of telephony applications. The functional requirements on how a set of telephony application logics are configured can be so various depending on manufacturer, product concept, service carrier, and so on. However, there is a commonality that all of the currently used telephony application logics can be generally described from the point of user's view, with a set of functional features that can be combinatorially synthesized from typical telephony services(i.e. voice/video telephony, CBS/SMS/MMS, address book, data connection, camera/multimedia, web browsing, etc.), and their possible connectivity. MNV DSL description acts as a backbone software architecture based on which the other types of telephony application logics are placed and aligned to work together globally.

• Convergence Security Journal
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• v.21 no.2
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• pp.57-66
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• 2021

Deep neural networks (DNNs) provide excellent performance for image, speech, and pattern recognition. However, DNNs sometimes misrecognize certain adversarial examples. An adversarial example is a sample that adds optimized noise to the original data, which makes the DNN erroneously misclassified, although there is nothing wrong with the human eye. Therefore studies on defense against adversarial example attacks are required. In this paper, we have experimentally analyzed the success rate of detection for adversarial examples by adjusting various parameters. The performance of the ensemble defense method was analyzed using fast gradient sign method, DeepFool method, Carlini & Wanger method, which are adversarial example attack methods. Moreover, we used MNIST as experimental data and Tensorflow as a machine learning library. As an experimental method, we carried out performance analysis based on three adversarial example attack methods, threshold, number of models, and random noise. As a result, when there were 7 models and a threshold of 1, the detection rate for adversarial example is 98.3%, and the accuracy of 99.2% of the original sample is maintained.

• Journal of Digital Forensics
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• v.12 no.3
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• pp.1-8
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• 2018

A random chat application is a type of social dating application that helps people find a lover or spouse by randomly connecting and providing services such as text, voice and video chat. Recently, there has been globally a rapid increase in its use due to the fact that it provides people to quick and convenient encounters at low cost. However, it is used as one of method to prostitute or to trade drugs and become a cause of violent crimes due to various criminal occurring after actual meeting between app users. For this reason, a random chat application is likely to provide proof of prostitution or drug trade and clues to arrest rape, kidnapping and murder suspects. Thus, it is necessary to analyse random chat applications from the viewpoint of digital forensics investigation, but there is no related research at all. Therefore, in this paper, we analyzed artifacts of 6 Korea random chat application's user behaviors; Ranchat, AngTalk, SsumgThing, DaTalk, EveryTalk and Sail. As a result, we found that it is remain on mobile device that time and contents of message transmission/reception, sender/receiver, friend profile and user account creation time when user is using the applications.

• Journal of Software Assessment and Valuation
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• v.17 no.1
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• pp.101-113
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• 2021

In this paper, we introduce a speaker overlap system and look at the process of converting the existed system on the specific framework of artificial intelligence. Speaker overlap is when two or more speakers speak at the same time during a conversation, and can lead to performance degradation in the fields of speech recognition or speaker recognition, and a lot of research is being conducted because it can prevent performance degradation. Recently, as application of artificial intelligence is increasing, there is a demand for switching between artificial intelligence frameworks. However, when switching frameworks, performance degradation is observed due to the unique characteristics of each framework, making it difficult to switch frameworks. In this paper, the process of converting the speaker overlap detection system based on the Keras framework to the pytorch-based system is explained and considers components. As a result of the framework switching, the pytorch-based system showed better performance than the existing Keras-based speaker overlap detection system, so it can be said that it is valuable as a fundamental study on systematic framework conversion.

• Journal of Practical Engineering Education
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• v.14 no.2
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• pp.367-376
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• 2022

In this study, the user experience was analyzed from the learner's point of view, focusing on the metaverse platform 'Spatial'. SUS(System Usability Scale) was used to evaluate the usability of the metaverse platform 'Spatial' in a college class, and the Magnitude estimation technique was used to evaluate the immersion and satisfaction with the class. In addition, a questionnaire survey was used to collect user experience opinions on the use of 'Spatial' as a teaching tool. Looking at the usability evaluation results of the 'Spatial' system, the students evaluated the usability, immersion, and satisfaction quite positively. Looking at the user experience of metaverse platform 'Spatial', it was found that students highly valued Metaverse as an educational tool that can provide a place for many people to gather and communicate even in a non-face-to-face space. Compared to other online platforms, metaverse has advantages in ease of use, interaction, immersion, and interest. In particular, in addition to keyboard, touch, and display, interaction using the five senses such as voice, motion, and gaze was recognized as a great advantage. On the other hand, it was found that high openness, freedom, and interest factors can both promote learning and inhibit learning. Nevertheless, it is judged that the metaverse platform 'Spatial' can be effectively applied in college classes because it enables various interactions between instructor and learner or between learner and learner.