• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.10
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• pp.1941-1948
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• 2016

The incident during commuting to school happened frequently in these days, such that the government announced the student commuting safety policy for addressing to implement the safety management system of the unsafer school commuting zone. In this paper, a commuting tracking system is proposed that notifies the location of vehicles and the boarding status of student using BLE beacon and smart phone GPS function. The commuting tracking system that gets the data from the system server of driver's smart phone GPS location and UUID of the beacon which had provided students has configured to provide notifications to parents and related administrators. It provides real-time information about whether a student boarding, boarding times and bus locations for parents and administrators. It verifies the disembarking time for each student and also provides to driver to secure if any student tries to board the wrong school bus and if any students is left behind in the bus.

• Journal of Digital Convergence
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• v.11 no.11
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• pp.775-781
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• 2013

N-screen is a Service that can be provide for One Service Multi Device. If the network is changed or if the device is changed after authentication the device fits seamlessly send footage. Security threats that occur here have a problem with user authentication. In this paper proposes available in a multi-device the authentication system. Homomorphic Encryption Algorithm of authentication scheme used. Among the authentication mechanism that already exists is a simple and lightweight authentication mechanism. In addition, N-screen service that uses multiple devices is simple authentication process of the device. Review the results of proposed authentication protocol, encryption algorithm to process a small storage capacity and is easy to work in low processor. And was suitable for use with multiple devices.

• Journal of Digital Convergence
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• v.14 no.7
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• pp.373-383
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• 2016

With mobile-epoch and emerging of Fin-tech, Bio-recognition technology utilizing bio-information in secure method has spread. Specially, In order to change convenient payment services and transportation cards, the combination of biometrics and mobile services are being expanded. The basic concept of authentication such as access control, IA&A, OpenID, OAuth 1.0a, SSO, and Biometrics techniques are investigated, and the protocol stack for security API platform, FIDO, SCIM, OAuth 2.0, JSON Identity Suite, Keystone of OpenStack, Cloud-based SSO, and AIM Agent are described detailed in aspect of application of AIM. The authentication technology in domestic and foreign will accelerate technology development and research of standardization centered in the federated FIDO Universal Authentication Framework(UAF) and Universal 2 Factor Framework(U2F). To accommodate the changing needs of the social computing paradigm recently in this paper, the trends of various authentication technology, and design and function of AIM framework was defined.

• Journal of Pharmacopuncture
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• v.24 no.2
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• pp.59-67
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• 2021

Objectives: We aimed to compare the external herbal dispensary (EHD) evaluation criteria for pharmacopuncture and the Korea Good Manufacturing Practice (KGMP) sterile medicine standards to contribute to the establishment of quality control criteria for pharmacopuncture. Methods: We obtained the KGMP standards from the Ministry of Food and Drug Safety and the pharmacopuncture certification criteria from the Ministry of Health and Welfare of South Korea. The EHD evaluation items were classified into three categories: facilities, quality control, and validation. The evaluation items were compared with the KGMP sterile medicine criteria to determine their conformance with each other, followed by a discussion among the committee of six experts and their consensus to suggest the items to complement the EHD evaluation criteria. Results: Among the KGMP sterile medicine criteria, 44 were related to the management of the facilities, and 32 pharmacopuncture evaluation items corresponded to these KGMP items (66.7%). Fifty-eight KGMP criteria were related to quality management, and 42 pharmacopuncture evaluation items corresponded to these KGMP items (72.4%). Twentyfive KGMP sterile medicine criteria were related to validation, and 11 pharmacopuncture evaluation items corresponded to these KGMP items (44.0%). Sixteen items under the pharmacopuncture EHD criteria corresponded to the KGMP sterile medicine criteria based on the consent of the experts. Among these, 4 were related to facility management, 6 were related to quality control, and 6 were related to validation. Conclusion: For the safety and quality control of pharmacopuncture, there is a need to select the criteria for the mandatory items among the proposed pharmacopuncture-EHD criteria laws and systems to ensure that the pharmacopuncture materials are produced under the pharmacopuncture-EHD in compliance with the relevant requirements. More studies are needed to secure the safety level of pharmacopuncture materials corresponding to that of conventional medicine.

• Fashion & Textile Research Journal
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• v.22 no.6
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• pp.789-802
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• 2020

This study analyzes the experiences of a color therapy program that uses color and fashion directing to influence the psychological characteristics of adolescents, such as emotional intelligence, self-efficacy, and color expression. The subjects of this study were 15 adolescents who participated in B-City Winter School (January 6-15, 2020). Pre- and post-tests for emotional intelligence and self-efficacy were conducted to confirm the effect of the color therapy program that indicated a significant difference at the p<.05 level. Statistical significance was confirmed through the GEE model for formal characteristics used in pre/post and fashion-directed color expression. Peter pre- and post-changes in vocabulary emergence were investigated with a Word Cloud analysis using R program 4.0.2 that extracted the lower and upper categories. The significance of this study is that by expressing both color expression and fashion direction together, a program was provided for adolescents to have richer emotional experiences and career-related experiences. This study examined changes within a short period of time in 15 subjects; consequently, a follow-up in-depth qualitative analysis of the program effect and a post-testing study on maintaining the long-term effect is expected to secure further validity and usefulness of the study results. In addition, new studies are expected to contribute to the development of differentiated fashion-related education programs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.742-756
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• 2022

A flood of information has occurred with the rise of the internet and digital devices in the fourth industrial revolution era. Every millisecond, massive amounts of structured and unstructured data are generated; smartphones, wearable devices, sensors, and self-driving cars are just a few examples of devices that currently generate massive amounts of data in our daily. Machine learning has been considered an approach to support and recognize patterns in data in many areas to provide a convenient way to other sectors, including the healthcare sector, government sector, banks, military sector, and more. However, the conventional machine learning model requires the data owner to upload their information to train the model in one central location to perform the model training. This classical model has caused data owners to worry about the risks of transferring private information because traditional machine learning is required to push their data to the cloud to process the model training. Furthermore, the training of machine learning and deep learning models requires massive computing resources. Thus, many researchers have jumped to a new model known as "Federated Learning". Federated learning is emerging to train Artificial Intelligence models over distributed clients, and it provides secure privacy information to the data owner. Hence, this paper implements Federated Averaging with a Deep Neural Network to classify the handwriting image and protect the sensitive data. Moreover, we compare the centralized machine learning model with federated averaging. The result shows the centralized machine learning model outperforms federated learning in terms of accuracy, but this classical model produces another risk, like privacy concern, due to the data being stored in the data center. The MNIST dataset was used in this experiment.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.2
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• pp.47-60
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• 2023

This study proposed to adopt consortium blockchain for the database in the research support system of national R&D projects in order to increase efficiency, to reduce administrative burden, and to promote transparent research environment focusing on servicing researchers. Specifically, storage methods were classified according to data characteristics. First, data that requires integrity and transparency is stored in the blockchain, Second, confidentiality and data that require modification and deletion are stored in the database, Third, data that requires confidentiality, integrity, and transparency at the same time stores the original in the database, and the hash value of the data is separately stored in the blockchain. If research support system adopts blockchain, it is possible to operate the system stably, to share quick exchange of information between research institutes, to reduce administrative burden, to improve transparency of process, to resolve asymmetry of information, and to secure integrity, confidentiality, and availability of data.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.63-71
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• 2020

In this paper, we propose a fire detection technology using YOLOv3 and EfficientDet, the most reliable artificial intelligence detection algorithm recently, an alert service that simultaneously transmits four kinds of notifications: text, web, app and e-mail, and an AWS system that links fire detection and notification service. There are two types of our highly accurate fire detection algorithms; the fire detection model based on YOLOv3, which operates locally, used more than 2000 fire data and learned through data augmentation, and the EfficientDet, which operates in the cloud, has conducted transfer learning on the pretrained model. Four types of notification services were established using AWS service and FCM service; in the case of the web, app, and mail, notifications were received immediately after notification transmission, and in the case of the text messaging system through the base station, the delay time was fast enough within one second. We proved the accuracy of our fire detection technology through fire detection experiments using the fire video, and we also measured the time of fire detection and notification service to check detecting time and notification time. Our AI fire detection and notification service system in this paper is expected to be more accurate and faster than past fire detection systems, which will greatly help secure golden time in the event of fire accidents.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.4
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• pp.771-782
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• 2021

Regular or irregular situations such as traffic accidents, damage to road facilities, maintenance or repair work, and vehicle breakdowns occur frequently on highways. It is required to provide traffic services to drivers by promptly recognizing these regular or irregular situations, various techniques have been developed for rapidly collecting data and detecting abnormal traffic conditions to solve the problem. We propose a method that can be used for verification and demonstration of unexpected situation algorithms by establishing a system and developing algorithms for detecting unexpected situations on highways. For the detection of emergencies on expressways, a system was established by defining the expressway contingency and algorithm development, and a test bed was operated to suggest a method that can be used for verification and demonstration of contingency algorithms. In this study, a system was established by defining the unexpected situation and developing an algorithm to detect the unexpected situation on the highway, and a method that can be used verifying and demonstrating unexpected situations. It is expected to secure golden time for the injured by reducing the effectiveness of secondary accidents. Also predictable accidents can be reduced in case of unexpected situations and the detection time of unpredictable accidents.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.1019-1034
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• 2022

Malware attacks become more prevalent in the hyper-connected society of the 4th industrial revolution. To respond to such malware, automation of malware detection using artificial intelligence technology is attracting attention as a new alternative. However, using artificial intelligence without collateral for its reliability poses greater risks and side effects. The EU and the United States are seeking ways to secure the reliability of artificial intelligence, and the government announced a reliable strategy for realizing artificial intelligence in 2021. The government's AI reliability has five attributes: Safety, Explainability, Transparency, Robustness and Fairness. We develop four elements of safety, explainable, transparent, and fairness, excluding robustness in the malware detection model. In particular, we demonstrated stable generalization performance, which is model accuracy, through the verification of external agencies, and developed focusing on explainability including transparency. The artificial intelligence model, of which learning is determined by changing data, requires life cycle management. As a result, demand for the MLops framework is increasing, which integrates data, model development, and service operations. EXE-executable malware and documented malware response services become data collector as well as service operation at the same time, and connect with data pipelines which obtain information for labeling and purification through external APIs. We have facilitated other security service associations or infrastructure scaling using cloud SaaS and standard APIs.