• Journal of information and communication convergence engineering
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• v.9 no.4
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• pp.358-362
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• 2011

The National Emergency Management Agency of South Korea has established a Disaster Victims Psychology Support Center. The Disaster Victims Psychology Support Center can enable victims who got psychological damage from disasters to return to their daily lives through healing activity, field visits and advice of experts. The previous Psychology Support Center System managed the information of disaster victims through an independent database. However, this paper proposes a system that is developed to identify medical institutions and mental health centers within a distance of radius, based on the potential Hot-Spot areas of disaster victims using the GIS Systems. The proposed system can efficiently support selection of appropriate institutions for disaster victims using their location and age, classification of damage, and damaged parts of the body. Also, this spatial analysis can assist to decide on a policy based on the location of disaster victims and the extent of damage. Therefore, this paper can provide the required information to support decision making based on the concentrated areas for disaster victims.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.7
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• pp.3191-3196
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• 2011

Smart home system are being installed in the most new construction of building for the convenience of living life. As smart home systems are becoming more common and their diffusion rates are faster, hacker's attack for the smart home system will be increased. In this paper, Risk level of smart home's to do respond to intrusion that occurred from the wired network and wireless network intrusion cases and attacks can occur in a virtual situation created scenarios to build a database. This is based on the smart home users vulnerable to security to know finding illegal intrusion traffic in real-time and attack prevent was designed the intrusion detection algorithm.

• Proceedings of the KIEE Conference
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• summer
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• pp.88-90
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• 2004

Data are a critical utility asset. Collecting correct data on site leads to accurate information. Data, when gathered with foresight & properly formatted, are useful to both existing database and easily transferable to newer, more comprehensive historical outage data. However, when investigating data items options, the task, can be an arduous one, often requiring the efforts of entire committees. This paper firstly discusses the KEPCO's past 10 years of historical outage data which include meterological data, and also by several elements of the National Weather Service, failure rate, outage duration, and probability classification, etc. Then, these collected data are automatically stored in an Outage Data Management System (ODMS), which allows for easy access and display. ODMS has a straight-forward and easy-to-use interface. It lets you to navigate through modules very easily and allows insertion, deletion or editing of data. In particular, this will further provide the KEPCO that not only helps with probabilistic security assessment but also provides a platform for future development of Probability Estimation Program (PEP).

• Korean Journal of Artificial Intelligence
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• v.9 no.2
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• pp.23-28
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• 2021

In this paper, we studied a system that can efficiently build security management for single-person households using Arduino, ESP32-CAM and PIR sensors, and proposed an Android app with an internet connection. The ESP32-CAM is an Arduino compatible board that supports both Wi-Fi, Bluetooth, and cameras using an ESP32-based processor. The PCB on-board antenna may be used independently, and the sensitivity may be expanded by separately connecting the external antenna. This system has implemented an Arduino-based Unauthorized intrusion system that can significantly help prevent crimes in single-person households using the combination of PIR sensors, Arduino devices, and smartphones. unauthorized intrusion system, showing the connection between Arduino Uno and ESP32-CAM and with smartphone applications. Recently, if daily quarantine is underway around us and it is necessary to verify the identity of visitors, it is expected that it will help maintain a safety net if this system is applied for the purpose of facial recognition and restricting some access. This technology is widely used to verify that the characters in the two images entered into the system are the same or to determine who the characters in the images are most similar to among those previously stored in the internal database. There is an advantage that it may be implemented in a low-power, low-cost environment through image recognition, comparison, feature point extraction, and comparison.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1494-1507
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• 2018

Intelligent human identification using face information has been the research hotspot ranging from Internet of Things (IoT) application, intelligent self-service bank, intelligent surveillance to public safety and intelligent access control. Since 2D face images are usually captured from a long distance in an unconstrained environment, to fully exploit this advantage and make human recognition appropriate for wider intelligent applications with higher security and convenience, the key difficulties here include gray scale change caused by illumination variance, occlusion caused by glasses, hair or scarf, self-occlusion and deformation caused by pose or expression variation. To conquer these, many solutions have been proposed. However, most of them only improve recognition performance under one influence factor, which still cannot meet the real face recognition scenario. In this paper we propose a multi-scale parallel convolutional neural network architecture to extract deep robust facial features with high discriminative ability. Abundant experiments are conducted on CMU-PIE, extended FERET and AR database. And the experiment results show that the proposed algorithm exhibits excellent discriminative ability compared with other existing algorithms.

• ETRI Journal
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• v.40 no.6
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• pp.745-758
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• 2018

A wide range of studies in various disciplines has focused on the Internet of Things (IoT) and cyber-physical systems (CPS). However, it is necessary to summarize the current status and to establish future directions because each study has its own individual goals independent of the completion of all IoT applications. The absence of a comprehensive understanding of IoT and CPS has disrupted an efficient resource allocation. To assess changes in the knowledge structure and emerging technologies, this study explores the dynamic research trends in IoT by analyzing bibliographic data. We retrieved 54,237 keywords in 12,600 IoT studies from the Scopus database, and conducted keyword frequency, co-occurrence, and growth-rate analyses. The analysis results reveal how IoT technologies have been developed and how they are connected to each other. We also show that such technologies have diverged and converged simultaneously, and that the emerging keywords of trust, smart home, cloud, authentication, context-aware, and big data have been extracted. We also unveil that the CPS is directly involved in network, security, management, cloud, big data, system, industry, architecture, and the Internet.

• Journal of Microbiology and Biotechnology
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• v.29 no.8
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• pp.1255-1265
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• 2019

To investigate the diversity of gastrointestinal microflora and lignocellulose-degrading enzymes in wild Asian elephants, three of these animals living in the same group were selected for study from the Wild Elephant Valley in the Xishuangbanna Nature Reserve of Yunnan Province, China. Fresh fecal samples from the three wild Asian elephants were analyzed by metagenomic sequencing to study the diversity of their gastrointestinal microbes and cellulolytic enzymes. There were a high abundance of Firmicutes and a higher abundance of hemicellulose-degrading hydrolases than cellulose-degrading hydrolases in the wild Asian elephants. Furthermore, there were a high abundance and a rich diversity of carbohydrate active enzymes (CAZymes) obtained from the gene set annotation of the three samples, with the majority of them showing low identity with the CAZy database entry. About half of the CAZymes had no species source at the phylum or genus level. These indicated that the wild Asian elephants might possess greater ability to digest hemicellulose than cellulose to provide energy, and moreover, the gastrointestinal tracts of these pachyderms might be a potential source of novel efficient lignocellulose-degrading enzymes. Therefore, the exploitation and utilization of these enzyme resources could help us to alleviate the current energy crisis and ensure food security.

• International journal of advanced smart convergence
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• v.10 no.3
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• pp.122-130
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• 2021

This study is about an Artificial Intelligence-based fake news identification system and its methods to determine the authenticity of content distributed over the Internet. Among the news we encounter is news that an individual or organization intentionally writes something that is not true to achieve a particular purpose, so-called fake news. In this study, we intend to design a system that uses Artificial Intelligence techniques to identify fake content that exists within the news. The proposed identification model will propose a method of extracting multiple unit factors from the target content. Through this, attempts will be made to classify unit factors into different types. In addition, the design of the preprocessing process will be carried out to parse only the necessary information by analyzing the unit factor. Based on these results, we will design the part where the unit fact is analyzed using the deep learning prediction model as a predetermined unit. The model will also include a design for a database that determines the degree of fake news in the target content and stores the information in the identified unit factor through the analyzed unit factor.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.3
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• pp.148-154
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• 2021

We propose the AI Smart Factory Model for integrated management of production processes in this paper .It is an integrated platform system for the production of food packaging containers, consisting of a platform system for the main producer, one or more production partner platform systems, and one or more raw material partner platform systems while each subsystem of the three systems consists of an integrated storage server platform that can be expanded infinitely with flexible systems that can extend client PCs and main servers according to size and integrated management of overall raw materials and production-related information. The hardware collects production site information in real time by using various equipment such as PLCs, on-site PCs, barcode printers, and wireless APs at the production site. MES and e-SCM data are stored in the cloud database server to ensure security and high availability of data, and accumulated as big data. It was built based on the project focused on dissemination and diffusion of the smart factory construction, advancement, and easy maintenance system promoted by the Ministry of SMEs and Startups to enhance the competitiveness of small and medium-sized enterprises (SMEs) manufacturing sites while we plan to propose this model in the paper to state funding projects for SMEs.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.759-764
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• 2021

Because sensitive and private information should be exchanged between electric vehicles and a V2G service provider, reliable communication channel is essential to operate Vehicle-to-Grid (V2G) system which considers battery of electric vehicles as a factor of smart grid. The block chain is a platform for cryptocurrency transaction and fully distributed database system running by only equivalent node in the network without help of any central management or 3^rd party. In this paper, the structure and operation method of the blockchain are investigated, and the application of the blockchain for the V2G system was also explained and analyzed.