• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.4
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• pp.91-96
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• 2018

Recently, as artificial intelligence technology becomes popular, demand for wireless traffic is rapidly increasing. In order to provide services in response to the increase in demand for wireless traffic, telecommunication companies are generalizing the installation of public APs. In order to provide convenience of using wireless APs between communication companies, it is necessary to share the use of APs in public places to efficiently use wireless resources in a public place, to pre-authenticate between wireless APs in a mobile communication service, So as to increase the convenience of the user. In this paper, we propose to share APs in public places through handoff between APs and pre-authentication between carriers in mobile communication services. The simulation results show that the handoff latency is improved by 35.1% and the bandwidth used by the AP selected by the pre-authentication method can utilize more bandwidth than the method of automatically selecting the AP.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.1
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• pp.157-167
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• 2020

The big data and artificial intelligence technology, which has provided the foundation for the recent 4th industrial revolution, has become a major driving force in business innovation across industries. In the field of information security, we are trying to develop and improve an intelligent security system by applying these techniques to large-scale log data, which has been difficult to find effective utilization methods before. The quality of security log big data, which is the basis of information security AI learning, is an important input factor that determines the performance of intelligent security system. However, the difference and complexity of log data by various product has a problem that requires excessive time and effort in preprocessing big data with poor data quality. In this study, we research and analyze the cases related to log data collection of various firewall. By proposing firewall log data collection format standard, we hope to contribute to the development of intelligent security systems based on security log big data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.213-216
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• 2018

As the advantages of smart devices based on artificial intelligence and voice recognition become more prominent, Virtual Assistant is gaining popularity. Virtual Assistant provides a user experience through smart speakers and is valued as the most user friendly IoT device by consumers. The purpose of this study is to investigate whether there are differences in people's perception of the key virtual assistant brand voice recognition. We collected tweets that included six keyword form three companies that provide Virtual Assistant services. The authors conducted semantic network analysis for the collected datasets and analyzed the feelings of people through sentiment analysis. The result shows that many people have a different perception and mainly about the functions and services provided by the Virtual Assistant and the expectation and usability of the services. Also, people responded positively to most keywords.

• Journal of the Semiconductor & Display Technology
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• v.18 no.3
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• pp.122-126
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• 2019

These-days technology related to IoT (Internet of Thing) is widely used and there are many types of smart system based IoT like smart health, smart building and so on. In smart health system, it is possible to check someone's health by analyzing data from wearable IoT device like smart watch. Smart building system aims to collect data from sensor such as humidity, temperature, human counter like that and control the building for energy efficiency, security, safety and so forth. Furthermore, smart city system can comprise several smart systems like smart building, smart health, smart mobility, smart energy and etc. In this paper, we propose multimedia IoT based intelligent emergency alarm system for smart city. In existing IoT based smart system, it communicates lightweight data like text data. In the past, due to network's limitations lightweight IoT protocol was proposed for communicating data between things but now network technology develops, problem which is to communicate heavy data is solving. The proposed system obtains video from IP cameras/CCTVs, analyses the video by exploiting AI algorithm for detecting emergencies and prevents them which cause damage or death. If emergency is detected, the proposed system sends warning message that emergency may occur to people or agencies. We built prototype of the intelligent emergency alarm system based on MQTT and assured that the system detected dangerous situation and sent alarm messages. From the test results, it is expected that the system can prevent damages of people, nature and save human life from emergency.

• The Journal of Bigdata
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• v.5 no.1
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• pp.139-145
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• 2020

In the era of the 4th Industrial Revolution, the use of information based on AI APIs has a great influence on industry and life. In particular, the use of artificial intelligence data in the medical field will have many changes and effects on society. This paper is to study the necessary components to implement the "Cognitive Health Advisor model (CHA model)" and to implement the "CHA model using chatbot" based on this. It uses the open Cognitive chatbot to analyze and analyze the health status of users changing in their daily lives. The user's health information analyzed by the biometric sensor and chatbot consultation delivers the information to the user through the chatbot. And it implements a cognitive health advisor model that provides educational information for users' health promotion. Through this implementation, it intends to confirm the possibility of future use and to suggest research directions.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.123-123
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• 2018

가뭄은 홍수와 달리 진행속도가 비교적 느리기 때문에 초기에 감지한다면 피해를 최소화 할 수 있다. 국내에서는 가뭄전망을 위해 물리적 기반의 기상-수문연계해석 시스템을 구축하여 월 내지 계절전망을 수행하고 있다. 물리적 기반의 가뭄전망은 수치예보모델의 불확실성을 가지고 있으므로 예보 정확도 개선의 측면에서는 통계적 모델을 같이 활용하는 것이 바람직하다. 최근 국외에서는 통계적 방법인 AI (Artificial Intelligence) 기술을 사용하여 가뭄을 전망하는 연구가 활발히 진행 중이나, 아직까지 국내에서는 관련연구가 미흡한 실정이다. 이에 본 연구에서는 ANFIS (Adaptive Neuro-Fuzzy Inference System) 기반의 댐 유입량 예측 모델을 구축하고 SRI (Standardized Runoff Index)를 활용하여 수문학적 가뭄전망을 수행하였다. 대상유역은 국내 주요 다목적댐이 위치한 충주댐 유역과 소양강댐 유역을 선정하였다. 수문 및 기상자료는 국토 교통부 및 기상청의 관측 댐 유입량, 관측 강수량, 관측 기온 및 장기기상예보 자료를 사용하였다. ANFIS 모델 구축을 위한 훈련 및 보정기간과 검정기간은 각각 1987~2010년과 2011~2016년을 선정하였다. 수문학적 가뭄전망은 지속기간 3개월의 1개월 전망 SRI3를 활용하였으며, SRI3는 관측유입량과 예측유입량을 결합하여 산정하였다. 댐 예측유입량 및 수문학적 가뭄전망의 정확도 평가를 위해 상관계수, 평균제곱근오차를 활용하였다. 댐 예측유입량 평가 결과 예측값과 관측값의 상관계수가 높게 나타났으며, 평균제곱근오차는 낮아 예측성이 뛰어났다. SRI3의 경우 관측값과 예측값의 가뭄발생시기가 유사하여 가뭄을 적절하게 반영하는 것으로 나타났다. 본 연구의 결과는 통계적 기반의 수문학적 가뭄전망기법을 개발하였다는 측면에서 의의가 있으며, 향후 물리적 기반의 가뭄전망정보와 결합한다면 보다 실효성이 향상될 것으로 기대된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.309-309
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• 2018

기후변화에 따른 강우의 규모와 발생빈도 증가로 농촌유역의 홍수 피해는 지속적으로 증가하고 있다. 하지만 우리나라의 홍수 피해 저감 대책은 도시지역의 대하천 주변으로 집중되어있으며, 소하천 및 농촌유역의 홍수 피해 저감에 대한 관리와 투자 노력은 부족한 실정이다. 특히, 최근 들어 갑작스런 집중호우 등으로 인한 농촌유역 돌발홍수 피해 사례가 증가하고 있으며, 이에 대응하기 위해서는 홍수 발생 등을 신속하게 파악하기 위한 돌발홍수 예경보 시스템 개발이 필요하다. 한편, 최근 산업의 혁신과 생산성 향상을 위한 새로운 패러다임으로 4차 산업혁명이 대두되고 있으며, 빅데이터와 인공지능 (Artificial Intelligence, AI)을 비롯하여 사물인터넷 (Internet of Things, IoT), 드론, 슈퍼컴퓨팅 등의 이른바 4차 산업혁명 기술을 활용한 연구가 수행되고 있다. 본 연구에서는 기후변화에 따른 농촌유역 홍수 피해를 저감하고 또한 사전에 대비하기 위해 빅데이터와 인공지능 등 4차 산업혁명 기술을 적용한 농촌유역 돌발홍수 예경보 시스템을 개발하고 그 적용성을 평가하고자 한다. 우선, 농촌유역의 홍수와 관련된 빅데이터 (기상 자료, 수문 자료, 기후변화 자료, 농업용 수리구조물 자료 등)를 토대로 정형 빅데이터와 비정형 빅데이터를 구분 추출하고 이를 연계 해석할 수 있는 시스템을 개발하였다. 추출한 정형 및 비정형 빅데이터를 활용하여 딥러닝을 기반으로 농촌유역의 홍수를 예측하고 홍수 예경보 기준에 따른 평가를 수행할 수 있는 시스템을 개발하였다. 과거 강우사상을 홍수 예경보 시스템에 적용하여 홍수 모의 결과를 도출하였으며, 재해연보 등과 비교 분석하여 시스템의 적용성을 분석하였다.

• Journal of Digital Convergence
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• v.19 no.10
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• pp.39-48
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• 2021

This study aimed to analyze the level of professors' understanding and perception of adaptive learning and proposed how college can implement successful adaptive learning in college classes. For research purposes, online survey was conducted by 162 professors of A university in capital region. As a result, professors seemed to feel pressure to provide students personalized feedback and gave concerned that students don't study enough in advance before participating in class. It was also found that professors realized that they have low level of understanding about adaptive learning, while they revealed intention to make use of adaptive learning in their class. They also answered that adaptive learning system is the most helpful support for encouraging professors to apply adaptive learning in real class. We proposed what is required to encourage professor to implement adaptive learning in their class.

• Journal of Digital Convergence
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• v.19 no.9
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• pp.181-188
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• 2021

Research and importance of legal services applied with AI so that it can be easily understood and predictable in difficult legal fields is increasing. In this study, based on the decision of the Tax Tribunal in the field of tax law, a model was built through self-learning through information collection and data processing, and the prediction results were answered to the user's query and the accuracy was verified. The proposed model collects information on tax decisions and extracts useful data through web crawling, and generates word vectors by applying Word2Vec's Fast Text algorithm to the optimized output through NLP. 11,103 cases of information were collected and classified from 2017 to 2019, and verified with 70% accuracy. It can be useful in various legal systems and prior research to be more efficient application.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.2
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• pp.149-160
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• 2021

In this study, artificial intelligence-based algorithms were proposed, which included a fuzzy search for matching suspects between current and historical crimes in order to obtain related cases in criminal history, as well as conditional generative adversarial networks for crime prediction system (CPS) using Timor-Leste as a case study. By comparing the data from the criminal records, the built algorithms transform witness descriptions in the form of sketches into realistic face images. The proposed algorithms and CPS's findings confirmed that they are useful for rapidly reducing both the time and successful duties of police officers in dealing with crimes. Since it is difficult to maintain social safety nets with inadequate human resources and budgets, the proposed implemented system would significantly assist in improving the criminal investigation process in Timor-Leste.