• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 1997.11a
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• pp.282-288
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• 1997

The purpose of this paper is to predict the situation of leak in closed space using an Artificial Neural Network (ANN). The existing system can't monitor the whole He situations with on/off signals. Especially the first stage of data determines the leak spot and intensity is disregarded in gas accidents. To complement these faults, a new prototype of monitoring system is proposed. Ihe system is composed of'sensing systenL data acquisition system computer, and ANN implemented in software and is capable of identifying the leak spot and intensity in closed space. The concentration of gas is measured at the 4 different places. The network has 3 layers that are composed of 4 input Processing Element (PE),24 hidden PEs, md 4 output PEs. The ANN has optimum condition through several experiments and as a consequence the recognition rate of93.75% is achieved finally

• Journal of Navigation and Port Research
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• v.32 no.2
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• pp.127-132
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• 2008

The safety degree of navigation for collision avoidance is closely related with the combination between mariner's behavior and navigational environment. The condition of navigational environment is mainly decided by navigable waters, ship traffic, rule of road, sea state, weather and so on. Especially, the condition of navigable waters and ship traffic in navigational environment are ones of the important factors to attain safe navigation when mariners are underway and crossing, head on or overtaking situation. Thus this paper is to analyze the characteristics of mariner's behavior for collision avoidance caused by ship traffic and navigable waters by analyzing the contents of questionnaire and the results of international collaborative research. As a result, it can be concluded that the density of ship traffic and the area of navigable waters affect mariner's ship handling for collision avoidance.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.1713-1716
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• 2012

최근 우리 사회에는 여성과 아동을 대상으로 하는 강력범죄들이 많이 발생하고 있어 개인 위치확인 및 안전에 대한 서비스들이 최근 재조명을 받고 있다. 하지만 서비스가 시작된 기간에 비해 아직도 높은 단말기 가격대와 월 사용료, 그리고 위험상황에서의 사용성은 상대적으로 미약한 발전을 이루었다. 그나마 스마트폰이 많이 보급되어 해당 서비스들의 가격이 조금씩 인하되고, GPS를 활용한 위치정보의 정확도를 개선하는 등 많은 노력을 기울이고 있지만 그 효과는 미비하다. 따라서 본 논문에서는 스마트폰의 다양한 센서를 이용하여, 스마트폰을 꺼낼 수 없는 위험상황에서 사용자가 위험상황에서 특정 이벤트를 발생시켜, 인지된 상태정보를 전송하는 방식을 분석하고, 이를 기반으로 앞으로 개인 안전에 대한 서비스가 발전할 방향을 제시하고자 한다.

• Journal of the Korea Safety Management & Science
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• v.23 no.2
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• pp.53-63
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• 2021

Small and medium-sized enterprises(SMEs) continue to adopt ICT to gain an edge in organizational innovation and competition. This has a management advantage, but it also brings vulnerabilities as to cyber security. Therefore, the purpose of this study is to conduct an exploratory study on the cyber security situation of SMEs. A survey was conducted on Korean SMEs to determine how well they are connected to ICT and how much they are exposed to cyber security threats. The results suggest two things. First, Korean SMEs are well connected to ICT, but there is a gap between the actual adoption and human recognition of its importance. Second, security threats and breaches affect the majority of SMEs, but several problems including costs have not been properly evaluated. The results of this study are expected to help improve the cyber security management system of Korean SMEs.

• Journal of Aerospace System Engineering
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• v.15 no.2
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• pp.36-44
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• 2021

Unmanned aerial vehicles (UAVs) are increasingly needed as they can replace manned aircrafts in dangerous military missions. However, because of their low autonomy, current UAVs can execute missions only under continuous operator control. To overcome this limitation, higher autonomy levels of UAVs based on autonomous situational awareness is required. In this paper, we propose an autonomous situational awareness software consisting of situation awareness management, threat recognition, threat identification, and threat space analysis to detect dynamic situational change by external threats. We implemented the proposed software in real mission computer hardware and evaluated the performance of situational awareness toward dynamic radar threats in flight simulations.

• Journal of Information Processing Systems
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• v.18 no.1
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• pp.26-47
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• 2022

Chatbots were an important research subject in the past. A chatbot is a computer program or an artificial intelligence program that participates in a conversation via auditory or textual methods. As the research on chatbots progressed, some important issues regarding them changed over time. Therefore, it is necessary to review the technology with a focus on recent advancements and core research technologies. In this paper, we introduce five different chatbot technologies: natural language processing, pattern matching, semantic web, data mining, and context-aware computer. We also introduce the latest technology for the chatbot researchers to recognize the present situation and channelize it in the right direction.

• Electronics and Telecommunications Trends
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• v.37 no.1
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• pp.1-9
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• 2022

With the development of artificial intelligence, many studies have focused on evaluating abnormal situations by using various sensors, as industries try to automate some of the surveillance and security tasks traditionally performed by humans. In particular, mobile robots using multimodal sensors are being used for pilot operations aimed at helping security robots cope with various outdoor situations. Multiagent systems, which combine fixed and mobile systems, can provide more efficient coverage (than that provided by other systems), but network bottlenecks resulting from increased data processing and communication are encountered. In this report, we will examine recent trends in object recognition and abnormal-situation determination in various changing outdoor security robot environments, and describe an outdoor security robot platform that operates as a multiagent equipped with a multimodal sensor.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.807-809
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• 2020

기존의 CCTV 관리 체계는 사건·사고에 대한 신속한 조치가 불가능하고 정황 파악이나 증거자료 확보 등 사후조치의 성격이 강하다. 본 논문에서는 Mask R-CNN(Regions with CNN)을 이용하여 CCTV가 읽어 들이는 객체가 응급상황인지 판단하는 방법을 제시한다. 사람으로 인식되는 영역을 다층 퍼셉트론(MLP, Multi-Layer Perceptron)으로 학습시켜 해당 대상이 처한 상황을 인지하고 응급상황으로 인식되는 상황이 지속될 경우 관리 모니터를 통해 사용자에게 알림을 준다. 본 연구를 통해 실시간 상호작용적인 CCTV 관리 체계를 구축하여 도움이 필요한 사람의 골든타임을 놓치지 않게 될 것으로 기대한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.426-428
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• 2022

화재 상황에서의 빠른 현장 파악은 인명피해를 줄이는데 중요한 요소이다. 기존 연구의 화재와 관련된 데이터셋들은 대부분 불과 연기를 라벨링하여 화재의 예방에 초점을 두고 있다. 본 연구에서는 화재 상황에서 사람과 소방관, 연기, 불을 탐지하는 Object detection 모델을 만들어 현장 파악에 더욱 도움을 주고자 하였다. 이를 위해 화재 상황 이미지 약 3000장을 수집하고 라벨링하여 데이터셋을 구성하였으며 이를 이용해 객체 검출 모델인 RetinaNet을 학습하였다. 또한, 화재 상황에서 Object Detection 모델의 성능을 향상시키기 위해 기존 모델인 RetinaNet에 Dehazing(FFA-Net), Smoke augmentation, semi-supervised(ISD) 방법을 적용하였고, semi-supervised 조건에서 mAP 63.7로 가장 높은 성능을 도출하였다.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2023.11a
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• pp.202-203
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• 2023

최근 화재 탐지 분야는 불꽃 연기의 특징과 인공지능 인식(Detection) 모델을 활용하여 탐지율을 높이려는 연구가 많이 진행되어 왔다. 기존 화재 탐지 정확도를 높이기 위한 모델 연구 이외에도 불꽃·연기의 특징을 다양한 방법으로 데이터 가공한 학습 데이터셋을 활용하는 연구들이 진행되고 있다. 본 논문에서는 화재 탐지시 불꽃/연기의 오탐지율이 높은 것을 확인하고 오탐지율을 낮추기 위해 화재 상황을 인식하여 분류하는 방법과 데이터셋을 제안한다. 제안한 모델은 동영상을 학습데이터로 활용하여 화재 상황의 특징을 추출하여 분류모델에 적용하였다. 평가는 한국정보화진흥원(NIA)에서 진행하는 화재 데이터셋을 이용하여 Yolov8, Slowfast의 모델 성능을 비교 및 분석하였다.