• 한국철도학회:학술대회논문집
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• 한국철도학회 2008년도 추계학술대회 논문집
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• pp.1354-1359
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• 2008

In this paper, we propose a vision based passenger monitoring system for passenger's safety in railway station. Since 2005, Korea Railroad Research Institute (KRRI) has developed a vision based monitoring system, funded by Korean government, for passenger's safety in railway station. The proposed system uses various types of sensors, such as, stereo camera, thermal-camera and infrared sensor, in order to detects danger situations in platform area. Especially, detection process of the system exploits the stereo vision algorithm to improve detection accuracy. The paper describes the overall system configuration and proposed detection algorithm, and then verifies the system performance with extensive experimental results in a real station environment.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2020년도 추계학술발표대회
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• pp.814-817
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• 2020

The number of single-person households is increasing every year, and there are also high concerns about the crime and safety of single-person households. In particular, crimes targeting women are increasing. Although home surveillance camera applications, which are mostly used by single-person households, only provide intrusion detection functions, this service utilizes AI image recognition technologies such as face recognition and object detection to provide theft, violence, stranger and intrusion detection. Users can receive security-related notifications, relieve their anxiety, and prevent crimes through this service.

• 한국지능시스템학회논문지
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• 제13권4호
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• pp.491-498
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• 2003

The advanced computer network technology enables connectivity of computers through an open network environment. There has been growing numbers of security threat to the networks. Therefore, it requires intrusion detection and prevention technologies. In this paper, we propose a network based intrusion detection model using FCM(Fuzzy Cognitive Maps) that can detect intrusion by the DoS attack detection method adopting the packet analyses. A DoS attack appears in the form of the Probe and Syn Flooding attack which is a typical example. The SPuF(Syn flooding Preventer using Fussy cognitive maps) model captures and analyzes the packet informations to detect Syn flooding attack. Using the result of analysis of decision module, which utilized FCM, the decision module measures the degree of danger of the DoS and trains the response module to deal with attacks. For the performance comparison, the "KDD′99 Competition Data Set" made by MIT Lincoln Labs was used. The result of simulating the "KDD′99 Competition Data Set" in the SPuF model shows that the probe detection rates were over 97 percentages.

• Journal of information and communication convergence engineering
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• 제7권1호
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• pp.7-12
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• 2009

The advanced computer network and Internet technology enables connectivity of computers through an open network environment. Despite the growing numbers of security threats to networks, most intrusion detection identifies security attacks mainly by detecting misuse using a set of rules based on past hacking patterns. This pattern matching has a high rate of false positives and can not detect new hacking patterns, making it vulnerable to previously unidentified attack patterns and variations in attack and increasing false negatives. Intrusion detection and prevention technologies are thus required. We proposed a network based hybrid Probe Intrusion Detection model using Fuzzy cognitive maps (PIDuF) that detects intrusion by DoS (DDoS and PDoS) attack detection using packet analysis. A DoS attack typically appears as a probe and SYN flooding attack. SYN flooding using FCM model captures and analyzes packet information to detect SYN flooding attacks. Using the result of decision module analysis, which used FCM, the decision module measures the degree of danger of the DoS and trains the response module to deal with attacks. For the performance evaluation, the "IDS Evaluation Data Set" created by MIT was used. From the simulation we obtained the max-average true positive rate of 97.064% and the max-average false negative rate of 2.936%. The true positive error rate of the PIDuF is similar to that of Bernhard's true positive error rate.

• 한국멀티미디어학회논문지
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• 제23권2호
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• pp.282-292
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• 2020

In order to minimize casualties from marine vessel accidents that occur frequently at home and abroad, it is important to ensure the safety of the passengers aboard the vessel in the event of an accident. There is an EPIRB system as a system for disaster preparedness in the marine situation currently on the market, but there is a problem that the price is very expensive. In order to overcome the cost problem, which is a disadvantage of previous system, LoRaWAN-based communication is used. LoRaWAN communication-based vessel positioning and risk detection system based on LoRaWAN communication transmits measurement data of each module using two Beacon and GPS modules to stably perform position measurement for both indoor and outdoor situations. The rider danger situation detection system can detect the safety status of the rider using the 3-axis acceleration sensor, collect data from the rider positioning system and the rider safety status detection system, and send to server using LoRa communication. When conducting communication experiments in the long-distance maritime situation and actual communication experiments using the implemented system, it was found that the two experiments showed over 90% communication success rate on average.

• 한국구조물진단유지관리공학회 논문집
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• 제17권5호
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• pp.113-121
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• 2013

본 연구의 복합 센서의 상태 판정 알고리즘을 적용한 노면결빙 예측 및 강설 감지 시스템은 기존 단일 센서 만을 이용하던 기존 방식에서 벗어나, 접촉식/비접촉식 센서 및 적외선 카메라를 통합 운영하여 분사 시스템의 분사 시기와 융설액 분사량을 최적 제어한다. 시스템에 적용된 상태 판정 알고리즘은 취득한 온/습도 데이터와 수분 감지 데이터, 관측된 도로 영상의 영상처리기술 등을 이용하여 노면결빙 위험상태와 강설 상태 뿐만 아니라 강설 강도까지 구분하여 판정을 수행한다. 제작된 시스템의 현장 적용 실험에서는 강설 상태 감지율 89% 습윤 상태 감지율 94%의 우수한 판정 결과와 신뢰성을 검증하였다.

• 전기전자학회논문지
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• 제24권4호
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• pp.1180-1183
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• 2020

최근 들어 IoT와 스마트홈의 발전에 따라 낙상 사고 감지, 화상 위험 감지와 같이 일상 생활에서의 안전 감지 기능이 많이 보급되기 시작했다. 이러한 안전 감지 기능은 대부분 인공지능에 의해 수행된다. 그러나 실험실 환경에서 안전 감지의 정확도만 평가하는 경우에는 실제로 일상 생활 환경에서 체감하게 되는 성능과 꽤 큰 차이를 보이는 경우가 많다. 본 논문에서는 이러한 문제점을 보완하기 위해 사용하는 두 가지 기법인 리빙랩과 혼동 매트리스를 소개한다. 리빙랩은 단순히 일상 생활환경의 모사를 넘어서 사용자가 직접 기술 개발 및 제품 설계에 참여할 수 있는 통로가 된다. 또한 혼동 매트리스에서 도출되는 다양한 성능 척도는 사용 목적에 적합하게 인공지능 시스템의 성능을 평가하는데 큰 도움을 준다.

• 정보보호학회논문지
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• 제23권6호
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• pp.1157-1167
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• 2013

최근 정보통신기술 발달과 함께 야기되는 불법적 도청으로 인한 기업의 정보유출 및 개인의 사생활 침해문제는 현대사회에서 중요한 문제로 인식되고 있다, 특히 스피커는 소리를 내는 장치임에도 불구하고, 공격자의 의도에 따라 마이크의 역할로서 작용 되어 도청 및 감청의 도구로 이용될 수 있다. 일반적으로 스피커를 도청장치로 이용할 수 있다는 인지가 적다는 점과 기존의 도청장치 탐지 장비로 쉽게 탐지하기 어렵다는 점은 도청도구로서 스피커를 더욱 위협적이게 한다. 이러한 점에서 악의를 갖는 공격자나 해커에 의해서 악용될 소지가 있다. 따라서 스피커를 이용한 도청피해를 최소화하기 위해서는, 스피커를 이용한 도청 위험성의 인지 및 예상되는 도청 시나리오에 대한 연구가 요구된다. 본 논문에서는 스피커를 이용한 도청방법과 실험을 통해 스피커가 음성수집 도구로 이용할 수 있다는 취약점과 그 위험성을 보이고자 한다.

• 전기학회논문지
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• 제65권7호
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• pp.1263-1269
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• 2016

Hearing impaired persons are exposed to the danger since they can't be aware of many dangerous situations like fire alarms, car hones and so on. Therefore they need haptic or visual informations when they meet dangerous situations. In this paper, we design a dangerous sound detection engine for hearing impaired. We consider four dangerous indoor situations such as a boiled sound of kettle, a fire alarm, a door bell and a phone ringing. For outdoor, two dangerous situations such as a car horn and a siren of emergency vehicle are considered. For a test, 6 data sets are collected from those six situations. we extract LPC, LPCC and MFCC as feature vectors from the collected data and compare the vectors for feasibility. Finally we design a matching engine using an artificial neural network and perform classification tests. We perform classification tests for 3 times considering the use outdoors and indoors. The test result shows the feasibility for the dangerous sound detection.

• 한국멀티미디어학회논문지
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• 제24권8호
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• pp.1026-1034
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• 2021

Tire wear and defect are important factors for safe driving condition. These defects are generally inspected by some specialized experts or very expensive equipments such as stereo depth camera and depth gauge. In this paper, we propose tire safety vision inspector based on deep neural network (DNN). The status of tire wear is categorized into three: 'safety', 'warning', and 'danger' based on depth of tire tread. We propose an attention mechanism for emphasizing the feature of tread area. The attention-based feature is concatenated to output feature maps of the last convolution layer of ResNet-101 to extract more robust feature. Through experiments, the proposed tire wear classification model improves 1.8% of accuracy compared to the existing ResNet-101 model. For detecting the tire defections, the developed tire defect detection model shows up-to 91% of accuracy using the Mask R-CNN model. From these results, we can see that the suggested models are useful for checking on the safety condition of working tire in real environment.