• 품질경영학회지
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• 제45권1호
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• pp.25-38
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• 2017

Purpose: Hot strip rolling mill consists of a lot of mechanical and electrical units. In condition monitoring and diagnosis phase, various units could be failed with unknown reasons. In this study, we propose an effective method to detect early the units with abnormal status to minimize system downtime. Methods: The early warning problem with various units is defined. K-means and PAM algorithm with Euclidean and Manhattan distances were performed to detect the abnormal status. In addition, an performance of the proposed algorithm is investigated by field data analysis. Results: PAM with Manhattan distance(PAM_ManD) showed better results than K-means algorithm with Euclidean distance(K-means_ED). In addition, we could know from multivariate field data analysis that the system reliability of hot strip rolling mill can be increased by detecting early abnormal status. Conclusion: In this paper, clustering-based monitoring and fault detection algorithm using Manhattan distance is proposed. Experiments are performed to study the benefit of the PAM with Manhattan distance against the K-means with Euclidean distance.

• 한국정보과학회논문지:정보통신
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• 제35권6호
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• pp.474-481
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• 2008

스캐닝 웜은 자기 스스로 복제가 가능하며 네트워크를 통해서 짧은 시간 안에 아주 넓은 범위에 걸쳐 전파되므로 네트워크의 부하를 증가시켜 심각한 네트워크 혼잡현상을 일으킨다. 따라서 실시간으로 스캐닝 웜을 탐지하기 위해 많은 연구가 진행되고 있으나 대부분의 연구가 패킷 헤더 정보를 이용하는 방법에 중점을 두고 있으며, 이 방법은 네트워크의 모든 패킷을 검사해야 하므로 비효율적이며 탐지시간이 오래 걸린다는 단점이 있다. 따라서 본 논문에서는 네트워크 트래픽량, 트래픽량의 미분값, 트래픽량의 평균 미분값, 트래픽량의 평균 미분값과 평균 트래픽량의 곱에 대한 variance를 통해 스캐닝 웜을 탐지하는 기법을 제안한다. 실제 네트워크에서 측정한 정상 트래픽과 시뮬레이터로 생성한 웜 트래픽에 대해 성능을 분석한 결과, 기존의 탐지기법으로는 탐지되지 않는 코드레드와 슬래머를 제안한 탐지기법으로 탐지할 수 있었다. 또한 탐지속도를 측정한 결과 웜 발생초기에 모두 탐지가 되었는데, 슬래머는 발생 후 4초만에 탐지되었으며, 코드레드와 위티는 발생한지 11초만에 탐지되었다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2009년도 제40회 하계학술대회
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• pp.2164.1_2165.1
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• 2009

In this study we suggest fire detection algorithm using fuzzy inference with input variables of temperature and smoke density to detect electrical fire of early stage. The algorithm consists of membership function of temperature and smoke density and fire probability. The antecedent part of the algorithm consists of temperature and smoke density, and the consequent part consists of fire possibility. The inference rules of the algorithm is estimated to input temperature and smoke density obtained by real fire. With the help of algorithms using fuzzy inference we may be diagnose electrical fire precisely.

• 한국콘텐츠학회논문지
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• 제9권12호
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• pp.504-514
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• 2009

최근 문화재나 시설물 관리를 위한 화재 감시 응용 시스템에 대한 요구가 증대되고 있다. 이러한 화재 감시 시스템은 화재 상황을 대처할 수 있도록 지원함으로써 피해규모를 축소시킬 수 있다. 그러나 기존 시스템은 일정 주기로 화재 감시를 수행함으로써 화재 판단을 지연시키는 단점이 존재한다. 또한 감시 상황을 확인 할 수 있는 사용자 인터페이스를 제공하지 않는다. 따라서 이러한 두 가지 문제점을 해결하기 위하여 첫째, 조기 위험상황 경보를 위해 새로운 화재 판단 알고리즘(Early Fire Detection Algorithm)을 제안한다. 이는 데이터 분포를 기반으로 하여 화재 판단 시작 주기를 동적으로 설정하기 때문에, 화재 판단 시간 측면에서 기존 알고리즘 보다 우수하다. 둘째, 제안하는 화재 판단 알고리즘을 통하여 사용자 인터페이스를 제공하는 화재 감시 응용 시스템을 개발한다. 마지막으로 성능 실험을 통해, 개발된 시스템이 다양한 화재 상황에서 조기 위험상황 경보를 위해 활용될 수 있음을 보인다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권12호
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• pp.6018-6033
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• 2018

Fires are a common cause of catastrophic personal injuries and devastating property damage. Every year, many fires occur and threaten human lives and property around the world. Providing early important sign for early fire detection, and therefore the detection of smoke is always the first step in fire-alarm systems. In this paper we propose an automatic smoke detection system built on camera surveillance and image processing technologies. The key features used in our algorithm are to detect and track smoke as moving objects and distinguish smoke from non-smoke objects using a convolutional neural network (CNN) model for cascade classification. The results of our experiment, in comparison with those of some earlier studies, show that the proposed algorithm is very effective not only in detecting smoke, but also in reducing false positives.

• 한국소음진동공학회논문집
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• 제14권12호
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• pp.1287-1294
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• 2004

Hub bearings not only sustain the body of a cat, but permit wheels to rotate freely. Excessive radial or axial load and many other reasons can cause defects to be created and grown in each component. Therefore, nitration and noise from unwanted defects in outer-race, inner-race or ball elements of a Hub bearing are what we want to detect as early as possible. How early we can detect the faults has to do with how the detection algorithm finds the fault information from measured signal. Fortunately, the bearing signal has Periodic impulse train. This information allows us to find the faults regardless how much noise contaminates the signal. This paper shows the basic signal processing idea and experimental results that demonstrate how good the method is.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2004년도 춘계학술대회논문집
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• pp.593-596
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• 2004

Hub bearings not only sustain the body of a car, but permit wheels to rotate freely. Excessive radial or axial load and many other reasons can cause defects to be created and grown in each component. Therefore, vibration and noise from unwanted defects in outer-race, inner-race or ball elements of a Hub bearing are what we want to detect as early as possible. How early we can detect the faults has to do with how the detection algorithm finds the fault information from measured signal. Fortunately, the bearing signal has periodic impulse train. This information allows us to find the faults regardless how much noise contaminates the signal. This paper shows the basic signal processing idea and experimental results that demonstrate how good the method is.

• 한국멀티미디어학회논문지
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• 제21권12호
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• pp.1431-1438
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• 2018

In this paper, we proposed a river water level detection and tracking of the river or dams based on image processing system. In past, most of the water level detection system used various water sensors. Those water sensors works perfectly but have many drawbacks such as high cost and harsh weather. Water level monitoring system helps in forecasting early river disasters and maintenance of the water body area. However, the early river disaster warning system introduces many conflicting requirements. Surveillance camera based water level detection system depends on either the area of interest from the water body or on optical flow algorithm. This proposed system is focused on water scaling area of a river or dam to detect water level. After the detection of scale area from water body, the proposed algorithm will immediately focus on the digits available on that area. Using the numbers on the scale, water level of the river is predicted. This proposed system is successfully tested on different water bodies to detect the water level area and predicted the water level.

• 디지털산업정보학회논문지
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• 제14권4호
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• pp.69-77
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• 2018

The malignant melanoma accounts for about 1 to 3% of the total malignant tumor in the West, especially in the US, it is a disease that causes more than 9,000 deaths each year. Generally, skin lesions are difficult to detect the features through photography. In this paper, we propose a computer-aided diagnosis algorithm based on deep learning for classification of malignant melanoma and benign skin tumor in RGB channel skin images. The proposed deep learning model configures the tumor lesion segmentation model and a classification model of malignant melanoma. First, U-Net was used to segment a skin lesion area in the dermoscopic image. We could implement algorithms to classify malignant melanoma and benign tumor using skin lesion image and results of expert's labeling in ResNet. The U-Net model obtained a dice similarity coefficient of 83.45% compared with results of expert's labeling. The classification accuracy of malignant melanoma obtained the 83.06%. As the result, it is expected that the proposed artificial intelligence algorithm will utilize as a computer-aided diagnosis algorithm and help to detect malignant melanoma at an early stage.

• 정보보호학회논문지
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• 제17권1호
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• pp.57-66
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• 2007

스캐닝 웜은 네트워크 관리자가 미처 대응하기 전에 넓게 전파되므로 차단하기 힘들고 그 피해가 상당히 크다. 따라서 자동으로 스캐닝 웜의 발생을 탐지하고 이에 대응할 수 있는 방법이 필요하다. 본 논문에서는 스캐닝 웜의 트래픽 특성을 분석하여 정상 트래픽과 이상 트래픽을 구분할 수 있는 탐지 알고리즘을 제안한다. 스캐닝 웜의 탐지를 위해 variance, VMR 및 correlation coefficient를 이용하는 방법을 제안하고, 시뮬레이션을 통해 기존의 방법과 성능을 비교하였다. 그 결과 기존의 방법에 비하여 간단한 계산을 통해 스캐닝 웜의 효율적인 탐지가 가능함을 확인하였다.