• 정보보호학회논문지
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• 제30권5호
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• pp.957-966
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• 2020

최근 산업기밀보호센터의 통계에 의하면 국내 기밀유출 사고의 경우 전·현직 직원에 의해 기업기밀유출의 약 80%를 차지하고 이러한 내부자에 의한 정보유출 사고의 대다수가 허술한 보안 관리체계와 정보유출 탐지기술의 이유로 발생하고 있다. 내부자의 기밀유출을 차단하는 업무는 기업보안 부문에서 매우 중요한 문제이지만 기존의 많은 연구들은 내부자에 의한 유출위협보다는 외부 위협에 의한 침입에 대응하는데 초점이 맞추어져 있다. 따라서 본 논문에서는 기업 내에서 발생하는 다양한 비정상 행위를 효과적이고 효율적으로 탐지하기 위해 내부정보 유출 시나리오를 설계하고 시나리오에서 도출 된 유출 징후의 핵심 위험지표를 데이터 분석(Data analytics)함 으로써 정교하지만 신속하게 유출행위를 탐지하는 모형을 제시하고자 한다.

• 한국컴퓨터정보학회논문지
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• 제24권9호
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• pp.21-27
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• 2019

In this paper, we propose a deep auto-encoder-based pipe leak detection (PLD) technique from time-series acoustic data collected by microphone sensor nodes. The key idea of the proposed technique is to learn representative features of the leak-free state using leak-free time-series acoustic data and the deep auto-encoder. The proposed technique can be used to create a PLD model that detects leaks in the pipeline in an unsupervised learning manner. This means that we only use leak-free data without labeling while training the deep auto-encoder. In addition, when compared to the previous supervised learning-based PLD method that uses image features, this technique does not require complex preprocessing of time-series acoustic data owing to the unsupervised feature extraction scheme. The experimental results show that the proposed PLD method using the deep auto-encoder can provide reliable PLD accuracy even considering unsupervised learning-based feature extraction.

• 한국소음진동공학회논문집
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• 제14권2호
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• pp.95-104
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• 2004

Leaks in underground pipelines can cause social, environmental and economical problems. One of relevant countermeasures against leaks is to find and repair of leak points of the pipes. Leak noise is a good source to identify the location of leak points of the pipelines. Although there have been several methods to detect the leak location with leak noise, such as listening rods, hydrophones or ground microphones, they have not been so efficient tools. In this paper, accelermeters aroused to detect leak locations which could provide an easier and more efficient method. Filtering, signal processing and algorithm of raw input data from sensors for the detection of leak location are described. A 120m-long and a 70m-long experimental pipeline systems are installed and the results with the systems show that the algorithm with the accelerometers offers accurate pinpointing for leaks location detection. Theoretical analysis of sound wave propagation speed of water in underground pipes, which is critically important in leak locating, is also described.

• 전기전자학회논문지
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• 제26권2호
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• pp.176-185
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• 2022

데이터 증강이란 다양한 데이터 변환 및 왜곡을 통해 데이터셋의 크기와 품질을 개선하는 기법으로, 기계학습 모델의 과적합 문제를 해결하기 위한 대표적인 접근법이다. 그러나 심층학습 이미지 생성 모델인 GAN 기반 모델에서 데이터 증강을 적용하면 생성된 이미지에 데이터 변환과 왜곡이 반영되는 증강 누출 문제가 발생하여 생성 이미지의 품질이 하락한다. 이러한 문제를 해결하기 위해 본 논문에서는 데이터 증강의 종류와 수에 관계없이 증강 누출을 방지하는 기법을 제안한다. 증강 누출의 발생 조건을 분석하였으며, 보조적인 데이터 증강 작업 분류기를 GAN 모델에 적용하여 증강 누출을 방지하였다. 정성적 정량적 평가를 통해 제안된 기법을 적용하면 증강 누출이 발생하지 않음을 보이고 추가적으로 생성 이미지의 품질을 향상시키며 기존 기법과 비교하여 발전된 성능을 보임을 입증하였다.

• International journal of advanced smart convergence
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• 제12권4호
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• pp.378-385
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• 2023

The importance of wearing respiratory protective equipment has been highlighted even more during the COVID-19 pandemic. Even if the suitability of respiratory protection has been confirmed through testing in a laboratory environment, there remains the potential for leakage points in the respirators due to improper application by the wearer, damage to the equipment, or sudden movements in real working conditions. In this paper, we propose a method to detect the occurrence of leak holes by measuring the pressure changes inside the mask according to the wearer's breathing activity by attaching an IoT sensor to a full-face respirator. We designed 9 experimental scenarios by adjusting the degree of leak holes of the respirator and the breathing cycle time, and acquired respiratory data for the wearer of the respirator accordingly. Additionally, we analyzed the respiratory data to identify the duration and pressure change range for each breath, utilizing this data to train a neural network model for detecting leak holes in the respirator. The experimental results applying the developed neural network model showed a sensitivity of 100%, specificity of 94.29%, and accuracy of 97.53%. We conclude that the effective detection of leak holes can be achieved by incorporating affordable, small-sized IoT sensors into respiratory protective equipment.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2018년도 학술발표회
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• pp.276-276
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• 2018

In this paper the potential of the Principle Component Analysis(PCA) technique that can be used to detect leaks in water pipe network blocks was evaluated. For this purpose the PCA was conducted to evaluate the relevance of the calculated outliers of a PCA model utilizing the recorded pipe flows and the recorded pipe leak incidents of a case study water distribution system. The PCA technique was enhanced by applying the computational algorithms developed in this study. The algorithms were designed to extract a partial set of flow data from the original 24 hour flow data so that the variability of the flows in the determined partial data set are minimal. The relevance of the calculated outliers of a PCA model and the recorded pipe leak incidents was analyzed. The results showed that the effectiveness of detecting leaks may improve by applying the developed algorithm. However, the analysis suggested that further development on the algorithm is needed to enhance the applicability of the PCA in detecting leaks in real-world water pipe networks.

• 정보보호학회논문지
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• 제26권3호
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• pp.799-808
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• 2016

본 논문은 국내 개인정보유출사고 발생의 패턴을 찾고 어떤 분포를 따르는지 확인한 연구이다. 이를 위해 2011년도부터 2014년도까지 언론에 보도된 개인정보유출사고를 사용하였다. 조사결과를 바탕으로 'K-S통계량' 방법론을 사용하여 개인정보유출사고의 통계적 분포를 추정하였고, 적합도 검정을 실시하였다. 그 결과 '유의수준 95%에서 포아송분포와 지수분포 모두 높은 적합도를 지닌다.'는 사실을 정략적으로 입증하였고, 이를 통해 1년에 평균 12번씩 대형 개인정보유출사고가 발생되어 언론에 보도되었다는 것을 확인할 수 있었다. 본 연구는 향후 기업 및 조직의 개인정보 유출사고의 발생예측 및 정보보호 투자금액선정 등 보안경제성 분석에 유용하게 활용될 것으로 전망된다.

• 상하수도학회지
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• 제32권5호
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• pp.461-470
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• 2018

In this study, the leak signal was measured by using an accelerometer to analyze the basic data and methodology for the development of the leak point estimation method in the water supply pipe. The measured results were analyzed by frequency analysis and cross-correlation analysis for leakage signals, and the error range was compared and analyzed with the actual leak point distance. As a result, it was confirmed that the vibration intensity due to leakage from the water leakage point was attenuated according to the distance. In the case of the ductile iron casting used in the experiment, the intensity of the signal at the 945 Hz, 1,500 Hz, 2,300 Hz band was increased with the change of the pressure in the pipe at 4mm of leakage hole. Also, it was confirmed that as the water pressure increases, the intensity of the leak signal increases but the similarity of the signal decreases. The results of this study confirm that the accelerometer sensor can be used efficiently for leak detection and it can be used as a basic data for the analysis for the development of leak point estimation method in the future.

• 한국신뢰성학회지:신뢰성응용연구
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• 제11권3호
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• pp.213-224
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• 2011

In this paper, we analyze statistically the data set of first leak time of water pipeline. We classify first the leak time data by pipe type, location, diameter of pipe and, length of pipe. We perform the analysis of variance to indicate that there are significant difference of mean of the time between levels of the factor and also compare the distribution of levels using the multiple box-plot. When there are the difference of the mean, we perform the least significant test to find out what levels of the facor has a different mean.

• 상하수도학회지
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• 제33권2호
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• pp.159-167
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• 2019

In this paper the potential of the principal component analysis(PCA) technique for the application of detecting leaks in water pipe networks was evaluated. For this purpose the PCA was conducted to evaluate the relevance of the calculated outliers of a PCA model utilizing the recorded pipe flows and the recorded pipe leak incidents of a case study water distribution system. The PCA technique was enhanced by applying the computational algorithms developed in this study which were designed to extract a partial set of flow data from the original 24 hour flow data so that the effective outlier detection rate was maximized. The relevance of the calculated outliers of a PCA model and the recorded pipe leak incidents was analyzed. The developed algorithm may be applied in determining further leak detection field work for water distribution blocks that have more than 70% of the effective outlier detection rate. However, the analysis suggested that further development on the algorithm is needed to enhance the applicability of the PCA in detecting leaks by considering series of leak reports happening in a relatively short period.