• Childhood Kidney Diseases
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• 제21권2호
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• pp.107-113
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• 2017

Purpose: This study aimed to evaluate the relationships between 99mTecnicium-dimercaptosuccinic acid (DMSA) scan findings and clinical parameters including age and fever duration. Methods: The positive rates for abnormal DMSA scans were analyzed according to the age of patients, fever duration prior to admission, and total fever duration. DMSA scan findings were divided into 3 categories: single defect, multifocal defects, and discrepant defects. We evaluated the detection rates of vesicoureteral reflux according to DMSA scan lesions. Results: Among a total 320 cases, 141 (44.1%) had abnormal DMSA scans. The infant group (0-1 year of age) had a shorter total fever duration, and a lower C-reactive protein (CRP) value and DMSA positive rate (39.8% vs. 60.6%, P=0.002) compared to children group (2-15 years of age). Patients with abnormal scans had a longer total fever duration and higher CRP compared to those with normal scans. The positivity rate of abnormal scans did not differ between the patients with a short fever duration prior to admission of ${\leq}2$ days and those with longer fever duration of ${\geq}3$ days. However, patients with longer total fever duration had a higher rate of abnormal DMSA scans (P=0.02). Among cases with a single defect, multifocal defects, and discrepant defects, vesicoureteral reflux was observed in 22.4%, 60% and 70.6% of cases, respectively (P=0.004). Conclusion: Although DMSA scan has limitations in early diagnosis, DMSA scan findings may aid in the prediction of the severity of systemic inflammation and detection of vesicoureteral reflux.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2008년도 하계종합학술대회
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• pp.669-670
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• 2008

In this paper, we present a real-time system to detect abnormal events on gas pipes, based on the signals which are observed through the audio sensors attached on them. First, features are extracted from these signals so that they are robust to noise and invariant to the distance between a sensor and a spot at which an abnormal event like an attack on the gas pipes occurs. Then, a classifier is constructed to detect abnormal events using neural networks. It is a combination of two neural network models, a Gaussian mixture model and a multi-layer perceptron, for the reduction of miss and false alarms. The former works for miss alarm prevention and the latter for false alarm prevention. The experimental result with real data from the actual gas system shows that the proposed system is effective in detecting the dangerous events in real-time with an accuracy of 92.9%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제5권2호
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• pp.313-329
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• 2011

To provide a seamless network to customers, Internet service providers must promptly detect and control abnormal traffic. One approach is to shorten the traffic information measurement cycle. However, performance degradation is inevitable if traffic measurement servers merely shorten the cycle and measure all traffic. This paper presents a software architecture that can measure traffic more frequently without degrading performance by estimating the level of abnormal traffic. The algorithm in the architecture estimates the values of the interface group objects in MIB by using the IP group objects thereby reducing the number of measurements and the size of measured data. We evaluated this architecture on part of Internet service provider's IP network. When the traffic was measured 5 times more than before, the CPU usage and TPS of the proposed scheme was 7% and 41% less than that of the original scheme while the false positive rate and false negative rate were 3.2% and 2.7% respectively.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2023년도 추계학술발표대회
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• pp.634-636
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• 2023

Surveillance systems play a pivotal role in ensuring the safety and security of various environments, including public spaces, critical infrastructure, and private properties. However, detecting abnormal human behavior in lowlight conditions is a critical yet challenging task due to the inherent limitations of visual data acquisition in such scenarios. This paper introduces a spatiotemporal framework designed to address the unique challenges posed by low-light environments, enhancing the accuracy and efficiency of human abnormality detection in surveillance camera systems. We proposed the pre-processing using lightweight exposure correction, patched frames pose estimation, and optical flow to extract the human behavior flow through t-seconds of frames. After that, we train the estimated-action-flow into autoencoder for abnormal behavior classification to get normal loss as metrics decision for normal/abnormal behavior.

• 전기전자학회논문지
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• 제13권2호
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• pp.140-149
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• 2009

최근 인터넷을 통한 각종 침해사고 및 트래픽 폭주와 같은 현상이 급격하게 증가함에 따라 네트워크의 비정상적 상황을 조기에 탐지하기 위한 보다 능동적이고 진보적인 기술이 요구되고 있다. 본 논문에서는 캠퍼스 네트워크와 같이 트래픽이 주기적인 특성을 띠는 환경에서 Fisher 선형 분류법(FLD)을 사용하여 트래픽을 두 개의 그룹으로 분류하고, 네트워크에 유입되는 트래픽이 어떤 그룹에 속하는지를 판별하는 기법을 제안한다. 이를 위해 WISE-Mon이라 불리는 트래픽 분석 시스템을 개발하여 캠퍼스 네트워크의 트래픽을 수집하고 이를 모니터링해서 분석을 수행한다. 생성된 트래픽의 training set을 이용하여 비정상 트래픽의 범위를 판단하기 위한 chi-square distribution을 유도하고, FLD를 적용하여 유입되는 트래픽을 두 그룹으로 분리하기 위한 초평면 (hyperplane)을 만든다. 또한 네트워크 내의 트래픽 패턴이 시간이 지남에 따라 계속적으로 변하는 상황을 반영하기 위하여 self-learning 알고리즘을 적용한다. 캠퍼스 네트워크의 트래픽을 적용한 수학적 결과를 통하여 제안하는 기법의 정확성과 신뢰도를 보여준다.

• 대한기계학회논문집B
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• 제41권5호
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• pp.303-309
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• 2017

본 연구에서는 비정상조직(파라핀)을 가진 생체조직에 광을 조사하고 그에 따른 조직의 표면온도와 비정상조직 주위에서의 온도분포를 실험과 해석적 방법을 통해 분석하였다. 파라핀을 이용하여 비정상 조직을 모사한 후 조사하는 광의 파장과 시간을 변화시키면서 조직 주위에서의 온도를 K형 열전대를 사용하여 측정하였다. 또한 전산열전달 기법을 이용하여 해석적으로 조직에 대한 온도분포를 예측하였다. 정상조직과 비정상조직의 주위에서의 온도는 차이가 있었으며, 비정상조직이 있는 경우 표면과 조직 주위의 온도가 높게 나타났다.

• 한국해양학회지:바다
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• 제26권4호
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• pp.307-326
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• 2021

상시 관측되는 조위관측소 해수위 자료는 결측값과 오측값을 포함하고 있으며, 그 중 오측 값은 이상값으로 분류되는 전처리 대상이다. 이러한 오측을 제거하기 위해 대표적으로 3𝜎 (three standard deviations) 규칙이 적용되어왔으나, 기상이변 등에 의한 극값이 존재하거나 3𝜎 범위 안에서도 오측이 존재하는 해수위 자료에는 그 적용이 어렵다. 본 연구에서 설계된 모델은 오측에 대한 사전 정보가 필요하지 않은 비주석 학습으로 구성되며, 재귀신경망과 앙상블 기법을 이용함으로써 실시간으로 수집되는 해수위 자료가 오측일 가능성을 발생한지 20분 이내로 제시한다. 검증이 완료된 모델은 평시 및 기상이변시의 정상값과 오측값을 잘 분리하며, 학습이 이뤄지지 않은 연도의 해수위 자료에서도 이상값 탐지가 가능함을 확인하였다. 본 연구의 관측 이상치 탐지 알고리즘은 조위관측소 해수위에 국한되지 않고 다양한 해양 및 대기자료의 이상치 탐지 인공신경망 모델에 확장 적용할 수 있다.

• 대한세포병리학회지
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• 제13권1호
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• pp.1-7
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• 2002

The significance of endometrial cells on cervicovaginal smears is underestimated. The aim of this study is to evaluate the detection rate of endometrial cells on cervicovaginal smears. The materials consisted of two groups. Group I was 701 cervicovaginal smears from patients with no gynecological problems. Group II was 208 cervicovaginal smears from patients with abnormal uterine bleeding followed by endometrial curettage; 31 cases of endometrial adenocarclnoma(CA), 19 cases of endometrial hyperplasia(HP), 83 cases of dysfunctional uterine bleeding(DUB), and 75 cases of normal endometrium. Cervicovaginal smears were reviewed according to the criteria of The Bethesda System. Endometrial cells were identified in 15 of 701 cases(2.1%) in group I and 64 of 208 cases(30.8%) in group II. Among group II, detection rate of endometrial cells was the highest in CA (51.6%) compared to HP(26.3%), DUB(41.0%), and normal endometrium(12.0%) (p<0.05). Cytologic atypia of endometrial cells was not found In group I, but was more frequently identified in CA(87.5%) than in HP(10.5%) or DUB(14.7%) (p<0.05). Exfollatlon of endometrial cells might be related to abnormal endometrial lesion, and reporting of endometrial cells in the cervicovaginal smear may increase a chance to detect endometrial lesions especially in patients with abnormal uterine bleeding.

• 한국기계가공학회지
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• 제18권6호
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• pp.82-90
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• 2019

To reduce emissions of marine pollutants, regulations are being tightened around the world. In the shipbuilding and shipping industries, various countermeasures are being put forward. As there are limits to applying countermeasures to ships already in operation, however, it is necessary for these vessels to use energy efficiently. The sensors installed on ships typically gather a very large amount of data, and thus a big data platform is needed to manage and analyze the data. In this paper, we build a big data analysis platform based on Hadoop and Spark, and we present a method to detect abnormal ship operation using the platform. We also utilize real ship operation data to discuss the data analysis experiment.

• 전력전자학회논문지
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• 제26권6호
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• pp.390-396
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• 2021

Lithium-ion batteries have been designed and used as battery packs with series and parallel combinations that are suitable for use. However, due to its internal electrochemical properties, producing the battery's condition at the same value is impossible for individual cells. In addition, the management of characteristic deviations between individual cells is essential for the safe and efficient use of batteries as aging progresses with the use of batteries. In this work, we propose a method to manage deviation properties and detect abnormal behavior in the configuration of a combined battery pack of these multiple battery cells. The proposed method can separate and detect probabilistic low-frequency information according to statistical information based on Z-score. The verification of the proposed algorithm was validated using experimental results from 10S3P battery packs, and the implemented algorithm based on Z-score was validated as a way to effectively manage multiple individual cell information.