• Journal of Communications and Networks
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• 제10권1호
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• pp.89-97
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• 2008

Anomaly detection systems playa significant role in protection mechanism against attacks launched on a network. The greatest challenge in designing systems detecting anomalous exploits is defining what to measure. Effective yet simple, Shannon entropy metrics have been successfully used to detect specific types of malicious traffic in a number of commercially available IDS's. We believe that Renyi entropy measures can also adequately describe the characteristics of a network as a whole as well as detect abnormal traces in the observed traffic. In addition, Renyi entropy metrics might boost sensitivity of the methods when disambiguating certain anomalous patterns. In this paper we describe our efforts to understand how Renyi mutual information can be applied to anomaly detection as an offline computation. An initial analysis has been performed to determine how well fast spreading worms (Slammer, Code Red, and Welchia) can be detected using our technique. We use both synthetic and real data audits to illustrate the potentials of our method and provide a tentative explanation of the results.

• 한국지능시스템학회논문지
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• 제18권2호
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• pp.200-205
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• 2008

데이터 분석을 위하여 데이터의 불확실성에 대한 측도로서 퍼지 집합에 대한 엔트로피를 소개하였고, 또한 데이터간의 유사도를 나타내는 유사측도를 구성하였다. 퍼지 소속 함수간의 유사측도는 거리측도를 이용하여 구성하였고, 제안한 유사측도를 증명을 통하여 확인하였다. 제안한 유사측도의 유용성을 확인하기 위하여 신뢰성 있는 데이터추출 예제에 적용하였다. 적용결과를 퍼지 엔트로피와 통계적 지식을 통하여 얻어진 이전의 결과와 비교하였다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2006년도 하계종합학술대회
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• pp.283-284
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• 2006

In this paper, we propose a novel segmentation and classification method using texture features for the document image. First, we extract the local entropy and then segment the document image to separate the background and the foreground using the Otsu's method. Finally, we classify the segmented regions into each component using PCA(principle component analysis) algorithm based on the texture features that are extracted from the co-occurrence matrix for the entropy image. The entropy-based segmentation is robust to not only noise and the change of light, but also skew and rotation. Texture features are not restricted from any form of the document image and have a superior discrimination for each component. In addition, PCA algorithm used for the classifier can classify the components more robustly than neural network.

• Structural Engineering and Mechanics
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• 제3권2호
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• pp.135-144
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• 1995

Since structural systems may fail in any one of several failure modes, computation of system reliability is always difficult. A method using numerical quadrature for computing structural system reliability with either one or more than one failure mode is presented in this paper. Statistically correlated safety margin equations are transformed into a group of uncorrelated variables and the joint density function of these uncorrelated variables can be generated by using the Maximum Entropy Method. Structural system reliability is then obtained by integrating the joint density function with the transformed safety domain enclosed within a set of linear equations. The Gaussian numerical integration method is introduced in order to improve computational accuracy. This method can be used to evaluate structural system reliability for Gaussian or non-Gaussian variables with either linear or nonlinear safety boundaries. It is also valid for implicit safety margins such as computer programs. Both the theory and the examples show that this method is simple in concept and easy to implement.

• 설비공학논문집
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• 제24권9호
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• pp.671-678
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• 2012

The objective of this study is to analyze theoretically the performance of an open air-cycle refrigeration system in which environmental concerns increase. The pressure ratio of the external compressor and efficiencies of the components that compose of the system are selected as important parameters. As the pressure ratio of the external compressor increases, the pressure ratio of the ACM compressor is determined high, the refrigerating temperature and capacity increase, the COP decreases, and the total entropy production rate increases. The effect of heat exchanger effectiveness and turbine efficiency on the performance are greater than that of the ACM compressor efficiency. Also the performance of the air-cycle refrigeration system with two heat exchangers has been enhanced like high COP and low total entropy production rate, compared to the system with one heat exchanger.

• 한국추진공학회:학술대회논문집
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• 한국추진공학회 2004년도 제23회 추계학술대회 논문집
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• pp.27-35
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• 2004

본 논문에서는 로켓 엔진의 고주파 연소불안정 현상이 연소현상과 맞물린 음향학적 현상이라는 점과 일반적으로 로켓엔진의 연소실 및 배기노즐이 원통형이라는 점을 고려하여 단면적이 변하는 원통형 관에서 음향, 엔트로피 및 와류 파동방정식의 해를 구하는 방법을 제시하였고 이를 통하여 엔트로피 및 와류파동이 음향파동에 미치는 영향을 정량적으로 해석 및 계산 할 수 있는 방법을 제시하였다.

• 정보보호학회논문지
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• 제17권2호
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• pp.73-81
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• 2007

본 논문에서는 스트림 암호 MICKEY의 두 가지 취약점에 대해서 논한다. 첫째, time-memory-data tradeoff 공격이 가능함을 보인다. 둘째, 상태 갱신 함수 (state update function)를 반복해서 적용할수록 내부 상태 (internal state)의 엔트로피가 감소하므로 다르게 시작된 키 스트림이 마침내 같아질 수 있다.

• Journal of Information Processing Systems
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• 제19권4호
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• pp.407-416
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• 2023

In order to effectively relieve the traffic pressure of the city, ensure the smooth flow of freight and promote the development of the logistics industry, the selection of appropriate port logistics location is the basis of giving full play to the port logistics function. In order to better realize the selection of port logistics, this paper adopts the entropy weight method to set up a multi-dimensional evaluation index, and constructs the evaluation model of port logistics location. Then through the actual case, from the environmental dimension and economic competition dimension to make choices and analysis. The results show that port d has the largest logistics competitiveness and the highest relative proximity among the three indicators of hinterland city economic activity, hinterland economic structure, and port operation capacity of different port logistics locations, which has absolute advantages. It is hoped that the research results can provide a reference for the multi-dimensional selection of port logistics site selections.

• ETRI Journal
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• 제46권3호
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• pp.513-525
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• 2024

Deep neural networks trained on labeled medical data face major challenges owing to the economic costs of data acquisition through expensive medical imaging devices, expert labor for data annotation, and large datasets to achieve optimal model performance. The heterogeneity of diseases, such as Alzheimer's disease, further complicates deep learning because the test cases may substantially differ from the training data, possibly increasing the rate of false positives. We propose a reconstruction-based self-supervised anomaly detection model to overcome these challenges. It has a dual-subnetwork encoder that enhances feature encoding augmented by skip connections to the decoder for improving the gradient flow. The novel encoder captures local and global features to improve image reconstruction. In addition, we introduce an entropy-based image conversion method. Extensive evaluations show that the proposed model outperforms benchmark models in anomaly detection and classification using an encoder. The supervised and unsupervised models show improved performances when trained with data preprocessed using the proposed image conversion method.

• 한국생산제조학회지
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• 제8권5호
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• pp.20-24
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• 1999

Grinding System is very difficult to examine closely wear phenomenon or dynamic characterastic because it is very complex and different from a general cutting system, Considering automatization and precision it is very important to examine closely grinding system. In this study grinding wheel surface is acquired by using computer vision system in order to explain wear and loading phenomenon. We investigate the relationship between wear and Fourier spectrum of acquired image and observe the entropy variation in the process of manufacturing.