• Journal of information and communication convergence engineering
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• 제7권3호
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• pp.268-274
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• 2009

Human beings can represent state of mind such as psychological state, personality or emotional trouble by the pictures painted on one's own initiative. But in general, it is hard to understand a consulter's unconscious state through one's objective and intentional descriptions only. So one's psychological state and emotional trouble can be understood and cured by color and location information of objects drawn in one's picture. By this reason, a consultant can help and settle a consulter's growth stages of life and emotional trouble through treatment by pictures. In this paper, we proposed a method to find out state of sensitivity by analysis of color and location information represented in a picture and fuzzy inference rules. We applied the proposed method to the states of sensitivity from color information proposed by Alschuler and Hattwick and the psychological states from location information proposed by Grunwald. In the experimental results by the two applications, we verified the proposed sensitivity analysis method is efficient.

• 디지털산업정보학회논문지
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• 제13권1호
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• pp.23-35
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• 2017

With the popularization of mobile devices, the number of social network service users is increasing, thereby the amount of data is also increasing accordingly. As Internet of Things environment is expanding to connect things and people, there is information much more than before. In such an environment, it becomes very important to recommend the necessary information to the user. In this paper, we propose a recommendation method that considers new users in IoT environment. In the proposed method, we recommend the information by applying the centrality-based social network analysis method to the recommendation method using the social relationships in the social IoT. We describe the seven-step recommendation method and apply them to the music circle scenario of the IoT environment. Through the music circle scenario, we show that we can recommend more suitable information to new users in the IoT environment than the existing recommendation method.

• 정보보호학회논문지
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• 제19권3호
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• pp.27-35
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• 2009

수집신호의 통계적 특성을 기반으로 하는 차분전력분석 (Differential Power Analysis, DPA) 방법은 암호시스템의 키를 해독하는 데 아주 효과적인 방법으로 알려져 있다. 그러나 이 방법은 수집신호의 시간적인 동기와 잡음에 따라 공격 성능에 상당한 영향을 받는다. 본 논문에서는 DPA에서 시간적인 동기와 잡음에 의한 영향을 동시에 효과적으로 극복하는 웨이블릿(Wavelet) 기반의 신호처리 방법을 제안한다. 제안된 방법의 성능은 DES 연산중인 마이크로 컨트롤러 칩의 전력소비 신호를 이용해서 측정한다. 실험을 통해 제안된 웨이블릿 기반의 전처리 시스템의 성능은 키 해독에 필요한 필요 평문의 수가 기존의 방법들이 필요로 하는 25%의 평문의 수로도 충분함을 보여주고 있다.

• Communications for Statistical Applications and Methods
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• 제9권2호
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• pp.491-503
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• 2002

In this paper, we propose a automatic document summarization method based on Principal Component Analysis(PCA) which is one of the multivariate statistical methods. After extracting thematic words using PCA, we select the statements containing the respective extracted thematic words, and make the document summary with them. Experimental results using newspaper articles show that the proposed method is superior to the method using either word frequency or information retrieval thesaurus.

• Journal of Communications and Networks
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• 제18권5호
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• pp.818-825
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• 2016

Network management and anomaly detection are challenges in high-speed networks due to the high volume of packets that has to be analysed. Flow-based analysis is a scalable method which reduces the high volume of network traffic by dividing it into flows. As sampling methods are extensively used in flow generators such as NetFlow, the impact of sampling on the performance of flow-based analysis needs to be investigated. Monitoring using sampled traffic is a well-studied research area, however, the impact of sampling on flow-based anomaly detection is a poorly researched area. This paper investigates flow sampling methods and shows that these methods have negative impact on flow-based anomaly detection. Therefore, we propose an efficient probabilistic flow sampling method that can preserve flow traffic distribution. The proposed sampling method takes into account two flow features: Destination IP address and octet. The destination IP addresses are sampled based on the number of received bytes. Our method provides efficient sampled traffic which has the required traffic features for both flow-based anomaly detection and monitoring. The proposed sampling method is evaluated using a number of generated flow-based datasets. The results show improvement in preserved malicious flows.

• 정보관리학회지
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• 제19권3호
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• pp.111-122
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• 2002

본 연구에서는 웹 검색 이용자들의 전반적인 검색 행태를 이해하기 위하여 국내에서 널리 사용되고 있는 웹 검색 서비스 네이버에서 생성된 검색 트랜잭션 로그를 분석하였다. 본 연구에서는 웹 검색 트랜잭션 로그 분석에 필요한 세션 정의 방법을 설명하고 로그 정제 및 질의 유형 분류방법을 제시하였으며, 한글 검색 트랜잭션 로그 분석에 필수절인 검색어 정의 방법을 제안하였다. 본 연구의 결과는 보다 효과적인 국내 웹 검색 시스템 개발과 서비스 구축에 기여할 것으로 기대된다.

• 정보보호학회논문지
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• 제24권6호
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• pp.1055-1063
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• 2014

전력소모량을 이용한 부채널 분석의 성능 향상을 위해 다양한 분석기법이 제안되고 있다. 이들 중, 사전처리 단계에서 적용 가능한 파형압축은 전력분석을 위한 소요시간을 단축하고 수집신호의 잡음성분을 줄이기 위해 널리 사용되는 방법이다. 본 논문에서는 영상처리 등에 많이 사용되고 있는 선형판별분석(Linear Discriminant Analysis)을 이용한 전력분석기법을 제안한다. 또한, 실험을 통해 기존의 파형압축방법 중 가장 성능이 좋은 것으로 알려진 주성분분석(Principal Component Analysis)을 이용한 방법과의 성능 비교를 통해 제안기법의 우수성을 증명한다.

• 한국IT서비스학회지
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• 제13권2호
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• pp.251-259
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• 2014

Outdoor cultural properties have been damaged by natural weathering and air pollution for a long time. For this reason, there are many characteristics it is hard to decipher some carved inscription on the surface of damaged outdoor cultural properties. Until now, Rubbed copy has been widely used to decode engraved inscription. A investigation for epigraph has been made by the rubbing that has resulted in a lower resolution from the viewpoints of extraction process and used materials. Rubbing's results are not satisfied in the damaged inscriptions which are weathered by natural environment and pollution for a long time and in the narrowed one. The main analysis presented in this paper is a cross section analysis method using 3d scanning technique for epigraph not read. Cross section analysis is a study on readability improvement method for ancient inscription of irregularity monument surface. Cross section analysis confirms information that separated the inscription information of monument and the ground information to read a ancient inscription and decode the inscription information. The proposed character identification method contributed to decoding an ancient inscription on Silla Monument in Jungseong-ri of Pohang.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권3호
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• pp.1670-1683
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• 2017

Mitotic event recognition is a crucial and challenging task in biomedical applications. In this paper, we introduce the slow feature analysis and propose a fully-automated mitotic event recognition method for cell populations imaged with time-lapse phase contrast microscopy. The method includes three steps. First, a candidate sequence extraction method is utilized to exclude most of the sequences not containing mitosis. Next, slow feature is learned from the candidate sequences using slow feature analysis. Finally, a hidden conditional random field (HCRF) model is applied for the classification of the sequences. We use a supervised SFA learning strategy to learn the slow feature function because the strategy brings image content and discriminative information together to get a better encoding. Besides, the HCRF model is more suitable to describe the temporal structure of image sequences than nonsequential SVM approaches. In our experiment, the proposed recognition method achieved 0.93 area under curve (AUC) and 91% accuracy on a very challenging phase contrast microscopy dataset named C2C12.

• 디지털산업정보학회논문지
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• 제19권4호
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• pp.1-11
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• 2023

Clustering analysis is used in various fields including customer segmentation and clustering methods such as k-means are actively applied in the credit card customer segmentation. In this paper, we summarized the input features selection method of k-means clustering for the case of the credit card customer segmentation problem, and evaluated its feasibility through the analysis results. By using the label values of k-means clustering results as target features of a decision tree classification, we composed a method for prioritizing input features using the information gain of the branch. It is not easy to determine effectiveness with the clustering effectiveness index, but in the case of the CH index, cluster effectiveness is improved evidently in the method presented in this paper compared to the case of randomly determining priorities. The suggested method can be used for effectiveness of actively used clustering analysis including k-means method.