• Proceedings of the Korea Information Processing Society Conference
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• 2003.05c
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• pp.1829-1832
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• 2003

본 연구는 유의어 매트릭스를 이용하여 질의의 확장을 통한 컴포넌트 검색 과정을 기술하였다. 컴포넌트 검색은 질의를 입력하면 질의의 확장이 이루어지고 컴포넌트 사이의 신뢰도를 측정하여 검색한다. 신뢰도 계산을 위해서는 질의와 컴포넌트 사이에 유사한가를 나타내는 동치관계, 클래스의 가중치와 동치관계 값을 이용한 포함관계, 그리고 유사도를 계산한다. 끝으로 이들 값을 이용하여 신뢰도를 계산한 후 이 신뢰도 값에 의하여 유사 컴포넌트들을 검색하여 유사도 우선순위로 컴포넌트가 검색된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.65-69
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• 2015

하천에서 유사이동은 하천환경과 하천형상을 결정하는 주요 요소이므로 이를 해석하는 것은 매우 중요하다. 그러나 유사이동은 일반적으로 이상흐름 (two-phase flow)이며 난류를 동반하기에 이를 해석하기에는 쉽지 않다. 이상흐름을 해석하는 방법으로는 유사를 연속상인 유사구름(sediment cloud)으로 표현하여 해석하는 Euler-Euler 모형이 있으며 입자를 직접 추적하여 해석하는 Euler-Lagrange 모형이 있다. 본 연구에서는 유사이동 해석을 위하여 Euler-Lagrange 모형을 사용하였으며 흐름의 진동성분을 고려하기 위하여 EIM (Eddy Interaction Model)을 사용하였다. 유체의 유속은 Dou (1987)가 제시한 경험식을 사용하였고 난류운동에너지와 소산률은 Nezu and Nakagawa (1993)가 제시한 식을 사용하였다. EIM에서 입자에 발생하는 와의 영향시간(eddy interaction time)을 계산하기 위해 Gosman and Ioannides (1983)가 제시한 eddy lifetime과 eddy crossing time을 사용하였다. 유사입자는 입자의 운동량방정식을 풀어 그 거동을 추적하였으며 일정 시간 후 입자의 수를 이용하여 농도를 계산하였다. 유체에 발생하는 유속의 진동성분에 의해 입자가 부상하고 중력에 의해 흐름에 따른 일정한 농도분포 형태를 가지는 것을 확인하였다. 유사의 입자크기와 흐름에 따른 농도분포를 계산하였으며, 이를 측정치와 비교하여 EIM의 적용성을 확인하였다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.3C
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• pp.77-82
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• 2005

Partial response maximum likelihood (PRML) detection using the Viterbi algorithm involves the calculation of likelihood metrics that determine the most likely sequence of decoded data. In general, it is assumed that branches at each node in the trellis diagram have same probabilities. If modulation code with minimum and maximum run-length constraints is used, the occurrence ratio (Ro) of each particular pattern is different, and therefore the assumption is not true. We present a calculation scheme of the likelihood metrics for the PRML detection using the occurrence ratio. In simulation, we have tested the two (1,7) run-length-limited codes and calculated the occurrence ratios as the orders of PR targets are changed. We can identify that the PRML detections using the occurrence ratio provide more than about 0.5dB gain compared to conventional PRML detections at 10/sup -5/ BER in high-density magnetic recording and optical recording channels.

• Journal of Korea Multimedia Society
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• v.5 no.5
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• pp.512-522
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• 2002

In this paper, we propose a hybrid method which is mixed with frame selection and weighting model rank method, based on GMM(gaussian mixture model), for real-time text-independent speaker recognition system. In the system, maximum likelihood estimation was used for GMM parameter optimization, and maximum likelihood was used for recognition basically Proposed hybrid method has two steps. First, likelihood score was calculated with speaker models and test data at frame level, and the difference is calculated between the biggest likelihood value and second. And then, the frame is selected if the difference is bigger than threshold. The second, instead of calculated likelihood, weighting value is used for calculating total score at each selected frame. Cepstrum coefficient and regressive coefficient were used as feature parameters, and the database for test and training consists of several data which are collected at different time, and data for experience are selected randomly In experiments, we applied each method to baseline system, and tested. In speaker recognition experiments, proposed hybrid method has an average of 4% higher recognition accuracy than frame selection method and 1% higher than W method, implying the effectiveness of it.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.2
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• pp.347-363
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• 2019

Malware has its own unique behavior characteristics, like DNA for living things. To respond APT (Advanced Persistent Threat) attacks in advance, it needs to extract behavioral characteristics from malware. To this end, it needs to do classification for each malware based on its behavioral similarity. In this paper, various similarity of Windows malware is estimated; and based on these similarity values, malware's family is predicted. The similarity measures used in this paper are as follows: 'TF-IDF cosine similarity', 'Nilsimsa similarity', 'malware function cosine similarity' and 'Jaccard similarity'. As a result, we find the prediction rate for each similarity measure is widely different. Although, there is no similarity measure which can be applied to malware classification with high accuracy, this result can be helpful to select a similarity measure to classify specific malware family.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2008.11a
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• pp.219-222
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• 2008

본 논문은 공간영역 상에서 다양하게 변형된 복제 영상과 원본 영상간의 통계적 특성을 이용하여 그 유사도를 측정하고 복제 여부를 판단하는 계층적 구조의 불법 비디오 감식 방법을 제안한다. 영상의 대표적 특성인 명암도에 따라 순위를 매김으로써 공간적 변형에 영향을 받지 않도록 하였으며, 데이터베이스에 저장된 방대한 양의 영상들에 대한 검색 시간과 계산량을 줄이기 위해 순위 표본 프레임을 이용하여 유사한 후보 영상군을 추출한다. 이러한 후보 영상군을 대상으로 일반적인 불법복제 비디오의 형태를 감안하여 각 프레임의 중앙 영역을 포함하여 통계 검정을 이용함으로써 복제 여부를 판단한다. 실험 결과 제안하는 방법은 기존 방법과 유사한 정확도를 보이며 동시에 선택된 순위 표본 프레임 수는 약 50% 가량 적게 추출되어 검색 시간과 계산량이 감소하였다. 또한 영상의 화질 열화, 대비 변형, 확대 및 축소, letterbox 등 다양한 공간적 변형에도 포괄적으로 복제 여부를 판단할 수 있음을 실험을 통해 확인하였다.

• Magazine of the Korea Concrete Institute
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• v.10 no.6
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• pp.253-260
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• 1998

An approximate method for the calculation of creep deflections of reinforced concrete beams under sustained service loads is proposed. The position of neutral axis and strain and stress distribution of fully cracked section after creep is determined from the requirements of strain compatibility and equilibruim of a section and then the long-term flexural rigidity of fully cracked section is determined based on the new neutral axis. The long-term flexural rigidity of uncracked section at the level of the reinforcenment. The approach of calculating long-term effective flexural rigidity and defections is similar to the current American Concrete Institue procedure for calculating effecitve moment of inertia and short-term deflections. The accuracy of the analysis is verified by comparison with several experimental mesurements of beam deflectons. The result is good between the theotetical values and mesured valus.

• Journal of KIISE
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• v.41 no.9
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• pp.728-743
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• 2014

Finger printing and sequence alignment are well-known approaches for document similarity comparison. A fingerprinting method is simple and fast, but it can not find particular similar regions. A string alignment method is used for identifying regions of similarity by arranging the sequences of a string. It has an advantage of finding particular similar regions, but it also has a disadvantage of taking more computing time. The Multi-Level Alignment (MLA) is a new method designed for taking the advantages of both methods. The MLA divides input documents into uniform length blocks, and then extracts fingerprints from each block and calculates similarity of block pairs by comparing the fingerprints. A similarity table is created in this process. Finally, sequence alignment is used for specifying longest similar regions in the similarity table. The MLA allows users to change block's size to control proportion of the fingerprint algorithm and the sequence alignment. As a document is divided into several blocks, similar regions are also fragmented into two or more blocks. To solve this fragmentation problem, we proposed a united block method. Experimentally, we show that computing document's similarity with the united block is more accurate than the original MLA method, with minor time loss.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04a
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• pp.830-832
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• 2003

본 논문에서는 교육용 컨텐츠 추천시스템의 정확도를 향상시키고자 사용자 모델 정보를 활용하여 기존의 협업여과 방법의 유사도 재산을 보완함으로써 추천의 정확도를 향상시키는 방법을 제안하고자 한다. 협업여과방법은 사용자의 평가와 비슷한 선호도를 가지고 다른 사용자의 평가를 기반으로 제품이나 항목을 예측하고 이를 사용자에게 추천한다. 그러나 협업여과방법은 일정 수 이상의 상품이나 항목에 대한 평가가 이루어져야 하며, 사용자의 평가가 적은 경우 희소성으로 인한 평가의 정확도가 낮아지는 단점을 기지고 있다. 본 논문에서는 인구 통계 정보를 이용한 가상 평가 점수를 반영하여 유사도 계산시 희소성을 낮춰 예측의 정확도를 향상시키고자 한다.

• Journal of the Korea Society of Computer and Information
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• v.14 no.5
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• pp.19-28
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• 2009

Prediction problem of the time-series data has been a research issue for a long time among many researchers and a number of methods have been proposed in the literatures. In this paper, a method is proposed that similarities among time-series data are examined by use of Hidden Markov Model and Likelihood and future direction of the data movement is determined. Query sequence is modeled by Hidden Markov Modeling and then the model is examined over the pre-recorded time-series to find the subsequence which has the greatest similarity between the model and the extracted subsequence. The similarity is evaluated by likelihood. When the best subsequence is chosen, the next portion of the subsequence is used to predict the next phase of the data movement. A number of experiments with different parameters have been conducted to confirm the validity of the method. We used KOSPI to verify suggested method.