• 정보보호학회논문지
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• 제21권5호
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• pp.3-13
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• 2011

2004년 Boneh et. al. 은 송신자가 전송한 검색어에 대한 암호문과 수신자가 제공한 쿼리 생성에 사용된 검색어의 동일성을 서버가 검사할 수 있는 PEKS 스킴을 제안하였다. 이후 Yang et. al. 은 서로 다른 공개키로 암호화된 암호문의 비교를 통해 암호화된 메시지의 동일성을 검사하는 기법인 Probabilistic Public Key Encryption with Equality Test(PEET)을 제안하였다. PEET 기술은 메시지 부분을 검색어로 대체해서 암호화할 경우 Keyword guessing attacks에 대한 안전성을 보장하지 못하고 검색가능 암호화 기법들에서 일반적으로 고려되고 있는 안전성의 기준인 IND-CPA 안전성을 제공하지 못한다. 본 논문에서는 Keyword guessing attacks에 안전하며 IND-CPA를 만족하는 고정된 검사자를 고려한 메시지 동일성 검사 공개키 암호시스템(public key encryption with equality test with designated tester. dPEET)을 제안한다.

• 한국인터넷방송통신학회논문지
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• 제16권6호
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• pp.153-158
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• 2016

다중홉 무선센서네트워크에서 플러딩(또는 브로드캐스팅) 전송은 경로 탐색, 주소설정, 메시지 전송과 같은 기본 작업이다. 그러나 플러딩 기법은 'Broadcasting Storm' 현상을 발생시키며, 메시지 충돌, 혼잡, 불필요한 네트워크 자원을 소모하게 된다. 따라서 이러한 단순 플러딩 기법의 단점을 보완하기 위한 다양한 Flooding 기법이 제안되었다. 이러한 플러딩 기법은 확률기반, 카운터 기반, 거리기반, 이웃 기반 기법으로 분류할 수 있다. 본 논문에서는 이러한 대표적인 플러딩 기법의 장단점을 소개하고, 컴퓨터 시뮬레이션을 통하여 단순 플러딩 기법을 포함한 각 기법의 성능과 효율성을 비교 분석하였다.

• Nuclear Engineering and Technology
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• 제47권2호
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• pp.187-195
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• 2015

Human-induced initiating events, also called Category B actions in human reliability analysis, are operator actions that may lead directly to initiating events. Most conventional probabilistic safety analyses typically assume that the frequency of initiating events also includes the probability of human-induced initiating events. However, some regulatory documents require Category B actions to be specifically analyzed and quantified in probabilistic safety analysis. An explicit modeling of Category B actions could also potentially lead to important insights into human performance in terms of safety. However, there is no standard procedure to identify Category B actions. This paper describes a systematic procedure to identify Category B actions for low power and shutdown conditions. The procedure includes several steps to determine operator actions that may lead to initiating events in the low power and shutdown stages. These steps are the selection of initiating events, the selection of systems or components, the screening of unlikely operating actions, and the quantification of initiating events. The procedure also provides the detailed instruction for each step, such as operator's action, information required, screening rules, and the outputs. Finally, the applicability of the suggested approach is also investigated by application to a plant example.

• 한국수학교육학회지시리즈A:수학교육
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• 제51권3호
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• pp.301-321
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• 2012

Since research has barely been done on the minority with low-achievement & low-SES in probability, this research attempted to search the change of their thinking level in the classes of probability and motivate them on the mathematical learning to feel confident in mathematics. We can say that the problems of the educational discriminations are due to the overlook on the individual conditions, situations, and environments. Therefore, in order to resolve some discrimination, 4 students who belonged to the minority group, engaged in the research, based on 10 units of the instructional materials designed for the research. As a result, for the student's thinking level, it was observed that they were improved from the 1st to the 3rd level in probability. Also, the researcher found that the adequate use of the encouragement, the praise, the direct explanation, and the scaffolding enabled them to prompt their learning motives and the increased responsibility on the learning. As time passed, the participants could share their mathematical knowledge and its concept with others, in the increased confidence.

• Smart Structures and Systems
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• 제32권1호
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• pp.61-81
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• 2023

This study presents an ensemble learning based Bayesian model updating approach for structural damage diagnosis. In the developed framework, the structure is initially decomposed into a set of substructures. The autoregressive moving average (ARMAX) model is established first for structural damage localization based structural motion equation. The wavelet packet decomposition is utilized to extract the damage-sensitive node energy in different frequency bands for constructing structural surrogate models. Four methods, including Kriging predictor (KRG), radial basis function neural network (RBFNN), support vector regression (SVR), and multivariate adaptive regression splines (MARS), are selected as candidate structural surrogate models. These models are then resampled by bootstrapping and combined to obtain an ensemble model by probabilistic ensemble. Meanwhile, the maximum entropy principal is adopted to search for new design points for sample space updating, yielding a more robust ensemble model. Through the iterations, a framework of surrogate ensemble learning based model updating with high model construction efficiency and accuracy is proposed. The specificities of the method are discussed and investigated in a case study.

• 한국음향학회지
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• 제25권4호
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• pp.151-158
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• 2006

본 논문은 필립스의 음악 검색 기법을 기반으로 필터 뱅크 에너지 변화량과 음악의 통계적인 특성을 이용한 오디오 핑거프린트 추출 방법을 제안하였다. 기존의 필립스 방식은 제한된 주파수 영역을 너무 많은 필터 뱅크로 분할하여 분석함으로써 밴드들 사이에 연계성 및 왜곡에 대한 민감도가 증가하는 특징을 보일 수 있다. 제안된 방법은 필터 뱅크의 밴드 수를 줄여 왜곡에 대한 강인성을 증진시키고, 필터 뱅크 에너지의 변화량의 부호와 크기 정보를 통계적 특성을 고려한 양자화 기법을 이용해 2비트로 할당함으로써 오디오 핑거프린트의 고유성을 확보하였다. 추출된 2비트는 4개의 레벨로 정보를 표현함으로 각 레벨 사이에 연계성이 존재하게 된다. 이 같은 레벨 사이의 연계성은 유사도 측정 시 이용될 뿐만 아니라 오디오 핑거프린트를 기준으로 검색 영역을 확장하는 제안된 방식에서는 효율적인 검색 영역을 선택할 수 있는 정보로 활용 되었다. 제안된 방식은 다양한 주변 잡음환경 (거리, 백화점, 자동차, 사무실, 식당)에서의 실험을 통하여 주변 잡음에 강인한 특성을 보일 뿐만 아니라 검색 속도 또한 향상되는 특징을 보였다.

• 한국음향학회지
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• 제24권3호
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• pp.178-185
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• 2005

본 논문에서는 내용기반 음악 정보 검색 방법으로써 멜로디의 시간 변화 특성과 통계적 특성을 모두 이용할 수 있는 hybrid 방법에 대해 제안하였다. 실제 방송 환경에의 적용을 위해 드라마 OST의 좁은 검색 범위뿐만 아니라 가요 1,005곡의 넓은 검색 범위에서도 제안한 방법을 이용하여 실험하였다. 제안된 방법은 특징 벡터로써 pitch와 MFCC(Mel Frequency Cepstral Coefficient)를 사용하여 음의 특성을 나타내었으며 멜로디를 표현하기 위해 피치 히스토그램과 VQ (Vector Quantization) 코드화한 MFCC의 템포럴 시퀀스를 이용함으로써 음악 검색 방법에 멜로디의 시간 변화 특성과 통계적 특성을 함께 적용할 수 있었다. 또한 pitch 히스토그램과 MFCC-VQ 템포럴 방법을 모두 사용한 hybrid 방식에 적절한 패턴 매칭 방법을 제안함으로써 기존의 각 단일 방식을 이용한 성능 결과 (MFCC-VQ 템포럴)와 비교하여 볼 때 드라마 OST 검색 범위에서는 평균 $9.9\%$, 가요 1,005곡의 검색 범위에서는 $10.2\%$의 오류 감소율을 나타내었다.

• 제어로봇시스템학회논문지
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• 제7권10호
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• pp.819-826
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• 2001

Genetic algorithms are becoming more popular because of their relative simplicity and robustness. Genetic algorithms are global search techniques for nonlinear optimization. However, traditional genetic algorithms, though robust, are generally not the most successful optimization algorithm on any particular domain because they are poor at hill-climbing, whereas simulated annealing has the ability of probabilistic hill-climbing. Therefore, hybridizing a genetic algorithm with other algorithms can produce better performance than using the genetic algorithm or other algorithms independently. In this paper, we propose an efficient hybrid optimization algorithm named the adaptive random signal-based learning. Random signal-based learning is similar to the reinforcement learning of neural networks. This paper describes the application of genetic algorithms and simulated annealing to a random signal-based learning in order to generate the parameters and reinforcement signal of the random signal-based learning, respectively. The validity of the proposed algorithm is confirmed by applying it to two different examples.

• 대한산업공학회지
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• 제32권1호
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• pp.9-17
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• 2006

This paper deals with a discrete simulation optimization method for designing a complex probabilistic discrete event simulation. The developed algorithm uses the configuration algorithm that can change decision variables and the stopping algorithm that can end simulation in order to satisfy the given objective value during single run. It tries to estimate an auto-regressive model for evaluating correctly the objective function obtained by a small amount of output data. We apply the proposed algorithm to M/M/s model, (s, S) inventory model, and known-function problem. The proposed algorithm can't always guarantee the optimal solution but the method gives an approximate feasible solution in a relatively short time period. We, therefore, show the proposed algorithm can be used as an initial feasible solution of existing optimization methods that need multiple simulation run to search an optimal solution.

• 한국안전학회지
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• 제30권3호
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• pp.100-106
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• 2015

A module or independent subtree is a part of a fault tree whose child gates or basic events are not repeated in the remaining part of the fault tree. Modules are necessarily employed in order to reduce the computational costs of fault tree quantification. This quantification generates fault tree solutions such as minimal cut sets, minimal path sets, or binary decision diagrams (BDDs), and then, calculates top event probability and importance measures. This paper presents a new linear time algorithm to detect modules of large fault trees. It is shown through benchmark tests that the new method proposed in this study can very quickly detect the modules of a huge fault tree. It is recommended that this method be implemented into fault tree solvers for efficient probabilistic safety assessment (PSA) of nuclear power plants.