• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.5 no.1
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• pp.29-37
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• 2007

A conventional single compartment model cannot simulate reasonably the migration phenomenon of contaminants through unsaturated zone, due to the intrinsic unrealistic assumption of the compartment model that contaminants entering a compartment are immediately and uniformly mixed. Although, a multi-compartments model, in which even physically identical layer is divided into multiple compartments, may be used for explaining the retardation of contaminant mass flux along with increasing number of compartments, its numerical modeling is usually time-consuming and appropriate analytical solutions have not been reported yet. In order to improve the conventional compartment models on contaminant migration through unsaturated zone, a series of analytical solutions for multi-compartments model were derived and a generalized constraint under which the results from multi-compartments model can be simply approximated by single compartment model was proposed. The simplified approximation method was verified by a simple numerical analysis on the constraint under hypothetical conditions. It was also proved that the influent contaminant transfer rate from the bulk unsaturated zone can be generally represented into a time-dependent nominal transfer rate rather than a constant. In addition, the nominal transfer rate turned out to be very sensitive to the contaminant transfer rate between compartments in unsaturated zone, but to be almost insensitive to the transfer rate from contaminated zone. It is expected that the simplified approximation method developed in this study can be used for rapid and reasonable estimation of the migration phenomenon of contaminant through unsaturated zone, instead of time-consuming multi-compartments modeling.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1077-1086
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• 2023

The searchable encryption model allows to selectively search for encrypted data stored on a remote server. In a real-world scenarios, the model must be able to support multiple search keywords, multiple data owners/users. In this paper, these models are referred to as Multi Ranked Searchable Encryption model. However, at the time this paper was written, the proposed models use fully-trusted trapdoor centers, some of which assume that the connection between the user and the trapdoor center is secure, which is unlikely that such assumptions will be kept in real life. In order to improve the practicality and security of these searchable encryption models, this paper proposes a new Multi Ranked Searchable Encryption model which uses random keywords to protect search words requested by the data downloader from an honest-but-curious trapdoor center with an external attacker without the assumptions. The attacker cannot distinguish whether two different search requests contain the same search keywords. In addition, experiments demonstrate that the proposed model achieves reasonable performance, even considering the overhead caused by adding this protection process.

• ICROS
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• v.6 no.5
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• pp.54-63
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• 2000

본 논문에서는 모델 불확정성을 가진 이산사건시스템의 강인 관리제어이론에 대해 알아본다. 지금까지 연구된 불확정성 이산사건시스템은 크게 두 부류로 구분된다. 첫째는 다중 모델의 집합으로 표현되는 불확정성 시스템으로서 제어 대상이 되는 시스템의 동적 특성이 몇 가지의 가능한 이산사건 모델들의 집합으로 기술되는 시스템이다. 두 번째는 비결정성 시스템으로서 시스템의 한 상태에서 하나의 동일 사건발생에 의해 천이되는 상태가 두가지 이상이 존재하여 상태천이에 있어 불확정성이 존재하는 시스템이다. 본 논문에서는 두가지 형태의 불확정성 이산사건 시스템들에 대한 강인 관리제어기 설계문제에 대해 살펴보고, 강인 관리 제어 이론에 대한 최근의 연구동향과 발전 방향에 대해 소개한다.

• The Journal of the Acoustical Society of Korea
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• v.23 no.6
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• pp.473-480
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• 2004

In this paper we propose an effective feature compensation scheme based on the speech model in order to achieve robust speech recognition. The proposed feature compensation method is based on parallel combined mixture model (PCMM). The previous PCMM works require a highly sophisticated procedure for estimation of the combined mixture model in order to reflect the time-varying noisy conditions at every utterance. The proposed schemes can cope with the time-varying background noise by employing the interpolation method of the multiple mixture models. We apply the‘data-driven’method to PCMM tot move reliable model combination and introduce a frame-synched version for estimation of environments posteriori. In order to reduce the computational complexity due to multiple models, we propose a technique for mixture sharing. The statistically similar Gaussian components are selected and the smoothed versions are generated for sharing. The performance is examined over Aurora 2.0 and speech corpus recorded while car-driving. The experimental results indicate that the proposed schemes are effective in realizing robust speech recognition and reducing the computational complexities under both simulated environments and real-life conditions.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.168-171
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• 2012

본 논문에서는 모바일 컴퓨팅 환경과 불확실성을 지원하는 다중추론지원 분산형 상황인식 시스템의 지식 베이스(KB: Knowledge Base)를 위한 모델로써 상황정보(OWL), 온톨로지 추론정보(OWL DL), 규칙 추론정보(SWRL), 베이지안 추론정보(PR-OWL)를 통합적으로 표현하는 UniOWL 통합상황모델을 제안한다. 제안한 통합상황모델은 상황정보와 다중 추론정보를 단일 구문, 즉 OWL 구문으로 표현하여 지식베이스 설계를 수월하게 하고 표현을 단순화하는 장점이 있다.

• The Journal of Society for e-Business Studies
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• v.26 no.1
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• pp.29-41
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• 2021

Recently, there are increasing attempts to utilize deep learning methodology in the fashion industry. Accordingly, research dealing with various fashion-related problems have been proposed, and superior performances have been achieved. However, the studies for fashion style classification have not reflected the characteristics of the fashion style that one outfit can include multiple styles simultaneously. Therefore, we aim to solve the multi-label classification problem by utilizing the dependencies between the styles. A multi-label recognition model based on a graph convolution network is applied to detect and explore fashion styles' dependencies. Furthermore, we accelerate model training and improve the model's performance through transfer learning. The proposed model was verified by a dataset collected from social network services and outperformed baselines.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1644_1645
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• 2009

본 논문은 비정상 시계열 예측을 위한 다중모델 퍼지 시스템과, 제안된 시스템의 최적화를 위한 유전 알고리즘의 응용을 다룬다. 일반적으로, 퍼지 예측시스템의 성능은 비선형 데이터가 가지고 있는 다양한 패턴이나 법칙성, 경향 등을 잘 분석하고 시스템에 반영함으로써 개선될 수 있다. 따라서, 본 논문은 원형 시계열의 특성을 보다 잘 반영할 수 있는 그들의 차분데이터를 시스템에 적용하며, 생성 가능한 차분 데이터들 중 원형 시계열의 특징에 가까운 일부를 추출하여 다중모델 퍼지 예측 시스템을 구현함으로써 다양한 원형시계열의 패턴이나 법칙성 등이 고려될 수 있도록 하였다. 다중 모델 퍼지 시스템의 각각의 예측기에는 구조가 간단한 k-means 클러스터링 기법을 적용하여 구현의 용이성을 꽤하였으며, 성능평가를 통해 선택된 최종 예측기는 RCGKA(real-coded genetic k-means clustering algorithms)를 통해 더욱 최적화된 규칙기반을 가지게 함으로써 예측성능이 개선될 수 있도록 하였다. 본 논문에 사용된 최적화 기법인 RCGKA에는 또한 성능이 우수한 다양한 유전연산자를 도입하여 더욱 예측기 성능이 강화될 수 있도록 하였으며, 시뮬레이션을 통해 제안된 예측시스템의 효용성을 증명하였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.458-461
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• 2004

오존 반응 메카니즘은 상당히 복잡하고 비선형적이기 때문에 오존 농도를 예측하는 것은 상당한 어려움을 안고 있다 따라서, 신뢰성 높은 오존 예측값을 구하는데 단일 예측모델만으로는 한계가 있으며, 이를 개선하기 위하여 다중 모델을 제안하였다. 입력데이터에 퍼지 클러스터링을 사용하여 고, 중, 저농도별로 그룹핑한 후, 그룹핑된 오존농도에 대해서 의사결정 트리를 사용하여 그룹핑된 오존데이터가 어느 정도 분류능력을 갖는지 파악하여, 오차가 가장 적은 분류특성을 갖는 그룹을 설정하여, 다중모델의 입력 데이터로 사용하여 모델을 형성하였다. 의사결정 트리를 이용하여 모델의 입력 데이터를 설정하는 것은 어떤 오존농도까지의 범위를 클래스로 설정하느냐에 따라서 모델의 성능과 고, 중, 저농도의 오존을 분류하는 성능이 달라지므로 본 논문에서는 퍼지 클러스터링을 이용하여 의사결정 트리의 클래스의 범위를 설정하여 예측 시스템을 구현하였다.

• Journal of The Korean Association For Science Education
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• v.34 no.5
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• pp.479-490
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• 2014

The purpose of this study is to develop an argument-based modeling strategy, utilizing writing and argumentation for communication in science education. We need to support students and teachers who have difficulty in modeling in science education, this strategy focuses on development of four kinds of factors as follows: First, awareness of problems, recognizing in association with problems by observing several problematic situations. Second is science concept structuralization suggesting enough science concepts by organization for scientific explanation. The third is claim-evidence appropriateness that suggests appropriate representation as evidence for assertions. Last, the use of various representations and multimodal representations that converts and integrates these representations in evidence suggestion. For the development of these four factors, this study organized three stages. 'Recognition process' for understanding of multimodal representations, and 'Interpretation process' for understanding of activity according to multimodal representations, 'Application process' for understanding of modeling through argumentation. This application process has been done with eight stages of 'Asking questions or problems - Planning experiment - Investigation through observation on experiment - Analyzing and interpreting data - Constructing pre-model - Presenting model - Expressing model using multimodal representations - Evaluating model - Revising model'. After this application process, students could have opportunity to form scientific knowledge by making their own model as scientific explanation system for the phenomenon of the natural world they observed during a series of courses of modeling.

• The Journal of the Acoustical Society of Korea
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• v.23 no.5
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• pp.390-399
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• 2004

In this paper, we design and implement a multiple-view VoiceXML editor to improve editing efficiency of the VoiceXML. The VoiceXML multiple-view Editor uses a MVC framework to support multiple views and paradigm. Our multiple-view editor consists of Model. View and Controller using MVC framework. A model, core data structure. is constructed of abstract syntax tree and abstract grammar. A view. user interface. is formalized in unparsing rules and unparser. A controller. to control model and view. is made of command interpreter and tree handler. The VoiceXML multiple-view editor overcomes a drawbacks of existing XML editors by showing document structure and context concurrently. as well as document flows. Our VoiceXML multiple-view editor. which MVC framework has been applied, provides various editing views concurrently to users. Thereby. it supports efficient and convenient editing environments for voice-web documents to users and it guarantees transparency of editors. as various views have a same consistent model.