• 트랜스-
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• 제14권
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• pp.65-92
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• 2023

The knowledge of the narrative agent not only constitutes the content and meaning of the narrative itself, but is also closely related to the emotional response of the recipient. Also, the disparity of knowledge between narrative agents is an important factor in making a narrative richer and more interesting. But It tends to be treated as a sub-topic of narration theory or genre/style studies rather than an independent subject of narrative studies or criticism. In this paper, I propose a model that can systematically and quantitatively analyze the knowledge of narrative agents. The proposed model consists of the knowledge structure that represents a narrative, the knowledge state that expresses the knowledge of narrative agent as a degree of belief, and the knowledge flow that means changes in the knowledge state according to the development of events. In addition, the formal notation of the knowledge structure and a probabilistic inference model that could obtain the state of knowledge were proposed, and the knowledge structure and knowledge flow were analyzed by applying the model to the actual narrative. It is expected that the proposed model will be of practical help in the creation and evaluation of narratives.

• 한국산업정보학회논문지
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• 제20권3호
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• pp.61-69
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• 2015

본 논문에서는 이산 웨이블릿 변환을 통해 추출된 시간 영역과 주파수 영역의 특징들을 활용하여 심박수변이도를 확률적인 지식으로 분석할 수 있는 방법을 제안하였다. 제안된 방법에서 지식획득 알고리즘은 규칙생성과 규칙평가 단계로 구성되어 있으며, 규칙생성에서는 ROC 분석을 통해 수치적인 속성값을 이산화된 구간으로 변환하고, 서로 다른 의사결정값을 포함하는 구간들 사이에 일관성 정도를 비교함으로써 감축된 규칙-집합을 생성한다. 이때 규칙-집합 내에 각 규칙에 대해서 확률적 해석을 위한 3가지 척도를 추정하였다. 제안된 모형의 효과성은 심혈관질환 병력을 가진 58명의 심전도 데이터로부터 심방세동을 식별할 수 있는 5가지 규칙을 생성하였고, 이들 규칙의 분별력을 평가하였다. 실험결과, 제안된 모형으로부터 생성된 지식은 4가지 성능평가 척도에 대해서 각각 93%의 정확도를 보여주었다.

• Steel and Composite Structures
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• 제21권1호
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• pp.93-107
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• 2016

Seismic fragility analysis is a probabilistic decision-making framework which is widely implemented for evaluating vulnerability of a building under earthquake loading. It requires ingredient named probabilistic model and commonly developed using statistics requiring collecting data in large quantities. Preparation of such a data-base is often costly and time-consuming. Therefore, in this paper, by developing generic seismic drift demand model for regular-multi-story steel moment resisting frames is tried to present a novel application of the probabilistic decision-making analysis to practical purposes. To this end, a demand model which is a linear function of intensity measure in logarithmic space is developed to predict overall maximum inter-story drift. Next, the model is coupled with a set of regression-based equations which are capable of directly estimating unknown statistical characteristics of the model parameters.To explicitly address uncertainties arise from randomness and lack of knowledge, the Bayesian regression inference is employed, when these relations are developed. The developed demand model is then employed in a Seismic Fragility Analysis (SFA) for two designed building. The accuracy of the results is also assessed by comparison with the results directly obtained from Incremental Dynamic analysis.

• 대한산업공학회지
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• 제42권4호
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• pp.249-255
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• 2016

Recently, transaction data is accumulated everywhere very rapidly. Association analysis methods are usually applied to analyze transaction data, but the methods have several problems. For example, these methods can only consider one-way relations among items and cannot reflect domain knowledge into analysis process. In order to overcome defect of association analysis methods, we suggest a transaction data analysis method based on probabilistic graphical model (PGM) in this study. The method we suggest has several advantages as compared with association analysis methods. For example, this method has a high flexibility, and can give a solution to various probability problems regarding the transaction data with relationships among items.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.2005-2010
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• 2004

As robots become more prevalent in human daily life, situations requiring interaction between humans and robots will occur more frequently. Therefore, human-robot interaction (HRI) is becoming increasingly important. Although robotics researchers have made many technical developments in their field, intuitive and easy ways for most common users to interact with robots are still lacking. This paper introduces a new approach to enhance human-robot interaction using a semantic symbol language and proposes a method to acquire the intentions of robot users. In the proposed approach, each semantic symbol represents knowledge about either the environment or an action that a robot can perform. Users'intentions are expressed by symbolized multimodal information. To interpret a users'command, a probabilistic approach is used, which is appropriate for interpreting a freestyle user expression or insufficient input information. Therefore, a first-order Markov model is constructed as a probabilistic model, and a questionnaire is conducted to obtain state transition probabilities for this Markov model. Finally, we evaluated our model to show how well it interprets users'commands.

• International Journal of Safety
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• 제2권1호
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• pp.50-56
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• 2003

There are a lot of uncertainties in the severe accident phenomena and scenarios in nuclear power plants (NPPs) and one of the major issues for severe accident management is the reduction of these uncertainties. The severe accident management aid system using Probabilistic Safety Assessments (PSA) technology is developed for the management staff in order to reduce the uncertainties. The developed system includes the graphical display for plant and equipment status, previous research results by a knowledge-base technique, and the expected plant behavior using PSA. The plant model used in this paper is oriented to identify plant response and vulnerabilities via analyzing the quantified results, and to set up a framework for an accident management program based on these analysis results. Therefore the developed system may playa central role of information source for decision-making for severe accident management, and will be used as a training tool for severe accident management.

• 대한공간정보학회지
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• 제18권4호
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• pp.61-69
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• 2010

비탈면 혹은 절성토지의 파괴로 사람과 재산에 심각한 피해를 입히기 때문에 미리 산사태 취약성 분석을 수행하여 개발 혹은 자연재해로부터 위험을 대비하는 것이 필요하다. 기존의 산사태 취약성 분석은 휴리스틱, 통계학적, 결정론적 혹은 확률론적 방법을 통해 이뤄졌다. 그러나, 적은 현장정보 등으로 분석의 신뢰도가 떨어지거나, 전문가의 경험과 지식을 기존 정량적인 해석모델에 반영하기 어려웠다. 본 연구는 산사태 취약성 분석에 대한 전문가 지식과 공간입력자료의 시맨틱을 추출하여 온톨로지 모델을 구축하고, 이를 베이시안 네트워크에 반영하여 확률적인 산사태 모델링을 제안하였다. 기존에 전문가 수작업으로 이뤄지던 베이시안 네트워크의 구조 생성을 온톨로지 모델의 지식추론으로 자동화하고, 현장정보뿐만 아니라 전문가 지식을 모델링에 반영하여 조건부 산사태 발생확률분포를 작성하였다. 이 결과를 GIS에 적용하여 산사태 취약성 지도를 작성하였다. 검증을 위해 충남 홍성일원의 오서산 지역에 적용한 결과 기존 산사태 발생흔적과 86.5% 일치하였다. 본 연구를 통해 일반 사용자도 전문가 도움 없이도 광역적인 산사태 취약성 분석이 가능하리라 기대된다.

• 한국인터넷방송통신학회논문지
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• 제17권2호
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• pp.263-269
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• 2017

'목표모델'은 대규모 시스템의 관리에 인간의 개입을 최소화하기 위한 대안으로 제시된 '자율제어 시스템'의 지식 베이스이다. '자율제어 시스템'은'목표모델'을 기반으로 '자율제어'의 네 단계인 '모니터링-분석-계획-실행'을 수행하기 때문에 대상 시스템의 '목표모델' 달성 비율을 정량화할 필요가 있다. 따라서 본 논문에서는 '목표모델'의 달성비율을 정량화하기 위한 '목표모델의 확률적 평가'를 나타낸다. 평가는 '목표 모델링 및 가중치 부여', '목표모델 모니터링', '목표모델 평가 및 분석' 총 3단계로 구성되어 있다. 연구를 통해 '자율제어 엔진'에 지식 베이스를 제공하고, 가중치를 적용한 '목표모델'을 평가함으로써 시스템의 신뢰성 향상이 가능하다. 사례연구로써 'Smart IoT Kit'에 '목표모델'을 만들어 적용하여 제안 연구에 유효성을 입증한다.

• Structural Engineering and Mechanics
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• 제25권3호
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• pp.331-345
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• 2007

Uncertainties enter a complex analysis from a variety of sources: variability, lack of data, human errors, model simplification and lack of understanding of the underlying physics. However, for many important engineering applications insufficient data are available to justify the choice of a particular probability density function (PDF). Sometimes the only data available are in the form of interval estimates which represent, often conflicting, expert opinion. In this paper we demonstrate that Bayesian estimation techniques can successfully be used in applications where only vague interval measurements are available. The proposed approach is intended to fit within a probabilistic framework, which is established and widely accepted. To circumvent the problem of selecting a specific PDF when only little or vague data are available, a hierarchical model of a continuous family of PDF's is used. The classical Bayesian estimation methods are expanded to make use of imprecise interval data. Each of the expert opinions (interval data) are interpreted as random interval samples of a parent PDF. Consequently, a partial conflict between experts is automatically accounted for through the likelihood function.

• Advances in aircraft and spacecraft science
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• 제2권2호
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• pp.217-232
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• 2015

To prevent over-testing of the test-item during random vibration testing Scharton proposed and discussed the force limited random vibration testing (FLVT) in a number of publications. Besides the random vibration specification, the total mass and the turn-over frequency of the load (test item), $C^2$ is a very important parameter for FLVT. A number of computational methods to estimate $C^2$ are described in the literature, i.e., the simple and the complex two degrees of freedom system, STDFS and CTDFS, respectively. The motivation of this work is to evaluate the method for the computation of a realistic value of $C^2$ to perform a representative random vibration test based on force limitation, when the adjacent structure (source) description is more or less unknown. Marchand discussed the formal description of getting $C^2$, using the maximum PSD of the acceleration and maximum PSD of the force, both at the interface between load and source. Stevens presented the coupled systems modal approach (CSMA), where simplified asparagus patch models (parallel-oscillator representation) of load and source are connected, consisting of modal effective masses and the spring stiffness's associated with the natural frequencies. When the random acceleration vibration specification is given the CSMA method is suitable to compute the value of the parameter $C^2$. When no mathematical model of the source can be made available, estimations of the value $C^2$ can be find in literature. In this paper a probabilistic mathematical representation of the unknown source is proposed, such that the asparagus patch model of the source can be approximated. The chosen probabilistic design parameters have a uniform distribution. The computation of the value $C^2$ can be done in conjunction with the CSMA method, knowing the apparent mass of the load and the random acceleration specification at the interface between load and source, respectively. Data of two cases available from literature have been analyzed and discussed to get more knowledge about the applicability of the probabilistic method.