• Korean Journal of Childcare and Education
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• v.11 no.5
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• pp.293-310
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• 2015

The purpose of this study is to investigate the differences that appeared in the episodes in understandings of the emotional display rules according to the types of emotions and subjects for expressing emotions. In addition, the interaction effects of intention reasoning types and gender on children's understandings of the real emotions and emotional display rules are explored. 144 4-5 year old children in Chungbuk province participated in the experimental interviews. The results are as follows. First, children comprehended the emotional display rules more clearly in a relationship with peers than adults. In terms of a type of emotion, it was the negative emotions rather than positives ones that those children understood better for real emotions and emotional display rules. Second, the main effect of the intention reasoning types on children's understanding of the emotional display rules appeared significant in all episodes. Especially, in negative emotion-peer episode, children with different types of intention reasoning showed a different level of understanding emotional display rules depending on gender of the children.

• Journal of the Korea Society of Computer and Information
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• v.16 no.1
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• pp.71-80
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• 2011

This paper provides a novel approach for developers to use face detection techniques for their applications easily without special knowledge by selecting optimal face detection algorithms based on fuzzy inference. The purpose of this paper is to come up with a high-level system for face detection based on fuzzy inference with which users can develop systems easily and even without specific knowledge on face detection theories and algorithms. Important conditions are firstly considered to categorize the large problem space of face detection. The conditions identified here are then represented as expressions so that developers can use them to express various problems. The expressed conditions and available face detection algorithms constitute the fuzzy inference rules and the Fuzzy Interpreter is constructed based on the rules. Once the conditions are expressed by developers, the Fuzzy Interpreter proposed take the role to inference the conditions and find and organize the optimal algorithms to solve the represented problem with corresponding conditions. A proof-of-concept is implemented and tested compared to conventional algorithms to show the performance of the proposed approach.

• Journal of the Korea Society for Simulation
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• v.25 no.3
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• pp.117-123
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• 2016

This paper presents inference methods for inner operations of a multi-server queue when historical data are limited or system observation is restricted. In a queueing system analysis, autocorrelated arrival and service processes increase the complexity of modeling. Accordingly, numerous analysis methods have been developed. In this paper, we introduce an inference method for specific situations when external observations exhibit autocorrelated structure and and observations of internal operations are difficult. We release an assumption of the previous method and provide lemma and theorem to guarantee the correctness of our proposed inference method. Using only external observations, our proposed method deduces the internal operation of a multi-server queue via non-parametric approach even when the service times are autocorrelated. The main internal inference measures are waiting times and service times of respective customers. We provide some numerical results to verify that our method performs as intended.

• Journal of Convergence for Information Technology
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• v.9 no.7
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• pp.57-66
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• 2019

This study is a descriptive research study for investigating the factors influencing clinical reasoning competency of senior grade nursing students. This study was targeted at 160 senior grade nursing students in M city and G city and data was collected from April 15, 2019 to May 15, 2019. The collected data was analyzed using the SPSS/WIN 21.0 program, and t-test, ANOVA, Pearson's correlation coefficient, and multiple regression analysis were carried out. There was a significant correlation between clinical reasoning competency, critical thinking disposition, metacognition, and empathy. The factors influencing the clinical reasoning competency included metacognition(${\beta}=.48$, p<.001), critical thinking disposition(${\beta}=22$, p=.021), and empathy(${\beta}=-.19$, p=.012). These variables explained 35.0% of the clinical reasoning competency. Based on the results of this study, a demonstration study for developing a convergence education program including metacognition, critical thinking disposition, and clinical reasoning competency and verifying its effect is necessary.

• The Journal of Korean Association of Computer Education
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• v.12 no.4
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• pp.65-75
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• 2009

The result verification is necessary to support a guarantee for the correctness of the task results be executed by any unspecified resources in desktop grid environments. Typically, voting-based and trust-based result verification schemes have been used in the environments. However, these suffer from two potential problems: waste of resources due to redundant replicas of each task and increase in turnaround time due to the inability to deal with a dynamic changeable execution environment. To overcome these problems, we propose a fuzzy inference-based replication scheme which can adaptively determine the number of replicas per task by using both trusty degree and result return probability of resources. Therefore our proposal can reduce waste of resources by determining the number of replicas meeting with a dynamic execution environment of desktop grids, not to mention an enhancement of turnaround time for entire asks. Simulation results show that our scheme is superior to other ones in terms of turnaround time, the waste of resources, and the number of re-replications per task.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.207-210
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• 2002

본 논문에서는 인간과 MR(Mobile Robot)이 일정한 거리를 유지하면서 인간을 추종할 수 있도록 퍼지 제어기를 이용한 지능적 추론 방법을 제안하였다. 로봇은 다중 초음파 센서와 PC 카메라를 사용하여 인간과 로봇의 거리와 위치를 인지하고 로봇의 진행 방향과 속도를 퍼지 추론하는 방법을 사용하였다. 먼저 초음파 센서와 카메라를 사용하여 주변 환경에 대한 정보를 획득하고 주변환경을 표현하는 것이 중요하다. 센서와 카메라에 의해 입수된 정보로부터 로봇을 제어할 수 있도록 속도와 방향을 이용하여 추론하고 로봇을 제어하였다. 논문에서 제안된 퍼지 로직 알고리듬의 유용성을 검증하기 위해 실제 Mobile Robot을 이용한 주행실험을 반복 시행하여 요구된 결과를 얻음으로써 퍼지로직 제어기의 우수성을 확인할 수 있었다.

• Journal of Information Technology Application
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• v.1
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• pp.3-27
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• 1999

최근 기업의 경쟁력 강화를 위하여 기업내의 지식을 중요한 자원으로 인식하고 활용하는 지식경영의 필요성이 강력히 대두되고 있다. 이러한 지식경영의 주요 활동을 지원할 구체적인 방법론으로 정보기술의 활용 방안이 다각도로 제시되고 있으나, 실제적인 연구는 아직 초보단계에 있다고 하겠다. 본 연구에서는 지식의 생성, 저장, 그리고 추출 및 활용이라는 지식경영의 주요 과제를 효과적으로 해결하는 방안으로써 인공지능기법인 사례기반추론과 유전자 알고리즘을 이용한 통합방법론을 제시한다. 본 연구에서 제시하고 있는 방법론은 생성된 지식의 표현, 저장, 그리고 추출에 사례기반추론기법을 활용하였다는 점 이외에 다음과 같은 두 가지 특징을 가지고 있다. 첫째로는, 해결하고자 하는 문제에 가장 적절한 과거 지식이 추출되도록 함으로써 활용 효과를 높일 수 있도록 하였다는 점이다. 둘째로는, 환경의 변화를 반영할 수 있는 방안을 제시하고 있다는 점이다. 본 인공지능 통합방법론은 신용평가부서의 지식관리모형을 통해 검증해 본 결과 그 효과가 입증되었다.

• The Journal of Information Systems
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• v.19 no.1
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• pp.19-33
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• 2010

생산 수율은 비선형관계를 지닌 여러 요인들에 의해 영향을 받기 때문에 반도체 생산의 경우 예측이 어렵다. 본 논문에서 저자들은 사례기반추론과 자기조직화신경망 기반의 데이터마이닝 기법을 활용하여 수율의 높고 낮음을 밝히는 지능화된 수율예측시스템을 제시한다. 이 시스템은 자기조직회신경망을 사용하여 생산 로트의 공정파라미터 패턴을 파악하고 속성가중치 기반의 사례기반추론을 통해 신규 로트의 수율 수준을 예측한다. 이때 속성가중치는 역전파인공신경망을 통해 계산된다. 웹기반 시스템이 개발되고, 반도체 생산 기업의 실제 자료를 적용하여 본 시스템의 효율을 검증하고 평가한다.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10a
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• pp.593-595
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• 1999

모형 검증을 통한 시스템 명세의 정형적인 검증은 상태 폭발 문제로 인해 많은 어려움을 겪고 있다. 여러 개의 병렬 프로세스로 구성된 시스템에서 지수적으로 증가하는 상태의 객수로 인해 현실적으로 모형 검증을 적용하는 것이 불가능한 경우가 많다. 이런 문제점을 해결하기 위해서 시스템을 모듈 단위로 생각하여 정형 검증을 시도하는 많은 연구가 수행되고 있다. 병렬성을 중요한 특성의 하나로 하는 Statecharts 또한 널리 사용되고 있음에도 불구하고 아직 모듈을 바탕으로 검증을 수행하려는 시도가 그리 많지 않다. 본 연구에서는 내장 소프트웨어 시스템에 널리 사용되는 Statecharts명세를 모듈을 바탕으로 검증하는 방법을 제시하고자 한다. 먼저 Statecharts에서의 모듈을 정의하고 그와 같은 정의를 바탕으로 여러 개의 모듈로 구성되어 있는 Statecharts 명세의 모듈 기반 검증 방법을 제안한다. 여기서 사용되는 모듈 기반 검증은 환경에 대한 가정이 만족된다면 모듈을 반드시 주어진 성질을 만족한다는 가정-보증 추론(Assume-Guarantee Reasoing)을 이용한다.

• Journal of KIISE
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• v.42 no.10
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• pp.1195-1206
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• 2015

For the management of a knowledge system, systems that automatically infer and manage scalable knowledge are required. Most of these systems use ontologies in order to exchange knowledge between machines and infer new knowledge. Therefore, approaches are needed that infer new knowledge for scalable ontology. In this paper, we propose an approach to perform rule based reasoning for scalable SHIF ontologies in a spark framework which works similarly to MapReduce in distributed memories on a cluster. For performing efficient reasoning in distributed memories, we focus on three areas. First, we define a data structure for splitting scalable ontology triples into small sets according to each reasoning rule and loading these triple sets in distributed memories. Second, a rule execution order and iteration conditions based on dependencies and correlations among the SHIF rules are defined. Finally, we explain the operations that are adapted to execute the rules, and these operations are based on reasoning algorithms. In order to evaluate the suggested methods in this paper, we perform an experiment with WebPie, which is a representative ontology reasoner based on a cluster using the LUBM set, which is formal data used to evaluate ontology inference and search speed. Consequently, the proposed approach shows that the throughput is improved by 28,400% (157k/sec) from WebPie(553/sec) with LUBM.