• Proceedings of the Korea Information Processing Society Conference
• /
• 2011.04a
• /
• pp.152-155
• /
• 2011

최근 사회생활의 다변화로 인한 개인의 질환을 예방하고 건강을 증진시키기 위한 개인 웰니스 관리는 현대 사회의 성인에게는 필수적인 자기 관리에 해당된다. 본 논문는 이러한 웰니스 관리에 적절한 상황 모델로써 상황 데이터를 추론할 수 있는 SWRL 상황규칙과 불확실성을 표현한 베이지안 네트워크를 포함한 통합 온톨로지 기반 상황모델을 제시하였다. 제안한 상황모델에 포함된 추론 규칙은 웰니스 관리에 필요한 상황 서비스를 수행하는 액션들을 정의한다. 즉 상황 온톨로지에 SWRL 규칙을 포함함으로써 주로 웹 시멘틱에 사용되고 있는 OWL 언어를 상황인식 분야의 지식 베이스 구축에도 적합하도록 하였다. 그리고 웰니스 관리를 위해 상황 온톨로지로 표현되는 원시 상황 데이터는 센서 부정확성, 또는 개인 판단기준 차이로 인해 불확실성을 포함하므로, 어떤 논리적 상황 데이터는 불확실성을 고려하여 추론되어야 하기 때문에 본 논문은 상황 온톨로지 및 SWRL 규칙과 함께 베이지안 네트워크를 함께 표현할 수 있게 하여 OWL 상황 온톨로지 기반 규칙 추론뿐만 아니라 확률 추론을 용이하게 하였다.

• Journal of KIISE:Databases
• /
• v.27 no.2
• /
• pp.141-150
• /
• 2000

There are three significant drawbacks in extant fuzzy rule-based expert system languages. First, they lack the functionality of composite object inference. Second, they do not support fuzzy reasoning semantically easy to understand and conceptually simple to use. Third, knowledge representation and reasoning style of their model have a great semantic gap with those of current database models. Therefore, it is very difficult for the two models to be seamlessly integrated with each other. This paper provides the formal specification of a fuzzy object inference model to solve the three drawbacks. GIS(Geographic Information System) application domain is used to demonstrate that our model naturally models complex GIS information in terms of composite objects and successfully performs fuzzy inference between them.

• Proceedings of the Korea Society for Industrial Systems Conference
• /
• 2001.05a
• /
• pp.184-197
• /
• 2001

In this paper, we provide the formal specification of a fuzzy object inference language and propose ICOT(Integrated C-Object Tool) as its implementation for knowledge-based programming with the disjunctive fuzzy information. The novelty of our model is that it seamlessly combines object inference and fuzzy reasoning into a unified framework without compromising a compatibility with extant databases, especially object-relational ones. In this model most of the object-oriented paradigm is successfully expressed in terms of relational constructs, tailoring fuzzy reasoning style to be well suited to the framework of the databases. It turns out to be useful in preserving its conceptual simplicity as well, since simple-to-use is one of important criteria in designing the databases. Additionally this model considerably enhanced the semantic expressiveness of data allowing disjunctive fuzzy information.

• 한국공간정보시스템학회:학술대회논문집
• /
• 2002.03a
• /
• pp.89-94
• /
• 2002

본 논문에서는 배전분야 설비관리 시스템을 대상으로 배전설비 고장시 GIS 기능을 이용하여 고장설비를 예측할 수 있는 방법과 구현 실례를 소개하고자 한다. 배전설비관리를 위한 지리정보 데이터 모델은 가공과 지중, 전기와 비전기, 점형과 선형의 특성을 가지는 배전설비의 특성을 분석하여 모델링된다. 모델링의 결과 생성된 데이터베이스는 실세계에 존재하는 대부분의 객체에 대한 정보를 포함하고 있으므로 매우 크고 그 구조 또한 복잡하다. 그러므로 응용프로그램이 필요로 하는 데이터를 추출하기 위하여 많은 시간이 요구된다. 그러나 고장복구업무를 위한 시스템은 사용자의 만족도를 위하여 추론의 정확성과 더불어 응답속도를 최소화하는 것이 필수조건이다. 이를 위하여 GIS 데이터베이스 모델을 좀 더 개량할 필요가 있으며, 본 논문에서는 이에 대한 한가지 방안으로 배전설비의 GIS 모델의 축약된 형태인 관계형 데이터베이스 모델을 제시한다. 고장점 추론은 이렇게 만들어진 축약모델을 이용하여 진행되며 고장신고 고객별로 회선, 개폐기, 변압기, 인입주 등 정보를 추출하고 추출된 설비들의 계통상 위치의 유사성을 추론하여 최종 예측점을 파악한다.

• Journal of Korea Water Resources Association
• /
• v.36 no.4
• /
• pp.587-596
• /
• 2003

Application of a catchment modelling system requires recorded information to ascertain the reliability and robustness of the predicted flow conditions. Where this recorded information is not available, the necessary information for reliable and robust predictions must be obtained from other available information sources. The alternative approach presented in this paper used inference models for getting this necessary information that is required to calibrate and validate the catchment modelling system for both an ungauged and a gauged catchments. In this study, inference models were developed for determination of control parameters of the Storm Water Management Model (SWMM), mainly based on landuse component of the catchment, which is a major factor to impact on quantity and quality of catchment runoff. Results from the study show that the new approach for determination of the spatially variable control parameters produced more accurate estimates than a traditional approach. Also, the number of control parameters estimated can be reduced significantly as the proposed method only requires determination of control parameters associated with each land use of the catchment while a traditional approach needs to assign a number of control parameters for a number of subcatchment.

• Journal of Intelligence and Information Systems
• /
• v.28 no.4
• /
• pp.309-328
• /
• 2022

This study empirically analyzed a Korean pre-trained language models (PLMs) designed for natural language generation. The performance of two PLMs - BART and GPT - at the task of abstractive text summarization was compared. To investigate how performance depends on the characteristics of the inference data, ten different document types, containing six types of informational content and creation content, were considered. It was found that BART (which can both generate and understand natural language) performed better than GPT (which can only generate). Upon more detailed examination of the effect of inference data characteristics, the performance of GPT was found to be proportional to the length of the input text. However, even for the longest documents (with optimal GPT performance), BART still out-performed GPT, suggesting that the greatest influence on downstream performance is not the size of the training data or PLMs parameters but the structural suitability of the PLMs for the applied downstream task. The performance of different PLMs was also compared through analyzing parts of speech (POS) shares. BART's performance was inversely related to the proportion of prefixes, adjectives, adverbs and verbs but positively related to that of nouns. This result emphasizes the importance of taking the inference data's characteristics into account when fine-tuning a PLMs for its intended downstream task.

• Journal of KIISE:Databases
• /
• v.35 no.6
• /
• pp.505-517
• /
• 2008

This paper proposes a rewriting algorithm of OWL-DL ontology query in SPARQL. Currently, to obtain inference results of given SPARQL queries, Web ontology repositories construct inference ontology models and match the SPARQL queries with the models. However, an inference model requires much larger space than its original base model, and reusability of the model is not available for other inferrable SPARQL queries. Therefore, the aforementioned approach is not suitable for large scale SPARQL query processing. To resolve tills issue, this paper proposes a novel SPARQL query rewriting algorithm that can obtain results by rewriting SPARQL queries and accomplishing query operations against the base ontology model. To achieve this goal, we first define OWL-DL inference rules and apply them on rewriting graph pattern in queries. The paper categorizes the inference rules and discusses on how these rules affect the query rewriting. To show the advantages of our proposal, a prototype system based on lena is implemented. For comparative evaluation, we conduct an experiment with a set of test queries and compare of our proposal with the previous approach. The evaluation result showed the proposed algorithm supports an improved performance in efficiency of the inferrable SPARQL query processing without loss of completeness and soundness.

• The KIPS Transactions:PartB
• /
• v.14B no.4
• /
• pp.281-286
• /
• 2007

This paper proposes an intelligent context awareness middleware(ICAM) for Ubiquitous Computing Environment. In this paper we have researched about the context awareness middleware. The ICAM model is based on ontology that efficiently manages analyses and learns about various context information and can provide intelligent services that satisfy the human requirements. Therefore, various intelligent services will improve user's life environment. We also describe the current implementation of the ICAM for service adaptation based on fuzzy inference that help applications to adapt their ubiquitous computing environments according to rapidly changing. For this, after defining the requirements specifications of ICAM, we have researched the inferred processes for the higher level of context awareness. The Fuzzy Theory has been used in process of inferences, and showed constructing the model through the service process. Also, the proposed fuzzy inferences has been applied to smart Jacky, and after inferring the fuzzy values according to the change of temperature, showed the adaptability of Smart Jacky according to the change of surroundings like temperature as showing the optimal value of status.

• Journal of the Korea Society of Computer and Information
• /
• v.14 no.8
• /
• pp.25-31
• /
• 2009

Widely used color information recognition methods based on the RGB color model with static fuzzy inference rules have limitations due to the model itself-the detachment of human vision and applicability of limited environment. In this paper, we propose a method that is based on HSI model with new inference process that resembles human vision recognition process. Also, a user can add, delete, update the inference rules in this system. In our method, we design membership intervals with sine, cosine function in H channel and with functions in trigonometric style in S and I channel. The membership degree is computed via interval merging process. Then, the inference rules are applied to the result in order to infer the color information. Our method is proven to be more intuitive and efficient compared with RGB model in experiment.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2004.10a
• /
• pp.463-466
• /
• 2004

본 논문에서는 각 입력 변수에 대하여 퍼지 공간을 분할한 퍼지 집합 기반 퍼지 추론 시스템을 제안한다. 퍼지 모델은 주로 경험적 방법에 의해 추출되기 때문에 보다 구체적이고 체계적인 방법에 의한 동정 및 최적화 쥘 필요성이 요구된다. 정보 granules는 근접성, 유사성 또는 기능성 등의 기준에 의해 서로 결합된 물체(특히, 데이터 점)의 연결된 모임으로 간주된다. 정보 데이터의 특성을 살리기 위해 HCM 클러스터링 방법에 의한 중심71을 이용하여 각 입력 변수에 대한 퍼지 집합 기반 전반부/후반부 구조 및 파라미터를 동정한다. 퍼지 추론 방법은 간략 및 선형 퍼지 추론을 수행하며 삼각형 멤버쉽 함수를 사용한다. 구축된 퍼지 모델은 유전자 알고리즘을 이용하여 전반부 파라미터를 최적으로 동정하며, 학습 및 테스트 데이터의 성능 결과의 상호균형을 얻기 위한 하중값을 가진 성능지수를 사용하여 근사화와 예측성능의 향상을 꾀한다. 또한, 제안된 퍼지 모델은 수치적인 예를 통하여 성능을 평가한다.