• 한국경영과학회지
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• 제33권2호
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• pp.27-44
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• 2008

Owl-based ontology is useful to realize the context-aware services which are composed of the distributed and self-configuring modules. Many ontology-based inference engines are developed to infer useful information from ontology. Since these engines show the uniqueness in terms of speed and information richness, it's difficult to ensure stable operation in providing dynamic context-aware services, especially when they should deal with the complex and big-size ontology. To provide a best inference service, the purpose of this paper is to propose a novel methodology of context-aware engine selection in a contextually prompt manner Case-based reasoning is applied to identify the causality between context and inference engined to be selected. Finally, a series of experiments is performed with a novel evaluation methodology to what extent the methodology works better than competitive methods on an actual context-aware service.

• 전자통신동향분석
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• 제34권4호
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• pp.23-31
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• 2019

Deep learning is a hot topic in both academic and industrial fields. Deep learning applications can be categorized into two areas. The first category involves applications such as Google Alpha Go using interfaces with human operators to run complicated inference engines in high-performance servers. The second category includes embedded applications for mobile Internet-of-Things devices, automotive vehicles, etc. Owing to the characteristics of the deployment environment, applications in the second category should be bounded by certain H/W and S/W restrictions depending on their running environment. For example, image recognition in an autonomous vehicle requires low latency, while that on a mobile device requires low power consumption. In this paper, we describe issues faced by embedded applications and review popular inference engines. We also introduce a project that is being development to satisfy the H/W and S/W requirements.

• 지능정보연구
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• 제6권1호
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• pp.1-16
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• 2000

본 논문은 웹 기반 전문가시스템의 구현 구조들을 분석, 장단점을 비교하고, 웹 서버의 부하를 절감할 수 있는 HTML을 이용한 역방향 추론기관의 구현 방안을 제시한다. 웹 환경 하에서 전문가 시스템을 구현하는 방안틀로는 CGI를 이용하는 방법, 웹 서버에 추론기관을 내장하는 방법, 외부 뷰어를 이용하는 방법, Java Applet을 이용하는 방법, 그리고 HTML을 이용하는 방법 등이있다. 이들은 시스템 개발, 시험, 확장, 이식, 유지보수의 용이성, 대규모 서비스 가능성 등에서 각기 장단점을 갖고 있다. 특히, HTML을 이용하여 역방향 추론을 구현하면 위의 장점들을 상대적으로 많이 누릴 수 있다. 따라서, 이 논문에서는 역방향 추론에서 진위형,OAV형 및 수치형 변수를 HTML과 JavaScript를 이용하여 표현하는 방안을 설명한 후 이를 바탕으로 HTML 방식의 전문가시스댐 설계 방법론을 제시한다. 또한, 기존 전문가시스템을 웹 기반으로 변환하기 위하여 기존 방식의 규칙베이스를 지식분석도로 바꾼 후 HTML 기반 전문가시스템을 생성하는 방법론도 설명한다.

• Asia pacific journal of information systems
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• 제13권1호
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• pp.213-230
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• 2003

Most of existing expert systems developed for Web use CGI-based techniques and this frequently makes them suffer from the overburden of commercial Web servers, which deal with large-scale services. However, since HTML-based inference technique represents expert's knowledge by hyperlinks among HTML documents, the hypertext function of the Web can perform the inference efficiently in terms of time and space without the help of additional inference engines. In spite of such benefits, when the expert's knowledge is relatively large and/or complicated, the HTML-based inference technique has usually become to have a hard time of dealing with a lot of HTML documents because generation and management tasks of the numerous HTML documents would cause big trouble to the knowledge engineer. To resolve this problem, we developed an automatic generation and management tool for Web-based inference sites, called WeBIS. With this tool, a knowledge engineer can input and edit expert's knowledge using Expert's Diagram on the GUI(Graphical User Interface) environment and automatically generate hyper-linked HTML documents for Web-based inference from the Expert's Diagram.

• 대한기계학회논문집
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• 제17권7호
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• pp.1813-1822
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• 1993

An algorithm of fuzzy variable structrue control is proposed to design a closed loop fuel-injection system for the emission control of automotive gasoline engines. Fuzzy control is combined with sliding control at the switching boundary layer to improve the chattering of the stoichiometric air to fuel ratio. Multi-staged fuzzy rules are introduced to improve the adaptiveness of control system for the various operating conditions of engines, and a simplified technique of fuzzy inference is also adopted to improve the computational efficiency based on nonfuzzy micro-processors. The proposed method provides an effective way of engine controller design due to its hybrid structure satisfying the requirements of robustness and stability. The great potential of the fuzzy variable structure control is shown through a hardware-testing with an Intel 80C186 processor for controller and a typical engine-only model on an AD-100 computer.

• Journal of Advanced Marine Engineering and Technology
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• 제27권1호
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• pp.42-48
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• 2003

In general many engineers can diagnose the fault condition using the abnormal ones among data monitored from a diesel engine, but they don't need the system modelling or identification for the work. They check the abnormal data and the relationship and then catch the fault condition of the engine. This paper proposes the construction of a fault diagnosis engine through malfunction data gained from the data fault detection system of neural networks for diesel generator engine, and the rule inference method to induce the rule for fuzzy inference from the malfunction data of diesel engine like a site engineer with a fuzzy system. The proposed fault diagnosis system is constructed in the sense of the Malfunction Diagnosis Engine(MDE) and Hierarchy of Malfunction Hypotheses(HMH). The system is concerned with the rule reduction method of knowledge base for related data among the various interactive data.

• Journal of Mechanical Science and Technology
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• 제14권6호
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• pp.655-665
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• 2000

We propose an efficient algorithm for the purpose of satisfying a wide range of design constraints represented with equality and inequality equations as well as production rules. The algorithm employs simulated-annealing and a production rule inference engine and works on design constraints represented with networks. The algorithm fulfills equality constraints through constraint satisfaction processes like variable elimination while taking into account inequality constraints and inferring production rules. It can also reduce the load of the optimization procedure if necessary. We demonstrate the implementation of the algorithm with the result on machine tool design.

• 한국안전학회지
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• 제39권1호
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• pp.62-69
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• 2024

Ensuring swift on-site access to fire engines is crucial in preserving the golden time and minimizing damage. However, various mobility constraints in alleyways hinder the timely entry of fire engines to the fire scene, significantly impairing their initial response capabilities. Therefore, this study analyzed the significant mobility constraints of fire engines, focusing on Seoul, which has many old town areas. By leveraging survey responses from firefighting experts and actual observations, this study quantitatively assessed the frequency and severity of mobility constraint factors affecting the disaster responses of fire engines. Survey results revealed a consistent set of top five factors regarding the frequency and disturbance level, including illegally parked cars, narrow paths, motorcycles, poles, and awnings/banners. A comparison with actual road-view images showed notable consistency between the survey and observational results regarding the appearance frequency of mobility constraint factors in vulnerable areas in Seoul. Furthermore, the study emphasized the importance of tailored management strategies for each mobility constraint factor, considering its characteristics, such as dynamic or static. The findings of this study can serve as foundational data for creating more detailed fire safety maps and advancing technologies that monitor the mobility of fire engines through efficient vision-based inference using CCTVs in the future.

• 정보과학회 논문지
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• 제42권8호
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• pp.998-1009
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• 2015

근래에 들어 풍부한 지식베이스를 구축하기 위한 대용량 RDFS 추론에 대한 관심이 높아지면서 기존의 단일 머신으로는 대용량 데이터의 추론 성능을 향상시키기에 한계가 있다. 그래서 분산 환경에서 의 RDFS 추론 엔진 개발이 활발히 연구되고 있다. 하지만 기존의 분산 환경 엔진은 실시간 처리가 불가능 하며 구현이 어렵고 반복 작업에 취약하다. 본 논문에서는 이러한 문제를 극복하기 위해 병렬 그래프 구조 를 사용한 인-메모리 분산 추론 엔진 구축 방법을 제안한다. 트리플 형태의 온톨로지는 기본적으로 그래프 구조를 가지고 있으므로 그래프 구조 기반의 추론 엔진을 설계하는 것이 직관적이다. 또한 그래프 구조를 활용하는 오퍼레이터를 활용하여 RDFS 추론 규칙을 구현함으로써 기존의 데이터 관점과 달리 그래프 구조의 관점에서 설계할 수 있다. 본 논문에서 제안한 추론 엔진을 평가하기 위해 LUBM1000(1억 3천 3백만 트리플, 17.9GB), LUBM3000(4억 1천 3백만 트리플, 54.3GB)에 대해 추론 속도를 실험을 하였으며 실 험결과, 비-인메모리 분산 추론 엔진보다 약 10배 정도 빠른 추론 성능을 보였다.

• 한국항공우주학회지
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• 제49권10호
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• pp.883-892
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• 2021

본 논문에서는 드론을 활용한 감시정찰 임무의 효율성을 향상하기 위해 드론 탑재장비에서 실시간으로 구동 가능한 딥러닝 기반의 객체 인식 모델을 개발하는 연구를 수행하였다. 드론 영상 내 객체 인식 성능을 높이는 목적으로 학습 단계에서 학습 데이터 전처리 및 증강, 전이 학습을 수행하였고 각 클래스 별 성능 편차를 줄이기 위해 가중 크로스 엔트로피 방법을 적용하였다. 추론 속도를 개선하기 위해 양자화 기법이 적용된 추론 가속화 엔진을 생성하여 실시간성을 높였다. 마지막으로 모델의 성능을 확인하기 위해 학습에 참여하지 않은 드론 영상 데이터에서 인식 성능 및 실시간성을 분석하였다.