• Title/Summary/Keyword: Knowledge based systems

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Identification and Control for Nonlinear Discrete Time Systems Using an Interconnected Neural Network

  • Yamamoto, Yoshihiro
    • 제어로봇시스템학회:학술대회논문집
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    • 2005.06a
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    • pp.994-998
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    • 2005
  • A new control method, called a simple model matching, has been recently developed by the author. This is very simple and be applied for linear and nonlinear discrete time systems with/without time lag. Based on this formulation, identification is examined in this paper using an interconnected neural network with the EBP-EWLS learning algorithm. With this result, a control method is also presented for a nonlinear discrete time system.

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Collision prediction and detection in a dynamic environment (동적 환경하에서의 충돌 예측 및 감지)

  • 한인환;양우석
    • 제어로봇시스템학회:학술대회논문집
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    • 1992.10a
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    • pp.309-314
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    • 1992
  • Many dynamic mechanical systems, such as parts-feeders, walking machines, and percussive power tools, are described by equations of motion which are discontinuous. The discontinuities result from kinematic constraint changes which are difficult to foresee, especially in presence of impact. A simulation algorithm for these types of systems must be able to algorithmically predict and detect the kinematic constraint changes without any prior knowledge of the system's motion. This paper presents a rule-based approach to the prediction and detection of kinematic constraint changes between bodies with arc and line boundaries. The developed algorithm's ability to accurately and automatically detect the unpredicted changes of kinematic constraints is demonstrated with a numerical example.

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Architecture for Complex Inference Method

  • Lim, M.H.;Leong, J.Y.
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1993.06a
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    • pp.989-992
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    • 1993
  • In this paper, we describe hardware architecture of fuzzy processors for reasoning involving fuzzy control“Heuristics”. This we believe will lead to fuzzy systems that are closer to the way humans process domain knowledge for decision making. One noticeable beneficial effect based on our notion of fuzzy heuristics is the significantly reduced number of rules required.

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($OntoFrame^{(R)}$;an Information Service System based on Semantic Web Technology (시맨틱 웹 기술 기반 정보서비스 시스템 $OntoFrame^{(R)}$)

  • Sung, Won-Kyung;Lee, Seung-Woo;Hahn, Sun-Hwa;Jung, Han-Min;Kim, Pyung;Lee, Mi-Kyung;Park, Dong-In
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2008.04a
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    • pp.87-88
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    • 2008
  • As an information service system based on semantic web technology, $OntoFrame^{(R)}$ takes aim at a framework for providing analysis and fusion services of academic information. It currently consists of three parts: ontologies representing knowledge schema derived from academic information, $OntoURI^{(R)}$ which makes academic information into knowledge, and $OntoReasoner^{(R)}$ which performs inference and search on the knowledge. Unlike existing search engines which provides simple search services, our system provides, based on semantic web technology, several semantic and analytic services such as year-based topic trends in academic information, related topics, topic-based researchers and institutes, researcher network, statistics and regional distribution of academic information.

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Technology Opportunity Discovery Based on Firms' Technologies and Products (기업의 보유 기술 및 제품에 기반한 기술기회발굴)

  • Park, Hyunseok;Seo, Wonchul;Coh, Byoung-Youl;Lee, Jae-Min;Yoon, Janghyeok
    • Journal of Korean Institute of Industrial Engineers
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    • v.40 no.5
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    • pp.442-450
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    • 2014
  • Technology opportunity discovery (TOD) based on technological capability is a process which identifies new product and technology items that can be developed by utilizing or improving a firm's existing products or technologies. By taking into consideration the investment risk of R&D and its practicality, developing technological capability-based TOD methodology is considered to be important for both business and research. To this end, we propose a technological capability-based TOD method and its system using TOD knowledge base. The method can support four types of TOD cases, which are based on a firm's existing technologies and products, and TOD knowledge base is developed by using function information extracted from patent documents. In this paper, we introduce the overall framework of the method and provide application examples on the four TOD cases using the prototype system.

Data-Based Monitoring System for Smart Kitchen Farm

  • Yoon, Ye Dong;Jang, Woo Sung;Moon, So Young;Kim, R. Young Chul
    • International journal of advanced smart convergence
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    • v.11 no.2
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    • pp.211-218
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    • 2022
  • Pandemic situations such as COVID-19 can occur supply chain crisis. Under the supply chain crisis, delivering farm products from the farm to the city is also very challenging. Therefore it is essential to prepare food sufficiency people who live in a city. We firmly insist on food self-production/consumption systems in each home. However, since it is impossible to grow high-quality crops without expertise knowledge. Therefore expert system is essential to grow high-quality crops in home. To address this problem, we propose a smart kitchen farm as a data-based monitoring system and platform with ICT convergence technology. Our proposed approach 1) collects data and makes judgments based on expert knowledge for home users, 2) increases product quality of the smart kitchen farms by predicting abnormal/normal crops, and 3) controls each personal home cultivation environment through data-based monitoring within the smart central server. We expect people can cultivate high-quality crops in thir kitchens through this system without expert knowledge about cultivation.

A Study on the Impact of Employee's Person-Environment Fit and Information Systems Acceptance Factors on Performance: The Mediating Role of Social Capital (조직구성원의 개인-환경적합성과 정보시스템 수용요인이 성과에 미치는 영향에 관한 연구: 사회자본의 매개역할)

  • Heo, Myung-Sook;Cheon, Myun-Joong
    • Asia pacific journal of information systems
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    • v.19 no.2
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    • pp.1-42
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    • 2009
  • In a knowledge-based society, a firm's intellectual capital represents the wealth of ideas and ability to innovate, which are indispensable elements for the future growth. Therefore, the intellectual capital is evidently recognized as the most valuable asset in the organization. Considered as intangible asset, intellectual capital is the basis based on which firms can foster their sustainable competitive advantage. One of the essential components of the intellectual capital is a social capital, indicating the firm's individual members' ability to build a firm's social networks. As such, social capital is a powerful concept necessary for understanding the emergence, growth, and functioning of network linkages. The more social capital a firm is equipped with, the more successfully it can establish new social networks. By providing a shared context for social interactions, social capital facilitates the creation of new linkages in the organizational setting. This concept of "person-environment fit" has long been prevalent in the management literature. The fit is grounded in the interaction theory of behavior. The interaction perspective has a fairly long theoretical tradition, beginning with proposition that behavior is a function of the person and environment. This view asserts that neither personal characteristics nor the situation alone adequately explains the variance in behavioral and attitudinal variables. Instead, the interaction of personal and situational variables accounts for the greatest variance. Accordingly, the person-environment fit is defined as the degree of congruence or match between personal and situational variables in producing significant selected outcomes. In addition, information systems acceptance factors enable organizations to build large electronic communities with huge knowledge resources. For example, the Intranet helps to build knowledge-based communities, which in turn increases employee communication and collaboration. It is vital since through active communication and collaborative efforts can employees build common basis for shared understandings that evolve into stronger relationships embedded with trust. To this aim, the electronic communication network allows the formation of social network to be more viable to rapid mobilization and assimilation of knowledge assets in the organizations. The purpose of this study is to investigate: (1) the impact of person-environment fit(person-job fit, person-person fit, person-group fit, person-organization fit) on social capital(network ties, trust, norm, shared language); (2) the impact of information systems acceptance factors(availability, perceived usefulness, perceived ease of use) on social capital; (3) the impact of social capital on personal performance(work performance, work satisfaction); and (4) the mediating role of social capital between person-environment fit and personal performance. In general, social capital is defined as the aggregated actual or collective potential resources which lead to the possession of a durable network. The concept of social capital was originally developed by sociologists for their analysis in social context. Recently, it has become an increasingly popular jargon used in the management literature in describing organizational phenomena outside the realm of transaction costs. Since both environmental factors and information systems acceptance factors affect the network of employee's relationships, this study proposes that these two factors have significant influence on the social capital of employees. The person-environment fit basically refers to the alignment between characteristics of people and their environments, thereby resulting in positive outcomes for both individuals and organizations. In addition, the information systems acceptance factors have rather direct influences on the social network of employees. Based on such theoretical framework, namely person-environment fit and social capital theory, we develop our research model and hypotheses. The results of data analysis, based on 458 employee cases are as follow: Firstly, both person-environment fit(person-job fit, person-person fit, person-group fit, person-organization fit) and information systems acceptance factors(availability perceived usefulness, perceived ease of use) significantly influence social capital(network ties, norm, shared language). In addition, person-environment fit is a stronger factor influencing social capital than information systems acceptance factors. Secondly, social capital is a significant factor in both work satisfaction and work performance. Finally, social capital partly plays a mediating role between person-environment fit and personal performance. Our findings suggest that it is vital for firms to understand the importance of environmental factors affecting social capital of employees and accordingly identify the importance of information systems acceptance factors in building formal and informal relationships of employees. Firms also need to reflect their recognition of the importance of social capital's mediating role in boosting personal performance. Some limitations arisen in the course of the research and suggestions for future research directions are also discussed.

A Knowledge-based Electrical Fire Cause Diagnosis System using Fuzzy Reasoning (퍼지추론을 이용한 지식기반 전기화재 원인진단시스템)

  • Lee, Jong-Ho;Kim, Doo-Hyun
    • Journal of the Korean Society of Safety
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    • v.21 no.3 s.75
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    • pp.16-21
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    • 2006
  • This paper presents a knowledge-based electrical fire cause diagnosis system using the fuzzy reasoning. The cause diagnosis of electrical fires may be approached either by studying electric facilities or by investigating cause using precision instruments at the fire site. However, cause diagnosis methods for electrical fires haven't been systematized yet. The system focused on database(DB) construction and cause diagnosis can diagnose the causes of electrical fires easily and efficiently. The cause diagnosis system for the electrical fire was implemented with entity-relational DB systems using Access 2000, one of DB development tools. Visual Basic is used as a DB building tool. The inference to confirm fire causes is conducted on the knowledge-based by combined approach of a case-based and a rule-based reasoning. A case-based cause diagnosis is designed to match the newly occurred fire case with the past fire cases stored in a DB by a kind of pattern recognition. The rule-based cause diagnosis includes intelligent objects having fuzzy attributes and rules, and is used for handling knowledge about cause reasoning. A rule-based using a fuzzy reasoning has been adopted. To infer the results from fire signs, a fuzzy operation of Yager sum was adopted. The reasoning is conducted on the rule-based reasoning that a rule-based DB system built with many rules derived from the existing diagnosis methods and the expertise in fire investigation. The cause diagnosis system proposes the causes obtained from the diagnosis process and showed possibility of electrical fire causes.

Enhancing Recommender Systems by Fusing Diverse Information Sources through Data Transformation and Feature Selection

  • Thi-Linh Ho;Anh-Cuong Le;Dinh-Hong Vu
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.17 no.5
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    • pp.1413-1432
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    • 2023
  • Recommender systems aim to recommend items to users by taking into account their probable interests. This study focuses on creating a model that utilizes multiple sources of information about users and items by employing a multimodality approach. The study addresses the task of how to gather information from different sources (modalities) and transform them into a uniform format, resulting in a multi-modal feature description for users and items. This work also aims to transform and represent the features extracted from different modalities so that the information is in a compatible format for integration and contains important, useful information for the prediction model. To achieve this goal, we propose a novel multi-modal recommendation model, which involves extracting latent features of users and items from a utility matrix using matrix factorization techniques. Various transformation techniques are utilized to extract features from other sources of information such as user reviews, item descriptions, and item categories. We also proposed the use of Principal Component Analysis (PCA) and Feature Selection techniques to reduce the data dimension and extract important features as well as remove noisy features to increase the accuracy of the model. We conducted several different experimental models based on different subsets of modalities on the MovieLens and Amazon sub-category datasets. According to the experimental results, the proposed model significantly enhances the accuracy of recommendations when compared to SVD, which is acknowledged as one of the most effective models for recommender systems. Specifically, the proposed model reduces the RMSE by a range of 4.8% to 21.43% and increases the Precision by a range of 2.07% to 26.49% for the Amazon datasets. Similarly, for the MovieLens dataset, the proposed model reduces the RMSE by 45.61% and increases the Precision by 14.06%. Additionally, the experimental results on both datasets demonstrate that combining information from multiple modalities in the proposed model leads to superior outcomes compared to relying on a single type of information.

Framework for Information Integration and Customization Using Ontology and Case-based Reasoning (온톨로지 및 사례기반추론을 이용한 맞춤형 통합 정보 생성 프레임워크의 제안)

  • Lee, Hyun-Jung;Sohn, M-Ye
    • Journal of Intelligence and Information Systems
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    • v.15 no.4
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    • pp.141-158
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    • 2009
  • The requirements of knowledge customization have increased as information resources have become more various and the numbers of the resources are increased. Even if the method for collecting the information has improved like Really Simple Syndication (RSS), information users are still struggling for extracting and customizing the required information through the Web. To reduce the burden, we offer the dynamic knowledge customization framework by using ontology-based CBR. The framework consisting of three phases is comprised of the conversion phase of web information as a machine-accessible case, the extraction phase to find a case appropriate for information users' requirements, and the case customization phase to create knowledge depending on information user's requirements. Newly, the dynamic and intensity-based similarity is adopted to support timely dynamic change of users' requirements. The framework has adopted to create traveler's knowledge to the level users wanted.

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