• Title/Summary/Keyword: domain-specific model

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ONTOLOGY DESIGN FOR THE EFFICIENT CUSTOMER INFORMATION RETRIEVAL

  • Gu, Mi-Sug;Hwang, Jeong-Hee;Ryu, Keun-Ho
    • Proceedings of the KSRS Conference
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    • 2005.10a
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    • pp.345-348
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    • 2005
  • Because the current web search engine estimates the similarity of documents, using the frequency of words, many documents irrespective of the user query are provided. To solve these kinds of problems, the semantic web is appearing as a future web. It is possible to provide the service based on the semantic web through ontology which specifies the knowledge in a special domain and defines the concepts of knowledge and the relationships between concepts. In this paper to search the information of potential customers for home-delivery marketing, we model the specific domain for generating the ontology. And we research how to retrieve the information, using the ontology. Therefore, in this paper, we generate the ontology to define the domain about potential customers and develop the search robot which collects the information of customers.

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An Analysis of Citation Counts of ETRI-Invented US Patents

  • Lee, Yong-Gil;Lee, Jeong-Dong;Song, Yong-Il
    • ETRI Journal
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    • v.28 no.4
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    • pp.541-544
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    • 2006
  • From its foundation until 2004, ETRI has registered over 1,000 US patents. This letter analyzes the characteristics of these patents and addresses the explanatory factors affecting their citation counts. For explanatory variables, research team related variables, invention specific variables, and geographical domain related variables are suggested. Zero-altered count data models are used to test the impact of independent variables. A key finding is that technological cumulativeness, the scale of invention, outputs in the electronic field, and the degree of dependence on the US technology domain positively affect the citation counts of ETRI-invented US patents. The magnitude of international presence appears to negatively affect the citation counts of ETRI-invented US patents.

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MULTI-LAYERED PRODUCT KNOWLEDGE MODEL (다중 레이어 기반 제품 지식 모델)

  • Lee J.H.;Suh H.W.
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 2005.06a
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    • pp.65-70
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    • 2005
  • This paper introduces an approach to multi-layered product knowledge model for collaborative engineering environment. The participants in collaborative engineering want to share and reason product knowledge through internet without any heterogeneity and ambiguity. However the previous knowledge models are limited in providing those aspects. In this paper, the collaborative engineering domain is analyzed and then the product knowledge is organized into four levels such as product context model, product specific model, product design model and product manufacturing model. The four levels are represented by first-order logic in layered fashion. The concepts and the instances of a formal ontology are used for recursive representation of the four levels. The instances of the concepts of an upper level like product context model are considered as the concepts of an adjacent lower level like product specific model, and this mechanism is applied to the other levels. These logic representations are integrated with the schema and the instances of a relational database. OWL representation of the four levels is defined through the integration of the logic representation and OWL primitives. The four product knowledge models have their major representation according to the characteristics of each model. This approach enables engineer to share product knowledge through internet without any ambiguity and utilize it as basis for additional reasoning.

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Frequency-Domain Circuit Model and Analysis of Coupled Magnetic Resonance Systems

  • Huh, Jin;Lee, Wooyoung;Choi, Suyong;Cho, Gyuhyeong;Rim, Chuntaek
    • Journal of Power Electronics
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    • v.13 no.2
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    • pp.275-286
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    • 2013
  • An explicit frequency-domain circuit model for the conventional coupled magnetic resonance system (CMRS) is newly proposed in this paper. Detail circuit parameters such as the leakage inductances, magnetizing inductances, turn-ratios, internal coil resistances, and source/load resistances are explicitly included in the model. Accurate overall system efficiency, DC gain, and key design parameters are deduced from the model in closed form equations, which were not available in previous works. It has been found that the CMRS can be simply described by an equivalent voltage source, resistances, and ideal transformers when it is resonated to a specified frequency in the steady state. It has been identified that the voltage gain of the CMRS was saturated to a specific value although the source side or the load side coils were strongly coupled. The phase differences between adjacent coils were ${\pi}/2$, which should be considered for the EMF cancellations. The analysis results were verified by simulations and experiments. A detailed circuit-parameter-based model was verified by experiments for 500 kHz by using a new experimental kit with a class-E inverter. The experiments showed a transfer of 1.38 W and a 40 % coil to coil efficiency.

A Research on Job Model Development for Data Convergent Talent (데이터 융합인재 직무모형 개발 연구)

  • Um, Hye Mi;Yu, Yun Hyeong
    • The Journal of Information Systems
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    • v.33 no.1
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    • pp.207-226
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    • 2024
  • Purpose This study aims to develop a job model for data convergent talents to meet the rapidly changing demands of the data industry. To create a job model, we first define and categorize data convergent talents with balanced competencies in data technology and domain knowledge, and then develop a job model by investigating job areas, scope, activities, and competencies. Design/methodology/approach The research is conducted using the following procedures and methodology. First, we conduct a current status survey on data talent demand, data talent policies, data talent programs, and curricula at home and abroad; second, we collect opinions on the jobs and competencies required for data convergent talents and curricula for talent development through in-depth interview with experts; and third, we present the job areas and job activities of data convergent talents derived from the previous status survey and expert opinions based on the National Competency Standards(NCS). Findings The research findings indicate that there are total of six job roles for data convergent talents, including data scientist, data planner, data architect, data developer, data engineer, and data analyst. It was observed that each of these roles requires the development of common competencies within their respective fields, followed by a need for further specialization into specific competencies within each professional domain.

Neural Network-based Decision Class Analysis with Incomplete Information

  • Kim, Jae-Kyeong;Lee, Jae-Kwang;Park, Kyung-Sam
    • Proceedings of the Korea Database Society Conference
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    • 1999.06a
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    • pp.281-287
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    • 1999
  • Decision class analysis (DCA) is viewed as a classification problem where a set of input data (situation-specific knowledge) and output data (a topological leveled influence diagram (ID)) is given. Situation-specific knowledge is usually given from a decision maker (DM) with the help of domain expert(s). But it is not easy for the DM to know the situation-specific knowledge of decision problem exactly. This paper presents a methodology fur sensitivity analysis of DCA under incomplete information. The purpose of sensitivity analysis in DCA is to identify the effects of incomplete situation-specific frames whose uncertainty affects the importance of each variable in the resulting model. For such a purpose, our suggested methodology consists of two procedures: generative procedure and adaptive procedure. An interactive procedure is also suggested based the sensitivity analysis to build a well-formed ID. These procedures are formally explained and illustrated with a raw material purchasing problem.

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Neural Network-based Decision Class Analysis with Incomplete Information

  • 김재경;이재광;박경삼
    • Proceedings of the Korea Inteligent Information System Society Conference
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    • 1999.03a
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    • pp.281-287
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    • 1999
  • Decision class analysis (DCA) is viewed as a classification problem where a set of input data (situation-specific knowledge) and output data(a topological leveled influence diagram (ID)) is given. Situation-specific knowledge is usually given from a decision maker (DM) with the help of domain expert(s). But it is not easy for the DM to know the situation-specific knowledge of decision problem exactly. This paper presents a methodology for sensitivity analysis of DCA under incomplete information. The purpose of sensitivity analysis in DCA is to identify the effects of incomplete situation-specific frames whose uncertainty affects the importance of each variable in the resulting model. For such a purpose, our suggested methodology consists of two procedures: generative procedure and adaptive procedure. An interactive procedure is also suggested based the sensitivity analysis to build a well-formed ID. These procedures are formally explained and illustrated with a raw material purchasing problem.

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Retrieval methodology for similar NPP LCO cases based on domain specific NLP

  • No Kyu Seong ;Jae Hee Lee ;Jong Beom Lee;Poong Hyun Seong
    • Nuclear Engineering and Technology
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    • v.55 no.2
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    • pp.421-431
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    • 2023
  • Nuclear power plants (NPPs) have technical specifications (Tech Specs) to ensure that the equipment and key operating parameters necessary for the safe operation of the power plant are maintained within limiting conditions for operation (LCO) determined by a safety analysis. The LCO of Tech Specs that identify the lowest functional capability of equipment required for safe operation for a facility must be complied for the safe operation of NPP. There have been previous studies to aid in compliance with LCO relevant to rule-based expert systems; however, there is an obvious limit to expert systems for implementing the rules for many situations related to LCO. Therefore, in this study, we present a retrieval methodology for similar LCO cases in determining whether LCO is met or not met. To reflect the natural language processing of NPP features, a domain dictionary was built, and the optimal term frequency-inverse document frequency variant was selected. The retrieval performance was improved by adding a Boolean retrieval model based on terms related to the LCO in addition to the vector space model. The developed domain dictionary and retrieval methodology are expected to be exceedingly useful in determining whether LCO is met.

Numerical simulation of the neutral equilibrium atmospheric boundary layer using the SST k-ω turbulence model

  • Hu, Peng;Li, Yongle;Cai, C.S.;Liao, Haili;Xu, G.J.
    • Wind and Structures
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    • v.17 no.1
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    • pp.87-105
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    • 2013
  • Modeling an equilibrium atmospheric boundary layer (ABL) in an empty computational domain has routinely been performed with the k-${\varepsilon}$ turbulence model. However, the research objects of structural wind engineering are bluff bodies, and the SST k-${\omega}$ turbulence model is more widely used in the numerical simulation of flow around bluff bodies than the k-${\varepsilon}$ turbulence model. Therefore, to simulate an equilibrium ABL based on the SST k-${\omega}$ turbulence model, the inlet profiles of the mean wind speed U, turbulence kinetic energy k, and specific dissipation rate ${\omega}$ are proposed, and the source terms for the U, k and ${\omega}$ are derived by satisfying their corresponding transport equations. Based on the proposed inlet profiles, numerical comparative studies with and without considering the source terms are carried out in an empty computational domain, and an actual numerical simulation with a trapezoidal hill is further conducted. It shows that when the source terms are considered, the profiles of U, k and ${\omega}$ are all maintained well along the empty computational domain and the accuracy of the actual numerical simulation is greatly improved. The present study could provide a new methodology for modeling the equilibrium ABL problem and for further CFD simulations with practical value.

Clustering-based Statistical Machine Translation Using Syntactic Structure and Word Similarity (문장구조 유사도와 단어 유사도를 이용한 클러스터링 기반의 통계기계번역)

  • Kim, Han-Kyong;Na, Hwi-Dong;Li, Jin-Ji;Lee, Jong-Hyeok
    • Journal of KIISE:Software and Applications
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    • v.37 no.4
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    • pp.297-304
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    • 2010
  • Clustering method which based on sentence type or document genre is a technique used to improve translation quality of SMT(statistical machine translation) by domain-specific translation. But there is no previous research using sentence type and document genre information simultaneously. In this paper, we suggest an integrated clustering method that classifying sentence type by syntactic structure similarity and document genre by word similarity information. We interpolated domain-specific models from clusters with general models to improve translation quality of SMT system. Kernel function and cosine measures are applied to calculate structural similarity and word similarity. With these similarities, we used machine learning algorithms similar to K-means to clustering. In Japanese-English patent translation corpus, we got 2.5% point relative improvements of translation quality at optimal case.