• Journal of Information Technology Applications and Management
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• v.12 no.4
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• pp.133-155
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• 2005

This Paper makes an attempt to suggest a process of automatically generating application software based on the Entity-Relationship model 1. The designer develops an E-R model of an real-world system. 2. The designer inputs the entity and relationship types, and attributes shown in the E-R model, and also the basic operations of the application system to the software generator. 3. The application generator produces database schema and link information between application programs, and then automatically generates a stereo-type application system. In order for the automated application generator to build the application system in a systematic way, four basic program generation rules have been suggested. A set of computer programs have been developed in order to show the applicability of the automated software generation process suggested in this paper. By following each rule with the generator, the designer can build an application in an efficient manner compared with traditional (manual programming) approaches. It has been demonstrated from the case study that the idea of applying an automated generator in systems development based upon the E-R model is feasible.

• The KIPS Transactions:PartD
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• v.11D no.1
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• pp.143-152
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• 2004

Recently, software programming method based on EJB for object oriented software design and implement has been used frequently. Usually, case that use permanent data that use Database in EJB base application is most. Part connected with Database-Access that take charge in Entity Bean class of server side creation program. In this paper using J2EE relationship DAO pattern class each separate. This is no much difference with existent pattern method, but in same pattern common classes are designed so that composition may be possible. And as well as use Entity Bean class that created each DBMS classes are different, is doing Rata Source so that connection work is available without alteration or creation of additional program in several DBMS environments.

• CELLMED
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• v.5 no.4
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• pp.27.1-27.6
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• 2015

Literatures and experimental studies have shown that Prunella has an effect on anti-hypertension, however, its components are complicated, so that it is still difficult to clear the specific roles of its various components in blood pressure regulation in. So we decide to systematically study the anti-hypertension mechanism of Prunella. We integrated multiple databases and constructed molecular interaction network between the chemical constituents of Prunella Vulgaris and hypertension based on entity grammar systems model. The network has 262 nodes and 802 edges. Then we infer the interactions between chemical compositions and disease targets to clarify the anti-hypertension mechanism. Finally, we found Prunella could influence hypertension by regulating apoptosis, cell proliferation, blood vessel development and vasoconstriction, etc. Thus this study provides reference for drug development and compatibility, and also gives guidance for health care at a certain extent.

• Korean Journal of Computational Design and Engineering
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• v.7 no.1
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• pp.9-17
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• 2002

The study is insufficient on process planning of the elliptical deep drawing product. Thus, in this present study, the expert system for elliptical deep drawing products was constructed by using process sequence design. The expert system was developed to be based on the general concept of each entity. The system was developed in this work consists of sixth modules. The first one is a shape recognition module to recognize non-axisymmetric products and to generate Entity_list. The second one is three dimensional (3-D) modeling module to calculate the surface area for non-axisymmetric products. The third one is a blank design module to create suggested blanks of three shapes with the identical surface area. The fourth one is shape design module based on the production rules that play the most important role in an expert system for manufacturing. The production rules are generated and upgraded by inter- viewing field engineers, plastic theory and experiments. The fifth and sixth ones are a graphic module to visualize results of the expert system and a post module to rise user's convenience, respectively. According to constructed the expert system for process sequence design, it was possible to reduce the lead time.

• The KIPS Transactions:PartD
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• v.11D no.3
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• pp.553-562
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• 2004

This paper constructs ERD thesaurus that is storing information about Entity Relationship Diagram(ERD), and proposes an ERD thesaurus-based methodology for view integration in an important conceptual design step in designing databases. To show the usefulness of proposed methodology, the prototype for view integration support system is implemented for the applied case. As a result, ERD thesaurus-based methodology is more effective than the existing methodologies for view Integration in the aspects of affinity analysis, semantic conflicts resolution, and view Integration processes. Therefore, our methodology is expected to be utilized in integrating the existing fragmented schema or designing a large database integration.

• Korean Journal of Computational Design and Engineering
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• v.11 no.6
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• pp.447-454
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• 2006

Although there are many needs to use 3D models in MEMS field, it is not easy to generate 3D models based on MEMS CAD. This is because MEMS CAD is based on 2D and their popular format-GDSII file format- has its own limits and problems. The differences between GDSII file format and 3D CAD system, such as (1) superposed modeling, (2) duplicated entity, (3) restricted of entity type, give rise to several problems in data exchange. These limits and problems in GDSII file format have prevented 3D CAD system from generating 3D models from the MEMS CAD. To remove these limits and solve problems, it is important to extract the silhouette of data in the MEMS CAD. The proposed method has two main processes to extract silhouette; one is to extract the pseudo-silhouette from the original 2D MEMS data and the other is to remove useless objects to complete the silhouette. The paper reports on the experience gained in data exchange between 2D MEMS data and 3D models by the proposed method and a case study is presented, which employs the proposed method using MEMS CAD IntelliMask and Solidworks.

• Annual Conference on Human and Language Technology
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• 2021.10a
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• pp.461-466
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• 2021

본 연구에서는 DECO(Dictionnaire Electronique du COreen) 한국어 전자사전과 LGG(Local-Grammar Graph)에 기반한 반자동 언어데이터 증강(Semi-automatic Symbolic Propagation: SSP) 방식에 입각하여, 핀테크 분야의 CS(Customer Service) 챗봇 NLU(Natural Language Understanding)을 위한 주석 학습 데이터를 효과적으로 생성하고, 이를 기반으로 RASA 오픈 소스에서 제공하는 DIET(Dual Intent and Entity Transformer) 아키텍처를 활용하여 핀테크 CS 챗봇 NLU 시스템을 구현하였다. 실 데이터을 통해 확인된 핀테크 분야의 32가지의 토픽 유형 및 38가지의 핵심 이벤트와 10가지 담화소 구성에 따라, DECO-LGG 데이터 생성 모듈은 질의 및 불만 화행에 대한 양질의 주석 학습 데이터를 효과적으로 생성하며, 이를 의도 분류 및 Slot-filling을 위한 개체명 인식을 종합적으로 처리하는 End to End 방식의 멀티태스크 트랜스포머 모델 DIET로 학습함으로써 DIET-only F1-score 0.931(Intent)/0.865(Slot/Entity), DIET+KoBERT F1-score 0.951(Intent)/0.901(Slot/Entity)의 성능을 확인하였으며, DECO-LGG 기반의 SSP 생성 데이터의 학습 데이터로서의 효과성과 함께 KoBERT에 기반한 DIET 모델 성능의 우수성을 입증하였다.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1987.10a
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• pp.1-1
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• 1987

• Bulletin of the Society of Naval Architects of Korea
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• v.35 no.3
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• pp.50-58
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• 1998

• Journal of KIISE
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• v.44 no.3
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• pp.306-313
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• 2017

Named entity recognition (NER) seeks to locate and classify named entities in text into pre-defined categories such as names of persons, organizations, locations, expressions of times, etc. Recently, many state-of-the-art NER systems have been implemented with bidirectional LSTM CRFs. Deep learning models based on long short-term memory (LSTM) generally depend on word representations as input. In this paper, we propose an approach to expand word representation by using pre-trained word embedding, part of speech (POS) tag embedding, syllable embedding and named entity dictionary feature vectors. Our experiments show that the proposed approach creates useful word representations as an input of bidirectional LSTM CRFs. Our final presentation shows its efficacy to be 8.05%p higher than baseline NERs with only the pre-trained word embedding vector.