• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.3 s.303
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• pp.9-16
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• 2005

For designing the database more efficiently, normailzation can be enforced to minimize the degree of unnecessary data redundancy and contribute to enhance data integrity. However, deep normalization tends to provoke multiple way of schema join, which could then induces response time degradation. To mitigate this sort of side effect that the normalization could brought, a number of field studies we observed adopted the idea of denormalization. To measure whether denormalization contributes to response time improvement, we in this paper developed two different data models about customer service system, one with perfect normalization and the other with denormalization, and evaluated their query response time behaviors. Performance results show that normalization case consistently outperforms denormalization case in terms of response time. This study show that the idea of denormalization, quite rarely contributes to that sort of improvement due ironically to the unnecessary data redundancy.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.26 no.3
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• pp.1-9
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• 2003

Databases are central to business information systems and RDBMS is most widely used for the database system. Normalization was designed to control various anomalies (insert, update, delete anomalies). However, normalized database design does not account for the tradeoffs necessary for performance. In this research, I model a database design method considering denormalization of duplicating attributes in order to reduce frequent join processes. This model considers response time for processing each select, insert, update, delete transaction, and for treating anomalies. A branch and bound method is proposed for this model.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.6 s.312
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• pp.17-25
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• 2006

As information systems to be computerized get significantly scaled up, data modeling issues apparently considered to be crucial once again as the early 1980's under the terms of data governance, data architecture or data quality. Unfortuately, merely resorting to heuristics-based field approaches with more or less no firm theoretical foundation of knowledge with regard to criteria of data design lead quite often to major failures in efficacy of data modeling. In this paper, we have compared normalization-critical data modeling approach, well-known as the Non-Stop Data Modeling methodology in the literature, to the Information Engineering in which in many occasions the notion of do-normalization is supported and even recommended as a mandatory part in its modeling nature. Quantitative analyses have revealed that NS methodology ostensibly outperforms IE methodology in terms of efficiency indices like adequacy of entity judgement, degree of existence of data circulation path that confirms the balancedness of data design and ratio of unnecessary data attribute replication.

• Annual Conference on Human and Language Technology
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• 2023.10a
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• pp.716-721
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• 2023

Inverse Text Normalization(ITN) 모델은 음성 인식(STT) 엔진의 중요한 후처리 영역 중 하나이며, STT 인식 결과의 가독성을 개선한다. 최근 ITN 모델에 심층신경망을 활용한 연구가 진행되고 있다. 심층 신경망을 사용하는 대부분의 선행연구는 문장 내 변환이 필요한 부분에 토큰 태깅을 진행하는 방식이다. 그러나 이는 Out-of-vocabulary(OOV) 이슈가 있으며, 학습 데이터 구축 시 토큰 단위의 섬세한 태깅 작업이 필요하다는 한계점이 존재한다. 더불어 선행 연구에서는 STT 인식 결과를 그대로 사용하는데, 이는 띄어쓰기가 중요한 한국어 ITN 처리에 변환 성능을 보장할 수 없다. 본 연구에서는 BART 기반 생성 모델로 생성형 ITN 모델을 구축하였고, Bi-LSTM 기반 보조 신경망 모델을 결합하여 STT 인식 결과에 대한 고유명사 처리, 띄어쓰기 교정 기능을 보완한 모델을 제안한다. 또한 보조 신경망을 통해 생성 모델 처리 여부를 판단하여 평균 추론 속도를 개선하였다. 실험을 통해 두 모델의 각 정량 성능 지표에서 우수한 성능을 확인하였고 결과적으로 본 연구에서 제안하는 두 모델의 결합된 방법론의 효과성을 제시하였다.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.11
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• pp.71-79
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• 2017

In this paper, we implemented the exchange rate forecasting neural network using heterogeneous computing. Exchange rate forecasting requires a large amount of data. We used a neural network that could leverage this data accordingly. Neural networks are largely divided into two processes: learning and verification. Learning took advantage of the CPU. For verification, RTL written in Verilog HDL was run on FPGA. The structure of the neural network has four input neurons, four hidden neurons, and one output neuron. The input neurons used the US $ 1, Japanese 100 Yen, EU 1 Euro, and UK £ 1. The input neurons predicted a Canadian dollar value of $ 1. The order of predicting the exchange rate is input, normalization, fixed-point conversion, neural network forward, floating-point conversion, denormalization, and outputting. As a result of forecasting the exchange rate in November 2016, there was an error amount between 0.9 won and 9.13 won. If we increase the number of neurons by adding data other than the exchange rate, it is expected that more precise exchange rate prediction will be possible.