• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.107-113
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• 2014

The derivation of optimal information from decision table is based on the concept of indiscernibility relation and approximation space in rough set. Because decision table is more likely to be susceptible to the superposition or inconsistency in decision table, the reduction of attributes is a important concept in knowledge representation. While complete subsets of the attribute's domain is considered in algebraic definition, incomplete subsets of the attribute's domain is considered in information-theoretic definition. Therefore there is a marked difference between algebraic and information-theoretic definition. This paper proposes a conditional entropy using rough set as information theoretical measures in order to deduct the optimal information which may contain condition attributes and decision attribute of information system and shows its effectiveness.

• The KIPS Transactions:PartD
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• v.11D no.6
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• pp.1287-1294
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• 2004

Recently relationships between reliability measures and the coverage have been developed for evaluation of software reliability. Particularly the mean value function of the coverage-based software reliability growth model is important because of its key role in rep-resenting the software reliability growth. In this paper, we first review the problems of the existing mean value functions with respect to the assumptions on which they are based. Then a new mean value function is proposed. The new mean value function is developed for a general testing environment in which imperfect fault detection and repeated construct execution are allowed. Finally performance of the proposed model is empirically evaluated by applying it to a real data set.

• KIISE Transactions on Computing Practices
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• v.24 no.2
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• pp.93-98
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• 2018

Recommendation systems are used to provide items of interests for users to maximize a company's profit. Matrix factorization is frequently used by recommendation systems, based on an incomplete user-item rating matrix. However, as the number of items and users increase, it becomes difficult to make accurate recommendations due to the sparsity of data. To overcome this drawback, the use of text data related to items was recently suggested for matrix factorization algorithms. Furthermore, a word-level convolutional neural network was shown to be effective in the process of extracting the word-level features from the text data among these kinds of matrix factorization algorithms. However, it involves a large number of parameters to learn in the word-level convolutional neural network. Thus, we propose a matrix factorization algorithm which utilizes a character-level convolutional neural network with which to extract the character-level features from the text data. We also conducted a performance study with real-life datasets to show the effectiveness of the proposed matrix factorization algorithm.

• Smart Media Journal
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• v.7 no.4
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• pp.17-23
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• 2018

We proposed a LSTM-based RNN model that can effectively perform the automatic spacing characteristics. For those long or noisy sentences which are known to be difficult to handle within Neural Network Learning, we defined a proper input data format and decoding data format, and added dropout, bidirectional multi-layer LSTM, layer normalization, and attention mechanism to improve the performance. Despite of the fact that Sejong corpus contains some spacing errors, a noise-robust learning model developed in this study with no overfitting through a dropout method helped training and returned meaningful results of Korean word spacing and its patterns. The experimental results showed that the performance of LSTM sequence-to-sequence model is 0.94 in F1-measure, which is better than the rule-based deep-learning method of GRU-CRF.

• Proceedings of the KAIS Fall Conference
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• 2009.05a
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• pp.835-838
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• 2009

본 논문은 환경공해의 주범인 자동차의 매연을 감소할 수 있는 방법에 대한 연구이다. 자동차 매연의 주요원인은 연료의 불완전 연소와 연료 자체의 특성때문이다. 따라서 본 논문에서는 가솔린/경유 차량을 CNG/LPG 차량으로 대체하고 CNG/LPG연료의 특성을 감안하여 연료의 공급과 분사량, 분사시간을 차량 ECU에서 보내지는 신호를 기본데이터로 차량의 가속과 감속 아이들 상태에서 차량을 적응적으로 최적화 할 수 있게 실시간으로 보정데이터를 계산한다. 본 논문의 결과로 기존의 구조변경차량의 출력에서 20%이상, 배기가스 에서 10% 이상의 향상되었다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.8A
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• pp.1358-1366
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• 2001

Variable Processing Variable Processing Gain 기법을 기반으로 하는 Multirate-DS/CDMA 시스템에서는 다중 사용자 간섭뿐만 아니라, 불완전한 전력제어로 인해 발생되는 현재 이동 통신 시스템에서의 원근효과와 유사한 간섭이 다른 데이터율 사용자 신호들 사이에서 발생하게 된다. 이러한 간섭은 이동통신 시스템 성능을 저하시키고, 용량을 감소시키게 되므로 이들 간섭을 효과적으로 제거할 수 있는 다중 사용자 검출 기법이 절실히 요구된다. 따라서 본 논문에서는 이러한 다중 사용자 간섭과 원근 효과를 효과적으로 제거하기 위한 하이브리드 다중 사용자 간섭 제거 기법을 제안한다. 하이브리드 다중 사용자 간섭 제거 기법은 하나 이상의 다중 사용자 검출 기법을 포함한다. 제안된 하이브리드 다중 사용자 간섭 제거 기법의 성능을 평가하기 위해 본 논문에서는 기존의 단일사용자 검출 기법 및 다중 사용자 검출 기법을 다시 한번 살펴보고, 이들 기존 검출기와 제안된 검출기를 컴퓨터 모의 실험을 통해 그 성능을 비교한다. 모의 실험에서는 AWGN 환경에서 각 SNR[dB]에 따른 BER 곡선을 조사하였다. 모의실험 결과로부터 기존의 병렬 간섭 제거 기법이나 역 상관 검출기보다 제안된 하이브리드 다중 사용자 간섭 제거 기법이 우수한 성능을 갖음을 알 수 있었다. 이는 어느 정도의 복잡도와 시간 지연에 대한 대가이다. 하지만 이 시간 지연은 검출구간을 고 데이터율 사용자 그룹에 맞춤으로써 어느 정도 해결될 것으로 보인다.

• The Transactions of the Korea Information Processing Society
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• v.4 no.1
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• pp.1-10
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• 1997

In this paper, for useful administration of the data which have ambiguities meaningfully and hard to treat, a new relation database model using an integrated fuzzy sets and rough sets relational database one. After proposing Fuzzy-rough relational database model on the base of integrated Fuzzy and Rough sets, Application of the examples of arithmetic is analyzed through the Access DBMS and the visual basic by composing and representing database based on fuzzy and rough sets which are characterized as fuzzy sets and rough sets on Pentium computer(166Mhz). This paper was induced to reduce the data incompleteness.

• The Journal of Bigdata
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• v.4 no.1
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• pp.129-142
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• 2019

Construction projects have experienced many failures due to incomplete production environments. Thus, the purpose of this study is to use ICT resources leased during the construction period at the construction site and to introduce the Mobile Cloud Computing Service, which utilizes Cloud Computing Service and mobile devices such as smart phones, tablet PCs, and notebooks instead of physically wired communication networks. The characteristics of Mobile Cloud, such as rapid accuracy, shared collaboration, and ubiquity, will affect the social network among various construction site participants. we conducted empirical research on the introduction of Mobile Cloud to promote information exchange and convergence among the participants and mutual trust, ultimately improving the project performance.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.11a
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• pp.153-155
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• 2018

정확한 영상 검색을 지원하기 위해 다양한 데이터와 방법들을 통한 메타데이터 생성 연구들이 이루어지고 있다. 자막 데이터를 기존의 키워드 기반의 메타데이터 생성 방법을 이용했을 경우, 구어체, 불완전 문장의 특징을 가진 특징을 반영하는데 어려움이 있었다. 또한, 단순히 키워드 매칭에 의존하기 때문에 문장에 중의적 단어가 포함되어 있을 경우에 검색 정확도가 떨어진다는 한계점이 있다. 따라서, 본 논문에서는 이러한 문제를 해결하기 위해 문장 전체를 특정 단위로 표현한 메타데이터를 생성한다. 이를 위해 비지도 학습인 RNN-LSTM 기반 네트워크를 이용하여 자막을 인코딩하고 장면 지식으로 생성하는 방법을 제안한다. 실험에서는 본 시스템을 통해 임의의 자막을 입력하고 유사도 기반의 결과 비교를 통해 자막 메타데이터의 정성적 평가를 수행하였다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.5
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• pp.592-601
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• 2016

The recurrent neural network (RNN) is a deep learning model which is suitable to sequential or length-variable data. The Long Short-Term Memory (LSTM) mitigates the vanishing gradient problem of RNNs so that LSTM can maintain the long-term dependency among the constituents of the given input sequence. In this paper, we propose a LSTM based language model which can predict following words of a given incomplete sentence to generate a complete sentence. To evaluate our method, we trained our model using multiple Korean corpora then generated the incomplete part of Korean sentences. The result shows that our language model was able to generate the fluent Korean sentences. We also show that the word based model generated better sentences compared to the other settings.