• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.5A
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• pp.485-494
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• 2011

In this paper, novel sub-space interference alignment algorithms are proposed to boost the capacity in multi-cell environment. In the case of conventional sub-space alignment, arbitrary reference vectors have been adopted as transmitting vectors at the transmitter side, and the inter-cell interference among users are eliminated by using orthogonal vectors of the chosen reference vectors at the receiver side. However, in this case, sum-rate varies using different reference vectors even though the channel values keep constant, and vice versa. Therefore, the relationship between reference vectors and channel values are analyzed in this paper, and novel interference alignment algorithms are proposed to increase multi-cell capacity. Reference vectors with similar magnitude are adopted in the proposed algorithm. Simulation results show that the proposed algorithms provide about 50 % higher sum-rate than conventional algorithm.

• Journal of Korean Association for Spatial Structures
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• v.8 no.3
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• pp.75-82
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• 2008

In this study, the modified genetic algorithm, D-GA, is proposed. D-GA is a hybrid genetic algorithm combined a simple genetic algorithm and the local search algorithm using direction vectors. Also, two types of direction vectors, learning direction vector and random direction vector, are defined without the sensitivity analysis. The accuracy of D-GA is compared with that of simple genetic algorithm. It is demonstrated that the proposed approach can be an effective optimization technique through a minimum weight structural optimization of ten bar truss.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2009.05a
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• pp.138-143
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• 2009

본 논문에서는 감성 공간에서 mean shift clustering과 user feedback을 이용하여 영상 검색 결과를 개선하기 위한 시스템을 제안한다. 제안된 시스템은 사용자 인터페이스, 감성 공간 변환, 검색결과 순위 재지정(re-ranking)으로 구성된다. 사용자 인터페이스는 텍스트 형태의 질의 입력과 감성 어휘 선택에 따른 user feedback에 의해 개선된 검색결과를 보인다. 사용된 감성 어휘는 고바야시가 정의한 romantic, natural, casual, elegant, chic, classic, dandy, modern 등의 8개 어휘를 사용한다. 감성 공간 변환 단계에서는 입력된 질의에 따라 웹 영상 검색 엔진(Yahoo)에 의해 검색된 결과 영상들에 대해 컬러와 패턴정보의 특징을 추출하고, 이를 입력으로 하는 8개의 각 감성별 분류기에 의해 각 영상은 8차원 감성 공간으로의 특징 벡터로 변환된다. 이때 감성 공간으로 변환된 특징 벡터들은 mean shift clustering을 통해 군집화 되고, 그 결과로써 대표 클러스터를 찾게 된다. 검색결과 순위 재지정 단계에서는 user feedback 유무에 따라 대표 클러스터의 평균 벡터와 user feedback에 의해 생성된 사용자 감성 벡터에 의해 검색 결과를 개선할 수 있다. 이때 각 기준에 따라 유사도가 결정되고 검색결과 순위가 재지정 된다 제안된 시스템의 성능을 검증하기 위해 7개의 질의의 각 400장, 총 2,800장에 대한 Yahoo 검색 결과와 제안된 시스템을 개선된 검색 결과를 비교하였다.

• Communications of the Korean Mathematical Society
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• v.15 no.1
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• pp.111-121
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• 2000

유클리드 공간의 정의와 평행이동 및 벡터의 성질을 현대적인 관점에서 살펴본다. 또 이를 이용하여 아핀 공간을 정의한다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2013.06a
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• pp.14-15
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• 2013

본 논문에서는 공간 계층 분해를 이용한 3 차원 메쉬 시퀀스 압축 기법을 제안한다. 제안하는 기법은 우선 각 점에 대한 시간적 궤적을 공분산 행렬로 표현하고, PCA(Principal component analysis)를 적용하여 시간 궤적에 대한 고유 벡터와 PCA 계수를 획득한다. 공간적인 예측을 통해 PCA 계수에 대한 벡터 차를 추출하고, 벡터 차와 그것에 대한 고유 벡터를 전송한다. 제안하는 방법은 PCA 계수 예측의 성능을 높이기 위해 점진적 압축에서 사용하는 공간 계층 분해 기법을 적용하여, 계수 예측에 효과적인 이웃 점을 지정하도록 한다. 또한, 이웃 점 개수를 사용자가 임의로 지정할 수 있도록 하여, 성능과 복잡도간의 트레이드 오프를 제어할 수 있도록 한다. 다양한 모델에 대한 실험 결과를 통해 제안하는 방법의 성능을 확인한다.

• Proceedings of the KIPE Conference
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• 2014.07a
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• pp.387-388
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• 2014

3상 AC/AC 2단계 직접형 전력변환시스템(TSDPC)의 제어는 입력전류공간벡터와 출력전압공간벡터를 합성한 공간벡터 PWM 스위칭패턴을 통해 제어를 하며, 이를 기반으로 계산된 유효벡터인가시간에 따라 TSDPC를 구성하는 다수 스위칭소자가 절환 됨을 통해 많은 스위칭손실이 발생하는 문제점이 있다. 본 논문에서는 스위칭 시퀀스를 간략화하여 스위칭 절환횟수를 줄임과 동시에 부분 영전류 스위칭 제어기법을 도입하여, 스위칭손실 저감 제어기법을 제안한다.

• The Journal of Society for e-Business Studies
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• v.19 no.3
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• pp.107-123
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• 2014

Current text mining techniques suffer from the problem that the conventional text representation models cannot express the semantic or conceptual information for the textual documents written with natural languages. The conventional text models represent the textual documents as bag of words, which include vector space model, Boolean model, statistical model, and tensor space model. These models express documents only with the term literals for indexing and the frequency-based weights for their corresponding terms; that is, they ignore semantical information, sequential order information, and structural information of terms. Most of the text mining techniques have been developed assuming that the given documents are represented as 'bag-of-words' based text models. However, currently, confronting the big data era, a new paradigm of text representation model is required which can analyse huge amounts of textual documents more precisely. Our text model regards the 'concept' as an independent space equated with the 'term' and 'document' spaces used in the vector space model, and it expresses the relatedness among the three spaces. To develop the concept space, we use Wikipedia data, each of which defines a single concept. Consequently, a document collection is represented as a 3-order tensor with semantic information, and then the proposed model is called text cuboid model in our paper. Through experiments using the popular 20NewsGroup document corpus, we prove the superiority of the proposed text model in terms of document clustering and concept clustering.

• The KIPS Transactions:PartB
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• v.18B no.2
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• pp.93-108
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• 2011

We propose a extended vector space model by using ambiguous words and disambiguous words to improve the result of a Korean document classification method. In this paper we study the precision enhancement of vector space model and we propose a new axis that represents a weight value. Conventional classification methods without the weight value had some problems in vector comparison. We define a word which has same axis of the weight value as ambiguous word after calculating a mutual information value between a term and its classification field. We define a word which is disambiguous with ambiguous meaning as disambiguous word. We decide the strengthness of a disambiguous word among several words which is occurring ambiguous word and a same document. Finally, we proposed a new classification method based on extension of vector dimension with ambiguous and disambiguous words.

• Smart Media Journal
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• v.7 no.4
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• pp.24-29
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• 2018

Text analysis technique for natural language processing in deep learning represents words in vector form through word embedding. In this paper, we propose a method of constructing a document vector and classifying it into spam and normal text message, using word embedding and deep learning method. Automatic spacing applied in the preprocessing process ensures that words with similar context are adjacently represented in vector space. Additionally, the intentional word formation errors with non-alphabetic or extraordinary characters are designed to avoid being blocked by spam message filter. Two embedding algorithms, CBOW and skip grams, are used to produce the sentence vector and the performance and the accuracy of deep learning based spam filter model are measured by comparing to those of SVM Light.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.4 s.310
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• pp.88-102
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• 2006

In multimedia information retrieval, multimedia data are represented as vectors in high dimensional space. To search these vectors effectively, a variety of indexing methods have been proposed. However, the performance of these indexing methods degrades dramatically with increasing dimensionality, which is known as the dimensionality curse. To resolve the dimensionality curse, dimensionality reduction methods have been proposed. They map feature vectors in high dimensional space into the ones in low dimensional space before indexing the data. This paper proposes a method for dimensionality reduction based on a function approximating the Euclidean distance, which makes use of the norm and angle components of a vector. First, we identify the causes of the errors in angle estimation for approximating the Euclidean distance, and discuss basic directions to reduce those errors. Then, we propose a novel method for dimensionality reduction that composes a set of subvectors from a feature vector and maintains only the norm and the estimated angle for every subvector. The selection of a good reference vector is important for accurate estimation of the angle component. We present criteria for being a good reference vector, and propose a method that chooses a good reference vector by using Levenberg-Marquardt algorithm. Also, we define a novel distance function, and formally prove that the distance function lower-bounds the Euclidean distance. This implies that our approach does not incur any false dismissals in reducing the dimensionality effectively. Finally, we verify the superiority of the proposed method via performance evaluation with extensive experiments.