• 한국환경과학회지
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• 제28권11호
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• pp.1047-1059
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• 2019

To address the increase of weather hazards and the emergence of new types of such hazards, an optimization technique for three-dimensional (3D) representation of meteorological facts and atmospheric information was examined in this study as a novel method for weather analysis. The proposed system is termed as "meteorological and air quality information visualization engine" (MAIVE), and it can support several file formats and can implement high-resolution 3D terrain by employing a 30 m resolution digital elevation model. In this study, latest 3D representation techniques such as wind vector fields, contour maps, stream vector, stream line flow along the wind field and 3D volume rendering were applied. Implementation of the examples demonstrates that the results of numerical modeling are well reflected, and new representation techniques can facilitate the observation of meteorological factors and atmospheric information from different perspectives.

• ETRI Journal
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• 제40권4호
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• pp.499-510
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• 2018

A vector space based augmented structural kinematic (VSASK) feature descriptor is proposed for human activity recognition. An action descriptor is built by integrating the structural and kinematic properties of the actor using vector space based augmented matrix representation. Using the local or global information separately may not provide sufficient action characteristics. The proposed action descriptor combines both the local (pose) and global (position and velocity) features using augmented matrix schema and thereby increases the robustness of the descriptor. A multiclass support vector machine (SVM) is used to learn each action descriptor for the corresponding activity classification and understanding. The performance of the proposed descriptor is experimentally analyzed using the Weizmann and KTH datasets. The average recognition rate for the Weizmann and KTH datasets is 100% and 99.89%, respectively. The computational time for the proposed descriptor learning is 0.003 seconds, which is an improvement of approximately 1.4% over the existing methods.

• Journal of Information Processing Systems
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• 제4권2호
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• pp.77-86
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• 2008

This research proposes a new neural network for text categorization which uses alternative representations of documents to numerical vectors. Since the proposed neural network is intended originally only for text categorization, it is called NTC (Neural Text Categorizer) in this research. Numerical vectors representing documents for tasks of text mining have inherently two main problems: huge dimensionality and sparse distribution. Although many various feature selection methods are developed to address the first problem, the reduced dimension remains still large. If the dimension is reduced excessively by a feature selection method, robustness of text categorization is degraded. Even if SVM (Support Vector Machine) is tolerable to huge dimensionality, it is not so to the second problem. The goal of this research is to address the two problems at same time by proposing a new representation of documents and a new neural network using the representation for its input vector.

• 한국정밀공학회지
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• 제20권5호
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• pp.47-56
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• 2003

Geometric cutting-simulation and verification play an important role in detecting NC machining errors in mold & die manufacturing and thereby reducing correcting time & cost on the shop floor. Current researches in the area may be categorized into view-based, solid-based, and discrete vector-based methods mainly depending on workpiece models. Each methodology has its own strengths and weaknesses in terms of computing speed, representation accuracy, and its ability of numerical inspection. The paper proposes a hybrid modeling scheme for workpiece representation with z-map model and discrete vector model, which performs 3-axis and 5-axis cutting-simulation via tool swept surface construction by connecting a sequence of silhouette curves.

• 대한수학회논문집
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• 제9권4호
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• pp.927-931
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• 1994

Let G be a cyclic group of order 2 and let $S^1$ denote the unit circle in $R^2$ with the standard metric. We consider smooth G-vector bundles over $S^1$ when G acts on $S^1$ by reflection. Then the fixed point set of G on $S^1$ is two points ${z_0, z_1}$. Let $E$\mid$_{z_0} and E$\mid$_{z_1}$ be the fiber G-representation spaces at $z_0$ and $z_1$ respectively. We associate an orthogonal G-representation $\rho_i : G \to O(n)$ to $E$\mid$_{z_i}, i = 0, 1$. Let det $p\rho_i(g), g \neq 1$, be denoted by det $E$\mid$_{z_i}$ since det $\rho_i(g)$ is independent of choice of $\rho_i$.

• 대한조선학회논문집
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• 제35권4호
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• pp.118-125
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• 1998

본 연구는 B-spline 근사법과 유전자 알고리즘을 이용하여 기하학적 경계 조건-양끝점의 위치 벡터 및 접선 벡터-을 만족하는 혼합 곡선 근사법에 의한 선형 표현을 내용으로 한다. B-spline 근사법을 이용하여 선형을 표현하고, 이들 곡선을 제어하는 조정점들이 기하학적 경계조건을 만족하도록 유전자 알고리즘으로 조정한다. 이 방법은 선형 생성시 순정 작업을 동시에 수행하므로 효율적인 선형 설계를 가능하게 한다.

• Journal of Information Processing Systems
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• 제13권4호
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• pp.863-875
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• 2017

The traditional text similarity measurement methods based on word frequency vector ignore the semantic relationships between words, which has become the obstacle to text similarity calculation, together with the high-dimensionality and sparsity of document vector. To address the problems, the improved singular value decomposition is used to reduce dimensionality and remove noises of the text representation model. The optimal number of singular values is analyzed and the semantic relevance between words can be calculated in constructed semantic space. An inverted index construction algorithm and the similarity definitions between vectors are proposed to calculate the similarity between two documents on the semantic level. The experimental results on benchmark corpus demonstrate that the proposed method promotes the evaluation metrics of F-measure.

• 응용통계연구
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• 제25권6호
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• pp.925-932
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• 2012

The article introduces a method to estimate a cointegrated vector autoregressive model, using mixed-frequency data, in terms of a state-space representation of the vector error correction(VECM) of the model. The method directly estimates the parameters of the model, in a state-space form of its VECM representation, using the available data in its mixed-frequency form. Then it allows one to compute in-sample smoothed estimates and out-of-sample forecasts at their high-frequency intervals using the estimated model. The method is applied to a mixed-frequency data set that consists of the quarterly real gross domestic product and three monthly coincident indicators. The result shows that the method produces accurate smoothed and forecasted estimates in comparison to a method based on single-frequency data.

• 한국정보과학회 언어공학연구회:학술대회논문집(한글 및 한국어 정보처리)
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• 한국정보과학회언어공학연구회 2022년도 제34회 한글 및 한국어 정보처리 학술대회
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• pp.115-121
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• 2022

질문에 답하기 위해 관련 구절을 검색하는 기술은 오픈 도메인 질의응답의 검색 단계를 위해 필요하다. 전통적인 방법은 정보 검색 기법인 빈도-역문서 빈도(TF-IDF) 기반으로 희소한 벡터 표현을 활용하여 구절을 검색한다. 하지만 희소 벡터 표현은 벡터 길이가 길 뿐만 아니라, 질문에 나오지 않는 단어나 토큰을 검색하지 못한다는 취약점을 가진다. 밀집 벡터 표현 연구는 이러한 취약점을 개선하고 있으며 대부분의 연구가 영어 데이터셋을 학습한 것이다. 따라서, 본 연구는 한국어 데이터셋을 학습한 밀집 벡터 표현을 연구하고 여러 가지 부정 샘플(negative sample) 추출 방법을 도입하여 전이 학습한 모델 성능을 비교 분석한다. 또한, 대화 응답 선택 태스크에서 밀집 검색에 활용한 순위 재지정 상호작용 레이어를 추가한 실험을 진행하고 비교 분석한다. 밀집 벡터 표현 모델을 학습하는 것이 도전적인 과제인만큼 향후에도 다양한 시도가 필요할 것으로 보인다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제14권4호
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• pp.240-248
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• 2014

We present a robust speaker identification algorithm that uses novel features based on soft bag-of-word representation and a simple Naive Bayes classifier. The bag-of-words (BoW) based histogram feature descriptor is typically constructed by summarizing and identifying representative prototypes from low-level spectral features extracted from training data. In this paper, we define a generalization of the standard BoW. In particular, we define three types of BoW that are based on crisp voting, fuzzy memberships, and possibilistic memberships. We analyze our mapping with three common classifiers: Naive Bayes classifier (NB); K-nearest neighbor classifier (KNN); and support vector machines (SVM). The proposed algorithms are evaluated using large datasets that simulate medical crises. We show that the proposed soft bag-of-words feature representation approach achieves a significant improvement when compared to the state-of-art methods.