• Title, Summary, Keyword: Vector Space Model

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The Study On the Effectiveness of Information Retrieval in the Vector Space Model and the Neural Network Inductive Learning Model

  • Kim, Seong-Hee
    • The Journal of Information Technology and Database
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    • v.3 no.2
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    • pp.75-96
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    • 1996
  • This study is intended to compare the effectiveness of the neural network inductive learning model with a vector space model in information retrieval. As a result, searches responding to incomplete queries in the neural network inductive learning model produced a higher precision and recall as compared with searches responding to complete queries in the vector space model. The results show that the hybrid methodology of integrating an inductive learning technique with the neural network model can help solve information retrieval problems that are the results of inconsistent indexing and incomplete queries--problems that have plagued information retrieval effectiveness.

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A Semantic Aspect-Based Vector Space Model to Identify the Event Evolution Relationship within Topics

  • Xi, Yaoyi;Li, Bicheng;Liu, Yang
    • Journal of Computing Science and Engineering
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    • v.9 no.2
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    • pp.73-82
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    • 2015
  • Understanding how the topic evolves is an important and challenging task. A topic usually consists of multiple related events, and the accurate identification of event evolution relationship plays an important role in topic evolution analysis. Existing research has used the traditional vector space model to represent the event, which cannot be used to accurately compute the semantic similarity between events. This has led to poor performance in identifying event evolution relationship. This paper suggests constructing a semantic aspect-based vector space model to represent the event: First, use hierarchical Dirichlet process to mine the semantic aspects. Then, construct a semantic aspect-based vector space model according to these aspects. Finally, represent each event as a point and measure the semantic relatedness between events in the space. According to our evaluation experiments, the performance of our proposed technique is promising and significantly outperforms the baseline methods.

Space Vector Modulation based on Model Predictive Control to Reduce Current Ripples with Subdivided Space Voltage Vectors (전류 리플 저감을 위한 세분화된 공간전압벡터를 이용한 모델 예측 제어 기반의 SVM 방법)

  • Moon, Hyun-Cheol;Lee, June-Seok;Lee, June-Hee;Lee, Kyo-Beum
    • The Transactions of the Korean Institute of Power Electronics
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    • v.22 no.1
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    • pp.18-26
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    • 2017
  • This paper proposes the model predictive control with space vector modulation (SVM) method for current control of voltage-source inverter. Unlike the conventional method using a limited number of voltage vectors by switching states, the proposed method can consider various voltage vectors to identify the optimized voltage vector. The various voltage vectors are obtained by subdividing existing voltage vectors. The optimized voltage vector that minimizes the cost function is selected and applied to the inverter by using the SVM. The various voltage vectors and SVM reduce current ripples in the output AC side of the inverter compared with the conventional method. The effectiveness and performance of the proposed method are verified through simulation and experiment with a three-phase two-level voltage-source grid-connected inverter.

A Linear Reservoir Model with Kslman Filter in River Basin (Kalman Filter 이론에 의한 하천유역의 선형저수지 모델)

  • 이영화
    • Journal of Environmental Science International
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    • v.3 no.4
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    • pp.349-356
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    • 1994
  • The purpose of this study is to develop a linear reservoir model with Kalman filter using Kalman filter theory which removes a physical uncertainty of :ainfall-runoff process. A linear reservoir model, which is the basic model of Kalman filter, is used to calculate runoff from rainfall in river basin. A linear reservoir model with Kalman filter is composed of a state-space model using a system model and a observation model. The state-vector of system model in linear. The average value of the ordinate of IUH for a linear reservoir model with Kalman filter is used as the initial value of state-vector. A .linear reservoir model with Kalman filter shows better results than those by linear reserevoir model, and decreases a physical uncertainty of rainfall-runoff process in river basin.

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Word Sense Similarity Clustering Based on Vector Space Model and HAL (벡터 공간 모델과 HAL에 기초한 단어 의미 유사성 군집)

  • Kim, Dong-Sung
    • Korean Journal of Cognitive Science
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    • v.23 no.3
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    • pp.295-322
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    • 2012
  • In this paper, we cluster similar word senses applying vector space model and HAL (Hyperspace Analog to Language). HAL measures corelation among words through a certain size of context (Lund and Burgess 1996). The similarity measurement between a word pair is cosine similarity based on the vector space model, which reduces distortion of space between high frequency words and low frequency words (Salton et al. 1975, Widdows 2004). We use PCA (Principal Component Analysis) and SVD (Singular Value Decomposition) to reduce a large amount of dimensions caused by similarity matrix. For sense similarity clustering, we adopt supervised and non-supervised learning methods. For non-supervised method, we use clustering. For supervised method, we use SVM (Support Vector Machine), Naive Bayes Classifier, and Maximum Entropy Method.

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A Semantic Text Model with Wikipedia-based Concept Space (위키피디어 기반 개념 공간을 가지는 시멘틱 텍스트 모델)

  • Kim, Han-Joon;Chang, Jae-Young
    • 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.

Speed Sensorless Vector Control Implementation of Induction Motor Using dSPACE 1104 System (dSPACE 1104 시스템을 이용한 유도전동기 속도 센서리스 벡터제어 구현)

  • Lee, Dong-Min;Lee, Yong-Suk;Ji, Jun-Keun;Cha, Gui-Soo
    • Proceedings of the KIEE Conference
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    • pp.1086-1087
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    • 2007
  • This paper presents a implementation of speed sensorless vector control algorithm of induction motor using MATLAB/SIMULINK. The proposed method utilize the combination of the voltage model based on stator equivalent model and the current model based on rotor equivalent model, which enables stable estimation of rotor flux. Estimated rotor speed, which is used to speed controller of induction motor, is based on estimated flux. The overall system consisted of speed controller with the most general PI controller, current controller, flux controller. Speed sensorless vector control algorithm is implemeted as block diagrams using MATLAB/SIMULINK. Realtime control is perform by dSPACE DS1104 control board and Real-Time-Interface(RTI).

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Category Factor Based Feature Selection for Document Classification

  • Kang Yun-Hee
    • International Journal of Contents
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    • v.1 no.2
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    • pp.26-30
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    • 2005
  • According to the fast growth of information on the Internet, it is becoming increasingly difficult to find and organize useful information. To reduce information overload, it needs to exploit automatic text classification for handling enormous documents. Support Vector Machine (SVM) is a model that is calculated as a weighted sum of kernel function outputs. This paper describes a document classifier for web documents in the fields of Information Technology and uses SVM to learn a model, which is constructed from the training sets and its representative terms. The basic idea is to exploit the representative terms meaning distribution in coherent thematic texts of each category by simple statistics methods. Vector-space model is applied to represent documents in the categories by using feature selection scheme based on TFiDF. We apply a category factor which represents effects in category of any term to the feature selection. Experiments show the results of categorization and the correlation of vector length.

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Korean Document Classification Using Extended Vector Space Model (확장된 벡터 공간 모델을 이용한 한국어 문서 분류 방안)

  • Lee, Samuel Sang-Kon
    • 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.

Fuzzy modeling using transformed input space partitioning

  • You, Je-Young;Lee, Sang-Chul;Won, Sang-Chul
    • 제어로봇시스템학회:학술대회논문집
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    • pp.494-498
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    • 1996
  • Three fuzzy input space partitoining methods, which are grid, tree, and scatter method, are mainly used until now. These partition methods represent good performance in the modeling of the linear system and nonlinear system with independent modeling variables. But in the case of the nonlinear system with the coupled modeling variables, there should be many fuzzy rules for acquiring the exact fuzzy model. In this paper, it shows that the fuzzy model is acquired using transformed modeling vector by linear transformation of the modeling vector.

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