• Title, Summary, Keyword: Vector Space Model

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A Tensor Space Model based Semantic Search Technique (텐서공간모델 기반 시멘틱 검색 기법)

  • Hong, Kee-Joo;Kim, Han-Joon;Chang, Jae-Young;Chun, Jong-Hoon
    • The Journal of Society for e-Business Studies
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    • v.21 no.4
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    • pp.1-14
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    • 2016
  • Semantic search is known as a series of activities and techniques to improve the search accuracy by clearly understanding users' search intent without big cognitive efforts. Usually, semantic search engines requires ontology and semantic metadata to analyze user queries. However, building a particular ontology and semantic metadata intended for large amounts of data is a very time-consuming and costly task. This is why commercialization practices of semantic search are insufficient. In order to resolve this problem, we propose a novel semantic search method which takes advantage of our previous semantic tensor space model. Since each term is represented as the 2nd-order 'document-by-concept' tensor (i.e., matrix), and each concept as the 2nd-order 'document-by-term' tensor in the model, our proposed semantic search method does not require to build ontology. Nevertheless, through extensive experiments using the OHSUMED document collection and SCOPUS journal abstract data, we show that our proposed method outperforms the vector space model-based search method.

An Improved Predictive Control of an Induction Machine fed by a Matrix Converter for Torque Ripple Reduction (토크 리플 저감을 위한 매트릭스 컨버터 구동 유도 전동기의 향상된 예측 제어 기법)

  • Lee, Eunsil;Choi, Woo Jin;Lee, Kyo-Beum
    • Journal of Institute of Control, Robotics and Systems
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    • v.21 no.7
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    • pp.662-668
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    • 2015
  • This paper presents an improved predictive control of an induction machine fed by a matrix converter using N-switching vectors as the control action during a complete sampling period of the controller. The conventional model predictive control scheme based matrix converter uses a single switching vector over the same period which introduces high torque ripple. The proposed switching scheme for a matrix converter based model predictive control of an induction machine drive selects the appropriate switching vectors for control of electromagnetic torque with small variations of the stator flux. The proposed method can reduce the ripple of the electrical variables by selecting the switching state as well as the method used in the space vector modulation techniques. Simulation results are presented to verify the effectiveness of the improved predictive control strategy for induction machine fed by a matrix converter.

Visualizing SVM Classification in Reduced Dimensions

  • Huh, Myung-Hoe;Park, Hee-Man
    • Communications for Statistical Applications and Methods
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    • v.16 no.5
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    • pp.881-889
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    • 2009
  • Support vector machines(SVMs) are known as flexible and efficient classifier of multivariate observations, producing a hyperplane or hyperdimensional curved surface in multidimensional feature space that best separates training samples by known groups. As various methodological extensions are made for SVM classifiers in recent years, it becomes more difficult to understand the constructed model intuitively. The aim of this paper is to visualize various SVM classifications tuned by several parameters in reduced dimensions, so that data analysts secure the tangible image of the products that the machine made.

A New Approach to Automatic Keyword Generation Using Inverse Vector Space Model (키워드 자동 생성에 대한 새로운 접근법: 역 벡터공간모델을 이용한 키워드 할당 방법)

  • Cho, Won-Chin;Rho, Sang-Kyu;Yun, Ji-Young Agnes;Park, Jin-Soo
    • Asia pacific journal of information systems
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    • v.21 no.1
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    • pp.103-122
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    • 2011
  • Recently, numerous documents have been made available electronically. Internet search engines and digital libraries commonly return query results containing hundreds or even thousands of documents. In this situation, it is virtually impossible for users to examine complete documents to determine whether they might be useful for them. For this reason, some on-line documents are accompanied by a list of keywords specified by the authors in an effort to guide the users by facilitating the filtering process. In this way, a set of keywords is often considered a condensed version of the whole document and therefore plays an important role for document retrieval, Web page retrieval, document clustering, summarization, text mining, and so on. Since many academic journals ask the authors to provide a list of five or six keywords on the first page of an article, keywords are most familiar in the context of journal articles. However, many other types of documents could not benefit from the use of keywords, including Web pages, email messages, news reports, magazine articles, and business papers. Although the potential benefit is large, the implementation itself is the obstacle; manually assigning keywords to all documents is a daunting task, or even impractical in that it is extremely tedious and time-consuming requiring a certain level of domain knowledge. Therefore, it is highly desirable to automate the keyword generation process. There are mainly two approaches to achieving this aim: keyword assignment approach and keyword extraction approach. Both approaches use machine learning methods and require, for training purposes, a set of documents with keywords already attached. In the former approach, there is a given set of vocabulary, and the aim is to match them to the texts. In other words, the keywords assignment approach seeks to select the words from a controlled vocabulary that best describes a document. Although this approach is domain dependent and is not easy to transfer and expand, it can generate implicit keywords that do not appear in a document. On the other hand, in the latter approach, the aim is to extract keywords with respect to their relevance in the text without prior vocabulary. In this approach, automatic keyword generation is treated as a classification task, and keywords are commonly extracted based on supervised learning techniques. Thus, keyword extraction algorithms classify candidate keywords in a document into positive or negative examples. Several systems such as Extractor and Kea were developed using keyword extraction approach. Most indicative words in a document are selected as keywords for that document and as a result, keywords extraction is limited to terms that appear in the document. Therefore, keywords extraction cannot generate implicit keywords that are not included in a document. According to the experiment results of Turney, about 64% to 90% of keywords assigned by the authors can be found in the full text of an article. Inversely, it also means that 10% to 36% of the keywords assigned by the authors do not appear in the article, which cannot be generated through keyword extraction algorithms. Our preliminary experiment result also shows that 37% of keywords assigned by the authors are not included in the full text. This is the reason why we have decided to adopt the keyword assignment approach. In this paper, we propose a new approach for automatic keyword assignment namely IVSM(Inverse Vector Space Model). The model is based on a vector space model. which is a conventional information retrieval model that represents documents and queries by vectors in a multidimensional space. IVSM generates an appropriate keyword set for a specific document by measuring the distance between the document and the keyword sets. The keyword assignment process of IVSM is as follows: (1) calculating the vector length of each keyword set based on each keyword weight; (2) preprocessing and parsing a target document that does not have keywords; (3) calculating the vector length of the target document based on the term frequency; (4) measuring the cosine similarity between each keyword set and the target document; and (5) generating keywords that have high similarity scores. Two keyword generation systems were implemented applying IVSM: IVSM system for Web-based community service and stand-alone IVSM system. Firstly, the IVSM system is implemented in a community service for sharing knowledge and opinions on current trends such as fashion, movies, social problems, and health information. The stand-alone IVSM system is dedicated to generating keywords for academic papers, and, indeed, it has been tested through a number of academic papers including those published by the Korean Association of Shipping and Logistics, the Korea Research Academy of Distribution Information, the Korea Logistics Society, the Korea Logistics Research Association, and the Korea Port Economic Association. We measured the performance of IVSM by the number of matches between the IVSM-generated keywords and the author-assigned keywords. According to our experiment, the precisions of IVSM applied to Web-based community service and academic journals were 0.75 and 0.71, respectively. The performance of both systems is much better than that of baseline systems that generate keywords based on simple probability. Also, IVSM shows comparable performance to Extractor that is a representative system of keyword extraction approach developed by Turney. As electronic documents increase, we expect that IVSM proposed in this paper can be applied to many electronic documents in Web-based community and digital library.

Speed Control of Induction Motor Using the Voltage Type Inverter with Speed Sensorless (속도검출기없는 전압형 Inverter에 의한 유도전동기 속도제어)

  • Seo Young-Soo;Lee Chun-Sang;Hwang Lak-Hoon;Kim Ju-Rae;Cho Moon-Tack
    • Proceedings of the KIPE Conference
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    • pp.430-433
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    • 2001
  • When the vector control, which does not need a speed signal from a mechanical speed sensor, it is possible to reduce the cost of the control equipment and to improve the control performance in many industrial application. This paper describes a rotor speed identification method of induction motor based on the theory of flux model reference adaptive system. The estimator execute the rotor speed identification so that the vector control of the induction motor may be achieved. The improved auxiliary variable of the two model are introduced In perform accurate rotor speed estimation. The control system is composed of the PI controller for speed control and current controller using space voltage vector PWM technique. High speed calculation and processing for vector control is carried out by TMS320C31 digital signal processor. Validity of the proposed control method is verified through simulation and experimental result.

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Spoken Document Retrieval Based on Phone Sequence Strings Decoded by PVDHMM (PVDHMM을 이용한 음소열 기반의 SDR 응용)

  • Choi, Dae-Lim;Kim, Bong-Wan;Kim, Chong-Kyo;Lee, Yong-Ju
    • MALSORI
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    • no.62
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    • pp.133-147
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    • 2007
  • In this paper, we introduce a phone vector discrete HMM(PVDHMM) that decodes a phone sequence string, and demonstrates the applicability to spoken document retrieval. The PVDHMM treats a phone recognizer or large vocabulary continuous speech recognizer (LVCSR) as a vector quantizer whose codebook size is equal to the size of its phone set. We apply the PVDHMM to decode the phone sequence strings and compare the outputs with those of a continuous speech recognizer(CSR). Also we carry out spoken document retrieval experiment through PVDHMM word spotter on the phone sequence strings which are generated by phone recognizer or LVCSR and compare its results with those of retrieval through the phone-based vector space model.

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Hybrid Fuzzy Least Squares Support Vector Machine Regression for Crisp Input and Fuzzy Output

  • Shim, Joo-Yong;Seok, Kyung-Ha;Hwang, Chang-Ha
    • Communications for Statistical Applications and Methods
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    • v.17 no.2
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    • pp.141-151
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    • 2010
  • Hybrid fuzzy regression analysis is used for integrating randomness and fuzziness into a regression model. Least squares support vector machine(LS-SVM) has been very successful in pattern recognition and function estimation problems for crisp data. This paper proposes a new method to evaluate hybrid fuzzy linear and nonlinear regression models with crisp inputs and fuzzy output using weighted fuzzy arithmetic(WFA) and LS-SVM. LS-SVM allows us to perform fuzzy nonlinear regression analysis by constructing a fuzzy linear regression function in a high dimensional feature space. The proposed method is not computationally expensive since its solution is obtained from a simple linear equation system. In particular, this method is a very attractive approach to modeling nonlinear data, and is nonparametric method in the sense that we do not have to assume the underlying model function for fuzzy nonlinear regression model with crisp inputs and fuzzy output. Experimental results are then presented which indicate the performance of this method.

A Study on Research Trends of Graph-Based Text Representations for Text Mining (텍스트 마이닝을 위한 그래프 기반 텍스트 표현 모델의 연구 동향)

  • Chang, Jae-Young
    • The Journal of The Institute of Internet, Broadcasting and Communication
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    • v.13 no.5
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    • pp.37-47
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    • 2013
  • Text Mining is a research area of retrieving high quality hidden information such as patterns, trends, or distributions through analyzing unformatted text. Basically, since text mining assumes an unstructured text, it needs to be represented as a simple text model for analyzing it. So far, most frequently used model is VSM(Vector Space Model), in which a text is represented as a bag of words. However, recently much researches tried to apply a graph-based text model for representing semantic relationships between words. In this paper, we survey research trends of graph-based text representation models for text mining. Additionally, we also discuss about future models of graph-based text mining.

A PROPOSAL OF SEMI-AUTOMATIC INDEXING ALGORITHM FOR MULTI-MEDIA DATABASE WITH USERS' SENSIBILITY

  • Mitsuishi, Takashi;Sasaki, Jun;Funyu, Yutaka
    • Proceedings of the Korean Society for Emotion and Sensibility Conference
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    • pp.120-125
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    • 2000
  • We propose a semi-automatic and dynamic indexing algorithm for multi-media database(e.g. movie files, audio files), which are difficult to create indexes expressing their emotional or abstract contents, according to user's sensitivity by using user's histories of access to database. In this algorithm, we simply categorize data at first, create a vector space of each user's interest(user model) from the history of which categories the data belong to, and create vector space of each data(title model) from the history of which users the data had been accessed from. By continuing the above method, we could create suitable indexes, which show emotional content of each data. In this paper, we define the recurrence formulas based on the proposed algorithm. We also show the effectiveness of the algorithm by simulation result.

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Automation of Expert Classification in Knowledge Management Systems Using Text Categorization Technique (문서 범주화를 이용한 지식관리시스템에서의 전문가 분류 자동화)

  • Yang, Kun-Woo;Huh, Soon-Young
    • Asia pacific journal of information systems
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    • v.14 no.2
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    • pp.115-130
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    • 2004
  • This paper proposes how to build an expert profile database in KMS, which provides the information of expertise that each expert possesses in the organization. To manage tacit knowledge in a knowledge management system, recent researches in this field have shown that it is more applicable in many ways to provide expert search mechanisms in KMS to pinpoint experts in the organizations with searched expertise so that users can contact them for help. In this paper, we develop a framework to automate expert classification using a text categorization technique called Vector Space Model, through which an expert database composed of all the compiled profile information is built. This approach minimizes the maintenance cost of manual expert profiling while eliminating the possibility of incorrectness and obsolescence resulted from subjective manual processing. Also, we define the structure of expertise so that we can implement the expert classification framework to build an expert database in KMS. The developed prototype system, "Knowledge Portal for Researchers in Science and Technology," is introduced to show the applicability of the proposed framework.