• Journal of Advanced Marine Engineering and Technology
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• v.38 no.9
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• pp.1146-1149
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• 2014

To discriminate polarities of stator windings for 3 phase induction motors terminal tags of which are not readable, it is possible to utilize the residual magnetic flux present at their rotors as well as to use the way based on external exciting current. The induced voltages are basically decided by parameters such as the quantity of residual flux, the rotator speed by hand force and the phase properties between stator windings. To adopt induced voltages by residual flux for polarity discrimination at sites, the measured voltages by multi-testers need to be readable in magnitude enough to discriminate winding condition with reasonable phase characteristics. This study focuses on the analysis of various connection cases in the expectation that the summing voltages induced by residual flux shall show zero in case of normal connections while the sum becomes greater indication if the connection is in wrong condition. The proposed method is applied to actual motors to disclose how effective it is for polarity discrimination at sites through comparison of output signals between normal and fault connections.

• Annual Conference on Human and Language Technology
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• 2013.10a
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• pp.113-115
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• 2013

다수의 상품평 요약은 인터넷 쇼핑몰 고객에게 편의를 제공할 수 있다. 본 논문에서는 상품평 요약 시스템의 성능 향상을 위한 방안을 제안한다. 시스템은 크게 상품평의 평가 항목 추출과 극성 사전 생성, 극성 판별 단계로 구성된다. 평가 항목 추출에서는 외부 연관도의 영향력을 줄이고, 극성 사전 생성에서는 단어 거리 평균을 적용한다. 제안한 방식을 사용하였을 때 평가 항목에 대한 문장의 극성 판별 시 90.8%의 정확율을 보였다.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.9
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• pp.1137-1140
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• 2014

Faulty electric motors onboard vessels with anomalies in windings or poor insulation are usually repaired at land based workshops and reinstalled in place by crew hands after receiving the repaired motors. Especially for 3 phase induction motors which need Y-${\delta}$ starters with 6 lead wires, it would happen that the polarities of stator windings cannot be well distinguished if the original tags of these wires are erased or not visible clearly, resulting in subsequent damage to the repaired motor due to extreme current flow when the power is given to the motor the stator windings of which are wrongly connected in the polarity. This study proposes an easy way to make correct connection in winding polarities without failures based on the electro-magnetically induced voltages on windings when a slight DC current is supplied to a winding coil by using an analog multi-tester. The proposed method is applied to actual motors and delves into the applicability for polarity discrimination through a few measurements onboard vessels.

• Annual Conference on Human and Language Technology
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• 2010.10a
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• pp.166-171
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• 2010

본 논문은 한국어 온라인 리뷰 문서의 오피니언 분류(Opinion Classification)에 있어 그 핵심 키워드가 형용사 (Adjective) 범주라는 점을 고려하여, 한국어 형용사를 <문맥에 의존하지 않는 절대 극성>과, <문맥에 의존하여 극성이 바뀌는 상대극성>으로 대분류한 뒤 그 각각의 의미 극성을 하위 분류하는 작업을 수행하였다. 기존의 연구에서 특징적인 오피니언 어휘 수십개에 의존하여 자동 분류를 시도하고자 하였던 문제점을 극복하기 위해서는 한국어 형용사 전체 범주에 대한 체계적인 극성 분류가 이루어져야 할 필요가 있으며, 여기서 특히 상세히 주목받지 못했던 상대 극성 어휘에 대한 본격적인 의미 분류가 요구된다. 본 연구에서 제시하는 형용사의 극성 분류는 기존의 이론 언어학적 형용사 의미 분류와 달리 온라인 오피니언 문서에서 도메인에 따라 나타나는 특징적 의미 유형을 결정하고, 이를 기준으로 온라인 오피니언 문서의 극성 판별에 효과적으로 적용할 수 있는 사전을 구축하였다는 점에서 의의를 가진다.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06c
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• pp.51-53
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• 2012

데이터 마이닝의 문서분류 기술에서 발전된 오피니언 마이닝은 이제 국외뿐만 아니라 국내의 학계 및 기업에서 중요한 관심분야로 자리잡아가고 있다. 오피니언 마이닝의 핵심은 문서에서 감정 단어를 추출하여 긍정/부정 여부를 얼마나 정확하게 자동적으로 판별하느냐를 평가하는 것이다. 국내에서도 이에 관련된 많은 연구가 이루어 졌으나 아직 실용적으로 적용할 만큼의 정확한 분류 정확도 보이지 않고 있다. 그 이유는 한국어의 경우 비문법적 표현, 감정단어의 다양성 등으로 인해 문서의 극성을 판별하기가 쉽지 않기 때문이다. 본 논문에서는 문법적 요소를 최대한 배제하고 단어 패턴의 빈도만을 고려한 영화평 분류기법을 제안한다. 제안된 방법에서는 문서를 단어들의 리스트로 추상화하여 패턴들의 빈도로 학습한 후 적절한 스코어 함수를 적용하여 문서의 극성을 판별한다. 또한 실험을 통해 제안된 기법의 정확도를 평가한다.

• The KIPS Transactions:PartB
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• v.17B no.1
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• pp.93-98
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• 2010

In this paper, we introduce a system that summarizes product evaluation through linguistic analysis to effectively utilize explosively increasing product reviews. Our system analyzes polarities of product reviews by product features, based on which customers evaluate each product like 'design' and 'material' for a skirt product category. The system shows to customers a graph as a review summary that represents percentages of positive and negative reviews. We build an opinion word dictionary for each product feature through context based automatic expansion with small seed words, and judge polarity of reviews by product features with the extracted dictionary. In experiment using product reviews from online shopping malls, our system shows average accuracy of 69.8% in extracting judgemental word dictionary and 81.8% in polarity resolution for each sentence.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.9
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• pp.1141-1145
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• 2014

When the stator windings of 3 phase induction motors are in wrong condition, the mutual inductive responses between windings can be utilized for the purpose of diagnosing motors in that fault windings affect even the responses by DC excitation. Three phase induction motors are supposed to generate consistent inductive voltages at the remaining windings when exciting DC current is given to one of 3 windings, while the inconsistence of their voltages indicates the existence of disorder at electric motors. This study describes how the exciting current to one of three windings cause the other windings to create induced voltages, analyzing responses by transfer functions, and discloses whether or not the balance relation at two windings is normal in the way of measuring the differential voltage of their outputs. For experiment, common analog multi-testers is used for applying exciting current and measuring the output signal to confirm whether the proposed method is useful enough to be able to discriminate wrong polarities of windings onboard vessels including also the case of exciting current by AC.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2012.06a
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• pp.127-127
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• 2012

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.5
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• pp.243-253
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• 2012

An opinion mining technique which was developed from document classification in area of data mining now becomes a common interest in domestic as well as international industries. The core of opinion mining is to decide precisely whether an opinion document is a positive or negative one. Although many related approaches have been previously proposed, a classification accuracy was not satisfiable enough to applying them in practical applications. A opinion documents written in Korean are not easy to determine a polarity automatically because they often include various and ungrammatical words in expressing subjective opinions. Proposed in this paper is a new approach of classification of opinion documents, which considers only a frequency of word patterns and excludes the grammatical factors as much as possible. In proposed method, we express a document into a bag of words and then apply a learning algorithm using a frequency of word patterns, and finally decide the polarity of the document using a score function. Additionally, we also present the experiment results for evaluating the accuracy of the proposed method.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.5
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• pp.563-569
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• 2012

The studies of opinion mining or sentiment analysis have been the focus with social web proliferation. Sentiment analysis requires sentiment resources to decide its polarity. In the existing sentiment analysis, they have been built resources designed with intensity of sentiment polarity and decided polarity of opinion using the ones. In this paper, I will present sentiment categories for not only polarity of opinion but also the basis of positive/negative opinion. I will define psychological emotions to primary sentiments for the reasonable classification. And I will extract the informations of sentiment from social web texts for the actual distribution of sentiments in social web. Re-classifying primary sentiments based on extracted sentiment information, I will organize sentiment categories for the social web. In this paper, I will present 23 categories of sentiment by using proposed method.