• Annual Conference on Human and Language Technology
• /
• 2012.10a
• /
• pp.107-112
• /
• 2012

본 논문은 일반 텍스트에 나타나는 경쟁 관계에 있는 고유명사들을 경쟁자로 자동 추출하는 방법에 대한 것으로, 규칙 기반 방법과 기계 학습 기반 방법을 모두 제안하고 비교하였다. 제안한 시스템은 뉴스 기사를 대상으로 하였고, 문장에 경쟁관계를 나타내는 명확한 정보가 있는 경우에만 추출하는 것을 목표로 하였다. 규칙기반 경쟁어 추출 시스템은 2개의 고유명사가 경쟁관계임을 나타내는 단서단어에 기반해서 경쟁어를 추출하는 시스템이며, 경쟁표현 단서단어는 620개가 수집되어 사용됐다. 기계학습 기반 경쟁어 추출시스템은 경쟁어 추출을 경쟁어 후보에 대한 경쟁여부의 바이너리 분류 문제로 접근하였다. 분류 알고리즘은 Support Vector Machines을 사용하였고, 경쟁어 주변 문맥 정보를 대표할 수 있는 언어 독립적 5개 자질에 기반해서 모델을 학습하였다. 성능평가를 위해서 이슈화되고 있는 핫키워드 54개에 대해서 623개의 경쟁어를 뉴스 기사로부터 수집해서 평가셋을 구축하였다. 비교 평가를 위해서 기준시스템으로 연관어에 기반해서 경쟁어를 추출하는 시스템을 구현하였고, Recall/Precision/F1 성능으로 0.119/0.214/0.153을 얻었다. 제안 시스템의 실험 결과로 규칙기반 시스템은 0.793/0.207/0.328 성능을 보였고, 기계 학습기반 시스템은 0.578/0.730/0.645 성능을 보였다. Recall 성능은 규칙기반 시스템이 0.793으로 가장 좋았고, 기준시스템에 비해서 67.4%의 성능 향상이 있었다. Precision과 F1 성능은 기계학습기반 시스템이 0.730과 0.645로 가장 좋았고, 기준시스템에 비해서 각각 61.6%, 49.2%의 성능향상이 있었다. 기준시스템에 비해서 제안한 시스템이 Recall, Precision, F1 성능이 모두 대폭적으로 향상되었으므로 제안한 방법이 효과적임을 알 수 있다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.11 no.3
• /
• pp.283-289
• /
• 2018

A PLL with an unipolar charge pump and a loop filter consisting of sample-hold capacitor and Fvco-sampled feedforward loop filter. The proposed PLL not only reduces the chip area by replacing the resistance to a switch and a small capacitor but also reduces the variation of ${\Delta}VLPF$ and ${\Delta}{\Delta}VLPF$ to 1/6 and 1/5 respectively. The variation of ${\Delta}VLPF$ is related to the phase noise of VCO output and that of ${\Delta}{\Delta}VLPF$ is proportional to reference spurs. It has been simulated and verified with a 1.8V $0.18{\mu}m$ CMOS process and shown a good phase noise characteristics. We plan to fabricate chip based on the simulations and check performance.

• Journal of the Korea Society of Computer and Information
• /
• v.17 no.9
• /
• pp.17-27
• /
• 2012

Open Market is one of the key factors to accelerate the profit. Usually retailers sell items in several Open Market. One of the challenges for retailers is to assign categories of items with different classification systems. In this research, we propose an item category recommendation method to support appropriate products category registration. Our recommendations are based on semantic relation between existing and any other Open Market categorization. In order to analyze correlations of categories, we use Morpheme analysis, Korean Wiki Dictionary, WordNet and Google Translation API. Our proposed method recommends a category, which is most similar to a guide word by measuring semantic similarity. The experimental results show that, our system improves the system accuracy in term of search category, and retailers can easily select the appropriate categories from our proposed method.

• Journal of Convergence for Information Technology
• /
• v.7 no.6
• /
• pp.53-59
• /
• 2017

This study was attempted to identify the domestic research related to depression and stress. The subjects of the analysis were 1,875 college degree theses thrown in the National Assembly Library searched by the depression and stress keyword as of November 30, 2016. The analysis method visualizes atypical data with Word Cloud, which is one of the text mining techniques. We also used the R'LDA package and LDA to classify treatment and subjects. As a result of the analysis, 233(12.4%) of the total papers with therapeutic keywords were found. Application of treatment methods was art therapy, music therapy, horticultural therapy, cognitive behavior therapy, clinical art therapy, cognitive therapy, psychological therapy, depression treatment, group therapy, laughter treatment sequence. The study subjects were adolescents, elderly, patient, mother, child, female, parents, and college students in order. The results of LDA topic analysis for adolescents were classified into four topics: self-support, treatment program, relationship effect, and variable study.

• Journal of the Korean Geosynthetics Society
• /
• v.22 no.2
• /
• pp.47-54
• /
• 2023

In this paper, we propose the technique of optimal search keyword extraction and retrieval for news article classification. The proposed technique was verified as an example of identifying trends related to North Korean construction. A representative Korean media platform, BigKinds, was used to select sample articles and extract keywords. The extracted keywords were vectorized using word embedding and based on this, the similarity between the extracted keywords was examined through cosine similarity. In addition, words with a similarity of 0.5 or higher were clustered based on the top 10 frequencies. Each cluster was formed as 'OR' between keywords inside the cluster and 'AND' between clusters according to the search form of the BigKinds. As a result of the in-depth analysis, it was confirmed that meaningful articles appropriate for the original purpose were extracted. This paper is significant in that it is possible to classify news articles suitable for the user's specific purpose without modifying the existing classification system and search form.

• The Journal of the Korea Contents Association
• /
• v.22 no.5
• /
• pp.1-15
• /
• 2022

In this study, text analysis was performed on the mealkit product review data to identify factors affecting the evaluation of the mealkit product. The data used for the analysis were collected by scraping 334,498 reviews of mealkit products in Naver shopping site. After preprocessing the text data, wordclouds and sentiment analyses based on word frequency and normalized TF-IDF were performed. Logistic regression model was applied to predict the polarity of reviews on mealkit products. From the logistic regression models derived for each product category, the main factors that caused positive and negative emotions were identified. As a result, it was verified that text analysis can be a useful tool that provides a basis for maximizing positive factors for a specific category, menu, and material and removing negative risk factors when developing a mealkit product.

• Proceedings of the Korea Society for Simulation Conference
• /
• 1999.10a
• /
• pp.218-223
• /
• 1999

현재의 발전소 보일러 제어방식은 프로세스 성격에 따라 제어루프의 상태변수들을 근간으로 세부적인 독립된 제어루프별로 제어를 시행하는 개별 루프를 채택하고 있다. 발전소에서 사용하는 PID제어 방식은 각 부시스템들의 상태변수, 출력변수가 다른 부시스템의 선행신호가 되거나 기준신호가 되어 피드백제오, 피드포워드 제어와 캐스케이드 제어 등의 신호로 작용한다. 선행신호와 상태 궤환 신호는 다른 부시스템의 상태변수를 측정함에 의하여 결정되며, 상호연결 함수는 경험에 의한 함수 설정에 의해 결정된다. 본 논문에서는 먼저 화력발전소 보일러 제어시스템을 플랜트 변수들 사이의 인과관계를 표현하는 신호흐름도의 보일러 모델과 실제 발전소에 적용되고 있는 드럼 수위 제어기에 대해 기술한다. 그리고 현장에서 취득한 운전데이터를 이용한 보일러 드럼수위 제어계통의 모델링에 관한 내용에 대해 기술하고, 마지막으로 컴퓨터 시뮬레이션에 의한 제어루프의 모의실험 결과를 통해 제어기의 설계와 제어루프의 효용성을 보이고자 한다.

• Annual Conference on Human and Language Technology
• /
• 2015.10a
• /
• pp.15-17
• /
• 2015

본 논문은 낚시성 기사 제목과 비낚시성 기사 제목을 판별하기 위한 시스템을 제시한다. 서포트 벡터 머신(SVM)을 이용하여 기사 제목을 분류하며, 분류하는 기준은 딥러닝 기법중의 하나인 워드임베딩(Word Embedding), 군집화 알고리즘 중 하나인 K 평균 알고리즘(K-means)을 이용한다. 자질로서 기사 제목의 단어를 사용하였으며, 정확도가 83.78%이다. 결론적으로 낚시성 기사 제목에는 낚시를 유도하는 특별한 단어들이 존재함을 알 수 있다.

• Journal of fish pathology
• /
• v.22 no.3
• /
• pp.275-282
• /
• 2009

In this study, effect of mating group, mating dam and mating sire was analyzed using olive flounder Paralichthys olivaceus challenged with Edwardsiella tarda. A challenge test was conducted using 1,708 olive flounder fingerlings obtained through $4{\times}4$ factorial cross from parents selected based on resistant degree. Regarding the means of resistant index, survival rate and days of survival by each mating group, the group(RR) produced by mating among resistant parents were 17.98, 13.77%, and 18.36 days, respectively; the group(SS) produced by mating among susceptible parents were 12.46, 2.71%, and 12.40 days, respectively. This study gives an indication that challenge test results may be successfully used as selection criteria for disease resistance to E. tarda in olive flounder, P. olivaceus.

• The KIPS Transactions:PartA
• /
• v.12A no.6 s.96
• /
• pp.539-546
• /
• 2005

Recently, as the number of Internet users are growing explosively, e-learning has been applied spread, as well as remote evaluation of intellectual capacity However, only the multiple choice and/or the objective tests have been applied to the e-learning, because of difficulty of natural language processing. For the intelligent marking of short-essay typed answer papers with rapidness and fairness, this work utilize heterogenous linguistic knowledges. Firstly, we construct the semantic kernel from un tagged corpus. Then the answer papers of students and instructors are transformed into the vector form. Finally, we evaluate the similarity between the papers by using the semantic kernel and decide whether the answer paper is correct or not, based on the similarity values. For the construction of the semantic kernel, we used latent semantic analysis based on the vector space model. Further we try to reduce the problem of information shortage, by integrating Korean Word Net. For the construction of the semantic kernel we collected 38,727 newspaper articles and extracted 75,175 indexed terms. In the experiment, about 0.894 correlation coefficient value, between the marking results from this system and the human instructors, was acquired.