• Title/Summary/Keyword: 확률 모델

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POS-Tagging Model Combining Rules and Word Probability (규칙과 어절 확률을 이용한 혼합 품사 태깅 모델)

  • Hwang, Myeong-Jin;Kang, Mi-Young;Kwon, Hyuk-Chul
    • Proceedings of the Korean Information Science Society Conference
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    • 2006.10b
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    • pp.11-15
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    • 2006
  • 본 논문은, 긍정적 가중치와 부정적 가중치를 통해 표현되는 규칙에 기반을 둔 품사 태깅 모델과, 형태 소 unigram 정보와 어절 내의 카테고리 패턴에 기반하여 어절 확률을 추정하는 품사 태깅 모델의 장점을 취하고 단점을 보완할 수 있는 혼합 품사 태깅 모델을 제안한다. 이 혼합 모델은 먼저, 규칙에 기반한 품사 태깅을 적용한 후, 규칙이 해결하지 못한 결과에 대해서 통계적인 기법을 사용하여 품사 태깅을 한다. 본 연구는 어절 내 카테고리 패턴정보에 따른 파라미터 set과 형태소 unigram만을 이용해 어절 확률을 계산해 내므로 다른 통계기반 접근방법에서와는 달리 작은 크기의 통계사전만을 필요로 하며, 카테고리 패턴 정보를 사용함으로써 통계기반 접근 방법의 가장 큰 문제점인 data sparseness 문제 또한 줄일 수 있다는 이점이 있다. 특히, 본 논문에서 사용할 통계 모델은 어절 확률에 기반을 두고 있기 때문에 한국어의 특성을 잘 반영할 수 있다. 본 논문에서 제안한 혼합 모델은 규칙이 적용된 후에도 후보열이 둘 이상 남아 오류로 반환되었던 어절 중 24%를 개선한다.

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Stochastic Pronunciation Lexicon Modeling for Large Vocabulary Continous Speech Recognition (확률 발음사전을 이용한 대어휘 연속음성인식)

  • Yun, Seong-Jin;Choi, Hwan-Jin;Oh, Yung-Hwan
    • The Journal of the Acoustical Society of Korea
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    • v.16 no.2
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    • pp.49-57
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    • 1997
  • In this paper, we propose the stochastic pronunciation lexicon model for large vocabulary continuous speech recognition system. We can regard stochastic lexicon as HMM. This HMM is a stochastic finite state automata consisting of a Markov chain of subword states and each subword state in the baseform has a probability distribution of subword units. In this method, an acoustic representation of a word can be derived automatically from sample sentence utterances and subword unit models. Additionally, the stochastic lexicon is further optimized to the subword model and recognizer. From the experimental result on 3000 word continuous speech recognition, the proposed method reduces word error rate by 23.6% and sentence error rate by 10% compare to methods based on standard phonetic representations of words.

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Evaluation of life Expectancy of Power System Equipment Using Probability Distribution (확률분포를 이용한 전력설비의 기대여명 추정)

  • Kim, Gwang-Won;Hyun, Seung-Ho
    • Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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    • v.22 no.10
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    • pp.49-55
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    • 2008
  • This paper presents a novel evaluation method of life expectancy of power system equipment. The life expectancy means expected remaining lifetime; it can be usefully utilized to maintenance planning, equipment replacement planning, and reliability assessment. The proposed method is composed of three steps. Firstly, a cumulative probability for future years is evaluated for targeted age year. Secondly, the cumulative probability is modeled by well-blown cumulative distribution function(CDF) such as Weibull distribution. Lastly, life expectancy is evaluated as the mean value of the model. Since the model CDF is established in the proposed method, it can also evaluate the probability of equipment retirement within specific years. The developed method is applied to examples of generators of combined cycle power plants to show its effectiveness.

Performance Comparison between Neural Network Model and Statistical Model for Prediction of Damage Cost from Storm and Flood (신경망 모델과 확률 모델의 풍수해 예측성능 비교)

  • Choi, Seon-Hwa
    • The KIPS Transactions:PartB
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    • v.18B no.5
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    • pp.271-278
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    • 2011
  • Storm and flood such as torrential rains and major typhoons has often caused damages on a large scale in Korea and damages from storm and flood have been increasing by climate change and warming. Therefore, it is an essential work to maneuver preemptively against risks and damages from storm and flood by predicting the possibility and scale of the disaster. Generally the research on numerical model based on statistical methods, the KDF model of TCDIS developed by NIDP, for analyzing and predicting disaster risks and damages has been mainstreamed. In this paper, we introduced the model for prediction of damage cost from storm and flood by the neural network algorithm which outstandingly implements the pattern recognition. Also, we compared the performance of the neural network model with that of KDF model of TCDIS. We come to the conclusion that the robustness and accuracy of prediction of damage cost on TCDIS will increase by adapting the neural network model rather than the KDF model.

Efficient Continuous Vocabulary Clustering Modeling for Tying Model Recognition Performance Improvement (공유모델 인식 성능 향상을 위한 효율적인 연속 어휘 군집화 모델링)

  • Ahn, Chan-Shik;Oh, Sang-Yeob
    • Journal of the Korea Society of Computer and Information
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    • v.15 no.1
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    • pp.177-183
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    • 2010
  • In continuous vocabulary recognition system by statistical method vocabulary recognition to be performed using probability distribution it also modeling using phoneme clustering for based sample probability parameter presume. When vocabulary search that low recognition rate problem happened in express vocabulary result from presumed probability parameter by not defined phoneme and insert phoneme and it has it's bad points of gaussian model the accuracy unsecure for one clustering modeling. To improve suggested probability distribution mixed gaussian model to optimized for based resemble Euclidean and Bhattacharyya distance measurement method mixed clustering modeling that system modeling for be searching phoneme probability model in clustered model. System performance as a result of represent vocabulary dependence recognition rate of 98.63%, vocabulary independence recognition rate of 97.91%.

Character spotting using image-based stochastic models (이미지 기반 확률모델을 이용한 문자검출)

  • 김선규;신봉기
    • Proceedings of the Korean Information Science Society Conference
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    • 2001.04b
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    • pp.484-486
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    • 2001
  • 본 논문에서는 의사 2차원 은닉 마르코프 모델의 구조로 생성한 마르코프 체인형 확률모형에 의한 인쇄체문자 이미지의 모델링에 대해 논한다. 이미지 데이터에서 바로 모델을 실시간 생성하며 문자 인식 및 검출에 응용할 수 있다. 실험에 의하면, 이 방법을 통해 특정 낱말이 포함된 문장에서 숫자를 인식, 한글을 검출할 수 있음을 확인하였다.

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Query Classification Based on Translation Probabilities of Similar Query Pair (유사한 질의쌍의 어휘 번역확률을 이용한 질의 분류)

  • Jin, Xueying;Jang, Kye-Hun;Lee, Kyung-Soon
    • Proceedings of the Korea Information Processing Society Conference
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    • 2010.04a
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    • pp.443-446
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    • 2010
  • 질의 분류에서 어휘의 다양한 표현으로 인한 어휘 불일치문제는 성능저하의 주요 원인이다. 본 논문에서는 야후!앤써 질의응답 아카이브를 이용해서 같은 카테고리의 질의-질의쌍들에 대해 어휘-어휘 번역확률을 계산하는 방법을 제안한다. 정보검색에서 우수한 성능을 보인 어휘 사이의 번역확률을 반영하는 번역기반 언어모델이 질의 분류에서 유효함을 확인하였고 언어모델과의 비교실험을 통해 성능향상을 보였다. 어휘관계를 측정하는 방법에서 번역확률 계산방법에 따른 성능측정에서 전체 질의-대답쌍들에 대해 번역확률을 계산하는 것보다 같은 카테고리에 속하는 질의-질의쌍들에 대해 번역확률을 계산하는 것이 분류를 위해 더 좋은 번역확률임을 확인하였다.

A Study on Generating Meta-Model to Calculate Weapon Effectiveness Index for a Direct Fire Weapon System (직사화기 무기체계의 무기효과지수 계산을 위한 메타모델 생성방법 연구)

  • Rhie, Ye Lim;Lee, Sangjin;Oh, Hyun-Shik
    • Journal of the Korea Society for Simulation
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    • v.30 no.2
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    • pp.23-31
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    • 2021
  • Defense M&S(Modeling & Simulation) requires weapon effectiveness index which indicates Ph(Probability of hit) and Pk(Probability of kill) values on various impact and environmental conditions. The index is usually produced by JMEM(Joint Munition Effectiveness Manual) development process, which calculates Pk based on the impact condition and circular error probable. This approach requires experts to manually adjust the index to consider the environmental factors such as terrain, atmosphere, and obstacles. To reduce expert's involvement, this paper proposes a meta-model based method to produce weapon effectiveness index. The method considers the effects of environmental factors during calculating a munition's trajectory by utilizing high-resolution weapon system models. Based on the result of Monte-Carlo simulation, logistic regression model and Gaussian Process Regression(GPR) model is respectively developed to predict Ph and Pk values of unobserved conditions. The suggested method will help M&S users to produce weapon effectiveness index more efficiently.

Durability Analysis and Development of Probability-Based Carbonation Prediction Model in Concrete Structure (콘크리트 구조물의 확률론적 탄산화 예측 모델 개발 및 내구성 해석)

  • Jung, Hyunjun
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.30 no.4A
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    • pp.343-352
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    • 2010
  • Recently, many researchers have been carried out to estimate more controlled service life and long-term performance of carbonated concrete structures. Durability analysis and design based on probability have been induced to new concrete structures for design. This paper provides a carbonation prediction model based on the Fick's 1st law of diffusion using statistic data of carbonated concrete structures and the probabilistic analysis of the durability performance has been carried out by using a Bayes' theorem. The influence of concerned design parameters such as $CO_2$ diffusion coefficient, atmospheric $CO_2$ concentration, absorption quantity of $CO_2$ and the degree of hydration was investigated. Using a monitoring data, this model which was based on probabilistic approach was predicted a carbonation depth and a remaining service life at a variety of environmental concrete structures. Form the result, the application method using a realistic carbonation prediction model can be to estimate erosion-open-time, controlled durability and to determine a making decision for suitable repair and maintenance of carbonated concrete structures.

Decision Making Support Model for Optimal Location of Anti-Tactical Ballistic missile (탄도미사일 방어무기의 최적배치를 위한 의사결정지원모델)

  • Yun Yong-Bok;Kim Gi-Beom;Jeong Bong-Ju
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • 2006.05a
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    • pp.1715-1721
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    • 2006
  • 기존의 탄도미사일 방어무기의 배치와 관련된 대부분의 연구들은 배치규모가 결정된 상태에서 후보지 중 최적위치를 구하는 것이 일반적이며, 방어확률이 최대가 되는 것을 목적으로 하는 확률적 부분담당모델의 개념을 적용한다. 본 연구에서는 무기의 도입 및 배치를 담당하는 의사결정자들에게 보다 많은 상황과 변수를 가정할 수 있도록 하는 의사결정모델을 제안한다. 모델에는 기존에 고려하지 않았던 후보지의 수준 및 방호시설의 최소방어요구수준 등이 포함되어 있으며, 모델은 의사결정자들이 결정하는 결정변수에 따라 각기 다른 방어무기의 위치와 규모 및 방어확률을 제시 하게 된다. 모델의 결과로 제시되는 내용은 무기체계의 최초소요제기 단계에서 필요규모와 위치를 결정하고 또한, 그 결과 값이 최초계획단계의 대략적 무기배치규모와 상이할 경우는 그 값이 필요성과 타당성을 가질 수 있는 수치적 분석을 제공해 준다.

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