• Title/Summary/Keyword: maximum entropy

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Analysis of an Inverse Heat Conduction Problem Using Maximum Entropy Method (최대엔트로피법을 이용한 역열전도문제의 해석)

  • Kim, Sun-Kyoung;Lee, Woo-Il
    • Proceedings of the KSME Conference
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    • 2000.04b
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    • pp.144-147
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    • 2000
  • A numerical method for the solution of one-dimensional inverse heat conduction problem is established and its performance is demonstrated with computational results. The present work introduces the maximum entropy method in order to build a robust formulation of the inverse problem. The maximum entropy method finds the solution that maximizes the entropy functional under given temperature measurement. The philosophy of the method is to seek the most likely inverse solution. The maximum entropy method converts the inverse problem to a non-linear constrained optimization problem of which constraint is the statistical consistency between the measured temperature and the estimated temperature. The successive quadratic programming facilitates the maximum entropy estimation. The gradient required fur the optimization procedure is provided by solving the adjoint problem. The characteristic feature of the maximum entropy method is discussed with the illustrated results. The presented results show considerable resolution enhancement and bias reduction in comparison with the conventional methods.

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Decreased entropy of unfolding increases the temperature of maximum stability: Thermodynamic stability of a thioredoxin from the hyperthermophilic archaeon Methanococcus jannaschii

  • Lee, Duck-Yeon;Kim, Kyeong-Ae;Kim, Key-Sun
    • Journal of the Korean Magnetic Resonance Society
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    • v.8 no.1
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    • pp.1-18
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    • 2004
  • A thioredoxin from hyperthermophile, Methanococcus jannashii (MjTRX) was characterized by use of the differential scanning calorimetry to understand the mechanisms of thermodynamic stability. MjTRX has an unfolding transition temperature of 116.5$^{\circ}C$, although the maximum free energy of the unfolding (9.9 Kcal/mol) is similar to that of E. coli thioredoxin (ETRX, 9.0 Kcal/mol). However, the temperature of maximum stability is higher than ETRX by 20$^{\circ}C$, indicating that the unfolding transition temperature increased by shifting the temperature of maximum stability. MjTRX has lower enthalpy and entropy of the unfolding compared to ETRX maintaining a similar free energy of the unfolding. From the structure and the thermodynamic parameters of MjTRX, we showed that the unfolding transition temperature of MjTRX is increased due to the decreased entropy of the unfolding. Decreasing the unfolded state entropy and increasing the folded state entropy can decrease the entropy of the unfolding. In the case of MjTRX, the increased number of proline residues decreased the unfolded state entropy and the increased enthalpy in the folded state increased the folded state entropy.

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Bayesian and maximum likelihood estimation of entropy of the inverse Weibull distribution under generalized type I progressive hybrid censoring

  • Lee, Kyeongjun
    • Communications for Statistical Applications and Methods
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    • v.27 no.4
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    • pp.469-486
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    • 2020
  • Entropy is an important term in statistical mechanics that was originally defined in the second law of thermodynamics. In this paper, we consider the maximum likelihood estimation (MLE), maximum product spacings estimation (MPSE) and Bayesian estimation of the entropy of an inverse Weibull distribution (InW) under a generalized type I progressive hybrid censoring scheme (GePH). The MLE and MPSE of the entropy cannot be obtained in closed form; therefore, we propose using the Newton-Raphson algorithm to solve it. Further, the Bayesian estimators for the entropy of InW based on squared error loss function (SqL), precautionary loss function (PrL), general entropy loss function (GeL) and linex loss function (LiL) are derived. In addition, we derive the Lindley's approximate method (LiA) of the Bayesian estimates. Monte Carlo simulations are conducted to compare the results among MLE, MPSE, and Bayesian estimators. A real data set based on the GePH is also analyzed for illustrative purposes.

A Study on the Entropy of Binary First Order Markov Information Source (이진 일차 Markov 정보원의 엔트로피에 관한 연구)

  • 송익호;안수길
    • Journal of the Korean Institute of Telematics and Electronics
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    • v.20 no.2
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    • pp.16-22
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    • 1983
  • In this paper, we obtained PFME(probability for maximum entropy) and entropy when a conditional probability was given in a binary list order Markov Information Source. And, when steady state probability was constant, the influence of change of a conditional probability on entropy was examined, too.

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Influence of Correlation Functions on Maximum Entropy Experimental Design (최대엔트로피 실험계획에서 상관함수의 영향)

  • Lee Tae-Hee;Kim Seung-Won;Jung Jae-Jun
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.30 no.7 s.250
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    • pp.787-793
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    • 2006
  • Recently kriging model has been widely used in the DACE (Design and Analysis of Computer Experiment) because of prominent predictability of nonlinear response. Since DACE has no random or measurement errors contrast to physical experiment, space filling experimental design that distributes uniformly design points over whole design space should be employed as a sampling method. In this paper, we examine the maximum entropy experimental design that reveals the space filling strategy in which defines the maximum entropy based on Gaussian or exponential. The influence of these two correlation functions on space filling design and their model parameters are investigated. Based on the exploration of numerous numerical tests, enhanced maximum entropy design based on exponential correlation function is suggested.

(Resolving Prepositional Phrase Attachment and POS Tagging Ambiguities using a Maximum Entropy Boosting Model) (최대 엔트로피 부스팅 모델을 이용한 영어 전치사구 접속과 품사 결정 모호성 해소)

  • 박성배
    • Journal of KIISE:Software and Applications
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    • v.30 no.5_6
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    • pp.570-578
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    • 2003
  • Maximum entropy models are promising candidates for natural language modeling. However, there are two major hurdles in applying maximum entropy models to real-life language problems, such as prepositional phrase attachment: feature selection and high computational complexity. In this paper, we propose a maximum entropy boosting model to overcome these limitations and the problem of imbalanced data in natural language resources, and apply it to prepositional phrase (PP) attachment and part-of-speech (POS) tagging. According to the experimental results on Wall Street Journal corpus, the model shows 84.3% of accuracy for PP attachment and 96.78% of accuracy for POS tagging that are close to the state-of-the-art performance of these tasks only with small efforts of modeling.

Minimum Variance Unbiased Estimation for the Maximum Entropy of the Transformed Inverse Gaussian Random Variable by Y=X-1/2

  • Choi, Byung-Jin
    • Communications for Statistical Applications and Methods
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    • v.13 no.3
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    • pp.657-667
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    • 2006
  • The concept of entropy, introduced in communication theory by Shannon (1948) as a measure of uncertainty, is of prime interest in information-theoretic statistics. This paper considers the minimum variance unbiased estimation for the maximum entropy of the transformed inverse Gaussian random variable by $Y=X^{-1/2}$. The properties of the derived UMVU estimator is investigated.

Syntax Analysis of Enumeration type and Parallel Type Using Maximum Entropy Model (Maximum Entropy 모델을 이용한 나열 및 병렬형 인식)

  • Lim, Soo-Jong;Lee, Chang-Ki;Hur, Jeong;Jang, Myoung-Gil
    • 한국HCI학회:학술대회논문집
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    • 2006.02a
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    • pp.1240-1245
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    • 2006
  • 한국어 문장을 구조 분석할 때에 모호성을 발생시키는 유형 중의 하나가 나열 및 병렬형이다. 문장 구조 복잡도를 증가시키는 나열 및 병렬형을 구조 분석 전에 미리 하나의 단위로 묶어서 처리하는 것이 문장 구조 분석의 정확도를 높이는데 중요하다. 본 연구에서는 형태소 태그를 이용한 기본 규칙으로 문장을 청크 단위로 분할하고 분할된 청크 중에서 나열형을 인식하여 해당되는 청크들을 하나의 나열 청크로 통합하여 청크의 개수를 줄인다. 병렬형에 대해서는 반복되는 병렬 청크의 범위와 생략된 용언을 복원한다. 이러한 인식은 첫 단계로 기호(symbol)를 중심으로 구축된 간단한 규칙으로 인식을 하고 이러한 규칙에 해당되지 않는 형태의 나열 및 병렬형은 Maximum Entropy 모델을 이용하여 적용한다. ME모델은 어휘자질, 형태소 품사 자질, 거리 자질, 의미자질, 구 단위 태그 자질(NP:명사구, VP:동사구, AP:형용사구), BIO 태그(Begin, Inside, Outside) 자질에 대한 ME(Maximum Entropy) 모델을 이용하여 구축되었다.

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