• The Korean Journal of Applied Statistics
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• v.19 no.3
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• pp.521-534
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• 2006

This paper considers a panel regression model with ill-posed data and proposes the generalized maximum entropy(GME) estimator of the unknown parameters. These are natural extensions from the biometries, statistics and econometrics literature. The performance of this estimator is investigated by using of Monte Carlo experiments. The results indicate that the GME method performs the best in estimating the unknown parameters.

• The Korean Journal of Applied Statistics
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• v.3 no.2
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• pp.91-105
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• 1990

Several practical prior distributions are derived using the maximum entropy principle. Also, an interactive method for estimating a prior distribution which uses the minimum cross-entropy principle is proposed when there are many prior informations. The consistency of the prior distributions obtained by the entropy principles is discussed.

• Communications for Statistical Applications and Methods
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• v.16 no.2
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• pp.265-275
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• 2009

This paper considers a linear regression model with a spatial autoregressive disturbance with ill-posed data and proposes the generalized maximum entropy(GME) estimator of regression coefficients. The performance of this estimator is investigated via Monte Carlo experiments. The results show that the GME estimator provides efficient and robust estimate for the unknown parameter.

• The Korean Journal of Applied Statistics
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• v.32 no.5
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• pp.763-782
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• 2019

This paper proposes a probabilistic document ranking model incorporating term dependencies. Document ranking is a fundamental information retrieval task. The task is to sort documents in a collection according to the relevance to the user query (Qin et al., Information Retrieval Journal, 13, 346-374, 2010). A probabilistic model is a model for computing the conditional probability of the relevance of each document given query. Most of the widely used models assume the term independence because it is challenging to compute the joint probabilities of multiple terms. Words in natural language texts are obviously highly correlated. In this paper, we assume a multinomial distribution model to calculate the relevance probability of a document by considering the dependency structure of words, and propose an information retrieval model to rank a document by estimating the probability with the maximum entropy method. The results of the ranking simulation experiment in various multinomial situations show better retrieval results than a model that assumes the independence of words. The results of document ranking experiments using real-world datasets LETOR OHSUMED also show better retrieval results.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.148-148
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• 2011

Riemann 문제는 천수방정식과 같은 쌍곡선형 방정식과 단일한 도약에 의해 불연속인 어떤 점의 좌 우에서 상수인 자료로 구성되는 초기치 문제로서 그 해법은 Godunov 방법과 같이 정확해에 의하면 정확 Riemann 해법, 근사 기법에 의하면 근사 Riemann 해법으로 불린다. 지금까지 이용되는 근사 Riemann 해법으로는 1981년에 P. L. Roe가 제안한 Roe의 선형화 기법과 1983년에 A. Harten, P. D. Lax, 그리고 B. van Leer가 제안한 HLL 기법의 수정 기법들이다. 최대 및 최소 파속만 고려하는 것으로 알려진 HLL 기법은 1988년에 B. Einfeldt의 제안에 의해 두 파속의 결정에서 Roe의 선형화 기법에 따른 고유치와 비교하는 것으로 수정되었다(HLLE 기법). 또한, 1994년에 E. F. Toro 등은 접촉파를 고려하기 위해 선형화된 지배방정식의 정확해로부터 중앙 파속을 고려하는 기법을 제안하였고, 이를 HLLC 기법으로 불렀다. 2002년에 T. Linde는 중앙 파속을 평가하기 위해 일반화된(수학적) 엔트로피 함수를 도입하였으며, van Leer는 이를 HLLL 기법으로 불렀다. 이 기법에서는 접촉파의 평가를 위해 보존변수에 대한 일반화된 엔트로피 함수로부터 중앙 파속이 유도되며, 이것과 특성 속도의 비교를 통해 최대 및 최소 파속이 결정된다. 따라서 이 기법에서는 모든 파속이 초기치로부터 결정되므로 HLLE 기법과 달리 Roe의 선형화 기법과 완전히 결별되고 HLLC 기법과 달리 정확해에 의존되지 않는 점에서 HLLL 기법은 모태인 HLL 기법의 온전한 계승으로 볼 수 있다. HLLL 기법은 여러 분야에 적용된 바 있으나, 수공학 분야에 적용된 사례는 알려진 바 없다. 이는 천수방정식에 대한 (물리적) 엔트로피 함수가 명확하지 않기 때문인 것으로 보인다. 이 연구에서는 보존변수로부터 정의되는 총 에너지를 일반화된 엔트로피 함수로 간주하여 모형을 구성하고, 정확해가 알려진 1차원 문제에 대해 적용성을 검토하였다. 정확해가 알려진 경우에 대해 모의한 결과, 1차 정도 수치해의 한계에도 불구하고, HLLL 기법의 결과는 대체로 정확해와 잘 일치하였으며 그 외의 HLL-형 기법의 그것에 비해 우수한 것으로 나타났다. 특히, 물이 빠져 바닥이 드러나는 상태에 대한 접촉 파속의 추정에서 Riemann 불변량을 이용하는 HLLC 기법에 비해 물이 빠지는 전선을 더 정확하게 포착하는 HLLL 기법의 결과는 매우 고무적이었다.

• Journal of KIISE:Databases
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• v.28 no.3
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• pp.324-333
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• 2001

In this paper we present a novel color image segmentation algorithm based on a Gaussian Mixture Model(GMM). It is introduced a Deterministic Annealing Expectation Maximization(DAEM) algorithm which is developed using the principle of maximum entropy to overcome the local maxima problem associated with the standard EM algorithm. In our approach, the GMM is used to represent the multi-colored objects statistically and its parameters are estimated by DAEM algorithm. We also develop the automatic determination method of the number of components in Gaussian mixtures models. The segmentation of image is based on the maximum posterior probability distribution which is calculated by using the GMM. The experimental results show that the proposed DAEM can estimate the parameters more accurately than the standard EM and the determination method of the number of mixture models is very efficient. When tested on two natural images, the proposed algorithm performs much better than the traditional algorithm in segmenting the image fields.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.5
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• pp.531-540
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• 2010

In this paper, we present a two-phase shallow semantic parsing method based on a maximum entropy model. The first phase is to recognize semantic arguments, i.e., argument identification. The second phase is to assign appropriate semantic roles to the recognized arguments, i.e., argument classification. Here, the performance of the first phase is crucial for the success of the entire system, because the second phase is performed on the regions recognized at the identification stage. In order to improve performances of the argument identification, we incorporate syntactic knowledge into its pre-processing step. More precisely, boundaries of the immediate clause and the upper clauses of a predicate obtained from clause identification are utilized for reducing the search space. Further, the distance on parse trees from the parent node of a predicate to the parent node of a parse constituent is exploited. Experimental results show that incorporation of syntactic knowledge and the separation of argument identification from the entire procedure enhance performances of the shallow semantic parsing system.

• Journal of the Korean Society of Hazard Mitigation
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• v.11 no.3
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• pp.229-235
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• 2011

In order to manage a sewer system effectively, flow conditions such as flux, water quality, Infiltration and Inflow (I/I), Combined Sewer Overflows (CSOs), etc need to be monitored on a regular base. Therefore, in sewer networks, a monitoring is so important to prevent the river disaster. Monitoring all nodes of an entire sewer system is not necessary and cost-prohibitive. Water quality monitoring points that can represent a sewer system should be selected in a economical manner. There is no a standard for the selection of monitoring points and the quantitative analysis of the observed data has not been applied in sewer system. In this study, the entropy method was applied for a sewer network to evaluate and determine the optimal water quality monitoring points using genetic algorithm. The entropy method allows to analyze the observed data for the pattern and magnitude of temporal water quality change. Since water quality measurement usually accompanies with flow measurement, a set of installation locations of flowmeters was chosen as decision variables in this study.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.257-262
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• 2009

This paper studies an automatic expansion of dialogue examples using the logs of an example-based dialogue system. Conventional approaches to example-based dialogue system manually construct dialogue examples between humans and a Chatbot, which are labor intensive and time consuming. The proposed method automatically classifies natural utterance pairs and adds them into dialogue example database. Experimental results show that lexical, POS and modality features are useful for classifying natural utterance pairs, and prove that the dialogue examples can be automatically expanded using the logs of a dialogue system.

• Journal of Environmental Impact Assessment
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• v.27 no.2
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• pp.203-214
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• 2018

The purpose of this study is to estimate the concentrated distribution area of nutria (Myocastor coypus) and potential suitable habitat and to provide useful data for the effective management direction setting. Based on the nationwide distribution data of nutria, the cross-validation value was applied to analyze the distribution density. As a result, the concentrated distribution areas thatrequired preferential elimination is found in 14 administrative areas including Busan Metropolitan City, Daegu Metropolitan City, 11 cities and counties in Gyeongsangnam-do and 1 county in Gyeongsangbuk-do. In the potential suitable habitat estimation using a MaxEnt (Maximum Entropy) model, the possibility of emergency was found in the Nakdong River middle and lower stream area and the Seomjin riverlower stream area and Gahwacheon River area. As for the contribution by variables of a model, it showed DEM, precipitation of driest month, min temperature of coldest month and distance from river had contribution from the highest order. In terms of the relation with the probability of appearance, the probability of emergence was higher than the threshold value in areas with less than 34m of altitude, with $-5.7^{\circ}C{\sim}-0.6^{\circ}C$ of min temperature of the coldest month, with 15-30mm of precipitation of the driest month and with less than 1,373m away from the river. Variables that Altitude, existence of water and wintertemperature affected settlement and expansion of nutria, considering the research results and the physiological and ecological characteristics of nutria. Therefore, it is necessary to reflect them as important variables in the future habitable area detection and expansion estimation modeling. It must be essential to distinguish the concentrated distribution area and the management area of invasive alien species such as nutria and to establish and apply a suitable management strategy to the management site for the permanent control. The results in this study can be used as useful data for a strategic management such as rapid management on the preferential management area and preemptive and preventive management on the possible spreading area.