• Title/Summary/Keyword: Entropy model

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Design of Experiment for kriging (크리깅의 실험계획법)

  • Jung, Jae-Joon;Lee, Chang-Seob;Lee, Tae-Hee
    • Proceedings of the KSME Conference
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    • 2003.11a
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    • pp.1846-1851
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    • 2003
  • Approximate optimization has become popular in engineering field such as MDO and Crash analysis which is time consuming. To accomplish efficient approximate optimization, accuracy of approximate model is very important. As surrogate model, Kriging have been widely used approximating highly nonlinear system . Because Kriging employs interpolation method, it is adequate for deterministic computer simulation. Because there are no random errors and measurement errors in deterministic computer simulation, instead of classical DOE ,space filling experiment design which fills uniformly design space should be applied. In this work, various space filling designs such as maximin distance design, maximum entropy design are reviewed. And new design improving maximum entropy design is suggested and compared.

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Convergence and Measurement of Inter-Departure Processes in a Pull Serial Line: Entropy and Augmented Lagrange Multiplier Approach

  • Choe, Sang-Woong
    • Industrial Engineering and Management Systems
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    • v.1 no.1
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    • pp.29-45
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    • 2002
  • In this study, we consider infinite supply of raw materials and backlogged demands as given two boundary conditions. And we need not make any specific assumptions about the inter-arrival of external demand and service time distributions. We propose a numeric model and an algorithm in order to compute the first two moments of inter-departure process. Entropy enables us to examine the convergence of this process and to derive measurable relations of this process. Also, lower bound on the variance of inter-departure process plays an important role in proving the existence and uniqueness of an optimal solution for a numeric model and deriving the convergence order of augmented Lagrange multipliers method applied to a numeric model. Through these works, we confirm some structural properties and numeric examples how the validity and applicability of our study.

An Improved Level Set Method to Image Segmentation Based on Saliency

  • Wang, Yan;Xu, Xianfa
    • Journal of Information Processing Systems
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    • v.15 no.1
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    • pp.7-21
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    • 2019
  • In order to improve the edge segmentation effect of the level set image segmentation and avoid the influence of the initial contour on the level set method, a saliency level set image segmentation model based on local Renyi entropy is proposed. Firstly, the saliency map of the original image is extracted by using saliency detection algorithm. And the outline of the saliency map can be used to initialize the level set. Secondly, the local energy and edge energy of the image are obtained by using local Renyi entropy and Canny operator respectively. At the same time, new adaptive weight coefficient and boundary indication function are constructed. Finally, the local binary fitting energy model (LBF) as an external energy term is introduced. In this paper, the contrast experiments are implemented in different image database. The robustness of the proposed model for segmentation of images with intensity inhomogeneity and complicated edges is verified.

Anomaly-based Alzheimer's disease detection using entropy-based probability Positron Emission Tomography images

  • Husnu Baris Baydargil;Jangsik Park;Ibrahim Furkan Ince
    • ETRI Journal
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    • v.46 no.3
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    • pp.513-525
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    • 2024
  • Deep neural networks trained on labeled medical data face major challenges owing to the economic costs of data acquisition through expensive medical imaging devices, expert labor for data annotation, and large datasets to achieve optimal model performance. The heterogeneity of diseases, such as Alzheimer's disease, further complicates deep learning because the test cases may substantially differ from the training data, possibly increasing the rate of false positives. We propose a reconstruction-based self-supervised anomaly detection model to overcome these challenges. It has a dual-subnetwork encoder that enhances feature encoding augmented by skip connections to the decoder for improving the gradient flow. The novel encoder captures local and global features to improve image reconstruction. In addition, we introduce an entropy-based image conversion method. Extensive evaluations show that the proposed model outperforms benchmark models in anomaly detection and classification using an encoder. The supervised and unsupervised models show improved performances when trained with data preprocessed using the proposed image conversion method.

THE RELATIVE ENTROPY UNDER THE R-CGMY PROCESSES

  • Kwon, YongHoon;Lee, Younhee
    • Journal of the Chungcheong Mathematical Society
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    • v.28 no.1
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    • pp.109-117
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    • 2015
  • We consider the relative entropy for two R-CGMY processes, which are CGMY processes with Y equal to 1, to choose an equivalent martingale measure (EMM) when the underlying asset of a derivative follows a R-CGMY process in the financial market. Since the R-CGMY process leads to an incomplete market, we have to use a proper technique to choose an EMM among a variety of EMMs. In this paper, we derive the closed form expression of the relative entropy for R-CGMY processes.

A Relation of Urbanization Entropy and Urban Heat Phenomenon (도시화 엔트로피와 도시 열현상과의 관계성)

  • Sangjun Kang
    • Journal of the Korean Regional Science Association
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    • v.39 no.3
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    • pp.3-12
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    • 2023
  • The issue to be discussed is set as the relationship between urban fragmentation and urban heat phenomena. The fragmentation is recognized as a negative form that commonly occurs in the process of urbanization. The purpose of this study is to examine the relationship between urbanization entropy and heat phenomenon by looking at the five major cities in Korea. The employed methods are InVEST Urban Cooling Model and MSPA (Morphological Spatial Pattern Analysis) by using the meteological data for the July 2018. The major results are as follows; First, a low rank correlation(rho=-0.3) is found in the relation between entropy and Cooling Capacity Index (CCi). Second, a very high level of rank correlation is observed between entropy and Average Temperature(℃)(rho=0.9). The implications are that 1) a city with a large degree of sprawling development can have a negative effect on urban heat phenomena; 2) the composition of land use including dispersion and concentration in non-urbanized areas, which has the characteristics of open space, can affect the urban thermal environment. Due to the limited number of case studies, it is appropriate to understand that a possibility, not generalization, is observed between entropy and heat phenomena in urbanized areas.

Maximum Entropy-based Emotion Recognition Model using Individual Average Difference (개인별 평균차를 이용한 최대 엔트로피 기반 감성 인식 모델)

  • Park, So-Young;Kim, Dong-Keun;Whang, Min-Cheol
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.14 no.7
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    • pp.1557-1564
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    • 2010
  • In this paper, we propose a maximum entropy-based emotion recognition model using the individual average difference of emotional signal, because an emotional signal pattern depends on each individual. In order to accurately recognize a user's emotion, the proposed model utilizes the difference between the average of the input emotional signals and the average of each emotional state's signals(such as positive emotional signals and negative emotional signals), rather than only the given input signal. With the aim of easily constructing the emotion recognition model without the professional knowledge of the emotion recognition, it utilizes a maximum entropy model, one of the best-performed and well-known machine learning techniques. Considering that it is difficult to obtain enough training data based on the numerical value of emotional signal for machine learning, the proposed model substitutes two simple symbols such as +(positive number)/-(negative number) for every average difference value, and calculates the average of emotional signals per second rather than the total emotion response time(10 seconds).

Discriminant Analysis of Binary Data by Using the Maximum Entropy Distribution

  • Lee, Jung Jin;Hwang, Joon
    • Communications for Statistical Applications and Methods
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    • v.10 no.3
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    • pp.909-917
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    • 2003
  • Although many classification models have been used to classify binary data, none of the classification models dominates all varying circumstances depending on the number of variables and the size of data(Asparoukhov and Krzanowski (2001)). This paper proposes a classification model which uses information on marginal distributions of sub-variables and its maximum entropy distribution. Classification experiments by using simulation are discussed.

Gaussian Mixture Model for Data Clustering using Fuzzy Entropy Measures (데이터 클러스터링을 위한 가우시안 혼합 모델을 이용할 퍼지 정보량 측정)

  • 임채주;최병인;이정훈
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2004.10a
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    • pp.335-338
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    • 2004
  • 본 논문에서는 기존의 정보량(Entropy) 기반 클러스터링 기법을 향상시키기 위한 방법으로서 퍼지 정보량을 이용하였다 가우시안 혼합 모델을 이용하면, 프로토타입의 목적 함수를 이용하는 클러스터링 기법보다 향상된 결과를 얻을 수 있고, Parameter의 조정이 요구되지 않는다. 그러나, 가우시안 혼합 모델의 사용은 주어진 패턴 집합을 클러스터링하는데 계산량의 증가를 초래하게 된다. 본 논문에서는 가우시안 혼합 모델의 정형화에 요구되는 계산량을 감소시키는 방법을 제시한다 또한 퍼지정보량(Fuzzy Entropy)을 적용하여 기존의 정보량 기반의 클러스터링 결과와 비교 분석하였다.

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ME-based Emotion Recognition Model (ME 기반 감성 인식 모델)

  • Park, So-Young;Kim, Dong-Geun;Whang, Min-Cheol
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2010.05a
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    • pp.985-987
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    • 2010
  • In this paper, we propose a maximum entropy-based emotion recognition model using individual average difference. In order to accurately recognize an user' s emotion, the proposed model utilizes the difference between the average of the given input physiological signals and the average of each emotion state' signals rather than only the input signal. For the purpose of alleviating data sparse -ness, the proposed model substitutes two simple symbols such as +(positive number)/-(negative number) for every average difference value, and calculates the average of physiological signals based on a second rather than the longer total emotion response time. With the aim of easily constructing the model, it utilizes a simple average difference calculation technique and a maximum entropy model, one of well-known machine learning techniques.

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