• Title/Summary/Keyword: active set

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The Effect of the Making Methods of Hollow Fiber Active Layer on Performance for Nanofiltration Helical Module (Nanofiltration Helical Module에서 Hollow Fiber Active Layer의 성형법에 따른 성능변화에 관한 연구)

  • ;Belfort, Georges
    • Membrane Journal
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    • v.7 no.2
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    • pp.95-109
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    • 1997
  • The effects of varing axial flow rate and solute concentration on the performance of both module sets made by different methods for active layer formation were compared and determined. All experiments were conducted simultaneously at the same transmembrane pressure and energy consumption per membrane area. In every comparative run between the presence of Dean vortices in a helical module and absence of such vortices in a linear module from the first module set, the solution fluxes and permeabilities were higher, and in some cases substantially higher for the vortex flow. With pure water, the permeabilities of both modules from the second module set were different and the flux in a linear module was 150% higher than in the helical module. This explained both module membranes were totally different.

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AN ACTIVE SET SQP-FILTER METHOD FOR SOLVING NONLINEAR PROGRAMMING

  • Su, Ke;Yuan, Yingna;An, Hui
    • East Asian mathematical journal
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    • v.28 no.3
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    • pp.293-303
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    • 2012
  • Sequential quadratic programming (SQP) has been one of the most important methods for solving nonlinear constrained optimization problems. Recently, filter method, proposed by Fletcher and Leyffer, has been extensively applied for its promising numerical results. In this paper, we present and study an active set SQP-filter algorithm for inequality constrained optimization. The active set technique reduces the size of quadratic programming (QP) subproblem. While by the filter method, there is no penalty parameter estimate. Moreover, Maratos effect can be overcome by filter technique. Global convergence property of the proposed algorithm are established under suitable conditions. Some numerical results are reported in this paper.

Sensitivity analysis on the active strategy set in the matrix game (행렬게임의 활성전략집합에 대한 감도분석)

  • 성기석
    • Korean Management Science Review
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    • v.9 no.1
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    • pp.87-92
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    • 1992
  • The purpose of this paper is to study the sensitivity analysis in the matrix game. The third type sensitivity analysis is defined as finding the characteristic region of an element of the payoff matrix in which the set of current active strategies is preserved. First by using the relationship between matrix game and linear programming, we induce the conditions which must be satisfied for preserving the set of current active strategies. Second we show the characteristic regions of active and inactive strategy. It is found that the characteristic regions we suggests in this paper are same with that of the type one sensitivity analysis suggested by Sung[3] except only one case.

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Compar ison of Level Set-based Active Contour Models on Subcor tical Image Segmentation

  • Vongphachanh, Bouasone;Choi, Heung-Kook
    • Journal of Korea Multimedia Society
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    • v.18 no.7
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    • pp.827-833
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    • 2015
  • In this paper, we have compared three level set-based active contour (LSAC) methods on inhomogeneous MR image segmentation which is known as an important role of brain diseases to diagnosis and treatment in early. MR image is often occurred a problem with similar intensities and weak boundaries which have been causing many segmentation methods. However, LSAC method could be able to segment the targets such as the level set based on the local image fitting energy, the local binary fitting energy, and local Gaussian distribution fitting energy. Our implemented and tested the subcortical image segmentations were the corpus callosum and hippocampus and finally demonstrated their effectiveness. Consequently, the level set based on local Gaussian distribution fitting energy has obtained the best model to accurate and robust for the subcortical image segmentation.

Video Segmentation using the Level Set Method (Level Set 방법을 이용한 영상분할 알고리즘)

  • 김대희;호요성
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.40 no.5
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    • pp.303-311
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    • 2003
  • Since the MPEG-4 visual standard enables content-based functionalities, it is necessary to extract video object from natural video sequences. Segmentation algorithms can largely be classified into automatic segmentation and user-assisted segmentation. In this paper, we propose a user-assisted VOP generation method based on the geometric active contour. Since the geometric active contour, unlike the parametric active contour, employs the level set method to evolve the curve, we can draw the initial curve independent of the shape of the object. In order to generate the edge function from a smoothed image, we propose a vector-valued diffusion process in the LUV color space. We also present a discrete 3-D diffusion model for easy implementation. By combining the curve shrinkage in the vector field space with the curve expansion in the empty vector space, we can make accurate extraction of visual objects from video sequences.

Image Segmentation with Energy Minimization Method (에너지 최소화 방법을 이용한 영상분할)

  • 강진숙;김진숙;차의영
    • Proceedings of the Korea Multimedia Society Conference
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    • 2002.05c
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    • pp.191-194
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    • 2002
  • 영상분할이란 영상 내에 존재하는 객체를 배경에서 분리해내는 것을 말한다. Active Contour 모델은 객체를 영상에서 분리하는 gradient 기반의 영상분할 방식이다. 전통적인 의미의 Active Contour 모델에서 사용한 gradient 함수 기반의 영상분할은 잡영이 많고 객체와 배경간 뚜렷한 경계가 없는 영상에서는 그 한계를 보이고 있다. 이에 본 논문에서는 이러한 Active Contour 모델의 단점을 극복하기 위한 방법으로 영상 내의 진화곡선에 의존하는 에너지 함수인 Mumford-Shah Functional을 이용한 방법을 제안한다. 이 방법은 영상 내의 Active Contour를 진화시켜 Mumford-Shah 함수의 에너지를 최소화시키는 Level Set 함수를 찾고 Level Set 함수에 의해 얻어진 부분영상에서 히스토그램을 이용한 임계치(thresholding) 방식을 사용하는 보다 효과적인 객체추출 모델이다.

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Control strategy of the lever-type active multiple tuned mass dampers for structures

  • Li, Chunxiang;Han, Bingkang
    • Wind and Structures
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    • v.10 no.4
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    • pp.301-314
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    • 2007
  • The lever-type active multiple tuned mass dampers (LT-AMTMD), consisting of several lever-type active tuned mass dampers (LT-ATMD), is proposed in this paper to attenuate the vibrations of long-span bridges under the excitation directly acting on the structure, rather than through the base. With resorting to the derived analytical-expressions for the dynamic magnification factors of the LT-AMTMD structure system, the performance assessment then is conducted on the LT-AMTMD with the identical stiffness and damping coefficient but unequal mass. Numerical results indicate that the LT-AMTMD with the actuator set at the mass block can provide better effectiveness in reducing the vibrations of long-span bridges compared to the LT-AMTMD with the actuator set at other locations. An appealing feature of the LT-AMTMD with the actuator set at the mass block is that the static stretching of the spring may be freely adjusted in accordance with the practical requirements through changing the location of the support within the viable range while maintaining the same performance (including the same stroke displacement). Likewise, it is shown that the LT-AMTMD with the actuator set at the mass block can further ameliorate the performance of the lever-type multiple tuned mass dampers (LT-MTMD) and has higher effectiveness than a single lever-type active tuned mass damper (LT-ATMD). Therefore, the LT-AMTMD with the actuator set at the mass block may be a better means of suppressing the vibrations of long-span bridges with the consequence of not requiring the large static stretching of the spring and possessing a desirable robustness.

Selection of An Initial Training Set for Active Learning Using Cluster-Based Sampling (능동적 학습을 위한 군집기반 초기훈련집합 선정)

  • 강재호;류광렬;권혁철
    • Journal of KIISE:Software and Applications
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    • v.31 no.7
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    • pp.859-868
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    • 2004
  • We propose a method of selecting initial training examples for active learning so that it can reach high accuracy faster with fewer further queries. Our method is based on the assumption that an active learner can reach higher performance when given an initial training set consisting of diverse and typical examples rather than similar and special ones. To obtain a good initial training set, we first cluster examples by using k-means clustering algorithm to find groups of similar examples. Then, a representative example, which is the closest example to the cluster's centroid, is selected from each cluster. After these representative examples are labeled by querying to the user for their categories, they can be used as initial training examples. We also suggest a method of using the centroids as initial training examples by labeling them with categories of corresponding representative examples. Experiments with various text data sets have shown that the active learner starting from the initial training set selected by our method reaches higher accuracy faster than that starting from randomly generated initial training set.

Adaptive Active Contour Model: a Localized Mutual Information Approach for Medical Image Segmentation

  • Dai, Shuanglu;Zhan, Shu;Song, Ning
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.9 no.5
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    • pp.1840-1855
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    • 2015
  • Troubles are often met when traditional active contours extract boundaries of medical images with inhomogeneous bias and various noises. Focusing on such a circumstance, a localized mutual information active contour model is discussed in the paper. By defining neighborhood of each point on the level set, mutual information is introduced to describe the relationship between the zero level set and image field. A driving energy term is then generated by integrating all the information. In addition, an expanding energy and internal energy are designed to regularize the driving energy. Contrary to piecewise constant model, new model has a better command of driving the contours without initialization.

Active Learning based on Hierarchical Clustering (계층적 군집화를 이용한 능동적 학습)

  • Woo, Hoyoung;Park, Cheong Hee
    • KIPS Transactions on Software and Data Engineering
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    • v.2 no.10
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    • pp.705-712
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    • 2013
  • Active learning aims to improve the performance of a classification model by repeating the process to select the most helpful unlabeled data and include it to the training set through labelling by expert. In this paper, we propose a method for active learning based on hierarchical agglomerative clustering using Ward's linkage. The proposed method is able to construct a training set actively so as to include at least one sample from each cluster and also to reflect the total data distribution by expanding the existing training set. While most of existing active learning methods assume that an initial training set is given, the proposed method is applicable in both cases when an initial training data is given or not given. Experimental results show the superiority of the proposed method.