• Title/Summary/Keyword: Initial set

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Development of a Core Set of Korean Soybean Landraces [Glycine max(L.) Merr.]

  • Cho, Gyu-Taek;Yoon, Mun-Sup;Lee, Jeong-Ran;Baek, Hyung-Jin;Kang, Jung-Hoon;Kim, Tae-San;Paek, Nam-Chon
    • Journal of Crop Science and Biotechnology
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    • v.11 no.3
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    • pp.157-162
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    • 2008
  • A total of 2,765 accessions were used as the initial set having both seed coat color and 100-seed weight data. As a result of molecular profiling using six SSR markers followed by stratification based on their usages, 335 accessions(12.1%) were selected by clustering based on UPGMA. Since 75 out of 335 accessions were mixed in phenotypic traits as a result of characterization, 260 accessions were finally set as a core set. This core set revealed nearly the same diversity compared with the other results on morphological traits of Korean soybean landraces. In total, 115 alleles(19.2 alleles per locus) were detected in the initial set and 79 alleles(13.2 alleles per locus) were detected in the core set. All 30 major alleles were present in the initial set and in the core set as well. In allele coverage, the core set was 71.4% of the initial set. These comparisons of number of alleles, gene diversity and coverage indicated that the core set represented the entire set well.

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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.

Level Set Based Shape Optimization of Linear Structures Using Topological Derivatives (Topological Derivative를 이용한 선형 구조물의 레벨셋 기반 형상 최적 설계)

  • Ha Seung-Hyun;Kim Min-Geun;Cho Seon-Ho
    • Proceedings of the Computational Structural Engineering Institute Conference
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    • 2006.04a
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    • pp.299-306
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    • 2006
  • Using a level set method and topological derivatives, a topological shape optimization method that is independent of an initial design is developed for linearly elastic structures. In the level set method, the initial domain is kept fixed and its boundary is represented by an implicit moving boundary embedded in the level set function, which facilitates to handle complicated topological shape changes. The 'Hamilton-Jacobi (H-J)' equation and computationally robust numerical technique of 'up-wind scheme' lead the initial implicit boundary to an optimal one according to the normal velocity field while minimizing the objective function of compliance and satisfying the constraint of allowable volume. Based on the asymptotic regularization concept, the topological derivative is considered as the limit of shape derivative as the radius of hole approaches to zero. The required velocity field to update the H -J equation is determined from the descent direction of Lagrangian derived from optimality conditions. It turns out that the initial holes is not required to get the optimal result since the developed method can create holes whenever and wherever necessary using indicators obtained from the topological derivatives. It is demonstrated that the proper choice of control parameters for nucleation is crucial for efficient optimization process.

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Optimization of Fuzzy Set-based Fuzzy Inference Systems Based on Evolutionary Data Granulation (진화론적 데이터 입자에 기반한 퍼지 집합 기반 퍼지 추론 시스템의 최적화)

  • Park, Keon-Jun;Lee, Bong-Yoon;Oh, Sung-Kwun
    • Proceedings of the KIEE Conference
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    • 2004.11c
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    • pp.343-345
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    • 2004
  • We propose a new category of fuzzy set-based fuzzy inference systems based on data granulation related to fuzzy space division for each variables. Data granules are viewed as linked collections of objects(data, in particular) drawn together by the criteria of proximity, similarity, or functionality. Granulation of data with the aid of Hard C-Means(HCM) clustering algorithm help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions and the initial values of polyminial functions being used in the premise and consequence part of the fuzzy rules. And the initial parameters are tuned effectively with the aid of the genetic algorithms(GAs) and the least square method. Numerical example is included to evaluate the performance of the proposed model.

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Setting an Initial Validation Gate based on Signal Intensity for Target Tracking in IR Image Sequences (적외선 영상에서 표적 추적을 위한 신호세기 기반 초기 유효게이트 설정 방법)

  • Yang, Yu Kyung;Kim, Jieun;Lee, Boohwan
    • Journal of the Korea Institute of Military Science and Technology
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    • v.17 no.1
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    • pp.108-114
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    • 2014
  • This paper describes a method to set an intensity-based initial validation gate for tracking filter while preserves the ability of tracking a target with maximum speed. First, we collected real data set of signal versus distance of an airplane target. And at each data point, we computed maximum distance the target can move. And a function is modeled to expect the maximum moving pixels on the lateral direction based on the intensity of the detected target in IR image sequence. The initial prediction error covariance can be computed using this function to decide the size of the initial validation gate. The simulation results show the proposed method can set the appropriate initial validation gates to track the targets with the maximum speed.

On the controllability of fuzzy differential systems with nonlocal initial conditions (비국소 초기 조건을 갖는 퍼지 미분 시스템에 대한 제어가능성)

  • 강점란;정두환;권영철
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2002.05a
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    • pp.272-275
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    • 2002
  • In this paper, we find the controllability conditions for the following fuzzy differential systems (equation omitted) where A(t), B(t) are continuous matrices, g is given function, U(t) is fuzzy set and target X$^1$ is fuzzy set.

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Finding an initial solution and modifying search direction by the centering force in the primal-dual interior point method (원쌍대 내부점기법에서 초기해 선정과 중심화 힘을 이용한 개선 방향의 수정)

  • 성명기;박순달
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • 1996.04a
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    • pp.530-533
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    • 1996
  • This paper deals with finding an initial solution and modifying search direction by the centrering force in the predictor-corrector method which is a variant of the primal-dual barrier method. These methods were tested with NETLIB problems. Initial solutions which are located close to the center of the feasible set lower the number of iterations, as they enlarge the step length. Three heuristic methods to find such initial solution are suggested. The new methods reduce the average number of iterations by 52% to at most, compared with the old method assigning 1 to initial valurs. Solutions can move closer to the central path fast by enlarging the centering force in early steps. It enlarge the step length, so reduces the number of iterations. The more effective this method is the closer the initial solution is to the boundary of the feasible set.

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A Study on Training Ensembles of Neural Networks - A Case of Stock Price Prediction (신경망 학습앙상블에 관한 연구 - 주가예측을 중심으로 -)

  • 이영찬;곽수환
    • Journal of Intelligence and Information Systems
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    • v.5 no.1
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    • pp.95-101
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    • 1999
  • In this paper, a comparison between different methods to combine predictions from neural networks will be given. These methods are bagging, bumping, and balancing. Those are based on the analysis of the ensemble generalization error into an ambiguity term and a term incorporating generalization performances of individual networks. Neural Networks and AI machine learning models are prone to overfitting. A strategy to prevent a neural network from overfitting, is to stop training in early stage of the learning process. The complete data set is spilt up into a training set and a validation set. Training is stopped when the error on the validation set starts increasing. The stability of the networks is highly dependent on the division in training and validation set, and also on the random initial weights and the chosen minimization procedure. This causes early stopped networks to be rather unstable: a small change in the data or different initial conditions can produce large changes in the prediction. Therefore, it is advisable to apply the same procedure several times starting from different initial weights. This technique is often referred to as training ensembles of neural networks. In this paper, we presented a comparison of three statistical methods to prevent overfitting of neural network.

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COMPLETE PROLONGATION AND THE FROBENIUS INTEGRABILITY FOR OVERDETERMINED SYSTEMS OF PARTIAL DIFFERENTIAL EQUATIONS

  • Cho, Jae-Seong;Han, Chong-Kyu
    • Journal of the Korean Mathematical Society
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    • v.39 no.2
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    • pp.237-252
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    • 2002
  • We study the compatibility conditions and the existence of solutions or overdetermined PDE systems that admit complete prolongation. For a complete system of order k there exists a submanifold of the ($\kappa$-1)st jet space of unknown functions that is the largest possible set on which the initial conditions of ($\kappa$-1)st order may take values. There exists a unique solution for any initial condition that belongs to this set if and only if the complete system satisfies the compatibility conditions on the initial data set. We prove by applying the Frobenius theorem to a Pfaffian differential system associated with the complete prolongation.

Automatic Local Update of Triangular Mesh Models Based on Measurement Point Clouds (측정된 점데이터 기반 삼각형망 곡면 메쉬 모델의 국부적 자동 수정)

  • Woo, Hyuck-Je;Lee, Jong-Dae;Lee, Kwan-H.
    • Korean Journal of Computational Design and Engineering
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    • v.11 no.5
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    • pp.335-343
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    • 2006
  • Design changes for an original surface model are frequently required in a manufacturing area: for example, when the physical parts are modified or when the parts are partially manufactured from analogous shapes. In this case, an efficient 3D model updating method by locally adding scan data for the modified area is highly desirable. For this purpose, this paper presents a new procedure to update an initial model that is composed of combinatorial triangular facets based on a set of locally added point data. The initial surface model is first created from the initial point set by Tight Cocone, which is a water-tight surface reconstructor; and then the point cloud data for the updates is locally added onto the initial model maintaining the same coordinate system. In order to update the initial model, the special region on the initial surface that needs to be updated is recognized through the detection of the overlapping area between the initial model and the boundary of the newly added point cloud. After that, the initial surface model is eventually updated to the final output by replacing the recognized region with the newly added point cloud. The proposed method has been implemented and tested with several examples. This algorithm will be practically useful to modify the surface model with physical part changes and free-form surface design.