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Journal of Meteorological Research 2021年第1期35卷 198-208页

作者： Haorui LI Daosheng XU Banglin ZHANG Guangzhou Institute of Tropical and Marine Meteorology Guangdong Provincial Key Laboratory of Regional Numerical Weather PredictionChina Meteorological AdministrationGuangzhou 510640

In traditional simulations of heavy rainfall events, the regional model is often initialized by using a global reanalysis dataset and a cold start method. An alternative to using global analysis data is to gradually introduce the analysis field via an incremental analysis update(IAU) method under the replay configuration. We found substantial differences in the forecast of a heavy rainfall event in southern China between a precipitation forecast using the traditional method and a forecast using the IAU method in the Tropical Regional Atmospheric Modeling System(TRAMS),based on the ECMWF global analysis. The IAU method is efficient in removing spurious high-frequency gravity wave noise, especially when the relaxation time is more than 90 min. The regional model needs to be pre-integrated for about 12 h to warm up the convective system in the background field. The improvement by the IAU method is supported by verification of simulations over 1 month(1–30 April 2019). In general, the IAU technique improves the initialization and spin-up process in the simulation of the heavy rainfall event.

关键词： incremental analysis update initialization replay

Dynamic Analogue initialization for Ensemble Forecasting

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Advances in Atmospheric Sciences 2013年第5期30卷 1406-1420页

作者：李珊容新尧刘赟刘征宇 Klaus FRAEDRICH Department of Atmospheric and Oceanic Sciences School of Physics Peking University Chinese Academy of Meteorological Sciences Center for Climatic Research and Department of Atmospheric and Oceanic Sciences University of Wisconsin-Madison Max Planck Institute for Meteorology

This paper introduces a new approach for the initialization of ensemble numerical forecasting： Dynamic Analogue initialization （DAI）. DAI assumes that the best model state trajectories for the past provide the initial conditions for the best forecasts in the future. As such, DAI performs the ensemble forecast using the best analogues from a full size ensemble. As a pilot study, the Lorenz63 and Lorenz96 models were used to test DAI＇s effectiveness independently. Results showed that DAI can improve the forecast significantly. Especially in lower-dimensional systems, DAI can reduce the forecast RMSE by ~50% compared to the Monte Carlo forecast （MC）. This improvement is because DAI is able to recognize the direction of the analysis error through the embedding process and therefore selects those good trajectories with reduced initial error. Meanwhile, a potential improvement of DAI is also proposed, and that is to find the optimal range of embedding time based on the error＇s growing speed.

关键词： initialization ensemble forecast analogue error growth

Stable initialization Scheme for K-Means Clustering

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Wuhan University Journal of Natural Sciences 2009年第1期14卷 24-28页

作者： XU Junling XU Baowen ZHANG Weifeng ZHANG Wei HOU Jun School of Computer Science and Engineering Southeast University Nanjing 211189 Jiangsu China State Key Laboratory of Software Engineering Wuhan University Wuhan 430072 Hubei China Department of Computer Nanjing University of Posts and Telecommunications Nanjing 210003 Jiangsu China

Though K-means is very popular for general clustering, its performance, which generally converges to numerous local minima, depends highly on initial cluster centers. In this paper a novel initialization scheme to select initial cluster centers for K-means clustering is proposed. This algorithm is based on reverse nearest neighbor （RNN） search which retrieves all points in a given data set whose nearest neighbor is a given query point. The initial cluster centers computed using this methodology are found to be very close to the desired cluster centers for iterative clustering algorithms. This procedure is applicable to clustering algorithms for continuous data. The application of the proposed algorithm to K-means clustering algorithm is demonstrated. An experiment is carried out on several popular datasets and the results show the advantages of the proposed method.

关键词： clustering unsupervised learning K-means initialization

initialization with rainfall derived diabatic heating

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Initialization with rainfall derived diabatic heating

A NEW initialization METHOD OF CLUSTER CENTERS

全国优秀青年气象科技工作者学术研讨会

作者： Leiming Ma (Shanghai Typhoon Institute, Shanghai, 200030, P.R.China)

In this paper, an idea is proposed to improve the initialization of tropical cyclone by the replacement of reference diabatic heating profile defined by satellite-derived rainfall, or hereafter RDH. T

关键词： rainfall diabatic heating typhoon initialization numerical simulation

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Journal of Electronics(China) 1999年第4期16卷 320-326页

作者： Pei Jihong Fan Jiulun Xie Weixin(School of Electronic Engineering, Xidian Univ., Xi’an 710071) (President Office, Shen-Zhen Univ., Shenzhen 518060) School of Electronic Engineering Xidian Univ. Xi’an Shen-Zhen Univ. Shenzhen

The problem of parameters selection for potential function used to initialize cluster centers is discussed, and two formulas are given for determining these parameters. Then a new potential function to initialize cluster centers is also given which is computational effective. Finally, a set of compared experiments is presented to show the effectiveness of the proposed methods.

关键词： Clustering initialization Potential function Nonlinear optimal

Research on initialization on EM Algorithm Based on Gaussian Mixture Model

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Journal of Applied Mathematics and Physics 2018年第1期6卷 11-17页

作者： Ye Li Yiyan Chen UK Systems Science Association London UK

The EM algorithm is a very popular maximum likelihood estimation method, the iterative algorithm for solving the maximum likelihood estimator when the observation data is the incomplete data, but also is very effective algorithm to estimate the finite mixture model parameters. However, EM algorithm can not guarantee to find the global optimal solution, and often easy to fall into local optimal solution, so it is sensitive to the determination of initial value to iteration. Traditional EM algorithm select the initial value at random, we propose an improved method of selection of initial value. First, we use the k-nearest-neighbor method to delete outliers. Second, use the k-means to initialize the EM algorithm. Compare this method with the original random initial value method, numerical experiments show that the parameter estimation effect of the initialization of the EM algorithm is significantly better than the effect of the original EM algorithm.

关键词： EM Algorithm Gaussian Mixture Model K-Nearest Neighbor K-Means Algorithm initialization

A New Weight initialization Method Using Cauchy’s Inequality Based on Sensitivity Analysis

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Journal of Intelligent Learning Systems and Applications 2011年第4期3卷 242-248页

作者： Thangairulappan Kathirvalavakumar Subramanian Jeyaseeli Subavathi 不详

In this paper, an efficient weight initialization method is proposed using Cauchy’s inequality based on sensitivity analy- sis to improve the convergence speed in single hidden layer feedforward neural networks. The proposed method ensures that the outputs of hidden neurons are in the active region which increases the rate of convergence. Also the weights are learned by minimizing the sum of squared errors and obtained by solving linear system of equations. The proposed method is simulated on various problems. In all the problems the number of epochs and time required for the proposed method is found to be minimum compared with other weight initialization methods.

关键词： Weight initialization Backpropagation Feedforward Neural Network Cauchy’s Inequality Linear System of Equations

THE APPLICATION OF TIDAL SIGNAL EXCLUSION SCHEME FROM initialization IN A GENERAL CIRCULATION MODEL

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Journal of Tropical Meteorology 2004年第2期10卷 210-215页

作者：杨学胜王军陈谊 Chinese Academy of Meteorological Sciences Beijing 100081 China Chinese

In this paper, some corrections was made to the assumption that the forcing is quasi-static, which is the basis of the nonlinear diabatic initialization scheme adopted by a global model T106L19. Thus the tidal signal is expressed and excluded from the initialization scheme. It shows that the new scheme captures the semi-diurnal pressure variation and is much closer to the uninitialized field. Compared with the standard initialization scheme, both the anomaly correlation coefficients and RMS of 500 hPa geopotential height simulated under the new scheme have improved significantly.

关键词： initialization tidal signals semidiurnal pressure variation

Study on the Hardware initialization of Embedded System

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International English Education Research 2014年第1期 1-3页

An embedded system generally comprises gffour parts （embedded microprocessor unit, peripheral hardware equipment, embedded operating system, and user application）, and its core function is to complete the control of different equipments as well as necessary monitoring and management measures. initialization is to set a variable as ＂default value＂, including system initialization, software initialization and hardware initialization. However, these three types of initialization are classified in accordance with different layers. This is studied in this paper.

关键词： Hardware of Embedded System initialization Questions

Online Self-calibration of Camera-IMU External Parameters and IMU initialization for Stereo VI-SLAM

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Online Self-calibration of Camera-IMU External Parameters an...