Robust Variance Components Estimation in the PERG Mixed Distributions of Empirical Variances—PEROBVC Method

A mixed distribution of empirical variances, composed of two distributions the basic and contaminating ones, and referred to as PERG mixed distribution of empirical variances, is considered. In the paper a robust inverse problem solution is given, namely a (new) robust method for estimation of variances of both distributions—PEROBVC Method, as well as the estimates for the numbers of observations for both distributions and, in this way also the estimate of contamination degree.

An Improved Double-Threshold Method Based on Gradient Histogram

This paper analyzes the characteristics of the output gradient histogram and shortages of several traditional automatic threshold methods in order to segment the gradient image better. Then an improved double-threshold method is proposed, which is combined with the method of maximum classes variance, estimating-area method and double-threshold method. This method can automatically select two different thresholds to segment gradient images. The computer simulation is performed on the traditional methods and this algorithm and proves that this method can get satisfying result. Key words gradient histogram image - threshold selection - double-threshold method - maximum classes variance method CLC number TP 391. 41 Foundation item: Supported by the National Nature Science Foundation of China (50099620) and the Project of Chenguang Plan in Wuhan (985003062)Biography: YANG Shen (1977-), female, Ph. D. candidate, research direction: multimedia information processing and network technology.

An artificial neural network approach for prediction of long-term strength properties of steel fiber reinforced concrete containing fly ash

In this study, an artificial neural network (ANN) model for studying the strength properties of steel fiber reinforced concrete (SFRC) containing fly ash was devised. The mixtures were prepared with 0 wt%, 15 wt%, and 30 wt% of fly ash, at 0 vol.%, 0.5 vol.%, 1.0 vol.% and 1.5 vol.% of fiber, respectively. After being cured under the standard conditions for 7, 28, 90 and 365 d, the specimens of each mixture were tested to determine the corresponding compressive and flexural strengths. The pa- rameters such as the amounts of cement, fly ash replacement, sand, gravel, steel fiber, and the age of samples were selected as input variables, while the compressive and flexural strengths of the concrete were chosen as the output variables. The back propagation learning algorithm with three different variants, namely the Levenberg-Marquardt (LM), scaled conjugate gradient (SCG) and Fletcher-Powell conjugate gradient (CGF) algorithms were used in the network so that the best approach can be found. The results obtained from the model and the experiments were compared, and it was found that the suitable algorithm is the LM algorithm. Furthermore, the analysis of variance (ANOVA) method was used to determine how importantly the experimental parameters affect the strength of these mixtures.

Influence of process parameters on deep drawing of AA6111 aluminum alloy at elevated temperatures

To gain a deep insight into the hot drawing process of aluminum alloy sheet, simulations of cylindrical cup drawing at elevated temperatures were carried out with experimental validation. The influence of four important process parameters, namely,punch velocity, blank holder force(BHF), friction coefficient and initial forming temperature of blank on drawing characteristics(i.e.minimum thickness and thickness deviation) was investigated with the help of design of experiments(DOE), analysis of variance(ANOVA) and analysis of mean(ANOM). Based on the results of ANOVA, it is shown that the blank holder force has the greatest influence on minimum thickness. The importance of punch velocity for thickness deviation is 44.35% followed by BHF of 24.88%,friction coefficient of 15.77% and initial forming temperature of blank of 14.995%. After determining the significance of each factor on forming characteristics, how the individual parameter affects characteristics was further analyzed by ANOM.

Research on Concrete Beam Crack Recognition Algorithm Based on Block Threshold Value Image Processing

To solve the problem that the digital image recognition accuracy of concrete structure cracks is not high under the condition of uneven ill umination and complex surface color of concrete structure,this paper has proposed a block segmentation method of maximum entropy threshold based on the digital image data obtained by the ACTIS automatic detection system.The steps in this research are as follows:1.The crack digital images of concrete specimens with typical fea-tures were collected by using the Actis system of KURABO Co,Ltd,of Japan in the concrete beam bending test.2.The images are segmented into blocks to dis-tinguish backgrounds of different grayscale.3.The max imum interclass average gray difference method is used to distinguish the sub-blocks and screen out the image blocks that need to be segmented.4.Segmentation is made to the image with 2D max imum entropy threshold segmentation method to obtain the binary image,and the target image can be obtained by screening the connected domain features of the binary image.Results have shown that compared with other algo-rithms,the proposed method can effectively decrease the image over-segmentation and under segmentation rates,highlight the characteristics of the target cracks,solve the problems of excessive difference between the identified length and actual length of cracks caused by background gray level change and uneven ilumnination,and effectively improve the recognition accuracy of bridge concrete cracks.

Tracking Target Identification Model Based on Multiple Algorithms

In view of current situation of bad data synchronization, image blurring and tracking station stability in tracking target identification, a kind of tracking target identification model based on multiple algorithms was put forward, firstly establishing the image degradation model, using the wavelet algorithm for image preprocessing, doing image edge segmentation by using Robert algorithm after pretreatment, then using the maximum variance threshold method for image threshold segmentation, then extracting target features from the segmented image, and finally using the ABS algorithm to finish target tracking. Experiments proved the proposed model practical and effective.

A review of 20 years of naive tests of significance for high-dimensional mean vectors and covariance matrices

We introduce the so-called naive tests and give a brief review of the new developments. Naive testing methods are easy to understand and perform robustly, especially when the dimension is large. We focus mainly on reviewing some naive testing methods for the mean vectors and covariance matrices of high-dimensional populations, and we believe that this naive testing approach can be used widely in many other testing problems.

DEM investigation of mixing indices in a ribbon mixer

Mixing index is an important parameter to understand and assess the mixing state in various mixers including ribbon mixers,the typical food processing devices.Many mixing indices based on either sample variance methods or non-sample variance methods have been proposed and used in the past,however,they were not well compared in the literature to evaluate their accuracy of assessing the final mixing state.In this study,discrete element method(DEM)modelling is used to investigate and compare the accuracy of these mixing indices for mixing of uniform particles in a horizontal cylindrical ribbon mixer.The sample variance methods for mixing indices are first compared both at particle-and macro-scale levels.In addition,non-sample variance methods,namely entropy and non-sampling indices are compared against the results from the sample variance methods.The simulation results indicate that,among the indices considered in this study,Lacey index shows the most accurate results.The Lacey index is regarded to be the most suitable mixing index to evaluate the steady-state mixing state of the ribbon mixer in the real-time(or without stopping the impeller)at both the particle-and macro-scale levels.The study is useful for the selection of a proper mixing index for a specific mixture in a given mixer.

Simultaneous optimization of multiple performance characteristics in WEDM for machining ZC63/SiC_p MMC

Abstract The compliance of an integrated approach, principal component analysis （PCA）, coupled with Tagu chi＇s robust theory for simultaneous optimization of cor related multiple responses of wire electrical discharge machining （WEDM） process for machining SiCp rein forced ZC63 metal matrix composites （MMCs） is investi gated in this work. The WEDM is proven better for its efficiency to machine MMCs among others, while the particulate size and volume percentage of SiCp with the composite are the utmost important factors. These improve the mechanical properties enormously, however reduce the machining performance. Hence the WEDM experiments are conducted by varying the particulate size, volume fraction, pulseon time, pulseoff time and wire tension. In the view of quality cut, the most important performance indicators of WEDM as surface roughness （Ra）, metal removal rate （MRR）, wire wear ratio （WWR）, kerf （Kw） and white layer thickness （WLT） are measured as respon ses. PCA is used as multiresponse optimization technique to derive the composite principal component （CPC） which acts as the overall quality index in the process. Consequently, Taguchi＇s S/N ratio analysis is applied to optimize the CPC. The derived optimal process responses are confirmed by the experimental validation tests results. The analysis of vari ance is conducted to find the effects of choosing process variables on the overall quality of the machined component.The practical possibility of the derived optimal process conditions is also presented using SEM.

Convergence analysis of self-tuning Riccati equation for systems with correlation noises

For linear discrete time-invariant stochastic system with correlated noises,and with unknown state transition matrix and unknown noise statistics,substituting the online consistent estimators of the state transition matrix and noise statistics into steady-state optimal Riccati equation,a new self-tuning Riccati equation is presented.A dynamic variance error system analysis(DVESA)method is presented,which transforms the convergence problem of self-tuning Riccati equation into the stability problem of a time-varying Lyapunov equation.Two decision criterions of the stability for the Lyapunov equation are presented.Using the DVESA method and Kalman filtering stability theory,it proves that with probability 1,the solution of self-tuning Riccati equation converges to the solution of the steady-state optimal Riccati equation or time-varying optimal Riccati equation.The proposed method can be applied to design a new selftuning information fusion Kalman filter and will provide the theoretical basis for solving the convergence problem of self-tuning filters.A numerical simulation example shows the effectiveness of the proposed method.