Mingjuan Zhao
email: azhaomingjuan110@163.com

Na Li

Longzhi Zhao
email: czhaolongzhi@163.com

Xiaolan Zhang
email: dzhangxiaolan85022@163.com

Numerical Simulations of compression properties of SiC/Al Co-Continuous Composites

Keywords: Composites, Compression properties, ANSYS, Loading velocity

The numerical simulations of property of SiC/Al co-continuous composites under compression with ANSYS software were carried out. Based on the simulation results, deformational behavior and distribution of stress and strain fields were analyzed. The effects of compression speed for material property was studied. The results showed that Al matrix and SiC reinforcement exhibit different mechanical behavior, large deformation appears inside Al matrix; the configuration of SiC has relatively great influence on intensity and distribution of stress within composites, its ultimate stress occurs near the interface of composites; when an external force is exerted on the composites, Al matrix and SiC reinforcement restrict each other to prevent from producing the strain; With the increase of loading velocity, the stress of matrix and the strain of reinforcement increase rapidly.

Introduction

With the fast development of research and applications technology of metallic matrix composite, the study on its macroscopical performance has to be paid increasing attention. The new composites have metal as a toughening phase and three dimension solid net framework structure as reinforcement, it has become the future trends of metallic matrix composites [START_REF] Wang | The Fabrication Method and Research Progress of the Reticulated Ceramic Reinforcement in Metal Matrix Composites[END_REF][START_REF] Zhang | ect: Research Status of Reticulated Ceramic/Metal Composite[END_REF]. The reinforcement of composites shows three-dimensional network structure, the distribution of holes is usually homogenized, and porosity is controllable [START_REF] Wang | Model of Compressive Strength of 3DNSRMMCS[END_REF]. The spatial topological structure has a number of special features, such as, light weight, high specific modulus and specific Strength, abrasion resistance, fatigue resistance, thermal shock resistance, and low coefficient of thermal expansion and so on. It has wide prospect to be applied in various industrial fields, such as aircrafts, automobile, electronics and machinery [START_REF] Qin | Metal-matrix Composites for Marine Applications: Development Status and Our Countermeasures[END_REF][START_REF] Yao | Wear Behavior of Ceramics Network Composites Fabricated by Die Casting Method[END_REF]. In this article, the compression performance of SiC/Al Co-Continuous Composites was simulated by the finite element analysis software ANSYS.

Creating the calculation model of composite material

When the compressive property of composites was simulated, we made some assumptions, an assumed equal division in the holes of SiC foam ceramics, ribs have continuity and size is same; SiC is absolutely continuous distribution in the composite materials; The physical characteristics of the SiC and Al have the properties of isotropy; There is a suitable interfacial bonding between reinforcement and matrix, it belongs to mechanical bond, that is, it depends mainly on mechanical forces of rough surface of reinforcement and contractile force of matrix which wrap up reinforcement and create friction, the forces combine reinforcement and matrix, other impurities is nonexistent at the interface. The model of SiC/Al Co-Continuous Composites should be preprocessed before numerical calculation. The primary attribute of SiC reinforcement is defined, elasticity modulus is 450Gpa, Poisson ratio is 0.17, density is 2.4 g/cm 3 , yield limit is 14 Mpa, shear modulus is 192.3 Mpa. Unit Type of reinforcement is defined as solid65. The primary attribute of Al matrix is defined, elasticity modulus is 24Gpa, Poisson ratio is 0.34, density is 0.6 g/cm 3 , yield limit is 7.6 Mpa, shear modulus is 80 Mpa. Unit Type of matrix is defined as solid45. In order to describe accurately the parameter changes, 16 stacked cells are adopted in numerical simulation. The macro-appearance and simulated model of SiC/Al Co-Continuous Composites are respectively showed in Fig. 1and

Analysis of simulation result

There is different Mechanical behavior between reinforcement and matrix to metal matrix composite materials. Simulation result of an intercepted cell was analyzed. The isopleths about displacement, stress and strain of the model are showed in Fig 3~8 .  This paper analyzes entire deformation, difference of elasticity modulus of two constituents cause difference of generated deflection. It can seems from Fig. 3, Al matrix in the hole of SiC foam has taken place large deformation, SiC plays a supporting role, the stress mainly distributed in SiC foam. SiC has a clean and distinct outline in Fig. 4 and Fig. 5. Reinforcement has three-dimensional connected structural characteristic in the composite materials, and reinforcement and matrix have continuity. When an external force is exerted on the composites, Al matrix and SiC reinforcement restrict each other to prevent from producing the strain; Furthermore, stress distribution has relatively uniform in the center of the interface stress, essentially the stress value is constant; but stress go up tremendously in near the interface boundary, the maximum stress appear in the interfacial boundary or edge. Additionally, according to the Fig 7~8 which described the isopleths of stress and strain in the different working face, analysis of structure of SiC foam shows that the structure leads to the distribution of stresses has symmetry, so it has a very significant effect on the distribution and size of stresses. The maximum stress occurred at the interfacial boundary between SiC reinforcement and Al matrix. This is also caused by mechanical properties of the reinforcement and matrix, such as, elasticity modulus, yield stress, it is only normal for them to have differences. When an external force is exerted on the composites, the different deformation causes the uneven stress distribution in materials, and furthermore, the deformation behavior of material shows a great difference in near interface of two constituents. This is caused due to the three-dimensional connected structural characteristic, the force on each rib affects other adjacent rib, and stress intensity is related to ribbed-orientation, as well. To sum up, the three-dimensional connected structural characteristic has changed the distribution of stresses of composites, which can restrict the extraneous stretch or compression, and this structural has immensely heightened the tensile strength or compression strength. Moreover, the degree of stress concentration shows that a suitable interfacial bonding can improve the material performance.

Fig.3. Isopleths of Y-direction

Influence of loading velocity on Compression Deformation Behavior

When temperature keep the constant, loading velocity is determined value, the deformation rate-time curve is shown in Fig. 9. It can be seen from the graph that as the time is reduced the deformation rate increases, and the trend of deformation reducing slow down, material shows a higher deformability. Based on the information provided by the Fig. 10, with the fast loading velocity growth, the stress of reinforcement and the strain of matrix are all increase, the strength of composites is also increases. Strength has been mentioned is manifested by material in the static compression. It can be seen in Fig. 10 that value of stress increases slowly with loading velocity raises, microcosmic principle is provided by engineering material can help to explain this, dislocation glide and plastic deformation need time, however, strain rate is too fast and there's not enough time to occur glide, so that materials exhibit rigid. Furthermore, loading velocity is too high, stress suddenly becomes more volatile causes the lower fracture toughness. In the paper perfect state is simulated, assumption has been made in previous paper, but it is difficult to obtain in the material fabrication processes, once the materials have microscopic imperfection, the fast pressure will exacerbate the breakdown speed of composites.

Conclusion

1)There is different Mechanical behavior between SiC and Al, difference of elasticity modulus of two constituents occasion difference of generated deflection, Al has taken place large deformation;

2)Due to structural characteristic of SiC, the force on each rib affects other adjacent rib;

3)When an external force is exerted on the composites, Al and SiC restrict each other to prevent from producing the strain; 4)With the fast loading velocity growth, the stress of reinforcement and the strain of matrix are all increase; the strength of composites is also increases.

Fig. 1 . 2 .

 12 Fig.1. Macro-appearance of SiC /Al Fig.2. The simulated model of SiC/Al Co-Continuous composites Co-Continuous composites

  Fig.2.

Fig. 4 . 5 .

 45 Fig.4. Isopleths of stress Fig.5. Isopleths of strain

Fig. 6 .Fig. 8 .

 68 Fig.6. Diagrammatic sketch of working plane

Fig. 9 .

 9 Fig.9. Deformation rate-Time curve Fig.10. 1-Stress-Speed curve of reinforcement 2-Strain-Speed curve of matrix


Daohong Kan
email: kandaohong@cau.edu.cn

Daoliang Li

Wenzhu Yang

Xin Zhang

An Online Image Segmentation Method for Foreign Fiber Detection in Lint

Keywords: Image segmentation, Thresholding, Cotton, Foreign fiber

The image of lint containing foreign fiber features that the background (cotton fiber) is homogeneous and has a normal gray-level distribution; the object (foreign fiber) is smaller, darker than the background but its gray-level distributes is a wide range. In this paper, a Background Estimation Thresholding(BET) method is presented to segment the objects from such kind of images. The segmented objects will be used to determine the existence and measure the quantity of foreign fiber. The experimental results show that the BET is effective and fast, and can be used in the online foreign fiber inspection in volumes of lints.

Introduction

Machine vision is a technology adopted newly in cotton trade and textile industry to evaluate the cotton quality [START_REF] Yang | A new approach for image processing in foreign fiber detection[END_REF][START_REF] Lieberman | Determining gravimetric bark content in cotton with machine vision[END_REF]. The foreign fiber in lint such as plastic film, fabric patch, hemp rope, hair, polypropylene twine, feather, and etc., will seriously affect the quality of final cotton textile product and can be detected and measured by machine vision technology.

China Agricultural University, together with China Cotton Machinery & Equipment

Corporation are developing a Foreign Fiber Inspection System(FFIS) to measure the content of foreign fibers in lint, according to which to grade and price, so that the cotton farmers and traders will be more willing to keep the foreign fibers away. FFIS is composed of hardware subsystem and software system, and the hardware system mainly includes lint feeder, opener, scanner, collector and computer, as shown in Fig. 1.

Lint is fed into the opener by the feeder to generate a 2mm thick, uniform lint slice, in which foreign fiber will be exposed sufficiently and can be detected easily by the scanner. The lint slice is pushed by rollers into an iamging alleyway, which is made of glass with high transparence, and 400mm in width, 4mm in thickness. The lint slice is imaged continuously by 2 scanners which are sensitive to visible light and ultraviolet radiation respectively. The image lines are assembled into frames and then transferred to the computer. Finally, the frames are processed online by the software subsystem. Image segmentation is a key step towards the foreign fiber inspection using machine vision technology. Mostly, the cotton fiber has high gray-level (bright) and the foreign fiber has low (dark), and thresholding is a reasonable choice for the segmentation of such kind of images. The thresholding method selects a gray-level T as the threshold based upon histogram (an approximation of gray-level probability distribution). If the gray-level g of a pixel is less than or equal to the T, the pixel will be marked as foreign fiber (object), otherwise as cotton fiber (background).

How to select a threshold appropriately is critical to thresholding. The commonly used technique is to select the optimal threshold according to a Criterion Function(CF).

Between-class variance [START_REF] Otsu | A threshold selection method from gray-level histograms[END_REF] proposed by Otsu is a widely used CF, which is based upon histogram. Otsu method met difficulties when applied to most foreign fiber images, in which the objects are thin and small, and the histograms are unimodal, or in which the gray-levels of objects vary largely, and the histograms extend to the low gray-level side just like a long tail. Hou's research [START_REF] Hou | On minimum variance thresholding[END_REF] shows that large differences in class variances or class probabilities will result in threshold bias towards the component with larger class variance or larger class probability. fuzziness CF [START_REF] Huang | Image thrsholding by minimizing the measures of fuzziness[END_REF], minimum error CF [START_REF] Kittler | Minium error thresholding[END_REF], and etc..

When thresholding according to CF, the image segmentation is considered as a classificaion problem. All of the CFs above assume that the gray-level probability distributions of objects and background are both normal, and the number of classes is predictable (that is how many kinds of objects in the background should be known before thresholding). FFIS can generate uniform lint slice and the lighting is well controlled. The gray-level of cotton fiber, which is background, distributes normally.

But the gray-level of foreign fiber, which is object, varies in a wide range and its distribution can not be considered as normal. The class number is also unpredictable in foreign fiber inspection because there may be or not be foreign fiber, and there may be one kind or many kinds of foreign fibers. So the two assumptions obstruct the application of such thresholding methods in foreign fiber inspection, which may occur in other similar situations.

A segmentation method may be suitable to some kinds of images, but there is not yet an universal method for all kinds of images. In this paper, a Background Estimation Thresholding(BET) method is developed specially for foreign fiber inspection. The experimental results show that the BET is effective and fast, and can be used in the online foreign fiber inspection in volumes of lint. The paper is organized as follows. In Section 2, we describe the BET method and its implementation. Section 3 gives the experimental results and the analysis comparing with valley-emphasis Otsu method.

Finally, the paper is concluded in Section 4.

BET method and its implementation 2.1 Method

FFIS scans the uniform lint slice under well controlled lighting and the gray-level of cotton fiber distributes normally. Foreign fiber is darker than cotton fiber and its gray-level is lower. The histogram of a lint image containing foreign fibers is close to normal but extends to the dark side like a tail.

Given a lint image with 256 gray-levels, the proportions of cotton fiber and foreign fiber are denoted by Pb and Po respectively, where Pb+Po=1. The gray-level distribution of cotton fiber can be formulated as:

) 2

) ( exp( 2 1 ) ( 2 2 σ μ σ π - - = x g p b (1)
where is mean and is standard deviation, g=0,1,2,…,255.

The gray-level distribution of foreign fiber, denoted by po(g), is unknown, which may be any form. For a threshold T, the lint image is segmented as follows. For every pixel, if its gray-level g<T, the pixel is marked as object, otherwise marked as background. The error ratio is given by: Actually, segmentation will fail for any threshold choice in the latter, which means thresholding is not suitable for such kind of images. Fortunately, most foreign fibers are separate from the cotton fiber in gray-level and can be segmented by thresholding.

⎪ ⎪ ⎪ ⎩ ⎪ ⎪ ⎪ ⎨ ⎧ ∑ ≥ < ∑ = = - = 255 1 0 ) ( ), ( ) ( , ) ( ) | (
White polypropylene twine is one inseparable from cotton fiber and can not be ignored in foreign fiber inspection in China. The ultraviolet scanner in FFIS is equipped specially for the detection of foreign fibers like this, which is separable from cotton fiber in the ultraviolet image.

Here we describe how to estimate the mean and standard deviation of Pb(g), which is called background estimation. The cutton fiber is dominant in lint image and its gray-level distribution is normal, it is reasonable to take the gray-level with maximal probabilty in histogram as the estimation of mean . Most foreign fibers are darker than cotton fiber and all pixels with gray-level between ~ 255 are considered as background. These pixels constitute one half of the background samples, and then the another half can be mirrored centering at means . The standard deviation can be evaluated finally by Bayersian Estimation based on the background samples.

BET method first do background estimation, and then select a threshold T to control the error ration of (2.a) under a predefined level (acquired by experiments). The background of lint image is stable and its gray-level distribution can be estimated, BET actually extracts the background by a threshold T, which depends on Pb(g) and a predefined limitation of error ratio.

Implementation

The steps of segmentation by BET are as follows:

Step 1. Computing the histogram h(g), g=0, 1, …, 255;

Step 2. Mean estimation: =m, where { }

) ( max ) ( 255 0 g h m h g ≤ ≤ = ; Step 3. Standard deviation estimation: b g g b P D g g h D h g h P / ) )( ( 2 ) ( ) ( 2 255 1 2 255 1 =
∑ - × = + ∑ × = + = + = σ μ μ μ μ ;
Step 4. Computing T:

4 3 , ≤ ≤ = k k T σ
, where k is predefined;

Step 5. Segmentation: For every pixel in lint image, if its gray-level g<T, then it is marked as object (foreign fiber); otherwise marked as background (cotton fiber).

Experiment results

The lint images in our experiment can be divided into three categories, they are cotton fibers only, cotton fibers mixed with foreign fibers whose gray-levels distribute narrowly, and cotton fibers mixed with foreign fibers whose gray-levels distribute widely, which correspond to one class (unimodal), two classes (bimodal) and multiple classes (multimodal) in classification, respectively. The BET method is evaluated against all three categories of lint images. This section gives one example and its comparison with the valley-emphasis Otsu (VEOtsu) thresholding for each category.

At the end of this section, the execution speed of BET is also discussed. VEOtsu marks all pixels as object, where T=233. BET marks all pixels as background,

Unimodal

where the estimated =203.00, =6.87, predefined k=3.5, and the final T=179.

Both of VEOtsu and BET can segment the unimodal image correctly, but VEOtus sometimes does wrong marking and BET never.

Bimodal

Fig. 2 shows a lint image containing a black hair and a small patch of cotton leaf, whose gray-level distributions are similar by chance. It is bimodal and Fig. 4 shows its segmentations by VEOtsu and BET.

Multimodal

Execution speed

To find out the optimal threshold, the VEOtsu needs compute the CF, which includes 2 means and 2 variances, each for every 256 gray-levels. The BET needs find out the gray-level with the maximal probability and compute variance one time. The computation of VEOtsu is more than the BET's and Table 1 shows their execution time for 1 million threshold selections(Intel Mobile Pentium 4 CPU, 2.0 Ghz), where the BET reduces the computation of the VEOtsu to 35.8%.

Fig. 1 .

 1 Fig.1. Scheme of FFIS

Fig. 2 Fig. 2 (

 22 Fig. 2 (a) Lint containing a hair, (b) Segmentation by Otsu method, (c)(d) Histogram and its details at the bottom

  a) gives the error ratio where the background is marked as object and (2.b) gives the error ratio where the object is marked as background. Decreasing T can decrease (2.a) while increase (2.b), and vice versa. Pb(g) is a normal distribution and its mean and standard deviation can be estimated (described below), so (2.a) can be estimated. po(g) is unknown and therefore (2.b) can not be estimated. (2.a) can be estimated and controlled, it is realistic to select T based only on (2.a), which is a partial error ratio, not the whole error ratio commonly used. (2.a) can be controlled under an acceptable level by choosing T. For example, if a) will be under 0.005%Pb. Such threshold T locates near the darkest gray-level of background. Decreasing T further has little help to decrease (2.a) while increase (2.b) rapidly. If the background is separable by gray-level value from the object, or their histogram do not overlap or overlap little each other, a T near the darkest gray-level of background will keep the error ratios of (2.a) and (2.b) low both, and the segmentation succeeds. Otherwise if their histogram overlap each other, (2.a) will be low while (2.b) high, which means many object pixels are marked as background, and the segmentation fails.

Fig. 3 Fig. 3 (

 33 Fig.3shows a lint image containing cotton fibers only, whose histogram demonstrates a normal distribution (202.69 in mean, 6.88 in standard deviation). Otsu method will split it at the mean and half of the image will be considered as foreign fiber. It will cause the measurement results meaningless because most lint images do not conrain foreign fiber.

Fig. 4

 4 Fig. 4 Segmentation of Fig.2(a), (a) by VEOtsu, (b)by BET

Fig. 5 (

 5 Fig. 5(a) shows a lint image containing a feather and a small patch of cotton leaf, whose gray-level distributions in a wide range. Fig. 5(b) shows that the histogram drags a long but thin tail, which is multimodal. Fig. 5(c, d) are the segmentations by VEOtsu and BET respectively.

Fig. 5 (

 5 Fig. 5 (a) Lint containing feather, (b) Histogram in detail at the bottom, (c) Segmentation by VEOtsu, (b) Segmentation by BET

Table . 1

 . Execution time of VEOtsu and BET

	Method	Execution time (Sec.)
	VEOtsu	8.46
	BET	23.62

Acknowledgements

. This research was funded by National Natural Science Foundation of China (30971693) and Ministry of Education of People's Republic of China (NCET-09-0731).

Conclusions

The lint image containing foreign fiber features that the background (cotton fiber) is homogeneous and has a normal gray-level distribution; the object (foreign fiber) is smaller, darker than the background but its gray-level distributes is a wide range. The BET method can extract the foreign fibers from lint image well, which makes the detection and measurement by machine vision technology possible.

Comparing with other thresholding methods such as Otsu, the BET can segment image with higher performance where the existence or types of object are unpredictable, and is suitable for foreign fiber inspection and other similar applications.

The BET method can applied in foreign fiber inspection online in volumes of lints, where the simplicity and fast speed are greatly concerned.

The thickness of lint slice is not uniform fully and the BET segments some cotton fibers wrongly as foreign fibers. This problem is left unresolved and the related researches are going on.


Xin Zhang

Daoliang Li

Wenzhu Yang

Jinxing Wang

Shuangxi Liu

An efficient iterative thresholding algorithms for Color images of Cotton Foreign Fibers

The goal of color image segmentation is to divide the image into homogeneous regions. Thresholding is a commonly used technique for image segmentation. Thresholding assumes that image present a number of components, each of a nearly homogeneous value, and that one can separate the components by a proper choice of intensity threshold. In this paper, we present an efficient iterative algorithm for finding optimal thresholds. In the first step, color images were captured, and the edge of color images were detected by edge detection method. In the second step, color images were converted into a gradient map, and then the regular of experience values were analyzed, at last the best threshold of the gradient map was chosen by selecting the best experience value iteratively. The experiment results indicate that the best threshold selection of the gradient map can precisely segment the high-resolution color images of cotton foreign fibers.

Introduction

The foreign fibers in cotton refer to those non-cotton fibers and dyed fibers, such as hairs, binding ropes, plastic films, candy wrappers, and polypropylene twines, etc. Foreign fibers mixed with cotton during picking, storing, drying, transporting, purchasing and processing, are difficult to remove in spinning process, and can cause yarn breakage, even reducing the efficiency. Every low content of foreign fibers in cotton, especially in lint, will seriously affect the quality of the final cotton textile products, as they may debase the strength of the yarn, and are not easy to be dyed [START_REF] Yang | A new approach for image processing in foreign fiber detection[END_REF]. Therefore, various techniques have been employed to implement automatic inspection and removal of foreign fibers in lint, including ultrasonic-based inspection, sensor-based inspection, and machine-vision-based inspection, etc. In recent years, machine vision systems have been applied to textile industries [START_REF] Tantaswadi | Machine vision for automated visual inspection of cotton quality in textile industries using color isodiscrimination contour[END_REF][START_REF] Millman | Computer vision for textured yarn interlace (nip) measurements at high speeds[END_REF][START_REF] Abouelela | Automated vision system for localizing structural defects in textile fabrics[END_REF] for inspection and/or removal of foreign matters in cotton [START_REF] Lieberman | Determining gravimetric bark content in cotton with machine vision[END_REF] or wool [START_REF] Su | A machine vision system for on-line removal of contaminants in wool[END_REF].

The recognition of foreign fibers of targets is the key machine vision technology, in which image segmentation is an important step. Thresholding is a simple and commonly used technique for image segmentation (M. [START_REF] Fornasier | Adaptive iterative thresholding algorithms for magnetoenceophalography (MEG)[END_REF]. It aims to group the image pixels by checking the pixel intensities against a set of thresholds. Searching for optimal thresholds with respect to a particular objective function has always been one of the fundamental problems in image processing. Numerous methods have been proposed in the past [START_REF] Sankur | A survey over image thresholding techniques and quantitative performance evaluation[END_REF][START_REF] Tancredi | Accounting for threshold uncertainty in extreme value estimation[END_REF][START_REF] Yin | Maximum entropy-based optimal threshold selection using deterministic reinforcement learning with controlled randomization[END_REF][START_REF] Cheng | Thresholding using twodimensional histogram and fuzzy entropy principle[END_REF]. The methods mentioned above may work well in their specific context, but it's not capable to segment images of cotton foreign fibers because of the low contrast. Due to the uneven thickness of the layers of cotton and foreign fibers of different colors and shapes, using the above conventional image segmentation methods may lead to significant variations of segmentation for foreign fibers. It is hard to attain a satisfying result by using one segmentation method. In this article, a novel method based on mathematical morphology and iterative thresholding method is proposed to segment such low-contrast images.

Materials and methods

The foreign fibers used in this research were collected from cotton mills which included feathers, hair, hemp rope, plastic films, polypropylene twine, colored thread, cloth piece etc. Adequate pure lint with no foreign fibers was also prepared for making the lint layer. The experiment selected a sufficient amount of lint cotton which does not include foreign fibers.

Materials preparation

The Image Acquisition System is the most important part in this experiment platform. It consist mainly of two cameras, two light sources, one shaft encoder, one synchronizing amplifier, two image acquisition boards and a computer, as shown in Figure 1.

By observing the images obtained, it was easy to find that the opened foreign fibers appears in three typical forms, as shown in Fig. 2a-c, (1) sheet, such as plastic films, papers, etc., (2) wirelike, such as hair, color thread, etc., and (3) villiform, such as hemp rope, chemical fibers, etc.

Iterative method principle

The identification of cotton foreign fibers is done through real-time monitoring, however, when it is in different circumstances, it will have different light intensity. Therefore, the target segmentation for foreign fibers can not be through a fixed thresholding value to segment. In this case, the thresholding was automatically selected by iterative method in this paper.

Thresholding is the most widely used image segmentation method. Thresholding algorithm has histogram bimodal method(also known as the mode method), Otsu method and the iterative threshold method, etc. Histogram bimodal method is used to some simple images, which appear two separate peaks in histograms, and then the troughs which correspond gray value between two peaks was selected as threshold value.

Iterative threshold method is the improved method of histogram bimodal, and it is an automatic process of selecting threshold, which is described as follows:

(1) The gradient map can be acquired through the edge detection method of mathematical morphology. To calculate the min gray value 1 Z and max gray value k Z of this gradient map, defined thresholding to initial value:

0 1 2 k ZZ T   (1)
(2) Gray gradient map is divided into two parts, object and background, through the thresholding k T , and then calculating the target average gray value 0 Z and the background average gray value

B Z ; ( , ) 0 ( , ) ( , ) ( , ) ( , ) k k z i j T z i j T z i j N i j Z N i j       (2) ( , ) ( , ) ( , ) ( , ) ( , )
k k z i j T B z i j T z i j N i j Z N i j       (3)
In the above formulas, ( , ) z i j is ( , ) ij point on gray value of gray gradient map , ( , )

N i j is weight coefficient of ( , ) ijpoint, ( , ) N i j =1.0;
(3) Obtaining a new adaptation thresholding of cotton foreign fibers:

1 0 () k B T n Z Z     (4)
The value n of conventional iterative threshold method is 0.5, but this value is not suitable for images of cotton foreign fibers, hence, this paper presents the experienced value method for selecting value n .

Results and discussion

Seven typical foreign fibers, namely, plastic film, feather, polypropylene twine, hair, color thread, hemp rope and cloth piece, were selected for the experiments. Ten samples were prepared for each type of foreign fibers. That is to say, there are totally 70 foreign-fiber samples. Matlab 7.0 was used to implement and validate the algorithm. An Intel Core 2 Duo CPU personal computer with 2GB SDRAM was chosen as the test environment and Windows XP was selected as the operation system.

Analysis of the improved threshold selection of iterative method

In order to increase the range of differences as far as possible, the range is defined from 0.1 to 0.9.

Table1 to Table3 are the final iteration threshold next T and the previous threshold comparison of results between wirelike, villiform and sheet foreign fibers. 1 to Table 3 show that when the value n is between 0.3 to 0.9, no matter what wirelike, villiform and sheet foreign fibers in the previous thresholds are equal, that is, only when n > 0.2, the value T began to significantly change, therefore, n = 0.2 is the mutation point of thresholding selection.

The following images are intuitively verifying this experience value. The Fig. 3 are the typical images of wirelike, villiform and sheet form foreign fibers. The range of experience value is from 0.1 to 0.5, step is 0.1.The follows are the effected images.

(a1) (b1) (c1) (d1) (e1) (f1) (a2) (b2) (c2) (d2) (e2) (f2) (a3) (b3) (c3) (d3) (e3) (f3)
Fig. 3 Original color images and its effected images

(a1)-(a3) Original color images (b1)-(b3) n =0.1 (c1)-(c3) n =0.2 (d1)-(d3) n =0.3 (e1)-(e3) n =0.4 (f1)-(f3) n =0.5
From the above Fig. 3 shows that, when n > 0.2, the clarity of target image decreased gradually, and when n = 0.6 ~ 0.9, the effected images for all kinds of foreign fibers become increasingly unobvious , so that wirelike form foreign fibers is difficult to see the target image, thus this paper doesn't list this images. When n = 0.1, as it is too small, the binary images are acquired almost black.

From analysis above, it is concluded that , when n =0.2, the selected thresholding is fit for most gray gradient maps for foreign fibers, and then getting the most clearly binary object images, formula (13) can be written as:

1 0 0.2 ( ) k B T Z Z     (4)If 1 kk TT  
, it is terminated, or letting 1 KK  , go to step (2).

Analysis of optimal threshold selection

Fig. 4 are color images of hair and the plastic film for cotton-fiber and its histogram. Histogram analysis in Fig. 4 shows that almost all histograms are single peak. The main reason for the histogram being of single peak is that the content of the foreign fiber in lint is very low, and it leads to small number of pixels for foreign fibers in the image. Therefore, it can not be acquired thresholding according to double peaks method. The Otsu's method (Otsu, 1979), which is also called maximum between-group variance method, is a kind of adaptive thresholding method which has the optimal threshold according to the statistics. The defect of Otsu's method is when the target and background gray scale difference is not obvious; it will lead to error segmentation, even losing the whole image information. The segmentation of hemp rope for cotton foreign fibers belongs to this case. Fig. 5 is color image of hemp rope and its segmented image by Otsu's method for cotton foreign-fiber.

Based on above analysis of experimental and theoretical, in order to solve a problem when object and background gray scale difference is not obvious leading to loss of image information, this paper presents a new approach for image segmentation which includes several steps. In the first step, color images were captured, and the edge of color images were detected by edge detection method. In the second step, color images were converted into a gradient map, and then the regular of experience values were analyzed, at last the best threshold of the gradient map was chosen by selecting the best experience value iteratively.Fig. 5 are original color images of three typical foreign fibers and their gray gradient maps, and segmented images by iterative thresholding method.

(a) (d) (g) (b) (e) (h) (c) (f) (i)

Conclusion

In this paper, research and analysis of the relationship and regular of thresholding for final iteration and the previous iteration, which are after the iteration for wirelike, villiform and sheet foreign fibers. At last the experience value is determined, which is fit for the gray gradient map of most foreign fibers, thus the clear binary image is obtained. The segmentation results indicate that the method of this paper can content the premise accuracy for image segmentation of foreign-fiber.

The mandate of speed is a key factor for the online visual inspection system. Hence, in addition to ensuring the segmentation accuracy, algorithms with faster speed is now being studied.

FigFig. 2

 2 Fig. 1 The image acquisition system

Fig. 4

 4 Fig.4 Original color images and its histogram (a) original color image of blue polypropylene (b) histogram of blue polypropylene image (c) original color image of hemp rope (d) histogram of hemp rope image

Fig. 5

 5 Fig.5Original color images and its segmented image by iterative threshold method

Acknowledgements

The authors would like to thank The National Natural Science Foundation of the People's Republic of China (30971693), and New Century Excellent Talents of Ministry Education (NCET-09-0731) for their financial support.


Aiguo Ouyang

Chagen Luo

Chao Zhou

Surface Distresses Detection of Pavement Based on Digital Image Processing

Keywords: Digital Image processing, Crack detection, Edge detection, Image segmentation

Pavement crack is the main form of early diseases of pavement. The use of digital photography to record pavement images and subsequent crack detection and classification has undergone continuous improvements over the past decade. Digital image processing has been applied to detect the pavement crack for its advantages of large amount of information and auto detection. The application of digital image processing was reviewed in pavement detection, pavement distresses classification and evaluation. The key problems were analyzed, such as image enhancement, image segmentation and edge detection. Some of the latest algorithms and their applications were tested in this paper.

The results forcefully supported following conclusion: noise in pavement is effectively removed median filtering, the histogram modification technique is a useable segmentation approach, canny edge detection is the optimum identification of pavement distresses.

Introduction

Maintenance of pavements is an important aspect for the Departments of Transportation all over the world. The first step towards maintenance is the identification of pavement distresses and their documentation for further action. Pavement distresses are visible imperfections on the surface of the pavements. Accurate evaluations will result in a better chance that resources will be distributed normally. Thus, yield a better service condition [START_REF] Kim | Development of a Low-Cost Video ImagingSystem for Pavement Evaluation[END_REF]. Pavement could be evaluated through the different types of distress experienced, such as cracking, disintegration and surface deformation. At present, there were various methods for conducting distress surveys, recording and analysing distress survey data [START_REF] Cheng | Automatic pavementdistress detection system[END_REF]. Pavement engineers had long recognized the importance of distress information in quantifying the quality of pavements. Traditionally, pavement condition data are gathered by human inspectors who walk or drive along the road to assess the distresses and subsequently produce report sheets, but it is high cost and time consuming. Worse still, work has to be done along fast moving traffic. Such condition would endanger the safety of the personnel involved. Finally, large differences will exist between the actual condition and evaluation results because of the subjectivity of the evaluation process.

In the wake of tedious manual measurements and safety issues, a variety of types of methods have been devised to identify the cracks on pavements apart from the crude process of manual inspection. Image processing, ultrasonic detection and infrared detection, the most widely reported of the automated methods is that known as WiseCrax. Wisecrax [START_REF]NCHRP synthesis 334: Automated Pavement Distress Collection Techniques[END_REF] is an example of a commercially available device that uses infrared imaging to detect cracks on pavements. A camera is mounted on a vehicle with that takes pictures of the pavements continuously. Images are processed off-line overnight at the office workstation by a unique open architecture process using advanced image recognition software. Typical survey vehicle configuration consists of one or more downward-facing video cameras, at least one forward facing camera for perspective, and any number of additional cameras for the capture of right-of-way, shoulder, signage, and other information depending on agency requirements (as shown in Figure 1).

New methods are being devised to identify cracks more efficiently and get closer towards perfection as good as the human eye and are still in the process. Ye et al. [START_REF] Ye | Measurement of Surface Crack Width Based on Digital Image Processing[END_REF] presented a crack width detection method to extract the crack image of pavement distresses and calculate the crack width. Li et al. [START_REF] Li | A Pavement Crack Image Analysis Approach Based on Automatic Image Dodging[END_REF] proposed a pavement crack image analysis approach based on the image dodging to improve the reliability of the pavement crack recognition. Existing automatic real-time detection systems focus on the low identification rate and classification difficulty [START_REF] Wang | Developments of Research on Road Pavement Surface Distress Image Recognition[END_REF]. First, a lot of noise brings in pavement images caused by the road environment itself. Second, it is lack of easy and effective identification and classification algorithm [START_REF] Zhang | Study on Pavement Crack Identification and Evaluation Technology Based on digital Image Processing[END_REF]. Although some auto-inspection system is in application at present, the system with surface-scan camera has the problem of low distinguish, and the dynamic collecting image clarity is not very ideal [START_REF] Chu | The Automatic Search of Pavement Surface Distress Image Based on on-line Learning[END_REF]. No method has achieved completely satisfactory results [START_REF] Tsai1 | Critical Assessment of Pavement Distress Segmentation Methods[END_REF].

Survey Vehicle

In-Office

Significance of pavement distress information

Pavement distresses are visible imperfections on the surface of pavements. They are symptoms of the deterioration of pavement structures. Agencies that have implemented a Pavement Management System (PMS) collect periodic surface distress in formation on their pavements through distress surveys [START_REF] Haas | Modem Pavement Management[END_REF].

Distress information takes a vital role in quantifying the quality of pavements. This information has been used to document present pavement condition, chart past performance history and predict future pavement performance [START_REF] Shahin | Pavement Management for Airport, Roads and Parking Lots[END_REF]. Pavement distress information is also broadly used as the only quality measure of pavements in many PMS. This is particularly true for systems used by local governments and in urban areas where roughness measurements are not performed because of a lack of equipment availability, high cost or a lack of relative applicability.

Pavement Image Analysis

The purpose of image processing is to extract the distress features from the pavement image. Preprocessing is done by removing extraneous features that have higher pixel intensities than the mean pixel intensity in the image. In this process, all the pixels representing paint striping and surface textures brighter than the average background gray level are surpassed to the background.

The effect of pavement image processing is illustrated by the following example. This study utilised full programming language software MATLAB 7.10.0 (Mathwork, Natick, MA, USA) to enable a series of MATLAB statements to be written into a file and then execute them with a single command.

Image enhancement

Image enhancement is applied in an attempt to remove noise in pavement images. Noise reduction is one aspect of preprocessing phase of crack detection process. Filtering is the most common form of noise reduction. Median filtering is one of the most commonly used preprocessing techniques for crack detection today. Median filtering is therefore applied as pavement image enhancement technique as suggested by Jitprasithsiri [START_REF]Jitprasithsiri: Development of a New Digital Pavement Image Processing Algorithm for Unified Crack Index Computation[END_REF]. The median is much less sensitive than the mean to extreme values. Median filtering is better able to remove these outliers without reducing the sharpness of the image. Such a property of median filtering was explained by a classic example of salt and pepper noise which was a random addition of black and white pixels into a gray scale image. The result of median filtering in figure 6 indicated that noise was removed aptly by the median filtering compared with other operators.

Image segmentation

Image segmentation is the crucial step in automatic image distress detection and classification (e.g., types and severities) and has important applications for automatic crack sealing [START_REF] Mustaffara | Automated Pavement Imaging Grogram (APIP) for Pavement Cracks Classification and Quantification-A Photogrammetric Approach[END_REF]. The segmentation approach chosen was based on a histogram modification technique. This histogram modification was achieved through iterative clipping. At each stage in iterative clipping, more pixels were assigned to the background. This process continues until only distress features were left. The end result as shown in Figure 12 was an image in which the distresses were distinct and easily separable from the background. At this point, a threshold value could be determined automatically in order to isolate the distress features from the background. Transformation function in Figure 11 presented that the threshold value was near 0.6.

Canny edge detection

The Canny edge detector is a good edge detector among traditional edge detection algorithms [START_REF] Canny | A computational approach to edge detection[END_REF]. The gray scale pavement image was first smoothed using a Gaussian filter with a specified standard deviation to reduce noise (see figure 13), then gradients were calculated to determine edge points. Using two threshold values 0.5 and 0.4, different correlated edge points were linked together. The strength of the method is its ability to detect edges in the presence of noise and to detect weak edges. Preliminary results from canny edge detection in figure 14~16 showed that the optimum identification of distress was dependent on the parameters used in the algorithm and that the optimum parameters varied with each image. There was a problem of coming up with false distress boundaries when a very high standard deviation in Gaussian filtering was used. The distress may seem wider than it actually was resulting in false severity level detection.

Pavement distress classification and evaluation

Many protocols and definitions exist for pavement distresses classification and evaluation. One of the most widely used protocols is Strategic Highway Research Program Long-Term Pavement Performance (SHRP-LTPP) protocol [START_REF] Hawks | Distress Identification Manual for the Long-Term Pavement Performance Project[END_REF]. SHRP-LTPP first classifies the type of cracks according to their orientations, locations, and shapes and quantifies the severity and extent of the cracks according to their properties, such as width, length, and areas. Another important protocol is the World Bank's universal cracking indicator (UCI) [START_REF] Paterson | Proposal of Universal Cracking Indicator for Pavements[END_REF]. UCI defined a single number to indicate the severity of all the cracks in a pavement segment. For a single crack such as a longitudinal, transverse, or diagonal crack, its indicator was defined as the product of its width and length. For the block and alligator cracks, their indicators were defined by the area that contains the block or alligator cracks. The unified crack index (ASTM STP 1121) is a protocol similar to UCI. The standard crack density can be automatically determined by dividing the number of pixels for the cracks by the number of the total pixels of the pavement segment.

Conclusions and perspectives

As mentioned above, digital images lend themselves to automated analysis because of the ability to analyze variations in grayscale as those variations relate to pavement features. A major force behind the move toward digital imaging of pavements is the opportunity to reduce distress data from those images through automated methods.

With the fast development of analog videotaping, using digital imaging to capture pavement surface is becoming the preferred method. With advances in image sensors and computer technologies, the automation of data collection and analysis is a major goal of contemporary pavement management. Several automated analysis algorithms for pavement distress are in use and others are under development.

Fig. 1 .

 1 Fig. 1. Elements of a pavement imaging system.

Fig. 2 .

 2 Fig. 2. Original pavement grayscale image.

Fig. 3 .

 3 Fig. 3. Grayscale image with salt and pepper.

Fig. 4 . 5 .Fig. 6 .Fig. 8 . 9 .

 45689 Fig.4. Laplacian operator Fig.5. Gaussian operator

Fig. 10 .

 10 Fig. 10. Histogram of original image.

Fig. 11 .Fig. 12 .

 1112 Fig. 11. Transformation function. Fig. 12. Result of histogram modification.

Fig. 13 . 14 .

 1314 Fig. 13. Grayscale image by gaussian filtering Fig. 14.Threshold value=0.5.

Fig. 15 . 4 Fig. 16 .

 15416 Fig. 15. Threshold value=0.4 Fig. 16. Result of canny edge detection.

Acknowledgements

The authors gratefully acknowledge the financial support provided by National Science and Technology Support Program (2008BAD96B04), Key Laboratory of Ministry of Education for Conveyance and Equipment, East China Jiaotong University (Grant No. 09JD10).


Xinjian Zhou

Xiangbin Xu

Wei Zhu

Simulation and Optimization in Production Logistics Based on eM-Plant Platform

Keywords: Production logistics, eM-Plant, Simulation, Optimization

The optimization of production logistics system is an important way to improve enterprise' s productivity, cut down cost and improve equipment' s utilization. Production logistics system is highly randomized, complicated and discrete system, which can hardly be modeled and analyzed in the traditional way, so computer simulation technology and eM-Plant simulation platform were introduced in the paper to model and analyze production logistics system, more than modeling eM-Plant also can analyze production logistics system' s equipment' s utilization, working efficiency, production capacity statistics effectively, which can offer useful guideline for production logistics system optimization and scientific decision-making.

Introduction

Production logistics should feature characteristics such as continuousness, smooth, parallel, proportionality, balance, flexibility, but many enterprises has production logistics problem including low productivity, more WIPs, long production cycle and waste of resources. The computer simulation technology is a effective way to find and solve these problems, the article takes eM-Plant simulation software to set up X4105BC diesel engine' s production logistics and optimize simulation models, the simulation and verification can offer great decision making support for the assembly line' s production logistics optimization, which ensure the scientific management decisions.

2

The Assembly Line' s Production Logistics

Process Route

The X4105BC diesel engine is the main product of some manufacturing enterprises, the product mode of X4105BC diesel engine is in a multi-variety, small batch and make-to-order way. The whole Diesel assembly process includes 22 procedures, which is listed in Table .1.

Table 1. X4105BC diesel engine assembly route

Production Lline Layout

X4105BC diesel engine assembly line was built on a 108m-long non-power roller conveyor belt, the layout of the assembly line shown in Fig. 1.

Fig. 1 The layout of the assembly line

The Simulation Results by eM-Plant

Once the assembly line' s simulation model is completed, run the simulation model, eM-plant can offer many kinds of statistics table and chart for us to analyze and optimize. Here we take the workstation' s working balance histogram to analyze the workstation working load, which is show in Fig. 3.The blue heap in the workstation' s working balance shows one worker' s total working time, the red heap in the workstation' s working balance mean balance lost of the assembly line.

The Optimization and Simulation of Production Logistics

Assembly Line Process Route Optimization

After field investigation based on the eM-plant simulation results, we decomposition working procedures1 and divide it into 5 sub-working procedures, namely working procedures1,2,3,4,5. since working procedures 18,19 and 20,21 are completed by a workers, so we combine working procedures 18,19 and 20,21 into one. The assembly process route after adjustment is shown in Table 2.

Table 2. X4105BC diesel dngine assembly route after optimization

And the assembly sequence after Optimized is shown in Fig. 4.

Simulation Model after Optimization

After adjustment and optimization the assembly route includes 24 working procedures, which are divided into four workstations, each workstation need only one operating workers to complete assembly tasks. The optimized simulation model shown in Figure 5, 6,7,8,9. Return Oil Pipe assembly line, gzz1, gzz2, gzz3 and gzz4, means the four workstation after optimization respectively. workstation 1 include six working procedures, which are first wash, second wash, camshaft bushes, crank watts, crankshaft, cylinder. The Source1, Source2, Source3 mean the required component supply , the eM-plant simulation model of the Workstation 3 shown in Fig. 6.

Simulation Results After Optimization

Once the optimization simulation model is completed, run the simulation model, we can get the interesting statistics after the simulation, hear we take the workstation' s working balance as an example. After the optimization, the workstation' s working balance histogram is shown in Fig. 10. The blue heap in the workstation' s working balance histogram means all processing time for each workstations, the red heap means the total balance loss, it is clearly that after optimization,the assembly line' s balance lost is improved greatly and the working procedures processing is in a proportional way, which can lead a effective drop-down WIPs. After optimization, the number of workers in the assembly line can be cut down largely, and the worker' s working efficiency has greatly improved from 65.41% to 96.19% on the average. And also, the WIP' s number and occupation rate in each buffer has greatly reduced from 65.834 to 9.024 on the average, which speeds up the enterprise' s logistics working efficiency greatly.

Conclusions

Many enterprise are facing production logistics problem such as WIP controlling, long production cycle ,low working efficiency and devilry delay etc, Handling these problem always means production mode adjustment which results in production line shutdown and assembly line re-balance, this may bring enormous loss if not handled carefully and rightly. With professional simulation software such as eM-Plant,we can build production logistics simulation model in a modular, hierarchical way, eM-Plant ' s simulation mode and it' s statistics, data analysis and optimization capabilities can offer powerful support to for enterprise' s decision making.

3 Production Logistics Simulation Model 3 . 1 .

 331 Assembly Line Simulation Bbased on eM-PlantBased on the X4105BC diesel engine process route and assembly line layout, the simulation model of the Assembly Line X4105BC build in eM-Plant is showed in Fig.2

Fig. 2 .

 2 Fig.2. The eM-plant simulation model of X4105BC assembly line In the simulation model, the assembly buffers are modeled in EM-plant' s Buffer component, the Capacity property of the EM-plant' s Buffer can specify buffer maximal number parts which can accommodate; the workstations are modeled in EMplant' s ParallelProc component, the set-up and processing time property of the EMplant' s ParallelProc can specify the workstation' s setting-up and processing time; EM-plant' s WorkerPool component is used to model worker dispatching,EM-plant' s Broker component can set worker dispatching rules, witch can route worker from the WorderPool to the right workstations.

Fig. 3 .

 3 Fig.3. Assembly Line Balance Histogram

Fig. 4

 4 Fig.4 X4105BC' s assembly sequence diagram after optimization

Fig. 5

 5 Fig.5 X4105BC' s assembly eM-Plant simulation model after optimization Here the " Source"means diesel engine supply; " Line"means powered conveyor which transfer diesel engine between workstations, Drain means the completion of the

Fig. 6

 6 Fig.6 eM-Plant simulation model of workstation1 after optimization Workstation2 include six working procedures, which are piston, flywheel, oil pipes, cooler, side cover, flywheel and other six processes, the eM-plant simulation model of the Workstation 3 shown in Fig.7.

Fig. 7

 7 Fig.7 eM-Plant simulation model of workstation2 after optimization Workstation3 include six working procedures, which are oil pan, starter motor, cylinder head, water pump, high pressure oil pump diesel filter, how the gear into the trachea, the eM-plant simulation model of the Workstation 3 shown in Fig.8.

Fig. 8

 8 Fig.8 eM-Plant simulation model of workstation3 after optimization Workstation4 includes six working procedures, which are the arm rings, gear cover, injector cylinder covers, exhaust pipe, pulleys oil filter, oil pipes, the eM-plant simulation model of the Workstation 4 shown in Fig.9.

Fig. 9

 9 Fig.9 eM-Plant simulation model of workstation4 after optimization

Fig. 10

 10 Fig.10 Assembly line balance histogram after optimization

Acknowledgements

This study was supported by JiangXi Natural Science Foundation(No.2009GZS0015), JiangXi Educational Committee Foundation(No.GJJ10467), National High-tech Research and Development Program of China(863 program) (No. 2009AA04Z106) and Jiangxi Key Laboratory of Ministry of Education for Conveyance and Equipment.


Baifen Liu
email: bfliu@4y.com.cn

Ying Gao

Parameters turning of the Active-Disturbance Rejection Controller based on RBF Neural Network

Keywords: Active-Disturbance Rejection Control (ADRC), Radial Basis Function (RBF), Robustness

the Active-disturbance rejection control (ADRC) has the advantage of strong robustness, antiinterference capability, and it does not rely on the accurate math model of controlled plant. But the parameter self-turning of ADRC isn't as easy as PID controller because there are more parameters to turn in ADRC. In this paper the parameters are self-turning by the Radial Basis Function (RBF) Neural Network. The results of the simulation indicate that the controller has good anti-interference capability and fast response. The robustness of the system is improved.

Introduction

Proportional-integral-derivative (PID) controller, a technique that dates back to 1920s, is still the most widely used control technique in process control although the control hardware has already entered the digital era. To overcome the limitations of PID [START_REF] Suresh Kumar | Model reference linear adaptive control of DC motor using fuzzy controller[END_REF], model based control, such as model predictive control, has been successfully developed. Plenty of linear MPC [START_REF] Buchnik | Speed and position estimation of brushless DC motor in very low speeds[END_REF] applications can be found in various industries. The differential geometry method, the inverse system method, the output-tracked method have also been developed in the past years.

The theory of auto-disturbance rejection control proposed these years is a simple and practical method. It was invented by Professor Han Jingqing who served in Chinese Academy of Sciences. This method does not depend on a precise mathematical model of controlled object. It can estimate and compensate the influences of all internal and external disturbances in real time when system is activated. The control has the advantage of simple algorithm, strong robustness, fast system response and high anti-interference ability. At present, this method has been applied to a number of fields of top science and technology, such as robotics, satellite attitude control, missile flight control, the fire control of tank and the inertia navigation. However, the parameters of ADRC need to turn in these occasions. The study of the parameters self-turning is only at an exploratory stage at home and abroad.

RBF Basis function (RBF) neural network has the ability of expressing arbitrary nonlinear mapping, learning and self-adapting, and also has an effective control to complex and uncertain system. This method becomes independent of model. Neural network is applied to fields of technology, and it gains a number of valuable research results.

So in this paper, parts of the parameters in ADRC are turned by the RBF neural network. And the simulation results indicate the method is practical.

Active-disturbance rejection control

Many plants' model can be simplified as follows:

( ) ( 1)
( , , , , ) ( )

nn x f x x x t w t u       (1)
Where, w is external disturbance variable, u is control variable,

( , , , , )

n f x x x t   
is an uncertain plant. For the two-order plant, its standard ADRC controller structure is usually illustrated as Figure 1[3]. According to Figure 1, ADRC controller consists of three main parts: "Transition Process Arranged", "Nonlinear Feedback" and "Extended-State Observer".

Transition Process Arranged

e v v fh fhan e v r h v v h v v v h fh                (2)
Where,

0 v is the control objective, 1 v is the track signal of 0 v , ()
fhan  is a time optimal integrated function, which detailed expression is described as (3).

                                             (3)

Extended-State Observer (ESO)

                        (4)
Where, h is the control cycle.

Output of Nonlinear Feedback (NF)

1 1 1 2 2 2 0 1 1 1 2 2 2
,, ( ,,) ( ,, )

e v z e v z u fal e fal e                  (5) 1 sgn( ), ( , , ) /, fal                       (6)
Where, the sgn( )  is the symbolic function. And the other parameters can be found in [START_REF] Zhi-Cheng | Study on the adaptive fuzzy control for brushless DC motor[END_REF][START_REF] Yu | PMSM Control Research Based on Particle Swarm Optimization BP Neural Network[END_REF].

ADRC optimized by RBF Neural Network

The structure of ADRC based RBF neural network control system is shown in Figure 2.

RBF neural network

The RBF neural network is presented by J.Moody and C.Darken in the 1980s which is a feed-forward network with three layers [START_REF] Han | From PID to Active Disturbance Rejection Con-trol[END_REF][7]. It possesses the capability of local adjustment and can approximate any continuous function in any accuracy. The structure of a typical RBF neural network is shown as Figure 3. m is the number of hidden unit.

Control of RBF neural network

1) The input and output of controller. RBF neural network can get more information from speed regulating system to adapt the chances of condition with effective measures. The structure of neural network is determined by genetic algorithm.

The vector of input is:

( ) [ ( ), ( ), ( ) ( 1) / , ( )] T X k r k e k e k e k T e k     (9)
Where () rk is the given input;

() ek  is the sum of error.

2) The parameters of neural network Array of parameter denotes all the parameters of the hidden unit connected with output layer in RBF network. Applications of RBF network have much difficulty in determining the array and the number of the hidden unit. The selection of hidden unit has a very huge influence on mapping ability and effectiveness of network. If there are too few hidden unit, the network can't complete the mission of classification and mapping function; if there are too many hidden units, influence generalization ability and learning efficiency. In order to improve the performance of system, it is necessary to seek more suitable learning algorithm of RBF network to determine array of parameter; and the number of hidden unit.

3) Target function of neural network

2 1 ( ( ) ( )) 2 J y k yout k   (10)
Where () yk is the output of the target;

() yout k is the output of the network;

4) Self-turning of the parameters in NF

Use the gradient descent method to turn the parameters in NLESF:  is the learning rate.

Simulation and analysis

In this paper, the simulation model adopts a

We adopt proposed algorithm and controller in the simulation. The outputs of ADRC controller based on RBF identification are shown in Figure 4 . The simulation results are shown the outputs of the identification network can match the output of the closed-loop controlled plant well. The adaptive turning of NF in ADRC controller parameters are shown in Figure 5. At the same time Jacobian information of identification is shown in Figure 6 in simulation. From Figure 7, we can see that the system output trace the reference information well while using ADRC based on BP neural network. And in Figure 8, we can clearly see that the method of PID control cannot trace the reference signal as well as the previous method.

Figure 1 .

 1 Figure 1. The structure of Active-Disturbance Rejection Controller

Figure 2 .

 2 Figure 2. Schematic of ADRC based on RBF Neural Network

Figure 3 .

 3 Figure 3.Schematic of a RBF neural network Radial vector of RBF network adopts multivariate Gau-ssian function as radial basis function in this paper.



  output of hidden unit; j b is the width of hidden unit; j C is the center of hidden units; i X is the output vector;  is Euclid norm.For an input pattern i X , the output of single output networks are given by is the weight between the th j node in the hidden layer and the output unit;

1

 1

Figure 4 .Figure 5 .Figure 6 .

 456 Figure 4. Output of the target and the network


Jian Zhang

Deying Li

Jianyun Li

Longzhi Zhao
email: zhaolongzhi@163.com

Numerical Simulation of Temperature Field in Selective Laser Sintering

Keywords: Selective laser sintering, Numerical simulation, Temperature field, Sintering depth

The laser sintering process of multi-component powder W/Cu is simulated by ANSYS software based on the factors of radiation, convection and thermal physical parameters on temperature. The laser power and scanning velocity which are the key process parameters to affect directly in sintering molding are studied in paper. The results show that when the scanning velocity is constant, the sintering depth is rising with the increase of laser power; In addition, when the laser power is constant, the sintering depth is decreasing with the increase of scanning velocity. To select reasonable processing parameters and meet the requirements of sintering quality on the sintering depth, the parameters of laser power and scanning velocity are optimized by analyzing the sintering depth.

Introduction

W/Cu composite, a typical two-phase pseudo alloy, exhibits excellent electrical conductivity and thermal conductivity of Cu and high melting point and high hardness of W. It can be used as electrical contact and electrode material, military material for special purpose (such as rocket nozzle), and heat sink substrate with high air tightness packing of CPU and solid-state microwave tube, etc. So W/Cu composite has been widely used both in industry and military [START_REF] Liu | Synthesis and Sintering of Ultra-fine/nanometer W-10%Cu Composite Powder[END_REF][START_REF] Gu | Microstructures of Laser Sintered Micron/nano-sized Cu-W Powder[END_REF].

Selective Laser Sintering (SLS), which becomes a research focus recently, can manufacture directly the metal mold or parts prototype with the complex shape from 3D CAD model, to obtain the functional parts meeting the requirements. For the advantages of short manufacturing cycle, wide range of materials and others, SLS is expected to obtain the W-Cu components with high density and complex shape that other process is difficult to do. In this paper, the temperature field distribution of W-Cu mixed powder is studied by ANSYS numerical simulation, and the effects of laser power and scanning velocity on sintering depth are analyzed. The reasonable processing parameters are selected by sintering depth, which has great significance for the sintering process of W-Cu mixed powder.

Establishment of model

Establishment of mathematic model

The molten pool has heat change with the surrounding air and powder bed in SLS, and the thermal conduction can be expressed by equation (1) [START_REF] Zhang | Simulation of Temperature Field for Optimization of Processing Parameters of Selective Laser Melting Metal Powders[END_REF].

t T C q q z T y T x T k g c e ∂ ∂ = + + ∂ ∂ + ∂ ∂ + ∂ ∂ ρ ) ( 2 2 2 2 2 2 (1)
Where is effective thermal conductivity of powder; e k ρ is compacted density of powder; C is heat capacity of material; is dispersed heat to air; is laser power density. c q g q Assume that the initial temperature T0 of powder bed is evenly distributed before sintering, and the initial condition is shown as follows:

0 0 ) , , , ( T t z y x T t = = (2)
In order to simplify calculation, assume that there is no heat loss on the bottom of substrate. In SLS, there is heat dissipation by convection and radiation, the boundary condition is shown as follows.

q T T T T h z T k E E z e = - + - + ∂ ∂ - = ) ( ) (

Establishment of physical model

In order to simplify the sintering model and reduce the computing time, the finite element model is established in Fig. 1. The multi-component powder consist of W and Cu with a certain percentage, the size of which is 3.4mm×1.6mm×0.3mm.To ensure the enough mesh density in laser heating zone and heat affected region and reduce time, the different meshing sizes can be adopted to the substrate and sintered layers.

The meshing of sintered layers is smaller (0.1mm), while the meshing of substrate is larger.

The Gaussian energy distribution of laser is shown in Figure 2, the laser energy of center is the highest, and the laser energy is continuously reduced away from the spot center. The laser spot is divided into 4 × 4, as shown and the decay coefficients of 1, 2, 3 are 0.8359552, 0.5226424, 0.3267576 [START_REF] Ma | Parametric Finite Element Model of Temperature/stress Field Evolution by Metal Laser Solid Forming[END_REF]. In the sintering process, the laser energy obeys the Gaussian distribution.

) 2 exp( 2 2 2 2 ϖ πϖ r AP q - = (4)
Where is laser power density; q p is laser power; A is absorption rate of powder bed to laser; r is distance between any point on the powder bed and laser spot center; ω is radius of laser spot.

Results and analysis

The powder consists of W and Cu with a certain percentage, the melting points are 3380℃ and 1083℃, and the other thermal physical properties on temperature are shown in Table 1 and Fig. 3.

The process parameters to simulate that the spot diameter is 0.4mm, the scanning spacing is 0.3mm, the powder thickness is 0.1mm, the convection coefficient of air is 10, the effective radiation coefficient of powder bed is 0.8. Fig. 4 shows the effects of scanning velocity on pool depth. As seen from it, when the laser power is constant, the sintering depth is decreasing with the increase of scanning velocity. The sintering depth is 0.29mm at v=0.12m/s, while only is the sintering depth 0.076mm at v=0.20m/s. The reason is that when the laser power is constant and the scanning velocity is increasing, the input laser energy decreases. Meanwhile, the time that energy on the powder particles is decreasing. Especially at high scanning speed, the contact time between energy and powder particle is very short, even the powder particles could not be melt for the energy transfer around the powder particles without enough time. But the temperature is rising quickly at low scanning speed, which leads easily to greater heat accumulation effect, and results in larger temperature gradient.

The effects of laser power on pool depth are shown in Fig. 5. As seen from it, when the scanning velocity is constant, the sintering depth is rising with the increase of laser power. The sintering depth is only 0.08mm at p=195w, and the sintering depth is increasing to 0.21mm at p=235w. The reason is that when the scanning velocity is constant and the laser power is increasing, the output energy density is rising. But the excessive laser power makes the temperature so high that the powder layers have a greater contraction, and is easier to warping and cracking.

The laser power and scanning velocity are the key process parameters to affect directly in sintering molding. The higher scanning velocity can not only effectively suppress the expansion of molten pool and the phenomenon of "splashing", but also improve the forming efficiency. The reasonable laser power can not only ensure the layers bond well, but also reduce the contraction and warping. The sintering depth must be greater than the powder thickness, to ensue the adjacent layers bond well by penetrating the current layer. Otherwise the adjacent layers will be separated, and the poor strength and accuracy of forming will be obtained, even the forming could not be completed. Fig. 6 shows the temperature distribution of Z-direction at different time with a certain laser power and scanning velocity. As seen from it, the temperature of 0.1mm is obviously higher than the melting point of Cu (1356K) at t=0.05875s, it indicates that the sintering depth is greater than the powder thickness. Similarly, the sintering depth are about 0.14mm and 0.19mm at t=0.11125s and t=0.21125s, so the powder thickness is less than the sintering depth. At the current process parameters, the related joint between the first layer and substrate is strong, and the metallurgical bonding of layers is well.

The 3D parts in SLS are completed by lines to surface and layer by layer overlapped, and the following two conditions should be met to form solid structure by sintering [START_REF] Kumar | An experimental design approach to selective laser sintering of low carbon steel[END_REF][START_REF] Tolochko | Selective laser sintering and cladding of single-component metal powders[END_REF].

1) The degree coverage of energy should be required between scanning beams, that is, the scanning velocity could not be too high with a certain power density, ensure that the enough power input to make the powder in the current layer bond together and form the 2D surface structure without loose powder.

2) The powder thickness is less than the sintering depth, that is, the laser power should ensure the adjacent layers bond well by penetrating the current layer to form the 3D body structure. (1-t1=0.05875s, 2-t2=0.11125s, 3-t3=0.21125s

Conclusion

1) When the scanning velocity is constant, the sintering depth is rising with the increase of laser power; In addition, when the laser power is constant, the sintering depth is decreasing with the increase of scanning velocity.

2) To select reasonable processing parameters and meet the requirements of sintering quality on the sintering depth, the parameters of laser power and scanning velocity are optimized by analyzing the sintering depth.

T

  is temperature of metal powder at a certain moment; σ is Stefan-Boltzmann constant; is initial temperature;

Fig. 1

 1 Fig.1 Finite element model. Fig.2 Gaussian energy distribution of laser.

Fig. 3

 3 Fig.3 Thermal physical properties of W.

Fig. 4

 4 Fig.4 Effects of scanning velocity on pool depth. Fig.5 Effects of laser power on pool depth.

Fig. 6

 6 Fig.6 Temperature distribution of Z-direction at different time.

Table 1

 1 Thermal physical properties of Cu

	T/℃	20	100	200	400	500 600 800 1000
	C/J(kg•k) -1	385	388	392	400	409 414 420	445
	K/W(m•k) -1	405	404	399	379	355 342 329	313

Acknowledgement. This work was sponsored by Science and Research Foundation of East China Jiaotong University (01308013), the Natural Science Foundation of Jiangxi Province (09497, 2009GQC0014, 2008GZC0037) and Graduate Innovation Foundation of East China Jiaotong University (YC09C002).

Corresponding author: Deying Li; Tel: 13707096493; E-mail: lideyingecjtu@163.com


Siquan Hu
email: husiquan@ustb.edu.cn

Haiou Wang

Chundong She

Junfeng Wang

A Semantic Middleware of Grain Storage Internet

Keywords: Semantics, Middleware, Grain Storage Internet

The vision of Internet of Things (IOT) promises a prosperous future for grain storage quality control by integrating all sensors deployed in the individual grain bins into a global grain storage system. However, since the data format and the data semantic of the monitoring systems are not unified, heterogeneous systems equipped with sensor networks are pending integration and have difficulty in semantic interoperation. In this paper, we design a semantic middleware to promote the data interoperability of different grain storage systems. The proposed semantic middleware uses a multi-layer architecture and provides four basic services: semantic mapping service, query service, publish service and ontology management service. Raw sensor data are converted to RDF format by semantic annotation. Semantic data are stored in the knowledge base and can be queried and reasoned. The semantic interoperation middleware will promote data interoperability, information search and retrieval, automatic inference and extendibility in the global grain storage internet.

Introduction

Food supply security is a worldwide challenge nowadays; many countries have built grain storage basements distributed over the world. To ensure a healthy storage condition for grains, different IT infrastructures have been built to monitor the condition of temperature and humidity [START_REF] Armstrong | Wireless Data Transmission of Networked Sensors in Grain Storage[END_REF], [START_REF] Rehman | Towards Design of Context-Aware Sensor Grid Framework for Agriculture[END_REF]. The vision of IOT [START_REF] Katasonov | Smart Semantic Middleware For The Internet Of Things[END_REF], [START_REF] Yan | The Internet of Things: from RFID to the Next-Generation Pervasive Networked Systems[END_REF], [START_REF] Brock | On the Semantic Web of Things[END_REF] gives us a prosperous future that the storage quality and quantity of the grain products in the same country or transnational can be recorded and integrated into a global grain storage system based on the technology of sensor networks, internet, distributed database and cloud computing etc. However, at least currently there are many challenges to achieve such vision. One of the challenges is that these systems are designed, operated and maintained by different organization, the data format and the data semantic of the IT system are not unified and lack well-accepted standards, thus causes difficulty in interoperation and integration even in the same country.

The grains in storage are products of agriculture field, which requires the monitoring of soil condition, weather, water-flow, crop etc. For that purpose different sensors are needed. Either wired or wireless, these sensors connect to form a sensor network to gather the context data of the grain. Therefore, heterogeneous systems equipped with sensor networks, with vastly different capabilities such as temperature, humidity, light, gas and biology metrics measurements are pending integration. In this paper, we design a semantic middleware to promote the interoperation ability of the different grain storage system. The envisioned goal is to provide semantic interoperation middleware to the global grain storage network that will promote data interoperability, information search and retrieval, automatic inference, and extendibility.

Related work

In agriculture domain, sensor network based solutions have been used for several years for monitoring or controlling purposes. W. Zhang et al. at Carnegie Mellon University developed a small wireless sensor network to monitor plant nursery [START_REF] Zhang | Integrated Wireless Sensor/Actuator Networks in an Agricultural Application[END_REF]. Sensors were used to collect soil and air humidity, moister, temperature and light information. Yunseop Kim et al. [START_REF] Kim | Remote Sensing and Control of Irrigation System using a Distributed Wireless Sensor Network[END_REF] developed an electronically controlled sensor based irrigation system that provides the facility to monitor soil moister and temperature, weather information and sprinkler position remotely using the Bluetooth and GPS technologies. Tim Wark et al. created a pervasive, self configurable sensor based solution [START_REF] Wark | Transforming Agriculture through Pervasive Wireless Sensor Networks[END_REF] to analyze the behavior of animals and their control as well as the pastures assessment. However, all these projects are close loop applications which have weak interoperation ability.

To handle interoperation problem, Semantic Sensor Web [START_REF] Sheth | Semantic Sensor Web[END_REF] are proposed to integrate the Semantic Web with sensors networks [START_REF] Thirunarayan | Semantic Information and Sensor Networks[END_REF], [START_REF] Brunner | SEMbySEM: a Framework for Sensors Management[END_REF], which will bring benefits such as a more expressive graph based representation that models relationships as first class objects, the use of Uniform Resource Identifiers that allows all concepts to be independently accessible throughout the Web, and a triple-pattern encoding scheme that provides for simplified integration of heterogeneous datasets. Based on such mechanisms, the semantic middleware is proposed for different applications, such as E-Discovery, E-Government, Mobile computing [START_REF] Butler | Semantic Middleware for E-Discovery[END_REF], [START_REF] Sanchez1 | Semantic Interoperability Middleware -Cases and Applications in Electronic Government[END_REF], [START_REF] Corradi | Adaptive Semantic Middleware for Mobile Environments[END_REF].

Design of the middleware

The semantic middleware exploits ontology to support the semantic integration and functional collaborations between different information systems. Rather than assuming a specific technical architecture or environment such as Service Oriented Architecture (SOA), web services, the semantic middleware only assumes that collaborations are realized through the exchange of messages. This provides the flexibility of implementation when SOA or web services are not supported in many legend systems.

To maximize the reusability, a service-based architecture is exploited in our middleware design showed in Fig. 1. From the aspects of the users, four services are provided: Publish Service, Query Service, Semantic Mapping Service, and Ontology Management Service.

Both Publish service and Query Service interact with the data store through a unified Query Controller. Publish service will deliver the data set results to the web client under the default query configuration. The Query service allows users set flexible query conditions and makes various semantics related queries Semantic data exchange and query need a interface or no one knows how to connect to the semantic data store. The solutions are quite dependable, the popular solution is using reasoning server, but any kind of data exchange mechanisms is workable. In our design, the Query Controller is based on a reasoning server. The Semantic Mapping service helps to make semantic annotation to raw sensor data. This service includes a optional syntax processing layer and a required layers are semantic annotation layer. The syntax processing layer transfers the raw sensor data into XML files or streams if the input is not in XML-format. The Semantic annotation layer abstracts the processed outputs from the heterogeneous, low-level data sources such as sensors. Context information is added here through application specific ontology which can be plugged and automatically initiated without any further human intervention. The Semantic Mapping service analyzes the incoming XML message and store it into the ontology model in the form of ontology class individuals. After semantic annotation, the data is stored into data stores as triple statements which form the underlying graph of the model. Both the syntax mapping rules and semantic mapping rules can be stored in the instance KB (knowledge base) and managed by the Ontology Management service.

The Ontology Management service handles the ontology definitions and semantic rules. The ontology definition can be an owl file in the instance knowledge base, or mapping rules from XML to RDF stored in the KB. These mappings should include both the raw sensor data and the context of the data generation. The meta data of the sensor output are the necessary description about the raw data itself. Take raw data produced by a temperature sensor for example. We have to make it clear that the raw data is a temperature measurement, how accuracy it is, and in what conditions it is valid, which usually depends on the capability of sensing devices. Different sensing devices may have different kinds of meta data. The context information is about the context information in which the raw data was generated, for example, the location of sensor node, the ID of sensor node and the timestamp of data captured. The semantic rule is used to trigger the modification of ontology when some condition is met. For example, when the humidity is too high then fire an alert.

The Prototype Implementation

To evaluate the proposed design, we have implemented a prototype of the semantic middleware for the grain storage internet. The semantic framework is based on the Jena [15] which is a Java framework for building Semantic Web applications. It provides a programmatic environment for RDF, RDFS and OWL, SPARQL and includes a rule-based inference engine. The backend database is PostgreSQL [START_REF]PostgreSQL[END_REF]. The reasoning server is the open source Pellet [START_REF] Pellet | [END_REF] which provides standard reasoning services for OWL ontologies. It supports both the essential OWL DL reasoning and OWL 2 EL reasoning. The query controller use Pellet to represent and reason about information using OWL. An ontology improved on OntoSensor [START_REF] Russomanno | Building a Sensor Ontology: A Practical Approach Leveraging ISO and OGC Models[END_REF] is used in the prototype as an experimental ontology. OntoSensor references and extends the IEEE Suggested Upper Merged Ontology, which defines general concepts and associations.

In our implementation, the semantic mapping service will transfer the raw XML reading into RDF/OWL statements. For example, from a sensor node 1 equipped with a sensor board MTS310 (bordid=130) , we get a xml packet in following format. After mapping, the data is stored into the ontology models as following format, which is the output after semantic annotation. <IOnto: NodeVoltage rdf:ID="NodeVoltage_1"> <IOnto:hasUnit rdf:resource="#Volts"/> <IOnto:hasDoublVal rdf:datatype=" #double">3.2525.4 </IOnto:hasDoublVal> </IOnto: NodeVoltage > <IOnto:MTS310_Temperature rdf:ID="MTS310_Temperature_1"> <IOnto:hasUnit rdf:resource=" #degreeC"/> <IOnto:hasDoubleVal rdf:datatype=" #double">25.4 </IOnto:hasDoubleVal> <IOnto: MTS310_Light rdf:ID="MTS310_Light_1"> <IOnto:hasUnit rdf:resource=" #ADCReading"/> <IOnto:hasDoubleVal rdf:datatype=" #double">749 </IOnto:hasDoubleVal> </IOnto: MTS310_Light > <IOnto:measures rdf:ID="MTS310_1"> <IOnto:hasMeasure rdf:resource="NodeVoltage_1"/> <IOnto:hasMeasure rdf:resource="MTS310_Temperature_1"/> <IOnto:hasField rdf:resource="MTS310_Light_1"/> </IOnto:measures> From the output we can see the all sensor readings have been endowed formal semantics which provides the basis of semantic interoperability.

To verify the integration effectiveness of different grain storages using the semantic middleware prototype, two senor networks are deployed as Fig. 2. Each sensor network has 100 sensor nodes distributed in 2 grain storage bins. Each network has a gateway running the proposed middleware.

Conclusion and future work

The semantic integration of the global food storage system is an exciting vision that maximizes the utilization of the potential of data resources on existed food monitoring information systems. The semantic interoperability is a necessary prerequisite for automatic search, retrieval and processing of grain storage sensor data. This paper is a step further towards to unify the domain objects, sensor readings, time/space semantics, lifecycles of grains. The benefits of our work are to automatic the syntax and semantic annotation of sensor data and provide the semantic unification -a solid base for integration of heterogonous grain storage systems to form a global Internet of things on grain storage.

As for future work, we are planning to perform more comprehensive performance analysis by integrating more sensor network of grain storage in China and considering more real life scenarios and extending the system to the storage information system of other kinds of food. This effort will be a further step in the direction towards enabling semantic web to access and process the global food storage system.

Our paper demonstrates how semantic middleware can be applied to grain storage internet of things. There are at least two advantages of our approach towards building such a future internet. First, RDF as the common information representation enables the semi-structured data sources conveniently handled and additional sources added incrementally without need to modify a global schema. Second, a richer domain model can be provided by formal ontology definition with supporting inheritance hierarchies for classes and properties.

Fig. 1 .

 1 Fig. 1. The service architecture of the middleware.

Fig. 2 .

 2 Fig. 2. The sensor network deployment structure Every 30 seconds, the server in Net 1 sends a query to retrieve the readings of a random one of the node 101-200 which is in the Net 2, vice versa. The observed query results show the data can be access successfully cross the bounds of the subnets, thus the integration is seamless.

Acknowledgement

Project supported by the National High Technology Research and Development Program of China (2008AA01Z208 and 2009AA01Z405), the National Natural Science Foundation of China (60772150), and the Youth Foundation of Sichuan Province (2009-28-419) and the Applied Basic Research Program of Sichuan Province ( 2010JY0013).