Xiaoqing Yuan

, Liyong Liu

Daoliang Li
email: li_daoliang@yaohoo.com

P O Box

STUDY ON EVALUATION METHOD FOR CHINESE AGRICULTURAL INFORMATIZATION

Keywords: words:Agricultural informatization, Index system, Evaluation method

Abstract:According to China"s current situation in agricultural informatization development and the functions of the Ministry of Agriculture, and on a basis of the connotation of "agricultural informatization", this paper initially establishes a quantitative agricultural informatization evaluation index system which could reflect the actual level of every province"s agricultural informatization. The index system consists of 12 indexes selected from 3 aspects, which are agricultural informatization foundational support level, agricultural production and operation informatization level, as well as agricultural management and service informatization level. At the same time, on the basis of summarizing the accomplishments of other researchers, a set of calculation methods/algorithms are proposed in the paper to evaluate the agricultural informatization level.

INTRODUCTION

Informalization is the general trend of current world economic and social development. The rapid development and overall permeation of information technology will provide agriculture production and rural social and economic development of our country with the new development opportunities. So it is a very important act to vigorously promote agricultural informalization, make full use of information technology to transform traditional agriculture for developing modern agriculture, train new peasants and accelerate the construction of new socialist countryside. Especially, it has strategic significance on promoting agriculture productivity great-leap-forward development in our country (The Ministry of Agriculture, Apr.,2010).

In recent years, the Central Committee of the Party and the State Council put high values on the work of agricultural informalization, put forward series of policies, carried out a number of engineering projects. Driven by these engineering projects, the agricultural informalization infrastructure construction has been speeding up continuously, the agricultural informalization application level has kept improving, the construction of agricultural informalization has been enhanced obviously (Li.,Oct.,2009).

Agricultural informatization is a systematic engineering project, it requires large amount of investment and long construction period (Yin.,Apr.,2004). If there is no scientific agricultural informatization evaluation system to guide it, it will inevitably cause a great waste of resources. Therefore, establishing a scientific agricultural informatization evaluation system to make quantitative analysis of the level of agricultural informatization has far-reaching strategic significance and important practical significance (Chen.,Oct.,2007). It will help each province not only know explicitly the stage of their agricultural informatization level, but also improve the management and decision-making level of their agricultural informatization level.

Based on the above facts, this article will establish a quantitative evaluation index system that could reflect the actual agricultural informatization level of each province in China with the principles of integrity, operability, orientation and cohesion, according to China"s current situation of agricultural informatization development and the specific functions of the Ministry of Agriculture.

THE CONCEPT AND CONNOTATION OF AGRICULTURAL INFORMATIZATION 2.1 The Concept of Agricultural Informatization

Agricultural informatization is a component of informatization, "2006-2020 National Informatization Developmental Strategy" has given the following definition to the informatization: informatization is a historic process that can make full use of information technology, develop and utilize information resources, promote information exchange and knowledge sharing, improve the quality of economic growth, promote economic and social development and transformation. Agricultural informatization put more emphasis on information technology applied to agriculture. Based on this judgment and the function scope of the Ministry of Agriculture, this research makes the following definition for agricultural informatization: agricultural informatization is the degree and process of the making full development and use of various information technology equipments and information resources to apply modern information technology to all aspects of agricultural production and operation, and agricultural management by promoting the construction of information infrastructural facilities such as rural broadcasting and television network, telecommunication network and computer network.

The Main Content of Agricultural Informatization

Embarking from the definition of Agricultural Informatization and the current situation, the articles believes that the core content of Agricultural Informatization should include three closely interrelated aspects: agricultural informatization foundational support, agricultural production and operation informatization, agricultural management and service informatization.

Agricultural Informatization Foundational Support

Agricultural informatization foundational support is a very important foundation for Agricultural Informatization. Agricultural informatization foundational support mainly include three basic network systems, they are broadcasting and television network, telecommunication network, and computer network, which covers radio, television, telephone, mobile phone, computer and other information terminals. Meanwhile, agricultural informatization foundational support also includes the degree of attention each province pay to informalization. It is specifically embodied in investment on Agricultural Informatization.

Agricultural Production and Operation Informatization

Agricultural production and operation informatization is among the important terms proposed in Third Plenary Session of the Seventeenth Central Committee of the Chinese Communist Party, which mainly contains crop farming informatization, animal husbandry informatization, fishery informatization and agricultural products electronic transaction.

Agricultural management and service informatization

Agricultural management and service informatization needs to be realized by information technology. It mainly contains the agricultural E-government management level,proportion of agricultural informatization administrators in agriculture department,coverage of administrative village information service station,and proportion of rural information staff in agricultural population.

ESTABLISHMENT OF AGRICULTURAI INFORMATIZATION EVALUATION INDEX SYSTEM

Principles of Index System Design

The design of agricultural informatization evaluation index system is based on the following four principles.Integrity principle: The agricultural informatization evaluation index system covers 2 aspects, it could reflect not only the agricultural informatization foundational support level, but also the agricultural informatization application level.Operability principle: The design of the index system is based on current agricultural informatization development realities in China, values the index data availability. The data could be obtained from Statistics Bureau of every province or provided by marketing division of agriculture department of each province.Orientation principle: combined with the trends of agricultural informatization, some indexes are forward-looking ,which can guide each province"s development.Cohesion principle: The agricultural informatization evaluation index system is upward linked up with the national informatization evaluation index system and downward reflecting the agricultural informatization development characteristics.

Composition of Index System

China"s agricultural informatization evaluation index system refers to the national informatization evaluation index system, meanwhile it closely combined with China's current development situation of agricultural informatization and the function scope of the Ministry of Agriculture. The index system is aimed at reflecting the actual situations and characteristics of China"s agricultural informatization, and also promoting the agricultural informatization schedule gradually. Initial assumption of the index system contains the agricultural informatization foundational support level, the agricultural production and operation level, and the agricultural management and service level, it total, it is 12 specific indicators in 3 aspects, as shown in table 1. informatization index values, the standard index values could be adjusted appropriately.

Non-dimensional processing of the index values

Due to the differences among the units of each index, the evaluation results could not be compared and calculated directly. In order to solve the problem with different units that cannot be calculated comprehensively, we make date dimensionless, compare the actual value with the standard value and convert the actual values dimensionless in range from 0 to 100. Converted dimensionless values could be directly used to compare provincial agricultural informatization differences, and also could be used to calculate the final result of agricultural informationization evaluation with weighting values. The formula (1) is the index values non-dimensional formula.

i T = 100  i i P R (1)
In this formula,i =1,2…12,i is the number of the 12 indexes ; T i is the dimensionless value of the ith index; R i is the actual value of the ith index, got from the statistics; P i is the standard value of the ith index, determined by the discussion in above texts.

The calculation of the agricultural informatization evaluation result

The final agricultural information evaluation result comes out from weighting the 12 dimensionless values, it ranges from 0 to 100. The total agricultural information evaluation result is also called agricultural informatization index, used in horizontal and vertical comparison. It is useful to thoroughly understand each province"s agricultural informatization development level and effectively develop each province"s agricultural informatization work. Formula (2) is to calculate the final agricultural informatization evaluation result, it comes out directly from weighting the 12 dimensionless values.

I =    12 1 i i i W T (2)
In this formula, i =1, 2…12, i is the number of 12 indexes;I is the agricultural informatization index;Ti is the dimensionless value of the ith index;Wi is weight of the ith index.

Demonstration of Calculation

In order to examine the accuracy of the above calculation method, this section takes A province as a example to demonstrate the process of agricultural informatization index calculation method (as shown in table 2).

ACKNOWLEDGEMENTS

The authors thank Ministry of Agriculture of People's Republic of China for their financial support.

THE CALCULATION METHOD OF AGRICULTURAL INFORMATIZATION INDEX SYSTEM

The Determination of Index Standard Value

Index standard value, also called target value every index needs to achieve, is a periodical target description for agricultural informatization development. The selection of the values refers to the current values of provinces that have got a good development in agricultural informatization. But the standard values are higher than the current values from those provinces. These necessary values for evaluation index system could be used to definite the direction of agricultural informatization development. According to related state statistics and the understanding of each province"s agricultural informatization, we tentatively definite standard values for 12 indexes, as shown in table 2. After more accurate understanding on each province"s agricultural The final evaluation result of A province agricultural informatization index is:

CONCLUSIONS

Embarking from the connotation of Agricultural Informatization,this article presents an agricultural informatization level evaluation index system, which includes 3 one-level indexes and 12 two-level indexes. Meanwhile, this article proposes the agricultural informatization evaluation method in detail. The established agricultural informatization evaluation index system has strong operability and orientation, each index is measurable and quantitative with data from statistics bureau, and provincial agriculture department market department or information ceter of each province. The index of agricultural informatization obtained from calculating could definite every province"s agricultural informatization development level clearly. And the standard value of each index can provide each province necessary reference data for the agricultural informatization construction.


Xianfeng Xu

Zhaoxia Chen

Bailin He

Application of Improved BP Neural Network in Controlling the Constant-Force Grinding Feed

Keywords: BP neural network, Feature parameter, Constant-force grinding, Feed

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The grinding is a method of machining with high precision. As a kind of grinding, the constant-force grinding is advanced to form curve shapes, which controls the force in the grinding and makes the wheel contact the workpiece uniformly. The premise of realizing the constant-force grinding is to recognize the feed of the wheel. Great attention has been paid by the researchers to the ways of monitoring the grinding on line [START_REF] Zhang | An Efficient Method for Solving the Signorini Problem in the Simulation of Free-form Surfaces Produced by Belt Grinding[END_REF] [START_REF] Haolin | Determination of Frequency Calculation Range of Feature Parameters in Grinding State Monitoring[END_REF]. Generally, several feature parameters which can reflect the state pattern of grinding are the main basis for this work. Thus whether the on-line monitoring can be done depends on the effectiveness of the feature parameters. In some references the feature parameters are picked up only in time domain or in frequency domain [3] [4], and by this method the grinding feed cannot be recognized precisely inevitably. As BP neural network has the distinctive advantage in merging various feature parameters, this paper presents the application of improved BP neural network on the basis of picking up the feature parameters of the vibration signals in the grinding. The aim is to recognize the grinding feed, control the amount of feed automatically and realize the constant-force grinding.

Principle of BP Neural Network

BP neural network defines the learning process consists of the forward propagation of the signal and backward propagation of error. In the forward propagation, the input sample is transmitted from the input layer, after processed by every hidden layer in sequence, then to the output layer. If the actual output cannot match the expected one (i.e. teaching signal), the backward propagation of error begins. This propagation means passing the output error through the hidden layers to the input layer reversely in some way. At the same time, the error is allocated to all units of each layer to obtain the error signal of each layer, which is the basis of correcting every unit weight. This adjustment of the weight for each layer is cycled, which is just the training process of the network. The process ends off when the output error is diminished to what is accepted or when the learning times is up to what is set in advance.

The main structure of BP neural network is that, the network is composed of nodes at different levels and the outputs from the nodes at a certain level are passed to those at next level. These outputs are amplified, attenuated or inhibited for different connection weight. Except the input layer, the input to each node is the weighted sum of the output from the former node. The stimulation output of each node is determined by the input, the activation function and the threshold. Figure 1 shows the main structure of BP neural network. The learning algorithm of BP neural network is the gradient search rule to make the mean square deviation between the actual and expected outputs of the network least. As the error is propagated backward, it is corrected simultaneously. This is the learning process of the network, which contains two steps: forward calculation and backward adjustment. The activation function is S-typed:

e x x f - + = 1 1 ) ( . (1)
The input to node l in the hidden layer is expressed by

∑ = + = n j l j lj l x w x net 1 ) ( θ . (2)
The output from node l in the hidden layer is determined according to ( ( )

l l h s net x ) = . ( 3
)
The input to node k in the output layer is expressed by

∑ = + = L l k l kl k h v h net 1 ) ( θ . (4)
The output from node k in the output layer is determined according to ( ( )

k k ) y s net h = . ( 5
) m k x w s v s y L l n j k l j lj kl k , , 2 , 1 ) ) ( ( 1 1 L
= + + = ∑ ∑ = = θ θ . ( 6
)
For a continuous mapping , a sample is the data representing the input and output, which is and is fit for the relationship:

m n R R F → : ) , , , ; , , ( 2 1 2 1 m n o o o x x x L L ) , , , ( ) , , ( 2 1 2 1 m n o o o x x x F L L = . ( 7
)
The error of network can be calculated by following equation:

∑ ∑ = = = - = m k k m k k k e o y E 1 2 1 2 2 1 ) ( 2 1 . ( 8
)
(here, that is the difference between network output and mapping output.)

k k k o y e - =
To make the network master this sample, the error must be as little as possible. This can be realized by adjusting the weights v kl and w lj , the simplest method of which is gradient descent algorithm.

( ) (

)

l s l s s st k k k k k m k k st h h h net s e net net y y e e E δ ν ν = ′ = ∂ ∂ ∂ ∂ ∂ ∂ = ∂ ∂ ∑ =1 . (9) ( ) ( ) ( ) ( ) ( ) (
)

j i j i ki m k k j i ki k m k k ij k k k k k m k k ij x x x net s x x net s h net s e w net net y y e e w E δ ν δ ν = ′ = ′ ′ = ∂ ∂ ∂ ∂ ∂ ∂ = ∂ ∂ ∑ ∑ ∑ - - = 1 1 1 . ( 10)
)) ( ( ) (

1 x net s v i m k ki k i ′ = ∑ = δ δ . ( 11
)
The correction value and

kl v Δ lj w Δ
is is proportional to error function and the relationship can be expressed

t s kl h v ηδ = Δ . ( 12
) j i lj x w δ η = Δ . ( 13
)
The threshold θ is variable and also need correcting, the principle of which is the same as the correction of weights.

Because local minimums often occur in BP neural network, certain momentums should be added to avoid it. Meanwhile, the adaptive learning rate must be used to speed the training [START_REF] Ferrari | Smooth function: Approximation Using Neural Networks[END_REF] [START_REF] Hornik | Universal Approximation of An Unknown Mapping and Its Derivatives Using Multilayer Feedforward Networks[END_REF].

Analysis of Grinding Experiment

The grinding experiment is made on the CNC external cylindrical grinding to obtain constant-force grinding. In the experiment, the acceleration sensor is fixed on the side of grinding carriage and the samples are picked up with the high-speed A/D sampler, the frequency (fs) of which is 1MHz. The workpiece is cylindrical and made from GCY15.The wheel is made from red corundum the granularity of which is 80.First, the vibration signals are recorded when the feed is 1μm、2μm、3μm respectively. Then the feature parameters about different feed are picked up to train the network. Finally, the trained network is used to recognize the feed, ensuring it is 2μm.

Features pick-up of signals

Some work must be done to fulfill real-time monitoring. That is to analyze appropriate data, judge the current feed, and give commands the grinding tool should go ahead or back. For instance, if the current feed is calculated to be 3μm and the expected is 2μm, the wheel is controlled to retreat for 1μm. If the quantity of data analyzed each time is too big, the processing time is long and the operation is not live. If the quantity of data analyzed each time is too little, the features picked up are representative. After a lot of tests, the quantity of data analyzed each time is 500.

From the 500 data analyzed each time, some features should be picked up in time and frequency domains, to study the features of signals comprehensively. In time domain, they are mean and standard deviation. In frequency domain, they are maximum value of amplitude spectrum and frequency there. A part of feature parameters are showed in Table 1, 2 and 3.Tests show any one in the four kinds of features cannot recognize the feed individually, because they are overlapped in every feed. So it is necessary to integrate the four kinds of features to recognize the feed precisely.

Construction and Training of Improved BP Neural Network

The first thing is to determine the structure of the network. According to Kolmogov theorem [START_REF] Yang Yong | Handwritten Digit Recognition Based on BP Neural Network[END_REF], an N× (2N+1) ×M three-layered network is designed as recognizer, whose structure is shown in Figure 2. Here, N is the number of components of input vectors and M is the number of output categories. In this paper, N is 4 and M is 3.So in the network there are 4 input nodes, 9 hidden-layer nodes and 3 output nodes.

When the output node is expressed as (1,0,0), the feed is 1 μ m. Similarly, (0,1,0)means 2μm and (0,0,1)means 3μm. In BP network model, the activation function of hidden layers is tansig and the one of output layers is logsig. Because in the three elements of output vectors one is 1 and the other are 0, the outputs must be processed by competitive transferring function [8] [9]. In order to train the network more fully, 40,000 sampling data are chosen, and from every 500 sampling data the features are picked up. The adaptive learning rate is used and certain momentums are added to improve BP neural network, because the velocity of its converge is very slow, and the network falls into the situation of local extrema easily. Figure 3 shows the training error curve of the network. From Figure 3, it can be seen that the network converges is very well.

The

Verification of the Network

To verify the recognition ability of the improved BP network, the author collects corresponding data respectively when the feed is 1μm, 2μm and 3μm. After picked up, the feature parameters are inputted into the network. The network can recognize the feed quickly and precisely. The verification code in MATLAB is as following.

%Read the data used to test the network from file which is made from sampling data. pp=load('test.txt'); %According to the feature of network, pp must be transposed. pp=pp'; %Test the network tt=sim(net, pp); %The output should handled by competitive transferring function, so that the outcome can be 0 or 1 tt=compet(tt);

Ending

The feature parameters in time and frequency domain of grinding vibration signals are fused into the improved BP neural network successfully. The feed of grinding tool can be recognized precisely. This method provides a new clue of integrating feature parameters from different domains in the grinding .Also, this method makes it possible that constant-force grinding transforms from experience-control to autocontrol. Practice proves its good effect.

2 Figure 1 .

 21 Figure 1. The main structure of BP neural network

Figure 2 .

 2 Figure 2. The structure of the actual BP Neural network

Figure 3 .

 3 Figure 3. The training error curve of the network

Table 1 .

 1 Feature parameters for the 1μm feed

	mean	standard deviation	maximum value of amplitude spectrum	frequency(KHz)
	1.423	55.269	16.591	87.890
	0.383	50.913	16.278	83.984
	1.170	38.368	10.379	76.171
	0.570	47.805	13.018	87.890
		Table 2. Feature parameters for the 2μm feed
	mean	standard deviation	maximum value of amplitude spectrum	frequency(KHz)
	1.092	38.570	10.649	58.593
	0.566	39.267	10.842	72.265
	0.403	50.029	13.463	91.145
	0.266	49.210	20.331	82.465

  program is compiled in MATLAB. The main code including interpretation is as following.

	%Read the training data from file which is made from
	sampling data.
	p=load('tezheng.txt');
	%According to the feature of network, p must be
	transposed.
	p=p';
	%Construct the network
	net=newff(minmax(p),[9 3], {'tansig','logsig'},
	'traingdx');
	%Set the starting value of network
	net.LW{2,1}=net.LW{2,1}*0.01;
	net.b{2}=net.b{2}*0.01;
	%Set the parameter of the network
	net.performFcn='sse';
	net.trainParam.goal=0.015;
	net.trainParam.show=100;
	net.trainParam.epochs=5000;
	net.trainParam.mc=0.97;
	%Read the target value of the network.
	t=load('target.txt');
	%According to the feature of network, t must be
	transposed.
	t=t';
	%Training the network.
	[net, tr]=train(net, p, t);


Lunhui Xu
email: lhxu@scut.edu.cn

Fan Ye

Yanguo Huang
email: huangyanguo2004@126.com

Study on Autonomous Path Planning by Mobile Robot for Road Nondestructive Testing Based on GPS

Keywords: road nondestructive testing, GPS, path planning, mobile robot

This paper analyses the statements of video imaging, CT, infrared thermography and radiography applied in the road nondestructive testing, and design a path planning mobile robot with GPS positioning which can remarkably increase the efficient of road nondestructive testing. Besides, appropriate algorithm for nondestructive testing on the road autonomous mobile robot path planning is given. This method is simplicity, versatility, and efficiency. The mobile robot are selected for example and the simulation results represent the effectiveness of this method

Introduction

Because of the natural factor and long-term used, roads may be damaged. The damaged road is harmful to the safety of vehicles, so the regular road testing is necessary.

While testing the road's structure, the destructive testing may descend the traffic capacity and waste a lot of money and time. So, to meet the requirements of road development and ensure road traffic safety, nondestructive testing (NDT) is becoming more and more important. But the traditional method of road nondestructive testing is manual operation, which can only carry out a limited number of testing, and may not achieve long-term monitoring. In addition, traffic flow on major highways, testing personnel may be braved danger. Therefore, making the manual testing developed to automated testing technology, and low-speed, low accuracy developed to high-speed, high precision is critical important. So, a fast, accurate and nondestructive road testing method is needed urgently.

The constant improvement of science and technology promotes the growth of mobile robot for road nondestructive testing. Instead of human beings, mobile robot for road nondestructive testing can suffer a large number of works with strong intensity, long time, high repeatability and terrible environment. These objective requirements propel the widely development in the study of mobile robot. Path planning is one of the important core problem and technique in the research area of mobile robot that not only develops fast but also contributes a lot [START_REF] Shi | A Survey of Environment Modeling in Mobile Robot: Design and Research[END_REF]. Path planning by mobile robot refers that it can search for the best (or second-best) and reasonable moving strategy from the initial status (including position and angle) to the final status by following the planed path. It is the foundation for the mobile robot to execute different kinds of tasks. In this paper, appropriate algorithm for nondestructive testing on the road autonomous mobile robot path planning is given .The feasibility of the scheme and algorithm proposed in this paper was verified by the experimental prototype test data.

Current Statements of Road NDT

Currently, the major testing methods of nondestructive testing methods are including video imaging, CT, infrared thermography and radiography. The introductions of all of the methods are as follows:

Video imaging

Video imaging systems have become versatile data-gathering tools because of their high spatial resolution, near real-time image availability and computer compatibility. Current video imaging is widely used for non-Destructive testing. There are a lot of digital video imaging systems which can be used for the pavement broken testing such as PAVUE and WiseCrax [START_REF] Zhang | The Application of Digital Image Processing to Road Nondestructive Test[END_REF].

These systems are widely used for Nondestructive testing; the steps of using these systems are as flows: 1. Get the image of the pavement, and then input this digital images into computer; 2. Change the consecutive images into still images, use the image processing technology to analyze and classify the still images. 3. Evaluate the road damage level and parameter such as crack breadth, length and square and so on. Meanwhile, according to the GPS, passion the damage section.

Computed tomography (CT)

In highway construction, to ensure the quality of engineering, relative mass detection is needed in a wide range including tamping foundation, rolling sub grade and paving pavement. The traditional detection is to on-site observe the roads and sample from drilling for the laboratory analysis, so that the control parameter of engineering quality, such as thickness, depth, degree of compaction and strength, can be obtained. However, this routine method has some limitation. First, it can be omitted easily although the measured points are selected randomly so that they should be representative to some extent. Second, the problems about the damage of the pavement, such as crack and sag, can be easily found, while the inner problems of the roads are difficult to find out. Therefore, some nondestructive, fast and direct test equipment and technique that can show the inner status of the roads is necessary for the improvement of highway construction and maintenance level.

CT was invented in 1972. In the early stage it was only applied in X-ray, then the application was widely extended, including emission computed tomography (ECT), ultrasound computed tomography, magnetic resonance imaging (MRI) and so on. This technique was at first applied in the field of biomedicine. The basic method of CT is image reconstruction, which refers to the reconstruction of the cross-section image obtained from projections of measured objects by the computer. Application of CT in road quality testing is like perspective diagnosis of the roads, which can help us to detect the diseases promptly and have a direct view. This technique has been used in the pavement detection by ground penetrating radar. The road radar, which is based on the use of transient electromagnetic waves, emits high-frequency spike pulse electromagnetic waves to the underground by wide time-domain transmitting antenna. When waves meet different interfaces of dielectric during the underground transmission, reflection happens. Then it is received by receiving antenna and handled by the computer, which can analyze the obtained digital signal, so that we can get three dimensional computed tomography imaging for the underground target. Accordingly, we are able to clearly observe the cavity of sub grade, thickness and deformation of the pavement structural layer, even the water under the surface.

Infrared thermography (IRT)

Infrared thermography (IRT) has established as a fast and reliable tool in many areas of non destructive testing. In recent years, IRT has emerged as a widely used method for road nondestructive testing. The IRT is bound up with thermal conductivity. With the influence of heat source, objects' surface will have some thermal spread. The difference of the thermal distribution can be used to detect the material difference under the objects' surface. IRT can image and orient horizontal plane and holes under the road efficiently.

Compare with other NDT methods, IRT has following advantages: 1. Fast inspection rate, which can up to a few m 2 at a time; 2. No contact. No couplant needed although in some cases a black painting step is required to perform the inspection so that strictly speaking, a contact is thus present in this case; 3. Security of personnel. Since there is no harmful radiation involved, however high power external stimulation-such as powerful flashes-requires a shroud; 4. Results are relatively easy to interpret; 5. Unique inspection tool for some inspection tasks.

But on the other hand, there are some difficulties specific to IR thermography as follows:

1. Difficulty in obtaining a quick, uniform and highly energetic thermal stimulation over a large surface; 2. Effects of thermal losses (convective, radiative) which induce spurious contrasts affecting the reliability of the interpretation; 3. Cost of the equipment; 4. Capability of detecting only defects resulting in a measurable change of the thermal properties; 5. Ability to inspect a limited thickness of material under the surface; 6. Emissivity problems.

Radiography

Radiography is a method that makes use of strong radiation, such as X-ray, to detect the inner situation of the object. Now this technique has been applied in the nondestructive test of the concrete. According to the sensitive photograph obtained from the steel bar and its defect inside the concrete, quantitative assessment is available by using the digital image analytical technique.

Besides, other imaging technique, such as ultrasonic imaging, interference imaging, is widely used in the test of road material. The application of these technique remarkably increases the quality of the road nondestructive testing, thus has an important role in enhancement of the development of our highway and transportation.

The System and Mechanical Structure of the Mobile Robot

The system in introduction

The system structure of autonomous path planning mobile robot for road NDT shows in the Figure 1. The whole system using distributed control mode. First of all, used the GPS coordinate acquisition equipments to get the path information, then according to the coordinate information and the initial and final statement of the mobile robot, get robot motion curve function, and use the PC to figure out the kinematic parameter from the motion curve and kinematic model, send the parameter to the lower computer, which use AT89C51 single chip to control the step motors. Meanwhile, the NDT equipments on the robot sent the road information to the lower computer. After finish the whole route, the robot sent the road testing data back to the PC.

The structure of the mobile robot

The research object of this paper is based on the mobile robot which is researched and designed by SCUT. The mobile robot use differential drive method, there are three wheels on the robot, two wheels are controlled by two step motors at front of the robot, which are as the drive wheels, the other rear wheel using flexible wheel at back to support the body of the robot. The moving direction is controlled by speed differential of the front two wheels.

Fitting for the Path Moving Function

Here, we use the regression equation to fit the moving function of the planed path. Regression equation is a statistical technique which can be used to predict the value of one variable using the value of one or a quantitative variable. According to the relationship between the two variables, regression equation can be divided into linear and nonlinear. According to the number of the variable, regression equation can be divided into unary and multiple.

Linear multiple regression equation

Supposing y is a random scalar, which has linear relationship with independent variables 1 2 , ..., n x x

x , and according to the value of n samples 1 2 ( , ,... ; )

i i i n i x x x y ( 1, 2,... ) i n = ,
= = -- - -- ∑ L ( 2
)
Where Q is the function of 0 b , 1 b , 2 b ,…, m b . According to the extreme value theory of multivariable function [START_REF] Fiorini | Time Optimal Trajectory Planning in Kinematic Environments[END_REF], when Q get the minimum value:

0 1 1 2 2 1 0 0 1 1 2 2 1 2 ( ) 0 2 ( ) 0 n j j j m m j j n ij j j j m mj j i Q y b bx bx b x b Q x y b bx b x b x b = = ∂ ⎧ =- -- - -- = ⎪ ∂ ⎪ ⎨ ∂ ⎪ =- -- - -- = ⎪ ∂ ⎩ ∑ ∑ L L ( i =1、2、…、 m ) (3)
With above, (1) can be rewritten as :

+ Σ + Σ + + Σ = Σ ⎧ ⎪ Σ + Σ + + Σ = Σ ⎪ ⎪ Σ + Σ + + Σ =Σ ⎨ ⎪ ⎪ ⎪ Σ + Σ + + Σ =Σ ⎩ L L L L (4)
From the first equation of (4), we can get the following expression:

0 11 22 ˆˆˆm m b y bx b x b x = - - -- L ( 5)
Substituting ( 5) into(4), we have

1 1 1 11 12 1 2 2 21 22 2 1 2 ˆ ˆ ˆy m y m m m mm my m l b l l l l b l l l l l l l b - ⎡ ⎤ ⎡ ⎤ ⎢ ⎥ ⎡ ⎤ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ = ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎣ ⎦ ⎢ ⎥ ⎢ ⎥ ⎣ ⎦ ⎣ ⎦ L L M M L (6)
Where:

1 1 ( )( ) , , [1, ] ( )(
)

uv iu u iv v m uy iu u i m l x x x x i u v m l x x y y ⎧ = - - ⎪ ⎪ = ⎨ ⎪ = - - ⎪ ⎩ ∑ ∑
After this, the value of 0 b can be got when substituting 1 2 ˆˆ, ,... m b b b into equation [START_REF] Kim | Jun-Hooh: Globally Asymptotically Stable Tracking Control of Mobile Robots[END_REF]. So the linear multiple regression equation can be got as (1) shows.

Nonlinear regression equation

In statistics, nonlinear regression is a form of regression analysis in which observational data are modeled by a function which is a nonlinear combination of the model parameters and depends on one or more independent variables. The data are fitted by a method of successive approximations. Some nonlinear regression problems, such as hyperbola regression model, logarithmic regression model and polynomial regression model can be moved to a linear domain by a suitable transformation of the model formulation [START_REF] Zuang | Statistics Statistical[END_REF]. But others have to deal with nonlinear regression model such as exponential model and S curve model.

Kinematic Modeling for the Road NDT Mobile Robot

Mobile robot kinematic modeling is the foundation of its motor system control. Accurate kinematic model plays a key role in the motion control system.

Curve fitting of moving mobile robot

It can be seen from Figure .2, when robot reached the point n, the track for robot moving from point n to point n+1 can be derived by fitting the robot moving function based on point n to point n k + . In this paper, we takes k=5, and only use 3 order polynomial regression models to fit the curve. So, the form of the moving function is as follow:

2 3 0 1 2 3 ( ) n n n n y n b b x b x b x = + + + (7)
At this time, we can calculate the mobile control mode according to [START_REF] Fiorini | Time Optimal Trajectory Planning in Kinematic Environments[END_REF]. When the robot reached point

+ + + + + = + + + (8)

Fig.2. Curve fitting of the mobile robot

Just like formula [START_REF] Fiorini | Time Optimal Trajectory Planning in Kinematic Environments[END_REF], we can calculate the mobile control mode according to formula (8), and so on.

As above done, the mobile robot can move along the track which is planned beforehand by adjusting the robot curve. x y , and the angel of the robot and the horizontal plane is n ϕ( ) . In the ideal case, the direction of the moving velocity is the same as the moving curve tangent line. So: sin ( ) cos 0 , and the angel between the robot and the horizontal plane is 1 n ϕ( + ).

Moving modeling for the mobile robot

n n n dx dy n ϕ ϕ - = (9) from(9)
As shown in Figure .3, θ is the turning angel of the mobile robot, according to the lane Analytic Geometry [START_REF] Zuang | Higher Mathematics[END_REF], we can get:

θ ϕ ϕ + + + = - = - (11)
(11) Use the Pythagorean theorem, we can get : (2 sin )

2 x y R θ Δ +Δ = ( 12) Then 2 2 0 2sin 2 0 x y R θ θ θ ⎧ Δ +Δ ⎪ ≠ ⎪ = ⎨ ⎪ ⎪ +∞ = ⎩ (13)

Method of Calculating the Kinematic Parameter for the Mobile Robot

Kinematic system is one of the important core problems and technique in the research area of differential drive mobile robot control system, and the moving curve of the robot is decided by the robot moving system.

Generally speaking, all the moving tracks of the robot are made up of straight lines and curves. So even if the robot moves in different route, the control method is the same. Just like above, the robot's moving tracks can be control accuracy by controlling the ratio of the two driving wheels well. S is the right one.

The kinematic parameter of the robot when curvilinear moving

As shown in the Figure .3, in the case of curvilinear moving, when the robot is turning left, 1

n n ϕ ϕ + ≠ , , 0
θ ≠ the speed of the left wheel is not the same as the right one. So, at this time, from the meaning of k , we can get:

t ( ) 1 v v t S v v S R b b k R R θ θ ⋅ - = = = = = - ⋅ 1 1 1 2 2 2 (16)
Where: v 1 --the speed of the left wheel; Use(13)input (16) can get:

Δ + = - + Δ - (18)
When the robot is turning right:

2 2 t ( ) 2 s 1 v v t S in 2 1 b x y v v S R b b k R R θ θ θ ⋅ + = = = = = + = Δ ⋅ + Δ + 1 1 1 2 2 2 (19
Δ + = + + Δ - (20)
When the mobile robot is curvilinear moving, the distance of the robot moved is:

1 2 ( ) ( ) 2 2 S S R b R b S R θ θ θ + - + + = = = ( 21)

Example

In order to prove the accuracy of the model set above, we used the mobile robot and PC to simulate this kinematic model. Fist of all, got the information of the moving path, the sampling of the coordinate information is as shown in Figure .5. Because of the road friction, there is error exist during the testing. But the error is less than 0.2%. So, the model can be thought accuracy.

Sampling of the coordinate information

Conclusion

This paper analyses the situation about the methods of video imaging, CT, infrared thermography and radiography applied in the road nondestructive testing. Besides, appropriate algorithm for nondestructive testing on the road autonomous mobile robot path planning is given .The feasibility of the scheme and algorithm proposed in this paper was verified by the experimental prototype test data.

Fig. 1 .

 1 Fig.1. The system structure of autonomous path planning mobile robot for road NDT

  we can get the least squares estimation 0 1 2 ˆˆˆ, , ,... m b b b b . A multiple regression equation for predicting ŷ can be expressed as follows:

  .......................................

1 n

 1 + from point n , the new track for robot moving from point n+1 to point n+2 can be derived by fitting the robot moving function based on point 1 n + to 1 n k + + . The form of the moving function is as follow:

Fig. 3 .

 3 Fig.3. Mobile robot's moving curve is a circle with radius R.

Figure. 3

 3 Figure.3 shows that the mobile robot's moving curve is a circle with radius R.

  the mobile robot moves from O to O', and the coordinate of O'

Where

6. 1

 1 The kinematic parameter of the robot when straight-line moving As shown in Figure.4, in the case of straight-line moving, 1 and the speed of the left wheel is the same as the right one. If taking the speed ratio of the left wheel and right wheel as k , then k =1 in this case.

Fig. 4 . 1 S

 41 Fig. 4.The robot is in the case of straight-line moving At this time, the distance of the robot moved is:

1 S --the distance of the left wheel moved 2 v

 12 --the speed of the right wheel; S --the distance of the right wheel moved; b --the distance from the left wheel to the right one.

Fig. 5 .

 5 Fig.5. Sampling of the coordinate information

Fig. 6 .

 6 Fig.6. The path of the robot moves

Table 1 .

 1 Fitting out the moving function After this, use the kinematic model to get the kinematic parameters; the PC simulates picture shows in the Figure.6.

	Function	b0	b1	b2	b3
	1	1	1.02	-0.07 -0.11
	2	0.97	1.15	-0.22 -0.06
	3	0.83	1.48	-0.47	0
	4	0.53	2.04	-0.8	0.06
	5	0.03	2.75	-1.13	0.11
	6	-0.52	3.43	-1.4	0.15
	7	-0.83	3.75	-1.51	0.16
	8	-0.4	3.35	-1.39	0.15
	9	1.34	1.92	-1	0.11
	10	4.91	-0.71 -0.36	0.06
	11	10.47	-4.44	0.47	0
	12	17.63	-8.85	1.37	-0.06
	13	25.32	-13.2	2.2	-0.11

ABOUT THE AUTHOR(S)


Huaji Zhu
email: zhuhuaji@126.com

Huarui Wu

Xiang Sun

Reasons of the Incremental Information in the Updating Spatial Database

Introduction

How to distribute the new data in the updated topological database is becoming a hot topic in GIS domain [START_REF] Badard | Using XML for the exchange of updating information between geographical information systems[END_REF][START_REF]Some elements for modelling updates in topographic databases[END_REF]. Currently, the new data of topological database is distributed by the batch method which has such problems as the data volume distributed is very big and users are difficult to integrate the distributed data in their database [START_REF] Badard | Towards a generic updating tool for geographic databases[END_REF][START_REF] Claramunt | Toward Semantics For Modeling Spatio-Temporal Processes, Within GIS[END_REF][START_REF] Zhou | Event-based Incremental Updating of Spatio-Temporal Database[END_REF]. According to the Stat. by Raynal [START_REF]Some elements for modelling updates in topographic databases[END_REF], the change rate every year of the geographical objects in a geographical database is about 10%. For instance, comparing to the 1998 version of the 1.250000 topographical database (a kind of special geographical database), the change rates of all kinds of topographical feature in 2002 version of the same database are respective 9.7% for water feature (2.4% every year), 27.9% for district boundary feature (7% every year), 20.9% for road feature (5.2% every year), 34.4% for point residential feature (8.6% every year) and 75.3% for area residential feature (18.8% every year). According to above percentages, we can find that most features' change rates every year are less than 10% except for the area residential feature.

If the batch method is used, about 90% distributed data are invalid. In practical, users only focus on the changed data in the updated database, i.e. the data delta between old version and new version of the same database. So providing data delta not the whole new updated database can overcome above mentioned problems in the batch method [START_REF] Spéry | A Spatio-Temporal Model for the Manipulation of Lineage Metadata[END_REF][START_REF] Badard | Using XML for the exchange of updating information between geographical information systems[END_REF][START_REF] Lin | Modeling spatial database incremental updating based on base state with amendments[END_REF].

Data delta result from the change in real world. Different change results in different data delta, that is to say, every change type is corresponding to certain data delta. If only data delta is provided, the users sometimes don't know how to integrate the data delta in their database without knowing the reasons (change type) of the data Keywords: Incremental Information Spatio-temporal change Subjective changes Objective changes

ARTICLE INFO

In order to quickly extract and distribute the incremental information of spatial database, this work analyses the reasons of incremental information of spatial database with the instance of residential feature. Firstly, a new concept of increment information is proposed based on the spatio-temporal change type and data delta. The change type describes the semantics of increment information and data delta describes the content of increment information. Based on this concept, the types of residential change and relevant data deltas are put forward. There are two catalogs of change type, i.e. subjective change and objective change. The subjective change can be detailedly divided into 3 small change types and the objective change can be detailedly divided into 12 small change types. Additionally, the incremental information resulted from the objective change is formalized. Based on the above formalized changes, the automatic extraction of incremental information can be expediently programmed. The classifying result was applied to the incremental information automatic extracting system of 1.250,000 topographic databases. the data delta △. For instance, one reason is the disappearance of a residential feature in the real world. The second reason is that the area of a residential feature is less than the area threshold of certain scale because of shrinking. In the certain scale database, a feature can be collected in the database if its area is more than or equal to the area threshold, otherwise the feature should be abandoned [START_REF] Wang | Incremental Maintenance of Global Templates in Heterogeneous Data Integration System[END_REF][START_REF] Claramunta | Modelling concepts for the representation of evolution constraints[END_REF]. However, the problem will completely vanish if data delta and its change type are provided together. In this work, we call the combination of data delta and change type as increment.

Relationship between change and increment

The relationships among change, data delta and increment are showed in Fig. 1.

Firstly, we explain the concept "geographical feature". The concept commonly is called as geographical entity in the real world and as geographical object in the data world. The changes of geographical entities in the real world or the changes of data collection standards may result into update operations which modify the content of the database and produce the data delta. The data delta resulted from every update operation is

Reasons of increment

There are two reasons to generate the increment information, subjective reason and objective reason. The objective change is the changes of geographical features in the real world. The subjective changes result from the change of data model, the defined attribute set of every feature and so on.

Subjective changes

There are three kinds of subjective changes, the changes coming from the errors amendment, the changes coming from the change of correction rules and the change of database scheme. The subjective changes of spatial data are ruleless and have no uniform expression.

The errors amendment may generate the data delta. For example, the grade of a road is amended from the national road to provincial road. The data delta is 200542.Grade.-"national road"/+"provincial road". The 200542 is the ID number of road. The increment information is [error amendment,update, 200542.Grade.-"national road"/+"provincial road"].

The change of correction rules may generate data delta. For example, the district feature has the attribute of district code. The rule of the district code is different between the old database and new database. For example, the old district code of the "Zhuo zhou city" is 121024, but the new district code is 132402. In fact, the "Zhuo zhou city" has no change. But because the change of the code rules, the feature generates some increment information. Another example, in the old data of the 1.250000 topological database, the database field 'FName' denotes the name of the feature. The values of 'Fname' don't include the district grade. But in the new data of the 1.250000 topological database, the values of 'Fname' include the district grade. For instance, a village is called as "Chang Cao" in old data but is called as "Chang Cao Cun" in the new data.

The change of database scheme also may generate increment information. The topological database is established based on a data scheme. The database can be established based many kinds of data scheme. In the scheme, the attribute set of every feature can be changes according to the real world. So, the scheme of a topological database often changed. For example, the residential feature has a field "PYNAME" in the old data of the 1.250000 topological database. But in the new data, the field "PYNAME" is deleted.

Objective changes

There are many kinds of objective change in the real world. [START_REF] Claramunt | Toward Semantics For Modeling Spatio-Temporal Processes, Within GIS[END_REF][START_REF] Shu | Principles of Conceptual, Formal and Logical Spatio-temporal Data Modeling[END_REF] defined the event types expressing the changes. In the real world, different geographical feature with the same event may generate different changes. So, we give a new change expression based on the event type and the data delta. In this work, we only consider the simple events, that is to say, the event which affects an attribute of the geographical feature. But in the real world, there are many complex events. We can compound the changes generated from complex events based on the changes of the simple events. According to the alterant extent, the event affected the change is divided into the evolution event and the death/birth event. The death/birth event can cause the appearance of new feature or the disappearance of old feature. The evolution event may cause the change of the characteristics of feature. The evolution event can be future divided into spatial evolution event and theme evolution event.

[4, 5] defined eight kinds of death/birth event, including appearance, disappearance, split, unite, reallocate, replace and produce. Based on the practical facts, we define six kinds of death/birth event, including appearance, disappearance, divide, split, unite, and merge into, as showed in Fig. 2, the &n in figure is the identification ID.

Different death/birth processes correspond to different update operations. This means the different death/birth processes generate different increment information. The Fig. 2 shows six kinds of increment information. In Table 1, the increment information and update operations resulted from the death/birth changes are given. For instance, Fig. 2 1 shows the increment information and update operation of death/birth evenets.

Table 1

The increment resulted from the birth or death evenets. [START_REF] Claramunt | Toward Semantics For Modeling Spatio-Temporal Processes, Within GIS[END_REF][START_REF] Shu | Principles of Conceptual, Formal and Logical Spatio-temporal Data Modeling[END_REF] defined seven kinds of evolution event, including move, deformation, contraction, rotation, discoloration, density increase and texture change. Based on the practical facts, we define four kinds of evolution event, including move, contraction, expand and incomplete merge into, as showed in Fig. 3, the &n in figure is the identification ID. Table 2 shows the increment information and update operation of evolution evenets. The increment resulted from the evolution events. Table 3 gives the increment information of the attribute change.

Table 3

The increment resulted from the attribute evolution processes

Application to the Case Study

Based on the above method, we developed software to extract the increment information. The core component of the software is the rule database. The rules are established according to the change type and the corresponding increment information. Every change type can be expressed as one or more rules. For example, the "Expand" of the residential feature can be expressed two rules. Fig. 5 gives some instances of increment information. The flow of the software is divided into 6 steps.

1) Load data. Load the old and new data of the same feature and same area.

2) Data pretreatment. check the invalid data and amend it.

3) Compute change types. based on the old and new data, extract the change types of every feature.

4) Correspond rules. find the rules from database based the change types.

5) Extract increment information

Conclusions

The focus of this work was on how to help the exchange of updating information between a spatial data producer and a user. In order to more clearly distinguish the increment change of geographical object, we analyses the increment information of the geographical feature.

A new model of increment information is proposed based on the spatio-temporal change type and data delta.

  ABSTRACT delta. For instance, data delta △ = -[OID, O.A.V, O.S.G] denotes a deleted residential feature O, OID denotes the ID of O, O.A.V denotes the attribute set of O, O.S.G denotes the graphics of O. Several reasons can result in

  exclusive and certain. However, different change types correspond to different update operations. So the different change produces different data delta. In this paper, we define 4 kinds of update operations and relative data delta. The 4 update operations are object deletion, object creation, graphics update and attribute update. The data delta of the object deletion is △={-[OID, O.A.V, O.S.G] }. The data delta of the object creation is △ ={+[OID, O.A.V, O.S.G] }. The data delta of the graphics update is △={O.S.-G/+G'}, G is the value before change, G' is the value of after change. The data delta of the attribute update is △={O.A.-V/+V'}, V is the value before change, V' is the value of after change. By establishing the certain relationship between the change type and the set of update operations, the data delta of every change can be exclusively decided. So the increment which is composed of change type and data delta can be decided.

Fig. 1 .

 1 Fig.1. The relationship among change, data delta and increment

Fig. 2 .

 2 Fig.2. The change process resulted from the birth or death events

  , { Update the graphics of &17, Update the graphics of &18},{ O1.S.-G1/+G1',O2.S.-G2/+G2' } Current researches can't give the change type of attribute event. Based on the practical facts, we define two kinds of attribute change, i.e. the relative attribute change and the generic attribute change, as showed in Fig. 4.

Fig. 3 .

 3 Fig.3. The change process resulted from the spatial evolution events

  grade change, Update the Grade and Name Field of &19,{&19.Grade. -31080/+31091,&19.Name.change, Update the Name Field of &20 , O.Name.-Nanshiliju Town /+ Dongfeng Town

Rule 1 .

 1 IF feature R has the change "Expand" And R exists in the old database Then increment information=[ Expand, Update the graphics of R, R.S.-G/+G']. Rule 2. IF feature R has the change "Expand" And R doesn't exist in the old database Then increment information=[ Expand, Create R, +[R, R.A.V, R.S.G]]

Fig. 5 .Fig. 4 .

 54 Fig.5. The instances of increment information

  The model not only clearly represents the semantics of change but also describes the data delta of change. Based on the model, we can get the reason of the change. In this work, we define 15 kinds of change type, including 3 kinds of subjective change and 12 kinds of objective change. And the increment information expressions of every change are given. The method can be used to extract the increment information. But the method proposed in this work can't be used to express the increment information of subjective change.

Acknowledgment

This paper is supported by a grant from Beijing Nova program (Grant No. 2009B26), a grant from Beijing Academy of Agriculture and Forestry Sciences for Young Scholars and the National Natural Science Foundation of China (Grant No. 60871042).


Yang Yang

Yanqing Zhao

Application of CPLD in Pulse Power for EDM

Keywords: EDM, CPLD, VHDL language, pulse power 1

In order to improve the precision and surface quality of Electrical Discharge Machining (EDM), the paper studies the application of complex programmable logic device (CPLD) used in pulse power for EDM, according to the characteristics of the device, using VHDL language input and schematic input method to design control circuit for EDM pulse power.

Introduction

EDM is a special processing method, which makes use of the continuous pulse spark discharge generated by two poles in working fluid, relying on each discharge generates partial and instantaneous high-temperature to ablate down metallic material gradually, cutting into necessary shapes, it is also called as discharge machining or electric erosion processing [START_REF] Liu | Special machining[END_REF]. Pulse power supply as an important part of machine tools of EDM, provides the required breakdown voltage for processing media, and provides energy to ablate metal after breakdown, its performance is well or not will determine the stability of processing equipment and the height of production efficiency [START_REF] Gao | Research on a Micro Energy Pulse Power Supply for Micro-EDM[END_REF][START_REF] Han | Experimental Attempts of Sub-Micrometer Order Size Machining Using Micro-EDM[END_REF]. With the development of EDM application technology, the research on technology of pulse power is more and more in-depth.

In the 1980s, it advanced the high and low pressure complex pulse and combshaped pulse (pulse group). Subsequent, it advanced the controlled current rising edge pulse and equal energy pulse, and nowadays has proposed to a new technology which can achieve single discharge pulse detection. Now people study of the pulse power variable parameters more refined, parameter adjustment and digital level higher. In this case, if it continues to use discrete components or small and medium-scale integrated circuits as the basic original, obviously doesn't meet the design requirements of power nowadays with high integrated, fast changing, good controllability.

From 80 to 90 during the 20th century made the high and low pressure complex, comb-shaped pulse (pulse packet), the subsequent rising edge of control current, such as the energy pulse, and has proposed to achieve a single discharge pulse detection technology, people Pulse variable parameters of a more detailed, parameter adjustment and further quantify the number of higher degree. In this case, the EDM pulse power supply circuit such as to continue to use discrete components and small and medium-scale integrated circuits as the basic original, obviously does not meet today's power highly integrated, fast changing, good controllability of the design requirements.

Characteristics of CPLD devices

The first programmable logic device -PLD was produced in the 1970s, and it's output structure is programmable logic macrocell, because the design of hardware architecture was completed by the software, which like that workers designed the interior structure after the house completed, so its design is more flexible than pure software on digital circuits. But its structure is too simple so that they only can achieve smaller circuits. To make up for the flaw that PLD only can design smallscale circuit, in the middle of 1980s, the complex programmable logic device was invented-CPLD [START_REF] Liao | CPLD digital circuit design[END_REF][START_REF] Xue | Application of CPLD in pulse power of EDM[END_REF].

With programming flexibility, high integration, design and development period is short, wide scope of application, development tools is advanced, low costs of design and manufacturing, the experience of the designers requirements low, standard products without testing, confidentiality, price moderate, etc, and it can realize largescale circuit design, so the complex programmable logic device -CPLD is widely used in prototype of product design and production (In general under 10,000 pieces). Almost all the situation where small scale general digital circuits were applied can applied CPLD device. In recent years, because advanced integration process was Adopted, CPLD device was produced in quantity and its costs continue to drop. Integrated density, velocity, and has been greatly improved. And its integrated density, velocity, capability has been greatly improved. Because it has a lot of advantages, such as high reliability, excellent electromagnetic compatibility (EMC) and micropower consumption, CPLD has been widely used in the field of numerical control. What is particularly important is that CPLD has the function of online programming (ISP) (according to different requirements, just a software re-programming, you can complete the online programming), which makes function update and system debugging more convenient and greatly shorten the product development cycle, also it is great convenience to design and modify.

EDM pulse power circuit structure

This pulse power takes CPLD and MOSFET as the core component. Through the programming on the CPLD, it can realize the steering impulse adjustment. This pulse takes the driving of circuit applied to the MOSFET as the power switch, and provides pulse power for the processing circuit. The power schematic is shown in Figure 1. CPLD can construct digital logic integrated circuits for users' own needs, and it is suitable to be used to realize each kind of operation and combinatory logic. Furthermore, it can achieve large-scale circuit design for its many kinds of characteristics, such as programming flexibility, high integration, advanced develop kit, low design cost and so on. ALTERA's MAXII series chips are high speed and support for the global maximum input clock frequency up to 304 MHz. By using the chip to carry on the pulse control signal disposition, it can output the disposition information for the external instrumentation, simultaneously may receive the superior information. In the VHDL design, two common objects are signal and variable [START_REF] Zhao | VHDL and computer interface design[END_REF], signal is a means of date exchange in the design entity, using signal objects can connect design entity to form module, representative of a hardware circuit in the hardware connection, sometimes the signal will be integrated into register, while variable mainly stores temporary data locally, it is a local variable. Within a process, signal processing is delayed, namely only after the arrival of the next clock for signal processing, but the variable processing is real-time. Therefore, after entering the process, the signal of On-time and Off-time need to be converted to variable form. Variable of Time-Count judging and variable of Count computing are real-time .When Time-Count equals 0, that is, the high pulse or low pulse is a clock cycle, and the program directly jumps out from process. The minimum output pulse increased to a clock cycle by the pulse entering output value .after using QUARTUSII simulation, waveform is shown in Figure 4.

Conclusions

The designed control circuit has many advantages, such as stable performance, strong anti-interference ability, precise pulses, good scalability, fully embodies the characteristics of CPLD devices.

The CPLD devices applied to the design of EDM pulse power, improved the flexibility of circuit design, reduced PCB area, increased power system reliability, shortened product development cycle, and reduced design costs and application costs, it can meet the increasingly complex electronic processing equipment control circuit applications.

Fig. 1 .

 1 Fig. 1. Schematic diagram of pulse power

Fig. 2 .

 2 Fig. 2. Pulses control program principle framework time parameters for pulse high and low, mainly through the multiplexer time-sharing operation to the next level. The multiplexer selection is carried out by the output

Fig. 3 .

 3 Fig. 3. Pulses control program flow chart

Fig. 4 .

 4 Fig. 4. Pulses width 10ns and inter-pulse 50ns simulation graph CPLD uses the source crystal oscillator to provide clock pulses, taking into account the conduction ability of MOSFET .we choose 100M crystal which can provide minimum pulse period of 10ns


Yantao Dou

Xiaoli Xu

Wei Wang

Siqin Pang

AE feature analysis on welding crack defects of HG70 steel used by truck crane

Keywords: HG70 steel, Welding crack, Acoustic Emission

In present paper, using Acoustic Emission (AE) as a ultrasonic technique, Welding crack has been investigated on the HG70 steel used by truck crane during three-point bending test was carried out.

The study shows that the activity of AE increases greatly during the welding crack initiates and propagates, and the distributing range of AE characteristic parameters can be fixed. AE signals of welding crack initiation and expansion are typical sudden signals which have a different spectrum energy distribution range with that of unstable crack propagation.

Introduction

As an important loading and transporting machine, truck crane is widely used in various fields of national economy. To achieve high efficiency and large scale production, truck cranes are often overloaded, which result in accidents frequently. The main cause lies in the damage of structural parts, for example, boom fracture brought about by fatigue crack or weld defects.

The current popular detecting technologies include macroscopic artificial inspection, ultrasonic testing, magnetic particle testing, radiographic testing, penetrant testing, stressstrain testing and so on, which often fail to have a complete and real-time detection of crane metal structures since they are often used as periodic testing or testing afterwards [START_REF] Wu Zhanwen | Application status of acoustic emission technology to crane's nondestructive test[END_REF]. Acoustic Emission (AE) technology can just make up the deficiency of conventional detection methods. During the past 20 years, some research works have been carried out to the aerial personnel devices or crane structure [START_REF] Wu Zhanwen | Characteristics of Acoustic Emission Testing During the Propagating of External Crack on the Crane Box Beam[END_REF][START_REF]Standard Test Method for Acoustic Emission for Insulated and Non . Insulated Aerial Personnel Devices Without Supplemental Load Handling Attachments[END_REF][START_REF] Gordon | Assessing the structure integrity of crane booms using acoustic emission[END_REF].

The aim of this paper is to investigate the AE signals during three-point bending test of welding specimens made of HG70 steel. The typical characteristic parameters and waveform features of the AE signals are collected and extracted at the three development stages of welding crack, namely as crack initiation, growth and fracture [START_REF] Ennaceur | Cherfaoui Monitoring crack growth in pressure vessel steels by the acoustice mission technique and the method of potential difference[END_REF], and the crack position was located by way of linear location.

Preparation

Specimen Preparation

HG70 steel, commonly used in crane booms and legs, is chosen as the experimental material. Table 1 and Table 2 indicate its chemical composition and mechanics properties [START_REF] Guangmin | Weldability of HG70 steel Transactions of the china welding institution[END_REF]. The structure of welding specimens is shown in Fig 1 . The welding interface is located in the middle of the specimens. The specimens are loaded through three-point bending mode.

Loading course and process

The loading test is shown in Fig 3 . To eliminate the noise caused by the friction between the specimen and the support roller, a rubber pad is placed between them. At the beginning of the loading, the specimen is preloaded and unloaded three times to achieve a close contact between the specimen, rubber pad and the support roller. During the whole course, the specimens are always at the stage of elastic deformation. The speed of loading and unloading of the test machine is 1mm/min. The final testing aim is to bend the specimens at When first preloaded (0~200s), the specimen undergoes elastic deformation (disturbance below 1mm) with little release of strain energy and the deformation within the material will vanish after unloading, so only a few AE signals are created which include those created by the deformation of the rubber pad and the friction as well as the agreement between the specimen and the support roller. At the 2 nd and 3 rd stage of loading, holding load and unloading (400~ 1100s), no obvious AE signals can be seen. This is partly due to Kaiser Effect, but on the other hand, it signifies there is an agreement between the specimen, rubber pad and support roller where no friction noise is produced. At the last loading (after 1100s), AE hits rates and counts rates increase rapidly, which is caused by the stress concentration in the defect area resulting from the increase of load. In part of the specimens, yield deformation occurs and tiny crack appears and propagates. With the gradual release of internal energy, a large number of AE signals are created. At the elastic deformation stage, the value of AE characteristic parameters is rather low, with the amplitude below 60dB, energy below 10 3 eu and the counts below 100. When the loading comes to the stage of plastic deformation and crack initiation and propagation, AE hits gradually increase with fairly high amplitude, energy and counts. A number of high-amplitude and high-energy hits even occur simultaneously in some shorttime periods, which is caused by the propagation of welding crack. ) and time filtering (t ≥ 1130s) are adopted to extract the typical AE signals of welding crack initiation and expansion. The distribution and range of characteristic parameters are respectively shown in Table 3.

Analysis of AE characteristic parameters versus time

Analysis of AE signal features at the welding crack initiation and propagation stage 4.1. Analysis of cumulative hits and counts

Analysis of time domain waveform and frequency spectrum features at the stage of tip crack and crack propagation

The welding crack develops through three stages: initiation of tiny crack, crack stable propagation and rapid fracture [START_REF] Ennaceur | Cherfaoui Monitoring crack growth in pressure vessel steels by the acoustice mission technique and the method of potential difference[END_REF].

Conclusion

Acoustic emission during three-bending tests of welding specimens of HG70 steel were investigated and it was found that AE hits and counts can coincide with the bending and cracking of the specimens. Based on AE hits and counts as well as the loading-time curve of the specimens, welding crack can be dynamically monitored and evaluated.

By way of linear location, the welding crack in the specimens can be accurately located, and the distribution ranges of various AE characteristic parameters at the stage of crack initiation and propagation are tentatively fixed.

The AE signals at the stage of crack initiation and propagation are typical sudden signals whose frequency spectrum energy distributes between 80kHz and 380kHz. Obvious peak value is found at 100kHz and 300kHz. The AE signals at the stage of crack rapid propagation are continuous signals formed by several sudden signals with rich frequency components, but no obvious peak is shown in high frequency band.

Fig. 1 .

 1 Fig.1.Structure of welding specimens2.2. Test equipment and the placement of sensorsComputer controlled electro-hydraulic servo universal test machine of WDW4200 Series is adopted for the loading test, with its max. test force being 200kN, and the indicating accuracy: ±0.5%. Besides, AMSY-5 AE-System by Vallen and VSl50-M sensors are chosen for the test. The sensor's main monitoring

  crack can be seen by the eye. The AE signals during the whole course are collected and analyzed.

Fig. 2 .Fig. 3 .Fig. 4 .

 234 Fig.2. Placement of sensors

Fig. 5 .

 5 Fig.5. AE characteristic parameters versus time Fig 5 shows the variation of AE characteristic parameters in the loading.At the elastic deformation stage, the value of AE characteristic parameters is rather low, with the amplitude below 60dB, energy below 10 3 eu and the counts below 100. When the loading comes to the stage of plastic deformation and crack initiation and propagation, AE hits gradually increase with fairly high amplitude, energy and counts. A number of high-amplitude and high-energy hits even occur simultaneously in some shorttime periods, which is caused by the propagation of welding crack.

Fig 6

 6 Fig 6 exhibits its loading-time curve during the test, whereas Fig 7 & 8 respectively show the cumulative hits and counts of AE signals. A comparative study of the three Figs shows the findings: the curve indicates that the specimen enters the stage of part plastic deformation at around 1130s(I). A tiny crack occurs in the welding area and grows gradually due to the stress concentration, which dwindles the load-carrying cross section. When the loading comes to 1570s (II), the crack expands with a snap and the carry capacity falls instantly. The welding crack expands again at 1865s (III). Fig 7&8 show the two instant increases in the value which coincide with the time of the instant fracture of the metal. The findings prove that AE technology can accurately monitor the expansion of welding crack.

Fig. 6 .

 6 Fig.6. Loading-time Curve

Fig. 7 .Fig. 8 .

 78 Fig.7. Cumulative hits versus time

Fig. 9 .Fig. 10 .

 910 Fig.9. AE location

  Fig 11 exhibit the waveform of AE signals at the first and second stage. The time domain waveform Fig shows that the AE signals are typical sudden ones. The frequency spectrum energy of the signals ranges from 80kHz to 380kHz with three major frequency bands: the 1 st band centers on 100kHz, the 2 nd from 150kHz to 200kHz and the 3 rd on 300kHz. The 1 st and 3 rd band see rather high energy and obvious peak value.

Fig. 11 .

 11 Fig.11. Waveform and frequency spectrum of initiation of tiny crack At the moment of rapid fracture, a few signals of full amplitude (almost 100dB) and high energy (10 7 ~ 10 8 eu) are created as shown in Fig 12. The time domain waveform shows that they are continuous signals formed by several sudden signals which are created by several crack sources. These crack sources are caused by the rapid propagation of the welding crack. As shown in the frequency spectrum Fig 12(b), the frequencies are of rich components and well-distributed between 85kHz and 330kHz. The peak frequencies occur at 100kHz and 200kHz. No obvious peak is shown in high frequency band.

Fig. 12 .

 12 Fig.12. Waveform and frequency spectrum of rapid fracture

Table 1

 1 Chemical composition of HG70

			Chemical Composition (weight fraction %)
	C	Si	Mn	P	S	Mo	Cr	Nb	V
	0.06	0.29	1.56 0.014 0.008 0.33	0.61	0.04	0.04

Table 2

 2 Mechanics properties of HG70

Table 3 .

 3 Range of the AE characteristics of welding crack

Acknowledgments

This study was funded by National High Technology Research and Development Program of China (2008AA042801, 2008AA042803).


Haijiang Tai

Daoliang Li

Yaoguang Wei

Daokun Ma

Qisheng Ding

A Simple Temperature Compensation Method for Turbidity Sensor

Keywords: Turbidity Sensor, Temperature compensation, software compensation

Abstract. Turbidity sensor for water treatment applications are based on scattered light measurement. The electrooptical characteristic of light emitter and detector has a close relationship with the environmental temperature. Fluctuations in water temperature can potentially affect electronic components and cause output signal errors in turbidity sensor. Decrease or eliminate temperature error is necessary. With this background we have taken the temperature experiment of turbidity sensor developed by us. 11 different concentrations were measured, ranging from 0 to 100NTU. Each solution was cooled to 1℃, and then gradually heated to 40℃. The output signal of sensor increased according to the rise of temperature found from the experiment. In order to compensate the temperature errors, we developed a novel method to take temperature compensation by using software. The method carried by MCU which is used to compensate the measurement errors by soft programming is simple and effective; it can improve the error compensation precision on sensors greatly.

Introduction

Along with the development of society, the effect of water is strengthening in people's live and industrial production day after day. Turbidity is regard as a key technical parameter in the water quality measurement. As an instrument measuring turbidity, turbidity sensor is widely used in water analysis and treatment processes (e.g., Gong Dian-ting et al., 2008; Brasington, J. et al., 2000; Gippel, C.J., 1995; Jack [START_REF] Lewis | Turbidity-controlled suspended sediment sampling for runoff-event load estimation[END_REF]. Turbidity sensor for water treatment applications are based on scattered light measurement. When a light beam from the light emitter (for example, light emitting diodes(LED)) passed through a water sample, particles in the light path change the direction of the light and scatter it, scattered light received by the detector (for example, photodiode, phototriode and photoresistance ). But the electrooptical characteristic of light emitter and detector has a close relationship with the ※ Corresponding author, Email: dliangl@cau.edu.cn temperature. Fluctuations in water temperature can potentially affect electronic components and cause output signal errors in turbidity sensor [START_REF] Lawler | A simple and inexpensive turbidity meter for the estimation of suspended sediment concentrations[END_REF]). In the practical work, due to the sensor's environment temperature changed greatly, more heat output caused by variation in temperature can bring about a big measurement error, static characteristic of the sensor will also be influenced. Consequently, temperature compensation must be adopted. Temperature compensation technologies include (1) parallel temperature compensation; (2) zero point temperature compensation; (3) temperature compensation based on software (Du Yong-ping and He Xiao-ying, 2009; J.A. Siqueira [START_REF] Siqueira Dias | Electronic technique for temperature compensation of fibre Bragg gratings sensors[END_REF]. In theory it can be compensated completely by using parallel temperature compensation, but in practical it is only proximate compensation; zero point temperature compensation have high accuracy, but it is difficult to find the material; it is a simple, effective method to take temperature compensation by using software, and an approach to increase the accuracy of sensor measurement (Xu Jun, 2002; Matej [START_REF] Možek | Digital Self-Learning Calibration System for Smart Sensors[END_REF]. As long as the mathematical model of measuring temperature error is established accurately, temperature compensation of sensor can realize well. In this work, the ambient-temperature influence on a turbidity sensor, which was maintained with a constant temperature difference from that of the solution, was analyzed and a simple temperature compensation method proposed.

Temperature effect mechanism

The sensor is consist of many parts, electronic components (such as resistor, capacitor) of signal conditioning circuit are not effected by temperature change. But the electrooptical characteristic of light emitter and detector has a close relationship with the temperature.

Temperature influence on LED

LED will suffer high power dissipation and self-heating at work, and its luminous intensity has a close relationship with the environmental temperature. Along with the increase in environmental temperature, there is a decrease in radiation intensity of LED, and the peak wavelength shifts to longer wavelength (B.P.J. de Lacy [START_REF] De Lacy Costello | Highly sensitive room temperature sensors based on the UV-LED activation of zinc oxide nanoparticles[END_REF]. Radiation intensity was proportional to the logarithm of environmental temperature. Compared with the environmental temperature at 25℃, When the environmental temperature at 40℃, relative light output will decreased to 70%~80%, the accuracy of turbidity sensor will be influenced directly (Sheng Qiang and He Xiao-gang, 2007). Under the constant input current, output optical power of LED decreased according to the rise of temperature, as Fig. 1 shows. Fig. 2 shows the peak wavelength shifts and relative light output of each color LED according to the rise of temperature shown in Fig. 3.

Materials and methods

Chemical and equipment

All solutions were prepared with filtered water and analytical grade chemicals. A 400NTU Formazin stock suspension was prepared by mixing equal volumes of 100mg/mL hexamethylenetetramine; (CH 2 ) 6 N 4 and 10mg/mL hydrazine sulfate solution N 2 H 4 H 2 SO 4 , after that, the mixture was let to stand for 24 h at 25±2℃in a topaz bottle before the measurements. Filtered water was used as a carrier. In this work, measuring cylinder and Bunsen beaker were used to dilute Formazin standard solutions, and prepared the water samples. A fridge to cool the water samples, and a constant water bath was used to change the temperature of water samples.

Turbidity sensor

The turbidity sensor used in this work was developed by us. The sensor mainly contained signal conditioning model, power source model, optical measurement system, constant-current source, temperature sensor, RS485 field bus, microprocessor and Transducers Electronic Data Sheets (TEDS), Hardware structure of the turbidimeter is shown in Fig. 5.

Temperature sensor

Signal

Methods

To measure the response of the sensor to different temperature, Turbidity standard solutions of Formazin were adjusted with filtered water into water samples of 0-100NTU. 11 different concentrations were measured, ranging from 0 to 100NTU. Take Bunsen beaker filled with Formazin standard solution and cooling to 1℃, then immerse the sensor in the solution. The Bunsen beaker and sensor were then placed in a water bath at room temperature. Solution in the Bunsen beaker was gradually heated to 40℃and the output recorded at 5s intervals.

Results and discussion

Calibration of sensor

Tab.1 shows the relationship between the turbidity of water samples prepared by diluting turbidity standard solutions of Formazin and the values measured by the sensor under environment temperature 20℃. As can be seen, the solutions measured gave nearly linear relations between the concentration of the standard solutions and the measured turbidity.

Temperature effect

Fig. 7 shows the relationship between temperature and the output signal of sensor. As the figure shows, the experimental curves seem to be similar in shape to each other, but had larger values as solution concentrations increased. The output signal of sensor increased according to the rise of temperature found from the experiment. But the output signal of sensor decreased according to the rise of temperature found from the experiment at initial stage, this result probably arose from the temperature between the sensor and solution failed to come up to equilibrium, the temperature of sensor is higher then solution.

Temperature compensation

As Fig. 7 shows, when the temperature was in the range of 1~40℃, the relationship between the output signal of sensor and temperature can be given by

p nx mx y    2 (1)
Where y , is the output signal of turbidimeter, x is temperature, m 、 n and p is coefficient. Consequently, a general curve can be obtained as Fig. 8 shows. on the curve in Fig. 8 can be used to solve the quadratic equation [START_REF] Dian-Ting | Measurement of CLand SO 4 2in boric acid by turbidimeter[END_REF]. But it is difficult to solve the quadratic equation ( 1) directly. Another form of the quadratic equation ( 1) as follow can be used.

c x x b x x x x a y       ) ( ) )( ( 2 1 0 (2)
Where a , b and c is coefficient.

Substitute the quantitative data of

            0 1 0 1 x x y y b    b x x y x x y y x x y c ) ( ) ( 0 2 0 0 1 0 1 0 2 0        
When the temperature is 20℃, output signal of sensor is given by

c x b x x a y       ) 20 ( ) 20 )( 20 ( 2 1 0 (3)
Then substitute the equation (3) into calibration equation of the sensor which obtained at temperature of 20℃. Reference value of the solution can be acquired finally. The above-mentioned method is designed to program, and be carried by MCU which is used to compensate the measurement errors by soft programming.

Conclusion

In the detection system, the high measurement accuracy is required, and decrease or eliminate temperature error is very important. By the experiment, the following results were obtained:

(1) The output signal of sensor increased according to the rise of temperature found from the experiment, and a quadratic equation give the best fit.

(2) The method carried by MCU which is used to compensate the measurement errors by soft programming is simple and effective; it can improve the error compensation precision on sensors greatly.

Fig. 1 .Fig. 2 .Fig. 3 .Fig. 4 .

 1234 Fig. 1. Temperature characteristic of LED (T2>T1)

Fig. 5 .Fig. 6 .

 56 Fig. 5. Hardware structure of the turbidimeterThe sensor is based on a high-power infrared light emitting diode (LED). The reason for select IR LED as following: (1) Turbidity measurement affected by organic substance in water samples, the light wavelength below 500nm can be absorbed seriously; (2) According to ISO 7027 using light with λ >800 nm can minimize interferences caused by the presence of dissolved light-absorbing substances(Dag Hongve and Gunvor Åkesson, 1998); (3) Water samples colour absorbs light of certain wavelength, and affect measurement precision, near infrared light can minimize interferences caused by water samples colour. Consequently, the LED used in this work is designed for maximum emittance at 940 nm. The detector is a matching, high sensitivity Silicon Photoelectric Generator with peak sensitivity also at 940 nm. Both the emitter and detector provide a narrow and closely matched spectrum. The electric circuit used to drive the sensor and to treat the signal is shown in Fig.6. Conversion of the Silicon Photoelectric Generator current and amplification of the voltage is achieved by using one wide range of input offset, low offset voltage drift, high input impedance, extremely low power, and high gain quad operational amplifiers (TLC27L4). Power requirements for the sensor are +3.3V for the operational amplifiers.

Fig. 7 .

 7 Fig. 7. Relationship of temperature and output signal of sensor

Fig. 8 .

 8 Fig. 8. General curve of the relationship between temperature and output signal of sensor

  , the three coefficients can be obtained, and the results as follow:

  Calibration equation of the sensor fitted according to the principle of least square is

	y		0003 . 1	x		0181 . 0	, and the correlation coefficient
	is Tab.1. Relationship of measured turbidity and concentration of turbidity standard 3 999 . 0 R . Water samples /NTU measured values /NTU correlation coefficient R 2 5 5.50 0.9993 10 10.55 20 19.42 30 29.39 40 39.37 50 49.35 60 59.88 70 71.52 80 80.38 90 90.92 2  100 98.68

Acknowledgements. This work was supported by 863 programs "Research and Application of Digital Integrated System for Intensive Aquaculture" (2007AA10Z238), 948 Project of China Agriculture Ministry (2010-Z13). And the programs "Development and Applications of sensor network applied to monitor bloom of blue-green algae in Taihu Lake" (2010ZX03006-006)


Jing Du

Daoliang Li

Hongwen Li

Lifeng Shen

P O Box

Application of Grey Prediction Model in Rural Informatization Construction

Keywords:

Grey system theory has been widely applied to many domains such as economy, biology, agriculture, control and so on. Based on the theory of grey system, this paper established GM(1,1) grey predict modeI for the first time to forecast Chinese every hundred rural households owned computers and every hundred rural households owned mobile phone. The predicting results are almost close to the actual values, and this shows that the model is reliable. Finally ,these models are used to forecast the two factors in the future years. The research provides a new seientific method for predicting rural informatization development.

Introduction

Agriculture and rural informatization is an important component of the national economy informatization. Setting up prediction model is very significant for the social and economic systems. Because rural informatization is a society-economynature complex system, there are many kinds of factors afflecting rural informatization level, and it is difficult to confirm all influencing factors. Besides, some factors are indefinite, some are definite but difficult to be described quantitively and some are quantified but they may have randomness. Therefore, we can think that rural informatization is a gery system.

Since grey system analysis method has fine adaptability to actua1 situation when information is incomplete, rural informatization system can be studied using grey theory, among which single sequence one order GM(1,1) model can be applied. The following work will introduce the principle of grey topologica1 forecast and a grey predicting model is set up. This model can be fully processed at a quantity level on the basis of history data to realize a scientific prediction [START_REF] Jihu | Forecasting model of coal requirement quantity based on grey system theory[END_REF] .

The basis of grey prediction method

Grey system theory

The grey theory, developed originally by Deng (1987), is a multidisciplinary and generic theory that deals with systems that lack adequate information and/or have only poor information. The fields covered by the grey theory include systems analysis, data processing, modeling, prediction, decision making and contro1. The grey theory mainly conducts systems analysis with poor, inadequate or uncertain messages. Grey prediction models have been extensively used in many applications [START_REF] Liu | Application of improved grey model in the settlement prediction of roadbed foundation[END_REF] .

In contrast to statistical methods, the weight of the original series in the time series grey model, called GM(1,1), has been proven to be more than four. This model is a time series forecasting mode1, encompassing a group of differential equations adapted for parameter variance, rather than a first order differential equation. Its difference equations have structures that vary with time rather than being general difference equations. In addition assumptions regarding the statistical distribution of data are not necessary when the grey theory is applied. The accumulated generation operation (AGO) is one of the most important characteristics of the grey theory, and its main purpose is to reduce the randomness of data. In fact, functions derived from AGO formulations of the original series are always well fired to exponential functions [START_REF] Julong | Grey system essential method[END_REF][START_REF] Jia | Application of Improved Grey Prediction Model to[END_REF][START_REF] Qihua | Grey forewarning and prediction for mine water inflowing catatrophe periods[END_REF] .

Grey GM (1,1 ) prection model

The GM(1,1) model is one of the most frequently used grey forecasting models. This model is a time series forecasting model, encompassing a group of differential equations adapted to parameter variance, rather than a first-order differential equation. Its difference equations have structures that vary with time rather than being general difference equations. Although it is not necessary to employ all the data from the original series to construct the GM(1,1) model, the weight of the series must be more than four. In addition, the data must be taken at equal intervals and in aconsecutive order without by passing any data. The GM(1,1) model constructing process is described below [START_REF] Jia | Application of Improved Grey Prediction Model to[END_REF] : Denote the original data sequence by

(0) (0) (0) (0) ( (1), (2), , ( )) X X X X n  (1)
The AGO formation of (0) X is defined as:

(1) (1) (1) (1)
( (1), (2), , ( )) X X X X n  (2)
Where

(1) (0) (1) (0)
(1)

(1) and

( ) ( ), 1, 2, , 1 k X X X k X m k n m      (3)
The GM(1,1) model can be constructed by establishing a first-order differential equation for (1) X as:

(1)

(1)

dX aX dt    (4)
Where, α is the grey coefficient,  is the grey control parameter.

If the estimating parameter vector 5 is defined as:

ˆa          (5)
And it use the least square method,

1 ˆT T n B B B Y        (6)
Then, the solution of Eq.( 4) can be obtained by using the least square method. That is,

ˆ( 1) ( ( 1)

)e + , 1, 2, , ˆk X k X k n           (7)
Where

1 ˆˆ, T T T n B B B Y               (8)
And

(1) (1)
(1)

(1) (1)

1 (1) (2) 1 2 1 (2) (3) 1 2 1 ( 1) ( ) 1 2 X X X X B X n X n                                       (9)
And Between 2000 and 2008, the every hundred rural households owned computers,the every hundred rural households owned mobile phones in China are listed in Table1.

Conclusion and Recommendations

Based on the grey system theory, two predicting model of rural informatization infrastructure factors have been set up. From the predicting results, its forecasting precision is higher. And the model needs fewer indexes, and thus is simple and convenient to calculate in practical application.

The research results show that when the grey forecasting technology is used in the middle and short-term prediction, this method has higher confidence and can be as a quantitative forecasting method of rural informatization infrastructure factors.

The study of this paper offers a new method and approach to predict for rural informatization development. But the establishment of grey predicts system is not once and for all. The accuracy of predict result is based on the amount and reliability of information we have had. We think it is essentially accurate and reliable for predicting the next period after a new grey prediction system has been estab1ished [START_REF] Wu | Prediction Model Based on the Grey Theory for Tackling wax deposition in oil Pipelines[END_REF] . We should modify grey parameters in time and establish new prediction model based on the risk of new information we have got. Then the credibility of prediction result can be greatly improved with the new model.

Table 1 .

 1 Annual values of the two rural informatization factor between 2000-2008Based on the data in Table1 and the above principle of grey forecasting, we can get the coefficients in the model through calculation. And then we get grey forecasting model GM(1,1) of every hundred rural households owned computers and every hundred rural households owned mobile phones.

	Time	Every hundred rural households owned computers	Every hundred rural households owned mobile phones
	2000		0.5		4.3
	2001		0.7		8.1
	2002		1.1		13.7
	2003		1.4		23.7
	2004		1.9		34.7
	2005		2.1		50.2
	2006		2.7		62
	2007		3.68		77.84
	2008		5.3578		96.13
	By using DPS9.05, The predicted values obtained by the proposed GM(1,1) model
	and their respective errors are shown in Table2 and Table3.
	Table2. Model operation results of every hundred rural households owned computers
	Model parameter
	a=0.006531		b=0.156794
	X(k+1)= -23.680009exp(-0.006531k)+24.006336
	No.	Actual value	Predicted value	Error	%
	2001	0.7	0.792	-0.022	-3.3033
	2002	1.1	1.2313	-0.0812	-7.5411
	2003	1.4	1.5064	-0.1066	-8.4386
	2004	1.9	1.8782	0.11	5.7485
	2005	2.1	1.9008	0.2217	10.859
	2006	2.7	2.7173	0.1202	4.6554
	2007	3.68	3.989	-0.1551	-4.3817
	2008	5.3578	5.8581	-0.0911	-1.7495

X is obtained from Eq. (7). Let

X be the fitted and predicted series,

Applying the inverse AGO, then have

ˆˆ( 1) (

Where

ˆˆ( 1), (2), , ( ) X X X n are called the GM(1,1) fitted sequence, while

ˆ( 1),

are called the GM(1,1) prediction values. After the above model is generated and developed, further tests are necessary to underst and the error of forecasted value, as compared to the actual value. To demonstrate the efficiency of the proposed forecasting model, we adopted the residual error test method to compare the actual value and the forecasted value. Here, Equations ( 14) and (15) are used to compute the residual error of the Grey forecasting.

Error

Grey prediction of rural informatization infrastructure

Based on the characteristics of rural informatization, this paper choose two factors to be predicted, is every hundred rural households owned computers, the other is every hundred rural households owned mobile phones [6].

The