Yongbin Liu

Qingbo He

Ping Zhang

Zhongkui Zhu

Fanrang Kong

Health Status Identification of Connecting Rod Bearing Based on Support Vector Machine

Keywords: Support Vector Machine, Internal Combustion Engine, Connecting Rod Bearing, Fault Diagnosis

Connecting rod bearing (CRB) is an important component which joins the reciprocating and rotating movements together in the internal combustion engine (ICE). It is very difficult to identify health status of CRB because of variable working process, complicated excitation and distribution sources, and lack of fault samples. Support vector machine (SVM), which has excellent capability in small data case, was introduced to identify the health status of CRB. In this paper, faults of the CRB were simulated in an ICE with the type of EQ6100. Vibration features were extracted from vibration signals acquired from the shell of ICE. And a SVM multi-classifier was designed to identify health status of CRB by using the radial basis kernel function. Experimental results indicated that the presented fault diagnosis method could effectively recognize different conditions of CRB.

Introduction

Internal combustion engine (ICE), a complicated mechanical system, is composed of different parts, in which connecting rod bearing (CRB) is an important one joining the reciprocating and rotating movements together. It is difficult to identify the health status of CRB for several reasons, such as variable working process, complicated excitation and distribution sources, and lack of fault samples [START_REF] Wu | Research on Fault Diagnosis of Engine by Ant Colony Support Vector Machine[END_REF][START_REF] Jia | Noise Diagnosis Research Based on Wavelet Packet and Fuzzy Cclusters about Connecting Rod Bearing Fault[END_REF]. One crucial task in CRB fault diagnosis is the feature classification based on vibration signal. For this problem, methods based data learning have been investigated for fault diagnosis, such as neural network [START_REF] Rao | Neural Networks Applied for Fault Diagnosis of AC Motors[END_REF], Bayesian classification [START_REF] Sahin | Fault Diagnosis for Airplane Engines Using Bayesian Networks and Distributed Particle Swarm Optimization[END_REF], etc. However, the vibration samples that represent the condition are difficult to collect in practice, which make the methods mentioned above are limited to being used. Therefore, it is critical to select an excellent classifier for reliable fault diagnosis. Support vector machine (SVM), a machine learning method based on the statistical learning theory, was presented by Vapnik et al. [START_REF] Vapnik | The Nature of Statistical Learning Theory[END_REF][START_REF] Vapnik | Statistical Learning Theory[END_REF][START_REF] Vapnik | Support Vector Method for Function Approximation Regression, Estimation, and Signal Processing[END_REF]. The SVM implements the structural risk minimization principle which leads to the preferable generalization capability. Kernel function is used to map a sample input space into a high-dimensional feature space through some nonlinear mapping. With SVMs, nonlinear dependence between factors and objects can be described. Because of the excellent capability in small data case and in limiting over learning, SVM has been used in many applications, such as pattern recognition [START_REF] Liu | A Novel And Quick SVM-Based Multi-Class Classifier[END_REF], regressive analysis [START_REF] Vapnik | Support Vector Method for Function Approximation Regression, Estimation, and Signal Processing[END_REF], etc. Especially, it shows advantages in fault diagnosis as described by the referred studies [START_REF] Hu | Fault Diagnosis of Rotating Machinery Based on Improved Wavelet Package Transform and SVMs Ensemble[END_REF][10][START_REF] Yuan | Support Vector Machines-Based Fault Diagnosis for Turbo-pump Rotor[END_REF][START_REF] Yang | A Fault Diagnosis Approach for Roller Bearing Based on IMF Envelope Spectrum And SVM[END_REF]. In this paper, we address extraction vibration signal features of connecting rod bearing, and health status identification of connecting rod bearing using the SVM. The organization of this paper is arranged as follows. The theoretical background of the proposed method is introduced in Section 2. The fearture extraction method and feature analysis are described in Section 3. The application example is presented in Section 4. Finally, conclusions are drawn in Section 5.

Support Vector Machine

SVM is a machine learning tool that is especially fit for classification in small-sample cases due to its good generalization capability. The basic idea of SVM for pattern recognition is to map data to a high-dimensional space by a nonlinear mapping, and find an optimum linear separating hyperplane with the maximal margin in this higherdimensional space [START_REF] Vapnik | Statistical Learning Theory[END_REF][START_REF] Vapnik | Support Vector Method for Function Approximation Regression, Estimation, and Signal Processing[END_REF]. However, the calculation complexity does not increase almost in the high-dimensional space as a kernel function is used. "one-against-one" and "oneagainst-all" are the most commonly used methods of SVM classifier [START_REF] Liu | A Novel And Quick SVM-Based Multi-Class Classifier[END_REF]. Multiclassification can be obtained by combining several binary classifiers. Considering the need of recognizing multiple statuses in fault diagnosis of ICE, one-against-all multi-class classifier is constructed in this paper and described in detail as follows [START_REF] Vapnik | The Nature of Statistical Learning Theory[END_REF].

Given a training set  

, , 1,2, ,

i i x y i l 
 , where l is the number of samples,

n i x R  is input data and   1, 1 i y    is output class data.
Finding an optimum separating hyperplane is equivalent to solving the following quadratic programming problem:

, , min in original space can be mapped to high-dimensional feature space using the function     . In feature space, optimum separating hyperplane can be found by maximizing the margin 2/||w||. The decision function can be finally written as:

w b  1 1 2 l T i i w w C     (1) S.t.   ( ) 1 , 0. T i i i i y w x b            
1 sgn ( ) ( ) l T i i i j i y x x b               (2)
S.t. 1 0 , 1 ,2 , ,; 0.
i l i i i a C i l a y             where the project function   i x 
could be conveniently replaced by a kernel function

      , i j i j K x x x x    .
Then the decision function can be rewritten as:

1 sgn ( , )

l i i i j i y K x x b           (3)
There are several commonly used kernel functions: linear function, polynomial function, and radial basis function (RBF). The radial basis function was described as follows:

  2 ( , ) exp - - i j i j K x x x x   (4)
where 0

  , and  is the kernel parameter.

For classable k-class statuses of connecting rod bearing of internal combustion engine, a multi-class classifier can be constructed to identify health statuses of CRB using k classifiers, each of which is trained by binary-class data as above. To identify the health status of CRB, the feature vectors of a sample are firstly sent into the classifier 1. If the output of the decision function is 1, the sample will belong to class 1 and the process of classification is over. Otherwise, the feature vectors are secondly sent into the classifier 2. Then the following process does the same as above until the classification is finished. If the final output is -1, the sample will not be included in the k class statuses.

Signal Acquisition and Feature Analysis

Signal Acquisition

Experiments were conducted on an internal combustion engine with the EQ6100 type. Faults were set to simulate abnormal statuses of connecting rod bearing in advance. Four wear conditions including normal wear, slight wear, medium wear and severe wear were set on the fifth cylinder of the ICE which has total six cylinders. As listed in Table 1, the clearances between bearing and axle neck corresponding to four statuses were set using plug gauges except for normal condition. In the experiments, each time only one fault was set in order to make the simulated fault as real as possible and reduce disturbances as much as possible. Vibration signals were acquired by B&K4384 model accelerometer mounted on the shell of the fifth cylinder of the ICE. Then, the signals were amplified by B&K2635 model charge amplifier and saved in computer through the data acquisition card. The rotating speed of the engine was kept at 1000 rpm without loads, and the sampling frequency was set to be 10 kHz.

Feature Analysis of Vibration Signals

Due to the complex and stochastic nature of internal combustion engine, there are many excitation sources and complex interferences in its working process, such as transformation between rotating and reciprocating movements, ignitions and explosions, some unbalance impacts, differences and interferences in different working cycles, etc. The connecting rod bearing is a critical component which joins the rotating and reciprocating movements. The vibration signals of CRB are modulated and attenuated by multiple parts before being transferred to the sensor. Therefore, the measured vibration signals include lots of disturbance noise. To make the signal analysis results equitable, the measured signal firstly needs to be preprocessed to minimize the noise caused by the sensor itself and machine working environment. This can be done based upon the meanvariance standardization method as described below. Given the measured vibration signal is one-dimensional data 1 2 { , , , }

n x x x 
, where n is the length of sample. The mean of the sample can be written as follows:

1 1 n i i x x n    (5)
And the variance of the sample can be written as:

  2 1 1
1

n i i x x n       (6)
Then the sample is standardized as follows:

' i i x x x    (7)
After mean-variance standardized, vibration signals will have zero mean and unit variance. In general, it is difficult to classify the vibration signals of CRB into different statuses using time-domain features only. The features in frequency-domain can also reflect different statuses of CRB to a certain extent. For example, the energy distribution in some typical frequency band could reflect CRB condition. Therefore, vibration features in both time-domain and frequency-domain were extracted to identify the health status of CRB comprehensively. These features include: Time-Domain Features: absolute mean, maximum peak, the root mean square, the variance, peak-peak, the kurtosis, the skewness, the square root, the crest factor, the shape factor, the kurtosis factor, the impulse factor, and the tolerance factor;

Frequency-Domain Features: average frequency, stabilization factor of spectrum peak, as well as five spectral energy values on five equally divided frequency bands. The total 20 features described above, which calculated according to the reference [START_REF] Chen | Machine Condition Monitoring and Fault Diagnosis Technology[END_REF], were extracted from vibration signals of internal combustion engine. Thus, the health status of connecting rod bearing could be diagnosed comprehensively by a combination of these features. Features as illustrated in Fig. 1 were extracted from vibration signals of connecting rod bearing with four conditions (normal wear, slight wear, medium wear and severe wear). As shown in Fig. 1, the horizontal ordinate represents sample numbers, in which 50 samples for each type of signal are analyzed. It can be seen that these features are different from each other. The 20 features represent different physical signification respectively and the sensitivity and regularity of each feature is different. It is difficult to find which feature is more sensitive and stable intuitively than others in representing the status of CRB. In order to avoid the scale effect of the features and make all the features hold in a uniform scale, features were also preprocessed with the mean-variance standardization method.

SVM for Fault Diagnosis of CRB

After being preprocessed by the mean-variance standardization method, typical vibration signals of the CRB are shown in Fig. 2 with four wear conditions including normal wear (denoted as C1), slight wear (denoted as C2), medium wear (denoted as C3) and severe wear (denoted as C4). As seen from Fig. 2, it is difficult to find the difference among the four conditions. The corresponding frequency spectra are illustrated in Fig. 3, from which we can see that the maximum spectral energy all locate around 1400Hz on four conditions. The energy is distributed widely on the spectrum except the severe wear condition, the energy of which is concentrated at 1400 Hz. From the frequency-domain, it is also difficult to see intuitively the difference among the four conditions.

A set of data which is comprised of fifty vibration signals were acquired from each condition and twenty vibration features are extracted from each signal. Therefore, we can construct a 50×20 vibration feature matrix for each condition. Fig. 1 illustrates the 20 vibration features on four conditions respectively. From Fig. 1, it is difficult to find which feature is more sensitive and more stable than others in representing the status of connecting rod bearing. We selected the fore-half of the feature matrix of each condition, i.e., a 25×20 sub-matrix, to train the SVM classifier and used the left half of the matrix for testing.

It is well known that the classification performance of the SVM depends on what kernel function is used to a certain degree. In the training, we compared linear kernel function, polynomial kernel function and radial basis kernel function. The training results using the three kernel functions are illustrated in Table 2, where the training time is denoted by TT, and the correct recognition rate is denoted by CRR. The parameters of the kernel functions are taken as: power index d=3 for the polynomial kernel function and 2.17

 
for the radial basis function. It can be seen from Table 2 that recognition accuracy rates are almost the same when using all three kernel functions. But the training time of SVM classifier based on radial basis function is much less than that based on the other two functions. Meanwhile, the radial basis kernel function has a less parameter advantage over the polynomial one. The complexity of SVM would be affected directly by the numbers of parameter. Therefore, in this paper, we constructed the SVM classifier using radial basis kernel function to identify the status of connecting rod bearing. The essential problem of machine condition monitoring and fault diagnosis is pattern recognition [START_REF] Hu | Fault Diagnosis of Rotating Machinery Based on Improved Wavelet Package Transform and SVMs Ensemble[END_REF]. The correct feature recognition is the basis of identifying the status of machine. The performance of the classifier will directly affect the fault diagnosis of machine. Therefore, the three-layer back propagation neural network (BPNN) and knearest neighbor classification (KNN) were used to compare with one-against-all SVM multi-classifier. In the training process of the BPNN, the transfer functions were chosen as log-sigmoid function and tan-sigmoid function respectively. Training times was set to 200, and network performance object was set to 0.001. Euclidean distance and one nearest neighbor were used in the KNN classification. When training the SVM classifier, the least square SVM algorithm was used. Radial basis function was selected as the kernel function with the kernel parameter γ being equal to 2.17. Table 3 shows the training time and correct recognition rate of the four statuses by using 3-layer BPNN, KNN and SVM classifier respectively. As seen from table 3, the correct recognition rates are almost the same on normal wear condition using BP neural network and SVM classifier. However, on other three conditions, the correct recognition rate of SVM classifier is higher than the rate of BPNN in evidence. Moreover, the training time of BPNN is far greater than SVM classifier used. Although the computer time of KNN is similar to SVM, its correct recognition rate is much lower than that of the other two methods. The results illustrates that the SVM is effective to identify the health status of connecting rod bearing of ICE.

Conclusions

This paper addresses on identification of the CRB health status of the ICE by using limited samples. By comparing, the SVM multi-classifier based on radial basis kernel function is chosen for identifying the health status of CRB. Furthermore, considering the training time and correct recognition rate in comparison with the BP neural networks, the SVM classifier based on statistical learning theory is especially fit for fault diagnosis in small-sample cases.

The results in this study validate the effectiveness of applying SVM multi-classifier to identifying the health status of connecting rod bearing. This illustrated that the SVM has great practical value and provide an efficient method for intelligent diagnosis of ICE.

1 NFig. 1 .

 11 Fig. 1. Features of vibration signals of CRB.

Fig. 2 .Fig. 3 .

 23 Fig. 2. Vibration signals of CRB on different conditions.

Table 1 .

 1 Faults setting.

	Status	Clearance(mm)
	Normal Wear	0.04-0.098
	Slight Wear	0.15
	Medium Wear	0.30
	Severe Wear	0.50

Table 2 .

 2 Diagnosis results by SVM with different kernel functions.

	Status of CRB	Linear Function TT CRR	Polynomial Function TT CRR	Radial Basis Function TT CRR
	C1	0.849s	100%	0.822s	99.4%	0.632s 99.3%
	C2	0.799s 98.3%	0.843s	98.3%	0.527s 97.8%
	C3	0.833s 99.5%	0.830s	99.1%	0.590s 98.5%
	C4	1.486s 98.2%	0.849s	97.5%	0.591s 96.7%

Table 3 .

 3 Comparision of BPNN, KNN and SVM results.

	Status of	KNN		BPNN	SVM
	CRB	TT	CRR	TT	CRR	TT	CRR
	C1	0.921s	71.1%	7.023s	98.5%	0.632s	99.3%
	C2	0.824s	73.3%	7.316s	90.7%	0.527s	97.8%
	C3	0.989s	77.3%	6.088s	90.8%	0.590s	98.5%
	C4	0.709s	79.5%	8.487s	90.4%	0.591s	96.7%

Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (No.50905021) and by the Fundamental Research Funds for the Central Universities in China.


Lingxian Zhang

Juncheng Ma

Daoliang Li

Zetian Fu

GIS-based evaluation on the eco-demonstration construction in China

Keywords: Evaluation system, Eco-demonstration construction, AHPCI, Sustainable development

The ecological construction for demonstration area is an idealized model for the establishment of rural sustainable development in the countyscale. This paper presents a GIS-based evaluation system of ecological construction for demonstration area. It includes an index system with the 4 secondary indices and 24 factor indices, and a method of ecological construction for demonstration area utilizing multi-attribute decision models of AHPCI method for comprehensive performance assessment. Taking Xinyang city of Henan province in China as a case study, the comprehensive assessment result were found to be coincident with practical situation, so it proves that the GIS-based evaluation system was fit for county-level ecological construction assessment for demonstration area as a beneficial reference framework elsewhere.

Introduction

Since United Nations Conference on Environment and Development (UNCED) in 1990s, the sustainable development concept has gradually been accepted all over the world with respect to almost all aspects of human development [START_REF] Liang | Comprehensive Evaluation of the Sustainable Development of an Ecological Demonstration District[END_REF]. The ecodemonstration area is regarded as an important way to maintain regional sustainable development in many countries. The ecological construction for demonstration area is an idealized model for the establishment of rural sustainable development in the county-scale. Eco-demonstration area is a compound ecosystem of society, economy and nature, which is relatively independent and exoteric [START_REF] Yang | Building High Quality Ecological Demonstration Areas[END_REF]. The eco-demonstration concept lies close to the UN Millennium Project [START_REF]UN Millennium Project: Investing in Development: A Practical Plan to Achieve the Millennium Development Goals[END_REF] and aims at making a contribution to this programme. Eco-demonstration construction refers to the application of ecological principles to the development of human ecosystems in order to achieve sustainability. It consists of three components: ecological engineering, ecological institutional reestablishment and ecological cultural remolding.

Since 1980, a vigorous campaign for the ecological construction for demonstration area has appeared in China, and many eco-polis, eco-counties, eco-villages as well as eco-families had sprung up all over the country [START_REF] Hu | Exploring eco-construction for local sustainability: An eco-village case study in China[END_REF]. In 1996, the Ministry of Environmental Protection of China(MEPC) carried out the activities of the national creation of eco-demonstration area, and a lot of eco-demonstration areas had been created in our country, as not only promoted coordination of economy, society and environmental protection in pilot region but also have positive effect on surrounding areas [START_REF] Yan | Evaluation Index System and Development Sustainability of Nongan County Ecology Demonstration Zone in Jilin Province[END_REF]. The evaluation of sustainable development for eco-demonstration area is the base of perfecting the sustainable development theory for eco-demonstration area and guiding its construction [START_REF] Chen | Degree and Its Application of Sustainable Development of Ecological Demonstration Areas[END_REF].

To promote the benign development of eco-demonstration area construction, this paper promoted an evaluation index system of eco-demonstration area construction based on criterion for eco-demonstration area construction issued by MEPC, and developed a GIS-based assessment model utilizing the Analytical Hierarchy Process (AHP) and Competitiveness Index (AHPCI) method.

Evaluation methodology for intra-county eco-demonstration construction 2.1 Evaluation index system of intra-county eco-demonstration construction

Taking the criterion of the national creation of eco-demonstration area and goal of eco-demonstration construction into account, the paper presented a set of quantitative and hierarchical index system with the 4 secondary indices, and 24 factor indices by integrating the assessment goal and the standards of eco-demonstration construction announced by MEPC according to the design principles and requirements of the index system (Table 1). In the index system, the comprehensive level of sustainable development for ecodemonstration areas is regarded as the general goal. In order to realize the regional sustainable development, the basis of resource and environment that support the development of social and economy is needed. Therefore, this paper divided the general goal into 4 sub-goals of economic development, social progress, ecological construction and environmental protection, which were further divided into the third level indices of national income, economic structure, quality of life, harmony, resource utilization, environmental management and protection. According to the requirements and principles, the general goal was divided into 24 indices.

Assessment model of the intra-county eco-demonstration construction

Constructing the hierarchical structural model. Following AHP, the level of sustainable development for eco-demonstration area is divided into three hierarchical structural levels from the bottom to the top. In the top is the general goal of the hierarchical structure, namely the overall objective layer with only one key element for the comprehensive level of sustainable development for eco-demonstration. The intermediate level is the criterion layer with several intermediate elements, such as economic development, social progress, ecological construction and environmental protection. The bottom, namely influence factors layer, is composed of various kinds of factors in evaluation system. There exist specific logic relationships between neighbor layers. The hierarchical structural model for evaluating the ecodemonstration construction was showed in Fig. 1.

Presenting the comparison matrix by layers. The comparison matrix below provides a measure of the weight factors for decision making, which denotes the relative importance of the element of each layer shown in the hierarchical structural model. Often from top to bottom, the comparison matrix could be established with one element of the above layer pairing and comparing with all the elements of the neighboring below layer according to the relationship between the elements or the layers. Between layer A and B showed in Fig. 1, the comparison matrix of A-B layer can be established (Table 2), whose elements are evaluated using a 9-point scale.

The comparison matrix is a square matrix as

n n ij ) (B A  
, and:

1 B B ; B 1 B ; 0 B jj ii ji ij ij     (1)
Where B ij is the pairing comparison of element i with element j in the B layer of the A-B comparison matrix.

Similarly, the comparison matrix of B-C and C-Q layers can be obtained in the same way used in forming the comparison matrix of A-B layers. Data on paired comparison matrices were collected from reviewers. Scores of B ij were estimated on the average by experts. The participating decision makers provided paired comparisons for each level of the hierarchy in order to obtain the weight factors of each element on that level, and with respect to one element in the next higher level [START_REF] Saaty | Theory and applications of the analytic network process[END_REF].

C 6 C 5 Q 63 Q 62 Q 64 Q 61 Q 53 Q 52 Q 51 C 4 C 3 Q 42 Q 41 Q 33 Q 32 Q 34 Q 31 C 2 C 1 Q 23 Q 22 Q 24 Q 21 Q 13 Q 12 Q 11 Q 73 Q 72 Q 74 Q 71
In ecological evaluation for eco-demonstration construction based on rural sustainable development, we adopt the evaluation standard of the comprehensive level of sustainable development (Table 1). In order to better carry out analysis and comparison, this paper has adopted a methodology of the comprehensive evaluation index to calculate the weight of every index to comprehensive level of sustainable development for an eco-demonstration area. Each index is translated into a dimensionless index score such as GDP per capita, amount of fertilizer application etc.; then weighted in order produce a measure of the comprehensive level of sustainable development for the eco-demonstration area.

The specific methods of evaluation are as follows: a) For the positive index, let

      j min j max j min Q x x x x ij ij        j ij j x x x max min   (2) a) For the negative index, let       j j ij j ij x x x x min max max Q        j ij j x x x max min   (3)
Where j is a sequence number for evaluation subordinate systems, i is a serial number of indices in a subordinate system; ij Calculating by weight in term and layer from the beginning of the specific index, we obtained the overall value of the comprehensive level of sustainable development. This can be expressed as follows:

) Q W ( W CI ij m 1 j ij n 1 i i      ( 4
)
Where CI is competitiveness index, representing the comprehensive level of sustainable development for eco-demonstration areas; n is the number the component factors of the comprehensive level of sustainable development for eco-demonstration areas, here n for 4; m represents sub-factor in number i component factor of the comprehensive level of sustainable development for eco-demonstration areas; ij Q is the standardized value of number j sub-factor of number i component factor; ij W is the weight of number j sub-factor of number i component factor.

By taking various statistical yearbooks, bulletins and other relative data, and national and international corresponding index standards into account, we designed a four-stage classification standard with the critical value and values of upper and lower limit of the CI of the comprehensive level of sustainable development for ecodemonstration areas (Table 3).

Design of GIS-based evaluation system

According to the evaluation algorithm of the comprehensive level of sustainable development for eco-demonstration areas aforementioned, this paper put forward a GIS-based evaluation system of ecological construction for demonstration area, which was designed three modules, including GIS operating, thematic evaluation and auxiliary module, in virtue of the geographic information system technology (Fig. 2).

Fig.2.

Function frame of GIS-based evaluation system.

Results and discussion

General situation of study area

This paper took evaluation of the sustainable development level for ecodemonstration area at Xinyang city of Henan province in China as an example to test the effectiveness of the AHPCI method. Xinyang city is located in south part of Henan province, P.R China. It consists of 8 counties and 2 districts, with the population of 7.8 million, the total area of 18000 km 2 . Only one of Xinyang city, Luoshan county, was identified as the national ecodemonstration area in 2004. By comparing CI of the 9 counties at Xinyang city (two districts as one), we could conveniently analyze the construction performance of ecodemonstration area at Luoshan county, and provided a beneficial reference framework for the eco-demonstration construction elsewhere.

Weight calculation and consistency test

From Fig. 1, the comparison matrix of A-B layer would be considered consistent (and accepted) with a CR (consistency ratio) lower than 0.1. Similarly, we could obtain the weight of the evaluation attribute layer 4 and5). From Table 4, we could find that in the second layer, economic development had the maximum weight (0.455), while in the third layer, C 2 (economic structure) had the maximum weight (0.364). 5 showed obviously the weight of 24 evaluation indices of the fourth layer to the general objective of the comprehensive level of sustainable development for ecodemonstration, of which unit GDP energy consumption (0.105) reached the largest influence and the second was the proportion of poor people (0.098), while the least influence was obtained by tourist district environmental compliance rate (0.004).

Intra-county comprehensive level of sustainable development for ecodemonstration

Fig. 3 and Fig. 4 showed the intra-county comprehensive level of sustainable development for eco-demonstration at Xinyang. Xinyang district gets the maximum (0.8757), and Shangcheng ranks the second (0.8149) followed by Xinxian (0.8024), Huangchuan (0.7928), Gushi (0.7846), Luoshan (0.778), Guangshan (0.769), and Xixian (0.7388, minimum). The comprehensive level of Huangchuan, Shangcheng, Guangshan, Xinxian, Luoshan, Xinyang and Gushi are at the level I of the classification standard while Huaibin and Xixian reach at the level II, as is found to be coincident with practical situation.

Conclusion

Analyzing the literature and combining with the reality, this paper sets 24 evaluation indices according to the principles of evaluation and conditions specified, and establishes the evaluation system of eco-demonstration construction by the AHPCI method as well as develops an evaluation system of intra-county eco-demonstration construction.

The results show that the model of evaluation system of eco-demonstration area reflects the comprehensive level of the 9 counties in Xinyang city and corresponds with the reality, which offers the basis to construction of eco-demonstration sustainable development and contributes to the construction of national ecodemonstration.

The evaluation system of eco-demonstration sustainable development is proved to be reliable, which presents a tool for the evaluation of eco-demonstration sustainable development.

Fig. 1 .

 1 Fig.1. Hierarchical structural model for evaluating the eco-demonstration construction.

xx

  is the original values of indices in a hierarchy of i and j, respectively; ij Q is only the standard transformation values of ij x ; max are lower limit or upper limit values of indices in a evaluation subordinate system, respectively.

  C 1 -C 2 to B 1 , C 3 -C 4 to B 2 , C 5 -C 6 to B 3 ; and the weight of the evaluation index layer Q 11-Q 13 to C 1 , Q 21 -Q 24 to C 2 , Q 31 -Q 34 to C 3 , Q 41 -C 4 , Q -Q 53 to C 5 , Q 61 -Q 64 to C 6 , Q 71 -Q 74 to B 4 (wholly passed the consistency test, and accepted) (Tables

Fig. 3 .

 3 Fig.3. Intra-county comprehensive level at Xinyang.

Fig. 4 .

 4 Fig.4. Thematic evaluation intra-county comprehensive level at Xinyang.

Table 1 .

 1 Index systems and its standard for the intra-county eco-demonstration construction.

	Primary index	Second index Third index	Fourth index Name	Reference value Code Upper limit Lower limit
					GDP per capita	Q 11
	development for Eco-demonstration	Comprehensive Level of sustainable	Economic development	National income Economic structure	Disposable income of urban residents Per capita annual net income of peasantry Unit GDP energy consumption(ton/ten thousand) Unit GDP water consumption (cubic meter/ten thousand) Proportion of investment in in GDP (%) environment protection	Q 12 Q 13 >4000 1600 Q 21 1.3-1.4 1.5-1.6 Q 22 <200 <600 Q 23 1 1.2
					Water productivity (kilogram/cubic meter) Q 24	>1.5	0.9

Table 2 .

 2 A-B comparison matrix.

Table 3 .

 3 Classification standard of the comprehensive level evaluation[START_REF] Li | The integrated eco-environment assessment of the red soil hilly region based on GIS-A case study in Changsha City, China[END_REF].

			Qualitative evaluation of
	Level	Critical value	comprehensive level of
			sustainable development
	Ⅰ	> 0.75	Very good
	Ⅱ	0.50-0.75	Better
	Ⅲ	0.25-0.49	General
	Ⅳ	< 0.25	Poor

Table 4 .

 4 Single ranking of the assessment factors.

	Index	B 1 0.455	B 2 0.263	B 3 0.141	B 4 0.141	Single ranking
	C	0.200				0.091
	C	0.800				0.364
	C		0.750			0.197
	C		0.250			0.066
	C			0.250		0.035
	C			0.750		0.106

Table 5 .

 5 Total ranking of the factors.

	Index	C 1 0.091 0.364 0.197 0.066 0.035 0.106 0.141 C 2 C 3 C 4 C 5 C 6 B 4	Total ranking
	Q 11	0.539	0.049
	Q 12	0.164	0.015
	Q 13	0.297	0.027
	Q 21	0.445	0.105
	Q 22	0.283	0.092
	Q 23	0.165	0.060
	Q 24	0.107	0.039
	Q 31	0.589	0.086
	Q 32	0.193	0.070
	Q 33	0.149	0.034
	Q 34	0.069	0.014
	Q 41	0.250	0.049
	Q 42	0.750	0.098
	Q 51	0.608	0.021
	Q 52	0.272	0.010
	Q 53	0.12	0.004
	Q 61	0.542	0.037
	Q 62	0.233	0.025
	Q 63	0.14	0.015
	Q 64	0.085	0.009
	Q 71	0.081 0.011
	Q 72	0.428 0.060
	Q 73	0.301 0.042
	Q 74	0.189 0.027

Acknowledgement. Thanks are due to National Science & Technology Support Program (2008BAB38B06) for financial support.


Qiang Zou

Hui Fang

Fei Liu

Wenwen Kong

Yong He

Comparative Study of Distance Discriminant Analysis And Bp Neural Network for Identification of Rapeseed Cultivars Using Visible/Near Infrared Spectra

Keywords: comparative study, BP neural network, distance discriminant analysis, visible/near infrared spectra,rapeseed cultivars

The potential of visible/near infrared spectra as a method of nondestructive discrimination of various rapeseed cultivars was evaluated, discrimination ability of distance discriminant analysis (DDA) and BP neural network (BPNN) for identification of rapeseed cultivars was shown in this article. The spectral curves ranging from 350 to 2500 nm of rapeseed cultivars were obtained by VIS/NIR spectroscopy, and the principal component analysis (PCA) was applied to perform the clustering analysis. The first 6 principle components (PCs) extracted by PCA were employed as the inputs of DDA and BPNN, respectively, and then two different discrimination models for rapeseed cultivars were built. Forty-five samples from each species and a total of 225 samples from 5 categories rapeseed were extracted. One hundred fifty samples were elected randomly as training sets to set up the training model which was validated by the samples of prediction sets formed by the remaining 75 samples. The result error of BPNN model was set to be ±0.15, and the result indicated that no samples exceeded threshold value, therefore the distinguishing rate was 100%. The result of DDA model displayed that the recognition rate of 100% was achieved. Although the methods mentioned in the presented paper were good approaches for nondestructive discrimination of rapeseed cultivars, DDA model with prediction functions was more intuitive than BPNN and convenient to machine recognition.

Introduction

Oilseed rape, being one kinds of major oil crops and nectar plants in China, is herbaceous cruciferous crops and is expanding rapidly as a rotation crop following rice [START_REF] Liu | Determination of acetolactate synthase activity and protein content of oilseed rape (Brassica napus L.) leaves using visible/near-infrared spectroscopy[END_REF]. In 2000, rapeseed was the third leading source of vegetable oil in the world, after soy and palm and is the world's second leading source of protein meal. Oil content of oilseed rape depends on rapeseed cultivars greatly, so discrimination of rapeseed cultivars is a very important part of detection for oilseed rape's quality.

Randomly Amplified Polymorphic DNAs (RAPDs) were used to discriminate among 23 cultivars of oilseed rape (Brassica napus) in 1994 [START_REF] Mailer | Discrimination among cultivars of rapeseed (brassica-napus L.) using DNA polymorphisms amplified from arbitrary primers[END_REF]. In 2007, Principles and advantages of particular marker techniques and application of molecular markers in rapeseed cultivar identification were discussed by Curn V [START_REF] Curn | Fingerprinting of Oilseed Rape Cultivars[END_REF]. Because of operational complexity and difficulty for RAPDs and molecular markers, it is very inconvenient to use it. Thus, it is interested in the possibility of on-line non-destructive sorting process for identification of rapeseed cultivars.

Nowadays, visible and near infrared (Vis/NIR) spectroscopy is widely employed as a method for measurement of quality in many fields, such as agriculture, pharmaceuticals, food, textiles, cosmetics, and polymer production industry [START_REF] Yan | The Foundation and Application of Near Infrared Spectroscopy Analysis[END_REF]. Some reports are available on use of visible and near infrared (VIS/NIR) spectroscopy for identification of cultivar [START_REF] He | Discrimination of varieties of apple using near infrared spectra based on principal component analysis and artificial neural network model[END_REF][START_REF] Romdhane | The potential of combined infrared and fluorescence spectroscopies as a method of determination of the geographic origin of Emmental cheeses[END_REF]. Study of rapeseed using visible and infrared spectroscopy has been reported [START_REF] Huang | Rapeseed nitrogen status estimation of Vis-NIR spectra based on partial least square and BP neural network[END_REF][START_REF] Gan | Establishment of math models of NIRS analysis for oil and protein contents in seed of Brassica napus[END_REF][START_REF] Ding | Analysis of erucic acid and glucosinolate in intact rapeseed by fourier transform near infrared diffuse reflectance spectroscopy[END_REF][START_REF] Velasco | Nondestructive assessment of sinapic acid esters in Brassica species: I. Analysis by near infrared reflectance spectroscopy[END_REF]. However, these papers are just related to physical and chemical analysis, such as oil content, sinapic acid esters assessment.

The performance of a classification or discrimination depends on the separability of the classes. This suggests that the centres of clusters within the measurement space should be sufficiently separated [START_REF] Younes | Identification of Seeds by Colour Imaging :Comparison of Discriminant Analysis and Artificial Neural Network[END_REF].

However, study of identification with linear identification model have shown that linear discriminator do not often yield satisfactory performances [START_REF] Kim | Linear and non-linear pattern recognition models for classification of fruit from visible-near infrared spectra[END_REF]. Thus, comparsion of non-linear model such as BP neural network (BPNN) and linear model such as distance discriminant analysis (DDA) for discrimination of rapeseed cultivar was studied. BPNN, one kind of neural network widely used by researchers,consisting of three elements including input layer, hide layer and output layer, are computationally robust with having the ability to learn and generalize from examples to produce meaningful solutions to problems even when the input data contain errors or are incomplete [START_REF] Nasseri | Optimized scenario for rainfall forecasting using genetic algorithm coupled with artificial neural network[END_REF]. DDA, which can indentify samples species on the basis of distance between the samples and training set,is a supervised learning technique of statistical pattern recognition. The same as other math models, the critical step of establishing both analysis methods is to get appropriate input variables. Because of large amounts of data, raw spectra data can not to be as inputs of model directly. And excessive input spectra data can result in lager number of iterative training [START_REF] Tang | Identification of official rhubarb samples based on IR spectra and neural networks[END_REF], also it leads to the over-regression between the prediction and the training samples [START_REF] Shao | A new approach to discriminate varieties of tobacco using vis/near infrared spectra[END_REF]. Principal component analysis (PCA) provides a good solution dealing with these problems for us.

The objective of this study was to investigate the potential utilization of VIS/NIR spectroscopy combined with chemometric or statistical methods to discriminat the rapeseed culvers with different attributes. PCA was used to extract principal components (PCs) from a large number of spectra data.

Then, BPNN and DDA model were established, respectively. And the quality of two discriminant model was assessed in this paper.

Material and methods

Instrument and system set-up

In this research, the ASD FieldSpe Pro FRTM Spectrometer from ASD (Analytical Spectral Device)

was applied. This spectrograph has high sensitivity range from 350 to 2500 nm. The interval of sampling is 1.4 nm and the sensitivity is 3.5 nm range from 350 to 1000nm. From 1000 to 2500 nm, the interval of sampling is 2nm. The software of ASD View Spec Pro, Unscramble V9.6 (CAMO, PROCESS, AS, OSLO, Norway), DPS (data procession system for practical statistics) and SAS (Statistical Analysis System) were used in this study.

Samples and measurements

In this study, a total of 225 samples of five kinds of rapeseed, as showed in Table 1, were from farm of Zhejiang University, Hangzhou (30 0 10'N, 120 0 12'E). In order to reduce the error of operation and Environmental differences, the uniform petri dish (diamitor: d = 65 mm, height: h = 1.4 mm) was chosen to bloom rapeseed which covered the bottom of petri dish.

Pretreatment of original spectral data

The pretreatment methods, such as multiplicative scatter correction (MSC), Savitzky-Golay (SG) smoothing, Standard normal variate (SNV) and so on, usually are used to reduce the error which contains in original spectral data. Chu, Yuan and Lu [START_REF] Chu | Progress and application of spectral data pretreatment and wavelength selection methods in NIR analytical technique[END_REF] uesd the smoothing method of Savitzky Golay (SG) with segment size 3 and default polynomial order zero to decrease the noise. It had been proved that many high frequency could be eliminated [START_REF] He | Discrimination of varieties of tea using near infrared spectroscopy by principal component analysis and BP model[END_REF]. The standard normal variate (SNV) was used to used to remove the multiplicative interferences of scatter, the change of light distance, and particle size [START_REF] Barnes | Standard normal variable transformation and de-trending of near infrared diffuse reflectance spectra[END_REF]. Both pretreatment methods were used in this study. The pre-process and calculations were carried out using a statistical software package named Unscrambler V9.6 for multivariate calibration. To avoid the low signal-noise ratio, the first 100 wavelength values and the last 200 wavelength values were removed, only the wavelength ranging from 450 to 2300 nm was used in this investigation [START_REF] Bao | Optimal mix forecasting method based on BP neutral network and its application[END_REF][START_REF] Qi | Quantitative analysis using NIR by building PLS-BP model[END_REF].

Method

Two kinds of methods for clustering, visualization of VIS/NIR spectra and classification the different varieties rapeseed, by PCA, BPNN and DDA model, were showed and compared with each other. The features of spectra data after pretreatment were visualized by principal component analysis in PCs space, then the PCs closely correlative with the categories of these samples, were used as the inputs of an BPNN model and DDA model, respectively, for classification of rapeseed cultivars.

Results and discussion

The reflectance spectra data of rapeseed cultivars

All the achieved spectra data were averaged, and changed to the ASCII code, which was used for building the reflectivity matrix for later use. Fig. 1 shows the typical curves of the reflectance spectra of each variety of rapeseed. The trend of all spectra curves was similar, but the differences of wavelength values ranging 450mm from 1000mm among the different rapeseed cultivars were existed after comparing in detail. The mainly reason might be differences in apparent color of different rapeseed cultivars in visible spectrum. At the same time, it can be found that there was not remarkable difference in the other spectral range, some tiny difference can be detected, which make it possible to discriminate the different varieties.

Data visualization by PCA

The spectra data matrix after pretreatment contained 1851×225 numbers, it was difficult to use it as input of model. Thus, PCA was used to extract PCs to enhance the fetures of spectra data and reduce dimensionality of matrix. 20 PCs were extracted from 225 spectra curves of rapeseed. The PCA scores graph shown in Fig. 2, which was organized according to the number of the rapeseed, was built using PC1 with PC2. It was easy to draw that the rapeseed was closely clustered and the differences among most kinds of rapeseed were displayed except the QZ3 and QZ5, which were mixed with each other. QZ varieties and QY varieties were separated by CP2-axis. The reason could be that the differences of their variety characteristics were apparent. Although the principal component analysis can qualitatively distinguish between different varieties of rapeseed, but could not give a quantitative identification model. samples. The first six PCs, which can explain the 99.817% of variables, were elected as inputs to build BP neural network model. The numbers from 1 to 5 stood for varieties of five kinds of rapeseed. So, the output value 1 was QZ2 rapeseed, 2 stood for QZ3 rapeseed, 3 was denoted as QZ5 rapeseed, QY14 rapeseed was stood for by 4 and 5 was denote as QY241 rapeseed.

With errors comparison, the best 3-layer neural network structure was determined to build BPNN model after adjusting by many times [START_REF] Yin | Analysis on the ultra-spectral characteristics of water environmental parameters about lake[END_REF][START_REF] Zhao | A new approach to the fast measurement of content of amino acids in cordyceps sinensis by ANN-NIR[END_REF][START_REF] Li | Discrimination of Varieties of Paddy Based on Vis/NIR Spectroscopy Combined with Chemometrics[END_REF]. The optimal nodes of the hidden layer of neural network was 8 and the maximum number of iterations was 2000. The parameter of sigmoid and the least training speed were set as default values of 0.9 and 0.1, respectively. The residual error was set as 0.00001. The threshold value of prediction error was ±0.15. If error exceeded the threshold, the prediction result was wrong. The prediction error of each sample was shown in Fig. 3 for the training set and prediction set. The result showed that the errors were close to 0. Identification result for training set and the prediction set was shown in Table 3, and the recognition ratio of 100% was achieved.

3.4

Clustering with DDA model DDA, a linear discriminant method, is a multivariate technique, which can allocate new objects to populations previously defined threshold error of recognition and separate objects from distinct populations [START_REF] Molfetta | A structure-activity relationship study of quinone compounds with trypanocidal activity[END_REF]. In this study, there were five varieties of rapeseed that were classified through distance discriminant analysis. PCs, inputs of BP neural network model, were used as inputs of DDA model. SAS statistical software was used to calculate the spectra data. The discriminant functions of DDA model achieved by SAS were listed in Table 4. The more information applied by functions. The greater the absolute value of coefficients was, the greater the contribution of this variable on the function was, and vice versa. For example, the absolute value of PC6 was maximum and the absolute value of PC3 was minimum in discriminant function for QZ2, so variable PC6 make the greatest contribution for function and PC3's was the smallest. PP: posterior probabilities.

The rule for discriminating the varieties of prediction samples is that: the probability for a sample with G possible varieties belonging to each category is pi (i = 1, 2,…, G). Assume that pS is maximum among pi (i = 1, 2,…, G), it means pS = max{p1, p2, …, pG}, ( 1≤S≤G). And then, it is concluded that this sample belongs to S variety. In this work, the posterior probability of prediction samples was calculated. Each sample was classified accurately and their posterior probabilities for correct cultivars were 1.00. The accuracy rate was 100% and result was displayed in Table 5. Thus, the stepwise discriminant analysis model was realistic, correct and valuable.

Methods comparison

There were two methods for discrimination of rapeseed cultivars using VIS/NIR spectroscopy, BPNN and DDA. Two methods are ordinary used in many fields, such as food, chemical industry, and so forth.

BPNN could be used for prediction the content of a component of a substance, while DDA is generally applied for identification of category of unknown samples based on samples with known types. 225 samples from 5 rapeseed categories were analyzed for comparison of two analysis methods.

Because of generalization ability and fault tolerance of BPNN model, a very few error data for samples has little effect on rules between inputs and outputs. But BPNN model for identification of rapeseed cultivars wasted a plenty of training time, this is its shortcoming. For obtaining the rules of inputs and outputs we wish, model parameters, such as residual error, least training speed and maximum number of iterations must be adjusted repeatedly, whereas for DDA model PCs can be finished using SAS statistical software without adjusting time.

Although the recognition ratios of 100% for two methods were acquired, the result of DDA model with SAS was more credible. Recognition ratio of BPNN model depended largely on the threshold of error of prediction value. In this study, the threshold was set to be ±0.15, so recognition ratio of 100% was achieved. If the threshold was set to be ±0.1, the recognition ratio maybe reduce. The reliability of recognition rate without giving authentication probability using BPNN model should be analyzed again.

While posterior probabilities, 1.00 for all samples including training set and prediction set, were got by discriminant analysis using SAS and it meant that its recognition ratio's reliability was good.

Finally, although the methods mentioned in this study were good approaches for non-destructive identification of rapeseed cultivars, DDA with prediction functions was more intuitive than BPNN and more convenient to machine recognition.

Conclusion

A rapid and non-destructive approach for identification of rapeseed cultivars was presented. And rapeseed with different varieties was classified by two methods, chemometrics and statistical methods, using VIS/NIR spectroscopy. In this study, quantitative analysis for the varieties of rapeseed was made, by combining with SG smoothing, SNV, PCA, BPNN and DDA, relative between reflectance spectra and rapeseed cultivars was built. The BPNN and DDA model displayed an excellent data prediction performance, and the recognition rate of 100% was achieved using two methods. BPNN model should be adjusted repeatedly, compared with DDA, it was not convenient to analyze the spectra data for identification. So DDA was a better method for discrimination of varieties and discriminant analysis with prediction functions was more intuitive than BPNN for identification of rapeseed cultivars.

Fig. 2 .

 2 Fig. 2. Scatter plot (PC1 ×PC2) of 225 rapeseed samples

Fig. 3 .

 3 Fig.3. The result error value of prediction results of training set and prediction set by BPNN model.

Table 1 .

 1 The varieties of the sample in the research

	Varieties	Class	From	Number of samples
	Qingza2(QZ2)	Qing Za	Zhejiang University	45
	Qingza3(QZ3)	Qing Za	Zhejiang University	45
	Qingza5(QZ5)	Qing Za	Zhejiang University	45
	Qingyou14(QY14)	Qing You	Zhejiang University	45
	Qingyou241(QY241)	Qing You	Zhejiang University	45

Table 2 .

 2 Table 2 listed the accumulative reliabilities of PC1, PC2, PC3, …, PC10. It could be find that the accumlative reliabillities of the first 6 principal components was 99.817% and the rest of accumlative reliabillities just was 0.183%. it meant that, 6 PCs contained all the informatins of variables, to some extent. Thus, 6 principal components could provide good discrimination of varieties. PCs and accumulative reliabilities

	PC	PC1	PC2	PC3	PC4	PC5	PC6	PC7	PC8	PC9	PC10
	Ar(%)	91.213	97.224	98.905	99.658	99.758	99.817	99.838	99.854	99.865	99.871

Fig. 1. VIS/NIR reflectance spectra of five varieties of rapeseed PC: Principal components; Ar: accumulative reliabilities.

Table 3 .

 3 Classification and prediction rate of BPNN model

			Classification			Prediction
	Class	NO.	False NO.	Accuracy rate(%)	NO.	False NO.	Accuracy rate(%)
	QZ2	30	0	100	15	0	100
	QZ3	30	0	100	15	0	100
	QZ5	30	0	100	15	0	100
	QY14	30	0	100	15	0	100
	QY241	30	0	100	15	0	100
	Total	150	0	100	75	0	100

Table 4 .

 4 Discriminant functions of DDA model

	Varieties	Functions
	QZ2	Y 1 (x)=-201.44725-66.17473PC1+204.59991PC2-5.21388PC3+264.62153PC4 -109.86786PC5+314.48234PC6
	QZ3	Y 2 (x)=-290.19263-59.33282PC1+413.00733PC2-685.86068PC3+412.97573PC4 +71.88543PC5+257.70740PC6
	QZ5	Y 3 (x)=-119.06113+0.61504PC1-154.57826PC2-1.45941PC3-362.24558PC4 +737.24938PC5-76.96774PC6
	QY14	Y 4 (x)=-267.17728-8.88928PC1+422.19211PC2-491.43925PC3+285.38541PC4 +93.56473PC5+427.92977PC6
		Y 5 (x)=-918.38635+135.18458PC1-868.94013PC2+1112PC3-607.09393PC4
	QY241	-59.34903PC5-931.26578PC6

Table 5 .

 5 Classification and prediction rate of DDA model

			Classification				Prediction
	Class	NO.	False NO.	PP(%)	Accuracy rate(%)	NO.	False NO.	PP(%)	Accuracy rate(%)
	QZ2	30	0	100	100	15	0	100	100
	QZ3	30	0	100	100	15	0	100	100
	QZ5	30	0	100	100	15	0	100	100
	QY14	30	0	100	100	15	0	100	100
	QY241	30	0	100	100	15	0	100	100
	Total	150	0	-	100	75	0	-	100

Acknowledgements

Funding for this research was provided by Science and Technology Department of Zhejiang Province (Project No. 2009C12002), National Natural Science Foundation (Project No. 60802038), National High Technology Research and Development Program of China (863 Program, Project No. 2006AA10Z234).


Liyong Liu

, Xiaoqing Yuan

Daoliang Li
email: li_daoliang@yaohoo.com

P O Box

AN EMPIRICAL RESEARCH ON THE EVALUATION INDEX REGARDING THE SERVICE QUALITY OF AGRICULTURAL INFORMATION WEBSITES IN CHINA

Keywords: agricultural information website, information service, evaluation, index system

Agricultural information website, which takes the mission of providing valid information for the expansive agricultural information demanders, is an important carrier for the state to enact its strategy of agriculture informatization. This paper establishes a set of evaluation measures regarding the service quality of agricultural information websites by setting up the index system of agricultural information websites, evaluates comprehensively agricultural information websites in the following four respects: the range of content, the operation, the benefits and the costs, and finally conducts an empirical research on 13 agricultural information websites in China to test and justify these measures.

INTRODUCTION

According to Gu Shen and An Baojun (2005), an agricultural information website should have agriculture industry as its main content; or both its target client and its main content or service involve agriculture industry. The website itself should at least meet one of these three requirements: (1) a website with independent domain and host server; (2) an independent website using webhost if without independent domain; (3) a website with virtual directory and independent complete information system if without independent domain or host server (Hu Dehua, Wang Jinhua, 2007).

THE EVALUATION INDEX SYSTEM 2.1 Constructing evaluation items

Constructing evaluation items lays the basis for designing the evaluation index system of agricultural information websites. In accordance with the characteristics of agriculture websites, by breaking down the evaluation aims, four first-class evaluation indexes can be established: the range of content, the operation, the benefits and the costs [START_REF] Jia | The Status Quo and Analysis of Agricultural Information Website Construction in China[END_REF]; then sixteen second-class indexes can be established, such as the practicability, comprehensiveness, the accuracy, etc.; and finally thirty third-class indexes, such as satisfying the real demand for agricultural information (Li, 2008), targeting recipients, click rate, etc.

Distributing the weight of indexes

Among the first-class indexes, the range of content should be considered as the primary factor during the evaluation process, and the operation should be the second one. The benefits should be the third one due to the effectiveness and development consideration while evaluating the quality of websites. In addition, because of the development cost issue of websites, the cost should be regarded as the fourth factor. The weight of both the first-class indexes and the second-class indexes is decided through expert evaluating method and the results are shown in the table 1:

Three levels of grading standards

Evaluation objects can be rated into four levels as A, B, C, and D, each level is given different score scale. Therefore the evaluation criteria of websites are refined greatly. As shown in table 2, such a method is simple and clear and easy to operate.

Grading criteria of evaluation objects (agriculture websites)

Criteria of preparing index system refers to grading requirements for defining evaluation indexes. The evaluation system is divided into four levels: A (91.00 -100.00), B (81.00 -90.00), C (71.00 -80.00), and D (40.00 -70.00), the scale of each level will be determined, and the evaluation results will be calculated by corresponding mathematical formula.

EVALUATION METHOD

Mathematical modeling of evaluation indexes

The overall objective of evaluation can be embodied in evaluation indexes which include sub-objectives (major evaluation respects) and factors (evaluation items). The series of index sets constitute the evaluation index system. The mathematical relation of the overall evaluation objective, sub-objectives and evaluation factors, namely the structure of the evaluation index system, is illustrated by the DirectionalTree Graph as shown in Chart 1 [START_REF] Zhou | Constructing the Evaluation System on Information Resources of Medical Websites by Applying Mathematical Model[END_REF].

In Chart 1, Z refers to the evaluation object, which is the ultimate question solved by the evaluation system; F refers to the range of content in the first-class indexes; S is the operation in the second-class indexes (The benefits and costs are left out since this chart in this article is only for the purpose of brief explanation). DirectionalTree can be mastered quickly and easily applied to concrete problems as it is a simple and manifest calculation model solving this kind of mathematical problems, its calculation process is easy to understand [START_REF] Yang | The Application of AHP in the Evaluation of Agricultural Information Websites[END_REF]. Taking DirectionalTree's advantages above, we analyzed and calculated agricultural information websites by this model, and got the final results.

Analyzing and calculating process 3.2.1 Parameters description

Z:The overall index

Z i :first-class indexes(Z 1 ,Z 2 ,Z 3 ) Z ij :second-class indexes(Z 11 -Z 1n ,Z 21 -Z 2n ,Z 31 -Z 3n ) Z ijk :third-class indexes(Z ij1 -Z ijn ,Z i1k -Z ink ,Z 1ik -Z njk )
α: Level correction factor(αA:0.0101,αB :0.0112,αC :0.0139,αD :0.0145) NOTE: Considering the impact of the evaluator's subjective factors, we magnify the evaluation results properly and determine α in the following way: the full score of each level is presumed as a, then α = a/(a-1)-1.

β: weight of indexes on each level

calculation process

Step 1: It is demonstrated by Table 5 that six websites including China Agriculture Web, China Agricultural Products Information Web, China Forage Industry Information Web, China Plant Industry Web, China Livestock and Veterinary Web, and Shiji Rural Information Web all got high scores over 90. Hence they can be classified into A-class agricultural information websites. Particularly, these six websites all have an outstanding performance in terms of the range of content, the operation and can be viewed as the model of rural information websites. However the other seven websites such as China Agricultural Information Window, Zhongshan Agricultural Information Web and Chengde Agricultural Information Web score less. Some specific respects of these websites remain to be improved although they meet the basic needs of rural information demanders.

Z ij = ∑Z ijk •β ijk Step 2: Z i = ∑Z ij •β ij =∑∑Z ijk •β ijk β ij Step 3: Z=α∑Z i •β i =α∑∑∑Z ijk •β ijk β ij β i

THE EMPIRICAL CALCULATION OF THE MODEL

In addition, scores and ranking based on each index are listed in It can be interpreted from these tables that in terms of the range of content, all these websites score relatively equal and most of them is capable of providing real, accurate and comparatively extensive and deep agricultural information. This suggests that Chinese agricultural information websites' amount and depth of information are increasing annually.

In terms of the operation, each website performs well. For instance, China Agriculture Web, China Plant Industry Information Web and China Livestock and Veterinary Web have a reasonable navigation design, evenly distributed resources, friendly user interface and convenient function of search and download while China Livestock and Veterinary Web has a very good navigation design and users can retrieve information clearly as long as they login to the website.

As far as the benefits are concerned, users can easily judge which website does well or not since this index has a strong objectivity. For example, Chinese Academy of Agricultural Sciences doesn't score very due to its lacking of functions of registration, forum and etc. in spite of its favorable impression on users.

As for the costs, all of these websites have lower requirements for users' internet condition and the connection fee of each website is not high either. And these websites would benefit from these to expand their user scope.

CONCLUSION

The evaluation method for the service quality of agricultural information websites constructed by this research is a relative scientific and impartial method by establishing the evaluation principles and method and applying to 13 agricultural information websites in random. It does not look at a website only from a single respect nor roughly evaluate a website from all respects. On the contrary, it evaluates the quality of a website based on the most important 30 respects and furthermore it is simple and clear, easy to learn and use, and can be used by anyone on any condition to evaluate a website scientifically and objectively.

Chart 1

 1 DirectionalTree Graph

Table

  The content and weight of first-class and second-class indexes

	First-class	Second-class	Third-class	Weight
				Satisfying the real demand for agricultural information	0.5
		Practicability 0.3	Targeting recipients	0.3
				Effectiveness level	0.2
				Variety of agricultural information	0.5
		Comprehensiveness 0.2	Depth of agricultural information	0.35
	The range of			Availability of original document	0.15
	content (0.4)	Accuracy 0.25	Objectivity Information standards	0.6 0.4
				Reliability	0.6
		Authority 0.05	Reputation of information providers	0.4
		Timeliness 0.05	The longest update cycle	1.0
		Specificity 0.05	distinctiveness	1.0
		Stability 0.1		Providing information stably Opening speed of the website	0.6 0.4
				Internal linkage	0.55
		Navigation design 0.3	Relativity of the linked resource	0.45
		Resources 0.2	organization	Categories in terms of agricultural subjects Balance of all kinds of agricultural information resources	0.5 0.5
				Menu design	0.4
	The operation (0..3)	User interface 0.2	User interface	0.35
				User guide and help information	0.25
				Search method	0.4
		Search function 0.15	Search speed	0.25
				Search range	0.35
		Connectivity 0.15	Response speed Download speed	0.5 0.5
	The benefits	Interactivity 0.5	Registration, forum	1.0
	(0.2)	Effectiveness 0.5	(global) click rate	1.0
	The costs	Technical support 0.5	Software and hardware requirements	1.0
	(0.1)	Connection cost 0.5	Connection fee	1.0

Table 2

 2 Three levels of grading standards

	Three levels of grading	A	B	C	D
	Score scale	90-99	70-89	60-69	40-59

  13 agricultural information websites are selected for empirical research in this paper, which are: China Agriculture Web, Zhongshan Agricultural Information Web, China Livestock and Veterinary Web, China Forage Industry Information web, China Fruits web, China Agricultural Products Information Web, China Agricultural Products Information Window, Chengde Agricultural Information Web, China Plant Industry Information Web, Chinese Academy of Agricultural Sciences, Wanquan Agricultural Information Web, Zhengding Agricultural Information web and Shiji Rural Information Web. The scores and rank are listed in table 3, table 4 and table 5 as below.

Table 3

 3 Scores of agricultural information websites based on second-class indexes

								Website
	Index
		1	2	3	4	5	6	7	8	9	10	11	12	13
	Practicability	28.53	27.00	29.37	29.07	28.17	28.56	28.41	27.96	28.20	27.60	26.76	27.42	27.24
	Comprehensiveness	18.84	14.80	19.24	18.61	14.43	19.26	17.71	18.82	16.60	18.23	17.51	17.68	17.90
	Accuracy	24.10	21.25	24.05	24.30	21.25	26.65	22.75	22.80	23.75	23.45	24.45	22.95	21.30
	Authority	4.70	4.15	4.86	4.55	4.06	4.75	4.12	4.480	4.50	4.83	4.57	4.30	4.56
	Timeliness	4.90	4.45	4.95	4.95	2.95	4.95	4.90	4.950	4.25	4.25	4.75	4.75	4.70
	Specificity	4.75	3.25	4.45	2.95	3.15	4.90	4.50	4.450	4.75	4.40	4.65	4.70	4.65
	Stability	9.48	8.90	9.80	9.52	4.98	9.12	8.98	9.26	9.50	9.54	9.32	9.34	9.24
	Navigation design	28.34	27.83	28.97	28.91	26.76	26.67	22.50	27.69	24.52	27.83	26.57	26.25	27.83
	Resources organization 19.00 17.00 19.40 18.70 17.20 18.7	19.00	19.50	15.00	17.60	18.00	14.40	17.60
	User interface	19.60	17.21	17.36	12.52	14.02	19.31	19.50	17.07	17.69	17.50	16.00	17.37	17.90
	Search function	14.55	13.96	14.48	14.65	8.39	13.64	13.31	8.85	13.46	8.85	12.44	13.29	13.85
	Connectivity	13.65	11.63	12.53	12.38	7.88	11.03	8.78	11.85	10.50	13.88	11.03	13.80	13.80
	Interactivity	49.00	47.00	34.50	49.00	47.50	49.50	48.50	29.50	45.00	29.50	29.50	40.00	46.00
	Effectiveness	48.00	44.50	49.00	48.50	42.50	44.50	41.00	32.50	48.00	46.00	45.00	34.50	42.50
	Technical support	46.50	47.50	47.50	48.50	46.00	46.00	45.00	47.50	40.00	47.00	45.00	47.50	47.50
	Connection cost	47.00	47.50	47.50	48.50	47.50	46.00	45.00	47.50	45.00	47.50	45.00	47.00	47.50
	Note: 1 China Agriculture Web, 2 Zhongshan Agricultural Information Web, 3 China Livestock and Veterinary Web,
	4 China Forage Industry Information web, 5 China Fruits Web, 6 China Agricultural Products Information Web, 7
	China Agricultural Products Information Window, 8 Chengde Agricultural Information Web, 9 China Plant
	Industry Information Web, 10 Chinese Academy of Agricultural Sciences, 11 Wanquan Agricultural Information
	Web, 12 Zhengding Agricultural Information Web, 13 Shiji Rural Information Web

Table 4

 4 Scores of agricultural information websites based on the first-class indexes

			Index
	Website	The range of	The	The	The
		content	operation	benefits	costs
	China Agriculture Web	38.12	28.54	19.4	9.35
	Zhongshan Agricultural Information Web	33.52	26.29	18.3	9.5
	China Livestock and Veterinary Web	38.69	27.82	16.7	9.5
	China Forage Industry Information Web	37.58	26.15	19.5	9.65
	China Fruits Web	31.6	22.28	18	9.35
	China Agricultural Products Information Web	39.28	26.8	18.8	9.2
	China Agricultural Products Information	36.55	24.92	17.9	9

table 6 ,

 6 table 7, table 8 and table 9 below.

Table 6

 6 Scores and ranking based on the range of content

	Ranking	Website	Score
	1	China Agriculture Web	28.54
	2	China Plant Industry Information Web	27.84
	3	China Livestock and Veterinary Web	27.82


Ping ' An Liu

Jianquncheng

Zhigang Lai

Calculation and Analysis of Double-Axis Elliptical-Parabolic Compond Flexure Hinge

Keywords:

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

Introduction

Flexible hinge eliminates its idling running and mechanical friction by using small angle elastic deformation and self-recovery characteristics in the transmission process , and can get highter resolution of displacement. It is been widely used in various occations, such as small angular displacement, high-precision rotation of occasions, especially in precision measurement, positioning and other fields because of its small-volume, gapless, no mechanical friction,no lubrication, in-gage and high sensitivity of the transmission structure [START_REF] Li | Based on generalized geometric error model of the micro-robot accuracy analysis[END_REF][START_REF] Pina | Synchrotron radiation beam line in the flexible hinge of[END_REF][START_REF] Sun | A kind of two-dimensional micro-nano positioning stage design and analysis[END_REF] .

People have been made a series of studies on the single-axis oval, chamfered, parabolic and hyperbolic flexure hinge [START_REF] Feng | Biaxial elliptical flexure hinge design and calculation[END_REF][START_REF] Jianhua | Cosh flexible hinge stiffness is derived[END_REF] since 1965 Paros and Weisbord derived circular flexure hinge flexibility formul. However,they have done little reaserch about the compound flexure hinge in addition to Chengui Min's [START_REF] Guimin | straight round elliptical flexure hinge of compound[END_REF] on the right circular and oval compound flexure hinge. Elliptical flexible hinge has a lager range of rotation but lower accuracy,and Parabolic with small range of rotation but high precise compared to the Circular incision hinge; for both accuracy and range of motion exercise, combining the elliptic and parabolic type flexure hinge will take the advantages of both flexible hinges, so this paper will present elliptic -parabolic compound flexible hinge.

Elliptie -parabolic compound flexible hinge

Elliptic and parabolic confound flexible hinge is composed of two half elliptical and parabolic form, as shown in figure1, oval-side as a fixed end and the Parabolic side as the rotation-side, and make the following assumptions:

(1)Elliptic and parabolic incision have the same thickness,and both are t.

(2)Elliptic and parabolic are tangented at the intersection, or smooth over.

(3)Two incision length in the X direction are m。 (4)Ellipse major axis m is greater than the minor axis n, when m=n,that is circurlar and parabolic compound flexible hinge.

1 1 2 1 0 0 0 0 z z M Y X x M C C y C C F C x F θ θ - ⎧ ⎫ 1 y ⎧ ⎫ ⎧ ⎫ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ = ⎨ ⎬ ⎨ ⎬⎨ ⎬ ⎪ ⎪ ⎪ ⎪⎪ ⎪ ⎩ ⎭ ⎩ ⎭ ⎩ ⎭ (1)
According to reciprocal theorem known

1 1 1 1 1 1 2 z z y C M C F θ = +
,it can be obtained from the left of formula (1) with the use of cartesian displacement:

1 1 1 1 1 1 z z y x u M u y F u x F θ ⎧ ∂ = ⎪ ∂ ⎪ ⎪ ∂ ⎪ = ⎨ ∂ ⎪ ⎪ ∂ ⎪ = ∂ ⎪ ⎩ (2)
By the material mechanics, without considering the shear and torsion, the deformation energe of the compound flexible hinge is:

2 2 1 1 2 ( ) ( ) X Z z l l F M U d x EA x EI x d x ⎡ ⎤ = + ⎢ ⎥ ⎣ ⎦ ∫ ∫ (3) and 1 x x F F = , 1 1 z y Mz M F x = + , ( ) ( ) A x bt x = , ( ) 3 ( ) ( ) / 12 I x b t x =
The coordinate system as shown in figure 2, variable thickness equations of dual-axis flexure hinge as follow: ( )

2 2 2 2 ( ) 0 2 2 , n t n m x x m t x x m t px m m x l ⎧ ⎡ ⎤ + - - ⎪ ⎢ ⎥ ⎪ ⎣ ⎦ = ⎨ ⎪ ⎡ ⎤ + - < ≤ ⎪ ⎣ ⎦ ⎩ ≤≤ (4)
To the type of substitution (1) (2) (3) can obtain the flexibility formulas:

⎧ ⎛ ⎞ + ⎫ -- + -+ ⎪ ⎜ ⎟ ⎪ ⎜ ⎟ - + ⎪ ⎪ ⎝ ⎠ = + ⎨ ⎬ - + ⎪ ⎪ ⎪ ⎪ ⎭ ⎩ m (5) ( ) ( ) ( ) ( ) 3
⎫ ⎪ ⎧ + ⎛ ⎞ + + + + ⎪ ⎪ = - + + ⎜ ⎟ ⎨ ⎬ ⎜ ⎟ + + -- + ⎝ ⎠ ⎪ ⎪ ⎩ -- ⎪ ⎭ m (7)
From the formulas of elliptical-parabolic compound hinges which have deduced,it can easily see that the flexibility is inversely proportional to the thickness B and modulus of elasticity E.

The verification of Closed-loop compliance formula

Design two dual-axis elliptical -parabolic compound hinges, its parameters as follows:

thickness B is 5mm, length of oval long axle 5mm, the minor axises 3mm and 5mm, the thinnest thickness in the middle is 1mm, the total width 10mm. FEM experimental 4), analytical method(AM) and finite element results were shown in Table 1. Known from the data in the table, finite element method(FEM) and the exact formula is less than 10% deviation.

Performance Analysis

From the formulases ( 5) to (7),it is easy to know that the formula of flexibility is inversely proportional to modulus E and thickness B; the relationship between the CX, CY, Cθ-M and the short axis length n、incision thickness t were shown in figure 567. (2) The relationship between flexibility Cy and t、 n as shown in Figure 6,Cy will reach maximum when t equals to 0.2,and Cy will change sharply as t in the range of 0.2-0.4 and decrease smoothly when t is greater than 0.4, Cy has no significant change with n.

(3)From the figure 7,we can see that the Cθ-M obtains maximum when t equals to 0.2 and decreases as n increase,especiallly when t in the range of 0.2-04, it decreases sharply; when t is greater than 0.6,the Cθ-M decreases slowly and close to zero.

From the anlysis above all,this conpound hinge has large rigidity in the direction of x -axis and good flexibility in y axis direction and rotate around Z axis,so it is very suitable as rotation flexibility hinge.

Conclusions

This paper presents a new type of compound hinge oval -parabolic compound flexible hinge. And analized its structure, its flexibility formulas were deduced based on card's second law and vertified by the finite element software,find the errors are within 8%, which demonstrates the correctness of the flexibility formulas. Then analyzed the relationship between the flexibility and the thickness B and the short axis lengh n with matlab software,and got some conclusions, it could be a theoretical references in future industry and agriculture fields.

Fig. 1 .

 1 Fig. 1. elliptic -parabolic hinge pro/e model

Fig. 2 .Fig. 3 .

 23 Fig. 2. the structure parameters of compound hinge

  parameters are: modulus of elasticity:E = 110 ×10 9 Nm -2 ,Poisson ratio:μ= 0. 3. parabol parameters:p=1/16 Fx1 = Fy1 = 1 N Mz1 = 1 N.m.

Fig. 4 .

 4 Fig. 4. the ansys geometric model of compound Models with different parameters in the finite element software to verify compliance formulas, choose the shell63 finite element, making finite element analysis to compound flexure hinge( As shown in figure 4), analytical method(AM) and finite

Fig. 5 .Fig. 6 .Fig. 7 .

 567 Fig. 5.the relationship between Cx and n、t

Table 1 ,

 1 analytical and finite element calculation about flexibility

* Supported by Natural Science Foundation of Jiangxi,China,NO.

2008GZC005