Jiahong Li

Heng Li

Qiang Fu
email: cfuqiang@neau.edu.cn

Computer computing and simulation-In view of the leaves' categories, shapes and mass

Keywords: computer, simulation, leaves, mathematic model

Leaf is the important part of a tree. This paper mainly studied its categories, shapes and mass, then established four mathematic models to describe and analyze them. Firstly, this paper analyzed the reasons why leaf shapes are different by the theory of mechanics. The mechanics model explained the structure and forming principles of a leaf. Secondly, in order to classify the leaves accurately, this paper selected digital image processing and built two classified criterion: ratio of leaf area to its perimeter and ratio of maximal length to maximal width. After edge extraction and Fourier fitting. Thirdly, this paper utilized the connection between illumination intensity and leaf area to explain how the distribution of leaves and branches affect leaf area. According to the stem height at each branch tip, this paper got the relation between the branches, then established a model of leaf area to proof that the leaf shape has a relation with the tree profile and the branches structure. Finally, this paper set up leaf mass model to compute the total leaf mass, based on the leaf area index. It was calculated by choosing digital image processing technique. Using the leaves mass, this paper analyzed the relevancy of the mass and the size characteristics of the tree.

Introduction

Leaves are the important parts of a tree. The classification, shapes and mass of leaves have big relations with a tree. The leaves are the places where plants can absorb the carbon and keep the carbon-water balance; they are also the survival foundation of the plant, so the form and physiology characteristics of the leaves will become important growth indexes of the tree undoubtedly. The leaves have the important denotation to the update of the vegetation, the community and the ecological system [START_REF] Niklas | A mechanical perspective on foliage leaf form and function[END_REF]. The types, shapes and mass of the leaves contact to the climate and its growth closely. Through the leaves, we can also learn about the relationship between plants and the global carbon cycle, so its significance is far-reaching. Nowadays, someone researched tree structure, obtaining the tree model based on L system and real-time rendering technology conclusion; someone imitated the dynamic model of veins, describing the importance of veins to leaf characteristics; someone applied physical methods to mimic the change process of the leaf shapes. But few has a systematic, integrated and comprehensive research about shape and mass of leaves. In this paper, problems which we need to solve is the diversity of the shape, the effect of the distribution of leaves and branches to the leaf shape, the relation between the leaf shape and the outline of the tree and the structure of the branches and how to calculate the weight of the leaves, we should establish the corresponding models to describe leaf shape and the mass of the tree.

There are the main steps in this paper, [START_REF] Niklas | A mechanical perspective on foliage leaf form and function[END_REF]This paper use some mechanics knowledge which related to the growth of the leaf to describe the leaf shape,then analyze the reasons of shape diversity about the leaves;

(2) This paper simulation the leaf shape according to the Fourier series, then classified the kinds of the leaves according some index in the final figures;

(3)This paper utilized the expression about the light interception ratio   LIR and the leaf area index   LAI , explaining the blade shape is effected by the distribution of the leaves and tree; By considering the sun light (photosynthesis) affects the leaf shape, we established the model to show the shape has some relations with the outline of tree and the structure of the branches;

(4) To measuring relative parameters easily, this paper selected digital image processing to calculate the leaf area index. So that we can weigh the mass of leaves by leaf area index and projected area. Confirming relations between the leaf mass and the mass, height, volume of the tree, we took correlation analysis and qualitative analysis to accomplish them.

To achieve the goal of simulating the relation between parameters, we did simulating experiment several times.

Analyses and Modeling

The Leaves Categories

The Leaf Variety

There are no two leaves exactly the same. The same tree's growths of the leaves are not completely the same. This paper mainly studied the leaf shape's diversity of a tree. Through understanding the structures of the leaves and the influence of environment to the leaves, we use the opinion of mechanics [START_REF] Chen | Describing shape changes in the human mandible using Elliptic Fourier functions[END_REF] that the force can be divided into the internal force and the external force. It is similar to the growth of the leaves which is effected by the external environment and the inherent factors itself. So the leaves stand its weight and external force produced by the environment. Then the petiole will bend and twist, so it can be regarded as a cantilever beam mechanical operation approximately. So we establish the model:

Nomenclature

mg F 

The gravity expression of a leaf  

       x 0  y  max    x then, x x x              2 max 2 2 x x x              2 min 2 2 ,thus max 1    , 0 2   , min 3    According to the third strength theory          3 1
,in the cell of the body, when the total maximum stress minus minimum stress, the result is less than the allowable stress. Considering the effect of the load from the outside, we should change the third strength theory into the following expression:

           3 1 k (1)
Due to the leaf must meets the strength theory to keep standing on the branch, we can get:

      S k L F x f , , , ,    (2)
So, the mechanics of the relevant knowledge, analyzes the specific reason for the diversity of leaf shapes. The differences of blade shapes are effected by

  S k L , , , ,

 

and other factors.

Image Processing

Boundary extraction

There is a very practical operation called boundary in the image processing. After extracting the boundary of image, we can do the further operation, such as image segmentation, extraction of the location and the skeleton extraction and so on. In this paper, we extracted an image of the leaf, then extract the edge. The process can be described as follow:

31 21 11      x  F 1 F 2 F  leaf Fig.3.
The main steps of image processing Therein, in the image after introduction, we extracted the edge of the leaf image to meet the requirements of leaf image. Then the graphics morphology processing is based on Mathematical Morphology (Mathematical Morphology) set theory method. IN this model, we applied to "corrosive" and "inflation" which are the morphological processing method [START_REF] Daliri | Robust symbolic representation for shape recognition and retrieval[END_REF]. Image processing the rapid expansion of the corrosion, morphology method is characteristic: it USES the rectangular element structure on the additional two sliding window that is vertical and horizontal two directions of the sliding window, with rectangular structural elements through traverse the whole image move corrosion, inflation in the process of operation, through the level and the vertical sliding window a record has been compared with local information, so that in the future can be directly used in calculating the extraction, in order to achieve reduce and eliminate the comparison of the calculation of the repeat redundant, save the computational time, and presents the existing technology results which can compared with experimental. Thus, we will get the leaves of the main context extracted.

Fourier Series Fitting

After the boundary extraction, we use the Fourier series [START_REF] Zheng | Leaf Vein Extraction Based on Gray-scale Morphology[END_REF] image analysis fitting out the boundary of the blade, the coefficients of the normalization of the leaves to final simulation blade shape relevant model. Among them, Fourier series fitting has successfully used by many scholars to achieve closed boundary characteristics, elliptic Fourier descriptor is using the stack to approximate the object boundary curves,

Because the blade of the image is a continuous border and have closed cycle, so Fourier series can be used to approximate the boundary to a closed boundary, the border in the x y direction of Fourier series could start for as follows:

                1 0 2 sin 2 cos n n n T t n b T t n a A t x   (3)                 1 0 2 sin 2 cos n n n T t n d T t n c C t y   (4) In the mathematical expressions                T t n T t n t x n T a p p k p p p n 1 1 2 2 2 cos 2 cos 2    (5)                k p p p p p n T t n T t n t x n T b 1 1 2 2 2 sin 2 sin 2    (6)                k p p p p p n T t n T t n t y n T c 1 1 2 2 2 cos 2 cos 2    (7)                k p p p p p n T t n T t n t y n T d 1 1 2 2 2 sin 2 sin 2    (8)                       k p p p p p p p p t t t t t x T A 1 1 2 1 2 0 2 1  (9)                       k p p p p p p p p t t t t t y T C 1 1 2 1 2 0 2 1  (10)   2 1 2 2 p p p y x t      (11)     p i i p t t 1            
* * n a 、                                     1 1
Where     * 1 * 1 * 1 * 1 1 arctan 0 0 arctan 2 1 a c x y            (15)     2 0 1                       1 1 1 1 1 1 1 * 1 * 1 * 1 * 1 cos sin sin cos d c b a d c b a     (16)
At the same time, half the size of the long axis for (2)When used for reconstruction of the harmonic times are over 10 times, errors can be ignored, and can be accurately rebuild original shape [START_REF] Royer | Phenotypic plasticity of leaf shape along a temperature gradient in Acer rubrum[J][END_REF] Series

    2 1 2 * 1 2 * 1 * 0 c a E   (

Classification Criterion

Many criterions for classifying leaves used digital morphological features, and the main criterions is ratio of leaf area to its perimeter AP and ratio of maximal length to maximal wide LW [START_REF] Sarkar | Rapid identification of molecular changes in tulsi (Ocimum sanctum Linn) upon ageing using leaf spray ionization mass spectrometry[END_REF]. Leaf area, its perimeter, maximal length and maximal wide both can't measure leaves shape alone. Since they are affected by leaf size [START_REF] Franz | Shape description of completely visible and partially occluded leaves for identifying plants in digital images[END_REF]. But their ratio can do. With them, classification leaves became quickly and accurate.

Leaf area ( LA ): the value of leaf area is easy to calculate, if only counting the mount of pixels of binary value 1 on smoothed leaf image.

Leaf perimeter ( LP ): leaf perimeter is calculated by counting the number of pixels consisting leaf edge. (19) According to the classification criterion, we classified leaves by program. To improve the accuracy, we selected Fourier series to fit the edge of leaves. The advantage of Fourier series is outstanding in nonlinear fitting.

Maximal length ( max

The Leaves Shapes

Minimum Overlapping

From the angle of the light energy,we considered the situation that the leaves have the overlapping, then we introduce the light interception ratio (LIR) [START_REF] Sato | Experimental analysis of moisture dynamics of litter layers-the effects of rainfall conditions and leaf shapes[END_REF] which is the proportion of incoming of irradiation intercepted by the canopy, is generally computed by using the turbid medium analogy which supposes a uniform distribution of leaf area in the canopy. This analogy has been proved to be very robust since the Beer-Lambert equation is fairly insensitive to violations of the uniform assumption. See for example [START_REF] Li | A Leaf Vein Extraction Method Based On Snakes Technique[END_REF] the light interception ratio is thus classical given by equation

) exp( 1 kLAI LIR    (20)
Where, LAI The leaf area index whose original definition is the total one-sided area of photosynthetic tissue per unit ground surface area. k Is a fixed numerical value, standing for the extinction coefficient for the Beer Law, related to the leaves' orientation.

Where,

PA
) FS PA exp( 1 TOE AOE k    ) * exp( 1 FS ls la k    (23)
In the expression, TOE, k, ls and FS are permanent only AOE and la are variation. So there are some relations between them. When the la is bigger,the shape turns to be large and the AOE also becomes plentiful. So the sentence that "shapes -minimize‖ overlapping individual shadows that are cast, so as to maximize exposure‖ was right.

Distribution of the Leaves and the Branches

The leaves will get more energy through the exposure in the sun if necessary. The distribution of the leaves and the branches will decide whether the leaves need energy. When the distribution is sparse, the leaves will expose mostly, then the leaves needn't change their shapes to receive the sunshine; when the distribution is dense, the leaves need to change too small to receive the sunlight. So when the leaves change to receive the sunlight, the shapes of the leaves will change as follow. Through the opinion, we can answer to the question 2 that the distribution of leaves within the"volume"of the tree and its branches effect the shape.

Outline of the Tree and The Structure of the Branches

Leaf areas

The leaves area distribution for the absorption of light by function have very important influence, so the outline and the branches of the tree structure will affect the growth of the leaves, from cross-sectional area of tree branches and leaves area and branch prediction model is out of the length : Crown length of each tree was divided into 0.1-m sections and branch leaf area within each section expressed as a proportion of total leaf area. These data were fitted to a two-parameter cumulative Weibull function using the NLIN procedure of the SAS software package (SAS Institute Inc., Cary, NC) [START_REF] Gunawardena | Programmed cell death remodels lace plant leaf shape during development[J][END_REF]. The cumulative foliage distribution function took the form:

Based on (5) the cumulative proportion of leaf area model can be reached

                                                

 

Estimated parameters For 1 < α < 3.6, the probability distribution is mound shaped and positively skewed; if α = 3.6, the distribution is approximately normal and if α > 3.6, the distribution becomes negatively skewed. The β parameter describes the scale of the distribution and has been interpreted as leaf area density or leaf area per unit height (Gillespie et al. 1994). Which reflects the outline of the outline of tree branches and structure and crown have relationship, the shape of the crown has influence for leaves' shape.

The proportion of leaf area at a given crown location was estimated from the fitted Weibull [START_REF] Mori | Crown profile of foliage area characterized with the Weibull distribution in a hinoki (Chamaecyparis obtusa) stand[END_REF] function:

                                                    1 exp 1 exp exp w x w x la la x (26)
Where x la The predicted leaf area la Total tree leaf area Crown volume was estimated by calculating the horizontal projection length of each branch at its maximum stem height, which is related to the branches of the tree. The branches of the different Angle will influence the leaves accept photosynthesis. Photosynthesis decided to the leaf to sunshine absorption and their own manufacturing organic matter, for the leaves the veins which influence the shape of leaves are the influence of photosynthesis determined to the movement of nutrients and carbon dioxide absorption.

So the outline of the tree branches and the distribution of the shape have influence for the leaves.

Section 3 the Leaves Mass

Calculating the Mass

Improving the production of orchard, protecting trees and the ecological environment require us to monitoring trees growth condition. One of monitoring indexes of trees is the mass of leaves [12]. The mass usually calculates by the total leaves area ( PA ) and its surface density ( SLM ), just is SLM PA LM *  Since SLM relates to the tree species and its environment of its location. So SLM needs to measure when counting LM . Calculating PA needs leaf area index ( LAI ) and projection area of crown ( PA ) [13]. Projection zone is usually treated as ellipse. So PA is easy to gain.

Based on the Digital Cover Photography

There are many ways to measure LAI . Among those ways, graphic processing has many advantages, such as no damage to trees and high precision. Using graphic processing to measure LAI , we should define crown porosity  . LAI can be expressed as follow:

  k IM LAI C  ln   (28)
Where, k leaf inclination angle distribution.

2.3.3Analysis of the model

After LM calculated, we confirm the connection between LM and the size characteristics of the tree by Correlation analysis. The result is showed in table1. According it, we can know the mean crown radius correlation obvious. And then we inferred the qualitative relationship by fitting (fig. 7.).

Table 1 The connection between leaf mass and some characteristics of tree (1)There are many reasons that lead to the difference between leaves. Some of them are primary causes, another are not. In order to search for the primary causes, we selected the mechanics structure of leaf. In the mechanical angle, we established the mechanism model based on leaf forming and analyzed reasons which affects leaf shape, then we found out the influence factors. To classify leaves exactly, we defined the classification criterions which are chosen from image processing technology to accomplish the goal.

(2)Leaf incidence and internodes can affect the rate of leaf coverage, enlarging the exposure. At the same time, the rate of leaves coverage should make production of photosynthesis equal or large to the production of transpiration. So leaf area and distribution of leaves and structures are affected each other.

(3)The mass of leaves was calculated. With the mass, we analyzed and confirmed the relativity between mass and some of the size characteristics of the tree.

(4)The cover rate of the leaves will content the photosynthesis and the transpiration for the final purpose. The use of light and carbon for leaves influence the leaf shape. The distribution of leaves and branches influence the light absorbed by the leaves, so the shapes of the leaves are affected by the distribution of leaves and branches.

(5)The mass of the leaves is determined by the canopy height, volume and radius, so they have the certain relations.

 1 F 2 FFig. 1 . 2 .

 1212 Fig.1. The cell of the leaf body Fig.2. Simulation tree 31 21 11

  Extract edgeFit the edge curves with Fourier series Calculate AP and LW Match leaves to classify them Load leaf image

the direction of y incremental 0 AFig. 4 .

 04 Fig.4. Leaves simulation process

5 .

 5 17) Through the type Calculated about starting point and the rotation Angle of the normalized coefficient; Then to scale to normalization, to get the coefficient of half the size of the divided by long axis can get the result; On to the translation of the normalization, just ignore dc component 0 A and 0 C . The connection between errors of Fourier fitting and series is shown in figure 5: Errors of Fourier fitting Fig. Error analysis of Fourier fitting notes: (1)With elliptic Fourier description method for describing the shape of need only a descriptor can complete complex shape description

L

  ): maximal length is the maximal distance between arbitrary two point on leaf edge.Maximal wide ( max W ): it is the longest line which is perpendicular to the main vein. Maximal length and maximal wide is shown in figure6

Fig. 6 .

 6 Fig.6. Maximal length and maximal wideTo avoid leaf size affecting result, we used AP and LW as criterion. They expressed as follows:

hh

  The stem height at each branch tip 0 Height of branch emergence  Branch angle from the vertical br l Branch length

l

  Cumulative proportion of leaf area rh Relative crown height

IM

  Is foliage cover in image. It counts by the fraction of pixels not in any gaps; C IM is defined as the ground fractional of the vertical projection of solid crown which include the porosities.

Fig. 7 .

 7 Fig.7. The relationships between dry leaf mass and ground crown distance

  Stands for projection area of leaves and it equals to the leaf area   la multiplied by leaf sum  

		ls . So
		PA 	leaf	area*	leaf	sum		la	*	ls	(21)
	FS	Stands for floor space
	AOE	Stands for absorbed optical energy
	TOE Stands for total optical energy
	Then,
			LAL		PA	LIR		AOE
					FS				TOE		(22)
	we can obtain

Acknowledgment

Funds for this research was provided by the special scientific research funds for Ministry of water resources public welfare industry (201301096), the studying funds of Key laboratory of water saving in Heilongjiang province (2012KF503), the development potential of groundwater resources in Heilongjiang provincial water resources bureau(201318).


Bolin Shi

Houyin Wang

Lei Zhao

Ruicong Zhi

Zhi Li

Lulu Zhang

Nan Xie

Study on Key Technology for the discrimination of Xihu Longjing Tea Grade by Electronic Tongue

Keywords: Electronic Tongue, Longjing Tea, Grade, Shifting Calibration, Repeatability, Abnormal Sample

Electronic tongue has the characteristics of sensitivity and instability. However the technical specification for its research has still not been formed. In this paper, it was introduced the key technology of electronic tongue qualitative discriminant analysis to micro-difference samples. The research objects were four different grades of Xihu Longjing Tea with less difference within small producing area in Hangzhou of Zhejiang Province in China. According to the research, the stability of equipment had not been shown until the fifth repetition for the same sample by the electronic tongue. Finally, the signal from seventh repetitive test was selected to represent the intelligent taste fingerprint for this sample. The fingerprints of electronic tongue collected at different days showed linearity drifting for the same sample. The special tea samples were set into each series of experiments to be considered as the reference sample. All samples' signals were calibrated with the difference value between the reference sample fingerprint of corresponding test to the designated reference sample. Principal component analysis (PCA) results showed that the same grade samples were clustered, while the different grades samples were more dispersion and non-overlapping. Through Mahalanobis Distance and Residual Method, the four abnormal tea samples were rejected from highest and 1st grade respectively. The electronic tongue's Longjing Tea Grade models were built by soft independent modeling of class analogy (SIMCA). The discrimination accuracy for tea sample grade were both 100% for correction set and prediction set. Through this study, it was established the technical specification and flow for the quick detection of tea by electronic tongue, which including the determination on intelligent taste spectrum repeatability performance, system error calibration for spectrum drifting, rejection for abnormal tea sample based on taste spectrum and establishment for the quality judgment model . The technical specification provides the theoretical base for reasonable use of electronic tongue.

Introduction

The 30~48% contents of tea are water-soluble substances, which represents the taste of tea soup and directly reflect the tea quality and grade (Rahim etc., 2014). Currently, the commonly used analysis technologies for flavoring matter of tea are liquid chromatography, spectroscopy, mass spectra and nuclear magnetic resonance method as well as the combination and cooperation between them. However, the flavoring ingredients are extremely complex. The whole quality of tea is not composed by certain or some kinds of taste matters but the comprehensive performance by dozens, even by over hundreds kinds of flavoring matters. The researchers were difficult to detect these ingredients one by one, and they can only figure out some ingredients with main flavor, which makes it difficult to test the taste of tea comprehensively (Shi Bolin etc., 2009). The electronic tongue is a kind of electronic intelligent identification system developed by simulating human body's taste mechanism and is a kind of novel food analysis, identification and testing technology developed within recent years. Being different from the common analyzer, the result obtained by the electronic tongue is not the qualitative or quantitative for the certain kind or some kinds of ingredients in the tested sample but is the overall taste information of sample which is also called as "Taste Fingerprint" data (Jiang Sha etc., 2009). IVARSSON etc. utilized the electronic tongue to appraise nine different kinds of tea and obtained the satisfied results through combining the pattern recognition method with multivariate analysis and principal component analysis. Lvova etc.(2003)used the electronic tongue microsystem with full solid status to quantitative determine the multiple ingredients of green tea from Korea. Chen Quansheng etc. (2008)utilized the electronic tongue technology to perform the classification and identification study for pan-fired green tea with different grades through combining the identification method for K nearest neighbor domain and neutral network mode. He Wei etc. (2009) applied the electronic tongue technology into the grading and classification study for Pu'er tea to study the correlation with sensory evaluation result. In the former tea quality study, the electronic tongue was employed to test the difference among different types of tea (such as, red tea, black tea and green tea) (Wu Jian etc., 2006; Liu Shuang etc., 2014) or the difference among the same kind of tea with various geographic origins (such as, green tea classification in Zhejiang, Fujian, Anhui, Jiangxi Province) (Wu Xinyu etc., 2007; Runu Banerjee etc., 2012). Since the quality difference was greater among samples in the these studies, it was easy to obtain the satisfied prediction result. Although these studies were specially taken into account the excavation of electronic tongue signal and the application of electronic tongue, the corresponding technical specification and systematic key technology solution on electronic tongue application were not formed in accordance with the electronic tongue technology advantages and the existing technology level. In order to conduct the rapid detection research of electronic tongue for different grades of Xihu Longjing Tea, three key elements should be taken into account. They were repeatability performance analysis of intelligent taste spectrum for electronic tongue, error calibration for spectrum drifting system and rejection for outlier tea samples based on taste spectrum. It was helpful to explore the solution on repeatability and reproducibility of electronic tongue instrument as well as the importance of selecting representative samples in classified model. This paper revealed the key technology and technical specification in the rapid detection of electronic tongue and reflected the value of electronic tongue test for micro-difference sample (such as Xihu Longjing Tea with different grades) in the practical application.

Experiments and Methods

Sample preparation

Xihu Longjing And the sample was tested by electronic tongue two hours later (Xue Dan etc., 2010).

Intelligent Taste Acquisition Method

ASTREE electronic tongue made by Alpha MOS in France was adopted, mainly including automatic liquid sampler LS16, electrochemical transducer array (including 7 chemical sensors and 1 reference electrode) and data acquisition unit. Prior to tea sample testing, the electronic tongue was subjected to such procedures as selfinspection, activation, training and calibration so as to ensure the equipment reliability and stability. Every fingerprint of sample was collected according to the flow of " Tea Soup Sample (120s) (10s) (10s)". Each sample was repeatedly tested for seven times and provided with two same cleaning fluids (Figure 1). After all samples were detected, the probe of electronic tongue sensor was stopped in the ultrapure water. Finally, the sensors were completely cleaned based on the setting cleaning procedure. The detection parameters of electronic tongue were presented in Table 1.

Multivariate Statistic Method

Mahalanobis was widely applied in the outlier judgment, because the method was used to measure whether the sample affects the whole samples set (Shi Bolin etc., 2010). If the Mahalanobis value of certain sample was too high, the regression model would have greater dependency on this sample, which would affect the stability of model. In other words, this sample was an outlier sample. Principal component analysis(PCA)was used for data dimension reduction (Shi Bolin etc., 2012). PCA is a statistical technique that is used to analyze the interrelationships among a large number of variables and to explain these variables in terms of a smaller number of variables, called principal components, with a minimum loss of information. The principal component scoring not only reflected the similarity and peculiarity among tea samples, but also revealed the internal characteristics and clustering information of samples. It represented that whether the sample had greater difference in various categories of sample set, and whether the automatic clustering phenomenon was formed in accordance with the quality characteristics among samples. Soft independent model classification analysis (SIMCA) was adopted to establish the classification identification model with different grades for Xihu Longjing Tea (Shi Tea Sample Cleaning Fluid No.1 Cleaning Fluid No.2

Bolin etc., 2011). The foundation of SIMCA was principal component analysis (PCA). Each class of samples had an independent model which was trained by PCA.

Meanwhile the residuals of each class were generated. The unknown sample was brought into every class to compare the residuals in different models. All algorithms were programmed by Matlab 7.0.

Results and Discussion

Selection of Stable Tea Spectrum for Electronic Tongue

The response value that the electronic tongue obtained was the difference value between electric potential of tested sample and electric potential of reference electrode ( R-R0,where R0 was electric potential value for reference electrode, and R was electric potential value of sensor of tested sample ). Figure 2 was the response curve of certain sample on seven sensors within 120s, from intense response originally to stability finally. The response on the final stage included the whole information for tea sample. Moreover, the steady value at 120 s reflected a minimum relative standard deviation (RSD) for the same sample, and a maximum distinction for the different samples. Therefore, the steady value at 120 s was the characteristic response signal for subsequent study.

Fig. 2. The response curve for electronic tongue of a certain Sample

For electronic tongue equipment, it was required to guarantee the repeatability of detection signal, otherwise, the reliability of testing data may be queried. The animal's gustation can perfectly catch the taste characteristics of sample after taste training continuously. Similarly, the taste fingerprint from electronic tongue would be more reliable after repeated testing. In the previous researches, the fingerprint information of electronic tongue was used by only one time test result or the mean response signal after three-time repetition. In this study, 7-time tests were repeated simultaneously for each sample. Figure 3 was the PCA scoring for four different grades samples after 7-time repeated tests. The figure showed that the signal repeatability of electronic tongue was poor in the former three-time testing. However, with the increase of repetition time, the testing results were tended to be stable continuously. Especially, the testing results for final three times showed good repeatability to the same sample and great difference among the various grades samples. Therefore, the electric potential value at 120s acquired during 7th repetition was selected to be the original information of intelligent taste for Xihu Longjing Tea.

In the repeatability test of electronic tongue, the signal choosing was solved.

- T-3

J-5 J-4 J-6 J-7 J-3 T-2 J-2 T-1 J-1 2-7 2-5 2-6 2-4 2-3 2-2 2-1 1-6 1-5 1-4 1-7 1-2 1-3 1-1 PC2 PC1
Fig. 3. PCA scores of four grading samples under seven repeating determination (The Highest Grade, Special Grade, 1 st Grade and 2 nd Grade Products were expressed as "J", "T", "1" and "2" in figure.)

System drifting correction for electronic tongue spectrum of tea

In order to give play to the advantage of electronic tongue's rapid testing, a long-term prediction model should be established based on lots of samples that had the characteristics of representativeness, typicality and comprehensiveness, so as to make sure of the model robustness and stability. These tea samples were usually detected for several days by electronic tongue. In the same day, the 7th repeated result was used as the sample fingerprint to solve the repeatability. Due to influence by various complicated factors such as environment, the spectrums of the same sample were shifted seriously in different days test, leading to bad reproduction. Therefore, the results would be meaningless if the original data was used directly and the accurate conclusion would be difficult to be made in this case. The same experimental operation was adopted on each tea soup sample during the whole test period, lasting for a month and acquiring one time every week. The principle analysis was applied to deal with the original data of 4 grades obtained in 4 different days. Figure 4 showed that the samples belonging to the same grade did not gather together properly and but had great dispersion; while for the samples of different grades, there was obvious trend of intersection and serious overlapping, which would not be helpful to the discrimination. Besides, contribution ratios of first principle component and second principle component were only 50% and 25%, showing that the cohesiveness of original data was worse and not suitable for the tea grade discrimination.

-2000-1600-1200 -800 -400 0 400 800 In order to solve the problem of error at different times, the reference sample was introduced during the data collecting. 1st Grade tea soup was chosen as the reference sample whose preparation method and acquisition method kept the same as other experimental samples. The reference sample was arranged in each sequence experiment, which means for each experiments, there was always a corresponding reference existing. It was necessary to conduct the signal drifting treatment to remove the drifting by utilize the value of reference sample gained in each measurement before modeling. The method was shown below: firstly, fixed the response value of reference sample on certain sequence as the reference value(REF), then figured out different value (△i=refi-REF) of response value of other sequence reference samples with REF, and then subtracted the value of the corresponding reference samples from the original data of the samples. In this way, the drift could be minimized as largely as possible and data by corrected in this way could be used to build the reliable prediction mode. Figure 5 was the PCA scoring figure of the samples calibrated by the correction. When compared with the results before the correction, data after correction showed that the cohesiveness of the same grade of tea samples was enhanced and the discreteness between different grade samples was improved, which brought more Highest Grade Special Grade 1 st Grade 2 nd Grade significant difference between different grads of samples. By analyzing the value range of the horizontal and vertical coordinates of the figure before and after calibration, we could find that after the correction, the values of PC 1 and PC 2 were reduced. But the contribution rates of the first principal component and the second principal component were improved largely, whose contribution rates were 78% and 12%respectively, and the cumulative contribution rate reached 90%, representing the main information of original data. Meanwhile, the cumulative contribution rate of the first four principal components was 97%, representing that these PCs containing 97% of sensor information. According to the main principle of PCA, it could be known that the first four principal components represented the structure characteristics of electronic tongue data of the samples, which could be helpful to reduce the data dimensionality and simplify the data. The above proved that the problem of system drifting for electronic tongue at different times was solved, showing good test reproducibility of the electronic tongue.

-

Rejection for Abnormal Sample Point of Tea

The sample set used for pattern recognition was required to be provided with representativeness and correctness of original data, and without anomalism. The existence of the outlier would affect, even change the distribution trend of original data leading to the effect of the prediction model accuracy. From figure 5 it could be found there outliers in highest grade and 1st grade marked by the separation from the cluster. By analyzing (Figure 6) the samples of four grades with Mahalanobis distance in combination with residual errors, No. 54 sample was doubted as the outlier considering its largest Mahalanobis value, meaning that it was apart from the set. Meanwhile, the residual value of No. 50 was largest, which meant the prediction for this sample was inaccurate. In other words, the model cannot be able to explain the Special Grade 2 nd Grade sample properly, so it should be treated as abnormal sample, too. However, there was not similar phenomenon for 2nd Grade and Special Grade samples. In order to comprehensively analyze the abnormal sample point, the principal component analysis and calculation of Mahalanobis distance value for the 1st Grade samples and highest Grade samples were conducted separately. From Figure 7, it could be found that the samples of No.47, No.50 and No. 54 were the three maximum Mahalanobis distance samples in 1st Grade, being considered to be the abnormal sample points. However, No. 52 sample not only detached from the samples set in the score figure but also had larger Mahalanobis distance value and residual value, also being of abnormal sample point. From Figure 8, it could be found that the Mahalanobis distance of No.9 sample in the competitive product sample set was maximum, being of abnormal sample point, but the samples of No.40, No.41 and No.42 were far from the samples set in the scoring chart, also being judged to be of abnormal sample points. So the quantity of abnormal sample points to be removed was 8 in total, including four primary samples respectively: 1-47, 1-50, 1-52 and 1-54; and four competitive product samples respectively: J-9, J-40, J-41 and J-42. Table 2 was the number of the samples before and after outlier elimination for each grade, and the final division of the sample set. The quantity of sample used for discriminating the grade of tea were 209 in total, among which, two-thirds were randomly chosen to be served as calibration set samples to establish the qualitative classification model so as to enable the modified samples to not only have good representativeness but also broaden the prediction range of the model to strengthen the adaptive capacity of the model. The rest one-thirds were used for prediction set samples to inspect the veracity and reliability of the established model.

Residual

Value identifying the different grades of tea samples, but the component of tea soup was complex, whose information were inevitably mixed together. Through choosing repetitive signal of electronic tongue, after calibrating signal drift at different time and eliminating outlier tea samples, the performance of PCA for extracting the independent information from large numbers of data was improved effectively. According to Figure 9, the score chart of PC 1 and PC 2 could make the distinction between different grade of samples more obvious (the cumulative contribution rate of the first two principal components had been up to 90%), among which the performance of PC 1 was the greatest, being able to distinguish different grade samples obviously. With the decrease of grade level, the score value in PC 1 increased gradually; while the variance of score value between principal component 3 and principal component 4 was very small (the cumulative contribution rate of principal component 3 and principal component 4 was 6%).

Conclusions

The study chose the graph of samples in the seventh repetition to stand for the intelligent taste fingerprint of the sample since in this case the stability and authenticity of the tea soup could be guaranteed effectively. The corresponding reference on each sequence was adopted to be served as the basis of calibration to solve the problem of system error generated by linear drifting which guaranteed the stability of the graph signal, making Mahalanobis distance and residual method could be run quickly, factually and accurately, eliminating the abnormal samples in tea sample set. Finally, we used SIMCA modeling with of the selected number of principal components, and the accuracy of discrimination for Xihu Longjing Tea reached 100%. In conclusion, the study focusing on three key points of electronic tongue: repeatability, reproducibility and representative. By handling these three points we could obtain signal graph with high signal to noise ratio, and the authenticity of the characteristics of the samples. The study developed the process of confirming the repeatability, correcting graph drift system error, eliminating the outlier samples and building the discrimination model with technical specifications and procedure. All these will provide the theory basis for application of electronic tongue in other fields..

Fig. 1 .

 1 Fig.1. The steps of sample test

Fig. 4 .

 4 Fig. 4. PCA Scores of Original Date from Four Grading

Fig. 5 .

 5 Fig.5. PCA Scores Under Sensors Modification

Fig. 6 .

 6 Fig.6. PCA Scores (a) and Mahalanobis Residual (b) before Outlier Elimination

Fig. 7 .Fig. 8 .

 78 Fig.7. PCA Score (a) and Mahalanobis Residual (b) Before Outlier Elimination of Grade-I Sample

Fig. 9 .

 9 Fig.9. PCA Scores between Different PCs after Outlier Elimination: (a) PC1-PC2, (b) PC3 -PC4 SIMCA discrimination model was built based on the basis of PCA analysis, where the choice of principal component numbers was very important to SIMCA modeling. The best principal component number of models for the different grades was determined

Fig. 10 .

 10 Fig.10. Relationship between PRESS and Number of PCs under Grading Model

Table 1 . The detection parameters of electronic tongue

 1

	Parameter Items	Parameter Value
	Activation, Training and Calibration Solution	0.0lmol/L HCl
	Cleaning Solution	Ultrapure Water
	Cleaning Time	10 s
	Stirring Speed	1 r/s
	Volume of Sample	25 mL
	Volume of Sample Cup	40 mL
	Sampling Time	120 s
	Repeated times of Determination	7 times
	Sampling Temperature	25℃ for Room Temperature

Table 2 .

 2 Quantity for Different Sample Sets of Graded Model

				Mahalanobis Value
		After Rejection
	Name of specimen clustering	Sample quantity	Sample quantity	Total	sample	Before Rejection
		of training set	of prediction set	amount
	Highest Grade	33	17	50		54
	Special Grade	36	18	54		54
	1 st Grade	34	16	50		54
	2 nd Grade	36	19	55		55
	Quantity of all graded sample	139	70	209		217
	3.4. Grading Model Establishment of Xihu Longjing Tea Based on Electronic
	Tongue
	Although the individual sensor was provided with high sensitivity and selectivity for

Table 3

 3 was the final model result. As what was found in the table, the electronic tongue sensor could distinguish the four grades of Xihu Longjing Tea effectively, not only for cross-validation for 139 modeling samples(33 Pcs. of Highest Grade, 36 Pcs. of Special Grade, 34 Pcs. of 1st Grade, 36 Pcs. of 2nd Grade) reaching 100%, but also for 70 prediction samples with the unknown grade level (17 Pcs. of Highest Grade, 18 Pcs. of Special Grade, 16 Pcs. of 1st Grade, 19 Pcs. of 2nd Grad) reaching 100%, and the models were provided with good adaptability and robustness. So the samples of same grade gather into a group severally, and the samples of different groups didn't overlap each other to reflect the good susceptibility of electronic tongue.

Table 3 .

 3 Results of SIMCA Modeling for Four Grades of Samples

			Integral Identification Ratio(%)
	Model Type	Number of PC
			Correction Set	Prediction Set
	Highest Grade, Special Grade, 1 st Grade, 2 nd Grade	2,1, 1, 3	100	100

Acknowledgment

This study had been sponsored by Beijing-sponsored programs for excellent talents training (No. 2012D009999000001), National High Technology Research and Development Plan (863 Plan) for the Project of No. 2011AA1008047..


Zhengqi He

Dechun Huang
email: bhuangdechun@hhu.edu.cn

Changzheng Zhang

Bo Wang

Zhijie Ma

Evaluation Research of the Influence of Small Hydropower Station for Fuel Project on Social Development Impact --taking Majiang of Guizhou Province as an example

Keywords: small hydropower station for fuel project, social development, cloud model, composite entropy weight method

To change energy construction in the backward areas and protect the ecological construction achievements of conversion of cropland to forest, China starts the small hydropower station for fuel project, which has promoted the local social development. To evaluate the influence of the small hydropower station for fuel project on social development in the backward areas, this paper has a comprehensive consideration of the social environment and ecological environment, establishes the relative evaluation index system about influence, introduces the normal cloud model, focusing on the association of fuzzy and random problems in the artificial intelligence, into the evaluation model, and establishes a comprehensive evaluation model based on the normal cloud model and composite entropy weight. Taking the pilot Majiang County of Guizhou Province as an example, this paper evaluates the influence of the small hydropower station for fuel project on social development in the backward areas, and the results are made that the small hydropower station for fuel project has played a role in promoting the social development, and with the implement of the project, the effects become more and more obvious.

Introduction

Water resources and forest resources are abundant in Southwest China, but for the long-term construction lag of the substitute energy in the rural areas, firewood is still the energy commonly used, which causes soil erosion, low forest coverage rate, deterioration of the ecological environment, and some important achievements of ecological construction like conversion of cropland to forest and natural forest protection cannot be consolidated. To solve the problem, the government has invested lots of money to start the small hydropower station for fuel project. China is rich in the small hydropower resources, and the exploitable capacity is 1.28 billion kilowatts with great exploitation potential, though the exploitation rate is only 36.6% at present. After years of development, the small hydropower has become an important basis and strong driving force for China's economical and social development in the rural areas. Developing the small hydropower station for fuel project reasonably and efficiently can provide farmers with living fuel and rural energy steadily for a long period, protect the ecological construction achievements of conversion of cropland to forest, reduce farmers' burden of cutting firewood and make great contribution to social development. But a quantitative research is urgently needed to evaluate the project impact on social development and how it can change people's living standard and ecological environment in the backward areas.

Literature review

The small hydropower station for fuel project, in a certain sense, belongs to poverty alleviation project. The organizations like World Bank and Asian Development Bank has a deep research on the poverty alleviation project that they participated in. Dani.A [START_REF] Ma | Social Analysis Sourcebook: Incorporating Social Dimensions into Bank-Supported Projects[END_REF] considers how to realize the development target for the better mainly through the characteristic analysis of local social culture and organizational structure, and studies the influence of poverty alleviation project on changing demographic, social and cultural characteristics, production activities and residents' living conditions in the backward project areas. Another group of scholars mainly study the patterns for poverty alleviation, taking the pro-Poor Tourism pattern as an example, Caroline Ashley [START_REF] Ashley | Pro-poor tourism: putting poverty at the heart of the tourism agenda[END_REF] pointed out that tourism development for poverty alleviation can create employment and income opportunities, promote small business management and improve the living standard of the poor. The domestic scholars mainly focus on the patterns for poverty alleviation and poverty alleviation benefits. Wang Sangui [START_REF] Sangui | Anti-poverty and Government Intervention[END_REF], who began to study the patterns for poverty alleviation in the early ninety's, pointed out that the economical development patterns can be divided into relying on resources type, asset accumulation type and technology driven type; starting from the benefit evaluation of the poverty alleviation project, Lin Boqian [START_REF] Boqiang | Income Distribution Analysis of Investment Projects and Poverty Alleviation Benefit Assessment [J][END_REF] emphasized that poverty alleviation is the most difficult challenge in the process of social development, and he believed that poverty alleviation benefits should be evaluated by both of number measuring method and monetary measuring method, and he proposed the benefit distribution analysis (BDA); taking conversion of cropland to forest project in the West China as an example, Kong Fanbin [START_REF] Kong Fanbin | Social Influence Research on Conversion of Cropland to Forest in West China[J][END_REF] studied its social impact and had a comprehensive exposition under the background of national microeconomic policy, which offered a way for the scholars to study the social impact evaluation of some important projects.

The current domestic research on the small hydropower station for fuel project mainly concentrates on feasibility evaluation and social development effect evaluation. Liu Tao [START_REF] Tao | Feasibilty Evaluation of the Small Hydropower Station for Fuel Ecological Protection Project Based on Grey AHP[END_REF] believes that the project is a public welfare project which focuses on the ecological benefits, and the normal small hydropower evaluation indexes and evaluation methods are unsuitable for the feasibility evaluation of the small hydropower station for fuel project. Shi Canxi [START_REF] Shi | Comprehensive Evaluation of the Social Benefits of the Small Hydropower[END_REF] constructed three aspects of evaluation indexes during the research on the social impact evaluation, including political benefits, economic benefits and other social benefits ( including environmental benefits and spiritual benefits), but using forest coverage rate as the only index evaluation can't truly reflects the environmental benefits; Zou Tifeng [START_REF] Tifeng | Analysis of the Ecological Environment Protection Problems of the Small Hydropower Development[END_REF] noticed the influence of the small hydropower on the ecological environment, and according to the situations like the disordered exploitation caused by the small hydropower development fever, he pointed out various problems that affects the ecological environment during the construction process and proposed reason analysis, and offered a series of policy recommendations that coordinate the small hydropower development and the ecological protection, but he just did the research on the ecological environment without considering other indexes.

All the researches fail to build a perfect evaluation system and comprehensively evaluate the influence of the small hydropower station for fuel project on the social environment and ecological environment. Thus, this paper builds the evaluation index system of the project effect on social development from the two aspects of social environment and ecological environment, proposes the comprehensive method which combines the normal cloud model with composite entropy method, and finally has an empirical analysis through the example of Majiang County of Guizhou Province.

The evaluation index system construction of social development influence of the small hydropower station for fuel project on backward areas.

The small hydropower station for fuel project aims to improve rural energy structure, consolidate ecological construction achievements, improve rural living conditions, and improve farmers' living standard and habitat environment. Some changes have been taken place in the project areas since the project was implemented, and many aspects should be considered to evaluate the project effect on social development. According to the evaluation index system about social development built by Pan Ane [START_REF] Ane | Comprehensive Evaluation of Economic and Social Development of Wuhan City Based on Principal Component Analysis[END_REF], other aspects of indexes as well as the single aspect of economic development should be considered to evaluate the social development. Therefore, apart from the economic development, this paper has introduced living quality index and population quality index and reduces the three indexes to the social environment index, together with the ecological environment, building the comprehensive evaluation index system about social development. In the social environment, the economic level is measured by per capita GDP, per capita disposable income, unemployment rate and the third industry proportion in GDP; the living standard is measured by the Engel coefficient, consumer price index, per capita daily leisure time and small hydropower proportion in energy utilization; population quality is measured by natural growth rate of population, per capita schooling years and average span. In the ecological environment, water environment, atmospheric environment, solid waste and environment protection are measured by the eight indexes: per capita annual wastewater emission, sewage treatment rate, per capita annual toxic gas emission, number of polluted days in a year, per capita annual solid waste production, harmless treatment rate of daily garbage, forest coverage rate and environment investment proportion in GDP. Table 1 is the evaluation index system.

4

The comprehensive evaluation model based on the normal cloud model and composite entropy method

weight determination based on the composite entropy method

To have a scientific determination of evaluation index weight, this paper combines the subjective weight with the objective weight according to the tendency coefficient method [START_REF] Yi | A study on Supply Chain Coordination of Engineering Project Manufacturing System with Large Logistics its Synergetic Evaluation[END_REF] of Yan Yi, If the subjective weight of the evaluation index j is j W (1) and the objective weight of which is ) 2 ( j W , then the combined composite entropy coefficient is:

j j j W W W (2) (1) ) - (1    
 is the tendency coefficient, which expresses the important and being tended degree of the subjective weight and the objective weight and to make a better use of the advantages, this paper takes  =0.5, that's to say the composite weight is the arithmetic average value.

Entropy method [START_REF] Sui | Science and Technology Evaluation Model and Empirical Study Based on Entropy Method[END_REF] is a kind of objective weight method, and when using the entropy method, this paper uses 0-1 standardization to have a normalized processing of the data in the judgment matrix R, and haves the max r and min r adjustments of about 20% to avoid the zero in the standard matrix. The subjective entropy takes the expert scoring method, that's to say by anonymous advice from the relative experts, we can collect, analyze and induce the experts' opinion and determine each index weight according to most experts' experience and subjective judge.

The comprehensive evaluation model

The cloud model is proposed by the Chinese Engineering academician Li Deyi, and it's a conversion model used to deal with the randomness and fuzziness which widely exist in the qualitative concept and it connects the qualitative concept with its qualitative numerical value representation. The digital characteristics in the normal cloud reflects the quantitative concept and quantitative characteristics, which is represented by expectation Ex, entropy En and excess entropy He [START_REF] Deyi | The Universality of the Normal Cloud Model[END_REF]. According to the digital characteristics (Ex, En, He) in the normal cloud,we use the normal cloud generator to have the cloud droplets, and then finish the mapping from qualitative analysis to quantitative analysis. [START_REF] Shouxin | A Subjective Trust Evaluation Method Based on Cloud Model[END_REF] In the comprehensive evaluation, weight is the key evaluation content, and this paper determines the size of index weight by the composite entropy method. The comprehensive evaluation model and its steps are as follows:

(1) Establish the factor domain of the evaluation object

  n u u u U , , , 2 1   , establish the comment domain   m v v v V , , , 2 1   ; (2) Calculate index weight   n w w w W , , , 2 1
  by the composite entropy method;

(3) Have a single factor evaluation between factor domain U and comment domain V, and establish fuzzy relation matrix R. This paper calculates the membership degree of the evaluation factor by the normal cloud model. Let the upper boundary value and the lower boundary value of grade j a and b, which corresponds to factor i, then the quantitative concept grade j can be represented by the normal cloud model, and among them The excess entropy He expresses the uncertain measure of entropy, which reflects the condensation degree of cloud droplets, the larger the excess entropy value is, the thicker the cloud is, and vice versa. This paper takes the value of excess entropy He according to experience.

(4) Determine the membership degree matrix

  m n ij z Z  
of cloud model of every grade corresponded to each index. What needs to be noticed is that in order to enhance the evaluation credibility, we need to have a repeated function of normal cloud generator N times, and calculate the average comprehensive evaluation value under different membership degree situations:

   N k k ij ij N z z 1 ;
(5)get the fuzzy subset B by the fuzzy conversion of weight set W and membership degree matrix Z:

  m b b b Z W B , , , 2 1     
In the equation:

   n i ij i j z w b 1 , j b
expresses the membership degree of the objects to be evaluated to the jth comment. According to the maximum membership degree principle, the ith grade which corresponds to the maximum membership degree is chosen as the comprehensive evaluation result.

5

Social development influence evaluation of the small hydropower station for fuel project in backward areas.

Date source

Majiang county of Guizhou Province is one of the pilot of China's small hydropower station for fuel project, and the project was officially started in 2005, therefore all the data are derived from the statistical yearbook and local survey research in Majiang county of Guizhou Province from 2006 to 2010.

The empirical results and analysis

On the basis of the evaluation index system of the social development influence on the small hydropower station for fuel project, the evaluation criteria of the project needs to been determined to get the evaluation results conveniently.

According to evaluation index system and evaluation index criteria, this paper expresses the corresponding grades of each index with the relative normal cloud model by the formula ①-③( see appendix 1). Assume that N=100, we can use the positive generator algorithm to generate the membership degree matrix, substitute the data of years in the positive generator of the grade cloud model described above, and calculate the average comprehensive evaluation value under the different membership degree situations by a repeated calculation of 100 times. According to the evaluation factor quantitative data of years, get each index weight by composite entropy method: W={0.0465,0.0476,0.0392,0.0392,0.0451,0.0453,0.0565,0.0554,0.0549,0.0640,0.0550,0.0481,0.0558,0.0519,0.0519, 0.0483,0.0563,0.0615,0.0776}.

Finally, we can have a fuzzy conversion of weight set W and membership degree matrix Z according to step(5)and get the membership degree of the object to be evaluated to the j th comment and according to the maximum membership degree principle, the i th evaluation grade, which is corresponded by the maximum membership degree , was chosen as the comprehensive evaluation result(table2). According to the evaluation result table and run chart, the small hydropower station for fuel project plays a significant role in promoting the social development in Majiang County.(take the year 2012 as an example, the huge promotion: 0.6101, the greater promotion: 0.3517), from the implement of the project in 2006, the huge promotion effect has increased to 0.6101, comparing to 0.1831 in 2006. From the data analysis, it can been seen that with the implement of the small hydropower station for fuel, the local living quality index A 2 , the atmospheric environment index B 2 and solid waste index B 3 has changed obviously, and because of the usage of the small hydropower, the living quality has been improved, farmers have more leisure time with enhanced subjective happiness, and for the decrease of the firewood combustion, the atmospheric environment and solid waste pollution is improved, which just fits the purpose of the small hydropower station for fuel project implement, improving the living quality of people in backward areas and improving the ecological environment.

Conclusion

This paper applies the normal cloud model, which studies the association problems of fuzziness and randomness, into the social development influence evaluation to realize the uncertain mapping of evaluation factor value to comment, randomness as well as fuzziness can be considered in the social development influence evaluation and qualitative analysis and quantitative analysis can be combined. Build the evaluation index system with the starting point of social environment and ecological environment, and determine every index weight by the combination of the subjective and objective composite entropy method, we can have a more scientific reflection of every index weight , and have an evaluation of the influence of the small hydropower station for fuel project in Majiang County of Guizhou Province on local social development.

The results show that: firstly, under the common effects of many factors, the small hydropower station for fuel has improved the local ecological environment, controlled the air pollution and reduced the solid waste pollution. Secondly, the implement of the project has improved the rural energy structure, consolidate the ecological construction achievements, improved the rural living conditions and farmers' living quality. Thirdly, the project has a promote role in widely improving the living quality, further enhancing the economic level and meeting the expected target of the project.

①

  For the boundary value is a transition value from one grade to another grade, and it's a fuzzy boundary which belongs to both of the two corresponding grades, that's to say the membership degree of the two grades is equivalent, therefore it has:

Fig. 1 .

 1 Fig.1. Run chart of the influence of the small hydropower station for fuel project on social development effect

Table 1 .

 1 The evaluation index system of the social development influence of the small hydropower station for fuel project on

	backward areas

Table 2 .

 2 The evaluation results of the influence of the small hydropower station for fuel of different years on social development

year Membership degree of the normal cloud evaluation results of The normal cloud model Huge promotion Greater promotion General promotion

					Smaller
					promotion
	2006	0.1831	0.0932	0.2046	0.5482	Smaller
	2007	0.1903	0.1844	0.4641	0.3149	General
	2008	0.2117	0.4842	0.2107	0.2014	Greater
	2009	0.4209	0.5119	0.1015	0.1004	Greater
	2010	0.6101	0.3517	0.0189	0.0338	Huge

Acknowledgment

Funds for this research was provided by the Special Program For Key Program for International S&T Cooperation Projects(2012DFA60830).


Quan Wu

Hualang Hu

Yanxia Liu

Danqiong Wang

Xinyu Duan

Lijuan Jia

Yajuan He

Study on Survey Methods for Crop Area Change Reasons at National Scale

Keywords: Crop area, Crop area change, Survey index, Driving factor, Grey system, Whitenization weight function, Estimate, RS

Remote Sensing Application Centre (RSAC) has been working on monitoring the main crop planting areas as an operational task and a research project based on its system for several years. A problem has been proposed from the RSAC'S technology system, which is that the monitoring result is only an estimate to main crop planting areas without quantitative explanation to crop area change reasons. The study as a project approved by RSAC attempts to solve the problem. A survey index system about crop area change reasons has been established by questionnaire and expert consultation. Through further research, 9 driving factors of crop area change have been set up by assembling 5 survey indexes. Based on triangular whitenization weight function of grey system theory, the crop Area Change Driving Model (ACDM) was designed. The factor weights and factor grey classes were produced through expert consultation. Heilongjiang province as the experiment region and single cropping rice as the experiment crop were selected in this study. The experiment result produced by ACDM is consistent with the estimate produced in monitoring crop area by RSAC in 2013.

Introduction

Crop area change is an important project paid much attention by governments in the world because it is closely related with food security. Sampling methods are widely used to estimate main crop area change by many countries and regions, such as US, China, EU, etc. [START_REF] Wu Quan | Sampling methods using RS and GPS in crops acreage monitoring at a national scale in China[END_REF][2] [START_REF] Wu Quan | A Sampling Design for Monitoring of the Cultivated Areas of Main Crops at National Scale Based 3S Technologies in China[END_REF]. Monitoring Agriculture with Remote Sensing (MARS) is a project facing Europe in order to obtain crop yield information constituted by European Union Committee [START_REF] Bangjie | Monitoring the agricultural condition using RS[M[END_REF]. It is a kind of three-stage sampling based on unsupervised classification with multitemporal RS data. In America, the prediction of total crop yield is acquired from crop acreage and crop yield per unit. The crop acreage data is mainly gotten by June Agricultural Survey(JAS). Two different sampling units used by JAS are area frame covering America and name list frame consisted of the names of registered farmers [START_REF] Bangjie | Monitoring the agricultural condition using RS[M[END_REF]. In China, the operating prediction of crop area change is mainly provided by RSAC [START_REF] Wu Quan | The applications of 3S operational monitoring system of main crops acreage in China[END_REF]. RSAC adopts two methods to obtain the acreage of main crops such as wheat, corn, cotton, soybean, rice, etc.. One method is stratified sampling with RS and the other is ground random sampling using GPS [START_REF] Wu Quan | Sampling methods using RS and GPS in crops acreage monitoring at a national scale in China[END_REF] [START_REF] Wu Quan | Theory of double sampling applied to main crops acreage monitoring at national scale based on 3S in China[END_REF]. RSAC has been working on monitoring the main crop area change as an operational task and a research project based on the complete organization system and the maturity of a technical system for several years. RSAC submits monitoring results to Ministry of Agriculture at prescribed time according to the crop monitoring calendar every year [START_REF] Wu Quan | The methods for monitoring land-use change with RS at non-large scale[M[END_REF]. The monitoring results are only estimates for interannual variation of crop areas while the explanation for the reasons of crop area change is qualitative not quantitative [START_REF] Wu Quan | The Quantificational Evaluation of a Sampling Unit Error Derived from Main Crop Area Monitorings at National Scale Based 3S in China[END_REF] [START_REF] Wu Quan | A study of two methods for accuracy assessment to RS classification[END_REF]. At the same time, at the beginning of the year planting intent survey is done by relevant departments of the government in China, which is a kind of sampling survey to peasant households through the interview. Although the interannual variation of crop areas can be calculated based on data from investigated peasant households, there is not quantitative interpretation for the reasons of crop area change.

Objectives

It is the first objective of the research to theoretically establish a set of survey index system for crop area change reasons. Going a step further, the ultimate goal is to find driving factors of crop area change and set up the Area Change Driving Model (ACDM) for estimating the trend of crop area change based on the driving factors. So, the reasons of crop area change will be explained quantitatively.

Materials and Methods

The study was acted as a research project authorized by CAAE (Chinese Academy of Agricultural Engineering), launched by RSAC at the beginning of 2013. In order to achieve the research goal RSAC form a group to carry out the work through selecting a study region and a crop for experiment. The members of the group come from the team specialized in monitoring Agricultural condition in RSAC, which have rich experience in Agricultural survey.

The study region and crop for experiment

Taking into account the combination of research and operational task, Heilongjiang province acted as study region was selected and single cropping rice acted as experimental crop was chosen from several crops. RSAC monitors single cropping rice area of Heilongjiang every year. The monitoring result can be used to test the study result. The following figure shows the spatial distribution of single cropping rice of Heilongjiang province in 2010.

Candidate indexes for estimating the reasons of crop area change

Because RSAC's operational system of crop area change monitoring has been working well for many years while the members of the study group are also main participants for monitoring task [START_REF] Wu Quan | The applications of 3S operational monitoring system of main crops acreage in China[END_REF], the candidate indexes can be produced by the researchers based on rich experience to Agricultural survey. The indexes are the purchase price and sales revenue of main crops, production cost, natural disaster, field management, occupation of cultivated land, labor transfer, irrigation and water conservancy, Agricultural mechanization, tax and subsidy, planting structure adjustment, crop rotation, land transfer, product sales, etc..

Survey indexes for estimating the reasons of crop area change

When the candidate indexes were built, the next step is to extract survey indexes from them. The extraction rules are some characteristics about the indexes, which are integrity, importance, quantifiability and availability. How the job for extracting indexes was done? Based on analysis about the extraction rules to every index, the study group designed survey forms to investigate peasant household in the study region. There are 100 peasant households, distributed on 57 counties, which were surveyed. After this step, three survey indexes were picked up from 14 candidate indexes, which are production cost, yield, purchasing price. The indexes forming an index system can be classified into three categories, which are investment, production and yield. The process of extraction survey indexes from candidate indexes is shown below by figure 2.

Knowledge Experience

The candidate indexes Integrity/Importance/ Quantifiable/Availability

The peasant household survey

The survey indexes Analysis Fig. 2. The extraction flow of survey indexes from candidate indexes

Driving factors for estimating crop area change

The driving factors are the main contradiction of the existence and development of things. It determines the direction of the development of things. They are the index which have greater influence to interannual variation of crop area, or directly influence planting intentions of peasants. According to the survey indexes system mentioned above, the driving factors were produced based on further research. They are price difference, cost difference, and per unit yield difference, which are divided into 7 factors. The table 1 shows the situaton.

Table 1. Driving indexes for paddy area change in Heilongjiang province

The driving factors The meanings of driving factors Marks

Sale

The difference of purchasing price of rice between the current year with last year X 1

The difference of purchasing price of rice between the last year with the year before last X 2

The difference between the purchasing price of three year average to rice with the average purchasing price of the main crops X 3

Investment

The difference of production cost of rice between the current year with last year X 4

The difference of production cost of rice between the last year with the year before last X 5

The difference between the three year average production cost of rice with the average production cost of the main crops X 6

Yield

The difference of rice yield between the current year with last year X 7

Crop Area Change Driving Model (ACDM)

Uncertainty is usually due to internal and external disturbances and the level of human understanding to things. Probability statistics, fuzzy mathematics and grey system theory are the three most common types of uncertainty system research.

Grey system theory focuses on the study of probability statistics and fuzzy mathematics to solve difficultly uncertain problems originated from the small sample and poor information [START_REF] Sifeng | Grey system theory and application[M[END_REF]. Uncertainty phenomenon is more obvious in Agricultural production system. On the one hand, the grey information widely exists in the system. On the other hand, this "grey" information" is not easy to gain. The research on the reason of crop area change has obvious advantages through the application of grey system theory. This research adopts the method of grey clustering evaluation to establish a driving model of crop area change.

                                  1 1 1 1 1 1 1 1 , , , , , , 0 )
( k j k j k J k j k j k j k j k j k j k j k j k j k j x x x x x f             (1) Where ) , , 2 , 1 ( m j j   is survey index, ) , , 2 , 1 ( s k k   is grey class, ) , , 2 , 1 ( s 
n i i   belonging to grey class k is k i  . j ij m j k j k i x f       ) ( 1 (2)
Where

) ( ij k j x f
is whitenization weight function of index j belonging to k subclass. j  is weight of j index in comprehensive clustering. The formula of object i belonging to k grey class is shown below.

 

k i k i s k      1 max (3)
Through expert consultation with experience, the weight of every driving factor and the grey class centre of every factor can be determined.

3.6

Experiment result

The values of survey indexes. The surveyed crops are not only rice but other main crops which are soybean and corn acted as main crops in the region. Based on peasant household survey the average values of 3 survey indexes were gained. The data came from 3 years when there were not serious drought and snowstorm in this region from 2011 to 2013. This table shown below is presented the survey result to rice production region in study region. The observed values and weights of driving factors. Using the data in the table 2, it is easy to calculate the values of the driving factors for estimating rice area change.

The seven driving factors were used to estimate rice area change with ACDM in 2013.

The values of the seven driving factors are shown in table 3. The weights of driving factors are obtained by expert investigation, which is also listed in table 3.

                         0 , 20 , 20 20 , 50 , 30 50 0 , 50 , 0 ) ( 1 2 , 1 x x x x x x f                        20 , 0 , 20 20 0 , 20 , 20 20 20 , 20 , 0 ) ( 2 2 , 1 x x x x x x f                     50 , 20 , 30 50 20 , 0 , 20 50 , 0 , 0 ) ( 3 2 , 1 x x x x x x f (4) (5)

Analysis for the experiment result

Using the formula (3), the result of the experiment is

  5790 . 0 max 2 1 1 3 1       k k . This
suggests that the rice area of study region from 2012 to 2013 is unchangeable. Because , it demonstrates the rice area has increasing trend. In 2013, the RSAC's monitoring result is that the rate of area change is 2.1%, which confidence interval is from -7.24% to 12.36% based on 95% degree of confidence. This shows the result of the experiment is consistent with the monitoring result produced by RSAC.

Conclusion and problems

Based on triangular whitenization weight function grey clustering can be used to establish ACDM. The model accurately reflects change situation of rice area from 2012 to 2013 in Heilongjiang province, Referencing RSAC' monitoring result. The survey indexes and driving factors founded in the study better reflect objective condition of reasons to rice area change, which better meet demands of the model. Including survey indexes and driving factors, the ACDM prototype suits not only for rice but also for other main crops, such as winter wheat, soybean, corn, etc.. At the same time, the ACDM prototype suits not only for Heilongjiang province but also for other regions, such as northeast China, southwest China, etc.. Obviously, the parameters of the model designed in this study need to be recalculated because of different crops and different regions.

Fig. 1 .

 1 Fig. 1. The spatial distribution of single cropping rice of Heilongjiang province

  Grey clustering includes grey relational clustering and grey whitenization weight function clustering. The grey whitenization weight function clustering includes grey fixed weight clustering, grey regulable weight clustering and whitenization weight function clustering based on endpoint or centre of triangle. The study adopts whitenization weight function clustering, based on triangular centre, acted as ACDM prototype. The general form of based on centre triangular whitenization weight function clustering is shown below.

  function belonging to grey class k of observed value x of index j . The Comprehensive clustering coefficient of object

Table 2 .

 2 The survey result to main crops in Heilongjiang province

	Crops	Years Yield(Kg/mu)	Cost(Yuan/mu)	Purchase price(Yuan/50 Kg)
		2011	466.7	742.0	128.0
	Rice	2012	471.5	874.8	140.0
		2013	483.0	1031.4	150.0
		2011	112.7	271.0	200.0
	Soybean	2012	116.0	306.5	225.0
		2013	110.0	346.7	231.0
		2011	505.6	398.2	86.3
	Corn	2012	482.2	451.6	89.3
		2013	494.8	512.1	96.5

Table 3 .

 3 The survey values and weights of driving factors

	Driving factors	X 1	X 2	X 3	X 4	X 5	X 6	X 7
	The observed values	10	12	-15	157	133	502	12
	The weights	0.20	0.15	0.10	0.20	0.15	0.10	0.10
	The grey class centres of 7 driving factors. In order to establish ACDM with the
	driving factors mentioned above based on its prototype, the first step is to determine
	the grey class centers of 7 driving factors. The result was gained through study and
	expert investigation, which is shown in table 4 below. Three grey classes on behalf of
	decreasing, unchanged and increasing situation of rice area from 2012 to 2013 were
	designed, whose centers are respectively marked by 1  , 2  and 3  . The 0  and
	4  are centers of grey class continuation.

Table 4 .

 4 The grey class centers of driving factors Using the values of grey class centers of every driving factor in table4, the ACDM can be easily established based on its prototype presented by formula (1). Due to space limit, only the ACDM of the first and second driving factors is listed below.

	Grey class centers	X1	X2	X3	X4	X5	X6	X7
		0	-50	-50	-30	300	300	700	-20
		-20	-20	-20	200	200	600	-10
	1
			0	0	-10	150	150	500	0
		2
		20	20	0	100	100	400	10
		3
			50	50	10	50	50	300	20
		4
	Setting up ACDM.

( 6 )

 6 The grey clustering based on ACDM. When all ACDMs are finished, based on the models the next step is calculate the value of whitenization weight function of every driving factor with survey values in table 3, which are 3 figures presented 3 grey classes. When values of whitenization weight function of 7 driving factors, listed in table 5, are worked out, based on formula (2) the final step is calculate comprehensive coefficient with the weights of driving factors in table 3. The result is shown in the last column in table 5.

Table 5 .

 5 The comprehensive coefficient of each grey class

	Grey classes	X1	X2	X3	X4	X5	X6	X7	X
	1	0.0000	0.0000	0.0500	0.0280	0.0000	0.0020	0.0000	0.0800
	2	0.1000	0.0600	0.0500	0.1720	0.0990	0.0980	0.0000	0.5790
	3	0.1000	0.0900	0.0000	0.0000	0.0510	0.0000	0.0800	0.3210

Acknowledgement

This paper is supported by the project named Study on survey methods for crop area change reasons at national scale, authorized by CAAE in 2012.


Guojian Xian
email: xianguojian@caas.cn

Ruixue Zhao
email: bzhaoruixue@caas.cn

Xianxue Meng
email: cmengxianxue@caas.cn

Yuantao Kou
email: dkouyuantan@caas.cn

Liang Zhu
email: zhuliang@caas.cn

Linking and Consuming Agricultural Big Data with Linked Data and KOS

Keywords: Big Data, Linked Data, Knowledge Organization System (KOS), Semantic Web, Scientific Data

This paper gives brief introduction about the big data, linked data and knowledge organization systems (KOS) and their relationships. As the authors mainly focus on the variety and value characteristics of big data, the linked data and KOS technologies are used to link and consume the large amounts of literature and scientific data in agricultural research community. The results show that it is a good way to describe, connect, organize, represent, visualize and access to big data effectively and semantically based on the linked data and KOS technologies.

Introduction

Big data is now one of the hottest topics. Nowadays, we are generating huge amount of data every day, and the total volume of data would be doubled every 18 months, as for the rise of multimedia, social media, and the Internet of Things. It is true that of our activity, innovation, and growth are more and more based on the big data [START_REF] Manyika | Big data: The next frontier for innovation, competition, and productivity[END_REF].

When talking about big data, people are likely to focus on technical issues, such as scalability, performance and how to deal with large quantities of heterogeneous data, but pay less attention to the connections, interoperability of data in disparate sources, and how to make sense of all the large data pools either. Actually, there are both latent and actual links, which are worth enriching, utilizing and publishing along with the raw dat. In most cases, it is the connections inside and outside of big data where the real value lies.

The formalized, structured and organized nature of linked data and its specific applications, such as the linked knowledge organization systems (KOS), have the potential to provide a solid semantic foundation for the classification, connection, representation, visualization of big data.

The reminder of this paper will firstly give a conceptual analysis of big data, linked data and KOS. And then we will illustrate how to create and consume semantic big data in agricultural research community, utilizing linked data technologies and knowledge organization systems as new tools for the describing, linking, organization, representation, visualization and access to big data.

Big Data, Linked Data and KOS

Big data always refers to large, diverse, complex, longitudinal, distributed data sets generated from instruments, sensors, internet transactions, email, video, click streams, and other digital sources. While there is no generally agreed understanding of what exactly is big data, an increasing number of V's has been used to characterize different dimensions and challenges of big data: volume, velocity, variety, value, and veracity [START_REF] Hitzle | Linked Data, Big Data, and the 4th Paradigm[END_REF][START_REF] Bizer | The Meaningful Use of Big Data: Four Perspectives -Four challenges[END_REF]. These terms means to the growing volume of different types of structured and unstructured data, the complex and heterogeneous nature and machine-processability. The organization, exploration, management, preservation, visualization and access to and use of these types of data pose technological and computational challenges. Linked Data defines a set of guidelines, best practices or patterns for exposing, publishing, sharing, and connecting pieces of data, information, and knowledge on the Semantic Web using URIs and RDF [START_REF] Heath | Linked Data: Evolving the Web into a Global Data Space (1st edition)[END_REF][START_REF] Bizer | Linked Data -The Story So Far[END_REF], which takes the WWW's ideas of global identifiers and links and applies them to (raw) data, not just documents [START_REF]Linked Data -Connect Distributed Data across the Web[END_REF][START_REF]The Rise of the Data Web[END_REF].

Linked data can be effectively used as a broker, mapping and interconnecting, indexing and feeding real-time information from a variety of sources, and inferring relationships from big data analysis that might otherwise have been discarded, which is made all the more valuable by the connections (links) that tie it all together than the sum of its volume, velocity and variety.

Semantic text analysis, natural language processing, data mining and data visualization are the typical challenges in addressing the management and effective use of big data [START_REF] Suchanek | Knowledge harvesting in the big-data era[END_REF]. The knowledge organization systems, such as thesauri, classifications, subject headings, taxonomies, and folksonomies would increasingly important roles. As W3C's standard, the Simple Knowledge Organization System (SKOS) standard, aims to build a bridge between the world of KOS and the linked data community [START_REF] Dolan-Gavitt | Virtuoso: Narrowing the Semantic Gap in Virtual Machine Introspection[END_REF]. SKOS-based linked controlled vocabularies can provide a framework rich in semantics to effectively manage big data through combining, aligning and cross-linking multiple KOSs in order to automatic or semi-automatic analyzing, indexing and organizing text, and develop faceted, categorized or hierarchical views of big data [START_REF] Shiri | Linked Data Meets Big Data: A Knowledge Organization Systems Perspective[END_REF]. A typical and successful example of combining the big data, linked data and KOS together is the new semantic web platform version of Agris: OpenAgris [START_REF] Anibaldi | Migrating bibliographic datasets to the Semantic Web: The AGRIS case[END_REF]. Based on nearly 4 million structured bibliographical records on agricultural science and technology, and AGROVOC vocabularies, alignment between KOS and ontologies, the OpenAgris also aggregates various open access data sources available on the Web, providing much data as possible about a topic or a bibliographical resource [START_REF] Celli | Pushing, Pulling, Harvesting, Linking: Rethinking Bibliographic Workflows for the Semantic Web[END_REF][START_REF]AGROVOC Linked Open Data[END_REF].

Linking Agricultural Big Data

The 4th paradigm of science -data intensive scientific discovery has emerged within the last years, which means scientific innovations and breakthroughs will be powered by advanced computing capabilities that help researchers manipulate and explore massive datasets [START_REF] Hey | The fourth paradigm: data-intensive scientific discovery[END_REF]. As for the several V's characteristics of big data, different disciplines highlight certain dimensions and neglect others. Fox example, people working on sensor and the internet of things may care more about the velocity, super-computing would be mostly interested in the volume dimension, and the research community pay more attentions to variety and value dimensions of big data.

The Chinese governments always pay high attentions to the agricultural science and technology innovation and the development of modern agriculture. With more investments are input to this sector, a large amount of agricultural big datasets are produced, such as the 3S data, scientific research data, and academic achievements (e.g. papers, books, proceedings, reports). These data have different formats, conceptualizations or data models, temporal and spatial dependencies [START_REF] Janowicz | The digital earth as knowledge engine[END_REF]. Effective usage of these big data is very important to promote and advance the research work in a new round.

The following parts of this paper will explain how to describe, organize, integrate, and consume parts of these agricultural big data with linked data and KOS technologies, as we just focus on the variety and value dimensions of big data.

Linking Chinese Agricultural Thesaurus to other KOS

As KOS can play important role to addressing some challenges of the big data, such as text analysis, natural language processing, data mining and data visualization of big data, the priority of our work is to convert the Chinese Agricultural Thesaurus (CAT) as linked data and link to other KOS.

CAT was developed by Agricultural Information Institution of CAAS in early 1990s

and kept maintenance all the time. So far, CAT contains more than 60,000 Chinese descriptors and non-descriptors, most have corresponding English translations, and also include about 130,000 semantic relationships, such as UF, BT, NT and RT.

We describe CAT's concepts and their semantic relationships with SKOS and SKOS-XL standards. In addition, we map and link CAT to other well-known KOS such as AGROVOC, NALT, EUROVOC and LCSH, as shown in Fig. 1.

Fig. 1 The SKOS Model of CAT and Link to Other KOSs

We also develop a SKOS-based CAT linked data web system, providing services such as HTTP URI dereference, CAT concepts browsing and navigation, SPARQL query endpoint and RDF Triples Dumps, as shown in Fig. 2. This work could greatly improve the CAT's visibility, accessibility and interoperability with other systems, and lay fundamental base to describe, organize and link other agricultural information resources semantically as well.

Fig. 2 The Linked Open data of SKOS-Based CAT

Linking Agricultural Literature and Scientific Research Data

As the current global research data is highly fragmented, by disciplines or by domains, from oceanography, life sciences and health, to agriculture, space and climate [21].

When it comes to cross-disciplinary activities, building specific "data bridges" are becoming accepted metaphors for approaching the data complexity and enable data sharing.

The millions of books, journals, proceedings and bibliographic records of the China National Agricultural Library, and also over 700 scientific datasets holds in the National Agricultural Scientific Data Sharing Platform, are the most valuable data materials for agricultural research communities. What we want to do most here is connect these literature and research data together in a light-weighted semantically way. Fig. 3 shows the available and linkable data resources we could access to.

Fig. 3 Available and Linkable Agricultural Data Resources

We analyze and abstract the main classes and properties from the literature and bibliographic records of the China National Agricultural Library, and also reuse the widely used vocabularies and ontologies such as DCMI[15], BIBO [START_REF] Darcus | Bibliographic ontology specification[END_REF], etc., to formally describe and model these classes, properties and their semantic relationships.

The following table shows the mapping result of the journal article (bibo:AcademicArticle) and its properties to common vocabularies and ontologies. As for most of the data resources mentioned above are stored in relational database, so we construct and publish the semantic agricultural linked data based on the open source software D2R Server [START_REF]D2R Server-Publishing Relational Databases on the Semantic Web[END_REF].

Fig. 6 The Linked Data Publishing Platform Based on D2R

Consuming Linked Agricultural Big Data

One effective way to explore and mine the linked big data is following the linked data rules and utilizing some semantic web technologies, here we do not care too much about the volume of big data and neither the computing performance. We pay more attentions to the variety and value of the big data.

The architecture and function models of domain knowledge service system have been designed, which totally driven by the linked data, as shown in Fig. 7. A prototype system was realized based on some key technologies such as SPARQL, Virtuoso [START_REF] Dolan-Gavitt | Virtuoso: Narrowing the Semantic Gap in Virtual Machine Introspection[END_REF] and so forth, see Fig. 8. Some import service functions have been provided in this system, such as integrated browsing and discovery of domain knowledge, dynamic facet navigation and searching, SPARQL query endpoint, HTTP URI dereferencing, downloading RDF triples. Fig. 7 The Architecture of Knowledge Service System Driven by Linked Big Data Fig. 8 The Domain Knowledge Service Prototype System Driven by Linked Data

The results of this study proves that it is one of the best practices to applying the ideas, principles and methodologies of linked data, to describe, organize and merge the huge amount of agricultural information resources in a more fine described, formally structured and semantically linked way. Linked data would play a great role to increase the popularity, visibility, accessibility and value of agricultural big data resources. The knowledge organization systems in SKOS formats would help us to align and match concepts to develop a broad and high level analytical framework for managing, representing and mining big data. As massive amounts of data are available, linked and identifiable via URIs, big data, linked data and KOS would be an integral part of the future web infrastructure, the web of data, global data space and semantic web is beginning to take shape.

Fig. 4

 4 Fig. 4 Bibliographic Records and Linking Model The most interested and meaningful work we done is integrating and linking the SKOS-based CAT and several kinds of literature to the scientific research datasets. So far, we have modeled and linked about 700 core metadata of the scientific research datasets, and also some particular datasets hold the data about the institutes, researchers and projects of agricultural related domain, by reusing the well-known vocabularies or ontologies (e.g. VIVO, SWRC, FOAF)[19].The multidimensional semantic linking model covering the scientific data, literatures, and thesaurus we designed is as shown in Fig 5.

Fig. 5

 5 Fig. 5 Multidimensional Linking Model Covering the Scientific Data, Literatures and KOS

Further

  research work are needed because we at present just now focus on the variety and value dimensions of big data, we should actually address the volume, velocity issues in practice based on the cloud computing and other efficient big data infrastructure and technologies.

Table 1 .

 1 The Core Properties of Journal Article

	Property	Type	Available Vacobulary
	title	DataProperty	dc:title、swrc:title
	alternateTitle DataProperty	dcterms:alternative、prism:alternateTitle
	author	ObjectProperty	dc:creator、foaf:maker、swrc:creator
	keywords	ObjectProperty/ DataProperty	dc:subject、swrc:keywords、 prism:keyword
	abstract	DataProperty	bibo:abstract、dcterms:abstract、 swrc:abstract
	language	DataProperty	dc:language、swrc:language
	startPage	DataProperty	bibo:pageStart、prism:startingPage
	endPage	DataProperty	bibo:pageEnd、prism:endingPage
	totalPage	DataProperty	bibo:numPages、prism:pageCount
	DOI	ObjectProperty	bibo:doi、prism:doi

Acknowledgements

The related research work of this paper was support by the National Key Technology R&D Program "Knowledge Services Application and Demonstration Based on STKOS" (2011BAH10B06) during the Twelfth Five-year Plan Period.