Jianglin Qin

Donald Rundquist

Anatoly Gitelson

Zongkun Tan

Mark Steele

Jianglin Qin1

Donald Rundquist2

Anatoly Gitelson2

Zongkun Tan1

Mark Steele2

A Non-Linear Model of Nondestructive Estimation of Anthocyanin Content in Grapevine Leaves with Visible/Red-Infrared Hyperspectral 1

Keywords: Grapes, Hyperspectral, SVM (Support Vector Machine), Anthocyanin

. It documents the facts that such an approach is more suitable for developing simple hand-held field instrumentation for accurate nondestructive Anth estimation and for analyzing digital airborne or satellite imagery to assist in making informed decisions vineyard management.

digital photographs were acquired for each group of sampled leaves using an olympus C-175 4.0 megapixel camera.

Pigment Extraction

Analytical extraction of anthocyanins is problematic since the pigment in solution absorbs at 525 nm, the same band used for analytical chlorophyll extraction. To separate absorption by the two pigments, a process of pheophytinization is conducted. The process is performed by acidifying the neutral solution to convert chlorophylls to pheophytins, which while in solution, will absorb at 654 nm and 648 nm. In a neutral solution, anthocyanins are colorless. The acidification causes the red anthocyanin absorption feature to develop. This, in addition to the conversion of chlorophylls to pheophytins, facilitates analytical extraction of anthocyanin For each leaf sample, two to three 1-cm diameter discs were cut from the marked area. Pigments contents were calculated in a two step procedure. The punches were weighed and ground in 100% methanol using a mortar and pestle. The leaf tissue was ground until the pulp turned white in color and all pigments were suspended in the solution. The resulting homogenate was centrifuged in test tubes for 6 minutes. Absorption spectra of the solution were recorded using a cary spectrophotometer. The cary spectrophotometer was configured to measure absorption of the sample at 1 nm intervals between 400 nm and 800 nm.

A Non-Linear Model of Nondestructive Estimation of Anthocyanin Content in

Grapevine Leaves with Visible/Red-Infrared Hyperspectral 5 chlorophylls a and b and carotenoid contents were calculated from the spectra using coefficients described by Lichtenthaler [START_REF] Lichtenthaler | Chlorophyll and carotenoids: Pigments of photosynthetic biomembranes[END_REF]. Once chlorophyll absorption spectra were collected, chlorophylls were converted to pheophytins by adding HCL to each sample to produce a 99.9% methanol 0.1% HCL solution.

Absorption spectra of the converted solution were recorded using the cary spectrophotometer and absorption by anthocyanins was calculated using coefficients described by Lichtenthaler [START_REF] Lichtenthaler | Chlorophyll and carotenoids: Pigments of photosynthetic biomembranes[END_REF]. Laboratory analytical anthocyanin of 144 leaves yielded an adequate range of pignment concentrations, ranging from 1.34 mg/m 2 to 64.74 mg/ m 2 with a mean of 11.71 mg/ m 2 . The range of Anthocyanin in this study was similar to that observed by Gamon and Surfus [START_REF] Gamon | Assessing leaf pigment content and activity with a reflectometer[END_REF] .

Measuring Leaf Reflectance

Spectral-reflectance measurements were collected, at leaf level, for each of the five grape cultivars noted above using "leaf clip" with 2.3 mm diameter bifurcated fiber optic attached to both an ocean optics UBS2000 radiometer and to an ocean optics LS-1 tungsten halogen light source. The ocean optics UBS2000 radiometer uses a charged coupled device(CCD) to measure radiance with a spectral resolution of 1.5 nm across 2024 individual spectral channels ranging from 350nm to 1010nm in wavelength. The instrument has a 12 bit radiometric resolution; thus it records levels of reflectance ranging from 0 to 4095. The an ocean optics LS-1 light source uses a regulated power supply and a tungsten halogen filament bulb burning at 3100K to output a steady beam of light with a spectral range between 260 and 2500nm. In this study, the light source was turned on at least 15 minutes prior to scanning to allow the bulb and filament to warm and stabilize.

The plastic leaf clip, used to position the ocean optics fiber against individual grape leaves, consisted of a black polyvinyl chloride(PVC) attachment and a bifurcated glass fiber optic with a diameter of 2.3 mm. This fiber optic is transmissive between 400 and 1000 nm and was positioned at a 60° angle relative to the leaf surface in order to minimize the spectral range of the instrument, was held against the abaxial side of the leaf to reduce extraneous reflectance from the reflect light being transmitted through the leaf.

The ocean optics radiometer was calibrated prior to each data-collection session using a Labsphere spectral reference panel(North Sutton, NH) with a nominal reflectance of 99% between 250 and 2500nm. First, the reflectance panel was held tightly against the optic, and a spetral scans were taken prior to each data-collection session. The sensor was operated using the CALMIT Data Acquistion Program, which uses a single calibration scan collected at the time nearest to the acquisition of target scans to compute reflectance. To insure that a calibration scan used for reflectance calculation was accurate, at least three additional reflectance-panel scans were recorded for each dataset. The fourth calibration scan was plotted and compared to the plots of the previous three, it was used in subsequent processing. The reflectance spectrum was calculated as a ratio of leaf radiance of the calibration standard at wavelength . JiangLin Qin1,2, Donald Rundquist2, Anatoly Gitelson2, Zongkun Tan1,Mark Steele2 A total of 6 reflectance measurements were acquired within the marked homogenous area of each leaf using the ocean optics UBS2000 radiometer. Care was taken to distribute the location of the spectral scans throughout the entire marked area superimposed on the leaf in order to acquire an accurate representation of greenness. No data were taken at leaf edges where stain was present due to ink from the darker. The average of the six scans per sample was calculated in order to establish a single representative reflectance spectrum per leaf. Each spectral profile was numbered to correspond with the leaf from which it was acquired. The sensitive channel is also variation between grape species: From the Fig. 2, we find very apparent variation in sensitivity between grape species. St.Croix is much more variable, there are many lower points on its sensitive curve, they are about at 499, 554, 625, 661, 693,750 nm. Normally situation, DeChaunac and Edelweiss is much more sensitive, and Lacrosse is lower sensitive, but it is not always right, we still need to confirm by caculating coefficient r 2 -value. The lower points is also different between grape species, for example, there is a lower point at 693.52 nm for St.Croix, but that similar lower point for Lacrosse is changed to point 686.75 nm, and for DeChaunac and Edelweiss, there is similar lower point may be changed to 721.71 nm and 737.96 nm. Another example, there is a lower spectral fields about in 625-661 nm for St.Croix; but it is changed to spectral fields 652.88 to 686.41 nm for DeChaunac; and it is changed to 532.9 to 640.43 nm for Lacrosse; and there is not apparent similar lower spectral fields for Edelweiss.

The Procedure for Developing the Quantitative Model

Then, by detail analysis the datum and figures of every growth stage, we also find significantly different sensitivity between grape species. But unfortunately, if we only pay attention to the rough-black line, e.g if we ignore the situation of the difference between grape species , you shouldn't get those findings, the result will became worse.

The sensitivity of spectral channels is changed and/or shifted according to growth-stage. From growth stage of June 5,2007 to that of June 28,2007, the shape of all the sensitive curve is changed a lot. Taken St.Croix and DeChaunac as example, the similar lower points at about 550 and 680 nm is still retained, but the exact point is changed and corresponding sensitivity is also changed. And the shape of all curve is changed. While in June 5,2007, the St.Croix is very sensitive at point 550nm, but DeChaunac is not very sensitive at 550 nm. However, while the growth stage move to June 28, 2007, the most sensitive channel of St.Croix and DeChaunac is changed to point at 693.86 and 506 nm separately.

Normally, the sensitivity of separate species is much more high than that of no considering species difference situation: Detail analysis of the datum and graphs of all growth stage shows that in the most situation and/or growth stages, the sensitive curve of separate species is usually under the curve of the rough-black one. It indicates that modeling on separate species will be much more effective, or we should use some kind of non-linear technique and taking consider of species and growth stage difference to get more effective model.

Interaction of anthocyanin, chlorophylls, carotenoids and other foliar chemistry in absorption and Reflectnce. A Non-Linear Model of Nondestructive Estimation of Anthocyanin Content in

Grapevine Leaves with Visible/Red-Infrared Hyperspectral 9

Absorption feature are broadened by multiple scattering and often interfere with one another. Organic compounds also absorb in similar wavebands, so that a wavelength is never uniquely related to a chemical. For instance, both of Anth, Chl and Car is absorb at about 550,680 nm, it makes the problem very intractable. From Fig. 3a&3b We can infer that the new model cover advantages in many methods, include MARI, ARI,3 bands model, because we can introduce as many indices as possible into the new model. And by learning procedure, the new model optimizes its accuracy and ability of its generalization or ability of prediction.

SVM Approach to Establishing Non-Linear Model

Support Vector Machine is a kind of high-degree non-linear method [START_REF] Vapnik | Statistical learning theory[END_REF][START_REF] Joachims | Making large-scale SVM learning practical[END_REF][START_REF] Joachims | Learning to Classify Text using Support Vector Machines Methods, Theory, and Algorithms[END_REF], it also possesses good robustness and lots of another good feature, those characteristics make it become one of the most prominent machine learning technique for high dimension sparse data. The principle of the technique is summarized below.

Taking measured Anthocyanin content Y i and its corresponding factors <x i1 ,x i2 ,…..,x im > T , or we can note factor vector as X i , it is belong to  m , here m = 25 represents factors number, i≤n represents sample number, as a pair dataset, normally it is called as sample.

The learning task is defined a fixed array X of n points(X 1 , X 2 ,…., X n ). Each data point has a desired Y i . For training, the learner receives the labels Y i for a random subset Y l ,

Y l  [1, 2, …, n]  Y l  = l (2)
Of l<n (training) data points. Here  Y l  represents the number of element of subset Y l . The goal of the learner is to predict the labels Y i of the remaining (testing) points in X as accurately as possible.

On another aspect, we can say that the principle of SVM is based on the hypothesis that all samples possess the similar features, so we can establish model from its subdatasets(learning) that can predict the remaining(testing) subdatasets. In our case, anthocyanin , Chlorophoylls and Cartotenoids reflect and absorpt at fixed channel of light spetrum in the same grape species and similar growth stage, their reflectance and absorption also follow some rules, so we can infer that every sample possesses the similar feature. Our training goal is to extract the rule, e.g. the relationship of anthocyanin content and factors, which is hidden behind subdatasets (learning), so that we can we can predict the remaining(testing) subdatasets. In our research, we randomly sort all samples, and then cut about two third as the training datasets (90 samples), remaining points as the testing datasets (54 samples).

We

Here ω is Weigh Vectors, b is Deviation Vector. And φ(X) is project function from  m space to  space and can be replaced by a kernel function, here we select Gauss kernel that is expressed by

k(x ik, x ij ) = exp(-‖x ik -x ij ‖ 2 / (2δ 2 ) ) (4)
Hereδ is the parameter of rbf kernel.

Modeling effect from our training datasets

The 5b, we also find that the Error is not apparently increased with the increasing of Ant content. This characteristic shows that the effect of the model is better than normally statistical method. Here is the format of calculating Error, it is recorded as Er i shortly.

Er i = abs(Y _predicted i -Y _original i ).

(

) 5
Here i=1,2,…,n. From Fig. 5b, we can find that almost all Er i is under about 1.69 mg/m 2 line. r 2 -value of 0.252 shows that the Er i is not apparently relative to the Anth content increase. This phenomenon shows that almost all useful information is transformed into SVM model, the Noise is "pure Noise".

To estimate how sensitive is each of predicted Anth content evaluated in this study, to change in reality Anth content(e.g.,Measured Anth content in Fig. 5a), the Noise Equivalent Anth content(NE ΔAnth ) was calculated [START_REF] Viña | New developments in the remote estimation of the fraction of absorbed photosynthetically active radiation in crops[END_REF]. From Fig. 5b&Fig.6b, we can see that that the Average Error is not apparently relative to the Anth content increase,even if Anth content is beyond 45 mg/m 2 ,it is great interest for detection of heavily plant environmental stress. We also found that the senisitive channel is variation between grape species and growth-stages.

Conclusion

Outperformance of the new methods is partially due to that it removed the affect of anthocyanin, carotenoids and it removed the affect by the scattering of the grape leaf internal structure and leaf surface.

The new methods was found to be capable of accurately estimating Anth contents across the whole Anth range , thus the new methods is likely to allow accurate Anth determination in Vitis vinifera vines, especially can used for quantitative assessment of the early stage of plant environmental stress.

Acknowlegements

Fig. 1 .

 1 Fig. 1. Leaves, as photographed by means of the digital camera, were selected to provide samples of the various cultivars. Measurement areas were delineated on the leaves themselves. These Saint Croix leaves were sampled on July 10, 2007.

4. 1 4 . 1 . 1

 1411 To create variable set for the new model. Confirming sensitive spectrum and/or factors. The relationship between the measured anthocyanin content and spectral reflectance at visible wavelengths (400-750 nm) is a complex non-linear form. The first task in developing an appropriate quantitative model to summarize that relationship is to determine which specific wavelengths in the visible spectrum have the greatest sensitivity for detecting subtle changes in anthocyanin content. The sensitivity calculation first makes use of the standard error in the regression of two datasets. In our case, the first dataset was the anthocyanin content associated with the sampled leaves corresponding to each of the five cultivars at every one of 4 growth stages. Each data-collection campaign in the field was considered as a separate growth stage (5 total field campaigns during 2006 and 2007). For the year of 2006, the first stage represents May 26, 2006,19 samples for Edelweiss and 25 samples for StCroix; the second stage represents June 1 and 9,2006, 10 samples for StCroix, Edelweiss, stpepin; the fourth stage represents September 19,2006,22 samples for stcroix. For the year of 2007, the second stage represents June 5, 2007, the third stage represents June 28,2007, every stage includes 6 samples for DeChaunac, Edelweiss, Lacrosse, StCroixper.The anthocyanin content datasets for the five cultivars were regressed against visible spectral reflectance, with the latter interpreted for our study as the wavelength region between 400 and 750 nm. The purpose of the many regression calculations was to select the most useful portions of the visible range for estimating Anthocyanin content. As an illustration of the procedure, consider Fig.2 where the data collected on June 28, 2007 are shown. Notice that the x-axis is the visible spectrum as defined above. The y-axis represents the standard error of the respective calculations associated with all 5 field campaigns. The solid black line represents the result for all samples combined (4 grape cultivars times 6 scans per leaf) for one growth stage. The red-line represents the series of standard errors for the A Non-Linear Model of Nondestructive Estimation of Anthocyanin Content in Grapevine Leaves with Visible/Red-Infrared Hyperspectral 7 DeChaunac grape cultivar, green represents Edelweiss, turquoise represents Lacrosse, and purple represents Saint Croix . The lowest points along the respective lines indicate the spectral regions or wavelengths where the greatest sensitivity occurs.

Fig. 2 .

 2 Fig. 2. The graphic illustrates the variation in the sensitivity of the various spectral regions to changes in Anth content. The solid black line represents the result for all samples combined (4 grape cultivars 6 times scans per leaf) for one growth stage. The red-line represents the series of standard errors for the DeChaunac grape cultivar, green represents Edelweiss, turquoise represents Lacrosse, and purple represents Saint Croix . The data shown here are from leaves sampled on June 28, 2007. The next step in the procedure was to calculate the correlations (R) and coefficients of determination (R 2 ) between anthocyanin and reflectance for those locations along the lines with the lowest standard errors. This was done merely to confirm the selection of wavelengths that will be used in the final model. The result of this latter calculation was the selection of fifteen spectral regions for use in the modeling procedure. The fifteen selections include the following (wavelengths in nm): 400.3 to 404.8;450.22 to 453.19;494.61 to 499.38;533.65 to 536.18; 550.62 to 553.14;641.13 to 643.55;676.2 to 679.95;682.33 to 686.07;689.8 to 692.51; 696.56 to 699.93; 716.71 to 719.71;724 to 726;721.04 to 722.71;739.94 to 742.57;748 to 750. Next, the reflectances for the individual spectral ranges noted above are integrated by means of averaging these reflectance over the wavelength ranges (e.g., 400.3 to 404.8 nm, which contains band centers at 400.3;400.68;401.06; 401.43; 401.81; 402.19; 402.57; 402.95; 403.32; 403.7; 404.08; i.e., eleven individual channels). The averages became input variables to the developing

Fig. 3 . 3 1 )

 331 Fig. 3. The graphic illustrates that the reflectance and absorption of the Anth content affects by Chl and Car content. Both of Figures illustrates that in lower Anth content, the Chl and Car variates sharply, especially for the Anth content ≤15mg/ m 2 After researching above in detail, we confirm following sensitive spectrum as part of our modeling factors. There are totally fifteen spectral fields (see above), each spectral field takes the average value of the nearest several channels. Some of them are Chl or Car sensitive spectrum. For instance, 550.62 to 553.14,641.13 to 643.55, 689.8 to 692.51,696.56 to 699.93.So that the affection of Chl and/or Car can be compensated in the new model.

Fig. 4 .

 4 Fig. 4. The graphic illustrates that in some case, 3 band model/or MARI can improve the Linear feature between Anth content and original data space m in local fields, e.g. subdimension. It can makes a asymptote form of Fig.4b to be a near linear form of Fig.4a.It also makes that the scattering points in Fig.4b shrinks closed to the curve in Fig.4a. Especially in lower Anth content points. The data shown here are from leaves sampled on June 9,2006 of Stpepin. From Fig.4a&4b which the data is taken from June 9,2006 of Stpepin, we can see that by introducing 3 band model/or MARI as index, the Linear feature between Anth content and original data space  m in local fields has been improved ; in lower value points of Anth content, the points become much more closed to the curve, e.g. shrunk to the curve(Fig.4a), which shows that Errors will

  use SVM (SVM Light V6.01) programmer to train the training datasets, and get the model, then use the testing datasets to test the model. Next we optimizes its accuracy and ability of its generalization or ability of prediction as likelihood as possible. Finally we had gotten best fit model, the function between Y i and <x i1 , x i2 ,…..,x im > T Y = ω•φ(X) + b

  effect of the model that we get from training dataset is shown in the Fig. 5a&5b. By the procedure of the training, we have established a kind of project from  m space to  space. While in  m space, the points of original datasets of factors are existed as a curve fields, now they are shown in a linear form, and existed in a narrow channel. The strength of SVM model for the measured Anth content and predicted Anth content was very significant, its r 2 -value of 0.97289 (calculated according format). Fig.5b shows Error distribution of the model, Average Error is 1.3823 mg/m 2 , e.g. 13.32%. From the Fig.

NE

  We are thankful that the study is supported by the International Cooperation and Exchange Division, Guangxi Education Department, to support Jianglin qin as a visiting scientist working with CALMIT Faculty, and acknowledge that this study has been funded by the Ministry of Science & Technology of China (No.2008BAD08B01).The first author is grateful to Donald Rundquist, Anatoly Gitelson, Mark Steele, Christopher Harkins, and Rebecca Briles of CALMIT for providing the essential raw data, and their constructive comments and suggestions.

, we can see that in some cases, while the Anth content is very lower, but Chl and/or Car maybe very high. For example, Edelweiss in June 5, 2007, Ant content is 2.36 mg/m 2 , but Chl and/or Car content is 454.5 and 97.6 mg/ m 2 separately. However, the same grape species Edelweiss in May 26, 2006, Ant content is the similar 2.364 mg/ m 2 , but Chl and/or Car content is 24.49 and 11.22 mg/m 2 separately. So the similar Anth content, but Chl and Car content is variable a lot, from 454.5 and 24.49 mg/m 2 for Chl and from 24.49 to 11.22 mg/m 2 for Car. As for sensitive channel of at 550.62 to 553.14 and 676.2 to 679.95 nm, the reflectance is 11.78 and 3.2 for Edelweiss in June 5, 2007 individually, and is 23.57 and 5.19 for Edelweiss in May 26,2006 separately, it is drastically variable from 11.78 to 23.57 for band of 550.62 to 553.14 nm and from 3.2 to 5.19 for band of 676.2 to 679.95 nm. So, in case of Anth lower content, the reflectance of spectrum maybe drastically variable, and so as for the absorption. The relationship between them is a very complex non- linear form, non-linear adjustment is necessary.

Table 1 . r 2 -value between Anthocyanin content Vs part of Indices

 1

By introducing SVM, we had developed a more accurate quantitative model to like leaf Anth content in grape with spectral reflectance in visible/Red-Infrared region. The fifteen important spectral regions in visible/Red-Infrared region were identified: 400.3 to 404.8;450.22 to 453.19;494.61 to 499.38;533.65 to 536.18; 550.62 to 553.14;641.13 to 643.55;676.2 to 679.95;682.33 to 686.07;689.8 to 692.51; 696.56 to 699.93; 716.71 to 719.71;724 to 726;721.04 to 722.71;739.94 to 742.57;748 to 750. are most responsive to changes in leaf Anth content in grape.

JiangLin Qin1,2, Donald Rundquist2, Anatoly Gitelson2, Zongkun Tan1,Mark Steele2

JiangLin Qin1,2, Donald Rundquist2, Anatoly Gitelson2, Zongkun Tan1,Mark Steele2

JiangLin Qin1,2, Donald Rundquist2, Anatoly Gitelson2, Zongkun Tan1,Mark

We can conclude that the performance of predicting ability of the SVM is very well.

14 JiangLin Qin1,2, Donald Rundquist2, Anatoly Gitelson2, Zongkun Tan1,Mark Steele2 Fig. 6. The graphic illustrates the predicting performance. Fig. 6a shows that all scattering points are existed between two parallel narrow lines, and its r 2 of 0.96176 (calculated according format) is similar as that of the modeling in Fig. 5a. In Fig. 6b, its Average Error is 3.618 mg/m 2 ,e.g 25.99%, the Er i is increased a little with the increase of the Anth content. The poor performance of the normally statistics methods is due to that it cannot describe the complex relationship of the Anth content and its reflectance, and the variation in species and growth stage. They cannot fully employ the wealth information of the hyperspectral

Comparison with the existing methods

A simple description about the theoretical principle over procedures

The learning procedure of SVM guarantees structural risk minimization of the model, and optimizes its accuracy, so SVM usually exhibit good generalization performance and high accuracy. The procedure in 4.2 item can ensure evenly distributed samples, so that the learning subset of samples can cover the  m space much more perfectly, and finally to ensure ability of generalization is strong.

Result and Discussion

Red/green [START_REF] Gamon | Assessing leaf pigment content and activity with a reflectometer[END_REF],Modified ACI(e.g. λNIR/  λ530 ) [START_REF] Van Den Berg | Nondestructive estimation of anthocyanin content in autumn sugar maple leaves[END_REF],ARI [START_REF] Gitelson | Optical Properties and Nondestrstructive Estimeation of Anthocyanin Content in Plant Leaves[END_REF],MARI [START_REF] Gitelson | Optical Properties and Nondestrstructive Estimeation of Anthocyanin Content in Plant Leaves[END_REF][START_REF] Gitelson | Three-band model for nonvasive estimation of chlorophyll, carotenoids, and anthocyyanin content in higher plant leaves[END_REF] are some traditional methods to estimate pigment content in leaves nondestructively. The authors of [START_REF] Mark | Research note nondestructive estimation of anthocyanin content in grapevine leaves[END_REF] used those indices to estimate Anth content in grape leaves with the same data of this study. They found that Anth prediction by Red/green was poorest with determination coefficient(r 2 ≈0.76) and highest RMSE (7.63 mg/ m 2 );Anth prediction by ARI was much more accurate that the Red/green(r 2 ≈ 0.96,RMSE<2.93 mg/ m 2 ); and MARI was the most accurate in Anth prediction(r 2 ≈0.97,RMSE<2.23 mg/ m 2 ).

In this study, we found that both of SVM and MARI/ARI has a coefficient of determination r 2 ≈0.96 for the whole Anth ranging.The Noise equivalents of Anth estimation by both the MARI and ARI has shown in [START_REF] Mark | Research note nondestructive estimation of anthocyanin content in grapevine leaves[END_REF]. This study uses the same data, but NE of SVM has average NE = 1.65 mg/m 2 ,for the whole range from 1.34 mg/m 2 to 64.74 mg/ m 2 , which is lower than that of MARI=2.23 mg/ m 2 and ARI=2.93 mg/ m 2 in [20],e.g. the result has been improved a lots.

For the Anth≤15 mg/m 2 , the NE△ Anth of MARI or ARI was about 2.23 to 2.93 mg/m 2 , but NE△ Anth of SVM was 1.00mg/m 2 ,thus SVM was much more accurate (less noisy) than both MARI and ARI, it is very sensitive for early detection of plant environmental stress.


Pengbo 苗蓬勃

Miao

薛龙 Long Xue

刘木华 Muhua Liu

黎静 Jing Li

王晓 Xiao Wang

罗春生 Chunsheng Luo

The detection of early-maturing pear's effective acidity based on hyperspectral imaging technology

Keywords: :early-maturing pear, hyperspectral image, effective acidity, partial

The hyperspectral imaging technology is used to detect early-maturing pear's effective acidity nondestructively, and effective prediction model is established. 145 pears' hyperspectral images are obtained in the wavelength range of 400nm-1000nm. Total 145 pears are separated into the calibration set (77 samples) and prediction set (68 samples). Early-maturing pear's effective acidity partial least squares (PLS) prediction model is built in different range of spectrum band. By comparison, the range 498 nm -971 nm was selected in using partial least squares (PLS) to build early-maturing pear's effective acidity prediction model. The experimental results show that, PLS prediction model of early-maturing pear's effective acidity has the best effect in this range of wavelength. The correlation coefficient R between early-maturing pear's actual effective acidity and predicted effective acidity is 0.9944 and 0.9233 for calibration set and prediction set respectively, the root mean squared error of prediction samples (RMSEP) is 0.022 and 0.072 for calibration set and prediction set respectively.

Introduction

Hyperspectral imaging technology is applied in research of detecting agricultural products' quality these years; it has much potential in nondestructive inspection area. Xuehua pear's sugar content and water content's sensitive moisture absorption spectrum was picked up by hyperspectral imaging system, artificial neural networks is used to establish Xuehua pear's sugar content and moisture content prediction model, in this study, the feasibility of nondestructive testing of Xuehua pear's quality in using hyperspectral imaging technology was discussed [START_REF] Hong | Nondestructive Detection of Xuehua Pear's Quality Based on Hyperspectral Imaging Technology[END_REF]. Hyperspectral imaging technology was used to predict apple's internal quality including firmness and soluble solid [START_REF] Qin | Measurement of the Optical Properties of Apples Using Hyperspectral Diffuse Reflectance Imaging[END_REF].Spectral bands 640-750 nm had the best effect for detecting tomatoes' damage; this result was based on the application of partial least-square method and genetic algorithm on hyperspectral data [START_REF] Xing | Wavelength Selection for Surface Defects Detection on Tomatoes by Means of a Hyperspectral Imaging System[END_REF]. The prediction model was obtained based on BP neural network for detecting apples' maturity and soluble solids content from wavelength 500nm to 1000nm by using hyperspectral imaging technology [START_REF] Lu | Imaging Spectroscopy for Assessing Internal Quality of Apple Fruit[END_REF]. As people's life quality improved, consumer choose fruit pay more attention in internal quality such as sugar content, acidity value etc. not only in the size, color and appearance these external quality. For the quality of fruit acidity, if there is too low, the fruit will lack of flavor [START_REF] Ying Yi-Bin | [END_REF].

The acidity which people in the taste mainly depends on the acid in the state of ions, which is effective acidity, usually use pH to express and not depend on the amount of acid [START_REF] Wu | Food inspection technology[END_REF]. So, this study selects one kind of early-maturing pears-Cuiguan pear as samples to inspect effective acidity. Early-maturing pears' hyperspectral images are collected in wavelength 400-1000nm, and partial least squares (PLS) is used to establish Early-maturing pears' effective acidity prediction model.

Methods and Procedure

Fruit Samples

This study selected the common early-maturing pears in south -Cuiguan pear.

Cuiguan pears are wide in northern area of Jiangxi province; it has the Characteristic of early maturity, adaptability, good quality and good harvest [START_REF] Du | Main techniques for forest cultivation[END_REF]. Cuiguan pears are round in general, and mature in early August. Its flesh is white and tender, its core is small, and has a good taste.

Fresh early-maturing pears are purchased from local market for this experiment, samples are randomly selected in size and shape representatively in early August 2009. Then pears had been kept in cold storage at 4℃. Samples had 145 pears in all which are separated into the calibration set (77 samples) and prediction set (68 samples).

Instrument Setup

Early-maturing pears hyperspectral imaging experiment device is shown in fig. 1, this platform mainly include hyperspectral camera(ImSpector,V10E,Finland), light source, manual lifting device, electronic mobile platform and computer with Spectralcube software. Two 500W tungsten halogen lamps are light source.

Hyperspectral Image Collections

The exposure time of hyperspectral camera should be predefined before acquiring hyperspectral data to ensure the image is clear and the speed of mobile platform should be set reasonably to avoid distortion of image size and the spatial resolution.

Early-maturing pear samples are took from the refrigerator and put in the tray under temperature of 24℃, after the water on surface evaporated, early-maturing pears samples are in accordance with experimental environment, then the numbers could be marked on samples respectively. Standard white hyperspectral image is collected by using standard white board in the same condition with collecting samples' images.

Then the standard black hyperspectral image is collected by covering the lens. The collection of pear samples' hyperspectral reflection images is according to the numbers marked in advance. After collecting Every 10 samples' images, the standard white and black images should be collected again.

Effective Acidity Measurement

PH=4 standard buffer solution is used in this experiment. Using potassium hydrogen phthalate 10.21 grams dissolved in distilled water of 1000mL and set aside. The instrument for inspecting effective acidity is PHS -3B precise digital acidometer (HongYi Instrument Co., LTD Shanghai), which is sensitive and reliable and has accurate reading. Specific operating processes are as follows:

(1) The electrode on acidometer should be connected first, and turn on the power for 30 minutes for preheating. After adjusting the temperature compensation knob, electrode are immersed in the buffer solution. Digital display of pH value on electrode is 4 (pH value of buffer solution) by adjusting potential regulator.

(2) Pulp of early-maturing pear sample is peeled, and mashed with the mortar for pear fluids.

(3) Electrode is washed with distilled water and dried with filter paper firstly, and washed with sample's some fluids once more, then inserted into the sample's fluids that is prepare to be examined. When the reading on acidometer's display screen stable, the corresponding sample's effective acidity is acquired.

Early-maturing pears' effective acidity statistics are acquired through effective acidity measurement method as shown in table 1. 3 Spectral image preprocessing

Image Preprocessing

In the continuous working process, the luminous intensity of light source will gradually decrease. Hyperspectral camera's stability and repeatability will be affected because of light attenuation in the study. The images obtained would contain much noise in the band which has weak light intensity because of the uneven distribution of light intensity under the band and dark current in sensor. So image calibration is needed [START_REF] Leng | A Positioning Method of Calibrating Symbols Based on High Noise Background Image[END_REF]. The standard white board is used for light calibration and standard black board is used for eliminating dark current noise in equipment etc.

Sample's original hyperspectral images turn into the absolute images after preprocessing (as shown in fig. 2) in using calibrated formula.1 [START_REF] Liu | The Detection of Residues Fruit and Surface Contaminant Based on Laser and Hyperspectral Technology[END_REF]. Early-maturing pear samples' images are processed for compression by using the nearest neighbor domain method in ENVI software, and make x and y directions scale down for 0.5. Data could be reduced for this method, so image reading and data processing could be quick and could save storage space in the same time. After the image processing, early-maturing pear's hyperspectral image is as fig. 3 shown.

() ( ) ( ) ()() y x I y x I y x I y x I y x I black white black sample norm , , , , , - - = (1)
• Fig. 3 The hyperspectral image at 723nm

Region of Interest (ROI) Selection

Early-maturing pear's effective acidity measuring area is the reflecting area in filming. So visible region on early-maturing pear sample's image is selected as interested region when process the image.

The region of interest has strong brightness, which could wipe out the effect from pear's edge and provide better data relative with early-maturing pear's quality highly [START_REF] Li | The No Divided Space Color Image Retrieval Method Based on Thumbnail and EMD[END_REF].Region of interest of early-maturing pear sample is selected as shown in fig. 4.

Early-maturing pear sample's reflected data could be generated from this area directly after the selection of region of interest (ROI), in order to analysis and process data later. From the table, predicted model established by PLS in spectral band 498nm -971nm has better effect, and has better characteristic parameter compare with the models in other bands. Therefore, the spectral band 498nm -971nm is selected as effective band for establishing early-maturing pear's effective acidity predicted model.

Effective Acidity's Calibration Model

The quantity of early-maturing pear samples for building effective acidity predicted model is 77. Partial least squares (PLS) in software of Unscrambler is applied in excellent spectral band from 498nm to 971nm. Through the internal cross validation, relevance between predicted effective acidity value and actual of calibration set is shown in fig. 5.

Model Prediction

Another 68 early-maturing pears are samples in prediction set for predicting the stability of the model and the accuracy of the predicted results. The predicted results obtained from Unscrambler analysis software shown in fig. 6.

Conclusion

Hyperspectral imaging system is used for detecting early-maturing pear's effective acidity which is based on hyperspectral imaging technology. Early-maturing pear's hyperspectral images are processed, the data from that are calculated by partial least squares (PLS) in the software Unscrambler for the study of detecting early-maturing pear's effective acidity.

The study indicates that, the wavelength from 498 nm to 971 nm is the best band to build early-maturing pear's effective acidity prediction model. The correlation coefficient R between early-maturing pear's actual effective acidity and predicted effective acidity is 0.9233 for prediction set; the root mean squared error of prediction samples (RMSEP) is 0.072 for prediction set. So the early-maturing pears' effective acidity prediction model has good prediction effect and good stability. Further studies are needed to evaluate the potential of using hyperspectral technology to detect more samples for improving the model's accuracy.

Fig. 1

 1 Fig.1 Hyperspectral imaging platform sketch, 1. Hyperspectral camera, 2. Light source, 3. Manual lifting device, 4. Electronic mobile platform 5.Computer

(formula. 1 )

 1 In formula.1, I sample is original image, I black is standard black board image, I white is standard white board image, I norm is the normalized data. Effective value from 0 to 1 could be obtained by calibrating the original image. And the image calibration can reduce distraction due to such as uneven light these factors for hyperspectral image. a. before preprocessing b. after preprocessing using formula 1

Fig. 2 .

 2 Fig.2. The Vis-NIR Spectrum of sample

Fig. 4

 4 Fig.4 Region of Interest at 723nm

  2.

Fig. 5 .

 5 Fig.5. The calibration model of Cuiguan pear's effective acidity By analyzing the figure, the predicted model of early-maturing pear's effective acidity is built under spectral band of 498nm-971nm. The correlation coefficient R between early-maturing pear's actual effective acidity and predicted effective acidity is 0.9944, the decisive coefficients R 2 is 0.989, standard error of calibration (SEC) is 0.022 and root means standard error of calibration (RMSEC) is 0.022 in calibration set.

Fig. 6 .

 6 Fig.6. The prediction model of Cuiguan pear's effective acidity 68 early-maturing pears are samples in prediction set for detecting the accuracy of model (shown in fig.6). From the figure, the correlation coefficient R between early-maturing pear's actual effective acidity and predicted effective acidity is 0.923, the decisive coefficients R 2 is 0.835, standard error of calibration (SEC) is 0.022 and root means standard error of prediction (RMSEP) is 0.072 in prediction set.

Table . 1

 . Statistics of effective acidity measurements of Cuiguan pear samples (pH)

		Mean	SD※	Min	Max	Samples
	Calibration set	5.24	0.201	4.73	5.62	97
	Predication set	5.267	0.180	4.96	5.52	68

Table . 2

 . The model comparison in different bands

	No.	Correlation	R-Square	RMSEC ※	SEC※	Bias	Band
	1	0.7824	0.6136	0.6248	0.6286	1.724e-08	399.2nm-1000.1nm
	2	0.8203	0.6728	0.5749	0.5784	-5.745e-09	447.5nm-917.6nm
	3	0.9561	0.9142	0.2944	0.2961	-1.281e-06	477.9nm-993.6nm
	4	0.9695	0.9399	0.2462	0.2477	-1.55e-06	493.1nm-980.5nm
	5	0.9372	0.8783	0.3506	0.3527	-3.441e-06	600.8nm-917.6nm
	6	0.9944	0.9888	0.022	0.022	-8.670e-07	498.2nm-971.5nm
	7	0.9491	0.9000	0.3164	0.3183	-1.476e-06	518.6nm-971.8nm
	8	0.8873	0.7874	0.4635	0.4664	-5.429e-06	652.6nm-810.5nm
	9	0.9068	0.8224	0.4237	0.4262	-2.172e-05	704.8nm-810.5nm
	10	0.9371	0.8783	0.3506	0.3527	-3.441e-06	704.8nm-917.6nm
		RMSEC ※ : Root means standard error of calibration.
		SEC

※ : Standard error of calibration.

Acknowledgement

This work was supported in part by the National Natural Science Foundation of China (No. 30760101) ,Jiangxi Provincial Department of Science and Technology (No. 2009BNB05705) and Jiangxi Provincial Department of Education (No.GGJ08513).


Qingfeng Ding

Fengping Yang

Qixin Zhu

Research of Subway's train control system Based on TCN

Keywords: TCN, RTP, Event arbitration, Network simulator 1

Abstract:Contraposing the subway car control equipment being scattered, the subway train controlnetwork system based on TCN RTP is designed. By constructing the virtual network environmentnetwork with simulation software NS2 and writing message data of transmission layer protocols.Through testing train control network communication efficiency, the influent factors of communication efficiency is known.Train control telecommunications devices based on TCN RTP communication function is developed.Using XML technology and JAVA language JDOM analytical mode, the compatible TCCL cross-platform configuration software of other train control communication equipments is designed.Through the train control communications equipment configuration of the basic functions and setting device configuration of network communication operation parameters to the purpose.

Network control is a way that control scattered train control equipments, so network controlling the control device of train control system is out of natural selection and necessary requirement,which has important theoretical significance and application value to actively promote the localization process of train control technology . This paper mainly reseach the following issues of subway train control system and communications network based on TCN.

( 1 ) RTP (Real-Time Protocol) packet transmission and verification.The data communication frequency and message priority between the Multifunction Vehicle Bus (MVB) and Wire Train Bus (WTB) are different, research transmission Methods of three types of real-time information:process data, message data and monitoring data in the TCN network.

( 2 ) Train control communications network performance. Setting up the TCN test environment to analyze factors of the network operation performance ,such as loss rate of the network node information packet, delay and jitter rates.

(3)Achieve train control device configuration capabilities.Select the structure and design of configuration file markup language to complete the basic functions of control equipment, network topology, to achieve device configuration.

2.RTP communication modeling

RTP communication model uses the publisher / subscriber communication structures. IEC 61375-1 defines the statute of the corresponding abstract model RTP, which services in the train control system. MVB and WTB transmission three types important information of process data, message data and monitoring data, which is a cycle process of data communication mode compared with sporadic transmission message data. The transmission of RTP messages uses by the master scheduling mechanism. General Event uses by the polling way. When more than one device requests to respond ,it will collide, then the bus master boots arbitration mechanism. Event arbitration is distinguished by event priority to achieve efficient incident response. Because the frequency of data communication and message priorities between MVB and WTB are different, the paper focuses on the methods to transmit of the three types of real-time information in the TCN network.

(1) Messages transmission mapping of RTP model achieve more specific.The application layer protocol data unit is specifically defined as PDU (Protocol Data Unit),which is coded by the presentation layer, then directly map to the data link layer and physical layer.Namely the transport layer and network layer are empty. The purpose of this mapping is to avoid the propagation delay caused by communication stack, thus ensuring the rapid transfer of packets. The role of presentation layer is encoding the application layer PDU to fit transmission in the TCN network, RTP message transmission encoding selects using ASN.1 Basic Encoding Rules (BER).The flowchart of codec is shown in Figure 1. (2) The transmission of RTP messages uses by the master scheduling mechanism.Bus master releases main frame to poll dispatch communication. When multiple devices respond to the message data request ,it will collide and then the bus master starts arbitration mechanism. This recursive algorithm is choosen to realize arbitration address and to complete the multi-device response. Figure 2

3.Study on network performances

In the course of train control equipment, communications and news service in real-time agreements are more important. Though studing the Transport Layer message data , write message data layer protocol design,simulate in NS2 (Network Simulator, version2), build a virtual network environment to simulate the configuration of this agreement to make the nodes more freely communicate, and test efficiency of communication, research influencing factors of the communications efficiency .Figure 3 shows for the frame work of tesing bed based on NS2. This paper studies the following two elements.

(1)According to contents of the TCN RTP, design news transport layer protocol by using C + +; use NS2 to build a virtual environment TCN network topology, and add the preparation of the agreement to a virtual network node,test communication of the nodes by using the protocol.

(2)Test the running performance of agreement to run. Awk software is used to analyze data on the node, including loss rate of the message data packet, delay and jitter rate, and test the effectiveness of network operations.Comparative analysis is not clearly defined in TCN real-time protocol, including the initiative problem of implicit solution.

4.Train control equipment configuration function design

TCCL(the Train Control_equirement's Configuration Language) ,on one side, which can describe the basic functions of train control equipment and can access basic information for device plug and play, is the premise equipment interoperability; on the other side , through the configuration file ,can configure the basic functions of train control equipment and set equipment operating parameters to achieve device configuration.

Configuration options

TCCL file uses XML markup language and the object model involves three levels. The first-level is related with train control systems, describes device capabilities, network topology and the key data format, indicates parameter characteristics of the train control system device.The second level is related with train control equipment.The third related the object by the communication model. Implementing Configurator in TCCL XML parser has the following two common methods [START_REF]《IEEE Standard for Communication Based Train Control system(CBTC) performance and Functional Requirements》IEEE Std1474[END_REF].

(1)Using Microsoft's msxml4.dll components. There are usually two ways parsing XML documents. One is the DOM (Document Object Model) ,another method is SAX (Simple API for XML) approach.

(2)Using JDOM based on JAVA language. JDOM is a open-source project, which is based on tree structure, using pure JAVA technology implementation on the XML document analysis, formation, and a variety of serialization operation. JDOM services directly JAVA programming. In view of openness and portability features, the research chooses Configurator JDOM as analytical tool.

Configuration feature

According to the requirements of TCN, the paper design flow chart of train control communications equipment configurator show in Figure 4. In accordance with the above analysis, train control equipment configuration features are included in the following parts [START_REF] Lin Zhi-Ming | Design and achievement of configuration tools based on SCL model[END_REF].

(1) Type information into train control equipment, documents, check whether the configurator database with the corresponding device type information, and if not, suggest new types information into devices; If already have, then prompte whether the need compare between new and old files after import, or directly covering .

(2) TCCL Configurator import information file ,then first verify the legitimacy of the file. Verify the legality including three main elements.First, XML syntax validation, whether the XML file configuration accord to file format. Second ,TCCL syntax verification, testing whether the configuration information file TCN given specifications; third,verify the device type, testing configuration information file if they meet specified type of device capabilities. If the detect is inconsistent with some of the actual situation,then give warning.

(3)Configurator pre-generate configuration information of a particular type of train control device according to device type information files, parse input information stratification into the corresponding information object according to the information model.In the configuration, each object is available separately edited by the configuration which check the necessary restrictions; configurator can create the configuration information file of entirety train control devices according to all the configuration information of each train control device, and can be exported as needed. TCN configuration tool parses the input configuration information into the corresponding information object based on TCN information model, then reflect in the configuration interface; which edits each object and realizes a wide variety response constraints.The final configuration complete and then export TCCL file.

Conclusion

With the rapid development of high-speed railway in China, TCN in the electric multiple unit (EMU) also be applied gradually, train network control systems network architecture of China's CRH1, CRH3 developed and manufactured use the TCN standard.The widely using of TCN offers localization process of train control system a broad market prospect. At the first of the paper, design configuration files of the train control system; then ,establish communication characteristics of the object model according TCN RTP, using the NS2 construct test TCN network environment; finally ,complete communications network transmission verification.

Fig. 1 .

 1 Fig.1. the flow chart of the ASN.1 Encoder/Decoder

  below shows for the arbitration process under device address [2].

Fig. 2 .

 2 Fig.2. Event Arbitration

Fig. 3

 3 Fig.3 the framework of testing bed based on NS2

Fig. 4 .

 4 Fig.4. Flow chart of configurator operation

Acknowledgement

The work described in this paper has been supported by National Natural Science Foundation of China(60964004 )and Foundation of JiangXi Educational Committee (GJJ09203) and Foundation of East China Jiaotong University (09DQ03).


Tao Shen

Jinheng Zhang

Junqiang Wang

Research on Non-point Source Pollution Based on Spatial Information Technology: A Case Study in Qingdao

Keywords: non-point source pollution, modified universal soil loss equation, spatial information technology

The paper discussed the research course of the non-point source pollution and the application of spatial information technology to the non-point source pollution. Then we collected some spatial information data such as terrain, soil, water system, land use and so on, used MUSLE equation supported by geographic information system to get the risk distributing map of the non-point source pollution in Qingdao. The study met with good results. The result showed that the spatial information technology is a powerful tool to research the non-point source pollution and it can powerfully provide the decision support for management and control of the non-point source pollution.

Introduction

Non-point source pollution refers to the atmosphere, surface and soil pollutants (municipal solid waste, rural livestock manure, chemical fertilizers in farmland, pesticides, heavy metals and other toxic substances or organic compounds) take the rain as the carrier and migrate to the surface runoff in the process of runoff generation and conflux under the action of eluviation and erosion of the rainfall runoff, thereby coming into receiving waters such as rivers, lakes, reservoirs, ocean waters and so on and causing pollution [START_REF] Wang | Theory and Methods of Quantitative Study on Non-Point Pollution[END_REF]. At present, the control and management of the point source pollution such as industrial and living pollution has gradually strengthened and achieved remarkable results as people deepened understanding of the importance of their hazards, and non-point source pollution impacting on the environment have become increasingly prominent [START_REF] Vladimir | Handbook of Nonpoint Pollution Sources and Management[END_REF]. Currently, many studies have confirmed that non-point source pollution has become the major sources for the world-wide surface water and groundwater pollution, such as in the United States, there are approximately 60% pollution of the rivers and 50% pollution of the lakes related to the non-point source pollution [START_REF]National Water Quality Inventory: Report to Congress Executive Summary[END_REF]. In China, non-point source pollution is increasingly serious, the non-point source pollution in Taihu Lake and Dianchi Lake has become one of the important causation of the main lake water quality deterioration [START_REF] Jin | China Lake Environment[END_REF].

* Corresponding Author: E-mail:zjh-nhl@163.com

The study of the non-point source pollution in foreign started in the 50 to 60 years of the 20th century and the United States, Britain, Japan and other developed countries take the lead in this study. Particularly in the United States, there is a long research history, the United States is on of the few countries in the world carrying out the national system control study of the non-point source pollution [START_REF] Wang | Non-point source pollution and its management[END_REF]. The U.S. Department of Agriculture and other research institutions have developed a series of experience statistical model foe agricultural non-point source pollution, including SCS curve code and universal soil loss equation (USLE) [START_REF] Wischmeier | Predicting rainfall erosion losses. a guide to conservation planning[END_REF]. In the late 90s of the 20th century, a number of very large and powerful watershed model has been developed. These models are all large professional software integrated spatial information processing, database technology, mathematical calculation and visualization.

In these models, more well-known are the BASINS developed by the United States Environmental Protection Agency, the SWAT [START_REF] Arnold | A comprehensive surface-groundwater flow model[END_REF] developed by Arnold and the AnnAGNPS jointly developed by the U.S. Natural Resources Conservation Service and Agricultural Research Service. These models are widely used in the study of the continuous simulation to the regional rainfall -runoff, soil erosion and solute transport. They can be used to estimate the pollution load in order to identify the main pollution factors and critical source areas, and combined with the control and management measures.

The study of the non-point source pollution in China began in the late 80s of the 20th century. In this period, the study mainly focus on the study in the macroscopic characteristics, the pilot study in quantitative model of the agricultural non-point source pollution load [START_REF] Xia | Calculation of non-point source pollution load in the watershed mode[END_REF] and the investigation of the lake eutrophication. As our country is not enough aware of the harm of the non-point source pollution, moreover the study of the non-point source pollution disjoints with the total amount control programs of the pollutants from the non-point source pollution, research means of our country isolated, scattered and mainly in the field of artificial simulation study and experiment on spot [START_REF] Shan | Study on phosphorus load from a watershed with rainfall simulation method[END_REF], and the non-point source pollution control measures are very weak. Although some studies have dealt the content of non-point source pollution load assessment, modeling and GIS integration technology [START_REF] Cai | Prediction on soil nutrients losses at typical small wate rshed of three gorges area with gis[END_REF], there are very few participants, lack systematic research and do not extend deep into the management and policy research. In recent years, with the understanding of non-point source pollution, the level of our study is also rising. The research on urban runoff pollution in Beijing [START_REF] Bao | China's non-point source pollution of water environment research and prospects[END_REF] and the following study in the Yuqiao reservoir, Dianchi Lake, Taihu Lake, Chaohu Lake, Jinjiang River, Suzhou River, the Miyun Reservoir achieved fruitful results and accumulated useful experience [START_REF] Liu | Watershed non-point source pollution identification and quantification in the Yuqiao Reservoir Basin[END_REF][START_REF] Liu | Discussion on charge policy for governance of agricultural non-point source pollution in dianchi watershed[END_REF][START_REF] Pan | Effect of longterm fertilization practices on mobility of phosphorus in a huangnitu paddy soil receiving low P input in the Taihu lake region, Jiangsu province[END_REF][START_REF] Yan | Study on agricultural movement of Chaohu lake basin and non-point source pollution[END_REF][START_REF] Wang | Research of non-point sources pollution loading in suzhou creek[END_REF].

Spatial information technology is a new technology rising in the 60 years of this century and developed rapidly in

China after mid-70s, which mainly includes the theories and technologies of the remote sensing (RS), geographic information systems (GIS), Global Positioning System (GPS), and combined with computer technology and communication technology. This technology puts up spatial data collection, measurement, analysis, storage, management, display, dissemination and application.

With the development and wide application of spatial information technology, remote sensing and geographic information systems has become an important research technology of non-point source pollution. Remote sensing and geographic information systems can provide the accurate spatial information to monitor non-point source pollution and provide the surface characteristics of the the location of pollution. Remote sensing can also provides land use, vegetation cover DEM and other basic data to various non-point source pollution model, geographic information systems has strong spatial and temporal advantage in managing and analyzing geographic data, GPS data, soil data, weather data, environmental data, remote sensing data and agricultural management data. The study of non-point source pollution often requires large quantities of measured data, but nowadays the information available to both quantity and quality are still not satisfied, a lot of information to rely on field measurements is very difficult to study [START_REF] Zhen | Advances and prospects for nonpoint source pollution studies[END_REF]. The emergence of remote sensing technology provides an effective means to solve these problems and creates favorable conditions for the development of these models because remote sensing is macroscopic, short-periodic and human, material, financial, and time savings. Geographic information system has powerful data processing capabilities, enabling people to analyze and process the geo-spatial data collected by the RS technology, and promote the development of the model. Non-point source pollution combined with RS and GIS technology has become a current focus of international research.

The research process

It is an effective technique to use the spatial information model to analyze the spatial and temporal distribution of non-point source pollution. USLE, the widely applied empirical model since the 60 years of the 20th century, only requires a few parameters to effectively describe and evaluate the non-point source pollution distribution after integrated with GIS. Sivertun [START_REF] Sivertun | A GIS method to aid in non-point source critical area analysis[END_REF] developed a modified USLE model(MUSLE) to achieve the rapid identification of risk areas of the non-point source pollution on the GIS platform, and so extended the USLE applications. In this paper, we make Qingdao as the research object and use MUSLE model to study the non-point source pollution in this region.

Overview of the study area

The study area, Qingdao is situated between 119°30'~121°00'E longitude and 35°35'~37°09'N latitude. Qingdao extends over a total area of10654 km2. Qingdao receives an average annual rainfall of 775.6 mm. Mountainous, plain regions, hilly region and lowland occupy the watershed the 15.5%, 37.7%, 25.1% and 21.7% of land area receptivity.

Research Methods

This research uses MUSLE equation, which improved on the basis of USLE(Universal Soil Loss Equation)

developed by the United States Department of Agriculture (USDA) [START_REF] Sivertun | A GIS method to aid in non-point source critical area analysis[END_REF]. It will help to apply GIS to evaluate the non-point source pollution risk area. The expression is as follows:

U W S K P     (1)
In the formula (1): P is non-point source pollution risk, which expresses the risk of soil erosion and contaminants eluviations; K is the soil factor; S is the slope factor; W is the channel factor; U is the land-use factor.

Compared to the original USLE and other non-point source pollution model, the risk map generated by MUSLE equation does not give the actual pollution load information, but it can be used to identify the high erosion risk or impact area to the surface water quality in the study area. MUSLE model can be based on normal commercial GIS platforms (such as ArcView and ArcGIS) to apply and analyze. It usually uses the grid data to compute and the model requires spatial data as input parameter, including DEM, land use maps, soil maps and water maps. The process is shown in Figure 1.

compute using model 2.3.1 Data Preparation

This research requires some spatial data including DEM, land use map, soil map and river map. All input data are used Albers Equal-Area Conic projection. Due to land-use data is interpreted from the TM remote sensing image, which grid size is 30m × 30m, so other spatial data are all turned to the same size. USLE equation is recommended for small area and the grid size used in this study basically meets the need.

(1) Soil map: the main soil classes in the study area are decided by the classification value given by McElroy et al [START_REF] Mcelroy | Loading functions for assessment of water pollution from non-point sources, 1 st ed[END_REF], as shown in Table 1.

(3) Land use maps: the main land use classes in the study area are decided by the classification value given by McElroy et al [START_REF] Mcelroy | Loading functions for assessment of water pollution from non-point sources, 1 st ed[END_REF], as shown in Table 2.

The results

In this study, the model calculation is based on ArcGIS 9.2 platform, the main application of the GIS includes conversion function between vector data and raster data, re-classification function, search distance function and grid data computing function. First of all, we convert all layers to 30m × 30m ESRI Grid format, then calculate in turn the slope length factor-S value (Figure 2), soil factor-K value (Figure 3), land-use factor-U value (Figure 4) and river factor-W value( Figure 5), which are the input parameters of the MUSLE model. according to statistical rules, using the mean and 1 and 2 standard deviations, we make the result into four categories for statistical and mapping and get the non-point source pollution risk classification tables (Table 3) and distribution map ( Figure 6).

Conclusion and Outlook

This study describes the development process and the basic method of the study of the non-point source pollution and introduces the application of the spatial information technology in the non-point source pollution research. On this basis, we collect a variety of spatial data of the study area and use the MUSLE equation to divide the study area into safe region, low risk region, middle risk region and high risk region. The results show that the more serious regions of the non-point source pollution in the study area distribute in mountainous and hilly areas, where is steeper, has more developed river system and is prone to runoff, so these area has higher risk to bring non-point source pollution.

In this study, we use the spatial model to study the non-point source pollution. It is showed that the spatial model requires few amount of data, saves time and labor, is simple and with full scientific. Although it can not calculate a specific non-point source pollution load, it gives the risk area of the non-point source pollution. So this study provide a strong basis to facilitate the non-point source pollution management and control for environmental managers and policy makers. We can see that the spatial information technology is a powerful tool to study the non-point source pollution and will be indispensable and play an important role in the future.

Acknowledgments

Project supported by the Science and Technology Projects of Qingdao (09-1-1-53-nsh and 08-2-1-36-nsh).

Figure 1 .

 1 Figure 1. The processing map of the non-point source pollution risk based on spatial information technology

Figure 2 .Figure 3 .Figure 4 .Figure 5 .

 2345 Figure 2. The map of slope length factor

Table 1 .

 1 Soil class in the study area Slope length map: in the ArcGIS 9.2, we load the Spatial Analyst module. The slope map is generated after filling depression for DEM and named slope, then we generate flow accumulation map in the Hydrology module.Before get slope length map, we must set the grid value of the rivers, lakes and other water bodies as 0 in order to avoid producing high values which do not meet the fact. The treated accumulation map is named Accumulation.

	Class	Grid value
	Till	0.380
	Water	1.000
	Gravel	0.200
	Clay	0.450

Table 2 .

 2 Land use class in the study area

	Class	Grid value
	Water	0.000
	Forest	0.005
	Pasture	0.010

Table 3 .

 3 the non-point source pollution risk classification tables

	Risk	Grid value
	classification

Figure 6. the non-point source pollution risk distribution map of the study area


Dongxian He
email: he_dongxian@hotmail.com

Daoliang Li

Jie Bao

Shaokun Lu

A CDMA-based Soil-quality Monitoring System for Mineland Reclamation

Keywords: CDMA service, Mineland reclamation quality evaluate, Soil water content, VPN connection

After the mining of mineral resource, mineland resources utilization became increasingly complicated. Soil and planting information can be used as the indicators to evaluate mineland reclamation level. In order to obtain the soil and planting information in real time, a CDMA-based soil-quality monitoring system was developed to wirelessly monitor air/soil environment and plant image in remote or local area. As a system test, a monitoring system equipping one network camera and four sensors including air temperature, relative humidity, soil temperature, and soil water content was deployed at a national mineland reclamation demonstration in Fuxin, Liaoning and communicated with a remote server in Beijing using CDMA service with IPsec-based VPN connection. Via a one-year testing, the CDMA-based soil-quality monitoring system as a dynamic infrastructure and available tool showed a good performance for mineland reclamation quality evaluate.

Introduction

After the mining of mineral resource, mineland resources utilization became increasingly complicated. In order to obtain the mineland detail information in real time for the reclamation in land administrative department of mining enterprise concerned, soil and planting information changes of mineland area is very important and can be used as the indicators to evaluate mineland reclamation level. How to get the indicator information and how to sign the reclamation quality is concerned to sustainable development of mineland reclamation utilization. Therefore, efficiency and low cost way to get mineland reclamation quality change is essential and required for mining enterprise and land administrative department. Mining land reclamation by the quality indicator technology developed in recent decade is based on wireless network technology, GIS, and remote sensing technology can be used as the integration charge of indicators of reclamation quality index. In the past 10 years, mineland reclamation in the quality indicator technology has carried out a significant development and has achieved remarkable achievements [START_REF] Bradshaw | The use of natural processes in reclamation -advantages and difficulties[END_REF][START_REF] He | An environmental measurement system using wireless networks and web-server-embedded technology[END_REF][START_REF] Chena | Assessing wastewater reclamation potential by neural network model[END_REF][START_REF] Fukatsu | Field monitoring using sensor-nodes with a web server[END_REF][START_REF] Sevkiye | An analysis on the efficient applicability of the land readjustment (LR) method in Turkey[END_REF][START_REF] Anderson | Influence of reclamation management practices on microbial biomass carbon and soil organic carbon accumulation in semiarid mined lands of Wyoming[END_REF][START_REF] Philip | Changes in the extent of surface mining and reclamation in the central appalachians detected using a 1976-2006 Landsat time series[END_REF][START_REF] Li | Calcareous sodic soil reclamation as affected by corn stalk application and incubation: a laboratory study[END_REF]. Therefore, development of a dynamic monitoring system of mineland reclamation for obtaining the soil and planting information in an efficiency and low cost way is becoming required and becoming more important.

2

Materials and Method

System Configuration

The CDMA-based soil-quality monitoring system consists of a CDMA module with IPsec-based VPN function (InRouter210C, Beijing Inhand Co., China), a network bullet camera (IP7330, Vivotek Co., Taiwan), a web-server-embedded chip (PICNIC2.0, TriState Co., Japan), switching hub (TL-SF1005D, TP-Link Tech. Co., China), a sensor module, and a solar power supply module (SPV-DCRC-20W, Beijing Sangpu Co., China) (Fig. 1). The sensor module is including temperature (PT1000, Hayashi Elect. Co., Japan), relative humidity (CHS-UPS, TDK Co., Japan), soil temperature (PT1000, Hayashi Elect. Co., Japan) and soil water content (EC-5, Decagon Co., US). The power supply module consists of a power control device, two 20W monocrystalline silicon photovoltaic panels, a 75AH battery (LC-X1275ST 75AH, Panasonic Co. Japan), and a timer (ZN48T-12V, Symore Co. China). The realtime data and images collected by the sensors and network camera are dynamically and wirelessly transported to a remote server via TCP/IP. The CDMA-based soilquality monitoring system was operated between appointed duration by a timer.

Topological Architecture

Each CDMA-based soil-quality monitoring system as an isolated local area network is connected to Internet via CDMA service by Telecom China (Fig. 1). That means the monitoring system as a sensing network node can be constructed to a large-scale wireless sensing network under CDMA signal covered areas. The monitoring system is also easy to increase sensor channels and network cameras as well as the CDMA bandwidth depending. In this monitoring system, four sensor channels were integrated into the web-server-embedded chip within one LAN IP and each camera has a LAN IP. All data and images are communicated by dynamic HTTP file and identified dynamically and storied by one to five remote servers. The CDMA module will be dynamically connected to the remote servers via IPsec-based VPN security technology. In order to identify the specified the remote servers, the remote VPN routers have to use a dynamic domain connection or a fixed global IP to support the remote VPN calling. The monitoring systems deployed anywhere will become to a local network connection because of the IPsec-based VPN tunnels connected. Therefore, all accredited users or clients can visit or manage the wireless sensing devices anywhere and anytime under Internet environment.

Testing Environment

A CDMA-based soil-quality monitoring system was deployed in a national mineland reclamation demonstration zones in Fuxin, Liaoning province for obtaining air and soil environment, and plant images. In this testing, one remote server with IPsecbased VPN router (BV-601, Nesco Co., China) is deployed in China Agricultural University located in Beijing, China. The testing experiments were conducted for one year.

Results and Discussion

Dynamic Image Transportation

A Java applet program was installed in the remote server to download image from the network camera via HTTP every minute and stored in the remote server. In order to monitor more areas, the network camera has setup in two benchmark level for measuring height of plants and leaf area index (Fig. 2). The image processing results will be described by other papers and omitted in this paper. All accredited users can visit or manage the network camera modules and any clients can visit the real-time or historical images from website anywhere and anytime under Internet environment. Therefore, this network camera module with an enough-quality dynamic image requirement at low cost is suitable to the monitoring system.

Zoom Out Zoom Out Zoom In Zoom In

Dynamic Environment Data Transportation

The environmental data such as temperature, relative humidity, soil temperature, and soil water content were dynamically storied or issued in webpage or Extensible Markup Language (XML) file by a special JAVA applet program in the remote servers (Fig. 3). The 10-bit analog signals of the sensors were obtained by the webserver-embedded chip without any memory and firmware. Therefore, the legacy problem of the USB-based or RS232-based devices caused from the firmware update can be solved in this monitoring system. The environmental data were also used to combine with image processing results such as the shape and color features to construct an artificial neural network model based on back-propagation algorism for identifying the growth measurement and nutrition detection. The sensor module is easy to increase the sensor channels with a little influence in data communication, because the narrow CDMA bandwidth will strongly influence the image communication in this monitoring system.

Network Communication and Electronic Consumption

The monitoring system were communicated with the remote server in Beijing by a 20-30 Kbps access speed with over 25-27 signal quality level of CDMA services. The real-time image with 640*480 pixels resolution captured by the network camera and compressed to JPEG file were dynamically transported to remote server during the system operation. The all data and images obtained at same time were identified by IP which is transferred to take turns and with omitted disconnecting IP. In this case, less than 2% of packet loss rate was resulted. Local user can visit the remote server via LAN, remote users can visit the remote server through Internet. These two methods have nothing to do with the CDMA. Therefore, remote user access speed is limited by the bandwidth of Internet hotspot such as ADSL network in this case. The remote server can support simultaneous accesses of 20 remote users. However, the numbers of concurrent local users do not have measurements and no restrictions on. The monitoring system using 12V DC power supply and timed operation by solar supply system consisting of two 20W solar panels and a 75Ah battery were consumed about 22W capacity with 1.8A electric current. This power supply module can drive the monitoring system for about 10 hours in sunshine day and for about 5 hours in cloudy day. Therefore, four hours operation divided to two hours in morning or afternoon were connected in this system testing.

Conclusion

The real-time environment data and images were dynamically collected in remote area by the CDMA-based soil-quality monitoring system. This CDMA-based soilquality monitoring system based on web-server-embedded technology and CDMA service with IPsec-based VPN function as a node infrastructure is useful to construct a ubiquitous wireless sensing network in high-security for mineland reclamation.

Fig. 1 .

 1 Fig. 1. The CDMA-based soil-quality monitoring system.

Fig. 2 .

 2 Fig. 2. The dynamic images obtained by the CDMA-based soil-quality monitoring system.

Fig. 3 .

 3 Fig. 3. Environmental data measured by the CDMA-based soil-quality monitoring system.

Acknowledgments

This work has been supported by International Technology Cooperation Program (2007DFA91050).


Lingxian Zhang

Xue Liu

Zetian Fu

Daoliang Li

Evaluation of the rural informatization level in the central China based on Catastrophe Progression Method

Keywords:

This paper developed a methodology based on the catastrophe theory for estimating the rural informatization level in central China, and took evaluation of the rural informatization level among the six provinces of central China as an example to test the effectiveness of the method. Taking data from reference and constructing the hierarchy based on catastrophe progression method, it was calculated the scores of rural informatization level among the six provinces of central China using normalizing formula. The results are found to be coincident with practical situation, so it proves the catastrophe progression method works well.

Introduction

As rural informatization is an important component of the national informatization development process, it is the basis for decision-making to make an objective assessment and analysis to national and regional level of development of rural informatization.

There have many methods for multiple object comprehensive evaluation [START_REF] Bazewicz | Information systems paradigms for design, engineering and education, Cybernetics and Systems'94[END_REF][START_REF] Yu | An integrated evaluation system for personal informatization levels and their maturity measurement: Korean motors company case[END_REF][START_REF] Chang | Research on integrated information system of regional enterprise informationization market[END_REF][START_REF] Hu | A study on the informatization evaluation index system of manufacturing enterprises and evaluation standard[END_REF], such as factor analysis, RITE's Index of Information, Information Society Index(ISI), UN's Information Utilization Potentials, fuzzy evaluation, analytic hierarchy process [START_REF] Zhang | E-learning adoption intention and its key influence factors based on innovation adoption theory[END_REF] and so on. But some methods have defects with more subjectivity on weight decision or too complex processes in calculation. The paper introduces the main steps and idea of a method using catastrophe theory (CT), namely, called catastrophe progression method (CPM). Its characteristic is combining catastrophe theory with fuzzy mathematics, and only considers the relative importance of the indices, so the method avoids the subjectivity for weight decision. Catastrophe progression method also has advantages in solving problems of fuzzy multiple object decision because the catastrophe progression is a multidimensional fuzzy membership function. With these characteristics, the method is easier and the results are more precise [START_REF] Li | Application of catastrophe progression method to evaluation of regional ecosystem health[END_REF].

2 Lingxian Zhang, Xue Liu, Zetian Fu, Daoliang Li *

The evaluation methodology for the rural informatization level in central China

Evaluation index system of the rural informatization level

According as the composition project of national informatization index issued by Information & Industry Ministry of China on July 29th, 2001, taking the literature [START_REF] Huang | Study on analysis evaluation for China's rural informatization in six provinces of the central[END_REF] from reference, this paper presents the evaluation index system for the rural informatization level in the central China, which includes the 5 categories (secondary indices) of the Economic strength, Information infrastructure, Information terminal equipment, Human resource and Information utilization, and 14 tertiary indices (Table 1).

Assessment model of the rural informatization level

Evaluation of the rural informatization level in the central China based on Catastrophe Progression Method 3

Establishment of the hierarchical structural model. According to the regulation of indices grouping aforementioned, we constructed the catastrophe progression model for informatization level of rural areas in central China by four hierarchies, which was broken down in a manner as shown in Fig. 1. Confirming the catastrophe type in every catastrophe subordinate system. Catastrophe theory analyses critical degenerate points of the potential function, where not just the first derivative, but one or more higher derivatives of the potential function are also zero. There are seven fundamental types of Catastrophe models presented,three types of which, such as elliptic umbilic catastrophe, hyperbolic umbilic catastrophe, and parabolic umbilic catastrophe, are under behavior variation of two dimension. Among seven fundamental types,only four common catastrophe types are cusp catastrophe, swallowtail catastrophe and butterfly catastrophe, respectively, which potential functions are as follows [START_REF] Huang | Application of catastrophe progression method to sustainable usage of water Evaluation of the rural informatization level in the central China based on Catastrophe Progression Method 9 resource[END_REF]: In the catastrophe progression model for informatization level assessment of rural areas, the catastrophe type was confirmed in every evaluation index layer (see Fig. 1). There were three catastrophe type, such as cusp catastrophe (one index divided into two sub-indices), swallowtail catastrophe (into three sub-indices) and butterfly catastrophe (into four sub-indices).

Fold catastrophe: ax x x f   3 ) ( . Cusp catastrophe: bx ax x x f    2 4 ) ( . Swallowtail catastrophe:   cx bx ax x x f     2 3 2 1 3 1 5 1 . Butterfly catastrophe:   dx cx bx ax x x f     

Normalizing the control variables of the catastrophe model

Standardizing data. The standardized data are in the value range of from 0 to1. Equation ( 1) is adopted if the index is positive, equation ( 2) is applied if negative. a) For The-Larger-The-Better indices, let

      j min j max j min x x x x y ij ij        j ij j x x x max min   (1) b) For The-Smaller-The-Better indices, let       j j ij j ij x x x x y min max max        j ij j x x x max min   (2)
Where j is a sequence number for catastrophe subordinate systems, i is a serial number of control variables in a catastrophe subordinate system; ij x is merely the values of x corresponding to control variables in a hierarchy of i and j, respectively; or upper limit values of x corresponding to control variables in a catastrophe subordinate system, respectively. Formulas for normalization of control variables. For the cusp, fold, swallowtail, and butterfly catastrophe system, the decomposition forms of the equation of the bifurcation point set and the formulas are showed in Table 2.

  ax x x f   3 2 3x a   a x a  Cusp 2   bx ax x x f    2 4 3 2 8 , 6 x b x a    3 , b x a x b a   Swallowtail 3   cx bx ax x x f     2 3 5 2 1 3 1 5 1 4 3 2 3 , 8 , 6 x c x b x a      4 3 , , c x b x a x c b a    Butterfly 4   dx cx bx ax x x f      2 3 4 6 2 1 3 1 4 1 6 1 5 4 3 2 4 , 15 , 20 , 10 x d x c x b x a       5 4 3 , , , d x c x b x a x d c b a    
Principles of finding values of catastrophe subordinate functions. During the process of computation, we must follow two principles, i.e., a complementary and a non-complementary principle. The non-complementary principle means that the smallest of the state variable values corresponding to the control variables (x a , x b , x c and x d ) is chosen as the state variable value of the whole system; However, the complementary principle implies x=(x a +x b +x c +x d )/4. Hierarchically doing the calculation in the same way, the value of the overall catastrophe subordinate function can be found [START_REF] Zhang | Application of the catastrophe progression method in predicting coal and gas outburst[END_REF].

Results and discussion

In the light of the evaluation methodology of rural informatization level aforementioned, the paper take evaluation of rural informatization level among the six provinces of central China as an example to test the effectiveness of the Catastrophe Progression Method , and referred to interrelated data on Beijing and nationally average in the mainland of China.

The central China is a central region, including six provinces, such as Shanxi, Anhui, Jiangxi, Henan, Hubei, and Hunan (see Fig. 2), with total population of 360 million people in the end of 2008. It plays an important role on the pattern of economic and social development in China, which land area is 1.03 million square kilometers, Gross Regional Product of which reached 6.3188 trillion RMB Yuan in 2008, accounting for 19.3% of GDP in China. Taking data from reference [START_REF] Huang | Study on analysis evaluation for China's rural informatization in six provinces of the central[END_REF], the region′s overall number of communication facilities was in place of four indices of information infrastructure in rural areas, telecommunications and other information transfer service employees in place of indices about the number of rural information service personnel, the proportion of local comprehensive internet users in place of the proportion of internet users in rural areas.

Table 3 showed some practical index values of the rural informatization level among the six provinces of central in 2006, Table 4 showed the standardized data of practical index values for the rural informatization level from the six provinces of central China transformed by equations ( 1) and (2). At the same time, the scores of rural informatization level among the six provinces of central China were calculated using normalizing formula (see Table 5).

Conclusion

The paper take evaluation of rural informatization level among the six provinces of central China as an example to verify the effectiveness of the method. Based on catastrophe progression method, it calculated the scores of rural informatization level among the six provinces of central China using normalizing formula. The results are found to be coincident with practical situation, so it proves the catastrophe progression method works well. From the calculation process, it was found that catastrophe progression method works simply and quickly. So the method was proved feasible in practice and provides us a new way in solving the problems of multiple objects comprehensive evaluation [START_REF] Li | Application of catastrophe progression method to evaluation of regional ecosystem health[END_REF].

Fig. 1 .

 1 Fig.1. Catastrophe progression model for rural informatization level in the central China.

.

  Where f(x) is the potential function of the state variable x, and a, b, c and d are control variables of the state variable.

x

  max are lower limit Evaluation of the rural informatization level in the central China based on Catastrophe Progression Method 5

Fig. 2 .

 2 Fig.2. Space diagram of the six provinces of central China.

Table 1 .

 1 Evaluation index system for the rural informatization level in the central China.

	Evaluation objective	Category	Indices	Index abbreviation
			GDP per capita	GDP
		Economic strength	Revenue per capita Per capita income of rural households	RP PI
	informatization	Information infrastructure	Long-distance telephone exchange capacity Mobile switching capacity Length of long-distance optical cable Internet broadband access ports	TC MC OC IB
	level	Information	Color television ownership	TO
	of rural areas	terminal equipment Human resource	Computer ownership Mobile telephone ownership Percentage of literate population to total aged 15 and over Number of rural information service employees	CO MO LP IEm
		Information utilization	Proportion of per capita information consumption expenditure of rural households Proportion of Internet users in rural areas	IEx IU

Table 2 .

 2 Catastrophe models under behavior variation of one dimension.

	Catastrophe type	Dimensions of control variation	Potential function	Bifurcation set	Normalization formula
	Fold	1

  Lingxian Zhang, Xue Liu, Zetian Fu, Daoliang Li *

	Shanxi	0.800	1	Hubei	0.773	2
	Anhui	0.222	6	Hunan	0.714	4
	Jiangxi	0.722	3	Nationally average	0.793

Acknowledgments. Thanks are due to National Science & Technology Support Program (2008BAB38B06) and Project from the Ministry of Agriculture of China (The twelfth five-year plan of agriculture and rural informatization development in China (2011-2015)) for financial support.

Evaluation of the rural informatization level in the central China based on Catastrophe Progression Method 7

Table 3. Collected data from the six provinces of central China [START_REF] Huang | Study on analysis evaluation for China's rural informatization in six provinces of the central[END_REF].

Region x 11 x 12 x 13 X 21 X 22 X 23 X 24 X 31 X 32 X 33 X 41 X 42 X 51 X 52 5 showed catastrophe progression values of the rural informatization level among the six provinces in central China. From Table 5, catastrophe progression values (CPV) of the rural informatization level averaged 0.793 in the mainland of China; CPV is bigger in only one province of the six provinces in the central China than nationally average in the mainland of China. CPV is larger in Beijing Municipality than in each region of the six provinces in the central China. Among the six provinces in the central China, CPV of Shanxi province is first with 0.800, Hubei province second with 0.773, Jiangxi province third with 0.722, Hunan province fourth with 0.714, Henan province and Anhui province least with 0.354 and 0.222, respectively. The results are found to be coincident with practical situation.