Hongbo Qiao
email: qiaohb@126.com

, Weng Mei

Yafei Yang

Wang Yong

Jishuai Zhang

Y U Hua

STUDY ON RELATIONSHIP BETWEEN TOBACCO CANOPY SPECTRA AND LAI

Keywords: Tobacco, spectra, LAI

N nutrition is one of the most important limitation factors for crop growth and yield. Precise and timely monitoring and detection of crop N nutrient conditions is necessary for improving the efficiency of N nutrition using and crop management, reducing environmental pollution caused by over nitrogen fertilizer application. In this paper the canopy reflectance spectra during the whole growth period on tobacco field plots treated with different nitrogen levels were periodically and continually measured. LAI of tobacco of several important growth periods was meanwhile measured. The results showed that tobacco canopy spectral reflectance of different growing periods changed regularly with the increase of nitrogen fertilizer application.The canopy spectral reflectance increased in 7l0~1000 nm,while decreased in 460~680 nm. There was high correlations between NDVI and LAI. The research supply theoretical foundation for tobacco N management.

Introduction

Crop canopy spectrometry is a harmless remote determination technique. It is a current effective remote monitoring method of crop growth state. The reflectance spectral curve of plant has significant characteristics. For same plant, spectral curve varies with fertility levels and development stage [START_REF] Shen | Review of the application of vegetation remote sensing[END_REF] . Thus, spectral curve characteristics could reflect plant growth state.

Biochemical parameters of plant canopy estimated through remote technique can understand the productivity of plant and the effectiveness of soil nutrients [START_REF] Curran | Remote sensing of foliar chemistry[END_REF][START_REF] Hinzman | Effects of nitrogen fertilization on growth and reflectance characteristics of winter wheat[END_REF] . Svetlana et.al. [START_REF] Svetlana | Changes in the first derivatives of leaf reflectance spectra of various plants induced by variations of chlorophyll content[END_REF] determined the chlorophyll content in leaf of wheat, maize, beet and grape, established the first-order differentiation of canopy spectrum and the regression equation model of chlorophyll content, which could estimated the chlorophyll content in different crop species. Daughtry et.al. [START_REF] Daughtry | Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance[END_REF] determined the canopy spectrum of maize at 8 nitrogen fertilizer levels, and the screened MCARI and NIR/Green vegetation index were significantly related to the chlorophyll content in leaf. The chlorophyll content in canopy was very sensitive to nitrogen reserve in soil [START_REF] Hinzman | Effects of nitrogen fertilization on growth and reflectance characteristics of winter wheat[END_REF] . Thus, chlorophyll content could be used to monitor the abundance and deficiency of nitrogen in soil. Elizabeth et.al. [START_REF] Elizabeth | Non-destructive estimation of potato leaf chlorophyll from canopy hyperspectral reflectance using the inverted PROSAIL model[END_REF] estimated the chlorophyll content in potato through PROSAIL model, which could predict the nitrogen content in soil at late growth stage.

LAI is one of most basic parameters to reflect the structure of vegetation canopy, and is also key determination factor of biomass and yield [START_REF] Bouman | Linking physical remote sensing model with crop growth simulation models, applied for sugar beet[END_REF] . Wu et.al. [START_REF] Wu | Assessing broadband vegetation indices and QuickBird data in estimating leaf area index of corn and potato canopies[END_REF] established the correlation model of vegetation index with maize/potato LAI based on QuickBird satellite image, and modified soil adjusted vegetation index (MSAVI) was more accurate (R 2 =0.63 and R 2 =0.79). in the study of Daughtry et.al. [START_REF] Daughtry | Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance[END_REF] , in maize at different nitrogen fertilizer levels, the reflectance varied with the increase in LAI and chlorophyll content (at maximum extent near red edge 715nm), and moved toward long wave. Hung et.al. [START_REF] Hung | Assessment of rice leaf growth and nitrogen status by hyperspectral canopy reflectance and partial least square regress[END_REF] established the partial least square regression model of rice reflectance spectrum with canopy LAI (R 2 >0.79) to better evaluate the growth state of rice mainly within near-infrared, visible light and red edge 707nm range.

Above studies verified the estimation of chlorophyll and LAI through remote technique, mainly for wheat and maize, but seldom for tobacco. Tobacco is harvested through leaf, and it is significant to monitor its LAI and chlorophyll content. This study tested tobacco under different fertilization conditions, defined the change law of chlorophyll content/LAI/canopy spectral characteristics, determined the correlation of vegetation index with chlorophyll content and LAI at different waveband combinations, confirmed whether canopy spectrum could be used to estimate LAI and chlorophyll content, and provided technological means for quick harmless large-area obtaining of information on the growth state of tobacco.

Materials and Methods

Experiment Design

5 levels of nitrogen fertilizer: N0 (0kg/hm2 ), N1 (22.5kg/hm 2 ), N2 (45kg/hm 2 ), N3 (67.5kg/hm 2 ) and N4 (90kg/hm 2 ). 3 levels of organics: Low (225 kg/hm 2 ), middle (375kg/hm 2 ) and high (525kg/hm 2 ). No base fertilizer, repeat each treatment for 3 times, and randomly divide into 45 blocks (12m×6m). Qin tobacco #96, at a row spacing of 1.2m and a plant spacing of 0.6m.

Experiment 1: At National Modern Tobacco Demonstrative Area in Mianchi County, Sanmenxia City, Henan, China (north latitude 34º37´52.96″ and east longitude 111º44´28.24″). Surface layer 0~20cm, soil organics 12.3g/kg, total nitrogen 0.74mg/kg, effective phosphorus 9.7mg/kg, quick-acting potassium 120mg/kg, and soil pH8.2.

Experiment 2: At Xuedian Village, Zhuyang Town, Lingbao City, Henan, China (north latitude 34º17´13.56″ and east longitude 110º44´29.04″). Surface layer 0~20cm, soil organics 11.9g/kg, total nitrogen 0.8mg/kg, effective phosphorus 7.2mg/kg, quick-acting potassium 132mg/kg, and soil pH7.9.

Spectral Data collection

This study made spectrometry at characteristic growth stage of tobacco: Seedling stage (June 20, 2009), resettling growth stage (July 19, 2009), vigorous growth stage (August 11, 2009), early-maturity stage (September 5, 2009) and maturity stage (September 23, 2009). Canopy spectrometer adopted portable hyperspectral radiometer of American ASD Co. (HandHeld), spectral range 325~1050nm, spectral resolution 1.4nm, field angle 25º. Make spectrometry in 3 tobacco plants of same growth state at each treatment, determine canopy spectral reflectance at 10:00~14:00 in sunny windless climate, with sensor probe vertically downwards and at 60cm vertically away from canopy top. Calibrate standard white plate before each determination, record data in 20 groups, and regard their mean as spectral reflectance value of this treatment.

Determination of Leaf Area and Chlorophyll Content

LAI=Length×width×0.6345 [START_REF] Liu | Tobacco Cultivation[END_REF] Chlorophyll content: SPAD-502 chlorophyll detector of Japan MINOLTA Co. was used to determine spectrum and chlorophyll content in tobacco, i.e. Specialty Products Agricultural Division (SPAD). According to the absorptivity of leaf chlorophyll to colored light, SPAD-502 chlorophyll detector determined the chlorophyll content in leaf by determining the intensity of emission light at a certain wavelength and that of light through leaf. SPAD-502 chlorophyll detector had 2 emission light sources to emit red light of maximum 650nm and infrared light of maximum 940nm respectively. Chlorophyll absorbed red light of 650nm, but did not absorb infrared light of 940nm which was emitted and received to mainly eliminate the influence of leaf thickness on determination results. After reaching leaf, some of red light was absorbed by leaf chlorophyll, and the remaining passed through leaf and was converted into electric signal through receiver. The chlorophyll content in leaf was determined by comparing the intensity of emission light with that of light received by receiver (SPAD unit):

SPAD = Klg 0 0 / / t t IR IR R R ⎡ ⎤ ⎢ ⎥ ⎣ ⎦
Wherein, K: Constant; IRt: Intensity of received 940nm infrared light; IR0: Intensity of emission infrared light; Rt: Intensity of received 650nm red light; and R0: Intensity of emission red light.

Data Analysis

This study analyzed the correlation of several spectral parameters with chlorophyll content and LAI in tobacco canopy. This study selected significantly-related sensitive waveband and spectral parameters, established chlorophyll content and LAI monitoring model through regression analysis, and optimized the equation through estimated standard error (SE) and determination coefficient (R 2 ). Spectral parameters were shown in Table 1. Table 1.Hyperspectral parameters used in this study

Results and Analysis

Raw Spectral Characteristics of Tobacco Canopy

Tobacco grew slowly, rapidly and then slowly at early, middle and late growth stage respectively. For example, the leaf area and plant height of tobacco increased slowly at <30, 30~60 and >60 days after the transplantation respectively, i.e. overall like S-shaped growth curve.

RED)] - (NIR * 8 - 1) NIR * (2 - 1 NIR * (1/2)[2 = MSAVI
At different growth stages, spectral curve of tobacco canopy changed at both same and different tendency, i.e. same at overall tendency but greatly different at local waveband. As shown in Fig. 1, at seedling stage, the LAI and chlorophyll content was very small, and the reflectance of visible light (450~760nm) was higher than that at other growth stages due to mulching film. Thus, the canopy reflectance of visible light and near-infrared light was mainly influenced by the content of chlorophyll a/b, carotenoid and xanthophylls, and the optical characteristics of leaf tissue respectively. With growth progress, there was a continuous increase in tobacco biomass and chlorophyll content, and an increase in LAI. Canopy spectral reflectance decreased to a certain extent within visible light, increased rapidly within near-infrared area, peaked at vigorous growth stage, and gradually decreased at maturity stage with partial leaf harvest. different N levels(Pre-mature stage) As shown by data at 4 growth stages, the spectral characteristics of tobacco canopy changed roughly at same tendency, but at significant difference for different nitrogen fertilizer levels (and very significantly even at early-maturity stage (Fig. 2), i.e. P>0.05 at t test (760nm, 800nm, 850nm and 960nm respectively).

Correlation of Vegetation Index with Chlorophyll Content and LAI

As shown by Table 2, LAI was very significantly related to RVI and NDVI (green); and significantly related to NDVI, SAVI and RDVI respectively. NDVI (green) was a spectral parameter closely related to leaf chlorophyll content and LAI.

Regression Equation of NDVI (green) with Chlorophyll Content and LAI

According to the NDVI (green), regression equation was established for NDVI (green) with LAI. As shown by regression equation with an independent variable of NDVI (green) (Table 3), a regression equation could be established for LAI. Determination coefficient (R 2 ) was 0.568 significantly for the regression equation of LAI, and regression coefficient was also significant. Thus, the established regression equation was effective, and NDVI (green) could be used to estimate LAI.

Conclusion and Discussion

Nitrogen is necessary for the growth, development, quality and yield of tobacco. However, excessive nitrogen caused spindly growth, formed black tobacco leaf, and greatly lowered tobacco quality. LAI is important index of tobacco photosynthesis and metabolism, and closely related to final yield and quality. Thus, the fertilization state of field and the growth information of tobacco must be obtained rapidly to support in time the decision on production management. Hyperspectral remote technique is a determination technique to rapidly obtain the growth state of crop.

This study preliminarily described the spectral characteristics of tobacco canopy under different fertilities at different fertilization levels at different growth stages, analyzed the correlation of vegetation index with LAI based on canopy spectrum, and established estimation model. Under different fertilization conditions, the spectral reflectance of tobacco canopy was significantly different, NDVI (green) was significantly related to LAI, and the regression coefficient, association probability and determination coefficient were all significant for established regression. Thus, the screened variable of hyperspectral characteristics was reliable, and relevant model could be used to estimate chlorophyll content and LAI. In the past, many studies were made on wheat, maize and rice to screen corresponding characteristic variable and statistical model [START_REF] Daughtry | Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance[END_REF][START_REF] Feng | Monitoring Leaf Nitrogen Concentration by Hyperspectral Remote Sensing in Wheat[END_REF][START_REF] Vaesen | Ground-measured spectral signatures as indicators of ground cover and leaf area index: the case of paddy rice[END_REF] . The ratio of near-infrared light to green/red light could significantly improve the sensitivity to parameters of canopy structure. The information of near-infrared light could standardize the established vegetation index, eliminate the influence of canopy structure as far as possible (such as leaf structure, leaf direction and radiation angle), and effectively highlight the information of sensitive waveband (such as green/red light) [START_REF] Xue | Relationship Between Spectral Vegetation Indices and LAI In Rice[END_REF] . In this study, the screened NDVI (green) was also based on vegetation index of near-infrared and green lights, and proven also effective for tobacco. In wheat study of Aparicio et.al. [START_REF] Aparicio | Relationship between growth traits and spectral veg2etation indices in Durum wheat[END_REF] , the correlation of LAI with vegetation index was not affected by planting measures, species or planting area. But such results on tobacco should be verified through further test.

In this study, SPAD-502 chlorophyll detector was used to rapidly harmlessly determine the chlorophyll content in tobacco. In soybean/maize study of John et.al. [START_REF] John | Calibration of the Minolta SPAD-502 leaf chlorophyll meter[END_REF] , the actual amount of chlorophyll extract was very significantly related to the detected data through SPAD chlorophyll detector (R 2 =0.94). Such results appeared in the study of Carlos et.al. [START_REF] Carlos | Inter-relationships of applied nitrogen, SPAD, and yield of leafy and non-leafy maize genotypes[END_REF] . In the study of Zeng Jianmin et.al. [START_REF] Zeng | Chlorophyll Content Determination and its Relationship with SPAD Readings in Flue-cured Tobacco[END_REF] , tobacco chlorophyll was extracted through 95% water solution of acetone and anhydrous ethanol (2:1 volume); the chlorophyll content in leaf and SPAD value were very significantly positive correlation; and the chlorophyll content in leaf was estimated through SPAD value to further track the nitrogen nutritional state in tobacco.

In this study, canopy hyperspectral monitored the chlorophyll content and LAI tobacco, rapidly effectively monitored the growth state of tobacco, and provided technological support for tobacco planting and fertilizer management. Of course, the fitting equation should be further verified through the tests at different ecosystems and different species, so as to popularize the model and provide theoretic support for large-scale monitoring.

  -RED)/(NIR+RED) NDVIgreen=(NIR-Green)/(NIR+Green) SAVI=1.5*(NIR-RED)/(NIR+RED+0.5) OSAVI=(NIR-RED)/(NIR+RED+0.16) MCARI=[(R 700 -R 670 )-0.2*(R 700 -R 500 )]*(R 700 / 670 )

Fig 1 .Fig 2 .

 12 Fig 1.Canopy spectral characteristic curves of tobacco at different growth stages

Tab 2 .Tab 3 .

 23 Correlation analyses between vegetable index and chlorophyll, LAI (n=90) Regression equations of chlorophyll and LAI by NDVI(green)

+ +

Acknowledgements

This work was financially supported by the National Key Technology Research and Development Program of China (No. (2006BAD08A01)) and Henan Tobacco Company Projects Grant (HNKJ200814) .


Rong Dai
email: dai_yun2002@sina.com

Performance Forecasting of Piston Element in Motorcycle Engine Based on BP Neural Network

Keywords: Motorcycle, Engine piston, BP neural network, Forecast

The piston performance affects the performance of the Motorcycle. In order to forecast the piston performance effectively, the performance forecast model based on BP neural network is presented. According to the characteristics of piston performance, the training samples are made up of the orthogonal experimental data, which are utilized to ensure higher generalization of BP neural network. And then the trained BP neural network is used to forecast test example. At the same time, the value of test samples is compared with the output of BP network model results. The results show that the output precision of BP neural network is high, and using the BP neural network to forecast the piston performance is practicable and effective.

Introduction

Piston of motorcycle engine is an important part. Its design that is good or bad is related to the engine fuel consumption, power, oil consumption, noise and many other indicators. In the practice of design piston, piston is often developed through repeated tests and experiments relying on experience, this method requires a long development cycle and more cost. Design errors of other theoretical approaches are worse, and they are not competent for the practice of piston design. Parameters that are affecting the performance of pistons are more, their values are difficult to determine. In order to seek a better performance of the piston, and often orthogonal test approach can be adopted for experimental design and analysis. Orthogonal test method not only can reduce the times of tests, but also can fully reflect the impact of each factor to achieve more reasonable optimization program with less cost. Although the prediction method based on neural networks can not determine the certain mathematical function, but it can give the algorithm and the structural parameters, the connections rights that inherent laws between input and output are reflected are determined by training, the network output can be predicted through them. The neural network can not only predict the experimental results, but also establish the foundation for performance simulation analysis. Because of possessing these advantages, many scholars have conducted the application research in neural network forecasting, such as literature [START_REF] Jiang | Application of Artificial Neural Network to the Saccharification Technology of Monascus Waxberry Wine Optimization[END_REF][START_REF] He | A Method for Estimating Product Assembly Cost Based on Neural Networks[END_REF][START_REF] Genel | Application of Artificial Neural Network for Predicting Strain-life Fatigue Properties of Steels on the Basis of Tensile Tests[END_REF]. Based on the above analysis, this paper uses BP neural network method to predict the performance of motorcycle engine piston. In order to overcome the shortcomings that BP network is difficult to be trained, network can be trained by using orthogonal experimental data to reduce the training samples and to improve the quality of training, and the output of network is analyzed comparatively to verify the accuracy of the network.

2

BP neural network

BP neural network structure

The basic element of neural networks is processing nodes, after adding the input obtained from other nodes with a weight value, which produces the output value through the activation function. Processing nodes are organized into a layer, which generally associated with next layer fully, and there is no interconnection in one layer, as is shown in Fig. 1. The input layer as inputting data to the network has not any operations in this layer. After the input layer, followed by one or more actual processing layers, the final processing layer is called the output layer that provide output data, between the input layer and output layer is a layer called the hidden layer. With multi-layer structure (usually three), this structure can allow the network to identify the nonlinear data.

Using neural network to process nonlinear data includes two phases: training and recall. The typical process is training network by back-propagation algorithm, until the error value between desired output and actual output of the network reach the objective minimum. Once training is completed, the network can process data through the recall as a non-linear processor [START_REF] Wang | Artificial neural network theory and applications[END_REF][START_REF] Guo | Neural Network and Its Application in Mechanical Engineering[END_REF][START_REF] Gao | Artificial Neural Network Theory and Simulation Instance[END_REF].

BP learning rule

BP algorithm consists of two parts: signal forward transmission and error backpropagation. In the forward course, the input signal pass to the output layer from the input layer, hidden layer. the state of neurons in each layer only influences the state of next layer neurons. If the output layer has not the expectable output, then calculated the error of output layer, and then turned back propagation through the network connection, the error signals along the original path are anti-passed back to modify the weight of every layer neurons to achieve the desired goal. BP network, the whole learning steps:

① Initialization, to the various connection weights W ij , V jt and the threshold θ

=

, the connection weights W ij and the threshold θ j calculate the input value Sj (activation value)of middle layer neurons, then using Sj through the activation function

x e x f - + = 1 1 ) ( (1)
Calculate the output value bj of each element in the middle layer. Where:

å = - × = n i j i ij j a W s 1 q
④ Using the output value b j of middle layer, the connection weights V jt and the thresholds γ t, calculate input value l t (activation value) of each element in the output layer, then using l t calculate each element's response c t of the output layer by activating function,

) ( t t l f c = (3) Where: å = - × = p j t j jt t b V l 1 g (t = 1,2, ... q) ⑤ Using the expectable output mode [ ] k n k k k y y y Y ,...
) ) 1 ( t t t k t k t c c c y d - × - = (t = 1,2, ... q) (4) ⑥ With t d , V jt , b j ,
calculates the correction error of the middle layer:

) 1 ( 1 j j q t jt k j b b V dt e - ú û ù ê ë é × = å = (j = 1,2, ... p) (5)
⑦ With k t d , b j , V jt , γ t ,calculates the new connection weights and thresholds between middle layer and output layer next time:

j k t ji ji b d a N V N V × × + = + ) ( ) 1 ( (6) k t t t d a N N × + = + ) ( ) 1 ( g g (7)
Where, N is times for learning, a is learning coefficient.

⑧ With k j e , k i
a , W ij , θ j , calculates the new connection rights between input layer and middle layer next time:

k i k j ij ij a e N W N W × × + = + b ) ( ) 1 ( (8) k j j j e N N × + = + b q q ) ( ) 1 ( (9)
⑨ Randomly selects the next learning model couple for the network, return to the ③step, until all m-model couple have been trained completely. ⑩ Again from the m-learning couple randomly select a model couple , return to the ③step, until the network global error function E is less than a pre-set limit value (network convergence), or learning times are more than the preset value ( network failed to converge). For the whole input mode, the network's global error E:

åå å å = = = = - = - = = m k q t t k t m k k m k k c y c Y E E 1 1 2 1 2 1 ) ( 2 1 ) ( 2 1 (10)
⑪End of the study In the above learning steps, ③-⑥ as "forward propagation process" of input learning mode, ⑦-⑧ as "back-propagation process" of the network error, ⑨ and ⑩ as the completion of the training and the convergence process. Fig. 2

j k t ji ji ji ji b d a N V N V N V N V × × + - - + = + )) 1 ( ) ( ( ) ( ) 1 ( h (11) k i k j ij ij ij ij a e N W N W N W N W × × + - - + = + b l )) 1 ( ) ( ( ) ( ) 1 ( (12)
3 Performance prediction model for engine piston Based on BP neural network

Construction of prediction model

Selecting a 4-cylinder gasoline engine piston as a research object, the piston diameter about 47.49 ~ 65mm, piston skirt length about 50.1mm, the piston head has burning chamber. According to the theoretical calculations and past experience, consideration in the design should be focused on the clearance between the piston skirt and cylinder liner, the clearance between piston head and cylinder liner, eccentricity of piston pin, length of piston skirt, cylinder liner surface roughness RMS(root-mean-square) for the impact of friction power, noise, power and fuel consumption [START_REF] Wang | Orthogonal Test and Design of a Diesel Piston[END_REF]. In the orthogonal experimental design, selecting experimental factors and levels is shown in Table 1according to work experience.

In the orthogonal test, we should try to select the smallest orthogonal table [START_REF] Chen | Experiment design and analysis[END_REF], which can reduce the workload of sampling and trial. So orthogonal experiment of the piston selects L 16 (4 5 ) orthogonal table, such as Table 2.The number of test samples is 16, the times of tests is also 16 less than full-factorial test times:4 5 =1024, which reduce 1008 times (at the same time reducing 1008 samples) and greatly reduce trial costs, time and workload. To find out the influence of various factors for the indexes, the test results in Table 2 (measured value)are analyzed, the difference is greater, the impact of factors on the indicators is the greater. Such as friction power, the difference analysis result is shown in Table 3. According to the difference value sorting in decreasing, the order of various factors affecting friction power is obtained: A> B> C> D> E. The smaller is the friction power, the better is performance. The optimal project of friction power indicator is that: A 4 B 4 C 3 D 2 E 3 . Not only the times of orthogonal tests can be reduced, but also the synthetical impacts of different factors on performance can be fully realized through it. However, the implicit knowledge that is implied in these data need to be mined further in order to improve the system intelligent level. In the working of the piston, its performance (friction power, noise, power and fuel consumption) mainly is affected by the factors those are the clearance between piston skirt and cylinder liner, clearance between piston head and cylinder liner, eccentricity of piston pin, length of piston skirt, cylinder liner surface roughness RMS. Therefore, the friction power, noise, power and fuel consumption are investigated as the indexes.

The performance of piston is comparatively analyzed through testing, then the structure of BP neural network prediction model is finally identified as: 5×11×4 (respectively, the input layer nodes, hidden layer nodes and output layer nodes). The input variable of every node in the input layer are respectively the clearance between piston skirt and cylinder, or clearance between piston head and cylinder liner, or eccentricity of piston pin, or length of piston skirt, or cylinder liner surface roughness RMS. The output variables of every node in the output layer respectively are friction power, or noise, or power, or fuel consumption.

Training samples will affect the generalization ability of the network, which is generally selected to reflect the circumstances and relation, and can serve as an effective adjustment of connection weights. Orthogonal test method adopts "orthogonal table" to arrange a multi-factor experiment and analysis scientifically. Its advantage is mainly that in a lot of testing projects(also known as test conditions), a few testing projects owing to the strong representation are selected, Each testing project has a strong representation to reflect fully the general situation in the region preferred. Present research [START_REF] Huang | Neural Network Optimal Design Based on Orthogonal Experiment Method[END_REF] also shows that, because orthogonal test method is a balanced dispersion and neat comparability, network structure constructed by using orthogonal test method can effectively reduce training samples and improve the training accuracy. Therefore, in order to select the small training samples for containing enough information, prediction model structure of piston performance based on BP network adopts No. 1 to NO.14, NO.16 samples in Table 2 to construct learning samples. At one time, NO.15 sample is tested as a predictor, not used for training the network. Learning samples, prediction sample and learning outcomes are shown in Table 2.

Implementation of BP neural network prediction model

Learning samples (input, output) in the Table 2 are normalized, x b ji = (x ji -x ji.min ) / (x ji.max -x ji.min ), in the equation, x b ji is NO. i input variable of NO. j sample in the normalized samples, x ji is NO. i input variable of NO. j sample in the raw data.; x ji.max and x ji.min are respectively the maximum and minimum of NO. i input variable of NO. j sample in the raw data. To enable the data uniformly distributed in [0,1] ,then neural networks can be trained, activation functions of the hidden layer and output layer are also Sigmoid function (see formula [START_REF] Jiang | Application of Artificial Neural Network to the Saccharification Technology of Monascus Waxberry Wine Optimization[END_REF]). Learning coefficient between input layer and hidden layer is taken 0.6, learning coefficient between hidden layer and output layer is taken 0.5, momentum coefficient is taken 0.6, the global error E is taken 0.001, error function tend to stabilize after 4416 times iteration, BP program is shown in Fig. 3, the final learning outcomes are shown in Table 2. At the time, according to the connection weights and the threshold determined by the network model to reflect the inherent law of input samples, the corresponding prediction model will be obtained, and thus parameters and network structure determined by the model is regarded as the actual prediction model parameters. 0.13 0.20 0.027 0.74 After the above algorithms constantly adjust the parameters of the network, complicated function that affects indicators and performance can be obtained to meet the accuracy requirements. Such a function can not be expressed in simple function expression, which can only be stored in the computer in the form of a network with weight value and threshold as parameters.

Predictive testing sample is utilized to predict in the trained BP network, the relative errors between prediction results and measured values are shown in Table 4.

It is discovered that in Table 2 and Table 4, fitting accuracy and prediction accuracy based on BP network are high, the relative errors are less than 1% to meet the required precision. Because the neural network can approximate a nonlinear function to any precision, it can establish the function among the friction power, power, fuel consumption, noise and various factors. Network prediction results also show that the forecasting performance of the established network is better, performance of the engine piston can be well predict, to a certain extent, which can improve prediction accuracy.

Conclusion

To a large extent, performance of piston directly impact on the motorcycle engine, there are many factors affecting the performance of the piston, and their values are more difficult to determine, so effective ways are urgently needed to find for identifying the reasonable combination values of the various factors, so as to shorten the product development cycle. Based on this, the relationship between factors and performance indicators is established through the use of BP neural network, which has been a high precision network output. The foundation for the simulation analysis of various factors is established through the BP neural network prediction model that has been trained, i.e. the impact of different combination on performance has been studied. The results show that adopting BP neural network approach to predict the performance of the piston is feasible and effective.

Fig. 1 .

 1 Fig. 1. BP neural network

, 2 1 =

 1 , the network actual output c t , calculates correction error k t d of each element in the output layer:

(

Fig. 3 .

 3 Fig. 3. BP neural network training and prediction

Improvement of the training function BP

  network training adopts gradient descent method in order to obtain minimum in error flat, which has its limitations and inadequacies, mainly reflected in: 1) the learning rate is too small lead to the training time was too long; 2) local minimum. In order to overcome the slow convergence of the algorithm, there are many scholars who have done research and made a number of improved BP algorithm, such as the weight adjustment algorithm with momentum term, namely, momentum term is added in the weight adjustment algorithm formula (11), (12):

		start
	Connection weight and threshold
		initialization
	Provide learning model couple to
		network
	Calculate input and output of
		middle layer neurons
	Calculate input and output of each
		cell in the output layer
	Calculate correction error of each
	cell in the output and middle layer
		Adjust connection weight
		between middle and ouput
	layer ,adjust output threshold of
		output layer neurons
	Updating learning and input mode
		couple
	no	all m-model couple are
		training completely
		yes
		Updating learning times
	no	Global error<E or
		learning times>N
		yes
		Study end
	Fig. 2. BP network learning process diagram
	2.3
		shows BP
	network learning process diagram.

Table 1 .

 1 Factors and level table

	factors

Table 2 .

 2 Piston

		orthogonal experiment results and BP neural network training and testing
			samples and learning results
			measured values			based on BP network
	No.	A B C D E friction	power	fuel	Noise	friction	Power	Fuel	Noise
		power	/kw	consum	dB(A)	power	/kw	consumptio	dB(A)

Table 3 .

 3 The results of difference analysis

				Friction Power
		A	B	C	D	E
	K 1	2.585	2.565	2.375	2.245	2.267
	K 2	2.643	2.417	2.313	2.212	2.260
	K 3	2.257	2.167	2.197	2.302	2.255
	K4	1.623	1.958	2.222	2.347	2.325
	R	1.020	0.607	0.178	0.135	0.070

Table 4 .

 4 Prediction results of BP network

			Based on BP Network
	Sample No.	Friction power relative error (%)	power relative error (%)	fuel consumption relative error (%)	noise relative error (%)
	15

ACKNOWLEDGEMENT

This work is supported by "the Fundamental Research Funds for the Central Universities."(N0.XDJK2009C005) and supported by "the Doctoral Fund of Southwest University"(N0.SWU109043).


Xueyan Sui

Xiaodong Zhang

Shaokun Li

Zhenlin Zhu

Bo Ming

Xiaoqing Sun

Extracting winter wheat planting area based on cropping system with remote sensing

Keywords: Shandong province, winter wheat, rice wheat, area, remote sensing 1

Winter wheat is one kind of important crop in China. It's planting area is one key element to explain yield change. To obtain winter wheat planting area as soon as possible can provide scientific reference for our country's making related policy. Basing on the cropping system in Shandong province, winter wheat is divided into two kinds "winter wheat sowed by machine-maize" and "people broadcast winter wheat-rice". Using MODIS data, NDVI characters of winter wheat, garlic, greenhouse vegetable, from sowing till overwintering stage were analyzed. Together with NDVI characters of former stubble crops in middle September, extracting requirements were set up for winter wheat planting area which was sowed by machine this year. In view of the spectrum similarity between rice wheat and greenhouse vegetable from sowing stage till overwintering stage, rice wheat planting area of former year was extracted relying on the character of biomass rapid growth at jointing stage. Because of the "people broadcast winter wheat-rice" cropping system is very fixed in Shandong province, then the rice wheat planting area of former year can take the place of the rice wheat planting area this year. Two kinds of winter wheat area were merged, and tested by 284 groups of located spots data, the accuracy reached 94.01%. The result showed that it is feasible to extract winter wheat area before overwintering stage, and the time is 4 months earlier than using jointing stage NDVI.

Introduction

Shandong Province located in the Huang-Huai Plain is the major winter wheat producing area in China. It is important to grasp winter wheat area timely and accurately for the country's food security. In the past, winter wheat area mainly relied on primary agricultural production sectors' artificial survey and then statistics level by level. Limited resources and subjectivity led to poor data, which brought some impact to government's decision on agriculture production. With the development of science and technology, In 1980s, remote sensing had become one tool of agriculture resource investigating and crop growth monitoring with characters of obtaining data objectively, accurately and timely [START_REF] Mei | Introduction to Remote Sensing[END_REF].The key to extract winter wheat area is the data resource and the choice of phase. In 1990s, using NOAA satellite data, Maoxin Wang set up regression equation of winter wheat area and the pixel number whose NDVI (Normal Differential Vegetation Index) difference between November and October was greater than zero [START_REF] Wang | Winter wheat sown area estimation using NOAA AVHRR data[END_REF]. A large population but less land area, complex cropping system and NOAA satellite data's lower spatial resolution resulted that the extracting accuracy of winter wheat was not high.

In the 21st century, with the food strategy advancement, some regions extracted winter wheat area with TM and spot data [START_REF] Murakami | Crop discrim-ination with multitemporal SPOT/HRV data in the Saga Plains, Japan[END_REF][START_REF] Qi | Selection of optimum periods for extracting winter wheat based on multi-temporal remote sensing images[END_REF] which holds higher spatial resolution than NOAA data. Extracting methods included visual interpretation [START_REF] Gu | Consistency study between MODIS and TM on winter wheat plant area monitoring--a Case in small area[END_REF], supervised classification [START_REF] Feng | Monitoring planting area and growth situation of irrigation-land and dry-land winter wheat based on TM and MODIS data[END_REF], unsupervised classification [START_REF] Huang | Monitoring on wheat area using TM in some areas of Jiangsu province[END_REF][START_REF] Li | A study on classification and monitoring of winter wheat growth status by Landsat/TM image[END_REF] and pure pixel identification based on spectral library [START_REF] Chen | Remote sensing method of pure crop pixel identification and planting area estimation based on spectral library[END_REF]. Although resolution being enhanced and data processing technology being improved have made the accuracy reach over 90%, While TM data has lower time resolution and easily influenced by weather, what's more the cost is high, then TM data can only be used to invest winter wheat areas of small regions but not large areas.

Compared with NOAA data and TM data, MODIS data has higher time and middle spatial resolution. The data sharing service has made MODIS data be used to remote sensing monitoring winter wheat more and more [START_REF] Yang | Extraction of multi-crop planting areas from MODIS data[END_REF][START_REF] Wu | Linear mixture modeling applied to remote sensing monitoring of winter wheat areas[END_REF][START_REF] Yan | Extracting winter wheat area using temporal sequence of Ts-EVI [J][END_REF][START_REF] Qiao | Application of EOS/MODIS-NDVI at different time sequences on monitoring winter wheat acreage in Henan Province[END_REF]. Yigang Jing set models and extracted winter wheat area with accuracy of over 91% using the NDVI changes of March, May and June [START_REF] Jing | Study on extracting winter wheat area using EOS/MODIS data [J][END_REF]. Jinqiu Zou extracted area using the EVI (Enhanced Vegetation Index) difference between May and October, and the error rate was -0.04% [START_REF] Zou | Method for extracting winter wheat area using Terra/MODIS data and its accuracy analysis[END_REF]. Wenpeng Lin used NIR, RED, BLUE and ESWIR 4 bands MODIS data of October and December to class 6 kinds of surface features with the method of fuzzy ARTMAP, and the accuracy reached 80.3% [START_REF] Lin | Study on crop information extraction based on MODIS spectral analysis[END_REF]. The main method of comparing spectral changes in the key growth periods after sowing for extracting winter wheat area was used frequently using MODIS data [START_REF] Zhang | Crop acreage change detection based on phenology model Transactions of the CSAE[END_REF]. Over time, the amount of information increased, and the accuracy enhanced [START_REF] Wang | Study on Extracting Winter Wheat Planting Area Based on MODIS Data by Spectral Mutation[END_REF][START_REF] Wang | Study on the Planting Area Extraction of Winter Wheat Based on MODIS Data[END_REF][START_REF] Xu | Remote sensing monitoring of winter wheat areas using MODIS data[END_REF]. The paper studied one method with high accuracy to extract winter wheat area at early periods, combining with the former stubble crops based on cropping system in Shandong province.

Data acquisition

Winter wheat is sowed in middle to late October in Shandong province, and tillers in early to middle December.

In middle to late October, most vegetables in greenhouse are transplanted, and grow fast after rejuvenation in December, and the change of NDVI is similar with that of winter wheat, which influences the extracting accuracy of winter wheat area [START_REF] Lin | Study on crop information extraction based on MODIS spectral analysis[END_REF]. Shandong province is also the major producing area of garlic. It is sowed in late September to early October, the growing condition is very similar to winter wheat in winter, and thus it is another important obstacle.

In order to exclude the interferences of greenhouse and garlic, discussion group located 284 spots of six kinds of surface features which included winter wheat, greenhouse vegetable, garlic, village, uncovered cotton filed, tree, in Shandong province, in late October, 2008. Downloaded MOD09Q1 data of the 284 spots of middle January, middle April, middle September, middle October and middle December, form MODIS data sharing platform-ftp://e4ftl01u.ecs.nasa.gov/.

Data analysis

This part analyzed surface features' NDVI sequence, and set up identification conditions to extract

winter wheat area preliminarily.

We calculated the located spots' NDVI and the average of middle September, middle October and middle December for six kinds of surface factures separately, and then drew line chart (fig. 1). The NDVI of cotton filed dropped as the season went on, and it was similar to village and tree. Among the three periods, the NDVI in September was the highest for winter wheat, greenhouse vegetable and garlic, at that time, winter wheat filed and garlic filed are all planted maize, and greenhouse is still planted vegetable. The gain period of garlic is in late May, which is earlier than winter wheat. After garlic the filed is planted with early mature maize, and the maize is gained in middle to late September. While winter wheat is gained in middle June, then the filed is planted with maize. Winter wheat field's maize is at filling period, so the NDVI average 0.71 in middle September is higher than that of garlic filed. Till middle October, maize is gained, and winter wheat is sowed.

Garlic is sowed in late September to early October, and the time of emergence is earlier than winter wheat.

Greenhouse vegetable is transplanted, and grows faster than winter wheat and garlic with good conditions. Thus greenhouse vegetable's NDVI is higher than garlic field's, and garlic field's NDVI is higher than winter wheat field's. In middle December, winter wheat steps into tillering stage, and reaches growing peak before winter.

Garlic's biomass and greenhouse vegetable's biomass all continues to increase. So in middle December, the three kinds surface features' NDVI are all higher than that in middle October, but lower than in middle September. According to the analysis above all, preliminary identification conditions were set up to extract winter wheat area: NDVI middle September > NDVI middle December > NDVI middle October ,and NDVI middle September >0.

Test on identification result

Used the identification conditions to extract winter wheat area, and tested the result with 284 groups of located spots' data (table 1). The result was listed in table 1. Among 284 located spots, 23 winter wheat spots were identified to be other surface features. 10 other surface features spots were identified to be winter wheat, which included 3 greenhouse vegetable spots, 3 garlic spots, 1 tree spot and 3 village spots. Imported the 284 groups of located spots into ENVI, and inspected one by one, then discovered that the wrong identifications can be divided into two kinds of situations: one was located spots were mixed pixels of winter wheat with greenhouse vegetable, garlic, tree or village (fig. 2); the other was rice wheat spots were identified to be other surface features (fig. 3). A large population but less land area and complex cropping system in Shandong province make the phenomenon of mixed pixels inevitable. Rice wheat was not identified, showed the identification conditions didn't contain rice wheat information. Only extracted "winter wheat sowed by machine-maize" area, so the main task remained was to extract "people broadcast winter wheat-rice" area.

The extraction of rice wheat area

Calculated 16 rice wheat spots' average NDVI of the 3 periods, compared with common winter wheat. As fig. 4, fig5 showed, in September, rice is growing in rice wheat field, and maize is growing in winter wheat field, and the NDVI of rice is lower than that of maize. In middle October rice is gained, and rice wheat is at trefoil stage which was broadcasted not long ago, whose NDVI is higher than normal wheat. What's more rice wheat field has adequate water supply, so rice wheat growing condition is better than normal winter wheat till middle December. During the analyzing progress, narrowing NDVI threshold of the 3 periods was tried to extract rice wheat area, but failed for the NDVI similarity between rice wheat and greenhouse vegetable.

Winter wheat goes into overwintering stage In January, and jointing stage in April, growing condition is influenced by air temperature; rice wheat and normal winter wheat are similar to each other. So it is possible to extract all winter wheat area depending on NDVI fast increasing over the period from middle January to middle April, that is to say, from overwintering stage to jointing stag. This method is popular now. The extraction of all winter wheat

The two parts S1 and S2 of winter wheat were merged, which contained the normal winter wheat area extracted with the data of September, October, and December, 2008, and the rice wheat area extracted with the data of January, April, and September, 2008. The total winter wheat area of 2008-2009 was 4480666.67 hm2, tested by the located spots' data the accuracy was 94.01%(fig. 7).

Result and discussion

Winter wheat in Shandong province can be divided into two kinds from a cropping system point of view, "mechanical sowing winter wheat-maize" and "people broadcast winter wheat-rice". The paper studied NDVI characters of winter wheat, garlic and greenhouse vegetable from seeding time October to overwintering stage December. The study showed that 3 kinds of surface features' NDVI changing tendencies are similar to each other, and it is difficult to extract winter wheat area. Together with the difference of the previous crop's NDVI, the normal winter wheat of "mechanical sowing winter wheat-maize" was extracted. But the rice wheat NDVI of "people broadcast winter wheat-rice" is similar to greenhouse vegetable NDVI, so it is impossible to extract rice wheat area just depending on the change of NDVI. Based on the special growing environment of rice wheat, rice plant region vector in Shandong province was set up. Among this region, rice wheat area of the previous year was extracted using the fast NDVI increase at jointing stage than overwintering stage. The two parts winter wheat area were added up, and got the total area in late December.

This study used middle spatial resolution and high time resolution MODIS data to settle the interference of garlic and greenhouse vegetable to winter wheat area extracting in Shandong province. The extracting time was 4 months in advance than using jointing stage NDVI character [START_REF] Qin | Winter wheat yield estimation based on high and moderate resolution remote sensing data at county level[END_REF][START_REF] Chen | Analysis on the Multi-temporal Features of MODIS/NDVI in the Growing Period of Winter Wheat and Its Application in Ground-object Identification[END_REF], and the accuracy reached 94.01%, so the method can meet the demand of large area.

Natural environment decides cropping system, while the improvement of agricultural production technology, and many other economical factors' all influence the reform of cropping system. So using the previous year rice wheat area to take the place of the current year will affect the accuracy of total winter wheat area. In future research, rice field's spectrum should be studied, and assisted with NDVI change character of rice wheat from seeding to overwintering stage to extract rice wheat area, in order to enhance the total winter wheat area extraction accuracy.

Fig. 1

 1 Fig. 1 NDVI line chart of 6 kinds of surface features in the three periods

Fig. 2 Fig. 3

 23 Fig. 2 Spots of rice wheat tested to be others

Fig. 4 Fig. 5

 45 Fig. 4 NDVI line chart of rice wheat, normal winter wheat, greenhouse vegetable in the three periods

Fig. 6

 6 Fig. 6 The extracted area of rice wheat of 2007-2008

Fig. 7

 7 Fig. 7 The extracted area of winter wheat of Shandong province of 2008-2009

Table 1.

 1.

	. . .The test results checklist
	Surface features	The number of spots to be tested	The number of spots tested to be Winter Wheat	The number of spots tested to be others	Wrong test reasons
	winter wheat	161	138	23	7 mixed pixels 16 rice wheat
	cotton	24	0	24	--
	greenhouse vegetable	31	3	28	mixed pixels
	garlic	11	3	8	mixed pixels
	tree	26	1	25	mixed pixel
	village	31	3	28	mixed pixels

Fund projects: National Science & Technology Pillar Program (2007BAH12B02); Shandong Academy of Agricultural Sciences innovation program (2007YCX026); Shandong Province Science & Technology development projects (2009GG10009007);


Tingxiang Jin
email: txjin@126.com

, Gailian

, Chunxia Li

Hu

Influences of temperature of vapour-condenser and pressure in the vacuum chamber on the cooling rate during vacuum cooling

Keywords: Temperature, Pressure, Vapour-condenser, Vacuum cooling

The temperature of vapour-condenser below 0℃ and the final pressure in the vacuum chamber below 0.61kPa during vacuum cooling were experimentally analysed in this paper. The temperature of vapour-condenser, -2 ℃, -35℃, -39℃ and -71℃, and the final pressure in the vacuum chamber, 0.3kPa, 0.4kPa, 0.5kPa and 0.61kPa, were chosen. The experimental results showed that the cooling rate varies with the temperature of vapour-condenser and the final pressure in the vacuum chamber. Water vapour becomes the frost on the surface of vapour-condenser when the initial temperature of vapourcondenser is below 0℃, which is helpful to trap water vapour for vapourcondenser. In addition, the formation mechanism of frost at the surface of vapour-condenser was analysed in this paper. The cooling time for vacuum cooling can be reduced when the final pressure in the vacuum chamber varied from 0.4kPa to 0.61kPa. However, the surface temperature of cooked meat occurred freezing when the final pressure in the vacuum chamber was 0.3kPa. Therefore, in order to reduce the cooling time and avoid freezing, the temperature of vapour-condenser should be set around -30℃~-40℃ and the final pressure in the vacuum chamber can be defined at from 0.4kPa to 0.61kPa.

Introduction

Vacuum cooling is a rapid evaporative cooling method. Vacuum cooling has been successfully used to cool vegetables and flowers since the 1950s [START_REF] Briley | Vacuum cooling of vegetables and flowers[END_REF]. In the recent years, for the safety of foods, a rapid cooling treatment after cooking process should be used to minimize the growth of surviving organisms. Compared with the conventional cooling methods including air-blast, slow-air and water-immersion cooling, vacuum cooing has many advantages. Therefore, many researches have highlighted the applications of vacuum cooling for the cooked meats [START_REF] Mcdonald | The effect of injection level on the quality of a rapid vacuum cooled cooked beef product[END_REF][START_REF] Burfoot | Effect of cooking and cooling method on the processing times, mass losses and bacterial condition of large meat joints[END_REF][START_REF] Desmond | Effect of rapid and conventional cooling methods on the quality of cooked ham joints[END_REF]. In addition, heat and mass transfer characteristics during vacuum cooling have been investigated [START_REF] Sun | Heat transfer characteristics of cooked meats using different cooling methods[END_REF]. Predictive models can provide much valuable information for the cooling process of large cooked meat joints under broad experimental conditions within a short time. Wang and Sun have developed a mathematical model for describing the vacuum cooling process of the large cooked meat joints [START_REF] Wang | Modelling vacuum cooling process of cooked meat-part 1: analysis of vacuum cooling system[END_REF][START_REF] Wang | Modelling vacuum cooling process of cooked meat-part 2: mass and heat transfer of cooked meat under vacuum pressure[END_REF][START_REF] Da-Wen Sun | CFD predicting the effects of various parameters on core temperature and weight loss profiles of cooked meat during vacuum cooling[END_REF][START_REF] Da-Wen Sun | CFD simulation of coupled heat and mass transfer through porous foods during vacuum cooling process[END_REF].

; 3 - ⋅ m - λ thermal conductivity, ; 1 1 1 - - - ⋅ ⋅ ⋅ s K m J Subscripts fr -frost layer; v -vapour; - ice ice layer;
A vacuum cooler is a machine to maintain the defined vacuum pressure in a sealed chamber, where the boiling of the water in the cooked meats occurs to produce the cooling effect. Theoretically, only the speed of vacuum pump is high enough to produce the defined vacuum pressure in the vacuum chamber. However, at a low pressure, the volume ratio of steam and water is very large. For example, when the pressure is 1073 Pa, the corresponding saturation temperature is 8℃, the specific volume is 120.851 kg m 3 . If the entire vapour is evacuated only through the vacuum pump, the speed of vacuum pump should be very large, many vacuum pumps are required in the vacuum cooler, which is obviously unsuitable. In order to remove the large amount of water vapour and keep the cooling cycle within a reasonable length of time, the vapour-condenser is used to economically and practically handle the large volume of water vapour by condensing the vapour back to water and then draining it through the drain valve. The vacuum pump and the vapour-condenser in the vacuum cooling system are used to remove the water vapour evaporated from the cooked meats. Wang and Sun [START_REF] Wang | Effect of operating conditions of a vacuum cooler on cooling performance for large cooked meat joints[END_REF] analysed the effect of operating conditions of a vacuum cooler on the cooling performance for large cooked meat joints by a validated mathematical model, they concluded that the temperature of the vapour-condenser should be above 0℃, because water freezes on the outside surface of the condenser when the temperature is below 0℃. It is well known that the boiling point changes as a function of saturation pressure, for a boiling temperature of 0℃, the saturation pressure will be 609 Pa. Therefore, in order to avoid freezing, the final pressure in the vacuum chamber is usually above 609 Pa. On the base of previous literatures, in the current study, vacuum cooling of cooked meats were conducted to analyze the effects of the final pressure in the vacuum chamber below 609 Pa and the temperature of vapour-condenser below 0℃ on the cooling rate of cooked meats.

2

Materials and methods

Samples preparation

The raw bone-out pork used in experiments was brought from a local supermarket. Then, the samples were cooked in water through the oven (Type of the oven is RF-P130Y, China) until the samples were at a uniform temperature. Then, the cooked meat was put and cooled in the vacuum chamber.

Experimental setup

A laboratory-scale vacuum cooler as shown in Fig. 1 was built by Shanghai Pudong Freezing Dryer Instruments Co. Ltd. (Shanghai, China). Vacuum cooler has four basic components: a vacuum chamber, a vacuum pump, a vapour-condenser and a refrigeration system. The volume of vacuum chamber was approximately 0.3m 3 . The rotary vane vacuum pump (Type 2XZ-2) with the pumping speed of 7.2m 3 h -1 and rotary speed 1400 rev min -1 was used to evacuate the air in the vacuum chamber and the vapour evaporated from the products from atmospheric pressure to the defined vacuum pressure. The final vacuum pressure in the vacuum chamber is regulated by the bleeding valve. The vapour-condenser is an evaporator in the refrigeration system and a condenser capturing water vapour evaporated from the cooked meats during vacuum cooling. The cooling coil of vapour-condenser is set up in a stainless cylindrical steel, which is enclosed with 30 mm thickness polyurethane foam to prevent heat transfer. The stainless cylindrical steel with vapor-condenser is defined as cold trap.

Data collection

A set of T-type copper-constantan thermocouples with an accuracy of ℃ are used to record the temperature distribution of cooked meats and the temperature of the cold trap. The pressure sensor (model CPCA-130Z), a capacitance membrane gauge with an accuracy of Pa was used to measure the vacuum pressure in the chamber. The data collection and control signals, such as pressure and temperature were conducted by I-7000, a family of network data acquisition and control modules. The control module was connected with software called "King of Combination" (Beijing Asia Control Automatic Software Co. Ltd.). In order to eliminate the error of the second conversion, the temperatures received by the computer were demarcated by a second scale standard mercury thermometer with a measurement range 0~100℃. The effect of temperature of vapour-condenser on cooling rates during vacuum cooling

The vapour-condenser, which is an auxiliary vacuum pump, is normally used to remove the large amount of water vapour generated by condensing the vapour back to water and draining the water out of the vacuum chamber. The effect of temperature of vapour-condenser on cooling rates is shown in Fig. 2. It can be seen from Fig. 2 that the cooling rate of cooked meat can increase with the reduction of temperature of vapour-condenser. During vacuum cooling, the temperature of vapour-condenser of below 0℃ was used. However, if the temperature of vapour-condenser was too low, the cooling rate of cooked meat can decrease. If 0.15 kg of cooked meat was cooled, the average temperature of cooked meat can be reduced from 61.4℃ to 4.7℃ within 30 min at the vapour-condenser temperature of -2 ℃ . In addition, when the temperature of vapour-condenser was further reduce from -2℃ to -39℃, the total cooling time can be obviously reduced from 30 min to 20 min. On the other hand, if the mass of cooked meat was 0.5 kg, the temperature of vapour-condenser was reduced continuously to -71℃, the average temperature of cooked meat decreased only from 74℃ to 26.1℃ within 42 min. In the same mass of cooked meat, the average temperature of cooked meat can be reduced from 60.1℃ to 5.7℃ within 50 min at the vapour-condenser temperature of -35℃. During vacuum cooling, the mass of cooked meat has also an effect on the cooling rate, which is accorded with Wang and Sun's [START_REF] Wang | Effect of operating conditions of a vacuum cooler on cooling performance for large cooked meat joints[END_REF] experimental result. Wang and Sun [START_REF] Wang | Effect of operating conditions of a vacuum cooler on cooling performance for large cooked meat joints[END_REF] think that the temperature of vapour-condenser should be set at around 2.5℃ above 0℃ in order to avoid freezing of water on the outside surface of the cold trap. However, author thinks that the temperature of vapour-condenser should be set below 0 ℃. Because when the temperature of vapour-condenser is below 0℃, the water vapour become frost through solidify on the surface of the vapour-condenser, water vapour can be easily trapped in the vapour-condenser. It can be found that the different temperatures of vapour-condenser below 0℃ have an effect on the cooling rate of cooked meat. However, if the temperature of vapour-condenser is too low, the cooling time can be increase adversely, which can be expressed by the formation of the frost on the surface of cold trap.

Fig. 2. Effect of temperature of vapour condenser on cooling rate during vacuum cooling

The formation mechanism of the frost on the surface of vapour-condenser

Fig. 3 shows the formation process of frost. The sensible heat is transferred from the water vapour in the vapour-condenser to the frost surface by the temperature difference driving force between the water vapour and the frost surface. Some of the transferred moisture deposits on the frost layer, causing the frost layer to grow. The remainder diffuses into the frost layer. The heat of sublimation caused by the phase change of the added frost layer is transferred through the frost layer. The latent heat and sensible heat transferred from the water vapour are then transferred through the frost layer by conduction. The water vapour diffusing into the frost layer changes phase within the frost layer. The frost density increases as a result of this process.

The frost layer is a porous medium composed of ice crystal and air. The ice crystal has different shapes during the formation of the frost layer. Ice crystal shapes are classified into main forms: plate-like forms and column-like forms. The microscopic structure of ice crystal is shown as in Fig. 4. (2) Column-like forms: (e) needle crystal, (f) hollow column, or sheath-like crystal. [START_REF] Na | New model for frost growth rate[END_REF] During the formation of the frost, the mass flux through water vapour diffusing into the frost layer can be calculated by the Clapeyron-Clausius equation. The expression is as follows [START_REF] Kondepudi | Performance of finned tube heat exchangers under frosting conditions[END_REF]:

[ ] ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ - - - ⎥ ⎥ ⎦ ⎤ ⎢ ⎢ ⎣ ⎡ ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ + - + = ice r f ice v v vs v ice r f ice v fr fr vs fr v v P h D v v RT h Q m ρ ρ ρ ρ λ 1 ) ( 1 ) ( 5 . 0 2 0 & (1)
Where is the refrigeration load of vapour-condenser; 0 Q vs h is the sublimation heat of ice; R is the gas constant; fr T is the surface temperature of the frost layer; The temperature of vapour-condenser is below 0℃, The water vapour evaporated from the cooked meats will become the frost at the surface of vapour-condenser. Fig. 5 shows that the comparison of the cold trap between before and after vacuum cooling of cooked meat. It can be obviously found that the frost occurs at the surface of cold trap after vacuum cooling of cooked meat.

The latent heat of sublimation is released in the cold trap, which can increase the temperature of vapour-condenser. The variation of temperature of vapour-condenser is shown in Fig. 6. It can be seen from Figs. 2 and6 that when the initial temperatures of cold trap were at -2℃, -39℃ and -35℃, respectively, the temperatures of cooked meat were reduced to about 5℃, the temperature of vapour-condenser was reduced from -2℃, -39℃ and -35℃ to -70.4℃, -70.4℃ and -71.9℃, respectively during vacuum cooling. It can be found that the temperature of vapour-condenser can be reduced continuously during vacuum cooling. Because the condensation ability of vapour-condenser is not smaller than the required one, the condenser can efficiently condense all the generated water vapour during the cooling. However, when the initial temperature of cold trap reached at -71℃, the temperature of cold trap had no variation during vacuum cooling, which can be seen from Fig. 6. At the same time, the temperature of cooked meat reduced only from 74℃ to 26.1℃ within 42 min, which is because the frost and ice at the surface of cold trap result in a significant heat resistance.

3.3

The effect of the pressure in the vacuum chamber on the cooling rate during vacuum cooling Fig. 7 gives the effect of the pressure in the vacuum chamber on the surface temperature of cooked meat during vacuum cooling. Four different final pressure, 0.3kPa, 0.4kPa, 0.5kPa and 0.61kPa, in the vacuum chamber were chosen during vacuum cooling. It can be seen from Fig. 7 that if the final vacuum pressure in the vacuum chamber is 0.61kPa, the surface temperature of cooked meat can be reduced from 50℃ to 5.2℃ within 40 min. When the final vacuum pressure in the vacuum chamber was reduced from 0.61kPa to 0.5kPa, the surface temperature of cooked meat can be reduced from 43.5℃ to 4℃ within 31 min. The final vacuum pressure in the vacuum chamber was kept at 0.4kPa, the surface temperature of cooked meat varied from 51.2℃ to 5.6℃ within 18 min. In the same case, the mass of cooked meat is 0.3kg, the final vacuum pressure in the vacuum chamber varied from 0.4kPa to 0.61kPa, the cooling time will increase from 18 min to 40 min. If the mass of cooked meat increased from 0.3kg to 0.6kg, the final vacuum pressure in the vacuum chamber is further reduced from 0.4kPa to 0.3kPa, the surface temperature of cooked meat can be reduced from 41.6℃ to 2.7℃ within 25 min. On the other hand, during vacuum cooling, it can be found when the final vacuum pressure in the vacuum chamber was 0.3kPa, the minimum surface temperature of cooked meat was -0.5℃, which shows that water freezes on the surface of cooked meat and has a negative effect on the cooked meat. It is well known that the boiling point changes as a function of saturation pressure, for a boiling temperature of 0℃, the saturation pressure will be 609Pa. However, the experimental results show that the surface temperature of cooked meat is above 0℃, when the vacuum pressure in the vacuum chamber is between 0.4kPa and 0.6kPa. If the vacuum pressure in the vacuum chamber is further reduced to 0.3kPa, the surface temperature is below 0℃. This means that the vacuum pressure in the vacuum chamber can be reduced to below 0.6kPa. At the same time, it should be noted that the vacuum pressure in the vacuum chamber should be above 0.4kPa.

Conclusion

The temperature of vapour-condenser below 0℃ and the final pressure in the vacuum chamber below 0.61kPa during vacuum cooling were experimentally analysed. The temperature of vapour-condenser, -2℃, -35℃, -39℃ and -71℃, and the final pressure in the vacuum chamber, 0.3kPa, 0.4kPa, 0.5kPa and 0.61kPa, were chosen during vacuum cooling. The experimental results showed that water vapour becomes the frost on the surface of vapour-condenser when the initial temperature of vapour-condenser is below 0℃, which is helpful to trap water vapour for vapourcondenser. However, if the temperature of vapour-condenser is reduced continuously to -71℃, the cooling rate will not increase. Therefore, the temperature of vapourcondenser should be set around -30℃~-40℃. At the same time, it can be also found that the cooling time for vacuum cooling can be reduced when the final pressure in the vacuum chamber varied from 0.4kPa to 0.61kPa. In addition, the surface temperature of cooked meat was above 0℃. However, the surface temperature of cooked meat occurred freezing when the final pressure in the vacuum chamber was 0.3kPa. It can be suggested that the final pressure in the vacuum chamber can be set between 0.4kPa and 0.61kPa. In a word, it is feasible that the temperature of vapourcondenser below 0℃ and the final pressure in the vacuum chamber below 0.61kPa. In order to reduce the cooling time, the temperature of vapour-condenser should be set around -30℃~-40℃ and the final pressure in the vacuum chamber can be defined at from 0.4kPa to 0.61kPa.

Nomenclature 0 Q

 0 -cold load of vapour-condenser,W ; v R -gas constant for water vapour, Greeks ρ -density, kg

Fig. 1 .

 1 Fig. 1. Schematic diagram of the vacuum cooler system

Fig. 3 .Fig. 4 .

 34 Fig. 3. The formation process of the frost

5 .Fig. 6 .Fig. 7 .

 567 Fig. 6. The variation of temperature of vapour condenser in different experimental conditions

ACKNOWLEDGEMENTS

Funding for this research was provided by Henan Provincial Department of Education (P. R. China).


Yun Qiu
email: qiuyun@mail.caas.net.cn

Guomin Zhou

Research of pest diagnosis system development tools based on binary tree

Keywords: pest diagnosis, binary tree, CLIPS system, knowledge system, knowledge expression

A visual pests diagnosis system development tools based on binary tree (abbreviation: knowledge system development tools) was developed by techniques of computer visualization, binary tree, XML database and reasoning. This tool combined tree knowledge expression with rule-based reasoning machine, proposed the knowledge acquisition and the reasoning technique based on binary tree, and solved the problem of knowledge acquisition used for pests diagnosis with an expert system development tools--CLIPS. The reasoning machine with stateless continuous reasoning technology can improve the efficiency of knowledge acquisition and system reasoning. In this article, we introduced the components and working principles of the pest diagnosis system development tools, and described its usage with a simple example.

Introduction

Pest is one of the most terrible threats to crops. The statistics shows that there are 236 million hectares fields are threatened by pests and about 15 percents food are lost in our country every year. An effective measure to reduce crops lost and improve its quality is to get accurate information in time by pest diagnosis and take proper measures in the growth period. Taxa Key and Classification Tree which can be combined into a binary tree key by optimization analysis (shown in Figure1) are often used to summarize the experience of pest diagnosis by plant protection experts. The system based on Binary Tree Key usually includes a series of questions (named "decision node"), the decision makers will answer each questions in sequence till the terminal object (named "answer node or leaf node") is identified.

Fig.1. structure of Binary Tree Key

The development process of expert system which can be expressed by binary tree is shown in Fig. 2.

Materials and methods

Introduction of CLIPS system

"CLIPS" is the abbreviation of "C Language Integrated Production System". It is a rule-based general expert system development tools developed by National Aeronautics and Space Administration (NASA) in 1985. As a knowledge engineering language, CLIPS inherits its traditional writing habits and operating characteristics, and has the basic characteristics of production systems. After deeper development by NASA, CLIPS has several knowledge expression methods and fuzzy reasoning function. The knowledge expression methods include the production system (rule-based), the framework structure, the object-oriented and process programming. The fuzzy reasoning function may improve the accuracy of reasoning results. In addition, CLIPS system has a good openness that can integrate with other systems easily. A dynamic linked functions library--clips.dll is provided in the software package of CLIPS development environment. The mixed programming can be completed by calling functions in clips.dll. For example, we can embed calculation unit into CLIPS to improve its poor calculate function as well as embed CLIPS into calculation unit to improve its poor logical reasoning.

There are three basic elements used for expert system development is provided by CLIPS.

(1) fact-list and instance-list. They usually consist of some self-defined facts and include the data needed for reasoning.

(2) knowledge-base. It includes all rules that usually formed by self-defined.

(3) inference-engine. It reasons according to reasoning mechanisms and control procedures overall.

The fact in fact-lists is the basic form of the data in CLIPS system. Each fact is information in fact-lists. Weather the rule condition is satisfied or not depends on the state fact. A fact is made up with a series of domains separated by spaces. The fact can be inserted or deleted from fact-list before reasoning, and it also can be inserted and deleted as an act section of rule during reasoning. Forward reasoning is the only rule adopted in CLIPS, it forms in "IF …THEN…". Its implementation technology is the famous Rete Algorithm, also named net-matching algorithm. To run the program of expert system is to fire and run each "IF… THEN …" rule defined in program till getting the specific conclusion. The necessary condition to fire the rule is each pattern in "IF" sentence matched with the facts in fact-list. It's called be-activated rule. Be-activated rules may not fire immediately, thus a waiting agenda is required. The waiting agenda will fire the rule with the highest priority and run the action in "THEN" sentence. As thus, CLIPS includes four parts shown in Fig. 3.

Components of development tools

Knowledge System Development tools is composed of two sections. One is knowledge editor functions for knowledge editing and uploading. Another is knowledge reasoning components and knowledge service webpage, they plays a role in receiving input information, calling knowledge from knowledge base to reason, and returning results to users by webpage.

Working principles of development tools

Knowledge expression is very important in the knowledge system. However, the knowledge expression method above is difficult for common users. In order to solve this problem a visual editing method is chosen, so the common users can express knowledge by some simple icons. Users can express knowledge by icons. After finished knowledge editing, the development tools will translate knowledge expressed by icons into knowledge expressed by rules. The work principle of development tools is show in Fig. 5. In fact, it isn't as complicated as the structure shown in Fig. 6. The development tool has packaged some main functions into COM and provides a standard ASP script. Therefore, users can run the system by the program sentence as "ks run.asp?kdb = name of knowledge base & title =name of system " without compiling complicated program.

Results and Analysis

An example of pest diagnosis

Suppose that we will develop a networked pest diagnosis system named "pest diagnosis experiment system", the development process is shown in Fig. 7.

Fig.7. the development process of knowledge system

According to the process above, knowledge management is the first step. Let's suppose that there is only one pest diagnosis knowledge information in the system, the decision rule is: if it occurs under cotton root then the pest is cutworm, else the pest is bollworm. As shown in Fig. 8.

Fig.8. chart of pest diagnosis

In this case, we can resort to knowledge system development tools. The pest diagnosis chart can be generated in the knowledge editing area with the icons in After editing the nodes and their interface information, saving the knowledge in server with the icon of (net write) and named "test". And then generate the rule-based information base that can be recognized by reasoning machine with the icon of (knowledge base). Lastly, the knowledge base for "pest diagnosis experiment system" is founded and transmitted to the server. The webpage of knowledge service system is shown in Fig. 10 after inputting the address of "http://127.0.0.1/ks/webapp/ks_run.asp?kdb=test&title= pest diagnosis experiment system" in URL of the browser. Click the button of "next" to reason. As thus, the pest diagnosis experiment system is finished.

Result for application

With the knowledge system development tools, we have developed several knowledge systems successfully, such as "the rice diseases diagnose system", "the rice pests diagnose system", "the cotton diseases and pests diagnose system", "the apple tree diseases and pests diagnose system", and so on. The application shows that the user-friendly interface makes the development tools easy to operate. The speed for knowledge generation using this knowledge system is about 35% faster than traditional way and the accuracy is also increased. Thus, experts of pants protection could generate a pests diagnose system knowledge system based network after simple training. This system is feasible and has a high practical value.

Conclusion and discussion

In this passage, we develop the visual pests diagnosis system development tools based on binary tree using techniques of computer visualization, binary tree, XML database and reasoning. This tool combined tree knowledge expression with rule-based reasoning machine, proposed the knowledge acquisition and the reasoning technique based on binary tree, and solved the problem of knowledge acquisition used for pests diagnosis with an expert system development tools--CLIPS. The reasoning machine with stateless continuous reasoning technology can improve the efficiency of knowledge acquisition and system reasoning. Application results show that the speed and accuracy of this system are greater than the traditional way. Thus, the development cycle of diseases and pests diagnose system is shortened, and promoted the information technology to be widely used in agriculture.

Fig. 2 .

 2 Fig.2. the development process of expert systemAs shown above, program compiling is a complicated work must be done by professional training engineers. Can we develop a tool to package these difficult programs and make program compiling automatically? The answer is positive. A

Fig. 4 .Fig. 3 .

 43 Fig.4. the structure of development tools There are three main functions with knowledge system development tools: (1) visual knowledge editing function which can edit the knowledge in books and

Fig. 5 .Fig. 6 .

 56 Fig.5. the work principles of development tools After being edited and translated, knowledge is formed into rule-based ones and stored in knowledge base of web server. The reasoning machine implemented with COM+ technology of Microsoft can call the knowledge stored in knowledge base. The user interface is realized by ASP script. (Fig.6)

  line . The pest diagnosis char in Fig.9 is similar to Fig.8.

Fig. 9 .

 9 Fig.9. the picture of knowledge system development toolsThe decision information provided to users to find the sites of pests in Fig.8can be inputted and edited with knowledge system development tools. Clicking the

Fig. 10 .

 10 Fig.10. the node edit picture of knowledge system development tools

Fig. 11 .

 11 Fig.11. the system of knowledge service system

  The rule-based knowledge expression method of CLIPS is used in this development tools. Each node is a fact. The information style in answer nodes and decision nodes is different, so the different models are needed. The model of answer nodes is expressed as: node <name> answer <value>). In this model, <name> is the name of answer node, and <value> is the answer in storage nodes. The model of decision nodes is expressed as: (node <name> decision <question> <yes-node> <no-node>).

	(deftemplate rule		(defrule goal-satified ""
	(multislot if)		(declare (salience 30))
	(multislot then))		?f <-(goal is ?goal)
	(defrule propagate-goal ""	(variable ?goal ?value)
	(goal is ?goal)		(answer ? ?text ?goal)
	(rule (if ?variable $?)	=>
	(then ?goal ? ?value))	(retract ?f)
	=>		(format	t
	(assert	(goal	"%s%s%n" ?text ?value))
	is ?variable)))		……………

In this model, <name> is the name of decision node, and <question> is the question proposed by this node, <yes-node> and <no-node> are the nodes performed respectively for positive and negative answers. An example of knowledge expression is shown below:

Acknowledgements: Our research is funded by the "863" project of research and application of field crop diseases intelligent diagnosis system (Number: 2007AA10Z237).


Weichuan Liao

Remote Control System Based on Compressed Image

Keywords: Remote control, Server, Client, Compressed image

Client computer controlling remote server computer or devices is usual for industry and agriculture automation. A monitor software system adopted C/S frame based on new algorithms of compressing image including improved LZ77, LZSS, LZW is designed. The client computer obtains compressed desktop image of remote server then decompresses the image on the client window to fulfill some mouse and keyboard actions of remote servers. Advantage of the control system by the new compress algorithms is that size of compressed image is smaller and system response time is shorter.

Introduction

Remote control system software is developed by C/S frame, which adopted new algorithms of compressing image. Client computer can perform some mouse and keyboard actions of multiple remote servers to control remote terminal devices. First, Compressed desktop bitmap of remote server is transformed to a view of client computer, then the client decompresses the bitmap and displays the bitmap in the view, so the client can fulfill all kinds of operations of servers ,such as starting or shutting down computer, opening files of servers etc. The client can monitor multiple remote servers by creating one view for every server. This remote control system takes shorter response time and less network traffic than other popular remote control systems.

Frame of Remote Control System

The Other control of actions, such as keyboard actions have the similar procedures will be not explained any more.

Conclusions

Our remote control system compares with some popular remote control systems in Chinese market by local network, takes less response time.

Response time of several systems

Monitor System Average Response Time Here, we explained main parts of a remote control system which adopted new image compressing algorithms. This remote control system takes shorter response time and less network traffic than other similar remote control systems.

Figure 3

 3 Figure 1 System Flow Chart

Acknowledgments. This work was partially supported by the Jiangxi Province Science Foundation No.2008GZS0012

Design of Server

Because of adopted new compressing algorithms, Size of compressed image is smaller and system response time is shorter.