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

Jingchao Fan

Lin Hu

Guomin Zhou

Research on the "Three Networks in One" Orchard Production Information Service System

Keywords: information technology, orchardist hotline, orchard production, remote diagnosis, last kilometer

This system has, by the comprehensive use of various latest information technologies and active gateway technologies, linked the wireless communication network, fixed-line telephone network and the Internet network (Three Networks) together to realize the seamless bridging of information, so as to broaden the service scope of the application system on the internetwork. The agricultural experts and/or agricultural technology promoters could provide the information service to the grassroots orchardists by use of this system in an even more convenient and speedy manner and the grassroots orchardists could also obtain the information on the internetwork in a flexible and convenient form, so that the "problem of last kilometer" in the dissemination of orchard information has been effectively solved.

Proposition of the Problems

Since our implementation of the reform and opening-up policies, China's orchard production has seen a rapid development. In 2010, China's fruit cultivation area reached 11.5440 million hectares and the output thereof achieved 214.0140 million tons [START_REF]Network. China's Fruit Production Situation over the Years[END_REF] . China's fruit production has ranked the first place in the world (2) . However, there also exist some problems, which have restricted the healthy and sustainable development of the orchard industry. The first is the poor quality and low ratio of high-quality fruits. At present, China's ratio of high-quality fruits is less than 10% of the total output and the top-grade fruits up to the export standard is less than 5% of the total output [START_REF] Lifei | An Analysis on China's International Competitiveness in Fruits[END_REF] , which has resulted in the low market competition and low benefit.

The second is the backward sustainable development of the orchard industry and consciousness of the safety production and the weak consciousness of the non-polluted production, which has resulted in such a serious phenomenon in which the farm-oriented chemical inputs have polluted the environment and the pesticide residue remains on the fruits or the heavy metal exceeds the standard. The third is the imbalanced variety structural ratio, in which the excessive development of a single variety could hardly satisfy the market demand of the diversified consumption but has increased the pressure of marketing. The existence of these problems have been caused because of various reasons, but one of the main reasons should be the lack of the effective route to timely obtain the new technology and market information, which has resulted in the backward technology, sluggish information and blind production for the entire orchard producers.

With the continuous improvement of China's comprehensive national strength and the continuous development of the project of "Coverage of Every Village", China's vast countryside has basically realized the entrance of telephone and television into the households in the villages. At the same time, with the rapid development of China's wireless communication project, it is no longer the dream for the better-off orchardists to have their own mobile phones. Telephone and mobile phone have gradually become the tools for the broad grassroots orchardists to learn new knowledge and new technology. Then, it is a problem that should be solved in China's agricultural information technology as to how the agricultural experts or agricultural technology promoters could make full use, in an even more convenient and speedy manner, of these advanced communication tools to pass on the agricultural technology to the orchardists and solve the problems encountered by the orchardists in their production. It is, therefore, of important practical significance in respect to the promotion of China's orchard production to research and use the information technology to solve such problems as of the sluggish dissemination and low dissemination efficiency of the information on the orchard production and set up a bridge of communication between the agricultural experts and the orchardists.

Research on the Mode of the Orchard Production Information Service System

The main situation of the telecom facilities in rural area

Since our entry into the 21 century, China's government departments at different levels have attached great importance to the construction of the agricultural information network, in which China's 31 provincial-level agricultural departments (exclusive of Hong Kong, Macao and Taiwan) have established their provincial-level agricultural information network platforms and most of the provinces have established their agricultural information centers. Throughout the country, there are more than 2,000 cities and/or counties that have opened up their agricultural information service websites. Until 2010, the agricultural websites have reached 31,108 [START_REF]'s Republic of China[END_REF] . China has preliminarily formed an agricultural information network system to cover the whole country.

According to the latest statistical results released by China Internet Network Information Center (CNNIC) in January 2012, up to the end of December 2011, the scale of the Chinese netizens had broken through 500 million and reached 513 million, of which the scale of the rural netizens was 136 million, accounting for 26.5% of the total netizens [START_REF]China's Statistical Report on the Internet Network Development Situation[END_REF] . As compared with that in 2006, the rural netizens had increased by 112.89 million and the proportion in the total netizens had gone up to 26.5% from the 1.6% in 2006. This could demonstrate the remarkable increase of the rural residents to approach the Internet. However, as seen from the netizens occupational structure, such netizens as engaged in the agricultural labor accounted only for 7.2% of the total rural netizens (as shown in Figure 1) [START_REF]China's Investigation Report on the Rural Internet Development Situation in 2009[END_REF] , which was still at a quite low level. It could be seen from it that in addition to the weak consciousness of the Chinese farmers to obtain the information by use of the Internet network, the websites are quite short of such information as being interesting, understandable and learnable for the farmers, and the farmers have quite insufficient means to obtain the website information.

As to how to make a fine job in running such websites as favored by the broad orchardists and at the same time to enable more and more orchardists to obtain the website information and to really make the orchard production technology information enter into the households in the villages, it is the first problem for the current solution to solve the "last kilometer" in the orchard informatization.

Because the per capita annual income in the rural areas is comparatively lower and the ability there to pay for the consumption is limited, the Internet-approaching equipment for the netizens in the rural areas is relatively deficient. However, with the increasing convenience of the mobile phone approaching the Internet, the mobile phone has been favored by the farmers because of its lower price. In 2009, the proportion of the rural netizens to use the desktop computer as the Internet-approaching terminals has declined to 68%, whereas the proportion of the netizens to approach the Internet by use of the mobile phone has seen a rapid increase.

At present, China's mobile phone users for the Internet approach have reached 233 million, of which the rural mobile phone users for the Internet approach are about 71.89 million, accounting for 67.3% of the total rural netizens (as shown in Figure 2) [START_REF]China's Investigation Report on the Rural Internet Development Situation in 2009[END_REF] . Since 3G network has already covered all the county towns and most of the townships throughout the country, the mobile phone has become the mainstream of the Internet-approaching terminals for the rural netizens. connected with the telephone, and nearly half of the countrywide townships have established the township information service station and county-township-village three-level information service system. China's telecommunication facilities have reached the objective of "entrance into the households in the villages" [START_REF]and Information Technology of the People's Republic of China. China's National Statistical Bulletin on the Telecommunications Industry[END_REF] .

68 Information Center that the main route for 88.3% of the non-netizens to obtain the information is TV, 35.9% of the non-netizens to obtain the information is newspaper, and the proportion of such non-netizens whose main routes to obtain the information are broadcast, book or magazine is not more than 13% (as shown in Figure 3) [START_REF]China's Statistical Report on the Internet Network Development Situation[END_REF] . It is thus clear that most of the non-netizens are to obtain the information through the TV.

11.1% To make full use of China's existing telecommunication facilities and good use of the television and the SMS called as the "Fourth Medium" is the best route to solve the "last kilometer" in the orchard informatization. For this purpose, we have designed such a "Five-in-One" system mode as of the Internet, 3G network, hotline, SMS and video play (as shown in Figure 4). We have developed the networked By use of their mobile phones and/or telephones as the terminals, the farmers could interact with the application on the Internet network and realize the acquisition of information whenever and wherever possible. Module. Through the Internet or the mobile phone or telephone, the orchardists could directly communicate with the system to inquire any knowledge required to know in the production and any problems that can be solved according to certain rules. The "Three Networks in One" Orchard Production Information Service System consists of such three parts as of China Orchardist Hotline Website (as shown in Figure 6), Orchardist Hotline Background Maintenance System (as shown in Figure 7)

Construction of the Non-polluted Orchard Production Managerial Knowledge System

Implementation of the System

and Digital Orchard Remote Diagnosis System (as shown in Figure 8).

Conclusion

At present, the "Three Networks in One" Orchard Production Information with the orchardists in the promotion pilot zone to realize the "all-weather" information service for the orchardists, which is greatly welcome by the orchardists in the promotion pilot zone. It has realized the rapid promotion in a large area of the orchard new technology, so as to provide the convenient means of decision-making and management for the grassroots technical personnel and broad orchardists in the non-polluted orchard production. It has realized the universality and foolproof of the non-polluted orchard production technology, so as to make it be visible, tangible and usable for the broad orchardists and become the "stationed orchard senior expert" in the orchard zone. All of these have fundamentally improved the technical level of the broad orchard producers and made up such a series of malpractice as resulted from the imperfection of the orchard technology promotion system and the shortage of the orchard technical personnel, so that the "problem of last kilometer" in the dissemination of orchard information has been effectively solved.

Figure 1 .

 1 Figure 1. Rural Netizens Occupational Structure

Figure 3 .Figure 4 .

 34 Figure 3. Main Routes for the Non-Netizens to Obtain Information

  have researched and developed the "Instant Messaging Components" and established the "Non-polluted Orchard Remote Real-time Consultation System". We have provided the orchardists with the on-line consultation and the interface to conduct the "one-to-one" on-line communication with the orchardists and orchard experts. Based on the GSM text message transceiver and OCX control and by use of the API functions provided by the GSM module, we have researched and developed the "Non-polluted Orchard Production SMS Transceiver System". And furthermore, by use of CCITT No. 7 signaling, by means of the digital signal processing (DSP) technology and by use of the universal telephone voice player, we have researched and developed the "Non-polluted Orchard Production Telephone Voice Play System".

Figure 5 .

 5 Figure 5. Functional Diagram of the "Non-polluted Orchard Production Knowledge System" The Non-polluted Orchard Production Managerial Knowledge System (as shown in Figure 5) includes: Orchard Production Technical Knowledge Browsing Module, Orchard Construction Consultation Module, Orchard Annual Production Management Scheme Consultation Module, and Disease, Pest and Weed Prediction and Forecasting

Figure 7 .Figure 6 .Figure 8 .

 768 Figure 7. Interface of the Orchardist Hotline Background Maintenance System

4. 1 .

 1 China Orchardist Hotline WebsiteChina Orchardist Hotline Website (http://www.cart.net.cn/) is the web portal of the "Orchard Production Information Service System" on the Internet. (As for the Android-edition mobile phone users, please visit http://android.cart.net.cn, but for the wap-edition mobile phone users, please visit http://wap. cart.net.cn.) The Website has integrated the "Non-polluted Orchard Production Managerial Knowledge System", "Apple Tree Plant Diseases and Insect Pests Expert System" and "Digital Orchard Remote Diagnosis System" to provide the information service of public benefit for the orchardists throughout the country. The Website includes such columns as of the recommendation of the latest fruit trees and nursery stock, recommendation of competitive orchard, practical cultivation technique, market price quotes, as well as the expert on-line consultation, expert system and training classroom.

4. 2 .

 2 Orchardist Hotline Background Maintenance System By use of the C/S structure, programmed with the C++Builder language and working in the environment of Windows Operating System, the Orchardist Hotline Background Maintenance System is composed of such three parts as of telephone consultation, SMS and information base maintenance. (1) Telephone Consultation and Audio Service: It consists of such four modules as of telephone attendance, running conditions statistics, broadcasting list maintenance and voice base maintenance. (2) SMS: It includes such five parts as of text message receiving, draft mailbox, text message en route, text message sent and user address list maintenance. (3) Information Base Maintenance: This module is a background maintenance for the database of China Orchardist Hotline Website, which contains such functions as of the addition, modification, deletion and details viewing for the data set of each database, and is at the same time characterized by the data synchronization, which means to make the database of the server synchronous with the data on the customer terminals so as to realize the release on the Website of the data maintained by the agricultural experts (or agricultural technology consultant).4.3. Digital Orchard Remote Diagnosis System By use of the B/S structure, the Digital Orchard Remote Diagnosis System consists of such three parts as of the remote consultation, video conference and knowledge inference. (1) Remote Consultation: It contains such functions as of the image-text communication, video consultation, document transmission, electronic whiteboard, viewing and analysis of the information on the orchard cultivation environment. (2) Video Conference: It is designed to provide the real-time on-line consultation function for the experts and orchardists through the construction of the virtual video conference room. (3) Expert System: It is designed to collate the experts' experience and knowledge to form a knowledge inference machine, so as to solve, in the limited steps, the problems that the orchardists may encounter.

  System is mainly applied in Xingcheng and Suizhong of Liaoning Province and the outer suburbs of Beijing. This System has linked the wireless communication network, fixed-line telephone network and the Internet network together to realize the seamless bridging of information, so as to broaden the service scope of the application system on the internetwork. It has closely connected the experts of the Institute of Pomology (IP) of the Chinese Academy of Agricultural Sciences (CAAS)

  Rural Netizens Internet-approaching EquipmentIt is discovered from the statistical findings of China Internet Network

	80.0%	.0%	67.3%
	60.0%
	40.0%
		22.2%
	20.0%
	0.0%		0.2%
	Figure 2.

Acknowledgments

The Orchard Production Information Service System has been accomplished in the support of the National High-Tech ( 863) Program's Project of "Remote Intelligent Diagnosis System for the Field Crop Plant Diseases and Insect Pests (2007 AA10Z237)" and the Agricultural Research Findings Commercialized Project of "Remote Diagnosis System for the Orchard Plant Diseases and Insect Pests and Its Pre-production (2009 GB23260457)", for which we would like to extend our heartfelt thanks.


Xiong Shen

Xiao Zheng
email: bzhengxiao@whpu.edu.cn

Zhiqiang Song

Dongping He

Peishi Qi

Rapid Identification of Waste Cooking Oil with Near Infrared Spectroscopy Based on Support Vector Machine

Keywords: near infrared spectroscopy, waste cooking oil, support vector machine, parameters optimization

The qualitative model for rapidly discriminating the waste oil and four normal edible vegetable oils is developed using near infrared spectroscopy combined with support vector machine (SVM). Principal component analysis (PCA) has been carried out on the base of the combination of spectral pretreatment of vector normalization, first derivation and nine point smoothing, and seven principal components are selected. The radial basis function (RBF) is used as the kernel function; the penalty parameter C and kernel function parameter γ are optimized by K-fold Cross Validation (K-CV), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), respectively. The result shows that the best classification model is developed by GA optimization when the parameters C = 911.33, γ= 2.91. The recognition rate of the model for 208 samples in training set and 85 samples in prediction set is 100% and 90.59%, respectively. By comparison with K-means and Linear Discriminant Analysis (LDA), the result indicates that the SVM recognition rate is higher, well generalization, can quickly and accurately identify the waste cooking oil and normal edible vegetable oils.

Introduction

Catering waste oils include drainage oil (in narrow sense), hogwash fat (waste cooking oil) and fried old oil. After pickling, washing, decoloration, deodorization and other processing, the catering waste oils often close to or completely achieve the national Hygienic Standard of Edible Vegetable Oil in sensory index and conventional typical properties, which consumers and government supervisors are difficult to identify by the sense of the sights and smell. At present, a complete set of testing technology standard of identification of the catering waste oil hasn't been established domestically or abroad. The Ministry of Health is requesting proposals for proposals from the public. Near Infrared Spectroscopy (NIR) technology is a nondestructive testing technique rapidly developed in recent years [START_REF] Wanzhen | Modern Near Infrared Spectroscopy Analytical Technology[END_REF]. The domestic scholars make use of NIR qualitative analysis to research the types of edible oil [START_REF] Jingzhu | Application of NIR technology on identifying types and determining main fatty acid content of edible vegetable oil[END_REF][START_REF] Fuli | Rapid discrimination of edible oil by near infrared transmission spectroscopy using clustering analysis[END_REF][START_REF] Juan | Principal component analysis of 6 kinds of vegetable oils and fats by near infrared spectroscopy[END_REF], however, qualitative analysis for catering waste oil is still limited. Support Vector Machine (SVM) is a new kind of machine learning algorithm based on the minimum principle of statistical learning theory and structural risk, which has advantages of simple structure, strong generalization ability and others. It presents many unique advantages in solving problems of pattern recognition in small sample, nonlinear, high dimension, local minimum [START_REF] Xuegong | Introduction to Statistical Learning Theory and Support Vector Machines [J[END_REF]. The methods combined SVM with NIR have been applied successfully in identifying the category of tea, milk powder, apple and others [START_REF] Quansheng | Indentification of Authenticity of Tea with Near Infrared Spectroscopy Based on Support Voctor Machine[END_REF][START_REF] Zhao Jiewen | Application of Support Vector Machine to apple classification with near-infrared spectroscopy[END_REF][START_REF] Jingzhu | Applied Study on Support Vector Machines in Identifying Standard and Sub-standard Milk Powder with NIR Spectrometry[END_REF][START_REF] Meiying | Identification of Chaotic Optical System Based on Support Vector Machine[END_REF]. The objective of this study is to develop a classified model for catering waste oil and four normal edible vegetable oils by combining SVM with NIR. This model provides a new approach to fast and effective identification of catering waste oil.

2

Experiments and Methods

Experimental Samples

Catering waste oils used in this experiment include drainage oil and hogwash fat obtained through different degree of refining of decoloration, deodorization, and normal edible vegetable oil which are of different brands or the same brand of different batches in major supermarkets. The samples make up of the following table 1

Experimental Methods

Adopt SupNIR-5700 NIRS (Focused Photonics (Hangzhou), Inc.) to collect NIR spectra of all samples. Spectral measurement of samples uses random RIMP software and its testing method is: transmission, measurement range: 1000~1800nm, scanning speed:10 times/sec, spectral resolution: 6nm, temperature of sample cell: 60°C, testing method: load the sample into the three-quarters of sample bottle, and then place the sample bottle into the sample cell. Stabilized in constant temperature for 5min, the bottle is taken out to check if there exist bubbles. It starts to collect spectrogram if there is no bubble, and each sample averages out three times.

Use NIRS random RIMP software and MATLAB7.8 to collect spectra and convert data format, use chemometrics software Unscrambler X 10.1 to pretreatment the spectral data and analyse principal component, and use SVM pattern recognition and regression software package designed by a professor Lin Zhiren from National Taiwan University to build SVM models in MATLAB7.8 and parameters optimization.

Results and Discussion

Pretreatment for Spectral Data

Besides samples' information collected through NIRS, it contains other irrelevant information and noise, therefore, it is very important and necessary to pretreatment spectra before developing model. Many kinds of methods for spectral pretreatment, including mean centralization, normalization, Savitzky-Golay smoothing, Savitzky-Golay first derivation and second derivation and so on, have been tried in this study. The attempted result indicates that NIR obtains the best pretreatment effect by combining vector normalization with Savitzky-Golay first derivation and nine-point smoothing. Fig. 1 shows raw and spectra after pretreatment respectively.

Extraction of Spectral Principal Component

Analyze the principal component of spectra after pretreatment, as shown in Fig. 2-a, the X-axis stands for the first principal component (PC1), Y-axis represents the second principal component (PC2). The figure shows the good effect of sample distribution. This experiment proves that principal component can reflect most of information when principal component's accumulative contributing rate is above 95% and principal component scree plot (as shown in Fig. 2-b) is quite smoothing. Therefore, this paper selects the previous seven principal components (accumulative contributing rate is 96.56%) as SVM input.

SVM Model Building and Parameter Optimization

Libsvm includes two classification models: C-SVC and nu-SVC. Based on oneagainst-one algorithm solving multi-classes pattern recognition, this paper uses C-SVC to establish classification modeling. It needs to select kernel function and parameters when using SVM for pattern recognition. At present there is no unified international model, so we could only use experience or experimental comparison. Typically, using RBF kernel function often gets better simulation results [START_REF] Meiying | Identification of Chaotic Optical System Based on Support Vector Machine[END_REF], and reduces complexity of computation during the training process. Therefore, this paper makes use of RBF kernel function to establish identification model.

It is very important to select penalty parameter C and kernel function parameter γ in RBF kernel function. C is used to measure the size of the penalty, γ is used to control function regression error and directly influence the initial characteristic value and feature vector. The research respectively uses K-CV, GA and PSO algorithm to optimize the models of C and γ to reach the highest accuracy of classification of training set under the best parameters C and γ. However, it cannot guarantee the testing set to reach the highest accuracy of classification. Fig. 3 shows the results of three parameters optimization. Fig. 3-a gives the optimization results using K-CV parameter optimization. Fig. 3-b gives the optimization results of fitness curve using GA parameter optimization, where the maximum number is 100, the population size is 20, the crossover probability is 0.8, the range of parameters C and γ are 0-1000, other parameters are by default. Fig. 3-c gives the optimization results of fitness curve using PSO parameter optimization, where the maximum number of iterations is 100, the initial population size is 20, the learning factor c 1 =1.5, c 2 =1.7, the range of parameters C and γ are 0-1000, other parameters are by default.

Use the default parameters (C = 1, γ= 1 / K = 0.1429) and optimal results of three different parameters to respectively establish the SVM recognition model, which are analyzed in Table 2. From the table 2, it is clear that SVM model recognition rate of the default parameters is very low, and almost four kinds of normal edible vegetable oils can be classified as catering waste oils; recognition rate of SVM model increases significantly about 90% after optimal results of different parameters of K -CV, GA and PSO. The learning ability and generalization ability of SVM classifier with optimal parameters C and γ can keep a balance and avoid the occurrence of learning state and non-learning state. Examples show that SVM classification model established when GA optimal parameters C = 911.331, γ = 2.91045, recognition rate of the 208 training sets and 85 predicting sets is 100% and 90.59% respectively, only occurs four blend oils mistaken for catering waste oil, four hogwash oils for blend oils. In the meantime, compared with methods of kmeans clustering and LDA, the recognition rate of GA-SVM model is higher than those about 10%. Therefore, SVM model is superior to the methods of k-means clustering and LDA.

Conclusions

The research uses GA-SVM to establish NIR classification model for catering waste oil and four normal edible vegetable oils, and determines the appropriate model parameters. The recognition rate of the established models is achieved respectively 100% for training set and 90.59% for predicting set, the recognition rate and generalization ability of GA-SVM of NIR classification model is higher than conventional analysis model, which can rapidly and accurately identifies the catering waste oil.

The sample source of catering waste oil in the research is limited and cannot completely represent diversity and complexity of catering waste oil. In addition, the law breakers usually add catering waste oil to qualified edible vegetable oil according to a certain proportion, and then sell the fake oil, therefore, it needs to further collect representative adulterated samples in the future.

It is essential to keep developing new methods of qualitative classification to research, and constantly strengthen the maintenance for the models of qualitative classification; in addition, a rapid portable detecting instrument for testing catering waste oils based on the models of NIR quantitative classification needs to be developed in order to protect the security of food production, to provide a more reliable basis for food supervisions and to prevent catering waste oils back to the table.

  (a) Raw spectra (b) Pretreatment spectra Fig. 1. Conventional and spectra after pretreatment

  (a) PCA SCORE (b) Explained Variance Fig. 2. PCA SCORE and explained variance

Fig. 3 .

 3 The results of three parameters optimization

Table 1 .

 1 : Composition of the experimental samples

	Training set	Predicting set	In total

Table 2 .

 2 Different parameters-analysis of SVM modeling results

		Default		K-CV		GA		PSO
		(C=1, γ=0.1429)	(C=1024, γ=0.03125)	(C=911.331, γ=2.91045)	(C=2287.16, γ=0.01)
		Returning	Predicting	Returning	Predicting	Returning	Predicting	Returning	Predicting
		error	error	error	error	error	error	error	error
		number	number	number	number	number	number	number	number
	The first category	0	0	2	0	0	4	2	1
	The second category	40	19	1	0	0	0	1	0
	The third category	26	7	0	0	0	0	0	0
	The forth category	15	5	0	0	0	0	0	0
	The fifth category	20	6	20	6	0	4	20	6
	Recognition rate	51.44%	56.47%	88.94%	92.94%	100%	90.59%	88.94%	91.76%

Acknowledgment

Funds for this research was provided by the National Science and Technology Plan Projects (2009BADB9B08), the major projects foster special of food nutrition and safety of Wuhan Polytechnic University (2011Z06), the entrust projects of Wuhan PASHUN Group green energy technology Co., LTD, and the postgraduate 2010 innovation fund of Wuhan Polytechnic University(2010cx005).


Mingfei Zhang

Huiping Xue

Lianzhi Wang

Daoliang Li

A Decision Support System for Fish Feeding Based on Hybrid Reasoning 1

Keywords: Knowledge Representation, Case-based Reasoning, Decision Support System

Considering the complex of decision-making process of rainbow trout feeding, a rainbow trout feeding decision support system is built by integrating case-based reasoning and rule-based reasoning with XML. This paper describes the theory of hybrid reasoning, and several experiment results with their own interpretation. The web based feeding decision support system is proved to have improved the feeding, and lessened the difficulty of obtaining expert knowledge in practice.

Introduction

With the Development of computer network, on-line expert decision support system plays a more and more important role [START_REF] Shim | Past, present, and future of decision support technology[END_REF] [START_REF] Omar F E | A web-based muti-perspective decision support system for information security planning[END_REF] . As a key part of decision support system, the knowledge database provides important foundation for decision [START_REF] Klein | A KB/DSS for financial analysis and planning[END_REF] . How to convert the expert knowledge and experiment into cases and store them in database, and work with reasoning mechanism that generate fast and correct decision information, is a key issue in practice.

The rainbow trout cultivation is complex and it is difficult to abstract the feeding knowledge. In conventional feeding decision support system, people input the quantity, growth stage, and middleweight of fish, and the information of bait feed and nutritional needs, then calculate the feeding scheme using decision model, which mainly accomplished by linear programming, RBR(Case-based Reasoning), and CBR(Case-based Reasoning). RBR is easy understanding, and can efficiently present knowledge, and the disadvantages are: difficult to get and define rules, not obvious relationship between the rules, inconvenience of process knowledge, inadvisable management and maintenance, lack flexibility in reasoning. The advantages of CBR includes: fast speed of reasoning, easy build and maintain case base, more flexible, strong self learning ability; CBR also has its limitations: not easy to express, sensitive to noise, and lack correction mechanism.

In this paper, we tried to combine RBR and CBR, and build a feeding decision support system of rainbow trout. In many cases, a simple CBR method or RBR method cannot ensure the accuracy of result when solving problems. So, the combination of RBR and CBR reasoning is a better model, in which they not only play the respective advantages, but also compensate shortcomings for each other. There are many repeats between the CBR case attributes and the RBR knowledge rules, which contribute to combining reasoning. Depending on the unified representation format (XML, Extensible Markup Language), RBR and CBR perform a seamless connection in the reasoning process. Through realizing a reasoning scheduling system, CBR can visit and call RBR auxiliary unit and RBR correction unit at each stage when they are required. Therefore, whether the RBR is used and when it is used depends on whether the CBR system request for reasoning scheduling module and when request. This ensures the parallel execution of the same knowledge in rules and cases during reasoning process, and reduces the search times, improves the efficiency, but also keeps the independence of unit itself and the entirety of system strategy.

2

Knowledge Pepresentation Based On XML

Features of XML

In decision support system, there are many traditional methods to present knowledge, such as logic, production, frame, semantic network, object oriented, agent, rough set theory, and etc. Those single methods cannot meet the needs of artificial system, and people have put forward many new ways to present domain knowledge, which combined different ways [START_REF] Corchado | A hybrid case-based model for forecasting[END_REF] . In this paper, because of the difficulty of structurization and management of mixed knowledge, we adopted the XML to present knowledge. The advantage of XML is arithmetical, easy organized and management, therefore nowadays more and more researchers use XML to present knowledge and solve problems [START_REF] Mehdi | A new method for knowledge representation in expert system's (XMLKR)[END_REF] .

As an open standard, XML enables organizations and persons to build standard set that is suitable in different situations. Secondly, XML is separate, and the storage format of data does not have to follow the display format. The self-describing feature makes XML better in describing complex data relations. Those properties also make applications that based on XML more correct and effective to search data, and pay no attention to irrelevant content [START_REF] Li Guo-Liang | Keyword searches in data-centric XML documents using tree partitioning[END_REF] . XML has many other advantages, such as good format, abundant display style and convenient data processing ability. All this features can help the rainbow trout feeding decision support system provide easy, capable and exact services for users.

Representation format of rules and cases based on XML

The specific feeding cases described by XML are shown below, in which the attributes of case involves caseName, caseID, caseDate, caseTime, growPeriod and their own weight. The attributes of including water temperature, DO(dissolved oxygen), the content of ammonium, pH, water quality, content of protein in feed, salinity of water, average length of fish, average weight of fish. The case results are feeding rates and feeding advices.

<case caseName="案例1" caseID="2" caseTime="14:00" caseDate="2009- We use the production rule "IF-THEN" to describe the feeding rule, whose type consists with feature attributes stored in database. In the process of reasoning, we achieved one time retrieval, and at the same match and judge case and rule, finally, generated the hybrid feeding advice. The rules are presented as follows.

<rule name ="规则2" type="DO"> <premise unit="mg/L"> <DO restrain="小于" nested="and">7</DO> <DO restrain="大于" nested="no">5</DO> </premise> <conclusion>溶氧不足,需要及时补氧!</conclusion> </rule> 3 Reasoning Based On Case

Case reasoning process

Case based reasoning process can be summarized by three steps [START_REF] Marling C | Case-based reasoning integrations[END_REF] :

(1) Retrieve historical experiments, and find similarities with current questions.

(2) Search the most similar cases from database.

(3) Learn from retrieved cases Meanwhile, many actions, such as retrieve, reuse, revise and save must be done in CBR:

Retrieve: using similarity match to search the most similar cases from database Reuse: using the solutions of similar cases to solve current questions.

Revise: if the historical solution does not fit current questions, adapt it as a base solution to new questions Save: the adapted solution become a new case, and save it to database, mainly the worthy experience and knowledge.

CBR is mainly consist of case presentation, case index, cases storage, case retrieve, case revise and inductions. Aiming at the particularities of hybrid reasoning feeding decision support system, we will discuss the case presentation and case index as below.

3.2

Case presentation based on XML

CaseBase={case 1 ,case 2 , …,case i },
where case i =case(F i ,S i ), present the i-th case of database.

F i =(f i1 ,f i2 ,…,f in )
present the feature sets of case i, f in present the n-th feature of case Ci S i =(s i1 ,s i2 , …,s in ) present the solution sets of case Ci s in present the n-th solution of case Ci. In rainbow trout feeding decision support system, the case database is consist of many cases, and each case includes three child elements, which are respectively case attribute, feature attribute and solution. The case attribute classifies the case, aiming at structuring an efficient case database in indexing and retrieving. The feature attribute plays an import role in reasoning process, and the retrieval of match case mainly depends on the similarity match of feature attribute, which contains feature name and feature value. The solution is the treatment method or conclusion of corresponding cases.

Case index

After saving cases, the system will retrieve and match the case. How to quickly and correctly obtain the case solution is not only an important aspect of evaluation, but also a core part of case based reasoning system. In the process of retrieval, efficiency is not only related to the retrieval algorithm, but also closely with the case database structure. Efficiency is even more critical considering the increasing size of the case database.

Generally, the retrieval indexes of case database are grouped by single index and multi-level index. It is comparatively simple to implement single index, and fit for the case database where not many cases are involved in early phases and the index can be set according to the feature attribute of problems. Therefore, we can have a retrieve and index in accordance with the attribute of the different stages of rainbow trout in the feeding decision system.

In the context of large scale of cases, the multi level index technology presumes there are N cases in the whole database, described as CaseBase={case 1 ,case 2 , …, case i }. At first, we set top level indexes of case attributes according different stages of rainbow trout, and get the first layer child case of m-classes: IndexCaseBase=〈Icase 1 , Icase 2 , …, Icase m 〉, m≤n Then we chose the feature attribute to build the second index that weights more by using clustering method, and built child case of lower levels. As the first index, the mclasses first layer case database contains many specific cases, Icase i = 〈 case i1 , case i2 , …, case ir 〉, r is the case number in i-class layer cases. Finally, those specific cases cluster by the value scale of feature attribute and form the second index. The left layer index can be recursively built by choosing the minor important feature attribute according to the method described above. Fig. 1 shows the case database structured by index tree, using the methods and layer index mechanism, and case feature attribute with value ranges.

Fig. 1 Multi index of case database 4 Combination of Case Reasoning And Rule Reasoning

Considering the Complexity of decision content and the inner shortage of different decision methods, current decision support system more and more depends on multi methods in combination. Decision support system combined with different decision methods performed great effect on different decision content [START_REF] Zhuang Z Y | Combining data mining and casebased reasoning for intelligent decision support for pathology ordering by general practitioners[END_REF][ 9 ] [START_REF] Liu K F R | Integrating case-based and fuzzy reasoning to qualitatively predict risk in an environmental impact assessment review[END_REF] .This paper combined RBR auxiliary unit and RBR revised unit of CBR that are needed in different stages, and integrated them into a unified module. By implementing and visiting the reasoning dispatch system, we can combine CBR and RBR into the greatest extent, without breaking the dependence and completeness of every reasoning unit. In the reasoning process, whether and when to use the RBR completely depend on the request by CBR system towards the reasoning dispatch system, and once called, this not only insures the parallel implementation of same knowledge in rules and cases, decreases the retrieve count and improves the efficiency, but also keeps the integrity of system strategy and unit itself. Fig. 2 shows the hybrid reasoning frame.

Fig. 2 combined RBR and CBR

In a fine-feeding decision support system that combines rules and cases, the knowledge database is composed of a group of rules and cases respectively contain cultivation rule knowledge, and feeding case sample. In the reasoning process, the feeding case sample and cultivation rule knowledge support each other, and perform specific decision task together. In accordance with the hybrid reasoning frame combining rules and cases, the specific decision task will be performed as follows:

(1) According to the main impact factors that influence the growth of rainbow trout that described above, the inputted cultivation information of new case should includes: (2) According to the layered structure, we retrieve the first layer classification index in accordance with cultivation stages, and then retrieve the adult case in accordance with the cultivation stage of adult fishes. In the process of retrieval, by calling the RBR auxiliary selection module, and judging from the water temperature and dissolved oxygen in multi layer index, the retrieve will be accelerated.

(3) Calculate the similarity of retrieved case data and new case, and match the case database by KNN (K Nearest Neighbors). If there is a case that exists in case database: CaseExist=〈e 1 , e 2 , …, e n 〉, and a new case :CaseNew=〈f 1 , f 2 , …, f n 〉, S i represents the similarity of feature i. If the values of feature were quantitative, then S i =1-abs(f ie i )/e i ; otherwise, if the values of feature were same ,then S i =1; else S i =0.

Hence, the similarity can be calculated as formula (1):

% 100 ) / ) ( (      i i i w S w S ( 1
)
When the similarity of case match is less than 0.75, after calculating by the rainbow trout feeding decision support system, all the results will be shown as Fig. 3.

Fig. 3 Results of similarity match

(4) In the reasoning process, the frame dispatch engine provides cultivation advice by calling rule reasoning module according to the cultivation information input .The advantage of hybrid feeding is that 1-time retrieval of case in decision process will serve the purpose. The RBR and CBR were performed on one round, with cross support and cross-dependence in decision reasoning process. For example, according to input cultivation information, the system gives following advices:

Feeding time: feed 2 times once a day.

Feed time: 9 :30, feed amount percentage:50%;

Feed time: 15:00, feed amount percentage: 50%.

Particles Fodders feeding advices: 5mm pellet feed (5) Revise and reuse the case result by calling RBR auxiliary module. If the similarity of retrieval results cannot meet the expectations, and there is corresponding warning information on feeding, we can adjust in new case in view of the warning information, and add it to the database for reversion which will finally be evaluated by experts.

For the cases with retrieval failure, we directly add it to the case database, and get it reversed to experts. For example, when current water temperature is lower than normal temperature that fits for rainbow trout, the cultivator can adjust the water temperature to maintain a healthy cultivation environment according warning information.

Conclusion

This paper presents a feeding decision-making method of combining the CBR and RBR, which made it more efficient in decision retrieval by using the mechanism of case index, and unified the expression forms of rules and cases by using XML to represent knowledge. The decision system combined rule reasoning and case reasoning work in practice has shown that it enhances the professionalism of feeding, and reduces the expert cost in cultivation.

Fund Project: Beijing Municipal Natural Science Foundation (4092024)


Huiping Xue

Lianzhi Wang

Daoliang Li

Design and Development of Dissolved Oxygen Realtime Prediction and Early Warning System for Brocaded Carp Aquaculture 1

Keywords: data correct, neural network, decision tree, dissolved oxygen realtime prediction and early warning

In aquaculture process, common water quality monitoring system just collect data in real-time, and data-monitoring that have been collected is time postponed. It is necessary to conduct prediction and early warning on quality in accordance with its historical state and current state. This paper aims to conduct prediction and warning in terms of the DO content in carp aquaculture using neural network and decision tree, and try to complete the dissolved oxygen Real-time prediction and early warning system through prediction and early warning model research, system design, and system development. The effect in practical application shows that the system can use the two methods to predict DO content, and conduct early warning by value prediction and rule based reasoning.

Introduction

Dissolved Oxygen (DO) is one of the key parameters in intensive aquaculture, the dissolved oxygen content plays a decisive role in affecting the aquatic feeding rate, feed utilization rate and feed conversion rate, and so, it is necessary to monitor the dissolved oxygen. At present, the DO monitor and prediction in pond breeding process cannot meet the need of real production [START_REF] Von Metzen | A Wireless System for Monitoring Polymer Encapsulations[END_REF] , further research is still necessary. In carp breeding process, the equipment that control DO is operated by hand, which is not only waste of cost, but also badly delayed in time. Opening aeration equipment in the case of hypoxia condition has occurred, will inevitably cause losses. The traditional warning and control method just compare the current DO content and specified standards, and open aeration device if it does not meet the requirements, which means bad occurrence is not avoided by precaution.

In this paper, we use the data checking and correction method to ensure the data accuracy and effectiveness that obtained. The method of compensation calculation was established after communication with culture experts, and it is based on the practical experience in breeding.

The system was deployed in Yixing agriculture network platform and Xiaotangshan carp farms, and the results of testing and operation achieved a desired effect comparatively. The real-time dissolved oxygen prediction and warning made breed staff can aware of the dissolved oxygen content for the moment and a period of time later (10minute, 30minute, and 60minute).

Theory

Data Check and Correct

Remote data checking module will check the obtained data, and call modification module and check data if the abnormal data was found, and make it into reasonable range. The specific processes is, firstly, census data and analyze the data distribution, find the maximum and minimum value, secondly, communicate with the breeding experts and after comprehensive analysis, get the range of parameters. The proportion of parameter changes is calculated by following formula:

                   i i i y y y abs 1 max (1)
Among them: (2) Among them: y represents the to-be-checked data, base represents the number of compensation, i y represents the historical data, i w represents the weight. The number of compensation refers to the number of data that referenced in order to correct and compensate data when the data is missing or error.

Real-time Prediction and Warning of DO Natural Network

Neural network simulates human brain memory and learning activities, and can solve problems such as classification, identification and prediction [START_REF] Caudell | Neural network architecture for linear programming, Neural Networks[END_REF] . BP algorithm is a kind of feedback neural network algorithm, and is realized by multiple iterations. A learning process is composed by forward propagation of data input and backpropagation of error. In this paper, we considered the actual situation of carp breeding, and take three layer network structures.

Decision Tree

Decision tree is a kind of association rules, and is based on the inductive learning method as the given sample, using up-side-down recursive manner to produce tree structure that similar to flowcharts [START_REF] Abdelhalim | A New Method for Learning Decision Trees from Rules, Machine Learning and Applications[END_REF] 。 From the root node, choose the most appropriate described attributes as branching attribute with reference of the given metric standard, and establish branches down according to different values. The advantages of decision tree classification method are: less time-consuming, simple and intuitive model, easy to understand. The C4.5(an improved algorithm of ID3) algorithm can not only deal with discrete description attribute, but also continuous description attribute. It selects the attribute that have the maximum information gain ratio and classify the training samples, aims at minimizing the system entropy when branching, and improve the computing speed and accuracy. The simulation results that using decision tree to predict dissolved oxygen content are shown as Fig. 1.

Fig. 1 DO predict results of decision tree

DO Warning

Warning refers to measure a factor in present and the future, and predict abnormal state and possible injury [START_REF] Han | Research on early warning system of water quality safety based on RBF neural network model[END_REF] 。On the early warning process, firstly, we analyze the surrounding environment and conduct early warning on object's internal factors in quality and quantity. Secondly, identify the trends, speed and range. Finally, do the warning and remind for events that might occur and measures that can be taken. The DO warning means to analyze and evaluate the DO content of water body in a certain period of time, and determines the change trends of dissolved oxygen content, and predicts the abnormal condition that maybe happen. In the process of freshwater pond breeding, the water quality warning is sudden, lagged, complex, concentrated, and dynamic [START_REF] Wang | AWQEE-DSS: A Decision Support System for Aquaculture Water Quality Evaluation and Early-warning[END_REF] 。 This paper do the numerical prediction according to the prediction results of neural network and decision tree, and also calculate the warning state through rule based reasoning of the various parameters of water quality and their respective effect relationship.

System Design

Data collection and database design

The real-time database is a supporting part in development of real-time control and data acquainting system [START_REF] Huiming | The researching and application of historical data processing in real-time database system, Computer Application and System Modeling (ICCASM)[END_REF] , which can help user collect and store data in real time and provide effective data sources for information mining in the upper layer software.

Real-time database also contributes to the monitoring and optimization control, and also provides real-time data service, data management, scheduling, data analysis, decision support and remote online browsing for the enterprise production process.

The database call relation of this system is shown as Fig. 2:

Water data

Meteorologi cal data

BP key parameter

Time(hour)

Decision tree prediction

Neural network prediction

Warning line

Predict results

Predict results

Neural network Warning results

Decision tree warning results

Early warning rules

Rule based reasoning result of early warning

User information table register、view、 delete

Fig. 2 database design

The Frame of Real-time Prediction and Early Warning System

The Real time prediction and early warning system can automatically monitoring, predict and warn on irregular data, and at the same time respond to users' requests such as viewing, modification, and so on. The workflow of this system varies by role. First it verifies the user's jurisdiction, then responses to different service contents according to different roles. Fig. 3 shows the framework of system.

Common user: Users request for viewing the data and perform prediction, and the system responds to them. If user views the historical data, then the system calls the database of historical data directly and displays the corresponding results on the HTML page. If user views the predicted or early warning information, then the system first do the calculation process and gets the needed data, then store it into database and finally show them on a page after calling the database. The aim is: the database can save each step during handling process, which not only provides a guarantee for users' viewing, but also provides traceability data and log records when error occurs in future.

System Testing and Running

In prediction module, the default parameter list is stored in the database, and is read from database when in use. This can not only ensure that the breeding expert modifies the parameter into reasonable value according to their own culture experience, but also make interaction with the system possible, while the system is able to respond to the requirements of experts and calculate a more accurate prediction value. After reading the relative prediction parameters, system performs the dissolved oxygen content prediction modules in different times, and gives the corresponding prediction value. The prediction results for 10 minutes are shown Fig. 4.

Fig. 4 dissolved oxygen prediction curve in 10 minutes

In addition, the user can view the prediction results of dissolved oxygen content in 10 minutes, 30 minutes, and 60 minutes. Fig. 5 shows the predict value of DO content using BP algorithm.

Fig. 5 predict results

In the parameter management module, the expert can view and modify the critical parameters, including learning rate, momentum factor, the number of hidden layer nodes, learning steps, and training times, and etc.

Conclusion

Taking the carp breeding as research object, this paper analyzed DO prediction and warning method in the practical breeding process, and developed a real-time prediction and warning system using Java programming technology. This paper has certain guidance significance in the practice of aquaculture production, and at the same time has reference value for other species culturing. In addition, breeding needs experts to be participating, especially in making the inference rules.

iy

  represents the obtained data in time t ,

Table 1

 1 Default parameter list of predict module

	Predict time	Default sample number when	default learning times
		training
	10min	500	3000
	30min	400	7000
	60min	300	10000

Fund Project: Beijing Municipal Natural Science Foundation (4092024)

Acknowledgements

Research work of this paper was supported by The Beijing Natural Science Fund Project (No 4092024) .Thanks to the domain experts in Beijing Freshwater Fisheries Technology and Engineering Research Center for their long term support.

Fig. 3 system framework

Expert: Expert is responsible for updating the local database, modifying the relevant parameters of prediction and warning, such as parameters of neural network prediction, rule base of warning reasoning.

Administrator: The administrator is mainly responsible for the users data maintenance work, including viewing user information, deleting users and other functions.

In short, the system implement functions according to the request of users, while keeping a real-time prediction and warning function, and perform remote monitoring data, predicting dissolved oxygen content and executing early warning functions no matter whether there is any manual interaction.

4

System Development and Implementation

MVC Model

MVC is the abbreviation of Model, View and Controller [START_REF] Wojciechowski | MVC model, struts framework and file upload issues in web applications based on J2EE platform[END_REF] . Both The model and controller can update the view layer content, and the controller can also change the state of the model. The view layer passes the user's operation to the controller and at the same time gets a new state from the model. The view layer is the pages that user can see, usually made of HTML, JSP, ASP, which contains form, servlet path that processing page request,i.e. the URL address of action.

Control layer processes business of view and model layer, and reads the page information, in response to user request and interactive model, and finally return information to page, which was generally handled by the configuration file web.xml.

Model layer is mainly composed of Java files, classes and servlet definition files. According to the research object, the JSP page consists with user needs, and the model is responsible for realizing the various functional models, and servlets (control layer) response page and call for the corresponding model.

The Implementation of DO Real-time Predict and Early Warning System

The prediction module includes two kinds of methods: neural network prediction and decision tree prediction. These serve for dissolved oxygen prediction, and belong to the same prediction module, including performing prediction, saving the results, showing the results. However, they are different in the principle of realization.

Neural Network Prediction Module

Neural network prediction module learns historical data, and input the recently acquired data to the network, then predicts the DO content in the next period. The module needs to be normalized .Neural network prediction process is divided into two main parts: preparation stage and working stage. In the preparation stage, there are two parallel activities: access to data and normalize, instantiate prediction class and acquaint parameters .In the working stage, it trains model firstly, and then uses the trained model to perform the final prediction, and save the prediction result into database, so that it contributes to the viewing and using.

Decision Tree Predict Module

This paper established the three-layer decision tree prediction module according to the prediction model previously built, and depth of 1, 2, 3 of the decision tree was set up and to predict dissolved oxygen content. Each prediction result was stored into the database, so users can view it. The implementation process of this module is divided into two parts: calculate the expected information that training set needed when doing classification, and information acquisition ratio of each attribute when the training set is partite.

DO Early Warning Module

Warning module includes two kinds of methods: according to the prediction value and rule reasoning, in which the prediction value is divided again for the predicted values of the neural network and decision tree to predict value. According to the prediction value of early warning, each alert level is stored into database. When performing the warning, system reads warning line from database, and then the prediction value and early warning limits are compared, and finally the result of early warning is given.

When reasoning according to the rules, the system reads reasoning rules from database, calculates and infers, and gets the result of early warning. In addition, the role of experts can also modify warning rules.


Fengyun Wang

Lin Mei

Wenjie Feng

Lei Wang

Limin Wang

Huaijun Ruan

A Greenhouse Control with Sectional-control Strategy Based on MPT Intelligent Algorithm

Keywords: Greenhouse, MPT, Sectional-control, Fuzzy

The greenhouse are classified as complex systems such as large lag, multi-input multi-output (MIMO), non-linear and difficult to create mathematic model and so on, so it is difficult to implement classical control methods for this kind of process. A new sectional-control strategy is put forward. It uses different control modes for different deviation domains. The new strategy is based on MPT intelligent control algorithm which improves on the traditional PID algorithm and adds in self-adapting, fuzzy control, expert self-tuning etc intelligent control functions. When the deviation exceeds a certain domain, fuzzy control is used to prevent the saturated integral; when the deviation reduces to within a certain domain, MPT algorithm is used to reduce the overshooting during response process and eventually eliminate the residual. The application in greenhouse control shows that the sectional-control strategy makes the output tracing the set value correctly.

Introduction

The agricultural greenhouses were used to protect the crop against the weather changes. With technical progress, the greenhouses have become a production means used to control the crop environment in order to obtain higher quality [START_REF] Trabelsi | Fuzzy identification of a greenhouse[END_REF] . To achieve environmental conditions favorable for plant growth, greenhouses are designed with various components, structural shapes, and numerous types of glazing materials. They are operated by hand or by control system differently according to each condition.

The control object of greenhouse is the environment system which factors such as humidity, temperature, CO 2 density and so on are affected not only by external climate, but also by the interaction between the environment and the plant. In control system, they are also controlled by the control components [START_REF] Dingcheng | SVM Regression Modeling for Greenhouse Environment[END_REF] . Therefore, the greenhouse system is a non-linear, multi-input multi-output (MIMO) complex system they present time-varying behaviors and they are subject to pertinent disturbances depending generally on meteorological conditions [START_REF] El Ghoumari | Non-linear constrained MPC: real-time implementation of greenhouse air temperature control[END_REF] . All these make it difficult to describe a greenhouse with analytic models and to control them with classical controllers.

The traditional PID algorithm was widely used because it needn't know the model of controlled object. But it is difficult to realize the high control accuracy when reduce the overshooting. In our case, we put forward a new MPT algorithm which improves the PID algorithm and adds in self-adapting, fuzzy control, expert selftuning etc control functions. On the base, a new sectional control strategy is created. When the deviation is beyond a certain domain, fuzzy control rules are used to eliminate the integral saturation. When the deviation is within the domain, MPT is used to reduce the overshooting and eventually eliminate the residual. The feature of sectional strategy is that it needn't consider the accurate mathematical model of controlled object. Finally its application in greenhouse is given and the results show that it can obtain the ideal effects.

2

MPT algorithm

The simplified expression of traditional PID algorithm is as follows:

Output= Proportional action( P )+Integral action(

I )+ Derivative action ( D ) (1)
In traditional PID control system, it is difficult to reduce the overshooting at the same time improving the control accuracy. The main reason is the integral action. If it reduces the integral action, the residual isn't easy to eliminate, and when there is disturbance, the speed to eliminate the error is slow. If it enhances the integral action, the oscillating process intensifies and it is hard to avoid overshooting. Therefore, PID algorithm is partly improved as follows:

Output= Proportional action ( P ) + Integral action ( I ) + Derivative action (

D ) + Derivative-integral action (  I ) (2)
Because derivative-integral action is added in formula (2), the integral saturation is greatly improved. But we can see from formula (2) that there is a new parameter (  I ), therefore these parameters must affect each other to make the new parameter more difficult to confirm. Through serious research and experimental analysis, the ratio between proportional action and derivative action is same to the ratio between the integral action and derivative action and the best ratio is related to the lag time of controlled object. If the lag time is longer, the response of proportional action decreases and the response of derivative action increases. The relationship is as follows:

Proportional action= ) / 1 ( t K (3) Derivative action= d t K ) / 1 1 (  (4)
Where, K -Coefficient; t -Ratio between lag time and control period, 1  t ; d - Derivative action. Then the formula of improved algorithm of PID is:

Output=       ] ) / 1 1 ( / 1 [ ) / 1 ( ] ) / 1 1 ( / 1 [ d t t M d t t P (5)
In formula (5), P is used to regulate the derivation and proportion. When P increases, it increases the derivative time and decreases the proportional band at the same time. Verse On the contrary, when P decreases, it decreases the derivative time and increases the proportional band.

M is similar to integral time to regulate the integral action and derivative-integral action.

t is used to regulate the mutual proportion between derivative action and proportional action.

If 1  t , the derivative action is zero. If 0  M
, the integral action is zero. Thus the control parameters are reduced to 3. Because the traditional PID parameters are only according to the algorithm self and its feature is that it needn't know the accurate model of controlled object. Because three improved parameters differ from the original parameters, the improved PID algorithm is defined as MPT control algorithm and the implication of three parameters as follows:

M

M50 is retaining parameter. The definition of M50 is the difference between stable measured value when the output value is 50% and stable measured value when the output value is 0%. Smaller the M value is and stronger the integral action is. Larger the M value is and weaker the integral action is (integral time adds). If 0  M , the integral action is cancelled.

P

P is rate parameter. P is inverse proportional to the change of measured value when the output changes 100% within one control period. The definition as follows: / 100  P change of controlled parameter every second. The best value of P is usually confirmed by self-tuning method. P value affects the proportional action and derivative action. Proportional action and derivative action increase proportionally with P value increasing. Proportional action and derivative action weaken correspondingly with P value decreasing. P is unrelated to integral action.

T

T is lag time parameter. T is the time required when the output rises to 63.5% of maximum value and its unit is second (s). T is introduced and set correctly to overcome the overshooting and oscillation completely and make the response at the best speed. T affects the proportional action and derivative action. Smaller T value is, stronger the proportional action is and weaker the derivative action is. Larger T value is, the proportional action weakens and derivative action enhances. If T is less than or equal to control period, the derivative action is cancelled completely and when the regulation becomes proportional or proportional-integral.

Intelligent Sectional strategy

The intelligent control is with some intelligent characteristic as follows: (1) Integrate the human's experience with control theory. ( 2) With the capacity of on-line studying, modifying and creating new knowledge. (3) Deal with qualitative and quantitative information and fuzzy and accurate information. ( 4) With stronger logical reasoning and analytical decision-making than traditional control [START_REF] Wu Hongxin | Intelligent control based on description of plant characteristic model[END_REF] . The intelligent control is the development of traditional control theory and combination of control theory, computer technology and artificial intelligent technology. Therefore it is with capacity of self-adapting, self-studying and self-organization [START_REF] Fourati | A greenhouse control with feed-forward and recurrent neural networks[END_REF] .

Self-adapting

The greenhouse is a complex control object with severe non-linear characteristic and intercoupling between parameter and parameter which is solved by self-adapting control mode. The feature of self-adapting is that when the controlled deviation is larger than the estimated error, the self-adapting system doesn't modify MPT parameters but modify the output to lower the error. Though the modified range is limited, the phenomenon that original correct controlled parameters are modified to wrong value doesn't happen to make the system respond rapidly which greatly improve the control accuracy.

Self-tuning

In the intelligent algorithm, the traditional PID algorithm parameters have been modified to MPT parameters. In order to confirm MPT parameters, a set of selftuning expert system is introduced. Because MPT parameters are described for controlled object, its self-adapting and self-tuning are simpler than traditional PID parameters and accurate. In general case, if the operation of self-tuning is correct, its success rate is almost 100%.

The process of self-tuning uses position control to regulate the system. After the oscillation, the lag time parameter T is confirmed by the period and the rate parameter P is confirmed by oscillation amplitude. It isn't easy to confirm the parameter M directly. For temperature, it is usually assumed that the measured value is 25℃ when the output is zero and parameter M is confirmed in accordance with the output when oscillating. For linear input, M value is its scale range. Therefore the best values of parameter M and T can be obtained by self-tuning, but parameter M is only rough. In addition, if the rate parameter or lag time is very long, self-tuning may also increase the control period to make the system conform to the actual requirements of controlled object.

Fuzzy control

The classical fuzzy controller uses the fuzzy set theory to directly convert the language rules formed by expert knowledge or operator's experience into automatic control strategy (usually the fuzzy rules table) which doesn't rely on the accurate mathematic model of controlled object but use its language knowledge model to design and modify the control algorithm [START_REF] Huaguang | Current state and development multivariable fuzzy control (I)[END_REF][START_REF] Trabelsi | Fuzzy identification of a greenhouse[END_REF] .

When the controlled deviation is very large, it hasn't too large significance for the output by formula regulation which inversely produces integral saturation. Though it uses MPT algorithm, if the controlled parameters are set incorrectly, the integral saturation or over-integral phenomena will also occur. Therefore when the controlled parameters are beyond the proportional band, the fuzzy control is used to confirm the output which can obtain ideal control effect.

Sectional strategy

The sectional strategy adopts different control modes in the deviation domain. Its block diagram is shown in Fig. 1. When the deviation is larger than a certain domain, the fuzzy control is selected by operation mode selector to overcome integral saturation and confirm the output by fuzzy rules. When the deviation reduces within a certain domain, selector switches to MPT algorithm to decrease the overshooting during responding and eventually eliminate the residual. Therefore the sectional strategy integrates the merits of MPT algorithm and fuzzy control to improve the sensitivity and accuracy and obtain the ideal control effect. The application is in a new built phalaenopsis greenhouse to effectively combine phalaenopsis planting with greenhouse control design to realize the optimal growth environment of phalaenopsis. The parameters that affect the growth of phalaenopsis are light intensity, temperature and humidity etc. The main parameters we will discuss hereinafter are light intensity and temperature.

4.1.1

Temperature There is a critical temperature, 25℃, between the vegetative growth and reproductive growth of phalaenopsis and the critical state is during the flower spikes appearing. During the period of peduncle growing out, the temperature should be kept at 20~25℃ in daytime and 22~18℃ in nighttime. This period will last 20~30 days until the peduncle grows out. The sensitivity to temperature for various phalaenopsis is various. The minimum temperature for phalaenopsis is 2~3℃ in dry environment and 15℃ under general humidity condition. During vegetative growth phase, the temperature is usually over 25℃.

Light intensity

The light needed by phalaenopsis growth is lower and continuously increases with its growth. Light intensity in various growth phases is shown in Table 1.

Humidity

The requirements of humidity for seedling and flowering of phalaenopsis are various. The ideal humidity during the period of seedling is from 70% in daytime to 90% in nighttime. During this period, the higher the humidity is and the quicker the seedling grows but easier the germ grows. The ideal humidity during the period of mature plant is from 60% in daytime to 80% in nighttime.

Control system

Hardware

The system is composed of measuring loop, controller and output loop. It may include one computer and printer depending on the requirements. In according to the production requirements, the measuring loop mainly measures the light intensity, temperature and humidity. So the sensors are light intensity sensor, indoor and outdoor air temperature sensors, soil temperature sensor, and indoor and outdoor air humidity sensor and soil humidity sensor. The controller mainly collects the signals from the sensors, then carries out A/D converting and analysis at last makes decision to the output loop, drives the relay after D/A converting so as to control the control equipments and adjust the environment conditions. The output loop mainly includes the inner and outer shade net motors, side window motor, wet curtain motor, fan, circulating motor and spray irrigation trolley.

4.2.2

Software

The system adopts MPT intelligent algorithm based sectional strategy. The develop environment for controller is Windows2000 +KeilC7.01+Wave+Labtool48. The operating enviroment is W77E58 + 32KBFlash+watchdog+extending parts. The software realizes the parameters set, data and time display, alarm information hint and online state indication etc [START_REF] Efré N Fitz-Rodrí Guez | Dynamic modeling and simulation of greenhouse environments under several scenarios: A web-based application[END_REF] .

Application and conclusion

The system was installed in the phalaenopsis greenhouse in Jining Agricultural Hitech Demonstration Zone. The ideal values of parameters are set in according to the phalaenopsis growth model [START_REF] Zhang Xiaoyan | Design of implementation on dynamic vegetation model of greenhouse phalaenopsis[END_REF] and expert knowledge library [10] . Fig. 2 When it didn't take warming measures, the greenhouse only kept the inner temperature by the heat preservation of itself in night and adjusted its temperature by side window, wet curtain and fan etc. It didn't take supplementary lighting measure in night and controlled the light intensity by inner and outer shade net. We can see from the curve that the sectional strategy based system can trace the set values well.

Fig. 1 4 Application in greenhouse 4 . 1

 141 Fig. 1 Block diagram of sectional strategy

  is the comparison curve between actual measured values by the indoor and outdoor air temperature sensors and the ideal value from zero clock on 24 th , Sep., 2006 to zero clock on 25 th , Sep., 2006. Fig. 3 is the comparison curve between actual measured values by the indoor and outdoor light intensity sensors and the ideal value from zero clock on 24 th , Sep., 2006 to zero clock on 25 th , Sep., 2006.

Fig. 2 Fig. 3

 23 Fig.2 Comparison curves between the measured temperature in greenhouse, outdoor temperature and ideal value

Table 1 .

 1 Light intensity in various growth phases

	Growth phase	Bottle seedling	Little seedling	Middle seedling	Big seedling Mature plant
	Light intensity (lux)	5000~7000	7000~10000 10000~15000 15000~20000 20000~40000

National science and technology support plan of P. R. China


Jianlun Wang
email: wangjianlun@cau.edu.cn

Yu Han

Zetian Fu

Daoliang Li

Jianshu Chen

Shuting Wang

Edge Geometric Measurement Based Principal Component Analysis in Strawberry Leaf Images

Keywords: Greenhouse strawberry leaves, Shape feature description, Principal Component Analysis, Edge geometric measure

Edge geometric measurement analysis is an important method of image understanding and portraying the target feature. In this paper, we compress 17 interrelated shape descriptors which are based on edge geometric measure into 6 independent components, and discuss their meanings by using principal component analysis. The analyses in this article provide guidance for the shape feature optimization and accurate identification for greenhouse strawberry leaves images successfully.

Introductions

The performance comparison of several common description of shapes which are used in identify the leaves of strawberry grown in the greenhouse。Strawberries have a short growth cycle, less disease, easy to manage, high nutritional value, economic value advantages, widely grown in the greenhouse [START_REF] Sun | Strawberry cultivation technology in greenhourse[END_REF]。With the popularity of hardware and image processing technology matures, many field of agricultural engineering applications [START_REF] Jiang | The computer image technology in Agricultural Engineering[END_REF] have used a variety of techniques based on computer vision, such as automatic classification of the fruits and vegetables [START_REF] Liu Etc | Fruit shape classification based on wavelet descriptors[END_REF], quality traceability [START_REF] Zhang | Forest products traceability system research[END_REF], the robot picking [START_REF] Cui | Image processing technology in the application of agricultural robot[END_REF], growth status monitoring, and the early warning of pest and disease [START_REF] Xiang | Agricultural pest image remote transmission system research and design[END_REF]. Among them, the target recognition are facing with many problems of different varieties of agricultural crops, complexity of the imaging background, range of issues such as characterization as a vital process in image processing, so it is difficult to form a high recognition rate of the general algorithm.

Therefore, this paper according to greenhouse strawberry leaves, summarized 17 ways of shape description. On the high dimensional feature dimension reduction is compressed into 6 comprehensive index by using the principal component analysis , and has carried on the detailed explanation to provide guidance in getting more effective characteristics selection and comprehensive evaluation in practical application.

The shape descriptors in target region

The main difficulties in shape quantitative description is the lack precise and uniform definition. ASM think that shape parameter which are used to describe the micro-structure have some common in no dimension, quantitative description and sensitive to shape change [START_REF] Zhang | Image engineering (Book) -image analysis Second Edition[END_REF]. This paper will divide geometry characteristic which are commonly used into the following several forms:Geometric description of the target boundary(Including the ratio between perimeter and area, fineness ratio, eccentricity, etc)、Measure of the target area and its surround polygon(Including Surrounded by rectangular compact, surrounded by polygonal compact degree, etc)、Geometric description of the target area (Including Centroid offset rate, Area symmetry, Circumference symmetry, etc)、Invariant moments(Hu Invariant moments)。We will give explanation about it one by one below:(Among them,A represents the area , represents the circumference , L represents the length of bounding rectangle,W represents the width of bounding rectangle,Aconvex is reprensents target convex hull area):

(1) Perimeter area ratio:P/A,Range in (0,1)。

(2) Fineness ratio:4*Pi*A/(P*P),Range in (0,1)。Get the maximum values(1) while contiguous area is round。

(3) Exterior ratio : W/L , Used to describe the shape of the target after plastic deformation, Take the maximum value(1) of a square and circular target and it will be reduced with slender degree about slender target。 (4) Eccentricity:The eccentricity of target which is equivalent to an ellipse(E), and the equivalent ellipse is defined as a voal which has the same centroid and secondary center moment with target。 (5) Skeleton eccentricity:Eccentricity of skeleton equivalent ellipse (E),and skeleton equivalent ellipse is defined as a fitting ellipse which has the same skeleton with target。Generally,In order to avoid the influence of finely branch in skeleton, the first to do is deburring skeleton and then fitting ellipse。 (6) Compactness of bounding rectangle:A/(L*W),Reflects the bump on the bounding rectangle of the target area. are represents the area size of left and right side of symmetry axis , regional symmetry in the area. It's the performance of regional symmetry in the area.If the region is completely symmetrical ,the maximum value will be 1.

(10) Perimeter symmetric degrees : min(Pleft,Pright)/max(Pleft,Pright),Pleft and Prightare represents the perimeter size of left and right side of symmetry axis , regional symmetry in the area. It's the performance of regional symmetry in the perimeter.If the region is completely symmetrical ,the maximum value will be 1.

(11) Hu Invariant moments:Ming-Kuei Hu apply algebraic invariant moment to pattern recognition and regard seven moment features about rotation and scaling translation invariant in the target area as the target shape characteristics。

Statistical analysis about the descriptor of shape

Statistical frequency of the above shape descriptor as follows to show:

Principal component analysis of the shape factor

Principal component analysis is a statistical method which is first proposed by Pearson in 1901, and then developed by Hotelling (1933). Its main purpose is that to reduce the number of variables, and make change for the formation of a linear combination of a few independent variables (principal components), but by the difference of the linear combination of ingredients into the largest, makingthe original multi-dimensional characteristics of these principal components show the greatest individual differences. In short, Principal component analysis is a number of variables into a few principal components ( i.e. integrated variable ) dimension reduction statistical method.

The general steps are as follows:

(

1) The standardization of original data

First of all to the standardization of original data, each index data of the sample is mean 0, variance 1.

    17 , 2 , 1 , , 2 , 1       j n i j j ij ij      ij
as the i sample of the j shape descriptor value,  j as mean value of the j shape descriptor value,  j as variance of the j shape descriptor value. In the principal component analysis of SPSS statistical software, standardization is automatically executed.

(2) Calculate the indicators of the correlation matrix R (

  17 , 2 , 1 , , , 2 , 1 )
( ) ( ) )( ( 1 1 2 2 1                j n i x

3) Seek the correlation matrix R eigenvalue and characteristic vector

The characteristic equation is expressed as , Commonly used Jacobi method to calculate the eigenvalues and the corresponding eigenvectors, and the size of the eigenvalues in descending order. The correlation matrix are given below:

  p i p k k , , 2 , 1 1 i      
The cumulative variance contribution rate:

  p i p k k i k k , , 2

Experiments and results

The first principal component eigenvalue is 6.783, the variance contribution rate is 39.9%, the entire data of standard variant is 39.9%, only relying on the first principal component can not reflect most information of original data. The first principal component in geometric invariant feature has high positive loads, in Appearance ratio and bounding rectangle compactness has high negative loads, the centroid offset rate has low positive loads, in the area and perimeter symmetric rate has a low negative loads, in other variables have similar secondary loads. That is to say, large (small ) to the first principal components tend to be large ( small ) geometric moment invariants, then appearance ratio and bounding rectangle compactness tend to have smaller values ( large). As a result of invariant moments are based on the regional global statistical feature, with similar but different sets of images ( like the fruit with similar to leaves )has better identification of. Therefore, the first principal component can be used to characterize the morphology of the more slender, not completely symmetrical, with concave boundary, and the global features of difference image sets with strong discernment.

The second principal component feature value is 3.334, the variance contribution rate of 19.614%, the entire data of standard variation is 19.614%.Through the observation to load factor matrix in table 4, we find that the second principal component in most variables are approximately equal to the load, so can be used as a comprehensive metric variables, reflect almost all the features of the original nature.In addition, because the main components in the bounding rectangle of compactness and area symmetrical rate have a low normal load and the perimeter symmetric rate with a low negative load. Therefore, while the second principal component analysis in comprehensive measure all the features, it slightly weak impact of foliage bending on region side.

The third principal component feature value is 1.504, showing that the entire data of standard variation is 8.847% separately. The third principal component in the area of symmetric rate ratio has high loads, in circumference symmetrical ratio, fineness rHu1 and Hu2 moments have high positive load.To the leaf image, if the edge has some curl, it may not affect the perimet circumference, but it changed the blade on the imaging surface of two-dimensional area, causing the image has high perimeter symmetric rate, and low rate of area of symmetry. Therefore, the higher of the third The sixth principal component feature value is 0.848, showing the entire data of standard variation is 4.985% separately. In the perimeter symmetric rates on sixth principal components have higher negative loads, and in Hu1 and Hu2 with higher positive load, in other variables are not high load. Because Hu1 and Hu2 are calculated by using image moment of order two , and the image of two order moment is not included details of the general information,in the geometry mean variance, if we consider only the order for 2 sets of the moment,the original image is completely equivalent to the image centroid as the center and has the same two order moments of the ellipse. Therefore, the higher of the sixth principal component values tend to express elliptical rather than circular, may have side edges curled area,but the direction and size of the show is weak.

From the above analysis we can see that the first six principal components of the cumulative variance contribution ratio reach to 87.1%, it can be very good summary of the data group. And the six main component covers the area of concave and convex of elongated,the complexity symmetry of shape ,the centroid position and direction, as well as statistics based on geometric moment invariants and other aspects of information, a reasonable description of the region shape characteristic, the original 17 features integrated into 6, while retaining the original variables most of the information under the premise, realize dimension compression of the data.

Conclusion

This article from the 17 dimensions of the quantitative describe the single plane shape characteristics, including the target boundary geometric description (perimeter area ratio, fineness ratio, eccentricity and so on ),the target area and its surrounding polygon metric ( bounding rectangle compactness, encircle polygon compact degree and so on ), the target area ( centroid offset ratio, area of symmetry,perimeter symmetry degree and so on) invariant moment ( Hu ), covering graphics elongated, convexity, complexity, symmetry, centroid direction, ellipse, compact plumpness, number of shape, geometric invariant moment etc.. Then through by the method of principal component analysis to do variable compression on these characteristics, explaining the he first six principal components which cumulative variance contribution rate reach to 87.1%, in which elongated, convexity and geometric invariant moment contributed to the first principal component is larger; the second principal component can be used as a comprehensive measure factor, each characteristics of second main components in the contribution rate is almost equal; the third principal component more reflect the region of the ellipse and elongated; the fourth principal component in global invariant moment statistical properties of the performance is not outstanding, but focused on the expression of slim and compact plumpness; the higher of the fifth principal component values tend to areas near left lower direction of the center of mass, which leaves the image on the right is fine, but left is wide, image centroid shift; sixth main components on the ellipse has stronger descriptive power, but the direction and size of the description of is a little weak.

( 7 )

 7 Compactness of bounding polygon:A/Aconvex,Reflects the bump on the minimum convex hull of the target area。 (8) Centroid offset rate:The ratio of the distance between centroid to boundary in the upper-left corner and lower right corner to the upper left corner,it reflects the relationship of the centroid position in the target area。 (9) Area symmetrical degrees:min(Aleft, Aright)/max(Aleft, Aright), Aleft and Aright

  of I rows and j columns representing related elements, namely the index I and index J correlation coefficient.



  Generally it takes the cumulative contribution rate of 85% ~ 95% m eigenvalues corresponding to the former m variables as main components, number of principal components for M.Variance and the cumulative variance contribution rate as shown in the following table:

  principal component values tend to express has an elongated oval blades, and the blade edge may appear in curling.The fourth principal component feature value is 1.405, showing the entire data of standard variation is 8.264% separately. The fourth principal component in encircle polygon compactness and perimeter symmetric rate have high positive loads, in appearance ratio and the circumference area ratio have high negative loads, in the centroid offset ratio with low negative loads,at the moment invariant part has lower positive load. High bounding polygon compactness and perimeter symmetric ratio both show that the region is filling full, is more symmetrical convex polygon, and the appearance ratio and perimeter area ratio with higher load, the principal component has higher differentiate different width of the ability to target.To a certain extent, the fourth principal components can be associated with the first principal component is complementary, it weakens the global moment invariant statistical characteristics influence, but strengthen the shape complexity and symmetry as well as the appearance of the elongated convex characteristics. The fifth principal component feature value is 0.933, showing the entire data of standard variation is 5.489% separately. On the center of mass migration rate of fifth principal components have significantly high positive loads the area of symmetric rate has high positive load, in addition, the other variables with have smaller load value.In general, low centroid rate figure centroid bias top right (according to the centroid offset rate calculation formula can be seen ), high centroid rate figure centroid bias bottom left. Value of the larger of the fifth principal components tend to say mass at the bottom left of the symmetrical region.

Table 2 .

 2 The correlation matrix of shape characteristics about SPSS

(4) Calculate the variance contribution rate and cumulative variance contribution rate Contribution rate:

Table 3 .

 3 Variance contribution rate and cumulative variance contribution rate of shape features about SPSS.From cumulative variance contribution ratio of table 3 and each component eigenvalue scatter diagram of figure1show that 1-6 cumulative variance contribution rate has reached 87.1 percent, After the sixth component feature value is going to be more smaller, and the transform slow. Therefore, here take first 6 comprehensive variables as main component.

		Total Variance Explained
	Component	Initial Eigenvalues	Extraction Sums of Squared Loadings

Table 4 .

 4 Principal component loading matrix L about SPSS

		Component Matrix(a)
	Shape descriptor				Component
		F1	F2	F3	F4	F5	F6
	Perimeter area ratio	0.502 -0.492	0.070 -0.208	0.225 -0.243
	Fineness ratio	-0.560	0.410	0.387	0.314 -0.026 -0.061
	Appearance ratio	-0.633	0.550	0.320 -0.314	0.003	0.208
	Eccentricity	0.594 -0.568 -0.322	0.295 -0.011 -0.221
	Skeleton eccentricity	0.536 -0.449 -0.113	0.272	0.055	0.071
	Bounding rectangle compactness	-0.745	0.059 -0.430	0.302	0.018	0.125
	Bounding polygon compactness	-0.536	0.476	0.055	0.564 -0.120	0.004
	Centroid offset rate	0.223	0.357	0.318 -0.070	0.772 -0.103
	Area symmetric rate	-0.274	0.243 -0.569	0.334	0.481	0.257
	Perimeter symmetric rate	-0.314 -0.121	0.406	0.548	0.033 -0.499
	Hu1 Moment	0.643 -0.430	0.328	0.215	0.102	0.338
	Hu2 Moment	0.395 -0.464	0.487	0.308 -0.041	0.475
	Hu3 Moment	0.860	0.479 -0.048	0.024 -0.047 -0.049
	Hu4 Moment	0.863	0.476 -0.045	0.030 -0.045 -0.050
	Hu5 Moment	0.793	0.545 -0.035	0.123 -0.093	0.019
	Hu6 Moment	0.846	0.495 -0.021	0.110 -0.080	0.022
	Hu7 Moment	0.843	0.518 -0.039	0.082 -0.069 -0.011

Calculate the feature vectors to determine the linear expression of the principal component index of the original data.Table 5 .

 5 Characteristic vector of correlation matrix about SPSS

				Eigenvectors
		F1	F2	F3	F4	F5	F6
	P/Area	0.19	-0.27	0.06	-0.18	0.23	-0.26
	Fineness ratio	-0.22	0.22	0.32	0.26	-0.03	-0.07
	Min/MaxLen	-0.24	0.30	0.26	-0.26	0.00	0.23
	Eccentricity	0.23	-0.31	-0.26	0.25	-0.01	-0.24
	Skeleton eccentricity	0.21	-0.25	-0.09	0.23	0.06	0.08
	Bounding rectangle	-0.29	0.03	-0.35	0.25	0.02	0.14
	compactness
	Bounding polygon	-0.21	0.26	0.04	0.48	-0.12	0.00
	compactness
	Centroid offset rate	0.09	0.20	0.26	-0.06	0.80	-0.11
	Area symmetric rate	-0.11	0.13	-0.46	0.28	0.50	0.28
	Perimeter symmetric	-0.12	-0.07	0.33	0.46	0.03	-0.54
	rate
	Hu1 Moment	0.25	-0.24	0.27	0.18	0.11	0.37
	Hu2 Moment	0.15	-0.25	0.40	0.26	-0.04	0.52
	Hu3 Moment	0.33	0.26	-0.04	0.02	-0.05	-0.05
	Hu4 Moment	0.33	0.26	-0.04	0.03	-0.05	-0.05
	Hu5 Moment	0.30	0.30	-0.03	0.10	-0.10	0.02
	Hu6 Moment	0.32	0.2 7	-0.02	0.09	-0.08	0.02
	Hu7 Moment	0.32	0.28	-0.03	0.07	-0.07	-0.01
	(8) F 1*6 =X 1*17 * E 17*6
	E 17*6 as eigenvector matrix in table 5,F 1*6 as the principal component vector,
	X 1*17 as the original shape descriptor vector