Guisheng Fan

Yonghong Han

Min Song

Experimental Study on the reasonable inbuilt-ring Depth of soil one-dimensional Infiltration Experiment in Field *

Keywords: Double-Ring Infiltrometer, Inbuilt-Ring Depth, Infiltration Rate; Lateral Infiltration, Soil Moisture Infiltration

Based on the field double-ring infiltration experiment under the condition of the same soil and with the different inbuilt-ring depths, the changes of soil cumulative infiltration and infiltration rate with inbuilt-ring depths were analyzed and a reasonable inbuilt-ring depth of the filtration test in field was confirmed. The results indicate that the measured precision of soil infiltration parameters is influenced by the inbuilt-ring depths. Namely, within a certain inbuilt-ring depth range, infiltration rate shows a trend of decreasing with the increasing of inbuilt-ring depth. nevertheless, when the inbuilt-ring depth reaches 19cm to 22cm the Infiltration rate curves are basically coincident. Therefore, the reasonable inbuilt-ring depth of the infiltration experiment in field should be between 19cm and 22cm. Through comprehensive consideration of the measurement accuracy, test instrument cost and test labor intensity, the reasonable inbuilt-ring depth of infiltration experiment in field can be targeted for 19cm if plough depth is less than 20cm; otherwise, the inbuilt-ring depth can be 20cm to 22cm.

Introduction

Double-ring infiltrometer is the most commonly used to measure the infiltration parameters of field soils. Its working principle is that the condition of one-dimensional vertical downward motion of soil moisture is formed in the inner rings with the help of the homocentric outer rings embedded in certain depth below the surface, and through measuring the infiltrating water amount in the inner rings the infiltration process and parameters of soils are obtained. The buried depth of homocentric inner and outer rings below the surface is defined as inbuilt-ring depth. At present, the international standards about double-ring infiltration apparatus is as follows, inner ring is 20cm high and inner diameter is 26.2cm; outer ring is 20cm high and inner diameter is 60cm; both the inbuilt-ring depth are 10cm. several decade field infiltration test datum obtained by writer with the standard double-ring infiltration apparatus showed that the height of the standard ring is not enough for obtaining the accurate test datum, the inbuilt-ring depth is lacking and the soil infiltration capacity measured is deflective. Therefore, writer had processed a set of d double-ring infiltration apparatus with different ring heights and had performed a series of soil infiltration experiment with different inbuilt-ring depths in the same soil conditions, in order to attempt to explore the reasonable inbuilt-ring depth of double-ring infiltration experiment under the agricultural soil condition. Based on field experimental datum of soil infiltration, the change of soil accumulation infiltration and infiltration rate along with inbuilt-ring depth and the reasonable inbuilt-ring depth was measured. The results provided will be of important values for enhancing the measurement precision of field soil infiltration parameters.

Experiment condition 2.1 Soil condition

The tests measuring the influence of inbuilt-ring depth to soil infiltration ability was conducted in Shanxi Province center irrigation experimental station. The station is located in the middle reach of Taiyuan basin where the terrain is flat. The quality of soil material is mainly constituted of the River alluvial and proluvial deposit, and the soil type is moisture soil. The burial depth of Ground water table is between 1.5 and 3.5 meters. The soil plough layer depth is between 18cm and 22cm.

The dry density of the surface soils is 1.05g/cm 3 ,the volumetric water content is 15.9%; in the 10~20cm soil layer, the soil dry density is 1.346g/cm 3 and the soil volumetric water content is 21.6% ; in the plough bottom layer, the soil dry density is 1.408g/cm 3 , and the volumetric water content is 27.1%; the soil dry density at underside of the plough bottom layer is 1.358g/cm 3 . the texture of 0-20cm surface soil is loam, clay content ( grain size < 0.002mm ) is 22.3%, silt content ( 0.002mm <grain size < 0.05mm ) is 47.5%,sand content ( grain size > 0.05mm ) is 30.2% ; the texture of 20-40cm soil layer is clay loam, clay particle content is 38.2%, silt content is 54.1%,and sand content is7.7% ; the texture of 40-60cm soil layer is sandy loam, clay content is 18.1%, silt content is 45.7%,and sand content is 36.2%.

Experiment equipment

Three pieces of equipment were used in the tests. One is the international standard double-ring infiltration experiment which was manufactured by Northwest water resources research institute, the height of inside and outside rings is separately 20cm and 26cm, the inbuilt-ring depth into soils is 10cm.It is made of stainless steel. There are two stainless steel slices fixed symmetrically on both sides of the inside ring to control inbuilt-ring depth. Other two infiltration equipments is self-regulating, the inside ring diameter is 26cm,outside ring diameter is 64.4cm,the maximal inbuilt-ring depth of inside and outside rings can respectively reach 20cm and 25cm.

Experiment program

In the selected test field, a set of soil infiltration tests were proceeded under the same soil condition, the inbuilt-ring depth is singly 10cm、13cm、16cm、19cm and 22cm.The aim of the experiment was to reveal influence of inbuilt-ring depth to soil infiltration rate, and to ascertain the reasonable inbuilt-ring depth of field infiltration experiment. To ensure the consistency of the tested soil conditions, five infiltration tests with different inbuilt-ring depth were arranged within small plot with 3m×3m scale.

The tested time was identified as 90min (At this time, soil moisture infiltration rate of all tests reach relatively stable rate), in the ahead two minutes after infiltration begin, the long of each observed time interval was 30 seconds; within 2-15 minutes was 60-180 seconds; within 15-60 minutes was 300 seconds; after 60 minutes was 600 seconds.

Experimental results and analysis

Influence of inbuilt-ring depth on infiltration processes

Fig. 1 shows soil cumulative infiltration amount and infiltration rate curves with 10cm and 22cm inbuilt-ring depths in the same soil condition where moisture content, soil structure, content of organic matter, salinity content were the same .From fig. 1 it can be aware that the influence of inbuilt-ring depth on soil infiltration processes and capacity was obviously. In Fig. 1-a, it shows that the soil cumulative infiltration curve with 10cm inbuilt-ring depth is all along above the cumulative infiltration curve with 22cm inbuilt-ring depth, the cumulative infiltration amount within 90 minutes of the former is 23.3cm which is more twice than 8.381cm of the latter. Thus it can be seen that infiltration amount of the former was much larger than the latter. In Fig. 1-b, the soil infiltration rate with 10cm inbuilt-ring depth is bigger than with 22cm inbuilt-ring depth, the relative steady infiltration rate of the former is 0.1889cm/min which is three times more than 0.0535cm/min with 22cm inbuilt-ring depth. By the above contrast of the infiltration rate and cumulative infiltration amount, it is showed that the measured infiltration capacity with small inbuilt-ring depth is greater than with big inbuilt-ring depth. therefore, inbuilt-ring depth had an distinct affect on the measured value of soil infiltration capacity. The deviation value should be occurred by the different lateral infiltration amount round the infiltration rings with different inbuilt-ring depths.

Fig. 2 indicates the lateral infiltration processes generating in double-ring infiltration test. Along with the happening of lateral infiltration on outer ring boundary, the advancing speed of infiltration stream vertical front in outer ring slows down, while the advancing of infiltration stream vertical front in inner ring keeps normal, which leads to the results that on the same horizontal surface, the soil moisture content in inner ring exceeds that in outer ring, a horizontal matrix potential gap between the inner ring and the outer ring is formed, and then infiltration stream moves from the inner ring to the outer ring. Namely, one-dimensional vertical infiltration condition is broken and lateral moving of infiltration stream in the inner ring occurs.

By above analysis, following conclusion is easily gained. Theoretically, no matter how much inbuilt-ring depth is, as long as infiltration time is enough long, lateral infiltration will emerge in both inner and outer rings. Here the key problem is explaining why the lateral infiltration of shallow inbuilt-ring depth is bigger than deep inbuilt-ring depth. Hereon and Green-Ampt infiltration pattern is introduced to explain the phenomenon. Model sketch map is showed in The total water potential on the surface is H ,on the wetting front was

))

( ( t z s f f   , therefore, water potential gradient is ) ( / ] )) ( ( [ t z H t z s f f f   
.The flux from surface into soil, that in essence is the infiltration rate ) (t i of the surface was gotten by Darcy law.

              ) ( 1 ) ( ) ( ) ( t z H s K t z H s t z K t i f f s f f f s (1)
Formula [START_REF] Li | Influence of organic matter content on infiltration capacity and parameter in field soils [J][END_REF] shows the relation between infiltration rate ) (t i and wetting front ) (t z f .Here:

s K is saturated hydraulic conductivity.
Formula (1) is vertical infiltration rate formula. A formula of horizontal infiltration rate ) ( ' t i representing lateral infiltration can be derived from vertical infiltration rate formula. Because the lateral infiltration is horizontal, the gravity potential gradient in horizontal direction is inexistent. Therefore, formula (1) could be turned into as follow:

          ) ( ) ( ) ( ' t t z H s K t i x f f s (2)
Next, On the basis of formula (2), it is analyzed why lateral infiltration of shallow inbuilt-ring depth is bigger than deep inbuilt-ring depth (taking 10cm and 22cm for instance).As it is seen from formula (2) that the value of ) ( ' t i is depended on the two items s K and ) (

) ( t t z H s x f f   
. The effect of the two items to lateral infiltration rate are separately analyzed as follow.

⑴ Soil saturated hydraulic conductivity is related to soil texture and soil volume weight, the value becomes smaller when soil becomes dense and viscous. When inbuilt-ring depth is 10cm, Lateral infiltration occurs at 10cm depth, where the soil layer is located in plough layer, the soil type is loam, the soil volume weight is 1.05g/cm 3 , the soil texture was lighter and volume weight was smaller. When inbuilt-ring depth is 22cm, lateral infiltration occurs at 22cm depth, where the soil layer is located in plough pan, the soil type is clay loam, the soil volume weight is 1.408g/cm 3 , the soil texture was heavier and volume weight was bigger. Therefore,

x f x f        (3)
By synthesizing above analyses, it can be achieved that the lateral infiltration rate with 22cm inbuilt-ring depth is smaller than with 10cm, namely, ) ( ' 22 t i  ) ( ' 10 t i .Meanwhile, because the starting time of lateral infiltrating with 22cm inbuilt-ring depth is posterior to with 10cm, the sustain time for lateral infiltrating is shorter, and the soil area of the lateral infiltrating with 22cm inbuilt-ring depth is smaller than with 10cm. Therefore, the infiltration rate with 22cm inbuilt-ring depth was smaller than with 10cm, and the lateral infiltration rate with shallow inbuilt-ring depth is bigger than with deep inbuilt-ring depth. ⑵ In Fig. 6, the steady infiltration rate at 90 minutes presented a decreasing trend with the increasing of inbuilt-ring depth, last, the curve tended to horizontal. As Fig. 6 showed, f 0 of 19cm is 0.0538cm/min and f 0 of 22cmis 0.0489 cm/min.

Quantitative influence of Inbuilt-ring depth on infiltration capacity

According to Fig. 5 and Fig. 6, following outcome can be got. After inbuilt-ring depth reaches more than 19cm, the cumulative infiltration amount and infiltration rate at 90min all tends to basically stable, which shows that when the inbuilt-ring depth reaches more than 19cm, soil moisture infiltration has reached a relatively stable level.

Reasonable inbuilt-ring depth

Factors which should be considered in determining reasonable inbuilt-ring depth should be as follows:

⑴ Certain measurement precision should be guaranteed. The measured value should be close to the true value. Upper segment analysis shows that when the inbuilt-ring depth into soil were 19~22cm ,the measured infiltration rate already have tended to steady, therefore, the measured value with 22cm inbuilt-ring depth could be as approximation to true value.

⑵ Production cost of measuring apparatus should be considered. If inbuilt-ring depth into soil were too shallow, the production cost of measuring apparatus could be reduced, but the required measuring precision could not be achieved; if depths were too deep, though measuring precision could be achieved, production cost of measuring apparatus would increase greatly.

⑶ Difficulty and Labor intensity of driving rings into soil should be considered. If the inbuilt-ring depth was deep, thickness of ring walls should be increased, so the difficulty and labor intensity of driving rings into soil increased.

To determine reasonable inbuilt-ring depth the above three factors must be synthetically considered.

Fig. 7 shows cumulative infiltration amount curve at each given time with different inbuilt-ring depths. In Fig. 5, H 10 signified cumulative infiltration amount at 10min after tests begin, and so on.

From Fig. 5,it could be discovered that at every given moment, the cumulative infiltration amount presents the same variation trend. Namely Along with increasing of the inbuilt-ring depth, the every given moment, the cumulative infiltration amount decreases, and when depth reach 19cm~

22cm the curves became parallel to the horizontal coordinates. The measured Value with 22cm inbuilt-ring depth could be considered as standard in order to calculate other depths corresponding relative errors. Table 1 presents the relative error with different inbuilt-ring depth corresponding each given time cumulative infiltration relative to 22cm inbuilt-ring depth. From Table 1, it can be discovered that the relative error of cumulative infiltration amount reduces along with the increase of inbuilt-ring depth; for the same inbuilt-ring depth, the relative error of cumulative infiltration amount reduces along with increase of infiltration time. When the depth achieved above 19cm and infiltration time reached more than 60min,the relative error of cumulative infiltration amount is less than 1.49%.Above analysis makes clear that sideward infiltration aroused by lacking depth has reached negligible level when inbuilt-ring depth reached more than 19cm.Therefore,19~22cm could be defined as reasonable inbuilt-ring depth from the requirements of test precision.

Through analysis above, measurement accuracy would also improve slightly through further enlarged depths, but would increase apparatus production cost, difficulty of inbuilt-ring and labor intensity during the experiments greatly. The following suggestions were putted forward combining years of field infiltration experience of author: if the buried plough bottom was less than 20cm,the inbuilt-ring depth could be 19cm;if the buried plough bottom was more than 20cm,the inbuilt-ring depth could be 20~22cm.

Conclusion

⑴Adopting double-ring infiltration apparatus to measure field soil infiltration parameters, inbuilt-ring depth has obvious effect on measuring accuracy. The inbuilt-ring depth ruled by international is smaller and the measured infiltration rate value is bigger than the truth-value.

⑵ Measuring error decreases gradually along with the increase of the inbuilt-ring depth.

When the depth reached 19cm~22cm, its relative error could be ignored.

⑶Considering measurement precision, instrument cost and labor intensity, if the buried plough bottom is less than 20cm, the reasonable inbuilt-ring depth should be 19cm; if the buried plough bottom is more than 20cm, the reasonable inbuilt-ring depth should be 20~22cm.

Fig. 1

 1 Fig.1 Soil accumulative infiltration amount and infiltration rate curves with different inbuilt-ring depths

Fig. 2

 2 Fig.2Sketch map of lateral infiltration generating process in union thimbles infiltration experiment Specific process of the lateral infiltration is as follows. Before moisture infiltration front arrives below interface of ring (such as 10cm), due to the hinder of inner and outer impervious ring wall, moisture infiltration belongs to the strict one-dimensional vertical infiltration, where no lateral infiltration takes place. After moisture infiltration front gets to 10cm depth, lateral infiltration begins to occur firstly in outer ring. The dynamical mechanism is that horizontal water potential gradient which is caused by inner and outer moisture content difference of outer ring boundary wall drove soil moisture migrate from outer ring inside to outer ring outside, namely, the moisture content in outer ring inside boundary overtops the moisture content in outer ring outside boundary, soil matrix potential of outer ring boundary inside is higher than outside.

Fig. 3 .Fig. 3

 33 Fig.3.In Fig.3, Zf 22 and Zf 10 respectively are infiltration wetting front position with 22cm and 10cm inbuilt-ring depth, △x 22 and △x 10 respectively are sometime lateral infiltration travel length of 22cm and 10cm, i.e. horizontal infiltration distance △x which changes with time.

  depth H is definite value, soil water suction f s in wetting front is related to soil moisture content, the value of f s becomes smaller along with increasing of moisture content. When inbuilt-ring depth is 10cm, the soil moisture content at 10cm depth where lateral infiltration occurs initially is 15.9%, while the soil moisture content at 22cm depth where lateral infiltration occurs initially is 27.1% .Therefore, position. the value of 22cm and 10cm inbuilt-ring were 0.22 and 0.1.So following formula (3) is got.

Fig. 4

 4 Fig.4 shows five sets of accumulative infiltration curve and infiltration rate curve under the same soil conditions and with different Inbuilt-ring depths.

Figure 4 -Fig. 4 ⑷Fig. 5 andFig. 5

 4455 Figure 4-a Changing curves of the cumulative infiltration amount at given time with different depths

Fig. 7

 7 Fig.7 Changing curves of the cumulative infiltration amount at given time with different depths

Tab 1

 1 Relative error of the cumulative infiltration amount at given time under different depths relative to 22cm depth

					Relative error(%)
	Inbuilt-ring depth (cm)			the cumulative infiltration amount at given time
		H 10	H 20	H 30	H 40	H 50	H 60	H 70	H 80	H 90
	10	78.61	107.53	126.37	140.21	150.99	159.62	166.81	172.85	178.03
	13	32.64	54.48	68.35	78.37	86.03	92.10	97.11	101.27	104.81
	16	7.76	14.57	19.70	23.82	27.24	30.11	32.58	34.74	36.64
	19	11.94	7.89	5.36	3.49	2.07	0.93	0.03	0.82	1.49
	22	0	0	0	0	0	0	0	0	0


Dandan Li

Daoliang Li

Yingyi Chen

Li Li

Xiangyang Qin

Yongjun Zheng

A Bayesian Based Search and Classification System for Product Information of Agricultural Logistics Information Technology

Keywords: Agricultural products logistics, Web mining, information technology, classification of web text

In order to meet the needs of users who search agricultural products logistics information technology, this paper introduces a search and classification system of agricultural products logistics information technology search and classification. Firstly, the dictionary of field concept word was built based on analyzing the characteristics of agricultural products logistics information technology. Secondly, the system used meta-search engine to search related pages on the Internet based on keywords collections, and then used Web mining to analyze and filter the relevant pages. Finally, classify the agricultural products logistics information technology by web text classification according to different users' needs. The results showed that the system could efficiently and accurately search the required information, and classification with good results.

Introduction

Agricultural products logistics refers to moving material objects and related information from producer to consumer physically for meeting customer's needs and achieve the value of agricultural products [START_REF] Liu | Modern technology and management of agricultural product logistics[END_REF].

It mainly includes agricultural production, purchase, transport, storage, loading and unloading, handling, packaging, circulation, processing, distribution, information activities and many other aspects. Each aspect will be involved in many information technologies and products, also new technology will come out continually, and relevant information on the network has become increasingly rich. People want to know the existing information technology and products and hope to fully use them, but in the sea of information in the network, it is a great difficulty to find the information needed quickly and accurately.

According to the above requirements, Web mining and Web text classification were used to retrieve and classify agricultural product logistics information technology. Web mining can generally be divided into three types [START_REF] Velasquez | Extracting significant Website Key Objects: A Semantic Web mining approach[END_REF], which are Web content mining, Web structure mining and Web usage mining. Web content mining is a process of getting useful knowledge from the summary and the document content of pages, generally, including text files and multimedia documents mining [START_REF] Oguz Mustapasa | Implementation of Semantic Web Mining on E-Learning[END_REF][START_REF] Oguz Mustapasa | Implementation of Semantic Web Mining on E-Learning[END_REF].

Web text classification is an important technology of text mining, which refers to that each document of documents collection, will be included in a pre-defined category [START_REF] Chen | Feature selection for text classification with Naive Bayes[END_REF][START_REF] Lo | Web service quality control based on text mining using support vector machine[END_REF]. At present, the main classification algorithm includes the decision tree based on inductive learning, the K-nearest neighbor based on vector space model, Bayes classification based on probabilistic models, neural * Corresponding author, Tel: +86-10-62736385, Fax: +86-10-62736591, Email: zyj@cau.edu.cn networks, the support vector machines based on statistical learning theory, etc. [START_REF] Ayse | A Web page classification system based on a genetic algorithm using tagged-terms as features[END_REF].

The aim of this paper is to design a system of agricultural products logistics information technology search and classification based on the above analysis. Firstly, the dictionary of field concept word was built based on analyzing the characteristics of agricultural products logistics information technology. Secondly, the system used meta-search engine to search related pages on the Internet based on keywords collections, and then used Web mining to analyze and filter the relevant pages. Finally, classify the agricultural products logistics information technology by web text classification according to different users' needs. The classified information can be a good decision support for the future.

Materials and Methods

Systems framework analysis and design

The agricultural product logistics information technology search and classification system was designed to help users in the field of agricultural logistics to search required information from the Internet and make full use of this information more easily. The system's main functions included the following aspects:

1) According to the user's search request, match the agricultural product logistics concept word dictionary which the system has been built, and get effective keywords collection.

2) Considering the custom search scheme of the system, use meta-search technology to search the page information that meet the search request from the Internet.

3) Use web services method based on semantic vector model to judge match degrees between the information searched from the Internet and demanded by users. Then filter out irrelevant information, and store useful information.

Analysis of agricultural products logistics information technology

Agricultural product logistics technology refers to the machine, equipment, facilities and other hardware and software and a variety of opportunities method which are used in the process of agricultural product from producers to consumers. Informatization of agricultural products logistics namely agricultural product logistics technology is applied to the logistics field. Agricultural product logistics can be divided into agricultural product production logistics, sales logistics and waste logistics according to supply chain function; and can be divided into food logistics, economic crops logistics, fresh food logistics, livestock product logistics, aquatic product logistics, forest product logistics and other agricultural product logistics according to concrete object; while, can be divided into room temperature chain logistics, cold chain logistics and fresh chain logistics and so on accordance with the logistics storage and transportation conditions. Agricultural product logistics information technology is very different, for that the system provided a diverse and differentiated information services according to the different demand and the needs of different levels to achieve the system's utility and meet the information needs of individual use.

According to the above analysis of agricultural product logistics information technology fields, the field concept word dictionary has been built. The system can obtain a valid set of search keywords by extracting valid search terms from users' input, semantic analyzing and matching with the the concept diction. This will reduce irrelevant information from the returned search results.

Search method framework

As the general search engine is limited coverage of the entire web, and search results will return many useless information, so the system selected meta search engine technology. Because the system is aim to search agricultural products logistics information technology, users' needs are logistics technology and product-related information, such as technical characteristics of the products, scope and price information, and so on. Based on the above considerations, the system specifically customized search scheme, defining a term including three words such as warehouse management system, system features and the price. When a keyword is inputted, for example, warehouse management system, the system will search uses the term which the keyword relevant in the Internet and return relevant information to the user. Users send search requests to meta search engine [START_REF] Salah Hamdi | A semantic map based meta-search engine for the purpose of web information customization[END_REF], and the meta search engine send the actual search requests to multiple search engine according to the users' requests, and multiple search engines perform search requests from the meta search engine, and sent search results to the meta-search engines by the response form. The meta-search engines send the obtained and search results to the actual users.

The system filters valuable information by judging relevance between the search results and user's queries to. There are many kinds of methods or models to judge relevance between the search results and user's queries, such as vector-based, based on probability, fuzzy set, latent semantic models, and so on. Here draw lessons from the vector model based on semantic Web service matching method for decision making [START_REF] Mao | Web services matchmaking approach based on semantic vector space model[END_REF].

In the model, data items to be matched compose of the concept a public body, and set the concept of space vector (

In the formula, l is the shortest path length between i c and t c ,h is the depth of the deepest common ancestor concept of i c and t c ,α ∈ (0,1), β ∈ (0,1) are the Impact coefficient of the two factors which are the length of the shortest path and the concept depth to the concept of semantic similarity.

Finally, the formula of the matching degree of the data item j d and j q , being the same with the cosine of two data items vectors, is as follows. Through this method, the system can effectively extract relevant information of agricultural logistics information technology from the results of general search engines.

            t i q i t i j i t i q i j i j j j w w w q d q d q d Sim 1 2 , 1 2 , 1 , , ) , ( . (3)

2.4Automatic text classification

After searching agricultural products logistics information technology and related information, the system would automatically classify the obtained information. In the process of Web text classification, including the four key steps as follows, namely the text pretreatment, the text says, characteristic dimension reduction, training methods and classification algorithms. The text pretreatment process take out some HTML or XML tags, a key link of Chinese text classification of is the Chinese automatic segmentation.Web document content is described in natural language, computer difficult to handle its semantic, to facilitate the computer process, so must transform the content of the text features into the computer can process format. After word segmentation and removing stop words and high frequency from the training text and the text to be classified, the dimension of vector space and category vector for said text is very big, so the need for feature dimension reduction

) ( j xk c t ) , 2 , 1 ; , , 2 , 1 ( mx j n k     .
Step 2, Calculate conditional probability of each entry in the category, the formula as follows.

m n mp n c t P xj kj j xk    ) | ( . (4)
m is a constant, called the equivalent sample size; P is the priori estimates of the probability to be defined.

Step 3, Calculate conditional probability of text x, the formula as follows.

       n k j xk j x j x j x j x c t P c t P c t P c t P c t P n 1 ) | ( ) | ( ) | ( ) | ( ) | ( 2 1  . (5)
Step 4, Calculate the probability of text x belong to category j c , the formula as follows.

) ( ) | ( ) ( ) | ( x j x j x j T P c T P c P T c P  . (6)
and

) | ( ) ( ) ( 1 j x m j j x c T P c P T P    . ( 7
)
Step 5, Take text category when

)} | ( , ), | ( ), | ( max{ 2 1 x mx x x T c P T c P T c P  . ( 8
)
as the category of text x.

This system takes two indexes for classification of evaluation methods, namely the accuracy and recall ratio. Let the correct number of text classification be num1, and let the actual number of text classification be num2, and let the number of should have text be num3.

The definition of accuracy, accuracy num2 num1 

The definition of recall ratio, recall ratio num3 num1 

Results and analysis

The test aimed to prove the search and classification effect of the system. The search object of this system was agricultural product logistics information technology and related product, including the transportation, loading and unloading handling, storage, packaging, circulation processing, collection and processing of information and containers unitization in agricultural logistics activities. Here selected three items as search request, and compared the precision ratio with general search engines.

Precision ratio is the ratio of effective search page to the total number of pages. The results are shown in table 1. 1 shows that the precision ratio of the system is improved than the precision ratio of general search engines.

Through detailed analysis of agricultural logistics information technology and the characteristics of the products, the system divided agricultural product logistics information technology into seven parts according to the function, as follows.

(1) Transportation, including rail, road, water transport, air and pipeline transportation.

(2) Material handling, including loading and unloading machinery, transportation machinery and material handling machinery, such as forklifts, automated guided machines, lifts, stackers, etc.

(3) Storage, including storage of materials, storage equipment, such as automated warehouses, shelves, trays, temperature and humidity control equipment.

(4) Packaging, including filling machines, sealing machines, labeling machines, sterilization machine, and multi-functional packaging machinery.

(5) Distribution processing, means the professional machinery and equipment used in the activity such as packaging, split, measurement, sorting, assembling, pay the price stickers, labels pay and so on.

(6) Information collection and processing, including computer and network related hardware and software, information identification devices, communication equipment. [START_REF] Ayse | A Web page classification system based on a genetic algorithm using tagged-terms as features[END_REF] The equipment of container unitization includes containers, trays, slide, FIBC, container network goods bundle, container handling equipment, transport equipment, container, and container identification system.

The system pre-set six categories such as transport, handling, storage, packaging, distribution processing, information collection and processing. and then selected the 600 piece of pages in the above searched page, the 480 of them as a training text, the other 120 as a test text. The test results of the system automatically classified shown as Table 2. A system of agricultural products logistics information technology search and classification was designed in order to meet the needs of users in the field of agricultural products logistics information technology. Firstly, the dictionary of field concept word was built based on analyzing the characteristics of agricultural products logistics information technology. Secondly, the system used meta-search engine to search related pages on the Internet based on keywords collections, and then used Web mining to analyze and filter the relevant pages. Finally, classify the agricultural products logistics information technology by web text classification according to different users' needs. The results showed that the system could search the required information efficiently and accurately, and classification with good results.

4 )Fig. 1 .

 41 Fig.1. System workflow chart.

jd

  and q are the model of data item vector and inquires the vector,

  . The job of training algorithm is to statistics each text corresponding word table in training set of documents, calculate category vector matrix simultaneously normalization, finally save the table get from the training, namely classification knowledge base. The classification algorithm was designed based on the classification knowledge base. Now many text classification algorithm has come up and improvement, such as based on group classification method, multiple classifier fusion method, based on RBF network text categorization model, latent semantic classification model , K-neighbor algorithm and support vector machine, etc.This paper adopted the bayes classification algorithm[START_REF] Han | Using the revised EM algorithm to remove noisy data for improving the one-against-the-rest method in binary text classification[END_REF].The bayes classification algorithm as follows,Step 1, Take out entry from the entry set x T one by one, and match with the word in the feature vocabulary, if xk t and then take out next entry until is null, at last, get the entry set classified is  

Table 1 .

 1 The comparison of search results.

	Search request	Precision ratio of general	Precision ratio of this
		search engines	system
	Automatically lead machine	90.3%	92.5%
	Automation warehouse	91.2%	93.7%
	Warehousing management system	90.5%	93.2%
	Table

Table 2 .

 2 The test results of the system automatically classified

	Category	Accuracy	Recall ratio
	Transportation	91.5%	90.3%
	Material handling	95.2%	94.6%
	Storage	90.1%	89.7%
	Packaging	92.6%	93.2%
	Distribution processing	87.9%	88.6%
	Information collection and processing	94.7%	93.5%

Acknowledgements

This work was supported by Special Fun for Agro-scientific Research in the Public Interest (200903009). The research was also financially supported by the national science and technology support plan (2009BADC4B01).


Shengping Liu
email: spliu@caas.net.cn

Yeping Zhu

Shijuan Li

Research on Agent-based Bee Product Traceability Platform and Barcode system

Keywords: bee product, traceability system, barcode

Establishing agricultural product quality safety traceability platform and barcode system have become a worldwide trend. Bee product traceability platform that can be used to identify all aspects from farm to table has been laid out based on China law and global standards. An integrated bee product traceability system which involving all of the process along the supply chain can be applied to increase consumer confidence of bee products by making traceability data accessible to the consumer. In this bee product traceability platform, barcode system is of enormous significance and due to its applications in the all tracing stage of platform, standard barcode system architecture and infrastructure become very important.

In this paper, we study safety situation of bee product quality, research status of bee product traceability and barcode system. We construct bee product traceability platform by adopting agent technology, build barcode system by adopting GS1 global traceability standard as well as realize standardization barcoding of bee product data content during four links: materials collection, purchase, processing and sales. Applying barcode system and traceability platform in bee products distribution process can realize bee products tracking and tracing, offer strong guarantee for Chinese bee product quality safety and improve competitiveness of bee products in international market.

Introduction

Current Situation of Bee Products Quality Safety

The annual capacity of honey is about 293,000 tons at present in China, which including royal 3,000 tons jelly honey bee, 3,000 tons pollen and 350 tons propolis, the total output value of honey is about 8 billion RMB and annual export value is about $133 million [START_REF] Li | Status quo of quality safety of bee products and construction of whole-process traceability system[END_REF]. As advantage products in our country, bee products have important function for export, human health. Bee products quality safety problems are not only in relation to the bee industry development, but also in relation to consumer safety and the improvement of bees' international competitiveness. Consequently, Study the quality safety problems of bee products has important meaning to promote bee industry development [START_REF] Zhu | Design of bee products quality monitoring information service platform[END_REF].

In recent years the rapid growth of bee products processing industry, and meanwhile an extension of the industrial chain and bees industry itself exists many factors that caused various quality problems of bee products, which directly affect bee products export, and also shock Chinese domestic market. EU and some countries took trade barriers like forbid export and stop selling to Chinese honey in 2002 [START_REF] Liu | Research on data acquisition system for agent-based bee product traceability platform[END_REF], which made great loss to Chinese beekeeping industry, seriously impacted normal development of beekeeping industry, and also caused Chinese government departments to pay attention to bee products quality problems. The key problem of bee products quality safety is doping fake and drug residue in China, and these kinds of problems have not been able to get fundamental solution [START_REF] Zhu | Agent-based bee product quality control system[END_REF]. The production and purchase link of Chinese bees industry especially the raw honey are loose management, small scale, and bad degree of organization, but effective supervision need reliable technical means, so the demand for bee products' quality safety traceability system is more pressing.

Current Situation of Bee Products' Traceability System Research

Bee products are particular agricultural products, so all countries in the world pay very attention on the quality safety, and the traceability research of bee products is actively developing at home and abroad. On the law level, EU Traceability Regulations-Regulation (EC) No 178/2002 article 18 Traceability, trade operator must ensure product's traceability. Our country "Food Safety Law" and "Produce Quality Safety Law" also provide legal protection for the implementation of traceability system on legal level [START_REF] Zhu | Design of agent-based agricultural product quality control system[END_REF].

EU sixth framework program put forward the standard of record information during honey sales chain traceability process, the content of record is to achieve good traceability purpose, and detailed information that should be recorded in honey sales chain. Greece and Hungary proposed joint and implemented "Trace Honey" project, Hungary-Greece food network traceability and transparency research raised pilot research for honey production and processing and sales chain management. Argentina Agriculture Food Sanitation and Quality Bureau(SENASA) put forward honey refining room is the core of the traceability system; Refining room, beekeeping producers and honey barrels are required to pass through SENASA registered commercial Apitrack traceability system has been promoted in many countries [START_REF] Borkovcova | Quality and safety of Czech honey[END_REF][START_REF] Casillas | Factors that influence the quality and safety of honey[END_REF][START_REF] Mahaman | A diagnostic expert system for honeybee pests[END_REF][START_REF] Montet | Future topics of common interest for EU and SEA partners in food quality, safety and traceability[END_REF]. Ontario, Canada agricultural food and rural affairs traceability pilot plan, also including bee products.

Due to long industry chain, no specified industry standard and many influencing factors for quality safety, currently research work of Chinese bee products quality safety tracing system is mainly conducted by Bee Research Institution and Agriculture Information Research Institution of Chinese Academy of Agricultural Science [START_REF] Yue | Multi-agent quality of bee products traceability model based on roles[END_REF].

This article, through the analysis of the Chinese bee products quality safety and bar code system research status, proposes to introduce modern information technology into bee industry in order to realize whole process trace of bee products, uses GS1 standard to set up bee products traceability bar code system, and constructs bee products trace platform which is based on Agent according to industry characteristics.

Research Status of Barcode System

Unified code system is the foundation of launching bee product traceability and the premise of information exchange and process in trace process. Bee product traceability barcode must have some standard principles: uniqueness, stability, versatility, extensibility and applicability. In trace process trace key points mainly include raw honey gathering barcode, honey purchase barcode, process barcode and the products barcode. All these key elements must be bar-code uniformly and standardly so that information of different trace link is unique identification. In this research GS1 system is used for realizing uniform and standard barcode of bee product.

GS1 System

The GS1 System is an integrated system of global standards that provides with accurate identification and communication of information regarding products, assets, services and locations. It is the most widely implemented supply chain standards system in the world. It is the foundation of a wide range of efficiency-building supply chain applications and solutions and is composed of the following two areas: GS1 barcodes and GS1 traceability. GS1 unified label system performs an important function for physical implementation of agricultural products barcode, especially for agricultural products' standardized management in logistics links. GS1 system has been widely used in the logistics and retai industry of the global supply chain. It reduces system operation cost and avoids causing supply chain's uncertainty. Adopting global unified identification system can realize rapid and accurate seamless links between Information flow and material flow. In general most research organizations have carried out traceability barcode system research according to pork, beef, lamb, grain and oil, fruit and vegetable [START_REF] Pouliot | Traceability, liability, and incentives for food safety and quality[END_REF]. However, research about bee product traceability barcode system has not been reported. Developing bee product barcode system is a difficult task because that collection source of bee product scattered and honey adulteration are widely spread.

Agent-based Bee Products Traceability Platform

Agent theory and technology, as a part of distributed artificial intelligence, has developed rapidly since it arose from 1970s, it is a popular direction of artificial intelligence now [START_REF] Zhu | Application of Agent in agricultural expert system inspection software[END_REF][START_REF] Zhu | Application of the agent in agricultural expert system inspection software[END_REF]. The researches on agent have received a great deal of attention because of its characteristics, such as autonomy, collaboration, intelligence, mobility, and the potential to build complex systems [START_REF] Bordini | Programming multi-agent systems in agentspeak using Jason[END_REF]. In recent years agent theory and technology research have been rapidly developed, many studies, which including agent-oriented development language, development framework, and method, decision support system and collaboration work model, make good progress. Agent and multiagent system technology have given us a new way to look at distributed systems and provided a path for more robust intelligent applications [START_REF] Bordini | Verifying Multi-Agent Programs by Model Checking[END_REF]. In food processing, the term traceability refers to the recording through means of barcodes & other tracking media, all movement of product and steps within the production process. One of the key reasons is in instances where an issue of contamination arises, and a recall is required. Where traceability has been closely adhered to, it is possible to identify, by precise date & location which goods must be recalled, and which are safe, potentially saving millions of dollars in the recall process. Traceability within the food processing industry is also utilized to identify key high production & quality areas of a business, versus those of low return, and where points in the production process may be improved.

This research has built agent-based bee products traceability platform which based on agent theory and quality tracing method. In figure 1, on account of the characters of independent interaction among the key control points in the bee product traceability platform, a development platform and multiple principal body system of bee product traceability are designed with agent-based method, the platform agent is adopted to perform data acquisition, bar code manufacturing, data inquiry, information surveillance and task management; and task management agent is employed to realize bee farm management, supplier management, processing management and sales management. And data acquisition system developed in this paper is managed by data acquisition agent, the data thus obtained is transmitted via ACL language to the platform management agent to achieve data sharing and surveillance, as well as to fulfill the purpose of bee product information traceability.

Users

Barcode System of Bee Product Traceability

Bee product traceability platform mainly consists of the following flows (Figure 2): tracking process (clockwise direction) begins from the origin of honeybee, covering a couple of key control points like place of production, distributor, processing plant and seller, so raw material purchase process, honeybee processing process, product selling process and retail process are traced. Tracing process (counter-clockwise direction) is traced back from customers, including the tracing of selling, production, raw material and place of origin. By means of the bee product traceability platform, tracking and tracing of several key control points like place of origin, distributor, processing plant, seller and customer can be realized, and bee product quality safety can be safeguarded. Fig. 2. Bee product traceability road map.

In various links of bee products tracking and tracing, GS1 system was used for barcoding and providing uniform code standards. Standardization bar code can provide accurate, safe and reliable traceability information. 13) AI( 10) AI( 7030) AI( 13) AI( 10) AI( 01) AI( 10

Honey Gathering Process

When bee farmers finished gathering honey in apiary, they attach different apiary honey traceability label to different honey barrels. Table 3 shows barcode information and label text information in apiary traceability links. And Figure 3 indicates apiary traceability barcode label which including necessary text and barcode. The traceability information can be used in information exchange between apiary and purchase trader or between apiary and processing factory.

Purchase Process

When purchase traders buy honey from bee keeper, they need to attach purchase traceability label to their honey barrels. Table 4 displays barcode information and label text information in purchase link. And Figure 3 shows purchase traceability barcode label which consists of necessary text and barcode. The traceability information can be used in information exchange between purchase trader and processing factory. 13)+AI [START_REF] Yue | Multi-agent quality of bee products traceability model based on roles[END_REF] (7030)156420804020 01( 13)090621 [START_REF] Yue | Multi-agent quality of bee products traceability model based on roles[END_REF]

Processing Process

Processors package honey products according to consuming unit or distribution unit as well as transport products through highway, aviation or sea transportation. Table 5 presents barcode information and label text information in process traceability link. And Figure 4 shows process traceability barcode label which is made of necessary text and barcode. The traceability information can be used in information exchange between processing factory and seller.

Sale Process

When dealer affords product to consumer necessary traceability information includes bar code for commodity and traceability code. Table 6 displays barcode information and label text information in sale traceability link. And Figure 4 demonstrates shows sale traceability barcode label which composed by necessary text and barcode.

Through querying traceability barcode in website, consumer can get all traceability information of bee product.

Conclusions

Agricultural products quality safety becomes the hot topic which the people at home and abroad generally pay attention to in recent years. As ways of guaranteeing quality safety, tracing and tracking technology have been widely used in the developed countries such as America, Canada, Japan, New Zealand and so on. In recent years Chinese government has been paying much more attention to food quality safety and also has done a lot of work for quality safety and traceability. As the largest beekeeping country in the world, beekeeping is Chinese traditional industry, in addition honey is important traditional product of foreign exchange income. So how to use tracing and tracking technology to further strengthen bee product safety control and management is an urgent affair. On the basis of analyzing the present situation of Chinese bee product quality safety, this paper proposes developing bee product traceability platform and barcode system and finally realizes bee product general quality control. 1) This paper first analyzes bee product quality safety status and research situation of bee product traceability system. We should develop bee product traceability barcode system and quality safety platform which are suitable for Chinese situation.

2) Secondly based on GS1 system we respectively encoded in four links of bee products circulation. Unique and standardization bee product barcode system are used in traceability platform.

3) Finally we developed agent-based bee product quality traceability platform which could realize farm-to-table traceability.

Fig. 1 .

 1 Fig. 1. Agent-based Bee products traceability platform.

Fig. 3 .

 3 Fig. 3. Honey gathering label and Honey purchase label.

Fig. 4 .

 4 Fig. 4. Product process label and Commodity sale label.

Table 1 .

 1 Symbols and abbreviations. Table1shows symbol and abbreviation in GS1 system.

	Symbol	Abbreviation
	AI	GS1 system Application Identifier
	GLN	GS1 system Global Location Number
	GMP	Good Manufacturing Practice
	GS1	Global Solution 1
	GS1 system	Unique global identification system
	GTIN	GS1 system Global Trade Item Number
	ID	Identification
	ISO	The international Organization for Standardization
	n2 or n14,etc	GS1 identifier numbers consisting for 2 or 14, etc digits
	SSCC	GS1 system Serial Shipping Container Code

Barcode Research Situation of Agricultural Product Traceability System At

  present agricultural product traceability systems are widely developing in at home and abroad. Zheng has analyzed traceability chain of cooking oil products and designed cooking oil product system based on batch management, meanwhile, zheng proposed barcoding rules of core link which included origin, process and final product[START_REF] Zheng | Construction of traceability system for quality safety of cereal and oil products[END_REF][START_REF] Liu | Study on quality safety traceability systems for cereal and oil products[END_REF]. Liu has encoded Fruit and vegetable products. According to characteristic of lamb industry chain as well as actual information level of lamb manufacturing enterprises, Wang has developed lamb barcode system. Xiong has realized pork quality safety traceability. Deng has studied producing area code of agricultural product, divided tea planting area and uniformly encoded tea. Sheng has developed beef traceability barcode system.

Table 2 .

 2 Barcode system information of bee product supply chain. Table2shows information exchange process of bee products traceability links.

	Supply chain	Gathering	Purchase	Process	Sale
	Diagram
				
	Label type	Apiary label	Purchase label	Process label	Logistics label
	Code system	EAN-128	EAN-128	EAN-128	EAN-128
		AI(7030)
	Key information	AI(

Table 3 .

 3 Barcode and information of gathering process.

	Data content	Data name	UCC/EAN-128	Example	Categorisation Shall may
	Gathering process
	Traceability code of gathering honey	PROCESSOR+ PACAK DATE + LOT	AI(7030)+AI(13 )+AI(10)	(7030)156510183010 01(13)080527(10)011	
	Apiary code	PROCESSOR	AI(7030)	n4+n3+an…27	
	Production date	PACK DATE	AI(13)	n2+n6	
	Lot number	LOT	AI(10)	n2+an…20	
	Global location number of honey	GLN	AI(414)	n3+n13	
	Honey kinds	REF.TO SOURCE	AI(251)	n3+an…30	
	Honey weight	NET WEIGHT	AI(3100)	n4+n6	

Table 4 .

 4 Barcode and information of purchase process.

	Data content	Data name	UCC/EAN-128	Example	Categorisation Shall may
	Purchase process
	Traceability code of purchase	PROCESSOR+	AI(7030)+AI(
	honey	PACAK DATE + LOT

Table 5 .

 5 Barcode and information of processing process.

	Data content	Data name	UCC/EAN-128	Example	Categorisation Shall may
	Processing process
	Traceability code of process honey	PROCESSOR+ PACAK DATE + LOT	AI(7030)+AI(13 )+AI(10)	(7030)156510106030 01(13)100310(10)001	
	Global trade number	GTIN	AI(01)	n2+n14	
	Lot number	LOT	AI(10)	n2+an…20	
	Package date	PACK DATE	AI(13)	n2+n6	
	Production date	PROD DATE	AI(11)	n2+n6	
	Date of minimum durability	BEST BEFORE	A(15)	n2+n6	
	Global location number of honey	GLN	AI(414)	n3+n13	
	Honey kinds	REF.TO SOURCE	AI(251)	n3+an…30	
	Global location number of supplier	GLN	AI(412)	n3+n13	
	Processor name text	Text information	--	Hangzhou Changqing	

Table 6 .

 6 Barcode and information of sale process.

	Data content	Data name	EAN/UPC	Example	Categorisation Shall may
	Sale link
	Bar code for commodity	EAN-13	EAN/UCC-13 6901234567892	
	Commodity traceability barcode	PROCESSOR+PAC AK DATE + LOT	EAN-128	51010604001100706105	
	Processor name text	Text information	--	Hangzhou Changqing	
	Production date text	Text information	--	2010-07-06	
	Lot number text	Text information	--	105	
	Honey origin text	Text information	--	Sichuan	
	Net weight text	Text information	--	1.5 kg	
	Honey kinds text	Text information	--	Sophorae honey	

Acknowledgments

This research was supported by the National Natural Science Foundation of China (Grant No.60972154).


Juncheng Ma

Daoliang Li

Yingyi Chen

Li Li1

Fei Gao

Lingxian Zhang

A Collision Detection-Based Wandering Method for Equipment Deploy Scene in Land Reclamation Area of Mining Dump

Keywords: Collision Detection, Bounding Volume nestification, OBBs, AABBs

Aiming at developing a simple and efficient collision detection method to support the wandering in equipment deploy scene in land reclamation area of mining dump. This paper presents an efficient algorithm for collision detection in the waste dump land reclamation equipment deploy scene using a bounding volume nestification which consists of an oriented bounding boxes (OBBs) enhanced with axially aligned bounding boxes (AABBs). This approach combines the compactness of OBBs and the simplicity of AABBs.

The majority of distant objects are separated using the simpler AABB tests. The remaining objects are in close proximity, where OBBs test is needed to detect.

Implementation results show that our algorithm achieves considerable speedup in most cases.

Introduction

With the development of virtual techniques, collision detection is widely used in areas such as robotics, computer graphics, animation, computer games, virtual reality, simulation and haptic rendering (Tomas Möller, 2002). The application of collision detection technique can improve the authenticity of 3D scene and avoid the phenomenon of go through a wall or go into the land when come into buildings and slopeland. There are many collision detection algorithms with different characteristics, but they all face the same problem: how to improve the real time and accuracy of detection.

The topographic condition of the waste dump is complicated with several sloping land that emerged in stair distribution and there are many pits and ditches. During the land reclamation monitoring of the waste dump, a large number of monitoring equipment is deployed which is irregularly shaped, of small size and easy neglected.

Thus, a collision detection algorithm that is both real time and accurate is badly need in the wandering of the waste dump land reclamation equipment deploy scene.

Bounding volumes algorithm has proved to be the most widely used in contemporary systems. In this method, a simple bounding volume with slight volume is occupied to bound the complicated object, then the intersect test of the objects is

2.Related work

Research about collision detectionproblems started from the 1970s, after thirty years of research, domestic and foreign scholars in the field of collision detection have done a lot of work, formatting some of mature technology. Space decomposition method and the hierarchical bounding volumes method became the most widely used method in collision detection for two Geometries.

In Space decomposition method, the entire virtual space is divided into a small volume of the cell, only applying intersection test to the same cell or adjacent cells of

Collision detection algorithm method for equipment deploy scene

Based on the characteristics of AABBs and OBBs, the topographic condition of the waste dump and the character of the equipment, this paper presents a bounding volumes nestification of AABBs and OBBs. The basic idea is: two levels of bounding volumes are set for the object, the outer bounding volume choose the bounding volumes that allow the simplest overlap tests while the inner bounding volumes select the volumes that fit the object more tightly. When the intersect test occurs, the outer bounding volume whose intersect test is simple is used to detect preliminarily so that the majority of distant objects are separated. The remaining objects are in close proximity where the OBBs are used to tested. This approach combines the compactness of OBBs and the simplicity of AABBs so that the overlap calculation is less and the efficiency is improved.

When the nestification on the equipment is constructing, because of the poor compactness of AABBs and the slender shape of the equipment, the nestification of AABBs and OBBs still exist a lot of space at the. Consequently, the algorithm is divided to 3 steps to ensure the accuracy.

(

1)AABBs-AABBs overlap test

This test is very simple due to the construction of AABBs, only when the two shadows overlap that the two AABBs cast on the three coordinate axes, the two AABBs are detected intersection.

(2)AABBs-OBBs overlap test

The AABBs can be seen as special OBBs so that the AABBs-OBBs overlap test is the same as the OBBs-OBBs: separating axis theory. If the shadows that two OBBs cast on the same axis(may not the coordinate axis), the axis is called a separating axis.

If a separating axis between two OBBs exists, this two OBBs don't overlap. There are only 15 potential axes for OBBs. We take one of them to explain the test process.

Fig.2.OBB-OBB overlap test

A, B are two OBBs, a i , b i is half the length of the border of A, B (i =1, 2, 3); A i 、B i is the unit vector along the axis(i =1, 2, 3); T is the distance of the center of A, B; L is the unit vector parallelled to the separating axis. r A is the sum of the shadows that a i cast on the direction of L, r B is the sum of the shadows that b i cast on the direction of L. We just need to compare |T•L| with ( r A + r B ) to detect whether the two OBBs overlap.

3 1 A i i i r a A L    , 3 1 B i i i r b B L    (1)
Consequently, whether the two OBBs overlap is detected by ( 2), L is one of the 15 separating axes.

33 11 i i i i ii T L rA rB a A L b B L          (2)
When (2) holds, the two OBBs do not overlap. Otherwise the shadows that A, B cast on the other 14 separating axes should be calculated and compared by ( 2) till there exists one axis that meets(2). If there is no such a separating axis, the two OBBs overlap.(Fan Xiaoping, 2011).

(3)OBBs-OBBs overlap test

The same method in step( 2) is occupied in this step. If the two OBBs overlap, the objects collide. If the two OBBs do not overlap, even though step(2)passed, the object do not collide.

Results

We have implemented our collision detection algorithm in C++ and Multigen Vega Prime API on an AMD Athlon(tm) 2.4 GHz PC with a 1.0 GB main memory.

Vega Prime is a cross-platform and extensible developing environment, which contains complete C++ API. A typical Vega prime program consist of five parts:

(1)Initialize. Invoke vp::initialize() to initialize Vega prime.

(2)Define. Create the objects that the program needed though code or ACF file.

(3)Configure. The system is configured at this stage according to the parameters set beforehand.

(4)Run loop. All the threads are occupied to render the scene.

(5)Shutdown. Free the memory and stop the threads inprocess.

MFC contains powerful application structure based on windows, and offers a large number of windows and events management function.

Conclusions and future work

We have presented a bounding volumes nestification of AABBs and OBBs collision detection algorithm that uses both OBBs and AABBs bounding volumes. We have shown how to combine the compactness of OBBs and the efficient overlap test for AABBs. We have implemented this algorithm C++ and Multigen Vega Prime API.

Implementation results show that our nestification makes favorable speed up.

In future work, we plan to introduce a general selection model for separating axes to further reduce the calculation amount in the OBBs-OBBs overlap test. Furthermore, we plan to design a collision detection algorithm intelligent model. Based on different kinds of objects, the collision detection algorithm design beforehand will be chosen.

  carried only when the bounding volumes overlap. The bounding volumes that are widely used are Spheres, axially aligned bounding boxes (AABBs), oriented bounding boxes (OBBs) and discrete orientation polytopes (k-DOP)(J.A. Corrales,2011).

Fig. 1 .

 1 Fig.1.Two-dimensional sketch map of bounding boxes Spheres and AABBs allow the simplest overlap tests while OBBs and K-DOP fit volumes more tightly(Jung-Woo Chang et al, 2010). In this paper, we propose a bounding volumes nestification based on conventional approach that combines the simplicity of AABBs and the compactness of OBBs to produce an efficient algorithm.

Fig. 3 .

 3 Fig.3.Wandering rendering in Fuxin Haizhou dump During the wandering in the equipment deploy scene in land reclamation area of mining dump, viewpoint firstly contact with the ground, at the same time ,collision detection occurs. When the viewpoint come into the deployed equipment, t collision detection occurs between the viewpoint and the equipment. Consequently the viewpoint can't go on wandering unless its changes its direction.

  the geometric object. typical examples of Space decomposition is kd-tree (JLBenile, 1975), octree (H. Nobor et al, 1989), BSP tree (B. Nayl et al, 1990), tetrahedral mesh and the regular grid and so on. Space decomposition method is usually applied to the collision detection for more evenly distributed geometric object between the in sparse environment. In 1976, Clark put up with bounding techniques, characterized by the

simplicity and compactness (J.M.

Snyder, 1995)

. The basic idea is to use slightly larger size but geometrically simple bounding box to give an approximate description of complex geometric objects (I.J.

Palmer, 1995)

, Axis-Aligned Bounding Boxes (A.

[START_REF] Smith | A simple and Efficient Method for Accurate Collision Detection among Deformable Polyhedral Objects in Arbitrary Motion[END_REF]

, and oriented Bounding Box (Gottschalk, 1996) and so on. With further studies,many new algorithm were proposed, such as fixed direction hull(Wei Yingmei, 2000) object-oriented collision detection method(Wang Zhaoqi et al, 1998).

In practice, collision detection was widely used in many areas. Kuan-Chen Lai et al (2009) applied the collision detection techniques to virtual construction stimulation. He proposed VC-COLLIDE method that support real-time rendering and effectively reduced the collision detection computation. Ehsan Arbabi et al (2009) proposed two kinds of collision detection methods for medical diagnosis and surgical planning. Many scholars improved the conventional method to meet the need of all kinds of need. Jung-Woo Chang (2009) proposed triangle-triangle intersection test algorithm based on the OBB bounding box. Jung-Woo Chang (2010) proposed a OBB-sphere bounding volume hierarchy and experiment 5 potential separating axes for OBBs.

  The document/view structural framing is becoming the mainstream structural framing for developing Windows application. So we use MFC to invoke Vega Prime, which implements the virtual wandering in equipment deploy scene of Mining Dump.

Acknowledgement

This work was supported by Project of National Natural Science Foundation of China (40901279). The research was also financially supported by Chinese Universities Scientific Fund (Project No. 2011JS143).


Fangming Zhang
email: fangmingzhang@126.com

Ximing Feng

Yuan Li

Xiuqin Rao
email: xqrao@zju.edu.cn

Di Cui

A Low-cost Positioning System for Parallel Tracking Applications of Agricultural Vehicles by Using Kalman Filter

Keywords: Positioning system, GPS, Kalman filter, parallel tracking, low-cost

A position-velocity (PV) model and a multi-sensor system, consisted of a consumer application GPS, a MEMS gyro, two encoders, and a turning angle sensor, was constructed for the positioning system. The two encoders augmented the positioning accuracy greatly that the fluctuation of vehicle position was greatly smoothed comparing with a GPS-only system. The minimal fluctuation was falling from 2.21 m to 0.52 m (east direction), from 0.68 m to 0.23 m (north direction). The maximum XTE was reduced from 2.5 m to 0.77 m, and the RMS value was improved to 0.22m. The GPS bias error was the major difficulty to produce better performance.

Introduction

Parallel tracking is the main operation method of agricultural vehicles. Global Positioning System (GPS) acts as an important role in navigating agricultural vehicles with parallel tracking. Some researches [START_REF] Cui | Sensor fusion for field robot localization[END_REF][START_REF] Noguchi | Development of Robot Tractor Based on RTK-GPS and Gyroscope[END_REF][START_REF] Bell | Automatic tractor guidance using carrier-phase differential GPS[END_REF] have been reported to use high-accuracy GPS receivers, Real Time Kinematic Global Positioning System (RTK-GPS) or Carrier-Phase Differential GPS (CPD-GPS), to develop automated agricultural vehicles. However, both RTK-GPS and CPD-GPS are too expensive for its actually application in agriculture. Low-cost consumer application GPSs are now widely used in automobile industry, i.e., car navigator, path tracking, but their position accuracy, 2-3 meters, could not be satisfied with requirement of agriculture application. 1Kalman filter has been extensively used to smooth raw DGPS signals [START_REF] Han | Kalman filtering of GPS positions for a parallel tracking application[END_REF][START_REF] Guo | Real-time tractor position estimation system using a kalman filter[END_REF], which improved the positioning accuracy, and more importantly, it provided reliable positioning information during a short period of time when the GPS signal is lost. For example, Will [START_REF] Cui | Sensor fusion for field robot localization[END_REF] constructed a position-attitude (PA) model-based extended Kalman filter. Han et al. [START_REF] Han | Kalman filtering of GPS positions for a parallel tracking application[END_REF] set up a position-velocity (PV) model for the Kalman filter. Guo et al. [START_REF] Guo | Real-time tractor position estimation system using a kalman filter[END_REF] integrated IMU and DGPS data and formed a position-velocity-attitude (PVA) model for fusion algorithms. Fiengo et al. [START_REF] Fiengo | A hybrid procedure strategy for vehicle localization system: Design and prototyping[END_REF] developed a model for vehicle by combinating of a GPS, speed sensor, and a gyro sensor. Guo [START_REF] Guo | Kalman filtering for GPS/magnetometer integrated navigation system[END_REF] developed a GPS/IMU/magnetometer integrated system with Kalman filtering for vehicles. The author [START_REF] Zhang | Vision-based Position System for Agricultural Vehicle Using Extended Kalman Filter[END_REF] had integrated a vision sensor and two encoders to construct an extended Kalman filter. Those results showed that fusion system could decrease the cost of sensors while kept the necessary accuracy for agricultural applications.

As sensor technology is developing rapidly, low-cost positioning system shows possible and attractive for agricultural vehicles. Rong Zhu et al. [START_REF] Zhu | A linear fusion algorithm for attitude determination using low cost MEMS-based sensors[END_REF] developed an extended Kalman-based fusion algorithm for attitude estimation by using inexpensive micromachined gyroscopes, accelerometers and magnetometers. Akira Mizushima et al. [START_REF] Mizushima | Development of a low-cost attitude sensor for agricultural vehicles[END_REF] used low-cost sensors, three vibratory gyroscopes and two inclinometers, to estimate tilt angles (roll and pitch) by least-squares method. The drift error of the gyroscopes was estimated using the inclinometers. Ndjeng et al. [START_REF] Ndjeng | Low cost IMU-Odometer-GPS ego localization for unusual maneuvers[END_REF] solved the problem of outdoor vehicle localization with Interacting Multiple Model (IMM) and Extended Kalman Filter (EKF) approaches, which allows the method to be optimized for highly dynamic vehicles with low-cost IMU-odometer-GPS composition. Zhi Shen et al. [START_REF] Shen | Low cost two dimension navigation using an augmented Kalman filter/Fast Orthogonal Search module for the integration of reduced inertial sensor system and Global Positioning System[END_REF] integrated low-cost sensors, a MEMS-grade gyroscope, a vehicle builtin odometer, and a GPS to provide 2D navigation for land vehicles. Fast Orthogonal Search is suggested for modeling the higher order of reduced inertial sensor system RISS errors.

The objective of this research was developing a positioning system with low-cost guidance sensors for agricultural parallel tracking application.

Materials and Methods

A multi-sensors system was constructed on a rice transplanter (ZP60, ISEKI, Japan) as shown in figure 1, which consists of a consumer application GPS receiver (U-blox LEA-5S, Zoglab Inc., China), a MEMS gyro as heading angle sensor (GX1, Xunjie Inc.,China),two encoders as speed sensor (E6B2-CWZ6C, Omron Inc., Japan), and a precision potentiometer as turning angle sensor (Copal N35,Japan). The antenna of the U-blox GPS was mounted on a rigid frame in the front the vehicle 2.5 m above ground level. The receiver transmitted data at 1 Hz with Baud rate of 9600 bps. A RTK-GPS (S82E, South surveying & mapping instrument Inc, china) was used to record track of the vehicle with its antenna mounted beside the U-blox one. The gyro, measured angular velocity for yaw direction at maximum ability of ±70°/s, was installed on the body of vehicle. Two encoders, outputted 360 pulses per round, were driven by the left and right rear wheel through a pair of gear transmission with rate of 1:1. The potentiometer, whose resistance is 5KΩ within rotary angle of 345°, was installed under the turning axis. A computer system consists of one embedded central computer (ECC) (ARM S3C2440 Developing board, Tianxiang Inc., China) and five electric circuit units (ECU) (PIC 16F873A Developing board, Microagriculture Inc., China), which were connected by a RS-485 net. The 1st ECU acted as transferring data from the GPS receiver to the ECC. The 2nd and 3rd ECUs acted as sampling tracking speed from the left encoder and right encoder at the left and right rear wheel correspondingly. The 4th ECU acted as sampling turning angle from the potentiometer and sampling heading angle from the gyro sensor, and the last one acted as controlling a step motor to steering the rice transplanter. The outputs of these sensors were acquired through a 10-bit analog/digital converter. The total cost of this attitude sensor was approximately $250, only 1/5 of a DGPS. The GPS signal triggered all ECUs to begin sampling signal from corresponding sensors at the same time. When the ECU1 received the '$GPGGA' frame, it extracted latitude and longitude and then sent them into the RS-485 net, which could be received by the ECC and all other ECUs, while the latter would sampled signal from corresponding sensors. The ECC processed the latitude and longitude after a button in its monitor, 'START', was pressed, and it got all sensor data by serial communication between one ECU and itself. The ECC transformed those data into significative decimal value, such as turning angle, left wheel speed. All data were then sent to the Kalman Filter for further processing.

A local coordinate system was set up that the 1 st point in every test was thought as the origin point, and the x coordinate pointed to east direction and y coordinate pointed to north direction. Coordinate x and y of any position were transformed from the latitude and longitude of GPS according to Chang's method [START_REF] Chang | Research of GPS used in Farm machines guidance system[END_REF]. Furthermore, the rice transplanter was modeled as a three-tyre vehicle as shown in figure 2. Relying on kinematics analysis, a position-velocity (PV) model was set up for the Kalman filter. It shows the angle between the road and y-axis is fixed value, α, while the heading angle, Ψ, offset, e, and speed, v 1 and v 2 , changed when driving the vehicle. The discrete Kalman filter recursive equations are [START_REF] Han | Kalman filtering of GPS positions for a parallel tracking application[END_REF]:

RTK-GPS U-blox GPS

k k k k w X X    1 (1) k k k k v X H Z   (2)
where Xk is the (n  1) process state vector at time tk k is the (n  n) state transition matrix k w is the (n  1) process noise vector with a known covariance Qk Zk is the (m  1) measurement vector at time tk Hk is the (m  n) measurement connection matrix vk is the (m  1) measurement noise vector with a known covariance Rk. Equation 1 is the process model, and equation 2 is the measurement model. Since the objective of this study was to improve the 2-D positioning accuracy, four state variables were set as following:

T k k k k k v y x X ] , , , [   (3)
where

x k and y k are local coordinates to be estimated.

Ψ k is heading angle to be estimated.

v k is velocity to be estimated. The state transition matrix was:

                     1 0 0 0 / ) tan( 1 0 0 ) cos( 0 1 0 ) sin( 0 0 1 W T c T c T k k k      (4)
The raw receiver outputs, x GPS and y GPS transformed from latitude and longitude by GPS, heading angle from gyro, speed from encoders, and turning angle from potentiometer, are the measurement variables. The measurement vector and the measurement connection matrix are:

T k Turntyre GPS GYRO GPS GPS k v y x Z ] , , , , [    (5)                  0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 k H (6)
The numerical solution to the discrete Kalman filter model is a step-wise procedure [4] :

Step 1: Compute the Kalman gain, Kk:

1 ] ) 1 | ( [ ) 1 | ( ) (       T T H k k P H R H k k P k K (7)
where Kk is the Kalman gain, P k,k-1 is the initial error covariance matrix, and R is the covariance matrix for the measurement noise vector.

Step 2: Update the estimate, k k X , ˆ, with the measurement, Zk:

)] 1 | ( ) ( )[ ( ) 1 | ( ) | ( ˆ     k k X H k Y k K k k X k k X (8) where 1 , ˆ k k X
is the updated estimate, and is a priori estimate.

Step 3: Compute the error covariance, Pk,k, for the updated estimate:

) 1 | ( ] ) ( [ ) | (     k k P H k K I k k P (9)
where Pk,k-1 is a priori error covariance matrix.

Step 4: Project ahead:

) | ( ) ( ) | 1 ( ˆk k X k k k X    (10) k T Q k k k P k k k P      ) ( ) | ( ) ( ) | 1 ( (11)
where

k k X , 1
ˆ and P k1,k are the projected estimate and projected error covariance matrix that the next iteration requires.

In the application of the above procedure, three matrices, the process noise covariance matrix Qk, the measurement noise covariance matrix Rk, and the initial error covariance matrix P k,k-1 , need to be defined prior to the start of the iteration. We derived these matrices by trial :

              01 . 0 0 0 0 0 1 . 0 0 0 0 0 2 0 0 0 0 2 ) 1 | ( k k P (14)
The updated estimate, equation 8, is the best estimate of the current position.

The first experiment was done on a road (50 meter long) located in campus of Ningbo Institute of Technology, Zhejiang University on Jan. 29, 2011, where tracks of right wheel were drew by Chalks, and another one was done on a road (90 meter long) located in Jiangshan, NingBo on July 1, 2011, where tracks of the center of the vehicle were recorded by the RTK-GPS. The vehicle was driven along two parallel rows, transecting approximately 2.2 m in the former experiment, and 4.0 m in the later one. Data were recorded with a 1-s interval in all experiments. Every experiment was repeated 2 times with 3 travel speeds, 0.25m/s, 0.73 m/s, and 1.1 m/s.

One program, including serial communication and Kalman filter module, was written, compiled and run real time in the ECC, and another Matlab program was written for data analysis. The record of chalk or RTK-GPS was used as a baseline (reference) to evaluate the performance of the filters. A cross-track error (XTE) [START_REF] Han | Kalman filtering of GPS positions for a parallel tracking application[END_REF] is defined as the distance between the currently measured GPS position and the desired track. Minimal fluctuation was defined as coordinates jumping in east direction or north direction to evaluate performance of the kalman filter when RTK-GPS was not available.

Results and Discussion

Figure 3 shows one results by drawing line on ground. It shows that the Kalman filter improves the positioning system. The state variables, coordinates x, y, and heading angle Ψ were smoothed. In these experiments, the minimal fluctuation was falling from 2.21m to 0.52 m (east direction), from 0.68 m to 0.23 m (north direction). However, the absolute bias still could not be evaluated by drawing line on ground. The truth was exposed when receiving position data both from the U-blox GPS and the RTK-GPS at the same time. Figure 4 shows one result of this kind of experiments. The first point got by the RTK-GPS was also the original point of the Ublox coordinates system, where the first point got by U-blox deviated to coordinates (2.610747, 2.771199). Though positioning coordinates were smoothed by the Kalman filter, they deviated in most time that the mean bias was 2.32m, and the RMS is 0.72m, and the difference between the maximum and minimum XTE is 5.30m. The two lines formed by the U-blox GPS are not parallel. Compared with the former experiments, bad performance might be caused by close distance of the two antennas. Results shows high accuracy sensors improve performance of positioning system. A close look at the GPS data we found the least GPS data changing is 1.5 m in x axis, and 0.45 m in y axis, the original GPS output could hardly be used to navigate an agriculture vehicle. After Kalman filter, the average data changing decreases to 0.45 m in x axis, and 0.20 m in y axis. This improvement owes to two encoders as they provide high accuracy tracking distance. The turning angle sensor is another high precision sensor; however, its output was not used as state variable yet, so it hardly contributed to improve positioning system. If it will be added as a state variable in future work, it will then bring more positive effect.

Another way to improve accuracy is speeding up the update of sensors. The maximum ability of this GPS receiver is 4Hz, while the working frequency was 1Hz only in this experiment due to stability of serial communication. Moreover, the gyro could update its output in 10 Hz. So software should be improved in future work to obtain more accuracy positioning data.

This experiment also certified that combine of some low cost guidance sensors could produce high accuracy position. The most expensive sensor in this system is the gyro, $100, and the total sensor cost is less than $250. It is important to use one or two low cost, high precision sensor, such as encoder, to improve the performance. The experiments showed that Kalman filter does not work when the vehicle is turning around which Han [START_REF] Han | Kalman filtering of GPS positions for a parallel tracking application[END_REF] mentioned. In our experiments, estimated position might move to wrong direction when starting turning, and heading angle will lag behind the gyro data greatly. Solution to this difficulty is closing the Kalman filter when turning, and initializing it when it goes into a new row. If using a local coordinate system, we could set the first GPS data in the first row as the origin, and set the initial state variable with changed x and y, as well as adjust the state transition matrix, whose sign of encoder should be reversed.

Conclusions

A low-cost positioning system, consisted of a consumer application GPS receiver, a MEMS gyro, two encoders and one turning angle sensor, was developed to improve positioning accuracy of the GPS by using Kalman filter. A computer system embedded on vehicles was constructed, which composed of an ECC (ARM embed computer), 5 ECUs (PIC16 microcomputer), and a RS-485 net. Local coordinates, heading angle, and vehicle speed were set as state variables in the Kalman filter. Experiment results show positioning coordinates got by the GPS were improved after filter processing that they were smoothed, but bias of the GPS made the estimated coordinates uncertainty. The minimal fluctuation was falling from 2.21 m to 0.52 m (east direction), from 0.68 m to 0.23 m (north direction). The maximum XTE was reduced from 2.5 m to 0.77 m, and the RMS value was improved to 0.22m. However, the Kalman filter could not remove bias of GPS. In addition, the proposed Kalman filter makes inaccurate position estimates when turning around. Further work should be on reducing the GPS bias error for parallel tracking applications.

Fig. 1 .

 1 Fig. 1. (a) configuration of the multi-sensor system; (b) the test platform on the ISEKI ZP60

Fig. 2 .

 2 Fig. 2. The vehicle model, which is transformed into three-tyre vehicle, locates on the local coordinates. Point 'C' represents the estimated position point.

Fig. 3 .

 3 Fig. 3. Experiment data by drawing line on ground, (a)original GPS position, estimated position and track of right wheel; (b) original gyro data and estimated heading angle; (c) left encoder and right encoder data; (d) turning angle sensor data

Fig. 4 .

 4 Fig. 4. experiment data by using the two GPSs together. The track go and back are nearly parallel, but whether the U-blox GPS or the estimated coordinates are not parallel.

This paper is supported from the Science Foundation of NingBo (Projects No.

2010A610140)

Acknowledgments. The authors wish to express their sincere thanks for the financial support from the Science Foundation of NingBo (Projects 2010A610140), Agriculture R&D project of Yingzhou (3rd batch, 2010), Machine R&D project of Ningbo Agriculture Machinery Bureau (2010-2011), and Initial Research Foundation of NIT.