Zhou Xin-Yang
email: axinyang87@126.combwyrf@163.com

Wang Yang-Ren

professor, Ph.D Yang-Ren Wang

Effect of Water and Nitrogen Stresses on Correlation among Winter Wheat Organs

Keywords: words:growth correlation,water and nitrogen stresses, winter wheat, leaf to root ratio, root nitrogen uptake

Correlation among root, stem, leaf and grain is the basis for the establishment of photosynthate partitioning model, and is of great significance for rational regulation of plant morphology and population structure and improvement of crop economic production. The paper is based on experiment results of dry mater weight of root, stem, leaf, and grain of winter wheat of 5 treatments with different water and fertilizer level, and experiment results of soil moisture and soil nitrogen (NH 4+ -N 和 NO 3-+N). The experiment was carried out in the Farmland Water Cycling Experiment Station of Tianjin Agricultural University in 2008-2009. The relationships between leaf to root ratio, stem to leaf ratio, and grain to stem ratio with root nitrogen uptake was established, respectively. The results indicted that the leaf to root ratio, and stem to leaf ratio was more sensitive to water and nitrogen stresses than that of grain to stem ratio. Both the leaf to root ratio and stem to leaf ratio increased with the root nitrogen uptake, and had positive correlations with root nitrogen uptake with the correlation coefficients above 0.91. The correlation between grain to stem ratio and root nitrogen uptake was not significant within the given range of water and nitrogen stresses. The ratio of grain to stem under water and nitrogen stresses was approximately equal to that without water and nitrogen stresses.

Introduction

Various parts of winter wheat (including root, stem, leaf and grain) integrate to a whole, where each organ has its unique function with close links between different organs. This phenomenon is known as correlation among plant organs [START_REF] Xu Ke-Zhang | Plant physiology[END_REF]. Correlation among root, stem, leaf and grain is the basis for the establishment of photosynthate partitioning model, and is of great significance for rational regulation of plant morphology and population structure and improvement of crop economic production. Davison (1969) republished correlation model between root and shoot in dry matter, the model included nitrogen uptake in previous results. The other previous results presented only correlation models among plant organs and these models are all without both water stress and nutrient stress. For example Michaelis-Menten's formula [START_REF] Liu Tie-Mei Xie Guo-Sheng | Agricultural System Simulation[END_REF]), Thomas, et al. (1997), in additional biomass partitioning indexes [START_REF] Liu Tie-Mei Xie Guo-Sheng | Agricultural System Simulation[END_REF]), or partitioning coefficients (P.M. [START_REF] Driessen | Qiu Jian-jun et translate, land use system analysis[END_REF], which imply correlation relationships among plant organs, between crop organs. Based on field experiment results, this paper republished the correlation models between the ratio (the leaf to root ratio, stem to leaf ratio and grain to stem ratio) with nitrogen uptake of winter wheat under water and nitrogen stresses conditions.

Materials and Methods

The experiment of water and fertilizer in winter wheat double factors was carried out at the water cycling experiment bases of Tianjin Agricultural University in 2009. The station located at 116°57'E and 39°08'N, elevation 5.49m, groundwater table 3.70-2.06m. The type of winter wheat is 6001, Growth period was from October 7, 2008 to June 15, 2009. The soil bulk density in 0-100cm is 1.43 g / cm 3 . The number of experiment treatments was 11 with 3 replications, and the area of each experiment plot is 66.7 m 2 . The dry matter data of roots, stems, leaves, grain, soil moisture and nutrient (include ammonium nitrogen NH 4 + and nitrate NO 3 + ) were measured for all treatments. These treatments include the combinations of high water (4 times water during all growth period, 60mm each time) and high fertilizer (base fertilizer: 750kg/ha compound fertilizer with potassium sulfate type with 15% nitrogen, 15% phosphorus, 15% potassium, and over 45% total nutrients, topdressing: 225kg/hm 2 urea with 46.2% total nitrogen), medium water (3 times) and medium fertilizer (450kg/ha compound fertilizer, 150kg/ha urea), medium water and low fertilizer (150kg/ha compound fertilizer, 75kg/ hm 2 urea), and without water and fertilizer.

The test depth of soil moisture and nitrogen is 0-160cm which is divided into eight layers, 20cm each layer. From sowing to harvest, eight times was sampled to test nitrogen in indoor method, and soil moisture is tested 22 times using the neutron probe. The root was sampled using root drilling with the inner diameter of 5cm and the drill bit length of 10 cm, cleaned, oven-dried and weighted to obtain the dry weight. Root samples were taken in the ridge and between two ridges, and their mean value is used to calculate the dry weight of the root. Sampling depth is 0-60cm before jointing and 0-100cm after jointing stage, 10cm a layer. Dry matter weight was tested nine times.

The Calculation of Nitrogen Uptake by Winter Wheat Root

Nitrogen uptake amount is calculated from the root water uptake multiplied by the soil water nitrogen concentration in different soil layers, which is

ij t i j ij j ij C z S t x  / 1 . 0 ) ( 1 4 1         (1)
Where ) (t x = cumulative nitrogen uptake by crop root from sowing to the day t ,kg/hm 2 ; ij S = root water uptake on day i and soil layer j, 1/d; ij C = soil nitrogen content (the sum of nitrate and ammonium nitrogen) on day i and soil layer j, mg/kg; j z  = soil depth of layer j, cm, and j z  = 20cm in this study; i = days number from sowing date, d; j = soil layer numbers, which is j = 1, 2,

3



= soil volumetric water content on day t and at depth z, cm 3 /cm 3 ; r z = crop root depth at time t, cm; wp  = soil moisture content at the wilting point, cm 3 /cm 3 ; cr  = critical soil moisture content affecting the root water uptake, cm 3 /cm 3 . In this cr

 = f  75 . 0
, where f  = soil moisture content at field capacity and f  = 36 cm 3 /cm 3 within the 0-100cm soil layer. Following Driessen and Konijn (1997),

) 1 ( ) ( ) ( ) (t LAI K p p e t ET t T      (4)
Where   t LAI = Leaf area index; K = extinction coefficient which is taken as 0.6 in this paper;

  t ET p =     t ET t K o c  ,   t ET p = Crop potential evapotranspiration, mm/d;    t ETo
Reference crop evapotranspiration, mm/d, which can be calculated from the Penman-Monteith equation [START_REF] Richard G Allen | Guidelines for computing crop water requirements FAO Irrigation and Drainage[END_REF]

); ) (t K c = crop coefficient.
In days without LAI measurement, LAI can be estimated from linear interpolation of two adjacent measurements.

) (t K c can be calculated from (5) (Kang, et al. 1994),        0 0 ) ( ) ( ) ( ) ( LAI t LAI K LAI t LAI b t aLAI t K co c (5)
Where a, b, 0 LAI , and co K are constants, which are 0.21, 0.42, 4.2, and 1.30, respectively.

In Eqs. ( 1) and (2), soil moisture content and soil nitrogen content can be obtained from measured values in the field test. In days without measurement, soil moisture content and soil nitrogen content of different soil layers can be obtained from linear interpolation of measurement. Then the amount of daily root water uptake and uptake can be calculated. Using Eq. ( 1), the cumulative nitrogen uptake of winter wheat can be obtained for different irrigation and fertilization treatments. From Eq.(1), the cumulative nitrogen uptake considers both root water uptake and soil nitrogen content, and can reflects soil moisture and soil nutrient status to some extent. Therefore, cumulative nitrogen uptake can be used for the quantitative representation of nutrient and water stresses effects on plant growth. In this paper, cumulative nitrogen uptake is taken as the moisture-nutrient stresses index to analyze the effect of water and nitrogen stresses on correlation among winter wheat organs, and to establish the relationship between stem to leaf ratio (

) (t K sl ), grain to stem ratio ( ) (t K s ), leaf to root ratio ( ) (t K lr )
and root nitrogen uptake. The above ratios are defined as

) ( ) ( ) ( t W t W t K l s sl  , ) ( ) ( ) ( t W t W t K s s    , ) ( ) ( ) ( t W t W t K r l lr  , where ) (t W l , ) (t W s , ) (t W  and ) (t W r
are dry weights of leaf, stem, ear and root, kg/hm 2 . Because the grain test is more difficult, especially before maturity, and ear dry weight test is easier; ear dry weight rather than grain dry weight was used. This is appropriate because the correlation between the maturity grain dry weight and ear dry weight is significant. For example, sampling data on June 15, 2009 resulted in y=0.7872x-117.79, R 2 =0.981, where y= grain dry weight, x= ear dry weight, kg/hm 2 .

Results and Discussion

4.1 Effect of water and nitrogen stresses on winter wheat root water uptake and nitrogen uptake, yield and total dry matter weight Amount of winter wheat root water uptake and nitrogen uptake are calculated from Eqs (2) and ( 1) (Table 1) by using 5 treatments. For different treatment, amount of winter wheat root water uptake and nitrogen uptake have great differences, which includes not only the impact of water shortage on the amount of root water uptake, but also the effect on leaf area index difference on winter wheat root water uptake. As can be seen from Figure 2, due to water, nitrogen double stresses, leaf area indexes have greater difference under different treatments. Leaf area indexes of high water high fertilizer and medium water and fertilizer treatments are greater, and their maximum reach 6.53 and 4.96 which appeared in about 0.7 ( relative growth period). LAI is smaller for medium water and low fertilizer treatment, and smallest for the medium water and zero fertilizer treatment. Thus, to some extent, root nitrogen uptake amount is a better representation for the degree of water and nitrogen stresses.

The relationship between leaf to root ratio, stem to leaf ratio, stem to grain ratio and root

nitrogen uptake of winter wheat

The relationship between leaf to root ratio and root nitrogen uptake

The relationship between leaf to root ratio and root nitrogen uptake can be expressed as the power function

    b lr t ax t K 
, where a and b are two regression coefficients, and the regression is given in Table 2 using 5 treatments. The regression equations for some testing time do not reach a significant level, but the leaf to root ratio increases with root nitrogen uptake and its power index is around 0.5 with the average value of 0.5460. The coefficient a decreases with the relative growth period. By fixing b=0.5, the regression analysis of    

The relationship between stem to leaf ratio and root nitrogen uptake

The relationship between

) ( ) ( t K t K sl slm and ) ( / 1 t
x is found to be linear (Fig. 1) , where ) (t K slm is the stem to leaf ratio under high water high fertilizer treatment. From the linear regression results, the relationship between stem to leaf ratio and root nitrogen uptake can be expressed as

) ( / 496 . 27 0123 . 1 ) ( ) ( t x t K t K slm sl   (6)
Figure 1 Relationship between stem to leaf ratio and root nitrogen uptake

The relationship between grain to stem ratio and root nitrogen uptake

Relative grain to stem ratio is defined as the grain to stem ratio under varying conditions divided by the ratio under high water and high fertilizer condition. The relationship between relative grain to stem ratio and root nitrogen uptake is shown Fig. 2. As seen from Figure 5, the relative grain to stem ratio (

) (t K y s r  
) remains constant for different root nitrogen uptake. Linear regression of . It shows that the effect of water and nitrogen stresses on grain to stem ratio is not significant. For winter wheat, in a certain water and nitrogen stresses range (root nitrogen uptake range in 57-306kg/hm 2 ), the relative grain to stem ratio is not related to root nitrogen uptake, and the value is 0.98 and very close to 1. It indicates that the grain to stem ratio is not affected by water and nitrogen stresses and its value is equal to grain to stem ratio without water and nitrogen stresses. The above results do not mean that grain growth and stem growth were not related to water and nitrogen stresses, and only shown that the ratio of the dry weight of grain to stem did not change or the grain and stem weights increase or decrease in the same degree for winter wheat suffering from water and nitrogen stresses.

In the above analysis, relative ratios with respect to with the high water high fertilization treatment (regarding as no water and nitrogen stresses) were used. As a result, data from different treatment and time can be combined in models, which can be used for correlations analysis of water and nitrogen stresses on the whole growth period of winter wheat.

Validation

Validation of the relationship between the leaf to root ratio with root nitrogen uptake

Leaf to root ratio can be calculated from Eq.( 5) for other different treatments. The scatter plot of

   R R
and reached a very significant level. This shown that the formula (6) expressed the effect of water and nitrogen stresses on stem to leaf ratio of winter wheat.

The ratio increases with root nitrogen uptake.

Conclusions

The paper is based on the experiment results of the dry mater weights of root, stem, leaf, grain of winter wheat of 5 treatment with different water and fertilizer levels, and the synchronization testing information of soil moisture and soil nitrogen (NH 4 + -N 和 NO - (1) The water and nitrogen uptake by roots, to a certain extent, can be reflected the degree of water and nitrogen stresses and can be taken as the index to describe the effect of water and nitrogen stresses (2) Leaf to root ratio and stem to leaf ratio are more sensitive to water and nitrogen stresses than grain to stem ratio, and have significant correlations with root nitrogen uptake, and increase with root nitrogen uptake. For the range of water and nitrogen stresses in this study, grain to stem ratio is not correlated with root nitrogen uptake.

(3) In this paper, the range of water and nitrogen stresses is greater, with the root water uptake varying from 151.9 to 299.4mm, root nitrogen uptake from 67.8 to 496.7 kg/hm 2 , and corresponding crop yield from 3.491 to 6.362 t/hm 2 , the total dry matter from 12.015 to 20.355 t/hm 2 .

  Potential transpiration rate can be expressed as[START_REF] Shao | Soil-Plant-Atmosphere Continuum Water Transmission Theory and Its Application[END_REF]),

Figure 2

 2 Figure 2 Relationship of grain to stem ratio and root nitrogen uptake

Figure 3 4 . 3 . 2

 3432 Figure 3 Scatter plot of simulated and measured values of leaf to root ratio4.3.2 Validation of the relationship between stem to leaf ratio with root nitrogen uptakeStem to leaf ratio under various root nitrogen uptake conditions can be obtained from Eq. (6). The scatter plot of measured and calculated stem to leaf ratios (Fig 4) shown that the simulated and measured values are close to the 1:1 line with the correlation coefficient   449 . 0 33 , 2 9167 . 0 01 . 0

Figure 4

 4 Figure 4 Scatter plot of simulated and measured value of stem to leaf ratio

3 -

 3 +N). The experiment was carried out in farmland water cycling experiment bases of Tianjin Agricultural University in 2008-2009. The relationship between leaf to root ratio, stem to leaf ratio, grain to stem ratio and root nitrogen uptake was respectively established. The results show that:

Table 1 Amount of winter wheat root water uptake and nitrogen uptake in the whole growing period under different treatments

 1

	Treatment	Root water uptake /mm	Root nitrogen uptake /kg/hm 2	Grain yield /t/hm 2	Total dry matter /t/hm 2
	High water and high fertilizer	299.4	496.7	6.362	20.355
	Medium water and fertilizer	231.8	187.4	6.297	16.245
	Medium water and low fertilizer	195.4	95.1	5.522	17.895
	Medium water zero fertilizer	166.9	85.5	4.461	14.085
	Without water and fertilizer	151.9	67.8	3.491	12.015

  Using 2 to approximate the power index 1.9884, the relationship between leaf to root ratio and root nitrogen uptake can be expressed by equation[START_REF] Shi | A mechanistic model describing the photosynthate partitioning during vegetative phase[END_REF]. Relationships of leaf to root ratio and root nitrogen uptake at different growing period

			Table2. Time/year-month-day	) growth period ( 0584 . 0 t x K lr = ) ( t Growth days Relative	5 . 0	 t	2  a	b	Squared correlation coefficient R 2	(5) F(1,2)
			2008-12-17		71	0.268		1.3258	0.4164	0.3191	0.937
			2009-03-27		171	0.367		0.5652	0.3754	0.3741	1.793
			2009-04-16		191	0.488		0.1642	0.4875	0.5933	4.376
			2009-05-07		212	0.655		0.0864	0.5658	0.7373	8.420
			2009-05-14		219	0.722		0.0990	0.5196	0.7929	11.486
			2009-05-21		226	0.789		0.0467	0.6612	0.8591	18.292
			2009-05-27		232	0.852		0.0271	0.7575	0.7894	11.245
			2009-06-04		240	0.936		0.0645	0.5022	0.5864	4.253
			2009-06-15		251	1.055		0.0281	0.6285	0.6702	6.096
		Note: significant F>F 0.05 =18.5, not significant F<F 0.1 =8.53.
									t Klr 	ax	t	5 . 0	results in
	a		0584 . 0 t	9884 . 1 

.

Acknowledgment

Funds for this research was provided by the Natural Science foundation Projects (50679055), the Tianjin Science and Technology Development project (10JCYBJC09400).


Xia Geng

Yong Liang
email: byongl@sdau.edu.cn

Qiulan Wu

Application of a Logical Reasoning Approach Based Petri Net in Agriculture Expert System

Keywords: first-order predicate logic, Predicate/transition system, goal guiding, graphic reasoning, backward reasoning, agriculture expert system

First of all, a goal-guiding graphic reasoning approach that based on the predicate/transition system has been proposed for the first-order predicate logic. In process of reasoning, the premise is separated from the conclusion, which has been taken as the beginning of the backward reasoning that is purposeful and effective as well. Next, this reasoning approach has been applied in the agriculture expert system to present a method of solving problem, providing a new way for studying the reasoning mechanism of the agriculture expert system.

Introduction

Agriculture expert system in agriculture is to widely apply the accumulated knowledge and experiences of agricultural experts by using computer techniques which can overcome the limit of time and space, so as to turn these knowledge and experiences into productivity [START_REF] Hui | The development of expert system and its application in the agriculture[END_REF]. To construct a good reasoning mechanism is the basis of agriculture expert system. Reasoning means the process of searching the answers from knowledge base for the given problems when domain-specific knowledge has been stored into the base in a certain form. A reasoning process is to determine whether the given proposition is contained in the selected sets of the first-level of facts and clause rules [START_REF] Liang | Study and Realization on Chemical HAZOP Expert System Based on Petri Net Modeling[END_REF][START_REF] Zhao | Citrus Cultivation and Management Expert System for Construction and Implementation[END_REF]. In the reasoning processes of agriculture expert system there already have applied many approaches. Recent years, the expert system based Petri net and its application have become one of the research hotspots in the field of intelligent control and intelligent system [START_REF] Liang | Study and Realization on Chemical HAZOP Expert System Based on Petri Net Modeling[END_REF]. Yet, it is not common that the Petri net model has been employed by agriculture expert system, and, theoretical system has not been established. The first-order predicate logic has already enjoyed a wide application in computer science. For its safety and reliability, the practical results of some other logic theories turn out that, the applications in which these logic theories had been employed can get essential conclusions no more than the application in which only the first-order predicate logic had been employed, and can not get a better intelligent system [START_REF] Yuping | Deduction property of some logics applied to computer science[END_REF]. Petri net offers a new way to study the first-order predicate logic reasoning. Not only the Petri net itself can provide an intuitive semantic frame for traditional logic symbols, but also its properties can be used for the randomness of logic reasoning, which finds a way for realizing the machine reasoning, and which increases the chances for dealing with reasoning problems by using different and effective ways. For the first-order predicate logic reasoning, predicate/transition system (Pr/T system) of the high-level Petri net can be utilized to build the model. Based on the model, researches of logic reasoning are divided into two types. ① For the set of Horn clauses, the algorithm of computing T-invariants had been proposed [START_REF] Peterka | Proof procedure and answer extraction in Petri net model of logic programs[END_REF][START_REF] Chuang | Logical inference of Horn clauses in Petri net models[END_REF][START_REF] Chuang | Application of Petri nets to logical inference of Horn clauses[END_REF]; four reasoning algorithms that can support the conclusions obtained and can be popularized to the first-order predicate logic had been proposed, through the improved strategies of resolution refutation [START_REF] Yi | New methods of logic inference of Petri net based on resolution refutation[END_REF]. ② For the set of non-Horn clauses, an efficient algorithm and the backward and forward approaches of analyzing Tinvariants had been put forward in accordance with the necessary and sufficient condition of contradiction which is contained in the set of non-Horn clauses [START_REF] Chuang | Petri net models and efficient T-invariant analysis for logical inference of clauses[END_REF][START_REF] Chuang | Logical inference of clauses using T-invariant of Petri nets[END_REF]; for propositional logics, the reasoning process is turned to solve the non-negative integer solutions of linear equations of the incidence matrix, and this principle can be applied for predicate logic [START_REF] Chongyi | Petri Nets Theory and Application[END_REF]. However, the existing reasoning methods of the first-order predicate logic that based on Pr/T system are equal to the traditional resolution refutation method, in which premise and conclusion are put together to make up the reasoning. In such a way, some heuristic information are not easily to be used for reasoning process, where a large number of useless steps may exist and the reasoning processes are inefficient. Therefore, this paper proposed, by borrowing ideas from the and/or resolution refutation reasoning and based on the Pr/T system, a goal guiding graphic reasoning approach of realizing the backward reasoning, which is applied in the agriculture expert system.

Basic Concepts

We assume that our readers know well the knowledge of agriculture expert system, Petri net, the first-order predicate logic and reasoning. For simplifying the description, we just list some related concepts and terms here. ( , ,..., )

n n f v v
v is also a term of D , and, no other terms exist.

(2)The n-ary vector 12 , , , n v v v  of which the components of terms of D is called the n-ary tuple of D , where n>1.

(3)The sum that multiple n-ary tuples of D are connected by "+" is called as the nary symbolic sum of D , symbolic sum for short. When 0 n  , it is called as the empty symbolic sum, which is represented with " NULL " or "< >". (4)The symbol "+" is commutative.

Definition 2 [START_REF] Chongyi | Petri Nets Theory and Application[END_REF]: Given that 0 ( , ; , , , , , , )

S T F S T F D V A A A M 
is the Pr/T system, which meets the following: ( Ms is the n-ary symbolic sum. When describing a logic problem, the first-order predicates can be divied into two types: describing the premise and describing the conclusion. In general, this paper adopts the method proposed in literature [START_REF] Xia | Modeling of first-order predicate expression by using Predicate/transition system[END_REF] to build the Pr/T system model for the first-order predicate. Definition 3: Assume that P and Q are predicates that describe the premise and conclusion respectively.

Given that 1 0 ( , ; , , , , , , ) ( , ; , , , , , , )

S T F S T F D V A A A M 

S T F S T F D V A A A M 

are Pr/T net systems corresponding to P and Q respectively, then 1  is called as the premise Pr/T net system, or called as the premise net for short, and 2  is called as the conclusion Pr/T net system, or called as the conclusion net. Definition 4: Assume that 0 ( , ; , , , , , , )

S T F S T F D V A A A M 

is the conclusion net, then tT  means the target transition. Definition 5: The two-ary tuple of ( , ) N S F  which meets the following conditions is called as Predicate-and/or graph, or called as Pre-and/or graph for short.

(1)

S  2 (2) () F S S  (3) ( ) ( ) dom F cod F S 
where, ,;,,,,,, )

( ) { : ( , ) } dom F x S y S x y F      ( ) { : ( , ) } cod F x S y S y x F      (
S T F S T F D V A A A M 
is a Pr/T net system, and ,;,,,,,, )

S S T F S S T F D V A A A M         
is a two-level place Pr/T subnet of ∑, if and only if all of the following conditions are met:

(1)

2 1 2 { , , , } ( 0} m S s s s S m    ;
(2)

2 { , } T t t T t S     
, and

T  is not null set; (3) 1 { , , { }} S s s S t T which makes s t        ; ( 4
)
1 2 1 2 1 2 1 2 {( , ) , ,( , ) } F x x x x S S T x x F      
, for

1 2 1 2 1 2 ( , ( , ) ( , ) FF x x F A x x A x x      ) , , 12 12 :, SS A S S   
where  is a dynamic predicate set of D ;

:

T A T true    , for tT   , () T At  is a static predicate, 0 : MS     .
2 S is the output place set of   , and 1 S is the input place set of   . If ∑ itself is a two-level place Pr/T subnet, then this ∑ is called as a two-level place Pr/T net. For example, fig. 1 shows the basic net of a two-level place Pr/T net. In particular, both 2 S and 1 S in definition 7 can be null set, but because that no independent node shall exist in the net, they can not be null set at the same time. Besides, provisions ( 2) and (3) of definition 7 do not require T  and 1 S to be the maximal set. Therefore, even 2 S has been determined, the obtained two-level place Pr/T subnet may not be the only one. ,;,,,,,, )

S T F S T F D V A A A M  is a Pr/T net system, for tT  , if t   , then make 12 { , , , } ( 1)
m t s s s m   ; define 1 1 2 2 { ), ), , ( )} mm s X s X s X ( (
as the output predicate set of the transition t (where,

( 1,2, , ) i X i m  is a term of ( , ) Fi A t s ). Similarly, if t   , then make 12 { , , , }( 1) n t s s s n      ; define 1 1 2 2 { ), ), , ( )} nn s Y s Y s Y    ( (
as the input predicate set of the transition t (where, (2) Each directed edge of the Pr/T net system has a ( 1) nn -try tuple, and no symbolic sum forms that consist of ( 2) mm n-try tuples.

( 1,2, , ) i Y i n  is a term of ( , ) Fi A s t  ).
S T F S S T F D V A A A M    is a two-level place Pr/T subnet,

Pre-and/or Graph Description of the Pr/T Net System

Assume that ∑ is a two-level place Pr/T net which contains ( 1) rr transitions. According to the definition of the two-level place Pr/T net, ∑ can be shared composition of r two-level place Pr/T nets, each of which contains only one transition [START_REF] Changjun | Dynamic invariance of Petri net[END_REF].

Pre-and/or Graph of the Two-level Place Pr/T Net System Which Contains Only One Transition

The ratio of the numbers of transition's input place and output place is represented by : ( , 0) m n m n  . According to the different value of : mn , the discussion can be divided into the following: [START_REF] Hui | The development of expert system and its application in the agriculture[END_REF] 1: ( 1) nn , as shown in fig. 2(a), and its Pre-and/or graph is shown in fig. 2(b). The Pre-and/or graph of fig. 9 can be composed by r Pre-and/or graphs described in conditions (4) of section 3.1. From the above ,we know that theorem 1 is true. (over) When B will appear r times in the Pre-and/or graph in conditions 1) and 2). In this paper, we regulate that in the reasoning process by referring to the Pre-and/or graph, only one (set) output place inherits the "and" or "or" relations of the original Pre-and/or graph, and these relations should be drawn up in the graph. For the rest 1 r  (set) output places, although they also inherit the original "and" or "or" relations, they do not need to be drawn up for conciseness of the graph.

4

Graphic Reasoning of the First-order Predicate Logic

The Goal Guiding Graphic Reasoning Approach of the First-order Predicate Logic

We assume that the general form of the first-order predicate logic that needed to be proved is: A , respectively, and merge the same places to obtain the premise net, which is assumed to be

1 1 1 1 1 1 1 01
( , ; , , , , , , )

S T F S T F D V A A A M 

. Build the Pr/T net systems for predicate formulas which are after the quantifiers of B to obtain the conclusion net, which is assumed to be ( , ; , , , , , , ) [START_REF] Xia | Modeling of first-order predicate expression by using Predicate/transition system[END_REF]. Rename some variables to so that the same variables will not appear on the input/output arcs of different transitions; (2)the atomic predicate formula set of all the "terminational nodes" in subgraghs is equal to the input predicate set of the target transition i t after a certain replacement. then, it proves that the reasoning of the target transition i t is successful. Otherwise, the reasoning is unsuccessful, which proves that B is not an effective conclusion of Step5 End the reasoning. In the above-mentioned approach, if the atomic predicate formula which is added to the Pre-and/or graph

S T F S T F D V A A A M 

Step2

, , ,

2 ( 1,2, , ) i G i T 
is equal to an existing atomic predicate formula of i G after replacement, then the merging should be made under the condition that no new end-node is added to i G . When making merging, the following should be met:

(1) If both nodes are marked as the "terminational nodes", then these two nodes needed to be merged, otherwise not;

(2) In order to keep the original reasoning relations, the two nodes are not really merged into one node, but connected through a dotted line.

Step 1 regulates that the same variable shall not appear on the input/output arc of different transitions in 1  and 2  . Therefore, if a replacement exists in Step 3.3, then it must be consistent with the replacement in Step 3.2.3. It can be known from section 3.1 that, when Pre-and/or graph

2 ( 1, 2, , ) i G i T 
has ( 2) nn "terminational nodes", then there are n terminational nodes that represent the "and" relation between predicates,and there are directed path from these "terminational nodes" to the beginning of reasoning. Thus, for the target transition i t we assume its input predicate set to be { ( ), ( ), , ( )}( 1)

1 1 2 2 { ( ), ( ), , ( )}( 0)
i m m O Q X Q X Q X m  .
When the two conditions in Step 3.3 are met, then the following proposition is true:

1 1 2 2 1 1 2 2 ( ) ( ) ( ) ( ) ( ) ( ) n n m m P X P X P X Q X Q X Q X       
,i.e. the reasoning of the target transition i t is successful, and Step 3.3 is correct. other steps, it is obvious that they are effective and reasonable. Hence, for the first-order predicate logic, the proposed goal-guiding graphic reasoning approach is also effective and reasonable.

Application Example

Example: if

( ( ) ( ) ( )) ( ( ) ( )) x C x W x R x x C x Q x       , prove that ( ( ) ( ))
x Q x R x  . Proof of the example: According to literature [START_REF] Xia | Modeling of first-order predicate expression by using Predicate/transition system[END_REF] and Step1 of the goal-guiding graphic reasoning approach, we get the premise and conclusion nets, as shown in fig. 11(a) and 11(b), respectively. The reasoning process of the target transitions 11(c) from the top to bottom. Because the two conditions in Step3.3 of the goal-guiding graphic reasoning approach are satisfied, the reasoning of 1 t  and 2 t  can be considered to be successful, and replacements in the reasoning process are consistent, thus the conclusion is true.

1 t  and 2 t  is shown in fig.

Application of the Goal-guiding Graphic Reasoning Approach in Agriculture Expert System

At present, production rule has become a knowledge representation mode which enjoys the most artificial intelligent application, and which has been employed in many successful expert systems to represent knowledge [START_REF] Liang | Study and Realization on Chemical HAZOP Expert System Based on Petri Net Modeling[END_REF]. In this paper, we assume that the agriculture expert system use the production rule representation. for instance: IF wz= suburbs, and nyhxptr=large THEN it means a large quantity of carbon emission per unit area The goal-guiding graphic reasoning approach can not only prove the already known results, but also solve questions in agriculture expert system. Specific steps as follows:

Step1 Build proper predicate formulas for production rules of the knowledge base and conclusions to be solved; Step2 According to literature [START_REF] Xia | Modeling of first-order predicate expression by using Predicate/transition system[END_REF] and Step1 of the goal-guiding graphic reasoning approach, build the Pr/T net model for production rules of the knowledge base and conclusions to be solved, to get the premise net Step4 If the question that need to be solved is an effective conclusion, then the value of the variable in 1  that obtained by replacement in the reasoning process is just the answer of the question; if the question is not an effective conclusion, then there is no answer for the question. Example Take the judgment of several common pest and disease damages during the cotton seedling period. In such an agriculture expert system knowledge base, the production rule representation of syndromes and diseases is described as follows [START_REF] Qiuhong | Discussion on the agricultural expert system based on the production rule and principle attributed[END_REF]: Diseases during the cotton seedling period: IF systemic and cotyledon foliage and appear one of syndromes of yellow net, purple plague, ralstonia solanacearum and yellows. THEN the cotton would be withered. IF local and cotyledon foliage and appear water-soaked dots or small spots and turbid juice when wiped on the glass. THEN it shows the angular. IF local and cotyledon foliage and appear pyorrhea or incrustation on the extended scab. THEN it shows the angular. IF local and rhizome and appear tawny and annular constriction. THEN it shows the seedling blight. IF local and root and appear arachnoid tomentum with soil particles but not the cotton fiber. THEN it shows the seedling blight. IF local and rhizome and burst of long-thin spindle-shaped fibers. THEN it shows the anthracnose. IF local and cotyledon foliage and appear small dots with ashen in the center and dullred in outer area. THEN it shows the anthracnose. IF local and rhizome and appear dark brown long round spot and constriction. THEN it shows the redroot. Here we do not list the diseases during cotton budding, blossing and boll opening periods. Table 1 lists the relations between the predicates and syndromes, in which x represents a disease. x is blight x is anthracnose x is red rot Given: during the seeding period, some rhizomes appear burst of long-thin spindleshaped fibers, and the disease is not systemic but local, question: what is the conclusion?

Step1: Build corresponding predicate formulas for production rule representation of the knowledge base, here lists parts of them:

( ) ( ) ( ) ( ) ( , ) Q x Q x Q x P x R x A     6 2 3 9 2 ( ) ( ) ( ) ( ) ( , ) Q x Q x Q x P x R x A     6 2 4 10 2 ( ) ( ) ( ) ( ) ( ) ( , ) Q x Q x Q x P x P x R x A      6 2 3 5 6 1
Build predicate formulas for the conclusion that needs to be solved:

( ) ( ) ( ) ( ) ( , ) Q x Q x Q x P x R x y     6 2 3 9
, where, ( , ) R x y represents that the disease x is y. Step3: The reasoning process is shown in fig. 12(c), from which we know that the question that needs to be solved is an effective conclusion. Step4: In the reasoning process, replacement A2/y had been used, which indicates that A2 is the answer of this question, namely, anthracnose is the conclusion.

Conclusions

In this paper, for the first-order predicate logic, a goal guiding graphic reasoning approach that based on the predicate/transition system has been proposed,and the approach has been applied in the reasoning process of agriculture expert system. Compared to other previous work,the paper has the following significance and innovation:

(1)The reasoning process of the approach proposed in this paper is started with the conclusion, so the approach is purposeful and effective with reducing many useless steps.

(2)For the approach in this paper,in process of reasoning, the premise is separated from the conclusion, avoiding the disadvantage that the causal relationship will be covered by traditional reasoning methods, therefore in this approach, the knowledge is highly readable and some heuristic information can be used in the reasoning process.

(3) When agriculture knowledge has been stored into the base in the form of the production rule, the proposed approach can be used to answer questions of the agriculture expert system, which provides a new way for studying the reasoning mechanism of the agriculture expert system.

Definition 1 [ 8 ]:

 18 Given that D is the nonempty finite set, and V is the nonempty finite symbol set. If all of the symbols of V set are representatives of the elements of D set, then V can be deemed as a variable set of D , and these symbols are the variables of D . (1)Both the elements and variables of D are called the D -terms of D . If () n f is an equation of D with n unknowns, and 12 , ,..., n v v v are terms of D , then () 12

Fig. 1 .

 1 Fig. 1. The basic net of a two-level place Pr/T net Definition 7: Assume that 0 (, ; , , , , , , )

Fig. 2 .

 2 Fig. 2. 1:( 1) nn and its Pre-and/or graph (2) For 0 :( 1) nn , its Pre-and/or graph is similar to the fig.2(b), only that the atomic predicate formula ()A AX is replaced by " NULL ". (3) :1( 2)mm  , as shown in fig.3(a), and its Pre-and/or graph is shown in fig.3(b).

Fig. 3 .Fig. 4 . 1 :PFig. 6 .Fig. 8 .Fig. 10 .

 3416810 Fig. 3. :1( 2) mm  and its Pre-and/or graph

1 r 12 {

 112  , the output place B of transition or

  first-order predicate formulas, and B should be represented by prenex normal form without universal quantifiers. Specific steps of the goal guiding graphic reasoning approach of the first-order predicate logic are shown as follows:Step1 Build the Pr/T net systems for

  replacements in reasoning process are coincident, then it proves that B is an effective conclusion of 12 , , , m A A A , else, it is not an effective conclusion.

Fig. 11 .

 11 Fig. 11. Premise and conclusion nets and the reasoning processesIn fig.11(c), a dotted line represents a predicate formula of the premise net which is obtained by replacing the predicate formula of the corresponding node. In essence, the two nodes connected by the dotted line represent one node.

1  and the conclusion net 2 

 12 ;Step3 Do Step2-Step4 of the goal-guiding graphic reasoning approach;

1  2 

 12 Step2: Part of the Pr/T net of corresponding production rules is shown in fig.12(a), and Pr/T net of the conclusion is shown in fig.12(b).

Fig. 12 .

 12 Fig. 12. Premise and conclusion nets and the reasoning processes

  Elements of input/output predicate set are called the input/output predicate of transition t .

	Definition 8: Assume that	(		, ; , , , , , , )
		1	2	0

  where,

	set is successfully matched with the two-level place Pr/T subnet ∑, if and only if both
	of the following conditions are met:
	(1) The place set corresponding to the set P meets 1 2 { , , , } n P P P	2  ; S
	(2) Exist a replacement  , which makes
	tT  ,	X	( , ) ( 1,2, , ) A t P i n  	.
			i	F	i
	For the purposes of simplifying discussion, we assume that:
	(1) In definition 8, predicate symbols of the predicate formula set	P are different;
					S is the input predicate set of ∑ and	S is the output predicate
					1	2
	set. For any atomic predicate formula set,	{ ( ), ( ), , ( )} P P X P X P X 	, the formula
						1	1	2	2	nn

  Add the Pre-and/or graph of

			For each target transition i t ( 1 i  to 2 T ), initialize the corresponding Pre-
	and/or graph	( 1,2, , ) G i T 	to be null;
							i	2
	Step3 for (	1 i  to	T ){
								2
	Step3.1
	(1) If the output predicates of	t belong to a non-null set, then we assume the
								i
	predicate set to be	Q	{ ( ), ( ), , ( )}( 1) P X P X P X n  , and these	n atomic
								i	1	1	2	2	n	n
	predicate formulas, which are taken as the beginning of the reasoning, are represented
	by	n end-nodes of	G . If	tT 	and	t  , then another end-node marked with
								i	1
	" NULL " should be added to	G , which is also the beginning of reasoning;
								i
	(2) If the output predicates of the target transition	t belong to a null set, then only
								i
	one end-node that marked with "	NULL " should be established as the beginning of
	reasoning;
	Step3.2
	If ( any subset of	Q can not successfully match with any two-level place Pr/T subne
								i
	in the premise net	 ), we consider that	B is not an effective conclusion of
								1
	12 , , , A A	m A , then goes to Step 5.
	else{
	While ( a subset of	Q is successfully matched with a certain two-level place Pr/T
								i
	subnet	  in the premise net	 ), do{
					1			1
	Step3.2.1 1   into	i G (if a node is involved in
	( nn	2)		times of successful matching processes, then copy it for	n times in	G to
								i
	make the Pre-and/or graphs adding into	G independent with each other);
								i
	Step3.2.2 Obtain a new set of end-nodes;
	Step3.2.3 If the atomic predicate formula of an end-node is the input predicate of the
	target transition	t after replacement, then this end-node is marked as "terminational
								i
	node";
	Step3.2.4 Given that	Q is the atomic predicate formula set of non-terminational
								i
	end-nodes.
	}
	};
	Step3.3
	If there is in	G a subgragh	G  , which meets:
							i	i
	(1)the atomic predicate formula set of the output predicate set of the target transition
	t is equal to that of the reasoning beginnings of
	i

i G  ;

Table 1 .

 1 Relations between the predicates and syndromes

	Predicate Syndrome	Predicate Syndrome
	Q1(x)	Systemic	P4(x)	pyorrhea or incrustation on
	Q2(x)	Local		the extended scab
	Q3(x)	Rhizome	P5(x)	tawny
	Q4(x)	cotyledon foliage	P6(x)	annular constriction
	Q5(x)	root	P7(x)	arachnoid tomentum with
	Q6(x)	seeding period		soil particles
	Q7(x)	budding period	P8(x)	cotton fiber
	Q8(x)	blossing period	P9(x)	burst of long-thin spindle-
	Q9(x)	Boll-opening period		shaped fibers
	P1(x)	yellow net, purple plague,	P10(x)	small dots with ashen in the
		ralstonia solanacearum and		center and dull-red in outer
		yellows		area
	P2(x)	water-soaked dots or small	P11(x)	dark brown long round spot
		spots	P12(x)	constriction

Acknowledgment

This research was funded by the National High Technology Research and Development Program of China (863 Program) under Grant 2013AA102301.


Xingzhen Xu

Shuangxi Liu

Weishi Cao

Peng Fa

Xianxi Liu

Jinxing Wang

Research on the knowledge based parameterized CAD system of wheat and rice combine chassis

Keywords: Knowledge base, Agricultural machinery chassis, Parametric modeling, Secondary development

In this paper, through using Pro/Toolkit to do secondary development of Pro/E, it completes the parametric modeling of every key parts of rice and wheat combined harvester chassis by using the object-oriented technology, and combines with Microsoft Access database to store the relevant knowledge. Meanwhile, it collects lots of related knowledge about rice and wheat combine harvester chassis from various channels and establishes a knowledge base of key components of the chassis after sorting them which can achieve the purposes of rapid designing chassis.

Introduction

In recent years, product development gradually transformed from the data intensive to knowledge intensive which will make the CAD system develop towards digitization, integration and intelligent direction. [START_REF] Chi | Knowledge-based Construction of Knowledge Base of Parametric Design System for Steering Gears[END_REF] As a creative and complex activity, product design needs a lot of knowledge, a large number of historical data query, parts typical structure, process plan, evaluation results of manufacture and test, calculation parameters and performance test parameters. The knowledge is scattered storage in each designers' minds, drawer and archives which can not be merged into geometric model in traditional CAD system. So it is impossible to realize the reuse of knowledge-based resources that reduces design efficiency greatly. [START_REF] Li | The application of Knowledge based Engineering on CAD[END_REF] Knowledge-Based Engineering (KBE) technology is a new intelligent design method towards modern design requirements. [START_REF] Zhi | The Knowledge Representation and Reuse in Product Design[END_REF] With the technology of parametric design based on knowledge we can greatly improve the design speed, improve product design quality reduce the research cost and shorten the development period. The knowledge base is both the foundation of applying all kinds of design knowledge and also the key technology which can realize the knowledge based CAD system. [START_REF] Zhongtu | Research on the Key Issues of Knowledge based CAD System[END_REF] The knowledge base supports rapid design, knowledge reuse and sharing in the process of product development. By using the object-oriented technology to realize the parametric modeling of every key parts of rice and wheat combined harvester chassis, using Pro/Toolkit to realize the secondary development of Pro/E and combining with Microsoft Access database to store the relevant knowledge, this paper designs the knowledge based parameterized CAD system of wheat and rice combine chassis.

2

The general structure of chassis parameterized CAD system

The main idea of parametric design

In the process of the traditional design, geometric model of parts is fixed size, so it is complex to modify the shape of parts that even a small detail modifications need redrawing. The product structure is determined in the process of parametric design. It designs different specifications of the products according to different structure parameters which are determinate by specific conditions and specific parameters. Its basic task is to replace the formal parameters of original graphics with the one concrete structure parameters that accord with the graphics we should design. Its concrete structure parameter is in correlation with specific products. The basic process of the program parameters design includes that: creating original graphics, determining the graphic parameters, determining the relation between the original graphics parameters and the specific structure parameters by professional knowledge, completing the design drawings and related documents. The whole process requires a database and database management system to storage and management the various kinds of data and graphics. By using the technology of parametric design can easily modify graphics. And designers can also inherit the experience and knowledge of past design. Then designers can concentrate on creative concept and overall design to give full play to creativity and improve the efficiency of design.

System synthesis

The system consists of three modules, as shown in Figure 1. They are the parametric module of Pro/E, the parametric module based on knowledge, database management module of Access database. The Pro/E module includes the creation of threedimensional model and the implementation of model parameter-driven. The parametric module based on knowledge includes the parametric design verification module, product examples and specific model, the knowledge of parameters variation and parts constraints. The database management module of Access database includes system authority management, model base management and database management.

The system overall design concept

Create three-dimensional models by interactive mode and build design parameter under Pro/E. Then retrieve the design parameters of models and provide editing functions of parameters and regeneration functions of the new three-dimensional model according to the new design parameter.

By using Microsoft Access as the database, parts model library and parts parameters library which store two-dimensional models, three-dimensional models and parts related knowledge, parameter respectively are established in this system. The database connects to Visual Studio 2008(VS) by the corresponding database interface and realizes parts invoking, parts parameter matching and parts management by programming with the language of Visual C++. Through the MFC programming under VS, this system creates a user interface to facilitate the operation and management of parts. Meanwhile with the Pro / E links to VS via DLL mode, we complete the Microsoft Access database, VS and Pro / E tripartite interaction and achieve the overall design of the system. The knowledge based parameterized CAD system is shown as Fig. 2.

The realization of knowledge based parameterized CAD system

The system mainly includes two aspects: parameterization implementation and database connectivity. Interactively create parts of original three-dimensional model, and use the parameter setting function of Pro/E to establish the design parameters and dimension relations under the Pro / E. And then establish the correlative forms in the Microsoft Access relational database. Using Visual C++ to map a Crecordset class object for interactive, then retrieve the design parameters of the model by the Pro/Toolkit application, finally input parameters and regenerate the model according to the data source object. The Access database and Pro/E is connected via VS2008 platform.

The realization of parametric system

Using Pro/ Add dialog resources in the menu of resources under VS. And add the appropriate button controls in the dialog box, complete ID and attribute settings. After finish designing dialog interface, double-click interface to generate CDBDlg dialog class.

Set up the data table class

In this paper, we connect databases by setting up the data table class using ODBC. Using CRecordset class to bind the data table, click [Add Class] under the menu [Project] in VS, select the MFC ODBC user and then choose the added data source DB, define the name of data class and complete the work of adding class.

The key program code of connection

Through the MFC programming, database is connected to Pro/E and the dialog function is completed, the key part of the code is as follows:

The knowledge database

This paper collects lots of related knowledge about rice and wheat combine harvester chassis from various channels and establishes a knowledge base of key components of the chassis after sorting it. The knowledge database consists of case library, rule library, parameter library and material library. Part of the knowledge of wheat and rice combine chassis is shown in figure 5.

Conclusions

Based on above studies, we develop a rice and wheat combine harvester chassis parametric knowledge base. The implementation of the system adopts four basic modules: the user interface module using VC++6.0 to develop with VS2008 as a platform, 3D model library module created by Pro/E, Pro/Toolkit program modules to achieve Pro/E system menu loading and parametric, knowledge base module in which the Microsoft Access as the database.

Fig. 1 .

 1 Fig.1. Parameterized CAD system of wheat and rice combine chassis

Fig. 2 .

 2 Fig.2. The knowledge based parameterized CAD system

Fig. 3 .

 3 Fig.3. The connection process of data source 3.2.2 Create MFC program and design dialog Build a new MFC DLL program under VS, complete the basic setup and add Pro/Toolkit header files and initialization function user_initialize () and termination functions user_terminate () in the item master file.Add dialog resources in the menu of resources under VS. And add the appropriate button controls in the dialog box, complete ID and attribute settings. After finish designing dialog interface, double-click interface to generate CDBDlg dialog class.3.2.3 Set up the data table classIn this paper, we connect databases by setting up the data table class using ODBC. Using CRecordset class to bind the data table, click [Add Class] under the menu [Project] in VS, select the MFC ODBC user and then choose the added data source DB, define the name of data class and complete the work of adding class.

1

 1 BOOL CDBDlg::OnInitDialog() { CDialog::OnInitDialog(); //connect to database if(!m_Set.Open()) { AfxMessageBox("Database connection failed!"); SendMessage(WM_CLOSE,0,0); return FALSE; } m_List.SetExtendedStyle(LVS_EX_FULLROWSELECT|LVS_EX_GRIDL INES); //insert column m_List.InsertColumn(0,"Number",LVCFMT_LEFT,50); m_List.InsertColumn(1,"FileName",LVCFMT_LEFT,80); m_List.InsertColumn(2,"Path",LVCFMT_LEFT,150); ShowList(); return TRUE; // return TRUE unless you set the focus to a control // unusual:OCX Property page should return FALSE The system interface This paper designs the knowledge based parameterized CAD system of wheat and rice combine chassis and realize the Microsoft Access database, VS2008 and Pro/E tripartite interaction. A screenshot of system interface is shown in Figure 4.

Fig. 4 .

 4 Fig.4. The system interface

Fig. 5 .

 5 Fig.5. Combine working parameters and engine parameters

Table 1 .

 1 The model file directory After design data tables, create a new data source DB in Administrative Tools under the menu of Control Panel, and connect to the database created. The process is shown in Figure 3.

	Toolkit procedure development technology to carry on secondary
	development in Pro/E, this method consists of four steps:
	1. Write source files (Source code and resource files)
	2. Compile and link, create the executable file (DLL or EXE)
	3. Register Pro/Toolkit application
	4. Run the application
	3.2 Database connection
	3.2.1 Establish the database tables and data sources
	Design a data table by Microsoft Access, as shown in Table1.

Acknowledgment

Funds for this research were provided by the National Science and Technology Plan Projects, the major projects foster Agricultural machinery professional chassis digital design and complex piece of lean manufacturing (2011BAD20B01).


Yinchi Ma1

Wen Ding

Design and Implementation of Aquarium Remote Automation Monitoring and Control System

Keywords: aquarium, remote, automation, monitoring and control 1

come

Introduction

At present, there is a wide variety of aquarium in the market. And some multifunction aquariums such as controlling water temperature, increasing oxygen, and filtering, feeding, and other functions have appeared. But most functions need to be operated 1 Ma Yinchi (1982 -), Beijing Fisheries Research Institute, Engineer, Master, graduated from Beijing Normal University, State Key Laboratory of Remote Sensing Science, mainly engaged in research of agriculture remote sensing and fisheries information technology. manually in site. With the progress of modern electronic information and automation technology, in some fields such as intelligent manufacturing, precision agriculture, and household, have been able to realize the remote video monitoring, environmental parameters monitoring, equipment condition monitoring and controlling [START_REF] Guoxiang | Development and application on GSM-based monitoring system for digital agriculture[END_REF]. These technologies have had some simple application on the aquarium equipment, but the multi-functional integration application is still a blank in this area. Tu used the on-line video to monitor the oil contamination level [START_REF] Qunzhang | Study on a New Technique of On-line Monitoring of Oil Contamination Level Using Computer Vision Technology[END_REF]. Peng, Yao, Zhang and Yu monitored and controlled the aquarium equipments based single-chip computer technology, but not realized the remote control and the on-line remote video monitoring [START_REF] Guoping | The Design of Automatic-Control Instrument for the Aquarium[END_REF] - [START_REF] Hongjie | Design of the automation oxygen controller based single chip microcomputer[END_REF]. Ge realized the remote control of the aquarium equipments based RS-485 bus. But the dynamic IP problem was not solved [START_REF] Hua | The Realization of the Remote Communication between PC and the Controller in Aquarium Based on RS-485 Bus [J[END_REF]. Yang realized to control the oxygen adding equipment of the fish pond based GSM and WIFI technology [START_REF] Shifeng | Wireless system for monitoring and control of dissolved oxygen in fish pond water[END_REF]. Through analysis and study of the most aquarium controlling equipment application status at present, people can't stand work of the aquarium, and also cannot monitor or control the electrical equipment of the aquarium in real-time. These problems cause a lot of trouble for daily maintenance, and even cause some economic losses [START_REF] Wei | Technology of Breeding Seawater Fancy Fishes in Household Aquarium[END_REF]. In this article, we propose a system solution method that can realize remote automation monitoring and controlling of the aquarium. By applying IMagineWorld center platform, RS485 protocol software and I/O alarm output control technology, the system can realize to view video of the aquarium, to monitor temperature of the water, to add oxygen, and to warm or filter the water based through the Internet. At the same time, The dynamic IP problem is also solved which makes the system convenient for simple household application..The design and implementation of this system will have an important innovation meaning for the design and manufacture area of the aquarium.

System Composition

The System is composed of the monitoring module, the control module, the video server, the center server platform and the client.

The monitoring module monitors the real-time video and the water temperature of the aquarium.

The control module is designed to control the light equipment, the heating equipment, the oxygen adding equipment and the filtering equipment.

The video server is the core part of the system, and we have improved and expanded its function. The I/O alarm output function of the video server is encapsulated into the system I/O control unit. The Unit receives all kinds of orders sent from the center server platform, and controls the equipment belonging to the control module in the aquarium. At the same, the unit encodes the data from the temperature sensor and uploads the data to the center sever platform through the RS485 transparent protocol. The real-time video data of the aquarium gathered by CCD sensor is compressed and coded, and then is also uploaded to the center server platform automatically. These functions are encapsulated into the data service unit.

The IMagineWorld management software is installed on the center server platform. The software can deal with the temperature and video data sent from the video server, and put forward these data to the request client. At the same time, all the monitoring and controlling orders by user are also sent to the control unit of the remote video server through the platform.

The system client is divided to two kinds. For normal PC users, we design an internet server based on the ASP technology on the center server platform. The users can visit the system and use all functions through internet by IE explorer. For other mobile users, we design client software running on the Android mobile operation system platform. The users can use the system through GPRS network or WIFI network by the client. Considering the safety and privacy of users, the system has a strict user authentication mechanism. All the monitoring and controlling operations from IE or the client must base on the legitimate login.

The structure design of the system is shown as follow (Fig. 1).

Fig. 1. System Structure Design

Hardware

The system hardware consists of the control module, the monitoring module, the video server and the web server.

Based on the current popular aquarium equipment, the control module consists of the light, the heating equipment, the filtering equipment and the oxygen adding

Software

The network system service is setup on the center server platform, and the IMagineWorld software is also setup on it. IMagineWorld is the core software of the center server platform, and is responsible for storing and forwarding of the real-time video. After the system administrator checks the count of the user who owns the system equipment, he will add the device ID into the system service list. The device ID is the only identifier of the system equipment. Only the system equipment whose device ID recorded in the system service list can upload the real-time video to the center server platform, then store and forward the video to the remote client through the center server platform.

The network users can visit the system through internet by IE, and they can monitor and control the aquarium by the IE widgets. We setup a set of rules for the user management. User must register a count on the network system first, and then the system administrator will check it. The center server platform can bind one or more sets of the system equipment for every user, and the user can switch among these equipments through the client or IE interface.

Aiming at the development of smart mobile terminals, for the convenience to monitor and control the remote aquarium anytime and anywhere, we develop a client for this system based on Android smart mobile operating system platform. For the legal users who have registered successfully, they can download and installed the client on their phone or pad running the Android system. And then they can login and use the system freely. The mobile client not only provides all the functions of the web system, but also provides image snapshot and video record function. The center server compresses the transfer data, and optimizes the network resource occupancy and data traffic. So users can operate the system through GPRS or WIFI network smoothly.

Conclusions

In this paper, we make detail design and implementation for remote automation monitoring and controlling of the aquarium. The structure of the system is simple and the performance is reliable. After strict system test, the system has obtained good effect. The aquarium water temperature data can be sent to the user client in real-time through the internet. Users can remote control the heating device due to the temperature data initiatively. And at the same time, users can remote control the oxygen adding device and the filtering device due to the video data from the CCD sensor initiatively.

Monitoring and controlling the aquarium in real-time can ensure the breeding security and reduce the breeding risk and loss. The application of this system can eliminate the influence of human factors during the breeding process, and increase efficiency significantly. For home and commercial places aquarium users, the beautiful and lightweight hardware structure design is important. And the software interface is also required to be friendly and operable. We will optimize and improve the system for these requirements. Industrial hardware structure design of the system can make it product and apply at scale. And the system will have a more practical usage. To expand the control circuit and do modular design will allow users to easily customize their own remote monitoring and controlling module in this system. And to expand the operating system platform of the mobile client will strengthen the portability of the system software. At the same time, we also design a particular system that can be used in large breeding sink to expand the system application.

  equipment. These equipments are controlled by an independent circuit. There are control buttons on the control panel of the aquarium. And every control circuit is connected to the I/O control unit of the video server. This design realized the local control and the remote control at the same time. At present, the monitoring module is composed by the water temperature sensor and the real-time video CCD sensor. The tiny custom water temperature sensor is applicable to the household aquarium. The DS18B20 component uses the waterproof packaging technology. The sensor is connected to the video server by leading wire. Its testing range is between -55℃~125℃. And the accuracy of the sensor is±0.5℃ working under the -10℃~85℃ condition. The Temperature data is conversed to RS485 signal through the video server. And then the video server sends these data to the remote center service platform by internet or GPRS network. User can set up the time interval of the water temperature refreshment in the system, and the temperature data can be refreshed on the client or IE automatically. The video acquisition device encapsulates a color CCD sensor with 420 line horizontal resolution. It uses PAL or NTSC signal mode. The camera has wide-angle function and can eliminate spherical deformation of the video. The camera base uses the sucker structure, and the reinforced sucker can fix the camera on the outer wall of the aquarium hardly. User can change the location and angle of the camera due to requirement. The camera is connected to the video server by 1.0Vp-p/75Ω/BNC interface.In recent years, the video server has been widely used in remote monitoring and security area. It is a kind of embedded device to compress and deal with the audio and the video data. It is composed of AV compression and encoder device, input and output channel, network interface, AV interface, RS485/RS232 serial interface, protocol interface and software interface, etc. This system is equipped with a high quality video server with high quality SOC computing chip which is stable and reliable, and has low power consumption. The video server uses the H.264 video coding technology with a high compression ratio and high image quality. The video server supports NTSC or PAL video formats and its image resolution can reach 704*576 pixels, and the video compression rate is 32kbps~4Mbps, the most high frame rate can be up to 30 frames per second. The video server can support 4 alarm input signal and 4 alarm output switch signal. It has the RJ45 adaptive 10M/100M Ethernet port, and supports transparent RS485 reading and writing protocol. The video server has a dynamic DNS client inside, and can be configured as the secondary domain name. This makes it particular convenient to the family dynamic IP users. The video server uses the DC12V/2A ballast for power supply.We design an I/O power controller for the video server, so as to control the switch of the aquarium equipment in remote way. The main structure components are composed of a transparent cover, an upper cover body, a fixing rail and a terminal block, etc. All the built-in electric switch components adopt with 9mm module electric equipment, and are fixed on the fixing rail. The Grounding is firm and reliable, and can be convenient to use. The controller is equipped with 4 groups of 10A socket, 12V/5A switch power and 4 12V relays. The 4 groups of I/O switch control the 4 relays, and the 12V switch power supplies power to the RS485 water temperature sensor and the 4 relays. The inside circuit structure of the I/O power controller is shown as follow (Fig.2).

Fig. 2 .

 2 Fig. 2. Inside Circuit Structure of I/O Power Controller


Yujian Yang

Huaijun Ruan

Yan Tang

Wenxiang Zhao

Xueqin Tong

Study on Conditional Autoregressive Model of Per Capita Grain Possession in Yellow River Delta

Keywords:

In the paper, we reviewed hierarchical statistical modeling about conditional autoregressive models method for spatial data located in Yellow River Delta. Moreover, we also proposed a new method about the evaluation and prediction of per capita grain possession on county-level in the highefficiency ecological zone. With the support of MCMC approaches and conditional auto-regressive (CAR) Model, we estimated the posterior distribution through iterative sampling among per capita grain possession and the correlated factors. As a useful tool, the spatial bayesian model appeared to gain insights into per capita grain possession on spatio-temporal variability related to the grain sowing area, efficient irrigation area, agriculture machinery conditions. The study results showed that auto correlation characteristics and the posterior density distribution were described by the quantity method with CAR model, correspondingly the posterior mean of observed value and the posterior mean of predicted value of per capita grain possession were developed in Yellow River Delta. The posterior probability was more evident statistical significance on basis of prior information and sample characteristics in credible interval. Apparently, three factors of the grain sowing area, efficient irrigation area and agriculture machinery resulted in complex random effects on per capita grain possession. The results also indicated that bayesian method was not only the more quantitative evaluation, but also the more estimated accuracy for the per capita grain possession on country level in Yellow River Delta.

Introduction

In the recent decades, hierarchical models had drawn the attention of scientists in many fields and were especially suited to studying the spatial variables. The approach to spatial modeling was fairly effective to evaluate the variables, and the merits of solving the flexibility and stability for high-dimensional spatial model or spatiotemporal model were very evident with bayesian hierarchical model. The bayesian estimation indicated significance for this variable whereas maximum likelihood estimates did not, and the bayesian considered the prior information regarded as the prior distribution for the variables, had the highest posterior density. So the posterior distribution was given the better precision. Recent development of the spatial bayesian hierarchical framework had gained increasing popularity in many domains. The spatial effects can be explicitly expressed in the model by assuming a prior distribution with its parameters specified from the information of spatial neighbors from spatial bayesian models. Based on a statistical point of view, however, the observations were not independent since proximity in space playing a key role in complicated spatio-temporal model. The importance of space and the problem of parameter heterogeneity were emphasized by Martin Feldkirche(2007), Doppelhofer et al. (2004) and Fernandez et al. (2001), In particular, hierarchical bayesian modeling allowed borrowing of strength across similar (say, geographically adjacent) countries, hence improved estimation prediction and mapping of underlying model features driving the data[ [START_REF] Feldkircher | A Spatial CAR Model applied to a Cross-Country Growth Regression[END_REF][START_REF] Doppelhofer | Determinants of long-term growth: A bayesian averaging of classical estimates (bace) approach[END_REF][START_REF] Fernandez | Model uncertainty in cross-country growth regressions[END_REF] . CAR model is more suitable for scientific research. Anselin(2002), Haining(2003) on one hand. From the model perspective, on the other hand, the computerization of Spatial weight matrix was emphasized in CAR model, undoubtly, the quantitative degree was improved in variable computerization process. Moreover, distance range attenuation function was used in spatial weight matrix, which was determined by the correlated prior knowledge, and estimation of semivariogram plot or correlated plots. Apparently, considering of prior knowledge improved the predication accuracy of model variables [START_REF] Anselin | under the hood:issues in the specification and interpretation of spatial regression models[END_REF][START_REF] Haining | Spatial data analysis theory and practice[END_REF] .

The Yellow River Delta was formed on basis of the yellow river alluvial plain and the adjacent coastal regions in Shandong province, including Dongying city, Binzhou city, Hanting district, Shouguang city, Changyi city in Weifang city, Laoling and Qingyun in Dezhou city, Gaoqing country in Zibo city and Laizhou city in Yantai city. The total area is 26500Km2. In 2009, the construction of high-efficiency ecological zone has been officially christened, in 2011, national science and technology demonstration of modern agriculture in Yellow River Delta was established to enforce the total development of agriculture. Due to the bayesian model provided the flexibility to accommodate that possibility, and the flexible nature of the model also made it possible to consider the inclusion of additional spatial (e.g., grain production) variables when appropriate. The study was carried out to analyze the location and interaction of taking the case of per capita grain possession, conditional autoregressive models of per capita grain possession considering of the typical variable heterogeneity and the better prediction were achieved by the bayesian method.

2

Material and Methodology

Data Acquisition and preprocessing

It was necessary to preprocess the spatial data before Bayesian inference Using Gibbs Sampling (GeoBUGS) analysis, which attributed the model of WINBUGS software. log transformation on per capita grain possession was used to ensure a constant variance. The map was comprised of 19 country units. The data came from Shandong Statistical Year Book-2008, Chinese county (city) social economic statistical yearbook-2008 [6] . Considered of the special data format for GeoBUGS, firstly the transformation from *.shp format was carried out to ensure the following steps in the study. In general, 4 steps were carried out to data transformation and correlated computerization. The first step, the first line contains the key word "map" followed by a colon and an integer, N, where N was the number of distinct areas in the map. The second steps, the import file is a 2 column list giving: (column 1) the numeric ID of the area, this must be a unique integer between 1 and N; the areas should be labelled in the same order as the corresponding data for that area appears in the model. The third step, it begins with a line containing the key word "regions". The fourth step, the final part of the import file gives the co-ordinates of the polygons. However, they are not used by GeoBUGS, so can be arbitrary. Subsequent rows contain a 2-column list of numbers giving the xand y-coordinates of the poly. The polygon coordinates can be listed either clockwise or anticlockwise. In the process, polygons should be separated by a line containing the key word END [7] .

Spatial Model Framework of Conditional autoregressive model

In CAR model, the expected value of the response variable was regarded as being conditional on the recorded values at all other locations, we may smooth the observed value of per capita grain possession by fitting a random effect on poisson model allowing for spatial correlation, using the intrinsic CAR prior proposed by Besag(1974), York and Mollie (1991) [START_REF] Horabik | 2nd International Workshop on Uncertainty in Greenhouse Gas Inventories, International Institute for Applied Systems Analysis[END_REF] . For the risk evaluation of per capita grain possession, the expected value of the response variable in this model was regarded as being conditional on the recorded values at all other locations. CAR was also a popular hierachical spatial model for use with areal data, or popular auto-gaussian model, the implementation of the CAR model was convenient in hierarchical bayesian settings because of the explicit conditional structure. The model may be written as:

) Poisson( ~i  i O (1) i i i b x p     10 / log log 1 0 i    (2)
where 0  is an intercept term representing the baseline (log) relative risk of per capita grain possession across the study region, nearby areas [START_REF] Arab | Hierarchical Spatial Models[END_REF] , we may assume a CAR prior for these terms. Intrinsic CAR model given referred to the following description:

)) /( 1 , ( ~i i i i m N      (3)
Where, 3), the prior was a pairwise difference prior, identified only up to an additive constant. This impropriety was typically ignored, since the posterior for  will typically emerge as proper even though this prior is not. In order to clarify the parameterisation and the suitable hyperpriors for the parameters of this model, application of proper CAR model required the user to specify unnormalized weights, must always be used, so the new description was chosen an alternative parameterisation Stern and Cressie (1999), as the following illustrated.

T p i i i ) ,..., , ,..., ( 1 1 1          , i m is
i ii E M / 1  (the inverse of the expected count in area i , 2 / 1 ) / ( i j ij E E C 
for neighboring areas i , j and 0 otherwise. So we used the dflat() distribution in WINBUGS developed the prior information for the intercept term in a model including CAR random effects [START_REF] Jiang | Bayesian analysis of within-field variability of corn yield using a spatial hierarchical model[END_REF] . MCMC procedure was now automated within WinBUGS, its spatial package GeoBUGS can be used to carry out bayesian computations, this software packages now routinely used by practicing biostatisticians and spatial workers. The adjacency matrices were edged from the GeoBUGS module including an option to produce a data file containing the adjacency matrix for any map loaded on the system. We edit ed the adjacency map of Yellow River Delta to include these areas as neighbors. In the article, the sum of 80 neighboring areas was given by spatial CAR models to the risk evaluation of per capita grain possession on country-level in Yellow River Delta.

3

Results and Analysis

Bayesian posterior analysis of CAR Model parameters

There was a large and growing literatures on bayesian analysis and Markov chain Monte Carlo (MCMC) method. MCMC method offered an iterative computational method suitable for solving this problem. We obtained posterior estimation of key fixed effects, smoothed maps of both frailties and spatially varying coefficients. MCMC also provided a mechanism for taking spatially dependent samples from probability distributions in situations, where the usual sampling was difficult, if not impossible. Many auto-models fall into this category, particularly because the normalizing constants for their joint or posterior probability distributions were either too difficult to calculate or analytically intractable. MCMC approaches can be utilized to estimate the posterior distribution through iterative sampling. The series of results were detailed given in the contexts.

There was no evidence of drift in the study, the mixing was quite good with the full range of each parameter covered in relatively few simulations. The plot also showed the median and 95% credible interval for each parameter. Trace plots can be usefully displayed individually, as shown here. Well-behaved MCMC output was characterized by parallel-chain trace plots showing each sampler in a set of parallel simulations (started at over-dispersed points in parameter space) has moved freely around a common parameter space, with no sign of being stuck within a localized region, and with no systematic trends. The iteration trace of alpha0, alpha1, tau and the trace plots about sigma parameter were analyzed in the article. The iteration results indicated that the markov chain based gibbs sampler of model parameters presented the convergence, including the two groups of initial values of sigma parameter according to the trace plot after 10000 iterations [START_REF] King | Markov Chain Monte Carlo Random Eects Modeling in Magnetic Resonance Image Processing Using the BRugs Interface to WinBUGS[END_REF][START_REF] Huiming | The economical model of Bayesian computation[END_REF] . After that the gibbs sampler, we also developed the correlogram plots the autocorrelations of the chain for each parameters of interests (alpha0, alpha1, sigma and tau) to understand the convergence of the parameters in detail.

Figure1. Parameters posterior kernel density of CAR Model

Figure1 was produced by computerization and was showed by the densities of sections of the chains for the parameters alpha0, alpha1, sigma and tau, and was produced an approximate visual kernel estimate of the posterior density or probability function. The posterior summary was estimated by the MCMC output: mean, standard deviation, MC (Monte Carlo) error, and selected percentiles (default values are 2.5%, 50%, and 97.5%). It also provided iterations finally kept for estimation. MC error was estimated using the batch mean method, the posterior mean table of variables was showed inTable1. 1 summarized the results for the key variables. The density estimated of the posterior distributions for the prior, the bayesian estimation of 4 parameters(alpha0, alpha1, sigma and tau) was 0.625, -7.651, 0.3259 and 11.71, the confidence intervals of the parameters were (0.2977, 0.9819),(-12.15, -3.518),( 0.1828, 0.5231), respectively [START_REF] Huiming | The economical model of Bayesian computation[END_REF] .The literature reviews showed that MCMC was an effective method for bayesian computerization on the premise of the Markov convergence, so it was significant for estimation of simulation sampler, the iterations times to reject the influence of the initial values, it was suitable for 10000 iterations to estimate the parameters of bayesian model in the study considering of these factors, correspondingly, the posterior mean plots were given, the high sustainable status of model were resulted from the data transformation, or policy and the micro-structure of market usually caused the results [START_REF] Ma | Spatial Methods in Geographic Administrative Data Analysis[END_REF] .

Table1. Posterior mean table of variables

The posterior mean of per capita grain possession

Posterior density plots of parameters were estimated from posterior sample using gibbs sampler, the results generally confirmed our previous conclusion, the bayesian analysis of the coefficient alpha0, alpha1, sigma and tau was given independent non informative normal priors while the standard deviation of the random effect, a spatial bayesian hierarchical approach was employed to simulate the relationships of 3 factors to per capita grain possession. In the study, according to the series of random numbers relevant to the uncertainty of per capita grain possession, the results was expressed to estimate the risk, maps of posterior mean of observed value and predicted value for the models in the paper.

Figure2

The posterior mean of observed value per capita grain possession on country level in Yellow River Delta Figure3 The posterior mean of predicted value of per capita grain possession on country level in Yellow River Delta

The posterior mean of observed value per capita grain possession observation in Yellow River Delta, respectively. Hekou district and Dongying district (<250 kg per capita grain possession), Zhanhua country, Lijin country, Kenli country and Bincheng district(250 kg -500 kg per capita grain possession), Huimin country, Wudi country, Shouguang city, Hanting district and Laizhou city (500 kg -750 kg per capita grain possession),Qingyun country, Laoling city, Yangxin country, Zouping city, Boxing country, Guangrao country Changyi city (750kg -1000kg per capita grain possession), Gaoqing country(>1000 kg per capita grain possession), as illustrated in Figure2.

The posterior mean of predicted value about per capita grain possession was computerized on country level in Yellow River Delta, respectively. Hekou district and Dongying district (<250kg per capita grain possession), Zhanhua country, Lijin country, Kenli country and Bincheng district(250kg-500kg per capita grain possession), Wudi country, Shouguang city, Hanting district and Laizhou city (500kg -750kg per capita grain possession), Huimin country, Qingyun country, Laoling city, Yangxin country, Zouping city, Boxing country, Guangrao country, Changyi city and Gaoqing country (750kg -1000kg per capita grain possession), as shown in Figure 3.

These results indicated that the observed values were consistent with the predicted values from the posterior predictive distribution and the spatial maps of CAR model, and the parameters responded to the characteristics of model computerization. three types of the grain sowing area, efficient irrigation area and agriculture machinery resulted in complex random effects on per capita grain possession was given to estimate the CAR model, the proposed model gave a quantitative result. Our results generally agreed with previous research findings in the literature, referred to GWR results in another paper [START_REF] Yang | A geographically weighted model of the regression between grain production and typical factors for the Yellow River Delta[END_REF] .

Conclusions and Discussion

As mentioned above, we had shown the application of spatial conditionally autoregressive model to examine the random effects on per grain possession, and we had also estimated per capita grain possession a set of 19 heterogeneous countries in Yellow River Delta. The Bayesian hierarchical approach distinguished itself by being able to explicitly model the spatial association of grain production data, the bayesian results showed the observed value and predicted value of per capita grain possession can be fitted using MCMC based on CAR model, the flexibility and stability estimation offered by model established were improved. 4 parameters in the model (including alpha0, alpha1, sigma and tau) showed that the posterior probability was more evident statistical significance on basis of prior information and sample characteristics in confidence interval. So CAR of per capita grain possession on country-level in Yellow River Delta was not only the more estimated accuracy, but also the more quantitative evaluation depending on bayesian method. The Bayesian model provided the flexibility to accommodate that possibility. The flexible nature of the model also made it possible to consider the inclusion of additional spatial (e.g., soil or topographic) variables when appropriate.

  i x is the covariate in district i , with associated regression coefficient 1  and i b is an area-specific random effect capturing the residual or unexplained (log) relative risk of per capita grain possession in area i . We often think of i b as representing the effect of latent (unobserved) risk factors. The distribution sets the value of i b equal to zero for areas i that are islands, hence the posterior relative risks depend on the overall baseline rate 0 and the covariate i x . To allow for spatial dependence between the random effects i b in

1 ,

 1 the number of adjacent regions of region the average of the adjacent regions values. This model corresponded to a multivariate normal distribution for full rank covariance matrix. This arose due to the translation invariance clearly visible in formular (