Zhiqiang Wu

Xianfu Cheng

Junping Yuan

Applying Axiomatic Design Theory to the Multi-objective Optimization of Disk Brake 

Keywords: Axiomatic design, Multi-objective optimization, ANOVA, Disk Brake

The multi-objective optimization involves multiple, competing functionality requirements, which is mainly limited to downstream detailed design. Axiomatic design provides the theory to design a complex system top down and deals with multiple functional requirements (FR). It has demonstrated its strength in various types of design tasks. In fact, the objective function is a FR and those variables affecting the objective function are the design parameters (DPs). This paper presents an application of axiomatic design to multi-objective optimization. First, identify the relationship between FRs and DPs in terms of contribution of each DP to each FR by using orthogonal experiment and analysis of variance (ANOVA); then identify important design parameters to a FR and classify design variables into groups based on uncoupled design philosophy; and then establish the function dependence table, and sequentially optimize every objective function. An application in a disk brake design is used to demonstrate the use of the proposed method in dealing with real-world design problems. The results show that the proposed method provides a promising approach to optimize multiple, competing design objectives.

Introduction

Multi-objective optimization is generally more difficult to achieve each objective optimum because of tradeoff between the various objectives, so the absolutely optimal solution may not exist. Axiomatic design approach has demonstrated its strength in various types of large-scale system design, including vehicles, aircrafts, manufacturing facilities, and so on [START_REF] Suh | Axiomatic design: advances and applications[END_REF] . Liu [START_REF] Liu | Improving an existing design based on axiomatic design principles[END_REF] , Hwang [START_REF] Hwang | Robust design of the vibratory gyroscope with unbalanced inner torsion gimbal using axiomatic design[END_REF] and Jeff [START_REF] Jeff | Applying axiomatic design theory to the evaluation and optimization of large-scale engineering systems[END_REF] apply independent axiom of axiomatic design theory to improve and optimize multi-objective and largescale engineering systems. In this paper, a systematic method is presented for applying independent axiom to multi-objective optimization problems. Firstly, identify the relationship between FRs and DPs in terms of contribution of each DP to each FR by using orthogonal experiment and analysis of variance; secondly, important design parameters to a specific objective are identified and could be grouped into one set of parameters by means of the "uncoupled" philosophy of axiomatic design; then establish the function dependence table, and optimize every objective function in sequence.

Multi-objective optimization formulation

The mathematical model of multi-objective optimization is generally expressed as

Min:   T 2 1 ) ( ) ( ) ( ) ( x x x x m f f f F , , ,   s.t. 0 ) (  x j g q j , , 1   0 ) (  x k h p k , , 1   (1)
where, x=(x 1 , x 2 , …,x n ) T is n-dimensional design variables; F(x) is the vector of objective functions; f i (x) is the ith sub-objective function and m is the number of subobjective function; g i (x) is the jth inequality constraint and q is the number of inequality constraint functions, h k (x) is the kth equality constraints and k is the number of equality constraint functions.

In the above equation, achieving the optimum of each objective is generally difficult. Specially, when there are tradeoffs between the various objectives, namely, the optimization problem has conflicting goals, it is impossible to make each subobjective simultaneously attain optimum. Thus, it should be required that the optimal solutions of all sub-objectives are balanced, so that the totally satisfactory solutions can be obtained. Compared to single objective optimization, the theory and computational methods on multi-objective optimization are not perfect. Therefore, it is necessary to introduce other relative design theories, such as axiomatic design, to further promote multi-objective optimization theory and methodology.

Optimization design based on "uncoupled" philosophy

In fact, the objective function is a FR and those variables affecting the objective function are the DPs already mentioned. But, in this case, the FR must be able to be expressed by the DPs in the form of a mathematical equation. It is obvious that optimization design is the method mapping between one FR and a set of DPs at a lower or the lowest level of FR and DP hierarchies in the process of axiomatic design [START_REF] Chen | Identifying the relationship among design methods-key to successful applications and developments of design methods[END_REF] . For the design with only a FR, FR is clearly independent, and the optimal value of FR can be easily obtained by adjusting the corresponding DPs. Here, optimal design is very effective. For those with multiple objective functions, it is difficult to tune the corresponding DPs to simultaneously obtain the optimal solutions of FRs. For example, in order to obtain the optimum of FR1, the value of DPs should be tuned. In order to improve the FR2, the value of DPs should be further tuned, but the FR1 will be changed. Therefore, it is needed to return to tune the DPs to optimize FR1; on the contrary, the FR2 will be changed. So again, this is a trade-off among multiple objectives.

In the axiomatic design, uncoupled or decoupled design is a good design [START_REF] Suh | Axiomatic design: advances and applications[END_REF] . The functional forms of the relations with two functional requirements are as follows:

For uncoupled design:

) ( 1 1 1 x f f  , ) ( 2 2 2 x f f  For decoupled design: ) ( 1 1 1 x f f  , ) , ( 2 1 2 2 x x f f 
where f 1 and f 2 are functional requirements and x 1 and x 2 are corresponding design parameters. For uncoupled design, the design parameters can be determined separately. For decoupled design, x 1 and x 2 should be determined sequentially. In the first step of the decoupled design, the value of x 1 can be get by optimizing f 1 , and x 1 is fixed in the next step as x 1 * . In the second step, the value of x 2 can be obtained by optimizing f 2 . The solution obtained from the above process could not be the optimum when x 1 is not fixed in the second step. Therefore, the decoupled design may not be good one when the design solution is calculated by a mathematical optimization. It is necessary to make the design to be uncoupled design.

In the multi-objective optimization problems, there are usually two different solving methods. The one is that the most important FR is selected as an objective function and the other FRs are ignored. Thus multiple FRs are transformed into single FR, and optimization is effective to determine the optimal value of FR and the value of corresponding DPs, but the other FRs can't be optimized. The other is that every FR is weighted to form the aggregate objective function. Although it is also single FR optimization, it can't optimize FRs directly. Additionally, it is difficult to identify the weight. Therefore, in order to satisfy the independence axiom, FRs should be redefined or DPs should be reselected, and the different FRs is controlled by corresponding DPs independently. For uncoupled design, each sub-objective of multiobjective optimization is independent mutually, which is equivalent to single objective optimization.

For coupled design problem: ) , (

2 1 1 1 x x f f  , ) , ( 2 1 2 2 x x f f 
. If the influence of x 1 on f 1 is far greater than that of x 2 on f 1 , and the influence of x 2 on f 2 is far greater than that of x 1 on f 2 , the problem can be considered as a nearly uncoupled problem. Thus the axiomatic design process can be applied. When the influence of design parameters on the rest of design objectives can't be ignored, it is a strong coupling problem that can't be dealt with by axiomatic design.

Generally, there are more design variables than objective functions in optimization design. However, according to axiomatic design theory, the number of design variables (design parameters) must be the same as that of the objective functions (functional requirements in axiomatic design). Therefore, these designs are generally redundant, or may be coupled, and they violate the independence axiom and are not good designs. When the number of design variables is large, the variables can be grouped to have similar characteristics via the sensitivity information. That is, important variables to a specific objective function can be grouped into one set of parameters, and the number of the groups is the same as that of the objective functions. Thus the design is rearranged to be a nearly uncoupled design. The pertinence is backed up by the Theorem 8 in axiomatic design theory.

Theorem 8: Independence and Tolerance.

A design is an uncoupled design when the designer-specified tolerance is greater than

                  n i j j j j i i P P R R 1 ) / ( n i , , 2 , 1   . ( 2
)
in which case the non-diagonal elements of the design matrix can be neglected from design consideration.

A multi-objective optimization problem will be transformed into single objective optimization problem in sequence by utilizing the method of large-scale optimization system or decomposition and coordination method of multi-disciplinary optimization. As the optimal solution of each sub-objective function may be not the optimum of multi-objective optimization, among of sub-objective function should be coordinated. According to optimization method on decomposition and coordination, the design variable of the previous objective function is fixed in the next objective function in the optimization process; the next objective function only optimize those design variables that is classified into this group, and the optimal variables will be returned to the previous objective function. By an iterative manner again and again, the optimization process continues until it satisfies the convergence condition. The key question of the "decomposition/coordination" optimization is how to identify the corresponding design variables and constraints of each objective function. In largescale / multi-disciplinary optimization, the design variables are grouped according to the locations of parts in structural design or experts-based domain knowledge. In this research, the decomposition is based on the relationship between DPs and FRs, which is defined as "logical decomposition".

Optimization design method using independent axiom

The steps of multi-objective optimization design using independent axiom in axiomatic design can be given as in the following:

Step 1. Select design variables and identify objective functions and constraints. Consider an optimization design problem that consists of six design variables, three objective functions and three constraints functions which is given by the following design equation.

  0 ) , , , ( 0 ) , , ( 0 )
, ( s.t. ) ( ) ( ) ( M in ) ( M in 6 5 3 1 3 5 4 1 2 2 1 1 T 3 2 1     x x x x g x x x g x x g f f f F x x x X , , (3)
The vector of design variables is

  T 6 5 4 3 2 1 , , , , , x x x x x x  x .                                           6 5 4 3 2 1 1 1 1 1 1 0 1 1 1 0 0 1 0 0 1 1 1 1 FR3 FR2 FR1 x x x x x x (4)
where FRi is the ith functional requirement, namely design objective. The sign "1" means a relation exists and "0" means there is no relation between FR and design variable.

If the design matrix is rearranged into a new matrix by making it as diagonal matrix as possible, it shows the optimization objectives are independent, then turn to step 4.

Step 3. Use orthogonal experiment and ANOVA to identify the contribution of each design variables to objective functions, and determine the principal design variables and ignore those design variables with small influence. In design matrix, the sign "  " means design variable has significant effect on FR and "+" means design variable has little effect on FR. The equation ( 4) is rewritten as Rearrange the design matrix to make it as diagonal matrix as possible.

                                                      
                                                       6 3 5 4 2 1 0 0 0 0 0 FR3 FR2 FR1 x x x x x x (6)
The design variables are grouped and the number of the groups is the same as that of optimization objectives. Thus, the design will be nearly uncoupled design. According to equation (6), the design variables are classified into three groups. The design parameters DP1 include x1 and x2, DP2 include x4 and x5, and DP3 include x3 and x6. Then the formula can be rewritten as Step 4. Suppose x p and x q lie in the same group f i (x), if one constraint is only relative to x p and x q , this constraint will be classified into this group. All other constraints are classified into different groups by analogy, and then go to step 6. Otherwise, go to the next step.

                                        ) , ( 3
Step 5. According to the relationship between the design functions (including the objective function and constraint functions) and design variables, establish the dependence matrix on the design functions and design variables, which is named as the function dependence table (FDT). If a constraint function relates to one design variable, the corresponding position in FDT is marked as "1", otherwise marked as "0". Here, the contribution values of design variables on the constraint function are not considered. The relationship between the objective function and design variables is still marked as "×" or "+". Table 1. Function Dependence Table

x 1 x 2 x 4 x 5 x 3 x 6 f 1 × × + + g 1 1 1 g 2 1 1 g 3 1 1 1 1 f 2 + × × + g 2 1 1 1 g 3 1 1 1 1 f 3 + + + × × g 3 1 1 1 1
In any case, the constraints must be met, so they can be classified into the different groups. Namely, if a constraint relates to design variables of multiple groups, it can be respectively classified into those groups. As shown in table 1, g 1 (x) only relate to {f 1 (x); x 1 , x 2 }, it will be classified into this group; g 3 (x) relate to design variables of three groups {x 1 ; x 5 ; x 3 , x 6 }, it will be respectively classified into these groups.

Step 6. If the design functions of each group are independent, the multi-objective optimization problem can be solved by single objective optimization manner. Then go to step 10. However, in many cases, even if the objective functions are independent, the constraint functions are often closely linked to each other. So, it can not be solved in single objective optimization manner and should be optimized in sequence.

Step 7. Determine the order of each group and optimize them in sequence. According to the order of optimization, if the optimization objective and constraints in one group not only relate to the design variables in this group, but also they relate to that in other group, the design variables in this group is only optimized and that in other group will be fixed. In the next group, the values of design variables obtained from the previous group are fixed. Each group is optimized in sequence, and set k = 1.

Step 8. Start from the first group continuously, the design variables in other groups are fixed. The other groups will be optimized in sequence and set k =k+1.

Step 9. Determine the optimization results whether meet the convergence criteria.

If        1 1 k k k x x x
, then return to step 10, where  is the convergence precision and is usually set Step 10. Output the optimal solution  

* * F , x
. The algorithm flow of optimization design using "uncoupled" idea of axiomatic design is shown in figure 1.

Case study

In the optimization design for caliper disc brake used in automobile, the short braking time and small nave parameter under controllable temperature are required. When the vehicle is full-loaded the load of a single wheel W is 3400N; the vehicle's running v is 160km/h; the radius of a wheel r is 350mm; the number of brakes (i.e. number of wheels) m is 4; the allowed maximum diameter for brake disk [D max ] is 300mm; the allowed maximum temperature [T max ] is 260º C; the material of brake disc is steel and the original temperature T i is 35º C; the diameter of nave D h is 75mm; the friction coefficient between pad and brake disc μ is 1 and the adhesion coefficient between tire and road surface φ is 1; the allowed maximum pressure of pad [p max ] is 3MPa; the allowed maximum oil pressure of hydro-cylinder [p 0max ] is 7MPa; the thickness of hydro-cylinder wall t c is 6.5mm.

The structural relationship between caliper and brake disc is shown in fig. 2. The circular friction surface of pad is discretized as an arc concentric with the disc, as shown in fig. 3. Considering that asymmetrical wear process lead to pv´ (unit-pressure × Rotate-speed) become balance gradually on the whole fiction plane, so we suppose that pv´ =C=const.

The force of pad acting on disc: The friction torque of braking:

ldr r C F d R d R     2 / 2 / (8) where                Rr d r R r r l 2 ) 2 / ( cos 2
     2 / 2 / 2 2 d R d R f FI prdA T   (9)
where

    2 / 2 / 1 2 d R d R dr I l I ,     2 / 2 / 1 d R d R dr r l I .
During the process of breaking the power dissipation of pad and brake disc is as much as kinetic energy of auto vehicle. Then the braking time can be derived:

mg n I F v t a     0 2 2 4   . ( 10
)
where W a is total weight of a car (N); n 0 is the rotation speed of brake discs or wheels when start braking (r/min); F is thrust force of oil cylinder,

                                                0
T T x x c J E g Wr I p D g p x x I x x g p x g D x g D t x x g x x x g D x x g i p c     x x x x x x x x . ( 11
)
where J is mechanical equivalent of heat and equals to 1 N•m/J;c is specific heat capacity and equals to 472.8 J/kgº C; ρ is density and equals to 7.8×10-6kg/mm 3 ; E is power dissipation of friction torque and equals to ) 2 /( 2 mg v W a  .

(3) Objective functions To assure the safety of vehicle, it is essential to improve the work efficiency of brakes and to shorten the braking time, so minimizing braking time should be considered as optimization objective. In addition, minimizing thickness and temperature of the brake disc can be also considered as other two objectives. i.e.: Min ) (x

F = Min   T 3 2 1 ) ( ) ( ) ( x x x f f f , , . ( 12
)
where

f 1 ( x )=t, f 2 ( x )=a, f 3 ( x )= 5 2 3 4 x x c J E   .

Computing process and results

(1) Identify the relationship between optimization objectives and design variables in terms of design matrix, and then rearrange the design matrix to make it into a nearly diagonal matrix or triangular matrix.

                                          3 5 6 4 2 1 1 1 0 0 0 0 0 1 0 0 0 0 0 0 1 1 1 1 FR3 FR2 FR1 x x x x x x . ( 13
)
As FR1 is independent of FR2 and FR3, and FR2 is only related to x 5 , so we only need to determine the contribution of x 3 and x 5 to FR3. Since there are three subobjectives in optimization design, the design variables can be grouped and the number of the groups is three. (2) Identify the contribution of x 3 and x 5 to FR3 using orthogonal experiment and ANOVA. Three levels are determined for both x 3 and x 5 , as shown in table 2, and the orthogonal table

) 3 ( 4 9 L is selected. The results of ANOVA show that x 3 has great influence on FR3 and x 5 has small influence on FR3. Then the equation ( 13) can be rewritten as

                                 ) ( 3 DP ) ( 2 DP ) , , , ( 1 DP 0
0 1 0 0 0 1 FR3 FR2 FR1 3 5 6 4 2 1 x x x x x x . (14)
(3) Establish the function dependence table. The FDT of disc brake is shown in Table 3.

Table 3 FDT of disc brake x 1 x 2 x 4 x 6 x 5 x 3 f 1 1 1 1 1 g 1 1 1 g 2 1 1 1 g 3 1 1 g 5 1 g 6 1 1 1 1 g 7 1 1 1 1 f 2 + 1 g 8 1 1 f 3 + × g 2 1 1 1 g 4 1 g 8 1 1
(4) According to table 3, f 1 (x) is independent of f 2 (x) and the relation between f 3 (x) and f 2 (x) is nearly uncoupled. Thus, f 1 (x) and f 2 (x) can be optimized simultaneously. But both the constraint g 2 in the group {f 1 (x)} and g 8 in the group {f 2 (x)} relate to x 3 in the group {f 3 (x)}, x 3 should be fixed in the optimization process of f 1 (x) and f 2 (x). When f 3 (x) is optimized, x 1 , x 2 and x 5 should be fixed.

Continue the next iteration until convergence criterion is satisfied. Then the optimal solutions are obtained.

The initial sizes of disc brake and the results of the weighted optimization method and the proposed method in this paper are summarized in table 4, here weighting factors w 1 , w 2 and w 3 are 0.495, 0.495 and 0.01 respectively. According to the results and process of calculation, it shows that results obtained from the weighted optimization method will change with different weighting factors, so it is difficult to balance every objective. The optimal results based on the proposed method are more satisfactory, which consider the influence of design variables to design objectives and make the design to be nearly uncoupled design. The values of the first two objectives are smaller except the third objective, which is consistent with the importance of the first two objectives.

Conclusions

In this paper, a new method of multi-objective optimization is presented. The concept of contribution of design variables to design objectives is used to identify the relation between them. Following the "uncoupled design" idea of axiomatic design, the design matrix is rearranged to be as diagonal matrix as possible. If the design is a nearly uncoupled design, an iterative method is suggested. The important design variables to a specific objective are identified and can be grouped into one set of parameters, and then establish the function dependence table and optimize every objective function in sequence. The optimization design of disc brake has been solved to show the validity of the proposed method.

.

  Otherwise return to step 8 and continue for the next cycle.

  Fig.1The flow diagram of optimization design using "uncoupled" idea of axiomatic design

  is diameter of piston (mm); p 0 is oil pressure (MPa) are shown in fig. 2 and fig. 3. (2) Constraint functions

Table 2

 2

		level of factor (unit: mm)
	Factor	1	Level 2	3
	x 3	200	250	300
	x 5	4	5	6

Table 4

 4

					the optimal results
		x1(mm)	x2(mm)	x3(mm)	x4(mm)	x5(mm)	x6(MPa)	f1(x)=t (s)	f2(x)=a (mm)	f3(x)=T (º C)
	Initial value	100	60	280	40	5	3	7.241	5	378.938
	Weighted optimization	100.016	60.036	280.145	40.077	7.272	3.516	6.153	7.272	207.484
	Proposed method	100.052	60.121	300	40.258	5.848	4.727	4.535	5.848	224.997

 Foundation: Project supported by the National Natural Science Foundation of China (51165007).


Ph. Lilong Chai

Ph.D Chengwei Ma

Gangyi Xu

Mingchi Liu

Yong Xu

Energetic and Exergetic performances simulating of GSHP greenhouse cooling system in northern China

Keywords: greenhouse, Ground source heat pump, cooling, Exergy efficiency, COP

The Energetic and Exergetic performances evaluating models were built for a agricultural greenhouse cooling with groundwater-style GSHP system (GSHPs) in Northern China. The Exergy destruction (Ex dest ), Exergy efficiency(ε) and coefficient of performance (COP) were estimated and presented based on summer cooling test monitored data and the simulation models. Analyzed results indicate that the evaporator has the highest Exergy efficiency compared with other sections of the system, which reached 92% during cooling test that means the evaporator has a brilliant performance of reversibility in cooling mode. The condenser and compressors have lower reversibility relatively. Generally, GSHPs has an Exergy efficiency of 36%. Besides, COP of GSHPs cooling in greenhouse reached 3.09, which is higher than 2.0 of conventional air source heat pump systems COP by 50%.

Introduction

The utilizing of renewable and clean energy source, such as solar energy and shallow geothermal energy for building air-conditioning were conducted more frequent with the strengthen of global warming and environmental pollutant [START_REF] Zheng | Modeling of standing column well in ground source heat pump system[END_REF][START_REF] Mao | The design and test of greenhouse solar energy heating system[J][END_REF][START_REF] Michopoulos | Three-years operation experience of a ground source heat pump system in Northern Greece[END_REF][START_REF] Ptasinski | Exergetic evaluation of biomass gasification[END_REF]. The shallow geothermal energy (the solar energy stored in shallow soil layer and groundwater) has been well developed and applied in cooling and heating industrial and civilian constructions by making use of ground source heat pump (GSHP) technology [START_REF] Zeng | A finite line-source model for boreholes in geothermal heat exchangers[END_REF][START_REF] Hamada | Improved underground heat exchanger by using no-dig method for space heating and cooling[END_REF], which has been proved with the characteristics of effective, environmental friendly and multi-functional of buildings heating, cooling and dehumidifying [START_REF] Li | Simulation and experiment on the thermal performance of Uvertical ground coupled heat exchanger[END_REF][START_REF] Karlsson | Capacity-controlled ground source heat pumps in hydronic heating systems[END_REF], which is indeed one of the most fast growing clean energy utilizing air-conditioning technologies at present. By the end of 2005, the utilization scale of ground source heat pump air-conditioning in resident or office buildings had reached 3000×10 4 m 2 in mainland China [START_REF] Xu | Investigation and analysis on utilization of ground source heat pump in China[END_REF], and some research on GSHP technology in agricultural facilities cooling or heating were conducted in recent years [START_REF] Chai | Energy consumption and economic analysis of ground source heat pump used in greenhouse in Beijing[END_REF][START_REF] Fang | Application of ground-source heat pump and floor heating system to greenhouse heating in winter[END_REF]. Although the energy saving performance of GSHPs in greenhouse cooling or heating has been estimated in past studies with many methods [START_REF] Dincer | The role of Exergy in energy policy making[END_REF][START_REF] Sciubba | Emergy and Exergy analysis, Complementary methods or irreducible ideological options[END_REF][START_REF] Winter | Energy efficiency, no, It's Exergy efficiency[END_REF], most of analysis were limited in first law of thermodynamics, which describes the heat quantity transfer process in GSHPS, such as the COP was calculated based on this method. However, the quality of energy utilizing was taken into consideration in Carnot Cycle based heat transferring process such as heat pump system [START_REF] Miu | Refrigeration Compressor[END_REF]. The quality analysis of the energy was defined as Exergy analysis that is based on second law of thermodynamics and is the combination of Enthalpy and Entropy. Exergy can be described as the maximum amount of work that a thermodynamic system can achieved when it is reach equilibrium with the reference environment [START_REF] Zhang | Brief Manual for Air Conditioning Worker[END_REF][START_REF] Yan | Engineering Thermodynamics[END_REF]. Exergy analysis method has been utilized in various fields [START_REF] Som | Thermodynamic irreversibilities and Exergy balance in combustion[END_REF]. Dincer [START_REF] Dincer | The role of Exergy in energy policy making[END_REF] has come up with that the potential usefulness of Exergy in addressing and solving environmental problems as well as attaining sustainable development is crucial. Ozgener and Hepbasli [START_REF] Ozgener | Exergoeconomic analysis of a solar assisted ground-source heat pump greenhouse heating system[END_REF] have assessed the relations between thermodynamic losses and capital costs for devices, and suggested possible generalizations in the relation. Generally, the Exergy analysis method was proved to be a more comprehensive method in assessing the energy transferring [START_REF] Bisio | Work and entropy production aspects of irreversible processes in closed and steady-state open systems[END_REF][START_REF] Ebru | A comparative study on exergetic assessment of two ground-source (geothermal) heat pump systems for residential applications[END_REF]. Therefore, the combined method of energetic and exegetic performances study on agricultural greenhouse cooling need to be built for the utilizing of GSHPs. The objects of this study is but not limited to (1) build the energetic and exergetic performance estimating method and simulation models for agricultural greenhouse cooling with GSHPs. (2) presents GSHPs greenhouse cooling performances based on the theories of first and second laws of thermodynamics.

Materials and Methods

GSHP greenhouse cooling system

The GSHPs and tested greenhouse (Fig. 1) were located in the in Haidian district (latitude 39°40' N) of Beijing of China. The GSHPs system had three principal circuits, as shown in Fig. 2.

(i) Groundwater drawing and refilling part (there are five wells with the depth of about 100m, one water drawing well, four water refilling wells. The groundwater drawing and refilling circuit consisted of one water drawing well, four water refilling wells, a water drawing pump, and a water refilling pump. Each water pump had 11 kW rated input power and 33.2 m3 h-1 maximum water flow rate. The distances between the drawing well and refilling wells ranged from 100 to 300 m;

(ii) GSHP energy enhancing circuit, which consisted of four Danfoss Hermetic scroll compressors connected in series. GSHP unit contained about 58 kg R22 refrigerant and had total capacity of 380 kW for heating and 450 kW for cooling, the rated power of the electric motor for each compressor was 16.08 kW;

(iii) Heat transportation and fan coil heating units. This circuit contains many water circulating pipes and two water pumps. During summer cooling test, the GSHPs operated cooling cycle, the water valves of b, c, e and h were turned on, and keep the valves of a, d, f and g shut off. In groundwater circle, the groundwater was drawn out from the well and pumped into heat exchanger connected with condenser via valve c, and then was recharged back via valve h to refilling wells after heat exchange in condenser. In greenhouse and building heating circle, the cold circulating water was sent to greenhouses and office buildings to cooling the house via valve e after released heat exchange in evaporator, and then was sent back via valve b to the heat exchanger connected with evaporator.

Energetic performance simulating models

The coefficient of performance (COP) was often used as indicator to analyze the GSHP system under cooling operation, which is an important index for evaluating the energy efficiency in a thermodynamics system. Referred to previous research [START_REF] Dincer | The role of Exergy in energy policy making[END_REF][START_REF] Ebru | A comparative study on exergetic assessment of two ground-source (geothermal) heat pump systems for residential applications[END_REF], the energy transfer and performance analytical model of the system can be put forwarded as Eqs. ( 1) - [START_REF] Li | Simulation and experiment on the thermal performance of Uvertical ground coupled heat exchanger[END_REF]. The cooling COP of the GSHP unit, total system and greenhouse subsystem are analyzed in the study.

Groundwater, ) (

9 10 well well h h m Q   (1)
Where, Q well is the groundwater heat transfer rate in condenser, in kW; m well is the mass flow rate of underground water ,in kg• s -1 ; h 9 is the specific enthalpy of underground water entering condenser, in kJ• kg -1 ; h 10 is the specific enthalpy of groundwater leaving condenser, in kJ• kg -1 .

Condenser,

) ( 3 2e ref cond h h m Q   (2)
Where, Q cond is the heat rejection rate in the condenser, in kW; m ref is the mass flow rate of refrigerant R22, in kg• s -1 ; h 2e is the specific enthalpy of refrigerant leaving compressor/entering condenser in isentropic process , in kJ• kg -1 ; h 3 is the specific enthalpy of refrigerant leaving condenser /entering throttling valve, in kJ• kg -1 .

Evaporator,

) ( 4 1 ref evap h h m Q   (3)
Where, Q evap is the heat transfer rate in the evaporator, in kW; h 1 is the specific enthalpy of refrigerant leaving evaporator /entering compressor, in kJ• kg -1 ; h 4 is the specific enthalpy of refrigerant leaving throttling valve /entering evaporator, in kJ• kg - 1 .

Compressor,

) (

1 2e ref c e com p h h m W     (4)
Where, W comp is the power input to the compressors in kW; η e is the compressor iso-entropy work efficiency; η c is the compressor cubage work efficiency. In this study the product of η e and η c was considered as 75% as manufacturer rated.

Throttling valve,

) ( 3 4 ref thro h h m Q   (5)
Where, Q thro is the heat transfer rate in the throttling valve, in kW.

) (

5 6 wa cir h h m Q   (6)
Where, Q fc is the total heat transfer rate of circulating water system, in kW; m wa is the mass flow rate of circulating water, in kg• s -1 ; h 5 is the specific enthalpy of circulating water leaving evaporator, in kJ• kg -1 ; h 6 is the specific enthalpy of circular water entering evaporator, in kJ• kg -1 . Circulating water system via greenhouse, ) (

7 8 g wa, g fc, h h m Q   (7)
Where, Q fc,g is the cooling capacity of fan-coil units in greenhouse, in kW; m wa,g is the mass flow rate of circulating water in greenhouse, in kg• s -1 ; h 7 is the specific enthalpy of flowing in water in greenhouse, in kJ• kg -1 ; h 8 is the specific enthalpy of flowing out water in greenhouse, in kJ• kg -1 .

The COP was usually applied as an indicator for assessing the performance of GSHPs in civilian and industry buildings heating and cooling research [START_REF] Yan | Engineering Thermodynamics[END_REF][START_REF] Ozgener | A parametrical study on the energetic and exergetic assessment of a solar-assisted vertical ground-source heat pump system used for heating a greenhouse[END_REF]. The greenhouse heating COP of GSHPs was estimated with Eqs. ( 8) - [START_REF] Chai | Energy consumption and economic analysis of ground source heat pump used in greenhouse in Beijing[END_REF].

comp evap GSHP COP W Q  (8) e , 11 nm ij ij EE    (9)
g sys e

COP 3600

E E  (10)
Where, E i,j is the electricity energy consumed by equipment j at time i, kWh, the electricity energy consuming equipment in greenhouse cooling including GSHP compressors, water pumps, and fan coil units; E e is total electric energy consumed by GHSPs, kWh; E g is cooling quantity in greenhouse provided by GHSPs, kJ; COP GSHP is cooling COP of GSHP unit, dimensionless; COP sys is greenhouse cooling COP of GSHPs, dimensionless.

Exergic performance simulating models

Exergy analysis is based on the second law of thermodynamics, which takes the entropy portion into consideration by including irreversibilities, and is the combination between enthalpy and entropy. Exergy reflects both quantity and quality of thermodynamic process [START_REF] Dincer | The role of Exergy in energy policy making[END_REF][START_REF] Sciubba | Emergy and Exergy analysis, Complementary methods or irreducible ideological options[END_REF]. The Exergy efficiency (Ex dest ) and Exergy destruction rate (ε) are core content of Exergy analysis, which was applied in this study to demonstrate the performance of GSHPs in greenhouse and buildings cooling. The estimation of Exergy transferring in each section of GSHPs was conducted with Eqs. ( 11)- [START_REF] Yan | Engineering Thermodynamics[END_REF].

Compressor,

) ( ) ( 1 2i 0 1 2i 1 2 s s T h h ex ex      (11) Condenser, ) ( ) ( 3 2i 0 3 2i 3 2 s s T h h ex ex      (12) Throttle valve, ) ( ) ( 4 3 0 4 3 4 3 s s T h h ex ex      (13) Evaporator, ) ( ) ( 1 4 0 1 4 1 4 s s T h h ex ex      (14)
Heat exchanger of circulating water and evaporator, ) ( ) (

s s T h h ex ex      (17)
Where, ex 1 ~ 10 represent the specific Exergy at each position of 1~10 in Fig. 2, in kJ• kg -1 ; h 1 ~ 10 are the specific enthalpy at each position of 1~10, respectively, in kJ• kg -1 ; s 1 ~ 10 are the specific entropy at each position of 1~10, respectively, in kJ• kg - 1 •K -1 .

Exergy destruction rate

Exergy destruction rate embodies the quantity of devalued energy [START_REF] Sciubba | Emergy and Exergy analysis, Complementary methods or irreducible ideological options[END_REF][START_REF] Ozgener | A parametrical study on the energetic and exergetic assessment of a solar-assisted vertical ground-source heat pump system used for heating a greenhouse[END_REF], which was estimated with Eqs. ( 18) -( 23) in each section of GSHPs.

Compressor,

) (

T Ex Q m ex ex T     (23)
Where, Ex dest,comp , Ex dest,evap , Ex dest,cond , Ex dest,thro , Ex dest,cir are exergy destruction rate in compressor, evaporator, condenser, throttling valve, circulating water system, respectively, dimensionless. T 5 , T 6 , T 9 are water temperatures at positions 5, 6 and 9, respectively, in K.

Exergy efficiency

The Exergy efficiency of various sections and total system of GSHPs were estimated with the Eqs. ( 24

                 Ex Ex
                     Ex Ex Ex Ex Ex Ex Ex Ex Ex Ex (30) Where, ε comp ,ε evap ,ε cond ,ε thro ,ε cir ,ε hp ,ε
sys are Exergy efficiencies of the compressor, evaporator, condenser, throttle, circulating water system, GSHP unit and GSHP system respectively, dimensionless.

Data acquisition

The following data was collected in this test, (1) Measuring mass flow rates of the water by using rotameters (Beijing jingyuan liquid apparatus company, China). Rotameters were installed at inlet of all water flow tubes; (2) Measuring the temperature of the water inlet and outlet of the condenser, evaporator, and the greenhouse by using resistance thermometers PT1000 (Jingyuan liquid apparatus company, China, error limitation is ± (0.3+0.005△ t ℃)), each couple of thermometers were installed at inlet and outlet of water flow respectively;

(3) Measuring the temperature and humidity of air inside and outside of greenhouse by using temperature-humidity sensors RS-11 (ESPEC, Japan), the error limitation is ±0.5 ℃ in temperature and ±5% in relative humidity; (4) Recording input power of different electric motor driving equipments by using ammeters (Shanghai Huaxia ammeters manufactory). All the simulation calculation was conducted in Micro-soft Excel file. The results was calculated by following assumptions (i) The temperature at 1, 2, 3 and 4 points in GSHP unit was gained from manufacturer (Chongke High-Technology Development Co.,Ltd, Beijing,China), and assumed that the running was kept on steadily, as well as the temperature at 1,2,3 and 4 points. (ii) In this study, the restricted dead state was taken to be the state of environment, the temperature and the atmospheric pressure are 0 ℃ and 101.325 kPa, and don't take the Exergy destruction of water pumps and pressure change into account for the limited condition.

Results and Discussion

The Energetic performance of GSHPs

For a great deal of water was needed for staff living and farm irrigation at experiment station in summertime, the pumped underground water were not backfilled into well during the test, and the Pump3 was not used in fact, so neglecting the effect of Pump3 and backfilled well. The refrigeration capacity of evaporator Q evap is 190kW, and the actual heat transfer rate between evaporator and circular water system was 164.15kW on average according to the calculation based on the measurements made during Aug.13-18th, 2007, the power input is 53.32kW. Besides, the refrigeration capacity rate to greenhouse during test is 40.18 kW on average. Detailed information about the energy input and COP calculated results as shown in Table 1. The greenhouses cooling COP with GSHPs is 3.09, which is higher than air source heat pumps of 2.0 by more than 50% [START_REF] Ozgener | A parametrical study on the energetic and exergetic assessment of a solar-assisted vertical ground-source heat pump system used for heating a greenhouse[END_REF].

Exergetic performances

Exergy analysis reflect the quality and quantity of a thermodynamic system, it is closely connected with each element of GSHP system, including compressor, evaporator, condenser and throttle valve. Exergy efficiencies and Exergy destruction are two of important indexes for evaluating energy utilizing system. The state of circulating water, groundwater and R-22 refrigerant was monitored or calculated according the test during Aug. [START_REF] Sciubba | Emergy and Exergy analysis, Complementary methods or irreducible ideological options[END_REF][START_REF] Winter | Energy efficiency, no, It's Exergy efficiency[END_REF][START_REF] Miu | Refrigeration Compressor[END_REF][START_REF] Zhang | Brief Manual for Air Conditioning Worker[END_REF][START_REF] Yan | Engineering Thermodynamics[END_REF][START_REF] Som | Thermodynamic irreversibilities and Exergy balance in combustion[END_REF]2007, as shown in Table 2. [START_REF] Dincer | The role of Exergy in energy policy making[END_REF][START_REF] Ozgener | A parametrical study on the energetic and exergetic assessment of a solar-assisted vertical ground-source heat pump system used for heating a greenhouse[END_REF][START_REF] Zhao | The thermodynamic analysis of water source heat pump[END_REF] under rated compressor summer operating efficiency of 75%.

In Exergetic performance simulating, the reference temperature T o was set as averaged outdoor air temperature 306.25. The Exergetic performances including of Exergy destruction rate and Exergy efficiency of GSHPs were listed in Table 3. In GSHPs, the section of evaporator shows highest Exergy efficiency of 92.5, which indicates that this section has higher energy utilizing quality. The condensers Exergy efficiency is about 65.8%. According to the Ozgener [START_REF] Ozgener | A parametrical study on the energetic and exergetic assessment of a solar-assisted vertical ground-source heat pump system used for heating a greenhouse[END_REF] reported result, the condenser has the Exergy efficiency of 80.5% when the GSHPs in heating mode, and 28.6% for evaporator. Therefore, the conclusion of higher Exergy efficiency for condenser and lower Exergy efficiency for evaporator in winter heating mode, and lower Exergy efficiency for condenser and higher Exergy efficiency for evaporator in summer cooling mode could be given. Throttling valve consist of many capillaries, the Exergy destruction rate was high theoretically. As the R22 passing throttling valve was considered as adiabatic process, so the destruction of Exergy of R22 was caused by the increase of Entropy. Besides, as there no energy input in throttle valve, so the Exergy efficiency of throttling valve could be positive infinity or negative infinity, the study on it is insignificance. If the throttle valve was replaced with Expansion valve, the Entropy increase of R22 would be declined, but the cost of GSHPs would increase. The key object of energetic and exergetic performance analysis is to balance the relationship between economics and thermodynamics. Generally, the total Exergy efficiency of GSHPs in greenhouse cooling is 36%, which is higher than published value of 15% [START_REF] Zhao | The thermodynamic analysis of water source heat pump[END_REF] in residential buildings cooling. However, the Exergy efficiency of GSHPs in this study has the potential to be improved by reducing the Exergy destruction of water circulating and fan-coil units operating.

Conclusions

The following conclusions were drawn based on the results in this study, 1) The Enegetic and Exergetic simulation models and performance evaluating method were built in this study for greenhouse GSHPs performance analysis;

2) According to simulated results, total Exergy efficiency of GSHPs in greenhouse cooling is 36%, which is higher than published value of 15% in residential buildings cooling. The Exergy efficiency of GSHPs in this study has the potential to be improved by reducing the Exergy destruction of water circulating and fan-coil units operating.

3) The Energetic and Exergetic performances of GSHPs are associated with the economical performance closely, the optimizing of the Exergy reversibility and COP usually at the cost of economic performance degradation. The key object of energetic and exergetic performance analysis is to balance the relationship between economics and thermodynamics.

Fig. 1 Fig. 2

 12 Fig.1 GSHPs and agricultural greenhouse in test.

Table 1

 1 Cooling performance of the GSHPs in greenhouse(Aug.[START_REF] Sciubba | Emergy and Exergy analysis, Complementary methods or irreducible ideological options[END_REF][START_REF] Winter | Energy efficiency, no, It's Exergy efficiency[END_REF][START_REF] Miu | Refrigeration Compressor[END_REF][START_REF] Zhang | Brief Manual for Air Conditioning Worker[END_REF][START_REF] Yan | Engineering Thermodynamics[END_REF][START_REF] Som | Thermodynamic irreversibilities and Exergy balance in combustion[END_REF] 2007)

	Parameters	GSHPs-Greenhouse section	GSHPs-whole system
	Fan-coil unit cooling rate,kW	40.2	157.2
	Heat exchanging rate of Groundwater,kW	-	131.7
	Compressors energy input rate,kW	8.1	32.2
	Water pumps energy input rate,kW	4.6	18.5
	Total energy input rate,kW	13.0	53.3
	COP of Heat pump unit	-	5.90
	COP of GSHPs	3.09	2.95

Table 2

 2 State parameters of Liquids in system(Aug.13-18th, 2007)

	Positions	Liquids	Mass flow rate m, kg• s -1	Temperature t, ℃	Specific enthalpy h, kJ• kg -1	Specific entropy s, kJ• kg -1 •K -1
	1	R22 refrigerant in vapor	1.145	10.0	409	1.738
	2	R22 refrigerant in vapor	1.145	65.0	430	1.738
	3	R22 refrigerant in Liquid	1.145	35.0	243	1.146
		R22 refrigerant in
	4	mixture of vapor and	1.145	3.0	243	1.158
		liquid
	5	Total circulating water	15.03 3	10.1±0.50	42	0.152
	6	Total circulating water	15.03 3	12.7±0.55	53	0.190
	7	Greenhouse inlet circulating water	0.923	10.2±0.48	43	0.154
	8	Greenhouse outlet circulating water	0.923	20.8±0.83	88	0.305
	9	Groundwater	9.220	14.1	59	0.211
	10	Groundwater	9.220	17.6	74	0.261
	Note, The state parameters value at different thermodynamic positions was calculated
	according to the recorded data during the test and published value

Table 3

 3

		Exergetic performances(Aug.13-18th, 2007)
	Elements	Exergy destruction rate, kW	Exergy destruction efficiency,%	Exergy efficiency,%
	Compressor	8.14	25.3	74.7
	Evaporator	1.83	7.5	92.5
	Throttle valve	11.22	-	-
	Condenser	5.84	34.2	65.8
	Circulating water system	6.26	61.7	38.3
	GSHP unit	27.03	42.0	58.0
	GSHP system	33.29	64.0	36.0

Acknowledgments

This study was supported by the earmarked fund for Modern Agro-industry Technology Research System(CARS-25-D-04)

This study was supported by the earmarked fund for Modern Agro-industry Technology Research System(CARS-25-D-04)


Xiaolei Chen

Ruijuan Chi

Jianqiang Wang

Changle Pang
email: pangcl@cau.edu.cn

Qing Xiao

Driver Safe Speed Model Based on BP Neural Network for Rural Curved Roads

Keywords: rural curved road, driver behavior, adhesion workload, yaw rate, BP Neural Network

In order to improve the safety and comfort of the vehicles on rural curved roads, the paper proposed a safe curve speed model based on the BP Neural Network. A series of drivers' manual operation state data during cornering were gathered and observed according to the driver experiments under real traffic conditions. Three factors, referring to the speed calculated based on road trajectory parameters, the adhesion workload and the yaw rate computed from the processed data, were used as inputs of the model to obtain the target vehicle speed. Finally, tests verify the applicability of the modified model. It indicates that the developed speed model can adjust to the individual curve speed behavior of each driver.

Introduction

Curves of the rural roads are those places where the accidents take place more frequently than other parts of the road due to their intricate road condition. Recently in China, statistics on traffic accidents from the Chinese Ministry of Transportation in 2009 showed that 25,146 road accidents occurred on curved road, which is up to 10.56% of the total accidents [1]. Besides, the occurrence of these accidents is increasing. Mistakes during steering is one of the main factors, which fundamentally results from the driver's incorrect recognition or misjudgment of road condition, vehicle performance or surrounding environment, that normally let the driver enter a corner at a high speed [START_REF] Lee | Speed Control Method for Vehicle Approaching and Travelling on a Curve[END_REF].

In order to solve the aforementioned problems, many studies have been conducted to calculate the safe speed for cornering and several models have been developed during the last few decades [START_REF] Stamatiadis | Analysis of Inconsistencies Related to Design Speed, Operating Speed and Speed Limits[END_REF][START_REF] Pomerleau | Run-off-road Collision Avoidance Using IVHS Countermeasures[END_REF][START_REF] Sledge | Formulas for Estimating Vehicle Critical Speed from Yaw Marks[END_REF][START_REF] Zhong | The Two-step Model of Operating Speed on Horizontal Curves Based on Driver's Information Collection and Processing[END_REF][START_REF] Mopu | Seńalización Vertical. Instrucción Decarreteras. Norma 8.1-IC[END_REF][START_REF] Misaghi | Modeling Operating Speed and Speed Differential on Two-Lane Rural Roads[END_REF]. According to [START_REF] Stamatiadis | Analysis of Inconsistencies Related to Design Speed, Operating Speed and Speed Limits[END_REF], a predicting model of the 85th percentile speed was developed by Krammers in 1993, and Polus built four speed models based on numbers of speed data to estimate the operating speed for the two-lane rural roads. Another curve safe speed model which was used in accident reconstructions was developed in [START_REF] Sledge | Formulas for Estimating Vehicle Critical Speed from Yaw Marks[END_REF].Taking the acceptable lateral acceleration into account, a safe speed was presented by the Spanish Road Administration [START_REF] Mopu | Seńalización Vertical. Instrucción Decarreteras. Norma 8.1-IC[END_REF]. It was found in [START_REF] Misaghi | Modeling Operating Speed and Speed Differential on Two-Lane Rural Roads[END_REF] that a differential speed model of the 85th percentile speed and the operating speed had been suggested for the two-lane rural roads according to the experiment data. In general, all the speed models above had determined the safe speed according to the road trajectory, yet considering the driver's individual characteristic. Sometimes it would make the drivers feel uncomfortable when cornering. As a result, the curve safe speed model should take into account of the individual characteristic of each driver.

Meanwhile, some researches had been undertaken focusing on the driver behavior on curved roads in recent years [START_REF] Ke | Analysis of Relation between Curve's Radius and Steering Speed on Expressway[END_REF][START_REF] Abdelwahab | Geometric Design Consistency Based on Speed Change on Horizontal Curves[END_REF][START_REF] Zhang | Study on Mental and Physiological Effects of Horizontal Radius of Expressway on Driving Reaction[END_REF][START_REF]Canadian Researchers Test Driver Response to Horizontal Curves[END_REF][START_REF] Mcfadden | Evaluating Horizontal Alignment Design Consistency of Two-lane Rural Highways: Development of New Procedure[END_REF]. According to [START_REF] Ke | Analysis of Relation between Curve's Radius and Steering Speed on Expressway[END_REF], the lateral force coefficient is a very important parameter to the safety and comfort of driving. It was indicated in [START_REF]Canadian Researchers Test Driver Response to Horizontal Curves[END_REF] that drivers chose their comfortable speed mainly depending on curvature and driving conditions. Besides, the comfort of driving was bound up with the value of lateral acceleration. However, the common objective of existing researches regarding driver behavior on curved roads is to meet the requirement of geometric design of highways. Therefore existing models may be unsuitable to improve the safety and comfort on curved roads.

In this study, a driver safe speed model on curved roads is developed based on real experimental data gathered from a series of driver behavior experiments. With a view of adapting to driver's individual curve speed behavior, the Neural Network method is used in the driver safe speed model. Tests verify the applicability of the modified model.

Driver Behavior Experiments

Data Acquisition System

As shown in Table 1, three types of data, which could reflect driver behavior and vehicle states, were recorded with the data acquisition system. The data were collected from CAN bus of the instrumented vehicle and some appendix sensors. Besides, the curvature and the friction coefficient of the road could be estimated in accordance with the GPS data and the vehicle states.

Fig.1. Data acquisition system

As indicated in Fig. 1, the data acquisition system consists of a battery, an industrial computer, and a driving recorder which were installed in the car boot. A monitor was fixed on the back of the co-pilot seat for the experiment recorder. In addition, four cameras were fixed in the system to record different images in real time. The data collection frequency of this system was 30 Hz.

Experiment Route and Design

Considering the requirement and purpose of the experiments, the sample route was required to be roads with regular curves. So we chose a section of a highway that started from the south entrance of the Beijing-Tibet highway to Badaling as the experiment area, as shown in Fig. 2.

Fig.2. Experiment route

A sample of 18 participants with full China driving license was enrolled to accomplish the experiments from Beijing. These drivers were acquainted with the road conditions and were required to drive with a safe and comfortable speed in free traffic flow. Data were collected and saved with the data collection system. It should be noted that only part of each experiment data from the curve entry to the curve exit were used for the analysis.

Data Observation

Direct data could not satisfy the requirements of the model due to error and noise. The observation of the original data, such as longitudinal acceleration, lateral acceleration, and yaw rate, was taken place in the next section.

Kalman Filter of the Original Data

The Kalman Filter is a set of mathematical equations that provides an efficient computational (recursive) means to estimate the state of a process, in a way that minimizes the mean of the squared error [START_REF] Welch | An Introduction to the Kalman Filter[END_REF]. It is widely used in many fields because of its powerful features.

Assume that the original data is k

x , and develop it by the formula of Tailor in time domain. The state equation is shown in Eq.1 taking k x and k x as state variables.

1 k k k X X Aw    (1)
Where,

,

T k k k X x x    , = 1 0 1 T     , A= 2 2 T T        , kk wx   is the random noise,
T  is the sampling time, and T  =1/30 s.

The measurement equation is shown in Eq.2.

1 k k k z HX n   (2)
Where, k z is the measurement value of the data,

  1 0 H  , and k n is the measurement noise.
The optimal estimation of the parameters based on Kalman Filter is shown in Eq.3.

ˆˆ( )

k k k k k k X X K z H X     (3)
Where, ˆk X is the optimum estimate of k X , ˆk X  is the priori estimate of k X , k K is the Kalman gain, and k z is the measurement vectors.

The recursive process of the system is as follows:

1 ˆkk XX     (4) 1 TT k k k P P AQ A     (5) 1 (
)

TT k k k k k k k K P H H P H R     (6)
(1 )

k k k k P K H P   (7)
Where,

Error Compensation of the Original Data

In order to reflect the real states of the vehicle, the deviation compensation between the measurement data and the real values caused by installation error, the roll and the pitch of the vehicle is extremely essential.

The influence of the roll and pitch of the vehicle to the parameters is shown in Fig. 4. The approximation of the roll angle can be estimated with the model [START_REF] Li | Cooperative Control of Vehicle Stability Based on AFS and DYC[END_REF]. It can be considered that cos r   1 and sin r   0 when the value of the roll angle is small. Eq.8 can be converted to the form as Eq.9.

rr ys r fr ma h

K K mgh       (9)
The actual values of the parameters can be calculated with Eq.10, Eq.11 and Eq.12. The data which had been compensated are more close to the actual values, and can be used for practical application.

Safe Curve Speed Model Based on BP Neural Network

BP Neural Network, which has flexible topology structure and self-adaptive capacity and has redundant organized fashion and perseverance, is widely used for the nonlinear system in many fields [START_REF] Wei | Prediction of Energy Production and Energy Consumption Based on BP Neural Networks[END_REF].

Drivers as a complex nonlinear system adjust their appropriate speed according to many effect factors, such as driving conditions, their own states when cornering etc. In this study, a curve speed model which can adapt to the driver's individual characteristic is established based on the BP Neural Network because of its superiority on intricate system. As shown in Fig. 5, three variables are taken as input data. A two layers feed-forward BP Neural Network is used to simulate the motion state of the vehicle on curved roads. The output data of the model is the target vehicle speed. ()

1 c g v c      (13)
Where, v c is the safe speed on curved road, c is the curvature,  is the road superelevation angle, and  is the friction coefficient.

The second one is the yaw rate collected from the real road experiments. The last one is the adhesion workload which can reflect the ratio of the friction coefficient used by drivers. It is varied with different characteristics of the drivers, and the calculative method of it is shown in Eq.14. Where,  is the adhesion workload, a x is the longitudinal acceleration, a y is the lateral acceleration, and  max is the maximum friction coefficient.

Firstly, calculate the values of the speed, adhesion workload, and the yaw rate based on a fifty seconds length of data chosen from the abovementioned experiments. Then, normalize the input data and the output data into the range 0-1, and take them as the training set to train the networks. After that, set up the parameters of the networks. The number of the hidden layer nodes is set to 60 considering the complexity of the driver's characteristic. The transfer functions of the hidden layer and the output layer are "logsig" and "pureline", respectively. The root mean square error value used for the stopping signal was set to 0.001, and the number of epochs was set to 100. All the iterations realize the target mean square error in less then 7 epochs. The output data could be converted to the target speed based on antinormalization.

Evaluation and Discussion

The evaluation was carried out based on the data gathered from real road experiments. Firstly, several parameters, such as speed (v c ), adhesion workload, yaw rate etc was calculated or processed according to the actual data of the drivers on curved roads. Secondly, train the Neural Networks with the information above to make it adapt to the driver's characteristic. Finally, when the vehicle driving into the curve, the model began to work and provided a safe and comfortable speed for the driver.

The comparison result of the real speed and the target speed known as the output of the model is shown in Fig. 6.

Discussion

Two factors referring to the road trajectory (e.g. curvature, the road superelevation angle, and the friction coefficient) and the characteristic of driver (e.g. the adhesion workload and the yaw rate) were considered in the curve speed model in this paper. However, there are several parameters that had not been taken into account, including the visibility, the slope angle of the road and the initial velocity etc. Besides, the driver behavior experiments were carried out with no vehicles ahead, the driver's characteristic might change when following other cars. A further study with accordance into these factors will be done in the next reach.

In addition, some of the drivers participated in the experiments didn't obey the rules strictly we had made earlier, which might impact the data validity. Besides, the sample size of the experiments also has influence on the result. It is assumed that the result would be improved by increasing the amount of the experimental data.

Conclusion

This paper focused on a driver safe speed model on rural curved roads by considering the driver's curve speed characteristic. From what had been discussed above, the conclusions could be drawn as follows:

(1) The developed safe speed model is proposed on account of the actual driver behavior experimental data, and is able to provide a more suitable speed for drivers when cornering.

(2) The processed parameters taken as the inputs of the model can reflect the real conditions of the rural curved roads and the actual curve speed behavior of the drivers.

(3) The driver safe speed model based on BP Neural Network is adaptive to the individual curve speed characteristic of each driver. It can improve the safety and comfort of the vehicle on curved roads.

kQ

  is the process noise covariance, k R is the measurement noise covariance. k P , k P  are the optimum estimate error covariance and the posteriori estimate error covariance, respectively. Their initial values are set to be zero matrixes. The comparison result of data before and after filtering is shown in Fig. 3 taking longitudinal acceleration as example.

Fig. 3 .

 3 Fig.3. Comparison of the data before and after filtering

Fig. 4 .

 4 Fig.4. Influence of the roll and pitch of vehicle to dataAs shown in Eq.8, a steady -state moment equation is determined from Fig.4based on the static suspension model.

K

   are the roll angle rigidity of the suspension, r  is the roll angle of the vehicle, ys a is the measurement lateral acceleration, and h is the distance between mass center and roll center.

l

  is the distance between mass center and installation position.

Fig. 5 .

 5 Fig.5. Driver safe speed model based on BP Neural NetworkThere are three factors taken as inputs of the BP Neural Network. The first one is the speed calculated considering road trajectory. As shown in Eq.13, three parameters are chosen to describe the curved road conditions.

Table 1 . Data collected based on real traffic experiments

 1

	Type	Variables
		Vehicle speed
		Yaw rate
	Vehicle states	Lateral acceleration
		Longitudinal acceleration
		Relative speed
		Steering wheel angle
	Driver operation	Brake pedal depression
		Gas pedal depression
	Road information	GPS coordinates Road conditions

Acknowledgements

The research reported herein was supported by the Chinese National Programs for High Technology Research and Development, No. 2011AA110402. The authors would like to thank Dezhao, Zhang (Tsinghua University) for his kind support for the paper preparation.


Chunhui Qi

Jianping Hu

Jun Ma

Jianbing Zhang

Solving Motion Law by Numerical Simulation on Bowl Seeding Transplanting Robot

Keywords: parallel mechanism, transplanting bowl seeding, trajectory planning, inverse position, numerical simulation

Abstract:

The paper using the inverse kinematics theory of the parallel mechanism , studied the movable needs of the transplanting robot,deduced the inverse kinematics formula of transplanting robot.Considered the demand of the "fetch and plant"operations,made an important research on the two trajectory planning methods of the moving platform: quintic and septic polynomial motion laws, acquired their variation rules of displacement, velocity, acceleration and saltus by using matlab software, and to make choice. After selected the law of motion equation, through numerical simulation of inverse kinematics by matlab, acquired kinematic parameters angular speed and acceleration of the two active motors,can be used as the priority basis for control the movement of the manipulator.

Preface

Pot seeding technology has many advantages, such as reduce man-hour, raise working efficiency and protect the agricultural ecology environment, and it has been used in production of vegetable and flower widely, transplanting bowl seeding is an important link. Although transplanting bowl seeding is easy, it needs amount of hand labour, the average speed of artificially transplanting is 800-1000 trees per hour, and the workers work continuously will get tired easily, a low production efficiency, and hard to realize large-scale operations. So, to research high-speed automatic bowl seedling transplanting machine is particularly important, it has important significance in improve labor productivity, reduce the labor intensity, promote potted tray seeding technology development. Currently, transplanting machines with the industrial robot as body have many shortcomings, such as many freedoms, complicated structure and so forth, have many difficults to fit for greenhouse automatic assembly line working facilities, so the type of automatic potted tray seedling transplanting machine fails to get promotion.

The paper will put forward a Two-DOF parallel transplanting robot of independent mechanical and electrical system, as shown in Figure 1, the mechanism consists of a five-bar parallel mechanism, two groups parallelogram chain and a moving platform, driven by the active motors, pneuncatic manipulator could achieve two-dimensional translational. Due to the driving motors fixed on the frame, driven arms are made into light bars, so could make the moving platform achieve a high speed and acceleration, to meet bowl seedlings transplanting demand of high speed and short to medium distance.

2 Select bowl seeding transplanting robot motion law

Motion law requirement of Two-DOF parallel transplanting robot

Two-DOF parallel mechanism used to pick up and placed the object, its main job is pick up object in the initial position, then move to the target position placed the object, kinematic sketch is shown in Figure 2. To make the moving platform under the condition of the maximum acceleration allowed, the fastest way to fetch seedling from a certainl position of the seedling tray, then set the seeding to the corresponding position of the plant seedling tray, must be based on position, velocity and acceleration to plan trajectory.

Motion trajectory planning on transplanting manipulator

In the process of transplanting bowl seedings, manipulator required fetch seeding and plant seeding the two motion process for each seeding transplanting. While each process, the manipulator would pass through rising, translation, decline three stages.

Transplanting motion requires the manipulator on the rise and decline section must keep a certain distance of straight-line movement, as AB and CD section shown in Figure 3: 2)First and second derivatives of displacement motion law must be continuous;

3)The third-order derivative of the displacement motion law f (the derivative of acceleration) is limited; otherwise it will cause shock;to ensure that the manipulator avoid to be impacted.

Mathematical Representation of the two motion laws

In less than a given maximum acceleration condition, base on the movement theory, there are many motion laws to meet the above requirements, such as sine law, cycloidal law, trapezoidal law, quintic polynomial motion law, septic polynomial motion law and so on. Because quintic and septic polynomial are more likely to achieve above motion requirements accurately, therefor, only conduct analysis on them. They are under the condition of known the displacement S and the maximum acceleration amax , the calculation formula is as follows:

T t ) 3 -3( T t ) 4 +3( T t ) 5 -( T t ) 6 ﹞ (7) a = 2 2 dt s d = 51 . 7 420a m ax ﹝( T t ) 2 -4( T t ) 3 +5( T t ) 4 -2( T t ) 5 ﹞ (8)

Simulation analysis of the two motion laws

Applied Mathematics software matlab, obtain the following motion law of Figure 4 to r-sgn(i)ee 1 -l 1 u i =l 2 w i [START_REF] Ji | Dynamics decomposition for Stewart platforms[END_REF] and

, u i =( cosθ 1i sinθ 1i ) T , w i =( cosθ 2i sinθ 2i ) T , e 1 =( 1 0 ) T , sgn(i)= 2 1 1 1       i i so: A i sinθ 1i + B i cosθ 1i + C i = 0 θ 1i =2artan i i i i i i B C B C A i A      2 2 2
) sgn( [START_REF] Lee | Dynamic analysis of a Three-Degrees-Freedom In-Parallel actuated manipulator[END_REF] among that, A i =-2l 1 y B i =-2l 1 (x-sgn(i)e)

C i = ex i l l e y x ) sgn( 2 2 2 2 1 2 2 2     

Velocity, acceleration model

First and second order derivation of time for formula (11), can get system speed and

acceleration model: Jv l 1 1 1    (13) ) ( 1 1 1 1      f l l Ja   (14)
In the formula, v and a are speed and acceleration of  , speed Jacobian matrix is:

T T T Qu w Qu w w w l J        2 2 1 1 2 1 1 1 (15) T Q         0 1 1 0 (16)

Numerical simulation curve of the two active motors

Set the manipulator horizontal moving distance 600mm, vertical moving distance 150mm, in the condition of a given mechanism parameter, inverse simulation by software matlab, acquire the following curves of the active motors: 2) Motion trajectory of transplanting robot was planned, conducted numerical analysis for quintic and septic polynomial by matlab software, selected the better motion polynomial.

3) Apply quintic polynomial motion law, under a certain condition of motor motion law, the operating cycle to be shorter, meet the requirement of high-speed robot for transplanting, can reduce the transplanting time of 0.07s, improve the transplanting efficiency.

Fig. 1 .Fig. 2 .

 12 Fig. 1. Schematic diagram of transplanting robot 1. parallel mechanism 2. motor 3.

Figure 3

 3 Figure 3 Manipulator trajectory of transplanting process

  Derivative of (6),acquire velocity, acceleration and saltus equation v=

  j=

Figure 7 。Figure 4

 74 Figure 7。

Figure 5

 5 Figure 5 Speed curve

Figure 6 Figure 7

 67 Figure 6 Acceleration curve

3 . 3 . 1

 331 Solving the active motor motion laws of the high-speed parallel transplanting robot Position inverse modelRefer to Figure2, in fixed reference coordinate system O-xy, establish equation:

Figure 8 Figure 9

 89 Figure 8 Angular velocity curve

Acknowledgements

This work was financially supported by Three Engineering of Agricultural Machinery, Jiangsu province(NJ2009-41), by Qing Lan Project of Jiangsu Province (Su teacher (2010)no.27) and the Priority Academic Program Development of Jiangsu Higher Education Institutions (Su financial teacher(2011)no.8).


Wanneng Yang

Chenglong Huang

Qian Liu

Development of an automatic control system for potgrown rice inspection based on Programmable logic controller

Keywords: Rice breeding, phenotyping inspection, automatic control system, programmable logic controller, phenomics

Rice improvement breeding is one of the most important research fields in China. With the development of modern rice breeding technology, hundreds to thousands of new varieties are produced daily, creating the impetus for rapid plant phenotyping evaluation. However, traditional measurement is inefficiency, contact-interferential, and lack-objectivity. Thus a high-throughput and automatic extraction system for rice plant is imperative. In this article we developed a rice phenotyping automatic extraction system and designed the automatic control for the system based on programmable logic controller (PLC). Subsequently, the prototype was test under industrial conditions continuous 24 h workdays and the error probability was less than 0.01%. In sum, base on PLC, we provide an efficient and stable automatic control system for pot-grown rice phenotyping inspection.

Introduction

Rice is the supreme production and consumption food in China, and thus rice improvement breeding is one of the most important research fields in China [START_REF] Zhang | Strategies for developing green super rice[END_REF]. During every breeding stage, plenty of phenotyping parameters, such as plant height, green leaf area, tiller numbers, and leaf temperature et al., must be extracted and evaluated prior to next breeding stage. With the development of modern rice breeding technology, hundreds to thousands of new varieties are produced daily, creating the impetus for high-throughput phenotyping evaluation [START_REF] With | Phenomics," plant scientists hope to shift breeding into overdrive[END_REF].

 Corresponding author.

However, the conventional measurement, including counting and recording, is still manual, which is inefficiency, contact-interferential, lack-objectivity and lackrepeatability. The prevalent commercial instruments, such as seed counter and leaf area meter, are inefficiency and single function. The Plant Accelerator (University of Adelaide, Australia), is designed for plant phenotyping with the throughput of 2400 plants per day. With the facility, the plant mass, tissue water content, leaf temperature, photosynthetic machinery, and protein content et al., are extracted equipped with color image, near IR imaging, far IR imaging, fluorescence imaging, and hyperspectral imaging technology, respectively [START_REF] Malte | The scanalyzer domain: greenhouse logistics as a planning problem[END_REF]. However, each imaging instrument is designed and fixed with one chamber, and thus five imaging chambers are necessary, which would lead unwanted time-consumption and unstable factors due to the interacting of each imaging chamber. What is more, the spatial and money cost would be incremental and unaccepted in some developing countries [START_REF] Mark | Breeding technologies to increase crop production in a changing world[END_REF].

Programmable Logic Controller (PLC) is widely used in agriculture automatic control, such as efficient management of greenhouse [START_REF] Sigrimis | Synergism of high and low level systems for the efficient management of greenhouses[END_REF], temperature-humidity control [START_REF] Jou | A boolean algebra algorithm suitable for use in temperaturehumidity control of a grafted seedling acclimatization chamber[END_REF], and so on. Previously, we designed a novel facility to count tillers equipped with X-ray CT, termed the high-throughput system for measuring automatically rice tillers (H-SMART) [START_REF] Yang | High-throughput measurement of rice tillers using a conveyor equipped with X-ray computed tomography[END_REF]. In order to incorporation with color imaging technology and far IR imaging technology, we developed a rice phenotyping automatic extraction system and this article focus on the design for the system automatic control based on programmable logic controller (PLC), which provides higher efficiency, higher spatial utilization, and lower cost. Development of an automatic control system for pot-grown rice inspection based on PLC

System description

The schematic drawing (top view) of the rice phenotyping automatic extraction system is shown in Fig. 1. In order to describe the imaging device of the system more clearly, the top of the chamber is treated transparently. The system consists of four units: imaging unit, industrial conveyor, chamber, and control unit.

Imaging unit and industrial conveyor

To acquire the different phenotyping parameters of the rice plant, three imaging devices, including color imaging camera, far IR camera, and X-ray system, are fixed into the imaging module. When the pot-grown rice plant (each pot with one pallet) is fed in the pot-input region, the pot is transported to the imaging region automatically.

In order to acquire image with different angles while the imaging devices remain stationary, a rotation platform is designed on the centre of the imaging region. The pot-grown rice plant is transported onto the rotation platform driven by the servo motor (MBDDT2210, Panasonic Corporation, Japan). What is more, before rotation, to jack the pot from the pallet, a jacking device, including jacking motor and ball screw, is designed and fixed under the rotation platform. Both the rotation platform and jacking device are driven via synchronous belts. The structure diagram of the jacking-rotation device is shown in Fig. 2.

After imaging acquisition, the examined rice is transported to the pot-discharged region. The motion direction of the conveyor is indicated as the arrow in Fig. 1.

Rotation platform

Jacking motor

Servo motor Ball screw

Synchronous belt

Chamber and control unit

For ensure the safety of the work area, a lead chamber is constructed for x-ray radiation shielding. And to avoid the interference of the external light, two traveling gates, which are open except the pot arrives at the rotation platform, are designed in the chamber. The whole system is controlled with PLC (CP1H-Y20DT-D, Omron Corporation, Japan) and computer workstation (HP xw6400, Hewlett-Packard Development Company, USA).

System control

As mentioned above, the automatic control core of the system are programmable logic controller (PLC) and computer workstation, which control the conveyer unit, imaging unit, and traveling gates.

Workstation communication

The system allows the user control the system via a friendly interface, provided in computer workstation. Once receiving the protocol "ON", transmitted by the workstation, PLC controls the system starts. Then the computer sends the instruction "R?" (every 2 seconds) and judges whether the pot arrives in the imaging region via the instruction returned by PLC. Once receiving the protocol "OF", representing the measurement task is finished, PLC controls the system stops. Additionally, the rotation angles and rotation speed could be predefined by the value sent by the workstation. The communication instructions are transmitted via the serial port (RS-232), and the communication protocol between workstation and PLC is illustrated in Table .1. Development of an automatic control system for pot-grown rice inspection based on PLC

Table 1. Communication protocol between workstation and PLC

Instruction

PLC control

Besides indirectly controlled by workstation, the system control is mainly designed and accomplished with PLC. As described in Fig. 3, photoswitch 1 and baffle 1 are used to block the path of next pot while the previous pot has arrived in imaging region. And the photoswitch 3 and baffle 2 are fixed to keep the awaiting-inspection pot in the centre of the rotation platform. The photoswitch 2 is used for detecting whether here is one pot in the coming pallet. In order to avoid the interference of the external light, two traveling gates are closed when the awaiting-inspection pot has in the imaging region. It is noticed that when the rice is jacked up for far IR imaging, the lamps must be turned off to eliminate the interference of the white fluorescent lights. After IR image acquisition, the lamps should be turn on for color imaging. The control flow chart based on PLC is shown in Fig. 4.

System starts

System reset and conveyor starts

The pot is detected by photoswitch 1

Results and discussions

To evaluate the efficiency and stabilization of the system, 12 pot-grown rice plants were fed and test in the system continuous 24 hours workday. Without manual intervention, these pots were measured successively and cyclically. After 3 days continuous test, there was no error and approximate 12965 pot-data were inspected and stored in the system. Thus, with the error probability less than 0.01%, the facility has the throughput of 4320 pots per continuous 24 hours workday. What is more, merging the three imaging technologies in one chamber, the system is designed with high spatial utilization. Correspondingly, the cost is lower with this compact design.

Conclusion

In this article, we demonstrate a rice phenotyping automatic extraction system and design the system automatic control based on programmable logic controller (PLC). Under the condition of industrial test, the facility provides high efficiency and high stability. Additionally, more modern imaging technologies, such as near IR imaging and hyperspectral imaging, could be incorporated into the system. In sum, base on PLC, we provide an efficient and stable automatic control system for pot-grown rice phenotyping inspection.

Fig. 1 .

 1 Fig. 1. Schematic drawing of the rice phenotyping automatic extraction system.

Fig. 2 .

 2 Fig. 2. Structure diagram of the jacking-rotation device.

Fig. 3 .

 3 Photoswitch 1 Baffle 1

Fig. 4 .

 4 Fig. 4. Control flow chart based on PLC.


Yongsheng Cao

Yeping Zhu

Bee Bee Bee Bee products products products products quality quality quality quality control control control control and and and and emergency emergency emergency emergency management management management management mechanism mechanism mechanism mechanism research research research research Based Based Based Based on on on on Multi-Agent Multi-Agent Multi-Agent Multi-Agent

Keywords: . . . . Multi-Agent, Bee products, quality control, emergency management, communication mechanism, distributed blackboard structure Maize, Purity identification, Density, Color, Core area 1

Based on the characteristics about distribution, intelligent, and coordination of Multi-Agent system, In this article, Agent theory and technology is applied to bee products quality control and emergency management system of information interaction and coordination, we constructed the bee products quality control and emergency management system of the frame structure, and put forward bee products quality control and emergency management system of the blackboard communication mechanism, and used the blackboard structure on the bee products quality control and emergency decision mechanism for research.

Multi-Agent Multi-Agent Multi-Agent Multi-Agent communication communication communication communication mechanism mechanism mechanism mechanism

The communication mechanism is Agent that in what ways does realize communication. Many of the Agent communication mechanism not only to realize the bottom of communication between the computer, but also to run in different network platform for communications connection, Agent in the environment of networks which position can be effective play its function and other collaborative interaction Agent. The communication mechanism design and choose plays an important role in between maintaining cooperation, to solve the conflict and to realize information exchange between agent, it will directly affect the overall efficiency of the system, robustness and expansibility.

At present, many of the main communication Agent system has there kinds: blackboard, federation, peer-to-peer [START_REF] George | Artificial intelligence[END_REF][START_REF] Long | Key Techniques to Realize Cooperation of Mobile Agent in MAS Environment[END_REF][START_REF] Trappey | Mobile agent technology and application for online global logistic services[END_REF]. The blackboard is actually a shared knowledge structure, it for each Agent provides a share data area, used in between Agent of information and data exchange and sharing. Each Agent can write directly to the blackboard information, also can from the blackboard read other Agent of information. Allow many Agent through to the blackboard content directly to read and write to distribute and get news [START_REF] Zhao Jian | An agent-oriented requirement analysis and modeling method[END_REF], it could achieve a send perform multiple tasks of communication. In the central control system, the blackboard way can share data structure and realizing efficient communication. The blackboard has advantages during the central control and high efficient, yet for complexity, interaction, and safety requirements high task, a blackboard way has some limitations.

Federation Federation Federation Federation

All members of agent constitute different groups on the basis of federation, each Agent group set a Agent medium, Each Agent only communication with in the group media Agent, media Agent according to the request of the Agent, the news sent to the agent in group. This method greatly reduces the direct communication link between Agent, but if the number of Agent less, frequent communication between Agent, through the media Agent communication efficiency but is not high.

peer-to-peer peer-to-peer peer-to-peer peer-to-peer

A peer-to-peer using TCP/IP protocol, establish the communication is the direct physical connection link. The packet to ensure the safe arrival, for TCP/IP protocol can realize the end-to-end confirmation. At present, most of the Agent platform established using direct communication mode. Using a peer-to-peer realize the physical connection between Agent for the sender Agent must know the receiving Agent physical location in the system.

Bee Bee Bee Bee products products products products quality quality quality quality control control control control and and and and emergency emergency emergency emergency management management management management communication communication communication communication mechanism mechanism mechanism mechanism based based based based on on on on the the the the Agent Agent Agent Agent

The core of Bee products quality control and emergency management is integration information resources and emergency management resources about our country different management and testing department, to achieve the emergency coordination effect between different departments, On the whole, enhance the system of the emergency response and decision making. Multi-Agent system has many good properties, it more suitable for the construction of bee products quality control and emergency management system. This section we will be structure bee products quality control and emergency management frame structure based on Agent, and use the Agent communication coordination technology research bee products quality control and emergency management system of communication mechanism.

3.1 3.1 3.1 3.1 Bee Bee Bee Bee products products products products quality quality quality quality control control control control and and and and emergency emergency emergency emergency management management management management system system system system structure structure structure structure based based based based on on on on Agent Agent Agent Agent Bee products quality control and emergency management system based on Agent, each Agent is real-time and autonomy basic modules, each Agent mutual cooperation and coordination, and compose a real-time function entity.

In the bee products quality control and emergency management system, Bee products quality control and emergency management organizations included ministry of agriculture, the national testing center, weather and environmental protection department and so on. They assume the bee products quality control and emergency management tasks. Every department is independent agent, and each main department Agent included many other auxiliary function agent, such as implementation agent and monitoring agent and so on. In the bee products quality control and emergency management organization, in order to better coordinate emergency management, optimize emergency management resources, to achieve the effective emergency decision knowledge together, we set up an emergency management center agent, the emergency management center agent include Central agent and Local agent, Every province is a Local agent, these agents jointly responsible for bee products quality control and emergency management in the system.

In figure 1 description of the typical distributed bee products quality control and emergency management structure based on Multi-Agent. Central management Agent in the global scheduling and decision-making position, then, according to China's bees products feature, The local agent of the province mainly complete emergency management functions (such as: daily management, emergency command). When need resources sharing and information collaboration between province, each of local Agent through communication network and information platform for exchange information. Central Agent plays a comprehensive coordination and decision-making role, integration and coordination each local Agent task. Through the argument that,each Agent by conditions and action of two parts, the condition is the use of Agent premise, is a set of information about the blackboard changes of judgement, action description of the operation about the Agent usually process. They are the whole emergency decision core part, they can understand crisis in time of the current state changes, can to the change of state to make timely response ability, to have Reasoning and prediction capability through the reasoning mechanism based on the knowledge and experience, to can response crisis situation change and independent judgment in no manual intervention case.

Conclusion Conclusion Conclusion Conclusion

With the fast development of the economy, bee products quality security is increasingly prominent, bee products related administrative departments for emergency management needs become very urgent. Considering the emergency management system of the distributed and autonomy characteristics , in this paper,

Modification

Variable in emergency blackboard

Start Emergency Prediction Agent

Analysis Emergency Event Input Event variable from emergency blackboard EmergencyPrediction Finish Agent theory and technology is applied to bee products quality control and emergency management system of information interaction and coordination, we constructed the bee products quality control and emergency management system of the frame structure, we put forward bee products quality control and emergency management system of the blackboard communication mechanism, and used the blackboard structure on the bee products quality control and crisis decision mechanism for research. The method provides a good strategy for emergency management resources scheduling and the implementation of the emergency measures, to provide strong support, to improve emergency management level, ensure the bee products quality security and stability.

In the future, we need to be further research bee products quality control and urgent management resources synergy optimization mechanism based on the communication mechanism collaborative optimization, to realize information resources and emergency management resources of emergency interact and collaborative allocation in the emergency management.

2. 1 .

 1 The 2.1.The 2.1.The 2.1.The blackboard blackboard blackboard blackboard

Fig. Fig.

  Fig. Fig. Fig. Fig. 6 6 6 6. . . . Emergency prediction Agent activity diagram

Ackonwledgemants Ackonwledgemants Ackonwledgemants Ackonwledgemants

This research was supported by National Scientific and Technical Supporting Programs Funded by Ministry of Science and Technology of China (nyhyzx07-041). National Natural Science Foundation about Agent-based quality control of agricultural products(60972154).

Emergency Emergency Emergency Emergency management management management management communication communication communication communication mechanism mechanism mechanism mechanism Based Based Based Based on on on on Agent Agent Agent Agent

Different local Agent distribution in different environment, Heterogeneous platforms and information produced some obstacles to information of interaction and coordinate. In order to promote the cooperation between local Agent, to realize the integration of heterogeneous knowledge, in this paper, we use distributed blackboard structure to describe emergency management of communication coordination mechanism. Distributed blackboard structure can provide concurrency control, interactive, real-time control etc.

Among them, each Agent are adopted the blackboard to transfer information form the communication interface between Agent, enhance the efficiency about cooperation and communication between Agent. Every function agent has a blackboard, the blackboard is communication and information storage area, and is an information communication interface channel with other agent. between between between between regional regional regional regional When department Agent can't alone finish emergency task, it through the blackboard structure to the local Agent sends a work request, if the local Agent think the request need other agent collaborative, the local agent release information on the blackboard, At the same time, the local agent analysis the task, send out task collaborative request to corresponding function agent, corresponding function agent received collaborative request, according to oneself circumstance to provide the service. Communication Agent is a control unit, to a certain extent, to realize department Agent communication request, equivalent to router, as shown in figure 2 shows.

Overall Overall Overall Overall situation situation situation situation decomposition decomposition decomposition decomposition collaborative collaborative collaborative collaborative mechanism mechanism mechanism mechanism

Central Agent establish an emergency task decomposition and task execution situation evaluation, first, overall emergency task decomposition, and assigned to each local agent, and then, appraise task execute condition, to more control effectively scheduling. The difference between overall situation decomposition collaborative mechanism and cooperative mechanism between regional, Central Agent monitoring the emergency blackboard information, if find local Agent is not able to accomplish task, must trans-regional information coordination, at this time, central agent control all emergency task, and then task decomposition, release the task decomposition to the local Agent, this is a cross-regional emergency "big synergy". Due to cooperative mechanism between regional, First, local agent accept department agent collaborative request, then build synergy between department Agent, and execute emergency task, as shown in figure 3. In short, in the emergency management system, Through emergency blackboard complete information transmission and interactive between each Agent, and in different situation use different cooperative mechanism, through the blackboard complete information for the sharing and exchange, and finally realize the emergency management system of the effective information synergy.

Bee Bee Bee Bee products products products products emergency emergency emergency emergency management management management management mechanism mechanism mechanism mechanism based based based based on on on on Agent Agent Agent Agent

The basic thought of the emergency management is based on the blackboard structure as problem solving mechanism, the Multi-Agent technology as a problem solving way. Agent will emergency decision problem solving process simulation for mathematical model, knowledge model and the man's judgment package unified format,and form problem solving knowledge module, in intelligent solving process, their external behavior is consistent, behavior act on their own and environment, and can respond to the environment, through operation the blackboard, and cooperating problem solving.

Emergency decision simulation intelligent blackboard model consists of three parts: the Emergency blackboard, the Emergency decision Agent, Emergency prediction Agent, shown in figure 4.

Central Agent Local Agent1

Communication network

Local Agent2

Local Agent3

1Emergency blackboard blackboard blackboard blackboard

Emergency blackboard is a global database , it store the original data, problem solving solutions. In Emergency decision, Emergency decision information defined as a group of variables, according to the variable physical meaning divided into two categories: state variables, events variables.

(1)State variables: included production data, processing data, testing data, geography data, climate data, and so on. The variable is a function of time, the value with the advancement of crisis changes, it Can be a (0, 1) between the quantitative value digital.

Events variables: Defined as all events in the process of the crisis happened; the variable is a function of time, the value with the advancement of crisis changes, it Can be a (0, 1) between the quantitative value digital.

The emergency information of emergency simulation through state variables to reflect.

The emergency participants perceive the current crisis through emergency state variables, and decision making based on state variables. Decision making behavior of participants to display a series of events, the occurrence of events led to the change of state variables, and promote the development of the crisis. Otherwise, even if no decision action, the state variables will still be the advancement of change over time. So decision action and time is direct reason to promote action and situation change. The situation is interaction between variables, the mutual influence, part of the trend of the change will cause other variable situation of a series of variable chain reaction.

Emergency Emergency Emergency Emergency decision decision decision decision Agent Agent Agent Agent

Emergency decision Agent mainly complete crisis situations of the situation analysis, risk assessment, strength analysis, scheme generation, scheme evaluation and so on. the operation modes is when the state variables change up to a certain value , and the value(between 0-1) match crisis decision Agent executive premise, the Agent will be motivated to perform some action. For each one of the Emergency decision work, Emergency decision to realize the function of the Agent essence of a crisis situation analysis, decision making, evaluation model.

Emergency

Emergency Emergency Emergency Emergency prediction prediction prediction prediction Agent Agent Agent Agent

Emergency prediction Agent main complete crisis analysis of results, crisis situation variables revision and so on, the operation modes is when the situation variables change up to a certain value , and the value(between 0-1) match crisis decision Agent executive premise, the Agent will be motivated to perform some action. For each one of the Emergency decision work, Emergency prediction to realize the function of the Agent essence of Crisis result analysis, variable modification, to understand that a self-learning model.

Modification

Variable in emergency blackboard

Start

Emergency decision Agent

Generate emergency decision scheme

Crisis analyse

Input

State variable from emergency blackboard scheme selection Finish