F Chaxel

E Bajic

Jacques Richard

Frederic Eddy Chaxe1

Bajic

Automotive Vehicle Data Management Based on Holon-Product Paradigm

This paper describes research currently being carried out in the area of Holonic Manufacturing Systems especially for Automotive vehicles data management. The originality of the presented work deals with the idea that process control may be achieved by machines, but also by the product carrying and managing its own information. Distribution of the information system onto each individual product requires tools and methods to manage and provide access to product related information for both automated systems and human operators. Automatic identification technology using electronic data carriers can provide such functionality and act as product information vectors. Our approach is based on STEP technology to define computer-aided tools for data structuring and management of product knowledge implemented on mobile database nodes circulating in an open environment (from manufacturing to recycling).

INTRODUCTION

Added value architectures (manufacturing, &er sales services, ...) are now often proposed more and more distributed [l].

This distribution is done by spliting both decision algorithms and information into several small sub-systems cooperating with each over. Each highly decentralized, modular, autonomous and cooperative sub-system supports a part of the global system. This paradigm is developped by the scientific community [2] under the words heterarchical [3], multi-agents, bionic [START_REF] Okino | Bionic Manufacturing System[END_REF] and more recently holonic [START_REF] Bongaerts | Schedule Execution for a Holonic Shop Floor Control System[END_REF].

Comparing to hierarchical architectures, expected advantages are [START_REF] Duffie | Won-hiemhical Control of Manufacturing Systems[END_REF] : 0 robustness due to the high degree of distribution 0 simplicity of each agents or holons because they just have to manage small systems 0 possible application of the "plug and play" concept with the agents in the system The three types of basic building blocks in an holonic manufacturing system (HMS) are: resource holons, order holons also call task holons and product holons [START_REF] Bongaerts | Schedule Execution for a Holonic Shop Floor Control System[END_REF]. A resource holon consists of a physical part, namely a production resource in the HMS. It is an abstraction for the production means such as machines, conveyors, human operators, . . . An order holon is an entity responsible for performing the work in a satisfactionary way. It captures all information related to a job [START_REF] Bongaerts | Schedule Execution for a Holonic Shop Floor Control System[END_REF]. Finaly product holon is an entity created by real or forecasted market demand. It holds all the necessary knowledge to assure the realisation and the maintenance of a product. This holon is an information and communication entity ( Our research work is particulary focused on this last holon: the product holon consided has an entity without any physical capability (information, communication and local processing capabilities only). Our contribution, showed in this paper, presents a framework which allows an implementation of the product holon in an open environment. Application case is focus on an automotive vehicle circulating in the manufacturing and then in the &er sales environment.

CONCEPT

Product knowledge is basically defined at the design level in words of fiinctionnality, shape, quality, manufacturing operations sequency, . . . This knowledge is constructed step by step and structured through a life cycle product information model as shown in Fig 2 [6].

This product knowledge has to be shared by all the computer tools used for the product design. In this field some work has be done in order to define both standardised product model, like STEP for instance, and integrated information infrastructure like AIT Integration Platform or Esprit The proposed concept is a logistic support to product holon data management and integration in order to solve both the vertical integration problem (link with the design level) and the horizontal integration problem by suplying a method and a tool where each electronic tag has to play the role of a vector of the information system which is in charge of the overall application management, and information coherency, consistency and reachability.

. PRODUCC HOLON INFORMATION MODELLING

One of the most significant approach today in product modelling is the development of I S 0 10303 standards called STEP [IO] (STandard for Exchange of Product model data) which define models, database access and neutral data files format for representation and exchange of product data

[ 1 I].
The goal is to define complete models for product life-cycle in a CIM context, as well as the means for exchange of data between enterprise functions along a product life-cycle definition (riom CAD to production, maintenance, . . .).

In this project, a now well known data object-oriented modelling language has been defined. EXPRESS is a formal language that models the knowledge about information used by an information system. EXPRESS provides the words, syntax and grammar needed to describe a universe of discourse in an uniform, precise and compact manner. An EXPRESS grammatical analyser could be simply programmed with standard Unix tools like LEX and YACC.

EXPRESS is designed to satis@ the five basic requirements [ 1 1 ] :

- These researches are the basis of our work, where we consider that product holon definition could result of a first phase supling both a product holon data model and holon data in the form of an EXPRESS data model and STEP files.

PRODUCT-DRIVEN ENVIRONMENT

ProductlProcess Interrelationship

In our model, every product is a real actor within its process able to :

manage itself its charactersitics like shape, design version, ...

sequency

has knowledge of its evolution through store its history in the holonic process. In such a case each product is the manager of its own information and gives them to the process &er it received a service request ("what is the next operation to be proceed on yourself' for example).

On the basis of a client/server relationship between product and process, user requests (i.e. process requests) are sent to an interface supporting product access methods in an objectoriented interrogation form such as Object-SeZectorMessuge (Parameters) or SystemA4essage (Parameters) :

-COLOR.GETVALUE ( ); -COL0R.PUTVALUE (Red);

The data storage medium, in our case, consists of identification tags carried by products or by pallets and also some optional network databases.

Product Model Translation for Manufacturing Control Services

The major problem, related to this model (Fig 3 .) is the integration of tags in the information system which necessitates :

reference conceptual models to put the information about -tools to implement these models and to access the the products themselves on tags; information.

For the purpose of our work, we assume that a logical data model of product for manufacturing operations exists and is defined in the EXPRESS language (this model could be a STEP application protocol or a specific model) Fig 4 1161 shows the STEP-based methodology allowing translation of product model, described in the EXPRESS formalism (entities, attributes, hnctions, rules) :

to a product-information access library supporting a direct requesting of target application on the EXPRESS schema data;

to the optional database structure definition.

The product definition within the STEP neutral file is used :

to create the tag memory image, for the first state of a product associated to the current phase of its life cycle in the manufacturing environment; The program code (in C and Java) is obtained by an automatic translation from the EXPRESS schema through a metatranslation model identimg semantic losts.

The resulting code has to be linked with a library providing the basic tags oriented functions liie Lockht.0, TAGS-IntO, TAGS-Str.0, ... and all the communication oriented hnctions like Send-Vafue(l, Send_Error(), ... in order to generate the Functional Application Interface.

Remote Database Reachability

Tags are of course limited in capacity, and storing complex structures (ENTITY) needs more memory than only storing the contents (Attributes value). Today, tags tend to have higher storage capacity (up to 128 KBytes) but in some cases (liie automotive industries) this may not be enough, so if it is relevant for the application a part of the data could be stored on other medium. A network database in clientlserver mode seems to be the natural architecture to manage the overflow of data :

we need to have a persistent computer storage in order to manage EXPRESS schema population and translation;

it stores and manages high volume of data; time response is efficient for most of the applications; and enterprises are already trained with such an architecture.

Our approach has the same philosophy as distributed database [ 171 that is, "users do not have to know where data are stored to access to them". The system performs data locating, formatting and transfening through the structure (see Fig 5 .). In fact, when we populate the schema, we specify where EXPRESS entities must be (on tags or remote database). Afterwards, the user can send a request to the system, without specirjling the location of the objects he wants to access.

The search for physical storage of objects is made after each user request reception, as shown in the model exposed in the if not, an identification of the product is made by reading its distinctive characteristics (product identification number, product type identification number) (3); -&er which, we get data from the database (4) and we realise the treatment (6).

Application Tool

Our prototype Tag-STEP (see Fig 6 .), running under MS-Windows, uses a standard computer to generate the MobIIe DAtabase Nodes structure. It is connected to a relational network database management system (ORACLE 7) through SQL-NET-TCP-IP-Ethernet protocols. The EXPRESS schema is converted in a relational schema which has to be put on the ORACLE database. In this case each EXPRESS entity is translated to a relational entity (a table) and to a view in order to manipulate sub-typing. Such a conversion has already been made by Eggers [18] at the National Institute of Standards and Technology.

Application

Mobne DAtabpse Nodes Tag-STEP The schema is also converted as a C programme to generate the Functional Application Interface as explained before. This program is able to read or write data independently on tags or on the database. To populate the schema we use a STEP neutral file provided by the product definition phases.

APPLICATION

To validate our approach, we have design an application concerning automotive vehicle data management covering a part of the live cycle fiom the factory to recycling.

This application is based on five application scenarios describing an usage of different vehicles. new customer services can be added in product usage as for instance product self configuration (seat position in a car for example). VI -438

CONCLUSION

Electronic data carriers for distributed manufacturing information management is taking more and more importance in industrial applications. This new approach for information management necessitates methodological framework for Information System implementation as well as interfacing tools based on international standard definitions. Our proposal dealing with STEP formal models has demonstrated the feasibility of high level information structuration, based on product data definition, with advanced management technics on attached escort memories. Attained benefits in CA4 environments are :

better data distribution in the overall plant allowing real time tracking of products. Each tag is an element of a distributed manufacturing database allowing immediate consultation by users (automated machine or human operator); simplification in the production machine structure and software capabilities. A generic interface is defined to link different kinds of users to the structure in the product coupled memory. There will be a gain in the production system modularity and modifiability, . . .

implicit fault-tolerance due to the data distribution on many media.

One of the most significant extension of this concept concerns it's application to cover the &U product life cycle data management from production to after sale services and recycling. This is actually done in the field of a research contract between the CRAN and a French cars manufacturer. Basidy, STEP will be the general framework used for vehicule data management and the link beetween different phasesie differents data modelswill be done through models described in EXPRESS-X [ 191 (creation of views on a model and mapping data from one model to another).

FigFig 1 .

 1 Fig 1. Resource and Product Holons structure.

Fig 3 .

 3 Fig 3. Procesdproduct relationship based on the client/server model. The schema Fig 3 points out the concept adapted from the clienthewer model we chose in order to respond to these objectives.In such a case each product is the manager of its own information and gives them to the process &er it received a service request ("what is the next operation to be proceed on yourself' for example).

Fig 4 .

 4 Fig 4. From EXPRESS schema to users requests.schema.

Fig 5 .

 5 Fig 5. State graph of product data remote access from a process point.

  Fig 5 :

-

  if object attributes are stored on the tag (Z), the message treatment is realised (6);

Fig 6 .

 6 Fig 6. Structure configuration with EXPRESS schema and STEP P21 neutral files

FactoryFig 8 .:

 8 Fig 8. : Application platform.

  modelling the things of interest; defining the constraints that are imposed upon those defining the operations in which those things participate;

	things;
	-readable by an human operator; -modelling in a computer sensible manner.
	For data exchange (one product model) between CIM
	modules two methods could be used, via a neutral file (STEP
	part 21) or via a STEP database interface SDAI [ 121 (Simple
	Database Access Interface).

The STEP project presently only specifies data modelling language, files format and data models with no considerations to implementation infrastructure. In this field, projects like Esprit IMPPACT [ 131 and Esprit NEUTRABAS [ 141 or CIM BIOSYS [ 151 have already obtained significant results re-used in project Esprit AIT (Advanced Information Technology in Design and Manufacturing) which is a European "user-pool" program.

  Due to the poor memory capacity of the currently escort memories, only a part of the overall information is used, when some Mega Bytes should be necessary . . .

	The target support escort memories supported by the present
	work are : (See fig 7.)
	0	Gemplus MPCOS 3DES 64Kbits Contact Card,	Fig 7. Target escort memories for Product life cycle
		Gemplus Gemeasy 64Kbits contactless Card 13.56	management
		Mhz
		Philips HiTag 125 KHz RFID	Results from this application are :
		Balogh Inductive tag 1,5 Mhz

-ISO-STEP based approach is coherent an pertinent with this framework;

individual product management and transport is convenient for after sale services and can accelerate drastically maintenance operations because each

--

product is always well known through its configuration data;


Arnaud Guibert

Luiz Lebensztajn

Olivier Chadebec

Jean-Louis Coulomb

Stochastic optimization of electrostatic devices thanks to an hybrid BEM/FMM method

teaching and research institutions in France or abroad, or from public or private research centers.

I. INTRODUCTION

Designing an industrial device needs to predict the effects of its functioning. For an electrostatic device, it means being able to compute the electric fields or capacitance for example. To do so, different modeling methods exist, such as the most used Finite Element Method (FEM) and the Boundary Elements Method (BEM). Those ones both offer advantages and drawbacks. BEM induces less post processing noise due to an only surface meshing of the structure but is numerically limited from using full interaction matrixes. Anyway, its use is well adapted to structure optimization as only surface meshing has to be modified during each step. To overcome this limitation, combining this to a Fast Multipole Method (FMM) would bring great numerical improvement for using a finer meshing.

II. HYBRID BEM / FMM IN ELECTROSTATIC

Let first introduce the application area of the work which is the capacitance optimization of an electrostatic device. We define the capacitance C of a structure as follows:

N i Q Q i C V V i    (1)
In this last equation, Q is the charge in Coulomb and V the potential in Volts, which repartition is the given information.

A. Use of a Boundary Element Method

In the right member of (1) is considered a surface meshing in N elements. On this meshing we consider a zero-order hypothesis where each quantity is constant by element. We also consider quantities at the geometric center of elements (point matching) which will define distances during integration. In most cases, structure geometry and voltage repartition are known, so only charges repartition has to be primarily computed. This can be done with a Boundary Element Method, building a N x N system:

     V Q A  . ( 2
)
The coefficients of matrix A can then be calculated thanks to the electrostatic equation:

, , 1 . 4 . . j i j j S ij A dS r    (3)
In this last equation ε is the domain permittivity and r ij the distance between the geometric center of element i and a varying point j of the source surface S j . This coefficient can also be approximated by a sum based on placing Gauss points (correctly weighted) on S j . After obtaining the full matrix A, the solution of (2) has several numerical difficulties when working with too many meshing elements. This problem can be overcome with a FMM.

B. Improvement thanks to a Fast Multipole Method

The FMM is a relative recent method that hierarchizes a meshing through a separation of proximate and far interactions. Different ways to build this meshing repartition have been developed but we will only focus on a fast adaptive multi-level one [START_REF] Carrier | A fast adaptative multipole algorithm for particle simulations[END_REF]. This aims to set a maximum number m of meshing elements in cubes. Practically, a first cube is built, containing all meshing elements (zero level). Then it is subdivided into eight new ones (level one) and only those who contain meshing elements are kept. If some cubes have more elements than m, they are subdivided once again until every lowest-level cubes have less or equal m elements (childless). After classifying all elements in this cubic hierarchy (Octree), a neighborhood study of each cube (of each level) is performed to separate most influencing cube towards others, creating 5 new groups [START_REF] Carrier | A fast adaptative multipole algorithm for particle simulations[END_REF].

At each cubic centre of childless element a multipole is computed from all charged elements contained inside. In spherical coordinates, this multipole creates an electrical potential V at a P point with the following equation:

  1 0 , ( ) . p m n m n n n n m n Y V P M r       (4)
In this last equation (r, θ, φ) are the P coordinates, p the multipole order and the M n m the coefficient (which have to be computed one time for each childless cube):

  0 . , . N m m n n i n i i i i M q Y        (5)
Optimization of electrostatic devices thanks to an hybrid BEM/FMM method A. Guibert (1) , L. Lebensztajn (1) , O. Chadebec (2) , J.-L. Coulomb (2) (1) Laboratório de Electromagnetismo Aplicado (LMAG) -Escola Politecnica da USP Av. Luciano Gualberto, 158, 05588-000 -São Paulo -SP -BRASIL (2) Grenoble Electrical Engineering Lab (G2ELab) -Grenoble-INP / Université Joseph Fourier / CNRS ENSE 3 , BP 46, 38402 Grenoble, FRANCE whereY n m are Legendre polynomial coefficients, q i the charge value of the i-element (for N contained in the cube) which has (ρ i , α i , β i ) spherical coordinates. After computing those coefficients, translation and conversion laws allow considering this multipole at a P point, which can be the center of another childless cube. In that way all elements far away from a childless cube can be approximated by locals written at its center, as shown in [START_REF] Carrier | A fast adaptative multipole algorithm for particle simulations[END_REF].

Remembering we want to get the capacitance of a structure from the knowledge of V repartition, we have to find Q, unknown vector of charge repartition on meshing elements. We have to write (4) for each meshing element, which give all far interactions. For close interactions (all other elements contained in the childless cube of P), a BEM is followed, creating a full square matrix for each childless cube. This can be summarized in the following system:

  .  far V P Q V Q (6)
In this last equation, P is a sparse matrix made of square sub matrixes and V far the potential created by far interactions thanks to (4).

C. Solving the system

We have to remark than system (6) is not directly solvable, as we do not have a direct interaction matrix like in [START_REF] Saad | GMRES: A generalized minimal residual algorithm for solving nonsymmetric linear systems[END_REF]. An iterative solver is needed, updating Q at each step. It is decided to choose the GMRES (General Minimum RESidual) one, well adapted to square symmetrical matrixes [START_REF] Saad | GMRES: A generalized minimal residual algorithm for solving nonsymmetric linear systems[END_REF]. The main goal is not to solve a system but minimizing the following residual norm:

    .    far r P Q V Q V (7)
Different methods can be used to improve this method, such as the Arnoldi process and the Givens rotation [START_REF] Belegundu | Optimization concepts and applications in engineering[END_REF]. A pre-conditioning of matrix P gives also better results by scaling P coefficients, thanks to the maximum and minimum eigenvalues. After N or less iterations, Q is obtained and the capacitance can be extracted.

III. USING HYBRID BEM/FMM IN OPTIMIZATION

Let focus on a particular electrostatic context as an applicative area. Main objective is to define some design criteria for minimizing an objective function based on capacitance computation:

      2 m OF C C  pp (8)
In this last equation p is a vector of geometric parameters of the structure. To find the best parameters p f from an initial p 0 , it is possible to use an iterative gradient based method, like the Steepest Descent [START_REF] Belegundu | Optimization concepts and applications in engineering[END_REF]. This leads to define a normalized search direction d k and a parameter α k , depending p k . The process is the following: The algorithm stops when the evaluation of OF(p k+1 ) is sufficiently proximate to OF(p k ). To realize step 2, a quadratic line search can be done [START_REF] Belegundu | Optimization concepts and applications in engineering[END_REF]. The main difficulty of this method is the evaluation of grad(OF(p k )). Let focus on the example of 2 similar plates (-0.5V and 0.5V), forming a capacitor with 3 geometrical parameters h, s and e: Let first consider the BEM to model the physical behavior of this capacitor. Computing the gradient of the objective function leads to get the capacitance one and so the charges one. This can be done by using the adjoint state method [START_REF] Rakotoarison | Formal sensivity computation of magnetic moment method[END_REF], which needs a matrix relation between V and Q. This relation, written in (2), has to be derived by all parameters, that is to say building a matrix from the derivation of the integral terms in (3) by h, s and e. This gives the following result, after 11 iterations: Another difficulty stands then in introducing the FMM in this process as the interaction matrix A is no longer available. This can be done by summing for each geometric center the gradient of the close interaction matrix (as the previous method) and the FMM gradient (thanks to a derivation of (4) and (5) by h, s and e).

IV. CONCLUSION This article has presented the use of an hybrid BEM/FMM method which allows computing quickly electrostatic quantities of a defined device. This method can be plugged into an optimization process, computing a chosen objective function. The optimization process chosen is deterministic: the Steepest Gradient. This iterative method requires the gradient of the modeling method which has to be carefully computed with the FMM.

1 .

 1 Compute d k = -grad(OF(p k )) / ||grad(OF(p k ))|| 2. Find α k that minimizes OF(p k + α k .p k ) 3. Update p k+1 : p k+1 = p k + α k .p k

Fig. 1 .

 1 Fig. 1. Capacitor example with 3 geometric parameters h, s and e

TABLE I THE

 I OPTIMIZATION PROCESS WHEN CM = 6.6PF (WITH BEM)

	Point	h (m)	s(m)	e (m)
	Starting	0.2	0.2 m	0.05
	Final	0.1027	0.1014 m	0.01987


This paper describes an original approach for determining independent loops needed for mesh-current analysis in order to solve circuit equation system arising in Partial Element Equivalent Circuit (PEEC) approach. Presented algorithm is well-suited for large degrees of freedom problems, saving significantly memory and decreasing the time of resolution.

I. INTRODUCTION

Node-voltage and Mesh-current analysis are two powerful methods which simplify circuit analysis substantially. However, let us point out that nodal analysis is unsuitable for circuits with many mutual inductances such as those provided by inductive low frequency PEEC method. In such cases, only independent loop analysis could reduce the circuit equations into the smallest number of unknowns and also provided a much better condition number for the system than those given by standard Kirchhoff's equations [START_REF] Kamon | Preconditioning techniques for constrained vector potential integral equations, with application to 3-D magnetoquasistatic analysis of electronic packages[END_REF]. Despite this clear advantage, this approach requires to identify an independent set of independent loops (or mesh), which is well known for being an uneasy task.

Many strategies has been proposed to determine independent loop matrix, such as general matrix solution by inspection of the circuit [START_REF] Pillage | Electronic Circuit and System Simulation Methods[END_REF] or graph algorithms [3][4]. Method proposed in [START_REF] Pillage | Electronic Circuit and System Simulation Methods[END_REF] is extremely time and memory consuming for complicated 3D structures due to the use of large scale matrix calculation and storage. The approach in [START_REF] Antonini | Hybrid Formulation of the Equation Systems of the 3-D PEEC Model Based on Graph Algorithms[END_REF] has better performance than the generic graph solution proposed in [START_REF] Rong | A novel graph partitioning technique for enhancing the computational efficiency of the loop-tree generalized PEEC modeling of 3D interconnects[END_REF] but its drawback is the dependence with the geometry discretization.

In this paper, a novel strategy coupling a general loop matrix method with a simple graph algorithm is presented. The proposed method is a generic circuit solution such as [START_REF] Pillage | Electronic Circuit and System Simulation Methods[END_REF][3] and requires similar computational complexity as [START_REF] Antonini | Hybrid Formulation of the Equation Systems of the 3-D PEEC Model Based on Graph Algorithms[END_REF]. An industrial application with 11,445 branches has been treated using our method.

II. PEEC MESH CURRENT PRINCIPE

A. Equivalent circuit formulation

By assuming quasi-static conditions and without any magnetic material, the classical PEEC method is derived from the equation governing the total electric field at a point r inside the conductors:

) ( ) ( 4
) ( 0 r r r r J r J φ π µ ω σ -∇ = ′ - ′ ⋅ + ∫∫∫ v dv j (1)
where J is the current density, φ is the scalar electric potential, σ is the material conductivity, µ 0 is the vacuum permeability and ω is the excitation pulse.

To compute the current in each branch, a mesh-based analysis is used [START_REF] Kamon | Preconditioning techniques for constrained vector potential integral equations, with application to 3-D magnetoquasistatic analysis of electronic packages[END_REF], where mesh is set of independent loop of branches in the graph representing the circuit. We can change (1) into a new equation:

s b m m m t b V MV I Z I M MZ = = = (2)
where Z b is a complex branch impedance matrix, Z m is a complex loop-based impedance matrix, I m is a vector independent loop-based currents, M is the transition matrix (branch -independent loop matrix) where each element is equal to -1, 0 or 1, V s is the vector of source voltages (most part of time equal to 0). Expression to compute each element in Z b can be found in [START_REF] Kamon | Preconditioning techniques for constrained vector potential integral equations, with application to 3-D magnetoquasistatic analysis of electronic packages[END_REF]. Let us notice that one fundamental loop may be composed of geometrical PEEC filament and external electric component (R, L, C, source current, source voltage)

B. Graph partition for independent loops identification

In this paper, the general circuit denotes the PEEC circuit, a sub-circuit is a group of independent loops and the circuit connecting all sub-circuit will be called super-circuit. The general circuit is then composed of sub-circuits and a super-circuit. The union of these both sets of independent loops is the complete set of independent loops of the general circuit (see Fig. 1). So if m denotes the number of independent loops: To identify all of fundamental loops, we have to: • Step 1: Partition general circuit into several sub-circuits and detects all fundamental loops in each of them.

• Step 2: Create the super-circuit from general circuit and sub-circuits and then search all fundamental loops in it. The partitioning of the general circuit into sub-circuits and super-circuit will be described in the next section.

An independent loops search algorithm for solving inductive PEEC large problems

IDENTIFICATION FUNDAMENTAL LOOPS ALGORITHM

A. Sub-circuits determination and their loops analysis

A very efficient way to search independent loops is to look for loops with the minimum number of branches. We want to determine all loops having k branches at the most. In a typical PEEC discretization, most part of independent loops is composed of 4 branches (see Fig. 2) so we chose k=4 to be sure that a large number of loops would be found rapidly. A Sub-circuit can be created by regrouping all small loops (with less than k branches) which share at least one branch with another small loop (see Fig. 3a). At the end of the process, some fundamental loops in the sub-circuit can miss. It remains to identify them.

To complete the missing loops, a graph algorithm is used to find the large-loops (with more than k branches) in the sub-circuit. An important number of loops has been already found, then building a spanning tree is very fast. The missing co-tree denotes the part of co-tree corresponding with missing loops. From this spanning tree and missing co-tree, all missing loops are determined thanks to Breath First Search algorithm (BFS) (see Fig. 3b,c).

B. Super-circuit loop analysis

In step 2, we need to search a sub-circuit internal path linking its connections with the super-circuit (see Fig. 4). This task is achieved thanks to BFS algorithm. Once all the paths have been found, a super-circuit is created, composed of the sub-circuit internal path and the branches which have been not included in sub-circuits. So, since this circuit has been reduced, the general matrix solution [START_REF] Pillage | Electronic Circuit and System Simulation Methods[END_REF] is efficient to identify the last fundamentals loops. 1 highlights the efficiency of the new method. With our new approach, the total time for the current distribution computation is 85s (PC Intel Core 2 Duo @2.66Ghz -2GB memory). The independent loops search algorithm is combined with an Adaptive Multi-Level Fast Multipole Method. 2.5s are needed to analyze the circuit, 7.5s to compute the near interactions, 75s for the iterative solving process. The memory requirement does not exceed 240 MB.

Fig. 1 .

 1 Fig. 1. Union of two set of loops in PEEC network

Fig. 2 .

 2 Fig. 2. Small loop of PEEC conductor meshing

  Graph of sub-circuit (c) Search of missing loop

Fig. 3 .

 3 Fig. 3. Sub-circuit loop analysis

Fig. 4 .

 4 Fig. 4. Reduction of general circuit to super-circuit IV. NUMERICAL RESULT Classical general matrix solution technique has been compared with this new approach. Both techniques have been implemented in commercial InCa3D software [5]. A LED headlight PCB with 11,445 branches and 4,762 fundamental loops has been modeled. The result in Table1highlights the efficiency of the new method.

Fig. 5 .

 5 Fig. 5. Car Headlight LED PCB modeling in InCa3D (courtesy of Valeo)TABLE I Time for determining a set of independent loop Classical general matrix solution [2] New algorithm Time(s) 2894 s 2.1 s


Vincent Ardon
email: vincent.ardon@cedrat.com

Olivier Chadebec

Jean-Michel Guichon

Patrice Labie

Yann Le Floch

Roger Franchino

Parallelized FMM and Dedicated PEEC Method to Model Industrial Power Interconnections -Application to Boost Chopper

This paper presents a novel version of a dedicated PEEC-based (Partial Element Equivalent Circuit) methodology applied to the modeling of interconnections of power electronics devices. Although this method is known, the originality of this work is its use to quickly model structures presenting an industrial complexity thanks to two separated and now parallelized Fast Multipole Methods (FMM) based on two different meshes. They respectively compute parasitic inductances and capacitances and lead to an equivalent circuit of the system. EMC performances of a structure can be evaluated with time and frequency domain analysis of this circuit. This approach is applied to model two static converters, boost choppers built with routing parts of an industrial variable speed drive.

I. INTRODUCTION

Since several years, power interconnections and consequently EMC modeling of industrial static converters are real challenges because of the frequency rise, the growing complexity of components, the closed environments and the strong electromagnetic couplings. The PEEC method based on integral equations appears to be efficient to model interconnections of large VLSI structures [START_REF] Ruehli | Survey of computer-aided electrical analysis of integrated circuit interconnections[END_REF]. However its use in power electronics needs some adaptations in order to get non-large models because EMC standards are focused on frequencies ranging from 10 kHz to 200 MHz. That was the purpose of our previous works [START_REF] Ardon | EMC Modeling of an Industrial Variable Speed Drive with an Adapted PEEC Method[END_REF] in which inductive and capacitive models were separately dealt with. Thus, it was possible to take advantage of both approaches and especially of the different meshes and hypothesis to extract equivalent elements that were finally coupled in a complete lumped element circuit.

The purpose of this article is to present some interesting improvements as a matter of time and memory consumption in the extraction of parasitic elements: the use of FMM algorithm [START_REF] Carrier | A fast adaptive multipole algorithm for particle simulations[END_REF] is generalized to the computation of inductances (L) and mutual-inductances (M) and secondly the parallelization of algorithms is applied during the integration, preconditioning and solving process. These enhancements make it possible to quickly process even larger industrial structures with few memory consuming.

In the second part of the article, the adapted PEEC approach is reminded and its improvements are detailed, with some numerical results on speed-up factors. The methodology is then applied to model a boost chopper, composing the industrial variable speed drive already modeled in [START_REF] Ardon | EMC Modeling of an Industrial Variable Speed Drive with an Adapted PEEC Method[END_REF]. New results, in frequency and time domains, obtained thanks to the improved techniques are finally presented in the last part of the paper.

II. EXTRACTION OF (R-L-M-C) EQUIVALENT CIRCUIT

A. Two separated FMM and preconditioned GMRES(m)

In our approach [START_REF] Ardon | EMC Modeling of an Industrial Variable Speed Drive with an Adapted PEEC Method[END_REF] a dedicated inductive mesh is used to compute parasitic equivalent resistances and inductances (R-L-M) by a full integral method and a second discretization is needed for the FMM-based capacitances (C) calculation. These two meshes are respectively adapted to skin depth and side effects to precisely model the current and surface charge distributions.

The use of FMM is now generalized for the inductive part in order to accelerate the computation of the vector magnetic potential A: a new multipole m n M is computed from the same spherical harmonics m n Y and scalar free- charges in [START_REF] Ardon | EMC Modeling of an Industrial Variable Speed Drive with an Adapted PEEC Method[END_REF] are replaced by current density vector J (1).

(

) ( )

       β α = ρ ϕ θ πε = ϕ θ ρ ∑ ∑ ∑ = - ∞ = - = + Nq 1 i i i m n n i i m n 0 n n n m 1 n m n m n 0 ) , ( Y r / , Y . 4 1 , , J M M A (1)
Since the "inductive" and "capacitive" multipoles are respectively vectors and scalars, the use of two different cube partitionings for the two meshes and two different iterative solvers is required. A preconditioned GMRES(m) [START_REF] Saad | GMRES: A generalized minimal residual algorithm for solving nonsymmetric linear systems[END_REF] algorithm is also used to solve the current distribution or the (R-L-M) inductive components.

As an example proving the computation enhancements, a 1.6 Gbytes of memory inductive problem solved without FMM can be now solved with only 230 Mbytes and five times faster (100 s). Another structure (induction heating device) has been simulated by FMM with 11 Gbytes of memory instead of more than 405 Gbytes without FMM.

B. Parallelization of algorithms

To take advantage of widespread multi-core computers, most algorithms have been parallelized, i.e. the computation of Gauss point tables, the integration of small full matrixes, the computation of scalar and vector potential V and A, the two GMRES(m) preconditioning processes, the matrixvector products during the GMRES(m) solving, etc. The average speed-up factors of all parallelized algorithms are shown in Fig. 1: for example, with two cores, the parallelized process time is divided by 1.8. The gap with the ideal factor of two is due to the increase of data exchanges between the processors.

III. APPLICATION TO BOOST CHOPPER All these methods are applied to model a boost chopper in two different configurations: the interconnections are routed on two or four copper layers. The device is composed by the mechanical part and two of the three phases of the industrial variable speed drive presented in [START_REF] Ardon | EMC Modeling of an Industrial Variable Speed Drive with an Adapted PEEC Method[END_REF].

A view of the capacitive and inductive meshes is given in Fig. 2. The extraction of inductive and capacitive equivalent parasitic elements is done by the two FMM and GMRES(m) algorithms. 35 and 40 capacitive regions are defined for the two prototypes and one parasitic capacitance is automatically connected between each pair of regions; the connection points depend on the distance between each region. Thus, a complete equivalent RLMC circuit is built and an equivalent impedance representation of it could possibly be exported to a circuit simulator.

In our study, the impedance spectrum of the two prototypes is firstly analyzed with the expedient of replacing all the electrical components (diode, transistor, inductances, etc.) by a short circuit, in order to focus on the parasitic RLMC behavior. This analysis points the different capacitive behavior of the routings, in fact the first frequency resonance of the two and four-layer structures appears at 58 MHz and 36 MHz, respectively. Time-domain simulations of the whole model in a circuit solver and measurement comparisons have then validated the electrical functioning of the two structures and consequently our model. The influence of interconnections during the switching can be pointed out. EMC performances can be easily investigated. For example the common mode voltage at the terminal of the LSIN resistance is plotted for the two prototypes in the figure 4. The prototype composed of four layers produces less common mode voltage than the other structure. In this last analysis, the minimum time step is 0.1 ns. One period of 65.5 µs is simulated in 4 minutes in the circuit solver Portunus [START_REF][END_REF].

In the extended paper, more details on these simulations and comparisons with measurements will be given.

IV. CONCLUSIONS

In this paper, a novel version of a dedicated PEEC method of industrial power interconnections is presented. Like for the capacitive modeling, the use of FMM and GMRES(m) algorithms for the inductive part makes possible to deal with larger structures with few memory consumption. Also the parallelization of most of algorithms further improves the time process.

Thus it is possible to quickly obtain small interconnection models of industrial complexity which include the main magnetic and electrical couplings. To finish, EMC performances of a complex device is evaluated in a circuit solver thanks to this approach.

Fig. 1 .Fig. 2 .

 12 Fig. 1. Average performances of all parallelized algorithms

Fig. 3 .

 3 Fig. 3. Impedance spectrum of the two prototypes (1-100MHz)

Fig. 4 .

 4 Fig. 4. Common mode voltage during the transistor switching


Tung Le Duc

Gérard Meunier

Olivier Chadebec

Jean-Michel A Guichon

New

A New Integral Formulation for Eddy Currents Computation in Thin Conductive Shells

d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

I. INTRODUCTION

In transient analyses of time-periodic electromagnetic field problems, several numerical methods to attain the steady state solutions efficiently have been proposed. For instance, the time-periodic finite-element method (TPFEM) [START_REF] Hara | Time-periodic finite element method for nonlinear diffusion equations[END_REF][2] and the harmonic balance method [START_REF] Yamada | Harmonic Balance Finite Element Method Applied to Nonlinear AC Magnetic Analysis[END_REF] can obtain steady state solutions directly. However, it requires huge computational costs because of the large coefficient matrix. On the other hand, although the time-periodic explicit error correction (TP-EEC) method [START_REF] Takahashi | Convergence Acceleration of Time-Periodic Electromagnetic Field Analysis by Singularity Decomposition-Explicit Error Correction Method[END_REF] can accelerate the convergence to a steady state drastically, their performance has not been discussed deeply from the theoretical standpoints. In addition, the shooting method [START_REF] Li | Numerically Efficient Steady-State Finite Element Analysis of Magnetically Saturated Electromechanical Devices[END_REF], which was originally developed for the electronic circuit analysis, appears similar to the TP-EEC method in the basis of error correction. Therefore, it is indispensable to investigate the advantages and disadvantages of the above methods and clarify their theoretical relationship.

In this paper, we first introduce the TP-EEC method taking account of both the dc and linearly-varying error components. Then, we compare the TP-EEC methods with the time differential correction (TDC) method [START_REF]Fast Analysis Method of Time-Periodic Nonlinear Fields[END_REF], which extracts fundamental harmonic components of steady state solutions by making use of the differential of transient solutions, from the standpoints of the computational costs and the scope of application. Numerical results that clarify the features of the above methods are also presented.

II. FAST STEADY-STATE ANALYSIS METHODS

A. TP-EEC Method

A nonlinear system of equations in the A-φ formulation in a quasi-static field is given by

f x x = ∂ ∂ + t C S ) ( , ( 1
)
where x is the unknown vector, and f is the right-hand-side vector. The number of unknowns is m. S(x) is generally nonlinear with respect to x because of nonlinear magnetic properties and C is constant. One or half period is divided into n time steps. In this paper, we treat electromagnetic field problems which satisfy x i = ±x i+n and f i = ±f i+n in the steady state. Here, the subscripts indicate the time step, and the upper and lower signs of ± correspond to the ordinary and half time-periodic conditions.

The key to the TP-EEC method is choosing the auxiliary matrix B [START_REF] Mifune | Convergence Acceleration of Iterative Solvers for the Finite Element Analysis Using the Implicit and Explicit Error Correction Methods[END_REF] to successfully extract poorly converged error components which correspond to the large time constants of an analyzed system. When we consider dc components as poorly-converged error, B which can extract dc error components is given by

[ ] T I I I B   = , (2)
where I is an m by m identity matrix and B is an nm by m matrix. By applying the SD-EEC method [START_REF] Mifune | Convergence Acceleration of Iterative Solvers for the Finite Element Analysis Using the Implicit and Explicit Error Correction Methods[END_REF]- [START_REF] Kameari | Improvement of ICCG Convergence for Thin Elements in Magnetic Field Analysis[END_REF] to the TPFEM with B in (2) and using the θ method for a time integration scheme, we obtain a linear system of auxiliary equations as follows [START_REF] Takahashi | Convergence Acceleration of Time-Periodic Electromagnetic Field Analysis by Singularity Decomposition-Explicit Error Correction Method[END_REF]:

( ) ( ) ( ) 0 0 0 1 1 1 r x x p = ± - =       +  = n n n i i C C C S  , ( 3)
) ( , ) 1 ( , ~i i i i i i S S t C S C t C S T x x ∂ ∂ = Δ + - - = Δ + = θ θ , (4)
( ) ( ) ( )

i i i t C S C x x x Δ + - - = θ 1 ~, i i i S C T = - ~ (5)
where p 0 indicates the extracted dc error component. The upper and lower signs of  and ± in (3) correspond to the ordinary and half time-periodic problems, respectively. When we take into account both dc and linearly-varying error components, B can be written as

              + - = I I h I n I I I B i     2 1 , ) 1 ( 2 n i n n i h i ≤ ≤ - = .(6)
Based on B in (6), the auxiliary system which can extract both dc and linearly-varying error components is given by ( ) ( )

        =                                             + +       -                   + +       -       +       +     = = = = 0 1 0 1 0 1 1 2 1 1 1 1 2 1 2 2 1 2 1 1 1 1 r r p p h C n h C n h S h C n h C n S h C S h C S n n i i i i i n n i i i i n n i i i n n i i     , ( 7
)
where p 1 indicates the linearly-changed error component. Although the error correction based on [START_REF] Mifune | Convergence Acceleration of Iterative Solvers for the Finite Element Analysis Using the Implicit and Explicit Error Correction Methods[END_REF]

has better

Comparison between Fast Steady-State Analysis Methods for Time-Periodic Nonlinear Magnetic Field Problems

Yasuhito Takahashi1 , Tadashi Tokumasu2 , Masafumi Fujita 2 , Shinji Wakao3 , Koji Fujiwara 1 , and Yoshiyuki Ishihara 1 performance than that based on (3), the coefficient matrix in ( 7) is nonsymmetric and therefore the double memory usage and a nonsymmetric iterative solver are required. The procedure of the TP-EEC method is as follows: (i) Perform a transient analysis starting with x 0 for one or a half period. (ii) Obtain the correction vectors by solving (3) or [START_REF] Mifune | Convergence Acceleration of Iterative Solvers for the Finite Element Analysis Using the Implicit and Explicit Error Correction Methods[END_REF]. (iii) Update x n by x n ← x n + p 0 or x n ← x n + p 0 + p 1 and substitute into x 0 . (iv) If the iteration does not converge, go to (i).

B. Time Differential Correction Method

First, we can remove the large high-order harmonic components in the steady-state solutions by integrating transient solutions as follows:

( ) ( ) φ θ φ φ θ θ φ θ φ θ - = ′ ′ =  - x x x sin 2 1 2 1 d , (8)
where θ =ω t and 2φ is the integral interval corresponding to the period of the high-order harmonic component. Then, we can eliminate exponentially-decreased error components corresponding to large time constants and extract fundamental harmonic components of steady state solutions by differentiating <x 1 > as ( )

2 old new sin θ φ φ φ θ d d x x - = - . (9)
When the steady state solutions do not include highorder harmonic components, averaging procedure using ( 8) is unnecessary. The TDC method has the advantage of low computational cost compared with the TP-EEC method which needs solving linear system of equations to extract error components. However, the TDC method is not suitable for the problems which include many high-order harmonic components in the steady state solutions such as rectangular wave form because it focuses on only the fundamental harmonic component. III. NUMERICAL EXAMPLES Fig. 1 shows the analyzed model. The number of elements and unknowns are 32,000 and 100,880, respectively. One period is divided into 40 time steps. All the computations were performed on Intel Core i7-975 Extreme Edition/3.33GHz,12.0 GB RAM. The relative errors of eddy-current loss W e with respect to those obtained from the TPFEM is shown in Fig. 2. Table I shows the number of time steps and the calculation time required for obtaining steady state solutions for a half period. We judge the steady state solutions are obtained when all the relative errors of W e in a half period are less than 1 %. Because this problem does not include large high-order harmonic components in the steady state solutions, the best performance is obtained from the TDC method. Although the TP-EEC method based on (7) needs large computational costs compared with that based on (3), it has better performance in convergence acceleration.

The error analysis of the TP-EEC methods based on (3) and [START_REF] Mifune | Convergence Acceleration of Iterative Solvers for the Finite Element Analysis Using the Implicit and Explicit Error Correction Methods[END_REF], the relationship between the TP-EEC method and the shooting method, and the advantage and disadvantage of the various fast steady-state analysis methods will be discussed in the full paper.

Fig. 1 .Fig. 2 .

 12 Fig. 1. Analyzed model (1/8 part of the whole model).

TABLE I

 I

	COMPARISON OF FAST STEADY-STATE ANALYSIS METHODS
		TP-EEC based on (3)	TP-EEC based on (7)	TDC
	Time steps	63	44	38
	CPU time [s]	170.3	140.2	108.2

Doshisha University, 1-3 Tatara Miyakodani, Kyotanabe, Kyoto 610-0321 Japan

Toshiba Corporation Power Systems Company, 2-4 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045 Japan

Waseda University, 3-4-1 Ohkubo, Shinjuku-ku, Tokyo 169-8555 Japan ytakahashi@mail.doshisha.ac.jp


Adrien Maglo
email: adrien.maglo@cea.fr

Quoc-Cuong Pham
email: quoc-cuong.pham@cea.fr

KaliCalib: A Framework for Basketball Court Registration

Keywords: Computing methodologies → Scene understanding sports-field registration, neural networks, computer vision

Figure 1: Baskeball court registration examples from the MMSPorts 2022 camera calibration challenge genearted by KaliCalib.

INTRODUCTION

The usage of data in team sport coaching is a growing trend. Player and ball positions are keys to the coaches for performance analysing, player scouting, train load monitoring or opponent analysis. Moreover, this data has also usage for entertainment. Indeed, virtual or augmented reality systems can enhance the viewing experience of a game by enabling replays with new point of views.

GPS tracking systems have been used for many years in outdoor sports. They are handy to use, even when a team is away his stadium. Yet, the coach can only gather data from his team and not the opponents. For indoors sports, tracking system exists but they require the arena to be specifically equipped. The only readily available sources to determine the player and ball position are often broadcast videos. Thus, automatic player and ball tracking in team sports has been a growing research interest in the past years.

To compute the position of elements in the court space from a video, the location of the court should be known in each frame when the camera moves. Sports-field registration aims at estimating the homography between the 3D position of a sport-field and the 2D position in the image space.

Sports-field registration can be a difficult task. Sport fields are often composed of large homogeneous areas. The only available common distinctive features between them are the rule lines and their intersections. During the game, some of these features can be occluded by the players. When the camera moves quickly to follow the action, the field lines can also be blurry, which hinders their detection. In some sports, like basketball, the color design of the courts can vary a lot between arenas. Some logos and advertisements can be displayed on the court ground, thus changing their appearance.

For the MMSports 2022 workshop, a camera calibration challenge for basketball is organized [START_REF] Van Zandycke | DeepSportradar-v1: Computer Vision Dataset for Sports Understanding With High Quality Annotations[END_REF]. The aim is to retrieve the camera calibration parameters from single images of basketball games. As, in the challenge, the calibration error is estimated only on the ground plane, we decided to estimate the calibration parameters with only points from this plane. This transforms the full calibration problem to a homography estimation.

We therefore propose KaliCalib, a new sports-field registration method based on the estimation by an encoder-decoder network of the positions of keypoints sampled with perspective-aware constraints. We also added the regression of the basket positions so as to help court localization. As the challenge training dataset was relatively small, we resorted to heavy data augmentation methods in order to generalize as much as possible to the test and challenge datasets.

In the first section of this paper, we review previous sportsfield registration methods that process single images and available public sports-field registration datasets. In the second section, we describe the method and our contributions for the participation to the challenge. Finally, in the last section we perform ablative studies and describe the results we obtained on the test and challenge sets.

RELATED WORK 2.1 Sports-field registration methods

Some previous work focused on the sports-field registration for video with initial annotations [START_REF] Gupta | Using line and ellipse features for rectification of broadcast hockey video[END_REF][START_REF] Hayet | Robust incremental rectification of sports video sequences[END_REF][START_REF] Okuma | Automatic rectification of long image sequences[END_REF] or without [START_REF] Wen | Court reconstruction for camera calibration in broadcast basketball videos[END_REF]. In this section, we focus on fully automatic sports-field registration for single images as it is the target task of the MMSports 2022 camera calibration challenge [START_REF] Van Zandycke | DeepSportradar-v1: Computer Vision Dataset for Sports Understanding With High Quality Annotations[END_REF].

The approach described in [START_REF] Farin | Robust camera calibration for sport videos using court models[END_REF] uses a Hough transform to extract tennis court lines and then computes the homography with the line intersection points. Farin et al. [START_REF] Farin | Fast camera calibration for the analysis of sport sequences[END_REF] resorted to a RANSAC-like algorithm to detect the court lines. The method of Yao et al. [START_REF] Yao | Fast camera self-calibration for synthesizing free viewpoint soccer video[END_REF] detects intersections between soccer field lines and computes a feature vector to associate them to template points. Homography candidates are generated and then evaluated using a back projection to the frame space. After having detected the soccer field lines and circles, the system of Cuevas et al. [START_REF] Cuevas | Automatic soccer field of play registration[END_REF] classifies them with a probabilistic decision tree. The homography is estimated with the intersection points between the classified elements.

Most of the recent approaches relies on a deep convolutional neural network to estimate the homography. Homayounfar et al. [START_REF] Homayounfar | Sports field localization via deep structured models[END_REF] uses a segmentation network to determine the play field surfaces, lines and circles. The matching between the images features and the template is formulated as a Markov random field solved by a branch and bound algorithm. Sharna et al. [START_REF] Rahul | Automated top view registration of broadcast football videos[END_REF] matches the soccer field lines detected with pix2pix [START_REF] Isola | Image-toimage translation with conditional adversarial networks[END_REF] and a pre-built dictionary of synthetic edge maps. The homographies corresponding to the matched maps are then later refined with a Markov random field optimization. In the approach of Sha et al. [START_REF] Sha | End-to-end camera calibration for broadcast videos[END_REF], the template matching is performed on segmented maps obtained with a U-Net network. Input image and template segmentations are transformed to features by a siamese network and compared with a 𝐿 2 distance. The template homography is then refined by a spatial transformer network. Ciopa et al. [START_REF] Cioppa | Camera calibration and player localization in soccernet-v2 and investigation of their representations for action spotting[END_REF] train the same model with a teacher-student approach, the teacher being a commercial tool. Chen and Little [START_REF] Chen | Sports camera calibration via synthetic data[END_REF] use two GAN models : the first one to segment the soccer play field and the second one to extract the field lines. Then a siamese network matches the detected line features with an entry from a feature-pose database. The final homography is generated by refining the mapping of the detected and retrieved edges images thanks to a Lucas-Kanade algorithm [START_REF] Baker | Lucas-kanade 20 years on: A unifying framework[END_REF].

Jiang et al. [START_REF] Jiang | Optimizing through learned errors for accurate sports field registration[END_REF] homography estimation framework is based on two models. The first network estimates the homography by regressing the real-world positions of four control points in the frame space. The second network estimates the re-projection error between the estimated field template and the real image. This allows to iteratively refine the initial homography estimation by minimizing this error. Fani et al. [START_REF] Fani | Localization of Ice-Rink for Broadcast Hockey Videos[END_REF] also regresses four control point for hockey ice-rink registration.

The framework of Citaro et al. [START_REF] Citraro | Real-time camera pose estimation for sports fields[END_REF] computes the homography by estimating the position of court specific keypoints with a U-Net network. Instead of using keypoint located on court corners, Nie et al. [START_REF] Nie | A robust and efficient framework for sports-field registration[END_REF] sampled them on a regular grid. They also use a RANSAC algorithm to estimate the homography. The model is trained with a pixel classification approach. Chu et al. [START_REF] Chu | Sports Field Registration via Keypoints-Aware Label Condition[END_REF] recently improved the keypoint detection by using dynamic filters.

KaliCalib is built upon the approach of Nie et al. [START_REF] Nie | A robust and efficient framework for sports-field registration[END_REF] without the homography refinement. We however resort to heavy data augmentation techniques, perspective aware keypoint sampling and basket localization to better fit the data of the MMSports 2022 camera calibration challenge.

Public datasets

Sports-field registration methods based on convolutional neural network need a significant amount of annotated images to be trained. Many authors resorted to private datasets to train and test their method. Chen and Little [START_REF] Chen | Sports camera calibration via synthetic data[END_REF] however publicly released their soccer WorldCup dataset. Composed of 395 images, it became the reference to compare recent sports-field registration approaches. Nevertheless, this small number of images can introduce biases in the training and evaluation of the appraoches. Chu et al. [START_REF] Chu | Sports Field Registration via Keypoints-Aware Label Condition[END_REF] therefore recently publicly released their TS-WorldCup datasets composed of 3812 soccer images. For its second edition, the Soccer-Net challenge [START_REF] Deliege | Soccernet-v2: A dataset and benchmarks for holistic understanding of broadcast soccer videos[END_REF] also publicly released a soccer-field registration dataset with 20028 annotated frames.

There are therefore currently a significant offer of public datasets to train and evaluate soccer-field registration systems. The situation is however not so satisfying for other sports. For the MMSports 2022 camera calibration challenge, the organizers released the first public dataset for basketball composed of 728 images.

PROPOSED METHOD 3.1 Overview

KaliCalib estimates the position in the frame space of keypoints sampled on the field template thanks to an encoder-decoder convolutional neural network [START_REF] Nie | A robust and efficient framework for sports-field registration[END_REF]. At inference time, this model generates heatmaps for each keypoint and the background. Keypoint positions are retrieved at the center of mass of the heatmaps. Since some of the positions may be inaccurate, a RANSAC algorithm [START_REF] Martin | Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography[END_REF] with DLT [START_REF] Hartley | Multiple view geometry in computer vision[END_REF] estimates a robust homography between the court space and the image space by excluding outliers. This process is summarized on figure 2.

Network design

KaliCalib's model generates heatmaps for each of the keypoints at a resolution of 1/4 of the input resolution. K different keypoints are sampled from the court template but the network also outputs a heatmap for the background that supervises the areas that do not contain any keypoint.

Our model has an encoder-decoder architecture similar to U-Net [START_REF] Ronneberger | U-net: Convolutional networks for biomedical image segmentation[END_REF]. The encoder is based on a ResNet-18 network [START_REF] He | Deep residual learning for image recognition[END_REF]. Dilated convolutions [START_REF] Chen | Rethinking atrous convolution for semantic image segmentation[END_REF] with a factor two and non-local blocks [START_REF] Wang | Non-local neural networks[END_REF] are used in the last two ResNet blocks. Nie et al. [START_REF] Nie | A robust and efficient framework for sports-field registration[END_REF] showed that they help to increase the receptive fields of local features. This improves keypoint localization in the case, for example, of occlusions or blur. The decoder is composed of four deconvolution layers followed by a Relu activation. We also add skip connections between the encoder and the decoder to recover fine-grained details in the heatmaps. The total number of parameters of our model is 14.5 millions.

Training

KaliCalib's network generates a softmax score for each pixel of the heatmaps that quantifies how much it corresponds to one keypoint or the background. It is trained with a cross-entropy loss:

𝐿 = - ∑ 𝑥 ∑ 𝑘 𝛼 𝑘 × 𝑝 𝑥,𝑘 × 𝑙𝑜𝑔(𝑝 𝑥,𝑘 )
where 𝑥 is a pixel position at the heapmap resolution, 𝑘 is a class (one of the keypoint or the background), 𝛼 𝑘 is a constant weight for the class 𝑘 and 𝑝 𝑥,𝑘 is the softmax score of the heatmap pixel at position 𝑥 for the class 𝑘.

Instead of using single activated pixel for each keypoint on the ground truth heatmaps, we use a disk with a radius of 10 pixels to increase the number of pixels to positively regress. Since the background class is still more represented than the keypoint classes, we set 𝛼 𝑘 = 1000 for the keypoint classes while 𝛼 𝑘 = 1 for the background class.

Localizing the baskets

The challenge input data provides the full camera calibrations parameters. As the dimensions of a FIBA basketball court are normalized, it is possible to retrieve the positions in the frame of the baskets. The baskets have very distinctive features that ease their localization. Even if our approach estimates a homography between the court and the frame space, we train our model to generate two additional heatmaps for the baskets. These points cannot be used to estimate the homography but this supplementary task helps the network to localize correctly the field keypoints.

Perspective aware keypoint sampling

Nie et al. [START_REF] Nie | A robust and efficient framework for sports-field registration[END_REF] regularly sample the keypoints in the court template. With the effect of the perspective, the field keypoints far from the camera are more concentrated than the keypoints near the camera. Having a bigger keypoint concentration in the area far from the camera means that the RANSAC will more likely estimate the homography with keypoints from this area despite less pixels were available to the network to estimate the keypoint heatmaps.

We therefore proposed a method to sample more uniformly the keypoints in the frame space, in the court width between the camera side and the opposite side. The differences between the two sampling method is shown on figure 3. We model 𝑤 𝑖 the 𝑖 𝑡ℎ distance between the keypoints in the court width axis and starting from the camera side by an arithmetic progression

𝑤 𝑖 = 𝑤 0 + 𝑖 × 𝑟
with 𝑤 0 the distance in the court width axis between the first two keypoints nearest to the camera and 𝑟 , the common difference.

Considering the sum of an arithmetic progression, we therefore have:

𝑊 = (𝑁 -1) 2 × 𝑤 0 + (𝑁 -2) × 𝑟 2 for 𝑁 ≥ 2
with 𝑊 the real-world width of the court and 𝑁 the total number of keypoints on the court width axis. We calculate 𝑟 by setting a value to 𝑤 0 with the following formula:

𝑟 = 2 𝑛 -2 ( 𝑊 𝑛 -1 -𝑤 0 )
We experimentally found that 1.75m was a good value of 𝑤 0 for the dataset.

Data augmentation

To increase variety in training images, we use data augmentation techniques. As in the training and test datasets, the images have been taken from the center of the court, it is possible to horizontally flip the images and the ground truth registration so the side of one team becomes the side of the other. Random image flips virtually increase the number of setups for each side. Contrary to other sports such as soccer, the color appearance of basketball courts can be very different between arenas. In order to make our network more robust to the court color design, we resorted to heavy brightness, contrast and color jittering.

EXPERIMENTS 4.1 Dataset

The DeepSportRadar Basketball Instants Dataset used in the challenge is composed of 480 images for the training, 164 for the validation, 84 for the testing and 84 for the challenge. All the dataset images, except the challenge ones, come by pair, one for each side of the court. Random crops are extracted from both views to dynamically generate the train, validation and test datasets.

It is quite challenging to learn robust basketball field registration with this dataset. The training set does not contain a wide variety of arenas. It is therefore hard to generalize to various court color designs. Besides, it is difficult to estimate the homography in some test and challenge images because only a small part of the court is visible or they contains lines from other courts as depicted on figure 4.

Implementation details

Our network was implemented with the Pytorch framework. It takes as input frames at the 960 × 540 resolution. We train it during 500 epochs with the AdamW optimizer, a batch size of 2 images and an initial learning rate of 10 -4 . After 2/3 of the epochs, the learning rate is divided by 10. The original ResNet-18 encoder layers that have not been modified [START_REF] He | Deep residual learning for image recognition[END_REF] have been pre-trained on ImageNet [START_REF] Russakovsky | Imagenet large scale visual recognition challenge[END_REF]. The non-local blocks of the encoder are initialized according to the Section 4.1 of the original paper [START_REF] Wang | Non-local neural networks[END_REF]. The decoder layers are initialized with default Pytorch uniform distributions. We normalize the input images with the ImageNet mean and standard deviation values. The size of a FIBA basket court is 28m by 15m. The number of court keypoints 𝐾 is set to 91. The RANSAC re-projection threshold is set to 35 pixels.

Regarding data augmentations, we use a random image flip probability of 0.5. For the color jittering, we use the torchvision 𝐶𝑜𝑙𝑜𝑟 𝐽𝑖𝑡𝑡𝑒𝑟 transform with a brightness parameter of 0.7, a contrast parameter of 0.5, a saturation parameter of 0.5 and a hue parameter of 0.5.

In the case KaliCalib outputs a degenerated estimation or no homography, the plausible average homography of the training set is returned. We detect degenerated homographies by re-projecting the frame points [240, 270] and [720, 270] in the court space and checking that their distance is below a threshold of 1800 cm. The camera calibration parameters are computed with the estimated homography thanks to the OpenCV function 𝑐𝑎𝑙𝑖𝑏𝑟𝑎𝑡𝑒𝐶𝑎𝑚𝑒𝑟𝑎 [START_REF] Bouguet | Camera calibration toolbox for matlab[END_REF][START_REF] Zhang | A flexible new technique for camera calibration[END_REF].

Metric

To evaluate the performance of our framework, we use the mean square error metric (MSE) as proposed by the challenge organizer. It is computed by re-projecting on the court space six frame points (left, center and right at the middle on bottom part of the frame). The frame error is defined as the square root of the mean squared distance been the six points projected with the ground truth calibration and the estimated calibration.

Results

A baseline approach was proposed by the challenge organizers. It is based on two models: the first one segments in the frame the 20 different court lines and the second one finds the line intersections and matches them with court points. Our lowest MSE values for all the frames were respectively on the test and challenge sets 126.61 cm and 140.14 cm. For the test set, 76.5% of the MSE measured on each image are inferior to one meter. The MSE for all the frames is divided by 4.7 compared to the baseline and by 2.1 compared to our implementation of Nie et al's method without the homography refinement. KaliCalib could not generate a homography for 2 images of the test set. An image is processed in about 9 ms on a system with an Intel Xeon Silver 4110 CPU and a NVIDIA GeForce GTX 1080 Ti GPU.

We performed an ablation study to measure the improvement of our contributions. The results, presented on table 1 shows the positive impact of our contributions. The color jittering reduces the error by about 18%, random image flips by about 30%, the perspective aware sampling of the keypoints by about 30% too and the basket localization by about 3%.

CONCLUSION

We presented in this paper KaliCalib, a new sports-field registration framework we developed to participate to the MMSports 2022 camera calibration challenge. On the challenge test set, KaliCalib divides the mean squared re-projection error by 4.7 compared to the baseline. As future work, we would like to test this approach on other datasets and study new network architectures to better capture the field features.

Figure 2 :

 2 Figure 2: Overview of KaliCalib, our sports-field registration framework during inference. An encoder-decoder network generates keypoint and background heatmaps to retrieve the keypoint positions in the image space. A RANSAC algorithm then estimates the homography between the 3D positions of the court and the 2D positions in the frame.

Figure 3

 3 Figure 3: a. Uniform grid sampling of the keypoints. b. Perspective aware grid sampling of the keypoints. The points nearest to the camera are more spread out than the point farthest to the camera in order to compensate for the too large distance variations in the image generated by the perspective effect. c. Input image. d. Keypoints ground truth obtained with a uniform samping. e. Keypoints ground truth obtained with the perspective aware sampling.

Figure 4 :

 4 Figure 4: Hard examples from the MMSport 2022 camera calibration challenge test set (a. and b) and the challenge set (c and d). The visible area of the court is sometimes small in the test set. Multi-sports courts or arena with different configuration in the challenge set have many lines that can hinders the basketball court localization.

Table 1 :

 1 Results on the test dataset of the MMSports 2022 camera calibration challenge. The result of Nie et al.'s method[START_REF] Nie | A robust and efficient framework for sports-field registration[END_REF] was obtained with our implementation that does not contain the homography refinement.

	Color	Random	P.A.	Basket MSE
	jittering	flip	sampling local.	(cm)
	x	x	x	x	126.61
		x	x	x	154.93
	x		x	x	181.09
	x	x		x	181.25
	x	x	x		130.68
		Nie et al. [21] -keypoints only 259.90
			Challenge baseline 592.48

ACKNOWLEDGMENTS

This work benefited from a government grant managed by the French National Research Agency under the future investment program (ANR-19-STHP-0006) and the FactoryIA supercomputer financially supported by the Ile-de-France Regional Council.


Eddy Bajic
email: bajic@cran.u-nancy.fr

Frédéric Chaxel

TOWARDS A HOLON-PRODUCT ORIENTED MANAGEMENT

Keywords: Information technology, Process control, Data models, Holonic manufacturing, STEP, EXPRESS, Product Data Management

This paper describes research currently being carried out in the field of holonic Manufacturing Systems as a step towards holonic concept partial validation. The originality of the presented work deals with the idea that process management may be achieved not only by machines, operators, but also by part and product itself, carrying and managing its own information. Presented research work deals with STEP methodology and formal models to provide a product-holon behavior validation case for manufacturing control, based on information management. This paper demonstrates the feasibility of high level information structuration, based on product data definition, with advanced management technics on attached escort memories. Distribution of the information system onto each individual product requires tools and methods to manage and provide access to product related information for resources, automated systems and human operators. Automatic identification technology using electronic data carriers, Radio Frequency transponder, can provide such functionality and act as product information vectors to provide a generic support for holon-product implementation.

INTRODUCTION

Since the last decade, tremendous changes have occurred in the field of manufacturing systems considering production control and organization. To manage product diversity and rapid changes in production demands, research effort of the eighties have been focused on manufacturing systems architectures satisfying productivity, flexibility, openness characteristics for integration aspect and cost reduction, widely based on automation increase [START_REF] Dilts | Whorms H.H. The evolution of control Architecture for Automated Manufacturing systems[END_REF]. Originally represented by the CIM concept (Computer Integrated Manufacturing), new approaches have emerged to remedy the limits hit by such hierarchically and centrally controlled organization suffering from rigidity to fast production changes and adaptations.

Then, priority have been put on decentralization and distribution of decision-making activities and information flow with an expected downsizing of manufacturing systems. Fully distributed structures were proposed [START_REF] Duffie | Non-hierarchical Control of Manufacturing Systems[END_REF] involving concurrent mechanism for job decision and coordination support, based on network communication procedures, contract nets. In this heterarchical architectures were promoted to provide manufacturing systems with decentralization, modularity, robustness and cooperative functionnalities.

Beyond that approach characterized by system-based functionnalities, strong demands appears for new formalizations of manufacturing systems to break off from conventional system organization to satisfy autonomy and individuality along with cooperation capabilities amongst all components of a manufacturing system. Emphasis was put on coordination, self-organization, hyper-flexibility, adaptation and part-orientation.

NEXT GENERATION OF MANUFACTURING SYSTEMS

Several new paradigms for manufacturing system description were formulated drawing inspiration from the real world mainly over the two aspects of social organization of human societies and natural rules and environment.

Biological metaphor have been proposed by [START_REF] Okino | Bionic manufacturing system[END_REF] to fit autonomous distributed manufacturing system description, so called Bionic Manufacturing System (BMS) in CAM-I/Japan. BMS concept lays on a system theory involving selforganization rules over distributed components with quasi-life functions copying genetic behavior, allowing freely adaptive and flexible connections in response to changes of system conditions. [START_REF] Ueda | Biological-oriented paragidgm for artifactual systems[END_REF] specified a model of BMS as a pseudo ecosystem built around works and manufacturing cells thus mimicking biological organisms carrying DNA (Deshoxyribo Nucleic Acid) gene type and BN (Brain and Neuron) type behavior and information. In a Bionic world environment, works are considered as organisms that grow up to become or generate products, and manufacturing cells are other type of organisms that process works. DNA type information is supporting inherited information and transformation as "growing up" objectives of products, while BN type information governs works activities. In this, a manufacturing system control behavior should be described by evolution strategies with mathematical model based expressions.

A social organization metaphor has been derived from the Hungarian journalist and philosopher Arthur [START_REF] Koestler | The ghost in the machine[END_REF] proposition, specified early in 1971 in the prophetic book "The ghost in the machine", as the holonic concept. Based on the observation of selfregulation capabilities of social organization, a social holonic behavior involves very few characteristics supported by elementary entities named Holon, acting and cooperating to assume living of complex and ruled organization named Holarchy. Koestler adds that holonic argument can be applied to biological, social or cognitive hierarchy which manifests rulegoverned behavior and structural consistency. The word Holon is build over the Greek root "Holos" meaning "Whole" completed by termination suffix "on" for "Particle" as encountered in the word neutron, to represent behavior properties of "a whole by itself and a part within other wholes".

The holonic metaphor have been applied to manufacturing system organization by the International joint program IMS (Intelligent Manufacturing System) with the thematic research consortium HMS (Holonic Manufacturing System) as a promising response to provide production systems with robustness and adaptability to condition changes and disturbances, modularity and flexibility capacities. Underlying principles of holonic application to manufacturing systems have been defined as follows by the HMS consortium [START_REF] Van Brussel | Architectural and system design issues in holonic manufacturing systems[END_REF] :

Holon : An autonomous and co-operative building block of a manufacturing system for transforming, transporting, storing information and physical object. Holarchy : An assembly of holons which act in cooperation having a specific set of objectives and common goal. Autonomy : Ability of an holon to control its own execution plan associated with its own strategy. Co-operation : Capabilities of systems entities to communicate, negotiate and execute actions plans in order to reach an objective.

The previously presented paradigms are actually drawing up the next generation manufacturing systems to come in the next decades. Scientist community research activities have to cope with formalization and resolution of the aiming capabilities of the real world to validate and prove efficiency within the production world.

The following paragraphs will focus on a validation approach of the holon concept supported by part product and resources cooperating in a manufacturing environment.

PRODUCT ORIENTED MANAGEMENT

A common characteristic is revealed in both approaches BMS and HMS, with the duality of elementary organisms performing in complex systems. An holon consists of an information processing entity allied to a physical processing entity, as also biological organisms are unified entity of information and substance. This characteristic contributes to the emergence of an autonomous behavior appearance in complex systems.

Three types of basic building blocks in an holonic manufacturing system are: resource holons, order holons also call task holons and product holons [START_REF] Van Brussel | Architectural and system design issues in holonic manufacturing systems[END_REF], co-operating to perform production (Fig 1 .)

A resource holon consists of a physical part, namely a manufacturing resource in the HMS, powered by physical capabilities. It is an abstraction for the production means such as production machines, conveyors, ... An order holon is an entity responsible for performing the work in a suitable and successfulness way. It captures all information related to a job [START_REF] Van Brussel | Architectural and system design issues in holonic manufacturing systems[END_REF].

Finally a product holon can be seen as an entity created by real or forecasted market demand. It holds all the necessary knowledge to assure the realisation of a product.

Local strategies

Information

Product holon

Resource holon Product information and knowledge is basically defined at the design level in words of functionality, shape, quality, manufacturing operations sequence, ... This knowledge is constructed step by step and structured through a life cycle product information model. This product knowledge must be shared by all the actors tools used for the product design. In this field some work has be done in order to define both standardised product model, like STEP for instance, and integrated information infrastructure like AIT Integration Platform or Esprit NEUTRABAS platform.

Physical capabilities

Inter -holon communication

At the manufacturing stage, this knowledge is conceptually duplicated to fit to each individual product or holon. One can say that product definition data set is cloned, and then follow it's own life. One of the major problem lies in the management of this information set associated to each holon. For instance [START_REF] Duffie | Non-hierarchical Control of Manufacturing Systems[END_REF] proposed some computers, connected with each others, in order to play the role of products data manager. Some other authors [START_REF] Upton | A Flexible Structure for Computer-Controlled Manufacturing Systems[END_REF] are proposing the use of electronic tags, but without any regards about integration problems.

In the following proposed approach, a product or any other holonic entity object carry its own information on a programmable tag which can be accessed in read/write mode by each user involved in its development process. Advantage of this approach is in machine capacities to focus on operations control (transport, measures, ...) and not on coherency maintenance of product related information, this point is conceptually assumed by each individual product.

A dialog can be imagined inbetween machine and product as follows : "Part 17843, where are you ?" say the machine , "I am currently at Machine 4 for a 10 minutes operation and I expect to be soon served by yourself " replies the product.

This leads to the emergence of claims contracts between customers and suppliers in a concurrent client/server formalism.

Unfortunately, electronic tags are by now not easy to integrate in an enterprise information system. It is for instance impossible to read or write structured information on these systems because they use elementary data handling protocol instead of high level and semantic information manipulation dialogue. In this field, the MONOLIN project (MObile NOdes in Logistics and Industrial Network, EP 6936) develops the basic requirements for a standard identification systems network interconnection interface allowing integration : the Escort Memory System -Application Programming Interface so called EMS-API [START_REF] Esprit | Deliverable 06 -MONOLIN Implementation Guide : Implementation Context and General Overview[END_REF]. Significant validation of a such approach can be foreseen, taking into account that ANSI X3T6 subcommitte is actually working on an Radio Frequency identification devices (RFID) interoperability standard, information structuration will follow (ANSI X3T6, 1996).

An

Holon-Product Information management approach is proposed in the following chapter as a logistic support to manufacturing system control. Holon-Product specification will assume both vertical integration (link with the design level) and the horizontal integration by supplying methodology and support tool allowing that each part carrying electronic tag, can act as a communication vector of process information system, in charge of the overall application management, and information coherency, consistency and reachability.

PRODUCT-HOLON INFORMATION MODELLING

One of the most significant approach today in product modelling is the development of ISO 10303 standards called STEP (STandard for Exchange of Product model data) which define models, database access and neutral data files format for representation and exchange of product data. The goal is to define complete models for product life-cycle in a CIM context, as well as the means for exchange of data between enterprise functions along a product lifecycle definition (from CAD to production, maintenance, ...).

EXPRESS is a formal modelling language, object oriented, that models the knowledge about information used by an information system. It provides the words, syntax and grammar needed to describe an application field. EXPRESS is designed to satisfy basic requirements as modelling of the information and processing objects; definition of the constraints rules; definition of the operations performed by objects; be readable by an human operator as also be automatically computed.

PRODUCT-DRIVEN MANUFACTURING

Product/Process Interrelationship

In the presented model, every product is acting as a real actor within its process able to :

• manage itself its characteristics like shape, design version, ... • has knowledge of its evolution through its operation sequence • store its history in the holonic process.

Figure 3 points out the concept adapted from the client/server model we chose in order to respond to these objectives. In such a case each product is the manager of its own information and gives them to the process after it received a service request : "What is the next operation to be proceed on yourself" for example.

On the basis of a client/server relationship between product and process, user requests -i.e. process requests -are sent to an interface supporting product access methods in an object-oriented interrogation form such as Object_Selector. Message (Parameters) or SystemMessage (Parameters) :

• COLOR.GETVALUE ( ); • COLOR.PUTVALUE (Red);
The data storage medium, in our case, consists of identification tags carried by products or by pallets and also an optional network database. Explanation about the choice of two possible storage medium is made in sub-paragraph 5.3.

Product Model Translation for Manufacturing Control Services

The major problem, related to this model (Fig 3 .) is the integration of tags in the information system which necessitates :

• reference conceptual models to put the information about the products themselves on tags;

• tools to implement these models and to access the information.

For the purpose of the presented work, it is assumed that a logical data model of product for manufacturing operations exists and is defined in the EXPRESS language (this model could be a STEP application protocol or a specific model).

Figure 4 shows the STEP-based methodology allowing translation of product model, described in the EXPRESS formalism (entities, attributes, functions, rules) :

• to a product-information access library supporting a direct requesting of target application on the EXPRESS schema data; • to the optional database structure definition.

The product definition within the STEP neutral file is used :

• to create the tag memory image, for the first state of a product associated to the current phase of its life cycle in the manufacturing environment; • if needed, to populate the previously created database schema. Tags are of course limited in capacity, and storing complex structures needs more memory than only storing the contents (Attributes value). Today, tags tend to have higher storage capacity (up to 128 Kbytes) but in some cases (like automotive industries) this may not be enough, so if it is relevant for the application a part of the data could be stored on other medium.

Remote Database Reachability

A network database in client/server mode seems to be the natural architecture to manage the overflow of data :

• we need to have a persistent computer storage in order to manage EXPRESS schema population and translation; • it stores and manages high volume of data; • time response is efficient for most of the applications;

• enterprises are already trained with such an architecture. The presented approach has the same philosophy as distributed database (Morris, 1992) that is, "users do not have to know where data are stored to access to them". The system performs data locating, formatting and transferring through the structure (see Fig 5 .). In fact, when we populate the schema, we specify where EXPRESS entities must be (on tags or remote database). Afterwards, the user can send a request to the system, without specifying the location of the objects he wants to access. The search for physical storage of objects is made after each user request reception, as shown in the state graph model exposed in the Fig 5.

Application Tool

A prototype Tag-STEP (see Fig 6 .), running under MS-Windows, uses a standard computer to generate a MobIle DAtabase Nodes structure. It is connected to a relational network database management system (ORACLE 7) through SQL_NET-TCP-IP-Ethernet protocols.

The EXPRESS schema is converted in a relational schema which has to be put on the ORACLE database. In this case each EXPRESS entity is translated to a relational entity -i.e. a table -and to a view in order to manipulate sub-typing. The schema is then converted as a C program to generate the Functional Application Interface as explained before. This program is able to read or write data independently on tags or on the database. To populate the schema we use a STEP neutral file provided by the product definition phases. Emerging technologies and standards for intelligent automatic identification systems are coming up with local processing capabilities, and also full object language characteristics of Express with methods and algorithms, should allow to upgrade this methodological approach for a full holonic characteristic implementation.

Expected benefits in manufacturing systems organisation and management are:

• better data distribution in the overall manufacturing system allowing real time tracking of products. Each holon-product is an element of a distributed manufacturing database allowing immediate consultation by other process actors; • simplification in the production machine structure and software capabilities. A generic interface is defined to link different kinds of users to the structure in the product coupled memory. There will be a gain in the production system modularity and modifiability, ... One of the most significant extension of this concept concerns it's application to cover the full product life cycle data management from production to after sale services and recycling. This is actually in progress in the field of a research contract between the CRAN and a French cars manufacturer, to provide a holonic approach car management all along the vehicle lifecycle [START_REF] Lonc | Design and exploitation of communicating escort memories for automotive applications[END_REF].

Basically, STEP could be the general framework used for vehicle data management and as recommended by AIT research program. Linking between different phases -i.e. different data models -will be realised through models described in EXPRESS-X, for the creation of views on a model and mapping data from one model to another.

Fig 1 .

 1 Fig 1. Resource and Product Holons structure.

Fig

  Fig 4. From EXPRESS schema to customers requests management at the shop floor.