Lisa Di-Jorio

Sandra Bringay

Céline Fiot

Anne Laurent

Maguelonne Teisseire

Sequential Patterns for Maintaining Ontologies over Time

Ontologies are known as a quality and functional model, allowing meta data representation and reasoning. However, their maintenance plays a crucial role as ontologies may be misleading if they are not up to date. Actually, this work is done manually, and raises the problem of expert subjectivity. Therefore, some works have developed maintenance tools but none has allowed a precise identification of the relations that could link concepts. In this paper, we propose a new fully generic approach combining sequential patterns extraction and equivalence classes. Our method allows to identify terms from textual documents and to define labelized association rules from sequential patterns according to relevance and neighborhood measures. Moreover, this process proposes the placement of the found elements refined by the use of equivalence classes. Results of various experiments on real data highlight the relevance of our proposal.

Introduction

Ontologies offer a generic model for knowledge representation. These special structures are widely used for capturing knowledge of a particular domain of interest, and for easing data manipulation and exchange. As this knowledge is constantly evolving, ontologies must be updated, by adding, deleting or replacing knowledge. Often, new knowledge is reported on textual documents. Ontology updating implies selection of interesting terms from various documents in a first time, and then a placement of these terms, either as new concepts, or as a new relation labels. For a human expert, regarding the quantity of data to mine, this is a difficult, time consuming and tedious work. Moreover, it differs from one expert to another one depending on their point of view.

One way to overcome expert subjectivity is the use of automatic tools. Mostly based on statistical or syntactic analysis, these tools focus on finding and adding new concepts. However, as far as we know, none of them allows detection of one important specific knowledge: the links between concepts, and the terms labeling them. These elements are the specificity of ontologies as they model semantic knowledge.

Expanding an ontology can be done through a feedback loop combining web mining and formal semantic [START_REF] Stumme | Semantic web mining: State of the art and future directions[END_REF]: data mining extracts new terms to add, and the placement is done using some semantic logic rules. With these new elements, the process is repeated as many times as possible. Most of the time, the user is involved into the loop, either to manually place a new discovered element, or to label a relation, or to validate an adding... However, applied data mining techniques often result in a large quantity of elements, making such tools inefficient. Furthermore, no tool automatically fills the entire process: that is, look for new concepts/relations and place them into the ontology at the right level of abstraction. We propose in this paper a fully automatic process to expand a given ontology, based on data mining techniques and on equivalence classes. Our contribution is twofold. On the one hand we propose a method to select new terms through sequential patterns mining and to categorize them as concepts or as relation labels. On the other hand we define an equivalence class based approach to allow the most precise term placement, and to process a large quantity of discovered data. Uncovered elements are grouped according to concepts and are added at the right place through labeled relations.

The rest of this paper is organized as follows. First, we give an overview of the problem statement, presenting current work (Section 2). Then, we detail our contribution, explaining each step of the ontology expanding process (Section 3). Finally, experiments described in Section 4 highlight the relevance of our proposal.

2 Related Work

Ontologies and Maintenance

It is a challenging issue to define precisely what is an ontology, since this term is used in many areas, from philosophy or linguistic to artificial intelligence. According to [START_REF] Gruber | A translation approach to portable ontology specifications[END_REF], an ontology can be viewed as "an explicit specification of a conceptualization". They allow both data exchange and human / machine readability. Often, ontologies describe objects of the real world as concepts, and formalize relations linking them either as hierarchical relations, or as semantical relations. Most of the time, these relations are labeled by a word or an expression. These relations stress the difference between ontologies and other structures giving a semantic description of concepts interaction.

Example 1 Let us consider the environmental area. Living beings drink water. Air and Water are specific to the Environment concept. Figure 2 illustrates part of this ontology.

All the approaches presented in this section follow two generic steps. First, documents are preprocessed, i.e. words are replaced by their lemma, that is the generic form of a word. For example, the word "is" will be replaced by "to be", allowing to consider words regardless of their declension. At this stage, lemmatized words are called terms. Among them are new potential candidates for the maintenance, such as new concepts or new relation labels. Then, enriching ontologies consist of selecting these terms as a first step. Lately, we have noticed two major tendencies for terms selection: statistical based methods, and syntactic based methods. We describe these methods in the following section.

Statistical Based Methods

Statistical methods consist of counting the number of occurrences of a given term in the corpus. The more frequent a term is (according to a measure), the more it will be considered as a candidate. In a statistical context, many measures have been proposed, mostly based on term distribution in the corpus. The easiest way is to count the number of apparitions of each term among the entire corpus [START_REF] Agirre | Enriching very large ontologies using the WWW[END_REF][START_REF] Faatz | Ontology enrichment with texts from the WWW[END_REF][START_REF] Parekh | Mining Domain Specific Texts and Glossaries to Evaluate and Enrich Domain Ontologies[END_REF]. More complex measures have also been defined. For instance, [START_REF] Xu | A domain adaptive approach to automatic acquisition of domain relevant terms and their relations with bootstrapping[END_REF][START_REF] Neshatian | Text categorization and classification in terms of multi-attribute concepts for enriching existing ontologies[END_REF] successively test mutual information, Tf.Idf, T-test or statistic distribution laws, resulting in a good terms selection. However, these measures never take the domain into account, nor terms appearing alone. To overcome the domain representation problem, [START_REF] Velardi | Using text processing techniques to automatically enrich a domain ontology[END_REF] proposes a new definition of mutual information. Whereas experiments show that representative terms are extracted, some relevant terms remain uncovered. Moreover, as these methods only select terms without any external information, it is impossible to distinguish concept terms from relation labels. So, all the extracted terms are considered as potential new concepts.

Then, discovered elements shall be placed into the ontology. Because of the quantity of extracted terms, this step cannot be manually done. To avoid a manual placement of a huge quantity of terms, [START_REF] Faatz | Ontology enrichment with texts from the WWW[END_REF] proposes to use term co-occurrence implying one or more existing concepts of the ontology. This involves knowing where a new concept should be added. However, they only bring the closest new concepts, and never add them at a precise level with a precise relation (ie, no relation label found). We thus argue that this method will not generate semantic knowledge.

Other approaches use data mining techniques, such as classification (grouping items in an already known class) or clustering (grouping items to, but in a class found during the process). [START_REF] Han | Centroid-based document classification: Analysis and experimental results[END_REF] and [START_REF] Neshatian | Text categorization and classification in terms of multi-attribute concepts for enriching existing ontologies[END_REF] bring new terms close to existing ones in the ontology thanks to a classification method. Similarly, [START_REF] Agirre | Enriching very large ontologies using the WWW[END_REF] and [START_REF] Parekh | Mining Domain Specific Texts and Glossaries to Evaluate and Enrich Domain Ontologies[END_REF] group extracted terms using a clustering method. Each cluster shows a possible relation between grouped terms. However, it is not possible to define what kind of relation it is, or to label it.

Therefore, statistical methods allow new term selection and correlation detection with existing terms, but do not place new terms directly into the ontology. Moreover, statistical methods ignore the linguistic structure of the analyzed sentences, which can give information about relations linking concerned concepts. Using syntactic methods can overcome the problem.

Syntactic Based Methods

Methods based on syntax consist of a grammatical sentence analysis, most of the time preceded by a part of speech tagging (POS) process. Syntactic methods suppose that grammatical dependencies reflect semantic dependencies [START_REF] Bendaoud | Construction et enrichissement d'une ontologie à partir d'un corpus de textes[END_REF][START_REF] Roux | An ontology enrichment method for a pragmatic information extraction system gathering data on genetic interactions[END_REF]. Thus, two syntagms are considered as concepts, and the gramatical relation linking them as a semantic relation. These considerations allow adding extracted terms as new concepts at the right place, linking them to the right existing concept.

However, these approaches suffer from the same problems as statistical ones: a huge quantity of related terms are extracted, as there exists more than one grammatical dependency into a sentence. Therefore, data mining techniques are also applied in some approaches. [START_REF] Bendaoud | Construction et enrichissement d'une ontologie à partir d'un corpus de textes[END_REF][START_REF] Maedche | Mining ontologies from text[END_REF][START_REF] Stumme | Semantic web mining: State of the art and future directions[END_REF] extract association rules from the syntactic dependencies. Association rules have been proposed by [START_REF] Srikant | Mining generalized association rules[END_REF] and allow strong correlation detection like "when the term A is employed, the term B is employed too". These correlations highlight frequent grammatical dependencies and thus are a good way to prune many insignificant dependancies. Some syntactic based work defines regular expressions in order to find terms corresponding to one and only one kind of relation. For instance [START_REF] Hearst | Automatic acquisition of hyponyms from large text corpora[END_REF] looks for hyponyms form large text corpora.

Even though syntactic based approaches automatically put new terms into the existing ontology, they do not label new relations. Once again, the extraction of a common semantic structure from a large text corpus is missed. Relation labeling has to be done by the user. Moreover, data mining techniques are always used as a second step of the generic enriching process whereas these techniques can be directly used to extract new terms. Indeed, efficient techniques have been developed, allowing among other options to restrict term selections by semantic or time constraints [START_REF] Fiot | Extended time constraints for sequence mining[END_REF][START_REF] Masseglia | Pre-processing time constraints for efficiently mining generalized sequential patterns[END_REF].

However no work uses sequential patterns in order to detect or add new elements, eventhough these structures have been proved to be efficient [START_REF] Jaillet | Sequential patterns for text categorization[END_REF] for large text databases mining. Sequential patterns lead to a finest text analysis as they store word apparition order and frequently co-occurring words. Moreover, sequential patterns keep a track of the document structure without requiring any external knowledge. We present in this paper a fully automatic approach based on sequential patterns extraction. Indeed, we propose to use them to discover new concepts and relations labels which link them to other concepts.

SPONTEX : Sequential Patterns For Ontology Expansion

Overview

In this section, we introduce SPONTEX, a new algorithm for expanding an ontology, by mean of sequential patterns. Our general process, described by Figure 1, starts from these patterns. However, taken as an ordered list of words, sequential patterns need to be transformed and refined to raise a new kind of knowledge: concepts and semantic relations. We called this labeled association rules.

Labeled association rules are generated through two steps: [START_REF] Stumme | Semantic web mining: State of the art and future directions[END_REF] words that may share semantic with an existing concepts are first selected and organized around this concept. These candidates constitute a neighbor set. In order to refine the search space, we define a new measure, called closeness. (2) Among these words are concept terms and relation labels. The generation of labeled association rules disambiguates the role of terms. At the end of the second step, the process is close to be finished: we know new concepts terms, the existing concept they can be attached to, and the label of the relation linking them. However, we have not organized our new terms around concepts so far. This is the aim of the third step. We propose to use equivalence classes. Every details and formalization are provided at section 3.6. Finally, we add mined elements to the existing ontology during step 4. In order to respect our formal ontology definition (following section), we check that we do not add a same word as a term concept and a label relation.

Sequential Patterns

Sequential patterns were originally introduced by [START_REF] Agrawal | Mining Sequential Patterns[END_REF]. They model an ordered list of itemsets usually associated to a given time period.

Let O be an object set and I be a set of items stored in a database DB. Each record E is a triplet (id-object, id-date, itemset) as illustrated by table 1

< a 2 < ... < a n such that s 1 ⊆ s a1 , s 2 ⊆ s a2 ,... s n ⊆ s an . S is then called a subsequence of S and S is a supersequence of S . For example, S = <(a)(b)> is a subsequence of S because (a) ⊆ (a) and (b) ⊆ (b c). On the other hand, <(b)(c)> isn't a subsequence of <(b c)>.
An object o supports a sequence S if and only if S is included into the data sequence of this object. The sequence support (also called frequency) F req() is defined as the number of objects of the database DB supporting S. Given a threshold minSupp, a sequence S is frequent if F req(S) ≥ minSupp. Extracting sequential patterns from a database like DB means finding all the maximal sequences (not included in others) which support F req() is at least equal to minSup. Each maximal sequence is a sequential pattern.

During this last decade, efficient algorithms for sequential patterns extraction have been proposed [START_REF] Agrawal | Mining Sequential Patterns[END_REF][START_REF] Masseglia | The PSP approach for mining sequential patterns[END_REF][START_REF] Zaki | SPADE: An efficient algorithm for mining frequent sequences[END_REF][START_REF] Di-Jorio | VPSP : extraction de motifs séquentiels dans weka[END_REF].

As sequential patterns were initially introduced to deal with market data, we need to transpose the context in order to apply extraction from a text documents database. Here, a date is represented by one or more sentences, and an item corresponds to a lemmatized word. Considering that a sentence is a unit of time, extracting the sequence <(living)(environment lake)(flood)> means that among all the documents, the word "living" frequently occurs in a sentence, followed by the co-occurrence of the words "environment" and "lake".

Formal Definition of an Ontology

We need to formally state what ontologies are, in order to properly use them during the adding process. In agreement with a common point of view, an ontology is constituted by concepts organized into a hierarchy. A concept is an object with associated terms describing their semantic. Computers infer knowledge starting from these concepts, and human understand their sense when reading the words associated to these ones. Moreover, our aim is to represent possible interactions between these concepts. This is done thanks to the definition of semantic relations. We use the following definition, initially proposed by [START_REF] Di-Jorio | Enrichissiment d'ontologie : Quand les motifs séquentiels labellisent des relations[END_REF]:

Definition 1. Let C be a concept set, T a term set, R c a relation (between concepts) set, R t a

relation (between terms) set and L a relation's label set (semantic name of a relation). An ontology O is defined by:

O = {C, T , R c , R t , L, < c , f tc , f rc } with : -< c : C × C is a partial order relation on C defining concept hierarchy, < c (c 1 , c 2 ) means that c 1 is more generic than c 2 -f tc : C → T

is the association function between a prefered term and a concept f rc : R c → C × C is an associative function between concepts

To avoid any confusion, we consider that an ontology concept is designed by one of its associated term. This term is said to be the preferred term of the concept. Talking about semantic relation amounts to use its relation label.

Example 2 Figure 2 shows a part of an ontology about atmospheric disturbances. Rectangles represent concepts, diamonds represent terms and ellipsis show relations.

The concept set C group {C1, C2, C3, C4}, the term set is T ={Atmospheric disturbance, Storm, Rainstorm, Rain, Drizzle}, and the relation set R c is formed by only one relation, which label is Lead to. "Atmospheric disturbance" is the preferred term of C 1 concept: when we talk about C 1 , we talk about all the atmospheric disturbances phenomema. f rc (Lead to) = (C 2 , C 4 ) is a relation meaning that storm leads to inundation. Concepts hierarchy < c is indicated by simple arrows and means that, for example, C1 is more specific than C2.

By keeping track of frequent words co-occurring and their order, sequential patterns are an efficient tool for data extraction. However, taken as an ordered list of words, they do not allow to directly infer semantic knowledge. We thus raise the following questions: how can we use them for an ontology updating process? How can we distinguish a word associated to a concept from a word associated to a relation? Among extracted sequential patterns are some already known terms, as they are already referenced by the ontology. We propose to refine the search space by only considering neighbors of these concepts. A neighbor of a given concept c o is a term that can be accessed by using one link (hierarchical or semantic) from c o : Definition 2. Let c o be a concept, the neighbor set V co of c o is defined as the concepts c and relations r set:

∀c ∈ V co , ∃r ⊆ R | f rc (r) = (c o , c) ∨ f rc (r) = (c, c o )∨ < c (c o , c)∨ < c (c, c o )
This notion allows the association of new terms extracted by sequential patterns with existing concepts. In the rest of this paper, a term candidate appearing in a sequential patterns is called an item.

Example 3

The neighbor set associated to the "Storm" concept is V storm ={"Rain", "Atmospheric disturbance", "drag"}, because f rc (Lead to)=("Storm", "Rain"), and < c ("Atmospheric disturbance", "Storm"). The term "Rain" represents one of the ontology's concept of the picture 2 and "Lead to" is a label of a relation of the ontology, whereas "Cause" or "Inundation" are items of the sequential pattern <(Rain)(Cause Inundation)>.

Constructing The Neighbor Set

Each sequential pattern containing a concept term may participate to the neighbor set construction. Obviously, we cannot consider that every item of such a sequence is a neighbor, as the set cardinality will quickly explode. Therefore we propose to use a measure based on sequence support in order to add an item as a neighbor of a known term. This measure, called closeness, indicates the neighborhood degree between a term and is defined as follow : Definition 3. Let S be a sequential pattern, i and c o two different items from this sequence such as c o ∈ T . The Closeness measure of the item i as a term or a relation label of the c o neighbor set is defined by :

Closeness(c o , i) = max        max( F req([(i co)]) F req([(co)]) , F req([(i co)]) F req([(i)]) ), max( F req([(i)(co)]) F req([(i)]) , F req([(i)(co)]) F req([(co)]) ), max( F req([(co)(i)]) F req([(co)]) , F req([(co)(i)]) F req([(i)]) )       
Here, word order does not infer on the neighbor set. Three configurations are possible : (1) the word frequently appears in the same sentence than the term, (2) the word frequently appears before the term and (3) the word frequently appears after the term. By keeping the best apparition proportion of a configuration order relative to only one item, we consider the influence of each item on another. Moreover, as we are only interested in the best configuration, we keep the maximum proportion rate among the tree possible configurations. Example 4 illustrates this idea:

Example 4 Table 3 shows extracted sequences from a document set.

Sequence F req [(rain inundation cause)] 0.4 [(rain inundation)(cause)] 0.3 [(rain)(inundation cause)] 0.3 [(rain)(inundation)(cause)] 0.2 [(rain cause)(inundation)] 0.5 [(rain)(cause)(inundation)] 0.3 [(inundation)(rain)(cause)] 0.5 [(cause)(rain)(inundation)] 0.3 [(inundation)(cause)(rain)] 0.3 [(inundation cause)(rain)] 0.3 Sequence F req [(inundation)(cause rain)] 0.2 [(rain inundation)] 0.5 [(rain)(inundation)] 0.5 [(inundation)(rain)] 0.6 [(rain cause)] 0.5 [(rain)(cause)] 0.6 [(cause)(rain)] 0.5 [(rain)] 1 [(inundation)] 0.7 [(cause)] 0.7 Table 3. Extracted sequences
The item "rain" already belongs to the ontology shown on the Figure 2 as a concept term. Let us compute "rain" and "inundation" closeness rate.

Closeness("rain", "inundation

") = max    max( F req([(inundationrain)]) F req([(rain)]) , F req([(inundationrain)]) F req([(inundation)]) ) max( F req([(inundation)(rain)]) F req([(rain)]) , F req([(inundation)(rain)]) F req([(inundation)]) ) max( F req([(rain)(inundation)]) F req([(rain)]) , F req([(rain)(inundation)]) F req([(inundation)]) )    (1)
= max(max( 0.5 1 , 0.5 0.7 ), max( 0.6 1 , 0.6 0.7 ), max( 0.5 1 , 0.5 0.7 )) = max(0.71, 0.86, 0.71) = 0.86

The building of the neighbor set is done through the algorithm 1, called Neighbor-Generation. Given a set of sequential patterns, an already known concepts set and a minimal closeness threshold fixed by the user, the algorithm returns the set V of all the neighbor sets V Ci of the ontology concepts. More formally: Note that building this neighbor set is only selecting new items to add to the ontology. However, we ignore if these items are concept terms or relation labels. For example, we do not know if the words "Inundations" or "Cause" selected in example 5 are new concept terms, or new relation labels. This will be determined through the extraction of labelled association rules, as it is explained in the following section.

∀V Ci ∈ V, V Ci = {(item j 1 , closeness(C i , j 1 )), ..., (item j n , closeness(C i , j n ))}

Extracting Labeled Relations

We notice that when a document address two concepts linked by a relation, the relation label is frequently employed in the same sentence than one of the two concepts. Therefore, we need to determine which item is frequently used in the same sentence than a known concept term. Sequential patterns provide this information, so we propose to exploit it with the following relationship measure: Definition 4. Let c o be a term such that V co ∈ V, i and j two items from V co such as i = j. Then, the relationship level of the item i as a relation label between c o and j and is defined by :

RL i (c o , j) = max        F req([(i j co)]) F req([(j co)]) , F req([(co)(i j)]) F req([(co)(j)]) , F req([(co i)(j)] F req([(co)(j)]) , F req([(j)(i co)]) F req([(j)(co)]) , F req([(j i)(co)] F req([(j)(co)])       
The relationship level represents the proportion of documents which employed terms c o and i in the same sentence, or terms c o and j in the same sentence. This proportion could be considered as a kind of confidence, as it represents the maximal probability that i co-occurs with c o knowing j or that i co-occurs with j knowing c o .

The relationship level is not bounded to confidence rating. As a relation between two concepts is frequently co-occuring with one of these concepts, RL permits to distinguish a word role as a concept or as a relation. If RL i (c o , j) > RL j (c o , i), this means that (i-c o ) and (i-j) co-occurs more frequently than (j-c o ) and (j-i). In that case, i is more likely to be a relationship according to our observations. This is why an item role is determined by the highter confidence, i.e, the higther RL.

Example 6 From the pattern set shown in Figure 3, two relationship levels can be computed: RL cause (rain, inundation) and RL inundation (rain, cause). RL cause (rain, inundation) represents the relationship level of "cause" as being a relation linking the concepts "rain" and "inundation"; and RL inundation (rain, cause) represents the relationship level of "inundation" as being a relation linking "rain" and "cause" concepts. Here, we only detail the RL cause (rain, inundation) calculation:

RL cause (rain, inundation) = max   

F req([(cause inundation rain)]) F req([(inundation rain)])

, F req([(rain cause)(inundation)])

F req([(rain)(inundation)])

,

F req([(rain)(cause inundation)]) F req([(rain)(inundation)

, F req([(inundation cause)(rain)])

F req([(inundation)(rain)

,

F req([(inundation)(cause rain)]) F req([(inundation)(rain)

  

= max(max( 0.4 0.5 , 0.4 0.5 ), 0.5 0.5 , 0.3 0.5 , 0.3 0.5 , 0.2 0.5 ) = max(0.8, 1, 0.6, 0.5, 0.33) = 1 In the same fashion, we find that RL inundation (rain, cause) = 0.8.

As RL inundation (rain, cause) < RL cause (rain, inundation), we can consider that "inundation" is a new concept, and "cause" is a relation linking it to the existing concept "rain".

Summarizing, at this stage, we have started from sequential patterns to select a set of potential new knowledge that can be linked to existing concepts. By building the neighbor set, we keept only interesting words. Thanks to the relationship level, we have decided if these new elements will be considered as new concept terms, or as new relation labels. We can now formelize a new kind of association rules integrating semantic knowledge, called labeled association rules: Definition 5. A labeled association rule or LAR, denoted by i r =⇒ j, define an item j implication by an item i according to the relation r.

Existence of such a rule between an item and a concept from the ontology indicates the existence into the ontology of a relation linking this concept to this item. Definition 6. The left part of a labeled association rule is the acting concept of the relation, and the right part is the receiving concept of the labeled relation.

A labeled association rule characterizes a relationship level as well as the relation direction. So, for each association of three items i, j and k with k corresponding to a concept c o of the ontology, we can determinate by the relationship level calculation if one of the others items i or j define a relation between c o and the third item.

The relation direction has been calculated during the relationship level determination. We define the implication rate of a labeled association rule. It represent the document proportion from the textual base for which having items c o and j imply having item i.: Definition 7. Let i, j, c o be a triplet such that i is the label of a labeled association rule between j and c o . The implication rate of a labeled association rule c o i =⇒ j is given by:

IR(c o i =⇒ j) = max ( F req([(co)(i j)]) F req([(co)(j)]) , F req([(co i)(j)]

F req([(co)(j)])

)

A high implication rate confirms that the relation i is a link between c o and j. Therefore, from a triplet formed by a candidate concept j, an item i and a concept c o , we compute the implication rates of the rules (j i =⇒ c o ) and (c o i =⇒ j). The rule having the best implication rate is kept while the other one is left.

In order to optimize the iteration number on sequential patterns and subsequences and consequently to reduce the execution time, we conceived Algorithm 2, named LARGeneration. This one computes through a single iteration the relationship level and the direction of the labeled association rules together.

Algorithm LARGeneration determines, from ontology concept neighborhood, the items which label relations or which are new terms.

This algorithm takes as input a prefixed tree called PSP. Proposed by [START_REF] Masseglia | The PSP approach for mining sequential patterns[END_REF], the prefixed tree is an efficient way to store sequential patterns. Each node is associated with an item. A PSP tree contains two kinds of edges: dashed edge representing the notion "and after" and plain edge representing the notion "in the same time". Leaves of a PSP are sequential patterns that can be read from the root to the leaves. This structure is output from our sequential pattern mining algorithm, and we choose to exploit it directly during the LAR mining process.

Given a set of all the neighbors and the PSP tree obtained from the sequential patterns extraction process, Algorithm 2 generate all possible labeled association rules. We iteratively compute the implication level RL j (c o , i) and RL i (c o , j) by combinating each item couples i, j of the neighbor set V co and a concept c o (lines 1-5). Once the item role is determined, we apply the implication rate calculation in order to fix the new LAR sense (line 6) and finally add it to the LAR set (line 7). At this stage, we have obtained a large quantity of new concepts and labels of relations which link them to existing concepts. It is now possible to add these new elements to the ontology. However, this means creating a concept for each selected term, and thus associating only one term by concept. We would overload the existing ontology with too many new concepts, missing benefit from concept philosophy (a concept allows for grouping equivalent words) and biasing user navigation during the validation step. Therefore, new terms need to be effectively grouped around common concepts.

Terms linked to a same concept through the same relation label share a common semantic. It is thus possible to exploit this property in order to group them by the use of equivalence classes. That way, homograph terms will be distinguished.

In the Set Theory, an equivalence class is defined on a set and through the definition of an equivalence relation. In our context, we consider the labeled association rules set E RAL , on which we define two binary relations. Definition 8. Let R (resp. S) be a binary relation on the labeled association rules set E such as:

R = {(a, b) | a, b ∈ E ∧ a.rec = b.rec ∧ a.label = b.label} S = {(a, b) | a, b ∈ E ∧ a.act = b.act ∧ a.label = b.label}
where RAL.act is the acting concept of the rule RAL, RAL.label is the rule label and RAL.rec is the rule recipient.

The relation R (resp. S) allows to select rules having the same label and the same recipient concept (resp. acting concept).

Proposition 1. Any relation R (resp. S) as defined in definition 8 is an equivalence relation, as R (resp. S) is reflexive, symmetric and transitive.

Proof. The proof is omitted as it is easy to see that all properties hold. These equivalence relations allow building equivalence classes from labeled association rules. These classes are then added as a concept into the existing ontology. =⇒ Submersion}, then we can build an equivalence class based on the label "cause" and on the concept which prefered term is "Rain":

[ cause =⇒ ] RAIN = RL
This allows us to group terms Inundation, Landslide, and Submersion under the same concept.

Expanding the Ontology

Adding new elements is the final step of our method, before validation by a human expert. We create new concepts around which are associated equivalent terms. We add then our new concepts linking them to existing ones by the mean of labeled relations.

Some classes give a different role to a same item, either as a term concept, or as a relation label. As in works using syntactic based methods, we consider that an item can only have one role into the same ontology. So, before adding a new equivalence class ce, the algorithm checks if the label relation linking ce to the ontology is not already considered as a concept term, and rejects it otherwise.

Example 8 Figure 3 shows the evolution of the ontology presented in figure 2. We notice that a new concept which prefered term is "Inundation" have been added, linked to the existing concept "Rain" using the relation label "Cause". Our method has been tested on the EMWIS ontology1 , Euro-Mediterranean System of Water Domain Information. EMWIS is an European project concentrating his efforts on developing an ontology about water domain. The major goal is to improve communication between all the water area protagonists. This ontology is formed by 1006 concepts organized around three hierarchy levels and 29 non-labeled relations.

Concepts have been grouped around 25 themes in order to ease navigation. We decided to use them during our experiments. We built for each concept term a query which have been ran on a search engine. We downloaded the first 20 retrieved documents, obtaining thus a thematic corpus.

Experiments presented here have been realized on the "Water Needs" thematic, composed by 136 concepts. This theme was chosen for its ontology representativity: it was the one grouping the highest number of concepts. Then, the textual corpus built from the Web have 2720 documents.

Preprocess and Sequential Patterns Extraction

The relevance of the extracted patterns depends on document preprocessing, which is done according to three steps: (1) content extraction, (2) lemmatization, and (3) items selection.

Content extraction erases noise in documents (advertising, images, hypertext links...) and only keeps the main page text. In our experiments, lemmatization have been realized with TreeTagger [START_REF] Schmid | Probabilistic part-of-speech tagging using decision trees[END_REF], which is able to process french and english texts. After this step, all words are in their generic form: they are now considered as terms.

In order to keep only relevant words, we used tf.idf measure [START_REF] Robertson | Relevance weighting of search terms[END_REF], allowing us to evaluate how important a word is to a document in a collection or corpus. At the end of this process, we obtain the most important terms according to tf.idf, and we keep terms already present into the ontology.

Sequential patterns have been extracted using a JAVA implementation of the algorithm VPSP [START_REF] Di-Jorio | VPSP : extraction de motifs séquentiels dans weka[END_REF], which combines prefixed tree projection of PSP [START_REF] Masseglia | The PSP approach for mining sequential patterns[END_REF] and memory projection of SPADE [START_REF] Zaki | SPADE: An efficient algorithm for mining frequent sequences[END_REF]. VPSP is a generate-prune algorithm which uses (k-1)-sequences to generate k-sequences, and after a frequency computation prunes sequences under minimal support fixed by the user. We adapted VPSP to keep in memory sequences of length 1, 2 and 3, necessary for the RL measure computation. We optimized this step in order to minimize required memory space, gaining the time which would have been used to compute again these information. For our dataset about Water Needs, we efficiently generated and stored about 14,000,000 of 3-sequences.

Results

Results obtained with various closeness clue highlight our proposal relevance. Indeed, we noticed that our method allows detection of a high number of new concepts, and proposes a correct placement of them into the existing ontology. We studied closeness clue value impact on the enrichment process, as it is a determining factor concerning the items selection. We noticed that the lower the closeness clue value, the larger the neighborhood set cardinality is. Consequently the higher neighbors we obtain, the higher is the combination possibilities to generate labeled association rules. This is why we decided to use the relation level to prune rules, whereas closeness clue is very low.

We noticed that closeness clue has a strong influence on the number of LAR generated: when it is low we obtain more rules with a 100% confidence. Actually, if occurrences of the triplet (concept relation item) are lower, then these element are more often found together, which explain these observations. We tested results obtained without using equivalence classes: this led to a new concept creation for each added terms, and mostly using a common relation label. Equivalence classes empirically prove to significantly improve results. First, we noticed that there is no loss of information: each selected term is placed into the existing ontology. Equivalence classes group terms sharing a common lexical field. As an example, we can see on the figure that terms "Basin", "Hydrology" or "River" are grouped under the same concept and linked as "Resource" to the "Water" concept. Secondly, adding elements driven by equivalence classes eases the navigation of the expanded ontology.

Furthermore, this method detects homographic terms. Homographics are words sharing the same spelling, but having different meanings. For example "shift" can be used as "a change" or as "a period at work". In our experiments, we found the term "Source" was associated to the financial area, whereas it was already associated into the ontology to the "Water" concept, meaning "a river origin". In such a case, equivalence classes lead to the placement of two distincts terms "Source", each one associated to a different concept.

Figure 6 shows the number of equivalence classes found according to the number of LAR. Curve with cross points represents classes based on receiving concept, whereas curve with triangular points represents acting based ones. Notice that our real dataset sample led to a high number of received based classes. The number of equivalence classes grows as the number of LAR grows, but the number of new added concepts is significantly reduded: as an example, we can see that 140 LAR led to the creation of half less new concepts (i.e. 80 concepts).

These results, i.e. high number of labeled association, let us choose a and therefore less selective minimal closeness clue. To obtain great quality results, we preferred to put a restriction on the implication level of our labeled association rules, keeping the more interesting ones.

Figure 4 presents some obtained results, realized with a minimal closeness clue fixed to 40%, and keeping labeled association rules having an implication level of at least 50%. Indeed, the chosen thematic -Water Needs -is large and then includes distant concepts. We fixed a large closeness value in order to catch less evident relations. Once labeled association rules have been generated, we noticed the high number of rules having an implication level superior to 80%, confirming that a threshold of 50% was sufficient for the extraction of most of the necessary rules to obtain relevant process result.

Figure 4 displays existing concepts, presented with dashed lines, and added element (concepts, relations and labels) by plain lines. A 40% minimal closeness value allowed to retrieve 303 of concepts/items pairs (or neighbor couples) potentially candidates.

From these pairs, 498 labeled association rules have been generated. We retained those having an implication level of at least 50% to build equivalence classes. The obtained results are coherent, as most of them have been correctly placed into the ontology. Elements added are rather general terms, which is a normal phenomenon considering that the built corpus cover a large part of the ontology. We can observe the high number of concepts added around the water concept, which seems normal as we have an ontology constructed for the water domain.

Finally, EMWIS ontology has 29 non-labeled relations. We observe that our method allowed to name one of them: the one linking "Hydrological basin" and "Watercourse". Whereas it seems to be limited, this result goes ahead compared to existing approaches. Moreover, this weak number of existing labelization can be explained by two reasons: firstly, the specific character of these relations, as they indeed concern only 0.02% of the total ontology and secondly, these relations link subconcepts at a very specific level of the hierarchy. Last, we noticed that these relations mainly concern other themes, such as politic or agriculture.

Ontologies are powerful structures, allowing knowledge representation and sharing. However, their relevance directly depends on their maintenance. Ontology updating mainly consists of adding concepts and/or relations and is mostly manually done. Some works propose tools for automatically updating ontologies. However, none of them have obtained complete results, because of their lack of automatism, i.e. the user is involved in the enrichment loop, or only one type of knowledge (mostly concepts) is considered; none allows automatic relation label detection.

In this paper, we proposed the use of sequential patterns, allowing term research and element extraction (concept or relation). Our proposal is fully automatic, as it places the user at the end of the process in order to validate obtained results. Our main contribution is the automatic discovery of relation label and new concepts, and a finest terms analysis by the mean of equivalence classes. We have described in details all the process steps. From sequential patterns, we determined items which can be used to expand the ontology through the building of neighbor sets. From these sets we construct a new kind of knowledge named Labeled Association Rules, allowing by their implication level to distinguish between concept and label. Finally, an equivalent classes based approach refined the results and drove to a coherent and readable adding to the ontology.

We noticed during our experiments the real quality of added elements: not only chosen elements are relevant but element grouping and placement is totally coherent too. This proves that sequential patterns allow to highlight semantically correlated terms, and that an equivalence based method correctly groups elements sharing a common lexical field. This results in the detection of homograph terms, and improves the ontology readability. Moreover, this opens some interesting perspectives. First of all, refined method for sequential patterns extraction have been proposed [START_REF] Srikant | Mining generalized association rules[END_REF][START_REF] Fiot | Extended time constraints for sequence mining[END_REF], such as the use of a minimal or maximum windowsize in order to restrict itemset selection to close or distant one. This work can be adapted in our context in order to select more closed patterns. Moreover, we could use an ontology driven extraction method to integrate the concept hierarchy during the mining process.

6 References

Example 5 ∅ 1 foreach s ∈ S do 2 foreach 5 Vc 12 -

 512512 This allows to store the closeness rate between a concept C i and an item j. Example 5 shows such a set V. Bold sequences from table 3 are sequential patterns. Algorithm 1 will successively test the following closeness threshold : Algorithm 1: NeighborGeneration Data: Sequential patterns set S, The pattern prefixed tree PSP, The ontology O minP rox the minimal closeness threshold fixed by the user Result: V, the set of all closeness relations V ← co ∈ C such as co ∈ s do 3 foreach i ∈ s such as i = co do 4 if P rox(c o , i) ≥ minP rox then Closeness(Rain, Inundation) = 0.86 -Closeness(Rain, Cause) = 0.71 With a minimal closeness threshold of 0.5, algorithm 1 will return the set V = {V C4 }, with V C4 = {(Inundation, 0.86), (Cause, 0.71)}.

Algorithm 2 : 1 foreach 5 ImplicationRate

 215 LARGeneration Data: The neighborhood set V, The pattern prefixed tree PSP Result: The labeled association rules set RAL RAL ←-∅ Vc o ∈ V do 2 foreach j ∈ Vc o do 3 foreach k ∈ Vc o such as k > j do 4 ral =Max(RLj(co, k), RL k (co, j))

Example 7

 7 Given an extracted LAR set RL = {Rain cause =⇒ Inundation, Rain cause =⇒ Landslide, Rain cause

  Fig. 3. Expanded Ontology

Fig. 4 .

 4 Fig. 4. Expanding Ontology Results

Fig. 6 .

 6 Fig. 5. Neighbor/minP

Table 1 .

 1 .An itemset is a non A transaction database example

	Sequential Pattern
		Extraction
	Texts	Neighborhood	SP		C1	Atmospheric disturbance
	4		Constitution
			1
					Storm	C2	C3	Rainstorm
	Element				Drizzle	Lead to
	Placing	3	LAR Extraction		Rain	C4
				2
				LAR	Relation	Hiérarchy	Relation
			Equivalence		Concept	Term	Prefered Term
			Class
			Extraction
	Fig. 1. General Process
	id-object id-date itemset
	1		1 a		id-object sequence
	1		3 b c		1	<(a)(b c)>
	2		2 d e		2	<(d e)(d)>
	2		3 d

Fig. 2. Ontology Example

empty set of items from I represented by (i 1 , i 2 , ..., i n ). A sequence S is defined as an ordered and non empty list of itemset <s 1 s 2 ...s n >. A n-sequence is a sequence of length n (containing exactly n items). Thus, DB associates a list of items to the object id-obj at the date id-date and can be represented in a object-sequence manner, as shown in table 2. In this table, the sequence S=<(a)(b c)> associated to object 1 means that the item a has been recorded, then b and c together. S is a 3-sequence.

Table 2 .

 2 A sequence database exampleGiven a sequence S =< s 1 s 2 ... s n > and a sequence S =< s 1 s 2 ... s m >, S is included into S if and only if there exist integers a 1

http://www.semide.net/portal_thesaurus


UNIVERSITÉ GRENOBLE ALPES UFR DE MÉDECINE DE GRENOBLE

Admis dans l'intimité des maisons, mes yeux n'y verront pas ce qui s'y passe ; ma langue taira les secrets qui me seront confiés et mon état ne servira pas à corrompre les moeurs, ni à favoriser le crime.

Je ne permettrai pas que des considérations de religion, de nation, de race, de parti ou de classe sociale viennent s'interposer entre mon devoir et mon patient.

Je garderai le respect absolu de la vie humaine.

Même sous la menace, je n'admettrai pas de faire usage de mes connaissances médicales contre les lois de l'humanité.

Respectueux et reconnaissant envers mes Maîtres, je rendrai à leurs enfants l'instruction que j'ai reçue de leurs pères.

Que les hommes m'accordent leur estime si je suis fidèle à mes promesses.

Que je sois couvert d'opprobre et méprisé de mes confrères si j'y manque.

ABBREVIATIONS AND ACRONYMS

GENERAL INTRODUCTION

The early 90s witnessed the development of Computer-Assisted Surgery (CAS) in a wide range of specialties including maxillofacial surgery (1). CAS could be defined by the integration of computer technologies within the patient surgical management in order to improve its planning, achievement and assessment accuracies. CAS is particularly useful for orthognathic surgery because the local anatomy is complex, complicating the planning treatment. Computer technologies are involved in several or in all of the stages of the patient management (2-4). The figure 1 shows this sequence.

Figure 1 Computer-Assisted Surgery sequence

Initially, digital technologies and computer software were used to make a diagnosis and to establish a treatment plan, integrating various imaging data, e.g. 3D CTscan, laser scan or Cone-Beam.

The second step was to improve the accuracy of the transfer of surgical planning to the operating room with an intra-operative assistance. This interactive assistance took different forms:

-A passive assistance to control the surgical gesture or its outcome with the use of Computer-Aided Designed/Computer-Aided Manufacturing (CAD/CAM) surgical guides or splints, patient-specific implants and navigation.

-A semi-active assistance to guide the surgical gesture to achieve the planned outcome as done by a navigation system used as a positioning tool.

-An active assistance with surgical robots wich could autonomously perform operative steps.

The final step is the post-operative assessment comparing the final surgical outcome to the planning and permitting the validation of the previous steps.

Computer-Assisted Navigation system has been gradually recognized in oral and maxillo-facial surgery (5-7). Indeed, it was reported that navigation could improve surgical reliability and patient safety (8).

The principle of Navigation is comparable to global positioning systems commonly used in automobiles and is composed of 3 primary components: a localizer, analogous to a satellite in space; an instrument or a surgical probe, representing track waves emitted by global positioning system unit in vehicle; and an imaging data set or a reference position, analogous to a road map.

The Navigation system allows real-time localizing of a surgical instrument in the operating field as well as representing its position in a virtual model. This model is created using the imaging data of the patient (image-based navigation) or a reference position of a structure previously recorded (imageless navigation).

There are different types of navigation systems according to the technology involved:

--A mechanical navigation

--An electromagnetic navigation --A video-optical navigation
Video-optical systems are the most popular.

The intra-operative assistance, as seen above, could be either passive or semiactive. Passive systems are now frequently used in particular for trauma or cancer and reconstructive surgery. Semi-active systems are more rare and there are only a few studies about them.

Our work was focused on the utilization and the benefits of navigation during orthognathic surgery.

We routinely use in our unit the Orthopilot TM Navigation System (ONS) for condyle repositioning during bilateral sagittal split osteotomy. Its accuracy and its benefit for anatomical and functional outcomes have been previously demonstrated by Bettega et al. (9)(10)[START_REF] Suojanen | The use of patient-specific implants in orthognathic surgery: A series of 30 mandible sagittal split osteotomy patients[END_REF]. This system has also been used to control the position of the maxilla during Le Fort I osteotomies.

Our present work is divided in three sections corresponding to three international publications. The first one dealt with the educational interest of the navigation system to learn condyle repositioning after mandibular sagittal split osteotomies.

This article was published in the International Journal of Oral and Maxillofacial Surgery [START_REF] Savoldelli | Dental occlusalsurface-supported titanium guide to assist cutting and drilling in mandibular bilateral sagittal split osteotomy[END_REF]. The second section evaluated the accuracy of traditional occlusal splints for maxillary repositioning in Le Fort 1 osteotomy. This article was published in the British Journal of Oral and Maxillofacial Surgery [START_REF] Mazzoni | Simulation-guided navigation: a new approach to improve intraoperative threedimensional reproducibility during orthognathic surgery[END_REF]. The last one concerned the improvement of this positioning thanks to the navigation system.

12.

Lartizien R, Zaccaria I, Savoldelli C, et al. Learning condyle repositioning during orthognathic surgery with a surgical navigation system. Int J Oral Maxillofac Surg. 2019 Feb 9. PubMed PMID: 30755359.

13.

Lartizien R, Zaccaria I, Noyelles L, Bettega G. Quantification of the inaccuracy of conventional articulator model surgery in Le Fort 1 osteotomy: evaluation of 30 patients controlled by the Orthopilot((R)) navigation system. Br J Oral Maxillofac Surg. 2019 Sep;57 (7):672-7. PubMed PMID: 31256987.

PART I

Learning condyle repositioning during orthognathic surgery with a surgical navigation system

PART II

Quantification of the inaccuracy of conventional model surgery in Le

Fort I osteotomy : evaluation of 30 patients controlled by the Orthopilot navigation system

PART III

Accuracy improvement of maxillary repositioning in Le Fort I osteotomy with Orthopilot TM Navigation System : evaluation of 30 patients ABSTRACT Traditional model surgery with facebow transfer is not very accurate. We aimed to demonstrate that the Orthopilot TM Navigation System (ONS) improves the accuracy of maxillary repositioning during Le Fort I osteotomy. 30 patients underwent Le Fort I osteotomy alone or associated to sagittal split osteotomy. The maxilla positioning was performed in two phases. First, the maxilla was positioned with the traditional occlusal splint and the position ("without ONS") was recorded by the ONS. In the second phase, ONS was used to improve positioning; and the final position ("with ONS") was recorded, after osteosynthesis. Positioning data were compared to planning data. Positioning data with and without ONS were also compared.

Accuracy was classified in distinct classes with three major criteria (conformity, nonconformity, failure) according to the discrepancies. Conformity rate was significantly greater with ONS (7% without ONS vs 27% with ONS; p=0.01). Failure rate was significantly lower with ONS (60% without ONS vs 23% with ONS; p=0.002).

Dispersions of discrepancies were usually lower in all direction with ONS.

Navigation reduced the risk of discrepancy without cancelling it, especially when large movements are planned. The ONS improved the accuracy of traditional occlusal splint during Le Fort I osteotomy. Keywords: orthognathic surgery; computer assisted surgery ; Le Fort I osteotomy ; maxillary repositioning ; accuracy ; 3D analysis Manuscript submitted to the International Journal of Oral and Maxillofacial Surgery.

We had explored different contribution of Orthopilot TM navigation system to orthognathic surgery. We will discuss the place of navigation in current clinical practice taking into account the new technologies currently available.

Navigation and Patient-specific implants

The development of computer-assisted surgery in orthognathic surgery was made possible by the lack of accuracy and reliability of traditional model surgery (1, 2) and by the emergence of new technologies. There are three main approaches: 1) 3D virtual planning and CAD/CAM occlusal splint; 2) 3D virtual planning, surgical guides and patient-specific implants (PSI); 3) 3D intraoperative navigation. The use of CAD/CAM occlusal splint avoid inaccuracies inherent to model surgery but there are still issues due to the occlusal splints (3,4). PSI and navigation could overcome this problem. One may wonder what the best method is.

It is difficult to compare precisely the two methods. The first difficulty is the multiplicity of the available systems. There is also a lack of consensus for accuracy assessment and the level of evidence of the current literature is very low. Several papers had reported that PSI could accurately transfer the surgical plan for maxillary repositioning (5-7) better than traditional (8) and CAD/CAM (9) occlusal splints.

However, we noticed that, as traditional model surgery, PSI are less accurate in the sagittal plan (5,9). We must keep in mind that, despite the statistical significance, the clinical benefits seemed less important (8). However, in most of these papers, the validity of the statistical analysis is questionable; especially when accuracy is evaluate by mean values comparison.

The reduction of operative time is another advantage of PSI. In their systematic review, Van den Bempt et al. (10) concluded: « the reduction in operating time gives this technique the potential to replace interocclusal splints as the standard of care ».

Ergonomics of current PSI are also notable. Concerning condyle repositioning during BSSO with PSI, there are only few studies assessing their accuracy (6) and no one evaluated the surgical outcomes. Moreover, Suojanen et al. [START_REF] Suojanen | The use of patient-specific implants in orthognathic surgery: A series of 30 mandible sagittal split osteotomy patients[END_REF] did not recommend the use of PSI for BSSO without splint due to poor fitting. PSI seemed to be more suitable in case of high oblique sagittal split osteotomy. New types of PSI for mandible are promising and need to be evaluated [START_REF] Savoldelli | Dental occlusalsurface-supported titanium guide to assist cutting and drilling in mandibular bilateral sagittal split osteotomy[END_REF]. It is also interesting to note that splint-less surgery with PSI do not take the occlusion as an intraoperative reference. Thus, performing maxillomandibular or mandibular procedure with PSI can lead to unacceptable final occlusion. That is why several authors (5,8) did not use PSI for BSSO. Another drawback of PSI is its lack of intraoperative flexibility. Indeed, unexpected events, such as bad-split for example, could make their use impossible. This is particularly critical because PSI allow inexperienced surgeon to perform orthognathic surgery alone. Moreover, their pedagogical interest was not demonstrated and there is a risk of loosing preoperative thinking. PSI are also costly but this issue can be tempered by the reduction of operative room occupation, nevertheless for the moment, the gain of time does not compensate the extra cost.

The use of navigation during Le Fort I osteotomies was assessed in several studies.

The accuracy for planning transfer with passive [START_REF] Mazzoni | Simulation-guided navigation: a new approach to improve intraoperative threedimensional reproducibility during orthognathic surgery[END_REF][START_REF] Badiali | Navigation in Orthognathic Surgery: 3D Accuracy[END_REF] or semi-active (15) systems was emphasized. Furthermore, Zinser et al. had shown that their system of semiactive navigation was more accurate than occlusal splints from traditional model surgery [START_REF] Zinser | A paradigm shift in orthognathic surgery? A comparison of navigation, computeraided designed/computer-aided manufactured splints, and "classic" intermaxillary splints to surgical transfer of virtual orthognathic planning[END_REF]. The outcomes achieved with ONS were also promising. However, there is still some difficulty to stabilize manually the maxilla during the osteosynthesis. The other drawback we have discussed previously is that 2D

planning could lead to inaccuracy, but this is now solved by the generalization of CT-scan 3D planning. Concerning the condyle repositioning, the improvement of anatomical and functional outcomes with ONS was demonstrated previously [START_REF] Bettega | Computer-assisted orthognathic surgery: clinical evaluation of a mandibular condyle repositioning system[END_REF][START_REF] Bettega | Computer assisted orthognathic surgery: Condyle repositioning[END_REF]. We also highlighted its pedagogical potential. The major drawback of navigation is the increase of surgical time. It needs only 5 to 10 minutes to install the navigation tools. However, the most time consuming is the time necessary to position accurately the bone fragment (maxillary or condyle); in some condition (mostly in asymmetric dysmorphosis), it is necessary to remodel bone pieces in order to optimize the positioning. The cost of the system is not a challenge; the hardware is now very common and not expensive and the cost of the consumable is very low (few euros per surgery). The problem is the availability of the system; this market is not yet considered by the industrial. The Orthopilot TM system is based on infrared technology; other systems are in study. In particular, electromagnetically systems were evaluated in several pilot-studies [START_REF] Berger | Electromagnetic navigated condylar positioning after high oblique sagittal split osteotomy of the mandible: a guided method to attain pristine temporomandibular joint conditions[END_REF][START_REF] Berger | Can electromagnetic-navigated maxillary positioning replace occlusional splints in orthognathic surgery? A clinical pilot study[END_REF]. Their benefit are not well established, they had, as the first infrared system, the major inconvenient to need a cable connection to the monitor. There accuracy is impaired by the proximity of metallic tolls, which is problematic in our surgery, and the field generator needs to be closed to the head, which is quite cumbersome.

However, there is a lack of study comparing navigation to PSI. During this work, we have also noticed that the primary outcome used in the major part of previous studies did not seem suitable for assessing accuracy. Indeed, as seen above, analysis based on mean or absolute mean of the discrepancy from the planned position could be confusing. Analysis of the distribution of discrepancies in each dimension given in those studies is more relevant. It would be interesting to evaluate the accuracy of PSI with the nine classes we used, and compare it to accuracy of ONS. Thus, we planned a prospective study on this subject. But, interestingly, while PSI are routinely used in orthognathic and reconstructive surgery, the ANSM did not give us the authorization to perform this study because the industrial could not prove the safety of PSI; especially concerning titanium degradation products after additive manufacturing process! One may lead to question about the difference between research and clinical practice.

Finally, the two approaches could be complementary. We can imagine a maxillafirst procedure with PSI for the maxilla and navigation for the condyle repositioning in order to add up the advantages of each method in terms of accuracy and clinical outcomes. However, the current high cost of such an approach may be an obstacle and could be reserved to difficult cases.

Perspectives for Orthopilot TM Navigation System

The most popular form of computer-assisted orthognathic surgery is the combination of virtual planning, surgical guide and PSI. Semi-active navigation systems dedicated to orthognathic surgery are still very uncommon. Our work highlighted different benefits of ONS in orthognathic surgery, as well as some limitations of its current configuration. New technologies will allow improving this device. The integration of 3D planning would enhance the accuracy of planning transfer, especially in case of Le Fort I osteotomy. By adapting the ancillary, it would also permit to expand the ONS applications such as to traumatology surgery for example. The recent years witnessed the emergence of augmented-reality (AR) [START_REF] Pietruski | Supporting mandibular resection with intraoperative navigation utilizing augmented reality technology -A proof of concept study[END_REF]. Although this technology needs some improvement, the first results are promising. It is quite possible to imagine an association of AR and navigation, enhancing the ergonomics of the system and the intraoperative perception of the surgeon. Overlay of planned position in the surgeon's visual field or discrepancies scores, via AR glasses, are some example of future possible evolutions.

Technologies miniaturization could even allow to place the optical localizer on the AR glass in order to reduce the issue of visual contact between those localizer and the reflective markers.

Conclusion

Through three international publications, we explored different benefits of the Orthopilot TM navigation system in orthognathic surgery. In a first work we evaluated the pedagogical interest of this navigation system in the learning of condylar repositioning during mandibular osteotomies. In a second work we measured, with this tool, the inaccuray of planning transfer of Le Fort I osteotomies via traditional occlusal splints, in order to evaluate our clinical practice. The last part of our work consisted in demonstrating that the navigation, used in a semi-active way, could improve the accuracy of maxilla repositioning during these same osteotomies. In view of current technological developments, it is quite possible to improve this navigation system by incorporating 3D planning data become current and optimizing ergonomics, including through augmented-reality.

CONCLUSION :

Au travers de trois publications internationales, nous avons exploré différents intérêts du système de navigation Orthopilot TM en chirurgie orthognathique. Dans un premier travail nous avons exploré l'intérêt pédagogique de ce système de navigation dans l'apprentissage du repositionnement condylien au cours des ostéotomies mandibulaires. Nous avons ensuite mesuré, avec cet outil, le degré d'imprécision du transfert de planification des ostéotomies de Le Fort I via les gouttières occlusales traditionnelles, de façon à évaluer notre pratique clinique. La dernière partie de notre travail a consisté à montrer que la navigation, utilisée de manière semi-active, pouvait améliorer la précision du repositionnement du maxillaire lors de ces mêmes ostéotomies. Au vu des évolutions technologiques actuelles, il est tout à fait envisageable d'améliorer ce système de navigation en y intégrant des données de planification 3D devenues courantes et en optimisant l'ergonomie, notamment par le biais de la réalité virtuelle.

  : 2019 CONTRIBUTION OF ORTHOPILOT TM NAVIGATION SYSTEM TO ORTHOGNATHIC SURGERY APPORT DU SYSTEME DE NAVIGATION ORTHOPILOT TM EN CHIRURGIE ORTHOGNATIQUE THÈSE PRÉSENTÉE POUR L'OBTENTION DU TITRE DE DOCTEUR EN MÉDECINE DIPLÔME D'ÉTAT Rodolphe LARTIZIEN THÈSE SOUTENUE PUBLIQUEMENT À LA FACULTÉ DE MÉDECINE DE GRENOBLE Le : 13/09/2019 DEVANT LE JURY COMPOSÉ DE Président du jury : Monsieur le Professeur Jean-Philippe GIOT Membres : Madame le Professeur Chloé BERTOLUS Monsieur le Professeur Georges BETTEGA, Directeur de Thèse Monsieur le Professeur Patrick GOUDOT Madame le Docteur Béatrice MORAND L'UFR de Médecine de Grenoble n'entend donner aucune approbation ni improbation aux opinions émises dans les thèses ; ces opinions sont considérées comme propres à leurs auteurs. [Données à caractère personnel] SERMENT D'HIPPOCRATE En présence des Maîtres de cette Faculté, de mes chers condisciples et devant l'effigie d'HIPPOCRATE, Je promets et je jure d'être fidèle aux lois de l'honneur et de la probité dans l'exercice de la Médecine. Je donnerai mes soin s gratuitement à l'indigent et n'exigerai jamais un salaire au dessus de mon travail. Je ne participerai à aucun partage clandestin d'honoraires.

AR : Augmented-Reality AVE : Anterior Vertical Excess BSSO : Bilateral Sagittal Split Osteotomy CAD/CAM : Computer-Aided Design / Computer-Aided Manufacturing CAS : Computer-Assisted Surgery ONS : Orthopilot TM Navigation System PSI : Patient-Specific Implants PVE : Posterior Vertical Excess Ra : axial Rotation Rf : frontal Rotation Rs : sagittal Rotation Tbf : back / front Translation Trl : right / left Translation Ttb : top / bottom Translation RESUME La

  chirurgie assistée par ordinateur a une place toujours plus importante dans notre

	ABSTRACT
	Computer-assisted surgery is increasingly widespread in our everyday practice. We
	pratique quotidienne. routinely use the Orthopilot TM Navigation System (ONS) during our orthognathic Nous utilisons en routine le système de navigation
	Orthopilot TM (ONS) au cours de nos chirurgies orthognatiques. Le but de notre surgeries. The aim of our work, through three international publications, was to
	travail, au travers de trois publications internationales, était d'explorer les différents explore the different interests of this tool. In a first part, we demonstrated the
	intérêts de cet outil. Dans une première partie, nous avons démontré l'intérêt pedagogical interest of the ONS to learn condylar repositioning during mandibular
	pédagogique de l'ONS dans l'apprentissage du repositionnement condylien au osteotomies. The use of ONS allowed trainees to improve their accuracy and
	cours des ostéotomies mandibulaires. L'utilisation de l'ONS permit aux jeunes surgical skills. In a second part, we measured, thanks to the navigation, the
	inaccuracy of planning transfer of Le Fort I osteotomies with traditional occlusal
	splints. We showed that the transfer was more inaccurate in the sagittal plane. This
	inaccuracy was also more important in case of larger maxillary movements. Finally,
	during the last part of this work, we demonstrated that the ONS, used semi-actively,
	could improve the accuracy of the maxilla repositioning during these same
	osteotomies. This work demonstrated many benefits of the ONS but also some of
	its current limitations. With the technological evolutions, it is quite possible to
	improve it by integrating 3D planning data and by optimizing the ergonomics,
	notably through the augmented-reality.	l'ONS en chirurgie
	orthognatique mais aussi certaines de ces limites actuelles. Avec les évolutions
	technologiques, il est tout à fait envisageable de l'améliorer en y intégrant des Key-words : orthognathic surgery ; computer-assisted surgery, surgical navigation ;
	données de planification 3D et en optimisant l'ergonomie, notamment par le biais de surgical planning ; surgical education ; Le Fort I osteotomy ; condyle repositioning
	la réalité virtuelle.
	Mots-clés : Chirurgie orthognatique; chirurgie assistée par ordinateur; navigation
	chirurgicale; planification chirurgicale; formation chirurgicale; ostéotomies de Le Fort
	I; repositionnement condylien

chirurgiens d'améliorer leur précision et leur gestuelle chirurgicale. Nous avons ensuite mesuré, grâce à la navigation, le degré d'imprécision du transfert de planification des ostéotomies de Le Fort I via les gouttières occlusales traditionnelles. Nous avons montré que le transfert était plus imprécis dans le plan sagittal. Cette imprécision était également corrélée à l'importance du déplacement du maxillaire. Enfin, lors de la dernière partie de ce travail, nous avons démontré que l'ONS, utilisé de manière semi-active, permettait d'améliorer la précision du repositionnement du maxillaire lors de ces mêmes ostéotomies. Ce travail a donc permis de démontrer différentes formes de l'apport de

  The manuscript was submitted in the International Journal of Oral and Maxillofacial Surgery and currently under review. We concluded with a discussion comparing navigation and patient-specific implants for orthognathic surgery and by the possible evolution expected with the new technologies currently available. 11. Bettega G, Leitner F. [Computer assisted orthognathic surgery: Condyle repositioning.]. Rev Stomatol Chir Maxillofac Chir Orale. 2013 Jul 17. PubMed PMID: 23871566. Chirurgie orthognathique assistee par ordinateur : le repositionnement condylien.

ABSTRACT

Condyle repositioning during bilateral sagittal splint osteotomy (BSSO) is a challenging step for the inexperienced surgeon. We aimed at demonstrating the benefit of navigation for learning the condyle repositioning. We treated 100 patients who underwent a BSSO. A trainee performed the condyle repositioning of one side in two phases. In the first one the trainee positioned without watching the screen of the Orthopilot Navigation system (ONS). In the second one, the trainee could use the ONS to replace the condyle. Heuristic, anatomical and functional scores of each phase were recorded. Heuristic (17% vs 75%; p<0.0001), anatomical (35% vs 86%; p<0.0001) and functional (14% vs 56%; p<0.0001) scores were significantly greater with the ONS. The ONS is a promising and original intra-operative learning tool for the repositioning of the condyle during BSSO.


Jules Verne

Pierre Palier
email: pierre.palier@cstb.fr

Anthony Couzinet

Jean-Paul Bouchet

Sylvain Aguinaga

Olivier Flamand

Philippe Delpech

RENEWAL OF A LARGE WIND TUNNEL FACILITY

The paper aims at showing the evolution of methods to design a large wind tunnel. The current Jules Verne facility was designed with a scale model of the wind tunnel which enabled hot wire local wind speed measurements. The new facility is designed according a numerical modelling approach which parameters were validated by PIV measurements in the reduced scale physical model.

INTRODUCTION

The Jules Verne climatic wind tunnel of CSTB has been operated for more than 25 years by now; with the complementary dynamic and thermal circuits the facility operates in various domains of expertise, building, civil engineering, energy production, transport… The renewal of the Jules Verne climatic wind tunnel is essential today to maintain its position amongst the major experimental facility at international level.

The modernization of the wind tunnel aims at meeting new societal challenges -energy and ecological transition, safe, clean and efficient energy production, health and wellness, smart, green and integrated transport, climate change ... -and contribute to the development of new induced markets.

These new challenges require the establishment and development of complementary scientific and technical competences related to the expected innovations along with accompanying training actions. On these issues, is added the development of digital technologies, data management and communication technologies, whether for simulations aspects of physical phenomena, as those related to building information modeling (BIM), GIS and Smart Grid.

The Jules Verne wind tunnel modernization project includes the following investments: -Optimization and adaptation of the geometry of the dynamic wind tunnel unit to optimize the aerodynamic field in order to address specific markets, -Adaptations of the climatic wind tunnel to meet the needs of key customers: variable geometry nozzles for testing large civil engineering structures (bridges), wind field over urban district; 4WD bench for 4x4, hybrid or full electric vehicles; load bench for off-road vehicles; moving belt floor for the railway industry, dust control systems, etc. -Regarding the health issue and operating safety of our facilities, it is also planned to replace the cooling tower refrigeration units (3 MW) by dry condensers, -Expansion of the control rooms, preparation halls, and availability of offices and meeting rooms to welcome outside researchers and corporate partners, -Digital simulation capabilities to complete the testing service offers with virtual simulations.

THE JULES VERNE CLIMATIC WIND TUNNEL

The Jules Verne climatic wind tunnel is composed of two aerodynamic units: the external circuit corresponding to the dynamic loop (SC1) which can be closed or opened following the kind of applications and the internal circuit corresponding to the thermal loop (SC2). These two wind tunnels are complementary to the test conditions they allow to reproduce and have independent propulsion systems: a single fan of 6.2 m diameter in SC2 and 6 fans of 3.2 m diameter in SC1. The various test rigs available afford to vary the operating conditions according to the technical capacities and the geometries of the structures tested: wind speed, ambient temperature, icing precipitation, solar illumination...

DYNAMIC LOOP IMPROVEMENTS OF THE WIND TUNNEL

The main modifications relate to the dynamic circuit of the installation. Indeed specific applications require to have a larger test rig. The geometrical changes have to integrate modularity in order to make it possible to arrange alternately a wide and low or high and narrow test rig.

Figure 3: Modification of the Jules Verne wind tunnel geometry So, a modification of the loop between the environment section and the high-speed section (figure 1) has been proposed; the final proposal is to lengthen the existing facility by replacing the elbow at 180 ° and the asymmetric nozzle contraction upstream of the high-speed vein by two 90 ° elbows containing a series of 9 or 10 vanes in each elbow depending on the width of the test section. This modification has several advantages which have been validated both numerically and experimentally: Uniformity of flow field is improved in the high-speed test section, tangential velocity components is drastically reduced and the separation flow in the environment test section is limited.

Thanks to optimization works of the flow conditions in the environment test section, new markets could be addressed by studying of large bridges, or ventilation at the scale of a district, or alternately on slender elements with a small scale like pylons, towers or wind turbines. In the high-speed test section, the modifications will allow to improve the accuracy of measurements on reduced scales (1/7th to 1/15th scale) for land vehicles applications, in particular rail vehicles.

Other modifications to the thermal circuit will be made during the modification of the installation: equipment of engine load benches to dissipate power on a brake, installation of a 4x4 test bench operational in the context of severe climatic environments reproduced in the wind tunnels, etc.

METHODOLOGY TO DEFINE THE NEW GEOMETRY

Analytical approach

Definition of guide vanes

Experimental investigations

The design and validation of the geometrical transformations of the aeraulic circuit of the installation made an intensive use of the CFD, as well as on PIV measurements on a scaled model (1/36) of the future facility. The scale of the model was chosen to allow the use of small fans adapted to the simulation of the air flow and constraints related to the implementation of the PIV measures.

Numerical simulations

The computational domain of the wind tunnel is based on the CAD files of the wind tunnel at scale 1.

ANSA software has been used to generate the computational grid. It lies on an unstructured mesh which is composed of tetrahedral volumic elements and prismatic elements layers in order to describe accurately the boundary layers (figure 6). Typically this kind of meshes contains approximately 3.5 million of cells. Numerical simulations have been performed using open source software OpenFOAM. RANS modelling approach is used to simulate the flow field in the wind tunnel. The choice of the turbulence model is based on comparisons with PIV measurements realized on the scaled mock-up. The comparison of the separation flow induced by the interaction between the diffuser (in diffuser test section cf. figure 1) and the elbow at 180° downstream of the fans proves to be a good parameter to evaluate the turbulence models.

Figure 7 compares results from PIV measurement, standard k-ε modeling and k-ε realizable modeling. The latter seems to reproduce the detachment flow in the diffuser more faithfully. Indeed, this kind of model are driven by a viscosity limiter function which tends to cut the turbulent viscosity in zones of adverse gradient inducing the detachment effects and making it more adapted to this flow configurations. The possibility of integrating vertical or horizontal "blowing nozzles" into the environmental test section has been studied numerically and experimentally. The provisions planned for these additional nozzles are (figure 9):

-vertical for the study of pylons, masts, wind turbines ...

-horizontal for studies with aero-elastic decks at large scales, districts, wind farms, The expertise carried out using numerical simulations aimed at checking the quality of the flow in the proposed test area and evaluating the average aerodynamic loading applied to these nozzles, the originality of which is based on the implementation of inflatable structures by assuming that these equipments are rigid and undeformable.

Vertical nozzle configuration

The main interest of using a vertical nozzle in the facility is to be able to test slender structures, pylons, mats, wind turbines, etc., in a high-speed wind stream without additional energy constraints. For this purpose, two vertical symmetrical profiles would be placed in the test section, allowing a cross section of about 50 m² (6 x 8.40 m).

The results of the CFD calculations of the velocity fields at the exit of the vertical nozzle show the existence of a bifurcation of the wind field towards the outside of the turn (Figure 10), which is homogeneous over the entire height of the section. The 9 inter-blade channels are then fed heterogeneously (supercharging channels 2-3-4-5). The shear zone is marked significantly from the nozzle outlet. It should be noted that this average representation should not hide the important effects in this zone which may impact the central zone of the jet. The qualitative comparison of the results obtained by numerical simulations and PIV on the 1 / 36th scale model (Figure 10) confirms this result, although numerical simulations tend to predict a more spread velocity gradient in the shear zone. From an aerodynamic point of view, it seems that this solution does not guarantee a clean flow at the nozzle outlet.

Aerodynamic loading of vertical nozzles

The parietal pressure fields are shown in figure 11. The bifurcation identified in the preceding paragraph induces a dissymmetry of the pressure field on each of the vertical nozzles (Figure 11). This results in a significantly different average force between the "inner" nozzle and the "outside" nozzle. The maximum effort recorded corresponds to an average load of 22 kg / m². The bifurcation is due to an imbalance of the pressure in the series of blading. In order to rebalance this pressure, one solution could be to close one or more inter-blade channels on the upper surface of the turn so as to straighten the flow at the nozzle outlet. Simply in a first step, we propose to close the three inter-blade channels at the outside of the turn as shown in figure 12. The implementation of such an adaptation could be easy by using a door system which could be open or closed depending on the test conditions. This first test was carried out numerically to estimate the potential of this adaptation. The effect is straightforward, but the operation seems tricky because the jet tends to bifurcate inwardly in this configuration. This bifurcation having been identified on the model, it is then proposed to study two configurations by closing two or three inter-blade channels as shown in figure 13.

Figure 13 compares two experimental solutions which aim at closing more or fewer inter-blade channels in order to check if the evolution of the jet at the nozzle outlet can be controlled. PIV measurements were carried out on the scale model to assess the effect of these changes on the mean velocity field. The PIV visualizations are complex to carry out in this area of the section because the reflections are numerous in the region of the turn. However, it is observed that a simple solution based on the closing the first two channels at the outer side of the turn and the first channel inside the turn makes it possible to obtain a symmetrical velocity field with respect to the axis of the nozzle.

Horizontal nozzle configuration

The purpose of this configuration is to create a test section that makes use of the maximum width of the test section by lowering the ceiling height from 8m to 3m. The section is then equal to 3 Х 15 meters (45 m2). This test section would be created from a single horizontal nozzle which can be placed at a distance from the downstream turn and the outlet of the fans. The contraction generated by the nozzle will reduce the heterogeneity of the velocity field that remains at the fan outlet. A cross-section of the environment section in the middle of the nozzle makes possible to identify the structure of the velocity field which naturally presents a sheared zone at the nozzle outlet (Figure 14). One can observe an overspeed at the trailing edge generating a low pressure zone. The velocity at the nozzle outlet is of the order of 0.68 times the maximum speed obtained in the wind tunnel circuit. It can be seen that the sheared zone impacts the testing these observations are confirmed by the velocity mappings at different heights of the test section. Again, this result is only a description of the mean field, the unsteady phenomena due to turbulence may impact the test zone. The size of the turbulent structures developing in the sheared zone can be of the order of magnitude of the height of the nozzle. Nevertheless, this field remains two-dimensional as shown by the different velocity fields obtained in the depth of the test section.

It could be envisaged to extend the trailing edge of the nozzle by a horizontal part which makes possible to protect the test zone from the sheared zone.

Aerodynamic loading of the horizontal nozzle

It is possible to represent the pressure field applied to the nozzle with a pressure coefficient based on a pressure at the surface and a reference speed (1).

(1)

The change in the pressure coefficient as a function of the height of the nozzle has a conventional pressure distribution (Figure 16). The pressure coefficient is maximum at the leading edge. The low pressure zone is located at the trailing edge of the nozzle (between 3 and 3,4 m). If the blowing nozzle is considered to be formed by a thin wall, the low pressure face of this wall has a constant distribution of the pressure field. This obviously has no physical reality if the inflatable nozzle fills all of the space at the rear of the nozzle.

The integration of the pressure field on the nozzle makes possible to obtain the components of the mean force applied to the nozzle. The effort recorded corresponds to an average load equivalent to that obtained on the vertical nozzle for the previous configuration, namely 26 kg / m².

Figure 1 :

 1 Figure 1: The Jules climatic wind tunnel composed of two units: dynamic and thermal

Figure 2 :

 2 Figure 2: Highly versatile use of the Jules climatic wind tunnel

Figure 4 :

 4 Figure 4: Definition of geometrical elements -guide vanes and contraction nozzle

Figure 5 :

 5 Figure 5: Model of the future wind tunnel made of transparent material in order to enable PIV measurements

Figure 6 :

 6 Figure 6: unstructured volumetric mesh based on tetrahedral elements with prismatic boundary layer at the walls

Figure 7 :

 7 Figure 7: Comparison of turbulence models to PIV measurements

Figure 8 :

 8 Figure 8: Visualization of the velocity field in the test section -SC1 vs future wind tunnel

Figure 9 :

 9 Figure 9: Fitting of vertical (left) or horizontal nozzles (right) in the dynamic unit of the Jules Verne wind tunnel

Figure 10 :

 10 Figure 10: Comparison of the wind velocity field downward the wind nozzle by PIV (left) and CFD (right)

Figure 11 :

 11 Figure 11: Evaluation and graphic representation of wind efforts applied to vertical nozzles

Figure 12 :

 12 Figure 12: Numerical test of blocking of the guide vanes channels

Figure 13 :

 13 Figure 13: Experimental test of blocking of the guide vanes channels

Figure 14 :

 14 Figure 14: Wind velocity profiles in a vertical plane, upstream the nozzle and in the test section. Evolution of the wind velocity with height at five locations in air flow: in the nozzle, near the trailing edge and three other locations near the nozzle outlet

Figure 15 :

 15 Figure 15: Locating the pressure taps on the horizontal nozzle

Paper presented at EACWE 2017, 7th European-African Conference on Wind Engineering, July 4-7, 2017, Liège, BEL

CONCLUSION

The results presented in the present study allowed us to obtain a great confidence in the both the numerical model and experimental model at reduced scale. As a consequence it is expected that the future configuration of the Jules Verne Wind Tunnel will meet the same specifications as the ones obtained through the monetization.

Additional measurements carried out in the actual Jules Verne wind tunnel are also available abut not presented here. They were used in order to understand the flow behavior downstream the fans. This section of the wind tunnel will be indeed used in addition with inflatable nozzles at it will presented in another paper.


Ashok Rajpurohit

Sébastien Joannès
email: sebastien.joannes@mines-paristech.fr

Faisal Islam

Vicky Singery

Steve Bucknell

Lucien Laiarinandrasana

Characterization and statistical analysis of single fiber strength of fibers at various processing stages

Keywords: Single fiber tests, Weibull distribution, Confidence interval, Carbon fibers, Glass fibers

Introduction

For numerous applications exploiting high performance composite materials, in-service safety and reliability assessment is a key challenge for engineers. Composite material performances rest largely on the mechanical behavior of its constituents and especially on the strength of technical fibers of which it is made. Researchers since long have been working on simulation and modelling tools to understand and predict the failure behavior of composites. It is essential that the simulation or modelling tools are provided with accurate input parameters. One of the most important input parameters for composite modelling tools is the single fiber strength and its distribution; and correctly capturing fiber strength statistics and failure kinetics is crucial to the success of composite stochastic strength models.

Single fiber test (SFT) is one of the most popular way of characterizing the fiber strengths, the others being bundle fiber test and fragmentation tests. SFT too has its own limitations: complex sample preparation, misalignments, lengthy and time-consuming process and prone to errors because of major human intervention [START_REF] Watanabe | Tensile strength distribution of carbon fibers at short gauge lengths[END_REF][START_REF] Naito | The effect of gauge length on tensile strength and weibull modulus of polyacrylonitrile (pan)-and pitch-based carbon fibers[END_REF]. The characterization of fibers through SFT is hence challenging, but it is the only unambiguous means of characterizing fiber strength data to feed into the micro-mechanical models. Despite great progress in characterization techniques, many obstacles remain to obtain accurate fiber strengths and most studies have reported the difficulty in obtaining fiber strength data which can accurately represent the entire fiber population for each application.

Research questions

Manual SFT has several limitations only some of which can be completely overcome. An automated SFT is hence the way forward to characterize and analyze the single fiber strength properties [START_REF] Islam | Towards accurate and efficient single fiber characterization to better assess failure strength distribution[END_REF][START_REF] Swolfs | A review of input data and modelling assumpion long. strength models for unidirectional fibre reinforced composites[END_REF]. In this work the challenges for automatic characterization of carbon and glass fibers will be addressed. Apart from this, the paper answers the very important but so far unanswered question: if the tensile strengths and Weibull parameter of fibers be identified from the raw bobbin or from the final reinforcement fabric? This work will help the modelling experts to choose the right input parameter value for the tensile strength and it will also help the textile manufacturer to understand if the manufacturing process and the process parameters (temperature, mechanical vibration, abrasion, etc.) are affecting the fiber strength distributions and to what level.

The work also explores the ideal distribution for single fiber tests. Experimental uncertainties are evaluated and provided.

Fiber strengths and distribution

The strength of a single fiber cannot be captured in one single average value. Single fibers normally exhibit a distribution of strengths that could be represented by using a probabilistic approach. The Weibull distribution is a good representation of the weakest link behavior of brittle single fibers [START_REF] Thomason | On the application of weibull analysis to experimentally determined single fibre strength distributions[END_REF]. Using the 2-parameter Weibull distribution assumes that fiber failure is controlled by the random distribution of a single type of defect. That being said, the classical 2-parameter Weibull distribution is not able to capture the non-linear deviation of the fiber strength distribution accurately while the 3-parameter Weibull distribution would assume that the minimum strength value in the data set represents the weakest fiber in the population which is often an incorrect assumption. Hence as suggested by Berger and Jeulin's [START_REF] Berger | Statistical analysis of the failure stresses of ceramic fibres[END_REF] and further by Joannes et. al. [START_REF] Islam | Towards accurate and efficient single fiber characterization to better assess failure strength distribution[END_REF] a Truncated Weibull distribution can be an ideal choice where; a truncation limit is set. This truncation limit is a limit below which the data is not available and that it is not necessarily a deterministic value and could be considered as a continuous variable.

Materials and methods

In this study we characterize carbon T700S and E-CR glass fibers in an automated SFT equipment from Dia-Stron Ltd. Carbon fibers are extracted i) from raw bobbin, 12k procured from Toray ii) from spread sheet/tape produced on a spreading machine using the 12k bobbins and iii) from the NCF produced using the manufactured spread tape on a multi-axial machine from LIBA. Similarly, glass fibers are extracted and tested at these three stages as shown for carbon fibers in

Fig. 1 Fig. 1

 11 Fig.1 Carbon fiber population at several processing stages

Acknowledgements: The research leading to these results has been done within the framework of the FiBreMoD project and has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 722626.