Ozan Caglayan
email: ocaglayan@gsu.edu.tr

Walid Aransa

Yaxing Wang
email: yaxing@cvc.uab.es

Marc Masana
email: mmasana@cvc.uab.es

Mercedes García-Martínez

Fethi Bougares

Loïc Barrault

Joost Van De Weijer

Does Multimodality Help Human and Machine for Translation and Image Captioning?

This paper presents the systems developed by LIUM and CVC for the WMT16 Multimodal Machine Translation challenge. We explored various comparative methods, namely phrase-based systems and attentional recurrent neural networks models trained using monomodal or multimodal data. We also performed a human evaluation in order to estimate the usefulness of multimodal data for human machine translation and image description generation. Our systems obtained the best results for both tasks according to the automatic evaluation metrics BLEU and ME-TEOR.

Introduction

Recently, deep learning has greatly impacted the natural language processing field as well as computer vision. Machine translation (MT) with deep neural networks (DNN), proposed by [START_REF] Kalchbrenner | Recurrent continuous translation models[END_REF][START_REF] Sutskever | Sequence to sequence learning with neural networks[END_REF] and [START_REF] Bahdanau | Neural machine translation by jointly learning to align and translate[END_REF] competed successfully in the last year's WMT evaluation campaign [START_REF] Bojar | Findings of the 2015 workshop on statistical machine translation[END_REF].

In the same trend, generating descriptions from images using DNNs has been proposed by [START_REF] Elliott | Multi-language image description with neural sequence models[END_REF]. Several attempts have been made to incorporate features from different modalities in order to help the automatic system to better model the task at hand [START_REF] Elliott | Multi-language image description with neural sequence models[END_REF]Kiros et al., 2014b;Kiros et al., 2014a).

This paper describes the systems developed by LIUM and CVC who participated in the two proposed tasks for the WMT 2016 Multimodal Machine Translation evaluation campaign: Multimodal machine translation (Task 1) and multimodal image description (Task 2).

The remainder of this paper is structured in two parts: The first part (section 2) describes the architecture of the four systems (two monomodal and two multimodal) submitted for Task 1. The standard phrase-based SMT systems based on Moses are described in section 2.1 while the neural MT systems are described in section 2.2 (monomodal) and section 3.2 (multimodal). The second part (section 3) contains the description of the two systems submitted for Task 2: The first one is a monomodal neural MT system similar to the one presented in section 2.2, and the second one is a multimodal neural machine translation (MNMT) with shared attention mechanism.

In order to evaluate the feasibility of the multimodal approach, we also asked humans to perform the two tasks of this evaluation campaign. Results show that the additional English description sentences improved performance while the straightforward translation of the sentence without the image did not provide as good results. The results of these experiments are presented in section 4.

Multimodal Machine Translation

This task consists in translating an English sentence that describes an image into German, given the English sentence itself and the image that it describes.

Phrase-based System

Our baseline system for task 1 is developed following the standard phrase-based Moses pipeline as described in [START_REF] Koehn | Moses: Open source toolkit for statistical machine translation[END_REF], SRILM [START_REF] Stolcke | Srilm -an extensible language modeling toolkit[END_REF], KenLM [START_REF] Heafield | KenLM: faster and smaller language model queries[END_REF]GIZA++ (Och and[START_REF] Franz | A systematic comparison of various statistical alignment models[END_REF]. This system is trained using the data provided by the organizers and tuned using MERT (Och, 2003) to maximize BLEU [START_REF] Papineni | Bleu: a method for automatic evaluation of machine translation[END_REF] and METEOR [START_REF] Lavie | Meteor: an automatic metric for mt evaluation with high levels of correlation with human judgments[END_REF] scores on the validation set.

We also used Continuous Space Language Model 1 (CSLM) [START_REF] Schwenk | Continuous space language models for statistical machine translation[END_REF] with the auxiliary features support as proposed by [START_REF] Aransa | Improving continuous space language models using auxiliary features[END_REF]. This CSLM architecture allows us to use sentence-level features for each line in the training data (i.e. all n-grams in the same sentence will have the same auxiliary features). By this means, better context specific LM estimations can be obtained.

We used four additional scores to rerank 1000best outputs of our baseline system: The first two scores are obtained from two separate CSLM(s) trained on the target side (i.e. German) of the parallel training corpus and each one of the following auxiliary features:

• VGG19-FC7 image features: The auxiliary feature used in the first CSLM are the image features provided by the organizers which are extracted from the FC7 layer (relu7) of the VGG-19 network [START_REF] Simonyan | Very deep convolutional networks for large-scale image recognition[END_REF]. This allows us to train a multimodal CSLM that uses additional context learned from the image features.

• Source side sentence representation vectors: We used the method described in (Le and [START_REF] Quoc | Distributed representations of sentences and documents[END_REF] to compute continuous space representation vector for each source (i.e. English) sentence that will be provided to the second CSLM as auxiliary feature. The idea behind this is to condition our target language model on the source side as additional context.

The two other scores used for n-best reranking are the log probability computed by our NMT system that will be described in the following section and the score obtained by a Recurrent Neural Network Language Model (RNNLM) [START_REF] Mikolov | Recurrent neural network based language model[END_REF]. The weights of the original moses features and our additional features were optimized to maximize the BLEU score on the validation set.

Neural MT System

The fundamental model that we experimented 2 is an attention based encoder-decoder approach [START_REF] Bahdanau | Neural machine translation by jointly learning to align and translate[END_REF] except some notable changes in the recurrent decoder called Conditional GRU.

1 github.com/hschwenk/cslm-toolkit 2 github.com/nyu-dl/dl4mt-tutorial

We define by X and Y , a source sentence of length N and a target sentence of length M respectively. Each source and target word is represented with an embedding vector of dimension E X and E Y respectively:

X = (x 1 , x 2 , ..., x N ), x i ∈ R E X (1) Y = (y 1 , y 2 , ..., y M ), y j ∈ R E Y (2)
A bidirectional recurrent encoder reads an input sequence X in forwards and backwards to produce two sets of hidden states based on the current input and the previous hidden state. An annotation vector h i for each position i is then obtained by concatenating the produced hidden states.

An attention mechanism, implemented as a simple fully-connected feed-forward neural network, accepts the hidden state h t of the decoder's recurrent layer and one input annotation at a time, to produce the attention coefficients. A softmax activation is applied on those attention coefficients to obtain the attention weights used to generate the weighted annotation vector for time t. The initial hidden state h 0 of the decoder is determined by a feed-forward layer receiving the mean annotation vector.

We use Gated Recurrent Unit (GRU) [START_REF] Chung | Empirical evaluation of gated recurrent neural networks on sequence modeling[END_REF] activation function for both recurrent encoders and decoders.

Training

We picked the following hyperparameters for all NMT systems both for Task 1 and Task 2. All embedding and recurrent layers have a dimensionality of 620 and 1000 respectively. We used Adam as the stochastic optimizer with a minibatch size of 32, Xavier weight initialization (Glorot and Bengio, 2010) and L2 regularization with λ = 0.0001 except the monomodal Task 1 system for which the choices were Adadelta, sampling from N (0, 0.01) and L2 regularization with λ = 0.0005 respectively.

The performance of the network is evaluated on the validation split using BLEU after each 1000 minibatch updates and the training is stopped if BLEU does not improve for 20 evaluation periods. The training times were 16 and 26 hours respectively for monomodal and multimodal systems on a Tesla K40 GPU.

Finally, we used a classical left to right beamsearch with a beam size of 12 for sentence generation during test time.

Data

Phrase-based and NMT systems for Task 1 are trained using the dataset provided by the organizers and described in Table 1. This dataset consists of 29K parallel sentences (direct translations of image descriptions from English to German) for training, 1014 for validation and finally 1000 for the test set. We preprocessed the dataset using the punctuation normalization, tokenization and lowercasing scripts from Moses. In order to generalize better over the compound structs in German, we trained and applied a compound splitter 3 (Sennrich and Haddow, 2015) over the German vocabulary of training and validation sets. This reduces the target vocabulary from 18670 to 15820 unique tokens. During translation generation, the splitted compounds are stitched back together.

Side

Vocabulary Words English 10211 377K German 15820 369K

Table 1: Training Data for Task 1.

Results and Analysis

The results of our phrase-based baseline and the four submitted systems are presented in Table 2. The BL+4Features system is the rescoring of the baseline 1000-best output using all the features described in 2.1 while BL+3Features is the same but excluding FC7 image features. Overall, we were able to improve test set scores by around 0.4 and 0.8 on METEOR and BLEU respectively over a strong phrase-based baseline using auxiliary features.

Regarding the NMT systems, the monomodal NMT achieved a comparative BLEU score of 32.50 on the test set compared to 33.45 of the phrase-based baseline. The multimodal NMT system that will be described in section 3.2, obtained relatively lower scores when trained using Task 1's data.

Multimodal Image Description Generation

The objective of Task 2 is to produce German descriptions of images given the image itself and one or more English descriptions as input.

3 github.com/rsennrich/wmt2014-scripts

Visual Data Representation

To describe the image content we make use of Convolutional Neural Networks (CNN). In a breakthrough work, Krizhevsky et al. [START_REF] Krizhevsky | Imagenet classification with deep convolutional neural networks[END_REF] [START_REF] He | Deep residual learning for image recognition[END_REF]. These networks learn residual functions which are constructed by adding skip layers (or projection layers) to the network. These skip layers prevent the vanishing gradient problem, and allow for much deeper networks (over hundred layers) to be trained.

To select the optimal layer for image representation we performed an image classification task on a subsection of images from SUN scenes [START_REF] Xiao | Sun database: Large-scale scene recognition from abbey to zoo[END_REF]. We extract the features from the various layers of ResNet-50 and evaluate the classification performance (Figure 1). The results increase during the first layers but stabilize from Block-4 on. Based on these results and considering that a higher spatial resolution is better, we have selected layer 'res4f relu' (end of Block-4, after ReLU) for the experiments on multimodal MT. We also compared the features from different networks on the task of image description generation with the system of Xu et al. [START_REF] Xu | Show, attend and tell: Neural image caption generation with visual attention[END_REF]. The results for generating English descriptions (Table 3) show a clear performance improvement from VGG-19 to ResNet-50, but comparable results are obtained when going to ResNet-152. Therefore, given the increase in computational cost, we have decided to use ResNet-50 features for our submission.

Multimodal NMT System

The multimodal NMT system is an extension of [START_REF] Xu | Show, attend and tell: Neural image caption generation with visual attention[END_REF] and the monomodal NMT system described in Section 2.2. [START_REF] Xiao | Sun database: Large-scale scene recognition from abbey to zoo[END_REF] for ResNet-50:

The colored groups represent the building blocks while the bars inside are the stacked blocks [START_REF] He | Deep residual learning for image recognition[END_REF]. Table 3: BLEU scores for various deep features on the image description generation task using the system of Xu et al. [START_REF] Xu | Show, attend and tell: Neural image caption generation with visual attention[END_REF].

The model involves two GRU layers and an attention mechanism. The first GRU layer computes an intermediate representation s j as follows:

s j = (1 -z j ) s j + z j s j-1 (3)
s j = tanh(W E[y j-1 ] + r j (U s j-1 )) (4) r j = σ(W r E[y j-1 ] + U r s j-1 ) (5)
z j = σ(W z E[y j-1 ] + U z s j-1 ) (6)
where E is the target word embedding, s j is the hidden state, r j and z j are the reset and update gate activations. W , U r , W r , U r , W z and U z are the parameters to be learned.

A shared attention layer similar to [START_REF] Firat | Multi-way, multilingual neural machine translation with a shared attention mechanism[END_REF] that consists of a fully-connected feedforward network is used to compute a set of modality specific attention coefficients e mod ij at each timestep j:

e mod ij = U att tanh(W catt h mod i + W att s j ) (7)
The attention weight between source modality context i and target word j is computed by applying a softmax on e mod ij :

α ij = exp(e txt ij ) N k=1 exp(e txt kj ) (8)
β ij = exp(e img ij ) 196 k=1 exp(e img kj ) (9)
The final multimodal context vector c j is obtained as follows:

c j = tanh( N i=1 α ij h txt i + 196 i=1 β ij h img i ) (10)
The second GRU generates s j from the intermediate representation s j and the context vector c j as follows:

s j = (1 -z j ) s j + z j s j (11)
s j = tanh(W c j + r j (U s j )) (12) r j = σ(W r c j + U r s j ) (13)
z j = σ(W z c j + U z s j ) (14)
where s j is the hidden state, r j and z j are the reset and update gate activations. W , U r , W r , U r , W z and U z are the parameters to be learned. Finally, in order to compute the target word, the following formulations are applied:

o j = L o tanh(E[y j-1 ] + L s s j + L c c j ) (15) P (y j |y j-1 , s j , c j ) = Softmax(o j ) (16)
where L o , L s and L c are trained parameters.

Generation

Since we are provided 5 source descriptions for each image in order to generate a single German description, we let the NMT generate a German description for each source and pick the one with the highest probability and preferably without an UNK token.

Data

The organizers provided an extended version of the Flickr30K Entities dataset [START_REF] Elliott | Multi30K: Multilingual English-German Image Descriptions[END_REF] which contains 5 independently crowd-sourced German descriptions for each image in addition to the 5 English descriptions originally found in the dataset. It is possible to use this dataset either by considering the cross product of 5 source and 5 target descriptions (a total of 25 description pairs for each image) or by only taking the 5 pairwise descriptions leading to 725K and 145K training pairs respectively. We decided to use the smaller subset of 145K sentences.

Side Vocabulary Words

English 16802 1.5M German 10000 1.3M The preprocessing is exactly the same as Task 1 except that we only kept sentence pairs with sentence lengths ∈ [3, 50] and with a ratio of at most 3. This results in a final training dataset of 131K sentences (Table 4). We picked the most frequent 10K German words and replaced the rest with an UNK token for the target side. Note that compound splitting was not done for this task. As we can see in Table 5, the multimodal system does not surpass monomodal NMT system. Several explanations can clarify this behavior. First, the architecture is not well suited for integrating image and text representations. This is possible as we did not explore all the possibilities to benefit from both modalities. Another explanation is that the image context contain too much irrelevant information which cannot be discriminated by the lone attention mechanism. This would need a deeper analysis of the attention weights in order to be answered.

Results and Analysis

Human multimodal description

To evaluate the importance of the different modalities for the image description generation task, we have performed an experiment where we replace the computer algorithm with human participants. The two modalities are the five English description sentences, and the image. The output is a single description sentence in German. The experiment asks the participants for the following tasks:

• Given both the image and the English descriptions: 'Describe the image in one sentence in German. You can get help from the English sentences provided.' • Given only the image: 'Describe the image in one sentence in German.' • Given only one English sentence: 'Translate the English sentence into German.'

The experiment was performed by 16 native German speakers proficient in English with age ranging from 23 to 54 (coming from Austria, Germany and Switzerland, of which 10 are female and 6 male). The experiment is performed on the first 80 sentences of the validation set. Participants performed 10 repetitions for each task, and not repeating the same image across tasks. The results of the experiments are presented in Table 6. For humans, the English description sentences help to obtain better performance. Removing the image altogether and providing only a single English description sentence results in a significant drop. We were surprised to observe such a drop, whereas we expected good translations to obtain competitive results. In addition, we have provided the results of our submission on the same subset of images; humans clearly obtain better performance using METEOR metrics, but our approach is clearly outperforming on the BLEU metrics. The participants were not trained on the train set before performing the tasks, which could be one of the reasons for the difference. Furthermore, given the lower performance of only translating one of the English description sentences on both metrics, it could possibly be caused by existing biases in the data set.

Conclusion and Discussion

We have presented the systems developed by LIUM and CVC for the WMT16 Multimodal Ma-chine Translation challenge. Results show that integrating image features into a multimodal neural MT system with shared attention mechanism does not yet surpass the performance obtained with a monomodal system using only text input. However, our multimodal systems do improve upon an image captioning system (which was expected).

The phrase-based system can benefit from rescoring with multimodal neural language model as well as rescoring with a neural MT system. We have also presented the results of a human evaluation performing the same tasks as proposed in the challenge. The results are rather clear: image captioning can benefit from multimodality.

Figure 2 :

 2 Figure 2: The architecture of the multimodal NMT system. The boxes with * refers to a linear transformation while Φ(Σ) means a tanh applied over the sum of the inputs. The figure depicts a running instance of the network over a single example.

  convincingly show that CNNs yield a far superior image representation compared to previously used hand-crafted image features. Based on this success an intensified research effort started to further improve the representations based on CNNs. The work of Simonyan and Zisserman[START_REF] Simonyan | Very deep convolutional networks for large-scale image recognition[END_REF] improved the network by breaking up large convolutional features into multiple layers of small convolutional features, which allowed to train a much deeper network. The organizers provide these features to all participants. More precisely they provide the features from the fifth convolutional layer, and the features from the second fully connected layer of VGG-19. Recently, Residual Networks (ResNet) have been proposed

Table 2 :

 2 BLEU and METEOR scores on detokenized outputs of baseline and submitted Task 1 systems. The METEOR scores in parenthesis are computed with -norm parameter.

				System Description	Validation Set	Test Set
						METEOR (norm) BLEU METEOR (norm) BLEU
				Phrase-based Baseline (BL)	53.71 (58.43)	35.61	52.83 (57.37)	33.45
				BL+3Features		54.29 (58.99)	36.52	53.19 (57.76)	34.31
				BL+4Features		54.40 (59.08)	36.63	53.18 (57.76)	34.28
				Monomodal NMT		51.07 (54.87)	35.93	49.20 (53.10)	32.50
				Multimodal NMT		44.55 (47.97)	28.06	45.04 (48.52)	27.82
		100
	Accuracy	85 90 95
		80	block2	block3	block4	block5
				ResNet Layers
	Figure 1: Classification accuracy on a subset of
	SUN scenes

Table 4 :

 4 Training Data for Task 2.

Table 5 :

 5 BLEU and METEOR scores of our NMT based submissions for Task 2.

	System	Validation	Test
		METEOR BLEU METEOR BLEU
	Monomodal	36.3	24.0	35.1	23.8
	Multimodal	34.4	19.3	32.3	19.2

Table 6 :

 6 BLEU and METEOR scores for human description generation experiments.

	Method BLEU-1 BLEU-2 BLEU-3 BLEU-4 METEOR
	Image + sentences	54.30	35.95	23.28	15.06	39.16
	Image only	51.26	34.74	22.63	15.01	38.06
	Sentence only	39.37	23.27	13.73	8.40	32.98
	Our system	60.61	44.35	31.65	21.95	33.59

m2cr.univ-lemans.fr

github.com/kelvinxu/arctic-captions

Acknowledgments

This work was supported by the Chist-ERA project M2CR 4 . We kindly thank KyungHyun Cho and Orhan Firat for providing the DL4MT tutorial as open source and Kelvin Xu for the arcticcaptions 5 system.


Amelia Carolina Sparavigna

Harold Grad and the Boltzmann Equation

Grad was a mathematician who specialized in the kinetic theory of gases and in the statistical mechanics applied to plasma physics and magnetohydrodynamics. Here we discuss in particular his article entitled Theory of the Boltzmann Equation, published in 1964.

In the Internet Archive, non-profit digital library which is providing free public access to collections of digitized materials, we can always find publications of great value for studies concerning the history of science. During a research made for finding articles and books on Ludwig Boltzmann, one of the fathers of the equilibrium statistical mechanics and, according to [1], the father of the non-equilibrium statistical mechanics, I found an interesting publication. It is entitled "Theory of the Boltzmann Equation", written by Harold Grad and published in 1964 by the Courant Institute of Mathematical Sciences, New York University. This publication diverted my research from Boltzmann into an investigation concerning the Grad's works, in particular his interest in the study of Boltzmann Equation (it could not be otherwise, for a person who investigates the thermal transport by means of this equation [2-6]). Harold Grad (1923Grad ( -1986) ) was born in New York City. He was a mathematician who specialized in statistical mechanics applied to plasma physics and magnetohydrodynamics [7,8]. Grad became bachelor in 1943,

schemes -a detailed discussion is given in [START_REF] Blank | Harold Grad[END_REF]. References to some of the articles written by Harold Grad are given in . Let us consider [START_REF] Grad | Theory of the Boltzmann equation[END_REF], the Grad's publication that we can find in Archive (references [START_REF] Grad | The guiding center plasma[END_REF][START_REF] Grad | Thermonuclear reaction rates in and electrical discharge[END_REF][START_REF] Grad | Theory of cusped geometries. I: General Survey[END_REF][START_REF] Grad | Accuracy and limits of applicability of solutions of equations of transport; dilute monoatomic gases[END_REF][START_REF]Singular and nonuniform limits of solutions of the Boltzmann equation[END_REF][START_REF] Grad | Classical plasma diffusion[END_REF][START_REF] Grad | Equations of flow in a rarefied atmosphere[END_REF] are mentioning some of the other Grad's works present in the same digital library). In the abstract of [START_REF] Grad | Theory of the Boltzmann equation[END_REF], Grad tells that he was presenting a survey of the theory of the Boltzmann equation for a dilute monoatomic gas and also an improved understanding of the significance of the approximate results that we can obtain from the equation, in the framework of a "general trend away from ad-hoc and toward more precise mathematical procedures". So we find in the Introduction, which is a survey on Boltzmann equation, several references concerning the solution of this equation. Then, he continues addressing the general problem of the existence of solutions. "The development of a comprehensive theory of existence and qualitative behavior of solutions of the Boltzmann equation serves several "practical" purposes". The first purpose is to find a help for recognizing "whether the solution of a specific problem is representative of more general cases or whether it may be exceptional. For example, … almost all the extrapolations that were drawn from a very ingenious explicit solution of an exact nonlinear Boltzmann equation have been found to be nonrepresentative of the behavior of the typical solution". To reinforce his point of view, he tells that "A more basic goal of a general theory is to determine whether or not there exist any solutions at all. Such a failure is always a conceptual possibility no matter how convincing are the physical arguments which suggest the validity of the equation and no matter how plausible are the results derived by nonrigorous approximate means". And this happens because, in the Boltzmann equation we have an "inherent sensitivity of mathematical structures to arbitrarily small perturbations, which are undetectable physically". For this reason, according to Grad, the validity of the Boltzmann equation "must be based on studies of internal mathematical consistency as well as on external confrontation with experiment". He also noted that, at the time, this was particularly true for the existence of nonlinear solutions. "There are powerful existence theorems for spatially homogeneous problems in which fluid dynamics plays no role. But the question of the existence of spatially dependent solutions is certainly as difficult as the corresponding question in nonlinear fluid dynamics, and this connection is, in fact, the most important heuristic argument for a belief in the reasonable behavior of solutions of the Boltzmann equation in the large". Another difficulty evidenced by Grad is the following: "there is no evidence at this time that even the linear equation has any validity for potentials of infinite total cross-section. We would expect no physical differences between an infinite range but rapidly decaying intermolecular potential and a closely approximating cutoff potential; but the mathematics seems to be extraordinarily sensitive to this point … (we must distinguish solution of the Boltzmann equation from solution of the integral equation which arises in transport theory and which is known to be insensitive to "small" changes in the potential)". It is clear from these sentences that the approach of a mathematician, as Grad was, to the Boltzmann equation is different from that of a physicist, who is involved in the study of the transport theory, such as the Boltzmann equation for the thermal transport [START_REF] Sparavigna | On the Boltzmann Equation of Thermal Transport for Interacting Phonons and Electrons[END_REF]. In [START_REF] Grad | Theory of the Boltzmann equation[END_REF], Grad is reporting the researches he made concerning the "significance of the Hilbert and Chapman-Enskog theories as formal asymptotic expansions of solutions of the Boltzmann equation". He tells that this significance was described qualitatively in 1958 [START_REF] Grad | Principles of the kinetic theory of gases[END_REF], but in the Section 3 of [START_REF] Grad | Theory of the Boltzmann equation[END_REF], it is described in full mathematical detail (to see how Hilbert was involved in the problem, I suggest article in Ref. [START_REF] Golse | The Boltzmann equation and its hydrodynamic limits[END_REF], where the reader can find the discussions on Chapman-Enskog expansion and the method of moments too).

According to Grad, the crucial point is the necessity to give an interpretation of "the variables that arise naturally in the Hilbert and Chapman-Enskog expansions as suitably modified macroscopic properties of the gas". "As a result of this generalized interpretation, the Chapman-Enskog expansion has been found to be representative of very general solutions of the Boltzmann equation in the limit of small mean free path". Then, we find the Section 3 of [START_REF] Grad | Theory of the Boltzmann equation[END_REF] devoted to the small mean free path, the Section 4 to the large mean free path and the Section 5 to the role of the spectrum in the interplay between the streaming term and the collision term of the Boltzmann equation. In reading these sections, any physicist who is working on the Boltzmann equation, fells that her/his comprehension of the Boltzmann equation has been increased. This is coming from the fact that the Grad's work is approaching the Boltzmann equation from a rather different point of view. It was that of a physical mathematician who aimed to study the equation in the framework of an "internal" mathematical consistency and an "external" confrontation with physical observable quantities.

Then he obtained his Master's Degree in 1945 from New York University. Grad did his doctoral work under Richard Courant and graduated in 1948, with a thesis on the "Approximation in the Boltzmann Equation by Moments". After the doctoral work, he continued to work at the Courant Institute of Mathematical Sciences of New York University

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Published on Friday 5th January, 2018 at 13:30:43.

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The complete electrification of aircraft power systems entails the implementation of smart logics for sharing the available energy among the loads, and the design of these logics requires the characterisation of the power absorption of each on-board system as a function of mission phase and aircraft operating point, also taking into account the level of criticality of the function implemented by the system itself.

The paper describes the models of the electro-mechanical systems used for the landing gear extension/retraction and the flight control actuation of a regional aircraft, with the basic objective of evaluating the power requests that have to be fulfilled both continuously and completely for these safetycritical equipments.

The Flight Control System (FCS) model is composed of both primary and secondary flight controls. The Landing Gear System (LGS) model refers to the landing gear of a typical turboprop regional aircraft, with a single nose and two main gears. Both the landing gears (LGs) and the control surfaces (CSs) are driven by Electro-Mechanical Actuators (EMAs). All the EMA models refer to actuators with a 3-phase synchronous brushless motor and mechanical transmission.

Simulation tests have been performed to assess the maximum power flows characterizing the systems, with reference to severe operative conditions.

INTRODUCTION

The design of modern airborne systems and equipments constantly tends towards both technology innovation and integration of functions. This trend has been recently emphasized by research programs oriented to the conversion of aircraft systems to the "all-electric" solution [START_REF] Emadi | Aircraft power systems: technology, state of the art, and future trends[END_REF][2][3] [START_REF] Rosero | Moving towards a more electric aircraft[END_REF], which, together with well-known advantages in terms of ecosustainability and cost reduction, point out important issues on power management for systems [START_REF] Maldonado | Power management and distribution system for a more electric aircraft (MADMEL) -Program status[END_REF][6] [START_REF] Anastasio | Power by wire meets environmental friendly requirements[END_REF]. Actually, in the conventional aircraft technological frame, different types of power are derived from the engine for supplying the on-board systems: pneumatic, hydraulic, mechanical and electrical. The All-Electric Aircraft (AEA) concept is instead based on the replacement of hydraulic and pneumatic power with the electrical one, so that the suggestive acronym PbW (Power by Wire) has been coined for referring to a solution that satisfies all power requests by cables. As for any technological innovation, new issues related to performances and reliability are expected, as well as systems interactions concerns in terms of electrical power flows. The challenging all-electric objective can be thus achieved by appropriately monitoring and managing the power requests (e.g. by temporarily reducing the power supplied to some systems during those flight phases in which the total request of electrical power could overcome the maximum available). Energy Management System (EMS) logics needed to be used, starting from the characterisation of the power absorption of each on-board system as a function of mission phase and aircraft operating point. In this context, a strong effort is required for system engineers to develop models that are capable of predicting the aircraft systems power flows.

Modelling and simulation activities play a key role in this design loop: firstly, because the power management can be simulated in operating conditions that are difficult to be tested in flight or via on-ground rigs, pointing out criticalities; secondly, because the electro-mechanical actuation is a novel technology for flight controls and the landing gears, and the influence on performances of electrical power quality, actuator thermal behaviour and power electronics efficiency needs to be investigated and predicted.

LANDING GEAR SYSTEM DATA

The model has been developed with reference to a LGS of a typical turboprop regional aircraft with one nose LG and two main LGs.

The LGS parameters have been defined starting from the basic design parameters reported in Table 1. Essentially, the basic scheme is composed of two main models:

Nose

an EMA model and a dynamics and kinematics model of landing gear (or control surface). It is worth noting the strong interaction existing between these two models. EMA model evaluates the actuator rod dynamics, whose acceleration, velocity and position are needed to the dynamics and kinematics model to provide the force acting on EMA itself. This interaction determines an algebraic loop in the Simulink scheme that has been solved by applying an integration step delay (memory block) on the force signal.

m q m q d d d d i p L dt di L i R V ω - + = (1) m b m d m d q q q q K i p L dt di L i R V ω ω + + + = (2)
where R is the motor phase resistance, pm is the number of magnet pole pairs, ωm is the motor shaft speed, Kb is the motor back-electromotive force coefficient, while Vd, Vq, Ld, Lq, id and iq are the voltages, the inductances and the currents along the direct and the quadrant axes respectively.

The motor torque is then provided by Eq. ( 3),

q b d m q d m i K i p L L T ] ) [( + - = (3)
and the system dynamics can be described with a single momentum equation, referred to the motor shaft, Eq. ( 4),

) sgn( m tot f m tot ext m m tot T B T T J ω ω ω - - - = (4) where 2 g tot m g J J J τ = + (5) s ext ext g l T F τ = (6)
In Eq. ( 4), Jtot is the EMA inertia, Btot is overall viscous damping coefficient, Tf_tot accounts for sliding friction effects (Coulomb, Stribeck, etc.), Text is the external loading torque, scaled to the motor shaft; while in Eqs. ( 5)-( 6), ls is the screw lead pitch, τg is the gearbox ratio, Fext is the external force (coming from the Concerning the EMA control, four simply proportional loops are used (position, motor speed, direct current and quadrant current), Eqs. ( 7)- [START_REF] Roskam | Methods for Estimating Drag Polars of Subsonic Airplanes[END_REF],

d i d i K V - = (7)
) (

q i q i q i i K V - = (8) ) ( m i m i q K i ω ω ω - = (9) ) ( a i a x i m x x K - = ω ( 10
)
in which Ki , Kω , Kx are the control gains of the current, speed and position loops respectively, and xa is the actuator position, Eq. [START_REF] Hoerner | Fluid-Dynamic Drag[END_REF].

s a m g l x ω τ = (11)
In addition, both voltage and current demands are limited, by saturating the related values to Vmax and imax respectively.

The power balance is given by Eq. (12). In Eq. ( 12), Pe (in) is the electrical power input, Pe (s) is the electrical power stored in the system (only related to inductance, no capacitors are considered), Pe (l) is the electrical power lost in the circuitry, Pm (s) and Pm (l) are the mechanical powers stored and lost respectively, while Pm (out) is the mechanical power output, Eqs. ( 13)-(18).

) ( ) ( ) ( ) ( ) ( ) (
q q d d in e i V i V P + = ) ( (13) q q q d d d s e i dt di L i dt di L P + = ) ( (14) ) ( 2 2 ) ( q d l e i i R P + = (15) m m tot s m J P ω ω = ) ( (16) m m tot f m tot l m T B P ω ω ω ) sgn( 2 ) ( + = (17) m ext out m T P ω = ) ( ( 18
)
As an example, table 1 reports the basic data of the EMA system referred to the aileron control surfaces. The LG mass and the aerodynamic force are assumed to be concentrated in the point H. The latter is modelled as a drag force, with a constant drag coefficient (derived from [START_REF] Roskam | Methods for Estimating Drag Polars of Subsonic Airplanes[END_REF][11]), not dependent on the LG stroke. For each LG (nose, left, right), the points H and 3 are assigned through their coordinates on a

Parameter

LG-fixed reference frame (C; XLG, YLG, ZLG -Figure 5), while the origin C of such a frame and the point 2 are assigned through their coordinates on the A/C body-fixed reference frame (O; XB, YB, ZB). The XLG axis of the LG reference frame coincides with the axis of rotation of the landing gear during extension and retraction. The axis ZLG has been chosen so that the XLG -ZLG plane contains the point H.

The orientation of the LG reference frame is defined by the Euler's angles with respect to the A/C reference frame (Figure 3). The CS rotation is actuated by an EMA acting on the above mentioned CS structure, and linked to the CS and to the respective wing structure by spherical hinges (points 2 and 3

Figure 7).

The force acting on the EMA rod is calculated by taking into account the CS weight, the aerodynamic effects (by means of the hinge moment calculation, which refers to point C Figure 7)

and the effects of CS acceleration due to A/C manoeuvres and CS rotation. The CS mass is assumed to be concentrated in the control surface's centre of gravity (i.e.: point H Figure 7).

For each CS, the points H and 3 are assigned through their coordinates on a CS-fixed reference frame (C;XCS,YCS,ZCS -Figure 6), while the origin C of such a frame and the point 2 are assigned through their coordinates on the A/C body-fixed reference frame (O;XB,YB,ZB, Figure 6).

The XCS axis of the CS reference frame coincides with the CS hinge axis (Figure 6). The axis ZCS is aligned with CS chord.

The position of the origin of the CS reference frame (point C Figure 6) is chosen in the middle point of the hinge segment.

The orientation of the CS reference frame is defined by the Euler's angles with respect to the body reference frame (φCS, θCS, ψCS). Such angles are defined accordingly to the classical texts of flight mechanics. Thus, the sequence of rotations which superimposes the body reference frame to the CS one is: ψCS, θCS, φCS.

The aerodynamic hinge moment on a generic control surface is given by:

) , , Re , ( 2 2 CS h CS CS H CS f f CS CS δ α M C s mac U ρ H CS CS = ( 19
)
where ρ is the air density, UCS is the local airspeed magnitude, CS f mac is the mean aerodynamic chord of the CS considered as an isolated wing, .

By applying these assumptions, the evaluation of hinge moments for the control surfaces is provided by the following relation:

0 1 2 ( ,Re ) ( ,Re , ) CS CS H H CS CS CS hCS CS hCS hCS C C b M α b M δ δ = + ⋅ + ⋅ (20) where ) Re , ( 1 CS

CS M b

is a constant coefficients evaluated as a function of M and ReCS, while 2 ( ,Re , )

hCS CS hCS b M δ is evaluated ( ) ( ) 2 2 , , 1
lin CS CS hCS lin hCS lin CS CS hCS double lin b M Re for δ δ δ δ b M Re for δ δ δ δ ⎧ ⎪ ≤ ⎪ ⎪ ⎨ ⎪ ⎡ ⎤ - ⎪ ⋅ + ≥ ⎢ ⎥ ⎪ - ⎣ ⎦ ⎩ (21) Z CS X CS Y B X B C Z B X B Y CS Z CS
Executive Secretariat 6 Rue Galilée 75016 PARIS Phone : 33 (0) 1 56 64 12 35 Fax : 33 (0) 1 56 64 12 31 E-mail : alexa.faucher@aaaf.asso.fr

In addition the product

CS h CS CS h δ M b ⋅ ) Re , (
) sin( ) Re , ( 2 CS h sat CS CS h CS CS h δ K δ M b ⋅ ≤ ⋅ (22)
The coefficients depending on the Mach number and the Reynolds number have been calculated by means of the ESDU method [START_REF]Hinge moment coefficient derivatives for trailing-edge controls on wings at subsonic speeds -Educational Science Data[END_REF][9] on the basis of the Net equivalent wing geometry. Referring to FCS simulation, the commands are provided in levelled flight conditions, at A/C speed of 100 m/sec, A/C load factor of 2.5 and with zero angle-of-attack and sideslip. The command deflections are characterized by large amplitudes and speeds (figure 12) and, for each CS, a motion inversion is imposed when the actuators achieves the maximum deflections (maximum hinge moments). In addition, such commands have been built in order to synchronize the control surfaces power peaks, thus to have an assessment of the maximum FCS power requests.

Even if it could be argued that these tests are referred to excessively severe conditions (e.g. contemporary extreme deflections on all CS), this approach provides an estimation of the maximum system power flows, by using the systems specifications only.

RESULTS

Figure 10 report the time histories of power flows for nose LG and left main LG during the extension and retraction phases.

The time history of the actual EMAs stroke is shown in Figure 9, together with the commanded ones. For the considered LGS configuration, the nose LG EMA works in brake mode (negative mechanical power) during all the extension phase and in motor mode during all the retraction phase. The behaviour of the main

LGs is slightly different because the EMA works in motor mode during the last part of the extension phase and in brake mode during the first part of the retraction phase.

In the test, the total maximum LGS power absorption ranges about 2.5 kW, and a similar value is generated by the EMAs in brake mode (to be dissipated or available for regeneration).

Considering that the LGs move at constant speeds, the power flows variations can be interpreted by using the results in terms of applied forces (Figure 11). For the nose LG, weight and drag contribute in equal measure, while for the main LGs the weight contribution is predominant. For all LGs, the inertial contributions demonstrate to be significant with respect to the other forces.

Figure 13 shows the power flows for each CS of the FCS. The models evaluate identical power flows for EMAs driving the CSs located symmetrically with respect to the A/C longitudinal plane (e.g. left and right ailerons), for the same commanded deflections. For this reason, all the power flows of figure 13, except those referred to rudder (single surface) are the sum of equal contributes. In addition, it is relevant to observe that the aerodynamic interaction among the different CS is neglected, while the flaps deflection modifies the total lift coefficient that affects the down-wash angle and the Elevators aerodynamic.

As for the LGS test case, Figure 13 highlights the phases in which the EMAs work in brake mode or in motor mode. In the test, the total maximum FCS power absorption ranges about

FLIGHT 1

 1 CONTROL SYSTEM DATAThe model is developed with reference to a FCS composed of the following control surfaces (Figure

Figure 1 :

 1 Figure 1: Flight control system layout BASIC SIMULATION MODEL Both FCS and LGS models have been developed by using for each landing gear and each control surface the basic Matlab-Simulink scheme depicted in Figure 2.

Figure 2 :

 2 Figure 2: Matlab-Simulink Basic model

  : alexa.faucher@aaaf.asso.fr dynamics and kinematics model), and Jm and Jg are the inertia of the motor shaft and gears, respectively.

  ).Each LG is represented by a generic rigid structure, which includes the fully extended shock absorber, bound to the A/C in the point C. The extension / retraction is obtained thanks to a rotation about an axis whose direction is assigned within the input parameters of the landing gear. The LG rotation is actuated by an EMA acting on the LG structure, and bound to the LG and to the A/C structure by spherical hinges (points 2 and 3 in Figure4).

Figure 3 Figure 4

 34 Figure 3 Landing gear system layout and body-fixed A/C reference frame

Figure 5

 5 Figure 5 Euler's angles of landing gear reference frame w.r.t. body reference frame FLIGHT CONTROL SYSTEM DYNAMICS AND KINEMATICS MODEL The same simplified architecture and the same kinematics (Figures 6 and 7) are considered for all the control surface

s

  is the span of the flapped portion of the aerodynamic surface under consideration (wing, horizontal tail or rudder) and CS H C is the hinge moment coefficient. The last coefficient is assumed to be a function of the Mach number (M), the Reynolds number ( CS Re ), evaluated by means of the mean aerodynamic chord of the aerodynamic surface containing the specific CS and the local airspeed, the local angle of attack ( CS α ) and the deflection of the control surface (

Figure 6 -Figure 7 -

 67 Figure 6 -Plain control surface general architecture

2

  is limited by the following saturation function:

Figure 8

 8 Figure 8 shows a diagram of the variation of the hinge moment coefficient (

Figure 8 -

 8 Figure 8 -Hinge moment coefficient with respect to control deflection (aileron example) SIMULATION TESTS Two simulation tests are presented in this section, as examples of assessment of LGS and FCS power flows in severe operative conditions. Concerning the LGS simulation, the test (Figure 9) is characterized by an extension phase and a retraction phase, performed in levelled flight conditions, at an A/C speed of 70 m/sec and an A/C load factor of 2.5. During the extension phase, the nose LG EMA retracts, while the EMAs of the main LGs extend.

Figure 10

 10 Figure9LGS position dynamics

Figure 11 Force

 11 Figure 11 Force applied to LGS

Table 1 :

 1 . Denti, G. Di Rito, R. Galatolo and F. Schettini Dipartimento di Ingegneria Civile ed Industriale -Università di Pisa, Via G. Caruso 8, 56122 -Pisa f.schettini@ing.unipi.it (contact author), e.denti@ing.unipi.it, g.dirito@ing.unipi.it, r.galatolo@ing.unipi.it

	Executive Secretariat
	6 Rue Galilée
	75016 PARIS
	Phone : 33 (0) 1 56 64 12 35
	Fax : 33 (0) 1 56 64 12 31
			Elevators
	Aileron	Spoilers
		Flaps	Rudder
		Flaps
			Spoilers
			Aileron

Basic requirements for the landing gear system ALL-ELECTRIC FLIGHT CONTROL SYSTEM AND LANDING GEAR SYSTEM MODELS FOR POWER ASSESSMENT STUDIES EE-mail : alexa.faucher@aaaf.asso.fr

ACKNOWLEDGEMENTS

This work was partially granted under the Green Regional Aircraft ITD, All Electric Aircraft domain, of the Clean Sky JTI, within the European framework Program 7. The authors gratefully acknowledge the support by Clean Sky, the European Commission and all the partners involved into the All Electric Aircraft domain.


Amelia Carolina Sparavigna

The Invention of Carborundum, the Synthetic Silicon Carbide

In origin, the semiconductor Silicon Carbide was known as the Carborundum. Here a short history of its invention from the pen of its inventor Edward Goodrich Acheson.

.

.

 [START_REF] Sparavigna | Using Google Ngram Viewer for Scientific Referencing and History of Science[END_REF].

Using the form of the Ngram Viewer, among the first publications on carborundum we find the book entitled "Carborundum: Its History, Manufacture and Uses", written by Acheson and published in 1893.

As previously told, Edward Goodrich Acheson (1856Acheson ( -1931) ) was the American chemist who invented the synthetic silicon carbide. He was the inventor of the Acheson process, still used to produce carborundum [START_REF] Weimer | Carbide, Nitride and Boride Materials Synthesis and Processing[END_REF] (let us note that when he invented the synthetic SiC he also invented the synthetic graphite). In [START_REF] Fitzgerald | The Carborundum Furnace[END_REF], it is shortly described the process for obtaining the carborundum from a mixture of silica and carbon. At relatively low temperatures the silica-carbon mixture yields a greenish-colored amorphous substance, but as the temperature is increased this is converted into crystalline silicon carbide. "If the temperature is increased considerably beyond that of its formation, carborundum is decomposed, the silicon being expelled as vapor and the carbon left behind in the form of graphite" [START_REF] Fitzgerald | The Carborundum Furnace[END_REF]. And therefore, in 1896, Acheson was granted a patent for the graphite manufacture too. Commercial production started in 1897.

As told by Wilder D. Bancroft, President of the American Chemical Society, in the course of an entertaining address: "To make carborundum he needed electrodes, so he invented graphite. A suitable refractory was needed to keep the heat in the furnace, and Mr. Acheson thereupon prepared siloxicon", a refractory material [START_REF] Acheson | A pathfinder: discovery, invention and industry; how the world came to have aquadag and oildag; also carborundum, artificial graphite and other valuable products of the electric furnace. The first of a series of educational biographical sketches of eminent inventors[END_REF]. "You may consider Mr. Acheson's discoveries as scientific inventions or as dividend payers. It makes no difference. They stand all tests, and they mark him as one of the great inventors of the world."

In the Internet Archive, the non-profit digital library which is providing free public access to collections of digitized materials, we can find what Acheson wrote about the discovery of carborundum [START_REF] Acheson | A pathfinder: discovery, invention and industry; how the world came to have aquadag and oildag; also carborundum, artificial graphite and other valuable products of the electric furnace. The first of a series of educational biographical sketches of eminent inventors[END_REF][START_REF] Acheson | Carborundum; its history, manufacture and uses[END_REF]. In [START_REF] Acheson | A pathfinder: discovery, invention and industry; how the world came to have aquadag and oildag; also carborundum, artificial graphite and other valuable products of the electric furnace. The first of a series of educational biographical sketches of eminent inventors[END_REF], we have a detailed report of the discovery. Acheson "did quite a great deal of experimenting during the Winter on various lines. I think it was in February, 1891, I was working on the making of rubber synthetically. I succeeded in producing a small piece, when at this critical moment Mr. John S. Huyler came from New York to see our plant. He was not pleased with the prospects. … With this, he left me to my own resources. His remarks discouraged me regarding rubber; I dropped the subject and resolved to endeavor to produce an artificial abrasive. … The value of a good abrasive was brought to my attention by a remark incidentally made in 1880 by Dr. George F. Kunz of Tiffany & Company, New York. I also remembered the observation of clay impregnated with carbon I made at Gosford, and I decided to make experiments on impregnating clay with carbon under the influence of electric heat. An iron bowl, such as plumbers use for holding their melted solder, was attached to one lead from a dynamo and filled with a mixture of clay and powdered coke, the end of an arc light carbon attached to the other lead was inserted into the mixture. The percentage of coke was high enough to carry a current, and a good strong one was passed through the mixture between the lamp carbon and bowl until the clay in the center was melted and heated to a very high temperature" (for carbon-arc lamps see [START_REF] Sparavigna | Carbon-Arc Light as the Electric Light of 1870[END_REF]). "When cold, the mass was examined. It did not fill my expectations, but I, by sheer chance, happened to notice a few bright specks on the end of the arc carbon that had been in the mixture. I placed one on the end of a lead pencil and drew it across a pane of glass.

It cut the glass like a diamond. I repeated the experiment, and collected enough of the material to test its abrasive qualities". Then, a "friend by the name of W. C. McCallister, a druggist of Monongahela, and I started for New York. On the way we coined a name for my new and, as yet, unnamed material. Under the impression, without any chemical analysis, that it was composed of carbon and corundum, I called it Carborundum" (the corundum is a crystalline form of alumina, the aluminium oxide). Then, Acheson "organized The Carborundum Company, and incorporated it for one hundred and fifty thousand dollars on the 21st day of September, 1891". In [START_REF] Fitzgerald | The Carborundum Furnace[END_REF] it is also told that "some months after the discovery of Carborundum", he learned from analyses that it was a compound of carbon and silicon, "and not alumina, the formula being SiC". This was the invention of carborundum as described by its inventor.

Figure 1 :

 1 Figure 1: Carborundum and Silicon Carbide in Google Ngram Viewer [6].

Some Acheson's publications and patents are given in [START_REF] Acheson | Carborundum and Siloxicon[END_REF][START_REF] Acheson | On carborundum[END_REF][START_REF] Acheson | Manufacture of graphite[END_REF][START_REF] Acheson | Deflocculated graphite[END_REF][START_REF] Acheson | Graphite: its formation and manufacture[END_REF][START_REF] Acheson | Method of graphitizing electrodes[END_REF][START_REF] Acheson | Producing pure electric-light carbon[END_REF][START_REF] Acheson | Process of making graphite[END_REF][START_REF] Acheson | Siloxicon-coated carbon-containing article[END_REF][START_REF] Acheson | Manufacture of earthenware[END_REF][START_REF] Acheson | Refractory body and process of making the same[END_REF][START_REF] Acheson | Thermo-electric generator[END_REF].

In the Internet Archive, we can find other publications interesting for our short history on carborundum. In [24], we can see the manufacture of Carborundum at Niagara Falls, with several illustrations, and in [START_REF] Burnham Jacobs | Abrasives & Abrasive Wheels, Their Nature, Manufacture and Use: A Complete Treatise[END_REF] a treatise on abrasives. In [START_REF] Packer | Radio For The Amateur: The Underlying Principles Of Receiving And The Construction And Operation Of Receiving Sets[END_REF], we find the material described for a radio detector. "The usual crystal detector uses a crystal of galena, which, while it is the most sensitive crystal that we can find, has one disadvantage, in that it is easily jarred out of adjustment".

In [START_REF] Packer | Radio For The Amateur: The Underlying Principles Of Receiving And The Construction And Operation Of Receiving Sets[END_REF], it is therefore suggested and discussed the carborundum as crystal detector.

Let us conclude by mentioning document [START_REF]Illegitimus non Carborundum, by Central Intelligence Agency[END_REF], from the Internet Archive too. The title of the document illustrates the use of the word carborundum in a pseudo-Latin aphorism "Illegitimi non carborundum", a phrase originated during World War II, which we can render as "don't let despicable persons grind you down". Of course, no use of carborundum as a verb is legitimate Latin, because carborundum is a noun, and not a gerundive of any verb. In any case, after this aphorism had been created, carborundum found its place in the popular culture too.

Information about this Article

Published on Sunday 7th January, 2018 at 17:01:05.

The full citation for this Article is:

Sparavigna, A. C. (2018). The Invention of Carborundum, the Synthetic Silicon Carbide. PHILICA.COM Article number 1212.


E Denti

F Schettini

G Di Rito

R Galatolo

Dynamic Simulation of the Electrical Energy Consumption of Future Regional All-Electric Aircraft

The paper presents a numerical tool named Shared Simulation Environment (SSE) developed within Clean Sky -Green Regional Aircraft (GRA) platform -All Electric Aircraft (AEA) domain.

In the All-Electric Aircraft an appropriate management of the electrical power available aboard is required to avoid excessive peaks of consumption with respect to generators capabilities.

The SSE aims at the dynamic simulation of the various onboard systems of the new all-electric GRA, with particular focus on the electrical energy absorption. Such a tool is developed in order to permit the design and the validation of the electrical energy management logics to be implemented aboard, which are under development within the project.

The paper briefly summarizes the scopes of GRA / AEA project and of the SSE tool. It describes the tool, the partners involved in its implementation, the energy management logics applied, the performances obtained and some examples of results.

INTRODUCTION

The goal of AEA project is to improve the environmental impact of aviation through a transition to all-electrical aircraft systems, in order to eliminate pneumatic power and hydraulic power [1].

Avoiding bleed air from the engine compressor increases the engine efficiency and to eliminate hydraulic power reduces the environmental impact of hydraulic fluids [START_REF] Howse | All Electric Aircraft[END_REF][START_REF] Emadi | Aircraft power systems: technology, state of the art, and future trends[END_REF][START_REF] Schettini | All-Electric Flight Control System and Landing Gear System Models for Power Assessment Studies[END_REF].

These objectives can only be achieved by properly monitoring and managing the electrical power requests through specific energy management logics to be implemented aboard the Allelectric aircraft to avoid temporary generator overloads, or possible lack of energy for powering safety-critical systems.

In this framework, the Shared Simulation Environment (SSE) tool, that is the numerical tool presented in this paper, is devoted to the simulation of the dynamic performance of the allelectrical on-board systems, with the particular aim of assessing the energy absorbed during the manoeuvres of the aircraft. The SSE is developed in order to permit the design and _______________________ ( 1 ) Corresponding author e-mail: e.denti@ing.unipi.it the validation of the electrical energy management logics under development within Clean Sky.

The models of on-board system are developed by dividing the work among Clean Sky partners, and also thanks to a Call for Proposals. And they are implemented using different simulation languages, and according to different levels of complexity.

The integration of the models in a single simulation environment (the SSE) is performed first at the University of Pisa, at a prototype level, and then thanks to a second Call for Proposals which aims at improving the SSE prototype, by transforming it in an high-quality simulation environment, having the features of an industrial software, to be used for the design and the validation of AEA plants.

The paper describes the architecture of SSE prototype and the methodologies used to integrate models developed in different simulation environments (AMESim, Dymola, Matlab/Simulink).

In addition, it illustrates the methodology used to have the models trimmed before the simulation starts, and the choice of key items, such as the integration solver and the time step. All these aspects have been treated with a special care to limit the computation time, in order to perform in reasonable times the large test campaigns needed for the validation of power management strategies.

PROJECT MANAGEMENT AND PARTICIPANTS

The AEA domain is lead by Alenia Aermacchi, and the

MODELS FEATURES

The models of the on-board systems are developed accordingly More precisely, UniPi is responsible for developing the SSE in a prototype form using Matlab/Simulink as the integration platform, then, thanks to the CfP 2011, an improved version will be developed. This will implement a high quality user interface, as well as co-simulation and multi-rate features, in order to improve as much as possible the software performance. The CfP 2011 was won by TWT GmbH Science & Innovation GMBH with a proposal named i-SSE [START_REF] Radermacher | Co-simulation for design, optimization and validation of all-electric aircraft systems[END_REF].

THE SSE PROTOTYPE

The SSE prototype is developed at UniPi using Matlab/Simulink as integration environment. It integrates the models at the functional level (Level 2), while the behavioural level models will be integrated into the final version of the SSE.

Figure 1 shows the high level scheme of the SSE prototype. A particular interface exists between EMS and ECS because it is not possible to obtain a reduction of the electrical power absorbed by the ECS by simply reducing the voltage available for the ECS itself. Actually, the EMS can ask the ECS for a reduction of the electrical power absorbed by the ECS itself (see Command to ECS in Figure 1). In its turn, the ECS sends warnings to the EMS when it cannot further reduce the power absorbed, because its performance can go out from the required range or because there is the risk of a compressor stall (ECS output flags).

Executive Secretariat 6 Rue Galilée 75016 PARIS Phone : 33 (0) 1 56 64 12 35 Fax : 33 (0) 1 56 64 12 31 E-mail : alexa.faucher@aaaf.asso.fr This approach, based on the progressive enabling, dramatically reduces also the possibility of incurring in the divergence of some systems during the trim-search phase.

MODELS INTEGRATION AND SIMULATION PERFORMANCE

The SSE prototype has to integrate into Simulink models written using Dymola/Modelica, AMESim and Matlab/Simulink. This can be done using two different methods. The first one is to convert Dymola/Modelica and AMESim models into Simulink S-Functions, and to import the S-Functions into Simulink. The second one is to use the co-simulation.

The main difference between the two methodologies is that in the first case all the models use the Simulink solver, while in the second case each model uses the solver of its own development environment.

Executive Secretariat 6 Rue Galilée 75016 PARIS Phone : 33 (0) 1 56 64 12 35 Fax : 33 (0) 1 56 64 12 31 E-mail : alexa.faucher@aaaf.asso.fr This is probably due to the fact that AMESim is able optimize the performance by automatically change the solver and the integration time step used, when it runs models developed in its own environment. This optimization is lost when the models are imported into Simulink.

The second method gave good results for both Dymola and AMESim.

For this reason, the SSE prototype uses both methods: it imports Dymola models as S-Functions and runs AMESim in co-simulation.

With this configuration we obtained quite good results, because the ration between computational time and simulation time is about 5. This means that a typical regional aircraft mission of 1.5 hours requires a computational time of about 7.5 hours.

This result has been obtained using the Simulink ode23 variable step solver.

This result can be considered acceptable for the SSE prototype, also because the manoeuvres to be simulated to validate the energy management logics will generally be limited to flight phases lasting a few minutes.

it is worth noting that fixed step integration methods are not suitable to be used because of the short integration step (between 10 -4 and 10 -5 seconds) required by the high frequency models (EPGS, FCS and LGS). This short step is used for all models, including those that could run with larger steps (ECS and CT), and it is not changed during the whole simulation. This has strong repercussions on the computation time.

EXAMPLE OF RESULTS

The SSE configuration used for the tests presented here, refers to the 90-pax turboprop regional A/C. In the following conditions: Max Continuous (Cruise), One Engine Operative, Two Electrical Generators Operative, ECS pack ON.

The EMS acts:

at High Frequency on SSPCs contactors which supply: the contrary, the action on the de-icing can reduce the supply voltage up to a value that is set at the 50% of the nominal voltage of 270 Volts. Finally, the action on the ECS is made through a specific interface, as explained above.

The two generators in parallel supply all loads. Such a configuration can be easily changed to test other architectures.

For example, one generator could supply the loads commanded by the EMS, and the other generator the other loads.

The EMS threshold was set at 140 KW. This value is based on the rough estimate of power absorptions shown in Table 4.

The central column in such a table reports an estimate of the continuous power absorptions in a cruise condition. The right column reports the maximum estimate peak loads, when FCS absorbs its maximum power [START_REF] Schettini | All-Electric Flight Control System and Landing Gear System Models for Power Assessment Studies[END_REF], the LG is actuated, and the de-Icing is on. The data reported in bold are those controlled by the EMS. The values reported in Table 4 were obtained partly by running system models and partly through the analysis of the requirements of these systems established by the partner responsible for of the systems themselves.

If we assume that in the continuous conditions we want to use 4).

This means that in the maximum peak conditions, without a proper action on the ECS, the systems would absorb the 97.5% Figure 4 shows the corresponding variation in the A/C cabin temperature around the target value o 23 degrees. It is possible to observe, also here, the cyclic behaviour due to the variations in ECS power.

It is worth noting that the total power in Figure 3 almost always remains below 145 KW, with the exception of fast peaks that reach 175 kW for very short times (less than 0.5 s). This seems to be a very good result although further tests with a more severe intervention of FCS must be done.

SSE FINAL IMPLEMENTATION

The behavioural models of LGS and FCS are developed within E-Bird project (CfP 2010). The remaining behavioural models are provided by the other organizations that participate into the GRA ITD. The final integration of SSE will be made within the iSSE project (CfP 2011) [START_REF] Radermacher | Co-simulation for design, optimization and validation of all-electric aircraft systems[END_REF].

CONCLUSIONS

The implementation of the SSE prototype is concluded even if extensive tests are still to be done to tune and validate the models. The activity showed that it is possible to integrate into Simulink the models developed using Dymola and AMESim, without particular problems. The integration can be done via cosimulation or by importing Dymola and AMESim models into Simulink as S-functions. However, the specific AMESim model that simulates the Cabin Thermal balance runs much better using the co-simulation approach because of the automatic optimization of the solver made by AMESim during the simulation. For this reason, the SSE prototype implements all the models in Matlab/Simulink (Dymola models are imported as S-functions) except for the Cabin Thermal model that runs in co-simulation. Under this configuration, the best results were obtained using the ode23 variable step solver, which lead to a value near to 5 for the ratio between the computational time and simulation time. This result is also due to the particular method used to find the initial trim of the systems. This method

  responsible for the implementation of one or more models. The Department of Civil and Industrial Engineering -Aerospace Division -of the University of Pisa (UniPi) is a member of Airgreen cluster, together with the Polytechnic of Turin and Piaggio Aero Industries S.p.A.

  All the models simulate the electrical current absorbed and the thermal flow of A/C systems, as functions of pilot commands, environmental conditions, flight conditions and the voltage made available by the EMS. In addition, the models provide further outputs (not shown in Figure1) characterizing system performances, which allow to assess if the system works in compliance with the assigned requirements. Such outputs are for example power absorbed, LGS extension/retraction velocity, FCS surface deflections, ECS mass flow. The block named Mission profile loads, as input data, the time histories of A/C manoeuvres, together with pilot commands and outside environmental conditions. Such data are obtained "offline" by a flight simulator, developed at UniPi, able to reproduce typical missions of a regional turboprop A/C by means of specific autopilots driven by a flight management system. The block named Cabin temperature controller regulates the temperature of A/C cabin. It reads the cabin reference temperature from the Mission profile block and the actual cabin temperature from the Cabin Thermal model block, and it sends proper commands to the ECS depending on the difference between the two temperatures. The command to the ECS is the target value, requested by the internal ECS controller, of the temperature of the air flow provided by the ECS to A/C cabin. The Cabin Thermal model evaluates the temperature inside the A/C cabin as a function of outside ambient conditions and of the air flow from ECS, by taking into account: the solar radiation, the thermal flows through the cabin walls, the heat produced by passengers, avionics and utilities, and the heat transport through the different A/C compartments. The electrical current absorbed by all the systems is fed back to the EPGS model which evaluates possible variations of the supply voltage with respect to the nominal value of 270 vols. On the basis of such a voltage and of the currents absorbed, the EMS model applies the energy management strategies and regulates the voltage supplied to the various systems. The EMS model developed by ALA in Dymola-Modelica includes EMS logics, some Solid State Power Controllers (SSPCs) and a High Voltage DC Bus-bar (on a single generation channel). It continuously monitors the EPGS current and its time derivative. When an overload condition is detected, the logics operate before on the SSPCs contactors to obtain a faster power absorption reduction (High frequency action). If the overload persists, the logics request the ECS to reduce the power absorbed by the ECS itself (Low frequency action) [6].

Fig. 1 :

 1 Fig. 1: SSE prototype high level scheme

Fig. 2 :

 2 Fig. 2: Dummy shift of input time histories Several tests have been performed to assess the SSE prototype performance in terms of the correlation among the computation time (the key performance parameter) and the methodology used for models integration, the solver and the integration step. The PC used for the tests is a dual-processor, 64 bit, six-core system, which is the high-end of workstations launched by Intel in 2009. The first method gave good results for Dymola but not for AMESim. Actually, the AMESim Cabin Thermal model runs very fast in AMESim (few seconds for 1000 seconds of simulation time), while the computation time dramatically increases when it is imported in Simulink via S-function.

  IPS (De icing function) -at Low Frequency (e) on the ECS The action of EMS on the first three loads -a), b), c) -can make the supply voltage of these loads vanish, if necessary. On

  of MCPR. (last row in Table 4) Conversely, the quick action of the EMS on the loads (a), (b), (c) and (d) can immediately reduce this percentage up to an estimated value of 87%. This is obtained by recovering 5 KW from (a), (b) and (c) and roughly 15 KW from (d).With a slower action, the EMS can then achieve a significant reduction of the power absorbed by the ECS. If we estimate this reduction in approximately 35 kW, the power consumption can drop up to the 80% of MCPR with the only action on the ECS, and the other loads can be restored.As an example of this behaviour, Figure3presents the results of a simulation carried out at sea level in a "cold day". The upper plots give the total power absorbed and those absorbed by Anti-icing, Equipment/Furnishing, Internal Lights, Entertainment, De-Icing, end ECS. The lower plots give the Electrical voltages supplying the same systems. The two plots in the right column are zooms in a shorter time-window. They are reported in order to show the action of the EMS on the voltages and the corresponding effects on the electrical powers absorbed.The powers absorbed by the other systems are not reported because they are constant and set to the following values (KW): to see that when the total power reaches the threshold of 140 kW (at about 780 s) the EMS immediately acts on the voltages of (a), (b), (c) and (d), and also it began to demand a reduction of the ECS power. When the ECS power is reduced, the EMS reactivates the other loads. Afterwards the EMS tries to increase again the power available for the ECS until the threshold is reached once more, and so on. This leads in this test to a periodic behaviour which starts at about 1600 seconds.

  Fig. 3: Simulation results

Fig. 4 :

 4 Fig. 4: SSE Simulation results

Table 3 :

 3 The block named Other Systems implements Utilities & Avionics models. The integration of the Fuel System (FS) model is not performed at this stage because the FS is not commanded by the EMS and it is not a key system for the purposes of the project. SW integration

	Models	Level 2	Level 3
	Electrical Power Distribution System (EPDS) Energy Management System (EMS)	Dymola Modelica
	Cabin Thermal model (CT)	AMESim
	Fuel System (FS)	Flowmaster	Not
	Utilities & Avionics	Dymola Modelica	necessary
	Electrical Power Generation and	Matlab
	Distribution System (EPGDS)	Simulink
	Ice Protection System (IPS)	Dymola Modelica
	Environmental Control System	Matlab	Matlab
	(ECS)	Simulink	Simulink
	Electrical Power Generation	Matlab	Sypnosis
	equipment (EPGS)	Simulink	Saber
	Flight Control System (FCS)	Matlab Simulink	Dymola Modelica
	Landing Gear System (LGS)	Matlab Simulink	Dymola Modelica
	Table 2: Simulation tools used
	SW	Integration environment	Responsibility
	SSE prototype	Matlab/Simulink	UniPi
	SSE final version	Dymola/Modelica	CfP 2011

It includes: -Electrical Power Generation System (EPGS) -Energy Management System (EMS) -Flight Control System (FCS) -Landing Gear System (LGS) -Ice Protection System (IPS) Executive Secretariat 6 Rue Galilée 75016 PARIS Phone : 33 (0) 1 56 64 12 35 Fax : 33 (0) 1 56 64 12 31 E-mail : alexa.faucher@aaaf.asso.fr -Environmental Control System (ECS) -Cabin Thermal model (CT)

  -mail : alexa.faucher@aaaf.asso.fr

	Executive Secretariat
	6 Rue Galilée
	75016 PARIS
	Phone : 33 (0) 1 56 64 12 35
	Fax : 33 (0) 1 56 64 12 31
	input EEquipments EPGS (internal loss)		Max continuous original 1.00	Max peak 2.00
		FCS		4.00	20.00
		LGS		0.00	t	3.00
	FS Time range added to let the 2.00 systems to reach the "trim" Avionics and External lights (AV) 2.00	2.00 2.00
	input	Anti Icing Equipment/Furnishing (a) 150 sec Internal lights (b)	50.00 modified 3.00 2.00	50.00 3.00 2.00
		Entertainment	(c)	1.00	1.00
		De-Icing ECS	(d) (e)	0.00 75.00	35.00 t 75.00
		TOTAL		140	195
	Maximum Continuous Power Rating of the generators (MCPR)	200	200
	Percentage used of MCPR		70%	97.5%
		Table 4: 90-pax -Systems Total Electrical Power
		and generators load (kW)
	the generators, this results in a value equal to 200 KW of the
	MCPR itself (penultimate row in Table

the 70% of the Maximum Continuous Power Rating (MCPR) of

ACKNOWLEDGEMENTS

This work was partially granted under the Green Regional Aircraft ITD, All Electric Aircraft domain, of the Clean Sky JTI,


Jihen Karoui

Marwa Graja

Mohamed Mahdi Boudabous
email: mehdiboudabous@gmail.commarwa.graja

Lamia Ha- Drich Belguith

Domain Ontology Construction from a Tunisian Spoken Dialogue Corpus

Keywords: Ontology, concept, term, semantic relation, spoken dialogue, Tunisian dialect

In this paper, we present a hybrid method for building a domain ontology from spoken dialogue corpus in Tunisian Dialect for the railway request information domain. The proposed method is based on a statistical method for term and concept extraction and a linguistic method for semantic relation extraction. This method consists of three fundamental phases, namely the corpus construction and treatment, the ontology construction and the ontology evaluation. The proposed method is implemented through the ABDO system to generate the RIO ontology which is used to semantically label Tunisian dialect utterances.

Introduction

During the last decade, ontologies are largely used in many fields such as: artificial intelligence, information retrieval, semantic Web, Natural Language Processing (NLP), etc. The purpose of ontology development for NLP system is to add a semantic level and then improve its quality. In fact, ontology allows representing knowledge explicitly.

Today, the main objective in the ontologies design field is to define shareable ontologies. However, the task of building ontologies is very expensive in terms of time, maintenance and updating which are performed manually. Consequently, automatic building starts to emerge in current research works which aims to create ontologies [START_REF] Ding | Ontology Research and Development: Part 1 -A Review of Ontology Generation[END_REF]. While the ontology building is a complex process, several actors are involved in the different stages of this process. For that, it is necessary to define methods or methodologies to assist the process for ontology building. Methodology gathers a method to build ontologies and a set of tools to automatically generate the ontology [START_REF]IEEE Standard for Developing Software Life Cycle Processes[END_REF].

The two most popular methodologies to built ontologies are from scratch and training methodologies. The first one consists in building ontologies manually without knowledge [3]. In the literature, the most popular methodologies from scratch are METHONTOLOGY [4] and SENSUS [START_REF] Swartout | Toward Distributed Use of Large-Scale Ontologies, Ontological Engineering[END_REF]. The second methodology consists in building ontologies from knowledge based on concept training. According to [START_REF] Maedche | Ontology Learning for the Semantic Web[END_REF], there are many learning approaches for each input types. We distinguish the training approaches from texts, dictionaries [START_REF] Hearst | Automatic Acquisition of Hyponyms from Large Text Corpora[END_REF], knowledge bases [START_REF] Suryanto | Discovery of Ontologies from Knowledge Bases[END_REF], semi-structured schemas [START_REF] Papatheodrou | Discovery of Ontologies for Learning Resources Using Wordbased Clustering, EDMEDIA[END_REF] and from relational diagrams [START_REF] Rubin | Automatic data acquisition into ontologies from pharmacogenetics relational data sources using declarative object definitions and XML[END_REF].

In this paper, we propose a hybrid method for semi-automatic building of domain ontologies. Our method belongs to the learning methodologies. In fact, this method is based on a spoken dialogue corpus in Tunisian Dialect for the railway request information domain. Our method generates the "Railway Information Ontology" (RIO) which is used to semantically label Tunisian dialect utterances.

This paper is organized as follows: the second section presents the proposed method for building a domain ontology. The third section presents an overview of the "Assistant for Building Domain Ontology" (ABDO) system which helps to generate RIO ontology. Conclusion is drawn in section four.

Proposed method for building domain ontology

In this section, we propose a semi-automatic method for building domain ontology from dialogue corpus in Tunisian dialect. This method is based on a statistical method for the extraction of terms and concepts, and a linguistic method for the identification of semantic relations between concepts. The proposed method consists of three fundamental phases, namely corpus construction and processing, ontology construction and ontology evaluation. These phases are illustrated in figure 1.

Corpus construction and processing

This phase is crucial because it represents the starting point on which are based the other phases of our method. It is composed of three steps which are corpus construction, corpus treatment and corpus normalization.

Corpus construction and treatment

Ontology construction

Ontology evaluation

Corpus construction

Building ontologies from texts requires a step of corpus construction which represents the domain knowledge [START_REF] Condamines | Sémantique et Corpus[END_REF]. Since we are interested in building domain ontology for railway information services in Tunisian dialect, we used the TuDiCoI (Tunisian Dialect Corpus Interlocutor) corpus [START_REF] Graja | Building Ontologies to Understand Spoken Tunisian Dialect[END_REF] which is a corpus of spoken dialogue in Tunisian dialect. It gathers a set of dialogues between the staff of the National Company of the Tunisian Railways and the customers who seek information about train schedules, fares, reservation, etc. Given the small size of this corpus, we have increased its size by manual transcription of recorded dialogues based on phonetics to highlight the Tunisian accent in the dialogue. The transcribed corpus consists of 1825 dialogues. These dialogues consist of 6533 customer utterances which represent 21682 words.

Study corpus construction

Lexical semantics patterns

Corpus treatment

The main idea of this step is to treat the corpus in order to standardize the domain terms. This requires the construction of five lexicons which are nouns base, verbs base, cities base, compound words base and stop words base. This processing step presents a major challenge because we are performing the Tunisian dialect which suffers from lack of tools and linguistic resources such as the morphological, syntactic and semantic analyzers as well as the lack of dictionaries. This step is carried out manually by the domain expert.

The nouns base represents a lexical dictionary which gathers synonyms and all different form of orthographic transcriptions, since the transcription has been done manually and given the lack of standard for the Tunisian dialect. For example, the word "reservation/"رسرفسٍون is transcribed in four different ways: ,"رسرفسٍون" ,"راسرفسٍون" ,"راسارفسٍون" ."رٌشرفسٍون" So, we tried to gather them in a single one. However, the verbs base consists of possible morphological transformations for verbs. Thereafter, we build the stop words base which consists of terms considered by the domain expert as useless. We also build compound words base which includes all possible compound words. For example, if we gather the two words " بٍز ", ,"بورقبو" we obtain the word " بٍز _ بورقبو " which represents a city name. Finally, we build the cities base which consists of all train station names.

Corpus normalization

The normalization step is based on all lexicons created in the corpus treatment step. This step consists in orthographic correction, replacing all words by their synonyms, remove stop words, detect compound words and perform morphological analysis of verbs and nouns in order to obtain a new version of the corpus called pretreated corpus. Figure 2 shows an extract of the corpus before and after treatment.

Ontology construction

The ontology construction phase consists of three steps: concept extraction, pattern definition and relations extraction.

Concept extraction

This step consists in extracting domain concepts specific to the railway information services from the TuDiCoI corpus based on statistical method. Indeed, domain terms have an important frequency given the important corpus size. Statistical study consists in calculating the frequency of each term of the pretreated corpus. Terms which have an important frequency represent terms of the domain concepts. The only problem is with the domain terms which have a low occurrence frequency. In this case, we have relied on the domain expert to specify the frequency threshold to be fixed. Indeed, the domain expert fixes the concept threshold and each term which has an occurrence value higher than this concept threshold is regarded as a domain concept. To justify this choice, we conducted an empirical study which consists of taking each time a small part of the corpus and calculating the frequency of the fixed concept threshold. The occurrence value of the concept threshold "رتور" increases with the size of the corpus but it still has the lowest occurrence frequency.

The result of this step is a list of domain terms. But it is so important to know that several set of terms represent the same concept. So, it is necessary to gather them and represent them by a single concept. For this, we have relied on the domain expert to define for each set of terms a well defined concept. Table 1 represents some candidate terms and their domain concepts.

Pattern definition

After domain concept extraction, we proposed a linguistic method for the extraction of semantic relations between concepts. In order to define the patterns, we make a linguistic study of the utterance structure. This enabled us to notice that utterance in a limited field respect lexico-semantic patterns in order to extract the semantic domain relations. In fact, this idea consists in using this kind of patterns is introduced by Hearst [START_REF] Hearst | Automatic Acquisition of Hyponyms from Large Text Corpora[END_REF]. Before starting pattern defini-tion, it is necessary to form a study corpus which represents approximately 30% of the pretreated corpus and which is used to manually define patterns. Note that a pattern can be detected from several utterances. In this case we must keep only one definition of this pattern. Defined patterns are validated by the human expert.

Relations extraction

This step consists of projecting the obtained patterns on all the corpus utterances to detect semantic relations. On this level, it is necessary to use the results obtained in the concept extraction and pattern definition steps in order to detect semantic relations between domain concepts. We noticed after this projection that the semantic relations are of two types namely the explicit semantic relations and the implicit semantic relations. An explicit relation is detected by a pattern which connects two domain concepts. This is explained by the following example: <C>hello how much round-trip train to Tunis</C>

بقداه><C ألً_رتور تونس إلى تزان ></Cسالم_علٍكن Request ticket_type city arrival train politeness

The corresponding pattern is: {Politeness, train, arrival, city, ticket_type, request}

Concept1 Relation Concept2

The implicit relations are detected by pattern projection and the intervention of the human expert. The following example explains this step: ساعتٍن_وأربعو><C عادي تزان </C> <C> normal train twenty past two</C>

Time train_type train

The corresponding pattern is: {train, train_type, time}

Concept1 Concept2 Concept3

The human expert indicates the existence of the semantic relation "train_have_type/"نوع_هن_تزان between the first concept "train/"تزان and the second concept "train_type/ نوع _ تزان " which connects the two concepts "train/"تزان and "train_type/."تزان_نوع Our method generates the RIO domain ontology which contains 14 concepts, 25 semantic relations and 387 instances.

Evaluation

The ontology evaluation is a critical phase. This phase can be achieved in deferent ways and with various manners. To evaluate the RIO ontology, we check its conformity with: our needs through an operational evaluation, its conformity with Gruber criteria and human expert evaluation.

Operational evaluation

To evaluate the RIO ontology, we exploit it for semantic annotation which aims to attribute semantic labels to transcribed utterances [START_REF] Raymond | Generative and Discriminative Algorithms for spoken language understanding[END_REF]. This method has already been introduced in [START_REF] Graja | Building Ontologies to Understand Spoken Tunisian Dialect[END_REF], which used a manually built ontology. Now, we are going to use the RIO ontology to check its fusibility for semantic annotation of the Tunisian dialect in the field of railway information. The semantic annotation based on the RIO ontology consists of attributing semantic label to each word based on ontology concepts. Then, we improve the semantic annotation by using ontology semantic relations in order to take into account the local context of each utterance. Indeed, semantic relations increase the level of understanding by highlighting relations between words in the same utterance. Before the annotation step, each utterance must be analyzed by the same treatments used in the first step to build the RIO ontology. So, we use the same bases created after corpus treatment step. The evaluation of the semantic annotation is given in term of F-measure and concept error rate (CER). Obtained results for semantic annotation are very encouraging since we have improved the F-measure in comparison with result obtained in [START_REF] Graja | Building Ontologies to Understand Spoken Tunisian Dialect[END_REF] which obtained an F-measure equals to 66%. In addition to that, semantic annotation based on RIO ontology is very interesting because we have dealt with speech phenomena which are incorporated in the RIO ontology.

Gruber criteria and human expert evaluation

After achieving the RIO ontology construction, we evaluate it according to the Gruber criteria which are clarity, extensibility, minimal ontological commitment, coherence and encoding minimum deformation. RIO ontology has provided clarity because all its concepts are represented with terms from the domain in Tunisian dialect. Also, the extensibility of RIO ontology is guaranteed in our method by the increase of the corpus size and by an infinite back cycle to the second phase to update the ontology. Equally, the minimal ontological commitment is ensured by the presence of all terms covering the studied domain in Tunisian dialect, this guaranteed the sharing of knowledge related to this domain. About the coherence criteria, the domain expert helped us to check the RIO ontology coherence. Finally, the encoding minimum deformation criterion is provided by the use of one of used terms as a label for concept. In fact, we try to keep the used terms of the studied domain in Tunisian dialect to nominate concepts. The satisfaction of all these criteria is noted by the human expert who has followed us during the construction process and evaluation of our ontology. Also, the human expert has manually evaluated and validated each step.

Implementation

To generate a domain ontology based on the proposed method, we have implemented the ABDO system which is an Assistant for Building Domain Ontology (see figure 3). This system provides interactive interfaces for all steps of the method to facilitate the intervention of the different actors. In addition, this system provides many interfaces for creating and updating standardization databases, for pattern definition and finally for databases creation into XML files. Also, the system helps the expert to add implicit relationships through automatic detection of not connected concepts. After connecting all concepts, the system generates the domain ontology in OWL language.

Conclusion

In this paper, we proposed a hybrid method for building domain ontology from a spoken dialogue corpus in Tunisian dialect. This approach combines a statistical method for term and concept extraction with a linguistic method for semantic relation identification. The generated ontology helps us to annotate semantically the Tunisian dialect utterances. As perspective we intend to integrate the RIO ontology into the understanding component of a dialogue system for railway request information to ameliorate semantic interpretation.

Figure 1 :

 1 Figure 1: The ontology construction process

Figure 2 .

 2 Figure 2. A sample of raw version from the corpus and its corresponding normalized version

Figure 3 .

 3 Figure 3. ABDO interface

Table 1 .

 1 Some domain concepts

	Concepts	Concepts translation	Terms	Terms translation
	هدٌنت	City	تونس صفاقس/ / سوسو	Tunis/Sfax/Sousse
	قطار	Train	تزان	Train
	تذكزة	Ticket	تسكزه	Ticket

Table 2 .

 2 Semantic labeling results using the RIO ontology

	#Train-ing words	#Test words	Correct prediction	Incorrect prediction	Reference con-cepts	CER F-measure
	5710	2104	1213	162	1718	9,4	78%
						%

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