Albert Gatt
email: albert.gatt@um.edu.mt

Nicolás Marín

François Portet
email: francois.portet@imag.fr

Daniel Sánchez
email: daniel@decsai.ugr.es

The role of graduality for referring expression generation in visual scenes

Keywords: Referring expression, fuzziness, linguistic description, visual scenes

Referring Expression Generation (reg) algorithms, a core component of systems that generate text from non-linguistic data, seek to identify domain objects using natural language descriptions. While reg has often been applied to visual domains, very few approaches deal with the problem of fuzziness and gradation. This paper discusses these problems and how they can be accommodated to achieve a more realistic view of the task of referring to objects in visual scenes.

Introduction

The aim of systems for automatically generating linguistic descriptions of visual scenes is to generate text describing the scene contained in an image, simulating the results provided by humans in the same task. In recent years, scene description has attracted a lot of attention as part of a growing trend to explore the interface between vision and language in Natural Language Generation (nlg) [START_REF] Reiter | Building Natural Language Generation Systems[END_REF], as is the case of other kind of data [START_REF] Kacprzyk | Computing with words is an implementable paradigm: Fuzzy queries, linguistic data summaries, and natural-language generation[END_REF][START_REF] Marín | On generating linguistic descriptions of time series[END_REF][START_REF] Ramos-Soto | On the role of linguistic descriptions of data in the building of natural language generation systems[END_REF].

Research in this area tends to focus on learning correspondences between parts of an image and textual descriptions to generate descriptive captions [START_REF] Mitchell | Midge: Generating Image Descriptions From Computer Vision Detections[END_REF][START_REF] Kulkarni | Baby talk: Understanding and generating simple image descriptions[END_REF][START_REF] Yatskar | See No Evil , Say No Evil : Description Generation from Densely Labeled Images[END_REF]. A somewhat different focus is offered by work on Referring Expression Generation (reg), one of the most important tasks in nlg. A referring expression is a noun phrase whose communicative purpose is to identify an object to the hearer, for which it must be a distinguishing description, i.e., it must be "an accurate description of the entity being referred to, but not of any other object in the current context set", the context set being "the set of entities that the hearer is currently assumed to be attending to" [START_REF] Reiter | A fast algorithm for the generation of referring expressions[END_REF]. The most standard way of approaching reg computationally is by seeing the problem as a content selection problem [START_REF] Krahmer | Computational generation of referring expressions: A survey[END_REF]. If a particular object is to be identified linguistically among a set of other objects, then the challenge is to find a set of properties of the object that identifies it uniquely and to do so in a way that matches what human speakers would do in a similar situation.

Formally, this means that there are two important criteria for a reg algorithm to be successful. Let re = {p 1 , . . . , p n } stand for a referring expression (a set of properties) returned by such an algorithm for some target referent o. The description re is considered accurate if, for all p ∈ re, o ∈ [[ p ]], that is, every property in the description is true of o. Furthermore, re is a successful description if it uniquely identifies o, that is:

p∈re [[ p ]] = {o} (1)
The choice problem represented by reg has been explored in a number of algorithms reviewed in detail in [START_REF] Krahmer | Computational generation of referring expressions: A survey[END_REF], though it has only recently been tackled in the context of complex visual scenes [START_REF] Elsner | Information Structure Prediction for Visualworld Referring Expressions[END_REF][START_REF] Kazemzadeh | ReferItGame: Referring to Objects in Photographs of Natural Scenes[END_REF]. Referring expressions in images use visual properties and concepts for characterizing univocally certain regions in the image, corresponding to objects or areas of interest.

In many cases these concepts and properties are fuzzy or gradable in nature, a factor that has been ignored in many reg algorithms, which typically treat a target's properties as symbols with a crisp extension. While exceptions to this trend exist [START_REF] Van Deemter | Generating referring that involve gradable properties[END_REF], they have focussed on prototypically gradable properties, such as an object's size. Yet, even properties such as an object's colour admit of boundary cases and gradations. Another simplification in existing reg models has to do with the notion of an object itself: frequently, reg algorithms assume that the relevant components of a visual scene are entities and their properties. However, even the concept of an 'entity' can be argued to be fuzzy. An example where this complication becomes an important issue is a weather forecasting application, which needs to refer to a geographical region in order to describe the weather conditions holding within it. As real-world applications show [START_REF] Turner | Selecting the Content of Textual Descriptions of Geographically Located Events in Spatio-Temporal Weather Data[END_REF], regions corresponding to a particular set of predicates describing the weather conditions will often not be easily identifiable using place names or simple expressions based on cardinal directions.

Thus, in addition to the problem of modelling the semantics of fuzzy concepts with respect to their correspondence with properties of objects [START_REF] Reiter | Choosing words in computergenerated weather forecasts[END_REF][START_REF] Cadenas | Context-aware fuzzy databases[END_REF], referring expressions using fuzzy concepts may also match objects in the scene to a certain extent, and this extent has to be calculated, introducing an additional complexity in the reg problem. In particular, distinguishability of an object with respect to others in terms of a referring expression involving fuzzy concepts turns out to be a matter of degree. In this paper we address this problem.

The paper is organized as follows: in Section 2 we briefly discuss fuzzy visual concepts. In Section 3 we deal with graduality in referring expressions. Implications of graduality in reg are discussed in Section 4. Section 5 concludes the paper.

Fuzzy concepts in visual scenes

Concepts related to low-level features like colour, texture, and shape are among the most employed in linguistic description [START_REF] Castillo-Ortega | Describing images via linguistic features and hierarchical segmentation[END_REF], and are usually affected by fuzziness. Colour terms used as adjectives like red, vivid yellow, etc. are paradigmatic examples of fuzzy concepts, and can be represented by means of fuzzy subsets of crisp colours in some colour space. That is, each crisp colour (usually represented by a triplet of numbers, whose domain and meaning vary from one colour space to another) has a membership degree to each colour term. Several proposals are available in the literature for determining the membership function representing each colour term [START_REF] Soto-Hidalgo | A new approach for defining a fuzzy color space[END_REF]. Once the terms are defined, they have to be matched to the objects under study. This is not a trivial issue because, unless all the pixels comprising the object have the same colour, approaches for associating colour terms to the whole object are necessary, like for instance the notion of colour dominance [START_REF] Chamorro-Martínez | Retrieving images in fuzzy object-relational databases using dominant color descriptors[END_REF]. Fuzzy approaches for defining shape (round, regular, convex, etc.) and texture properties (coarseness, contrast, orientation, etc.) are also available, see [START_REF] Chamorro-Martínez | A fuzzy approach for modelling visual texture properties[END_REF][START_REF] Chamorro-Martínez | Fuzzy sets on 2D spaces for fineness representation[END_REF] and references therein.

Not only properties, but regions themselves can be fuzzy as well, as they are usually defined in terms of their correspondence to properties [START_REF] Castillo-Ortega | Describing images via linguistic features and hierarchical segmentation[END_REF]. In this sense, a fuzzy region is defined as a fuzzy subset of pixels in the image. As an example of this, if we take the homogeneity in colour as the criterion for determining regions, we obtain fuzzy regions like the ones in the image in Figure 1(a), which shows a collection of cells as seen under the microscope. The regions corresponding to cells are characterized by being red but, whilst some of them have clear boundaries, some others do not, since the colour of some pixels has partial membership to red for some cells. Dozens of methods for fuzzy segmentation are available [START_REF] Prados-Suárez | Region-based fit of color homogeneity measures for fuzzy image segmentation[END_REF].

The lack of clear boundaries of fuzzy regions is another source of fuzziness for other properties which further complicates the task of calculating a region's other properties, including its size, shape, and its spatial relationships with other regions and with the image framework (location), among others. These concepts, which are themselves fuzzy even for crisp regions, have been an object of study with fuzzy techniques [START_REF] Hudelot | Fuzzy spatial relation ontology for image interpretation[END_REF][START_REF] Castillo-Ortega | Describing images via linguistic features and hierarchical segmentation[END_REF][START_REF] Buck | A memetic algorithm for matching spatial configurations with the histograms of forces[END_REF]. It is easy to see examples of concepts that are fulfilled only to some degree by objects in the image depicted in Figure 1(a), such as round shape, above (spatial relationship), in the middle (location), etc.

In order to illustrate our discussion, let us consider, for the objects in Figure 1(a), the fuzzy properties small, dark, and upperleft. Table 1 shows example fulfilment degrees of these properties for all the named objects in Figure 1(a). For the property small, a fuzzy subset on the domain of the proportion of pixels in the image has been employed. The property dark is calculated as the dominance of the property low luminance of the pixels in an object. The property upperleft is defined as a fuzzy region with square core and support, core with side 1/4 of the image, and support with side 3/8, with membership decreasing linearly. We omit more specific details about how these degrees have been calculated for reasons of space.

Gradation in referring expressions

When referring expressions are comprised of fuzzy properties, both their accuracy and their ability to discriminate an object become a matter of degree, contrary to the view usually assumed by reg algorithms (see Section 1). In this section we propose different ways to assess these, as well as some other properties which are also important when generating referring expressions and assessing their quality.

Accuracy

As we have noted, the accuracy of a referring expression with respect to a given object measures the extent to which what the expression says is true for the object, that is, the degree to which all the properties that appear in the expression hold for the object. When the referring expression is composed of fuzzy properties, to calculate the accuracy of the expression for a given object we first have to compute the fulfilment degree of each property; subsequently we have to appropriately aggregate these degrees into a single value.

Let us once again consider a referring expression re involving properties {p 1 , . . . , p n } and an object o. Let also p i (o) be the fulfilment degree of property p i for object o. The accuracy of re for object o is calculated as follows:

a re (o) = n i=1 p i (o) ( 2
)
where ⊗ is a t-norm.

As an example, consider again Figure 1(a) (Figure 1(b) assigns numbers to the cells) and the three referring expressions: The small object in the upper-left (re 1 ), The dark object in the upper-left (re 2 ), and The small and dark object(re 3 ).

Cell Dark Small Upperleft dark & small dark & upperleft small & upperleft 1 0,00 0,29 0 0,00 0,00 0,00 2 0,00 0,71 0 0,00 0,00 0,00 3 1,00 1,00 0,96 1,00 0,96 0,96 4 0,83 0,00 0,85 0,00 0,83 0,00 5 0,00 0,00 0,05 0,00 0,00 0,00 6 0,00 0,00 0 0,00 0,00 0,00 7 0,00 0,14 0 0,00 0,00 0,00 8 0,33 0,14 0 0,14 0,00 0,00 9 0,33 0,00 0,33 0,00 0,33 0,00 10 0,00 0,00 0,01 0,00 0,00 0,00 11 0,00 0,14 0 0,00 0,00 0,00 12 0,00 0,43 0 0,00 0,00 0,00 13 0,00 0,00 0 0,00 0,00 0,00 14 0,00 0,00 0 0,00 0,00 0,00 15 0,00 0,00 0 0,00 0,00 0,00 16 0,00 0,00 0 0,00 0,00 0,00 17 0,00 0,14 0 0,00 0,00 0,00 18 0,17 0,14 0 0,14 0,00 0,00 19 0,17 0,14 0 0,14 0,00 0,00 20 0,00 0,00 0 0,00 0,00 0,00 21 0,17 0,43 0 0,17 0,00 0,00 22 0,00 0,00 0 0,00 0,00 0,00 23 0,17 0,29 0 0,17 0,00 0,00 24 0,00 0,86 0 0,00 0,00 0,00 25 0,00 0,00 0 0,00 0,00 0,00 26 0,00 0,57 0 0,00 0,00 0,00 27 0,50 0,43 0 0,43 0,00 0,00 28 0,67 0,00 0 0,00 0,00 0,00 Table 1. Fulfilment degree of several fuzzy properties and accuracy of three referring expressions for the image in Figure 1(a).

According to data in table 1, and using the minimum as t-norm, we have that:

a c3 (re 1 ) = min(small(c 3 ), upperlef t(c 3 )) = 0.96 a c3 (re 2 ) = min(dark(c 3 ), upperlef t(c 3 )) = 0.96 a c3 (re 3 ) = min(small(c 3 ), dark(c 3 )) = 1

Referential success

As noted in Section 1, referential success is the extent to which the referring expression unambiguously identifies the intended referent to the reader or hearer. Consider again a referring expression re and a set of objects O = {o 1 , . . . , o m }. The referential success of re with respect to a referent object o i is the degree to which re is true for o i and false for the rest of objects, that is:

rs re (o i ) = a re (o i ) ⊗   oj ∈O∧j =i ¬(a re (o j ))   ∈ [0, 1] (3)
where ⊗ is a t-norm and ¬ is a fuzzy negation. Note that this notion of referential success is quite different from the more traditional definition given in reg (see 1 above). As an example, consider again Figure 1(a) and the three referring expressions in the previous section. In this case, if we once more use the minimum as t-norm and the standard fuzzy negation operator, we have that:

rs re1 (c 3 ) = min(a re1 (c 3 ), min cj =c3 (1 -a re1 (c j ))) = min(0.96, 1) = 0.96 rs re2 (c 3 ) = min(a re2 (c 3 ), min cj =c3 (1 -a re2 (c j ))) = min(0.96, 0.17) = 0.17 rs re3 (c 3 ) = min(a re3 (c 3 ), min cj =c3 (1 -a re3 (c j ))) = min(1, 0.57) = 0.57 As can be observed, though the three considered referring expressions are almost equally true for cell 3, they have rather different degrees of referential success. The two last expressions fail in the objective of unambiguously identifying the referent object to the reader. This is due to the fact that the accuracy of re 2 regarding cell 4 is also high (a re2 (c 4 ) = 0.83) and the accuracy of re 3 regarding cell 27 is not low (a re2 (c 2 7) = 0.43).

The presented measure is a natural extension of the conventional concept of referential success when the accuracy of the referring expression for a given object is fuzzy. However, this measure produces similar values when evaluating referring expressions independently of the number of objects they fuzzily refer to.

For example, consider the following sentences:

re 4 =The dark object.

re 5 =The object in the upper-left.

In both cases, the referential success with respect to cell 3 is rather low (rs re4 (c 3 ) = 0.17, rs re5 (c 3 ) = 0.15). However, if we do not have any other alternative, we will probably choose re 5 because, as can be seen in table 1, there are fewer objects for which re 5 is true to some extent than there are for re 4 . That is, the fuzzy sets of objects of which re 5 is to some extent true is more specific, i.e. there is less uncertainty about which referent is intended.

To asses this uncertainty, Fuzzy Set Theory offers a wide variety of measures of specificity of fuzzy sets. If O re is the fuzzy set of objects for which re is true to some extent, with O re (o) = a re (o), then the specificity of re, Sp(re), should satisfy, among others, the following:

-Sp(re) = 1 iff O re is a crisp singleton. -If O re = ∅, then Sp(re) = 0. -If card(O re ) ≤ card(O re )
and O re and O re are normal fuzzy sets, then Sp(re) ≥ Sp(re ), where card(•) is a suitable cardinality measure.

A multiobjective problem

In order to asses the goodness or adequacy of a referring expression, other quality dimensions can be considered, apart from the ones previously discussed, similarly to what happens in complete linguistic descriptions [START_REF] Marín | On generating linguistic descriptions of time series[END_REF]. The reg literature contains a number of such proposals. Thus, brevity has been proposed as a criterion of adequacy [START_REF] Dale | Cooking up referring expressions[END_REF] based on the conversational maxim, due to Grice [START_REF] Grice | Logic and conversation[END_REF],

that a cooperative communicator should say no more than is required for the purposes of the exchange. Similarly, humanlikeness has been explored based on a variety of psycholinguistic findings, such as for example the extent to which the properties selected in a referring expression are salient or 'preferred' by speakers [START_REF] Dale | Computational Interpretations of the Gricean Maxims in the Generation of Referring Expressions[END_REF][START_REF] Gatt | Introducing shared task evaluation to NLG: The TUNA shared task evaluation challenges[END_REF]. Finally, it has been argued that the use of certain properties in a referring expression depends on the relevance to the user given their current communicative task [START_REF] Jordan | Learning content selection rules for generating object descriptions in dialogue[END_REF] as well as their knowledge state [START_REF] Janarthanam | Adaptive Generation in Dialogue Systems Using Dynamic User Modeling[END_REF].

Though in the example used in this section it has been possible to determine the best referring expression for cell 3, in general, the problem of generating a referring expression for a given object does not always lead to a single best solution. It is quite normal to find that different measures of quality are correlated to different degrees; for instance, they might exert opposing influences, be contradictory, and/or have a negative correlation. For example, as we add properties to a referring expression, we increase the specificity but we also decrease brevity and probably also accuracy and ease of comprehension. Interestingly, empirical work comparing a large number of reg algorithms against shared datasets suggests that in fact, different evaluation metrics corresponding to different quality dimensions often do not correlate with each other [START_REF] Gatt | Introducing shared task evaluation to NLG: The TUNA shared task evaluation challenges[END_REF].

This means that there is in general no such thing as the optimum or best referring expression for a given object, and the referring expression generation is a multiobjective optimization problem, as explained in [START_REF] Castillo-Ortega | Quality assessment in linguistic summaries of data[END_REF][START_REF] Bugarín | Aspects of quality evaluation in linguistic descriptions of data[END_REF] for complete linguistic descriptions.

REG in the presence of graduality: a discussion

As we have noted, gradability and fuzziness have not received much attention in the reg literature. An exception is the work of van Deemter [START_REF] Van Deemter | Generating referring that involve gradable properties[END_REF], who proposes a semantics for gradable properties, such as those related to the size of an object, and an algorithm to incorporate these properties in a reg procedure. van Deemter proposes to represent gradable properties numerically, preceding the standard reg content determination procedure with a step in which such properties are converted into inequalities. For example, if o is defined as having a value n for its height, this would be 'unpacked' into a set of inequalities of the form n > m, for all m which are values of height that are less than n. A standard reg algorithm can now use these inequalities as it would any other property, assuming that content selection is followed by an appropriate realisation step to convert them into an appropriate natural language string. This approach is arguably less general than the one proposed in this paper. In particular, if we abandon the view that the relationship between objects and their properties is necessarily boolean (in the sense that an object either has a property or it doesn't) and adopt the more graded notion of degree of fulfilment, it is possible to treat all properties as potentially graded, or rather, all objects in a domain as having a particular property to different degrees. The contrast between the definition of accuracy and referential success given in ( 1) and (2-3) makes this explicit.

Discriminatory power

From the perspective of knowledge representation, this also eschews a core simplifying assumption made in many approaches to reg, namely, that properties are mutually exclusive. For example, consider an object's colour. In a domain in which some objects are red and some are pink, it is typically assumed that

[[ red ]] ∩ [[ pink ]] = ∅,
so that a reg algorithm that determines, for a target referent o, that o ∈ [[ red ]], can safely ignore all objects in the extension of pink, since the latter is a completely different property.

This assumption has had an important implication for the development of heuristics to control the search performed by a reg algorithm during content selection, especially where the notion of discriminatory power is concerned. Discriminatory power refers to the informativeness of a property, that is, the extent to which it is true of the target referent, but false of other objects in the domain [START_REF] Dale | Cooking up referring expressions[END_REF]. Thus, algorithms which define the quality of a referring expression in terms of brevity or informativity make use of this notion in trying to generate brief descriptions, by prioritising properties with higher discriminatory power, under the assumption that this will help the reader or hearer to identify the object more efficiently [START_REF] Dale | Cooking up referring expressions[END_REF][START_REF] Gardent | Generating Minimal Definite Descriptions[END_REF][START_REF] Frank | Predicting pragmatic reasoning in language games[END_REF]]. Returning to our example of colour, if the target referent happens to be the only red object in the domain, these algorithms will tend to select this property. The problem is that the object in question might also have a non-zero degree of fulfilment with respect to the property pink, based on the closeness of this colour to red, so that the discriminatory value of the property selected is not as clear-cut as was initially assumed.

Salience and contextual knowledge

Explicitly accounting for fuzziness also has implications for the way in which another important quality heuristic is modelled in a reg algorithm, namely, salience or preference. Many algorithms [START_REF] Dale | Computational Interpretations of the Gricean Maxims in the Generation of Referring Expressions[END_REF][START_REF] Kelleher | Incremental generation of spatial referring expressions in situated dialog[END_REF] give priority to properties, not based on discriminatory power, but on how salient, relevant or preferred they are. This notion of preference is usually defined by appealing to psycholinguistic findings, for example, the finding that speakers tend to avoid prototypically gradable properties such as size, unless they are absolutely required, in contrast for example to colour which, as we have seen, is used very frequently [START_REF] Pechmann | Incremental speech production and referential overspecification[END_REF][START_REF] Belke | Tracking the time course of multidimensional stimulus discrimination: Analyses of viewing patterns and processing times during "same"-"different" decisions[END_REF]. However, many such findings are based on experimental domains in which colours are maximally salient and distinct. A fuzzy approach can of course handle such cases, given that the fulfilment degree of an object with respect to a colour which is maximally salient in a domain would be higher than it would be in case the property is less salient, that is, closer to other colours. However, this approach would also enable more nuanced models, in which preference or salience can be computed in less straightforward cases, as in the example in Figure 1(a) above. Indeed, the human tendency to use colouring referring expressions decreases significantly in the presence of colours that are similar, or close to each other [START_REF] Viethen | The impact of colour difference and colour codability on reference production[END_REF]. Similarly, the tendency to use size increases, when the size difference between a target referent and its distractors is large [START_REF] Van Gompel | Overspecification in reference: Modelling size contrast effects[END_REF].

Note that a graded notion of salience is also more faithful to the findings in the vision literature, where many computational models of visual salience identify salient regions based on a combination of the properties we have discussed in this paper, treating these properties as gradable [START_REF] Itti | Computational modelling of visual attention[END_REF][START_REF] Erdem | Visual saliency estimation by nonlinearly integrating features using region covariances[END_REF]. The framework proposed here could potentially be adapted to the findings from the vision literature, by incorporating a psychologically plausible definition of salience into the computation of degree of fulfilment that determines the likelihood for a property to be selected by a reg procedure.

A further trend in computational research on vision is the incorporation of top-down, or contextual, knowledge [START_REF] Oliva | The role of context in object recognition[END_REF][START_REF] Cadenas | Context-aware fuzzy databases[END_REF]. Certain models [START_REF] Torralba | Contextual guidance of eye movements and attention in real-world scenes: The role of global features in object search[END_REF][START_REF] Kanan | SUN: Top-down saliency using natural statistics[END_REF] have successfully modelled human shifts of attention in visual scenes by incorporating both feature-based salience and knowledge of the type of scene being viewed. Thus, the salience of an object depends not only on the distinctiveness of its properties, but on human expectations concerning its location and function in a particular scene. Returning to our running example, when viewing an array of cells, such as those in Figure 1(a), a biologist's attention might be drawn less by their colour (perhaps because this is relatively unimportant, or expected, where cells and tissues are concerned), but by their other properties. Future research on reg will need to take into account these top-down, expectation-based mechanisms, which are also known to influence the way humans refer to objects [START_REF] Sedivy | Pragmatic versus form-based accounts of referential contrast: evidence for effects of informativity expectations[END_REF][START_REF] Westerbeek | Stored object knowledge and the production of referring expressions: the case of color typicality[END_REF]. It is an open question whether the computation of the degree of fulfilment of a fuzzy property can be modulated by such top-down considerations.

Spatial relations

Finally, another area in which fuzzy sets can play an important role in reg is in reference to objects using (spatial) relations to other elements of a scene. Since the early work of Dale and Haddock [START_REF] Dale | Generating referring expressions involving relations[END_REF], the problem has been mostly defined in logical terms: the elements of a scene that can serve as potential anchors for a target referent in a spatial relation such as in front of are themselves encoded explicitly in the reg input [START_REF] Areces | Referring expressions as formulas of description logic[END_REF], though there has been some work that takes a more realistic perspective in selecting salient landmarks [START_REF] Kelleher | Incremental generation of spatial referring expressions in situated dialog[END_REF][START_REF] Elsner | Information Structure Prediction for Visualworld Referring Expressions[END_REF]. Once again, the simplifying assumptions underlying reg algorithms can be relaxed using a fuzzy approach to spatial relations, especially where these are 'uncertain' and not necessarily mutually exclusive. For instance, in Figure 1(b) if the object to be identified is cell 1, it can be seen that it is above cell 6 but also overlaps with it to some degree. It is not clear whether a good referring expression in this case should combine the two relations (the small cell at the top which is above and slightly overlaps another), select only one, perhaps the most salient one, or simply rely on a non-relational frame of reference (the one at the top towards the middle). Many techniques have been described to handle fuzzy spatial relationships, and we believe that reg would benefit from an exploration of this literature. Some related work has shown that fuzzy sets can be useful in generating appropriate natural language descriptions of uncertain temporal relations between events [START_REF] Gatt | Multilingual generation of uncertain temporal expressions from data: A study of a possibilistic formalism and its consistency with human subjective evaluations[END_REF]; applying similar strategies to spatial relations is a promising way forward.

Conclusions

In this paper, we have tackled a classic problem in the generation of natural language from non-linguistic data, namely, referring expression generation. Focussing on visual scenes, we have sought to extend the remit of reg algorithms, by (a) abandoning some simplifying assumptions related to knowledge representation, especially the assumption that properties are crisp sets and that they are either true or false of an object; and (b) extending the notions of accuracy and referential success to deal with the more complex picture that gradability affords. Our discussion has also pointed to several directions for future work, notably the possibility of integrating the types of models proposed in this paper with findings from the vision literature concerning salience. We believe this is a promising direction in which to take research on reg; indeed, it potentially offers a way to bridge the gap between research on fuzzy sets, research on Natural Language Generation, and work in computer vision.

Fig. 1 .

 1 Fig. 1. An image with fuzzy properties and its labelled counterpart

Acknowledgments

This work has been partially supported by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund (FEDER) under project TIN2014-58227-P.


M Nassereddine
email: m.nassereddine@westernsydney.edu.au

J Rizk

A Hellany

M Nagrial

A Elrafhi

Z Obeid

K Hajar

Electrical Energy Management for Advance Smart Home Systems: Introduction

Keywords: Electrical Power, House Consumption, Smart Technology. I

Electrical power energy forms an indispensable part of human comfort. Numerous activities would have been impossible without the aid of electricity. The introduction of the PV solar system introduces additional challenges to engineers on how to increase the efficiency of the system. Micro grid PV generations spread world-wide by installing small size PV system on the roof top of residential properties. The maximum output of the PV system is at midday where working families are away from homes. Without the storage system, this energy can only be injected into the electrical grid. This paper introduces the concept of electrical management system for smart home. The proposed smart system allows percentage of the PV generated energy to be used during working hours. Also, the proposed system gives the individual a total control to maximize the use of generated energies, reduce the electricity bills and the impact on the environment. Case study is included

INTRODUCTION

Electrical energy forms an indispensable element when it comes to human comfort. Numerous activities wouldn't be possible without the aid of the electrical energy. The production and consumption of the electrical power impact on the human budget as well as the environment. To reduce the cost of the fossil fuel power, and to reduce the impact on the environment, the installation of renewable energy sources increased dramatically in the past two decades [START_REF]Solar Energy[END_REF]. Individuals are investing in micro-generation system that can be installed at their premises. For example, in Sydney, large number of houses installed small size PV solar panels on their roof to reduce the impact on climate change and reduce the electricity bill. According to the Australian Energy Council [START_REF]Clean Energy Council[END_REF], 1.42 million small-scale solar power systems were installed across Australia by the end of 2014. The high percentages of these installed systems are grid connected without energy storage banks.

This large number of small PV solar generation reduces the energy consumption that is generated by the fossil fuel. However, the PV solar installation didn't reduce the maximum demands required from the power stations during the peak hours (morning and night). The morning peak hours are usually between 06:00 and 08:00am; the sun energy at this time will be at its minimum and won't assist with the electrical power demands. Based on this information, the electrical generation utilities cannot reduce its electrical infrastructure.

The works in this paper introduce the smart management system that enhance the efficiency of using the sun energy and allow for the reduction of the electrical generation infrastructure. The paper shows the innovative architect diagram between the smart grid and advance home technologies that lead to higher electrical power system efficiency. The case study validates the theoretical discussion within the paper.

II. THEORETICAL STUDY

The increase in electrical power demands forces engineers to focus on increasing the efficiency and the reliability of the entire power system. Researchers use smart grid technologies to enhance the performance of the power system. The smart grid collects information from distributions, generations and transmission using the latest information and communication technologies [START_REF] Erol-Kantarci | Energy efficient information and communication infrastructures in the smart grid: a survey on interactions and open issues[END_REF]. The optimizations in smart grid network enhance the overall network performance by increasing its reliability [START_REF] Zhang | Trust system design optimization in smart grid network infrastructure[END_REF]- [START_REF] Xu | Towards a danish power system with 50% wind-smart grid activities in Denmark[END_REF].

The installation of the solar PV panels on houses introduces the micro-generation source at the house. The output of the solar panels depends on the sun radiation energy. Figures 1 and2 show the average hourly solar distribution and electrical loading for residential customers [START_REF] Mitchell | Optimisation of the applications of sustainable energy systems[END_REF], [START_REF] Mitchell | Simulation and optimization of renewable energy systems[END_REF]. The figures show that the generated power from the solar system without energy storage doesn't meet the electrical demands during peak hours for the residential house. The high cost of electrical storage prevent individual from investing into this technologies.

The paper idea is to enhance the electrical management system within residential houses. The system aims in maximizing the use of sun energy during the day and reducing the demands at evening and night.

A. House Hold Energy Consumption

The average working hours in Sydney Australia are between 08:30 and 17:00 Monday to Friday. During this period, home energy consumption is at its minimum. Sydney school hours are 07:40 to 15:00. After these hours, the following activities are responsible for increase in energy usage:

• Air-conditioning

• Cooking • Lighting • Washing • Drying
Performing any of the above activities between 11:00 and 15:00 on a sunny day, it reduces the evening energy consumption and the cost of energy bill for individual.

B. Proposed Architect Diagram

Figure 3 represents the proposed smart management architect diagram for individual houses. The system provides the individual with a predicted energy forecast from the solar system, based on hourly layout, and possible activities that can be performed to maximize the use of the solar energy. The proposed smart home management system has the following capabilities:

• Analyzes the weather forecast data (sunlight, humidity, heat, Etc…) for the area and determine its impacts on the output of the PV solar system.

• Provide the individual of the predicted electrical output from the installed PV system based on the weather forecast data. It allows the individual to plan certain activities based on the forecast data.

• Monitor the performance of the PV solar system. It logs all its operation and provides report to the individual when requested. The report can cover the used percentage of the generated power within the house.

• Assess the instantaneous electric power generated from the PV modules and advise on a possible use within the house

• Monitor the energy consumptions of the household electrical appliances (lights, oven, Aircondition, Etc...)

• Control the energy distribution between household appliances.

• Provides the individual with the opportunity to prioritize the use of generated power (create priority list).

• Manage the surplus generated power to reduce the peak demands in the afternoon. For example, the system can use the excessive energy to cool the house, complete the washing, drying, cooking, etc…

• Log all the proposed weekly activities and provide an efficiency report based on the weather forecast.

• Two ways data between the house and the utility electrical smart grid. The utility can use this system to inform the individual of proposed outages or system malfunction updates. Also, it is possible to predict the area electrical forecast based on the proposed weekly activities.

• For a PV system with electrical storage device, the utility can utilities the storage energy if required and approved by the owners. This principle might be adopted by the electrical companies as it might reduce the investment within the electrical infrastructure.

• For standalone system, it can manage the system to protect the battery:

o Setting the PV electrical energy priority to the load o Maximize the use of the surplus electrical power during the daylight and reduce the energy consumption during the night

• Access throughout smart decides such as mobile phones. Individual can track and monitor the system while away.

• Provide an overall performance report. This provides individual with the opportunity to enhance the system efficiency.

C. Calculations

The electrical energy consumed by the house can be computed using equation 1:

M G L O K P + + + + = ( 1
)
Where P is the total daily power consumed by the house (Wh) K is the air-condition power (Wh) O is the cooking power (Wh) L is the lighting power (Wh) M is the washing and cleaning machines power (Wh) G is the general power (Wh) (Fridge, hair dryer, mixer….) For working family, K, O, M, and G are usually consumed when people arrived home after work or school. Therefore, it is main impacts will be after 15:00 hours. Figure 4 shows the energy consumption of a standard residential house of 20kWh daily energy usage. Figure 5 shows the electrical power outputs for a 5kW PV solar system during summer and winter. The figure shows that major house electrical consumptions sit outside the PV generated power. This also confirms that PV outputs do not support the electrical peak maximum demands without the presence of the batteries storage. The following can be concluded from figure 5: • Shifting the morning or afternoon loads to midday on a sunny day will reduce the maximum demands from the utility grids

• It also reduce the electrical energy bills for the properties G and L form the main electrical power consumption for a working family between 09:00 and 15:00. Using the smart house management system to shift the following energy consumption to the midday activities:

• "M" run the washing machine, dryer and dishwasher between 11 :00 and 14:00 on a sunny day

• "K" run the air-condition between 13:00 and 15:00 on a sunny day. This will cool down the house and provide the individual with the opportunity to run the air-condition at lower power when they arrive home (save on electrical energy in the afternoon)

• "O" perform the cooking, especially the electrical oven, where possible between 11:00 and 14:00 on sunny day.

• The times and dates the machines should operate can be determined by the smart house management system and its ability to predict the PV electrical outputs.

Figure 6 shows the updated house hourly energy consumption against the PV solar outputs. It is clearly shown the reduction in energy consumption in the afternoon peak period. Depending on the installed PV solar system, the generated power could exceed the house requirements. However, the house maximum demands located outside the peak electrical output of the solar system. Therefore, without storage facility, the current technologies do not assist in utilizing the generated power to supply the household electrical requirements. As shown in figure 5, the generated power for a working family will be fed into the grid. This system assists the utilities in reducing the generated power during midday period for a sunny day.

The smart home energy management system will shift the power from the peak to midday when possible. Also it predicts the maximum power for the week by taking into consideration the forecast data. This arrangement increases the efficiency of the PV system and reduces the electricity bills. This increased efficiency reduces the environmental impact for power stations and reduces the cost of electrical infrastructure.

IV. CASE STUDY

The case study is based on the house loading showing in table 1. The house has 5kW PV solar system installed on the roof top. The elements of equation 1 as per table 1 are as follows:

P=16960Wh, K=7200Wh, O=4400Wh L=960Wh, M=2100Wh, G=2300Wh Figure 7 shows the standard hourly energy consumption against the 5kW solar system outputs for winter and summer. It is clearly shown that the generated PV power only feeds low percentage of the required household. The smart home energy system analyses the proposed weather forcast for the area. The predictied PV oputput power is shown in table 2. The individual chooses the following activities for the week during sunshine hours: Figure 8 represents the updated hourly consumption as per the proposed smart management concept. It is clearly shown the reduction from the peak afternoon slot. It should be noted that cooling or heating the house during the day using the PV generated power, will reduce the electrical energy consumption for the evening cooling or heating. Furthermore, the system ensures that washing, drying, dishwashing and part of the cooking will be done during peak sun energy. For example, in summer, the ducted air-condition can work on cooling the entire house during the afternoon using the generated electrical energy. This introduces the following benefits:

• Reduce the consumption of the fridge. (Fridge consumes higher electrical power when operating at higher temperature).

• Electronics appliances consume less energy when operating at lower temperature.

• The air-condition required less energy to keep the house cool in the evening and night time, especially that partial of the cooking is completed during the day.

Regarding the partial cooking, it could include the followings:

• Boiling red or white meats for major meals • 75% cooking of an oven meal

• Etc… V. CONCLUSION
This paper is an introduction to a sophisticated smart energy management system for residential properties. The proposed system has the abilities to forecast the outputs of the installed PV solar system. Furthermore, it provides the individual with the ability to enhance the system efficiency by maximizing the use of the sun energy.

The case study shows that it is possible to reduce the electrical peak demands for residential properties by using the smart energy management tools. This reduction decreases the required electrical power generated by fossil fuel plants. The outcomes of this arrangement have positive impacts on the environments as well as the electricity bill.

VI. FUTURE WORKS

At Western Sydney University, the following systems will be constructed during 2016:

• PV solar system to suit a DC load (with battery storage)

• PC solar system to suit an AC load

• Wind turbine

The installed system allows for the development and testing of the proposed smart energy management tools. Future publication will captures the findings of ongoing works.

Figure 1 .

 1 Figure1. Solar hourly energy distribution[START_REF] Mitchell | Optimisation of the applications of sustainable energy systems[END_REF]

Figure 2 .

 2 Figure2. Electrical loading for residential property[START_REF] Mitchell | Optimisation of the applications of sustainable energy systems[END_REF]

Figure 3 .

 3 Figure 3. Proposed smart home management system

Figure 4 .

 4 Figure 4. 20kWh house electrial hourly consumption

Figure 6 .

 6 Figure 6. Updated household energy consuption with energy shifting III. THEORETICAL DISCUSSION

Figure 7 .•

 7 Figure 7. Household electrical power consuptions against PV solar outputs

Figure 8 .

 8 Figure 8. Updated hourly energy consumption to suit the PV solar output

TABLE I

 I

	.	HOUSEHOLD ELECTRICAL LOADING
		DC Loading
	Product	Electrical Load	Electrical Load
	Description	(W)	Wh/day
	290L		900
	refrigeration
	Washing		500
	Machine
	Dishwasher		700
	Dryer		900
	1L Kettle	200	200
	TV	50	500
	LED light	10 light (12W)	960
	Air-conditioner	1200	7200
	Chargers	100	200
	Microwave	1000	500
	Oven	2200	4400
		Total kWh/day	16.960

TABLE II .

 II PREDICTED PV SOLAR SYSTEM WEEKLY OUTPUT

	Mon Tue	Wed Thu	Fri	Sat	Sun
	kWh 21.2	15.96 21.3	21.2	16.1	14.3	14.6

TABLE III

 III

		.	PROPOSED WEEKLY ACTIVITIES
		Proposed activities	Required PV outputs (kWh) (this includes general requirements)
	Mon Washing, Dishwashing condition, partial cooking Drying, Air-	10.1
	Tue	Dishwashing, condition, partial cooking air-	9.1
	Wed Dishwashing, condition, partial cooking air-	9.1
	Thu	Washing, Dishwashing condition, partial cooking Drying, Air-	12.3
	Fri	Dishwashing, condition, partial cooking air-	9.3
	Sat	Dishwashing, condition, partial cooking air-	13.6*
	Sun	Dishwashing, condition, partial cooking air-	13.6*
	* During the weekend, general activities at its maximum during the day (TV, Stereos light,….)


Rajneesh Chaudhary

K Ravi Kumar

Martin Seden

Bengt Rydholm

Jan-Erik A Eriksson

Hongliang Yang

Electromagnetic Devices for Continuous Steel Casting Tundishes

Keywords: Electromagnetic stirrer (EMS), Electromagnetic brake (EMBR), Temperature homogenization, inclusion removal, tundish heater

Electromagnetic (EM) devices such as electromagnetic stirrer (EMS) and brake (EMBR) are extensively being used in steel industry across various processes in furnaces, ladles and casters. The application of EM devices in tundish is not well known despite the fact that tundish offers huge potential to improve steel cleanliness and overall melt quality, due to the availability of large surface area, before the melt is solidified in the mold. This article focuses on to develop EMS and EMBR for tundishes by way of quantifying their benefits in the process. For slab casting, a "T" shape two strand tundish is considered and for billet casting a "V" shape six strand tundish is considered for the analysis. Simulations of various combination of EMS and EMBR configurations are carried out for both two and six strand tundish to obtain better fluid flow characteristics, melt thermal homogenization and steel cleanliness. EM devices when used in tundishes influence the melt to reduce the temperature variations from around 15-17K to 5-7K besides avoiding short circuiting, compositional segregation with better control on strand(s) temperature while significantly improving inclusion floatation to produce cleaner steel. A surface heater canbe included in the tundish along with EM device to maintain strand(s) temperature at desired level while achieving optimum superheat to cast better quality steel and avoiding nozzle freezing.

Introduction

Continuous casting of steel is time and energy intensive process and requires precise control to improve the steel quality and cleanliness. In continuous casting process,the melt flows from ladle to tundish and finally to caster loses heat throughout the path and also fromvarious vessels.Tundish gets molten steel (at ~1600 o C) from ladle in a batch wise process while supplying the same continuously to the caster. Tundish plays an important role in maintaining desired melt flow pattern and thermal homogenization at required melt temperature with desired steel cleanliness. As a current practice, the flow and temperature distribution in tundish are typically controlled with the help of physical tundish furniture such as dam, weir, turbulence inhibitor and impact pads [START_REF] Sahai | Tundish Technology for Clean Steel Production[END_REF]. Though tundish furniture is beneficial for melt flow and temperature distribution but limited to the extent of their effects on the flow characteristics and overall tundish operation.In order to improve flow characteristics, thermal uniformity, the efficient use of electromagnetic devices such as electromagnetic stirring (EMS) / braking (EMBR) is essential leading to the benefits in terms of steel quality and overall process efficiency. Besides, the number of non-metallic inclusions which are generated along the process have the potential to be efficiently removed in tundish with the help of EMS and EMBR due to the availability of large surface area which is typically missing in the caster. In this article, effect of EMS and EMBR devices on fluid flow and temperature distribution along with the tundish heater for their benefits are analyzed for slab and billet casting applications.

Modeling of Electromagnetic Tundish Slab and Billet Casting Tundish

The slab and billet casting tundish are modeled to analyze the benefits of EMS, EMBR and tundish heater in terms of temperature homogenization, melt outlet temperature and fluid flow distribution. For slab casting, two strand "T" shaped tundish is considered and for billet casting, six strand "V" shape tundish is considered and are shown in Figure 1 and 2 respectively. More details on dimensions of two and six strand tundish and thermo-physical properties of steel used for the analysis can be found elsewhere [START_REF] Warzecha | [END_REF][3].

Computational Model

The governing equations for the coupled flow-thermal-electromagnetic field are given in the following sub sections. The electromagnetic equations are solved in Opera/ELEKTRA for electromagnetic forces and magnetic fields which are then imported in FLUENT to model their influence on the flow-thermal phenomena in the tundish [START_REF] Opera | Vector Fields Software[END_REF][START_REF] Fluent | ANSYS Fluent theory guide[END_REF].

Modeling of Electromagnetic Field

Under quasi-static limit, Maxwell's equations are solved along with Ohm's law and total Lorentz force (combined EMS and EMBr) may be written as follows:

     Lorentz F J B ( 1
)
where J  is the induced electric current and B  is magnetic flux density.

Flow-Thermal Model

The flow-thermal physics is governed by x-, y-and z-momentum, continuity and energy equations [START_REF] Fluent | ANSYS Fluent theory guide[END_REF].

The continuity equation for incompressible flow is given as:

0     V
(2) The transient momentum equations (x-, y-and z-) in vector form can be written as:

       T t P t                             B Lorentz V VV V V F F ( 3
)
where  is the fluid density (kg/m 3 ),  is the dynamic viscosity (N-s/m 2 ),  V is the velocity vector (m/s), P is the pressure (N/m 2 ),

 Lorentz F
is the total Lorentz force generated by EMS and EMBR, t  is the eddy or turbulent viscosity (N-s/m 2 ). In this model, the density is assumed constant except its variation with temperature whose effect has been incorporated in the momentum equation through Boussinesq approximation as defined via   B F .

To maintain conservation of energy in the domain, an energy equation is also solved along with continuity and momentum equations to complete the flow-thermal part of the model. The scalar energy equation is given as:

      '' p p CT C T T q t               V (4)
where T is the temperature (K),  is the thermal conductivity (W/m K), p C is the specific heat (J/kg-K) and '' q is the volumetric heat generation (W/m 3 ) such as induction heating etc.

Boundary Conditions

The inlet velocity two and strand tundish for steady state operation is considered as 0.23 m/s and 2.2 m/s respectively. The inlet temperature of melt for steady operation is considered as 1873 K and 1823 K respectively for two and six strand tundish. A zero pressureoutlet boundary condition (=0 Pa) is used at all the outlets. All the side, bottom and walls are subjected to typical constant heat flux boundary conditions and can be found in elsewhere [3].

Validation

The developed computational model and numerical methodologies are validated to capture various physics in the tundish to make sure model captures the desired physics. The turbulent flow and thermal parts of the model are validated with the work of Damle and Sahai [START_REF] Damle | [END_REF]. To validate electromagnetic brake, a Hartmann flow is simulated and compared with the analytical solutions of Bershader and Landshoff [START_REF] Bershader | Magnetohydrodynamics: In Handbook of Fluid Dynamics[END_REF]. The model predictions for electromagnetic stirring in a box shape domain are compared with the experimental and numerical works of Dubke et al. [START_REF] Dubke | [END_REF] and Felten et al. [9]. Validation results are not reported here due to restriction ofmanuscript length and these results are report by Kumar and Chaudhary [START_REF] Kumar | Electromagnetic Tundish[END_REF]. Overall, the present numerical methodology seems to be very well capturing physics associated with flow, thermal and electromagnetics aspects needed to model the effect of electromagnetic devices in tundishes.

Results and Discussion

In this section, effect of EM devices on flow and temperature distributions and inclusions removal are compared for with and without EM devices for two and six strand tundish. The benefits of plasma heater are also quantified for the two strand tundish. A special configuration of EMS is used for tundish modeling. In case of two strand tundish, EMS is placed along the longer arm of the tundish and in case of six strand tundish, EMS is placed along the straight side of the tundish.EMBR is considered at the inlet, outlets and between inlet and longer arm side of the tundish (EMBR dam/weir) for two strand tundish. In case of six strand tundish, EMBR is considered only at the inletwith magnetic field parallel to the flow.

Flow and Temperature Distribution

For a T-shape two strand tundish, it is found that with 100% EMS intensity the maximum surface velocity below the top molten metal layer is very high (0.554 m/s) and may pose danger in terms of steel shearing off slag layer leading to emulsification with small surface waves. When the intensity is reduced, the surface velocity reduced almost linearly reaching less than 0.3 m/s at around 20% EMS intensity, which is less than safe range (0.2-0.3m/s) of maximum velocity suggested to avoid slag entrainment [START_REF] Sahai | Tundish Technology for Clean Steel Production[END_REF]. The similar trend for EMS intensity is observed in six strand tundish as well. Therefore, 20% EMS intensity is considered for the further investigations along with the EMBR for both two and six strand tundish.The maximum and minimum temperature difference across the two strand tundish is found as 14.87 K and 4.88 K for No EM and 20% EMS configuration respectively (Figure 3). The temperature variation is more at slag surface (top wall) due to stagnation regions and stronger cooling effects in that area. Interestingly, adding EMBR at inlet, outlet(s) and EMBR as tundish furniture, slows down flow field leading to stagnation and cold pockets around. The effect of EMS and EMBR is analyzed in six strand tundish as well and similar behavior like two strand tundish is observed. As can be seen in Figure 4, the maximum temperature difference is observed in No EM configuration (16.66 K) and minimum temperature difference is observed in 20% EMS (7.74 K), i.e. 20% EMS showing maximum thermal homogenization.

Inclusions Removal

The analysis is carried out for various particle sizes ranging from 5 μm to 300 μm viz. 5 μm, 10 μm, 25 μm, 50 μm, 100 μm, 200 μm and 300 μm. The separation rate is less at lower inclusion particle sizes and increases with particle diameter. Most of the particles bigger than 200 μm are removed at the steel-slag surface. The separation rate is higher for all the EM configurations compared to that of No EM configuration for lower particle sizes (up to 50 μm). This shows that EM configuration contributes significantly to encourage non-metallic inclusion floatation, particularly smaller sizes which are most critical to be removed, in the tundish. The EM device extends the residence time of particle in the domain by mixing the flow thus leading to higher chances of its removal from the tundish domain. The separation rate for EM configurations at larger particles is more or less similar as in No EM case. Among all the EM configuration, 20% EMS configuration shows better inclusion separation rate compared to No EM and 20% EMS with all possible EMBR configurations (Figure 5). In case of six strand tundish, 20% EMS configuration encourages the inclusion floatation by 6% compared to the No EM configuration at particle size of 5 μm. The 20% EMS with EMBR performs poor compared to 20% EMS and No EM configuration (Figure 6). This is due to the EMBR at inlet which directs the flow towards other end of the longer arm of the tundish instead of directing flow upwards or towards side.

Dynamic Simulation of Tundish with Plasma Heater

The dynamic simulation of tundish is carried out in order to understand the improvements and benefits in typical transient tundish operation during ladle changes by way of adding EMS and heaters. Heater in this case, when combined with EMS, helps in maintaining desired melt superheat which is a significant requirement at the steel plants. In this work, simulation of 20% EMS configuration has been performed for two strand tundish with typical process variables. The target is to maintain bulk temperature of 1890 K with outlet flow rates of 32.5 kg/s from each outlets.Figure 7 and 8 shows the melt temperature (inlet, bulk and outlets) and flow rate (inlet and outlets) respectively. As can be seen from Figure 7, there is a ramp-up period on the bulk and outlet temperatures in the beginning afterwards the bulk temperature of 1890 K is maintained throughout the casting time. Since, bulk temperature is controlled very closely at 1890 K, there are some variations on the tundish outlet temperatures giving peaks at ladle openings, mainly due to heat losses and some variations in the tundish. Outlet flow rate is targeted at 32.5 kg/s (same from both outlets) by changing pressure drop at the outlets and due to transient free surface dynamics, the flow rate fluctuates around 30kg/s. In this article, multi-physics modeling of slab and billet casting tundish were carried out to study the effect of electromagnetic devices on fluid flow distribution, temperature homogenization and inclusion separation in the tundishes. Two strand "T" shape tundish was considered for slab casting, and six strand "V" shape tundish was considered for the billet casting. Analysis were carried out for combination of various EMS and EMBR configurations for both two and six strand tundish. The effect of EMS suggest the needand advantages of stirring for both tundish configurations. The melt in the tundish with EMS gets homogenized thus reducing melt temperature difference significantly and inclusion separation rate increased especially for smaller inclusions. EMS with tundish heater helps in maintaining required uniform outlet temperature over time and across various strands in multi-strand tundish. The effect of EMBR at inlet, outlets and as tundish furniture needs further investigation to confirm their benefits in terms of avoiding vortexing and splashing while encouraging inclusion floatation.

Figure 1 Figure 2

 12 Figure 1 Schematic of two strand tundish

Figure 3 Figure 4

 34 Figure 4 Temperature distribution in six strand tundish (a) without EMS (b) with 20% EMS

Figure 5 Figure 6 Figure 7 Figure 8

 5678 Figure 5 Inclusions separation rate in two strand tundish


S Kunstreich
email: s.kunstreich@danieli-rotelec.fr

T Gautreau

J Y Ren

A Codutti

F Guastini

M Petronio

F Vecchiet

Experimental approach to develop Multi-Mode® EMB, an advanced electromagnetic brake for thin slab casters

Keywords: EMB, EM Brake, Thin Slab Caster, response surface methodology

 it describes in more detail the approach to develop a new EMB concept not only by simulations but by experimental work. Considering that no commercial software was available with reliable full coupling between electromagnetic and CFD codes and starting from our know-how in TSC process fluid-dynamics, we decided to design and realize an installation that simulates with cold melting metal at scale 1 the industrial operation of a thin slab caster. The result was not only the validation of the concept of the new EMB, but the development of the operating know how. The extensive measurement of liquid metal flow velocity and fluctuations within a large range of existing and future operating conditions of slab width and casting speed permits to develop in advance the knowledge, how to use the EMB control parameters for different casting conditions. That in turn permits to develop in advance the automation software for industrial operation. Without the full scale caster simulator, such task would require test casts of some hundred thousand tons that no steel making company would accept to do on its thin slab caster.

Introduction

The quality of continuously cast slabs is strongly related to the liquid steel flow in the mold. Various technologies of Electromagnetic Actuators (EMA) are currently used on continuous casting machines to control the steel flow. For conventional (medium to thick) slabs, different concepts using electromagnetic stirring (EMS) or electromagnetic braking (EMB) are competing , whereas for thin slabs, up to now conventional EMB like EMBr or EMBr-Ruler [START_REF] Hess | New Steel[END_REF] with one or two poles per mold are used. These provide only a global braking action, but the specific problems of high productivity thin slab casters require a more differentiated action. We have explained in a recent paper our concept why for medium and thick slabs we recommend EMS (our Multi-Mode® EMS technology) and why specifically for thin slabs we have developed a new EMB technology [START_REF] Kunstreich | 8 th European Casting Conference[END_REF][START_REF] Kunstreich | Proceedings of 1 st ESTAD and 31 st JSI conference[END_REF]. Due to the particular geometries of the funnel mold (Fig. 1) and the submerged entry nozzle (SEN) (generally with four ports, Fig. 2) of thin slab casters, the predominant flow pattern is the Double Roll characterized by two lateral recirculation loops toward the meniscus two lateral downward jets penetrating inside the liquid pool and one upward central returning flow (Fig. 2). Like in conventional slab casters, the lateral recirculation loops and the downward jets become too fast at high casting speed/high throughput and must be slowed down by a braking function, however in thin slab casters the aim is to differentiate the braking function between the upward and downward flow. Also like in conventional slab casters, right/left asymmetrical and unstable flow must be prevented by a stabilizing function that prevents bias flow and flow switching from right to left. However, unlike conventional slab casters, vertical meniscus fluctuations become more severe in thin slab casters due to the reduced mold volume, and that requires a damping function.

To address these specific flow problems of thin slab casters, an EMB with a multiple pole geometry and three distinctive functions braking, stabilizing and damping (Fig. 3) has been developed by Danieli Research Center and Danieli Rotelec and is commercialized under the name Multi-Mode® EMB.

The slab quality problems related to the flow control functions have been explained in more detail in a previous paper [START_REF] Kunstreich | 8 th European Casting Conference[END_REF]. The purpose of this paper is to show that both, extensive computer modeling as well as important experimental work are necessary to achieve such industrial development. Why modeling is useful but not sufficient ? EMB, in contrast to its name and to a widespread opinion, does not really brake the liquid steel velocity, but more likely deviates or deflects the steel flow.

With our purpose to develop a new EMB with distinctive functions, a multi-pole geometry is proposed. To find the 'good' pole geometry, we have started the development with modeling the concept, i.e. to compare and choose for a wide range of flow topology the number, size and position of the poles and the required ampturns of the coils.

Modeling is a powerful and useful tool to investigate and choose between various solutions for complex multi-physics problems. In our case, a one way modeling between Electromagnetic Brake and stationary fluid dynamics (CFD) provides a qualitative description on how the steel flow 'responds' to different magnetic configurations. However, it does not provide a reliable quantitative information. In order to increase the accuracy of such modeling and to approach reality, it is necessary to use transitory CFD method which is very time consuming compared to stationary method and which must be calibrated by experimental measurement data. Moreover, to describe the operation of a caster, the number of simulations would be too big and the time required to perform such work too long. As an example, if we consider just one mold design and one SEN geometry, we would have to compute at least 450 cases (5 casting speeds from 4 to 8 m/min times 9 slab widths from 1000 to 1800 mm times 10 different coil current settings). For all the above reasons, we felt necessary to validate the new EMB experimentally and decides to design, manufacture and operate a thin slab simulator plant with full scale EMB and full scale thin slab funnel mold.

Full scale caster simulator

Based on the concept development by modellisation, an EMB prototype was designed and manufactured by Danieli Rotelec in France, and a 1:1 scale low melting metal facility was designed and installed in the Danieli R&D Center in Italy (Fig. 4).

The steel flow conditions have been reproduced by a recirculation loop of cold melting bismuth-tin alloy (melting point 138°C), which has physical properties such as conductivity, density and viscosity very similar to liquid steel. On the top of the facility a small tundish and an industrial stopper provide the liquid to the SEN and the mold. Below the mold, the low metal alloy is collected in a special tank designed to prevent turbulence transmission up to the mold; it then is recirculated upwards by means of a centrifugal pump having an operating range from

Experimental Work

The experimental set up is composed of four sensors (Fig. 5). Two velocity sensors, so called "paddles", are immersed on each side of the SEN and monitor the right and left horizontal meniscus velocities. On the left side, vertical meniscus fluctuations are measured by recording the position of a floating sensor installed on the left side of the mold (Fig. 5&6). Each 40s sliding windows, the standard deviation (σ) of the meniscus waviness (horizontal fluctuations) is determined and is available online. On the right side, a meniscus topography is obtained by a laser scanner (Fig. 5&6).

Operational know how

The target of the experimental tests was not only the validation of the concept of the Multi-Mode® EMB, but also the development of the operating know how, i.e. the determination how to choose the EMB setting parameters (coil current of different poles) for different operation parameters of slab width and casting speed. Thanks to this unique caster simulator we have been able to produce experimentally a huge data base of meniscus velocity (Vm) and meniscus waviness (σ) as a function of different casting speeds, mold widths, SEN geometries and EMB coil current settings. This data base is then used to represent the meniscus velocity (Vm) and the meniscus waviness (σ) in a slab width / casting speed diagram for the natural flow obtained without EMB (Fig. 9 and 10) as well as for the 'forced' flow obtained with a given set of EMB parameters. This data base is further used with a dedicated software in a process computer that selects automatically the appropriate EMB settings according to the actual casting speed and mold width of a thin slab caster. Thus, we can use this powerful tool to automatically control the meniscus velocity (Vm) and the meniscus waviness (σ) whatever are the casting parameters. We hence have developed not only an experimentally validated new EMB but also the operational knowhow and the automatic process control. As a result, we obtain a meniscus velocity Vm controlled within 0,1 to 0,3 m/s and a meniscus waviness σ below 0,8 mm in the validated range of caster operations of casting speed from 4 to 10 m/min and slab width from 900 to 1800 mm. Please note that the simulated casting time of this experimental work represents something like 200.000 to 300.000 t of cast steel and be sure that no steel making company in the world would accept to use its thin slab caster for such long and expensive testing.

Fig. 1 :

 1 Fig. 1: Danieli's Funnel mold design for thin slab Casters Fig. 2: Double-Roll in Thin Slab Casters with 4 ports

Fig. 3 :

 3 Fig. 3: New Danieli Multi-Mode® EMB multipole configuration

Fig. 4 :

 4 Fig. 4: (a) Low melting metal simulation facility in Danieli R&D Center (Buttrio, Italy), (b) Detail of the small tundish 450 l/min to 1100 l/min (casting speed range from 4.2 to 11.7m/min). A real full scale copper mold with funnel geometry and variable width from 990 mm to 1800 mm (Fig. 4) is used. The slab thickness at narrow face has been kept constant at 102.5 mm. The MM-EMB is installed on both sides of the mold. Real refractory SEN with different geometries have been used for the tests.

Fig. 5 :

 5 Fig. 5: Experimental Set Up including right and left paddles and a floating sensor to acquire the meniscus waviness Fig. 6: Example of meniscus level and standard deviation of the meniscus level (σ) acquired on line during the test.

Fig. 7 :

 7 Fig. 7: Mold width 1530 and casting speed 6.2 m/min: damping and braking of two different meniscus velocities.

Fig. 8 :

 8 Fig. 8: Waviness Shape for mold width 1530, casting speed 6 m/min: (a) MM-EMB on (b) MM-EMB off.

Fig. 9 :

 9 Fig. 9 : Casting speed/slab width diagram -meniscus velocity (V M ) -Natural flow Fig. 10 : Casting speed/slab width diagram -meniscus waviness (σ) -Natural flow Conclusion An innovative EMB technology called Multi-Mode® EMB has been developed with distinctive functions of braking, stabilizing and damping the liquid steel in the moulds of thin slab casters. This development was possible only thanks to the combination of three mandatory activities: advanced computer simulations for concept development, experimental work with low melting metal for concept validation, full scale caster simulation for quantitative data base and automatic process control. The new Multi-Mode® EMB is able to keep the meniscus velocity (Vm) in a range of 0.1 to 0.3 m/s and the meniscus fluctuations (σ) below 0.8 mm for a caster operating in a range of casting speed 4-10 m/min and slab width 900-1800 mm.

Acknowledgment

The authors acknowledge with thanks the support of Danieli Research Center, Daniely Davy Distington and Danieli Automation who have financed the work.


Kyung Hyun Kim
email: khkim@kims.re.kr

M A Slazhniev

Se Won Kim

Hyun Suk Sim

Kwangjun Euh

Study on controlled segregation of Al-Sn alloys for bearings

Keywords: Segregation, distribution, microstructures, particles, bearing materials

In this study, Sn particles are finely and uniformly distributed in the Al-Sn alloy through a strong pulse treatment on the molten alloy. The Sn particle size in the conventionally cast Al-25%Sn alloy is about 80μm while that in the pulsetreated alloy is reduced to 45μm with a uniform distribution. Applying a strong pulse to an Al-Sn alloy causes a fine and uniform microstructure and the pulse-treated Al-Sn alloy is preferable for a metal bearing material where an excellent lubricity with a proper wear resistance are necessary.

Introduction

Most engine bearings have layered structure, which provides optimum combination of the bearing material properties: fatigue strength, seizure resistance, wear resistance, conformability, embedability, corrosion resistance, and cavitation resistance. The main functional bearing materials can be a mono-metal, a bi-metal, a tri-metal or a multi-layered clad metal and most of engine bearings are in the form of either bi-metal or tri-metal. Recently, enhanced properties of bearings are required with a development of the engine performance such as speed, compactness, and light weighting. The Al-Sn alloy should have an excellent corrosion resistant, thermal conductivity, and abrasion resistance. The Al-Sn alloys containing harmful Pb are cheaper than the Cu containing alloys and an economical technology should be applied to a manufacturing of bearing alloys. Typical engine bearings structures are presented in the Fig. 1. The bearing alloy has been developed to meet the demands such as the fatigue strength, lubricity, excellent thermal conductivity, wear and corrosion resistance as well as economical materials [1][2][3]. In order to improve the lubricity, the bearing material contains 10-14% of Sn in aluminum alloy. For a further improvement in lubrication, a higher Sn concentration is adapted with Al-20%Sn and Al-40%Sn alloys. Aluminum based bearing alloys commonly contain Sn (6-40%) as a soft component. Tin is distributed in aluminum matrix as a separate phase in a reticular structure along the edges of aluminum grains. Tin element imparts to the material anti-friction properties such as compatibility, conformability, embedability. Silicon has very high hardness and Si-related inclusions distributed through the aluminum matrix can serve as abrasive particles polishing the mating journal surface. During the engine operation, Si particles included in the aluminum matrix remove the ragged edges from the shaft surface. Aluminum matrix of engine bearing alloys can be strengthened by an addition of alloying elements such as Cu, Ni, Cr, Mn, Mg, and Zn. The Sn particles are crystallized with eutectic silicon phases, resulting in a uniform distribution of Sn particles with the addition of Si in an Al-Sn alloy. The addition of Cu and Si in an Al-Sn alloy increases the tensile and yield strength of the alloy. Wear resistance of the Al-Sn alloy having large network structure of Sn particles exhibits better anti-friction characteristics compared to that of the elongated Sn structure. Cu and Si elements are known to enhance the strength of the Al-Sn alloy and improve the bearing properties such as wear resistance and the performance in seizure resistance. In the as-cast condition, the AlSn20Cu material is characterized by an interdendritic distribution of the low melting point Sn-based phase which forms a three-dimensionally connected network around the Al-based dendrite arms. Tin in an aluminum is hardly dissoluble and the melting point of aluminum is 660℃ which is about 3 times higher than that of Sn. The density of Sn is 7.28g/cm 3 which is about 3 times higher than that of aluminum. Mushy zone of the Al-Sn alloy is very wide through the whole content of Sn and wider in a low Sn content alloy (Fig. 2). Therefore, grains of Al-Sn alloy become coarser and severe segregation is easily found in this alloy when it is produced by conventional melting and casting processes. Segregation in an Al-Sn alloy is caused by insoluble Sn phase and huge difference in melting temperature and density. Therefore, investigations on the manufacturing method for finely and uniformly distributed Sn particles in the Al-Sn alloy are required. This alloy can be produced in a strip form by the continuous casting but segregation problems have not been solved completely. As the Sn content increases, the lubricity is improved. However, increase in Sn content causes weakening of the grain boundary segregation of Sn in Al matrix, which can decrease the fatigue strength rapidly [4][5]. Electro hydro pulse treatment (EHPT) [6] and electromagnetic stirring process are able to influence the size and distribution of the intermetallic particles. This research is mainly focused on a fine and uniform distribution of Sn particles through the strong pulse process in a molten Al-Sn alloy. Fig. 5 is SEM micrographs of as-cast Al-25%Sn alloys showing the effect of pulse treatment on the distribution of Sn phases. The size of Sn particles in the conventionally cast Al-25%Sn alloy is somewhat coarse with an average diameter of 80μm (Fig. 5a). However, Sn particles in the pulse-treated specimens are greatly reduced to 45um with a uniform microstructure (Fig. 5b). By applying a strong pulse into an Al-Sn alloy, a fine and uniform structure was obtained and this alloy and it is applicable to an Al-Sn alloy for a metal bearing which requires excellent lubricity and wear resistance. The pulse treatment can be also applied to a conventional Al-Si-Mg-Fe casting alloy in which the size and distribution of the hard phases such as primary Si and Al-Fe-Si intermetallics are important. Especially, the Fe-containing compounds are harmful for the soundness of the casting. The pulse treated alloy shows shorter intermetallic compounds through the Al matrix (Fig. 6), which can improve the mechanical properties of the alloy. A microstructural analysis was done for the typical conditions of moderate heat removal in the mold with a low solidification rate and overheating above the melting point of metals. It is found the formation of semi dendrites of α-Al, and a cluster of α-Sn in the grain boundary with different sizes and shapes. Application of mechanical mixing with gas bubbling provided a moderate dispersion of Sn up to 60 vol.% of interdendritically distributed form, with a large size of Sn particles between 50 to 85 m. In this method, it is inevitable to form residual gas porosity during casting. However, application of an EMS-PMF showed a deeper mixing in particle dispersion of Sn with dense structures incorporating Sn of 5-40 m in an interdendritic distribution (Fig. 7).

Bi-metal bearing

Aluminium bearing alloy

Aluminium bounding layer

Steel back

According to the multiple additional electromagnetic stirring (20-30 cycles of stirring for 1 minute, at speeds of 0.5 to 2 m/s) the mass flow rate in the pulse processing zone is 0.5 to 2 kg / s due to electromagnetic forces (frequency 120 Hz) for a binary Al-Sn alloy. In a high Sn containing Al-Sn alloy such as Al-40%Sn, the pulse processing (1.25 kJ with a frequency range of 1 to 3 Hz) can significantly increase the efficiency of the treatment by enhancing the processed volume (without EMP only 20 kg over 1-3 minutes) which is associated with an output pulse in relation to the effective volume of the melt to process.

Compared with an electromagnetic stirring only, typical advantage of the pulse processing is deeper efficiency on mechanical and acoustic effects of compression-dilatation waves on redistribution, homogenization, crushing solids and inclusions containing silicon, iron and other complex heterogeneous phases (needle-like intermetallic compounds) (Fig. 6), as well as non-metallic inclusions in the molten alloy. Based on the idea of combining the pulse treatment with simultaneous contactless mixing of melts, segregation of the constituent phases can be successively prevented. Furthermore, it is possible to create novel equipment with an efficient technology for dispersion and stabilization of the emulsion of finely distributed Al-Sn alloys with production quantities (from 50 to 500 kg). Compared with the pulse-only specimen without electromagnetic stirring, the specimen processed with a combination of the pulse treatment and the electromagnetic stirring has smaller Sn particles with a more uniform distribution. In this specimen, the partitioning of the alloying elements within the parent Al phase and the Sn-rich regions is essentially the same as in the as-cast material.

Conclusions

Tin particles of Al-25%Sn alloy which is produced by conventional melting and casting are coarse to 80μm but those of the pulse-treated specimens are exhibited fine as 45μm with homogeneous microstructures. By adding a strong pulse in an Al-Sn alloy, fine and uniform structures are obtained. This alloy can be used for a metal bearing material which requires excellent lubricity and wear resistance. The best manufacturing method of Al-Sn alloy for a bearing material can be applied with a combination of strong pulse treatment and electromagnetic stirring at the same time in the molten alloy.

Fig. 1 :Fig. 3 : 4 :

 134 Fig. 1: Typical structure of metal bearing with Al-Sn alloy Fig. 2: Phase diagram of Al-Sn alloy

Fig. 5 :Fig. 6 :

 56 Fig. 5: Microstructure of Al-25%Sn alloy for metal bearing with pulse treatment

  Fig. 7: Microstructure of Al-25%Sn alloy for metal bearing with pulse treatment

Acknowledgment

The authors acknowledge financial support provided by the Ministry of Trade, Industry & Energy of Korea.


Iwai Kazuhiko
email: iwai@eng.hokudai.ac.jp

Evaluation of magnetic susceptibility difference of coagulated particles for crystal alignment

Keywords: crystal alignment, magnetic susceptibility difference, particles

published or not. The documents may come

Evaluation of magnetic susceptibility difference of coagulated particles for crystal alignment

Introduction

Crystal alignment is a powerful method for producing functional materials with crystallographically anisotropic nature. For example, crystallographically aligned graphite strongly enhances heat flow because thermal conductivity of graphite in a,b-axis is about two hundred times larger than that in c-axis. Thus, a lot of materials such as ceramics and polymers have been slip cast under the imposition of a magnetic field to achieve crystal alignment. In this process particles or powders have been mainly used as a raw material. Their magnetic susceptibility difference in magnetically major axis and magnetically minor axis is different from that of single crystal because they might coagulate and they might be polycrystal. Because magnetic susceptibility difference highly affects the minimum intensity of a magnetic field and alignment time in an alignment process, evaluation of distribution of magnetic susceptibility difference is essential for optimization of the alignment process. We evaluated the effective magnetic susceptibility difference distribution of bismuth particles using the measurement of in situ crystal-alignment behavior in the presence of a static magnetic field.

Experimental

Bismuth with a rhombohedral crystal structure was chosen as a sample material because it has a relatively large magnetic susceptibility difference of 5.3*10 -5 (-). Granular bismuth particles with a few mm size were grinded in a mortar to make them small particles. Median diameter of grinded particles were 12.5m.

Fig.1 Experimental procedure

A slurry composed of grinded bismuth particles and liquid polydimethylsiloxane with viscosity of 50 Pa.s was inserted into an acrylic vessel as shown in Fig. 1(a). Then the vessel was inclined with an angle of 45 degrees from the gravitational direction under the imposition of a 1T vertical static magnetic field as shown in Fig. 1(b), and was kept for a while for magnetic alignment of the bismuth particles. In the next step, the aligned angle of the vessel was changed from 45 degrees to 0 degree as soon as possible, and X-ray was emitted into the vessel through the one of its side surface, which was made of thin polymer film to minimize the decay of X-ray, and reflected X-ray intensity was measured by the detector for evaluation of the peak intensity of bismuth (110) plane as shown in Fig. 1(c). Detail of the experimental procedure is mentioned in reference [1]. Because the c-axis is magnetically major axis, it becomes parallel to the magnetic field direction under the imposition of a magnetic field. That is, planes parallel to the c-axis such as (110) plane also become parallel to the magnetic field direction if a magnetic field is imposed. Thus the (110) plane peak intensity can be considered as an index of the bismuth particle alignment.

A particle alignment behavior under the imposition of a magnetic field can be theoretically expressed as follows.

tanϕ tanϕ

(2)

where B is magnetic flux density, t is time, t E is relaxation time,  e is effective magnetic susceptibility difference of a particle,  is angle between magnetically major axis and magnetic field direction, is initial angle,  is viscosity of a surrounding liquid, is magnetic permeability in vacuum, respectively. Magnetic susceptibility difference can be calculated using equations ( 1) and ( 2) by substituting the measured alignment time. In this experiment, the measured (110) bismuth plane peak intensity gradually increased and finally became constant at time, t F as shown in Fig. 2. The reason for this is mentioned below. Driving force of a bismuth particle alignment under the imposition of a magnetic field is magnetic torque while resistant force of the alignment is viscous torque caused by a fluid motion surrounding the particle. The former depends not only on the coagulating condition of the bismuth particles but also number, size of crystals constituting a particle, while the latter depends on the shape and coagulating condition of the bismuth particle. Therefore, alignment time of each particle is different, and some particles aligns quickly while some particles aligns slowly, and finally their magnetically major axis becomes parallel to the magnetic field direction.

Fig.2 Time dependency of bismuth (110) peak intensity

A small increase in the peak intensity, dI in a small time between t and t+dt corresponds the amount of the particles having effective magnetic susceptibility difference of Δ , which is calculated using equation (3).

Δ 1 6 tanϕ tanϕ (3)
Thus, the relation between the effective magnetic susceptibility difference and its ratio in the particles can be evaluated. Relative magnetic susceptibility difference,  r defined as equation ( 4) is introduced for easy understanding of magnetic property of the bismuth particles.

Δ Δ Δ

(4)

The obtained distribution of the relative magnetic susceptibility difference in this experiment is shown in Fig. 3. Minimum of the relative magnetic susceptibility difference was 0.2 and maximum was unity in this experimental condition. Thus, some particles had a magnetic property similar to that of a single crystal while some particles were coagulated and/or had shapes with large viscous resistance. And for alignment of all the particles, it takes a few times longer time than single crystals because about 40% of the particles have the relative magnetic susceptibility of around 0.3. This is useful Bi(110)peak Intensity, I Time, t tF Δ I Δ t information for crystal alignment using a magnetic field.

Fig.3 Distribution of relative magnetic susceptibility difference

Conclusions

Effective magnetic susceptibility difference of grinded bismuth particles in magnetically major axis and magnetically minor axis was evaluated. This is useful information for optimization of crystal alignment process using a magnetic field.