Trung-Dung D Vu
email: trung-dung.vu@inrialpes.fr

Olivier Aycard
email: olivier.aycard@imag.fr

Nils Appenrodt
email: nils.appenrodt@daimlerchrysler.com

Online Localization and Mapping with Moving Object Tracking in Dynamic Outdoor Environments

In this paper, we present a real-time algorithm for online simultaneous localization and mapping (SLAM) with detection and tracking of moving objects (DATMO) in dynamic outdoor environments from a moving vehicle equipped with laser sensor and odometry. To correct vehicle location from odometry we introduce a new fast implementation of incremental scan matching method that can work reliably in dynamic outdoor environments. After a good vehicle location is estimated, the surrounding map is updated incrementally and moving objects are detected without a priori knowledge of the targets. Detected moving objects are finally tracked using Global Nearest Neighborhood (GNN) method. The experimental results on datasets collected from different scenarios such as: urban streets, country roads and highways demonstrate the efficiency of the proposed algorithm.

I. INTRODUCTION

Perceiving or understanding the environment surrounding of a vehicle is a very important step in driving assistant systems or autonomous vehicles. The task involves both simultaneous localization and mapping (SLAM) and detection and tracking of moving objects (DATMO). While SLAM provides the vehicle with a map of static parts of the environment as well as its location in the map, DATMO allows the vehicle being aware of dynamic entities around, tracking them and predicting their future behaviors. It is believed that if we are able to accomplish both SLAM and DATMO in real time, we can detect every critical situations to warn the driver in advance and this will certainly improve driving safety and can prevent traffic accidents.

In this context, the paper proposes to solve SLAM and DATMO in dynamic outdoor environments with a vehicle moving at high speed using laser sensor and odometry. Recently, there are considerable research efforts focusing on these problems [START_REF] Prassler | Navigating a robotic wheelchair in a railway station during rush hour[END_REF], [START_REF] Hähnel | Mobile robot mapping in populated environments[END_REF], [START_REF] Wang | Simultaneous Localization, Mapping and Moving Object Tracking[END_REF], [START_REF] Wolf | Mobile robot simultaneous localization and mapping in dynamic environments[END_REF]. However, for highly dynamic outdoor environments like crowded urban streets, there still remains many open questions. These include, how to represent the vehicle environment, how to obtain a precise location of the vehicle in presence of dynamic entities, and how to differentiate moving objects and stationary objects.

To model the environment surrounding the vehicle, we use the Occupancy Grid framework developed by Elfes [START_REF] Elfes | Occupancy grids: a probabilistic framework for robot percpetion and navigation[END_REF]. Compared with feature-based approaches [START_REF] Leonard | Simultaneous map building and localization for an autonomous mobile robot[END_REF], grid maps can represent any environment and are specially suitable for noisy sensors in outdoor environments where features are hard to define and extract. Grid-based approaches also provide an interesting mechanism to integrate different kinds of sensors in the same framework taking the inherent uncertainty of each sensor reading into account.

In general, in order to perform SLAM and DATMO from a moving vehicle, a precise vehicle localization is essential. Direct scan matching techniques like ICP [START_REF] Besl | A method for registration of 3d shape[END_REF] and its variants [START_REF] Rusinkiewicz | Efficient variants of the icp algorithm[END_REF] are popular ways to correct vehicle location from odometry. The most evident flaw of these ICP-style scan matching methods is that the measurement uncertainty is not taken into account. Especially, sparse data and dynamic entities in outdoor environment cause problems of correspondence finding in ICP-style methods which affect the accuracy of matching results.

To overcome these problems, we introduce a new fast incremental scan matching method based on a local grid map and it does not need to find corresponding features and can work reliably in dynamic environments. When good vehicle locations are estimated, by integrating sensor measurements we are able to build a consistent grid map surrounding of the vehicle. And by comparing new measurements with the constructed local vehicle map, dynamic objects can be detected and tracked.

The proposed algorithm for solving SLAM and DATMO is tested on data collected from the DaimlerChrysler demonstrator car equipped with a camera, two short range radar sensors and a laser scanner (Fig. 1). In addition, vehicle odometry information such as velocity and yaw rate are provided by the vehicle sensors. In this paper we use only data from laser scanner and odometry. Images from camera are for visualization purpose. Experimental results show that our algorithm can perform both SLAM and DATMO in real The rest of the paper is organized as follows. In the next section, we describe our approach to mapping and localization in dynamic outdoor environments. Algorithm for detecting and tracking moving objects is presented in Section III. Experimental results are given in Section IV and finally in Section V conclusions and future works are discussed.

II. LOCALIZATION AND MAPPING

For safety navigation applications, basically a good global map is not necessary, so that the problem of revisiting or loop closing in SLAM is not considered in this work. For this reason, we propose an incremental mapping approach based on a fast scan matching algorithm which involves in building a consistent local vehicle map. The map is updated incrementally when new data measurements arrive along with good estimates of vehicle locations obtained from the scan matching algorithm. The advantages of our incremental approach are that the computation can be carried out very quickly and the whole process is able to run online.

A. Notation

We denote the discrete time index by the variable t, the laser observation from vehicle at time t by the variable z t = {z 1 t , ..., z K t } including K individual measurements corresponding to K laser beams, the vector describing an odometry measurement from time t -1 to time t by the variable u t , the state vector describing the true location of the vehicle at time t by the variable x t .

B. Occupancy Grid Map

In the occupancy grid representation, the vehicle environment is divided into a two-dimensional lattice M of rectangular cells and each cell is associated with a measure taking a real value in [0, 1] indicating the probability that the cell is occupied by an obstacle or not. A high value of occupancy grid indicates the cell is occupied and a low value means the cell is free. Suppose that occupancy states of individual grid cells are independent, the objective of a mapping algorithm is to estimate the posterior probability of occupancy P (M i | x 1:t , z 1:t ) for each cell of grid M i , given observations z 1:t = {z 1 , ..., z t } at corresponding known poses x 1:t = {x 1 , ..., x t }.

In the literature, many methods are used for occupancy grid mapping, such as Bayesian [START_REF] Elfes | Occupancy grids: a probabilistic framework for robot percpetion and navigation[END_REF], Dempster-Shafer [START_REF] Pagac | An evidential approach to map-building for autonomous vehicles[END_REF] and Fuzzy Logic [START_REF] Oriolo | Fuzzy maps: a new tool for mobile robot perception and planning[END_REF]. Here we apply Bayesian Update scheme [START_REF] Thrun | Probabilistic Robotics (Intelligent Robotics and Autonomous Agents)[END_REF] that provides an elegant recursive formula to update the posterior under log-odds form:

log O(M i | x 1:t , z 1:t ) = log O(M i | x 1:t-1 , z 1:t-1 ) + + log O(M i | z t , x t ) -log O(M i ) (1)
where

O(a | b) = odds(a | b) = P (a | b) / (1 -P (a | b))
In [START_REF] Aycard | Pedestrian tracking using offboard cameras[END_REF], P (M i ) is the prior occupancy probability of the map which is set to 0.5 representing an unknown state, this makes this component disappear. The remaining probability P (M i | x t , z t ), is called the inverse sensor model. It specifies the probability that a grid cell M i is occupied based on a Fig. 2. Hit maps build directly from raw laser data collected from a vehicle moving along a straight street: with vehicle localization using odometry (left); and using results of scan matching (right). Note that the scan matching results are not affected by moving objects in the street. See Fig. 6 for the resulting occupancy grid map.

single sensor measurement z t at location x t . In our implementation, it is decided by the measurement of the nearest beam to the center mass of the cell. Note that the desired probability of occupancy, P (M i | x 1:t , z 1:t ), can be easily recovered from the log-odds representation. Moreover, since the updating algorithm is recursive, it allows for incremental map updating when new sensor data arrives. Fig. 3b shows an example of an occupancy grid map, incrementally constructed from laser measurements during the vehicle's movement. The color of each grid map cell indicates the probability that corresponding space being occupied: gray=unknown, white=free, black=occupied.

C. Scan Matching against Occupancy Grid Map

In order to build a consistent map of the environment, good vehicle localization is required. Because of the inherent error, using only odometry often results in unsatisfied maps (see Fig. 2 left). When features can not be defined and extracted, direct scan matching techniques like ICP [START_REF] Lu | Globally consistent range scan alignment for environment mapping[END_REF] can help to correct the odometry error. The problem is that sparse data in outdoor environments and dynamic entities make correspondence finding difficult. One more important disadvantage of the direct scan matching methods is that they do not consider dynamics of the vehicle. Indeed we have implemented several ICP variants [START_REF] Rusinkiewicz | Efficient variants of the icp algorithm[END_REF] and found out that scan matching results are unsatisfactory and often lead to unexpected trajectories of vehicle. This is because matching only two consecutive scans may be very hard, ambiguous or weakly constrained, especially in dynamic outdoor environment and when the vehicle moves at high speeds.

An alternative approach that can overcome these limitations consists in setting up the matching problem as a maximum likelihood problem [START_REF] Thrun | A real-time algorithm for mobile robot mapping with applications to multi-robot and 3d mapping[END_REF], [START_REF] Hähnel | Mobile robot mapping in populated environments[END_REF]. In this approach, given an underlying vehicle dynamics constraint, the current scan's position is corrected by comparing with the local grid map constructed from all observations in the past instead of only with one previous scan. Mathematically, we calculate a WeB1.29

xt = argmax xt {P (z t | x t , M t-1 ) . P (x t | xt-1 , u t )} (2)
In the equation ( 2), the term P (z t | x t , M t-1 ) is the measurement model which is the probability of the most recent measurement z t given the pose x t and the map M t-1 constructed so far from observations z 1:t-1 at corresponding poses x1:t-1 that were already estimated in the past. The term P (x t | xt-1 , u t ) represents motion model which is the probability that the vehicle is at location x t given that the vehicle was previously at position xt-1 and executed an action u t . The resulting pose xt is then used to generate a new map M t according to (1):

M t = M t-1 ∪ {x t , z t } (3)
Now the question is how to solve the equation ( 2), but let us first describe the motion model and the measurement model used.

For the motion model, we adopt the probabilistic velocity motion model similar to that of [START_REF] Thrun | Probabilistic Robotics (Intelligent Robotics and Autonomous Agents)[END_REF]. The vehicle motion u t is comprised of two components, the translational velocity v t and the yaw rate ω t . Fig. 4 depicts the probability of being at location x t given previous location x t-1 and control u t . This distribution is obtained from the kinematic equations, assuming that vehicle motion is noisy along its rotational and translational components. For the measurement model P (z t | x t , M t-1 ), mixture beam-based model is widely used in the literature [START_REF] Fox | Markov localization for mobile robots in dynamic environments[END_REF], [START_REF] Hähnel | Mobile robot mapping in populated environments[END_REF]. However, the model come at the expense of high computation since it requires ray casting operation for each beam. This can be a limitation for real time application if we want to estimate a large amount of measurements at the same time.

To avoid ray casting, we propose an alternative model that only considers end-points of the beams. Because it is likely that a beam hits an obstacle at its end-point, we focus only on occupied cells in the grid map. A voting scheme is used to compute the probability of a scan measurement z t given the vehicle pose x t and the map M t-1 constructed so far. First, from the vehicle location x t , individual measurement z k t is projected into the coordinate space of the map. Call hit k t the grid cells corresponding to the projected end-points. If this cell is occupied, a sum proportional to the occupancy value of the cell will be voted. Then the final voted score represents the likelihood of the measurement. Let P (M t i ) denote the posterior probability of occupancy of the grid cell M i estimated at time t (following (1)), we can write the measurement model under the sum following:

P (z t | x t , M t-1 ) ∝ K k=1 { P (M t-1 hit k t ) such that M t-1 hit k t is occupied } (4)
The proposed method is just an approximation to the measurement model because it does not take into account visibility constraints, but experimental evidences show that it works well in practice. Furthermore, with a complexity of O(K), the computation can be done rapidly.

It remains to describe how we maximize (2) to find the correct pose xt . Hill climbing strategy in [START_REF] Thrun | A real-time algorithm for mobile robot mapping with applications to multi-robot and 3d mapping[END_REF], [START_REF] Hähnel | Mobile robot mapping in populated environments[END_REF] can be used but may suffer from a local maximum. Exploiting the fact that the measurement model can be computed very quickly, we perform an extensive search over vehicle pose space. A sampling version of the motion model (Fig. 4 right) is used to generate all possible poses x t given the previous pose x t-1 and the control u t . The resulting pose will be the pose at which the measurement probability achieves a maximum value. Because of the inherent discretization of the grid, the sampling approach turns out to work very well. In practice, with a grid map resolution of 20 cm, it WeB1.29 is enough to generate about four or five hundreds of pose samples to obtain a good estimate of the vehicle pose with the measurement likelihood that is nearly unimproved even with more samples. The total computational time needed for such a single scan matching is about 10 ms on a conventional PC. An example of scan matching result is shown in Fig. 3. The most likely vehicle pose is obtained when the laser scan is aligned with the occupied parts of the map and at the same time the vehicle dynamics constraint is satisfied.

Besides the computational effectiveness, one attraction of our algorithm is that it is not affected by dynamic entities in the environment (see Fig. 2 right). Since we only consider occupied cells, spurious regions in the occupancy grid map that might belong to dynamic objects do not contribute to the sum (4). The voting scheme ensures that measurement likelihood reach a maximum only when the measurement is aligned with the static parts of the environment. To some meaning, measurements from dynamic entities can be considered as outliers. This property is very useful for moving object detection process that will be described in the next section.

D. Local mapping

Because we do not need to build a global map nor deal with loop closing problem, only one online map is maintained at each point in time representing the local environment surrounding of the vehicle. The size of the local map is chosen so that it should not contain loops and the resolution is maintained at a reasonable level. Every time the vehicle arrives near the map boundary, a new grid map is initialized. The pose of the new map is computed according to the vehicle global pose and cells inside the intersection area are copied from the old map.

III. OBJECT DETECTION AND TRACKING

After a consistent local map of the vehicle is constructed from SLAM, moving objects can be detected when new measurements arrive. The principal idea is based on the inconsistencies between observed free space and occupied space in the local grid map. If an object is detected on a location previously seen as free space, then it is a moving object. If an object is observed on a location previously occupied then it probably is static. If an object appears in a previously not observed location, then we can say nothing about that object.

Another important clue which can help to decide a object is dynamic or not is evidence about moving objects detected in the past. For example, if there are many moving objects passing through an area then any object that appears in that area should be recognized as a potential moving object. For this reason, apart from the local static map M as constructed by SLAM described in the previous section, a local dynamic grid map D is created to store information about previously detected moving objects. The pose, size and resolution of the dynamic map is the same as those of the static map. Each dynamic grid cell store a value indicating the number of observations that a moving object has been observed at that cell location.

From these remarks, our moving object detection process is carried out in two steps as follows. The first step is to detect measurements that might belong to dynamic objects.

Here for simplicity, we will temporarily omit the time index. Given a new laser scan z, the corrected vehicle location and the local static map M computed by SLAM and the dynamic map D containing information about previously detected moving objects, state of a single measurement z k is classified into one of three types following:

state(z k ) =    static : M hit k = occupied dynamic : M hit k = f ree or D hit k > α undecided : M hit k = unknown
where hit k is the coordinate of the grid cell corresponding to the end-point of the beam z k and α is a predefined threshold.

The second step is after dynamic measurements are determined, moving objects are then identified by clustering end-points of these beams into separate groups, each group represents a single object. Two points are considered as belonging to the same object if the distance between them is less than 0.3 m. Fig. 5 illustrates the described steps in detecting moving objects. The leftmost image depicts the situation where the vehicle is moving along a street seeing a car moving ahead and a motorbike moving in the opposite direction. The middle image shows the local static map and the vehicle WeB1.29 Fig. 6. Occupancy grid maps built with and without filtering out detected moving objects. location computed by SLAM and the current laser scan is drawn in red. Measurements which fall into free region in the static map are detected as dynamic and are displayed in the rightmost image. After the clustering step, two moving objects in green boxes are identified and correctly corresponds to the car and the motorbike.

Note that our map updating procedure makes use of results from moving object detection step. Measurements detected as dynamic are not used to update the map in SLAM. For unknown measurements, a priori we will suppose that they are static until latter evidences come. This will help to eliminate spurious objects and result in a better map. Fig. 6 shows two occupancy grid maps constructed from the same laser data in Fig. 2 with and without filtering out dynamic measurements. We can see that the left one built without the filtering step results in many fuzzy regions.

Once we are able to detect moving objects we need to track them in order to estimate their states and predict their behaviors in the future. Tracking multiple moving objects is a classical problem. In the general case this problem is very hard, however it has been shown experimentally that simple methods are good enough to cope with urban scenarios [START_REF] Wang | Simultaneous Localization, Mapping and Moving Object Tracking[END_REF]. In our current work, a simple object tracking scheme as described in [START_REF] Aycard | Pedestrian tracking using offboard cameras[END_REF] using Global Nearest Neighborhood (GNN) and Kalman filter is employed to track detected objects. A replacement using MHT [START_REF] Cox | An efficient implementation and evaluation of reid's multiple hypothesis tracking algorithm for visual tracking[END_REF] and Adaptive IMM [START_REF] Burlet | Adaptive interactive multiple models applied on pedestrian tracking in car parks[END_REF] is ongoing.

IV. EXPERIMENTAL RESULTS

Our proposed approach for SLAM and DATMO is tested on datasets collected with the DaimlerChrysler demonstrator car using only laser sensor and odometry. The maximum measurement range of laser sensor is 70 m with a horizontal field of view of 160 • and a resolution of 1 • . The vehicle was driven through different kinds of scenarios such as city streets, country roads and highways with a maximum speed of 120 kph and the data is collected every 40 ms.

In our implementation, the width and height of local grid map are set to 160 m and 200 m respectively, and the grid resolution is set to 20 cm. Every time the vehicle arrives at 40 m from the grid border, a new grid map is created. The local SLAM and DATMO is run for every new laser scan.

The results of local SLAM and DATMO are shown in Fig. 7. The images in the first row represent online maps and objects moving in the vicinity of the vehicle are all detected and tracked. The current vehicle location is represented by blue box along with its trajectories after corrected from the odometry. The red points are current laser measurements that are identified as belonging to dynamic objects. The green boxes indicate detected moving objects with corresponding tracks shown in dark-yellow. The second row are images for visual references to corresponding situations.

In Fig. 7, the leftmost column depicts a highway scenario where the demonstrator car is moving at a very high speed of about 120 kph while two cars moving in the same direction in front of it are detected and tracked. In the middle is the situation where the demonstrator is moving at 80 kph on a country road. A car moving ahead and two other cars in the opposite direction are all recognized. Note that the two cars on the left lane are only observed during a very short period of time but both are detected and tracked successfully. In the third situation, the demonstrator moving quite slowly at 25 kph in a crowded city street. Our system detects and tracks both the pedestrian moving in front of the vehicle and the car moving far ahead. Temporary stationary objects like another standing pedestrian and several other cars parked nearby are considered as static objects. In all three cases, precise trajectories of the demonstrator are achieved and local maps around the vehicle are constructed consistently. In our implementation, the computational time required to perform both SLAM and DATMO for each scan is about 20 -30 ms on a 1.86GHz, 1Gb RAM laptop running Linux. This confirms that our algorithm is absolutely able to run synchronously with data cycle in real time.

V. CONCLUSIONS AND FUTURE WORKS

We have presented an approach to accomplish online mapping and moving object tracking simultaneously. Experimental results have shown that our system can successfully perform a real time mapping with moving object detection and tracking from a vehicle moving at high speeds in different dynamic outdoor scenarios. This is done based on a fast scan matching algorithm that allows estimating precise vehicle locations and building a consistent map surrounding of the vehicle. After a consistent local vehicle map is build, moving objects are detected and tracked reliably.

Future works include integrating information from radar and exploring how radar can help to improve the moving object detection and tracking process. We also intend to incorporate object models and road models that give a more meaningful representation of detected objects with specific shapes and sizes instead of only sets of contour points as in our current work.

WeB1.29

Fig. 1 .

 1 Fig. 1. The DaimlerChrysler demonstrator car.

  time for different types of dynamic outdoor environments.

Fig. 3 .

 3 Fig.3. An example of scan matching. From left to right: reference image; local map created so far M t-1 and previous vehicle pose x t-1 ; laser measurement at time t; and matching result is obtained by trading off the consistency of the measurement with the map and the previous vehicle pose.

Fig. 4 .

 4 Fig. 4. The probabilistic velocity motion model P (xt | x t-1 , ut) of the vehicle (left) and its sampling version (right).

Fig. 5 .

 5 Fig. 5. Moving object detection example. See text for more details.

Fig. 7 .

 7 Fig. 7. Experimental results show that our algorithm can successfully perform both SLAM and DATMO in real time for different environments.

VI. ACKNOWLEDGMENTS

The work is supported by the European project PReVENT-ProFusion 1


Chellali Benachaiba

Benyahia Samia

IMAT3E'18 International Meeting on Advanced Technologies in Energy and Electrical Engineering I prefer: ORAL presentation Power management based on fuzzy controller of stand-alone hybrid photovoltaic/wind system

. This study puts forward the prime importance of Water/Power flows management optimization. For this purpose, a Fuzzy Logic-based Energy Management Strategy (FLEMS) is proposed. Thus, both electric and hydraulic subsystems are

strongly coupled in terms of energy making necessary to manage the power flows provided by renewable sources to optimize the overall system performance. In this paper, a kind of management strategies based on artificial intelligence control with the fuzzy logic controller consists of a "smart" power-sharing between the electrical and hydraulic networks with regard to the battery SOC and compared in the way they share the hybrid power sources between the storage devices and the electrical/hydraulic loads. A dynamic simulator of the hybrid energy system has been developed and tested using a MATLAB environment. Several tests are carried out using real meteorological data of the Adrar region and a practical load demand profile. The simulation results show that the "coupled strategy" clearly outperforms the classical management strategies.

Simulation and results

The simulation of the proposed hybrid Renewable sources has been accomplished using the real data of Atmospherics conditions (irradiance and wind speed) and PV-Wind power generation recorded every hour for one day the 30th July 2018 of Adrar region in Southwest Algeria. From 00.00 AM to 05.00 AM the system is in a critical period caused by the battery discharge, in this time the wind system is incapable to satisfy the power demand, the waterpump work in a fault condition and rescued for damage.

To solve this problem, we should keep the batteries instantly charged by the time.

The PV system work 12 hours in the day, the rest hours the wind system support the load alone, the batteries bank charged in the peak when the PV and Wind systems with a maximum in the day.

IMAT3E'18 International Meeting on Advanced Technologies in Energy and Electrical

Engineering

In this paper, a specific class of standalone battery-less PV/Wind desalination system has been presented. This issue has been addressed through a specific and optimized water and power flows management strategy based on the fuzzy logic theory.

The major scientific challenge that this work contributes to rising is the development of an optimized power management system, which could work in real-time for such an autonomous system especially dedicated to remote communities [START_REF] Ahmed | MPPT Technique for Standalone Hybrid PV-Wind Using Fuzzy Controller[END_REF], [START_REF] Harrou | Model-based fault detection algorithm for photovoltaic system monitoring[END_REF]. The sought objective through the developed this system is to produce fresh water as much as possible while taking advantage of the available electric power offered along wind speed and sun irradiation conditions. So, according to the input electric power, fuzzy rules have been used to identify the instantaneous power sharing between the two motor-pumps so that to maximize freshwater production according to the available generated power [START_REF] Saidi | Simulation and control of Solar Wind hybrid renewable power system[END_REF]. The power management system by fuzzy logic play important role in management of power generated by RES and the load presented in water-pump.

Implementing an autonomous water desalination system powered by RESs in remote areas is still being a problematic issue that needs to be carefully addressed to guarantee freshwater supply. Energy management represents the major problem of such a system and the proposed approach in this paper could be used to solve the problem in real-time, but an experimental validation needs to be carried out on the experimental test bench to confirm the simulation results and the feasibility of the proposed FLEMS.

Figure 1 .

 1 Figure 1. The irradiance of 1 day 30th July 2018.

Figure 2 .

 2 Figure 2. Wind speed of 1 day 30th July 2018.

Figure 3 .

 3 Figure 3. Wind speed of 1 day 30th July 2018.


Chellali Benachaiba

Benyahia Samia

IMAT3E'18 International Meeting on Advanced Technologies in Energy and Electrical Engineering Impact of load characteristics on photovoltaic PV system with P&O MPPT controller

In the last decade, the use of renewable energy resources instead of fossil fuels pollutants has increased exponentially. Photovoltaic energy generation is ever more important as a renewable resource since it does not cause in fuel costs, pollution, maintenance, and emitting noise compared to other renewable resources as more accessibility of solar irradiation.

This paper presents impact load characteristics on MPPT controller of R, RC, RL and RLC circuit load with Perturb and Observe Maximum Power Point Tracking (MPPT) algorithm for a stand-alone photovoltaic system and sees the comparison of each circuit load on the system and the algorithm of control. The different results of power, voltage and current are discussed and shown that the inductor it has a capital effect on Maximum power point (MPP) and system in general. The simulation results and a comparative analysis are discussed in this paper.

Simulation and results

In this part, the PV system simulation is realized to research the effects of the sliding mode MPPT controller parameters on the system performance. The PV system presented by

R, RL, RC loads:

We suppose that the irradiations trajectory mentioned takes this form: it decreases irradiation from 1 000 to 800 to 600W/m2 in steps of 200 units and we consider that the temperature is a constant (T= 25•C).

The simulation results show that the characteristic load it has a major effect on MPPT, moreover on Perturb and Observe controller.

IMAT3E'18

International Meeting on Advanced Technologies in Energy and Electrical

Engineering

The simulation of the PV array with a deference load R, RC, RL, and RLC shows the negative effect of the inductor load on the maximum power point of the system and the response of MPPT controller , and we can see that the load has a large importance to determine the maximum power point and a direct influence on the MPPT controller.

So the proposed siding mode controller under variable irradiations and characteristic load conditions are exhibiting an accurate control signal. This is clear if we examine Fig. 2 and 3, the sliding controller is adjusting in every case of the corresponding value of the control signal that extracts the maximum power from the PV panel. In Fig. 3, the proposed Perturb and Observe controller is proving its robustness against load change. The techniques have been implemented in standalone PV system as shown in the simulation model to study their individual efficiency and response towards the variation in solar irradiance and load. The comparison proves that the proposed MPPT technique has better tracking ability and speed of response towards the PV system irrespective of irradiance and load variation. Moreover, the optimal operating point does not oscillate around the MPP in case of R and RC Load. But in the case of RL and RLC load overtaking time to change irradiation caused by inductive.

Fig. 1 MPPTFigure 1 .

 11 Fig. 1

Figure 2 .Figure 3 .

 23 Figure 2. Output Power of the solar array

Table 1

 1 presents typical peak of Power, voltage, and current for each characteristic circuit load.

	Load Typical	peak	The voltage at peak	Current at peak power (IMPP)
		power (P) [W]		power (VMPP) [V]	[A]
	R	249		87.74	2.81
	RC	249.12		86.37	2.83
	RL	269.8		94.99	2.91
	RLC 269.8		94.99	2.91


Sébastien George
email: sebastien.george@insa-lyon.fr

Hervé Labas
email: herve.labas@free.fr

E-LEARNING STANDARDS AS A BASIS FOR CONTEXTUAL FORUMS DESIGN

Keywords: Asynchronous Discussion, Collaborative learning, Computer Mediated Communication, Distance Education/Distance Learning, E-learning Standards, Learner Communities and Peer-Support

This article argues that linking communication to learning activities offers an interesting approach to develop the efficiency of learning management systems in facilitating and increasing discussions between students.

We propose, present and elaborate a forum model that is based on two special features: a contextual view and forum structures. The contextual view of the forum, which is always visible, allows the user to focus on pertinent discussions. Two kinds of forum structures are offered: a content structure and a cognitive structure.

In this paper, we discuss the use of some e-learning standards as a basis for the design of the contextual forum tool. The description of the conceptual framework for online communications and the computing implementation aims at giving researchers some possible uses and recommendations in dealing with contextsensitive communication tools.

INTRODUCTION

In distance education, the emergence of learner communities has a favorable impact on learning conditions [START_REF] Lave | Situated learning: legitimate peripheral participation[END_REF]. In a socio-constructivist approach, interactions between learners play a dynamic role in individual learning [START_REF] Doise | The social development of the intellect[END_REF]. However, distance discussion tools are not always suitable for the emergence of learner communities. Some research studies have highlighted that the emergence of learner communities is rare in distance learning environments [START_REF] Gommer | Implementation of a digital learning environment: The real results[END_REF][START_REF] Hotte | Leadership and conflict management support in a cooperative telelearning environment[END_REF].

Forum tools currently used in online educational platforms are mostly generic and not specific to particular educational situations [START_REF] George | A contextual forum for online learning[END_REF]. More specifically, current distance learning systems do not recognized human communication process as a situated and contextualized process [START_REF] Jakobson | Closing Statement: linguistics and poetics[END_REF]. Our main idea is to define a conceptual framework to support situated and contextualized communication interactions during learning activities. The aim of our work is to design forum models and tools which are specific to distance learning systems.

The forum models depicted herein are intended to foster text-based asynchronous discussions (i.e., a forum) during learning activities that are not inherently collaborative.

During individual learning activities, providing only usual communication tools (email, forum, etc.) is not always sufficient to create interactions among learners. Usual communication tools could be appropriate if a collective learning activity is set-up.

Nevertheless, in distance education, learning activities cannot always be collaborative. The proposed approach aims at encouraging interactions during activities without collaboration overly scripted to learners.

In the rest of the paper we first describe the context and research issues about contextual forum before discussing the related research in the field of communication tools in educational context. We then present the design of two kinds of contextual forums, following by a study about their integration. We conclude with further work.

CONTEXT AND RESEARCH ISSUES

Low interaction levels between learners in forums might result from discussion tools and more particularly from their lack of connection with learning activities and learning contents: "the problem with content-related communication often is, that it doesn't occur because it is a separate activity that is not include in the course" (Gommer and Visser, 2001, p.438). We postulate that the separation between learning activities and communication is not favorable for the emergence of discussions. We interpret learning activities broadly and include reading an electronic document, doing an exercise or using a simulation. Our research question lies in determining how to link discussion activities to learning activities by developing well suited computer tools.

Research dealing with situated actions introduce the interesting idea that action is not simply the execution of a plan but includes the user's adaptation to the context [START_REF] Lave | Cognition in practice[END_REF][START_REF] Suchman | Plans and situated actions[END_REF]. The term 'situated action' underlies the idea that each action closely depends on the material and social circumstances in which it occurs.

Communication is a situated activity [START_REF] Lambert | A connectionist model of message design[END_REF]. Applying situated action theory to computer-mediated communication, [START_REF] Mantovani | Social context in HCI: a new framework for mental models, cooperation and communication[END_REF] concludes that users are social actors with their own aims and autonomy in situations, and it is technology which must adapt to them. In this sense, "the most effective way of clarifying the meaning of messages is to relate them to a shared context" (Riva, 2001, p.217). Situated action theory suggests that instructional communication should occur during an action or activity at the time when the user needs it. So if a user cannot communicate easily during an action or activity, the opportunity to communicate and share and exchange ideas will be lost. On the whole, our work concerns the design of human communication systems that attempt to respect human behavior and thought processes, that is to facilitate communication in action. These theoretical considerations guide the system design towards a more effective presentation of discussions. A more complete study of the underlying theories can be found in George (2006).

The forum model, named CONFOR (CONtextual FORum) is based on two special features: contextual view and structuring. The contextual view of the forum, always visible, allows learners to focus on pertinent discussions, i.e. on messages that correspond to their activities. Contextualization is common in annotation systems but not in forum tools. By adding this feature to forums, the intention is to closely link communications to learning activities. In order to provide this contextual view, discussions need to be structured. We suggest two means of structuring, according to (1) the content structure of an online course, and (2) the cognitive structure of an online course. The parts 4 and 5 respectively depict the design of these two ways of structuring contextual forum. We discuss before related works.

RELATED WORKS IN THE FIELD OF COMMUNICATION

TOOLS USED IN EDUCATIONAL CONTEXT

Knowledge Forum [START_REF] Scardamalia | Schools as knowledge building organizations[END_REF]) is a communication tool dedicated to the educational context. This tool is a collaborative database developed for the process of 'knowledge building': defining problems and hypothesizing, researching and collecting information, analyzing and collaborating. The aim of this system is to help classrooms to become knowledge-building communities, in which all students are producers of knowledge. The interface of Knowledge Forum allows users to post notes and to link them together. The students structure the notes themselves. This tool was designed to support a specific learning activity: problem-based learning. Therefore, the students' activity is by definition collaborative; the tool is used to resolve a problem in group. Our interest is different. Our work concerns more generic forums which aim at promoting discussion during learning activities where collaboration is not scripted. Moreover, the view of the Knowledge Forum is not contextual and the tool is not linked to other learning activities. [START_REF] Sampson | ASK-eEDCOM: enhancing educational portals through capturing collective knowledge of web-based learning communities[END_REF] suggests a tool named ASK e-EDCOM which allows web-based learning community members to discuss, comment and collaborate on a learning object. In this environment, every community member can offer a learning object and then this object can be discussed to be adapted to the community need. Our aim is quite different because we don't address autonomous learning communities' needs but virtual classrooms of learners that follow courses designed by teachers. So learners can comment learning objects or discuss the course organization but they can't add directly learning objects, that is the job of the instructional designer for instance. [START_REF] Nachmias | Learnet -A model for virtual learning communities in the world wide web[END_REF] have worked on a particular educational configuration based on CMC technology. They develop a model of networked learning which is named Learnet. This model contains variables classified under three dimensions: community, technology and pedagogy. Comparing to ASK e-EDCOM, the approach of Learnet is quite similar with the work we present because there's a pedagogical framework around the learning community. The model is useful to analyse virtual learning environments but no tool is offered to support CMC. Furthermore, the model should be update. For instance, the content structure of an online course is considered as a simple web structure (named hyper curriculum) and do not take into account actual standards.

A large body of literature related to computer-mediated communication in educational context promotes the emergence of learning communities. The special feature of the proposed contextual forum is to closely link discussion activities to learning activities using existing structuring of online courses.

DESIGNING AN ACTIVITY-BASED CONTEXTUAL FORUM

The activity-based contextual forum aims at showing a partial view of the forum, that is to say the topics directly linked to the activity done by a learner at any time. This approach implies focusing on different points:

• the choice of the relevant standard for the course structure and how it will impact the forum's structure;

• the structure importation method, based on the chosen standard; and,

• the way the forum view should be refreshed, in order to keep the link between the learner's activity and the related topics.

Standards for online course structures

To set-up an online course, it is necessary to describe how learning objects and resources are organized: which is the learning resources order or path, what are the combining of learning objects, and so on. This is the step of specification of a course structure. There are currently several ways to describe a course content structure. Standards such as AICC (Aviation Industry CBT Committee) and SCORM (Sharable Content Object Reference Model) are currently used by a lot of industrial and educational organizations. These kinds of standards allow a designer to describe courses with a classical structure that includes modules, chapters, parts, lessons, and so on. SCORM was developed after AICC, and found lots of its bases in the AICC standard.

Other standards exist to model learning activities. For example, work on Educational Modeling Language [START_REF] Koper | From change to renewal: educational technology foundations of electronic learning environments[END_REF] is at the origin of the IMS Learning Design (IMS LD) standards [START_REF] Ims | IMS learning design information model version 1[END_REF]. CONFOR was designed in the beginning of IMS LD, which is why it found its bases in SCORM as SCORM was more developed at the same time. We will focus on the SCORM structure before analyzing the interest of using IMS LD in the future.

With SCORM, educational content may be sequenced for the learner: "One activity in the process of creating and delivering learning experiences involves the creation, discovery and gathering together, or aggregation, of simple assets into more complex learning resources and then organizing the resources into a predefined sequence of delivery" (Dodds and Thropp, 2004a, p.18). The SCORM content aggregation model is a profile of the IMS Content Packaging specification, an IMS standard designed to describe a course in a package containing the course's files and an XML document describing the structure: organizations, items, resources and files (Dodds and Thropp, 2004a). The course structure (organization) contains pedagogical information that indicates how a learner will go through course activities. Thus, this organization described in the XML manifest file provides a way to retrieve easily a course structure and to use it for other means, such as a forum tool.

Concerning IMS LD, this standard is based on the learner's activity and describes pedagogical scenarios that he or she will follow during a session. Those scenarios introduce sequencing notions, pre-requisites, actor roles, and allow an elaborated pedagogical design. A deeper analysis could lead to a model allowing an IMS LD importation to get a forum structured as the pedagogical scenario. It is specified by the IMS Global Learning Consortium as follows: "Each activity refers to a collection of specific objects and services (called the 'Environment') needed to perform the activity. In order to support the description of individualized learning designs, learner Properties, Conditions, and Notifications are needed." (IMS, 2003, p.4). This approach allows for the possibility of integrating CONFOR as a service within this Environment, as it is designed according to e-learning standards.

Standards evolve. For instance, they are presently discussed [START_REF] Ims | IMS, ADL and IEEE LTSC will collaborate to update and standardize Content Packaging[END_REF] to be updated and standardized by IMS, ADL (SCORM authors) and IEEE LTSC (Learning Technology Standards Committee). Standards might evolve so as to lead to an almost universal way to describe a content structure. Standards offer a solid and sustainable basis for designing contextual forums. To conclude this part, CONFOR is not bound hand and foot with a particular standard and it will evolve in the same time than standards.

Contextual forum based on course structures

We designed a contextual forum tool based on a strong parallel between learning activities (course structures) and discussion activities. In this approach to forum structuring, each forum thread is linked to a course content item. Each root message of the forum is a reference to a learning activity. Thus, a reference could be for example the title of a course chapter or the number of an exercise. The forum is then hierarchically structured according to learning activities, by reference to the course structure. According to the kind of contextualization discussed, opening of an educational object leads to the opening of a forum view corresponding to the activity in progress. The goal is to focus learner exchanges on learning objects and specific learning activities.

Figure 1. Screen shot of CONFOR

The interface of the CONFOR tool is shown in Figure 1. The upper part of the window contains a learning activity of an online course. Under this course is the contextual view of the forum, which is automatically updated depending on the upper part. For instance, in Figure 1, a learner carries out the activity 2.2 of the module 2 of his/her course and s/he sees, at the same time, the messages of the forum that correspond to this activity (messages under the reference 'activity 2.2'). This contextual view of the forum is a part of a unique global forum. It should be noted that this global forum can be displayed in a global view (to see the entire tree of messages). In both views -contextual or global -the left part of the forum displays the list of the message titles and the references names. When user clicks on a message title, the content of the message is displayed on the right part of CONFOR.

The forum can be resized or put in an always-on-top window.

In order to provide the contextual display of the forum, we have to define references in connection with the online course structure. In this model, references contained in the forum are linked to the learning activities structure. References are dependent on the course structure designed by the author. We suggest an automatic procedure to add references in the forum. We describe this procedure in the next part.

Mechanism for importing course structure

As we initially chose SCORM for data importation, the design of the module to import course structures is essentially based on the analysis of XML manifest files. A class modeling can be obtained to virtually represent the course structure and then manipulate those objects in order to create a valid forum structure.

An objective of SCORM, as a profile of IMS Content Packaging, is to provide a broad sphere of activity to a course designer, by allowing recursive inclusions of manifest elements. Each manifest element describing a structure, a designer may aggregate many structures in only one package, and have lots of possibilities. As we seen on part 3.1, in a manifest element, structures are described with the organization elements, containing item elements. Each item can be linked to either a resource or a (sub)manifest package (Dodds and Thropp, 2004a). The possibility of recursive inclusions of manifests emphasizes the fact that a package can lead to many potential structures.

Therefore, we end on the following steps for a content packaging import:

1. Package selection and definition of import options.

2. First analysis to present the contained courses organizations.

3. Presentation of the structure to import, regarding on the user's selection. 4. Creation of the forum structure(s).

Using learner navigation to display contextual discussions

SCORM describes the way content objects should be launched by a LMS (Learning Management System). This part of the SCORM specification is known as the SCORM Runtime Environment [START_REF] Dodds | SCORM run-time environment[END_REF]. It essentially describes an interface between a SCORM compatible LMS and a content object. This interface consists of functions a content object can rely on to dialogue with the LMS. This Application Programming Interface (API) defines especially two functions, LMSInitialize() and LMSTerminate(), that a content object should call respectively at its beginning and at its end. This special feature can be used to reach the goal of updating the forum contextual view. Interfacing the forum tool with a SCORM compatible LMS presents no major problem.

Another approach is currently study to provide CONFOR as a Web Service, usable by any kind of LMS. This service would be totally independent of platforms. Some services (like create a contextual forum, add a message, etc.) could be call by any LMS that would only have to take care of the display part (user interface).

Results from an experiment

An assessment has been carried out at the Tele-university of Quebec to evaluate the contextual forum. CONFOR was assessed within an introductory course on "training in the workplace" offered to students registered in an undergraduate certificate program in business-oriented computer sciences. This course is an e-learning environment that integrates several learning objects linked together by an educational scenario [START_REF] Paquette | Organic aggregation of knowledge objects in educational systems[END_REF] The experiment lasted 8 months and about 70 students have taken the course. During the evaluation, two tutors supervised the students. The goal of the assessment was to study the use of CONFOR. More specifically, we wanted to test the utility and usability of the forum contextual display. For the purposes of the assessment, we used questionnaires, interviews, and regular observations coupled with computer traces analysis. It should be pointed out that the course used to assess CONFOR was newly offered by the Teleuniversity. For this reason, we could not directly compare the use of CONFOR with that of other forums used at the Tele-university, too many parameters being different.

The forum was divided into two categories for the purposes of this assessment: 'general' and 'course' The 'general' category is for discussions not dealing directly with course content. For example, students can use it to present each other or discuss administrative problems. The 'course' category has a reference-based structure. It is accessible in a global mode (in which students can view all messages, classified according to the course outline) and a contextual mode. We made the 'course' reference-based structure very detailed-down to the document level. In this way, all html pages and documents in .doc and .pdf formats are referenced in the forum.

The students and their tutors connected with the training environment 2,923 times and sent 437 messages to the forum. Message distribution by category is as follows: 167 messages for the 'general' category, and 260 for the 'course' category. Figure 2 provides details of the messages sent.

Figure 2. Messages sent in contextual and global modes

For the 'course' category, nearly 7.5 times more messages were sent in contextual mode than in global mode. Most users were thus using the forum's contextual display to send messages about the course. Also of note is the fact that a higher percentage of messages in the 'general' category (60.5 percent) were responses to other messages than in the 'course' category (44 percent). In our opinion, users tend to make use of the 'course' category's structure to make notes and annotations that do not always require responses. How the messages are read also differs depending on forum category and mode (Figure 3).

Reading of old messages

Reading of new messages

Figure 3. Messages read in contextual and global modes

In the 'course' category, there were over 4.5 times more messages read in contextual mode than in global mode. Comparing global mode with contextual mode, we note that contextual display favors the rereading of messages (46.6 percent of messages read were re-reads, compared with 36 percent in global mode).

To get feedback from students on their use of CONFOR, we sent them a survey questionnaire made up of closed, multiple-choice questions, with space to include comments. We received 27 responses to the questionnaire (39 percent of users). What follows is a brief summary of the qualitative results. The questionnaire responses indicate that the students are quite appreciative of the reference-based structure of the forum.

Similarly, they favor the forum's contextual display, i.e., the display of portions of the forum, depending on where one navigates in the course. The results also indicate that CONFOR is better at helping students find messages relevant to their activities, i.e., messages useful for the learning activity they are currently engaged in. Finally, students found that the forum fostered the organization of discussions. From the standpoint of usability, they had no trouble handling CONFOR; however, some lamented the lack of a search tool.

Since forums are also an important tool for e-learning tutors, we conducted semistructured interviews with them to obtain information on their use of CONFOR. Tutors found the interface simple and intuitive to use. Concerning utility, tutors appreciated having the forum and the course on the same page. They appreciated the ease of locating new messages, which facilitated their monitoring activities.

At this point, we can conclude that contextualization of discussions for learning activities is appreciated and has some potential. Users seem to appreciate the fact that communication and learning are integrated into a single space. Having access to the opinions of others, as they carry out their learning activities, motivates students to locate discussions that help them to understand and to build their knowledge. From this point of view, we can contend that this kind of forum has a positive effect on learning. More detailed results of the experiment could be found in [START_REF] George | Contextualizing discussions in distance learning systems[END_REF].

DESIGNING A KNOWLEDGE-BASED CONTEXTUAL FORUM

From the precedent work on activity-based contextual forum, an issue emerges: it would be a good idea to propose a different structuring of forum, by defining references in connection with knowledge dealt in online courses. From the first assessment of CONFOR, we observed that two messages could be situated in two different threads even though these messages dealt with the same content or with the same knowledge. So the goal became to design a structuring model based on knowledge representation while keeping the contextual view of forum. With this approach, the forum tool could provide some facilities for discussions convergent processes that are not supported in conventional threaded discourse environments [START_REF] Hewitt | Beyond threaded discourse[END_REF]. We then use e-learning standards and taxonomies in order to retrieve knowledge elements tackled in online courses.

LOM and Dewey Decimal Classification

In online education, the metadata are used to describe the courses and the learning objects.

They include a number of descriptors which are defined according to a standard, and which enable to make these courses and learning objects more easily accessible and usable (interoperable, reusable, long-lasting, adaptable) [START_REF] Downes | Learning Objects: resources for distance education worldwide[END_REF].

The standard LOM (Learning Object Metadata) is limited to a minimal set of essential characteristics to manage the learning objects, to seek them and evaluate them. A learning object is regarded here as "any entity, digital or non-digital, that may be used for learning, education or training" [START_REF] Hodgins | Draft standard for Learning Object Metadata[END_REF]. Others approaches exist to describe links between knowledge and learning contents. Among them, uses of ontologies are studied for the management of learning objects [START_REF] Aroyo | OntoAIMS: Ontological approach to aourseware authoring[END_REF][START_REF] Hayashi | A design environment to articulate design intention of learning contents[END_REF]. We choose to use LOM because it is a standard.

Nevertheless, the proposed model could be extended in the future to be compatible with other knowledge structuring.

In LOM, nine categories are taken into account in order to describe the educational resources, but according to our objectives, they are not all of the same importance. The ninth category, named "classification", is the category that particularly interests us in our context. This field ensures to classify and index educational objects according to a knowledge taxonomy. We studied various taxonomies which enable to describe the knowledge-elements approached in learning documents. We studied in particular the taxonomies used by the libraries: DDC (Dewey Decimal Classification), UDC (Universal Decimal Classification) and LCC (Library of Congress Classification). Among those we retain the DDC because it is flexible, simple to use and allows a classification of knowledge sufficiently fine for our work.

However, providing metadata is not always an easy work for authors of learning objects. From their point of view, this work requiring a literature study, which is not always in their field of competences, is tiresome and non-productive. We believe that it will be one of the major problems for the development of educational objects. However, we take as a working hypothesis that, to use CONFOR, each learning object will be well documented and described with LOM. If this work is not done by authors, information specialists could do it.

Contextual forum based on knowledge

We suggest a model of knowledge-based forum, in which the topics are organized according to a structure defined by the knowledge tackled in a course (see an example on Figure 4). With the attribute 'classification' of the LOM description of each educational object, the knowledge elements being consulted at a time can be identified. Therefore, a forum function can show in a contextual way all the topics corresponding to these knowledge elements. The learner may then consult, share and interact with other learners about the course content. The use of LOM within SCORM lets a course designer the possibility to describe each resource referenced in the course, by using the metadata element. This element allows any kind of metadata description, so LOM can be used in particular to classify each resource, by using its ninth element named classification. The classification element contains a knowledge identifier (named 'taxon') relative to a given taxonomy, as the Dewey Decimal Classification. Resources metadata definition gives all needed information to handle a knowledge structure: each taxon identifier can be analyzed to rebuild the entire knowledge structure for a given course. This structure is kept within a particular forum, and will therefore reference any discussion about a given 'knowledge element'.

As a remark, this mode encourages fulfilling every resource metadata by taking advantage of the classification definition, and getting the cross navigation, as interactions, richer.

Mechanism to detect knowledge elements consulted

As depicted in previous part, with the attribute 'classification' of LOM, we can identify the knowledge elements (KE) being consulted. Therefore, a function of the forum can show in a contextual way the topics corresponding to these KEs. In this way, a learner who wants to discuss about the knowledge-element number 1.1 (KE1.1) approached in a learning object 1 (LO1) will be able to see all the messages under the topic KE1.1 and will be able to post his/her message under this topic. Moreover, several KE could be shown at a time if the LOM description contains more than one Dewey Decimal Classification identifier. The learner may then consult, share and interact with other learners about the knowledge treated in the course.

The Figure 5 shows how a learner uses an online learning environment to consult his/her courses and to communicate with the forum. At a given time, s/he can open a particular learning object (1). A function of the system recovers automatically the DDC indexes of the KEs treated in this LO (2) using the LOM description. Then, the forum seeks the DDC indexes of each KE in the structure of the forum (3). The forum then opens in a contextual way (4) by showing the messages in relationship with the KEs treated in the LO.

Figure 5. Mechanism of the knowledge-based contextual forum

An advantage of this mechanism is that two students who work on two different LOs will be able to meet on the same forum to discuss a common KE.

TOWARDS AN INTEGRATED APPROACH FOR A CONTEXTUAL FORUM

Our current research concerns the integration of the two models presented above. Actually, using a singular approach has some limitations. In the first approach, contextual forum based on educational scenarios, some messages could be situated in different threads even though these messages dealt with the same content. In the second approach, knowledgebased contextual forum, general discussions about learning activities have no place in the knowledge structure.

The idea of integration consists in showing the learners a discussion thread corresponding to the current activity (to discuss about the organization inside the course for example) and also several discussion threads corresponding to knowledge at stake at a time (in order to discuss about the content). The Figure 6 depicts a model which takes into account these two levels of contextual discussions.

Figure 6. The integrated model of contextual forum

In this model, an educational object -or a resource -is referenced as an object of an educational scenario (in the upper part of Figure 6) and this object also deals with several knowledge elements described in its metadata (in the lower part of Figure 6). Knowledge elements could be defined by an ontology of a particular field or by a taxonomy like Dewey (DDC). Always in this model, each circle is then a discussion topic inside the forum. So, when a learner opens an educational object, the contextual forum displays automatically the activity topic and all the knowledge topics linked to the resource. However, with this integrated approach, the context is limited to the activity in progress and to concepts studied at a time. Regarding situated actions theory, we should extend the notion of context, taking into account more parameters such as learner's history or learner's goals. Then, we could use this information to better adapt displayed topics to each user. For example, carrying out the same activity, two learners would see different and specific discussions topics according to their past actions and to their personal characteristics.

CONCLUSION AND FUTURE TRENDS

Our research aims at proposing specific forum models and tools for online education in The last version of CONFOR will soon be tested in order to validate the integrating model.

Moreover, a long-term experiment with several groups of students will be conducted to measure the impact of CONFOR on the quality of learning. Another relevant element for measuring this impact concerns the integration of CONFOR in common learning platforms as an independent web service, to allow a wider use and experiment its functions at a large scale. The work of designing a CONFOR web service, easily pluggable in any learning management system, is currently done in cooperation with the Tele-University of Quebec.

The CONFOR model and tools are based on current emerging e-learning standards. In particular, CONFOR uses, in one hand, SCORM for the learning activity structure and, on the other hand, LOM combined with Dewey Decimal Classification for the knowledge structure. Nevertheless, CONFOR could easily evolve to take into consideration other standards, like IMS-LD or future standards [START_REF] Ims | IMS, ADL and IEEE LTSC will collaborate to update and standardize Content Packaging[END_REF].

Figure 4 .

 4 Figure 4. An example of forum that is structured according to knowledge elements

Finally

  , the Figure 7 shows the interface of the integrated contextual forum. Two thumbs enable to see either the activity-based contextual forum or the knowledge-based contextual forum.

Figure 7 .Figure 8 .

 78 Figure 7. The integrated interface of contextual forum

  order to foster discussions between learners. The work led to the idea of contextual display of forum messages. We suggest two versions of contextualization. The first one is based on a forum structuring according to online course structures. Some results of an experiment led us to study another forum structuring, by taking into account the cognitive structure of a course. The result is a discussion tool, named CONFOR, which displays to the learner an activity topic and several knowledge topics linked to the learning resource that is open.


Fabien Boux

Florence Forbes

Julyan Arbel

Emmanuel L Barbier

DICTIONARY-FREE MR FINGERPRINTING PARAMETER ESTIMATION VIA INVERSE REGRESSION

MR Fingerprint requires an exhaustive search in a dictionary, which even for moderately sized problems, becomes costly and possibly intractable. In this work, we propose an alternative approach: instead of an exhaustive search for every signal, we use the dictionary to learn the functional relationship between signals and parameters. This allows the direct estimation of parameters without the need of searching through the dictionary. The comparison between a standard grid search and the proposed approach suggest that MR Fingerprinting could benefit from a regression approach to limit dictionary size and fasten computation time.

INTRODUCTION

Magnetic resonance imaging (MRI) can map a wide range of tissue properties but is often limited to observe a single parameter at a time. In order to overcome this problem, Ma et al. introduced magnetic resonance fingerprinting (MRF), a procedure based on a dictionary of simulated couples (Y ,X ) of signals and parameters. Acquired signals called fingerprints are then matched to the closest signal in the dictionary in order to estimate parameters. This requires an exhaustive search in the dictionary, which even for moderately sized problems, becomes costly and possibly intractable .

We propose an alternative approach to estimate more parameters at a time. Instead of an exhaustive search for every signal, we use the dictionary to learn the functional relationship between signals and parameters.

METHODS

The dictionary is built using a biophysical model, g that allows to simulate a signal X from parameters Y, possibly corrupted by noise. To derive parameters Y from acquired signal X avoiding dictionary search, we propose to estimate the inverse function f=g , denoted by f . The parameters are then computed using Y =f (X). To achieve this, the dictionary is seen as a training dataset for a regression procedure. While the problem of regression has been extensively studied, the case where the input variable (X) dimension is far greater than that of the response (Y) remains a challenge. We propose to apply the GLLiM regression that bypasses the problems associated with high-to-low regression.

The experimental validation of our method is performed in the context of vascular fingerprinting and using the simulation tool developed by Pannetier et al. In this study, every brain voxel is characterized by two parameters: the blood volume fraction (BVf; [1, 25]%) and the mean vessel radius (R; [START_REF] Ma | Magnetic resonance fingerprinting[END_REF]10]μm). We generated dictionaries of 91 time-point signals, which represent the responses of brain tissue voxels simulated by a gradient-echo sampling of free induction decay and echo sequence .

We compared our regression approach to the reference method . We computed dictionaries of different sizes from 512 to 65536 randomly sampled. For each dictionary size, a function was learnt for the regression approach. Then 5000 additional independent test signals, with signal-to-noise (SNR) between 20 and infinity, were used to compute the normalized root mean squared error (NRMSE) between the true and estimated parameters. The whole procedure was repeated 60 times to compute an average NRMSE (figure 1).

RESULTS

Regarding computation time, both methods require a pre and post acquisition computations. In addition to the dictionary, our approach needs an additional step to estimate the function f (35s for a 23168-signals dictionary). The post-acquisition computation time of our approach does not depend on the dictionary size and is highly time efficient (21ms). However, the real-time grid search computation requires as many scalar product computations as signals in the dictionary (132ms for a 5824-signals dictionary and 505ms for a 23168-signals dictionary; time values were averaged on 10 computations of 10000 parameter estimations).

In figure 2, we present the average NRMSE as a function of the dictionary size for different SNR. As expected, NRMSE decreases when the dictionary size or the SNR increases. Regarding the impact of the dictionary size on the prediction quality, we observe that similar (often better) results can be obtained with the regression approach using much smaller dictionaries. For instance in the SNR=500 case (orange curves), to reach a NRMSE of 0.35, the reference method needs a dictionary of 65536 signals while the same NRMSE is already reached with the regression approach using a dictionary of size 512 for the grid search. This is futher visible on the second graph, which represents the ratio of NRMSE obtained with the two methods. NRMSE ratio in figure 2 tend to converge towards values within the interval [1.4, 2.2] for SNR between infinity and 20. Note the ability of the regression approach to obtain a low error for very small dictionary sizes when SNR is high.

DISCUSSION

The regression approach outperforms the grid seach appraoch, whose accuracy is limited by the grid step, as it cannot predict a value in a hole of the grid (figure 3). The main interest of our method is to overcome this problem by learning the inverse function from the dictionary.

CONCLUSION

These preliminary results suggest that MR fingerprinting could benefit from a regression approach to limit dictionary size and fasten computation time. This opens a possibility to introduce more parameters in the dictionary and benefit from multiparametric informations, a key to obtain fine diagnostics. Further evaluations are required with signals and dictionaries of higher dimensions.

FiguresFigure 1 :

 1 Figures

Figure 3 :

 3 Figure 3: The blue curve is the model g, the purple curve is the learnt inverse function f and black points are an observation Y =f(X) and the estimated parameter values from both proposed method Y =f (X) and reference method Y =Y such as X minimizes the scalar product <X |X > for i = {1, …, N}

estim true reg estim grid best best i https

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Acknowledgements

No acknowledgement found.


Bayesian Nonparametric Priors for Hidden Markov Random Fields

Hongliang Lü & Julyan Arbel & Florence Forbes Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, 38000 Grenoble, France

Résumé.

L'un des problèmes centraux en statistique et apprentissage automatique est de savoir comment choisir un modèle adéquat qui peut automatiquement s'adapter à la complexité des données observées. L'approche bayésienne non paramétrique est une solution intéressante pour gérer cette difficulté. Basés sur un espace de paramètres en dimension infinie, les modèles bayésiens non paramétriques sont flexibles et peuvent être relativement facilement utilisés pour apprendre des jeux de données complexes. Dans ce travail, nous abordons le problème de la détermination automatique du nombre de groupes en classification non supervisée lorsque les données à classer ne sont pas independantes mais modelisées à l'aide d'un champ de Markov. Plus precisément, l'estimation du nombre de groupes est évitée en considérant un modèle qui suppose un nombre infini de groupes. Nous montrons comment un champ aléatoire de Markov peut être combiné avec différentes lois a priori non paramétriques. Nous illustrons cela à l'aide d'un modèle de Potts combiné à un processus de Dirichlet et à un processus de Pitman-Yor. L'inférence de ces modèles est basée sur l'algorithme d'expectation-maximization variationnel en raison de son coût de calcul plus faible que l'approche Monte-Carlo par chaînes Markov (MCMC). L'approche proposée est appliquée à la segmentation d'images et quelques comparaisons et résultats préliminaires sont présentés et discutés.

Mots-clés. Champs de Markov cachés, méthodes bayésiennes non paramétriques, approximation variationelle, clustering, données spatiales, segmentation d'images.

Abstract. One of the central issues in statistics and machine learning is how to select an adequate model that can automatically adapt its complexity to the observed data. Bayesian nonparametric methods are thought of as one of the most promising candidates that are capable of handling such tasks. Based on an infinite-dimensional parameter space, Bayesian nonparametric models are highly flexible and thus can be readily used for parameterizing or learning about complex datasets. In the present work, we consider the issue of determining from the data the number of groups in a clustering task of non independent observations. The required guess on the number of clusters is avoided by considering models with an infinite number of clusters as suggested in Dirichlet Process Mixture models. We propose to combine a Markov random field model with different Bayesian nonparametric priors and illustrate such a combination on a Potts model combined with a Dirichlet process and a Pitman-Yor process. As regards inference, the variational expectation-maximization algorithm is adopted due to its lower computational cost with respect to its MCMC counterpart. Finally, the proposed framework is applied to image segmentation and some preliminary comparisons and results are presented and discussed.

Keywords. Hidden Markov random fields, Bayesian nonparametric methods, variational approximation, clustering, spatial data, image segmentation.

Introduction

Hidden Markov random field (HMRF) models are widely used for image segmentation or more generally for clustering data under spatial constraints. They can be seen as spatial extensions of independent mixture models. As for standard mixtures, one concern is the automatic selection of the proper number of components in the mixture, or equivalently the number of states in the hidden Markov field. Several criteria exist to select this number automatically based on penalized likelihood (eg. AIC, BIC, ICL etc.) but they usually require to run several models for different number of classes to choose the best one. Other techniques use a fully Bayesian setting including a prior on the class number. The most commonly used method in this case is reversible jump Markov chain Monte Carlo. Although simplifications in the inference have been proposed recently in [START_REF] Miller | Mixture models with a prior on the number of components[END_REF], the computational cost of reversible jump techniques remains high. In this work, we propose to investigate alternatives based on the field of Bayesian nonparametrics. In particular, Dirichlet process mixture models (DPMM) have emerged as promising candidates for clustering applications where the number of clusters is unknown. Most applications of DPMM involve observations which are supposed to be independent. For more complex tasks such as unsupervised image segmentation with spatial relationships or dependencies between the observations, DPMM are not satisfying. In many applications, data points are not independent and require models that account for dependencies. When clustering data, the goal is to produce a partition of the observations (eg. via a labelling of each data point) that accounts for the groups existing in the observed data. To address this goal, a probabilistic missing data framework can be considered assuming the existence of a set of missing variables representing a set of labels. Dependencies between labels are modelled via Markov random field (MRF) models. Starting from a Potts Markov random field (MRF) modelisation of spatial dependencies and following the line of DPMM, we propose to extend the standard finite state space Potts model to a countable infinite number of states. This requires the use of an appropriate prior on the state variable in the MRF formulation.

Bayesian Nonparametric Hidden Markov models

Attempts to build such infinite HMRF have been made using BNP priors before. In particular, we can distinguish attempts such as [START_REF] Sotirios | The infinite hidden Markov random field model[END_REF][START_REF] Sotirios | A Markov random field-regulated Pitman-Yor process prior for spatially constrained data clustering[END_REF][START_REF] Adelino | A Dirichlet process mixture model for brain MRI tissue classification[END_REF] from the work in [START_REF] Albughdadi | A Bayesian non-parametric hidden Markov random model for hemodynamic brain parcellation[END_REF][START_REF] Orbanz | Nonparametric Bayesian image segmentation[END_REF][START_REF] Da Xu | Bayesian nonparametric image segmentation using a generalized Swendsen-Wang algorithm[END_REF][START_REF] Sodjo | A generalized Swendsen-Wang algorithm for Bayesian nonparametric joint segmentation of multiple images[END_REF]. The approach in [START_REF] Sotirios | The infinite hidden Markov random field model[END_REF][START_REF] Sotirios | A Markov random field-regulated Pitman-Yor process prior for spatially constrained data clustering[END_REF][START_REF] Adelino | A Dirichlet process mixture model for brain MRI tissue classification[END_REF] differs in that it is not based on a generalization of the Potts model but on a transformation of an inference algorithm. More specifically in [START_REF] Sotirios | The infinite hidden Markov random field model[END_REF][START_REF] Sotirios | A Markov random field-regulated Pitman-Yor process prior for spatially constrained data clustering[END_REF], a mean field approximation is first considered and then transformed to account for an infinite number of states. In that sense it is closer to an Iterated Conditional Mode (ICM) algorithm, but does not provide a spatial generalization of the DPMM. Typically the simple Potts model considered in [START_REF] Sotirios | The infinite hidden Markov random field model[END_REF] (Section IV) cannot be extended to an infinite number of states. Other attempts to combine Potts and DP modelling includes the work in [START_REF] Timothy D Johnson | A Bayesian non-parametric Potts model with application to pre-surgical fMRI data[END_REF] but there the number of states is known to be three and the DP is used instead to model intensity distributions non parametrically.

In this work we build on the approach in [START_REF] Albughdadi | A Bayesian non-parametric hidden Markov random model for hemodynamic brain parcellation[END_REF] which differs from [START_REF] Orbanz | Nonparametric Bayesian image segmentation[END_REF][START_REF] Da Xu | Bayesian nonparametric image segmentation using a generalized Swendsen-Wang algorithm[END_REF][START_REF] Sodjo | A generalized Swendsen-Wang algorithm for Bayesian nonparametric joint segmentation of multiple images[END_REF] in that it uses a stick-breaking representation for the mixing weights while the latter use a partition model representation. In particular [START_REF] Da Xu | Bayesian nonparametric image segmentation using a generalized Swendsen-Wang algorithm[END_REF] generalizes [START_REF] Orbanz | Nonparametric Bayesian image segmentation[END_REF] and proposes a more efficient MCMC inference based on the Swendsen-Wang algorithm, while [START_REF] Sodjo | A generalized Swendsen-Wang algorithm for Bayesian nonparametric joint segmentation of multiple images[END_REF] extends this idea to Hierarchical Dirichlet process priors for multiple image segmentation. Indeed, although the most straightforward prior is the Dirichlet process (DP) prior, it may not be the best choice for segmentation applications and number of components selection. DP induces a highly peaked prior on the number of components which requires a reliable prior information on this number and this is often unavailable. Also, DP tends to produce tiny extra clusters that may be difficult to interpret. Gibbs-type priors [START_REF] De Blasi | Are Gibbs-type priors the most natural generalization of the Dirichlet process?[END_REF] are a recent generalization in particular of the Dirichlet and Pitman-Yor process priors. In this class of priors, the existence of additional parameters allows for a better control of the informativeness of the prior on the number of components, which is likely to provide much more flexibility. Segmentation with spatially dependent Pitman-Yor processes has been considered in [START_REF] Sudderth | Shared segmentation of natural scenes using dependent Pitman-Yor processes[END_REF] but using Gaussian processes.

In contrast to [START_REF] Da Xu | Bayesian nonparametric image segmentation using a generalized Swendsen-Wang algorithm[END_REF][START_REF] Sodjo | A generalized Swendsen-Wang algorithm for Bayesian nonparametric joint segmentation of multiple images[END_REF], we will therefore use a stick-breaking representation for the mixing weights, [potentially] thus providing a richer representation than partition models which integrate out the process. Also stick-breaking representations lead naturally to variational approximations for inference [START_REF] Blei | Variational inference for Dirichlet process mixtures[END_REF] which have the advantage to lower the computational cost in complex data segmentation and not to suffer from label switching complications. For such a generalization, we need to consider the Potts model in a less standard formulation than in [START_REF] Sotirios | The infinite hidden Markov random field model[END_REF][START_REF] Sotirios | A Markov random field-regulated Pitman-Yor process prior for spatially constrained data clustering[END_REF][START_REF] Adelino | A Dirichlet process mixture model for brain MRI tissue classification[END_REF].

Our goal is therefore to consider several such Gibbs-type priors in an HMRF context and to design the associated infinite HMRF models using a stick-breaking formulation. We will then compare both their respective theoretical properties and their practical performance assessed on various spatial data and image segmentation tasks.

Inference using variational approximation

A sampling (MCMC) based inference of similar DP and extensions with MRF models has been proposed in [START_REF] Orbanz | Nonparametric Bayesian image segmentation[END_REF][START_REF] Da Xu | Bayesian nonparametric image segmentation using a generalized Swendsen-Wang algorithm[END_REF]. In this work, we rather consider inference of the proposed models using a variational Bayesian approach. Variational algorithms based on truncation [START_REF] Blei | Variational inference for Dirichlet process mixtures[END_REF][START_REF] Foti | Stochastic variational inference for hidden Markov models[END_REF] are first considered but other (hybrid) variational approximations [START_REF] Wang | Truncation-free stochastic variational inference for Bayesian nonparametric models[END_REF] and fast implementations [START_REF] Guo | Boosting variational inference[END_REF] are also investigated. The reason for preferring a variational Bayesian approximation instead of Markov chain Monte Carlo inference, is the size and complexity of the data involved in common HMRF applications (e.g., image segmentation) which usually requires inference methods allowing for increased computational efficiency. However, it is interesting to compare such a variational procedure to its Markov chain Monte Carlo counterpart.