Vicente W C Briceño

Keywords: Eigenvalue analysis, grid code requirements, maximum wind power penetration, Monte-Carlo Simulation (MCS), Probability Density Function (PDF), small-signal stability, time domain simulation, uncertainty, Wind Turbine

Wind power is the fastest developing renewable energy source in power networks. The maximization of wind power penetration depends on constraints imposed by network topology, electrical and mechanical limits, temporal variations of wind power injections and load consumption. In order to determine the maximum wind power penetration, uncertainties must be considered. The proposed method uses stability criterion (time domain simulations and) mixed with stochastic simulation, under different contingency scenarios. It determines the Probability Density Function (PDF) of unstable cases using small signal stability according to wind power penetration. The study case was IEEE 9-bus transmission network and two contingencies were considered: three phases short-circuit for Fault Ride Through (FRT) capability, and loss of a generation unit. The loss of a machine is the worst limiting operational condition which determines wind power penetration of the network within the operational limits of wind turbines (WTs).

I. INTRODUCTION

ower networks are changing from a vertical operated structure, built-up around large conventional power plants, to more horizontal structures with connection of new loads and power sources. The integration of Renewable Energy Sources (RES) creates new technical problems during the planning studies such as: voltage and power variations, increase of short-circuit levels [START_REF] Moghimi | Stochastic technoeconomic operation of power systems in the presence of distributed energy resources[END_REF][START_REF] Briceno Vicente | Interval arithmetic for shortcircuit computation in mv radial networks with distributed generation[END_REF], harmonics coming from converters [START_REF] Compatibility | Part 2-12: Environment compatibility levels for lowfrequency conducted disturbances and signaling in public medium-voltage power supply systems[END_REF], flicker that affects power quality [START_REF] Standard | Measurement and Assessment of Power Quality of Grid Connected Wind Turbines[END_REF], possible resonances and instability issues. Wind power takes advantage of wind speed which is highly unpredictable (as a result of changes in weather conditions). Therefore, large wind power generation affects dynamic operation of power network; which has motivated the development of widely applicable studies and contingency analysis considering uncertainties to evaluate its impact in stability. In addition to variations of wind power injection, other electrical parameters are constantly fluctuating, and affect the network stability, such as load consumption which is highly unpredictable despite the advances in forecasting methods.

In recent years, probabilistic approaches have been used to evaluate the stability and reserve requirements of power networks with large amount of RES. The impact of stochastic uncertainty of WTs connected into the grid has been investigated using small-signal stability studies under an analytical probabilistic approach in [START_REF] Bu | Probabilistic analysis of small-signal stability of large-scale power systems as a ff ected by penetration of wind generation[END_REF]. In addition, other probabilistic assessment method has been proposed based on Eigenvalue analysis and numerical probabilistic approach to evaluate the impact of wind power in dynamics [START_REF] Rueda | Impacts of large scale integration of wind power on power system small-signal stability[END_REF], and steady-state voltage stability analysis in [START_REF] Vittal | A steady-state voltage stability analysis of power systems with high penetrations of wind[END_REF][START_REF] Zheng | Impact of wind generation uncertainty on power system small disturbance voltage stability: A pcm-based approach[END_REF]. The maximum wind power penetration using fixed-speed WT technology in island network has been determined using a different methodology of the Dynamic Maximum Available Power (DMAP) [START_REF] Najafi | Dynamic maximum available power of fixedspeed wind turbine at islanding operation[END_REF]. Probabilistic methodologies are applied in [START_REF] Matos | Probabilistic evaluation of reserve requirements of generating systems with renewable power sources: The portuguese and spanish cases[END_REF] to evaluate the reserve requirements of power systems with a large penetration of RES.

This paper proposes a methodology that focuses on transient stability of an island transmission network, where a large amount of wind power is produced. Two contingencies are studied in the dynamic simulation; these are a three phase short-circuit fault and the loss of a generation unit that affects the topology, and may cause the disconnection of other components. Additionally, other constraints such as bus voltage limitations, thermal line limits, synchronous machine loading capability, and WT grid code requirements influence the network dynamic behavior.

Load variation and wind speed are source of uncertainty and of perturbation during simulation. A probabilistic model of load variations is built from annual load measurements, while wind speed variations are modeled according to Mexican hat wavelet curve described in IEC 61400-1 [START_REF] Committee | Iec 61400-1: Wind turbines part 1: Design requirements[END_REF]. A random sampling of load probabilistic model is executed during the dynamic simulation based on the Monte-Carlo simulation method, which can be used to determine the PDF of unstable network cases. The dynamic simulation and small signal stability analysis of the network are run in Matlab using Power Systems Toolbox (PSAT) [START_REF] Milano | Power system modelling, Power System Modelling and Scripting[END_REF].

The proposed methodology determines the maximal wind

II. DEFINITIONS

A. Power System Stability

The power system stability may be defined as the property o f a p o w e r s y s t e m t o r e m a i n i n a s t a t e o f o p e r a t i n g equilibrium, under normal operating conditions or after being subjected to a disturbance and to regain an acceptable state of equilibrium [START_REF] Kundur | Power system stability and control[END_REF]. From this general definition, stability can be classified in several categories and subcategories in figure 1. In transient stability (rotor angle stability) synchronous machines must keep synchronism after a severe disturbance (single or multiphase short-circuit or a generator loss). While in small-signal stability, the disturbance is sufficiently small that the system response is linearized.

In the case of frequency stability, the power system faces active power deficit that can be compensated with energy stored in rotating masses of all generators, increasing the power generation of primary or secondary controlled units or with a re-dispatch of power generation.

In voltage stability, the power system must maintain steady voltages after being subjected to small disturbances, such as increasing load, change in solar PV output; or following large disturbances, as transmission line trip.

Other method to analyze dynamics and stability is by timedomain simulation which is a state space representation of the system. The system state and outputs are determined by inputs, state variables, and differential equations that describe the system dynamics.

B. Dynamic simulations

The power network solution is found by a temporal timestep computation of load-flow. The dynamic behavior of the power network is modeled with a set of nonlinear ordinary differential equations [START_REF] Kundur | Power system stability and control[END_REF].

) 1 ( ) , ( ) , ( u x g y u x f x = =
where x , u and y are the system's state variables, inputs and outputs respectively. The time domain simulation allows observing the evolution of electrical variables during the simulation time, considering changes of network topology or disturbances. The solution is found by an integration method, such as: forward Euler or trapezoidal rule.

C. Small-signal stability

If the derivatives of the state variables are not explicit functions of time, the stability can be calculated and analyzed with the traditional root-locus methods [START_REF] Kundur | Power system stability and control[END_REF]. The Eigenvalues are non-trivial solutions around an operating point calculated from ) 2 ( λΦ(t,to) Φ(t,to) A = where A is the state matrix. If A is real, the eigenvalues (λ) always occur in conjugate pairs λ = σ ± j ω where σ is the damping coefficient and ω the angular speed of oscillation.

III. ELECTRICAL MODELS

A. Load model

The load model can represent static load components, such as resistive and lighting loads, but also approximate dynamic components [START_REF] Kundur | Power system stability and control[END_REF]. The static characteristics of the load depend on the relationship between power and voltage. Load models f o r a c t i v e a n d r e a c t i v e p o w e r c a n b e e x p r e s s e d i n a n exponential form

(3) 2 0 0 1 0 0 k ) (V/V Q Q k ) (V/V P P = =
where P 0 , Q 0 and V 0 are the initial conditions of active, reactive power respectively, and voltage of the system and the coefficients define power (k1 = k2 = 0), current (k1 = k2 = 1) or impedance (k1 = k2 = 2). A constant power load model is considered in the study case presented in this article.

B. Wind speed model

Wind speed varies every minute, hour, day and season. If historical series of measurement of wind speed data are not available, it is necessary to consider a wind speed model. In this way, wind speed variation has been modeled by a Weibull Probability Density Function (PDF) [START_REF] Matos | Probabilistic evaluation of reserve requirements of generating systems with renewable power sources: The portuguese and spanish cases[END_REF], a composite wind speed model [START_REF] Najafi | Dynamic maximum available power of fixedspeed wind turbine at islanding operation[END_REF][START_REF] Committee | Iec 61400-1: Wind turbines part 1: Design requirements[END_REF] or a Mexican hat wavelet [START_REF] Standard | Measurement and Assessment of Power Quality of Grid Connected Wind Turbines[END_REF].

The wind speed model of Mexican hat wavelet allows evaluating critical variations of wind speed during a period of time. This deterministic wind gust model has been s t a nd a r d i z e d b y I E C [START_REF] Standard | Measurement and Assessment of Power Quality of Grid Connected Wind Turbines[END_REF]

a s t h e s e c o nd d e r i v a t i v e o f t he

Gaussian distribution function, described by

) 4 ( 2 2 2 0 2 0 1 σ ) t (t e σ t t ) w a v w g (v w a v (t) w v - - - - - + = ÷ ÷ ø ö ç ç è ae
where t 0 is the centering time of the gust, σ is the gust shape factor both in s, v g w is the peak wind speed value, and v a w the average speed value both in (m/s).

C. Wind power penetration

It describes how the total installed wind power capacity in a certain region is related to the total peak load in this region over a period of time. where τ Wind is the wind capacity penetration, P Wind the installed wind power capacity and P L,peak the peak load.

IV. STOCHASTIC SIMULATION

The stochastic simulations are performed using Monte Carlo Simulation (MCS) method to evaluate the impact of load variation and identifying unstable scenarios. This method can be used in deterministic and stochastic problems. It is used in stochastic problems to create a complex estimation or forecasting situation that involves a high degree of uncertainty. Additionally, this method is used as reference for other probabilistic approaches. A load random sampling is performed during the dynamic simulation that determines the behavior of the network under a confidence measure for each value. Then, a mathematical model for the variable of uncertainty is needed, generally a probability function. The method forecasts the likely outcome of an event.

A. Monte-Carlo Simulation

The MCS uses the exact equations of the problem; it substitutes parameters of uncertainty for Random Variables (RVs) that follow a Probability Density Function (PDF) and performs a big number of deterministic analyses with the random trials. The results of the method are usually used as reference for other approaches. In order to get a high degree of accuracy, it requires a minimum of computations, which can be very time consuming. The relationship between the sample size and the estimate of the accuracy from a sample is given by

) 6 ( 2 n z e =
where n is the sample size, and e is the margin of error, by which the estimation may be incorrect. The variable z is a function of how confident is the e estimation. Table 2 contains values of z for some common confidence levels [START_REF] Ellard | Sq course book[END_REF]:

B. Probabilistic load model

Load is very uncertain and difficult to model given its large number components, high distribution, variations with time of day and week, weather through time, and lack of information. Nevertheless, several Probabilistic Density Functions (PDFs) can be used to model power load behavior, for instance uniform, Weibull, normal, log-normal and beta [START_REF] Kundur | Power system stability and control[END_REF]. A simplified model of load consumption is considered with an uniform distribution varying from lower (P Lmin ) to upper (P Lmax ) values [START_REF] Kundur | Power system stability and control[END_REF].

C. Proposed method

This procedure shows the steps to find the probability of instability of power network. Previously, it must be established power network parameters, configuration, wind power penetration, wind speed parameters (t 0 , σ ), perturbation location at time (t p ) , n u m b e r r a n d o m t r i a l s ( n ) of load variation at time (t l ) and simulation time (t f ). The following steps must be followed:

1. Initialize time domain simulation (t i = 0) with a load flow.

2. Apply to time domain simulation load variation (t l ), Mexican hat wavelet (t 0 ), and perturbation (t p ).

3. Verify at every step of time t that wind generation and network components restrictions are accomplished, otherwise disconnect them.

4. Complete time domain simulation, run at the end of simulation time t = t f a small-signal stability analysis.

5. Execute next time domain simulation and a small-signal stability analysis until j > n.

When MCS is completed it can be established a PDF of instability of the power network under a percentage of wind power penetration and consider the risk associated to its probability.

V. STUDY CASE

The study case is WECS network composed by 3-machine, 3-loads and 9-bus taken from [START_REF] Milano | Power system modelling, Power System Modelling and Scripting[END_REF]. This is ring topology in high voltage 230 kV, it is comparable to the power system of Guadalupe island. The original system was modified with the insertion of a wind farm at bus 06. The network configuration is shown in figure 2 and the parameters are available in [START_REF] Milano | Power system modelling, Power System Modelling and Scripting[END_REF]. The time domain simulation allows to study transient, voltage, and frequency stability, in order to determine the maximum wind capacity penetration (τ Wind ) connected to bus 6. The wind capacity penetration is the ratio of the installed wind power capacity and the peak load. Stability criteria are: constant angle of rotor synchronous machines, voltage, and frequency permissible variations. Wind generation and component network constraints such as heat capacity of lines, maximum active, and reactive power of synchronous machines, must be respected.

Two severe contingencies are considered: three-phase short circuit and loss of a machine, under load and wind speed variations criterion N-1. The disturbances are described below:

1. A three phase short-circuit occurs in the middle of the line of bus 05 and 07 at time t p = 10s during 150 ms (about 8 cycles). The wind farm must be capable of withstanding the voltage dip during short-circuit to stay connected to the network.

2. The loss of synchronous machine of bus 02 occurs at time t p = 30s. Meanwhile, the wind farm must support large voltage and frequency variations; otherwise, it must be disconnected (criterion N-2).

The parameters of the models considered for wind speed Mexican hat wavelet are t 0 =15s, σ=3s, v a w /v g w =0.48, while load variations follow an uniform distribution model of parameters [P Lmin , P Lmax ] = [0.95 , 1.05] p.u. which occur at time t l =5s.

VI. RESULTS

The case of three phase short-circuit with τ Wind = 43%, did not represent a risk for network's stability under high wind power penetration; a stochastic analysis is not needed. The electrical variables such as bus voltages, frequency, active and reactive power were in the allowed range of values.

The case of loss of synchronous machine at bus 02 with τ Wind = 40% on the other hand, causes some unstable cases. The minimum frequency allowed to the wind farm is fixed to 48.5 Hz. The frequency goes down, exceeding the minimum frequency allowed and causing the wind farm disconnection. The maximum active power of synchronous machine 03 and line 05-04 are close to reaching their limits (2 p.u.), which means that the capacity wind power penetration cannot be greater. In addition, the power system becomes oscillatory after the disconnection of the wind farm in some cases of MCS with a sample size of n = 50. The results of bus frequency, voltages, machines active and reactive power are shown in figure 3.

Therefore, wind capacity penetration is decreased until all time domain simulations were stable after running MCS with a sample size of n = 50. The maximum capacity wind power penetration without causing network oscillations, after the machine loss and the wind farm disconnection, is τ Wind = 37%. In the time interval during which the wind does not produce power (wind speed is zero) frequency drops below 49 Hz. As a consequence, the w ind speed variation and low inertia of the wind farm may cause significant variations in frequency network, under disturbance or normal operating conditions. The network stable state is assured in front of the worst scenario of operation for this wind power penetration. The results of the electrical variables: synchronous machine frequencies, bus voltages, active and reactive power generated are shown in figure 4.

A small signal stability analysis is run at the end of time domain and MCS. In the case of τ Wind = 37% all poles are real and negatives, while τ Wind = 40% some of the closest poles to the imaginary axis become real and positive causing instability. The real positive values represent the oscillatory behavior of electrical variables. The results of Eigenvalue analysis are shown in figure 5.

The probability of network instability can be modeled using the results of eigenvalues analysis with a Bernoulli experiment [START_REF] Grinstead | Introduction to probability[END_REF]. The experiment models the probability of success or failure of the system to withstand disturbances, considering τ Wind from 37% to 43%. The number of success or failure corresponds to the number of unstable and stable cases a t t h e e n d o f t h e s i m u l a t i o n t i m e . T h e s u c c e s s i o n o f independent Bernoulli experiments allows the construction of a variable counting the number of unstable cases (success), using a binomial distribution. The results of PDF and CDF are shown in figure 6. The probability of instability (p) for the case τ Wind = 37% is zero; the system is always stable after MCS trials. As it can be seen in figure 6, the PDF of instable cases with τ Wind from 38% to 42% have the form of a discrete normal distribution, while for τ Wind = 43% the PDF is a discrete exponential. The wind power penetration increases the possibility of network instability, with it automatic disconnection of the network. The probability of instability of network according to τ Wind can be found in table 3.

VII. CONCLUSION

In order to determine the maximum wind power penetration of an interconnected network, a new method was proposed. The proposed method is based on Monte-Carlo simulation considering load, an interval of wind speed variations applied to the case of machine loss, which represented the base case to determine maximum wind power penetration. Two scenarios of disturbances were examined, three-phase short-circuit and loss of a machine. The case of three-phase short-circuit was evaluated using a deterministic method, and it did not pose a risk to the stability of the network.

The time domain simulation and small signal stability were used to establish a probability density function of the network unstable cases. Constraints of synchronous machines, lines, and regulatory dynamic wind farms limitations of voltage, active power, reactive power and frequency taken from Irish grid code were considered.

The Eigenvalues at the end of simulation time were used to determine the probability of network instability. The loss machine criterion N -1 risks the stability of the network by the disconnection of the wind farm, criterion N -2. The Eigenvalues of the network were calculated for different percentages of wind power penetration τ Wind f ro m 37% to 43%. Applying the proposed method with a wind power penetration of 37%, electrical variables always return to steady state without violating network constraints and real part of Eigenvalues analysis were always negative. A binomial distribution models the network's unstable cases of wind power penetration above 37%. The probability density function of unstable cases resembled a discrete normal distribution for the first cases of instability and to an exponential distribution for the last study case. The probability function allows associating a probability of instability or risk to a percentage of wind power penetration.

Fig. 1 .

 1 Fig. 1. Classification of power system stability

  T h e e q u a t i o n s h o u l d n o t b e u s e d f o r n ≤ 3 0 , o r probabilities that are extremely near zero or one unless a large number of samples are drawn.

Fig. 2 .

 2 Fig. 2. Single phase diagram of three generators system.

Fig. 3 .Fig. 4 .

 34 Fig. 3. Loss of synchronous machine at bus 02 with τWind = 40%.

Figure 5 :

 5 Figure 5: Eigenvalues for τWind = 37% and τWind = 40%

Figure 6 :

 6 Figure 6: PDF and CDF of unstable cases.

Table 2 :

 2 Values of z according to confidence level

	Confidence Level (%)	95	99	99.9
	z	1.96	2.58	3.29

TABLE 3 :

 3 PROBABILITY OF UNSTABLE CASES

	τWind (%)	38	39	40	41	42	43
	p	0.1613 0.1935 0.2258 0.7419 0.8065 0.9355


Xitong Jia

Xianye Ben
email: benxianye@gmail.com

Hui Yuan

Kidiyo Kpalma

Weixiao Meng

Macro-to-micro transformation model for micro-expression recognition

Keywords: micro-expression recognition, macro-to-micro transformation model, feature selection, singular value decomposition

 In this paper, a novel macro-to-micro transformation model was proposed to solve the problem that the labeled training micro-expression samples are limited to train a high performance model. The proposed model creatively used the macro-expression database for micro-expression recognition by means of transfer learning, which took advantage of the existing macro-expression database well. Furthermore, feature selection using Group LASSO was employed in the proposed model to improve the recognition accuracy.

Introductions

Emotion is an expression form of people's psychological activity, which directly reflects people's current psychological and mental states [START_REF] Bankó | Flawless visual short-term memory for facial emotional expressions[END_REF]. Under high pressure, people in stimulating source, such as the moment of effective stimulation source of voice, text, images, video and others, undergo rapid psychological changes. This is accompanied by short-term emotional and a subconscious revelation that does not appear under the control of thinking, called "micro-expression" [START_REF] Ben | Survey on Automatic Micro Expression Recognition Methods[END_REF]. Emotion is an expression of the inner psychological state, and micro-expression is the physical external manifestations of emotion. Emotion acts like the link that combines closely together the psychological state and the external manifestation. These three elements are interdependent and closely related. This makes it possible for us to judge people's emotions by studying the psychological behavior. So how to interpret the psychological behavior has become an important issue. In traditional sense, the research on micro-expression of psychological behavior depends on the professional personnel trained to judge and interpret, which inevitably adulterates a lot of subjective emotional factors. In this paper, we will try to analyze people's real emotional and psychological states through micro-expression signals relying on computer vision, pattern recognition and machine learning methods, which has a very important influence on theory and application.

As one of the most important forms of psychological behaviors, micro-expression is the revelation of a rapid and transient facial expression [START_REF] Ben | Survey on Automatic Micro Expression Recognition Methods[END_REF]. Micro-expression reflects patterns of facial muscle movement during various feelings periods and occurs with characteristics of short continued period, low intensity, and high difficulty to induce [START_REF] Shen | Effects of the duration of expressions on the recognition of microexpressions[END_REF], which makes it one of the most potential physiological characteristics in the field of spiritual psychology and sentiment analysis.

Although the duration of the micro-expression is short, it can reveal the true feelings of the heart, so as to provide a reliable basis to judge the spiritual state of people. At present, in psychology, micro-expressions are generally classified as six basic types named anger, fear, disgust, sadness, happiness, surprise [START_REF] Ekman | The Repertoire of Nonverbal Behavior: Categories, Origin, Usage and Coding[END_REF]. Due to the short duration of expression (1/25s-1/5s [START_REF] Ekman | Telling lies: Clues to deceit in the marketplace, politics, and marriage[END_REF]), the weakness of intensity (only reflected on part of the facial motor unit [START_REF] Ekman | Micro Expression Training Tools (METT)[END_REF]) and other factors, we face difficulties in micro-expression detection and recognition. However, with the rapid development of machine learning algorithms [START_REF] Du | A discriminative metric learning based anomaly detection method[END_REF][START_REF] Du | Target detection based on a dynamic subspace[END_REF][START_REF] Gong | Fick's Law Assisted Propagation for Semisupervised Learning[END_REF][START_REF] Gong | Deformed Graph Laplacian for Semisupervised Learning[END_REF][START_REF] Gong | Multi-Modal Curriculum Learning for Semi-Supervised Image Classification[END_REF][START_REF] Yang | A collaborative representation based projections method for feature extraction[END_REF] and face recognition algorithms [START_REF] Ding | Multi-Directional Multi-Level Dual-Cross Patterns for Robust Face Recognition[END_REF][START_REF] Ren | Incremental learning of bidirectional principal components for face recognition[END_REF][START_REF] Yang | Face recognition using adaptive local ternary patterns method[END_REF], micro-expression research has been paid more and more attention.

At present, the existing micro-expression databases (CASME I [START_REF] Wu | CASME database: A dataset of spontaneous micro-expressions collected from neutralized faces IEEE International Conference and Workshops on Automatic Face and Gesture Recognition[END_REF], CASME II [START_REF] Li | CASME II: an improved spontaneous micro-expression database and the baseline evaluation[END_REF], SMIC [START_REF] Li | A Spontaneous Micro-expression Database: Inducement, collection and baseline IEEE International Conference and Workshops on Automatic Face and Gesture Recognition[END_REF], Polikovsky [START_REF] Polikovsky | Facial micro-expressions recognition using high speed camera and 3D-gradient descriptor IET International Conference on Crime Detection and Prevention[END_REF][START_REF] Polikovsky | Facial Micro-Expression Detection in Hi-Speed Video Based on Facial Action Coding System (FACS)[END_REF]) of approximately seven kinds of micro-expressions. It is easy to cause the researchers to draw inaccurate conclusion of the psychological state on the subject being observed and not to efficiently find problems or avoid risks. Considering the above reasons, how to solve automatic labeling and recognition problems for the tested micro-expressions becomes a challenging task under a small number of labeled training micro-expression samples condition.

On the contrary, there are large amounts of macro-expression databases, each of which consists of vast labeled training samples compared with micro-expression databases. For instance, the CK+ database, expanded from Cohn-Kanade Dataset, includes 123 subjects, 593 image sequences. The last frame of each image sequence has the label of action units. In addition, among the image sequences, there are 327 sequences which have emotion labels, much more than that in the existing micro-expression databases. Thus, how to take advantage of the macro-expression databases at the area of micro-expression recognition has become an important direction of the research.

Transfer learning [START_REF] Tan | Transitive Transfer Learning[C]// The[END_REF], a kind of method solves the question that how to take advantage of limited data, it can transfer knowledge learned from the existing data to help the studying in the future. The purpose of transfer learning is to help learning tasks in a new environment by knowledge learned from an available environment, so it won't make the same distribution assumption, as machine learning does. Thus, by using the main idea of transfer learning, it is able to take advantage of the quantitative superiority of macro-expression to recognize the micro-expression.

In this paper, we proposed a macro-to-micro transformation model for micro-expression recognition, which aims at solving the difficulty of micro-expression recognition due to the limitation of data amounts. The proposed model combined LBP features of macro-expression and LBP-TOP features of micro-expression. Furthermore, feature selection was employed in the proposed model for removing redundant information. Finally, singular value decomposition is employed to achieve macro-to-micro transformation model. Extensive experiments on the popular CK+ macro-expression database and CASME2 micro-expression databases have shown that the proposed model outperforms the existing micro-expression recognition methods.

The macro-to-micro-expression model proposed in this paper established a bridge of relationship between macro-expression and micro-expression, which creatively combined the two kinds of features during the training process, and realized the micro-expression recognition by means of a linear transfer learning. Owing to the participation of macro-expression, the training sample set has been expanded and the available information is increased, thus the micro-expression recognition performance significantly improved.

The rest of this paper is organized as follows. Section 2 introduces the related works on recognition of micro-expression. The proposed macro-to-micro transformation model will be described in Section 3. Section 3 also includes the representation and selection of macro-expression and micro-expression features. Experimental results and analysis are given in Section 4. Section 5 concludes with a summary and a further discussion.

Related works

During the past years, many researches of micro-expression recognition appeared, most of which discussed the problem of micro-expression detection and recognition respectively. Given an expression video sequence, the purpose of detection is to judge whether the sequence consists of any micro-expressions, while recognition is to estimate the classification of micro-expressions appeared in the sequence. Figure 1 shows the existing micro-expression recognition methods, which are mainly divided into two categories: feature representation and multi-linear subspace learning methods.

The feature representation methods are used to find efficient and robust micro-expression features and use them in the original micro-expression video sequence. At present, the main micro-expression features include: Gabor features [START_REF] Wu | The Machine Knows What You Are Hiding: An Automatic Micro-expression Recognition System[END_REF][START_REF] Zhang | Micro-expression recognition system[END_REF], 3D HOG features [START_REF] Polikovsky | Facial micro-expressions recognition using high speed camera and 3D-gradient descriptor IET International Conference on Crime Detection and Prevention[END_REF], optical flow features [START_REF] Liong | Optical strain based recognition of subtle emotions IEEE International Symposium on Intelligent Signal Processing and Communication Systems[END_REF][START_REF] Liu | A Main Directional Mean Optical Flow Feature for Spontaneous Micro-Expression Recognition[END_REF] and LBP or LBP-TOP texture features [START_REF] Pfister | Recognising spontaneous facial micro-expressions[C]// International Conference on Computer Vision[END_REF][START_REF] Wang | Micro-Expression Recognition Using Robust Principal Component Analysis and Local Spatiotemporal Directional Features ECCV 2014 Workshops[END_REF][START_REF] Huang | Facial Micro-Expression Recognition Using Spatiotemporal Local Binary Pattern with Integral Projection The Workshop on Computer Vision for Affective Computing at ICCV[END_REF][START_REF] Wang | Efficient spatio-temporal local binary patterns for spontaneous facial micro-expression recognition[END_REF]. Gabor features methods are based on Gabor filters for representing face images in different directions and scales of the features; this primitive method can only deal with facial muscles which have great changes of expressions. Thus it is not the true sense of the detection and identification [START_REF] Wu | The Machine Knows What You Are Hiding: An Automatic Micro-expression Recognition System[END_REF][START_REF] Zhang | Micro-expression recognition system[END_REF]. 3D HOG method [START_REF] Polikovsky | Facial micro-expressions recognition using high speed camera and 3D-gradient descriptor IET International Conference on Crime Detection and Prevention[END_REF] captures micro-expression video sequences with a high-speed video camera and extracts 3D gradient histograms of ROI according to Facial Action Coding System (FACS). Optical flow method [START_REF] Liong | Optical strain based recognition of subtle emotions IEEE International Symposium on Intelligent Signal Processing and Communication Systems[END_REF][START_REF] Liu | A Main Directional Mean Optical Flow Feature for Spontaneous Micro-Expression Recognition[END_REF] extracts muscle movement information according to optical flow changes of facial action unit caused by muscle movement when micro-expression occurs. The classification and identification of the micro-expressions can be processed by this information. LBP-TOP [START_REF] Pfister | Recognising spontaneous facial micro-expressions[C]// International Conference on Computer Vision[END_REF][START_REF] Wang | Micro-Expression Recognition Using Robust Principal Component Analysis and Local Spatiotemporal Directional Features ECCV 2014 Workshops[END_REF][START_REF] Huang | Facial Micro-Expression Recognition Using Spatiotemporal Local Binary Pattern with Integral Projection The Workshop on Computer Vision for Affective Computing at ICCV[END_REF][START_REF] Wang | Efficient spatio-temporal local binary patterns for spontaneous facial micro-expression recognition[END_REF] is a kind of texture features to describe the micro-expression for the extraction of micro-expression from video stream texture features to be a sequence of three directions, using micro-expression feature fusion in three directions in the classification process. These methods greatly enhance the effect of micro-expression, but due to micro-expressions' short duration and their low intensity, these methods are not robust in the micro-expression identification and classification.

Subspace learning method is based on the needs of micro-expressions in video sequences interpolation into a certain number of frames, and then directly handle the stream video sequence of micro-expressions, which can mainly be divided into two categories: tensor based subspace learning method [START_REF] Wang | Micro-expression Recognition Using Color Spaces[END_REF][START_REF] Ben | Gait recognition and micro-expression recognition based on maximum margin projection with tensor representation[END_REF][START_REF] Wang | Face recognition and micro-expression recognition based on discriminant tensor subspace analysis plus extreme learning machine[END_REF] and sparse representation based on the tensor subspace learning theory [START_REF] Wang | STPCA: Sparse tensor Principal Component Analysis for feature extraction[END_REF]. Tensor-based subspace learning method extracts directly on the entire video sequence feature and retains the features of micro-expressions in video sequences of different directions, and to maintain the structure of micro-expression sequence, this method is vulnerable to face structure, pixel characteristics influence and may cause the wrong classification of micro-expression. In order to make feature extraction robust, the sparse technology needs to be used. Through the sparsity constraint, the micro-expressions which have been mapped have better classification ability, but this binding force is limited, still largely influenced by features of human faces, light, noise effects and it cannot really extract the micro-expression's feature robustly.

To sum up, micro-expression's duration is short and its intensity is weak. Hence the micro-expression features are vulnerable to the appearance features of faces, illuminations, noises and other factors. Thus, it results in difficulties on the feature representation and extraction of the micro-expression. At present, there are few samples in current micro-expression databases, and the number of existing methods of automatic micro-expression recognition is largely limited by the lack of labeled training samples and the difficulty of sample labeling. In this paper, a macro-to-micro transformation model was proposed to solve the problems above. The proposed model creatively used the macro-expression database for micro-expression recognition by means of transfer learning, which took advantage of the existing macro-expression database well. The model combined macro-expression features with micro-expression features in the process of training. Micro-expression samples were classified in the process of testing. Thus the relation between macro-expression and micro-expression was established to improve the micro-expression recognition accuracy.

Macro-to-micro transformation model

In this section, the proposed macro-to-micro transformation model is introduced in detail. In the data preprocessing, Local Binary Pattern (LBP) is employed to extract the macro-expression features and Local Binary Patterns from Three Orthogonal Planes (LBP-TOP) is employed to extract the micro-expression features from the video sequences. Then Group LASSO is used to respectively select useful features from LBP features and LBP-TOP features. Then, the two kinds of features were combined by the proposed model in the training process, thus the transformation between macro-expression and micro-expression was accomplished by SVD [START_REF] Muramatsu | View Transformation Model Incorporating Quality Measures for Cross-View Gait Recognition[END_REF].

Macro-expression feature representation

LBP is a kind of operator to describe the local textural features of an image, which has rotation and grayscale invariance. LBP operator is defined as follows, , 0 = 2 ( ),

P k P R k c k LBP s i i    where 1 if 0 () 0 else x sx      (1)
where P represents the number of adjacent pixels, R represents the radius of the local region, In a 3*3 region, assume that the center pixel is regarded as threshold, then the grey value of adjacent 8 pixels was compared with it respectively. If the grey value of the surrounding pixel is larger than that of the center pixel, mark the position to 1, otherwise, to 0. After the comparison, 8 pixels in the 3*3 neighborhood can produce an eight-digit binary number, and it usually converted to a decimal number called LBP code, a total of 256 kinds. Then the LBP code of the center pixel is obtained, which is used to reflect the textural information of that region.

In order to adapt to the different scales of textural feature, the region of 3*3 is expanded to an arbitrary neighborhood, and the square neighborhood was replaced by a circular neighborhood.

Thus the improved LBP operator allowed arbitrary number of pixels in the circular neighborhood within radius R. Then the LBP operator with P sampling points can be employed. Fig. 2 shows 8,1

LBP .

In an actual image, the vast majority of LBP patterns consist of only twice jumps as from 0 to 1 or from 1 to 0. Thus the Uniform Pattern is defined as, when the specific circulation binary number of a LBP operator only consists of only twice jumps, the binary of the LBP is defined as a Uniform Pattern LBP.

The Uniform Pattern LBP is defined as follows,

P kc k PR s i i U LBP PP            (2) 2 1 0 ( , ) ( ), 2 ( 1) 2, otherwise u
where

1 1 0 1 1 ( ) ( ) ( ) (
) P P c c k c k c k U s i i s i i s i i s i i              .
In this paper, the uniform 8,1

LBP is employed to extract the macro-expression feature. The dimension of each expression sample can be reduced to 59.

Micro-expression feature representation

The macro-expression features extracted by LBP were from each frame of the sequences, which ignores the temporal information of the expression. To solve this problem, LBP-TOP can extract the concatenation of LBP histograms on 3 orthogonal planes named XY, XT and YT.

Generally, an image sequence is considered as a stack of XY plane centered on time axis T, however, the image sequence can also be regarded as a stack of XT plane centered on axis Y or a stack of YT plane centered on axis X. Plane XT and YT provide plenty of temporal transition information, i.e., the facial movement displacement, where the position changes of eyes, nose, lips, cheek can be obtained. And plane XY includes the spatial information, i.e., expression, identity, emotion.

The micro-expression sequences include a video sequence which indicates several kinds of changes on one's face. The video sequence is short, and the micro-expression remains in a short duration and does not change significantly between each frame. The features extracted from the micro-expression sequences are supposed to be time-related, from which we can analyze the feature relationship between two adjacent frames. The Uniform Pattern of LBP-TOP histogram is cascaded according to the order of XY, XT, YT, thus the feature dimension will be 3 59  for each sample, while the dimension of macro-expression feature is 1 59  mentioned above. In order to keep both dimensions of the macro-expression and micro-expression consistent, the features of each plane are added together and the average of them is viewed as the final feature of micro-expression. Thus, the dimension of micro-expression feature is also 1 59  . Fig. 3 shows the description of micro-expression feature extracted by LBP-TOP.

Feature selection using group LASSO

For macro-expression and micro-expression sequences, the final purpose is to test the performance of features transferred from macro-expression to micro-expression. Obviously, some redundant features exist in the feature vectors. For instance, the typical features used to distinguish different kinds of emotions are mostly the movement changes of eyes, lips and cheek muscles, not all changes of a whole face. Thus, some areas of the face may be redundant for the recognition.

LASSO can get the globally optimal solutions of important factors for accurate estimation, which is defined as:

1 2 ˆ( ) arg min( ) LASSO YX          , (3)
where

1
represents the 1 -norm, and  represents a parameter for adjusting.

While LASSO has great computational advantages of feature selection and wonderful performance, it aims at selecting several single variables which represent the pixels of each image, not the general factor selection. Group LASSO [START_REF] Nie | Efficient and Robust Feature Selection via Joint ℓ2,1-Norms Minimization[END_REF] can solve the problem of feature selection with grouped variables based on Eq. ( 3). Given positive definite matrices 1 ,, J KK , the group LASSO estimation is defined as:

1 ˆ() 2 j JJ Group LASSO j j j K jj YX            , ( 4
)
where  is a parameter for adjusting and 1 2 ()

j j j j j K K     
, Group LASSO is stable with kinds of features and usually reaches reasonable convergence tolerance within iterations. Thus, it is used to select LBP features of macro-expression and LBP-TOP features of micro-expression in this paper. We know that there are some differences between macro-expression and micro-expression features, thus a connection between them should be explored. In this way, these features are Now the representation of vector x after a linear transformation D can be described as

1 1 2 2 ( ) ( ) x v x v v x v     (6)
where vectors 1 v and 2 v are orthogonal unit vectors, which means

1 1 2 2 1 1 1 2 2 2 ( ) ( ) ( ) ( ) Dx v x Dv v x Dv Dx v x u v x u          (7)
The vector inner product can be represented with vector transposition

T v x v x  Thus, 1 1 1 2 2 2 1 1 1 2 2 2 TT TT T Dx u v x u v x D u v u v USV      (8)
This suggests that an arbitrary matrix D can be transformed into three matrix as follows,

x k TT x x x x R k R R R x     (11)
Then the gallery micro-expression feature can be estimated by projecting y

R into the joint subspace  1 ˆ() TT y y x x x y R R R R R x    (12)
Finally, the nearest neighbor (NN) classifier is used to classify the probe micro-expression samples. Given a gallery macro-expression feature set  

i i i TT y x x x i i i dis y y dis y R R R R x     (13)
where i  is the label of the gallery macro-expression sample i

x .

The proposed macro-to-micro transformation model is summarized in Algorithm 1. In this paper, there are two popular databases, i.e, Extended Cohn-Kanade (CK+) [START_REF] Lucey | The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression[C]// Computer Vision and Pattern Recognition Workshops[END_REF] expression database and CASME2 micro-expression database [START_REF] Li | CASME II: an improved spontaneous micro-expression database and the baseline evaluation[END_REF] are used in the experiments.

The CK+ database contains 327 image sequences, and there are 7 kinds of expressions, i.e., anger, contempt, disgust, fear, happy, sadness and surprise in this database. There is only one frame in each sequence that is labeled as a specific expression, which is called the peak frame. For each expression sample, the last 5 frames are selected to represent the specific expression, thus we can more experimental expression samples.

The CASME2 database contains expression-labeled 157 image sequences in 6 kinds of micro-expressions, such as disgust, fear, happiness, repression, sadness and surprise.

In our experiments, some repetitive samples for the CASME2 micro-expression database were added to train the macro-to-micro transformation model in order to guarantee the same data amount as the selected samples from the CK+ database, because the samples in the CASME2 database are fewer than that in the CK+ database. Therefore, we can test the performance of micro-expression recognition by transfer learning from the macro-expression sample, at the same time, the number of training samples is expanded in another way. Three kinds of expressions including disgust, happiness, and surprise were selected from the CK+ expression database and micro-expression database, respectively. In addition, the facial regions across these two databases were aligned to remove the positional and scale variance based on eye positions, and further cropped to the resolution of 231*231 pixels. Each cropped image was then divided into different patches, the numbers of which were 5*5, 6*6, 7*7, 8*8, 9*9, respectively. In the next step, a uniform 8,1 LBP was used to extract the features of each patch. Therefore, a histogram with 59 bins was calculated for each patch. Thus, one expression sample in the CK+ database was transformed to a feature vector containing n*n*59-dimentional features, where n*n denotes the number of patches. Similarly, each image from sequences of the CASME2 database were cropped to the same resolution as CK+ database and also divided into the same number of patches as CK+ database. LBP-TOP was adopted to extract the micro-expression features. Although each sequence was transformed into a 3*59-dimentional vector, it was further transformed to the size of 59 dimensions by average processing of 3 planes. Thus, micro-expression sequences in the CASME2 database were transform to a feature of n*n*59 dimensions. In all, 885 samples from the CK+ database and 57 stochastic samples from the CASME2 database were selected for training.

Moreover, the number of samples in the CASME2 database was fictitiously expanded to 885 by means of duplication in order to ensure the same amount between macro-expression and micro-expression samples. We used the remaining 55 samples for testing from the CASME2 database, and repeat the test randomly for 20 times. We reported the average value of all test results.

Analysis of patch size results on the cross-database

The face images in the two databases were divided into 5*5, 6*6, 7*7, 8*8, 9*9 patches respectively, and the proposed method also implement feature selection with increasing amount of 40% to 90% of the original features. The results of different numbers of patches are shown in Fig. 5. We can see that the recognition accuracy is not satisfactory if there is only one patch. This is because that the holistic feature is not conducive to recognize expressions. Both of the dimensions of macro-expression and micro-expression features extracted by LBP and LBP-TOP were 59, and the features will be disturbed by the holistic redundant information. To solve the problem, each image of the sequences was divided into more patches. When the number of patches reached 8*8, the recognition accuracy reached 0.6244, improved about 22% compared with the situation of one patch.

Analysis of feature dimension results on the cross-database

The influence on the recognition accuracy of the patch size is shown in Section 4.1. The division of the images cannot wipe off the redundant information. Fig. 6 shows the recognition performance of the proposed model with different feature dimensions. To reduce the redundant information, SVD was employed for dimension reduction. From Fig. 6 we can see that compared to the recognition accuracy of without dimensionality reduction, the dimensionality reduction has a particular effectiveness for micro-expression recognition in most cases.

Analysis of patch selection results on the cross-database

Table 1 shows the recognition performance of the proposed model with varying patch selection degree and different numbers of patches. Group LASSO were used to select different numbers of patch features according to the degree of patches' importance. Table 1 gives the recognition accuracy results with different degrees of feature selection via Group LASSO, i.e., from the top 90% patches to 40% patches of all. From Table 1 we can see that the recognition accuracy rose in proportion after feature selection via Group LASSO. The highest recognition rate appeared in varying degrees of feature selection because of the quantitative difference among the patches. Mostly, when the degree of feature selection rose to 40%, the recognition rate reached a maximum. When the number of patches became 77  , the recognition rate reached 0.655 accordingly.

Comparison with other methods

We evaluated the performance of the proposed model (setting 77  patches and 40% of feature selection) by comparing with three state-of-the-art methods, such as Discriminant Tensor Subspace Analysis (DTSA) [START_REF] Wang | Face recognition and micro-expression recognition based on discriminant tensor subspace analysis plus extreme learning machine[END_REF], Facial Dynamics Map (FDM) [START_REF] Xu | Microexpression Identification and Categorization using a Facial Dynamics Map[END_REF], and LBP-TOP [START_REF] Pfister | Recognising spontaneous facial micro-expressions[C]// International Conference on Computer Vision[END_REF] on the CASME2 micro-expression database. The nearest neighbor classifier was used for classification.

The recognition rates of four methods are listed in Table 2.

From Table 2 we can see that the proposed model outperformed the other three state-of-the-art methods. Just as mentioned at the beginning, the limited micro-expression samples greatly reduced the micro-expression recognition and classification accuracy. However, the proposed model takes advantage of the macro-expression samples in the training stage, and exploits the ideas of transfer learning, which can solve the problem of the limited micro-expression samples.

Conclusion

In this paper, we proposed a macro-to-micro transformation model for micro-expression recognition to overcome the difficulty in micro-expression recognition due to the limitation of labeled trained data. LBP and LBP-TOP are employed for representing macro-expression features and micro-expression features, respectively. In the proposed model, each image of expression image sequences is divided into different sizes of patches to dilute the features in order to reduce the influence of redundant information. Afterwards, Group LASSO is used to select the most efficient patches from LBP and LBP-TOP features. This way, the most discriminative features for the micro-expression recognition can be selected and used in the subsequent processing. Finally, singular value decomposition is employed to achieve macro-to-micro transformation model.

According to our experiments, we determined the prime parameter settings and defined the rules of patch design and feature selection. As demonstrated by the experimental results, the proposed model outperforms the compared state-of-the-art methods. More importantly, the proposed model can take advantages from the trained macro-expression databases to the micro-expression recognition: this aspect will be investigated more deeply in future work to check its efficiency.

Besides this study, the feasibility of nonlinear transfer learning and complicated classifiers [START_REF] Gu | A Robust Regularization Path Algorithm for ν-Support Vector Classification[END_REF][START_REF] Gu | Structural Minimax Probability Machine[END_REF][START_REF] Gu | Incremental Vector Learning for Ordinal Regression[END_REF][START_REF] Gu | Incremental learning for ν -Support Vector Regression[END_REF][START_REF] Gu | Bi-parameter space partition for cost-sensitive SVM[END_REF][START_REF] Wen | A rapid learning algorithm for vehicle classification[END_REF] will be explored.

  value of the adjacent pixel and c i represents the grey value of center pixel.

3. 4

 4 Macro-to-micro transformation model Now there are two kinds of features, i.e. m X represents a macro-expression feature, and m Y represents a micro-expression feature, both of which are m-dimensional features.

2 

 2 associated with the training objects and target objects for recognition. Assuming that there are N pairs of samples, the proposed model is constructed by arraying the training and target objects' features. Suppose the training matrix M is given by the rows indicate the features and the columns indicate the samples of each kind of features. For a 22  matrix D, two mutually orthogonal unit vector 1 respectively represent the mould of the two vectors with different direction, which are also called as the singular value of matrix D. Thus

R

  the concatenated features in a joint subspace, and d represents the dimension of each feature. standard orthogonal basis of the subspace. Fig.4shows the visualization of SVD.For the training matrix D, the two different features can be projected into a joint subspace, where they are associated with each other.As is shown above, U consists of the two different features and it can be decomposed as follows represent the sub-matrices, and they are feature-independent matrices.Assuming that x represents a gallery macro-expression feature and y represents a probe micro-expression feature, the gallery macro-expression feature can be transformed to the probe micro-expression feature.LetT K SV and m k be the m -th row vector of K, thus a point within a joint subspace

  micro-expression feature sample y can be assigned to class

R 4 :

 4 of micro-expression samples.Output: The label of a probe micro-expression feature sample y from matrix U according to Eq. (10); Calculate a point within a joint subspace  according to Eq. (11); 5: Estimate the gallery micro-expression feature according to Eq. (12); 6: Recognize the probe micro-expression feature sample y according to Eq. (13).

Fig. 1

 1 Fig.1 Micro-expression recognition methods

Fig. 3

 3 Fig.3 Description of facial expressions with LBP-TOP

Table 1

 1 Recognition rates of the proposed model with feature selection

		90%	80%	70%	60%	50%	40%
	55 	0.586	0.589	0.576	0.563	0.565	0.591
	66 	0.600	0.608	0.611	0.611	0.613	0.589
	77 	0.588	0.562	0.599	0.612	0.588	0.655
	88 	0.625	0.598	0.596	0.633	0.598	0.637
	99 	0.573	0.561	0.628	0.637	0.603	0.578
	Table 2 Recognition rates of four methods
	The proposed	FDM	LBP-TOP	DTSA
	model
	0.655		0.426	0.418		0.366

Acknowledgment

We sincerely thank the Institute of Psychology, Chinese Academy of Sciences for granting us permission to use the CASME database. This project is supported by the Natural Science Foundation of China (Grant No. 61571275, 61571274, 61379095).


Annie Pauzie
email: annie.pauzie@ifsttar.fr.

Pauzié Annie

Safety of scholar trip: design of innovative support for bus drivers

Keywords: Human Factors, ICT Design, HMI, IVIS, Public Transport, Bus drivers' safety, Children safety, Human centred design Facteurs humains, Conception ICT, IHM, Transport Public, Sécurité des conducteurs de bus, Sécurité des enfants, Conception anthropocentrée

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

Introduction

Going to and from school with school transport is a daily activity done by a lot of children within Europe, with about 250 000 children in Sweden, 700 000 in Poland, 450 000 in Austria while there would be about 4.5 millions in Germany and 4 millions in France (Pauzié et al., 2010). Protecting children -one of the most vulnerable transport system's users -is of great importance for all societies, but solutions to increase safety of bus transport to school is a quite underinvested area in many EU-countries, even if several measures for children are recommended in the EU-report Road safety in school transport (2004). In terms of accidentology, the most frequent scenarios of accidents are the following:

• school bus manoeuvring at a slow speed on a square or on a parking space with the driver attention devoted to other pedestrians around the bus and to the surrounding traffic. • drivers missing the detection of a pedestrian located on the side of the bus in the blind spot.

• pedestrian running over by the bus with high gravity of injury [START_REF] Brenac | Typical accidents scenario involving pedestrians and elements for prevention (in French) INRETS report n° 256[END_REF].

• child falling down between the bus and the sidewalk, or falling dawn from the sidewalk to the road at the arrival of the bus or when it is leaving the stop (French National Council of Transport, C.N. T. report, 2003).

Generally speaking, data show that one of the most critical situations in terms of safety is dealing with pedestrians around bus stops, going in and out from the bus [START_REF] Vayre | The risk of accident for a Parisian pedestrian[END_REF]. Despite the several Information and Communication Technology (ICT) functionalities developed these last decades to improve road safety, the use of support systems for school transport related to children boarding, exiting or walking to/from or waiting at the bus stop, is quite scarce, a literature review showing only very few scientific papers evaluating such systems [START_REF] Bruneau | Evaluation of two school bus advance signalling devices: The eight-light system and hazard lights[END_REF][START_REF] Carson | Development and evalutation of an active warning device for school bus loading and unloading pints in the area of limited visibility[END_REF][START_REF] Johnston | An evaluation of electronic pedestrian detection systems for school buses[END_REF][START_REF] King | Review of safe school travel initiatives: flashing lights at school zones and high visibility bus strips[END_REF].

In this context, a system allowing to automatically detecting vulnerable road users around the bus and displaying information to the bus driver could have a great positive impact on safety. Nevertheless, a specific concern has to be dedicated to the fact that the information should be easy understandable and legible by the driver, in order not to interfere with the main driving activity management. Furthermore, the issue of integration of this information with other functionalities of the informative and assistive system potentially useful for the bus driver has also to be considered.

In this perspective, this research has been developed as part of the Safeway2School European project, focusing on the human centred design concept of a school bus Driver Support System (DSS), aiming at displaying informative and warning information to assist the driver in his/her task and to improve road safety of children on their scholar trip.

Human centred design approach for bus Driver Support System

Support system HMIs have to be designed aiming at supporting human capabilities and compensating human limitations, to reduce errors and to improve drivers' and children safety. In order to fulfil this goal, a human centred design approach for the school bus Driver Support System has been followed, according to these phases:

--Definition of use case for the innovative system --Ergonomic inspection of system --Survey to identify context of use among school bus drivers --Setting up of an ergonomic mock-up --Transcultural evaluation of the mock up functionalities among bus drivers

Definition of use case for the innovative system

A use case is used as a tool implemented in the system analysis to identify, clarify, and organize system requirements. The use case is made up of a set of possible sequences of interactions between systems and users in a particular environment and related to a particular goal and comprise a powerful tool to capture functional requirements for software systems to evaluate them.

The methodology followed on Safeway2School Use Cases consisted in identifying drivers' needs through focus groups investigation and questionnaires to experts in the area. Following this analysis, identification of DSS use cases has been conducted for the following functionalities: Intelligent Speed Adaption (ISA), Safety belt checker, Child approach to bus stop notification, Child at bus stop notification and Late arrival of the Child to the bus stop notification [START_REF] Chalkia | Use cases[END_REF].

Ergonomic inspection of system

Heuristic evaluation is a usability engineering method for finding and assessing usability problems that can be encountered while designing user interface as part of an iterative design process. Based upon ergonomic design literature (10 Nielsen's heuristics, Nielsen & Mack, 1994), three major criteria have been highlighted to fit ergonomic inspection in the context of the bus driver support:

Inter consistency: When using a set of systems/functionalities that is aiming for the same overall goal, it is important that this set of systems/functionalities is consistent and follows the same standards in order to make the task of the bus driver easier by, for example, reducing memory load and the likelihood of error due to faulty interpretation of system messages. It is also of importance that the systems/functionalities are consistent with the road network (e.g. the information displayed on the on-board system should strictly respect the road signs design in terms of colors and shape).

Conflict avoidance: Possible conflicts between the bus driver's main driving task and tasks related to use of the support system, as well as conflicts between the uses of two or more sources of information from the bus cockpit have to be inspected. Conflicts are divided into conflicts of information (e.g. incompatible messages from two sources) and conflicts of task (e.g. the main task of the bus driver and a system demand action at the same time).

Flexibility: Systems should meet requirements of the bus driver's level of experience and his needs. Therefore the possibility of shutting systems off and adjusting which information that should be apparent is needed. This criterion is important because the bus driver competence can vary in terms of experience and needs. (e.g., a navigation system that cannot be shut off would be problematic because the information would be superfluous for a school bus driver that has driven the same route for many years, thus being an unnecessary distraction). However, this criterion should take into account related safety issues since some systems safety benefits might overweigh the bus driver's preferred preferences.

These criteria constituted a theoretical framework and has been used for the future ergonomic design and evaluation of the DSS for the school bus drivers [START_REF] Dukic | Ergonomic inspection based on selected systems from the Benchmarking database[END_REF].

Survey to identify context of use among school bus drivers

A survey was conducted in two French bus companies, one managing exclusively school transport, covering rural school transport, as well as urban school transport, the other one based in the city in charge of urban public bus transport, with some of their lines stopping in front of schools. Overall, 28 drivers have filled out the questionnaire from the two bus companies, aged between 27 to 63 years, working in the companies between 1 to 32 years. The questions were related to the following aspects of the context: characteristics of the route, including visibility of bus stops, density of the traffic, speed and regulation, disturbance about children and pedestrians surrounding the bus, usefulness of identifying of children at bus stop and inside, opinion about information on seat belts fasten, support for management of time schedule and proposition of future technological solutions to assist their driving task. Interviews have been carried out in focus groups and through individual questionnaire.

The following table summarised some of the main results related to characteristics of the rural, urban school and public transport in addition to needs and requirements about potential ICT functionalities of a school bus DSS. This first investigation allowed showing that the driving context for school bus drivers is quite complex and under high constraints in terms of timing. The overall opinion of bus drivers about potential support brought by ICT functionalities is diversified and quite indecisive. Part of this uncertainty was linked to reluctance to modify activity as a consequence of the system implementation; part of it was linked to the difficulty to conceive in which way this system can be supporting the task.

Setting up of an ergonomic mock up

One of the objective of the mock up development was to overcome concern related to bus drivers' difficulty to conceive the future system, by illustrating in a concrete and dynamic way the DSS interface displaying a number of functionalities to be able to test their acceptability. Another one was for the human factors team to test various design options in a flexible way.

Based upon the previous data, several versions of the mock up display (2D, 3D, without pictograms) have been tested in the lab, and after several iterations, 3 versions of the dynamic ergonomic mock up have been selected to be presented to the bus drivers samples. To summarise, objectives of these mock-ups was:

• To test the integration and the organisation of the various available functions on the screen.

• To assess the HMI usability and acceptability by bus drivers in an iterative process by conducting a second survey in relation to the designs.

Evaluation of the mock up functionalities

A second survey has been conducted regarding the understandability, acceptability and usefulness of the warning and notification messages displayed on the dynamic ergonomic mock up. This survey has been conducted among a sample of school bus drivers in France, in Italy and in Sweden. The objective was to gather transcultural opinions and preferences of the school bus drivers in relation to the DSS concept (utility of each function during the route), as well as the human machine interface (easy or not to understand, to read and to perceive). Only some main results of this investigation are presented in this paper; due to limitation of space, only results from urban school transport company are presented in this paper.

French participants

The questionnaire has been filled in by 27 drivers, aging from 24 to 63 years and with working experience in the bus companies was between 2 and 28 years (figure 5).

Ranking of Functions : Urban School Transport

Italian participants

The age of these 10 drivers was between 30 and 45 years and their seniority in the bus company between 3 and 11 years (Figure 6).

Swedish participants

The average age of the10 drivers was 48,7 years with an average seniority in the bus company of 18 years (Figure 7).

Similarities between the 3 countries

Location of pedestrians/children around the school bus: most of the bus drivers have positive opinion about this function that was judged useful, especially when children are located in the rear area of the bus and when pedestrians and children are crowded around the bus. Some French bus drivers indicated they have enough experience not to rely on electronic support, some Swedish ones rejected the principle.

Unfasten automatically seatbelt: This function has been identified after the analysis of the first survey, where the French drivers explained that one of their concern was the difficulty to release quickly all the children from the bus in case of crash because of the fasten seat belts. They proposed a system allowing unfastening all the seat belts by a simple push of button. The relevancy of this proposition was confirmed widely by the Italian and the Swedish population in the framework of the second survey.

Zoom and road map: school bus drivers of the 3 countries have a similar mixed opinion about these functions: part of the sample was positive, part was neutral and part was positive. Indeed, the view of the successive bus stops located on a map has been found relevant by drivers with low experience or having a new route, while regular and experienced drivers found it unnecessary.

Differences between the 3 countries

Information about children/pedestrian waiting at bus stop: Swedish bus drivers appreciated this function; warning about existing children at bus stop would allow them to adapt their speed when getting close to the stop in dark condition with low visibility. Italian bus drivers judged this function useful especially for a driver having a new route. French bus drivers were in favour of using this function only in the case where the same bus stop was used by several bus lines.

Location of children close to the bus stop around the street corner (a badge allows each child to be detected by the driver when walking to the bus stop even if he/she is not still visible but close): this function was appreciated and judged useful by Swedish bus drivers in case of darkness and bad weather conditions. Indeed, in Sweden, the weather can be so cold that it is dangerous to let a child in the country side. Italian bus drivers were more or less in favour of using this function to avoid that children miss the bus. French bus drivers judged this information not being relevant because it could encourage children to be systematically late at bus stops.

Send pre-recorded SMS: sending automatic SMS was not evaluated as being useful by Italian and French, due to the will for this population to explain in detail reasons of the delay linked to the context, and raising the potential ambiguity of pre-recorded messages, while Swedish drivers identified this functionality as a good opportunity to gain time.

Call company, colleagues and school: the function of calling external actors was moderately appreciated by the school bus drivers, especially the French ones and even worst the Italian ones. All of these drivers were belonging to urban school transport, and the negative opinion came from lack of time and potential distracting effect of phone call in heavy traffic.

Warning about late or early arrival at bus stops: this function was poorly appreciated by drivers from the three countries, as the possibilities to catch delays is highly restricted (precisely defined route, speed limit, …), leading bus drivers to feel powerless. The only positively rated information was the one in case of early arrival at bus stop where bus drivers can regulate better the route, by waiting children and driving slowly. But in case of delay, bus drivers thought generally that this information can be a problem of distraction during driving and a stress factor.

Warning about delays on arrival at the terminus: While Italian and French bus drivers considered useful to have a warning in case of delays on arrival time at the school, to increase their awareness and to be able to adjust their speed when it is necessary, the Swedish sample of drivers considered that this warning is stressful, especially when it is not possible to catch the delay, and rejected it.

Eco driving: these functions (fuel consumption and change gear information) were more or less appreciated by Swedish bus drivers but not at all by French and Italian bus drivers. Indeed, some Swedish drivers judged it useful in order to take care of the environment, whereas French and Italian bus drivers considered these functions as optional. Some drivers thought that it could contribute to improve driving style. Furthermore, it has to be noted that some French school buses are now equipped by automatic gearbox, so the eco-driving advice related to the appropriate change of gear would be not useful for them.

Speed limit: this function was not appreciated by French and Swedish bus drivers, especially with an auditory alarm, the first one considering they never overcame speed limit in urban area and the second one considering it could scare children. However, some Italian bus drivers judged it useful for the safety of children on board.

Automatic counting of children while boarding inside the bus: the function was not considered as very useful, but was not rejected either by Italian and Swedish bus drivers, while the French drivers appreciated it. Considering the details of the results, it appears that there is an important difference between rural dedicated school bus and urban public transport school bus: in the first case, the driver knows the children, so this function is less useful than in the second case where the information of exact number of children allows to adjust the frequency of the bus on the line, and so increase the quality of the management and avoid to have crowded bus.

Conclusion

Following a human centred design process to develop a Driver Support System for school bus drivers, this investigation allowed first of all identifying the diversity of opinions related to the implementation of a DSS in relation to the cultural backgrounds of the bus population at the European level. Nevertheless, some of these functions were unanimously positively rated, mostly regarding road safety such as detection of pedestrians around the bus, and corresponding to a real issue in terms of road safety confirmed by the statistics in accidentology. The dynamic schema proposed as HMI for this functionality was well accepted, considered as being clearly understandable and a useful support, especially to overcome blind spots. Secondly, following questionnaires results and confirmed through exchanges and discussions with the drivers, it was identified that part of the level of acceptability of these functionalities is strongly connected with the high timing constraints and the stress of the task. The issues were managing and controlling both children safety and time schedule at the successive bus stops and at the final destination. Some of the functions were more or less rejected by some drivers with the concern of a potential impact by increasing their level of stress. This approach brought concrete data regarding the priorities of ICT functionalities to be implemented to support the school bus drivers based upon an analysis of their context, needs and requirements. The diversity of opinions linked to the cultural belonging at the European level has been also identified and will have to be taken into consideration in a perspective a future implementation of these functionalities.

Fig 1 :

 1 Fig 1: Average number of children (6-14 years old) killed during 2003-2006. Source: IRTAD

Fig 3 :

 3 Fig 3: Functionalities implemented in the DSS ergonomic mock-up (2D version) developed in the framework of Safeway2School project

Figure 4 :

 4 Figure 4: Test of ergonomic mock-up usability and acceptability by school bus drivers

Figure 5 :

 5 Figure 5: Ranking of functions by level of satisfaction for French urban school bus drivers

Figure 6 :

 6 Figure 6: Ranking of functions by level of satisfaction for Italian urban school bus drivers

Figure 7 :

 7 Figure 7: Ranking of functions by level of satisfaction for Swedish urban and rural school bus drivers

Acknowledgements

The SAFEWAY2SCHOOL project (Integrated system for safe transportation of children to school) is a European project funded by the European Commission DG RTD 2009-2012, involving the following partners: Amparo Solutions, CERTH/HIT, Fleetech, Ifsttar/LESCOT, IIID, ITS, KfV, LiU, MIZAR, UNIMORE, USTUTT, VTI. The Italian survey has been conducted by Chiara Ferrarini & Roberto Montanari , UNIMORE and the Swedish survey by Tania Dukic & Ana Annund, VTI.