David Inácio

João Murta Pina

José M Ceballos

Mário Ventim Neves

Alfredo Álvarez

Experimental Magnetic Field Mapping of a Polycrystalline Superconducting YBCO Disc for an Axial Flux Motor

Keywords: High temperature superconducting motor, polycrystalline YBCO, axial flux disc motor, magnetic field mapping

The method of creating large and complex-shaped melt-textured YBCO bulks based in artificial welding seeds opens the door to the study of applications with polycrystalline superconductor samples. In order to evaluate the superconducting quality of the bulks, Hall probe mapping can be applied in order to measure trapped flux field profiles after magnetisation. This paper describes the magnetic field mapping of an axial flux motor with a polycrystalline high temperature superconductor (YBCO) disc rotor at liquid nitrogen temperature (77 K). An axial flux hall probe is used and magnetic field profiles have been obtained for different polar configurations of a three-phase stator winding which will be operated at 50Hz. After the magnetization of the HTS disc trapped flux is measured, and its magnitude depends on the polar configuration and magnitude of the applied field.

Introduction

The application of melt-textured YBCO in power applications (motors, transformers, current limiters, etc.) requires manufacturing complex-shaped pieces, some with large sizes. Such large bulks are usually build using artificial welding of separate monograins from it, grown by the Top-Seeding technique. Different joining techniques have been proposed, based on the use of low-melting point welding agents like oxides [START_REF] Salama | Joining of high current bulk YbaCuO superconductors[END_REF] or YBCO/Ag composites [START_REF] Lo | Development of nonweak link bulk YBCO grain boundaries for high magnetic field engineering applications[END_REF]. Recently, new methodologies based on surface melting have been developed, leading to high quality welds [START_REF] Puig | Self-seeded YBCO welding induced by Ag additives[END_REF]. The need to evaluate the quality of the superconducting joins has stimulated the development of different experimental characterization methods, as Hall probe mapping magnetometry [START_REF] Chen | Joining of melt-textured YBCO: A direct contact method[END_REF]. The operation of the HTS disc motor is based in the vortex's dynamics within the HTS material due to the magnetic field variations imposed by the three-phase supply. The field in the HTS material depends on its magnetic history, exhibiting several magnetic excursion that defines the operation regimes of the motor: in the synchronous regime, the rotor is synchronous with the stator rotating magnetic field due to trapped flux; however, in the asynchronous regime the rotor's magnetic field slides over it and flux flow is present, leading to losses and, consequently, torque. For the asynchronous regime, the torque is given by (1) [START_REF] Inacio | Experimental Characterization of a Conventional (Aluminum) and of a Superconducting (YBCO) Axial Flux Disc Motor[END_REF], and is maximum when the rotor is all magnetized.

cicle H P p T / 2 2π = (1)
Since torque proportional to the losses in the HTS material, it is useful that as much HTS material as possible gets penetrated by flux in order to take advantage of the disc size. For that reason, a research question is defined, namely "Is it possible to magnetise all the HTS material?".

Relationship to Cloud-based Solutions

Mapping of trapped flux in an HTS disc material is showed in this paper. In order to observe if all the material is magnetized, several mappings were obtained for different poles configurations. All the obtained results could be shared in a cloud-based system allowing other researchers to perform analysis, and providing data backup an on-time data monitoring.

Hall Measurement System

In order to observe the magnetization of the rotor, a Hall probe magnetic mapping system was developed allowing obtaining field profile in a matrix of points. It consists in a three-dimensional computer-controlled positioner connected to an axial flux hall probe attached to a mechanical arm which, after the rotor's magnetization, collects the magnetic field data. The system is showed in Fig. 1.

Topology of the Motor

The built disc motor is composed by two 20 cm diameter semi-stators with 24 slots and 24 conventional cooper windings each one, a steel shaft, a rotor consisting of a 20 cm diameter YBCO disc, two support bearings and fixing screws. Spacing between the semi-stators and the rotor is regulated using adjustment nuts to ensure an airgap as small as possible. Dry bearings were used, after ultrasound treatment. The HTS material considered (YBCO) has a critical temperature of 90-92 K. For that reason, liquid nitrogen was chosen to cool the system due to it boiling temperature of 77 K.

Supply System

All the motor is submersed in liquid nitrogen during its operation. For that reason, the stator's cooper winding exhibit an electrical resistance significantly smaller when compared to room temperature, decreasing impedance. The input current was controlled by a 6 kVA DC power supply (0-15 V/0-400 A). The stators were wound with copper conductors with a 0.8 mm 2 cross section, allowing a maximum current of 8 A at room temperature. However, at liquid nitrogen temperature, this limit was considered superior.

As referred before, the rotor's magnetization was achieved using two semi-stators and a three-phase supply source, in bipolar and tetrapolar configurations, as showed in Fig. 3. In this figure a linear visualization of one semi-stator is illustrated.

Under normal operation the motor is supplied with a symmetric three-phase system as indicated in [START_REF] Lo | Development of nonweak link bulk YBCO grain boundaries for high magnetic field engineering applications[END_REF]. In order to verify trapped flux capability of the rotor, only a static magnetization with three DC currents was applied. Considering t = 0 s, the DC current in phase 1 is zero, in phase 2 is -Im⋅(√3/2) and in phase 3 is Im⋅ ( )

Data Acquisition System

The necessity of operation in cryogenic environment implies a specific support system made of bakelite to fix the Hall probe. Bakelite was chosen due to its low linear expansion coefficient. The probe supply was performed using a double layer cooper plate connected to an electronic module that allows probe calibration and data acquisition. The system is illustrated in Fig. 4.

Data acquisition

In order to obtain the matrix of magnetic field measurements, a three-dimensional computer-controlled positioner was used, allowing defining the xyz displacement and acquisition time. The data acquisition was performed as close as possible to the HTS disc, as detailed in Fig. 5. A data acquisition system was also used. Due to symmetry, measurements were performed only in half disc. In order to ensure a constant and secure distance to the probe, the disc was shimmed between the semi-stators. All the system for data acquisition (Hall probe + calibration) was connected to a magnetometer that allowed determining deviations between the beginning and the end of the recording data. The xy displacement step was 1.5 mm with an acquisition time of 0.5 seconds, to ensure the field stabilization. Thus, each measurement run took approximately two hours, allowing obtaining about 4500 points of field.

Obtained Magnetic Field Characteristic

The goal of the measurements was determining flux density distribution due to semistators excitation, at room temperature, as well as in rotor (trapped flux), at cryogenic temperature.

Magnetic Field Characteristic of Semi-Stators

These measurements were perfomed with a tetrapolar configuration and a maximum current of 11.5 A. From (2), for t = 0 s, the DC currents in phases 2 and 3 was -10 A and 10 A. These values were chosen in order to explore maximum current density of cooper windings, as the airgap was significantly increased to ensure scanning along

Magnetic Field Characteristic of Rotor

As in previous measurements, this was performed using the tetrapolar configuration but at cryogenic temperature. Since the motor is submerged in liquid nitrogen, the stator's windings allow higher currents. The maximum current considered in this test was 34.6 A, leading to DC currents in phases 2 and 3 of -30 A and 30 A. The axial flux hall probe was calibrated for liquid nitrogen temperature.

After the magnetization, the supply system was turned off. Trapped flux mapping is showed in Fig. 6 b). As it is possible to observe, profile is similar to Fig. 6 a As described in (1), the developed torque is proportional to the rotor's trapped flux. Thereby, higher values of current in the stators imply higher trapped flux, up to a certain limit. Magnetic induction is showed in Fig. 7 a) (trapped flux) in the rotor, in half the disc, for tetrpolar configuration and Im = 127 A. As is possible to observe, for a current 3.7 times higher, the trapped flux is, approximately, ten times higher. Measurements for the same current amplitude, but in octopolar configuration are showed in Fig. 7 b). It is possible to observe, in half disc, four poles, which means than in full disc eight poles are present, as expected. Nevertheless, for these values of current amplitude, magnetic saturation is present in the ferromagnetic circuit, which must be taken into account in a detailed analysis.

Conclusions

In this paper a preliminary study is presented, describing experimental mapping of the magnetic induction characteristics of axial flux disc motor with the rotor composed by polycrystalline high temperature superconductor. As torque is proportional to the number of pole pairs, these were changed by means of electrical connections in the armatures. Field profiles from rotor and stator were obtained at room and liquid nitrogen temperatures. Flux profiles allow verifying number of poles. Moreover, it is possible to conclude that entire disc rotor is magnetized. Thus, it is expected that, when the motor operate in asynchronous regime, a maximum "theoretical" torque may be achieved, since this is proportional to the losses in the superconducting material.

Fig. 1 .

 1 Fig. 1. Experimental apparatus for the static test of the HTS rotor.

Fig. 2 Fig. 2 .

 22 Fig. 2. Built motor: a) steel semi-stator; b) double cooper layer slot stator winding, c) polycrystalline YBCO rotor and d) full motor with two semi-stators.

Fig. 3 .

 3 Fig. 3. Linear visualization of electrical connections, considering only phase 1, for a) bipolar configuration and b) tetrapolar configuration.

2 3 ,

 3 where Im is the magnitude of the current.

Fig. 4 .

 4 Fig. 4. Detail of the bakelite mechanical support for data acquisition.

Fig. 5 .

 5 Fig. 5. Data acquisition system for recording data of the magnetic field mapping.

  disc. Flux density measurements of semi-stators excitation at room temperature are showed in Fig.6a) for the entire disc, where it is possible to observe that it exhibits a maximum of 0.04 T. Two poles pairs configuration are observed, as expected.

Fig. 6 .

 6 Fig. 6. Magnetic induction distribution, for tetrapolar configuration, a) due semi-stators supply, at room temperature and b) trapped in the rotor, after magnetization, at cryogenic temperature.

Fig. 7 .

 7 Fig. 7. Magnetic induction in half disc for a) tetrapolar e b) octopolar configurations, at liquid nitrogen temperature for Im = 127 A.

Acknowledgments. The authors thank to CTS -UNINOVA and FCT (CTS multiannual funding -PIDDAC Program funds) for the financial support for the work.


Hugo Serra

Ricardo Madeira
email: r.madeira@campus.fct.unl.pt

Nuno Paulino
email: nunop@uninova.pt.

Analysis of a Multi-Ratio Switched Capacitor DC-DC Converter for a Supercapacitor Power Supply

Keywords: DC-DC power converters, Switched capacitor circuits, Supercapacitors, Performance analyses, Design optimization, Efficiency

An energy harvesting system can use a supercapacitor in order to store energy; however, a voltage regulator is required to obtain a constant output voltage as the supercapacitor discharges. A Switched-Capacitor DC-DC converter allows for complete integration in CMOS technology, but requires several topologies in order to obtain a high efficiency. This paper presents the complete analysis of these topologies in order to determine expressions that allow to design and determine the optimum input voltage ranges for each topology. These expressions are verified using electrical simulations.

Introduction

In order to achieve infinite operation, electronic systems must obtain their energy directly from the surrounding environment [START_REF] Paradiso | Energy Scavenging for Mobile and Wireless Electronics[END_REF]. This kind of procedure is commonly known as energy harvesting. Depending on the environment where the system is located at there are different energy sources that can be harvested [START_REF] Chalasani | A Survey of Energy Harvesting Sources for Embedded Systems[END_REF]. In the case of systems with small size, the available energy is necessarily reduced, typically only providing a fraction of the necessary power needed for the operation of the system. Therefore, such a system has to be powered down while it harvests enough energy in order to work during a short time and then this cycle is repeated. This type of operation is useful for wireless sensor nodes, where the node only needs to report data periodically. This operation requires an energy storing device, which can be either a rechargeable battery or a supercapacitor. A rechargeable battery has the advantage of having a larger energy storing capacity, but its lifetime is seriously reduced by the number of charge/discharge cycles (typically the maximum number is around 1000 cycles). A supercapacitor has the advantage of having limitless charge/discharge cycles and being less expensive than a battery, but it stores much less energy than a battery for the same volume [START_REF] Simjee | Everlast: Long-life, Supercapacitor-operated Wireless Sensor Node[END_REF]. Depending on the mode of operation of the energy harvesting system, a battery, a supercapacitor or both can be used as the energy storing device. In the case of low power remote sensor that uses a power-down power-up cycle, it makes more sense to use a supercapacitor as the energy storage.

The energy stored in a capacitor is given by = 1 2 ⁄ • • , this means that as the capacitor supplies energy to the circuit its output voltage drops. As an example a 1 F supercapacitor charged to its maximum voltage (usually 2.3 V) can store 2.645 J, if this capacitor supplies energy for a circuit with a supply current of 20 mA during 10s, its output voltage would drop ∆ = • = 200 mV. Since the power supply voltage of the circuit should be constant it is necessary to have a voltage regulator between the supercapacitor and the circuit. In order to maximize the energy retrieved from the supercapacitor it is necessary for the voltage regulator circuit be able to both downconvert and up-convert the input voltage with an efficiency as high as possible. This prevents the use of a linear voltage converter because it can only step-down the voltage and its efficiency is reduced when the input voltage is much larger than the output voltage. The voltage regulator can be implemented using either an inductor based or a capacitor based DC-DC converter. The first option requires an inductor which is not feasible to implement in a CMOS integrated circuit. The second option can be fully integrated in a CMOS integrated circuit; however, switched-capacitor (SC) based DC-DC converters have maximum efficiency for a specific input output voltage ratio. This means that in order to maintain a high efficiency the SC voltage converter circuit has to change its topology as the input supercapacitor voltages decreases [START_REF] Ramadass | Voltage Scalable Switched Capacitor DC-DC Converter for Ultra-Low-Power On-Chip Applications[END_REF]. Therefore a careful analysis of each converter topology should be carried out in order to determine its efficiency as a function of the input voltage and the different parasitic capacitance of the circuit. This analysis will determine the voltages values that will result in a change in the voltage conversion factor.

Relationship to Cloud-based Solutions

Cloud-based solutions rely on several servers, which are connected together through specialized connections, to distribute data processing tasks among them to overcome limitations in environments where the processing and information storage capability is lacking.

Wireless sensor networks which are integrated into cloud services, e.g. wireless sensor networks for real-time data collection, usually rely on batteries to supply energy to the nodes of the network. Due to the limited lifecycle of batteries, the long term sustainability of the wireless sensor network is compromised. An alternative is to use supercapacitors that acquire energy through energy harvesting systems. Unlike a battery, supercapacitors can charge/discharge an unlimited amount of times, however, a voltage regulator is needed to obtain a constant output voltage as the supercapacitor discharges. This paper analyses several SC DC-DC converters topologies in order to determine the optimum input voltage ranges for each topology.

Analysis of the SC DC-DC Converter Topologies

As previously mentioned, a SC based DC-DC converter has maximum efficiency for a given conversion factor between its input and output voltage. This means that as the supercapacitor discharges and its output voltage drops while the output voltage remains constant, it is necessary to use different topologies for the SC converter circuit in order to maintain high efficiency. There is a compromise between the number of different topologies and the complexity of the circuit. In this case the input voltage can vary from 2.3V to 0 V and the output voltage should be held constant around 1 V. Assuming that a supercapacitor with 1F is used, the available energy, for the maximum voltage, will be 2.645 J. When the capacitor voltage is equal to 1 V the energy remaining in the capacitor is still 0.5 J and when it is equal to 0.7 V the remaining energy is only 0.245 J. This means that there is a small payoff in trying to up-convert input voltages smaller than 0.7 V, which would require a circuit with a voltage conversion factor larger than 2. After analyzing the efficiency of several voltage conversion circuits for the different input voltages, it was decided to use only 4 SC capacitor voltage converter circuits. These are depicted in Fig. 1 and correspond to the voltage conversion factors of 1/2, 2/3, 1 and 3/2 [START_REF] Ramadass | Voltage Scalable Switched Capacitor DC-DC Converter for Ultra-Low-Power On-Chip Applications[END_REF]. Each of these converters is responsible for a given range of the input voltage (where its efficiency is maximized) and its clock frequency is adjusted in order to obtain an output voltage of 1 V at its output, independently of the input voltage and of the load. These 4 topologies can be combined into a single circuit with 3 capacitors that can be configured to any of the 4 topology using switches. In order to determine what are the input voltage ranges that maximize the efficiency for the different topologies it is necessary to analyze the behavior of each SC converter circuit to determine its operating parameters (efficiency and output voltage) as function of the different design parameters (capacitance values, clock frequency, etc) and the different non-ideal effects (e.g. parasitic capacitance, switch ON resistance).

Analysis of the Efficiency of the 1/2 SC Converter

Assuming that the clock frequency is adjusted in order to have the desired output voltage (e.g. = 1 V) and that output decoupling capacitor is large enough, it is reasonable to assume that the output voltage value is constant. In this case, the schematics of the 1/2 SC converter circuit during phase and are shown in Fig. 2 and Fig. 3 respectively. In these schematics, and represents the bottom and top plate parasitic capacitances of and represents the gate capacitance of all the CMOS switches in the circuit. In each clock cycle, the switches drain a charge (∆

) from through the clock buffers. Using conventional switched-capacitor circuit analysis techniques [START_REF] Gregorian | Analog MOS Integrated Circuits for Signal Processing[END_REF] (Chapter 5) it is possible to determine the charge in each capacitor at the end of each clock phase. The resulting equations are shown in [START_REF] Paradiso | Energy Scavenging for Mobile and Wireless Electronics[END_REF] and these allow to determine Δ , Δ , Δ , and Δ .

! - ) + = ! + ) + $ , 0 = + $ , - = ! -) + + $ , ! + ) = ! - ) + + $ . (1)
The input (' ) and output (' ) power are calculated using Δ , Δ , Δ , and Δ and the values of and and are given by

( ' = = !Δ + Δ ) ) *+, , ' = = !Δ + Δ ) ) *+, . (2)
The efficiency (-) is defined as the ratio between ' and ' ,with = 1 V , it is given by.

-/ = + - -2 ! -2) !-/0 ! + + ) + 2 + (3)
The 1/2 SC converter circuit was simulated in Spectre with ideal switches and capacitors for different input voltages between 2 to 2.5 V and with fixed at 1 V. From these transient simulations its efficiency was calculated and compared to the efficiency calculated using [START_REF] Simjee | Everlast: Long-life, Supercapacitor-operated Wireless Sensor Node[END_REF]

Analysis of the Output Voltage of the 1/2 SC Converter

A similar analysis can be performed in order to determine the output voltage of the SC converter, assuming that its output is connected to a load resistor [START_REF] Paradiso | Energy Scavenging for Mobile and Wireless Electronics[END_REF]

The resulting equations are shown in [START_REF] Ramadass | Voltage Scalable Switched Capacitor DC-DC Converter for Ultra-Low-Power On-Chip Applications[END_REF]. Solving in order to 607 and then considering the steady state condition ( 607 = 60 -17 = ), allows to calculate [START_REF] Gregorian | Analog MOS Integrated Circuits for Signal Processing[END_REF]. In order to simplify (5) it is possible to assume that ≫ resulting in [START_REF] Carvalho | CMOS Indoor Light Energy Harvesting System for Wireless Sensing[END_REF]. Fig. 5 represents as a function of the frequency for = 1 : the solid lines are the plot of ( 6) and the marks correspond to the simulation results. These validate expression [START_REF] Carvalho | CMOS Indoor Light Energy Harvesting System for Wireless Sensing[END_REF]. This graph shows that it is possible to control the value of by adjusting the clock frequency.

= 2 ) *+, 1 ! + 2 ) *+, 1 !2 + ) ) 1 + 2 ) *+, 1 <2 C + + 2 + + 2 ) 1 > + ! + C ? ) + C ! + 4 + )AB (5) = !2 + ) ) *+, 1 1 + !4 + + ) ) *+, 1 (6)
Using ( 6) to calculate the clock frequency for a given results in

) *+, = 1 !2 ! -2 ) + ! - ) - ) (7)
Fig. 6 shows the plot of (7) for = 1 , ' = 10 mW (1 = 10 Ω) and ' = 100 μW !1 = 10 KΩ) . As approaches the conversion ratio ! = 2 ), the clock frequency becomes infinite.

Circuit Design

In the previous section a theoretical study of the 1/2 converter was carried out. The behavior of the other three converters was determined using the same methodology. The 1/1 converter, is only affected by the top plate parasitic capacitance ( ), because the bottom plate capacitance is shorted to ground in both phases. Since is charged and discharged in parallel with , there is no charge lost to ground through . This means that the efficiency of this converter is not degraded by . Only the gate capacitance from the switches causes a decrease in the efficiency.

The 2/3 and 3/2 converters have a similar topology except for the switches that connect to and to . In these converters, there are five parasitic capacitances: , , , and Q . Where the number 1, 2 and 3 refers to , and Q , respectively. The analysis showed that Q has no impact on the efficiency. Similarly to the 1/2 converter, this is because Q charges and discharges in parallel with Q in both phases. The other four capacitances result in a decrease in -.

In order to better understand the design constraints of the circuit, the circuit will be designed using a 130nm CMOS technology and the capacitors are implemented using MIMCAP including parasitics. Limiting the total area for the capacitors to 1 mm 2 results in a maximum capacitance value of 1 nF this means that 1/2 and 1/1 converters will have a capacitor of 1 nF; and the 2/3 and 3/2 converters will have 3 capacitors with 0.33 nF. This type of capacitors have a top and bottom plate parasitic capacitance of, approximately, 0.4% and 0.6% of the nominal value, respectively.

Equivalent equations to [START_REF] Carvalho | CMOS Indoor Light Energy Harvesting System for Wireless Sensing[END_REF] were obtained for the remaining 3 converters using the same methodology. These equations are used to calculate for the 4 converters for ' = 10 mV (1 = 100 Ω), is 2, 3/2, 1 and 2/3, according to the converter ratio.

is plotted in Fig. 7 using the parasitic capacitances values of the selected technology. This plot shows that the 2/3 and 3/2 converters need a higher clock frequency to have = 1 , these higher frequencies will lead to lower efficiency due to the power needed to drive the larger switches.

The analysis described in section 3.1 was applied to the remaining 3 converters and their efficiency was calculated. Fig. 8 shows the plot of R in function of G ST . The solid lines are obtained from the equations and the shapes are simulation results. These plots were obtain for U V = W. The efficiency graph of Fig. 8 was calculated independently of the clock frequency and ignoring the charge lost through the gate capacitance of the switches. Since the gate capacitance increases with the CMOS switch size, which increases with the clock frequency value, the efficiency also depends on the clock frequency. The clock frequency value for each converter as a function of the G HIJ in order to have G HIJ = G is shown in Fig. 9. This graph was obtained using similar expressions to (7) calculated for all the converters. The CMOS switches are constituted by NMOS and PMOS devices in parallel. These transistors are sized in order to have an ON resistance (1 ef ) value that results in a time constant compatible with the clock frequency value. In [START_REF] Carvalho | CMOS Indoor Light Energy Harvesting System for Wireless Sensing[END_REF] it is shown that 1 ef and for a settling error !ghhih) and a frequency clock !) *+, ) and the capacitance coupled to the switch ! ) are given by:

1 ef = 1 2 ) *+, ln l 1 ghhih m (8) = 2 n o n * ln 8 1 ghhih 9 ) *+, (9)
Where n o = 806.24 Ω ⋅ μm and n * = 7.1 fF/μm are coefficients derived by simulation Spectre [START_REF] Carvalho | CMOS Indoor Light Energy Harvesting System for Wireless Sensing[END_REF]. Replacing (9) in (3) resultsas function of , , ghhih, 1 and ) *+, . This now takes into account the effect of the frequency in the parasitic capacitances of the switches. Replacing (7) in this new expression results in in function of , , ghhih, 1 . This final equation describes the efficiency with the frequency adjusting to the value necessary to maintain = 1 V and the extra power consumed by the switches due to this adjustment. Fig. 10 shows the plot ofas function of with ' = 10 mW !1 = 100 Ω), = 1 V, settling error of 1%, = 0.6% and = 0.3% , for the 1/1 and 1/2 = 1 nF; and for the 2/3 and 3/2 = 0.33 nF. The plot shows the maximum achievable efficiency and the corresponding value of . Through this it is possible to determine the range of operation for each converter. For example, in this case the 1/2 converter works from 2.3 to 2.07 V, with a maximum of 84% and a minimum of 64% efficiency. The 2/3 converter works from 2.07 to 1.76 V, with a maximum of 65% and a minimum efficiency of 57%. The 1/1 converter works from 1.76 to 1.05V, with a maximum of 83% and a minimum efficiency of 57%. Finally, the 3/2 converter works from 1.04 to 0.67 V, with a maximum efficiency of 84%.

Frequency (Hz)

Vin (V)

Conclusion

This paper presented a theoretical analysis of a multi ratio SC DC-DC converter for a supercapacitor power supply of an energy harvesting system. The different SC circuits of the converter were analyzed in order to obtain expressions that allow computing their efficiency as a function of the input voltage. These expressions were verified using electrical simulations and then used to design the circuit for a 130 nm CMOS technology and determine the optimum input voltage ranges for each topology.

Fig. 1 .

 1 Fig. 1. Simplified schematics of the selected topologies for the SC converters

Fig. 2 .Fig. 3 .

 23 Fig. 2. Simplified schematic of the 1/2 SC converter during phase

Fig. 4 .

 4 Fig. 4. These simulation results prove that (3) accurately describes the behavior of the efficiency of the converter and validates the theoretical analysis.

Fig. 5 .Fig. 6 .

 56 Fig. 5. G HIJ as function of the clock frequency

Fig. 7 .

 7 Fig. 7. K NOP as function of the frequency clock, with X Y = W. Z% X , X \ = W. ]% X , ^NOP = WW _ and K LM = {a, . b, , W. Zc} according to the converter ratio.

Fig. 8 .

 8 Fig. 8. d as function of K LM for the four converters with X Y = W. Z% X and X \ = W. ]% X

Fig. 10 .

 10 Fig. 10. η as function of V {| for a P ~•€ = 10 mW !R ~•€ = 100 Ω), V ~•€ = 1 V, settling error of 1%, C ' = 0.6% C , C ? = 0.3% C , for the 1/1 and 1/2 C = 1 nF; and for the 2/3 and 3/2 C = 0.33 nF

  Fig. 9. Frequency clock as function of K LM , for a x NOP = W mW !^N OP = WW _) with K NOP = V.

	10 10											1			1/2		2/3	1/1		3/2
												0.9
												0.8
	10 9
												0.7
	10 8										Efficiency (%)	0.4 0.5 0.6
	10 7											0.3
												0.2
	10 6	0.6	0.8	1	1/2	1.2	1.4 2/3	1.6 1/1	1.8	2 3/2	2.2	0 0.1	0.8	1	1.2	1.4	1.6	1.8	2	2.2	2.4	2.6
																	Vin (V)


Nelson Santos
email: nsantos@deea.isel.ipl.pt

J Fernando
email: fernando.alves@tecnico.ulisboa.pt

Vasco Soares
email: vesoares@deea.isel.ipl.pt

Sónia F Pinto
email: soniafp@tecnico.ulisboa.pt

Duarte Sousa
email: duarte.sousa@tecnico.ulisboa.pt

Design of High Voltage Full-Bridge Inverter Using Marx Derived Switches

Keywords: Pulsed Power Systems, Marx Generator, Bipolar Pulses, High Voltage Inverter, Smart-grids

This paper presents a high-voltage (HV) inverter to generate bipolar voltages with variable duty-cycle and frequency for HV pulsed power or HV electrical network applications. Each one of the four HV inverter switches is built with a series stack of semiconductor devices, derived from the Marx generator concept, using small capacitors to equally share the voltage among the individual series stacked semiconductors. A sliding mode control is used to control the output voltage and a delay technique is used to reduce dU/dt at the HV inverter output and to balance the capacitor voltages. The design and structure of the HV inverter switches is described together with the delay technique. Steady-state and dynamic behavior is evaluated. Simulation results are presented (using MATLAB/Simulink software) and discussed.

Introduction

In power electronic applications solid state power semiconductor devices are used to transfer large amounts of power and energy, in a controlled way, sometimes for very short times, as in the case of pulsed power. In HV applications solid-state semiconductor devices are sought because they have lower power losses compared to older devices like spark gaps or vacuum tubes. However, for HV applications, a single power semiconductor device is still not able to hold-off the tens or hundreds of kV or allow the kA currents needed. Connecting power semiconductor devices in series or/and in parallel is a way to avoid these limitations [START_REF] Withanage | Novel voltage balancing technique for series connection of IGBTs[END_REF], although issues like voltage sharing and pulse synchronization remain.

For kV applications, such as switches, inverters in smart-grids, choppers or pulsed power, the semiconductor devices like MOSFET (Metal Oxide Semiconductor Field Effect Transistor), GTO (Gate turn-OFF Thyristor) or IGBT (Insulated Gate Bipolar Transistor) can be connected in series and operated synchronously as a single switch device. To ensure proper operation of the switch it is crucial to balance the voltage between all the series connected semiconductor devices by dividing the voltage equally between them, both in steady-state and during transients.

However, even small variations of devices static and dynamic parameters, the imperfect synchronization or delay of the gate drive voltages, thermal impedance variations and asymmetrical parameters in the snubber circuits for voltage sharing [START_REF] Robinson | Series connecting devices for high-voltage power conversion[END_REF], can impair the voltage balance among series semiconductor devices.

There are techniques to minimize the unbalanced voltages by using passive snubber circuits, active gate control circuits and voltage clamping circuits. Snubber circuits usually are RC (Resistor-Capacitor) or RCD (Resistor-Capacitor-Diode) in parallel with the collector-emitter of each IGBT of the stack, minimizing device switching losses while increasing the global losses and limiting the switching frequency. The active gate control proposes individual control of the signal gate of each device to dynamically balance the collector-emitter voltage. While this technique may not increase the switching loss or switching times, it presents some complexity in the drive circuit and may require additional small passive snubbers. Another approach, active voltage clamping, balances the voltage collector-emitter by using optimized snubbers (with low losses) and auxiliary circuits using usually extra semiconductor devices to improve the switching frequency restrictions [START_REF] Withanage | Novel voltage balancing technique for series connection of IGBTs[END_REF].

This paper proposes a full-bridge inverter with four HV switches using IGBT, each one made of switching cells derived from the Marx generator concept [START_REF] Redondo | Solid State Pulsed Power Electronics[END_REF], able to balance the voltages in the series cell stacks of each HV switch with efficiency. Performance evaluation is done using a typical inductive load at the output of the HV inverter and the voltage balancing is maintained, even with 10 % tolerance in the capacitor cells. Unsynchronized gate drive signals are used advantageously to reduce the output dU/dt, decreasing the load voltage stress and the electromagnetic interference problems [START_REF] Redondo | Rise time reduction in high-voltage pulse transformers using auxiliary windings[END_REF].

Relationship to Cloud-based Solutions

Research works, like the described in paper, need computers to perform numerical calculations and simulations. Sometimes, one computer is not enough as it takes too long to finish the simulations. Cloud technology can help and increase the productivity of research teams that locally do not have powerful enough resources due to budget constraints and strict control on the distribution of funds. Additionally, the physical implementation of prototypes can benefit from cloud-based solutions by connecting several teams in real time for development, analysis and sharing in the community. In return, HV full-bridge inverters will be key players in the supply systems sustainability of cloud data centers, its integration with renewable energy resources and power quality control in smart-grids.

Proposed Topology

Marx Derived Switch

The original Marx generator topology charges n capacitors in parallel and discharges them in series to obtain the HV UHV approximately equal to n times the charging power source ULV voltage = , without using step-up transformers. Figure 1 To reduce the losses, the impedances and spark gaps are replaced by power semi-conductors, as depicted in Fig. 1(b), increasing the efficiency, the performance and the pulse frequency. To use the Marx generator concept as a switch, it is necessary to charge the capacitors in series (diodes of S1 to Sn), to hold-off the HV, and to balance the capacitor voltage connecting them in parallel (diodes D1 to Dn-1). Therefore, in the Fig. 1(b) circuit load and HV source are interchanged (Fig. 1(c)) to obtain a series stack of switching cells for positive HV pulses. This technique provides HV handling using ULV (kV) rated semiconductor devices [START_REF] Redondo | Solid State Pulsed Power Electronics[END_REF].

Full-Bridge Inverter Topology

The full-bridge inverter uses four HV Marx generator based switches Fig. 1(c), each switch denoted by SMk with k ∈{1, …, 4}. The inverter outputs bipolar HV pulses from a single HV source Udc (Fig. 2

= = = for = +1 , = 0 for = 0 , ! , = - for = -1 ! , (1)
A sliding mode modulator [START_REF] Santos | Sliding Mode Control of Unified Power Quality Conditioner for 3 Phase 4 Wire Systems[END_REF], [START_REF] Silva | PWM Audio Power Amplifiers: Sigma Delta Versus Sliding Mode Control[END_REF], [START_REF] Silva | Sliding Mode Control of Voltage Sourced Boost-Type Reversible Rectifiers[END_REF] and [START_REF] Silva | Advanced control methods for power electronics systems, special issue on Modelling and simulation of Electrical Machines[END_REF] will be used to obtain the gate drive signals for the IGBT stacks SM1, …, SM4 in order to control the output voltage U0.

Operation of the Switch

Each switch SMk has two modes of operation, charging mode, if Skn are OFF and SCn are ON, and balancing mode, if Skn are ON and SCn are OFF. In the charging mode, the SMk switch is OFF and all capacitors C1 to Cn are charged in series through the SC1 to SCn by HV source Udc. Series charging leads to some voltage imbalance. Under the balancing mode operation (the SMk switch is ON) all the capacitors are connect in parallel through Sk1 to Skn and D1 to Dn, therefore equal voltage sharing is achieved. Assuming that an upper stack capacitor exhibits a voltage higher than those on the bottom, a current path is established that balances the voltages among the capacitors on the switch SMk (Fig. 3(a)). However, under opposite conditions, a lower stack capacitor cannot discharge to the remaining ones. This issue can be avoided by the replacement of diodes D1 to Dn by IGBTs (thus increasing the cost and control system complexity). Other options include the use of balancing resistors (which decrease the efficiency) or using, as in the present paper, an unsynchronized gate drive signal technique approach. A sequential gate driver delay (5 µs) on each one of the cells (Sk1, SC1 to Skn, SCn) is used on every transition mode operation, similar to the technique adopted on multilevel inverters, but for a short period of time. Figure 3 exemplifies the sequence delay technique adopted for one arm of the inverter to turn OFF SM1 and turn ON SM2 with IL > 0 and n = 3. To turn ON SM1 and turn OFF SM2 for IL > 0 there is the need to invert the initial sequence, that is, to operate the stages from St4 to St1. If IL < 0 it is necessary to change the drive sequence, leaving a slightly higher voltage on the C1 of SM1 and SM2 in order to guaranty the proper equalization.

Design Parameters

It is considered that the HV inverter provides energy to a resistive load through one low-pass filter (LC). Also, the system is design for a specific efficiency parameter ηT by assuming P0 as the nominal output power, PLC the filter power losses and PCS the switches power losses. Since the majority of the losses are due to IGBT switching effects, its relationship with maximum switching frequency and goal efficiency is (2).

1 -# $ # $ = % & % ' + % &( % ' (2)
a) The filter is designed to reduce harmonic voltage content. Its contribution to the overall losses shouldn't be greater than 0.3 % of the nominal output power. For a cut-off frequency fc and a damping ratio ξ the filter parameters are

) ' = 2+, , -= !./ 0 1 2 0 , 3 = 45 0
, where the ) ' is the angular frequency and Z is the characteristic impedance of the load output. The corresponding filter parameters for the inductance and capacitor values are 6 = ./ 0 1 78 9 2 0 , 3 = % 0 4;+, < 0 2 . It is considered that the losses in capacitor are negligible because most of them are due to the output current ripple Δ> [START_REF] Silva | Electrónica Industrial -Semicondutores e Conversores de Potência[END_REF]. With rC and rL as the equivalent serial resistance of the capacitor and the inductor the power ratio can be calculated [START_REF] Redondo | Solid State Pulsed Power Electronics[END_REF].

% & % ' = % ' ' ! + & Δ> ! 12% ' (3)
To achieve a specific performance losses criteria rL should obey the relationship presented in (4).

< % & ' ! % ' ! (4)
b) The efficiency of the IGBT is related to the semiconductor ON state and switching losses. The ON state losses PC1 are a function of the device ON state voltage Uce, the anti-parallel diode voltage drop Uf and the maximum current output I0. Assuming one single IGBT and the duty cycle δ', it can be written % & = @ A ' ′ + 8 A ' 1 -′ . Considering ′ = 1 2 ⁄ the total ON losses PC for the four switches with n cells each is [START_REF] Redondo | New technique for uniform voltage sharing in series stacked semiconductors[END_REF].

% & = 2 A ' D @ + 8 E (5)
The switching losses PS1 are related with the rise time tr, the fall time tf, and the switching frequency fs. Since the IGBT voltage is = ⁄ and I0 represents its current, in a single device the switching losses are % ( = A ' , F DG H IG J E ! .Taking into account the voltage Udc and the previous equation the total switching losses PS for the four switches with n cell each are [START_REF] Santos | Sliding Mode Control of Unified Power Quality Conditioner for 3 Phase 4 Wire Systems[END_REF].

% ( = 2 A ' , F D K + 8 E (6)
Since PCS should be no more than 1.7 % of P0, so [START_REF] Silva | PWM Audio Power Amplifiers: Sigma Delta Versus Sliding Mode Control[END_REF].

% &( % ' = 2 A ' , F D K -8 E + 2 A ' D @ + 8 E % ' (7)
For a specific target efficiency the average switching frequency fs must obey [START_REF] Silva | Sliding Mode Control of Voltage Sourced Boost-Type Reversible Rectifiers[END_REF].

, F < 2 LM 2 0 - !NO 0 D/ 9P I/ J E 2 0 !/ Q9 DG H RG J E 2 0 (8)
c) The Cn value on each cell is important to the proper operation of the stack and voltage protection purposes by limiting the dU/dt. Additionally, the inductance Lp must consider the dI/dt IGBT limitations. Assuming variations of ΔU & (%) on capacitor nominal voltages, on C1 to Cn, equations ( 9) and ( 10) can be presented. Equation [START_REF] Silva | Electrónica Industrial -Semicondutores e Conversores de Potência[END_REF] is also function of time delay td.

6 T = ΔU & / Q9 N K 2A ' (9)
3 N = U -1 ! W A ' ΔU & / Q9 N (10)
It should be noted that the energy stored in Lp boosts the voltage in capacitors C1 to Cn so a freewheel diode was added.

Simulation Results

The circuit presented in Fig. 2(a) was simulated using MATLAB/Simulink considering four HV switches with five cells each, n = 5. On the HV inverter output there is a LC filter with a cut-off frequency of fc = 500 Hz. For other general parameters it is considered P0 = 1 MW, U0 = 10 kV, R0 = 100 Ω, ηT = 98 % and ΔU & = 2 %. Figure 4(a) presents the inverter output voltage UL and the voltage U0 applied to the output resistor R0. The capacitor voltages (Fig. 4(d)) are very similar and balanced around 4 kV, even using randomly chosen capacitor values within the typical tolerance of 10 %. In The voltage variation at Fig. 4(c) is higher at C1 of SM1. At turn OFF there is a voltage increase of 2.5 % relative to its nominal value. At turn ON the voltage in C1 is 4.5 % higher to the same nominal value. Besides all the voltages sharing is achieved.

Conclusion

The full-bridge inverter proposed in this paper uses HV switches based on the Marx generator concept. Marx topology well-known toughness is suited for HV pulsed power or electrical network applications. A good voltage balancing was attained on each switch, with just 5 % difference even using 10 % of tolerance for the capacitors due to the use of just one extra diode (Dn) per cell. This is advantageous as the absence of resistors for balancing or capacitor discharging purposes means an improved efficiency. The inverter output dU/dt was also reduced and controlled using the proposed delay time unsynchronized gate drive technique.

  (a) shows an example of Marx generator for positive HV pulses where the capacitors C1, …, Cn are charged in parallel through the impedances Z1, …, Zn (resistive and/or inductive) when switches S1, ..., Sn are turned OFF and discharged in series into the load, when switches S1, ..., Sn are turned ON.

Fig. 1 .

 1 Fig.1. Marx derived topology; (a) Marx generator topology; (b) Solid-state HV positive pulse generator; (c) series switch for positive voltages.

  (a)). Each HV switch is composed by n series connected cells > / . Each cell contains two IGBT Skn, SCn with anti-parallel diodes (Fig. )). IGBT/diode hold-off voltages are imposed by capacitor Cn. The inter-cell diode Dn balances the Cn voltages.

Fig. 2 .

 2 Fig.2. Proposed Circuit; (a) HV Inverter; (b) single cell based in Marx generator Assuming the IGBTs Sk1, …,Skn at each switch SMk are driven simultaneously, the inverter output voltage UL depends on the states of SM1, SM2, SM3 and SM4, obtaining three voltage levels, according to (1).

Fig. 3 .

 3 Fig.3.Sequence delay technique for turn OFF SM1 and turn ON SM2 for n = 3 and IL > 0 The sequence delay technique begins at Stage 1 (St1) where the capacitors C1, C2 and C3 of SM2 are linked in series with Udc, and at same time C1, C2, C3 of SM1 and C1 of SM2 are connected in parallel for voltage balancing. Sequentially, follows the St2, then the St3 and finishes with St4 where the capacitors C1, C2 and C3 of SM1 are linked in series with Udc and C1, C2 andC3 of SM2 are connected in parallel for voltage balancing purposes. All the states for SM1 and SM2 for IL > 0 are resumed in Tab. 1.

Fig. 4 .

 4 Fig.4.Temporal evolution; (a) voltage output inverter UL and voltage U0; (b) voltage UL and current IL at output of inverter; (c) voltages cell of switch SM1; (d) voltages present in each cell in all four switches SM1, SM2, SM3 and SM4

Fig. 4 (

 4 b) it is shown an improvement of 80 % (for n = 5) reduction in the inverter output dU/dt regarding the synchronized gate driver technique. The output filter current exhibits smooth transitions around 100 A.

Table 1 .

 1 Stages transition for SM1 and SM2 for IL > 0

	Stage	ON at SM1	OFF at SM1	ON at SM2	OFF at SM2	ISM1	ISM2
	1	S11, S12, S13	SC1, SC2, SC3	SC1, SC2, SC3	S21, S22, S23	IL	0
	2	S12, S13, SC1	S11, SC2, SC3	S21, SC2, SC3	S22, S23, SC1	2/3IL	-1/3IL
	3	S13, SC1, SC2	S11, S12, SC3	S21, S22, SC3	S23, SC1, SC2	1/3IL	-2/3IL
	4	SC1, SC2, SC3	S11, S12, S13	S21, S22, S13	SC1, SC2, SC3	0	-IL

Acknowledgments. This work supported by Portuguese national funds through FCT -Fundação para a Ciência e Tecnologia, under Project PEst-OE/EEI/LA0021/2013.


Robin Burgess

António J Falcão

Tiago Fernandes

Rita A Ribeiro

Miguel Gomes

Alberto Krone-Martins

André Moitinho De Almeida

Selection of Large-Scale 3D Point Cloud Data using Gesture Recognition

Keywords: Point Cloud Data, Gesture Recognition, Large Scale Data

An essential task when visualizing and analyzing large-scale 3D point cloud data is the selection of subsets of that data. This presents two challenges, the need for a selection method that is independent of the size of the dataset and how to interact with a 3D space effectively in a digital world still rooted in 2D interaction and visualization. We present an interface for defining volumes to select 3D point cloud data that uses hand gesture control with a Leap Motion device. The use of volumes is scalable to very large datasets, and the use of the Leap Motion gives the user access to the third dimension, facilitating interaction with the point cloud data. We illustrate with a large astronomical data archive hosted on the cloud that is retrieved on as-needed basis.

Introduction

In an age when automated massive data analysis is becoming a mainstream activity, the task of visualization is essential for the understanding and critical interpretation of data and results -for science and mission analysis and control alike [START_REF] Bowman | 3D User Interfaces: Theory and Practice[END_REF][2] [START_REF] Hey | The Fourth Paradigm -Data intensive scientific discovery[END_REF]. Particularly, in Space related activities, most of the data leading to new scientific discoveries is expected to come from online archives collected through automatic instruments and all-sky surveys. Very few works address the topic [START_REF] Becciani | VisIVO-Integrated Tools and Services for Large-Scale Astrophysical Visualization[END_REF]. Unfortunately, no adequate visualization solutions are available today for interactively visualizing and analyzing such amounts of complex datasets. Within this context we present an interface for defining volumes to select 3D point cloud data that uses hand gesture control with a Leap Motion. The use of volumes is scalable to large-scale datasets, and the use of the Leap Motion gives the user access to the third dimension, facilitating interaction with the point cloud data.

When working with any data, it is often of interest to analyze the particular properties of subsets of that data. It is therefore necessary to be able to select the subset of interest. When properties of that data are already known, it is possible to use automatic processes to select it. Otherwise, some form of manual selection is necessary. If the data visualized is in the form of 3D cloud points, such data can only be selected in relation to its position, and therefore it is necessary to have an effective method of spatial selection available. A challenge when designing such a method is how to define a 3D space easily when traditional interaction tools, such as a mouse or a touchpad are naturally geared towards 2D interaction. In cases where the point cloud dataset is too large to process in a single device and the data is stored on a distributed server, any solution must avoid the possibility of processing large chunks of the dataset. Fig. 1. 3D selection using a touch-based interface [START_REF] Yu | Efficient Structure-Aware Selection Techniques for 3D Point Cloud Visualizations with 2DOF Input[END_REF] Our goal is to design an interactive spatial selection method for a large-scale 3D point cloud visualizer. The method had to provide the user with the ability to select areas of the point cloud of any size without requiring the processing of large amounts of data.

The existing work related to selection of 3D point cloud data is not very extensive, and as far as we have been able to ascertain, does not consider the size of the dataset used [START_REF] Fisher | Animation for Visualization: Opportunities and Drawbacks[END_REF]. One solution is to manually select 3D point cloud data by using a lasso to define a 2D area, as seen in Fig. 1, which then selects all points visible through it. This results in selections as seen in Fig. 2. This solution can easily result in selecting points that the user did not intend to select, and which then must be deselected similarly. Therefore, the selection becomes a complex multi-step process.

Fig. 2. 3D selection of galaxies using TeddySelection and CloudLasso techniques [5]

A paper by Lingyun Yu et al. [START_REF] Yu | Efficient Structure-Aware Selection Techniques for 3D Point Cloud Visualizations with 2DOF Input[END_REF] describes two solutions, TeddySelection and CloudLasso -two spatial, structure-aware selection techniques. They too rely on the use of a lasso, but they simplify the selection process by relying on algorithms to analyze the structure of the data and select a more limited area that is more likely to correspond to the user's needs. The performance of both techniques will scale with the number of particles to be selected, or even with the size of the whole dataset.

Another solution that has been used is the creation of volumes that encapsulate the point cloud data that the user wishes to select. One example is the 3D Point Cloud Editor [START_REF] Li | 3D Point Cloud Editor[END_REF], illustrated in Fig. 3. The volumes can be modified and multiple volumes can be combined using Boolean operations. This solution has the advantage of not requiring the processing of data to define the boundaries of its selection.

Fig. 3. Selection of points from a point cloud using a cuboid [6]

The Slice-n-Swipe [START_REF] Bacim | Slice-n-Swipe: A free-hand gesture user interface for 3D point cloud annotation. 3D User Interfaces (3DUI)[END_REF] solution uses a Leap Motion and has the user perform gestures to define cuts in the point cloud and select the desired half. It introduces a 3D element into the selection process but allows only fairly simple selections to be made.

Finally, another proposed solution [START_REF] Lubos | Touching the Cloud: Bimanual annotation of immersive point clouds[END_REF] uses a PrimeSense sensor for gesture recognition and an Occulus Rift for visualization, and selection is achieved by using the hand as a virtual brush to select the desired area of the point cloud.

Our solution combines gesture recognition to solve the problem of interaction with a 3D space by using a Leap Motion [9], with the data-independence of creating 3D volumes for selection. The Leap Motion provides the user with direct access to the third dimension, which otherwise can only be managed with complex multi-step processes or with the aid of data processing. This work is being developed in the context of a project, financed by the European Space Agency (ESA), denoted IVELA-Intelligent Visualization [START_REF]IVELA project[END_REF]. The project's main objective is to develop tools to support astronomers and physicists performing scientific visual analytics studies on gathered data, such as the mission GAIA [11]. The GAIA mission aims to map about a billion of the Milky Way's stars, and gather information about each of those stars, including their speed and direction. With this data they hope to learn more about the composition, formation and evolution of our Galaxy.

This paper is structured as follows. This first section and section 2 provide an introduction to the topic of research and its relation with the theme of the conference. Section 3 presents the prototype developed. Section 4 discusses the solution presented and its virtues and flaws. Section 5 briefly concludes the paper.

Contribution to Cloud-based Engineering Systems

As mentioned before, this work is being done as part of the development of an astronomical visualizer for large-scale stellar datasets [START_REF]IVELA project[END_REF]. Since a single computer cannot easily handle such large volumes of data, the data is stored in a cloud server and will be loaded as required. Scientists using the visualizer must be able to perform selections of clusters or regions of interest efficiently and effectively.

The Prototype

A prototype of the interaction system was created as a proof of concept of a tool for visual analytics. It was developed using the Unity game development system, developed by Unity Technologies [START_REF]Unity Technologies[END_REF] and programmed in C#. To allow interaction between the Unity application and the Leap Motion device [9], the Leap Motion C# API and Unity assets made by the Leap Motion company were used.

The prototype environment displays a randomly generated cluster of stars. Using both hands to interact with the prototype through the Leap Motion, the point cloud stars can be selected by creating spherical or cubic selection volumes.

Interaction

The prototype displays 3D models representing the hands, as seen in Fig. 4. The movements of these models mirror the movements of the hands and fingers detected by the Leap Motion. This allows the user to more easily relate the movements of their hands to the effects to the environment in the prototype. The 3D model and their movement scripts used were part of the functionalities provided by the Leap Motion team.

The Modes

The interface is separated into distinct interaction modes, as follows:

• Navigation Mode • Selection Mode • Creation Mode • Position Mode • Rotation Mode • Scale Mode
The user can choose which mode is active via a menu in the bottom of the screen, as shown in Fig. 4. By separating the various activities into different modes, the user can more freely interact with less risk of their gestures being misinterpreted.

The Menu

Using a simple command gesture, users can activate the menu at any point. The menu is composed of submenus that the user can activate by swiping left or right. Each of the submenus consists of a vertical list of buttons. To pick a menu option the user must move their hand up and down, highlighting the corresponding option, and then pinch their fingers together. Using the menus, the user is allowed to pick the active mode (Navigation, Selection, Creation, Position, Rotation or Scale) as seen in Fig. 4, switch to a mouse-based interface, pick the shape of the 3D volume they wish to create and change the visualization mode of the dataset.

Navigation Mode

Navigation within the prototype is based on the flight analogy. The user can move the camera forwards, backwards, left, right, up and down by moving the hand accordingly relative to the center of the Leap Motion's interaction volume, as well as rotate it along the horizontal and vertical axis by tilting the hand similarly.

Creation Mode

In creation mode either a spherical, cubical or cylindrical selection volume can be created. Two opposite sides of the volume are locked to the palms of two hands so that the user can freely move the volume and scale it by moving their hands within the 3D space. The user then locks the volume in place by pinching their index and thumbs together. This automatically switches the mode to the position mode. Fig. 5 shows a volume after creation.

Position Mode, Rotation Mode and Scale Mode

The position, rotation and scale modes allow the user to edit the active selection volume. In the position mode, the user can move the selection by making a grabbing gesture with their hand and moving it. The volume moves in whichever direction the hand is in relative to the center of the device's interaction volume.

In the rotation mode, the user can rotate the volume by holding a pinching gesture and dragging the hands around, causing the volume to rotate in a corresponding direction.

In the scale mode, the user can increase or decrease the size of the volume along one of three axes by holding one hand at a height above or below the other. Using a gesture command they can switch between the three axes

Selection Mode

In the selection mode, the user can select volumes by pinching the index and thumb finger. The volume closest to the hand is selected. The selected volume can be deleted with a gesture command or it can be edited in the position, rotation or scale modes.

Visualization

When creating and editing a selection volume, one of the difficulties is to know which data points the volume is actually selecting. As such, visualization plays an important part in the process and several visualization modes were made available to the user:

• All: All data points are drawn.

• Only Selected: Only selected data points are drawn.

• Only Not Selected: Only non-selected data points are drawn.

• Colors: All points are drawn, but are given a different color depending on whether they are selected or not.

Discussion

While the Leap Motion can facilitate selection by giving the user the ability to interact with the software in three dimensions, there are still some limitations to its usage. The first problem is that the visualization is still two dimensional. The lack of depth perception associated with this means that it can be difficult to make a mental connection between the visualized space and the user's hands, rendering the advantages of 3D interaction far less effective. However, the available visualization modes help to solve this problem. For example, if the color visualization mode is active, then as the volume's position is changed, the effect on the selection is clearly visible.

The second problem is that the Leap Motion still has important limitations in its tracking ability. For example, it cannot keep track of a hand while it is occluded by another hand, limiting certain gestures in one axis. It also is limited in how many gestures it can easily identify. These problems raise many difficulties in designing sophisticated scientific visual analytics tool that users can understand intuitively and use effectively, and, therefore it remains a simple interaction system. Indeed, we have not come across any user interface that uses the Leap Motion in a way that is not either trivially simple or very hard to master. However, new developments are expected in the near future and these might solve part of the pointed problems for developing a truly scientific visual analytics tool.

Despite its limitations, the use of the Leap Motion remains a more effective way of modifying the position of a 3D volume than any 2D interaction system. Any such system would require the user to refine the volume's position while changing position of the camera.

Conclusion

The selection method described in this paper is scalable, and intuitively simple to understand. Despite the current limitations of gesture recognition hardware and software, it holds a distinct advantage over systems that work purely with 2D tools.

However, there is still a long way to go before it can be considered a useful tool for any serious scientific work. Future developments could include the use of the Occulus Rift for improved visualization and the refinement of the gesture interface.

Fig. 4 .

 4 Fig. 4. The 3D representation of a hand.

Fig. 5 .

 5 Fig. 5. A cubic selection volume after being created.


Laplace Isabelle

Latgé-Roucolle Chantal
email: chantal.latge-roucolle@enac.fr

Deregulation of the ASEAN air transport market: measure of impacts of airport activities on local economies

Keywords: air trafic, economic impact, liberalization, econometrics, ASEAN

ASEAN Member States are currently in a step through liberalization of air traffic market in their region. The target is the 5 th freedom right for South-East Asia in 2020. Two opposite effects might be observed following the deregulation: one negative on flag carrier due to increase in competition, one positive on national and regional economies. One main issue concerns the impact of expected development of airport activity on national and regional economies. We propose an estimation of these impact, using a two stage econometric model applied to four ASEAN countries. We show that GDP is the most sensible to air traffic growth in region where only international airports are located, that is for region that exhibit the highest level of development. We show that up to the 5 th freedom right, given the expectation in tourism development, national GDP is expected to increase by 9% (Myanmar) to 51% (The Philippines) depending on the country. The magnitude of the impact depends on the tourism development expectation as well as on the tourism contribution to GDP. The analysis show then that economic benefit of air transport liberalization are non-negligible for the ASEAN countries. Given the magnitude of the estimated effect, the benefits would certainly overlap the negative effect of competition on the flag carriers.

Introduction

Historically air transport in South East Asia † is regulated on the basis of bilateral agreements which impose restrictions in operations in the region for non-ASEAN as well as ASEAN airlines. ASEAN Member States have decided to sign agreements which define the milestones for liberalization of air transport in the region. The idea of liberalizing the air travel sector came as early as 1995 in the ASEAN leaders' summit held in Bangkok. In 2004, the 10th air transport ministers' meeting in Phnom Penh decided upon an "Action Plan for ASEAN Air Transport Integration and Liberalization 2005-2015" (ASEAN, 2004). The objective was to establish a single aviation market by 2015. While not all of the countries have reached the same level of ratification, they however have all made steps towards greater liberalization. The first target was to open 5 th freedom right ‡ for the ASEAN region in 2015. Air transport liberalization up to the 5th freedom right, also named ASAM (ASEAN Single Aviation Market), will remove the frequency and capacity constraints existing in bilateral air service agreements between Member States. It will simplify and foster the mobility of ASEAN citizens inside the area.

Air transport liberalization leads to highest air traffic volumes. In the Current Market outlook produced by Boeing in 2014, it is argued that following the Japan-Taïwan Open Skies agreements, the number of destinations has double between these two countries on the two years period August 2011 and August 2013. In a deregulated environment, airlines have to adapt their strategies in response to highest competition. They have the freedom to vary fares, to develop their networks. Deregulation is a recognized driver of traffic and network growth. As a consequence of ASEAN deregulation, air traffic is then expected to increase meanwhile national carriers might be armed by stronger competition. Some of the Member States unwillingness to ratify the preliminary steps through liberalization might be due to this fear of competition for their flag carriers. According to Alan Tan Khee Jin (2013), some national airlines are afraid of increased competition in their markets, and have persuaded their governments to adopt a protectionist stance. In this context ASEAN member States face a tradeoff between the potential positive impact on the economy following air traffic increase and potential negative consequences on their flag carriers. One main issue for ASEAN Member States is therefore to be able to evaluate the potential impacts that the liberalization of air transport up to the 5th freedom right may have on their economy at national and regional levels. If this impact appears to be highly positive it might compensate the adverse effect supported by the national carriers and then encourage the States to accept more easily the different steps through liberalization. The relationship between economic growth and air transport development has been addressed in numerous reports or in the economic literature. Teresa Cederholm (2014) argues that globally and "according to the World Travel and Tourism Council (or WTTC), the travel and tourism industry's total contribution to the global economy rose to $6,990 billion, or 9.5% of the GDP (gross domestic product), and is expected to grow by 4.3% to $7,289 billion, or 9.6% of the GDP, in 2014" § . Similarly, the relationship between tourism activities and air traffic demand is recognized. On one side tourism is a driver for air traffic, on the other side, when the share of tourists arriving by air is negligible, as this is the case within the ASEAN region, the causality is reversed. The magnitude of this impacts depend on characteristics of the country at stake in particular in terms of economic development and tourists travel habits. ASEAN countries face different economic conditions as well as different level of development of the air transport activity. However, so far a few studies have analyzed and estimated the economic impacts of the air transport market liberalization for the ASEAN countries. The ECORYS (2012) study focuses on the economic impacts for Indonesia, Myanmar and the Philippines between 2015 and 2030. This study however only estimates part of the impacts due to ASAM (ASEAN Single Aviation Market): only economic impacts due to air traffic increase between ASEAN member states (and not with non-ASEAN member states) are addressed. Other studies have focused on the impacts of the air transport liberalization at a worldwide level such as thec one producd by InterVISTAS-ga (2006). These studies however weren not able to provide reliable results, for developing countries such as ASEAN member states, due to missing data. The method used in the InterVISTAS-ga (2006) study can be broken down in two steps. First, an econometric model is built and estimated to forecast air traffic between any two countries (or group of countries). This model is based on economic variables characteristics, trade level, geographic relationships and air service agreements characteristics. A general least square method using the GDP variable as a weighting factor is estimated. Then, the model is used to estimate incremental traffic from liberalization by changig the dummy variable "Air Service Agreement" from 0 to 1. Once these forecasts obtained, multiplier coefficients got from the ATAG ( 2004) study are applied on forecasted traffic levels to estimate the corresponding impacts, on GDP, employment, etc. A similar method is also applied in the ECORYS (2012) study. Such method however presents three main drawbacks. The first drawback is related to the missing modelling of the interdependent relationships between economic and air transport activities. So far, only few authors have studied this simultaneity issue because of the need to have panel datasets for a long period of time. Using data for Brazil from 1996to 2006, Marazzo et al. (Marazzo, Scherre, & Fernandes, 2010) show that GDP and air passenger traffic are co-integrated variables. Co-integration means that both data series present stationary linear combinations. In other words, co-integration also means that there is a long-run equilibrium linking both data series and generating a kind of coordinated movement over time. Evidence of such a long-run equilibrium relationship between economic growth and air passenger traffic is also shown by Hu, § http://marketrealist.com/2014/12/impact-travel-tourism-industry-economy/ Xiao, Deng, Xiao, and Shouyang (2015) on the Chinese domestic market between 2006 and 2012. Testing for cointegration requires first showing that both data series are non-stationary and then checking the linear combination between variables i.e. the existence of a long-term relationship. Both papers show the importance of taking intoaccount the double-causality between air passenger traffic and GDP when forecasting air traffic. The second drawback in the method applied in the ECORYS (2012) or in InterVISTAS-ga (2006), is the use of multiplier coefficients between forecasted air transport growth and economic indicators to estimate the economic impact of the obtained traffic forecasts. These multipliers provided by the ATAG (2004) study are average multipliers for five areas of the world (North America, Europe, Latin America, Asia-Pacific, Middle East and Africa). All these regions include countries with different air traffic and economic levels and features. Applying these average multiplier coefficients at a country level, fails to take into account the countries' heterogeneity and leads to under or over-estimation of the economic impact of the air transport activity at regional level. Finally in these studies, the economic impacts do not estimate the regional impacts of ASAM for each of the considered country due to airport activity. We aim to fill these gaps by providing an econometric model which controls for the double-causality between air passenger traffic and GDP. We propose a method to estimate regional and national economic impacts of air transport liberalization up to the 5 th freedom rights in ASEAN countries. We restrict the analysis to 4 among the 10 ASEAN countries: Lao PDR, Myanmar, the Philipines, and Vietnam. The impacts are estimated locally/regionally on the basis of traffic to and from the airports of city/region. Two complementary economic impacts are estimated: the current sensitivity of local/regional GDP to airport traffic growth; the potential impacts of air transport liberalization on GDP up to 2020. The methodology to reach these objectives is developped on the basis of: 1. the observed current situation of the countries: air transport market and socioeconomic indiators, 2. the expected future evolutions: developpement of air transport markets in terms of traffic or socioeconomic development. Section 2 describes the main characteristis of the countries in the scope of the analysis. The full methodology is explained in section 3. Section 4 presents the data used for the estimation. Finally, the results are presented in section 5.

Characteristics of the ASEAN countries

Lao PDR

Lao PDR is a small country, with 6.4 million inhabitants. Its GDP is the lowest in the ASEAN region, with a GDP amounting only to 0.37% of the ASEAN GDP. However, the Lao PDR economy is quite dynamic, with an annual GDP increase of 7.95% during the period 2004-2013 (Source: World Bank). Natural resources (forestry, agricultural land, hydropower, and minerals) represent more than half of the total wealth. In the recent years, the development of these resources drives the GDP rate of growth. Lao PDR welcomed 3.8 million tourists in 2013 (Source: Lao PDR Ministry of Tourism) which represents around 4% of the total tourists of the ASEAN region. The number of tourists in the country is increasing constantly with average growth reaching 17%per year between 2005 and 2013. Air traffic in Lao PDR is rather low and only represents 1% of the total passenger traffic in ASEAN in 2013 (Source: World Bank). The yearly traffic increase is however quite high with an average of 20% between 2009 and 2013 (Source Lao PDR Civil Aviation). Currently only two Laotian airports handle international traffic: Vientiane and Luang Prabang airports.

Myanmar

Myanmar is the largest country (mainland) of Southeast Asia with 676 577 km² and 60 million inhabitants, but represents only 2.43% of the total ASEAN GDP. Myanmar is however on a growing path with a yearly GDP increase of 7.3% between 2012 and 2013 (Source: World Bank). There are currently few tourists visiting the country but their number has grown by 93% between 2012 and 2013 to reach 2 million tourists. Due to this very strong increase, the yearly average rate of growth (between 2004 and 2013) in tourist numbers in the country is 18% (source: Myanmar Ministry of tourism). As for Lao PDR, the air traffic is low in Myanmar and only represents 1% of the total ASEAN passenger traffic in 2013 (Source: World Bank). The air traffic has nevertheless increased considerably (+229%) between 2007 and 2013. Three airports operate international traffic: Yangon International Airport, Mandalay International Airport and Nay Pyi Taw International Airport.

The Philippines

The Philippines have the second largest population in the ASEAN, after Indonesia, with 95 million inhabitants. Its GDP represents 10.8% of the total GDP in the area. GDP growth in the Philippines averaged 5.2% per year between 2004 and 2013 (source: World Bank). The Philippines welcomed 4.7 million tourists in 2013 (Source: Philippines Statistics Authority) which represents around 5% of the total tourists in the ASEAN region. While 2009 saw a slight decline in the number of tourists, the average tourist rate of growth reaches 8% per year between 2005 and 2013; this is equivalent to the average rate of growth in the ASEAN region. The air traffic operated to and from Philippines airports represents 10% of the total ASEAN passenger traffic in 2013 (Source: World Bank). The average yearly traffic growth between 2004 and 2013 is 10%. International air traffic is organized to and from Manila airport while eleven airports are able to receive international traffic.

Vietnam

The economy of Vietnam represents 5.5% of the total ASEAN GDP in 2013. It is a populated country, with 88 million inhabitants. GDP growth in Vietnam averaged 6.2% per year between 2004 and 2013 (source World Bank). This growth is explained by the accelerated growth in services (+6.6%), especially hotels and restaurants (+9%). Inflation averaged 6.6%, a steep decline from 18.6% in 2011 (Asian Development Outlook). Vietnam is ranked 5 th in ASEAN in terms of number of yearly tourists in ASEAN. The country welcomed 7.6 million tourists in 2013 (source Vietnam Ministry of tourism) which represents around 8% of the total tourists of the ASEAN region. The average tourist rate of growth reached 11% per year between 2005 and 2013. It is higher than the average rate of growth in the ASEAN region (8%). This increase stresses the increasing importance of tourism activity in the country. The air traffic operated to and from Vietnam airports represents 9% of the total ASEAN passenger traffic in 2013 (Source: World Bank). The average yearly traffic growth between 2009 and 2013 is 8%. The air transport market in Vietnam is growing very rapidly. International traffic comes into and goes out of Vietnam via three main international airports: Tan Son Nhat International airport, Noi Bai International airport and Da Nang international airport. The largest airport is Tan Son Nhat, which concentrates 45% of the total passenger traffic in Vietnam.

The four countries described above exhibit high heterogeneity in their socio-economic characteristics (in terms of level and/or rate of growth). The development of air transport is also different from one country to the other. The differences in geographic characteristic of the countries are additional elements that influence air transport development. Because of this high heterogeneity between countries, we estimate the impact of liberalization on GDP independently for each country. Technically we argue that the amount of individual available information is too light to be able to capture the specificities of each country into a global model.

Methodology

We develop an econometric model aiming to quantify the economic impacts of the air transport liberalization up to the 5 th freedom right in the ASEAN countries. We implement a two stages methodology. First, we estimate a model which expresses, for each country, the relationship between the economic development and the air traffic, in terms of number of passengers. The level of GDP is used as a proxy for the economic development. Then, based on tourism or passenger growth scenarios between 2015 and 2020, we use this econometric model to forecast the potential impact on GDP growth up to 2020. The analysis is implemented at regional level, focusing on regions that include at least one airport. The regional impacts depend on the socioeconomic characteristics of the region at stake.

First stage: estimation of the impact of air traffic on GDP

We express a linear relationship between the yearly regional GDP and the yearly number of passengers at airports. In order to control for the temporal effect, the estimation is performed using difference by difference methodology. The variables included in the model are therefore expressed in terms of yearly evolution. Socioeconomic differences between regional areas are considered by introducing airport fixed effects: any change in passenger or tourist number might have different impact on economic growth, depending on the airport characteristics. In particular whether the airport is domestic or international might be of particular importance. A dummy variable is introduced to distinguish between international and domestic airports. Yearly fixed effects are included when necessary to take into account some unobservable or non-measurable socio economic chocks or any particular event.

There is an uncertainty regarding the sense of causality between the GDP and the number of passengers. Is the evolution of passengers that affects the GDP? Or, is the evolution of GDP that impact the number of passengers? To control for the sense of causality, we use the usual instrumental variable econometric method. The instruments are the yearly number of tourists, the percentage of international air passengers into the corresponding region, and all the other exogenous variables of the model. The percentage of international air passengers is used to take into account the structure of air traffic on the different areas. The relationship between GDP and the number of passengers is expressed: where , ,  and  are the parameters to be estimated,  is the error of the model.  includes all the factors which impact the level of GDP and that we cannot observe.The instrumental variable method consists of estimating first the regional number of passengers on the different instruments. We use the following linear regression: 2where , , ,  and  are the parameters to estimate, and  is the error of the model. As in the previous estimation, the model is estimated using difference by difference estimation. The whole model (Equation 1 and Equation 2 together) is estimated using the model procedure in SAS where we specify 3SLS method which takes into account both endogeneity of some of the regressors and cross-equation correlation of the errors. The parameter of interest of this model is the parameter , in Equation 1, which is a measure of the sensitivity of GDP with respect to the number of passengers. This parameter allows the measure of elasticities: how many changes in GDP following a 1% change in the number of passengers? The interpretation suggested is the following: a sensitivity below 1% means that the major part of travellers spending is made at the airport. Percentages exceeding 1% shows that air travellers do not only spend money at the airport, but also around it when using other transports modes, or in the region thanks to their stay (restaurants, hotels, shops, and cultural sites). The econometric model is based on two main assumptions:

              effect fixed
                 effect fixed
 Regional GDP The objective is to estimate the impact of the air traffic development at the regional level. This implies to observe GDP at the regional level. For many countries, this information is unavailable and we make the strong assumption that regional GDP is proportional to the number of inhabitants in the region. The GDP per inhabitant is calculated at the national level and multiplied with the proportion of regional population. The regional populations are found through public sources for a unique year in general. We assume that the proportion of regional population remains constant during the period of analysis 2004-2013. The notion of region differs from one country to the other, and depends on the characteristics of the country as well as on the available data. Some of the country are analyzed at the regional level, some others at city level. The unique imperative is the location of at least one airport into the geographic area.

 Split between domestic and international traffic We include the percentages of international air passengers into the analysis of the evolution of passengers. This percentage is interpreted as the structure of air transport activity. It is assumed to remain constant over the period of analysis. We assume no evolution of the traffic structure between 2004 and 2013. Regarding international airport, we set the share of international passengers equal to its average level on the observed period 2004-2013. Regarding domestic airport, we keep the share of international passengers equal to zero.

Second stage: anticipation of the future -Forecasts of the impact of air traffic on GDP

Forecasting the air traffic impact on GDP up to 2020 requires including forecasts of the number of air passengers in Equation 1. Based on tourist predictions up to 2020, a preliminary step consists in using the relationship between passengers and the number of tourists (Equation 2) to assess the impact of the increase in the number of tourists on the number of air passengers. Then, the impacts on GDP are estimated by introducing the air passenger forecasts in Equation 1. The forecasts are implemented under assumption 2: the share of international passengers remains constant for the period of prediction. Some robustness analyses have been performed. We use the Monte Carlo technique of simulation available in the model procedure of SAS. It consists in assessing the different potential economic impact when modifying the value of the estimated errors and parameters of the econometric model inside their respective confident intervals. As a consequence we obtain a distribution of the regional impact rather than a single value. The final step through the measure of the potential air traffic growth on GDP is the comparison between GDP forecasts in 2020 and GDP observed in 2013, at regional and national levels.

Data

The quality of the analysis is highly dependent on the quality of the data provided by the different countries. The period of data required for the analysis is 2004-2013. The countries provided the best data that they have available. However, there is a large heterogeneity from one country to the other. We had to find alternative sources of data and/or needed to make strong assumptions to be able to reach the objectives ** . This is particularly the case for the anticipations of the future. Many different public sources or national reports were used to collect the full dataset required for the analysis. In particular macroeconomic data, GDP, population, trade come from these public sources. For the estimation of the model, two different types of data are required. The first type is related to air transport. The different countries provided the traffic at airport level, split into domestic and international traffic. Air traffic data is provided on a yearly basis. The second type of data is related to socio-economic indicators, in particular GDP and tourism activities. Most of the time, these indicators are collected at national level. Regarding the anticipation up to 2020, some countries, but not all, provided traffic forecasts in terms of air passengers and/or tourism forecasts in terms of number of international tourists. The methodologies proposed is based on a number of information, at the State, regional or airport level. For instance the analysis of air traffic variation on the GDP is implemented at the regional or airport city level. To assess this effect the regional/local GDP needs to be observed. The required data was unfortunately not always available. In this case we make some assumptions, which are based on our knowledge of the region under consideration, its socio-economic characteristics, as well as our experience from previous similar analysis. Concerning LaoPDR, yearly airport passenger traffic are provided by the Lao PDR Civil Aviation from 2009 to 2013; GDP and population are collected from the World Bank website; tourism activity statistics are provided by the Laotian Ministry of Tourism. Projections made by the Lao PDR Ministry of 4.7 million tourists by 2020 are also used. Air passenger data are not available before 2009. For LAO PDR the period of analysis is restricted to 2009-2013. Concerning Myanmar, yearly passenger airport are provided by the Myanmar Civil Aviation from 2003 to 2013; GDP and population are collected from the World Bank website; tourism activity statistics are provided by the Myanmar Ministry of Tourism. Forecasts of the international number of tourists have also been collected from the Ministry of Tourism which assumes that the country will welcome 7.849 million tourists by 2020. Concerning the Philippines, yearly passenger airport traffic are provided by Filipino Civil Aviation from 2004 to 2013; GDP and population are collected from the World Bank website; tourism activity statistics are provided by the Filipino Statistics Authority. The number of tourists in the country is 4.7 million in 2013. We, unfortunately, do not have any tourism forecasts nor traffics forecasts for this country. But we can observe during the five past years a 10% yearly rate of growth on average. We assume that this 10% average yearly growth will continue up to 2020. Concerning Vietnam, yearly passenger airport traffic are provided by the Vietnam Civil Aviation from 2009 to 2013; GDP and population are collected from the World Bank website; tourism activity statistics are provided by the Vietnam Ministry of Tourism. On its web portal (http://www.chinhphu.vn), the Socialist Republic of Vietnam government forecasts a 12% increase per year in the number of international tourists by 2020. We therefore use this assumption to run the econometric model. Air passenger data are not available before 2009. For Vietnam the period of analysis is restricted to 2009-2013.

Results † †

Thanks to the estimated parameters in Equation 1, regional GDP elasticities to airport traffic activity are measured. These elasticities represent the regional sensitivity to airport traffic growth. The second output of the model is the forecasted GDP growth for the period 2013-2020 linked with airport traffic forecasts and/or tourism development anticipations on the same period.

Elasticity of GDP with respect to airport traffic

Erreur ! Source du renvoi introuvable. presents the average regional GDP elasticities to airport traffic growth obtained from the joint estimation of Equation 1 and Equation 2. They are obtained by taking the average of the elasticities estimated for all the airports located in the regions. Regions are split into three categories: regions where ** The data building step are available on request. † † The result of the estimation of the different models are available on request. The quality of the estimations is validated thanks to usual statistical tests.

only domestic airports are located, regions where only international airports are located and regions with both domestic and international airports. Comparisons between average elasticities obtained in Lao PDR, Myanmar, the Philippines and Vietnam clearly show that regions where only domestic airports are located have the lowest elasticities while regions with only international airports have the highest ones. In these four countries, international airports are located either in capital cities or in regions with tourism activity. One main explanation of the strongest GDP sensitivity to international airport activity is related to the largest industrial and/or tourism development on that particular regions.

It is also particularly interesting to stress that Myanmar and the Philippines are the only countries with elasticities exceeding 1%. In Myanmar, as long as at least one international airport is located in the region, a 1% increase in the yearly passenger traffic at airports leads to a of 3% to 4% growth in regional GDP. In the Philippines, a 1% increase in the yearly passenger traffic in regions where only international airports are located leads to an increase of 1.56% of the regional GDP. Lao PDR and Vietnam GDP elasticities to airport traffic growth are always below 1% whatever the type of region into consideration. The regional economic growth in these countries is hence lesser sensitive to the airport activity growth than it could be in some regions of the Philippines or Myanmar.

5.2. Impact on GDP up to 2020 presents air traffic impact on GDP forecasted up to 2020. Figures represent the GDP growth between 2013 and 2020 based on the number of tourist forecasts and using Equation 2 in a first step to obtain the air passengers forecasts. Then, impacts on GDP are estimated by introducing these air passenger forecasts in Equation 1. The forecasted GDP growth at a country level, between 2013 and 2020, differs a lot between countries. Such increase is strongly related to two main effects: the current contribution of the tourism activity on national GDP and the forecasted tourism activity growth expected by each country up to 2020. The tourism activity contribution to GDP is in 2013, quite close for Lao PDR, the Philippines and Vietnam (between 4 and 4.6%) but is lower for Myanmar (only 1.6%). This low tourism contribution to GDP explains that despite Myanmar expect multiplying by 3 its number of tourists by 2020, the economic impact is expected to be moderate: 9%. The explanation of the moderate expected economic growth for Lao PDR (+16% up to 2020) is however more related to the moderate tourism activity growth expected by 2020 (+23% in number of tourists) combined with the low tourism activity in 2013. Besides, countries with an important tourism activity such as the Philippines and Vietnam should benefit of a GDP growth exceeding 20% between 2013 and 2020. The expected economic growth due to the ASEAN air transport liberalization is particularly important for the Philippines, which forecasts a tourism growth of 58% by 2020. At a regional level, it is particularly interesting to observe that for all countries, except Myanmar, the highest GDP growth should be in regions with domestic airports only. In other words, despite the fact that only international airports will be concerned by the air transport liberalization, domestic airports should also benefit from traffic increase at international airports which will play the role of national hubs. However, the stronger expected GDP growth in regions where only domestic airports are located does not mean that these regions will bring the highest contribution to the national GDP. The GDP level in these regions is generally lower than in regions with international airports where we observe the largest industrial and/or tourism development.

Conclusion

ASEAN Member States have decided to sign agreements which define the milestones for liberalization of air transport in the region. As a consequence of air traffic deregulation, national authorities face a trade-off between positive impacts on GDP and potential negative impacts, due to highest competition, on their flag carriers. One main issue concerns the impact of expected air traffic development on national economy. Can we quantify this impact?

Can it be possible that some regions experience different effects depending on the characteristics of their airports? Will these impacts be homogeneous among countries of the region? Given some forecast on air traffic growth and /or tourist activity could it be possible to measure the impact for the next 5 years?

We answer to these questions by quantifying the relationship between GDP and air traffic demand up to the 5th freedom right (2020) for some ASEAN member states, at regional and national levels. We focus hour analysis on four ASEAN countries: Lao PDR, Myanmar, The Philippines and Vietnam. These countries provide us information related to air traffic for the period 2003-2013. Additionally some forecasts are available in terms of tourism development up to 2020. On the basis of these information and some other information related to socio-economic data that we picked-up on different public sources, we estimate the relationship between air traffic and GDP. We use the number of tourists as one of the instrumental variable to correct the endogeneity between GDP and air traffic demand. This method is also useful to predict the forthcoming impact on GDP using the forecast of tourists for the next 5 years, through the impact on air traffic.

Thanks to the estimated model we can estimate the sensibility of regional GDP to air traffic increase and we forecast the impact of traffic increase on GDP up to the 5 th freedom right. Because of heterogeneity between the different countries, the model is estimated independently for each country. The estimations are statistically significant. We show that GDP is the most sensible to air traffic growth in region where only international airport is located, that is for region that exhibit the highest level of development. The elasticity is the highest in Myanmar where air traffic is the less developed and the lowest in Vietnam, whatever the region considered. Finally we show that up to the 5 th freedom right, national GDP is expected to increase by 9 (Myanmar) to 51% (The Philippines) depending on the country. The magnitude of the impact depends on the tourism development expectation as well as on the tourism contribution to GDP. The analysis show then the economic benefit of air transport liberalization are non-negligible for the ASEAN countries. Given the magnitude of the estimated effect, the benefits would certainly overlap the negative effect of competition on the flag carriers. The authorities should then encourage liberalization.

The main limits of the models developed lie in the assumptions that are necessary because of the lack of information on regional population and/or regional GDP. Moreover we have assumed that the structure of the traffic, that is the split between domestic and international traffic remains constant during the period of the analysis and for the period of forecast. Relaxing this assumption could allow to estimate more precisely the potential impact on the economy. Transportation Research Part E, 46, 261-269. Tan, A., Jin, K., 2013, Clear take-off on ASEAN Open Skies, Straits Times.

Fig. 1 .

 1 Fig.1. Evolution of air passenger and aircraft movement traffic in Lao PDR between 2009 and 2013 (Source: Lao PDR Civil Aviation)

Fig. 2 .

 2 Fig.2. Evolution of air passenger and aircraft movement traffic in Myanmar between 2003 and 2013 (Source: Myanmar Civil Aviation)

Fig. 3 .

 3 Fig.3. Evolution of air passenger and aircraft movement traffic in the Philippines between 2004 and 2013 (Source: The Philippines Civil Aviation)

Fig. 4 .

 4 Fig.4. Evolution of air passenger and aircraft movement traffic in Vietnam between 2009 and 2013 (Source: Vietnam Civil Aviation)

  economic and social benefits of air transport, Air Transport Action Group Boeing, 2014, Current Market Outlook. ECORYS, 2012, Provision of a Consultancy Study for EASA: Economic impact of the ASEAN Single Aviation Market. Hu, Y., Xiao, j., Deng, Y., Xiao, Y., & Shouyang, W., 2015, Domestic air passenger traffic and economic growth in China: evidence from heterogeneous panel models, Journal of Air Transport Management, 42, 95-100. InterVISTAS-ga., 2006, The economic impact of air service liberalization, Washington, USA.Ishutkina, M., Hansman, R., 2009, Analysis of the interaction between air transportation and economic activity: a world wide perspective, Cambridge, USA: MIT Internationam Center for Air Transportation (ICAT). Marazzo, M., Scherre, R., Fernandes, E., 2010, Air transport demand and economic growth in Brazil: A time series analysis.

Table 1 .

 1 Average regional GDP elasticity to airport traffic growth.

	Average regional GDP elasticity	Regions with domestic airports	Region with international	Regions with domestic and
	to airports' traffic growth	only	airports only	international airports
	Lao PDR	0.15%	0.68%
	Myanmar	0.52%	4.07%	3.01%
	The Philippines	0.10%	1.56%	0.27%
	Vietnam	0.02%	0.14%	0.04%

Table 2 .

 2 ASAM impact on GDP. Estimated GDP growth from 2013 to 2020.

	ASAM impact on GDP: Estimated GDP growth from 2013 to 2020	Regions with domestic airports only	Region only international	with airports	Regions with domestic airports and international	Country
	Lao PDR	+143%	+52%			+16%
	Myanmar	+11%	+19%		+7%	+9%
	The Philippines	+63%	+52%		+46%	+51%
	Vietnam	+52%	+51%		+25%	+22%