DETERMINATION OF THE FOREST ROAD NETWORK INFLUENCE ON THE SUPPLY CHAIN FOR FIREWOOD PRODUCTION BY DISCRETE EVENT SIMULATION

Simulation is the process of building a model of a real or proposed system to study the performance of the system under specific conditions. A simulation model could be classified as being static or dynamic, deterministic or stochastic and discrete or continuous [Bank 2005]. In the last decade, in several studies concerning forest operations, dynamic, stochastic and discrete simulations have been applied [Asikainen 1998; Wang 2003; Väätäinen 2010; Asikainen 2010a; Hogg 2010]. Dynamic because the effects of changing variables and the workflow of the system according to the simulation time are analyzed, stochastic because randomness of observation is considered; discrete because the state variable changes only at a discrete set of point in time. These types of simulations are generally called discrete event simulations [D-es] and are generally applied to analyze the behavior of a system defined as a collection of entities, usually workers and machines, that act and interact toward the accomplishment of some logical end over time [Law 1991]. Asikainen [1995] and Ziesak [2003] report some advantages of the D-es simulation techniques, one of which is the capability of this application to analyze discrete and complex real-world situations that cannot be solved by analytical operational methods because of various interactions between the system components. In addition the approach of D-es permits to describe the interactions between the system elements and model the effects of random processes. At a tactical level, many D-es models were developed for the analysis of the efficiency in transport interactions. The logistic of a chipping terminal was also modeled by a D-es model, which was based on a manufacturing simulator in order to compare different loading and transport technologies [Asikainen 1998]. In this study chipping into truck, chipping onto ground and loading using a wheeled loader, long-distance transport by truck with drawbar trailer, by truck with semitrailer and by truck with interchangeable platforms were considered. Väätäinen [2010] investigated through D-es the cost-effective patterns of harwarders for forest machine contractors in different logging structures and conditions. A D-es model was also programmed to find optimal set-ups for the supply chain of crushed material made from stumps at different road transport distances. The simulation model was based on the continuous supply of crushed material from landings to a district heating plant [Asikainen 2010]. Recently, a D-es model was developed to find the optimal setups for the timber yarding-processing-truck transport system in mountainous condition using a logging site and transport distance database as input [Asikainen 2010a]. At an operational level, applications of the D-es model also concern the analysis of interaction to study the cost-efficiency of a single machine or specific operation systems. By changing simulation inputs and observing the result outputs, machine and system behaviors can be studied and compared with different simulation runs. This allows largely deterministic base simulations that can be carried out to illustrate the effects of machine interactions. Also a groundbased timber harvesting system was modeled by an object-oriented model from felling to extraction in order to evaluate the interaction of stands, harvester treatments, machines and extraction patterns [Wang 2003]. Chipping operation systems in thinning were also analyzed by evaluating bin size, chipper productivity, in-field extraction distance and forest-road haulage distance [Talbot 2005], while multi-stem the mechanized harvesting operation was analyzed under the South Africa condition by comparing the current system to two hypothetical systems. One of the alternative systems was based on modifying the operation procedures but by considering the same machines and equipments of the current system, the second one by J. of Ag. Eng. Riv. di Ing. Agr. (2011), 4, 41-48

Introduction
Simulation is the process of building a model of a real or proposed system to study the performance of the system under specific conditions. A simulation model could be classified as being static or dynamic, deterministic or stochastic and discrete or continuous [Bank 2005].
In the last decade, in several studies concerning forest operations, dynamic, stochastic and discrete simulations have been applied [Asikainen 1998;Wang 2003;Väätäinen 2010;Asikainen 2010a;Hogg 2010]. Dynamic because the effects of changing variables and the workflow of the system according to the simulation time are analyzed, stochastic because randomness of observation is considered; discrete because the state variable changes only at a discrete set of point in time. These types of simulations are generally called discrete event simulations [D-es] and are generally applied to analyze the behavior of a system defined as a collection of entities, usually workers and machines, that act and interact toward the accomplishment of some logical end over time [Law 1991]. Asikainen [1995] and Ziesak [2003] report some advantages of the D-es simulation techniques, one of which is the capability of this application to analyze discrete and complex real-world situations that cannot be solved by analytical operational methods because of various interactions between the system components. In addition the approach of D-es permits to describe the interactions between the system elements and model the effects of random processes.
At a tactical level, many D-es models were developed for the analysis of the efficiency in transport interactions. The logistic of a chipping terminal was also modeled by a D-es model, which was based on a manufacturing simulator in order to compare different loading and transport technologies [Asikainen 1998]. In this study chipping into truck, chipping onto ground and loading using a wheeled loader, long-distance transport by truck with drawbar trailer, by truck with semitrailer and by truck with interchangeable platforms were considered. Väätäinen [2010] investigated through D-es the cost-effective patterns of harwarders for forest machine contractors in different logging structures and conditions. A D-es model was also programmed to find optimal set-ups for the supply chain of crushed material made from stumps at different road transport distances. The simulation model was based on the continuous supply of crushed material from landings to a district heating plant [Asikainen 2010]. Recently, a D-es model was developed to find the optimal setups for the timber yarding-processing-truck transport system in mountainous condition using a logging site and transport distance database as input [Asikainen 2010a].
At an operational level, applications of the D-es model also concern the analysis of interaction to study the cost-efficiency of a single machine or specific operation systems. By changing simulation inputs and observing the result outputs, machine and system behaviors can be studied and compared with different simulation runs. This allows largely deterministic base simulations that can be carried out to illustrate the effects of machine interactions. Also a groundbased timber harvesting system was modeled by an object-oriented model from felling to extraction in order to evaluate the interaction of stands, harvester treatments, machines and extraction patterns [Wang 2003].
Chipping operation systems in thinning were also analyzed by evaluating bin size, chipper productivity, in-field extraction distance and forest-road haulage distance [Talbot 2005], while multi-stem the mechanized harvesting operation was analyzed under the South Africa condition by comparing the current system to two hypothetical systems. One of the alternative systems was based on modifying the operation procedures but by considering the same machines and equipments of the current system, the second one by changing also the machine and the equipment [Hogg 2010].
Some of these works have addressed the development of D-es on a single component of a more complex supply chain [Asikainen 1998;Wang 2003;Talbot 2005;Hogg 2010] and only few have analyzed the interaction between more parts on a complex supply chain [Asikainen 2010a;Asikainen 2010b].
The study aims to develop a D-es model for evaluating the productivity of the wood supply chain for firewood production according to the current operational level from the harvesting site to the firewood production terminal. Therefore the D-es model will be applied to evaluate the productivity of the entire supply chain by considering the complexity of the transportation network with the support of the GIS network analysis according to different scenarios.
The forest plot (11.4 ha) consisted in a high stand forest almost exclusively of beech (Fagus sylvatica L.) and sporadic individuals of Norway spruce (Picea abies Karst.). The altitude ranged from 1248 m to 1398 m a.s.l. with a slope gradient ranging between 5 and 35%. The average growing stock was 423 m 3 ha -1 with a mean rate of growth equal to 7.6 m 3 ha -1 . The adopted silvicultural system was the shelterwood method and the analyzed operation regards a shelterwood selection cut with a cut mass of 776 m 3 on 11.4 ha (exploitation percent about 15%). The mean diameter at breast height of the cut trees was 24 cm and the average height was 22 m.

Study layout
The study evaluates a distinctive supply chain for firewood production. The case study can be considered a representative situation of the application of the full tree system (FTS) for firewood production from broadleaves high stands in medium gentle terrain. The supply chain was identified by four steps (Fig. 1): extraction (Step A), cross-cut operation (Step B), offroad transport (Step C) and on-road transport (Step D).
The study considers the processes and the workflow of the wood from the stump site to the terminal where wood in the form of logs and bundles of 1.1 m length assortments were cut and split into firewood.
The extraction (Step A) was performed by hauling the felled full trees from the stump to the nearest landings. The extraction operation was ground based and involved one agricultural tractor (67 kW) with a forest winch. This operation was performed by two operators, one driving the tractor and one preparing and hooking the loads (normally one full tree).
At the landing, four operators cross-cut full trees into logs (4-6 m length and > 20 cm diameter) and into small assortments of 1.1 m length considering three different classes of diameter at half length (< 10 cm, 10-15 cm, 15-20 cm) (Step B). One operator was involved in the piling operation of logs by using a loader (lifting moment 60 kN m and maximum reach of the boom 6.50 m) attached to an agricultural tractor (74 kW), which was located in front of the cross-cutting area. When full trees arrived to the landing, the same operator grappled and moved the unhooked load into the cross-cutting area (landing). At the same time the tractor with the forest winch went back to the stump area for a new extraction cycle. The same operator on the tractor with the crane pilled logs and bundles in piles (maximum 4-4.5 m height), which were located at the right and left side of the same tractor. Other two operators cross-cut the full trees while the remaining operator was mainly busy to sort, according to the diameters at half length (< 10 cm, 10-15 cm, > 15 cm), the small assortments into three different frames (one for each diameter) which were then used for assembling the bundles (70 cm diameter).
Logs and bundles were subsequently transported off-roads to the transshipment site along tractor roads by an agricultural tractor (81 kW) with a trailer with mechanical traction and a payload of 11 t (Step C). Transport on a public road to terminal was performed by a 4x2 WD truck (294 kW) and trailer with a total payload of 22 t (Step D).

Definition of the D-es model
The model was developed on the base of Figure 1 to meet the primary objective to evaluate the influence of the extension of a forest road network on the firewood supply chain. The model was developed as a dynamic, stochastic and discrete simulation model (D-es).
The D-es model was created using the Witness simulation modeling software [Lanner 2009], a visual entities-based simulation application for system dynamics models.
The model was therefore constructed interactively in three steps (definition step, detailing step and logic step) via graphic interfaces. According to the workflow of the supply chain, the names and quantities of the elements and the variables of the system were specified at the definition step. The detailing step allowed then to define the parameters of each element such as cycle times, setup times, transport capacities and delay times. The logic step determined then how each element operates and how its operation depends on the action of other elements or state variables.

Functions and parameters of the D-es model
Simulation calls for information about the variables and processes. For instance, production functions for the different working phases of the supply chain needs to be defined. To define the productivity for extraction, cross-cut operation, off-road transportation and on-road transportation time studies were sorted out in May and June 2009 and according to the basic time concepts proposed by Björheden [1991].
Extraction was studied adopting the stop-watch method. Data collection of the main factors affecting time consumption of extraction considered: volume (generally a single tree, V, m 3 ), loaded (LD, m) and unloaded driving (UD, m) distance and slope gradient (SL, %) of the trail. The load volume (V) was determined after the cross-cut process by measuring the diameter at half length and the length of each single log (log volume, LV) and by recording the number and the size of each small assortment (small assortment volume, SAV) according with the frame where they were sorted. The extraction distances and the slope gradients were measured by a laser rangefinder with an integrated compass allowing to measure the straight distance and slope within 400 m. The crosscut operation of full trees at landing was studied with the support of a digital video-camera and the work sampling method. The off-road and on-road transportation was also investigated by adopting the stopwatch method to determine the average speed for loaded and empty travel and for loading and unloading time. In the case of the on-road transportation the distance was always the same.

Statistical analysis
The time and motion study of the ground-based extraction was subdivided in travel loaded TL and in travel unloaded TU. For the travel loaded the productivity hypothesis was TL = f (V, LD, SL) and for the travel unloaded TU = f (UD, SL). The time and motion study of the cross-cut (CC) supposed the following productivity hypothesis CC = f (V, V_Type), where V_type corresponds to a factor equal to 1 in the case of a rate of small assortment higher than 10% on the total volume, otherwise the factor is 0.
According to Stampfer [2010], the variance analysis was used to quantify the influence of nominal and ordinal-scaled variables. Also the analyses for the three time and motion studies consisted in: estimation of significant effects of the co-variables and factors and an analysis of their significance, the evaluation of the non-linearity of the co-variable, an analysis of the interaction between factors and co-variables, parameter estimation of significant factors and co-variables and regression analysis. Further, the Box-Cox transformations procedure was applied to determine an optimal transformation of the tree volume for stabilizing the variance and making the deviations around the model more normally distributed.
The locking and unlocking time for each cycle time was verified in terms of distribution. The distributions were analyzed by the Kolmogorov-Smirnov test (K-S) at the confidence interval of 0.05 and supposed to be of the normal type.
Also for the collected data concerning the speeds of the off-road transportation and the on-road transportation and the un-loading and loading time were analyzed by the K-S test at the confidence interval of 0.05 and supposed to be of the normal type.
Randomly-occurring delays have an important influence on machine and operation performance. The frequency of the time between breakdowns and the breakdown times were also determined. The distributions of breakdown time and interval were studied by the K-S test. All the breakdown distributions were supposed to be of the Erlang type with shape factor k equal to 2 (bell-shaped distribution, strongly skewed to the left and with a shape similar to lognormal distribution) [Lanner 2009].

Model validation
The D-es model was built according to the investigated situation. During the model construction the logical proceeding of work sequences were tested by running the model step by step and observing the interaction between all the elements by graphic and value outputs.
According to Banks [2005], the validation ensures that the model is a realistic representation of the real system. The simulation model was verified with 15 daily firewood loads unloaded at the terminal, which were registered by the forest enterprise. The validation supposed for the daily average (5 working days, 2880 min) of unloaded firewood mass in term of logs and small assortments of 15 repetitions with a warmup period of 480 min and several different sets of random number streams.

Experimental design
The experiment design considered 8 different extensions (scenarios, SC) (Tab. 1) of the transportation network as a result of the increasing number of landings within the forest plot (Fig. 2).
The numbers of the loads (the felled trees) corresponds to the location of the 731 stump sites inside  the study area. As the precise location of all the stump sites was not fixed, the spatial distribution of the load was determined by the Generating random points tool of ArcGIS 9.3-ArcInfo which randomly places a specified number of points within an identified area [ESRI 2009]. As a consequence the volume of each load was determined by a random number generation function according to the distribution resulting by the 105 loads, which were observed during the time and motion study.
For each scenario, a routing analysis was thus carried out to determine the total transportation distance covered by extraction, off-road and on-road transportation to supply all the harvested wood (logs and bundles) to the final terminal. All the routes connecting each load to landings and also the landings to the transshipment sites and the transshipment sites to the terminal were calculated by ArcGIS network analysis [ESRI 2009] according to Djikstra's algorithm, which is implemented for determining a multiple origin-multiple destination or multiple closest facilities [Cavalli 2010].
The network analysis supported thus the definition of the input parameters about the transportation distance for each load according to the 8 scenarios.
Once all the parameters were defined, D-es simulation was launched to perform 15 repetitions for each scenario with a warm-up period of 480 min and several different sets of random number streams.
On the present work, all the statistical analyses were sorted out using SPSS statistic software [SPSS 2009]. Table 2 shows the results about the regression models and the distributions and the basic parameters used in the model for the simulations.

Simulation model parameters
The productivity resulting from the composed extraction model, including the regression model of the travel unloaded (TU) and the travel loaded (TL) and the distribution of the hooking and unhooking operations, is reported on Figure 3. This composed model concerning the Step A highlights the effects of the volume and the extraction distance on the effective productivity (calculated on the Productive System Hour without delays -PSH 0 ) (m 3 h _1 ) of the extraction system. Figure 4 reports the results (m 3 h _1 , PSH 0 ) of the regression model of the cut-crossing operation at landing according to the rate of small assortment resulting from the operation respect to the total volume of the load.

Influence of the road network extension on the transportation system productivity
The average output equal to 1.48 truck and trailer per day to the terminal of the 15 replications reported by the D-es model built according to the current state (corresponding to the Scenario B) was considered consistent with the real condition reporting an average of 1.52 truck and trailer per day to the terminal. For this reason the model has been considered reliable and hence it was used for the proposed aims. The influence of the increment of the forest road density and the extracted volume on the productivity was evaluated by the variance analysis and then by the linear regression analysis. The analysis of the variance reported that the extracted volume does not have a significant effect (p-value 0.067) on the productivity of the supply chain, hence the forest road density (DN) was assumed as the main variable influencing the productivity (p-value 0.001). For this reason a curve estimation procedure permits to estimate a significant (p-value 0.000) regression model at a confidence level of 95%. (Tab. 3) to fit the outputs resulting from the 8 Scenarios (Fig. 5).
From Figure 5 it appears that the increment of the secondary forest road density up to 25-30 m ha -1 could be considered a significant feature to increase the productivity of the supply chain. As can be seen, the current forest road density (Scenario B) is 14.48 m ha -1 . An increment of the current forest road extension to 20.00 m ha -1 (+38%, corresponding to 62 m in 11.4 ha) could correspond to a slight increment of the productivity up to 1.8-2.0%. Therefore by the same supposed increment of the forest road network, the sum of the extraction distance (travel unloaded and travel loaded) would decrease for the entire operation of about 18-20%, corresponding to a reduction of 30-34 km of covered distance.
It should, however, be noted that the curve reported in Figure 5 highlights a slightly positive influence when DN increases over 20-22 m ha -1 .

Discussion and conclusion
Simulation is the process of building a model of a real or proposed system to study the performance of its performance under specific conditions. The analysis of forest operations and wood supply chains has recently been applied in order to analyze discrete and complex real-world situations that could not be solved by analytical operational methods.
The study has presented a first approach of a D-es model integrated with a GIS network analysis to evaluate the influence of the interaction between three main transportation systems occurring during forest operations (extraction, off-road and on-road transportation) on the final productivity of a wood supply chain.
The results indicate that the increment of the forest road network may significantly increase the productivity of the wood supply chain. For the specific investigated condition, the increment of the forest road network showed only a slight increment on the productivity (up to 2%). Presumably this correlation depends on the characteristics of the investigated supply chain, which considered a harvesting site characterized by even terrain with an average slope gradient approximately equal to 18%.
According to the general indication reported by Baldini [2008] the current forest road density, evaluated in 14.48 m ha -1 and corresponding to Scenario B, could be considered a low index for a productive forest. Therefore the increment to 20-22 m ha -1 could be considered reasonable by taking into account that in average in Italy the forest road network is approximately about 18 m ha -1 and by considering that in even terrain with a slope gradient of 40% (higher than the investigated area) forest road density can be approximately to 25-30 m ha -1 [Hippoliti 2004].
The proposed study highlighted the possibility to integrate the D-es approach with the GIS analysis to support the complex evaluation of the wood supply chains from the stump site to the final processing terminal.
The main limitation of this study was due to the fact that the investigated area represented an operative condition located in medium gentle terrain. Future work should include the analysis of forest productive sites in steep terrain where the forest road density is more complex to evaluate and the extraction transportation network is mainly based on cable systems.

SUMMARY
In this study a Discrete-event simulation (D-es) has been developed to analyze the wood supply chain for firewood production in a mountain area in North-eastern Italy. The D-es is applied in the modeling of extraction (Full Tree System), processing of roundwood into wood assortments (cross-cut and sorting), offroad and on-road transport.
In order to estimate the productivity functions and parameters, field studies were conducted to gather data about the different operations linked in the model. Also a GIS network analysis was developed to integrate the spatial information onthe covered distance to the D-es model for each of the supposed Scenarios.
The results indicats that an increment of 5 m ha -1 of the forest road network could significantly increase the productivity of the wood supply chain up to 2%.


Power Electronics Application for More Electric Aircraft

In the competitive world of airline economics, where low cost carriers are driving dawn profit margins on airline seat miles, techniques for reducing the direct operating costs of aircraft are in great demand. In effort to meet this demand, the aircraft manufacturing industry is placing greater emphasis on the use of technology, which can influence maintenance costs and fuel usage. (Faleiro, 2005)

Introduction
In the competitive world of airline economics, where low cost carriers are driving dawn profit margins on airline seat miles, techniques for reducing the direct operating costs of aircraft are in great demand. In effort to meet this demand, the aircraft manufacturing industry is placing greater emphasis on the use of technology, which can influence maintenance costs and fuel usage. (Faleiro, 2005) There is a general move in the aerospace industry to increase the amount of electrically powered equipments on future aircraft. This trend is referred to as the "More Electric Aircraft". It assumes using electrical energy instead of hydraulic, pneumatic and mechanical means to power virtually all aircraft subsystem including flight control actuation, environmental control system and utility function. The concept offers advantages of reduced overall aircraft weight, reduced need for ground support equipment and maintenance and increased reliability (Taha,2007,Wiemer,1999.
Many aircraft power systems are now operating with a variable frequency over a typical range of 360 Hz to 800 Hz.
Distribution voltages for an aircraft system can be classified as: a. Nominal 115/200 V rms and 230/400 V rms ac, both one phase and three phase, over variable frequency range. b. Nominal 14, 28 and 42 V DC. c. High DC voltage which could be suitable for use with an electric actuator (or other) aircraft loads.
This chapter presents studies, analysis and simulation results for a boost and buck converters at variable input frequency using vector control scheme. The design poses significant challenges due to the supply frequency variation and requires many features such as: 1. The supply current to the converter must have a low harmonic contents to minimize its impact on the aircraft variable frequency electrical system. 2. A high input power factor must be achieved to minimize reactive power requirements. 3. Power density must be maximized for minimum size and weight.

Boost converter for aircraft application
A three phase boost converter which is shown in fig. 1 with six steps PWM provide DC output and sinusoidal input current with no low frequency harmonic. However the switching frequency harmonics contained in the input currents must be suppressed by the input filter. Referring to fig.1 after the output capacitor has charged up via the diodes to a voltage equals to 1.73V pk , the diodes are all reverse biased. Turning one of the MOSFETs in each of the three phases will cause the inductor current to increase. Assume the input voltage V a is positive, if S 2 is turned on, the inductor current increases through the diode D 4 or D 6 and the magnetic energy is stored in the inductor. Since the diodes D 1 , D 3 and D 5 are reverse biased, the output capacitor C dc provides the power to the load. When S 2 is turned off, the stored energy in the inductor and the AC source are transferred to C dc and the load via the diodes. When the AC voltage is negative, S 1 is turned on and the inductor current increases through the diode D 3 or D 5 . The same operation modes are involved for phase B and phase C (Taha., 2008;Habetler.,1993). Fig.2 and fig. 3 show different operating modes.

Buck converter for aircraft application
The buck 3-phase/dc converter is a controlled current circuit which relies on pulse width modulation of a constant current to achieve low distortion. As shown in fig. 4. The circuit consists of 3 power MOSFETs and 12 diodes, an AC side filter and DC side filter.
The AC side input and DC side output filters are standard second order low pass L-C filters. For the input filter, the carrier frequency has to be considerably higher than the filter resonance frequency in order to avoid resonance effects and ensure carrier attenuation. The Ac side filter is arranged to bypass the commutating energy when the MOSFETs are turning off and to absorb the harmonic for the high frequency switching. At the DC side, the inductor is used to maintain a constant current, this inductor can be relatively small since the ripple frequency will be related to the switching frequency. The magnitude and the phase of the input current can be controlled and hence the power transfer that occurs between the AC and DC sides can also be controlled. (Green et al., 1997). The input phase voltages V a , V b , V c and the input currents I a , I b , I c are assumed to be sinusoidal of equal magnitude and symmetrical V a = V pk sin (t) I a = I pk sin (t +φ) (1) V b = V pk sin (t -2/3) I b = I pk sin (t -2/3+φ) V c = V pk sin (t + 2/3) I c = I pk sin (t + 2/3+φ) Figure (5) shows 60 degrees of two sine waveforms.
T a = TM sin (t+φ) (4) The freewheeling time T f is equal to: Where: φ is the displacement angle, Vpk is the peak phase voltage,. M modulation index. T is the PWM switching period.
The general operation of the system is as follows: The switching of the devices is divided into six equal intervals of the 360 degrees main cycle. The waveforms repeat a similar pattern at each interval. At any time during the switching interval, only two converter legs are modulated independently and the third leg is always on. There are some time intervals that only one device is only on, thus providing a freewheeling for the DC current since at this time the energy stored on the DC inductor feeds the load.

Mode 1
With S 2 on and S 1 is modulated by reference T a , current flows in phase (a) and phase (b). I a >0 and I b <0, The bridge output voltage V L is connected to main line supply V ab which opposed by V DC is applied across the inductor. Current I L increases in the inductor. In this mode, only one MOSFET is on (S 2 ), the inductor current freewheels and the converter is disconnected from the mains and the DC voltage is zero.
Therefore the average voltage V L over one switching period T is: Where: V ab = V a -V b = 1.5V pk sin (t) + 0.866 V pk cos (t) By substituting equations 8, 10, 12 and 13 into equation 12 yeilds: By assuming an ideal power converter in which the power losses are negligible, the power nput is then equal to power output, and by assuming cosφ = 1 , The DC output voltage can be defined as :

Adaptive reactive power control using boost converter
In comparison to the operating frequencies of land based power systems, which is normally 50-60 Hz, the operation of aircraft power systems at these relatively high frequencies can present some technical difficulties. One area of importance is associated with the impedance of the potentially long cables (which may run along part of the wing and a large proportion of the fuselage). These cables connect electrical loads, such as electric actuators for aircraft flight surfaces, to the AC supply, or "point of regulation" (POR). In large modern aircraft, the cables can be in excess of 200 ft and contribute impedance which is dependent on the cable's inductance and resistance. The inductive reactance, X L , is proportional to the operating frequency of the power system and is given by X L = 2πfL, where f is the operating frequency and L is the inductance of the cable and therefore the reactance changes with operating frequency. (Taha M, Trainer R D 2004). As the connected load draws a current, the cable develops a voltage drop due to its impedance which is out of phase with respect to the voltage at the POR and has two detrimental effects: The voltage at the load is reduced below the regulated voltage at the point of regulation which is usually at the generator output.
The power factor of the load seen at the point of regulation reduces (even for a purely resistive load).
The voltage drop across the cable is clearly disadvantageous. The voltage drop may be tolerated and the connected loads have to be correspondingly down rated for the lower received voltage. Alternatively the voltage drop across the length of the cable is not allowed to exceed a threshold (typically 4 V) and it is necessary to provide cables that are both large and heavy such that their resistance remains low. Clearly space and weight are at a premium in aerospace applications. There can be significant weight saving if smaller, high resistance cables are used, particularly where low duty cycle, pulsed loads like electric actuators are supplied. The detrimental effects of such cables may be offset if the system designer uses the high inductive reactance present at the higher operating frequencies to affect voltage boost and power factor correction.
The simplest type of compensation for this type of problem is to connect a set of 3-phase capacitors (star or delta) at the point of connection of the load, in a similar way to ac motorstart capacitors. The capacitors can be used as a generator of reactive power but the beneficial effects are limited since the capacitive compensation is mainly controlled by the voltage magnitude and system frequency rather than the requirements of the load. Having noted the limitations of connecting shunt capacitors, there may be some applications where this type of compensation is applicable.
There is growing interest in the use of advanced power electronic circuits for aerospace loads, particularly in the motor-drives associated with electric actuators. The main two classes of converters currently being considered are active rectifiers and direct ac-ac frequency changer circuits (e.g. Matrix converters). Both types of converter can be made to operate with leading, lagging or unity power factor by suitable control of the semiconductor switching elements.
The current view in the aerospace industry appears to be that the operation of these converters should be limited to unity power factor and little (or no) work has been carried out to explore the true system level benefits of variable power factor operation. Fig.10 shows a basic circuit diagram for an electric actuator load incorporating an advanced power electronic converter with power factor control. It is clear that by controlling the power factor of the converter (shown leading), the effects of cable inductance can be eliminated so that the load as seen from the POR becomes unity power factor. Other operating power factors may be desirable in order to optimize the operation of the overall power system, including the generator loading.
Because the effects are proportional to the load current flowing through the cable and the system frequency, the reactive power compensation provided by the converter also needs to be variable.
The voltage magnitude at the load can be made the same as that at the POR. It could be beneficial in some applications to boost the input voltage by increasing the capacitive compensation provided by the power electronic converter.
The main benefit of using the advanced power electronic converter as a source of reactive power is to reduce (or eliminate) the voltage drop down the connecting cable. This gives us the possibility to use high impedance cables with benefits of reduced conductor diameter and significantly lower weight.
In order to understand the benefits of reactive power control, it is convenient to consider the flow of real and reactive current separately as shown in figure 10. Superposition can then be used to assess the net effect of both forms of current flow.
Therefore: i = i p + ji q = I (cosθ 1 + jsinθ 1 ) Where θ 1 = tan -1 (i q /i p ) Where θ 2 = tan -1 ((i q R + i p X L )/ (V + i p R -i q X L )) For unity power factor θ 1 should equals θ 2. Therefore: i q = ((V/ X L ) ± ((V/ X L ) 2 -4 i p 2 ) 1/2 )/2 In a practical system, ip could take the form of a current demand and iq would be a separate r e a c t i v e c u r r e n t d e m a n d t ha t i s m a d e t o v a r y a s a fun c t i o n o f i p a n d X L (frequency dependant). R and X L are cable dependant parameters.
Referring to Fig. 10, the inputs here are system frequency and load current, the output is Q demand, which is an input to the power electronic converter. The parameters of the cable are stored and used within the electronic circuitry to calculate the required compensation for the system under consideration.

DQ vector control for the converters
In the DQ vector control strategy the instantaneous 3 phase voltages and currents are transferred to a 2-axis reference frame system which rotates at the angular frequency of the supply. This has the effect of transforming the three phase AC quantities (representing rotating volt and current phasors in the stationary co-ordinate frame) into DC quantities in the synchronously rotating frame (Taha et al.,2002, Taha,. 2008). If the D axis is chosen to be aligned with the voltage phasor, the D and Q axis current components represent the active and reactive components respectively. Fig. 11 shows the schematic of the DQ control scheme implemented in the input converter.
The proposed control scheme consists of two parts: 1. An outer voltage controller.
2. An inner current controller.
The outer voltage controller regulates the DC link voltage. The error signal is used as input for the PI voltage controller this provides a reference to the D current of the inner current controller. The Q current reference is set to zero to give unity power factor. A PI inner current control is used to determine the demand of the stationary DQ voltage values (Taha M & Trainer R D 2004;Kazmierkoski et al., 1991). Each gain in the controller affects the system characteristics differently. Settling time, steady state error and system stability are affected by the amount of the proportional gain. Selecting a large gain attains faster system response, but cost of large overshoot and longer settling time. Application of the integral feedback drives the steady state error to zero. The integral term increases as the sum of the steady state error increases causing the error to eventually be zero. However it can cause overshoot and ringing.
Selection of the two gain constants is critical in providing fast system response with good system characteristics.

Power Electronics Application for More Electric Aircraft 319
The general formulas for DQ transformations are given as follows. We assume that the three-phase source voltages v a , v b and v c are balanced and sinusoidal with an angular frequency.
The components of the input voltage phasor along the axes of a stationary orthogonal reference frame ( , ) are given by: The input voltage can then be transformed to a rotating reference frame DQ chosen with the D axis aligned with the voltage phasor. The voltage components are given by: The same transformations are applied to the phase currents.
Let v a1 , v b1 and v c1 be the fundamental voltages per phase at the input of the converter. v a = Ri a + L di a /dt + v a1 where L is the value of input line inductance and R is its resistance of the inductor.
Taking the steady state DQ transformation for the inductor, the input voltage to the converter in the DQ reference frame is given by: The active and reactive powers are given by: Inverse DQ transformations then need to be applied to provide the three phase modulating waves (v aref , v bref and v cref ) for the PWM generation.
The main advantages of the DQ control are : 1. Direct control the active and reactive power. 2. Fast dynamics of current control loops.
The PWM generator based on a regular asymmetric PWM strategy.

Voltage Control
The DC side may be modelled by a capacitor C, representing the smoothing capacitors, and a resistor R, representing the load. This is shown in Figure 12.
v DC i C R The zero of the transfer function is where: Therefore: An approach to the controller design is to locate this zero to coincide with one of the poles, say at 1 s ω  , so as to cancel its effect. This gives: The second pole can then be placed at any desired location, to give the desired bandwidth. This gives the proportional and integral gains:

Current Control for boost converter
In this case the system is the line from the generator to the input converter, which may be modelled by an inductor in series with a resistor. The generator e.m.f. is assumed to have no dynamic effect, and so is represented as a short circuit. The system schematic is shown in Fig. 13.
V a I a LR Fig. 13. Schematic of current control for the boost converter.
The phase current is given by: The open loop transfer function relating the phase current to the phase voltage is, therefore: www.intechopen.com From this simple transfer function, it would appear that the PI controller proposed would suffice, driving the steady-state current error to zero and allowing the behaviour and bandwidth, (i.e. the positions of the poles, of the closed loop system) to be fully determined by choosing the proportional and integral gains. The D-axis and Q-axis currents are compared to their respective demanded values and the error is applied to individual PI controllers to give voltage demands referred to the D-axis and Q-axis. With the feedforward and dc-coupling terms, the transfer functions of the systems being controlled are: Again, these are first-order equations and similar to the voltage control loop, the PI controllers will drive the steady-state error to zero and enable the behaviour and bandwidth of the closed-loop system to be determined by placing the poles appropriately.

Current Control for buck converter
The idea of controlling the current of the AC side LC filter has been proposed as a way of suppressing the excitation of the resonance of this filter. In steady state and in the absence of distortion there are no current components to excite the resonance because the resonant frequency will have been chosen to fall between the fundamental and the switching frequency. During the transient, the resonance of the filter can be damped by choosing the characteristics impedance to match the resistance and the inductance.
In this case the system is the line from the generator to the input converter, which may be modeled by an inductor in series with a resistor and capacitor.. The system schematic is shown in Figure 14.
From this transfer function it would be appear that the PI controller proposed would suffice, driving the steady state error to zero and allowing the behaviour and bandwidth (position of the poles) of closed loop system to be fully determined by choosing the proportional and integral gains. The procedure for this is the same as that described above for voltage control.

Hardware design
All of the converters components had to be selected so that normal service maintenance would ensure the retention of their specified characteristics through the full range of operational and environmental conditions likely to be encountered through the life of the aircraft, or support facility, in which they are installed (Taha M 1999).

Capacitors
The choice of capacitors is very important for aerospace industry. Wet aluminum electrolytic capacitors are not suitable due to their limited operating temperature range and hence limited life. Equivalent series resistance is also a problem for these and other types of electrolytic capacitor and therefore alternative technologies, such as ceramic or plastic, are recommended.
Ceramic capacitors have a good lifetime, low series resistance and they work in high temperature conditions. On the other hand for a rating of a few hundred volts this type of capacitor has a very small value per unit volume and are only available in units of up to 20uF. The size and weight for this converter are very important. Therefore care was taken to choose the optimal value of the DC capacitor

Magnetic components
Another important factor is the design of the magnetic components. In order to achieve a small air gap, minimum winding turns, minimum eddy current losses and small inductor size, the inductor should be designed to operate at the maximum possible flux density. Also, care should be taken to ensure that the filter inductors do not reach a saturated state during the overload condition. As the cores saturate, the inductance falls and the THD rises.

Simulation results for boost and buck converter
The power conversion in the boost or buck converter is exclusively performed in switched mode. Operation in the switch mode ensures that the efficiency of the power conversion is high. The switching losses of the devices increase with the switching frequency and this should preferably be high in order have small THD therefore choosing the switching frequency poses significant challenges due to: 1. Supply frequency Variation (360 to 800 Hz).
For the boost converter the simulation carried out with a fixed switching frequency. However, for the buck converter. one of the method could be used is a variable switching frequency which depend on the input frequency. Trade off between the values of the filters and the switching frequency have been studies, in order to maintain the THD within the required value at different input frequency. Another method is to use the same switching frequency for different input frequency, here the highest input frequency should be considered.
The parameter values used for the simulation are shown in table 1. Fig. 15 to fig. 18 show, input AC voltage and current and Dc output voltage.
Boost converter Buck converter RMS phase voltage = 115 V RMS phase voltage = 115 V DC voltage setting = 400 V DC voltage setting = 42 V AC Input Filter = L ac = 100uH AC Input Filter = L ac = 150uH; C ac = 1μF Dc Output filter C dc = 50μF Dc Output filter = L dc = 1mH ; C dc = 50μF Load = 10 Ω Load = 0.5 Ω Switching freq for 800 input freq =20000 Hz Switching freq for 800 input freq =33600 Hz Switching freq for 360 input freq =20000 Hz Switching freq for 360 input freq =23760 Hz

Simulation results for the adaptive power control
Simulation has been done at 16 kW approximately. Fig. 19 shows the "per phase" parameter values used. Fig. 20 and fig. 21 show results for 360 Hz, the voltage drops to 107.5 V at the input filter of the converter. To compensate for the voltage drop across the cable, q ( reactive demand) has been set this gave leading power factor. Fig. 21 shows that the voltage Va3 increase to 111.3 V at 0.9 PF. Fig. 22 and Fig. 23 show results for 800 Hz, the voltage drops to 106 V at the input filter of the converter. Fig. 23 shows that the voltage Va3 increase to 1113 V at 0.9 PF.

www.intechopen.com
Va1 is the point of regulation voltage Va3 is the point of connection of the load L1 = 25uH,R1= 0.015 ohms is the generator inductance and resistance. L2 = 10uH,R2= 0.01 ohms is the cable inductance and resistance from the generator to contactor. L3 = 20uH,R2= 0.1 ohms is the cable inductance and resistance from the contactor to the load. L4 = 100uH,R4= 0.1 ohms is the inductance and resistance of the load converter input filter.

Conclusion
On the basis of the space vector concept a PWM controller was developed. It has been shown that sinusoidal modulation generated in a space vector representation with PI controllers give an adequate performance in steady state and transient condition fast. It has been shown that with the future use of advanced power electronic converters within aircraft equipment, there is the possibility to operate these at variable input frequency and keeping the input current harmonics low.
An AC/DC buck and boost converters with different input frequency and offers low THD ( less than 7%) has been described and simulated.
The operation and performance of the proposed topology was verified by simulating a 16 KW with a pure resistive load of 10 Ω and 400 dc voltage for the boost converter and a 3.5 KW with a pure resistive load of 0.5 Ω and 42 dc voltage for the buck converter. The input current is sinusoidal and power factor is unity. The DC voltage is well smoothed.
With the future use of advanced power electronic converters such as active rectifiers and matrix converters within aircraft equipment, there is the possibility to operate these at variable power factor in order to provide system level benefits. These include control of the voltage at the load and improvements in power factor seen at the POR.


COST CALCULATION MODEL FOR LOGISTICS SERVICE PROVIDERS

The exact calculation of logistics costs has become a real challenge in logistics and supply chain management. It is essential to gain reliable and accurate costing information to attain efficient resource allocation within the logistics service provider companies. Traditional costing approaches, however, may not be sufficient to reach this aim in case of complex and heterogeneous logistics service structures. So this paper intends to explore the ways of improving the cost calculation regimes of logistics service providers and show how to adopt the multi-level full cost allocation technique in logistics practice. After determining the methodological framework, a sample cost calculation scheme is developed and tested by using estimated input data. Based on the theoretical findings and the experiences of the pilot project it can be concluded that the improved costing model contributes to making logistics costing more accurate and transparent. Moreover, the relations between costs and performances also become more visible, which enhances the effectiveness of logistics planning and controlling significantly.

INTRODUCTION
Due to efficiency considerations the non-core activities have more and more often been outsourced in various industries in the last decades. Logistics is one of the non-core activities frequently outsourced. These logistics tasks are performed by logistics service providers (LSP). They operate independently from their clients and offer complex logistics packages including not only such classic services as transport or warehousing but also value added services like postponed manufacturing or assembling, etc.
Logistics has become one of the main factors determining the competitiveness of the economy. As a considerable part of logistics tasks are undertaken by LSP companies, they play a crucial role in making the operation of several industries more effective and efficient. That is why their operation shall also be made as efficient as possible. It means that the decisionmakers of LSP companies shall be aware of the main operational factors of logistics processes evaluated and monitored by management information systems.
It has been concluded by earlier research that the control of logistics costs will become increasingly important to firms seeking competitive advantage. Managers will require more accurate and focused costing information of logistics functions or services to ensure profitability. Success of these efforts will largely depend on the ability of the firm's cost accounting system to trace costs to specific logistics activities [1].
Effective capacity allocations in LSP companies require more detailed and reliable information on the operational costs and performances. Decision makers need accurate data on the costs and the profitability of logistics services, and additionally also on the cost efficiency of logistics performance generators. It is also important to explore the cause-effect chains in the technology and business processes so that the interventions aiming to improve the operational efficiency within the company or along the entire supply chain can be established on a sound methodological basis.
To meet the requirements set before, an improved cost calculation mechanism shall be introduced in logistics costing. The traditional costing methods may fail when applying them in logistics as they ignore the consideration of cause-effect relationships and use ad-hoc cost allocation factors in case of overheads. They can only be used when the relevance of overheads or indirect costs is low [2,3].
In this paper the logistics costing techniques applied in practice are investigated first. Then the basic methodological framework is built up through embedding the relevant outcomes of the related research. The proposed costing model for LSP companies is set up on the basis of the methodological framework and by considering practical experience. The model is then tested by an illustrative numerical example based on empirical observations. Note that the pilot calculation aims to demonstrate the usefulness and the applicability of the elaborated costing model rather than delivering exact cost and profit information for concrete decision-making purposes. In a real-life application the structure of the model should be adapted to the specific features of the examined LSP company.

BASIC HYPOTHESIS
Before defining the basic hypothesis it is worth analysing the results of related research presented in the literature. The aim of this analysis is to find relevant principles, models and their verifications, which can be used to establish a reliable and transparent cost calculation methodology for logistics processes.
Activity-based costing (ABC) is often regarded as an alternative solution to the technique of fixed overheads. It uses activities to trace indirect costs while traditional systems of cost calculation treat indirect costs as a homogenous lump to be allocated to products or services on a single-volume related base like direct costs. Although ABC was developed for the needs of manufacturing, it can be applied in logistics, too. To prove this fact, a costing model for the purchasing function has been elaborated by identifying the corresponding activities and cost drivers [4]. Another example is when the logistics costs classified as overheads were analysed in a manufacturing company by using ABC. It has turned out that the detailed information on logistics costs obtained in this way enables a more efficient management of logistics functions within production companies [5].
The need for more accurate logistics cost information than traditional systems can produce arises in several decision-making situations. This applies to distribution costs as well. Traditionally, distribution costs were allocated on the basis of simple valuebased factors. Although most of the applications dealing with ABC have their focus on manufacturing, this approach can be applicable in distribution logistics management as well and could yield more accurate logistics costs [6]. ABC applications can be found in a warehouse logistics environment, too. They increase the visibility of logistics costs and contribute to higher accuracy by measuring performances and using them for cost allocations. However, a case study with comprehensive and detailed data collection showed that single transaction based cost drivers may not be sufficient: automatic data collection can be a solution to this problem [7].
Some authors state that real life applications of ABC in transport or logistics are rarely presented in literature. A case study has proven that ABC can be helpful for transportation companies to determine the costs of their operation with higher correctness. Detailed process and cost driver mapping was carried out where ABC was combined with business process modelling and AHP (analytical hierarchy process). It turned out that the proposed approach is a more effective costing method than the existing traditional costing system [8]. Other case studies examined how to calculate the accurate costs by ABC for individual airplanes and flights by identifying the main activity items and cost drivers of each airplane and flight and using a flow chart for cost assignment [9].
ABC can even be extended to the supply chains. ABC integrated into supply chain management (SCM) identifies the driving cost factors affecting the key logistics activities and helps improve the allocation of logistics costs [10]. SCM requires more accurate costing information and ABC can significantly contribute to making SCM more effective by delivering reliable and detailed cost and profit data, giving a clear picture of where the resources are spent, understanding the cause-effect relations between costs and the demands for activities, etc. [11]. When determining the cost in supply chains intra-firm methods are not appropriate; thus, a new, two step ABC approach can be used consisting of design and operation phases; moreover, the costing information shall be standardised along the entire chain [12]. ABC can be a tool of evaluating the tactical production planning in supply chains by adding financial evaluation to physical parameters and using logistics process activities. Here the links between financial and physical flows are to be determined exactly [13].
Some attempts have already been carried out to depict the operation of LSP companies and create ABC models for them. It has been concluded that the traditional costing methods may not be sufficient for such business actors. A model including warehousing and transport was set up with proposals for activities and cost drivers. It has been found that no general models can be used as each company has its unique operational characteristics. At the same time, appropriate ABC models enable to identify unused capacity and conduct what-if analyses. Nevertheless, special attention shall be paid to the model trade-off: the costs of introducing ABC shall not exceed the benefits of additional costing information [14]. A basic ABC model of LSP companies using matrix algebra has also been worked out and tested through a case study defining sample activities and cost drivers [15].
Having reviewed the relevant literature, it can be concluded that ABC is regarded as the most appropriate tool for enhancing the capabilities of logistics costing. Nevertheless, business surveys consistently indicate that managers prefer to use full cost data to make decisions e.g. about prices or capacity allocations [16]. ABC can also be used for full cost allocation (FCA) purposes but it may not take into account the interactions between the units or entities applied for collecting and distributing indirect costs or overheads. Another variant of FCA can be the multi-level indirect cost allocation approach where the cost allocation mechanism relies on the operational model of the company depicting the organisational structure. This topic is not so often addressed in the logistics and transport sector as the implementation of ABC. Thus, it is worth investigating how a multi-level FCA can be adopted in logistics with special regard to logistics service providers.
The basic hypothesis of the research is that the costing system of LSP companies can be made more accurate, reliable and transparent by adopting an FCA approach based on multi-level indirect cost allocation.

RESEARCH METHODOLOGY
The principles of the proposed cost calculation model are defined on the basis of former, related research results incorporating also relevant ideas coming from the literature reviews [2,3,17]. The basic procedures and formulas have been elaborated by the author of this paper. Further general methodological details can also be found in the corresponding article analysing the improvement of logistics cost calculation in the frame of production costing [18].
The proposed methodology is similar to ABC but it uses organisational units, pieces of equipment or dedicated set of resources instead of activities. Moreover, it considers the hierarchical structure of such indirect cost generators, so that the relationships between these entities are also taken into account. Thus, the model needs sound information on the main features, i.e. organisational structures, operational rules, competences, etc. of the investigated business-technology system. The general structure of the multi-level full cost allocation model is shown in Figure 1.
The model depicts the operation of the examined company by revealing the so-called intern service connections represented by the performance flows. It consists of the cost objects arranged into a multi-level hierarchy, the profit objects and the performance relations between these entities. Note that the definition of model elements (objects) is different from the one of ABC: ABC uses cost/activity centres instead of cost objects and cost objects instead of profit objects.
Indirect costs are recorded in the cost objects. These are the so-called primary costs of the cost objects. Cost objects serve as the first collection points of the indirect costs. They can be organisational (business or technology) units, pieces of equipment or machinery, groups of staff, etc. contributing to the production of the profit objects or they might serve other cost objects. Each cost object shall be supplied with a performance indicator measuring its performance. As the cost objects have intern service relations with each other (as shown in Figure 1) their total costs also contain the so called secondary costs which can be allocated by using the rate of the relative performance consumption, called performance intensity. If we summarise these procedures into a single mathematical formula, the total cost of a cost object can be calculated as follows: where: Cco k -total cost of cost object k; Cp k -primary cost of cost object k; Csco i -total cost of serving cost object i; Psco i -total performance of serving cost object i; Pcons ki -performance consumption of cost object k at serving cost object i; pki -performance intensity of cost object k at serving cost object i; index i goes through the relevant serving cost objects.
Note that the cost of a cost object can be calculated only if the total cost data of all preceding cost objects are already available. This fact determines the sequence of the calculation steps. The cost efficiency of the cost object can be evaluated by calculating the average (or specific) cost: total cost divided by performance. A too high value of average cost may reflect a low level of capacity utilisation so it may be an important indicator for capacity reallocations. Specific cost values can also be used for preparing outsourcing decisions: the prices of extern services can be compared with the "intern price" of performance creation.
Direct costs are recorded in the profit objects. Profit objects are the products or services which induce revenues for the company. The indirect costs are allocated to the profit objects from the relevant serving cost objects (for the relationships see Source [17] basis of the relative performance consumption. It is a similar approach to the one presented in Equation (1) so the total cost of a profit object can be determined according to the following formula: where: Cpo j -total cost of profit object j; Cd j -direct cost of profit object j; Pcons ji -performance consumption of profit object j at serving cost object i; pji -performance intensity of profit object j at serving cost object i; index i goes through the relevant serving cost objects.
Revenues are also recorded in the profit objects. The margin of the profit object, as the indicator of its profitability, can be calculated by subtracting the total cost form the revenue. Another indicator of profitability is the cost-coverage ratio which can be determined through dividing the revenue by the total cost. The general data structure of the entities in the calculation scheme can be identified as indicated in Table 1.

RESEARCH RESULTS
To build up the cost calculation model of LSP companies the general model shall be transformed according to the operational characteristics observed. The business and technology models of several LSP running businesses in Hungary have been studied to identify the profit objects, to select the appropriate cost objects and to arrange them along cause-effect based performance chains. Another task was to add the suitable performance indicators and their measures to the cost objects. Figure 2 illustrates a possible cost calculation model of a certain (medium-sized) LSP company having the core activity of road haulage and carrying out some additional activities like warehousing. This is an LSP costing model containing the most common objects and relations. It is detailed enough for conducting exact calculations; however, this model is still a general scheme and should be adapted to the concrete business-technology model when applying it under real life circumstances. It could be even more sophisticated if more detailed or more accurate information was required.
The elementary profit objects in the sample model are the logistics services or service packages offered to the customers or clients. Their direct inputs represent the direct costs. They can be, for example, the extern logistics services purchased for completing one's own logistics services, individual cost elements like infrastructure user charges or even fuel costs depending on the data structure of the accounting system. Each cost item assigned to the logistics services directly can be regarded as direct cost.
The cost objects can be classified into three groups: 1. the cost objects representing the general management or background intern services in the company like the general, financial or human management units and the department for information technology (IT); 2. the cost objects representing the units of operative and tactical control or execution like service planning, transport control, maintenance, warehousing, sales and drivers. They are served by the cost objects of Group 1 and serve the cost objects of Group 3 or the profit objects; 3. the cost objects representing the assets (vehicle types) serving the profit objects.
There are additional intern services within Group 2 as service planning governs other objects in this group (excluding drivers) and transport control supervises drivers while maintenance serves warehousing. Based on the model described before, a numerical calculation can be performed if the necessary input data are accessible. In our example modified and assumed input cost and revenue data have been utilised while the missing performance data have been estimated due to data restriction problems in the competitive logistics market. Although the input data are estimated or modified, their relative order of magnitude is in general correct as they rely on practical experience. Nevertheless, the results of the pilot calculation must not be used for decision-making purposes directly. The sample calculation aims rather to prove the practical applicability and usefulness of the theoretical model. Of course, on the other hand, it also reflects the constraints of implementation.
The sample LSP company operating according to the business-technology model and presented in Figure 2 has ten service packages and runs three vehicle types (n =10 and x = 3). Table 2 contains the input data, i.e. direct costs and revenues, for the profit objects (logistics services 1-10) where th. MU = thousand monetary units. Table 3 contains the input data, i.e. primary costs and performances for the cost objects. Note that the sum of primary costs equals the total indirect cost of the company. Tables 4 and 5 contain the performance intensities, which are necessary for the cost allocations using Equations (1) and (2). log. serv. 10 160 520

Source: own estimation based on empirical information
Cost and revenue data can be obtained from the accounting systems, mainly from the general ledger. If the output data structure of the accounting system are not in line with the format of the requested input information, additional data transformation may also be necessary. Performance data can be extracted from the technology information systems or they might be made available by dedicated data collection procedures. The performance intensity data have been determined on the basis of cause-effect interactions presented in Figure 2. The entities receiving (consuming) performances can be found in the first column, while the entities providing (serving) performances are listed in the first row of the tables. Note that not only the performance data are to be measured but also the distribution of performance consumption has to be assessed for completing the cost allocations.
Having obtained or estimated the input data, the calculation procedure can be started. The first task is to calculate the secondary, the total and the average cost of the cost objects by using Equation (1). The results are listed in Table 6.
Let us see how to calculate the output data (results) of cost objects by using Equation (1). In case of cost object "financial management" the primary cost is 30 th. MU. There are two serving cost objects: -"general management" with a total cost of 20 th.
MU and 6% of its performance is consumed by "financial management";     Let us see another example. The primary cost of cost object "service planning" amounts to 30 th. MU. There are four serving cost objects: -"general management" with a total cost of 20 th.
MU and 21% of its performance is consumed by "service planning"; -"IT" with a total cost of 40 th. MU and 8% of its performance is consumed by "service planning"; -"financial management" with a total cost of 40.40 th. MU and 7% of its performance is consumed by "service planning"; -"human management" with a total cost of 25.80 th. MU and 3% of its performance is consumed by "service planning". So, the total cost can be calculated by extracting Equation (1): 30 + 20 * 0.21 + 40 * 0.08 + 40.40 * 0.07 + 25.80 * 0.03 = 30 + 11.00 = 41.00 th. MU. The average cost is 41.00 / 3,800 * 1,000 = 10.79 MU/hour, so one operational hour of service planning costs 10.79 MU. The cost of this cost object can be calculated only if the total cost data of all preceding cost objects, i.e. "financial management" and "human management" have already been calculated.
The total and average costs of other cost objects can be calculated similarly by taking into account the corresponding cause-effect chains. Special attention shall be paid to the ascending-descending relations, which determine the sequence of the cost object calculations.
The second task is to calculate the indirect cost, the total cost, the margin and the cost coverage of the profit objects by using Equation (2). The results are summarised in Table 7.
Let us see how to calculate the output data (results) of profit object "logistics service 1" by using  Equation (2). The direct cost is 180 th. MU. There are four serving cost objects: -"sales" with a total cost of 201.66 th. MU and 8% of its performance is consumed by "logistics service 1"; -"drivers" with a total cost of 777.52 th. MU and 7% of its performance is consumed by "logistics service 1"; -"vehicle type 1" with a total cost of 421.29 th. MU and 16% of its performance is consumed by "logistics service 1"; -"vehicle type 3" with a total cost of 579.16 th. MU and 13% of its performance is consumed by "logistics service 1". So the total cost can be calculated by extracting Equation (2) The indirect cost, the total cost, the margin and the cost coverage ratio of other profit objects can be calculated similarly by taking into account the corresponding performance service relations.

DISCUSSION
By implementing the proposed costing model it becomes possible to evaluate the cost efficiency of each cost object, i.e. organisational unit, activity area, vehicle, etc., of LSP companies. The current values can be compared to benchmarks, i.e. to earlier values of the same entity or to the values of similar entities in other LSP companies of similar size. If the cost efficiency of a certain cost object is poor in comparison to a benchmark, low capacity utilisation can be suspected. For example, such a situation may occur when a vehicle operates at a too high value of the indicator "MU / vehicle km". The problem can be swiftly identified so that interventions aiming to enhance the utilisation of capacities or rationalising the consumption of resources can be executed in time.
Outsourcing decisions can also be supported by cost object calculations as the average costs of cost objects can be compared with the prices offered by the potential extern service providers. Here, the outsourcing of background services, like for instance financial/ human management or maintenance can be considered in case of logistics service providers on the basis of average cost information.
The profitability and the cause-effect chains of creating logistics services become transparent and the cost values are more exact than in case of using traditional costing regimes, due to the traceable allocation of indirect costs. It is worth comparing the outcomes of the improved costing model with the results of the traditional cost calculation relying on a direct cost-based allocation of indirect costs, which means that indirect costs are allocated proportionally to the direct costs: Table 8 indicates the total cost of logistics services in the sample model calculated according to the traditional costing approach using Equation (3). The differences between the values of the two costing approaches are also indicated. Let us see as an example how to calculate the total cost of "logistics service 1". It is 180 + 3,380 * 180 / 2,340 = 440.00 th. MU, where 180 th. MU is the direct cost of this logistics service, while 3,380 th. MU is the total indirect cost and 2,340 th. MU is the total direct cost of the company. The values of other profit objects can be calculated similarly by inserting the actual direct cost value into the formula. The difference is (440.00 -393.26) / 393.26 * 100 = 11.89% and so on. In this example, differences can be seen between the values of the traditional and the improved cost calculation. The related real-life applications confirm the assumption that LSP companies with not homogeneous services may experience a similar situation [8]. Using the traditional approach can yield distortions in the allocation of resources or in the determination of prices. That is why it is important to use systematic, cause-effect based allocation of indirect costs instead of applying ad-hoc cost distribution techniques.
Another advantage of using the developed cost allocation method is that the causes of profitability can be accurately explored. For instance, the profitability of "logistics service 8" in our example may be negative because of the wrong setting of the price, which would lead to low revenues. Maybe the direct costs are too high due to the high prices of the extern logistics ser-vices purchased or the expensive infrastructure user charges, etc. It might also happen that some of the entities generating the indirect costs, e.g. the vehicles operate with a low capacity utilisation, or others, e. g. the maintenance unit, operate at a high resource consumption, etc. In possession of such additional information the decisions aiming to enhance the profitability of logistics services or the cost efficiency of organisational units or pieces of equipment can be supported in a more exact way.
Beside the advantages, the constraints and the conditions of introducing the model shall also be investigated. Difficulties may occur if the operation of the selected LSP company cannot be modelled perfectly. It means that some simplifications have to be accepted during the process modelling to make the allocation model consistent and calculable. Another problem may arise when the input data are not available at the required quality or they are too aggregate. Estimations may help eliminate the lacking or incomplete data bases. Nevertheless, the simplifications or estimations generally reduce the correctness of the calculation.
To run the model, often additional data collection procedures are to be carried out as some of the performance data are not covered by the enterprise information systems. Some input data, mainly cost items, may need extra transformations before feeding them into the model. The most difficult task is to measure the performance intensities, i.e. the distribution of performances or the performance consumption. So, one has to make every effort to automate and improve the measurement or the collection of input data as far as it is possible. However, it will probably increase the costs of the implementation. That is why it is important to find the balance or trade-off between accuracy and feasibility, as already mentioned in the literature review.

CONCLUSION
Logistics service companies play an important role in national economy. They contribute to making several industries more competitive, at the same time they shall keep an eye on their own competitiveness as well. To be competitive, LSP companies shall be aware of their costs, profits and performances. Decision makers need as accurate and detailed data of these key operation parameters as possible. Furthermore, they want to get an insight into the cause-effect mechanisms of logistics performance creation.
To meet the requirements set above, logistics costing systems shall be improved as traditional approaches may not be sufficient. One of the ways to enhance the correctness and reliability of logistics cost calculation is the adoption of the multi-level indirect cost allocation technique. This method is based on the causeeffect oriented cost allocation principle supported by the identification of intra-company performance generation and consumption while inter-company service contact points are also taken into account.
This research has yielded a cost calculation scheme for logistics service providers incorporating the principles and experiences of best practice and introducing an alternative way to logistics cost management. Of course, the proposed model is generalised so it needs to be adapted before applying it in practice: every LSP company has its special operational features which are to be included in the real-world calculation schemes. The developed sample model can serve as a starting point for further modelling initiatives.
Although no fully appropriate input data were available the illustrative example calculation has proven the advantages of the costing tool. The allocation of indirect logistics costs is traceable and transparent. Cost and profit data of logistics services have become more correct. Capacity problems can be identified and handled effectively due to the revealed information concerning the value chains. On the other hand, the implementation of the improved logistics costing methodology may need further considerable resources like extra data collection or transformation efforts. Also, the information quality may not reach the level of total accuracy due to the simplifications and estimations. Nevertheless, the improvement of accuracy concerning cost and profit information is still considerable in comparison with the outcomes of the traditional calculation methods.
Summarising the research results we can conclude that the basic hypothesis has been verified. Nevertheless, further research is needed to better understand the conditions and possibilities of the real-world utilisation of the developed costing model. So the model is intended to be run in a real enterprise environment as well -as far as possible.

ACKNOWLEDGEMENT
This work is connected to the scientific program of the "Development of quality oriented and harmonized R+D+I strategy and functional model at BME" project supported by the New Széchenyi Development Plan (project ID: TÁMOP-4.2.1/B-09/1/KMR-2010-0002). Furthermore, this paper is prepared with the support of the project "Modelling and multi-objective optimization based control of road traffic flow considering social and economic aspects" of OTKA (Project ID: CNK 78168).


Hemispherical Dielectric Resonator Antennas over Non-Planar Surfaces for Direction Finding Systems

In this paper, hemispherical DRA array mounted on or embedded in a hollow circular cylindrical ground structure is used for direction finding systems. In the proposed array only one DRA element is active and the others are parasitic. The d irection of the received signal will be determined by the relat ion between the received signal strengths at each element of the array.

Introduction
Switched beam array can be used in a lot of communicat ion applicat ions such as base station tracking in mobile co mmun ication, wireless networking, motor vehicles, aircrafts, robots, radar, security systems, and detection of transmitter direct ion as needed in d irection finding systems. In [1], a switched parasitic array (SPA) is considered by using parasitic elements (passive elements, PE) that can be shorted to ground by pin-diodes. The array is consisting of one fed element and several other parasitic elements. Switching between different identical patterns but pointing to different directions can be achieved using a coordinated shortening of the passive elements.
Beam steering that is affected by mechanical steering, require co mplex feeding network, high power to operate, and its angular resolut ion is related to the beamwidth of the antenna. That can be ach ieved in crossed loop arrays, a highly directional arrays and phased arrays. But controlled directional patterns with electronically steerable beam are more efficient and useful. The control of the position of the feed element in an array to change the direction of its beam reduces mult ipath fading and power consumption through increased antenna gain . A lso, this method, similar to the phased array , o ffers pro mis ing solut ion, p rov ides better p erfo r man ce an d red u ces co s t o f co mp o n en ts and assembly [2][3][4][5][6][7]. The d ielectric resonator antenna (DRA) has man y ad vant ag es includ ing wide band widt h and h igh radiat ion efficiency that are necessary for thoseapplicat ions [8][9][10]. The rad iation characteristics of cylindrical DRA and hemispherical DRA p laced on or embedded in superquadric cylindrical, circular cy lindrical and spherical ground planes are investigated in [11][12][13][14][15].
In this paper, the model for switched parasitic antenna array is considered. The hemispherical DRA array elements mounted on or embedded in circular cy lindrical ground plane for direction finding system are investigated. The DRA antenna elements are equally d istributed along the circu mference of a fin ite hollow circular conducting cylinder. The beam can be switched by exchanging the position of the feed so that the direction of the maximu m radiat ion in the horizontal plane is changed. The switching board was located inside the ground structure.
In this model, the radiation patterns of DRA elements are investigated to determine the angle of arrival of an incident wave using the relation between the received signal strength at each of the array elements. The finite element method (FEM) [16][17] is used to simulate the structure and the results are compared with that calculated by the finite integration technique (FIT) [18][19] for authentication. Figure.1 shows the hemispherical DRA element wh ich is placed on a hollow circular cylindrical perfect ly conducting ground plane. The hemispherical DRA ((Crystals (single, inorganic) Alkali halides, LiF)) with dielectric constant εr =8.9 is used. It has a radius a, of 1.88 cm (0.458λ). A coaxial probe with radius of 0.075 cm is used to excite the element. The probe is located off the center by df =1.288 cm with a height hP of 1.125 cm. The hemispherical DRA is designed to operate at 2.45 GHz (λ=12.245 cm). The radius of the circular cylinder is rg=7.5 cm (0.612λ), and the length, lg, is 10 c m. The simulated reflection coefficient as a function of frequency and the radiation patterns for one element mounted on hollow circular cylindrical ground plane at f=2.45 GHz are illustrated in Fig.2. The simulated results are calculated using the FEM and compared with that calculated by FIT method. Good agreement is obtained. It is obvious that the main-lobe in the xy-plane radiation pattern goes beyond the ±90oin addition to a significant back lobe. This is because the diffraction effect due to the finite size of the cylindrical ground plane. The radiation pattern in the y -z plane is symmet ric as expected with some high levels of the Eθ components in some directions. The backlobe is only about -6 dB relat ive to the maximu m in the front as indicated in the x-z p lane and the x-y plane p lots. This indicates a significant backward radiat ion, which is expected as the radius of the conducting ground plane cylinder is only 0.612λ. Figure.3 shows the switched-parasitic antenna array. It consists of four hemispherical DRA elements that are equally spaced and placed on a hollow circular cylindrical ground plane. The antenna array consists of one active element and three parasitic elements. The array is electronically switched sequentially to each of the four positions of the antennas and then detects the signal strengths v1, v2, v3 and v4 at each switch positions 0o, 90o, 180o, 270o, respectively. The radiation patterns in x-y p lane are calculated for all four directions as shown in Fig.4. This is almost the same pattern of a single element but switched in the four directions. The mutual-effect of the elements on each other is negligible. Surfaces for Direction Finding Systems   For more economy and for getting rid of the elements protrusion because of using the DRA elements sticking out of the surface, a hemispherical DRA array with elements embedded in hollow circu lar cylindrical ground plane structure loaded with protecting dielectric superstrate is proposed as shown in Fig.5. The superstrate layer directly loads the hemispherical DRA. It has been selected to be RT/duroid 5880 and to have a relative permittivity εrs= 2.2, a thickness hc = 2.22 cm and rc =2.96 cm. For one element, the simu lated reflection coefficient versus the frequency is shown in Fig.6, wh ich indicates a reduction of the resonant frequency to become 2.4 GHz due to the loading effect of the superstrate combined with the effect of the cavity used for embedding the DRA element. Radiat ion patterns in x-y plane for four and five elements uniformly distributed around the hollow circular cy lindrical ground plane are shown in Fig.6. For the case of four elements the switch positions produce the signal strengths (v1 or v2 or v3 or v4). By knowing the radiation pattern of the array, the exact determination of the angle of any signal source can be determined by simp ly ordering the two maximu m signal strengths. The relations between the values v1, v 2, v 3 and v4 are shown in Fig.7. For examp le if v 1 and v2 are the two largest signal strengths received in the scan and v1 >v2, then the source direction is located between 0o degree and 45o degrees. Then, using the branch in Fig .7 for v1/v2 or an equivalent formu la, the angle of arrival can be determined in a mo re precise way.

Conclusions
Hemispherical DRA array mounted on or embedded in a hollow circu lar cy lindrical ground structure is proposed for direction finding systems. The detected signal strengths v1, v2… at each switch positions, are used to get the exact determination of the angle of any incident signal, therefore it is quite suitable for the direct ion finding applicat ion. The proposed structures are simp le and not costly.