阅读说明：本技术 兼容不同代际车辆的ato停车控制方法 (ATO (automatic train operation) parking control method compatible with different inter-vehicle generations ) 是由 沈鹏翔 贾庆东 于 2020-12-23 设计创作，主要内容包括：本公开的实施例提供了兼容不同代际车辆的ATO停车控制方法。所述方法包括ATO系统根据ATO目标速度曲线以及列车当前速度,生成所述列车的停车控制指令并发送给所述列车；其中,在停车控制过程中,通过迭代自学习,学习所述列车的制动参数；根据学习得到的制动参数及列车速度和目标速度之间的差值,计算列车运行的推荐速度,控制列车按照推荐速度运行。以此方式,可以解决不同代际车辆制动力建立过程延时参数差异造成的ATO控车问题,解决精准停车阶段车速震荡不收敛、停车过标的难题。(The embodiment of the disclosure provides an ATO parking control method compatible with different vehicles of different generations. The method comprises the steps that an ATO system generates a stopping control instruction of a train according to an ATO target speed curve and the current speed of the train and sends the stopping control instruction to the train; in the parking control process, learning the braking parameters of the train through iterative self-learning; and calculating the recommended speed of train operation according to the learned brake parameters and the difference between the train speed and the target speed, and controlling the train to operate according to the recommended speed. By the method, the problem of ATO vehicle control caused by delay parameter difference in the process of building braking force of different vehicles at different generations can be solved, and the problems of non-convergence of vehicle speed oscillation and over-standard parking in the accurate parking stage are solved.)

1. An ATO parking control method compatible with different vehicles of different generations is characterized by comprising the following steps:

the ATO system generates a stopping control instruction of the train according to a target speed curve and the current speed of the train and sends the stopping control instruction to the train; wherein the content of the first and second substances,

in the parking control process, learning the braking parameters of the train through iterative self-learning;

and generating a control instruction of train operation according to the learned brake parameter and the difference between the train speed and the target speed.

2. The method of claim 1,

the target speed curve is a multi-target decision variable determined by the ATO system; establishing an objective function and a constraint condition when calculating a target speed curve; solving a target speed curve set meeting the conditions according to the determined target function and the constraint conditions; and generating target speed curves according to different emphasis points.

3. The method of claim 1, wherein generating the train stopping control command by the ATO system based on the ATO speed target profile and the train current speed comprises:

and generating a stopping control instruction of the train according to the ATO target curve and the current speed of the train based on the two-degree-of-freedom robust PID controller.

4. The method of claim 3, wherein learning the braking parameters of the train during the localized stop control through iterative self-learning comprises:

the positioning parking control adopts a distance control mode and carries out staged braking near a positioning parking point;

and designing a self-adaptive iterative learning controller according to the train operation dynamics model, and learning the braking parameters of the train in the staged braking process of the train.

5. The method of claim 4, wherein generating control commands for train operation based on the learned braking parameters and the difference between the train speed and the target speed further comprises:

and determining a feedforward value of brake delay in the accurate parking stage according to the response time of the train, and outputting the brake in advance of preset time.

6. The method of claim 5, wherein determining a feed forward value for the precise stopping phase brake delay based on the response time of the train comprises:

determining a feedforward value of braking delay according to the driver driving historical data; or the like, or, alternatively,

and determining a feedforward value of the braking delay through a regression prediction model.

7. The method of claim 6, wherein determining a feed forward value for braking delay through a regression prediction model comprises:

obtaining an input variable influencing a parking error and an output variable parameter representing a convergence speed; the input variable is a braking delay; the output variable is the error between the recommended speed and the target speed;

establishing a regression prediction model for obtaining convergence rate by inputting variable parameters, wherein the regression prediction model comprises a correction variable;

obtaining an optimized regression prediction model through multiple iterative training;

and calculating the input variable parameter when the value of the optimized regression prediction model is 0, updating the input variable parameter, and controlling by using the updated input variable parameter.

8. The method of claim 5, further comprising: in the precise parking phase, the parking position is determined,

when the train head is positioned on a platform rail, if the deviation between the train speed and the target speed is greater than a preset threshold value, outputting the minimum braking force; if the deviation is less than or equal to a preset threshold value, outputting a braking force of 0.5 times of the target braking rate;

when the train head is not positioned on the platform rail, if the deviation between the train speed and the target speed is greater than a preset threshold value, the traction force calculated by the PID controller is output; and if the deviation is less than or equal to a preset threshold value, outputting the idle line.

9. An electronic device comprising a memory and a processor, the memory having stored thereon a computer program, wherein the processor, when executing the program, implements the method of any of claims 1-8.

10. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the method according to any one of claims 1 to 8.

Technical Field

Embodiments of the present disclosure relate generally to the field of rail transit technology and, more particularly, to an ATO parking control method compatible with different agent vehicles.

Background

An ATO (Automatic Train Operation) is an important guarantee for smooth Operation, accurate and smooth stop of a Train. The ATO assists ATP (automatic train protection) work, receives information from the ATP, controls the train through a traction/brake line, keeps the train running at a reference speed, and realizes accurate stop.

In the existing rail transit, a link of electric-air conversion and a link of compressed air are still needed for applying a brake instruction and a brake force of a brake system (taking a microcomputer control straight-through type electro-pneumatic brake system as an example), namely, firstly, an electric signal is converted into a pre-control pressure signal through an electro-pneumatic valve (EP valve), and after the flow is amplified through a relay valve, compressed air can enter a brake cylinder according to the instruction, a piston rod of the brake cylinder is pushed to move, so that a brake shoe is driven to be attached to a tread or a brake pad to be attached to the brake disc, and finally, the brake force is formed through a wheel-rail.

For old vehicles, the delay of the building process of the braking force is generally high, a long time (1-3 s) is needed, the response is slow, the control precision is greatly influenced by factors such as air pressure, brake cylinder friction resistance and brake shoe performance, the braking force is unstable, the following consistency is poor, and no energy absorption device is arranged on a line. Therefore, there are problems of over-tagging and under-tagging. Particularly for different vehicles of different generations, the compatibility and precise parking of ATO cannot be realized.

Disclosure of Invention

According to an embodiment of the present disclosure, an ATO parking control scheme compatible with different agent vehicles is provided.

In a first aspect of the present disclosure, an ATO parking control method compatible with different agent vehicles is provided. The method comprises the following steps: the ATO system generates a stopping control instruction of the train according to an ATO target speed curve and the current speed of the train and sends the stopping control instruction to the train; in the parking control process, learning the braking parameters of the train through iterative self-learning; and generating a control instruction of train operation according to the learned brake parameter and the difference between the train speed and the target speed.

The above-described aspects and any possible implementations further provide an implementation in which the target speed profile is a multi-objective decision variable determined by the ATO system; establishing an objective function and a constraint condition when calculating a target speed curve; solving a target speed curve set meeting the conditions according to the determined target function and the constraint conditions; and generating target speed curves according to different emphasis points.

The above-mentioned aspects and any possible implementation manner further provide an implementation manner, where the generating, by the ATO system, the train stop control command according to the ATO target speed curve and the train current speed includes: and generating a stopping control instruction of the train according to the ATO target curve and the current speed of the train based on the two-degree-of-freedom robust PID controller.

The above-mentioned aspects and any possible implementation manner further provide an implementation manner, and the learning of the braking parameters of the train through iterative self-learning in the positioning and stopping control process comprises the following steps: the positioning parking control adopts a distance control mode and carries out staged braking near a positioning parking point; and designing a self-adaptive iterative learning controller according to the train operation dynamics model, and learning the braking parameters of the train in the staged braking process of the train.

The foregoing aspects and any possible implementations further provide an implementation, where generating a control command of train operation according to the learned braking parameter and the difference between the train speed and the target speed further includes: and determining a feedforward value of brake delay in the accurate parking stage according to the response time of the train, and outputting the brake in advance of preset time.

The above aspect and any possible implementation further provides an implementation in which determining a feed-forward value of the precise stopping phase braking delay from the response time of the train includes: determining a feedforward value of braking delay according to the driver driving historical data; or, determining a feedforward value of the braking delay through a regression prediction model.

The above aspect and any possible implementation further provides an implementation in which determining a feedforward value of the braking delay through a regression prediction model includes: obtaining an input variable influencing a parking error and an output variable parameter representing a convergence speed; the input variable is a braking delay; the output variable is the error between the recommended speed and the target speed; establishing a regression prediction model for obtaining convergence rate by inputting variable parameters, wherein the regression prediction model comprises a correction variable; obtaining an optimized regression prediction model through multiple iterative training; and calculating the input variable parameter when the value of the optimized regression prediction model is 0, updating the input variable parameter, and controlling by using the updated input variable parameter.

In the aspect and any possible implementation manner described above, there is further provided an implementation manner that, in the precise parking stage, when the train head is located on the platform rail, if a deviation between the train speed and the target speed is greater than a preset threshold, a minimum braking force is output; if the deviation is less than or equal to a preset threshold value, outputting a braking force of 0.5 times of the target braking rate; when the train head is not positioned on the platform rail, if the deviation between the train speed and the target speed is greater than a preset threshold value, the traction force calculated by the PID controller is output; and if the deviation is less than or equal to a preset threshold value, outputting the idle line.

In a second aspect of the disclosure, an electronic device is provided. The electronic device includes: a memory having a computer program stored thereon and a processor implementing the method as described above when executing the program.

In a third aspect of the present disclosure, a computer readable storage medium is provided, having stored thereon a computer program, which when executed by a processor, implements a method as in accordance with the first aspect of the present disclosure.

It should be understood that the statements herein reciting aspects are not intended to limit the critical or essential features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. In the drawings, like or similar reference characters designate like or similar elements, and wherein:

FIG. 1 illustrates a flow chart of an ATO parking control method compatible with different agent vehicles in accordance with an embodiment of the present disclosure;

FIG. 2 illustrates a schematic diagram of learning braking parameters of a train through iterative self-learning during a localized park control according to an embodiment of the present disclosure;

FIG. 3 is a schematic diagram illustrating the effect of generating control commands for train operation based on learned braking parameters and the difference between the train speed and the target speed according to an embodiment of the present disclosure;

FIG. 4 is a schematic diagram illustrating the effect of determining a feed forward value of a precise stopping phase brake delay according to a response time of a train and outputting a brake at a predetermined time in advance according to an embodiment of the disclosure;

FIG. 5 illustrates a block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure.

Detailed Description

To make the objects, technical solutions and advantages of the embodiments of the present disclosure more clear, the technical solutions of the embodiments of the present disclosure will be described clearly and completely with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are some, but not all embodiments of the present disclosure. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.

In addition, the term "and/or" herein is only one kind of association relationship describing an associated object, and means that there may be three kinds of relationships, for example, a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.

Fig. 1 shows a flow chart of an ATO parking control method 100 compatible with different agent vehicles in accordance with an embodiment of the present disclosure.

In a frame 110, the ATO system generates a positioning and stopping control instruction of the train and sends the positioning and stopping control instruction to the train according to the ATO target curve and the current speed of the train based on the PID controller;

in some embodiments, the ATO system calculates an ATO target curve based on the distance from the current location of the train to the destination, the route, and the vehicle performance; in particular, the amount of the solvent to be used,

the ATO system determines multi-target decision variables according to the actual constitution of a train traction and braking system, the parking precision, the punctuality, the comfort level, the energy conservation and other requirements, wherein the multi-target decision variables comprise fixed line speed limit, temporary speed limit, the maximum limit of a train, interval running time, parking precision, comfort level, energy consumption and other decision variables; establishing an objective function and a constraint condition when calculating a target speed curve; solving a target speed curve set meeting the conditions according to the determined target function and the constraint conditions, wherein a target programming method, a genetic algorithm, a particle swarm algorithm and the like can be adopted during solving; and generating different target speed curves according to different emphasis points.

In some embodiments, the ATO system determines a multi-objective decision variable based on the objective demand and establishes a multi-objective decision model based on the multi-objective decision variable; solving the multi-target decision model according to a target function and constraint conditions of the multi-target decision model to obtain a solution space of the multi-target decision model; and selecting a target solution from the solution space as a target speed curve according to the train operation requirement.

In some embodiments, the PID controller is a two-degree-of-freedom robust controller, and the ATO system controls the train to track the ATO target curve to run based on the two-degree-of-freedom robust controller, so that high-precision tracking of an ideal curve is realized, and the ATO automatic driving requirement is met. In a two-degree-of-freedom robust controller, a parameter for optimizing an external disturbance rejection characteristic and a parameter for optimizing a target tracking characteristic are independently adjusted, so that the external disturbance rejection characteristic and the target tracking characteristic are simultaneously optimized. Under the protection of an EBI emergency braking trigger speed curve of ATP, the ATO system calculates the recommended speed of train operation based on a two-degree-of-freedom robust controller, and controls the train to operate according to the recommended speed by outputting a traction braking state command and an analog value representing the magnitude of ATO traction/braking force. In some embodiments, the two-degree-of-freedom robust controller further calculates the recommended speed of train operation according to the calculated traction dynamics parameter and braking dynamics parameter and the track slope. And in the calculation process of the traction dynamics parameters and the braking dynamics parameters, taking a traction curve corresponding to the recommended speed of train operation as a compensation parameter.

At block 120, the braking parameters of the train are learned through iterative self-learning during the positional stop control.

As shown in the attached figure 2, the braking parameters of the train are learned by adopting a staged braking process through an iterative self-learning model based on a moving data window, so that the train braking parameters are obtained by using learning in the final accurate parking stage.

In some embodiments, the positioning and stopping control adopts a distance control mode, a braking mode is calculated according to the distance from a braking starting point to a positioning and stopping point, the speed of the train and the line condition, the speed of the train is reduced by braking in stages near the positioning and stopping point, and the error of the stopping position is continuously corrected, so that the precision of the positioning and stopping and the train control is ensured. For example, a plurality of sets of ground markers (transponders) for parking at a parking position are buried under the track within several hundreds of meters from the parking distance, for example, 350m, 150m, 25m, 8 m. When the train approaches a station, firstly detecting a marker which is farthest from a stop point 350m, and starting a vehicle braking curve; and when the train passes through a marker 8m away from the parking point, the braking curve is updated again, and once the vehicle alignment antenna is directly positioned on the ground alignment coil, the train is controlled to be accurately positioned and parked.

In some embodiments, due to the fact that old vehicles have various adverse factors such as common high delay of service braking and air braking, unstable braking force, poor following consistency, no energy absorption device in a line and the like, after an ATO (automatic train operation) control train enters a precise parking stage, frequent train speed oscillates back and forth around a target speed curve and cannot converge to a target speed, after electric-air conversion is completed, braking adjustment is more difficult under a pure air braking condition, and finally the problems of over-mark and under-mark are frequent.

In order to realize accurate stop of the platform of the ATO, the ATO vehicle control model needs to meet the following requirements: accurate distance measurement accuracy, and a fine train control process. And the impact of vehicle performance on ATO precision stops is reflected in: the controllable minimum step length of deceleration, the delay of a train braking system and the application of train braking. And performance parameters of different vehicles are different among different intergeneration vehicles, for example, the delay time of a train braking system is generally 1.20-1.75s, the air brake is 0.8-1.5s, and the difference between the delay characteristics of pure air brake and electric brake can reach 30% at most. For the ATO control technologies of different intervehicular vehicles, if the same ATO software version is adopted for control, but the performance parameters of different intervehicular vehicles are changed, the problems of over-mark and under-mark are frequent.

Therefore, a staged braking process is adopted, and the braking parameters of the train are learned through an iterative self-learning model based on a moving data window, so that the train braking parameters are obtained through learning in the final accurate parking stage. And then, estimating ATO output according to the train braking parameters obtained by learning and the difference value between the train speed and the target speed, so that the train speed is converged to the target speed as soon as possible.

And designing a self-adaptive iterative learning controller according to the train operation dynamic model, and enabling the operation speed and the operation displacement of the train to gradually track the given expected speed and the expected displacement in the staged braking process of the train. In this embodiment, a given desired speed may be gradually tracked in consideration of only the operating speed of the train. The controller includes a P-type feedback portion based on a velocity tracking error, a parameter estimation portion, and a time lag compensation portion. By adopting the self-adaptive iterative learning control algorithm and the parameter learning rate, the position tracking error and the speed tracking error of the train are smaller when the iteration times are larger.

In some embodiments, in order to realize the gradual tracking of the target speed by the variable expected speed, the change information of the expected speed is added into the parameter learning rate, and the iterative learning can respond to the change of the expected speed and perform additional advanced control on the train according to the related information of the change amount, so that the train is controlled more effectively to operate at the new expected speed.

In block 130, a control command for train operation is generated according to the learned braking parameter and the difference between the train speed and the target speed, and the train is controlled to operate at the recommended speed.

In some embodiments, a recommended speed of train operation is calculated based on the learned train braking parameters, and the train is controlled to operate at the recommended speed by outputting a traction braking status command and an analog value representing the magnitude of ATO traction/braking force, so that the train speed converges to the target speed as soon as possible, as shown in fig. 3 of the accompanying drawings.

In some embodiments, since the delay in the building process of the braking force of old vehicles is generally high, the braking delay of the vehicle causes overshoot and oscillation, which need to be eliminated. The response time of the train and the corresponding feed-forward value of the braking delay are determined according to the type of the vehicle. Generating a train operation control command according to the learned brake parameter and the difference between the train speed and the target speed, further comprising: and determining a feedforward value of brake delay in the accurate parking stage according to the response time of the train, and outputting the brake in advance of preset time.

In some embodiments, the feed forward value of the brake delay may be empirically determined to output braking at a predetermined time in advance, after which the train speed just matches the target speed, as shown in FIG. 4. For example, using expert experience, such as experience of a driver with a high driving experience, a feed forward value is added for a braking delay, braking is output in advance, overshoot and shock caused by vehicle braking delay are eliminated, and after the vehicle braking delay, the train speed is exactly matched with the target speed. For example, the driver driving data when the driver with rich driving experience drives the train is screened from the driver driving historical data; the braking timing of the driver is determined to determine the feed forward value of the braking delay. The situation that the output of ATO is increased due to overshoot is avoided, and the train speed vibrates for many times around the target speed is avoided. In some embodiments, the corresponding feed forward values are added separately for the trains for different braking phases (hybrid braking and air braking phases).

In some embodiments, a feed forward value for the brake delay may also be determined by a regression prediction model, and the brakes may be output a predetermined time in advance, after the vehicle braking delay, the train speed just matches the target speed. The method comprises the following steps:

obtaining an input variable influencing a parking error and an output variable parameter representing a convergence speed; the input variable is a brake delay (an electronic control switching delay time); the output variable is the error between the recommended speed and the target speed.

Establishing a regression prediction model for obtaining convergence rate by inputting variable parameters, wherein the regression prediction model comprises a correction variable; the method comprises the steps of obtaining an empirical formula model according to data preprocessing and analysis of an ATO system, and obtaining a regression prediction model according to the empirical formula model and a preset correction function. The data preprocessing and analysis includes performing machine learning. For example, an empirical formula model in which an input variable parameter of a certain vehicle is braking delay and an output variable parameter is an error between a recommended speed and a target speed can be obtained by performing data arrangement and multiple iterative operation tests through an ATO system, and then a corresponding prediction regression model is obtained.

And obtaining the optimized regression prediction model through machine learning and repeated iterative training. In some embodiments, a cost function is obtained by using a least square method to describe the closeness of the output of the prediction regression model to the actual output variable, and when the value of the cost function is minimum, the optimized regression prediction model is obtained.

And calculating the input variable parameter when the value of the optimized regression prediction model is 0, updating the input variable parameter, and controlling by using the updated input variable parameter so as to realize that the train speed converges to the target speed as soon as possible. In some embodiments, the resulting braking delay is stored in the ATO system so that the ATO system can make calls to achieve accurate stopping and eliminate the overshoot and oscillation when performing stopping control.

In some embodiments, the corresponding feed forward values are added separately for the trains for different braking phases (hybrid braking and air braking phases).

In some embodiments, different ATO versions are set for different agents based on the feed forward values.

In some embodiments, during the precise parking primary braking phase, policy adjustment is also required according to whether the train head is on the platform rail:

1) when the train head is positioned on the platform rail, traction is cut off, and the braking output value is adjusted according to the train speed and the target speed deviation: when the deviation is larger than a preset threshold value, outputting the minimum braking force; when the deviation is less than or equal to a preset threshold value, outputting a braking force of 0.5 times of the target braking rate;

2) when the train head is not positioned on the platform rail, when the deviation is greater than a preset threshold value, the traction force calculated by the PID controller is output; and outputting the idle line when the deviation is less than or equal to a preset threshold value.

According to the embodiment of the disclosure, the following technical effects are achieved:

the problem of accuse car that different inter-vehicle braking force build-up process delay parameter difference caused is solved, the speed of a motor vehicle vibrates not to converge, parks the mark difficult problem in accurate parking stage has been solved.

It is noted that while for simplicity of explanation, the foregoing method embodiments have been described as a series of acts or combination of acts, it will be appreciated by those skilled in the art that the present disclosure is not limited by the order of acts, as some steps may, in accordance with the present disclosure, occur in other orders and concurrently. Further, those skilled in the art should also appreciate that the embodiments described in the specification are exemplary embodiments and that acts and modules referred to are not necessarily required by the disclosure.

FIG. 5 shows a schematic block diagram of an electronic device 500 that may be used to implement embodiments of the present disclosure. The apparatus 500 may be used to implement the ATO system of fig. 1. As shown, device 500 includes a Central Processing Unit (CPU)501 that may perform various appropriate actions and processes in accordance with computer program instructions stored in a Read Only Memory (ROM)502 or loaded from a storage unit 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the device 500 can also be stored. The CPU 501, ROM 502, and RAM 503 are connected to each other via a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.

A number of components in the device 500 are connected to the I/O interface 505, including: an input unit 506 such as a keyboard, a mouse, or the like; an output unit 507 such as various types of displays, speakers, and the like; a storage unit 508, such as a magnetic disk, optical disk, or the like; and a communication unit 509 such as a network card, modem, wireless communication transceiver, etc. The communication unit 509 allows the device 500 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.

The processing unit 501 performs the various methods and processes described above, such as the method 100. For example, in some embodiments, the method 100 may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 508. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 500 via the ROM 502 and/or the communication unit 509. When loaded into RAM 503 and executed by CPU 501, may perform one or more of the steps of method 100 described above. Alternatively, in other embodiments, CPU 501 may be configured to perform method 100 in any other suitable manner (e.g., by way of firmware).

The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a load programmable logic device (CPLD), and the like.

Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

Further, while operations are depicted in a particular order, this should be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable subcombination.

Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.