Patent Application
20170283085
The present disclosure relates generally to assessing structural loads of an aircraft (and other aerospace vehicles) and, in particular, to determining the structural severity of ground or flight events on the aircraft.
Regularly-scheduled maintenance of aircraft and other similar manufactured products have both operational and economic impacts on the daily business affairs of the overall aircraft fleet. It is important to precisely determine desired times or intervals for performing maintenance tasks to efficiently run an airline. Undesirably, unscheduled maintenance tasks can disrupt operational schedules as a result of misdiagnosing the impact or severity of a ground or flight event on an aircraft (e.g., misdiagnosing a hard landing of an aircraft) or an inability to efficiently monitor the structural health of the aircraft.
In particular, misdiagnosed hard landings may significantly impact aircraft dispatch reliability as the inspection process for assessing damage of an allegedly heavy or hard landing event is both time consuming and costly. Empirical evidence shows that, depending on the platform, 90% of pilot-initiated hard landing inspections result in no signs of damage which resultantly causes a loss of revenue due to the down-time of the aircraft. Therefore, it is desirable to have a system and method that reduces unnecessary inspections by improving upon existing practices.
Example implementations of the present disclosure are directed to an improved system, method and computer-readable storage medium for structural load assessment of an aircraft. In particular, as opposed to subjective determinations or assessments, the system utilizes machine learning techniques and structural dynamics models for accurately assessing the impact of ground or flight events on an aircraft, based at least in part on flight parameters obtained during the ground or flight event. The system may then automatically perform or trigger maintenance activities as required for the aircraft.
In particular, the system may be configured to quickly and efficiently detect structural damage within an aircraft for ensuring the safety thereof. The system may reduce false alarms that cause unnecessary service interruptions and expensive maintenance actions. Accordingly, the system may maximize the use of available ground and flight load information for implementing a high probability of detecting structural damage within an aircraft while maintaining a low false alarm rate. The present disclosure includes, without limitation, the following example implementations.
In some example implementations, a method is provided for structural load assessment of an aircraft. The method may comprise receiving flight parameters related to at least one of a ground or flight event of an aircraft, and calculating a response load on the aircraft as a result of the ground or flight event. The response load may be calculated from the flight parameters using a machine learning algorithm and a structural dynamics model of the aircraft. The method may also comprise comparing the response load to a corresponding design load, and based at least in part on the comparison, determining the structural severity of the at least one ground or flight event on the aircraft. The method may also comprise automatically initiating a maintenance activity requirement for the aircraft in an instance in which the structural severity of the at least one ground or flight event causes a limit exceedance state of at least one of the aircraft or at least one structural element of the aircraft.
In some example implementations of the method of the preceding or any subsequent example implementation, or any combination thereof, calculating the response load includes calculating the response load using the machine learning algorithm comprising at least one of a Kalman filter algorithm or a heuristic algorithm, and in at least one instance updating at least one of the machine learning algorithm or the structural dynamics model based at least in part on at least one of flight test data or flight operation data.
In some example implementations of the method of any preceding or any subsequent example implementation, or any combination thereof, calculating the response load includes calculating the response load using the machine learning algorithm that is or includes a heuristic algorithm in which the heuristic algorithm is or includes at least one of an artificial neural network, Gaussian process, regression, support vector transform, classification, clustering, or principal component analysis algorithm.
In some example implementations of the method of any preceding or any subsequent example implementation, or any combination thereof, receiving the flight parameters includes receiving the flight parameters including at least one of a vertical sink rate, pitch altitude, roll angle, roll rate, drift angle, initial sink acceleration, gross weight, center of gravity, maximum vertical acceleration at or near at least one of the aircraft nose or a pilot seat, maximum vertical acceleration at the center of gravity, or ground speed of the aircraft.
In some example implementations of the method of any preceding or any subsequent example implementation, or any combination thereof, in the instance in which the structural severity of the at least one ground or flight event causes the limit exceedance state of at least one of the aircraft or at least one structural element of the aircraft, the at least one ground or flight event includes at least one of a hard landing, overweight landing, hard braking event, encounter with turbulence, extreme maneuvering, speed limit exceedance, or stall buffet condition(s) of the aircraft.
In some example implementations of the method of any preceding or any subsequent example implementation, or any combination thereof, further comprising transmitting information indicating the structural severity of the at least one ground or flight event to at least one of an external inspection system or a health monitoring system onboard the aircraft, the external inspection system and health monitoring system being configured to download the information thereto.
In some example implementations of the method of any preceding or any subsequent example implementation, or any combination thereof, receiving the flight parameters includes receiving the flight parameters from a control unit of a health monitoring system onboard the aircraft.
In some example implementations, an apparatus is provided for structural load assessment of an aircraft. The apparatus comprises a processor and a memory storing executable instructions that, in response to execution by the processor, cause the apparatus to implement a number of subsystems, such as an approximator, and analysis and maintenance engines, which may be configured to at least perform the method of any preceding example implementation, or any combination thereof.
In some example implementations of the apparatus of the preceding example implementation, at least the processor or a memory of the apparatus may be embedded in at least one of a health monitoring system onboard the aircraft, an external inspection system, database, or a portable electronic device.
In some example implementations, a computer-readable storage medium is provided for structural load assessment of an aircraft. The computer-readable storage medium is non-transitory and has computer-readable program code portions stored therein that, in response to execution by a processor, cause an apparatus to at least perform the method of any preceding example implementation, or any combination thereof.
These and other features, aspects, and advantages of the present disclosure will be apparent from a reading of the following detailed description together with the accompanying drawings, which are briefly described below. The present disclosure includes any combination of two, three, four or more features or elements set forth in this disclosure, regardless of whether such features or elements are expressly combined or otherwise recited in a specific example implementation described herein. This disclosure is intended to be read holistically such that any separable features or elements of the disclosure, in any of its aspects and example implementations, should be viewed as intended, namely to be combinable, unless the context of the disclosure clearly dictates otherwise.
It will therefore be appreciated that this Brief Summary is provided merely for purposes of summarizing some example implementations so as to provide a basic understanding of some aspects of the disclosure. Accordingly, it will be appreciated that the above described example implementations are merely examples and should not be construed to narrow the scope or spirit of the disclosure in any way. Other example implementations, aspects and advantages will become apparent from the following detailed description taken in conjunction with the accompanying drawings which illustrate, by way of example, the principles of some described example implementations.
Having thus described example implementations of the disclosure in general terms, reference will now be made to the accompanying drawings, which are not necessarily drawn to scale, and wherein:
Some implementations of the present disclosure will now be described more fully hereinafter with reference to the accompanying drawings, in which some, but not all implementations of the disclosure are shown. Indeed, various implementations of the disclosure may be embodied in many different forms and should not be construed as limited to the implementations set forth herein; rather, these example implementations are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art. For example, unless otherwise indicated, reference to something as being a first, second or the like should not be construed to imply a particular order. Also, for example, reference may be made herein to quantitative measures, values, relationships or the like. Unless otherwise stated, any one or more if not all of these may be absolute or approximate to account for acceptable variations that may occur, such as those due to engineering tolerances or the like. Like reference numerals refer to like elements throughout.
Example implementations of the present disclosure are generally directed to assessing structural loads of an aircraft and, in particular, to determining the severity of ground or flight events on the structure of an aircraft. Example implementations will be primarily described in conjunction with aerospace applications in which the aircraft may be composed of one or more structural elements, such as one or more materials, components, assemblies and sub-assemblies. It should be understood, however, that example implementations may be utilized in conjunction with a variety of other applications, both in the aerospace industry and outside of the aerospace industry. In this regard, example implementations may be utilized in conjunction with complex systems, vehicles or the like, such as in the case of aerospace, automotive, marine and electronics. For example, while the example implementations may be discussed or illustrated herein with reference to an aircraft, the present disclosure may be applied to a number of aerospace vehicles including aircrafts, spacecraft, and other vehicles not explicitly contemplated herein.
The system 100 may be generally configured to accurately assess structural loads on an aircraft as a result of flight events such as assessing the impact or severity of a landing on the aircraft. Among various benefits, the system may provide minimal false positive and zero false negative indications of severe flight events (e.g., hard landing, overweight landing, hard braking event, turbulence conditions, extreme maneuvering, speed limit exceedance, stall buffet conditions, and the like). The system may also increase reliability (e.g., the system utilizes machine learning algorithms and a structural dynamics model of the aircraft and does not solely rely upon measurements from sensors that may provide erroneous data or be susceptible to damage) for assessment of structural loads. The system may also provide for rapid and efficient computation of structural load assessments on-board an aircraft to determine the need for inspection. Individually or collectively these benefits may reduce the number of hours an aircraft may be off-line for inspection which in turn may save airline operators significant revenue, maintenance cost, and customer inconvenience.
The system 100 may include one or more of each of a number of different subsystems (each an individual system) coupled to one another for performing one or more functions or operations. As shown in
As explained in greater detail below, the approximator 102 may be generally configured to receive flight parameters related to a ground or flight event of an aircraft, and calculate a response load on the aircraft as a result of the ground or flight event, in which the response load may be calculated from the flight parameters using a machine learning algorithm and a structural dynamics model of the aircraft. The analysis engine 104 may be coupled to the approximator and generally configured to compare the response load to a corresponding design load, and based at least in part on the comparison, determine the structural severity of the ground or flight event on the aircraft. The maintenance engine 106 may be coupled to the analysis engine and generally configured to automatically initiate a maintenance activity requirement for the aircraft in an instance in which the structural severity of the ground or flight event causes a limit exceedance state of the aircraft or at least one structural element thereof.
According to example implementations of the present disclosure, the system 100 and its subsystems and/or components including the approximator 102, analysis engine 104, and/or maintenance engine 106 may be implemented by various means. Means for implementing the systems, subsystems and their respective elements may include hardware, alone or under direction of one or more computer programs from a computer-readable storage medium.
In some examples, one or more apparatuses may be provided that are configured to function as or otherwise implement the systems, subsystems, tools and respective elements shown and described herein. In examples involving more than one apparatus, the respective apparatuses may be connected to or otherwise in communication with one another in a number of different manners, such as directly or indirectly via a wired or wireless network or the like.
In more particular examples, the electronic device may be embedded in a health monitoring system onboard an aircraft, embedded in or coupled to a control unit of the health monitoring system. Or in some examples, the electronic device may be embodied in a fixed or mobile on-ground maintenance system coupleable (by wired or wirelessly) to the control unit of a health monitoring system onboard an aircraft. In some examples, the apparatus may be embodied within a database and/or other infrastructure which may allow further improvement of the probability of detecting structural damage and reduction of false alarms by leveraging historical data across a fleet of aircraft and across various aircraft types maintained by a ground fleet management support system.
The apparatus 200 may include one or more of each of a number of components such as, for example, a processor 202 (e.g., processor unit) connected to a memory 204 (e.g., storage device) having computer-readable program code 206 stored therein. In addition to the memory, the processor may also be connected to one or more interfaces for displaying, transmitting and/or receiving information. The interfaces may include an input interface 208, display 210 and/or communication interface 212 (e.g., communications unit).
The input interface 208 may be configured to manually or automatically receive information such as flight parameters from an aircraft. In some examples, the input interface may be coupled or coupleable to a control unit of a health monitoring system onboard the aircraft, and through which the approximator 102 of the system 100 implemented by apparatus 200 may be configured to receive the flight parameters from the control unit. The apparatus may implement the system further including the analysis engine 104 to determine the structural severity of a ground or flight event on the aircraft based on a response load on the aircraft, which may be calculated from the flight parameters using a machine learning algorithm and a structural dynamics model of the aircraft, as indicated above and described more fully below.
In some example implementations, the display 210 may be coupled to the processor 202 and configured to display or otherwise present information indicating the structural severity of the ground or flight event. Additionally or alternatively, in some example implementations, the communication interface 212 may be coupled to the processor 202 and configured to transmit information indicating the structural severity of the ground or flight event to at least one of an external inspection system or a health monitoring system onboard the aircraft, such as in the instance in which the structural severity of the ground or flight event causes the limit exceedance state of the aircraft or at least one structural element thereof.
For example, the displayed and/or transmitted information may be or include ground and/or flight load information (e.g. landing, hard braking event, turbulence, maneuvering, speed limit exceedance, stall buffet information, and the like), which may be used to direct inspections and therefore reduce inspection cost and time. In these examples, the external inspection system and health monitoring system may be configured to download the information thereto. In some implementations, the display 210 may be embedded within a flight deck of the aircraft such that the transmitted information may be visible to a pilot or other aircraft personnel within the flight deck via the display (e.g., visible display page within the flight deck of the aircraft).
Reference is now again made to
Any of a number of different flight parameters may be suitable for example implementations of the present disclosure. Examples of suitable flight parameters may be or include at least one of a vertical sink rate, pitch altitude, roll angle, roll rate, drift angle, initial sink acceleration, gross weight, center of gravity, control surface deflections maximum vertical acceleration near the nose of the aircraft or at a pilot seat, maximum vertical acceleration at the center of gravity, or ground speed of the aircraft. In some examples, the flight parameters may include sensor data recorded during a flight, including the ground or flight event, by various sensors and systems. In these example implementations, the flight parameters may be received automatically via the various sensors and systems. Examples of suitable sensors and systems include Avionics systems, Flight Controls systems, and/or other Flight Operations or Maintenance Operations systems or components thereof. Examples of suitable sensor data in addition to flight parameters may include strains and accelerations measured at key locations on the aircraft.
In some example implementations, the flight parameters may be recorded with appropriate sample rates for resolving proper peak values during a ground or flight event (e.g., landing, side or drag, turbulence, maneuvering, speed limit exceedance, stall buffet, and the like). For example, the flight parameters may be recorded at a minimum of eight (8) samples per second. In these example implementations, higher sampling rates may correlate to more accurate peak information being captured from the time varying flight parameter information. It should be noted that although flight parameters may be recorded in real-time during a ground or flight event, various functions of the system may be executed in real-time or after an occurrence of the ground or flight event (e.g., after touchdown during a landing).
In these implementations, the approximator 102 may process the flight parameters and return a single value or reduced set of values of one or more of the flight parameter recorded during the ground or flight event (e.g., touchdown during a landing). The single value or reduced set of values, in some instances, may be based at least in part on a maximum and/or minimum value of the flight parameter recorded during the ground or flight event. For example, the approximator may identify maximum or peak values of the flight parameters (e.g., left and right gear truck tilt, normal acceleration at center of gravity, rate of sink, pitch angle, roll angle, roll rate, drift angle, gross weight, center of gravity, normal acceleration at cockpit, equivalent airspeed, and the like) during the ground or flight event. In particular, in some implementations, the reduced set of values may be recorded during a specific time frame before and/or after the ground or flight event.
As indicated above, the approximator 102 may be configured to calculate the response load on the aircraft as a result of the ground or flight event. The response load may be calculated from the flight parameters and using a machine learning algorithm and a structural dynamics model of the aircraft, and in some examples may include one or more response loads at respective key distinct locations, as shown in
In at least one instance, the approximator 102 may be configured to update (e.g., automatically or in response to a manual trigger) at least one of the machine learning algorithm or structural dynamics model based at least in part on flight test data or flight operation data that may be maintained in a database as an integral part of the aircraft service system. In particular, the model may be generated, periodically updated, and verified from flight tests as well as historical flight data which may be stored and maintained in a database including architectural elements of the system conceived using processes described herein.
In some examples, the machine learning algorithm may be or include a Kalman filter algorithm and/or a heuristic algorithm. In these examples, the heuristic algorithm may be or include at least one of an artificial neural network, Gaussian process, regression, support vector transform, classification, clustering, principal component analysis algorithm, or the like. Other suitable heuristic algorithms include heuristic modeling techniques as disclosed in U.S. Pat. Pub. No. 2008/0114506 to Davis et al., the content of which is incorporated herein by reference in its entirety. In some example implementations, the heuristic algorithm may execute a high-order nonlinear curve fitting for calculating the response load from the flight parameters.
As shown in
In the illustrated example, a load prediction error may be modeled as a Gaussian distribution 502 having a known standard deviation, in which the safety margin may be a factor that is applied to each calculated response load for subsequently eliminating a false negative indication of a structural severity on the aircraft. For example, in an instance in which the ground or flight event is a landing, the safety margin may be implemented by applying a multiplier to the output variance and adding the resulting value to the mean load output. The safety margins may account for sources of error such as machine learning uncertainty, input measurement and down-sampling errors, and the like.
In order to accomplish this, the machine learning algorithm may be developed with noisy inputs to represent flight parameter measurement error and/or sampling error. A process for developing or generating the machine learning algorithm may comprise a plurality of steps including using in-service or flight test data sets to quantify an error distribution of each input due to sampling, building the noise or error into an analytical data set for developing a reduced-order heuristic load model (e.g., Monte Carlo simulation), and passing the noisy input information to the heuristic load model for training.
Once trained, a resulting prediction interval produced by the heuristic load model may intrinsically incorporate an additional error, caused by the input error, by widening an output distribution to account for flight parameter input scatter. A factor may be computed to reduce the probability of missing a hard landing. For example, using a discrete (e.g., binomial) probability distribution function, the factor for guaranteeing zero false negatives across a fleet of 30 aircraft for 30 years with a 95% confidence may be approximately 3. In service, the measured flight parameters may be applied to the heuristic load model to compute a mean response load output. The final load output reported for structural load assessment may be or include the mean value plus the factor multiplied by sigma to account for any input error and/or model uncertainty.
The approximator 102 may also be configured to calculate a response load on the aircraft as a result of the ground or flight event in which the response load may be calculated from the flight parameters. In some example implementations, the calculation of the response load on the aircraft may be or include a prediction of the response load based at least in part on the one or more flight parameters. The approximator may be configured to provide data (e.g., calculated response loads) to the analysis engine 104 for use in subsequently determining the structural severity of the ground or flight event of the aircraft.
The analysis engine 104 may be configured to compare the response load to a corresponding design load, and based at least in part on the comparison, determine a structural severity of the ground or flight event on the aircraft. The analysis engine may be coupled to the approximator 102 and/or the maintenance engine 106. The analysis engine may be configured to receive calculated response loads from the approximator for use in determining the structural severity of the ground or flight event on the aircraft.
In some implementations, comparing the response load to its corresponding design load or limit may include normalizing the response load with respect to the design load for determining the structural severity of the ground or flight event on the aircraft. For example, if the normalized load is greater than one (1), the analysis engine may determine that the ground or flight event severity is great enough to require structural inspection since the response load exceeded its design limit. Alternatively, if less than one (1), the analysis engine may determine that that the ground or flight event has not structurally impacted the aircraft.
In some examples, the analysis engine 104 may also be configured to calculate a residual life expectancy of the aircraft or at least one structural element thereof based at least in part on the structural severity of the ground or flight event on the aircraft. In these example implementations, the analysis engine may be configured to track historic flight event loads which may reduce scheduled maintenance inspection frequency and/or extend the life of the structural elements as a result of calculating the residual life expectantly or influencing future structural design for provided cost and weight savings.
The maintenance engine 106 may be configured to automatically initiate a maintenance activity requirement for the aircraft in an instance in which the structural severity of the ground or flight event causes a limit exceedance state of the aircraft or at least one structural element thereof. In further examples, the maintenance engine may be configured to automatically perform or trigger the maintenance activity itself for the aircraft. In some example implementations, in an instance in which the structural severity of the ground or flight event causes the limit exceedance state of the aircraft or at least one structural element thereof, the ground or flight event may include at least one of a hard landing, hard braking event, overweight landing, extreme maneuvering, speed limit exceedance, encounter with turbulence, stall buffet conditions, or the like.
In some example implementations, maintenance of a structural element may include inspection that may lead to repair or replacement of the part at its various locations and/or the repair or replacement work itself. In some example implementations, the maintenance engine 106 may be configured to automatically schedule the part for removal and/or replacement based at least partially on the structural severity of the ground or flight event on the structural element. The maintenance engine may determine a need or requirement for inspection after a ground or flight event (e.g., suspected hard or overweight landing), and further identify locations at which the inspection may be required.
As previously indicated, calculated response loads may be normalized with respect to the corresponding design loads for determining the severity of the structural event on the aircraft. In these example implementations, the normalized response loads may be grouped to represent a need or requirement for inspection across a general aircraft zone such as left main landing gear, right main landing gear, left engine strut, right engine strut, auxiliary power unit, empennage, forward fuselage, aft fuselage, and the like. For example, normalized response loads of all left main landing gear response loads (e.g., left gear vertical load, left gear drag load (aft, spin-up), left gear drag load (forward, spring-back), left drag brace tension, left drag brace compression, left side brace tension, left side brace compression, left gear beam vertical load) may be utilized to represent the need or requirement for inspection of the left main gear. The same rationale may be applied to the right main gear, forward body loads, aft body loads, left and right engine, and the like.
In some example implementations, the maintenance engine 106 may be operatively coupled to a display (e.g., display 210) configured to present to a user a Boolean flag identifying the need or requirement for maintenance or inspection within the aircraft. In these implementations, a Boolean flag may be presented for each general zone within the aircraft. For example, each aircraft inspection zones may have a corresponding line on the display in which a zero (0) or “NO” may indicate that no inspection is needed, and a one (1) or “YES” may indicate the need for maintenance or inspection within the aircraft zone.
Reference is now again made to
The processor 202 may be a number of processors, a multi-processor core or some other type of processor, depending on the particular implementation. Further, the processor may be implemented using a number of heterogeneous processor systems in which a main processor is present with one or more secondary processors on a single chip. As another illustrative example, the processor may be a symmetric multi-processor system containing multiple processors of the same type. In yet another example, the processor may be embodied as or otherwise include one or more application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or the like. Thus, although the processor may be capable of executing a computer program to perform one or more functions, the processor of various examples may be capable of performing one or more functions without the aid of a computer program.
The memory 204 is generally any piece of computer hardware that is capable of storing information such as, for example, data, computer programs (e.g., computer-readable program code 206) and/or other suitable information either on a temporary basis and/or a permanent basis. The memory may include volatile and/or non-volatile memory, and may be fixed or removable. Examples of suitable memory include random access memory (RAM), read-only memory (ROM), a hard drive, a flash memory, a thumb drive, a removable computer diskette, an optical disk, a magnetic tape or some combination of the above. Optical disks may include compact disk-read only memory (CD-ROM), compact disk-read/write (CD-R/W), DVD or the like. In various instances, the memory may be referred to as a computer-readable storage medium. The computer-readable storage medium is a non-transitory device capable of storing information, and is distinguishable from computer-readable transmission media such as electronic transitory signals capable of carrying information from one location to another. Computer-readable medium as described herein may generally refer to a computer-readable storage medium or computer-readable transmission medium.
The communication interface 208 may be configured to transmit and/or receive information, such as to and/or from other apparatus(es), network(s) or the like. The communication interface may be configured to transmit and/or receive information by physical (wired) and/or wireless communications links. Examples of suitable communication interfaces include a network interface controller (NIC), wireless NIC (WNIC) or the like.
The display 210 may be configured to present or otherwise display information to a user, suitable examples of which include a liquid crystal display (LCD), light-emitting diode display (LED), plasma display panel (PDP) or the like.
The input interface 212 may be wired or wireless, and may be configured to receive information from a user into the apparatus, such as for processing, storage and/or display. Suitable examples of user input interfaces include a microphone, image or video capture device, keyboard or keypad, joystick, touch-sensitive surface (separate from or integrated into a touchscreen), biometric sensor or the like. The user interfaces may further include one or more interfaces for communicating with peripherals such as printers, scanners or the like.
As indicated above, program code instructions may be stored in memory, and executed by a processor, to implement functions of the systems, subsystems and their respective elements described herein. As will be appreciated, any suitable program code instructions may be loaded onto a computer or other programmable apparatus from a computer-readable storage medium to produce a particular machine, such that the particular machine becomes a means for implementing the functions specified herein. These program code instructions may also be stored in a computer-readable storage medium that can direct a computer, a processor or other programmable apparatus to function in a particular manner to thereby generate a particular machine or particular article of manufacture. The instructions stored in the computer-readable storage medium may produce an article of manufacture, where the article of manufacture becomes a means for implementing functions described herein. The program code instructions may be retrieved from a computer-readable storage medium and loaded into a computer, processor or other programmable apparatus to configure the computer, processor or other programmable apparatus to execute operations to be performed on or by the computer, processor or other programmable apparatus.
Retrieval, loading and execution of the program code instructions may be performed sequentially such that one instruction is retrieved, loaded and executed at a time. In some example implementations, retrieval, loading and/or execution may be performed in parallel such that multiple instructions are retrieved, loaded, and/or executed together. Execution of the program code instructions may produce a computer-implemented process such that the instructions executed by the computer, processor or other programmable apparatus provide operations for implementing functions described herein.
Execution of instructions by a processor, or storage of instructions in a computer-readable storage medium, supports combinations of operations for performing the specified functions. In this manner, an apparatus 200 may include a processor 202 and a computer-readable storage medium or memory 204 coupled to the processor, where the processor is configured to execute computer-readable program code 206 stored in the memory. It will also be understood that one or more functions, and combinations of functions, may be implemented by special purpose hardware-based computer systems and/or processors which perform the specified functions, or combinations of special purpose hardware and program code instructions.
Many modifications and other implementations of the disclosure set forth herein will come to mind to one skilled in the art to which the disclosure pertains having the benefit of the teachings presented in the foregoing description and the associated drawings. Therefore, it is to be understood that the disclosure is not to be limited to the specific implementations disclosed and that modifications and other implementations are intended to be included within the scope of the appended claims. Moreover, although the foregoing description and the associated drawings describe example implementations in the context of certain example combinations of elements and/or functions, it should be appreciated that different combinations of elements and/or functions may be provided by alternative implementations without departing from the scope of the appended claims. In this regard, for example, different combinations of elements and/or functions than those explicitly described above are also contemplated as may be set forth in some of the appended claims. Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation.
