Patent Publication Number: US-2023145553-A1

Title: System for charging multiple power sources and monitoring diode currents for faults

Description:
FIELD OF THE INVENTION 
     The present invention generally relates to the field of power source structures. In particular, the present invention is directed to a system for charging multiple power sources. 
     BACKGROUND 
     Electric aircraft often rely on batteries in order to power the aircraft. If an electric aircraft experiences a failure in its power source system, the results can be catastrophic. Existing solutions do not provide an adequate way of eliminating points of failure in the power source system in order to preserve the safety of the aircraft. 
     SUMMARY OF THE DISCLOSURE 
     In an aspect, A system for charging multiple power sources, including a plurality of batteries; a plurality of diodes, each diode of the plurality of diodes electrically connected in series with at least a battery of the plurality of batteries, wherein each diode of the plurality of diodes restricts current flow into the plurality of batteries and the plurality of diodes permits current flow from the plurality of batteries. The system for charging multiple power sources further including a bus element, wherein the bus element electrically connects to the diodes in parallel; the bus element includes a cross tie element having an engaged state and disengaged state; and the bus element disconnects a first battery of the plurality of batteries from a second battery of the plurality of when the cross tie element is in the disengaged state, and connects the first battery to the second battery when the cross tie element is in the engaged state. Power source structure also includes a load electrically connected to the plurality of diodes. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       For the purpose of illustrating the invention, the drawings show aspects of one or more embodiments of the invention. However, it should be understood that the present invention is not limited to the precise arrangements and instrumentalities shown in the drawings, wherein: 
         FIG.  1    is a diagram of a system for charging multiple power sources; 
         FIG.  2    is a diagram of a system for charging multiple power sources having a positive bus element and a negative bus element; 
         FIG.  3    is a diagram of a system for charging multiple power sources wherein each battery is wired to diodes placed in parallel; 
         FIG.  4    is a block diagram of an exemplary flight controller. 
         FIG.  5    is a block diagram of an exemplary machine learning module. 
         FIG.  6 A  is an isometric view of a diode heat sink system; 
         FIG.  6 B  is a cross-section view of a diode heat sink system; and 
         FIG.  7    is a block diagram of a computing system that can be used to implement any one or more of the methodologies disclosed herein and any one or more portions thereof. 
     
    
    
     The drawings are not necessarily to scale and may be illustrated by phantom lines, diagrammatic representations and fragmentary views. In certain instances, details that are not necessary for an understanding of the embodiments or that render other details difficult to perceive may have been omitted. 
     DETAILED DESCRIPTION 
     At a high level, aspects of the present disclosure are directed to a power source structure incorporating diodes for use in an electric aircraft. In an embodiment, each battery or power source in the power source structure can be connected to two sets of a diode and a current sensor, the sets wired to the battery in parallel. In another embodiment, the batteries may be connected to a bus element. 
     Aspects of the current disclosure help improve the redundancy of the power systems of an electric aircraft. This is particularly important because if the power systems of an electric aircraft fail, those on board, as well as those on the ground may be placed in grave danger. Arranging batteries in parallel helps to provide needed redundancy if a battery were to fail and diodes can be used in order to prevent electricity from flowing from one battery in parallel to another of the batteries in parallel. 
       FIG.  1    is a diagram of system for charging multiple power sources  100  including a plurality of batteries  104   a - b,  cross tie element  108 , diode  112 , bus element  116 , and load  120 . Each battery of plurality of batteries  104   a - b  is an energy storage device for the purposes of this disclosure. Battery  104  may include an electrochemical cell configured to store potential electrical energy in the form of a chemical reaction. Battery  104  can be any energy storage element such as such as pouch cells, cylindrical cells, or electrochemical cells, for example. A battery may include, without limitation, a battery using nickel based chemistries such as nickel cadmium or nickel metal hydride, a battery using lithium ion battery chemistries such as a nickel cobalt aluminum (NCA), nickel manganese cobalt (NMC), lithium iron phosphate (LiFePO4), lithium cobalt oxide (LCO), and/or lithium manganese oxide (LMO), a battery using lithium polymer technology, lead-based batteries such as without limitation lead acid batteries, metal-air batteries, or any other suitable battery. Additionally, battery  104  need not be made up of only a single electrochemical cell, it can consist of several electrochemical cells wired in series or in parallel.  FIG.  1    depicts an embodiment of this disclosure where there are two batteries  104  wired in parallel. However, in other embodiments, there may be more than two batteries  104  wired in parallel. For instance, in some embodiments, there may be four batteries  104  wired in parallel. In other embodiments, there may be more than four batteries  104  wired in parallel. One of ordinary skill in the art, upon reviewing the entirety of this disclosure, would appreciate that any number of batteries  104  may be wired in parallel according to the energy needs of the particular application. Additionally, in a non-limiting embodiment, two or more batteries  104  may be wired in series, while wired in parallel to one or more other batteries  104 . One of ordinary skill in the art, upon reviewing the entirety of this disclosure, would realize that any number of wiring configurations for any number of batteries  104  would be suitable for this application. 
     With continued reference to  FIG.  1   , a plurality of batteries  104  may, in some embodiments, include a battery management component. The battery management component may check the voltage, charge state, capacity, health, temperature or any other battery measurement. In some embodiments, where the battery management component detects that one of the previously listed battery measurements indicates a fault, then battery management component may send an alert to the user, or may deactivate the battery for which a fault was detected. The battery management component may be implemented as disclosed in U.S. application Ser. No. 17/111,002, filed on Dec. 3, 2020, entitled “Systems and Methods for a Battery Management System Integrated in a Battery Pack Configured for Use in Electric Aircraft,” the entirety of which is incorporated herein by reference. 
     With continued reference to  FIG.  1   , a diode  112  is joined in series with each battery  104 . For the purposes of this disclosure, diode  112  can be any electronic component that provides low resistance to electronic current flowing on one direction and high resistance to electronic current flowing in the other direction. The low resistance is ideally very close to zero. The high resistance is ideally very high. As a non-limiting example, the high resistance may be in the range of 1000 ohms to 10 megaohms. In some embodiments, the high resistance may be higher than the previously listed range. Each diode  112  is configured such that it allows current to flow in the direction from battery  104  to bus element  116  but, conversely, restricts current flow from bus element  116  to the battery  104 . Diode  112  may be any type of diode. 
     With continued reference to  FIG.  1   , a bus element  116  is connected to each diode  112  in the plurality of diodes  112 . For the purposes of this disclosure, a “bus element” is an electrically conductive pathway connecting at least a component in a system configured to convey electrical energy between components. Bus element  116  may include one or more electrically conductive pathways configured to transfer electrical energy across the pathways to convey electrical energy from one component to one or more other components. Bus element  116  may include, without limitation, one or more metallic strips and/or bars. Bus element  116  may include a ring bus. Bus element  116  may be implemented as disclosed in U.S. application Ser. No. 17/348,240, filed on Jun. 15, 2021, titled “System and Method for Dynamic Excitation of an Energy Storage Element Configured for Use in an Electric Aircraft,” the entirety of which is hereby incorporated by reference. For the purpose of this disclosure, a “ring bus” is a bus element wherein circuit breakers are connected to form a ring with isolators on both sides of each circuit breaker. Ring bus may include a component configured to isolate a fault by tripping two circuit breakers while all other circuits remain in service; such a component may include a cross tie element as described in this disclosure. Bus element  116  may be disposed in or on a switchgear, panel board, busway enclosure, plurality of batteries  104 , any portion of electric aircraft, plurality of propulsors, or a combination thereof. Bus element  116  may also be used to connect high voltage equipment at electrical switchyards, and low voltage equipment in plurality of batteries  104 . Bus element  116  may be uninsulated; Bus element  116  may have sufficient stiffness to be supported in air by insulated pillars. These features allow sufficient cooling of the conductors, and the ability to tap in at various points without creating a new joint. Bus element  116  may include material composition and cross-sectional size configured to conduct electricity where the size and material determine the maximum amount of current that can be safely carried. Bus element  116  may be produced in a plurality of shapes including flat strips, solid bars, rods, or a combination thereof. Bus element  116  may be composed of copper, brass, aluminum as solid or hollow tubes, in embodiments. Bus element  116  may include flexible buses wherein thin conductive layers are sandwiched together; such an arrangement may include a structural frame and/or cabinet configured to provide rigidity to Bus element  116 . Bus element  116  may include distribution boards configured to split the electrical supply into separate circuits at one location. Busways, or bus ducts, are long busbars with a protective cover. Rather than branching from the main supply at one location, they allow new circuits to branch off anywhere along the route of the busway. Bus element  116  may either be supported on insulators, or else insulation may completely surround it. Busbars are protected from accidental contact either by an enclosure or by design configured to remove it from reach. Bus element  116  may be connected to each other and to electrical apparatus by bolted, clamped, or welded connections. 
     With continued reference to  FIG.  1   , bus element  116  is connected to a load  120 . Load  120  may, as a non-limiting example include any component of an electric aircraft that is powered by batteries. As a non-limiting example, load  120  may include a motor, a fan, a processor, a flight controller, avionics, actuators, a computer, lights, climate control, or any other appropriate load. As another non-limiting example, load  120  may include a flight component. Flight component may include power sources, control links to one or more elements, fuses, and/or mechanical couplings used to drive and/or control any other flight component. Flight component may include a motor that operates to move one or more flight control components, to drive one or more propulsors, or the like. A motor may be driven by direct current (DC) electric power and may include, without limitation, brushless DC electric motors, switched reluctance motors, induction motors, or any combination thereof. A motor may also include electronic speed controllers (not shown) or other components for regulating motor speed, rotation direction, and/or dynamic braking. While  FIG.  1    only depicts one load  120 , there may be any number of loads  120 . For instance, in one embodiment, there may be four different loads  120 . These loads  120  may be each wired to bus element  116 . One of ordinary skill in the art, upon reviewing the entirety of this disclosure, would recognize that any number of loads  120  or configurations of loads  120  are possible. 
     Further referring to  FIG.  1   , cross tie element  108  is an element of bus element  116 . As a non-limiting example, disconnection of a cross tie element  108  may isolate a single energy storage element of plurality of batteries  104  from all other batteries  104  electrically connected to bus element  116  and/or may isolate a first plurality of batteries  104  from a second plurality of batteries  104 . More generally, any number of cross tie elements  108  may operate to divide plurality of batteries  104  into various different groups and/or isolate any single battery  104  one by one or two or more at a time. Where cross tie element  108  separates a first battery  104  from a second battery  104 , either of first or second battery  104  may be part of a plurality of batteries  104  that remain interconnected and/or may be isolated from all other batteries  104 . Cross tie element  108  may be implemented, without limitation, as any switch, relay, or other circuit element capable of acting to open or close a circuit, such as without limitation electrically actuated switches, semiconductor circuit elements such as transistors, which may include field-effect transistors (FETs), metal oxide FETs (MOSFETs), bipolar junction transistors, or the like. 
     Referring now to  FIG.  2   , an exemplary embodiment of a system for charging multiple power sources  200  is depicted. System for charging multiple power sources  200  contains many of the same elements as system for charging multiple power sources  100 . Particularly, batteries  204   a - b,  cross tie element  208 , diode  212 , and load  216 , may be consistent with any of the respective elements consistent with this disclosure with reference to  FIG.  1   . 
     With continued reference to  FIG.  2   , system for charging multiple power sources  200  also may contain positive bus element  220 . Positive bus element may be consistent with bus element  116  in  FIG.  1   . Load  216  may be wired to positive bus element  220 . Alternatively, in another non-limiting embodiment, positive bus element  220  may be separately wired to each load  216  of which there may be a plurality. Positive bus element  220  may be wired to each of diodes  212 . 
     With continued reference to  FIG.  2   , system for charging multiple power sources  200  also may contain a negative bus element  224 . Negative bus element  224  may be consistent with the technical disclosure for bus element  116  in  FIG.  1   . Load  216  may be wired to negative bus element  224 . Alternatively, in another non-limiting embodiment, negative bus element  224  may be separately wired to each load  216  of which there may be a plurality. Negative bus element  224  may be wired to each of the batteries  204   a - b.  In some embodiments, negative bus element  224  may be wired to the negative end of batteries  204   a - b.  For the purposes of this disclosure, the “negative end” of batteries  204 . is the anode end of batteries  204   a - b.    
       FIG.  3    shows a diagram of an exemplary embodiment of system for charging multiple power sources  300 , system for charging multiple power sources  300  containing a first current sensor  304  and a second current sensor  308 . First current sensor may be wired in series to a first diode  312 . Second current sensor  308  may be wired in series to a second current sensor  308 . One end of first current sensor  304  and second current sensor  308  may be wired to battery  328   a - b  and first diode  312  and second diode  316  may be wired to bus element  320 , such that first current sensor  304  and first diode  312  are in parallel with second current sensor  308  and second diode  316 . Bus element may be wired to load  324  and load  324  may be also wired to the negative end of battery  328   a - b.    
     With continued reference to  FIG.  3   , the first current sensor  304  and second current sensor  308  can be any type of device adapted to measure current in an electric circuit, such as an ammeter. Furthermore, first current sensor  304  and second current sensor  308  could be any type of ammeter, such as a moving-coil ammeter, a moving magnet ammeter, an electrodynamic ammeter, a moving-iron ammeter, a hot-wire ammeter, a digital ammeter, or an integrating ammeter. One of ordinary skill in the art, upon reviewing the entirety of this disclosure, would realize that a wide variety of methods for measuring current in an electric circuit would be compatible with this disclosure. 
     With continued reference to  FIG.  3   , in the event that the first current sensor  304  detects a measurement of double the expected current and the second current sensor  308  detects a current measurement of zero, a diode failure may be signaled. In a non-limiting example, where first diode  312  and second diode  316  have the same specifications, then the expected current flowing through first current sensor  304  and second current sensor  308  may each be half of the current flowing from battery. However, if one of first diode  312  and second diode  316  has experienced partial diode failure, then the current flowing through the other diode may increase; system  100  may detect increase in current through one diode and decrease in current through another diode to identify partial failure. Thus, if first current sensor  304  detects a current measurement of higher than the expected current and second current sensor  308  detects a current measurement of less than the expected current, then it is likely that there has been a partial diode failure. If one of first diode  312  and second diode  316  has experienced diode failure, then the current flowing through the other diode may double. Thus, if first current sensor  304  detects a current measurement of double the expected current and second current sensor  308  detects a current measurement of zero, then it is likely that there has been a diode failure. Alternatively, in another non-limiting embodiment, diode failure may be signaled when first current sensor  304  and second current sensor  308  detect different current measurements. For the purposes of this disclosure, current measurements are “different” if the difference between the measurements falls outside of a tolerance. As a non-limiting example, the tolerance could be 1% of an expected current, 5% of an expected current, or any other tolerance sufficient to indicate diode failure. 
     With continued reference to  FIG.  3   , diode failure may be signaled in a variety of ways. In one non-limiting embodiment, diode failure may be signaled by communicating the diode failure to a user. For the purposes of this disclosure, “user” is anyone operating a system in which the disclosed power source structure is included. As a non-limiting example, where the power source structure, such as system for charging multiple power sources  300 , is located in an electric aircraft, a user may be the pilot of that electric aircraft. In one embodiment, signaling a diode failure may include sending an error message. This error message may be communicated to the user in a variety of ways, such as by displaying the error message on a screen, by displaying an alert on a screen, by turning on a warning light, or by way of a warning noise. As a non-limiting example, the error message may include a text message where the text message is displayed on a screen to alert the user. As another non-limiting example, the error message may include an audio message such as an alarm sound, loud beep, or any other noise sufficient to alert the user. These are merely examples; one of ordinary skill in the art, upon reviewing the entirety of this disclosure, would appreciate that there are a wide variety of ways to communicate an error message to a user. In another embodiment, signaling diode failure may include deactivating a battery. For example, is second current sensor  308  were to experience diode failure, then signaling a diode failure may include deactivating battery  328   a - b.  A variety of devices may be responsible for detecting and/or signaling a diode failure, including a computing device, a flight controller, and/or a battery management system. Additionally, a computing device, flight controller, and/or a battery management system may be implemented to monitor first current sensor  304  and second current sensor  308 . One of ordinary skill in the art, upon reviewing the entirety of this disclosure, would appreciate that a wide variety of devices would be suitable for this task. 
     Returning to  FIG.  3   , Battery  328   a - b  may be consistent with any previously described battery in this disclosure. First diode  312  and second diode  316  may be consistent with any previously described diode in this disclosure. Bus element  320  may be consistent with any previously described bus element in this disclosure. Cross tie element  332  may be consistent with any previously described cross tie element in this disclosure. 
     With continued reference to  FIG.  3   , bus element  320  may be wired to a load  324 . Load  324  may, as a non-limiting example be any component of an electric aircraft that is powered by batteries. As a non-limiting example, load  324  may include a motor, a fan, a processor, a flight controller, avionics, actuators, a computer, lights, climate control, or any other appropriate load. While  FIG.  3    only depicts one load  324 , there may be any number of loads  324 . For instance, in one embodiment, there may be four different loads  324 . These loads  324  may be each wired to bus element  320 . One of ordinary skill in the art, upon reviewing the entirety of this disclosure, would recognize that any number of loads  324  or configurations of loads  324  are possible. 
     With continued reference to  FIG.  3   , load  324  may be wired to battery  328   a - b  or to each battery  328   a - b.  While  FIG.  3    only depicts a single bus element  320 , a non-limiting embodiment may incorporate the negative bus element  224  and positive bus element  220  configuration from  FIG.  2   . For instance, load  324  may be wired to a negative bus element which is in turn wired to battery  328   a - b  or each battery  328   a - b.  Additionally, for example, a positive bus element may take the place of bus element  320  such that first diode  312  and second diode are wired to a positive bus element, and the positive bus element is wired to load  324  or each load  324 . 
     With continued reference to  FIG.  3   , in some embodiments several batteries  328  may be placed in parallel, each with a set of first current sensor  304 , second current sensor  308 , first diode  312 , and second diode  316 . While  FIG.  3    only shows two batteries  328   a - b,  one of ordinary skill in the art, upon reviewing the entirety of this disclosure, would appreciate that any number of batteries  328   a - b  could be included in system for charging multiple power sources  300 . 
     Moving to  FIG.  4   ,  FIG.  4    depicts an exemplary embodiment  400  of a flight controller  404 . First current sensor  304  and second current sensor  308  may be in electronic communication, as a non-limiting example, with flight controller  404 , or some other computing device. In an embodiment, flight controller  404  may be responsible for monitoring the current measurements of first current sensor  304  and second current sensor  308 . In some embodiments, flight controller  404  may also be responsible for signaling a diode failure as covered in this disclosure. In some embodiments, there may be a battery management system in electronic communication with each set of first current sensor  304  and second current sensor  308 . In other embodiments, there may be a single battery management system in electronic communication with all of the current sensor in the power source structure. Additionally, in yet other embodiments, a battery management system may also be in electronic communication with battery  328   a - b  or plurality of batteries  328   a - b  and may measure cell voltage, battery capacity, or any other appropriate battery measurement. In some embodiments, battery management system may signal diode failure, or, in another embodiment, battery management system may preform the monitoring of first current sensor  304  and second current sensor  308  and the signaling of a diode failure in conjunction with flight controller  404 , with which it may be in electronic communication. 
     With continued reference to  FIG.  4   , an exemplary embodiment  400  of a flight controller  404  is illustrated. As used in this disclosure a “flight controller” is a computing device of a plurality of computing devices dedicated to data storage, security, distribution of traffic for load balancing, and flight instruction. Flight controller  404  may include and/or communicate with any computing device as described in this disclosure, including without limitation a microcontroller, microprocessor, digital signal processor (DSP) and/or system on a chip (SoC) as described in this disclosure. Further, flight controller  404  may include a single computing device operating independently, or may include two or more computing device operating in concert, in parallel, sequentially or the like; two or more computing devices may be included together in a single computing device or in two or more computing devices. In embodiments, flight controller  404  may be installed in an aircraft, may control the aircraft remotely, and/or may include an element installed in the aircraft and a remote element in communication therewith. 
     In an embodiment, and still referring to  FIG.  4   , flight controller  404  may include a signal transformation component  408 . As used in this disclosure a “signal transformation component” is a component that transforms and/or converts a first signal to a second signal, wherein a signal may include one or more digital and/or analog signals. For example, and without limitation, signal transformation component  408  may be configured to perform one or more operations such as preprocessing, lexical analysis, parsing, semantic analysis, and the like thereof. In an embodiment, and without limitation, signal transformation component  408  may include one or more analog-to-digital convertors that transform a first signal of an analog signal to a second signal of a digital signal. For example, and without limitation, an analog-to-digital converter may convert an analog input signal to a 10-bit binary digital representation of that signal. In another embodiment, signal transformation component  408  may include transforming one or more low-level languages such as, but not limited to, machine languages and/or assembly languages. For example, and without limitation, signal transformation component  408  may include transforming a binary language signal to an assembly language signal. In an embodiment, and without limitation, signal transformation component  408  may include transforming one or more high-level languages and/or formal languages such as but not limited to alphabets, strings, and/or languages. For example, and without limitation, high-level languages may include one or more system languages, scripting languages, domain-specific languages, visual languages, esoteric languages, and the like thereof. As a further non-limiting example, high-level languages may include one or more algebraic formula languages, business data languages, string and list languages, object-oriented languages, and the like thereof. 
     Still referring to  FIG.  4   , signal transformation component  408  may be configured to optimize an intermediate representation  412 . As used in this disclosure an “intermediate representation” is a data structure and/or code that represents the input signal. Signal transformation component  408  may optimize intermediate representation as a function of a data-flow analysis, dependence analysis, alias analysis, pointer analysis, escape analysis, and the like thereof. In an embodiment, and without limitation, signal transformation component  408  may optimize intermediate representation  412  as a function of one or more inline expansions, dead code eliminations, constant propagation, loop transformations, and/or automatic parallelization functions. In another embodiment, signal transformation component  408  may optimize intermediate representation as a function of a machine dependent optimization such as a peephole optimization, wherein a peephole optimization may rewrite short sequences of code into more efficient sequences of code. Signal transformation component  408  may optimize intermediate representation to generate an output language, wherein an “output language,” as used herein, is the native machine language of flight controller  404 . For example, and without limitation, native machine language may include one or more binary and/or numerical languages. 
     In an embodiment, and without limitation, signal transformation component  408  may include transform one or more inputs and outputs as a function of an error correction code. An error correction code, also known as error correcting code (ECC), is an encoding of a message or lot of data using redundant information, permitting recovery of corrupted data. An ECC may include a block code, in which information is encoded on fixed-size packets and/or blocks of data elements such as symbols of predetermined size, bits, or the like. Reed-Solomon coding, in which message symbols within a symbol set having q symbols are encoded as coefficients of a polynomial of degree less than or equal to a natural number k, over a finite field F with q elements; strings so encoded have a minimum hamming distance of k+1, and permit correction of (q−k−1)/2 erroneous symbols. Block code may alternatively or additionally be implemented using Golay coding, also known as binary Golay coding, Bose-Chaudhuri, Hocquenghuem (BCH) coding, multidimensional parity-check coding, and/or Hamming codes. An ECC may alternatively or additionally be based on a convolutional code. 
     In an embodiment, and still referring to  FIG.  4   , flight controller  404  may include a reconfigurable hardware platform  416 . A “reconfigurable hardware platform,” as used herein, is a component and/or unit of hardware that may be reprogrammed, such that, for instance, a data path between elements such as logic gates or other digital circuit elements may be modified to change an algorithm, state, logical sequence, or the like of the component and/or unit. This may be accomplished with such flexible high-speed computing fabrics as field-programmable gate arrays (FPGAs), which may include a grid of interconnected logic gates, connections between which may be severed and/or restored to program in modified logic. Reconfigurable hardware platform  416  may be reconfigured to enact any algorithm and/or algorithm selection process received from another computing device and/or created using machine-learning processes. 
     Still referring to  FIG.  4   , reconfigurable hardware platform  416  may include a logic component  420 . As used in this disclosure a “logic component” is a component that executes instructions on output language. For example, and without limitation, logic component may perform basic arithmetic, logic, controlling, input/output operations, and the like thereof. Logic component  420  may include any suitable processor, such as without limitation a component incorporating logical circuitry for performing arithmetic and logical operations, such as an arithmetic and logic unit (ALU), which may be regulated with a state machine and directed by operational inputs from memory and/or sensors; logic component  420  may be organized according to Von Neumann and/or Harvard architecture as a non-limiting example. Logic component  420  may include, incorporate, and/or be incorporated in, without limitation, a microcontroller, microprocessor, digital signal processor (DSP), Field Programmable Gate Array (FPGA), Complex Programmable Logic Device (CPLD), Graphical Processing Unit (GPU), general purpose GPU, Tensor Processing Unit (TPU), analog or mixed signal processor, Trusted Platform Module (TPM), a floating point unit (FPU), and/or system on a chip (SoC). In an embodiment, logic component  420  may include one or more integrated circuit microprocessors, which may contain one or more central processing units, central processors, and/or main processors, on a single metal-oxide-semiconductor chip. Logic component  420  may be configured to execute a sequence of stored instructions to be performed on the output language and/or intermediate representation  412 . Logic component  420  may be configured to fetch and/or retrieve the instruction from a memory cache, wherein a “memory cache,” as used in this disclosure, is a stored instruction set on flight controller  404 . Logic component  420  may be configured to decode the instruction retrieved from the memory cache to opcodes and/or operands. Logic component  420  may be configured to execute the instruction on intermediate representation  412  and/or output language. For example, and without limitation, logic component  420  may be configured to execute an addition operation on intermediate representation  412  and/or output language. 
     In an embodiment, and without limitation, logic component  420  may be configured to calculate a flight element  424 . As used in this disclosure a “flight element” is an element of datum denoting a relative status of aircraft. For example, and without limitation, flight element  424  may denote one or more torques, thrusts, airspeed velocities, forces, altitudes, groundspeed velocities, directions during flight, directions facing, forces, orientations, and the like thereof. For example, and without limitation, flight element  424  may denote that aircraft is cruising at an altitude and/or with a sufficient magnitude of forward thrust. As a further non-limiting example, flight status may denote that is building thrust and/or groundspeed velocity in preparation for a takeoff. As a further non-limiting example, flight element  424  may denote that aircraft is following a flight path accurately and/or sufficiently. 
     Still referring to  FIG.  4   , flight controller  404  may include a chipset component  428 . As used in this disclosure a “chipset component” is a component that manages data flow. In an embodiment, and without limitation, chipset component  428  may include a northbridge data flow path, wherein the northbridge dataflow path may manage data flow from logic component  420  to a high-speed device and/or component, such as a RAM, graphics controller, and the like thereof. In another embodiment, and without limitation, chipset component  428  may include a southbridge data flow path, wherein the southbridge dataflow path may manage data flow from logic component  420  to lower-speed peripheral buses, such as a peripheral component interconnect (PCI), industry standard architecture (ICA), and the like thereof. In an embodiment, and without limitation, southbridge data flow path may include managing data flow between peripheral connections such as ethernet, USB, audio devices, and the like thereof. Additionally or alternatively, chipset component  428  may manage data flow between logic component  420 , memory cache, and a flight component  432 . As used in this disclosure a “flight component” is a portion of an aircraft that can be moved or adjusted to affect one or more flight elements. For example, flight component  432  may include a component used to affect the aircrafts&#39; roll and pitch which may include one or more ailerons. As a further example, flight component  432  may include a rudder to control yaw of an aircraft. In an embodiment, chipset component  428  may be configured to communicate with a plurality of flight components as a function of flight element  424 . For example, and without limitation, chipset component  428  may transmit to an aircraft rotor to reduce torque of a first lift propulsor and increase the forward thrust produced by a pusher component to perform a flight maneuver. 
     In an embodiment, and still referring to  FIG.  4   , flight controller  404  may be configured generate an autonomous function. As used in this disclosure an “autonomous function” is a mode and/or function of flight controller  404  that controls aircraft automatically. For example, and without limitation, autonomous function may perform one or more aircraft maneuvers, take offs, landings, altitude adjustments, flight leveling adjustments, turns, climbs, and/or descents. As a further non-limiting example, autonomous function may adjust one or more airspeed velocities, thrusts, torques, and/or groundspeed velocities. As a further non-limiting example, autonomous function may perform one or more flight path corrections and/or flight path modifications as a function of flight element  424 . In an embodiment, autonomous function may include one or more modes of autonomy such as, but not limited to, autonomous mode, semi-autonomous mode, and/or non-autonomous mode. As used in this disclosure “autonomous mode” is a mode that automatically adjusts and/or controls aircraft and/or the maneuvers of aircraft in its entirety. For example, autonomous mode may denote that flight controller  404  will adjust the aircraft. As used in this disclosure a “semi-autonomous mode” is a mode that automatically adjusts and/or controls a portion and/or section of aircraft. For example, and without limitation, semi-autonomous mode may denote that a pilot will control the propulsors, wherein flight controller  404  will control the ailerons and/or rudders. As used in this disclosure “non-autonomous mode” is a mode that denotes a pilot will control aircraft and/or maneuvers of aircraft in its entirety. 
     In an embodiment, and still referring to  FIG.  4   , flight controller  404  may generate autonomous function as a function of an autonomous machine-learning model. As used in this disclosure an “autonomous machine-learning model” is a machine-learning model to produce an autonomous function output given flight element  424  and a pilot signal  436  as inputs; this is in contrast to a non-machine learning software program where the commands to be executed are determined in advance by a user and written in a programming language. As used in this disclosure a “pilot signal” is an element of datum representing one or more functions a pilot is controlling and/or adjusting. For example, pilot signal  436  may denote that a pilot is controlling and/or maneuvering ailerons, wherein the pilot is not in control of the rudders and/or propulsors. In an embodiment, pilot signal  436  may include an implicit signal and/or an explicit signal. For example, and without limitation, pilot signal  436  may include an explicit signal, wherein the pilot explicitly states there is a lack of control and/or desire for autonomous function. As a further non-limiting example, pilot signal  436  may include an explicit signal directing flight controller  404  to control and/or maintain a portion of aircraft, a portion of the flight plan, the entire aircraft, and/or the entire flight plan. As a further non-limiting example, pilot signal  436  may include an implicit signal, wherein flight controller  404  detects a lack of control such as by a malfunction, torque alteration, flight path deviation, and the like thereof. In an embodiment, and without limitation, pilot signal  436  may include one or more explicit signals to reduce torque, and/or one or more implicit signals that torque may be reduced due to reduction of airspeed velocity. In an embodiment, and without limitation, pilot signal  436  may include one or more local and/or global signals. For example, and without limitation, pilot signal  436  may include a local signal that is transmitted by a pilot and/or crew member. As a further non-limiting example, pilot signal  436  may include a global signal that is transmitted by air traffic control and/or one or more remote users that are in communication with the pilot of aircraft. In an embodiment, pilot signal  436  may be received as a function of a tri-state bus and/or multiplexor that denotes an explicit pilot signal should be transmitted prior to any implicit or global pilot signal. 
     Still referring to  FIG.  4   , autonomous machine-learning model may include one or more autonomous machine-learning processes such as supervised, unsupervised, or reinforcement machine-learning processes that flight controller  404  and/or a remote device may or may not use in the generation of autonomous function. As used in this disclosure “remote device” is an external device to flight controller  404 . Additionally or alternatively, autonomous machine-learning model may include one or more autonomous machine-learning processes that a field-programmable gate array (FPGA) may or may not use in the generation of autonomous function. Autonomous machine-learning process may include, without limitation machine learning processes such as simple linear regression, multiple linear regression, polynomial regression, support vector regression, ridge regression, lasso regression, elasticnet regression, decision tree regression, random forest regression, logistic regression, logistic classification, K-nearest neighbors, support vector machines, kernel support vector machines, naïve bayes, decision tree classification, random forest classification, K-means clustering, hierarchical clustering, dimensionality reduction, principal component analysis, linear discriminant analysis, kernel principal component analysis, Q-learning, State Action Reward State Action (SARSA), Deep-Q network, Markov decision processes, Deep Deterministic Policy Gradient (DDPG), or the like thereof. 
     In an embodiment, and still referring to  FIG.  4   , autonomous machine learning model may be trained as a function of autonomous training data, wherein autonomous training data may correlate a flight element, pilot signal, and/or simulation data to an autonomous function. For example, and without limitation, a flight element of an airspeed velocity, a pilot signal of limited and/or no control of propulsors, and a simulation data of required airspeed velocity to reach the destination may result in an autonomous function that includes a semi-autonomous mode to increase thrust of the propulsors. Autonomous training data may be received as a function of user-entered valuations of flight elements, pilot signals, simulation data, and/or autonomous functions. Flight controller  404  may receive autonomous training data by receiving correlations of flight element, pilot signal, and/or simulation data to an autonomous function that were previously received and/or determined during a previous iteration of generation of autonomous function. Autonomous training data may be received by one or more remote devices and/or FPGAs that at least correlate a flight element, pilot signal, and/or simulation data to an autonomous function. Autonomous training data may be received in the form of one or more user-entered correlations of a flight element, pilot signal, and/or simulation data to an autonomous function. 
     Still referring to  FIG.  4   , flight controller  404  may receive autonomous machine-learning model from a remote device and/or FPGA that utilizes one or more autonomous machine learning processes, wherein a remote device and an FPGA is described above in detail. For example, and without limitation, a remote device may include a computing device, external device, processor, FPGA, microprocessor and the like thereof. Remote device and/or FPGA may perform the autonomous machine-learning process using autonomous training data to generate autonomous function and transmit the output to flight controller  404 . Remote device and/or FPGA may transmit a signal, bit, datum, or parameter to flight controller  404  that at least relates to autonomous function. Additionally or alternatively, the remote device and/or FPGA may provide an updated machine-learning model. For example, and without limitation, an updated machine-learning model may be comprised of a firmware update, a software update, an autonomous machine-learning process correction, and the like thereof. As a non-limiting example a software update may incorporate a new simulation data that relates to a modified flight element. Additionally or alternatively, the updated machine learning model may be transmitted to the remote device and/or FPGA, wherein the remote device and/or FPGA may replace the autonomous machine-learning model with the updated machine-learning model and generate the autonomous function as a function of the flight element, pilot signal, and/or simulation data using the updated machine-learning model. The updated machine-learning model may be transmitted by the remote device and/or FPGA and received by flight controller  404  as a software update, firmware update, or corrected autonomous machine-learning model. For example, and without limitation autonomous machine learning model may utilize a neural net machine-learning process, wherein the updated machine-learning model may incorporate a gradient boosting machine-learning process. 
     Still referring to  FIG.  4   , flight controller  404  may include, be included in, and/or communicate with a mobile device such as a mobile telephone or smartphone. Further, flight controller may communicate with one or more additional devices as described below in further detail via a network interface device. The network interface device may be utilized for commutatively connecting a flight controller to one or more of a variety of networks, and one or more devices. Examples of a network interface device include, but are not limited to, a network interface card (e.g., a mobile network interface card, a LAN card), a modem, and any combination thereof. Examples of a network include, but are not limited to, a wide area network (e.g., the Internet, an enterprise network), a local area network (e.g., a network associated with an office, a building, a campus or other relatively small geographic space), a telephone network, a data network associated with a telephone/voice provider (e.g., a mobile communications provider data and/or voice network), a direct connection between two computing devices, and any combinations thereof. The network may include any network topology and can may employ a wired and/or a wireless mode of communication. 
     In an embodiment, and still referring to  FIG.  4   , flight controller  404  may include, but is not limited to, for example, a cluster of flight controllers in a first location and a second flight controller or cluster of flight controllers in a second location. Flight controller  404  may include one or more flight controllers dedicated to data storage, security, distribution of traffic for load balancing, and the like. Flight controller  404  may be configured to distribute one or more computing tasks as described below across a plurality of flight controllers, which may operate in parallel, in series, redundantly, or in any other manner used for distribution of tasks or memory between computing devices. For example, and without limitation, flight controller  404  may implement a control algorithm to distribute and/or command the plurality of flight controllers. As used in this disclosure a “control algorithm” is a finite sequence of well-defined computer implementable instructions that may determine the flight component of the plurality of flight components to be adjusted. For example, and without limitation, control algorithm may include one or more algorithms that reduce and/or prevent aviation asymmetry. As a further non-limiting example, control algorithms may include one or more models generated as a function of a software including, but not limited to Simulink by MathWorks, Natick, Mass., USA. In an embodiment, and without limitation, control algorithm may be configured to generate an auto-code, wherein an “auto-code,” is used herein, is a code and/or algorithm that is generated as a function of the one or more models and/or software&#39;s. In another embodiment, control algorithm may be configured to produce a segmented control algorithm. As used in this disclosure a “segmented control algorithm” is control algorithm that has been separated and/or parsed into discrete sections. For example, and without limitation, segmented control algorithm may parse control algorithm into two or more segments, wherein each segment of control algorithm may be performed by one or more flight controllers operating on distinct flight components. 
     In an embodiment, and still referring to  FIG.  4   , control algorithm may be configured to determine a segmentation boundary as a function of segmented control algorithm. As used in this disclosure a “segmentation boundary” is a limit and/or delineation associated with the segments of the segmented control algorithm. For example, and without limitation, segmentation boundary may denote that a segment in the control algorithm has a first starting section and/or a first ending section. As a further non-limiting example, segmentation boundary may include one or more boundaries associated with an ability of flight component  432 . In an embodiment, control algorithm may be configured to create an optimized signal communication as a function of segmentation boundary. For example, and without limitation, optimized signal communication may include identifying the discrete timing required to transmit and/or receive the one or more segmentation boundaries. In an embodiment, and without limitation, creating optimized signal communication further comprises separating a plurality of signal codes across the plurality of flight controllers. For example, and without limitation the plurality of flight controllers may include one or more formal networks, wherein formal networks transmit data along an authority chain and/or are limited to task-related communications. As a further non-limiting example, communication network may include informal networks, wherein informal networks transmit data in any direction. In an embodiment, and without limitation, the plurality of flight controllers may include a chain path, wherein a “chain path,” as used herein, is a linear communication path comprising a hierarchy that data may flow through. In an embodiment, and without limitation, the plurality of flight controllers may include an all-channel path, wherein an “all-channel path,” as used herein, is a communication path that is not restricted to a particular direction. For example, and without limitation, data may be transmitted upward, downward, laterally, and the like thereof. In an embodiment, and without limitation, the plurality of flight controllers may include one or more neural networks that assign a weighted value to a transmitted datum. For example, and without limitation, a weighted value may be assigned as a function of one or more signals denoting that a flight component is malfunctioning and/or in a failure state. 
     Still referring to  FIG.  4   , the plurality of flight controllers may include a master bus controller. As used in this disclosure a “master bus controller” is one or more devices and/or components that are connected to a bus to initiate a direct memory access transaction, wherein a bus is one or more terminals in a bus architecture. Master bus controller may communicate using synchronous and/or asynchronous bus control protocols. In an embodiment, master bus controller may include flight controller  404 . In another embodiment, master bus controller may include one or more universal asynchronous receiver-transmitters (UART). For example, and without limitation, master bus controller may include one or more bus architectures that allow a bus to initiate a direct memory access transaction from one or more buses in the bus architectures. As a further non-limiting example, master bus controller may include one or more peripheral devices and/or components to communicate with another peripheral device and/or component and/or the master bus controller. In an embodiment, master bus controller may be configured to perform bus arbitration. As used in this disclosure “bus arbitration” is method and/or scheme to prevent multiple buses from attempting to communicate with and/or connect to master bus controller. For example and without limitation, bus arbitration may include one or more schemes such as a small computer interface system, wherein a small computer interface system is a set of standards for physical connecting and transferring data between peripheral devices and master bus controller by defining commands, protocols, electrical, optical, and/or logical interfaces. In an embodiment, master bus controller may receive intermediate representation  412  and/or output language from logic component  420 , wherein output language may include one or more analog-to-digital conversions, low bit rate transmissions, message encryptions, digital signals, binary signals, logic signals, analog signals, and the like thereof described above in detail. 
     Still referring to  FIG.  4   , master bus controller may communicate with a slave bus. As used in this disclosure a “slave bus” is one or more peripheral devices and/or components that initiate a bus transfer. For example, and without limitation, slave bus may receive one or more controls and/or asymmetric communications from master bus controller, wherein slave bus transfers data stored to master bus controller. In an embodiment, and without limitation, slave bus may include one or more internal buses, such as but not limited to a/an internal data bus, memory bus, system bus, front-side bus, and the like thereof. In another embodiment, and without limitation, slave bus may include one or more external buses such as external flight controllers, external computers, remote devices, printers, aircraft computer systems, flight control systems, and the like thereof. 
     In an embodiment, and still referring to  FIG.  4   , control algorithm may optimize signal communication as a function of determining one or more discrete timings. For example, and without limitation master bus controller may synchronize timing of the segmented control algorithm by injecting high priority timing signals on a bus of the master bus control. As used in this disclosure a “high priority timing signal” is information denoting that the information is important. For example, and without limitation, high priority timing signal may denote that a section of control algorithm is of high priority and should be analyzed and/or transmitted prior to any other sections being analyzed and/or transmitted. In an embodiment, high priority timing signal may include one or more priority packets. As used in this disclosure a “priority packet” is a formatted unit of data that is communicated between the plurality of flight controllers. For example, and without limitation, priority packet may denote that a section of control algorithm should be used and/or is of greater priority than other sections. 
     Still referring to  FIG.  4   , flight controller  404  may also be implemented using a “shared nothing” architecture in which data is cached at the worker, in an embodiment, this may enable scalability of aircraft and/or computing device. Flight controller  404  may include a distributer flight controller. As used in this disclosure a “distributer flight controller” is a component that adjusts and/or controls a plurality of flight components as a function of a plurality of flight controllers. For example, distributer flight controller may include a flight controller that communicates with a plurality of additional flight controllers and/or clusters of flight controllers. In an embodiment, distributed flight control may include one or more neural networks. For example, neural network also known as an artificial neural network, is a network of “nodes,” or data structures having one or more inputs, one or more outputs, and a function determining outputs based on inputs. Such nodes may be organized in a network, such as without limitation a convolutional neural network, including an input layer of nodes, one or more intermediate layers, and an output layer of nodes. Connections between nodes may be created via the process of “training” the network, in which elements from a training dataset are applied to the input nodes, a suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust the connections and weights between nodes in adjacent layers of the neural network to produce the desired values at the output nodes. This process is sometimes referred to as deep learning. 
     Still referring to  FIG.  4   , a node may include, without limitation a plurality of inputs x i  that may receive numerical values from inputs to a neural network containing the node and/or from other nodes. Node may perform a weighted sum of inputs using weights w i  that are multiplied by respective inputs x i . Additionally or alternatively, a bias b may be added to the weighted sum of the inputs such that an offset is added to each unit in the neural network layer that is independent of the input to the layer. The weighted sum may then be input into a function φ, which may generate one or more outputs y. Weight w i  applied to an input x i  may indicate whether the input is “excitatory,” indicating that it has strong influence on the one or more outputs y, for instance by the corresponding weight having a large numerical value, and/or a “inhibitory,” indicating it has a weak effect influence on the one more inputs y, for instance by the corresponding weight having a small numerical value. The values of weights w i  may be determined by training a neural network using training data, which may be performed using any suitable process as described above. In an embodiment, and without limitation, a neural network may receive semantic units as inputs and output vectors representing such semantic units according to weights w i  that are derived using machine-learning processes as described in this disclosure. 
     Still referring to  FIG.  4   , flight controller may include a sub-controller  440 . As used in this disclosure a “sub-controller” is a controller and/or component that is part of a distributed controller as described above; for instance, flight controller  404  may be and/or include a distributed flight controller made up of one or more sub-controllers. For example, and without limitation, sub-controller  440  may include any controllers and/or components thereof that are similar to distributed flight controller and/or flight controller as described above. Sub-controller  440  may include any component of any flight controller as described above. Sub-controller  440  may be implemented in any manner suitable for implementation of a flight controller as described above. As a further non-limiting example, sub-controller  440  may include one or more processors, logic components and/or computing devices capable of receiving, processing, and/or transmitting data across the distributed flight controller as described above. As a further non-limiting example, sub-controller  440  may include a controller that receives a signal from a first flight controller and/or first distributed flight controller component and transmits the signal to a plurality of additional sub-controllers and/or flight components. 
     Still referring to  FIG.  4   , flight controller may include a co-controller  444 . As used in this disclosure a “co-controller” is a controller and/or component that joins flight controller  404  as components and/or nodes of a distributer flight controller as described above. For example, and without limitation, co-controller  444  may include one or more controllers and/or components that are similar to flight controller  404 . As a further non-limiting example, co-controller  444  may include any controller and/or component that joins flight controller  404  to distributer flight controller. As a further non-limiting example, co-controller  444  may include one or more processors, logic components and/or computing devices capable of receiving, processing, and/or transmitting data to and/or from flight controller  404  to distributed flight control system. Co-controller  444  may include any component of any flight controller as described above. Co-controller  444  may be implemented in any manner suitable for implementation of a flight controller as described above. 
     In an embodiment, and with continued reference to  FIG.  4   , flight controller  404  may be designed and/or configured to perform any method, method step, or sequence of method steps in any embodiment described in this disclosure, in any order and with any degree of repetition. For instance, flight controller  404  may be configured to perform a single step or sequence repeatedly until a desired or commanded outcome is achieved; repetition of a step or a sequence of steps may be performed iteratively and/or recursively using outputs of previous repetitions as inputs to subsequent repetitions, aggregating inputs and/or outputs of repetitions to produce an aggregate result, reduction or decrement of one or more variables such as global variables, and/or division of a larger processing task into a set of iteratively addressed smaller processing tasks. Flight controller may perform any step or sequence of steps as described in this disclosure in parallel, such as simultaneously and/or substantially simultaneously performing a step two or more times using two or more parallel threads, processor cores, or the like; division of tasks between parallel threads and/or processes may be performed according to any protocol suitable for division of tasks between iterations. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which steps, sequences of steps, processing tasks, and/or data may be subdivided, shared, or otherwise dealt with using iteration, recursion, and/or parallel processing. 
     Referring now to  FIG.  5   , an exemplary embodiment of a machine-learning module  500  that may perform one or more machine-learning processes as described in this disclosure is illustrated. Machine-learning module may perform determinations, classification, and/or analysis steps, methods, processes, or the like as described in this disclosure using machine learning processes. A “machine learning process,” as used in this disclosure, is a process that automatedly uses training data  504  to generate an algorithm that will be performed by a computing device/module to produce outputs  508  given data provided as inputs  512 ; this is in contrast to a non-machine learning software program where the commands to be executed are determined in advance by a user and written in a programming language. 
     Still referring to  FIG.  5   , “training data,” as used herein, is data containing correlations that a machine-learning process may use to model relationships between two or more categories of data elements. For instance, and without limitation, training data  504  may include a plurality of data entries, each entry representing a set of data elements that were recorded, received, and/or generated together; data elements may be correlated by shared existence in a given data entry, by proximity in a given data entry, or the like. Multiple data entries in training data  504  may evince one or more trends in correlations between categories of data elements; for instance, and without limitation, a higher value of a first data element belonging to a first category of data element may tend to correlate to a higher value of a second data element belonging to a second category of data element, indicating a possible proportional or other mathematical relationship linking values belonging to the two categories. Multiple categories of data elements may be related in training data  504  according to various correlations; correlations may indicate causative and/or predictive links between categories of data elements, which may be modeled as relationships such as mathematical relationships by machine-learning processes as described in further detail below. Training data  504  may be formatted and/or organized by categories of data elements, for instance by associating data elements with one or more descriptors corresponding to categories of data elements. As a non-limiting example, training data  504  may include data entered in standardized forms by persons or processes, such that entry of a given data element in a given field in a form may be mapped to one or more descriptors of categories. Elements in training data  504  may be linked to descriptors of categories by tags, tokens, or other data elements; for instance, and without limitation, training data  504  may be provided in fixed-length formats, formats linking positions of data to categories such as comma-separated value (CSV) formats and/or self-describing formats such as extensible markup language (XML), JavaScript Object Notation (JSON), or the like, enabling processes or devices to detect categories of data. 
     Alternatively or additionally, and continuing to refer to  FIG.  5   , training data  504  may include one or more elements that are not categorized; that is, training data  504  may not be formatted or contain descriptors for some elements of data. Machine-learning algorithms and/or other processes may sort training data  504  according to one or more categorizations using, for instance, natural language processing algorithms, tokenization, detection of correlated values in raw data and the like; categories may be generated using correlation and/or other processing algorithms. As a non-limiting example, in a corpus of text, phrases making up a number “n” of compound words, such as nouns modified by other nouns, may be identified according to a statistically significant prevalence of n-grams containing such words in a particular order; such an n-gram may be categorized as an element of language such as a “word” to be tracked similarly to single words, generating a new category as a result of statistical analysis. Similarly, in a data entry including some textual data, a person&#39;s name may be identified by reference to a list, dictionary, or other compendium of terms, permitting ad-hoc categorization by machine-learning algorithms, and/or automated association of data in the data entry with descriptors or into a given format. The ability to categorize data entries automatedly may enable the same training data  504  to be made applicable for two or more distinct machine-learning algorithms as described in further detail below. Training data  504  used by machine-learning module  500  may correlate any input data as described in this disclosure to any output data as described in this disclosure. As a non-limiting illustrative example flight elements and/or pilot signals may be inputs, wherein an output may be an autonomous function. 
     Further referring to  FIG.  5   , training data may be filtered, sorted, and/or selected using one or more supervised and/or unsupervised machine-learning processes and/or models as described in further detail below; such models may include without limitation a training data classifier  516 . Training data classifier  516  may include a “classifier,” which as used in this disclosure is a machine-learning model as defined below, such as a mathematical model, neural net, or program generated by a machine learning algorithm known as a “classification algorithm,” as described in further detail below, that sorts inputs into categories or bins of data, outputting the categories or bins of data and/or labels associated therewith. A classifier may be configured to output at least a datum that labels or otherwise identifies a set of data that are clustered together, found to be close under a distance metric as described below, or the like. Machine-learning module  500  may generate a classifier using a classification algorithm, defined as a processes whereby a computing device and/or any module and/or component operating thereon derives a classifier from training data  504 . Classification may be performed using, without limitation, linear classifiers such as without limitation logistic regression and/or naive Bayes classifiers, nearest neighbor classifiers such as k-nearest neighbors classifiers, support vector machines, least squares support vector machines, fisher&#39;s linear discriminant, quadratic classifiers, decision trees, boosted trees, random forest classifiers, learning vector quantization, and/or neural network-based classifiers. As a non-limiting example, training data classifier  416  may classify elements of training data to sub-categories of flight elements such as torques, forces, thrusts, directions, and the like thereof. 
     Still referring to  FIG.  5   , machine-learning module  500  may be configured to perform a lazy-learning process  520  and/or protocol, which may alternatively be referred to as a “lazy loading” or “call-when-needed” process and/or protocol, may be a process whereby machine learning is conducted upon receipt of an input to be converted to an output, by combining the input and training set to derive the algorithm to be used to produce the output on demand. For instance, an initial set of simulations may be performed to cover an initial heuristic and/or “first guess” at an output and/or relationship. As a non-limiting example, an initial heuristic may include a ranking of associations between inputs and elements of training data  504 . Heuristic may include selecting some number of highest-ranking associations and/or training data  504  elements. Lazy learning may implement any suitable lazy learning algorithm, including without limitation a K-nearest neighbors algorithm, a lazy naïve Bayes algorithm, or the like; persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various lazy-learning algorithms that may be applied to generate outputs as described in this disclosure, including without limitation lazy learning applications of machine-learning algorithms as described in further detail below. 
     Alternatively or additionally, and with continued reference to  FIG.  5   , machine-learning processes as described in this disclosure may be used to generate machine-learning models  524 . A “machine-learning model,” as used in this disclosure, is a mathematical and/or algorithmic representation of a relationship between inputs and outputs, as generated using any machine-learning process including without limitation any process as described above, and stored in memory; an input is submitted to a machine-learning model  524  once created, which generates an output based on the relationship that was derived. For instance, and without limitation, a linear regression model, generated using a linear regression algorithm, may compute a linear combination of input data using coefficients derived during machine-learning processes to calculate an output datum. As a further non-limiting example, a machine-learning model  524  may be generated by creating an artificial neural network, such as a convolutional neural network comprising an input layer of nodes, one or more intermediate layers, and an output layer of nodes. Connections between nodes may be created via the process of “training” the network, in which elements from a training data  504  set are applied to the input nodes, a suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust the connections and weights between nodes in adjacent layers of the neural network to produce the desired values at the output nodes. This process is sometimes referred to as deep learning. 
     Still referring to  FIG.  5   , machine-learning algorithms may include at least a supervised machine-learning process  528 . At least a supervised machine-learning process  528 , as defined herein, include algorithms that receive a training set relating a number of inputs to a number of outputs, and seek to find one or more mathematical relations relating inputs to outputs, where each of the one or more mathematical relations is optimal according to some criterion specified to the algorithm using some scoring function. For instance, a supervised learning algorithm may include flight elements and/or pilot signals as described above as inputs, autonomous functions as outputs, and a scoring function representing a desired form of relationship to be detected between inputs and outputs; scoring function may, for instance, seek to maximize the probability that a given input and/or combination of elements inputs is associated with a given output to minimize the probability that a given input is not associated with a given output. Scoring function may be expressed as a risk function representing an “expected loss” of an algorithm relating inputs to outputs, where loss is computed as an error function representing a degree to which a prediction generated by the relation is incorrect when compared to a given input-output pair provided in training data  504 . Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various possible variations of at least a supervised machine-learning process  528  that may be used to determine relation between inputs and outputs. Supervised machine-learning processes may include classification algorithms as defined above. 
     Further referring to  FIG.  5   , machine learning processes may include at least an unsupervised machine-learning processes  532 . An unsupervised machine-learning process, as used herein, is a process that derives inferences in datasets without regard to labels; as a result, an unsupervised machine-learning process may be free to discover any structure, relationship, and/or correlation provided in the data. Unsupervised processes may not require a response variable; unsupervised processes may be used to find interesting patterns and/or inferences between variables, to determine a degree of correlation between two or more variables, or the like. 
     Still referring to  FIG.  5   , machine-learning module  500  may be designed and configured to create a machine-learning model  524  using techniques for development of linear regression models. Linear regression models may include ordinary least squares regression, which aims to minimize the square of the difference between predicted outcomes and actual outcomes according to an appropriate norm for measuring such a difference (e.g. a vector-space distance norm); coefficients of the resulting linear equation may be modified to improve minimization. Linear regression models may include ridge regression methods, where the function to be minimized includes the least-squares function plus term multiplying the square of each coefficient by a scalar amount to penalize large coefficients. Linear regression models may include least absolute shrinkage and selection operator (LASSO) models, in which ridge regression is combined with multiplying the least-squares term by a factor of  1  divided by double the number of samples. Linear regression models may include a multi-task lasso model wherein the norm applied in the least-squares term of the lasso model is the Frobenius norm amounting to the square root of the sum of squares of all terms. Linear regression models may include the elastic net model, a multi-task elastic net model, a least angle regression model, a LARS lasso model, an orthogonal matching pursuit model, a Bayesian regression model, a logistic regression model, a stochastic gradient descent model, a perceptron model, a passive aggressive algorithm, a robustness regression model, a Huber regression model, or any other suitable model that may occur to persons skilled in the art upon reviewing the entirety of this disclosure. Linear regression models may be generalized in an embodiment to polynomial regression models, whereby a polynomial equation (e.g. a quadratic, cubic or higher-order equation) providing a best predicted output/actual output fit is sought; similar methods to those described above may be applied to minimize error functions, as will be apparent to persons skilled in the art upon reviewing the entirety of this disclosure. 
     Continuing to refer to  FIG.  5   , machine-learning algorithms may include, without limitation, linear discriminant analysis. Machine-learning algorithm may include quadratic discriminate analysis. Machine-learning algorithms may include kernel ridge regression. Machine-learning algorithms may include support vector machines, including without limitation support vector classification-based regression processes. Machine-learning algorithms may include stochastic gradient descent algorithms, including classification and regression algorithms based on stochastic gradient descent. Machine-learning algorithms may include nearest neighbors algorithms. Machine-learning algorithms may include Gaussian processes such as Gaussian Process Regression. Machine-learning algorithms may include cross-decomposition algorithms, including partial least squares and/or canonical correlation analysis. Machine-learning algorithms may include naïve Bayes methods. Machine-learning algorithms may include algorithms based on decision trees, such as decision tree classification or regression algorithms. Machine-learning algorithms may include ensemble methods such as bagging meta-estimator, forest of randomized tress, AdaBoost, gradient tree boosting, and/or voting classifier methods. Machine-learning algorithms may include neural net algorithms, including convolutional neural net processes. 
     Moving to  FIG.  6 A , an isometric view of a diode heat sink system  600  is shown. Diode heat sink system  600  includes a heat sink  604  that is mounted on top of diode  608 . Heat sink  604  may be made out of a material with a high thermal conductivity value, such as, for example, copper or aluminum. Heat sink  604  helps to dissipate the heat created by diode  608 . Heat sink  604  may be any type of heat sink such as a passive heat sink, or an active heat sink. Active heat sinks can encompass heat sinks that utilize forced air or pumped liquid in order to provide enhanced cooling. In a non-limiting embodiment, a fan  612  may be configured to blow air over the heat sink  604  and diode  608 . Diode  608  may be consistent with any other diode described as part of this disclosure. 
     Moving to  FIG.  6 B , a cross-sectional view of an embodiment of diode heat sink system  600 . As with the embodiment in  FIG.  6 A , heat sink  604  is mounted on top of diode  608 . As shown in  FIG.  6 B , heat sink  604  may include fins in order to increase the heat transfer ability of heat sink  604 . 
     It is to be noted that any one or more of the aspects and embodiments described herein may be conveniently implemented using one or more machines (e.g., one or more computing devices that are utilized as a user computing device for an electronic document, one or more server devices, such as a document server, etc.) programmed according to the teachings of the present specification, as will be apparent to those of ordinary skill in the computer art. Appropriate software coding can readily be prepared by skilled programmers based on the teachings of the present disclosure, as will be apparent to those of ordinary skill in the software art. Aspects and implementations discussed above employing software and/or software modules may also include appropriate hardware for assisting in the implementation of the machine executable instructions of the software and/or software module. 
     Such software may be a computer program product that employs a machine-readable storage medium. A machine-readable storage medium may be any medium that is capable of storing and/or encoding a sequence of instructions for execution by a machine (e.g., a computing device) and that causes the machine to perform any one of the methodologies and/or embodiments described herein. Examples of a machine-readable storage medium include, but are not limited to, a magnetic disk, an optical disc (e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-only memory “ROM” device, a random-access memory “RAM” device, a magnetic card, an optical card, a solid-state memory device, an EPROM, an EEPROM, and any combinations thereof. A machine-readable medium, as used herein, is intended to include a single medium as well as a collection of physically separate media, such as, for example, a collection of compact discs or one or more hard disk drives in combination with a computer memory. As used herein, a machine-readable storage medium does not include transitory forms of signal transmission. 
     Such software may also include information (e.g., data) carried as a data signal on a data carrier, such as a carrier wave. For example, machine-executable information may be included as a data-carrying signal embodied in a data carrier in which the signal encodes a sequence of instruction, or portion thereof, for execution by a machine (e.g., a computing device) and any related information (e.g., data structures and data) that causes the machine to perform any one of the methodologies and/or embodiments described herein. 
     Examples of a computing device include, but are not limited to, an electronic book reading device, a computer workstation, a terminal computer, a server computer, a handheld device (e.g., a tablet computer, a smartphone, etc.), a web appliance, a network router, a network switch, a network bridge, any machine capable of executing a sequence of instructions that specify an action to be taken by that machine, and any combinations thereof. In one example, a computing device may include and/or be included in a kiosk. 
       FIG.  7    shows a diagrammatic representation of one embodiment of a computing device in the exemplary form of a computer system  700  within which a set of instructions for causing a control system to perform any one or more of the aspects and/or methodologies of the present disclosure may be executed. It is also contemplated that multiple computing devices may be utilized to implement a specially configured set of instructions for causing one or more of the devices to perform any one or more of the aspects and/or methodologies of the present disclosure. Computer system  700  includes a processor  704  and a memory  708  that communicate with each other, and with other components, via a bus  712 . Bus  712  may include any of several types of bus structures including, but not limited to, a memory bus, a memory controller, a peripheral bus, a local bus, and any combinations thereof, using any of a variety of bus architectures. 
     Processor  704  may include any suitable processor, such as without limitation a processor incorporating logical circuitry for performing arithmetic and logical operations, such as an arithmetic and logic unit (ALU), which may be regulated with a state machine and directed by operational inputs from memory and/or sensors; processor  704  may be organized according to Von Neumann and/or Harvard architecture as a non-limiting example. Processor  704  may include, incorporate, and/or be incorporated in, without limitation, a microcontroller, microprocessor, digital signal processor (DSP), Field Programmable Gate Array (FPGA), Complex Programmable Logic Device (CPLD), Graphical Processing Unit (GPU), general purpose GPU, Tensor Processing Unit (TPU), analog or mixed signal processor, Trusted Platform Module (TPM), a floating-point unit (FPU), and/or system on a chip (SoC). 
     Memory  708  may include various components (e.g., machine-readable media) including, but not limited to, a random-access memory component, a read only component, and any combinations thereof. In one example, a basic input/output system  716  (BIOS), including basic routines that help to transfer information between elements within computer system  700 , such as during start-up, may be stored in memory  708 . Memory  708  may also include (e.g., stored on one or more machine-readable media) instructions (e.g., software)  720  embodying any one or more of the aspects and/or methodologies of the present disclosure. In another example, memory  708  may further include any number of program modules including, but not limited to, an operating system, one or more application programs, other program modules, program data, and any combinations thereof. 
     Computer system  700  may also include a storage device  724 . Examples of a storage device (e.g., storage device  724 ) include, but are not limited to, a hard disk drive, a magnetic disk drive, an optical disc drive in combination with an optical medium, a solid-state memory device, and any combinations thereof. Storage device  724  may be connected to bus  712  by an appropriate interface (not shown). Example interfaces include, but are not limited to, SCSI, advanced technology attachment (ATA), serial ATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and any combinations thereof. In one example, storage device  724  (or one or more components thereof) may be removably interfaced with computer system  700  (e.g., via an external port connector (not shown)). Particularly, storage device  724  and an associated machine-readable medium  728  may provide nonvolatile and/or volatile storage of machine-readable instructions, data structures, program modules, and/or other data for computer system  700 . In one example, software  720  may reside, completely or partially, within machine-readable medium  728 . In another example, software  720  may reside, completely or partially, within processor  704 . 
     Computer system  700  may also include an input device  732 . In one example, a user of computer system  700  may enter commands and/or other information into computer system  700  via input device  732 . Examples of an input device  732  include, but are not limited to, an alpha-numeric input device (e.g., a keyboard), a pointing device, a joystick, a gamepad, an audio input device (e.g., a microphone, a voice response system, etc.), a cursor control device (e.g., a mouse), a touchpad, an optical scanner, a video capture device (e.g., a still camera, a video camera), a touchscreen, and any combinations thereof. Input device  732  may be interfaced to bus  712  via any of a variety of interfaces (not shown) including, but not limited to, a serial interface, a parallel interface, a game port, a USB interface, a FIREWIRE interface, a direct interface to bus  712 , and any combinations thereof. Input device  732  may include a touch screen interface that may be a part of or separate from display  736 , discussed further below. Input device  732  may be utilized as a user selection device for selecting one or more graphical representations in a graphical interface as described above. 
     A user may also input commands and/or other information to computer system  700  via storage device  724  (e.g., a removable disk drive, a flash drive, etc.) and/or network interface device  740 . A network interface device, such as network interface device  740 , may be utilized for connecting computer system  700  to one or more of a variety of networks, such as network  744 , and one or more remote devices  748  connected thereto. Examples of a network interface device include, but are not limited to, a network interface card (e.g., a mobile network interface card, a LAN card), a modem, and any combination thereof. Examples of a network include, but are not limited to, a wide area network (e.g., the Internet, an enterprise network), a local area network (e.g., a network associated with an office, a building, a campus or other relatively small geographic space), a telephone network, a data network associated with a telephone/voice provider (e.g., a mobile communications provider data and/or voice network), a direct connection between two computing devices, and any combinations thereof. A network, such as network  744 , may employ a wired and/or a wireless mode of communication. In general, any network topology may be used. Information (e.g., data, software  720 , etc.) may be communicated to and/or from computer system  700  via network interface device  740 . 
     Computer system  700  may further include a video display adapter  752  for communicating a displayable image to a display device, such as display device  736 . Examples of a display device include, but are not limited to, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasma display, a light emitting diode (LED) display, and any combinations thereof. Display adapter  752  and display device  736  may be utilized in combination with processor  704  to provide graphical representations of aspects of the present disclosure. In addition to a display device, computer system  700  may include one or more other peripheral output devices including, but not limited to, an audio speaker, a printer, and any combinations thereof. Such peripheral output devices may be connected to bus  712  via a peripheral interface  756 . Examples of a peripheral interface include, but are not limited to, a serial port, a USB connection, a FIREWIRE connection, a parallel connection, and any combinations thereof. 
     The foregoing has been a detailed description of illustrative embodiments of the invention. Various modifications and additions can be made without departing from the spirit and scope of this invention. Features of each of the various embodiments described above may be combined with features of other described embodiments as appropriate in order to provide a multiplicity of feature combinations in associated new embodiments. Furthermore, while the foregoing describes a number of separate embodiments, what has been described herein is merely illustrative of the application of the principles of the present invention. Additionally, although particular methods herein may be illustrated and/or described as being performed in a specific order, the ordering is highly variable within ordinary skill to achieve methods, systems, and software according to the present disclosure. Accordingly, this description is meant to be taken only by way of example, and not to otherwise limit the scope of this invention. 
     Exemplary embodiments have been disclosed above and illustrated in the accompanying drawings. It will be understood by those skilled in the art that various changes, omissions and additions may be made to that which is specifically disclosed herein without departing from the spirit and scope of the present invention. 
     Processor  704  may include any suitable processor, such as without limitation a processor incorporating logical circuitry for performing arithmetic and logical operations, such as an arithmetic and logic unit (ALU), which may be regulated with a state machine and directed by operational inputs from memory and/or sensors; processor  704  may be organized according to Von Neumann and/or Harvard architecture as a non-limiting example. Processor  704  may include, incorporate, and/or be incorporated in, without limitation, a microcontroller, microprocessor, digital signal processor (DSP), Field Programmable Gate Array (FPGA), Complex Programmable Logic Device (CPLD), Graphical Processing Unit (GPU), general purpose GPU, Tensor Processing Unit (TPU), analog or mixed signal processor, Trusted Platform Module (TPM), a floating point unit (FPU), and/or system on a chip (SoC). 
     Memory  708  may include various components (e.g., machine-readable media) including, but not limited to, a random-access memory component, a read only component, and any combinations thereof. In one example, a basic input/output system  716  (BIOS), including basic routines that help to transfer information between elements within computer system  700 , such as during start-up, may be stored in memory  708 . Memory  708  may also include (e.g., stored on one or more machine-readable media) instructions (e.g., software)  720  embodying any one or more of the aspects and/or methodologies of the present disclosure. In another example, memory  708  may further include any number of program modules including, but not limited to, an operating system, one or more application programs, other program modules, program data, and any combinations thereof. 
     Computer system  700  may also include a storage device  724 . Examples of a storage device (e.g., storage device  724 ) include, but are not limited to, a hard disk drive, a magnetic disk drive, an optical disc drive in combination with an optical medium, a solid-state memory device, and any combinations thereof. Storage device  724  may be connected to bus  712  by an appropriate interface (not shown). Example interfaces include, but are not limited to, SCSI, advanced technology attachment (ATA), serial ATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and any combinations thereof. In one example, storage device  724  (or one or more components thereof) may be removably interfaced with computer system  700  (e.g., via an external port connector (not shown)). Particularly, storage device  724  and an associated machine-readable medium  728  may provide nonvolatile and/or volatile storage of machine-readable instructions, data structures, program modules, and/or other data for computer system  700 . In one example, software  720  may reside, completely or partially, within machine-readable medium  728 . In another example, software  720  may reside, completely or partially, within processor  704 . 
     Computer system  700  may also include an input device  732 . In one example, a user of computer system  700  may enter commands and/or other information into computer system  700  via input device  732 . Examples of an input device  732  include, but are not limited to, an alpha-numeric input device (e.g., a keyboard), a pointing device, a joystick, a gamepad, an audio input device (e.g., a microphone, a voice response system, etc.), a cursor control device (e.g., a mouse), a touchpad, an optical scanner, a video capture device (e.g., a still camera, a video camera), a touchscreen, and any combinations thereof Input device  732  may be interfaced to bus  712  via any of a variety of interfaces (not shown) including, but not limited to, a serial interface, a parallel interface, a game port, a USB interface, a FIREWIRE interface, a direct interface to bus  712 , and any combinations thereof. Input device  732  may include a touch screen interface that may be a part of or separate from display  736 , discussed further below. Input device  732  may be utilized as a user selection device for selecting one or more graphical representations in a graphical interface as described above. 
     A user may also input commands and/or other information to computer system  700  via storage device  724  (e.g., a removable disk drive, a flash drive, etc.) and/or network interface device  740 . A network interface device, such as network interface device  740 , may be utilized for connecting computer system  700  to one or more of a variety of networks, such as network  744 , and one or more remote devices  748  connected thereto. Examples of a network interface device include, but are not limited to, a network interface card (e.g., a mobile network interface card, a LAN card), a modem, and any combination thereof. Examples of a network include, but are not limited to, a wide area network (e.g., the Internet, an enterprise network), a local area network (e.g., a network associated with an office, a building, a campus or other relatively small geographic space), a telephone network, a data network associated with a telephone/voice provider (e.g., a mobile communications provider data and/or voice network), a direct connection between two computing devices, and any combinations thereof. A network, such as network  744 , may employ a wired and/or a wireless mode of communication. In general, any network topology may be used. Information (e.g., data, software  720 , etc.) may be communicated to and/or from computer system  700  via network interface device  740 . 
     Computer system  700  may further include a video display adapter  752  for communicating a displayable image to a display device, such as display device  736 . Examples of a display device include, but are not limited to, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasma display, a light emitting diode (LED) display, and any combinations thereof. Display adapter  752  and display device  736  may be utilized in combination with processor  704  to provide graphical representations of aspects of the present disclosure. In addition to a display device, computer system  700  may include one or more other peripheral output devices including, but not limited to, an audio speaker, a printer, and any combinations thereof. Such peripheral output devices may be connected to bus  712  via a peripheral interface  756 . Examples of a peripheral interface include, but are not limited to, a serial port, a USB connection, a FIREWIRE connection, a parallel connection, and any combinations thereof. 
     The foregoing has been a detailed description of illustrative embodiments of the invention. Various modifications and additions can be made without departing from the spirit and scope of this invention. Features of each of the various embodiments described above may be combined with features of other described embodiments as appropriate in order to provide a multiplicity of feature combinations in associated new embodiments. Furthermore, while the foregoing describes a number of separate embodiments, what has been described herein is merely illustrative of the application of the principles of the present invention. Additionally, although particular methods herein may be illustrated and/or described as being performed in a specific order, the ordering is highly variable within ordinary skill to achieve methods, systems, and software according to the present disclosure. Accordingly, this description is meant to be taken only by way of example, and not to otherwise limit the scope of this invention. 
     Exemplary embodiments have been disclosed above and illustrated in the accompanying drawings. It will be understood by those skilled in the art that various changes, omissions and additions may be made to that which is specifically disclosed herein without departing from the spirit and scope of the present invention.