CN117033873B - Low-altitude atmosphere waveguide measurement method and system based on neural network - Google Patents
Low-altitude atmosphere waveguide measurement method and system based on neural network Download PDFInfo
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Abstract
The invention discloses a low-altitude atmosphere waveguide measurement method and a system based on a neural network, wherein the method comprises the following steps: setting an influence function of the square of the amplitude of the electric field on the electric polarization, fitting an adjustment factor in the influence function of the square of the amplitude of the electric field on the electric polarization by using a neural network, and calculating a nonlinear electric polarization response value according to the electric field at a space coordinate (x, y, z) at time t; setting an influence function of the square of the amplitude of the magnetic field on magnetization, fitting the influence function of the square of the amplitude of the magnetic field on magnetization by using a neural network, and calculating a nonlinear magnetization response value according to the magnetic field at a space coordinate (x, y, z) at time t; and setting a low-altitude atmosphere waveguide measurement model, and completing the measurement of the low-altitude atmosphere waveguide according to the nonlinear electric polarization response value and the nonlinear magnetization response value.
Description
Technical Field
The invention belongs to the technical field of low-altitude atmospheric waveguide measurement, and particularly relates to a low-altitude atmospheric waveguide measurement method and system based on a neural network.
Background
Low-altitude waveguide measurement is a technique for studying atmospheric parameters and phenomena at low-altitude levels in the atmosphere.
Low-altitude waveguide measurements typically include the steps of:
a transmitter: electromagnetic wave radiation is sent into the atmosphere using a specific microwave or millimeter wave radiation source.
Propagation path: electromagnetic waves propagate in the atmosphere and are affected by various factors in the atmosphere, such as humidity, temperature, and air flow. These factors change the propagation speed and path of electromagnetic waves in the waveguide.
A receiver: a receiver is remotely located for receiving electromagnetic waves that propagate back from the atmosphere.
And (3) data processing: the received signal data will be processed and analyzed to obtain information about the atmospheric parameters. This typically involves the use of radar and other sensor technology.
However, since the low-altitude layer has a lot of uncertainty, the low-altitude waveguide error measured by the sensor alone is large.
Disclosure of Invention
In order to solve the technical problems, the invention provides a low-altitude atmosphere waveguide measurement method based on a neural network, which comprises the following steps:
setting an influence function of the square of the amplitude of the electric field on the electric polarization, fitting an adjustment factor in the influence function of the square of the amplitude of the electric field on the electric polarization by using a neural network, and calculating a nonlinear electric polarization response value according to the electric field at a space coordinate (x, y, z) at time t;
setting an influence function of the square of the amplitude of the magnetic field on magnetization, fitting an adjustment factor in the influence function of the square of the amplitude of the magnetic field on magnetization by using a neural network, and calculating a nonlinear magnetization response value according to the magnetic field at a space coordinate (x, y, z) at time t;
and setting a low-altitude atmosphere waveguide measurement model, and completing the measurement of the low-altitude atmosphere waveguide according to the nonlinear electric polarization response value and the nonlinear magnetization response value.
Further, the low-altitude atmosphere waveguide measurement model includes:
,
,
wherein,e is the electric field at spatial coordinates (x, y, z) at time t, c is the speed of light,is of vacuum permeability->For the nonlinear electric polarization response value, H is the magnetic field at the spatial coordinates (x, y, z) at time t, +.>Is a nonlinear magnetization response value.
Further, calculating the nonlinear electric polarization response valueAnd the nonlinear magnetization response value +.>The method comprises the following steps:
，
，
wherein,as a function of the influence of the square of the amplitude of the electric field on the electric polarization, < >>Is the influence function of the square of the magnitude of the magnetic field on the magnetization.
Further, the square of the magnitude of the electric field has an influence function on the electric polarizationComprising the following steps:
,
wherein A, B, C and D are adjustment factors.
Further, the square of the magnitude of the magnetic field affects the magnetization functionComprising the following steps:
,
wherein A, B, C and D are adjustment factors.
Further, the neural network is a convolutional neural network or a recurrent neural network.
Further, the mean square error function is used as a loss function, and the difference between the low-altitude atmosphere waveguide measured by the low-altitude atmosphere waveguide measurement model and the true value is judged.
Further, a plurality of hidden neurons in the neural network are provided for optimizing the low-altitude atmospheric waveguide measurement model.
The invention also provides a low-altitude atmosphere waveguide measurement system based on the neural network, which comprises:
the nonlinear electric polarization response value calculating module is used for setting an influence function of the square amplitude of the electric field on the electric polarization, fitting an adjustment factor in the influence function of the square amplitude of the electric field on the electric polarization by using a neural network, and calculating a nonlinear electric polarization response value according to the electric field at the space coordinates (x, y, z) at time t;
the module is used for calculating a nonlinear magnetization response value, which is used for setting an influence function of the square of the amplitude of the magnetic field on magnetization, fitting an adjustment factor in the influence function of the square of the amplitude of the magnetic field on magnetization by using a neural network, and calculating the nonlinear magnetization response value according to the magnetic field at the space coordinates (x, y, z) at time t;
and the measurement module is used for setting a low-altitude atmosphere waveguide measurement model and completing the measurement of the low-altitude atmosphere waveguide according to the nonlinear electric polarization response value and the nonlinear magnetization response value.
Further, the low-altitude atmosphere waveguide measurement model includes:
,
,
wherein,e is the electric field at spatial coordinates (x, y, z) at time t, c is the speed of light,is of vacuum permeability->For the nonlinear electric polarization response value, H is the magnetic field at the spatial coordinates (x, y, z) at time t, +.>Is a nonlinear magnetization response value.
Compared with the prior art, the technical scheme of the invention has the following beneficial effects:
the method comprises the steps of setting an influence function of the square of the amplitude of an electric field on electric polarization, fitting an adjustment factor in the influence function of the square of the amplitude of the electric field on the electric polarization by using a neural network, and calculating a nonlinear electric polarization response value according to the electric field at a space coordinate (x, y, z) at time t; setting an influence function of the square of the amplitude of the magnetic field on magnetization, fitting an adjustment factor in the influence function of the square of the amplitude of the magnetic field on magnetization by using a neural network, and calculating a nonlinear magnetization response value according to the magnetic field at a space coordinate (x, y, z) at time t; and setting a low-altitude atmosphere waveguide measurement model, and completing the measurement of the low-altitude atmosphere waveguide according to the nonlinear electric polarization response value and the nonlinear magnetization response value. According to the technical scheme, the low-altitude atmospheric waveguide can be accurately measured.
Drawings
FIG. 1 is a flow chart of embodiment 1 of the present invention;
fig. 2 is a block diagram of a system of embodiment 2 of the present invention.
Detailed Description
In order to better understand the above technical solutions, the following detailed description will be given with reference to the accompanying drawings and specific embodiments.
The method provided by the invention can be implemented in a terminal environment, wherein the terminal can comprise one or more of the following components: processor, storage medium, and display screen. Wherein the storage medium has stored therein at least one instruction that is loaded and executed by the processor to implement the method described in the embodiments below.
The processor may include one or more processing cores. The processor connects various parts within the overall terminal using various interfaces and lines, performs various functions of the terminal and processes data by executing or executing instructions, programs, code sets, or instruction sets stored in the storage medium, and invoking data stored in the storage medium.
The storage medium may include a random access Memory (Random Access Memory, RAM) or a Read-Only Memory (ROM). The storage medium may be used to store instructions, programs, code sets, or instructions.
The display screen is used for displaying a user interface of each application program.
All subscripts in the formula of the invention are only used for distinguishing parameters and have no practical meaning.
In addition, it will be appreciated by those skilled in the art that the structure of the terminal described above is not limiting and that the terminal may include more or fewer components, or may combine certain components, or a different arrangement of components. For example, the terminal further includes components such as a radio frequency circuit, an input unit, a sensor, an audio circuit, a power supply, and the like, which are not described herein.
Example 1
As shown in fig. 1, an embodiment of the present invention provides a low-altitude atmosphere waveguide measurement method based on a neural network, including:
step 101, setting an influence function of the square of the amplitude of the electric field on the electric polarization, fitting an adjustment factor in the influence function of the square of the amplitude of the electric field on the electric polarization by using a neural network (the neural network is a convolutional neural network or a cyclic neural network), and calculating a nonlinear electric polarization response value according to the electric field at a space coordinate (x, y, z) at time t;
102, setting an influence function of the square of the amplitude of the magnetic field on magnetization, fitting an adjustment factor in the influence function of the square of the amplitude of the magnetic field on magnetization by using a neural network (the neural network is a convolutional neural network or a cyclic neural network), and calculating a nonlinear magnetization response value according to the magnetic field at a space coordinate (x, y, z) at time t;
specifically, the nonlinear electric polarization response value is calculatedAnd the nonlinear magnetization response value +.>The method comprises the following steps:
，
，
wherein,as a function of the influence of the square of the amplitude of the electric field on the electric polarization, < >>Is the influence function of the square of the magnitude of the magnetic field on the magnetization.
Specifically, the square of the magnitude of the electric field affects the electric polarizationComprising the following steps:
,
wherein A, B, C and D are adjustment factors.
Specifically, the square of the magnitude of the magnetic field affects the magnetization functionComprising the following steps:
,
where A, B, C and D are tuning factors, in low-altitude waveguide measurements, the nonlinear properties at different coordinates in the low-altitude are critical to the propagation and response of the waveguide. The influence function of the square of the amplitude of the magnetic field on the magnetizationThe situation under different magnetic field conditions at different coordinates in the low air can be better described.
And 103, setting a low-altitude atmosphere waveguide measurement model, and completing measurement of the low-altitude atmosphere waveguide according to the nonlinear electric polarization response value and the nonlinear magnetization response value, wherein a plurality of hidden neurons in the neural network are set for optimizing the low-altitude atmosphere waveguide measurement model, and a mean square error function is used as a loss function to judge the difference between the low-altitude atmosphere waveguide measured by the low-altitude atmosphere waveguide measurement model and a true value.
Specifically, the low-altitude atmosphere waveguide measurement model includes:
,
,
wherein,e is the electric field at spatial coordinates (x, y, z) at time t, c is the speed of light,is of vacuum permeability->For the nonlinear electric polarization response value, H is the magnetic field at the spatial coordinates (x, y, z) at time t, +.>Is a nonlinear magnetization response value.
Example 2
As shown in fig. 2, an embodiment of the present invention further provides a low-altitude atmospheric waveguide measurement system based on a neural network, including:
setting a first monitoring model module, which is used for setting an influence function of the square of the amplitude of an electric field on electric polarization, fitting an adjustment factor in the influence function of the square of the amplitude of the electric field on the electric polarization by using a neural network (the neural network is a convolutional neural network or a cyclic neural network), and calculating a nonlinear electric polarization response value according to the electric field at a space coordinate (x, y, z) at time t;
setting a second monitoring model module, which is used for setting an influence function of the square of the amplitude of the magnetic field on magnetization, fitting an adjustment factor in the influence function of the square of the amplitude of the magnetic field on magnetization by using a neural network (the neural network is a convolutional neural network or a cyclic neural network), and calculating a nonlinear magnetization response value according to the magnetic field at a space coordinate (x, y, z) at time t;
specifically, the nonlinear electric polarization response value is calculatedAnd the nonlinear magnetization response value +.>The method comprises the following steps:
,
,
wherein,as a function of the influence of the square of the amplitude of the electric field on the electric polarization, < >>Is the influence function of the square of the magnitude of the magnetic field on the magnetization.
Specifically, the square of the magnitude of the electric field affects the electric polarizationComprising the following steps:
,
wherein A, B, C and D are adjustment factors.
Specifically, the square of the magnitude of the magnetic field affects the magnetization functionComprising the following steps:
,
where A, B, C and D are tuning factors, in low-altitude waveguide measurements, the nonlinear properties at different coordinates in the low-altitude are critical to the propagation and response of the waveguide. The influence function of the square of the amplitude of the magnetic field on the magnetizationThe situation under different magnetic field conditions at different coordinates in the low air can be better described.
The monitoring module is used for setting a low-altitude atmosphere waveguide measurement model, and completing measurement of the low-altitude atmosphere waveguide according to the nonlinear electric polarization response value and the nonlinear magnetization response value, wherein a plurality of hidden neurons in the neural network are set and used for optimizing the low-altitude atmosphere waveguide measurement model, and a mean square error function is used as a loss function to judge the difference between the low-altitude atmosphere waveguide measured by the low-altitude atmosphere waveguide measurement model and a true value.
Specifically, the low-altitude atmosphere waveguide measurement model includes:
,
,
wherein,e is the electric field at spatial coordinates (x, y, z) at time t, c is the speed of light,is of vacuum permeability->Is nonlinear electric polarization response value, H is timeMagnetic field at spatial coordinates (x, y, z) at t,/v>Is a nonlinear magnetization response value.
Example 3
The embodiment of the invention also provides a storage medium which stores a plurality of instructions for realizing the low-altitude atmosphere waveguide measurement method based on the neural network.
Alternatively, in this embodiment, the storage medium may be located in any one of the computer terminals in the computer terminal group in the computer network, or in any one of the mobile terminals in the mobile terminal group.
Alternatively, in the present embodiment, the storage medium is arranged to store program code for performing the steps of the method of embodiment 1.
Example 4
The embodiment of the invention also provides electronic equipment, which comprises a processor and a storage medium connected with the processor, wherein the storage medium stores a plurality of instructions, and the instructions can be loaded and executed by the processor so that the processor can execute a low-altitude atmosphere waveguide measurement method based on a neural network.
Specifically, the electronic device of the present embodiment may be a computer terminal, and the computer terminal may include: one or more processors, and a storage medium.
The storage medium may be used to store a software program and a module, such as a neural network-based low-altitude air waveguide measurement method in the embodiment of the present invention, and the processor executes various functional applications and data processing by running the software program and the module stored in the storage medium, that is, implements the neural network-based low-altitude air waveguide measurement method. The storage medium may include a high-speed random access storage medium, and may also include a non-volatile storage medium, such as one or more magnetic storage systems, flash memory, or other non-volatile solid-state storage medium. In some examples, the storage medium may further include a storage medium remotely located with respect to the processor, and the remote storage medium may be connected to the terminal through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The processor may invoke the information stored in the storage medium and the application program through the transmission system to perform the steps of the method of embodiment 1.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
In the foregoing embodiments of the present invention, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the embodiments provided in the present invention, it should be understood that the disclosed technology may be implemented in other manners. The system embodiments described above are merely exemplary, and for example, the division of the units is merely a logic function division, and there may be another division manner in actual implementation, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or partly in the form of a software product or all or part of the technical solution, which is stored in a storage medium, and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a random-access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or the like, which can store program codes.
It is apparent that the above examples are given by way of illustration only and are not limiting of the embodiments. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary here nor is it exhaustive of all embodiments. While still being apparent from variations or modifications that may be made by those skilled in the art are within the scope of the invention.
Claims (7)
1. The low-altitude atmosphere waveguide measurement method based on the neural network is characterized by comprising the following steps of:
setting an influence function of the square of the amplitude of the electric field on the electric polarization, fitting an adjustment factor in the influence function of the square of the amplitude of the electric field on the electric polarization by using a neural network, and calculating a nonlinear electric polarization response value according to the electric field at a space coordinate (x, y, z) at time t;
setting an influence function of the square of the amplitude of the magnetic field on magnetization, fitting an adjustment factor in the influence function of the square of the amplitude of the magnetic field on magnetization by using a neural network, and calculating a nonlinear magnetization response value according to the magnetic field at a space coordinate (x, y, z) at time t;
setting a low-altitude atmosphere waveguide measurement model, and completing measurement of the low-altitude atmosphere waveguide according to the nonlinear electric polarization response value and the nonlinear magnetization response value, wherein the low-altitude atmosphere waveguide measurement model comprises:
,
，
wherein,for Laplace operator>An electric field at a spatial coordinate (x, y, z) at time t, c is the speed of light,/->Is of vacuum permeability->For the nonlinear electric polarization response value, H is the magnetic field at the spatial coordinates (x, y, z) at time t, +.>Is a nonlinear magnetization response value;
calculating the nonlinear electric polarization response valueAnd the nonlinear magnetization response value +.>The method comprises the following steps:
，
，
wherein,as a function of the influence of the square of the amplitude of the electric field on the electric polarization, < >>Is the influence function of the square of the magnitude of the magnetic field on the magnetization.
2. The method for measuring low-altitude atmosphere waveguide based on neural network according to claim 1, wherein the square of the magnitude of the electric field is an influence function of electric polarizationComprising the following steps:
，
wherein A, B, C and D are adjustment factors.
3. The method for measuring low-altitude atmosphere waveguide based on neural network according to claim 1, wherein the square of the magnitude of the magnetic field is an influence function of magnetizationComprising the following steps:
,
wherein A, B, C and D are adjustment factors.
4. The method for measuring low-altitude atmosphere waveguide based on a neural network according to claim 1, wherein the neural network is a convolutional neural network or a cyclic neural network.
5. The method for measuring low-altitude atmosphere waveguide based on neural network according to claim 1, wherein the difference between the low-altitude atmosphere waveguide measured by the low-altitude atmosphere waveguide measurement model and the true value is determined using a mean square error function as a loss function.
6. A neural network based low-altitude atmosphere waveguide measurement method according to claim 1, wherein a plurality of hidden neurons in the neural network are provided for optimizing the low-altitude atmosphere waveguide measurement model.
7. A neural network-based low-altitude atmospheric waveguide measurement system, comprising:
the nonlinear electric polarization response value calculating module is used for setting an influence function of the square amplitude of the electric field on the electric polarization, fitting an adjustment factor in the influence function of the square amplitude of the electric field on the electric polarization by using a neural network, and calculating a nonlinear electric polarization response value according to the electric field at the space coordinates (x, y, z) at time t;
the module is used for calculating a nonlinear magnetization response value, which is used for setting an influence function of the square of the amplitude of the magnetic field on magnetization, fitting an adjustment factor in the influence function of the square of the amplitude of the magnetic field on magnetization by using a neural network, and calculating the nonlinear magnetization response value according to the magnetic field at the space coordinates (x, y, z) at time t;
the measuring module is used for setting a low-altitude atmosphere waveguide measuring model and completing the measurement of the low-altitude atmosphere waveguide according to the nonlinear electric polarization response value and the nonlinear magnetization response value, wherein the low-altitude atmosphere waveguide measuring model comprises:
，
，
wherein,for the Laplacian, E is the electric field at the spatial coordinates (x, y, z) at time t, c is the speed of light, +.>Is of vacuum permeability->For the nonlinear electric polarization response value, H is the magnetic field at the spatial coordinates (x, y, z) at time t, +.>Is a nonlinear magnetization response value;
calculating the nonlinear electric polarization response valueAnd the nonlinear magnetization response value +.>The method comprises the following steps:
，
，
wherein,as a function of the influence of the square of the amplitude of the electric field on the electric polarization, < >>Square of the magnitude of the magnetic fieldAnd (5) affecting the function.
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| CN112560342A (en) * | 2020-12-15 | 2021-03-26 | 哈尔滨工程大学 | DNN-based atmospheric waveguide parameter estimation method |
| WO2022136146A1 (en) * | 2020-12-22 | 2022-06-30 | Ecole Polytechnique Federale De Lausanne (Epfl) | Optical computing and reconfiguring with spatiotemporal nonlinearities in waveguides |
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| SU1425564A1 (en) * | 1987-05-27 | 1988-09-23 | Воронежский государственный университет им.Ленинского комсомола | Device for measuring electromagnetic field in waveguide |
| DE19711158A1 (en) * | 1997-03-18 | 1998-09-24 | Ernst Dr Brinkmeyer | Waveguide grid measuring method |
| CN115004098A (en) * | 2019-12-03 | 2022-09-02 | 加州理工学院 | Entangled photon source capable of replacing pulse laser in non-ablation multiphoton and non-linear process |
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| WO2022136146A1 (en) * | 2020-12-22 | 2022-06-30 | Ecole Polytechnique Federale De Lausanne (Epfl) | Optical computing and reconfiguring with spatiotemporal nonlinearities in waveguides |
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