1. A method for rating the quality of a textile yarn, the method comprising:

determining a quality index of the textile yarn;

determining a raw cotton index of the textile yarn;

constructing a nonlinear relation between the process parameters and the raw cotton indexes;

supplementing the quality index through the raw cotton index;

determining a yarn usage for each textile yarn;

inputting the supplemented quality index and the yarn application into a prediction model for each textile yarn;

and taking the prediction grade output by the prediction model as the quality grade of the textile yarn.

2. The method of claim 1, wherein the quality indicator comprises at least one of a tenacity value, unevenness, hairiness, evenness, and composite rating data of the textile yarn.

3. The method of claim 1, wherein constructing a non-linear relationship between the process parameter and the raw cotton index comprises:

collecting technological parameters of the textile yarns;

according to the process sequence in the production process and the influence sequence of a single process on the raw cotton index, sequencing the combination of the process parameters of the textile yarns and the raw cotton index, and converting the textile yarns into a matrix formed according to the raw cotton index and the process parameters;

and inputting the matrix into a residual error network to determine the nonlinear relation between the process parameters and the raw cotton index.

4. The method of claim 1, wherein the predictive model comprises a residual network and a generalized recurrent neural network; the step of using the prediction grade output by the prediction model as the quality grade of the textile yarn comprises the following steps:

inputting the supplemented quality index and yarn use into the residual error network;

after multilayer convolution calculation and pooling are carried out on input data through the residual error network, the output data of the residual error network pooling layer are input into the generalized regression neural network;

and obtaining the prediction grade output by the generalized regression neural network as the quality grade of the textile yarn.

5. The method of claim 1, further comprising:

and enhancing the supplemented quality index and the data corresponding to the yarn application through a generative confrontation network so as to improve the number of samples.

6. The method of claim 1, further comprising: determining a level of the quality rating according to the yarn usage.

7. A device for rating the quality of textile yarns, characterized in that it comprises:

the index confirming module is used for confirming the quality index of the textile yarn; determining a raw cotton index of the textile yarn; constructing a nonlinear relation between the process parameters and the raw cotton indexes; supplementing the quality index through the raw cotton index;

a quality rating determination module for determining a yarn usage for each textile yarn; inputting the supplemented quality index and the yarn application into a prediction model for each textile yarn; and taking the prediction grade output by the prediction model as the quality grade of the textile yarn.

8. The apparatus of claim 7, wherein the quality indicator comprises at least one of a tenacity value, an irregularity, a hairiness, a evenness, and a composite rating data of the textile yarn.

9. A machine readable storage medium having instructions stored thereon, which when executed by a processor causes the processor to be configured to perform a method of rating the quality of a textile yarn according to any one of claims 1 to 6.

10. A processor characterized by being configured to carry out the method of quality rating of textile yarns according to any one of claims 1 to 6.

Background

The quality of the yarn directly affects the quality of the woven fabric, so that the quality of the cotton yarn needs to be quickly and accurately predicted before processing and production. There are many factors that affect yarn quality, including raw cotton properties and spinning process. The yarns have various quality indexes, the prior art usually only carries out prediction analysis on a single quality index, the influence degree of each quality index on the comprehensive quality rating of the yarns is different, meanwhile, the requirements of the final use of the yarns on each quality index are different, and objective comprehensive evaluation is difficult to make by manpower. The prior art generally measures a single quality index, does not comprehensively evaluate the yarn quality and does not consider the influence of the yarn application, so that the methods cannot give the comprehensive level of the yarn quality and have limited practical significance.

Disclosure of Invention

The embodiment of the application aims to provide a method and a device for grading the quality of textile yarns, a storage medium and a processor.

In order to achieve the above object, a first aspect of the present application provides a method for rating the quality of a textile yarn, comprising:

determining a quality index of the textile yarn;

determining a raw cotton index of the textile yarn;

constructing a nonlinear relation between the process parameters and the raw cotton indexes;

supplementing the quality index through the raw cotton index;

determining a yarn usage for each textile yarn;

inputting the supplemented quality index and the yarn application into a prediction model for each textile yarn;

and taking the prediction grade output by the prediction model as the quality grade of the textile yarn.

Optionally, the quality indicator comprises at least one of a single quality rating and a composite rating data of strength values, unevenness, hairiness, evenness, etc. of the textile yarn.

Optionally, constructing a nonlinear relationship between the process parameter and the raw cotton index comprises: collecting technological parameters of the textile yarns; according to the process sequence in the production process and the influence sequence of a single process on the raw cotton index, the combination of the process parameters of the textile yarns and the raw cotton index is sequenced, and the textile yarns are converted into a matrix formed according to the raw cotton index and the process parameters; and inputting the matrix into a residual error network to determine the nonlinear relation between the process parameters and the raw cotton index.

Optionally, the prediction model comprises a residual error network and a generalized recurrent neural network; the prediction rating output by the prediction model as a quality rating of the textile yarn comprises: inputting the supplemented quality index and yarn use into the residual error network; after multilayer convolution calculation and pooling are carried out on input data through the residual error network, the output data of the residual error network pooling layer are input into the generalized regression neural network; and obtaining the prediction grade output by the generalized regression neural network as the quality grade of the textile yarn.

Optionally, the method further comprises: and enhancing the supplemented quality index and the data corresponding to the yarn application through a generative confrontation network so as to improve the number of samples.

Optionally, the method further comprises: determining a level of the quality rating according to the yarn usage.

The present application provides in a second aspect a quality rating apparatus for textile yarns, comprising:

the index confirming module is used for confirming the quality index of the textile yarn; determining a raw cotton index of the textile yarn; constructing a nonlinear relation between the process parameters and the raw cotton indexes; supplementing the quality index through the raw cotton index;

a quality rating determination module for determining a yarn usage for each textile yarn; inputting the supplemented quality index and the yarn application into a prediction model for each textile yarn; and taking the prediction grade output by the prediction model as the quality grade of the textile yarn.

A third aspect of the present application provides a machine-readable storage medium having stored thereon instructions which, when executed by a processor, cause the processor to be configured to perform the above-described method of quality rating of textile yarns.

A fourth aspect of the present application provides a processor configured to perform the above-described method of quality rating a textile yarn.

The quality rating method of the textile yarns utilizes a deep learning technology combining ResNet50 and GRNN, simultaneously considers different purposes of the yarns, and constructs the nonlinear relation between the yarn quality index, the purposes and the comprehensive quality rating. Aiming at the condition of partial quality index value loss, a ResNet50-GRNN technology is utilized to construct a relation model of a raw cotton index, process parameters and the quality index, so that the process parameters can be optimized in advance pertinently to improve the related quality index, the waste of time and materials is reduced, the quality index value can be predicted to be used for completing the loss value, the comprehensive rating of the yarn can be output by inputting the quality index and the application by a user by utilizing the trained model, the weight of the quality index is prevented from being determined artificially and subjectively, and the client can easily select and purchase proper yarn according to the rating.

Additional features and advantages of embodiments of the present application will be described in detail in the detailed description which follows.

Drawings

The accompanying drawings, which are included to provide a further understanding of the embodiments of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the embodiments of the disclosure, but are not intended to limit the embodiments of the disclosure. In the drawings:

fig. 1 schematically shows a flow diagram of a method of rating the quality of a textile yarn according to an embodiment of the present application;

fig. 2 schematically shows a schematic structural diagram of a residual error network according to an embodiment of the present application;

figure 3 schematically shows a schematic view of a method of rating the quality of a textile yarn according to another embodiment of the present application;

fig. 4 schematically shows a block diagram of a construction of a device for grading the quality of a textile yarn according to an embodiment of the present application;

fig. 5 schematically shows an internal structure diagram of a computer device according to an embodiment of the present application.

Detailed Description

The following detailed description of embodiments of the present application will be made with reference to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the embodiments of the application, are given by way of illustration and explanation only, not limitation.

Fig. 1 schematically shows a flow diagram of a method of rating the quality of a textile yarn according to an embodiment of the present application. As shown in fig. 1, in an embodiment of the present application, there is provided a method of rating the quality of a textile yarn, comprising the steps of:

step 101, determining the quality index of the textile yarn;

step 102, determining a raw cotton index of the textile yarn;

103, constructing a nonlinear relation between the process parameters and the raw cotton index;

104, supplementing the quality index through the raw cotton index;

step 105, determining the yarn usage of each textile yarn;

106, inputting the supplemented quality index and the yarn application into a prediction model aiming at each textile yarn;

and step 107, taking the output prediction rating as the quality rating of the textile yarn.

First, the yarn quality index of each textile yarn evaluated by a plurality of detection mechanisms, which may be mechanisms such as the wurster, may be collected. The quality index comprises at least one of single quality rating and comprehensive rating data of strength value, unevenness, hairiness, evenness and the like of the textile yarns. The composite rating data may be 5%, 25%, 50%, etc. The processor can also obtain raw cotton indexes of each textile yarn, wherein the raw cotton indexes can comprise maturity of cotton fibers, length of the cotton fibers, impurity content and the like. The process parameters of each textile yarn are obtained, and the nonlinear relation between the process parameters of each textile yarn and the raw cotton indexes corresponding to the process parameters can be determined.

In one embodiment, constructing a non-linear relationship between the process parameter and the raw cotton index comprises: collecting technological parameters of the textile yarns; according to the process sequence in the production process and the influence sequence of a single process on the raw cotton index, the combination of the process parameters of the textile yarns and the raw cotton index is sequenced, and the textile yarns are converted into a matrix formed according to the raw cotton index and the process parameters; and inputting the matrix into a residual error network to determine the nonlinear relation between the process parameters and the raw cotton index. Specifically, the process parameters of each textile yarn may be collected, then the process sequence in the production process and the influence sequence of a single process on the raw cotton index are sequenced on the combination of the process parameters and the raw cotton index of all the textile yarns, and then the data corresponding to each textile yarn is converted into a matrix, for example, into an N × 1+ M matrix, where N may be represented as a process parameter and 1 represents a corresponding process parameter, so that the first column of the matrix corresponds to the sequence of the production process, M may be represented as a raw cotton index of the textile yarn, and each row of the matrix represents the process and the influence sequence of the process on the raw cotton index. Then, the data can be normalized, and then the matrix is input into a residual error network, so that the nonlinear relation between the process parameters and the raw cotton index can be determined according to the residual error network. Specifically, the process parameters and raw cotton indexes corresponding to each textile yarn can be obtained. And inputting a matrix consisting of the process parameters and the raw cotton index into a residual error network, wherein the residual error network can output a corresponding prediction rating. In this way, through inputting a plurality of matrix data, the nonlinear relation between the process parameters of the textile yarns and the raw cotton indexes can be finally counted.

Further, aiming at the problem that part of the yarns lack part of quality indexes, the raw cotton indexes and the process have important influence on the quality of the cotton yarns. For example, the maturity and impurity content of cotton fibers have great influence on the strength of yarns, the length of the cotton fibers has great influence on yarn evenness, important raw cotton indexes influencing various quality indexes can be screened out by expert grading and utilizing an analytic hierarchy process, and the corresponding relation between the important raw cotton indexes and the important raw cotton indexes is found out by adopting residual deep learning networks such as ResNet50 and the like, so that the missing quality indexes can be complemented by the raw cotton indexes. In one embodiment, the method further comprises: and enhancing the supplemented quality index and the data corresponding to the yarn application through the generative countermeasure network so as to improve the number of samples. When the number of effective data sources is few, the related data can be enhanced by using GAN (generative countermeasure network) to increase the number of samples, and when each network is trained, more sample data can be trained to improve the prediction accuracy and precision of the network model. In one embodiment, the method further comprises: the level of quality rating is determined according to the yarn usage. Further, the yarn usage of each yarn can also be acquired. Because the quality indexes corresponding to different purposes of the same yarn have different emphasis points, the quality indexes are finally reflected on the comprehensive quality rating, and the ratings of different purposes can be adjusted through expert grading. The rating is not adjusted under the condition of unknown use, and in order to avoid the influence of artificial subjectivity, a certain range of randomness can be added in the rating adjustment. Therefore, aiming at different purposes of the yarn, the original comprehensive quality rating is further subdivided and adjusted by referring to expert opinions, certain randomness is added, such as 25% of yarn rating, 10%, 15%, 20%, 30% of rating and the like, certain random values in-5%, 0 and 5% are added, the random values are used as the final rating of the yarn for algorithm training, and if the purposes are unknown, the rating is kept unchanged.

In one embodiment, the prediction model includes a residual network and a generalized recurrent neural network; the step of using the prediction grade output by the prediction model as the quality grade of the textile yarn comprises the following steps: inputting the supplemented quality index and the yarn application into a residual error network; after multilayer convolution calculation and pooling are carried out on input data through a residual error network, the output data of a residual error network pooling layer are input into a generalized regression neural network; and obtaining the prediction rating output by the generalized regression neural network as the quality rating of the textile yarns.

The residual network may be a ResNet50 neural network model and the generalized recurrent neural network may be a GRNN recurrent neural network. After the quality index is supplemented by the raw cotton index, the supplemented quality index and yarn application (such as ribbing, all cotton and the like) can be input into a residual error network, and a predicted value can be output through a full connection layer after multilayer convolution and pooling of the residual error network. The output data of the pooling layer of the residual error network can be used as the input data of the generalized recurrent neural network, calculation is performed through the mode layer and the summation layer of the generalized recurrent neural network, then the prediction rating output by the generalized recurrent neural network can be obtained, and the prediction rating can be used as the quality rating of the textile yarn corresponding to the input data, such as 5%, 10%, 15% and the like. Specifically, as shown in fig. 2, the convolutional layers of the residual network may include 3 layers, where the output data of the convolutional layer of the first layer is 1 × 1, 64; the output data of the second convolution layer is 3 x 3, 64; the output data of the third convolutional layer is 1 x 1, 64.

In one embodiment, as shown in FIG. 3, yarn quality indicators and composite rating data are collected and added to yarn usage, with the composite rating further subdivided and adjusted by experts, and not adjusted if usage is unknown. And the quality indexes, the raw cotton indexes and the process parameters influencing the indexes can be found out through expert scoring. And a GAN technology can be used for adding training samples, a ResNet-GRNN technology is used for constructing a non-linear model of the raw cotton index and the quality index, and a ResNet50-GRNN technology is used for constructing a non-linear model of the quality index/application and comprehensive quality rating. And inputting the quality index value and the application of the yarn to the model, so that the comprehensive quality rating of the yarn can be predicted through the model.

The quality rating method of the textile yarns utilizes a deep learning technology combining ResNet50 and GRNN, simultaneously considers different purposes of the yarns, and constructs the nonlinear relation between the yarn quality index, the purposes and the comprehensive quality rating. Aiming at the condition of partial quality index value loss, a ResNet50-GRNN technology is utilized to construct a relation model of a raw cotton index, process parameters and the quality index, so that the process parameters can be optimized in advance pertinently to improve the related quality index, the waste of time and materials is reduced, the quality index value can be predicted to be used for completing the loss value, the comprehensive rating of the yarn can be output by inputting the quality index and the application by a user by utilizing the trained model, the weight of the quality index is prevented from being determined artificially and subjectively, and the client can easily select and purchase proper yarn according to the rating.

In one embodiment, as shown in fig. 4, there is provided a quality rating apparatus for textile yarns, comprising:

an index confirmation module 401, configured to determine a quality index of the textile yarn; determining the raw cotton index of the textile yarn; constructing a nonlinear relation between the process parameters and the raw cotton indexes; supplementing the quality index through the raw cotton index;

a quality rating determination module 402 for determining a yarn usage for each textile yarn; inputting the supplemented quality index and the yarn application into a prediction model aiming at each textile yarn; and taking the prediction grade output by the prediction model as the quality grade of the textile yarn.

In one embodiment, the indicator confirmation module 401 is also used to collect process parameters of the textile yarn; according to the process sequence in the production process and the influence sequence of a single process on the raw cotton index, the combination of the process parameters of the textile yarns and the raw cotton index is sequenced, and the textile yarns are converted into a matrix formed according to the raw cotton index and the process parameters; and inputting the matrix into a residual error network to determine the nonlinear relation between the process parameters and the raw cotton index.

In one embodiment, the prediction model includes a residual network and a generalized recurrent neural network. The quality rating determination module 402 is further configured to input the supplemented quality index and yarn usage to the residual error network; after multilayer convolution calculation and pooling are carried out on input data through a residual error network, the output data of a residual error network pooling layer are input into a generalized regression neural network; and obtaining the prediction rating output by the generalized regression neural network as the quality rating of the textile yarns.

In one embodiment, the apparatus for rating the quality of the textile yarn further comprises a data enhancement module (not shown in the figure) for enhancing the supplemented quality index and the data corresponding to the yarn usage by the generative confrontation network to increase the number of samples.

In one embodiment, the quality rating determination module 402 is further configured to determine a hierarchy of quality ratings according to yarn usage.

The quality rating device for textile yarns comprises a processor and a memory, wherein the index confirmation module, the quality rating determination module and the like are stored in the memory as program units, and the processor executes the program modules stored in the memory to realize corresponding functions.

The processor comprises a kernel, and the kernel calls the corresponding program unit from the memory. One or more than one inner core can be arranged, and the quality grading method of the textile yarns is realized by adjusting the parameters of the inner core.

The memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip.

Embodiments of the present application provide a storage medium having a program stored thereon, which when executed by a processor, implements the above-described method for rating the quality of a textile yarn.

The embodiment of the application provides a processor which is used for running a program, wherein the program is used for executing the quality rating method of the textile yarns during running.

In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 5. The computer device includes a processor a01, a network interface a02, a memory (not shown), and a database (not shown) connected by a system bus. Wherein processor a01 of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises an internal memory a03 and a non-volatile storage medium a 04. The non-volatile storage medium a04 stores an operating system B01, a computer program B02, and a database (not shown in the figure). The internal memory a03 provides an environment for the operation of the operating system B01 and the computer program B02 in the nonvolatile storage medium a 04. The database of the computer device is used for storing data such as quality indexes of the textile yarns. The network interface a02 of the computer device is used for communication with an external terminal through a network connection. The computer program B02 is executed by the processor a01 to implement a method of rating the quality of a textile yarn.

Those skilled in the art will appreciate that the architecture shown in fig. 5 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.

The embodiment of the application provides equipment, the equipment comprises a processor, a memory and a program which is stored on the memory and can run on the processor, and the following steps are realized when the processor executes the program: determining the quality index of the textile yarn; determining the raw cotton index of the textile yarn; constructing a nonlinear relation between the process parameters and the raw cotton indexes; supplementing the quality index through the raw cotton index; determining a yarn usage for each textile yarn; inputting the supplemented quality index and the yarn application into a prediction model aiming at each textile yarn; and taking the prediction grade output by the prediction model as the quality grade of the textile yarn.

In one embodiment, the quality indicator comprises at least one of a single quality rating and a composite rating data of the textile yarn strength value, unevenness, hairiness, evenness, etc.

In one embodiment, constructing a non-linear relationship between the process parameter and the raw cotton index comprises: collecting technological parameters of the textile yarns; according to the process sequence in the production process and the influence sequence of a single process on the raw cotton index, the combination of the process parameters of the textile yarns and the raw cotton index is sequenced, and the textile yarns are converted into a matrix formed according to the raw cotton index and the process parameters; and inputting the matrix into a residual error network to determine the nonlinear relation between the process parameters and the raw cotton index.

In one embodiment, the prediction model includes a residual network and a generalized recurrent neural network; the step of using the prediction grade output by the prediction model as the quality grade of the textile yarn comprises the following steps: inputting the supplemented quality index and the yarn application into a residual error network; after multilayer convolution calculation and pooling are carried out on input data through a residual error network, the output data of a residual error network pooling layer are input into a generalized regression neural network; and obtaining the prediction rating output by the generalized regression neural network as the quality rating of the textile yarns.

In one embodiment, the method further comprises: the level of quality rating is determined according to the yarn usage.

In one embodiment, the method further comprises: and enhancing the supplemented quality index and the data corresponding to the yarn application through the generative countermeasure network so as to improve the number of samples.

The present application further provides a computer program product adapted to perform a program for initializing the following method steps when executed on a data processing device: determining the quality index of the textile yarn; determining the raw cotton index of the textile yarn; constructing a nonlinear relation between the process parameters and the raw cotton indexes; supplementing the quality index through the raw cotton index; determining a yarn usage for each textile yarn; inputting the supplemented quality index and the yarn application into a prediction model aiming at each textile yarn; and taking the prediction grade output by the prediction model as the quality grade of the textile yarn.

In one embodiment, the quality indicator comprises at least one of a strength value, unevenness, hairiness, evenness of the textile yarn, and composite rating data.

In one embodiment, constructing a non-linear relationship between the process parameter and the raw cotton index comprises: collecting technological parameters of the textile yarns; according to the process sequence in the production process and the influence sequence of a single process on the raw cotton index, the combination of the process parameters of the textile yarns and the raw cotton index is sequenced, and the textile yarns are converted into a matrix formed according to the raw cotton index and the process parameters; and inputting the matrix into a residual error network to determine the nonlinear relation between the process parameters and the raw cotton index.

In one embodiment, the prediction model includes a residual network and a generalized recurrent neural network; the step of using the prediction grade output by the prediction model as the quality grade of the textile yarn comprises the following steps: inputting the supplemented quality index and the yarn application into a residual error network; after multilayer convolution calculation and pooling are carried out on input data through a residual error network, the output data of a residual error network pooling layer are input into a generalized regression neural network; and obtaining the prediction rating output by the generalized regression neural network as the quality rating of the textile yarns.

In one embodiment, the method further comprises: the level of quality rating is determined according to the yarn usage.

In one embodiment, the method further comprises: and enhancing the supplemented quality index and the data corresponding to the yarn application through the generative countermeasure network so as to improve the number of samples.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.

The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.