阅读说明：本技术 字母向量学习方法、系统、存储介质及电子设备 (Alphabet vector learning method, system, storage medium and electronic device ) 是由 梁吉光 黄艳香 于 2021-08-17 设计创作，主要内容包括：本申请公开了一种字母向量学习方法、系统、存储介质及电子设备,方法包括：获取字典步骤：整理预训练中文字向量模型中的汉字并形成字典；获取二元组步骤：遍历所述字典中的汉字,获取所述汉字的拼音,并将所述汉字的拼音拆分构成字母串,根据所述汉字和所述字母串构建二元组；计算步骤：根据公式计算所述字母串中每个汉语拼音字母的在所述汉字中所分得的向量；获取字母向量步骤：根据汉字拼音字符串中每个汉语拼音字母在所述汉字中所分得的所述向量,计算获取汉字拼音字母向量。本发明主要考虑了汉字在发音方面的语义关系,丰富了只基于字或词粒度的向量表示。(The application discloses an alphabet vector learning method, a system, a storage medium and an electronic device, wherein the method comprises the following steps: a dictionary obtaining step: sorting Chinese characters in a pre-training Chinese character vector model and forming a dictionary; obtaining a binary group: traversing the Chinese characters in the dictionary, obtaining pinyin of the Chinese characters, splitting the pinyin of the Chinese characters to form letter strings, and constructing binary groups according to the Chinese characters and the letter strings; a calculation step: calculating the vector of each Chinese phonetic alphabet in the alphabet string, which is obtained in the Chinese character, according to a formula; acquiring an alphabetic vector: and calculating to obtain the Chinese character pinyin character vector according to the vector of each Chinese pinyin character in the Chinese character pinyin character string. The invention mainly considers the semantic relation of the Chinese characters in the aspect of pronunciation and enriches the vector representation only based on the character or word granularity.)

1. An alphabet vector learning method, comprising:

a dictionary obtaining step: sorting Chinese characters in a pre-training Chinese character vector model and forming a dictionary;

obtaining a binary group: traversing the Chinese characters in the dictionary, obtaining pinyin of the Chinese characters, splitting the pinyin of the Chinese characters to form letter strings, and constructing binary groups according to the Chinese characters and the letter strings;

a calculation step: calculating the vector of each Chinese phonetic alphabet in the alphabet string, which is obtained in the Chinese character, according to a formula;

acquiring an alphabetic vector: and calculating to obtain the Chinese character pinyin character vector according to the vector of each Chinese pinyin character in the Chinese character pinyin character string.

2. The alphabet vector learning method of claim 1, wherein the acquiring dictionary step comprises:

a model obtaining step: acquiring the pre-training Chinese character vector model;

finishing: and sorting the Chinese characters in the pre-training Chinese character vector model and forming the dictionary.

3. The alphabet vector learning method of claim 1, wherein the calculating step includes:

and (3) inverted arrangement: inverting the Chinese pinyin and the Chinese characters according to the character string formed by the Chinese pinyin characters corresponding to the Chinese characters;

vector calculation: and calculating the vector of each Chinese phonetic alphabet in the character string, which is obtained in the Chinese character, according to the inverted result and a vector calculation formula.

4. The alphabet vector learning method of claim 1, wherein the acquiring a bigram step includes:

if the Chinese character is a polyphonic character, the Chinese character can be split into a plurality of character strings formed by Chinese phonetic alphabet.

5. An alphabet vector learning system, comprising:

the dictionary acquisition module is used for collating Chinese characters in the pre-training Chinese character vector model and forming a dictionary;

the obtaining binary group module traverses the Chinese characters in the dictionary, obtains the pinyin of the Chinese characters, splits the pinyin of the Chinese characters to form letter strings, and constructs binary groups according to the Chinese characters and the letter strings;

the calculation module is used for calculating the vector of each Chinese phonetic alphabet in the alphabet string, which is obtained in the Chinese character, according to a formula;

and the letter vector acquisition module calculates and acquires the Chinese character pinyin letter vector according to the vector of each Chinese pinyin letter in the Chinese character pinyin character string.

6. The alphabet vector learning system of claim 5, wherein the retrieve dictionary module comprises:

a model obtaining unit, which obtains the pre-training Chinese character vector model;

and the sorting unit sorts the Chinese characters in the pre-training Chinese character vector model and forms the dictionary.

7. An alphabet vector learning system according to claim 5, wherein said calculation module includes:

the reverse arrangement unit is used for performing reverse arrangement on the Chinese pinyin and the Chinese characters according to the character string formed by the Chinese pinyin characters corresponding to the Chinese characters and the Chinese pinyin characters;

and the vector calculation unit is used for calculating the vector of each Chinese pinyin letter in the letter string, which is obtained in the Chinese character, according to the inverted result and a vector calculation formula.

8. The alphabet vector learning system of claim 5, wherein the obtain bigram module comprises:

if the Chinese character is a polyphonic character, the Chinese character can be split into a plurality of character strings formed by Chinese phonetic alphabet.

9. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the alphabet vector learning method of any one of claims 1 to 4 when executing the computer program.

10. A storage medium on which a computer program is stored, which program, when executed by a processor, implements the alphabet vector learning method according to any one of claims 1 to 4.

Technical Field

The invention belongs to the field of natural language processing, and particularly relates to an alphabet vector learning method, an alphabet vector learning system, a storage medium and electronic equipment.

Background

Wordebedding was originally started from english research, and its application in chinese was to replace the input of the algorithm model with chinese corpora and replace the english words in the original model with chinese characters or words for training. The method does not consider Chinese characteristics, for example, pronunciation of Chinese characters is fully considered in the construction process, namely pinyin of the Chinese characters, and the Chinese characters with the same pinyin (namely homophones) may have certain semantic relevance, for example, "he" and "she" are third-party names, but refer to male "he" and female "her" respectively.

Disclosure of Invention

The embodiment of the application provides an alphabetic vector learning method, an alphabetic vector learning system, a storage medium and electronic equipment, and aims to at least solve the problem that the existing alphabetic vector learning method does not consider Chinese characteristics.

The invention provides an alphabetic vector learning method, which comprises the following steps:

a dictionary obtaining step: sorting Chinese characters in a pre-training Chinese character vector model and forming a dictionary;

obtaining a binary group: traversing the Chinese characters in the dictionary, obtaining pinyin of the Chinese characters, splitting the pinyin of the Chinese characters to form letter strings, and constructing binary groups according to the Chinese characters and the letter strings;

a calculation step: calculating the vector of each Chinese phonetic alphabet in the alphabet string, which is obtained in the Chinese character, according to a formula;

acquiring an alphabetic vector: and calculating to obtain the Chinese character pinyin character vector according to the vector of each Chinese pinyin character in the Chinese character pinyin character string.

In the above method for learning alphabetic vector, the step of obtaining a dictionary includes:

a model obtaining step: acquiring the pre-training Chinese character vector model;

finishing: and sorting the Chinese characters in the pre-training Chinese character vector model and forming the dictionary.

The above alphabet vector learning method, wherein the calculating step includes:

and (3) inverted arrangement: inverting the Chinese pinyin and the Chinese characters according to the character string formed by the Chinese pinyin characters corresponding to the Chinese characters;

vector calculation: and calculating the vector of each Chinese phonetic alphabet in the character string, which is obtained in the Chinese character, according to the inverted result and a vector calculation formula.

The above alphabet vector learning method, wherein the step of obtaining the binary group includes:

if the Chinese character is a polyphonic character, the Chinese character can be split into a plurality of character strings formed by Chinese phonetic alphabet.

The invention also provides an alphabet vector learning system, which comprises:

the dictionary acquisition module is used for collating Chinese characters in the pre-training Chinese character vector model and forming a dictionary;

the obtaining binary group module traverses the Chinese characters in the dictionary, obtains the pinyin of the Chinese characters, splits the pinyin of the Chinese characters to form letter strings, and constructs binary groups according to the Chinese characters and the letter strings;

the calculation module is used for calculating the vector of each Chinese phonetic alphabet in the alphabet string, which is obtained in the Chinese character, according to a formula;

and the letter vector acquisition module calculates and acquires the Chinese character pinyin letter vector according to the vector of each Chinese pinyin letter in the Chinese character pinyin character string.

The above-mentioned alphabet vector learning system, wherein, the obtaining dictionary module includes:

a model obtaining unit, which obtains the pre-training Chinese character vector model;

and the sorting unit sorts the Chinese characters in the pre-training Chinese character vector model and forms the dictionary.

The above alphabet vector learning system, wherein the calculating module includes:

the reverse arrangement unit is used for performing reverse arrangement on the Chinese pinyin and the Chinese characters according to the character string formed by the Chinese pinyin characters corresponding to the Chinese characters and the Chinese pinyin characters;

and the vector calculation unit is used for calculating the vector of each Chinese pinyin letter in the letter string, which is obtained in the Chinese character, according to the inverted result and a vector calculation formula.

The above alphabet vector learning system, wherein the obtaining binary unit module includes:

if the Chinese character is a polyphonic character, the Chinese character can be split into a plurality of character strings formed by Chinese phonetic alphabet.

An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor, when executing the computer program, implements the alphabet vector learning method as in any one of the above.

A storage medium having stored thereon a computer program, wherein the program, when executed by a processor, implements an alphabet vector learning method as in any one of the above.

The invention has the beneficial effects that:

the invention belongs to the field of natural language processing in deep learning technology. The invention provides a Chinese pinyin letter vector learning method and device based on character pinyin, which mainly consider the semantic relation of Chinese characters in the aspect of pronunciation and enrich the vector representation only based on character or word granularity.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application.

In the drawings:

FIG. 1 is a flow chart of an alphabet vector learning method of the present invention;

FIG. 2 is a flow chart of substep S1 of the present invention;

FIG. 3 is a flow chart of substep S3 of the present invention;

FIG. 4 is a schematic diagram of the structure of the alphabet vector learning system of the present invention;

fig. 5 is a frame diagram of an electronic device according to an embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments provided in the present application without any inventive step are within the scope of protection of the present application.

It is obvious that the drawings in the following description are only examples or embodiments of the present application, and that it is also possible for a person skilled in the art to apply the present application to other similar contexts on the basis of these drawings without inventive effort. Moreover, it should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another.

Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the specification. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of ordinary skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments without conflict.

Unless defined otherwise, technical or scientific terms referred to herein shall have the ordinary meaning as understood by those of ordinary skill in the art to which this application belongs. Reference to "a," "an," "the," and similar words throughout this application are not to be construed as limiting in number, and may refer to the singular or the plural. The present application is directed to the use of the terms "including," "comprising," "having," and any variations thereof, which are intended to cover non-exclusive inclusions; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to the listed steps or elements, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. Reference to "connected," "coupled," and the like in this application is not intended to be limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" as referred to herein means two or more. "and/or" describes an association relationship of associated objects, meaning that three relationships may exist, for example, "A and/or B" may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. Reference herein to the terms "first," "second," "third," and the like, are merely to distinguish similar objects and do not denote a particular ordering for the objects.

The present invention is described in detail with reference to the embodiments shown in the drawings, but it should be understood that these embodiments are not intended to limit the present invention, and those skilled in the art should understand that functional, methodological, or structural equivalents or substitutions made by these embodiments are within the scope of the present invention.

Before describing in detail the various embodiments of the present invention, the core inventive concepts of the present invention are summarized and described in detail by the following several embodiments.

The first embodiment is as follows:

referring to fig. 1, fig. 1 is a flowchart of an alphabet vector learning method. As shown in fig. 1, the alphabet vector learning method of the present invention includes:

dictionary acquisition step S1: sorting Chinese characters in a pre-training Chinese character vector model and forming a dictionary;

get doublet step S2: traversing the Chinese characters in the dictionary, obtaining pinyin of the Chinese characters, splitting the pinyin of the Chinese characters to form letter strings, and constructing binary groups according to the Chinese characters and the letter strings;

calculation step S3: calculating the vector of each Chinese phonetic alphabet in the alphabet string, which is obtained in the Chinese character, according to a formula;

acquiring alphabet vector step S4: and calculating to obtain the Chinese character pinyin character vector according to the vector of each Chinese pinyin character in the Chinese character pinyin character string.

Referring to fig. 2, fig. 2 is a flowchart of the dictionary obtaining step S1. As shown in fig. 2, the acquiring dictionary step S1 includes:

model acquisition step S11: acquiring the pre-training Chinese character vector model;

finishing step S12: and sorting the Chinese characters in the pre-training Chinese character vector model and forming the dictionary.

Referring to fig. 3, fig. 3 is a flowchart of the calculating step S3. As shown in fig. 3, the calculating step S3 includes:

inverted step S31: inverting the Chinese pinyin and the Chinese characters according to the character string formed by the Chinese pinyin characters corresponding to the Chinese characters;

vector calculation step S32: and calculating the vector of each Chinese phonetic alphabet in the character string, which is obtained in the Chinese character, according to the inverted result and a vector calculation formula.

Wherein the step of obtaining the duplet comprises:

if the Chinese character is a polyphonic character, the Chinese character can be split into a plurality of character strings formed by Chinese phonetic alphabet.

Specifically, the method comprises the following specific steps:

s1: and acquiring a pre-training Chinese character vector model. The word vector model is stored in the form of < kanji, word vector >.

S2: and sorting the Chinese characters in the character vector model and forming a dictionary.

S3: traversing the Chinese characters in the dictionary obtained in the step S2, obtaining pinyin of the Chinese characters, splitting the pinyin of the Chinese characters into character strings formed by the Chinese pinyin characters, and forming a binary group < Chinese characters, { character strings formed by the Chinese pinyin characters } >.

Further, for convenience of description, the exemplary expression is given by taking the Chinese character "Ming" as an example. The 'clear' pinyin is 'ming', so the 'clear' pinyin can be split into letter strings { m, i, n, g } formed by the pinyin letters. Further, we get the doublet < Ming, { { m, i, n, g } } >.

Further, in the step S3, if the chinese character is a polyphone, a one-to-many strategy is adopted. One Chinese character can correspond to a plurality of pinyin, namely one polyphone can be split into a plurality of letter strings formed by Chinese pinyin letters.

Further, for convenience of description, the exemplary description is made by taking the Chinese character "say" as an example. The "say" is a polyphonic character having three pronunciations, which are "shuo" (one sound), "shui" (four sounds) and "yue" (four sounds).

Furthermore, the pinyin of the Chinese character 'say' can be split into character strings formed by pinyin letters { { s, h, u, o }, { s, h, u, i }, { y, u, e } }. Further, a doublet < so, { { s, h, u, o }, { s, h, u, i }, { y, u, e } } > is obtained.

S4: inverting the pinyin and the Chinese characters according to the character string formed by the Chinese characters and the corresponding pinyin characters obtained in the step S3, namely constructing the Chinese pinyin characters, { Chinese characters 1 formed by the pinyin characters, and Chinese characters formed by the pinyin characters.

Further, for convenience of description, it is assumed that only four Chinese characters are in the pre-trained Chinese character vector model in S1, which are "Ming", "explain", "you" and "Tian", respectively.

Further, { { m, i, n, g } } >, < say, { { S, h, u, o }, { S, h, u, i }, { y, u, e } } >, < you, { n, i } > and < day, { { t, i, a, n } } > are obtained according to the step S2 and the step S3. A

Further, the reverse arrangement of the pinyin letters and the chinese characters obtained in step S4 is as follows:

< a, { day } >)

< e, { say } >)

< g, { Ming } >)

< h, { say (sound shuo), say (sound shui) } >

< i, { ming, say (voice shui) }, you >

< m, { Ming } >)

< n, { you, day } >)

< o, { say (tone shuo) } >

< s, { say (sound shuo), say (sound shui) } >

< t, { day } >)

< u, { say (tone shuo), say (tone shui), say (tone yue) } >

< y, { say (tone yue) } >

S5: calculating the vector of each pinyin letter in the pinyin character string, which is obtained according to the Chinese character and the corresponding pinyin character string in the step S3, in the calculation method, the word vector is divided by the number of the pinyin letters corresponding to the Chinese character, and the calculation formula is as follows:

where wj is a Chinese character, Ci is a pinyin character in the pinyin corresponding to the Chinese character wj, and Vwj represents a word vector of the Chinese character wj.

Further, according to the method of calculating the vector of each pinyin character in the chinese pinyin character string divided in the chinese character in step S5, the vector of the pinyin character 'u' divided in the chinese character "say" and the vectors of the pinyin characters 'i' divided in the chinese character "say" (phonetic sui), the chinese character "day" (phonetic tianan) and the chinese character "you" (phonetic ni) are as follows:

further, vectors of the bopomofo 'e', 'g', 'h', 'i','m', 'n', 'o','s','t', 'u', 'y' and the like, which are divided in the chinese characters "ming" (sound), "day" (note), "you" (note) and "speech" (sound, shui, yue) can be obtained.

S6: calculating the vector of the Chinese phonetic alphabet according to the vector of each Chinese phonetic alphabet in the Chinese phonetic alphabet string calculated in the step S5, wherein the calculation method is as follows:

wherein f (Ci) refers to Chinese characters whose Pinyin contains the Pinyin letter Ci.

Further, the vector calculation method of the pinyin letter 'u' is as follows:

example two:

referring to fig. 4, fig. 4 is a schematic structural diagram of an alphabet vector learning system according to the present invention. As shown in fig. 4, an alphabet vector learning system of the present invention includes:

the dictionary obtaining module 11 is used for arranging Chinese characters in a pre-training Chinese character vector model and forming a dictionary;

the binary group obtaining module 12 is used for traversing the Chinese characters in the dictionary, obtaining the pinyin of the Chinese characters, splitting the pinyin of the Chinese characters to form letter strings, and constructing binary groups according to the Chinese characters and the letter strings;

a calculation module 13, which calculates the vector of each pinyin character in the character string, which is obtained in the Chinese character, according to a formula;

and the letter vector acquisition module 14 is used for calculating and acquiring the Chinese character pinyin letter vectors according to the vectors of each Chinese pinyin letter in the Chinese character pinyin character string.

Wherein, the obtaining dictionary module 11 includes:

a model obtaining unit 111 that obtains the pre-training chinese character vector model;

a sorting unit 112, which sorts the Chinese characters in the pre-training Chinese character vector model and forms the dictionary.

Wherein the calculation module 13 comprises:

a reverse arrangement unit 131 that reverses the chinese pinyin and the chinese characters according to the character string formed by the chinese pinyin characters corresponding to the chinese characters;

and a vector calculation unit 132, which calculates the vector of each pinyin character in the character string, which is obtained in the Chinese character, according to the inverted result and a vector calculation formula.

Wherein the get binary group module 12 comprises:

if the Chinese character is a polyphonic character, the Chinese character can be split into a plurality of character strings formed by Chinese phonetic alphabet.

Example three:

referring to fig. 5, this embodiment discloses a specific implementation of an electronic device. The electronic device may include a processor 81 and a memory 82 storing computer program instructions.

Specifically, the processor 81 may include a Central Processing Unit (CPU), or A Specific Integrated Circuit (ASIC), or may be configured to implement one or more Integrated circuits of the embodiments of the present Application.

Memory 82 may include, among other things, mass storage for data or instructions. By way of example, and not limitation, memory 82 may include a Hard Disk Drive (Hard Disk Drive, abbreviated to HDD), a floppy Disk Drive, a Solid State Drive (SSD), flash memory, an optical Disk, a magneto-optical Disk, tape, or a Universal Serial Bus (USB) Drive or a combination of two or more of these. Memory 82 may include removable or non-removable (or fixed) media, where appropriate. The memory 82 may be internal or external to the data processing apparatus, where appropriate. In a particular embodiment, the memory 82 is a Non-Volatile (Non-Volatile) memory. In particular embodiments, Memory 82 includes Read-Only Memory (ROM) and Random Access Memory (RAM). The ROM may be mask-programmed ROM, Programmable ROM (PROM), Erasable PROM (EPROM), Electrically Erasable PROM (EEPROM), Electrically rewritable ROM (EAROM), or FLASH Memory (FLASH), or a combination of two or more of these, where appropriate. The RAM may be a Static Random-Access Memory (SRAM) or a Dynamic Random-Access Memory (DRAM), where the DRAM may be a Fast Page Mode Dynamic Random-Access Memory (FPMDRAM), an Extended data output Dynamic Random-Access Memory (EDODRAM), a Synchronous Dynamic Random-Access Memory (SDRAM), and the like.

The memory 82 may be used to store or cache various data files for processing and/or communication use, as well as possible computer program instructions executed by the processor 81.

The processor 81 implements any of the above-described embodiments of the method of alphabetic vector learning by reading and executing computer program instructions stored in the memory 82.

In some of these embodiments, the electronic device may also include a communication interface 83 and a bus 80. As shown in fig. 5, the processor 81, the memory 82, and the communication interface 83 are connected via the bus 80 to complete communication therebetween.

The communication interface 83 is used for implementing communication between modules, devices, units and/or equipment in the embodiment of the present application. The communication port 83 may also be implemented with other components such as: the data communication is carried out among external equipment, image/data acquisition equipment, a database, external storage, an image/data processing workstation and the like.

The bus 80 includes hardware, software, or both to couple the components of the electronic device to one another. Bus 80 includes, but is not limited to, at least one of the following: data Bus (Data Bus), Address Bus (Address Bus), Control Bus (Control Bus), Expansion Bus (Expansion Bus), and Local Bus (Local Bus). By way of example, and not limitation, Bus 80 may include an Accelerated Graphics Port (AGP) or other Graphics Bus, an Enhanced Industry Standard Architecture (EISA) Bus, a Front-Side Bus (Front Side Bus), an FSB (FSB), a Hyper Transport (HT) Interconnect, an ISA (ISA) Bus, an Infini Band Interconnect, a Low Pin Count (LPC) Bus, a memory Bus, a microchannel Architecture (MCA) Bus, a PCI (Peripheral Component Interconnect) Bus, a PCI-Express (PCI-X) Bus, a Serial Advanced Technology Attachment (SATA) Bus, a Video Electronics Bus (audio Electronics Association), abbreviated VLB) bus or other suitable bus or a combination of two or more of these. Bus 80 may include one or more buses, where appropriate. Although specific buses are described and shown in the embodiments of the application, any suitable buses or interconnects are contemplated by the application.

The electronic device may learn based on the alphabet vector to implement the methods described in conjunction with fig. 1-3.

In addition, in combination with the method for learning an alphabet vector in the foregoing embodiments, the embodiments of the present application may provide a computer-readable storage medium to implement. The computer readable storage medium having stored thereon computer program instructions; the computer program instructions, when executed by a processor, implement any of the above-described embodiments of the method of alphabetic vector learning.

The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.

In summary, the beneficial effects of the invention are that the present disclosure provides a method and a device for learning chinese pinyin alphabet based on character pinyin, which mainly consider the semantic relationship of the chinese character in pronunciation and enrich the vector representation based on the character or word granularity only.

The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.