WO2018131749A1 - Deep learning-based self-adaptive learning engine module - Google Patents

Abstract

According to the deep learning-based self-adaptive learning engine module suggested by the present invention, the mechanism of the human brain for identifying a mission on its own by understanding a situation, and resolving the situation by creating a model can be effectively implemented by self-organizing a DNA mission and self-configuring an artificial neural network DNA model by combining self-adaptive technology and deep learning-based learning technology. Further, the present invention is implemented in the form of a module and can therefore be easily applied to various systems and, by performing self-adaptive learning by using structured data and unstructured data, can be applied for various purposes, such as understanding a situation, scheduling, decision making, prediction, recommendation, and taking a measure according to a situation, by making use of the learning result.

Description

Deep Learning based self-adaptive learning engine module

The present invention relates to a learning engine module, and more particularly, to a deep learning based self-adaptive learning engine module.

Research is ongoing to technologically implement the human brain mechanisms to make decisions by understanding and analyzing given or ongoing situations. In particular, as interest in artificial intelligence technology increases, AI technology is rapidly developing through deep learning based on artificial neural networks.

In addition, in software engineering, there is an increasing demand for self-adaptive technology for understanding a user's and device's situation and providing customized user services for a better user experience.

However, research on self-adaptive learning that combines deep learning-based learning technology or self-adaptation technology of artificial neural network and software engineering has hardly been conducted.

In particular, existing artificial neural network technology or self-adaptation technology has a limitation in that it is difficult to process unstructured data because it cannot effectively implement the human brain mechanism, and it is difficult to apply to various situations because it is developed in a specific field. There is a limit that is not efficient.

On the other hand, as the prior art related to the present invention, Korean Patent Publication No. 10-2012-0057319 (Invention: Intelligent sensor middleware structure that can be applied to various environments and self-adaptable for smart environment configuration, published date: June 2012 Publication No. 10-1999-0044063 (name of the invention: a method for providing a self-adaptive management service using an information communication network, publication date: May 07, 2001) and the like have been disclosed.

The present invention is proposed to solve the above problems of the proposed method, by combining self-adaptation technology and deep learning-based learning technology, by self-organizing the DNA mission and self-organizing the artificial neural network DNA model, Its purpose is to provide a deep learning-based self-adaptive learning engine module that can effectively understand the situation, build a model by itself, and effectively implement the human brain mechanism to solve the situation.

In addition, the present invention is implemented in a modular form, easy to apply to a variety of systems, self-adaptive learning using the structured data and unstructured data, utilizing the learning results to understand the situation and scheduling, decision and prediction, The purpose of the present invention is to provide a deep learning-based self-adaptive learning engine module that can be applied to various recommendations and situational actions.

Deep learning-based self-adaptive learning engine module according to a feature of the present invention for achieving the above object,

Self-adaptive learning engine module

A mission organization unit for processing the data to self-organize the DNA mission;

A model constructing unit for self-organizing a deep learning-based artificial neural network DNA model using the self-organized DNA mission; And

Including the model learning unit for self-learning the self-constructed DNA model is characterized by its configuration.

Preferably, the mission organization,

Self-organizing the DNA mission consisting of a sum of mission modules,

The mission module may be a function of elements extracted from the data and positions of organizations.

More preferably, the DNA mission,

It can be a combination of Blocks of Organization and Chains.

More preferably, the DNA mission,

It may include a Special DNA Mission consisting of a combination of chains.

Preferably, the mission organization,

When the organization's DNA mission changes at a certain point in time, when the mission for a member of the organization changes at some point, or when the member itself changes, and when an element exceeds a predetermined threshold over time, the tissue At least one of the blocks and chains of may be modified to self-organize DNA missions that change over time.

Preferably, the model component,

Self-construct the DNA model consisting of the sum of the functional submodels,

The functional submodel may be a function of elements extracted from the data and sequences of thoughts.

More preferably, the DNA model,

It may be a combination of Blocks of Function and Chains.

According to the deep learning-based self-adaptive learning engine module proposed by the present invention, by combining self-adaptive technology and deep learning-based learning technology, self-organizing the DNA mission and self-organizing the artificial neural network DNA model to understand the situation You can effectively implement the human brain mechanisms to identify missions, model and solve situations.

In addition, the present invention is implemented in a modular form, easy to apply to a variety of systems, self-adaptive learning using the structured data and unstructured data, utilizing the learning results to understand the situation and scheduling, decision and prediction, It can be applied to recommendations and situational measures.

1 is a diagram showing the configuration of a deep learning-based self-adaptive learning engine module according to an embodiment of the present invention.

2 is a diagram illustrating a method of self-organizing a DNA mission in a mission organization unit of a deep learning-based self-adaptive learning engine module according to an embodiment of the present invention.

3 is a view illustrating an example of a method for configuring a DNA mission in a mission organization unit of a deep learning based self-adaptive learning engine module according to an embodiment of the present invention.

4 is a diagram illustrating a method of self-configuring a DNA model in a model constructing unit of a deep learning based self-adaptive learning engine module according to an embodiment of the present invention.

<Description of the code>

100: self-adaptive learning engine module according to an embodiment of the present invention

110: Mission Organization

120: model component

130: model learning unit

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those skilled in the art may easily implement the present invention. However, in describing the preferred embodiment of the present invention in detail, if it is determined that the detailed description of the related known function or configuration may unnecessarily obscure the subject matter of the present invention, the detailed description thereof will be omitted. In addition, the same reference numerals are used throughout the drawings for parts having similar functions and functions.

In addition, in the specification, when a part is 'connected' to another part, it is not only 'directly connected' but also 'indirectly connected' with another element in between. Include. In addition, the term "comprising" a certain component means that the component may further include other components, except for the case where there is no contrary description.

1 is a diagram illustrating a configuration of a deep learning based self-adaptive learning engine module 100 according to an embodiment of the present invention. As shown in FIG. 1, the deep learning based self-adaptive learning engine module 100 according to an embodiment of the present invention may include a mission organization unit 110, a model constructing unit 120, and a model learning unit 130. It can be configured to include.

In other words, the present invention combines self-adaptive technology with deep learning-based learning technology to self-organize DNA missions and self-organize artificial neural network DNA models to understand the situation by themselves, identify missions and create models to solve the situation. The human brain mechanism can be effectively implemented. In addition, the present invention is implemented in a modular form is easy to apply to a variety of systems.

Hereinafter, each component of the deep learning based self-adaptive learning engine module 100 according to an embodiment of the present invention will be described in detail.

The mission organization unit 110 may process data to self-organize the DNA mission. In this case, the data may include not only structured data but also unstructured data such as natural language input by a person, and the mission organization unit 110 processes the data to construct a DNA mission using extracted elements. Can organize themselves. In some embodiments, a separate preprocessing module (not shown) may be included for processing data to extract elements.

More specifically, the mission organization unit 110 may self-organize the DNA mission composed of the sum of the mission modules, and the mission module may include elements of the elements extracted from the data and positions of the positions of the organization members. It can be a function. At this time, the position of the organization member may be predetermined.

Meanwhile, the DNA mission may be a combination of Blocks of Organization and Chains. That is, the mission organization unit 110 may use a block chain combination technique to organize a DNA mission by combining a block and a chain of tissue.

2 is a diagram illustrating a method of self-organizing a DNA mission in the mission organization unit 110 of the deep learning-based self-adaptive learning engine module 100 according to an embodiment of the present invention. The mission organization unit 110 of the deep learning-based self-adaptive learning engine module 100 according to an embodiment of the present invention includes a block of organization (Block i, Block j, Block k, etc.) as shown in FIG. 2. DNA missions can be constructed by combining chains (Chain l, Chain m, Chain n, etc.) through blockchain combination technology. The constructed DNA mission may be expressed as the sum of the mission module, which is a function of the position of the element and the tissue member.

In addition, the DNA mission may include a special DNA mission composed of a combination of chains. That is, according to the embodiment, the DNA mission may be composed of a combination of chains without block of tissue.

FIG. 3 is a diagram illustrating a method of constructing a DNA mission over time in the mission organization unit 110 of the deep learning based self-adaptive learning engine module 100 according to an embodiment of the present invention. . As shown in FIG. 3, the mission organization unit 110 of the deep learning-based self-adaptive learning engine module 100 according to an embodiment of the present invention is extracted from elements and data within a mission and mission of a predetermined organization. By comparing and evaluating elements, you can organize your own DNA missions that change over time.

There are three ways to self-organize DNA missions that change over time, as shown in FIG. That is, the mission organization unit 110 may change the DNA mission of the organization at a specific time point (①), if the mission of the organization member is changed or the member itself is changed at a specific time point (②), and in the passage of time. Therefore, when the element exceeds a predetermined limit (③), at least one of the block and chain of the tissue can be changed to self-organize the DNA mission that changes over time.

More specifically, as shown in the upper part of FIG. 3, when the DNA mission of the tissue already self-organized at a specific time point is changed (①), the mission organization unit 110 changes and combines both the block and the chain, Self-organizing DNA missions can change over time. In addition, as shown in the middle of FIG. 3, when a mission previously set for an organization member is changed or an organization member itself is changed (②), the mission organization unit 110 keeps the block of the organization as it is. By altering and recombining the chain, the DNA mission can be self-organized. Finally, as shown at the bottom of FIG. 3, when an element exceeds a predetermined threshold value (③) as time passes, the mission organization unit 110 automatically leaves the block of tissue intact. Chains can be altered and combined to self-organize DNA missions that change appropriately over time.

The model constructing unit 120 may self-configure the deep learning-based artificial neural network DNA model using a self-organized DNA mission. That is, the model constructer 120 may receive a DNA mission from the mission organizer 110 as an input and construct a DNA model that can be trained on a deep learning basis. As such, the model constructing unit 120 of the deep learning-based self-adaptive learning engine module 100 according to an embodiment of the present invention may generate a DNA model using a DNA mission that is self-organized using elements extracted from data. Because of the configuration, the model can be flexibly changed in accordance with the input data.

In addition, the model constructor 120 self-constructs a DNA model consisting of a sum of functional submodels, and the submodels are functions of elements extracted from data and sequences of thoughts. Can be.

The DNA model may be a combination of blocks of function and chains. That is, the model constructing unit 120 may use a block chain combination technique to organize a DNA model by combining a functional block and a chain.

4 is a diagram illustrating a method of self-configuring a DNA model in the model constructing unit 120 of the deep learning-based self-adaptive learning engine module 100 according to an embodiment of the present invention. The model configuration unit 120 of the deep learning-based self-adaptive learning engine module 100 according to an embodiment of the present invention, as shown in FIG. 4, blocks block and chains as shown in FIG. 4. Combination technologies can be combined to form functional submodels (Functional Submodel i, Functional Submodel j, Functional Submodel k, Functional Submodel m, Functional Submodel n, etc.) and to construct DNA models from the sum of the functional submodels.

The model learner 130 may self-learn a self-constructed DNA model. That is, the model learning unit 130 is configured to learn the DNA model configured in the model constructing unit 120, and can learn through an artificial neural network technology, and utilizes the learning results of the model learning unit 130. It can be applied to understanding and scheduling, decision making and prediction, recommendation and situational action.

The present invention described above may be variously modified or applied by those skilled in the art, and the scope of the technical idea according to the present invention should be defined by the following claims.

Claims (7)

As a self-adaptive learning engine module 100, A mission organization unit 110 for processing the data to self-organize the DNA mission; A model constructing unit 120 for self-organizing a deep learning-based artificial neural network DNA model using the self-organized DNA mission; And Deep learning-based self-adaptive learning engine module, characterized in that it comprises a model learning unit 130 for self-learning the self-configured DNA model. The method of claim 1, wherein the mission organization unit 110, Self-organizing the DNA mission consisting of a sum of mission modules, The mission module is a deep learning based self-adaptive learning engine module, characterized in that a function of the elements (Elements) extracted from the data (Positions of Organization). The method of claim 2, wherein the DNA mission, Deep learning-based self-adaptive learning engine module, characterized in that it is a combination of Blocks of Organization and Chains. The method of claim 2, wherein the DNA mission, Deep learning based self-adaptive learning engine module, characterized in that it comprises a special DNA Mission consisting of a combination of chains (Chains). The method of claim 1, wherein the mission organization unit 110, When the organization's DNA mission changes at a certain point in time, when the mission for a member of the organization changes at some point, or when the member itself changes, and when an element exceeds a predetermined threshold over time, the tissue Deep learning-based self-adaptive learning engine module 100, characterized in that by changing at least one of the block and chain of the self-organizing DNA mission that changes over time. The method of claim 1, wherein the model configuration unit 120, Self-construct the DNA model consisting of the sum of the functional submodels, The functional sub-model is a deep learning based self-adaptive learning engine module, characterized in that a function of the elements (Elements) extracted from the data (Sequences of Thought). The method of claim 6, wherein the DNA model, Deep learning-based self-adaptive learning engine module, characterized in that it is a combination of Blocks of Function (Chains) and Chains (Chains).

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