Neural Network Classification Apparatus for Space State Detection
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Solution Overview
Problem
Existing methods for determining the state of a predetermined space using light patterns, such as infrared light, are time-consuming and costly due to the complexity of light patterns involving direct and reflected light paths, requiring extensive programming to analyze and classify the state effectively.
Innovation Solution
A classification apparatus that integrates a neural network trained to classify space states using light projection and reception patterns, reducing the time and cost of creating a determination program by acquiring and processing light information to output classification results efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ordinary programming is used to create a determination program for analyzing light patterns, then the program can accurately detect the state of a predetermined space, but it requires more time and cost to create the program
Solution Approach 1:
The neural network is trained in advance using learning data that includes light projection patterns, light reception patterns, and corresponding space state information. This preliminary training enables the system to quickly classify space states without requiring time-consuming ordinary programming during actual operation.
Solution Approach 2:
The patent replaces the mechanical programming approach with a neural network-based machine learning system. Instead of manually programming determination logic, the system learns patterns automatically from training data, substituting traditional software development with automated learning.
2Measurement precision
If ordinary programming is used to create a determination program for analyzing light patterns, then the program can accurately detect the state of a predetermined space, but it requires more cost to create the program
Solution Approach 1:
The neural network is trained in advance using learning data that includes light projection patterns, light reception patterns, and corresponding space state information. This preliminary training enables the system to quickly classify space states without requiring time-consuming ordinary programming during actual operation.
Solution Approach 2:
The patent replaces the mechanical programming approach with a neural network-based machine learning system. Instead of manually programming determination logic, the system learns patterns automatically from training data, substituting traditional software development with automated learning.
3Loss of time
If a neural network is used to classify space states using light patterns, then the time and cost required to create a determination program is reduced, but the system complexity increases
Solution Approach 1:
The neural network performs self-learning by automatically processing learning data to extract patterns and relationships. The system serves itself by autonomously improving its classification capability without requiring manual programming or complex configuration, thereby reducing system complexity despite using advanced AI technology.
4Ease of manufacture
If a neural network is used to classify space states using light patterns, then the cost required to create a determination program is reduced, but the system complexity increases
Solution Approach 1:
The neural network performs self-learning by automatically processing learning data to extract patterns and relationships. The system serves itself by autonomously improving its classification capability without requiring manual programming or complex configuration, thereby reducing system complexity despite using advanced AI technology.
Data Source
AI summary
A classification apparatus includes: a specifying unit integrated with a neural network that has been trained to classify a state of a space using information indicating a light projection pattern and information indicating a light reception pattern; a light projection information acquisition unit configured to acquire information indicating a light projection pattern of light projected into a predetermined space, and output the acquired information to the specifying unit; and a light receiving unit configured to acquire information indicating a light reception pattern of light received from the predetermined space, and output the acquired information to the specifying unit, wherein the specifying unit outputs a classification result of classifying a state of the predetermined space, based on the information indicating the light projection pattern acquired by the light projection information acquisition unit and on the information indicating the light reception pattern of the light received by the light receiving unit.