AI Drying Control Using External Environment Inference

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Solution Overview

Problem

Conventional drying machines inaccurately predict drying time due to external environmental factors, leading to increased drying time and sensor distortion, which results in inefficient operation and power consumption.

Innovation Solution

The implementation of a method using artificial intelligence models, specifically neural networks, to analyze initial sensed values and external environment information, allowing the drying machine to adaptively control drying time and correct sensor distortions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Extent of automation

If conventional drying machines use internal sensors to determine drying time, then the drying process can be automated, but sensor distortion occurs due to external environmental factors leading to inaccurate drying time prediction

Engineering Contradiction:

Improveautomated drying processVSAvoidsensor measurement accuracy

Core Design Contradiction:

Extent of automationVSMeasurement precision

Solution Approach 1:

The patent introduces an intermediary AI model that acts as a mediator between the distorted sensor readings and the drying time determination. The AI model receives the initial sensed value and external environment information as inputs, processes them to infer the actual environmental conditions, and outputs corrected drying time predictions, thereby eliminating the direct harmful influence of external environment on sensor accuracy

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces the traditional mechanical/sensor-based drying time determination system with an AI-based computational system. Instead of relying solely on physical sensors that are susceptible to environmental distortion, the system uses machine learning models that can process and interpret sensor data in conjunction with external environment information to make more accurate predictions

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If drying time is extended to account for sensor errors, then measurement accuracy improves, but drying time increases unnecessarily

Engineering Contradiction:

Improvedrying time prediction accuracyVSAvoiddrying time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent performs preliminary action by acquiring external environment information and inferring actual environmental conditions before the drying process begins. The AI model uses the initial sensed value combined with external environment data to predict the accurate drying time in advance, allowing the system to set the optimal drying time before operation starts, thereby avoiding both under-drying and unnecessary extended drying time

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent implements a feedback mechanism where the AI model continuously uses external environment information to correct and refine drying time predictions. The system monitors the relationship between sensor readings and actual environmental conditions, and adjusts the drying time determination based on this feedback, ensuring accurate prediction without unnecessary time extension

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20250003133A1Drying machine for performing drying function on basis of external environmental information, and control method therefor

Publication Date: 2025.01.02 LG ELECTRONICS INC

AI summary

A method for controlling a drying machine for performing a drying function on the basis of external environmental information, according to one embodiment of the present invention, comprises the steps of: receiving initial sensing values before starting drying; receiving object-to-be-dried analysis information; acquiring the external environmental information by providing the initial sensing values and the object-to-be-dried analysis information to a first artificial intelligence model; and drying objects to be dried, on the basis of the acquired external environmental information.