AI-based laundry course recommending apparatus and method of controlling the same
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Solution Overview
Problem
Existing AI-based laundry course recommending systems require users to manually determine the type of laundry, leading to inaccuracies, and are limited by reliance on pre-learned models that do not adapt to new inputs.
Innovation Solution
An AI-based laundry course recommending apparatus that uses a detector to classify and photograph laundry at multiple angles, acquiring images to infer the type and material, and a processor to compare this information with user-input data to automatically recommend a suitable laundry course, utilizing machine learning and convolutional neural networks for accurate classification and course extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually determine the type of laundry, then the system can provide laundry course recommendations, but the accuracy of laundry type recognition deteriorates
Solution Approach 1:
The patent replaces the manual mechanical operation of users determining laundry type with an automated image recognition system using deep learning. The detector captures images of laundry, and the processor automatically classifies laundry types and materials through neural network algorithms, eliminating the need for users to manually identify and input laundry information while significantly improving recognition accuracy.
2Reliability
If the system relies on pre-learned models, then it can provide recommended courses, but the adaptability to new laundry inputs deteriorates
Solution Approach 1:
The patent implements a dynamic system where the deep learning model can be continuously trained and updated with new laundry data. The processor stores extracted laundry information in a database, which serves as training data for ongoing model improvement. This allows the system to maintain stability from the pre-trained model while adapting to new laundry types and materials through continuous learning cycles.
Solution Approach 2:
The system establishes a feedback loop where the processor extracts laundry information from images, stores it in a database, and uses this accumulated data to continuously retrain and improve the deep learning model. This feedback mechanism ensures the model adapts to new inputs while maintaining the reliability of established patterns.
3Measurement precision
If the system uses multiple angles and regions for laundry analysis, then the classification accuracy improves, but the device complexity increases
Solution Approach 1:
The patent employs a multi-functional detector system that integrates multiple cameras or a single camera capable of capturing images from multiple angles and regions. This universal detector performs several functions: capturing overall laundry distribution, identifying specific fabric regions, and providing multi-perspective views, all through a single integrated component that reduces overall system complexity despite the increased analytical capabilities.
Data Source
AI summary
Disclosed is an artificial intelligence (AI)-based self-control air conditioner. The AI-based self-control air conditioner includes a communication unit configured to receive an image including member data for identifying the member from an image acquisition apparatus corresponding to a group including at least one member, and a processor configured to recognize the member data from the received image, to acquire operation data including an operation condition of an air conditioner, which is desired by the member, based on the recognized data, to store member information including the member data and the operation data in a database, and to acquire and analyze the operation condition of the air conditioner, which is desired by the member, with respect to at least one member from a plurality of pieces of member information corresponding to the group stored in the database, wherein the air conditioner is autonomously driven according to control of the processor. Accordingly, the air conditioner learns members itself and controls an operation in an optimum state to reduce power consumption and is driven according to an operation condition set for each member to enhance personal convenience of the member.