Construction of Home Product Design System Based on Self-Encoder Depth Neural Network

Area of Science:

• Computer Science
• Artificial Intelligence
• Product Design

Background:

• Traditional home product design systems rely on shallow networks and basic IoT, leading to inefficiencies.
• Existing self-encoder technology in deep neural networks has significant drawbacks in computer vision, wasting resources and limiting learning.
• Deficiencies in traditional systems include poor learning efficiency and weak learning ability due to shallow networks and suboptimal self-encoder implementations.

Purpose of the Study:

• To develop an advanced home product design system leveraging deep neural networks and self-encoders.
• To enhance system sparsity and optimize the self-encoder structure for improved deep learning performance.
• To increase the accuracy and stability of internal feature classifiers and overall furniture design system performance.

Main Methods:

• Implementation of a home product design system based on a deep neural network with a sparse self-encoder.
• Optimization of the self-encoder's learning and training process to improve system sparsity.
• Integration of ZigBee and embedded technologies as the design carrier, focusing on simplicity, intelligence, and convenience.

Main Results:

• The proposed system demonstrates a noise processing level 4-5dB lower than traditional systems.
• Image classification accuracy is improved by approximately 4% compared to traditional design systems.
• The optimized system shows enhanced accuracy and stability in its internal feature classifier.

Conclusions:

• The developed home product design system offers significant advantages over traditional methods.
• Improved sparsity and hierarchical feature learning contribute to superior deep learning model performance.
• The system effectively enhances overall performance, accuracy, and stability in furniture design applications.