School of Art, Huzhou University, Huzhou, Zhejiang 313000, China.
Comput Intell Neurosci. 2022 Apr 21;2022:8331504. doi: 10.1155/2022/8331504. eCollection 2022.
The traditional home product design system mainly depends on relatively shallow learning network, relatively simple embedded technology and Internet of things technology. The traditional home design system mainly depends on the traditional self-encoder technology. When combined with the deep neural network, this technology has serious defects in the computer vision algorithm, resulting in the serious waste of the corresponding system storage and computing resources, the corresponding system learning efficiency is relatively poor and the learning ability is weak. Based on this, this paper will build a home product design system based on the deep neural network of self-encoder. By improving the sparsity of self-encoder in the process of learning and training, we can further improve the sparsity of the system and further optimize the structure of self-encoder in the design system, The performance of the deep learning model of the design system is further improved through the hierarchical features continuously learned by the self-encoder in the process of home case design. Based on the optimization of the home product design system in this paper, the system effectively improves and improves the accuracy and stability of the internal feature classifier of the system, and improves the overall performance of the furniture design system. In the specific system construction part, based on ZigBee technology and embedded technology as the design carrier, and adhering to the goal of simplicity, intelligence and convenience, this paper designs and constructs the home product design system. The experimental results show that the noise processing level of the proposed home product design system is lower than 4-5db compared with the traditional design system, and the corresponding image classification accuracy is about 4% higher than the traditional design system. Therefore, the experimental results show that the home design system proposed in this paper has obvious advantages.
传统的家居产品设计系统主要依赖于相对较浅的学习网络、相对简单的嵌入式技术和物联网技术。传统的家居设计系统主要依赖于传统的自编码器技术。当与深度神经网络结合时，该技术在计算机视觉算法中存在严重缺陷，导致相应系统存储和计算资源的严重浪费，相应系统的学习效率较差，学习能力较弱。基于此，本文将构建基于自编码器的深度神经网络的家居产品设计系统。通过改进自编码器在学习和训练过程中的稀疏性，可以进一步提高系统的稀疏性，并进一步优化设计系统中自编码器的结构，通过自编码器在家庭案例设计过程中不断学习的分层特征，进一步提高设计系统的深度学习模型性能。基于本文对家居产品设计系统的优化，系统有效地提高和改善了系统内部特征分类器的准确性和稳定性，并提高了家具设计系统的整体性能。在具体的系统构建部分，本文基于 ZigBee 技术和嵌入式技术作为设计载体，坚持简单、智能和方便的目标，设计和构建了家居产品设计系统。实验结果表明，与传统设计系统相比，所提出的家居产品设计系统的噪声处理水平低于 4-5dB，相应的图像分类精度比传统设计系统高约 4%。因此，实验结果表明，本文提出的家居设计系统具有明显的优势。
