用可调湿热的复合材料构建神经网络机器，实现智能遮阳。
A physical adaptive material motor unit neural network: a hygromorph composite material machine
- 基于木碳复合材料的仿肌腱结构，响应温湿度变化。
- 训练350+组数据，能逐步学习并预测遮阳行为。
- 可自适应优化配置，在不同环境达相似遮阳效果。
新材料科学的进步使结构可通过嵌入记忆与学习能力直接成为智能机器。本文提出一种物理自适应材料电机单元神经网络，利用新型可控致动器——由木材与炭黑组成的复合材料，对温度和相对湿度敏感。这些材料致动器被组装成类肌肉收缩的电机单元结构，构成可动态调节遮阳的智能机器，适用于建筑场景。该机器通过在多种环境条件下采集的350多个实验数据点训练的神经网络进行控制。我们提出一种新的数据感知反向传播训练方法，证明该机器能预测遮阳响应，并随数据库扩展逐步学习合适行为。此外，还验证了机器在两种不同条件下优化配置以实现相似遮阳输出的能力。
原文摘要 · Abstract (English)
Advances in novel materials science enable structures to function as intelligent machines by embedding memory and learning capabilities directly into materials. Our work introduces a physical adaptive material motor unit neural network,leveraging a new generation of controllable actuators composed of wood- and carbon black-based composites, sensitive to temperature and relative humidity. These material actuators are assembled into a motor unit-like structure inspired by muscle contraction trigger, forming an intelligent machine capable of dynamic shading control that can be used, for example, in buildings. The machine is governed by a neural network trained on over 350 experimental data points collected under diverse environmental conditions. By establishing a new data-aware backpropagation training, we show that the machine predicts shading responses and learns to predict appropriate behaviour incrementally as the database expands. We also demonstrate the ability of the machine to optimise configurations to achieve similar shading outputs under two distinct conditions.
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