基于深度强化学习的多感官艺术装置的热视觉协作优化控制系统.
1Department of Architecture and Environmental Engineering, Taiyuan University, Taiyuan, 030032, Shanxi, China. 18834128222@163.com.
Scientific reports
|November 3, 2025
概括
这项研究引入了多感官艺术装置的先进控制系统,增强了热视觉体验. 以注意力增强的深度强化学习模型显著提高了控制精度,并减少了动态艺术表达的响应时间.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 人工智能的人工智能
- 数字艺术和媒体
背景情况:
- 多感官艺术装置需要复杂的控制系统来实现沉浸式体验.
- 传统的控制方法与热和视觉元素的动态,实时优化作斗争.
- 整合各种不同的感官输入带来了重大的融合和处理挑战.
研究的目的:
- 开发一种注意力增强的深度强化学习 (DRL) 控制系统,以优化多感官艺术装置中的热视觉协作.
- 为了提高控制精度,减少响应时间,提高能源效率与现有方法相比.
- 在现实世界部署中验证系统的实际有效性和用户满意度.
主要方法:
- 采用了基于注意力的深度决定性政策梯度 (DDPG) 算法,并使用了动态模式加权.
- 结合卡尔曼和颗粒过的自适应传感器融合技术,以低处理延迟 (8 ± 2 ms) 实施.
- 一个分层的四层架构 (感知,融合,决策,执行) 被设计为高效的控制.
主要成果:
- 控制精度提高了65% (0.085 ± 0.012 RMSE) 和响应时间减少了40% (45 ± 8 ms).
- 通过智能热视觉协调,可以减少23%的能源消耗.
- 现实世界部署显示了高用户满意度 (4.1/5) 和系统可用性 (98.5%).
结论:
- 提议的注意力增强的DRL系统为艺术装置中的热视觉协作优化提供了卓越的性能.
- 该系统在保持艺术完整的同时,可以实现更复杂,更响应的多感官体验.
- 这项研究为先进的数字艺术技术和智能交互系统提供了基础.
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