Elevators are essential components of contemporary buildings, enabling efficient vertical mobility for occupants. However, the proliferation of tall buildings has exacerbated challenges such as traffic congestion with...Elevators are essential components of contemporary buildings, enabling efficient vertical mobility for occupants. However, the proliferation of tall buildings has exacerbated challenges such as traffic congestion within elevator systems. Many passengers experience dissatisfaction with prolonged wait times, leading to impatience and frustration among building occupants. The widespread adoption of neural networks and deep learning technologies across various fields and industries represents a significant paradigm shift, and unlocking new avenues for innovation and advancement. These cutting-edge technologies offer unprecedented opportunities to address complex challenges and optimize processes in diverse domains. In this study, LSTM (Long Short-Term Memory) network technology is leveraged to analyze elevator traffic flow within a typical office building. By harnessing the predictive capabilities of LSTM, the research aims to contribute to advancements in elevator group control design, ultimately enhancing the functionality and efficiency of vertical transportation systems in built environments. The findings of this research have the potential to reference the development of intelligent elevator management systems, capable of dynamically adapting to fluctuating passenger demand and optimizing elevator usage in real-time. By enhancing the efficiency and functionality of vertical transportation systems, the research contributes to creating more sustainable, accessible, and user-friendly living environments for individuals across diverse demographics.展开更多
针对节点数量较多、功能类型多样的控制器局域网(controller area network,CAN)中存在的管理问题,基于其协议技术标准和OSEK/VDX规范,提出并实现了一种网络管理的改进策略。该策略可实现直接网络管理中CAN节点的分组管理和合并。实验结...针对节点数量较多、功能类型多样的控制器局域网(controller area network,CAN)中存在的管理问题,基于其协议技术标准和OSEK/VDX规范,提出并实现了一种网络管理的改进策略。该策略可实现直接网络管理中CAN节点的分组管理和合并。实验结果表明,这种网络管理机制能够快速构建网络管理的逻辑结构,提高了CAN网络管理效率。展开更多
文摘Elevators are essential components of contemporary buildings, enabling efficient vertical mobility for occupants. However, the proliferation of tall buildings has exacerbated challenges such as traffic congestion within elevator systems. Many passengers experience dissatisfaction with prolonged wait times, leading to impatience and frustration among building occupants. The widespread adoption of neural networks and deep learning technologies across various fields and industries represents a significant paradigm shift, and unlocking new avenues for innovation and advancement. These cutting-edge technologies offer unprecedented opportunities to address complex challenges and optimize processes in diverse domains. In this study, LSTM (Long Short-Term Memory) network technology is leveraged to analyze elevator traffic flow within a typical office building. By harnessing the predictive capabilities of LSTM, the research aims to contribute to advancements in elevator group control design, ultimately enhancing the functionality and efficiency of vertical transportation systems in built environments. The findings of this research have the potential to reference the development of intelligent elevator management systems, capable of dynamically adapting to fluctuating passenger demand and optimizing elevator usage in real-time. By enhancing the efficiency and functionality of vertical transportation systems, the research contributes to creating more sustainable, accessible, and user-friendly living environments for individuals across diverse demographics.
文摘针对节点数量较多、功能类型多样的控制器局域网(controller area network,CAN)中存在的管理问题,基于其协议技术标准和OSEK/VDX规范,提出并实现了一种网络管理的改进策略。该策略可实现直接网络管理中CAN节点的分组管理和合并。实验结果表明,这种网络管理机制能够快速构建网络管理的逻辑结构,提高了CAN网络管理效率。
