Rashed Ahmed Nabih Zaki, Yarrarapu Manasa, Prabu Ramachandran Thandaiah, Raj Antony Gnana Sagaya, Edeswaran Logashanmugam, Kumar E Santosh, Aswitha K, Snehith N, Ahammad Shaik Hasane
Electronics and Electrical Communications Engineering Department, Faculty of Electronic Engineering, Menoufia University, Menouf, 32951, Egypt.
Department of CSE, Prasad V Potluri Siddhartha Institute of Technology, Vijayawada, India.
Sci Rep. 2024 Aug 20;14(1):19330. doi: 10.1038/s41598-024-69173-1.
Smart elevators provide substantial promise for time and energy management applications by utilizing cutting edge artificial intelligence and image processing technology. In order to improve operating efficiency, this project designs an elevator system that uses the YOLO model for object detection. Compared to traditional methods, our results show a 15% improvement in wait times and a 20% reduction in energy use. Due to the elevator's increased accuracy and dependability, users' qualitative feedback shows a high degree of pleasure. These results imply that intelligent elevator systems can make a significant contribution to more intelligent building management. Due to the elevator's increased accuracy and dependability, users' qualitative feedback shows a high degree of pleasure. These results imply that intelligent elevator systems can make a significant contribution to more intelligent building management. The successful integration of artificial intelligence (AI) and image processing technologies in elevator systems presents a promising foundation for future research and development. Further advancements in object detection algorithms, such as refining YOLO models for even higher accuracy and real-time adaptability, hold potential to enhance operational efficiency. Integrating smart elevators more deeply into IoT networks and building management systems could enable comprehensive energy management strategies and real-time decision-making. Predictive maintenance models tailored to elevator components could minimize downtime and optimize service schedules, enhancing overall reliability. Additionally, exploring adaptive user interfaces and personalized scheduling algorithms could further elevate user satisfaction by tailoring elevator interactions to individual preferences. Sustainable practices, including energy-efficient designs and integration of renewable energy sources, represent crucial avenues for reducing environmental impact. Addressing security concerns through advanced encryption and access control mechanisms will be essential for safeguarding sensitive data in smart elevator systems.
智能电梯通过利用前沿的人工智能和图像处理技术，在时间和能源管理应用方面具有巨大潜力。为了提高运行效率，本项目设计了一种使用YOLO模型进行目标检测的电梯系统。与传统方法相比，我们的结果显示等待时间缩短了15%，能源消耗降低了20%。由于电梯的准确性和可靠性提高，用户的定性反馈显示出高度的满意度。这些结果表明，智能电梯系统可以为更智能的建筑管理做出重大贡献。由于电梯的准确性和可靠性提高，用户的定性反馈显示出高度的满意度。这些结果表明，智能电梯系统可以为更智能的建筑管理做出重大贡献。人工智能（AI）和图像处理技术在电梯系统中的成功集成，为未来的研发奠定了有前景的基础。目标检测算法的进一步发展，如优化YOLO模型以实现更高的准确性和实时适应性，有望提高运行效率。将智能电梯更深入地集成到物联网网络和建筑管理系统中，可以实现全面的能源管理策略和实时决策。针对电梯部件的预测性维护模型可以最大限度地减少停机时间并优化服务计划，提高整体可靠性。此外，探索自适应用户界面和个性化调度算法，可以根据个人偏好定制电梯交互，进一步提高用户满意度。可持续实践，包括节能设计和可再生能源的整合，是减少环境影响的关键途径。通过先进的加密和访问控制机制解决安全问题，对于保护智能电梯系统中的敏感数据至关重要。
