School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China.
Department of Acupuncture Hebei Provincial Hospital of Traditional Chinese Medicine, Shijiazhuang 050011, China.
Sensors (Basel). 2024 Aug 21;24(16):5393. doi: 10.3390/s24165393.
With the increasing aging of the global population, the efficiency and accuracy of the elderly monitoring system become crucial. In this paper, a sensor layout optimization method, the Fusion Genetic Gray Wolf Optimization (FGGWO) algorithm, is proposed which utilizes the global search capability of Genetic Algorithm (GA) and the local search capability of Gray Wolf Optimization algorithm (GWO) to improve the efficiency and accuracy of the sensor layout in elderly monitoring systems. It does so by optimizing the indoor infrared sensor layout in the elderly monitoring system to improve the efficiency and coverage of the sensor layout in the elderly monitoring system. Test results show that the FGGWO algorithm is superior to the single optimization algorithm in monitoring coverage, accuracy, and system efficiency. In addition, the algorithm is able to effectively avoid the local optimum problem commonly found in traditional methods and to reduce the number of sensors used, while maintaining high monitoring accuracy. The flexibility and adaptability of the algorithm bode well for its potential application in a wide range of intelligent surveillance scenarios. Future research will explore how deep learning techniques can be integrated into the FGGWO algorithm to further enhance the system's adaptive and real-time response capabilities.
随着全球人口老龄化的加剧，老年人监测系统的效率和准确性变得至关重要。在本文中，提出了一种传感器布局优化方法，即融合遗传灰狼优化（FGGWO）算法，该算法利用遗传算法（GA）的全局搜索能力和灰狼优化算法（GWO）的局部搜索能力，提高老年人监测系统中传感器布局的效率和准确性。通过优化老年人监测系统中的室内红外传感器布局，提高老年人监测系统中传感器布局的效率和覆盖范围。测试结果表明，FGGWO 算法在监测覆盖率、准确性和系统效率方面优于单一优化算法。此外，该算法能够有效地避免传统方法中常见的局部最优问题，减少传感器的使用数量，同时保持较高的监测精度。该算法的灵活性和适应性为其在广泛的智能监控场景中的潜在应用奠定了基础。未来的研究将探讨如何将深度学习技术集成到 FGGWO 算法中，以进一步增强系统的自适应和实时响应能力。
