探讨物联网(Internet of Things,IoT)领域的两大关键技术,即窄带物联网(Narrow Band Internet of Things,NB-IoT)和增强型机器类型通信(enhanced Machine-Type Communication,eMTC),分析它们在不同应用场景下的实际应用和面临的挑战。...探讨物联网(Internet of Things,IoT)领域的两大关键技术,即窄带物联网(Narrow Band Internet of Things,NB-IoT)和增强型机器类型通信(enhanced Machine-Type Communication,eMTC),分析它们在不同应用场景下的实际应用和面临的挑战。详细介绍基于NB-IoT的智慧水表系统和基于eMTC的车辆跟踪系统的设计与实现,展示这些系统在提高城市管理效率、物流监控等方面的积极作用。针对网络覆盖与信号质量、数据安全与隐私保护、功耗与续航等关键技术挑战,提出相应的解决方案。最后总结NB-IoT和eMTC的广阔应用前景和市场潜力,并对未来技术发展和应用趋势进行展望。展开更多
The smart water meter in water supply network can directly affect water production and usage when faults occur.The traditional method of fault detection is inefficient with time lagging,which is not helpful for modern...The smart water meter in water supply network can directly affect water production and usage when faults occur.The traditional method of fault detection is inefficient with time lagging,which is not helpful for modernization of water supply system.The capability of automatic fault diagnosis of smart water meter is an important means to improve the service quality of water supply.In this paper,an automatic fault diagnosis method for the smart device is proposed based on BP neural network.And it was applied on Google Tensorflow platform.Fault symptom vectors were constructed using water meter status data and were used to train the neural network model.In order to improve the learning convergence speed and fault classification effect of the network,a method of weighted symptom was also employed.Experimental results show that it has good performance with a general fault diagnosis accuracy of 98.82%.展开更多
文摘探讨物联网(Internet of Things,IoT)领域的两大关键技术,即窄带物联网(Narrow Band Internet of Things,NB-IoT)和增强型机器类型通信(enhanced Machine-Type Communication,eMTC),分析它们在不同应用场景下的实际应用和面临的挑战。详细介绍基于NB-IoT的智慧水表系统和基于eMTC的车辆跟踪系统的设计与实现,展示这些系统在提高城市管理效率、物流监控等方面的积极作用。针对网络覆盖与信号质量、数据安全与隐私保护、功耗与续航等关键技术挑战,提出相应的解决方案。最后总结NB-IoT和eMTC的广阔应用前景和市场潜力,并对未来技术发展和应用趋势进行展望。
基金the Huaihua University Double First-Class initiative Applied Characteristic Discipline of Control Science and Engineeringthe Educational Cooperation Program of Ministry of Education of China(No.201801006090)the Hunan Provincial Natural Science Foundation of China(No.2017JJ3252).
文摘The smart water meter in water supply network can directly affect water production and usage when faults occur.The traditional method of fault detection is inefficient with time lagging,which is not helpful for modernization of water supply system.The capability of automatic fault diagnosis of smart water meter is an important means to improve the service quality of water supply.In this paper,an automatic fault diagnosis method for the smart device is proposed based on BP neural network.And it was applied on Google Tensorflow platform.Fault symptom vectors were constructed using water meter status data and were used to train the neural network model.In order to improve the learning convergence speed and fault classification effect of the network,a method of weighted symptom was also employed.Experimental results show that it has good performance with a general fault diagnosis accuracy of 98.82%.
