Intelligent Drying Rack System based on Internet of Things

Xing, Xiao; Zhang, Chong; Gu, Jieming; Zhang, Yixin; Lv, Xinrun; Zhuo, Zihan

doi:10.1088/1742-6596/1887/1/012002

Cited by 7 publications

(2 citation statements)

References 2 publications

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“…As shown in Figure 11, the APP can realize various intelligent control functions such as lifting, panning, rotating, and harvesting. The APP interacts with the weather API, and the users can obtain real-time weather data through the APP [21][22][23][24]. This system can receive control information from APP in real-time.…”

Section: Software Designmentioning

confidence: 99%

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An efficient intelligent control algorithm for drying rack system

Zhang

Liang

et al. 2023

IET Communications

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With the development of the drying rack system, users with limited time tend to use the fully functional drying rack system to realize various intelligent control functions. However, the existing control methods for drying rack system has some defects such as, low intelligence and system delay, which are unsuitable for most users. In this paper, an efficient intelligent control algorithm based on the back‐propagation (BP) neural network is proposed. In this system, STM32F103 is first utilized as the central controller and multiple sensors are used to collect environmental information. Then, the remote control, voice keywords, and buttons can be used to achieve intelligent control. Subsequently, a motor drive intelligence control algorithm based on the BP neural network (MCBP) is proposed to improve the accuracy of the intelligent control. Next, an Application (APP) that can display environmental data such as wind speed, temperature, and humidity is developed. The APP can realize various intelligent control functions such as lifting, panning, rotating, and harvesting. Finally, MCBP is compared with normal control and programmable logic controller control. The accuracy of the MCBP is higher than other two control methods. The final extensive experiments confirm the accuracy and efficiency of the proposed intelligent control algorithm.

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Section: Software Designmentioning

confidence: 99%

“…We compare MCBP with normal control (no neural network is added) and programmable logic controller (PLC) control [23][24][25][26][27]. We analyse the application principle of raindrop sensors, photosensitive sensors, and temperature sensors of the automatic drying rack control system based on PLC technology.…”

Section: Simulation Testmentioning

confidence: 99%