Temperature error correction based on BP neural network in meteorological wireless sensor network

Ma, Li; Wang, Baowei; Yan, Shuangshuang; Gu, Xiaodu

doi:10.1504/ijsnet.2017.10004215

Cited by 16 publications

(6 citation statements)

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“…The sink node can achieve the initial integration of the information and send the processed information to the gateway. The gateway generally has a very good long-distance communication module, which can realize the remote transmission of data, and finally the relevant information is received by the monitoring station [8][9]. The sensor node is a micro-embedded system at the monitoring site.…”

Section: Wireless Sensor Network Embodiment Structurementioning

confidence: 99%

Tunnel Health Monitoring System Based on Wireless Sensor Network

2021

IJETC

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At present, China is not only the country with the most highway tunnels, but also the country with the longest railway tunnels, but with the increase of tunnel use time, its structure is gradually aging, resulting in arch settlement, segment damage, misplacement, and leakage a series of problems; in order to solve these problems, regular monitoring and maintenance of the tunnel is needed, because manual monitoring and maintenance of the tunnel during the operation is very inconvenient. Based on the above background, the purpose of this paper is to study the tunnel health monitoring system based on wireless sensor networks. First, this article uses a modular approach to develop the system hardware platform, focusing on the design of the ZigBee wireless communication unit, power management unit, flash data storage unit, clock unit, etc.; then, in order to ensure the reliability of the sensor data, design correction error detection and detection algorithms are embedded in the underlying nodes to reduce errors in the storage and transmission of data. At the same time, for the interference of sensor data during acquisition, wavelet denoising is used to preprocess the data, and a random forest algorithm is used to establish the tunnel health monitoring and evaluation system analyzes and evaluates the tunnel status information. Finally, experimental verification is performed. The experimental results show that the wireless sensor data loss rate is 9% when a train passes, and the wireless sensor data loss rate is only 3% when no train passes. By optimizing the node arrangement and ad hoc network, the reliability of data transmission can be improved.

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Section: Wireless Sensor Network Embodiment Structurementioning

confidence: 99%

Tunnel Health Monitoring System Based on Wireless Sensor Network

2021

IJETC

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“…That is the input of the output layer. According to the basic principle of BP neural work, set the weight coefficient and the threshold [20].…”

Section: Network Trainingmentioning

confidence: 99%

Fitting analysis and research of measured data of SAW micro-pressure sensor based on BP neural network

Huang

et al. 2020

Measurement

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“…The back propagation (BP) neural network is a multilayer feedforward neural network with complex pattern classification capability and excellent multi-dimensional function mapping capability. The BP neural network iterates on the weights and thresholds of neurons by the principle of back propagation, each iteration of weight and threshold will be adjusted toward the direction of the output error reduction [29][30][31]. The BP neural network formula could be expressed as…”

Section: Hybrid Artificial Neural Network Modelmentioning

confidence: 99%

Nondestructive Evaluation of Thermal Barrier Coatings Interface Delamination Using Terahertz Technique Combined with SWT-PCA-GA-BP Algorithm

Wang

Yin

et al. 2020

Coatings

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Thermal barrier coatings (TBCs) are usually subjected to the combined action of compressive stress, tensile stress, and bending shear stress, resulting in the interfacial delamination of TBCs, and finally causing the ceramic top coat to peel off. Hence, it is vital to detect the early-stage subcritical delamination cracks. In this study, a novel hybrid artificial neural network combined with the terahertz nondestructive technology was presented to predict the thickness of interface delamination in the early stage. The finite difference time domain (FDTD) algorithm was used to obtain the raw terahertz time-domain signals of 32 TBCs samples with various thicknesses of interface delamination, not only that, the influence of roughness and the thickness of the ceramic top layer were considered comprehensively when modeling. The stationary wavelet transform (SWT) and principal component analysis (PCA) methods were employed to extract the signal features and reduce the data dimensions before modeling, to make the cumulative contribution rate reach 100%, the first 31 components of the SWT detail data was used as the input data during modeling. Finally, a back propagation (BP) neural network method optimized by the genetic algorithm (GA-BP) was proposed to set up the interface delamination thickness prediction model. As a result, the root correlation coefficient R2 reached over 0.95, the various errors—including the mean square error, mean squared percentage error, and mean absolute percentage error—were less than or equal to 0.53. All these indicators proved that the trained hybrid SWT-PCA-GA-BP model had excellent prediction performance and high accuracy. Finally, this work proposed a novel and convenient interface delamination evaluation method that could also be potentially utilized to evaluate the structural integrity of TBCs.

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Temperature error correction based on BP neural network in meteorological wireless sensor network

Cited by 16 publications

References 0 publications

Tunnel Health Monitoring System Based on Wireless Sensor Network

Tunnel Health Monitoring System Based on Wireless Sensor Network

Fitting analysis and research of measured data of SAW micro-pressure sensor based on BP neural network

Nondestructive Evaluation of Thermal Barrier Coatings Interface Delamination Using Terahertz Technique Combined with SWT-PCA-GA-BP Algorithm

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