An Data Correction Method for Hydrological Monitoring Based on Improved BP Neural Network

2013 ◽  
Vol 380-384 ◽  
pp. 879-883
Author(s):  
Jia Hua Zhang ◽  
Xin Wang ◽  
Yan Gu ◽  
Li Zhong Xu ◽  
Tang Huai Fan
Keyword(s):  
Neural Network ◽  
Flow Velocity ◽  
Back Propagation ◽  
Temporal Correlation ◽  
Sensor Data ◽  
Monitoring Station ◽  
Fusion Algorithm ◽  
Data Correction ◽  
Hydrological Monitoring ◽  
Station Unit

For hydrological monitoring, the missing and distorted sensor data may directly affect the reliability of the acquired information. To address such problems, an information fusion algorithm for sensor data correction based on the spatio-temporal correlation of hydrological monitoring information is proposed in this paper. A monitoring station unit whose core device is FPGA (Field Programmable Gate Array) is employed as hardware platform and fusion of the data collected by the monitoring station unit is performed using an improved BP (Back Propagation) neural network. This work uses the horizontal and vertical correlation of flow velocity distribution to correct flow velocity. The simulation experimental results show that this algorithm can be used for the correction of both random and gross error of sensor data.

scholarly journals Multi-Sensor Data Fusion Algorithm for Indoor Fire Early Warning Based on BP Neural Network

Information ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 59
Author(s):  
Lesong Wu ◽  
Lan Chen ◽  
Xiaoran Hao
Keyword(s):  
Neural Network ◽  
Early Warning ◽  
Back Propagation ◽  
Fire Detection ◽  
Back Propagation Neural Network ◽  
Standard Test ◽  
Sensor Data ◽  
Fusion Algorithm ◽  
Trend Extraction ◽  
Fire Scenarios

Fire early warning is an important way to deal with the faster burning rate of modern home fires and ensure the safety of the residents’ lives and property. To improve real-time fire alarm performance, this paper proposes an indoor fire early warning algorithm based on a back propagation neural network. The early warning algorithm fuses the data of temperature, smoke concentration and carbon monoxide, which are collected by sensors, and outputs the probability of fire occurrence. In this study, non-uniform sampling and trend extraction were used to enhance the ability to distinguish fire signals and environmental interference. Data from six sets of standard test fire scenarios and six sets of no-fire scenarios were used to test the algorithm proposed in this paper. The test results show that the proposed algorithm can correctly alarm six standard test fires from these 12 scenarios, and the fire detection time is shortened by 32%.

scholarly journals Multi-sensor Information Fusion Method Based on BP Neural Network

2016 ◽  
Vol 12 (05) ◽  
pp. 53 ◽  
Author(s):  
Lin Liandong
Keyword(s):  
Neural Network ◽  
Bayesian Inference ◽  
Information Fusion ◽  
System Development ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Fusion Algorithm ◽  
Temperature Detection ◽  
Posterior Expectation ◽  
Sensor Information

This study aims to solve the problem of multi-sensor information fusion, which is a key issue in the multi-sensor system development. The main innovation of this study is to propose a novel multi-sensor information fusion algorithm based on back propagation neural network and Bayesian inference. In the proposed algorithm, a triple is defined to represent a probability space; thereafter, the Bayesian inference is used to estimate the posterior expectation. Finally, we construct a simulation environment to test the performance of the proposed algorithm. Experimental results demonstrate that the proposed algorithm can significantly enhance the accuracy of temperature detection after fusing the data obtained from different sensors.

scholarly journals Dynamic Stress Measurement with Sensor Data Compensation

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 859
Author(s):  
Jingjing Gu ◽  
Zhiteng Dong ◽  
Cai Zhang ◽  
Xiaojiang Du ◽  
Mohsen Guizani
Keyword(s):  
Neural Network ◽  
Dynamic Stress ◽  
Stress Measurement ◽  
Rapid Change ◽  
Back Propagation ◽  
Environmental Parameters ◽  
Back Propagation Neural Network ◽  
Sensor Data ◽  
Bee Colony ◽  
Stability Of Solution

Applying parachutes-deployed Wireless Sensor Network (WSN) in monitoring the high-altitude space is a promising solution for its effectiveness and cost. However, both the high deviation of data and the rapid change of various environment factors (air pressure, temperature, wind speed, etc.) pose a great challenge. To this end, we solve this challenge with data compensation in dynamic stress measurements of parachutes during the working stage. Specifically, we construct a data compensation model to correct the deviation based on neural network by taking into account a variety of environmental parameters, and name it as Data Compensation based on Back Propagation Neural Network (DC-BPNN). Then, for improving the speed and accuracy of training the DC-BPNN, we propose a novel Adaptive Artificial Bee Colony (AABC) algorithm. We also address its stability of solution by deriving a stability bound. Finally, to verify the real performance, we conduct a set of real implemented experiments of airdropped WSN.

A Grain Output Combination Forecast Model Modified by Data Fusion Algorithm

2018 ◽  
Vol 27 (2) ◽  
pp. 303-315
Author(s):  
Xiang Wan ◽  
Bing-Xiang Liu ◽  
Xing Xu
Keyword(s):  
Neural Network ◽  
Data Fusion ◽  
Bp Neural Network ◽  
Particle Swarm Optimization Algorithm ◽  
Back Propagation ◽  
Forecast Model ◽  
Fusion Algorithm ◽  
Combination Model ◽  
Combination Forecast ◽  
Use Of Data

AbstractTo deal with the lack of accuracy and generalization ability in some single models, grain output models were built with lots of relevant data, based on the powerful non-linear reflection of the back-propagation (BP) neural network. Three kinds of grain output models were built and took advantage of – particle swarm optimization algorithm, mind evolutionary algorithm, and genetic algorithm – to optimize the BP neural network. By the use of data fusion algorithm, the outcomes of different models can be modified and fused together, and the combination-predicted outcome can be obtained finally. Taking advantage of this combination model to predict the total grain output of China, the results showed that the total grain output in 2015 was a bit larger than the actual value of about 0.0115%. It was much more accurate than the three single models. The experimental results verify the feasibility and validity of the combination model.

scholarly journals Machine Learning-Based Sensor Data Modeling Methods for Power Transformer PHM

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4430 ◽  
Author(s):  
Anyi Li ◽  
Xiaohui Yang ◽  
Huanyu Dong ◽  
Zihao Xie ◽  
Chunsheng Yang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
High Performance ◽  
Health Management ◽  
Power Transformer ◽  
Back Propagation ◽  
Cuckoo Search ◽  
Sensor Data ◽  
Maintenance Cost ◽  
Prognostic And Health Management

An emerging prognostic and health management (PHM) technology has recently attracted a great deal of attention from academies, industries, and governments. The need for higher equipment availability and lower maintenance cost is driving the development and integration of prognostic and health management systems. PHM models depend on the smart sensors and data generated from sensors. This paper proposed a machine learning-based methods for developing PHM models from sensor data to perform fault diagnostic for transformer systems in a smart grid. In particular, we apply the Cuckoo Search (CS) algorithm to optimize the Back-propagation (BP) neural network in order to build high performance fault diagnostics models. The models were developed using sensor data called dissolved gas data in oil of the power transformer. We validated the models using real sensor data collected from power transformers in China. The results demonstrate that the developed meta heuristic algorithm for optimizing the parameters of the neural network is effective and useful; and machine learning-based models significantly improved the performance and accuracy of fault diagnosis/detection for power transformer PHM.

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