Research on the Intelligent Plant Growth System Temperature Acquisition Based on the Data Fusion

2013 ◽  
Vol 846-847 ◽  
pp. 906-909
Author(s):  
Gao Li Chen ◽  
Li Guo Tian ◽  
Meng Li ◽  
Zhi Qi Liu
Keyword(s):  
Plant Growth ◽  
Data Fusion ◽  
Sensor Data ◽  
Fusion Method ◽  
Relevant Research ◽  
Multi Sensor Data Fusion ◽  
Temperature Detection ◽  
Growth System ◽  
System Temperature ◽  
Sensor Measurement

The growth of plants needs certain temperature conditions, carried out relevant research for intelligent plant growth systems of temperature acquisition. For plant growth cabinet temperature is by the influence of many factors, and multi-sensor measurement error caused by temperature detecting, using the distribution display method of temperature detection divorced value removing method and the Bayesian estimation of multi-sensor data fusion method. The experiment results show that the algorithm is reasonable and reliable, improving the accuracy of the temperature acquisition, and effectively eliminate the error caused by the failure sensor.

A Method for the Temperature Measurement of PCR Instrument Based on Multi-Sensor Data Fusion

2012 ◽  
Vol 462 ◽  
pp. 624-630
Author(s):  
Rong Rui Fang ◽  
Sheng Hu Xue ◽  
Zi Hong Ye ◽  
Xiao Ping Yu
Keyword(s):  
Data Fusion ◽  
Mean Value ◽  
Sensor Data ◽  
Fusion Method ◽  
Sampled Data ◽  
Fusion Technology ◽  
Weighted Mean ◽  
Real Time Quantitative Pcr ◽  
Multi Sensor Data Fusion ◽  
Sensor Detection

Method based on multi-sensor detection and data fusion technology is proposed for the temperature of real-time quantitative PCR reaction samples .The principle of Grubbs is used to eliminate the careless mistake data. Particularly, the fusion method based on weighted mean value and estimation in batches is used to process the sampled data, which gives error of indication. And then we can revise indication values.

Intelligent Temperature Control of Ceramic Kiln Based on Data Fusion Technology

2011 ◽  
Vol 128-129 ◽  
pp. 177-180
Author(s):  
Yong Hong Zhu ◽  
Jun Wan
Keyword(s):  
Control System ◽  
Data Fusion ◽  
Temperature Control ◽  
Intelligent Control ◽  
Sensor Data ◽  
Temperature Control System ◽  
Fusion Method ◽  
Fusion Technology ◽  
Multi Sensor Data Fusion ◽  
Kiln Temperature

At first, according to the feature of ceramic kiln, this paper studies a fundamental data fusion method which is applied to ceramic kiln temperature control system. This method is used to solve the parameter estimation problem in measurement noise environment. Then, it proposes a kind of intelligent control structure of ceramic kiln temperature control system based on multi-sensor data fusion technology. At last, this data fusion method is applied to intelligent temperature control system of ceramic kiln. The result shows that the method proposed is effective and feasible.

Identification of Freshwater Fish Meat Freshness Based on Multi-Sensor Fusion Technology

2013 ◽  
Vol 303-306 ◽  
pp. 912-917
Author(s):  
Xiao Jing Yang ◽  
Zheng Hu Yan
Keyword(s):  
Data Fusion ◽  
Freshwater Fish ◽  
Sensor Fusion ◽  
Fuzzy Theory ◽  
Sensor Data ◽  
Fusion Method ◽  
Fusion Technology ◽  
Standard Samples ◽  
Fish Meat ◽  
Multi Sensor Data Fusion

In order to explore the application of multi-sensor fusion technology in nondestructive testing of agricultural products and develop new detection methods for agriculture, multi-sensor information fusion technology are performed to identify freshness of freshwater fish meat. Different freshness of grass carp specimens were identified by multi-sensor fusion method which three characteristic data that are PH value, conductance and smell were measured, collected, and then fused by fuzzy theory method. During the experiment process the value of the total volatile basic nitrogen (TVB-N) of standard samples and test samples were measured. The freshness standard was established by the TVB-N value of standard samples. Correctness of the results of multi-sensor data fusion was verified by comparing the TVB-N value of the test samples with the freshness standard. The results show that the freshwater fish meat with different freshness can be identified correctly by multi-sensor data fusion method which is fuzzy theory and the accuracy rate is 94%.

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