Gas Identification Using Oxide Semiconductor Gas Sensor Array and Neural-network Pattern Recognition

2005 ◽  
Author(s):  
Hyung-Ki Hong ◽  
Hyun Woo Shin ◽  
Hycon Soo Park ◽  
Dong Hyun Yun ◽  
Chul Han Kwon ◽  
...  
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Gas Sensor ◽  
Sensor Array ◽  
Oxide Semiconductor ◽  
Semiconductor Gas Sensor ◽  
Gas Sensor Array ◽  
Gas Identification ◽  
Network Pattern

Gas identification using micro gas sensor array and neural-network pattern recognition

1996 ◽  
Vol 33 (1-3) ◽  
pp. 68-71 ◽  
Author(s):  
Hyung-Ki Hong ◽  
Hyun Woo Shin ◽  
Hyeon Soo Park ◽  
Dong Hyun Yun ◽  
Chul Han Kwon ◽  
...  
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Gas Sensor ◽  
Sensor Array ◽  
Gas Sensor Array ◽  
Micro Gas Sensor ◽  
Gas Identification ◽  
Network Pattern

An Odor Monitoring System Based on Differentiated Pattern Recognition Implemented a Semiconductor Gas Sensor Array

2013 ◽  
Vol 19 (10) ◽  
pp. 2901-2904 ◽  
Author(s):  
Eungyeong Kim ◽  
Jung Ho Lee ◽  
Beom Ju Shin ◽  
Seok Lee ◽  
Young Tae Byun ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Gas Sensor ◽  
Monitoring System ◽  
Sensor Array ◽  
Semiconductor Gas Sensor ◽  
Gas Sensor Array ◽  
Odor Monitoring

Pattern Recognition of a Gas Sensor Array Using Impedance

2000 ◽  
Vol 37 (6) ◽  
pp. 862 ◽  
Author(s):  
Lee Yun-Su ◽  
Joo Byung-Su ◽  
Choi Nak-Jin ◽  
Kang Bong-Hwi ◽  
Lee Duk-Dong
Keyword(s):  
Pattern Recognition ◽  
Gas Sensor ◽  
Sensor Array ◽  
Gas Sensor Array

scholarly journals The Effectiveness of Varroa destructor Infestation Classification Using an E-Nose Depending on the Time of Day

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2532
Author(s):  
Andrzej Szczurek ◽  
Monika Maciejewska ◽  
Żaneta Zajiczek ◽  
Beata Bąk ◽  
Jakub Wilk ◽  
...  
Keyword(s):  
Gas Sensor ◽  
Varroa Destructor ◽  
Sensor Array ◽  
Measurement Data ◽  
Time Of Day ◽  
Semiconductor Gas Sensor ◽  
Bee Colony ◽  
Gas Sensor Array ◽  
Array Measurement ◽  
Method Of Assessment

Honey bees are subject to a number of stressors. In recent years, there has been a worldwide decline in the population of these insects. Losses raise a serious concern, because bees have an indispensable role in the food supply of humankind. This work is focused on the method of assessment of honey bee colony infestation by Varroa destructor. The approach allows to detect several categories of infestation: “Low”, “Medium” and “High”. The method of detection consists of two components: (1) the measurements of beehive air using a gas sensor array and (2) classification, which is based on the measurement data. In this work, we indicate the sensitivity of the bee colony infestation assessment to the timing of measurement data collection. It was observed that the semiconductor gas sensor responses to the atmosphere of a defined beehive, collected during 24 h, displayed temporal variation. We demonstrated that the success rate of the bee colony infestation assessment also altered depending on the time of day when the gas sensor array measurement was done. Moreover, it was found that different times of day were the most favorable to detect the particular infestation category. This result could indicate that the representation of the disease in the beehive air may be confounded during the day, due to some interferences. More studies are needed to explain this fact and determine the best measurement periods. The problem addressed in this work is very important for scheduling the beekeeping practices aimed at Varroa destructor infestation assessment, using the proposed method.

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