Limits...
Classifying Sources Influencing Indoor Air Quality (IAQ) Using Artificial Neural Network (ANN).

Saad SM, Andrew AM, Shakaff AY, Saad AR, Kamarudin AM, Zakaria A - Sensors (Basel) (2015)

Bottom Line: Basically, the proposed system consists of sensor module cloud (SMC), base station and service-oriented client.The SMC contain collections of sensor modules that measure the air quality data and transmit the captured data to base station through wireless network.The results showed that the system is able to measure the level of air quality and successfully classify the sources influencing IAQ in various environments like ambient air, chemical presence, fragrance presence, foods and beverages and human activity.

View Article: PubMed Central - PubMed

Affiliation: Center of Excellence for Advanced Sensor Technology (CEASTech), Universiti Malaysia Perlis (UniMAP), Taman Muhibbah, Jejawi, 02600 Arau, Perlis, Malaysia. shaharil85@gmail.com.

ABSTRACT
Monitoring indoor air quality (IAQ) is deemed important nowadays. A sophisticated IAQ monitoring system which could classify the source influencing the IAQ is definitely going to be very helpful to the users. Therefore, in this paper, an IAQ monitoring system has been proposed with a newly added feature which enables the system to identify the sources influencing the level of IAQ. In order to achieve this, the data collected has been trained with artificial neural network or ANN--a proven method for pattern recognition. Basically, the proposed system consists of sensor module cloud (SMC), base station and service-oriented client. The SMC contain collections of sensor modules that measure the air quality data and transmit the captured data to base station through wireless network. The IAQ monitoring system is also equipped with IAQ Index and thermal comfort index which could tell the users about the room's conditions. The results showed that the system is able to measure the level of air quality and successfully classify the sources influencing IAQ in various environments like ambient air, chemical presence, fragrance presence, foods and beverages and human activity.

No MeSH data available.


Related in: MedlinePlus

System architecture for real-time IAQ monitoring.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4481943&req=5

sensors-15-11665-f001: System architecture for real-time IAQ monitoring.

Mentions: The proposed system has been designed in such a way that it is able to monitor air quality in both indoor and outdoor environments. Nine sensors have been used to capture data for the nine parameters required in this project. Figure 1 shows our proposed system architecture for real time IAQ monitoring. The proposed system consists of sensor module cloud (SMC), base station and service-oriented client. The SMC contains an array of sensor modules that captures the air quality data. The data are then transmitted to the base station through wireless connection where the data are stored in a server. The server functions as data logger to keep track of data received in base station. It stores the data in database, processes the data, performs analysis and provides information about IAQ information through web service. The web service enables the clients or users to be informed about the IAQ level in real-time.


Classifying Sources Influencing Indoor Air Quality (IAQ) Using Artificial Neural Network (ANN).

Saad SM, Andrew AM, Shakaff AY, Saad AR, Kamarudin AM, Zakaria A - Sensors (Basel) (2015)

System architecture for real-time IAQ monitoring.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4481943&req=5

sensors-15-11665-f001: System architecture for real-time IAQ monitoring.
Mentions: The proposed system has been designed in such a way that it is able to monitor air quality in both indoor and outdoor environments. Nine sensors have been used to capture data for the nine parameters required in this project. Figure 1 shows our proposed system architecture for real time IAQ monitoring. The proposed system consists of sensor module cloud (SMC), base station and service-oriented client. The SMC contains an array of sensor modules that captures the air quality data. The data are then transmitted to the base station through wireless connection where the data are stored in a server. The server functions as data logger to keep track of data received in base station. It stores the data in database, processes the data, performs analysis and provides information about IAQ information through web service. The web service enables the clients or users to be informed about the IAQ level in real-time.

Bottom Line: Basically, the proposed system consists of sensor module cloud (SMC), base station and service-oriented client.The SMC contain collections of sensor modules that measure the air quality data and transmit the captured data to base station through wireless network.The results showed that the system is able to measure the level of air quality and successfully classify the sources influencing IAQ in various environments like ambient air, chemical presence, fragrance presence, foods and beverages and human activity.

View Article: PubMed Central - PubMed

Affiliation: Center of Excellence for Advanced Sensor Technology (CEASTech), Universiti Malaysia Perlis (UniMAP), Taman Muhibbah, Jejawi, 02600 Arau, Perlis, Malaysia. shaharil85@gmail.com.

ABSTRACT
Monitoring indoor air quality (IAQ) is deemed important nowadays. A sophisticated IAQ monitoring system which could classify the source influencing the IAQ is definitely going to be very helpful to the users. Therefore, in this paper, an IAQ monitoring system has been proposed with a newly added feature which enables the system to identify the sources influencing the level of IAQ. In order to achieve this, the data collected has been trained with artificial neural network or ANN--a proven method for pattern recognition. Basically, the proposed system consists of sensor module cloud (SMC), base station and service-oriented client. The SMC contain collections of sensor modules that measure the air quality data and transmit the captured data to base station through wireless network. The IAQ monitoring system is also equipped with IAQ Index and thermal comfort index which could tell the users about the room's conditions. The results showed that the system is able to measure the level of air quality and successfully classify the sources influencing IAQ in various environments like ambient air, chemical presence, fragrance presence, foods and beverages and human activity.

No MeSH data available.


Related in: MedlinePlus