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 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.

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.


Block diagram of base station.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-11665-f004: Block diagram of base station.

Mentions: The base station in IAQ monitoring system contains two components which are wireless transceiver and a server that act as a data logger. The base station is responsible for managing, collecting and recording the data before displaying it on a computer screen and on the web service. The wireless transceiver unit is similar to the sensor module, which contains IRIS mote—ATmega1281 low power microcontroller and AT86RF230 radio frequency (RF) [29]. It also contains Future Technology Devices International (FTDI) device which emulates RS 232 transmission protocol and communicates with Data Processing Module (DPM). DPM is responsible for processing and writing the air quality data into the database. At the same time, DPM sends the data to the Web Service which allows users to access the information in real-time. A simple database is based on a SQLite format which is used to log the data for further processing (if necessary) on the server system. SQLite has been selected instead of alternatives like MySQL simply due to the fact that it is easier to setup and it uses single file storage. However, due to its limitation of storage, the DPM has been programmed to create one SQLite file which contains one week of data, and this process is repeated for the following weeks. Figure 4 shows the block diagram of the base station.


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)

Block diagram of base station.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-11665-f004: Block diagram of base station.
Mentions: The base station in IAQ monitoring system contains two components which are wireless transceiver and a server that act as a data logger. The base station is responsible for managing, collecting and recording the data before displaying it on a computer screen and on the web service. The wireless transceiver unit is similar to the sensor module, which contains IRIS mote—ATmega1281 low power microcontroller and AT86RF230 radio frequency (RF) [29]. It also contains Future Technology Devices International (FTDI) device which emulates RS 232 transmission protocol and communicates with Data Processing Module (DPM). DPM is responsible for processing and writing the air quality data into the database. At the same time, DPM sends the data to the Web Service which allows users to access the information in real-time. A simple database is based on a SQLite format which is used to log the data for further processing (if necessary) on the server system. SQLite has been selected instead of alternatives like MySQL simply due to the fact that it is easier to setup and it uses single file storage. However, due to its limitation of storage, the DPM has been programmed to create one SQLite file which contains one week of data, and this process is repeated for the following weeks. Figure 4 shows the block diagram of the base station.

Bottom Line: Basically, the proposed system consists of sensor module cloud (SMC), base station and service-oriented client.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.

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.