A Method for Selecting Optimal Number of Sensors to Improve the Credibility

Abstract

With the development of sensors, it is possible to embed many sensors within a certain space, which makes the monitor and alarm system with multisensor possible. There are two important parameters in a monitor and alarm system, namely, the false alarm rate and the missed alarm rate. In this work, a method for selecting optimal number of sensors in the sensor array is presented to improve the credibility. The influence factors of the weights and the false alarm rate and the missed alarm rate of one sensor and total number of sensors are discussed. An experimental setup was developed. The monitoring methods of common strategies and the proposed optimal number of sensors strategy are compared graphically by the receiver operating characteristic curves and the area under receiver operating characteristic curve values. The receiver operating characteristic curves graphically prove that the optimal number of sensors’ method presents the best performance, and it is shown that the optimal number of sensors’ method has the highest area under receiver operating characteristic value (0.9631). This method may aid future users of the monitor and alarm system by providing an optimal number of sensors strategy for high credibility.

A Review of State-of-the-Art Low-Cost Sensors for Detection of Indoor Air Pollutants that Correlate with Health

“…Four major types of automated calibration approaches have been developed and tested till date: a) Uni-Gas Uni-Sensor Calibration; b) Uni-Gas Multi-sensor ENB Calibration; c) IoT-based Networked Multi-Gas ENB Calibration; and d) Climate Smart Heterogeneous ENB Calibration. The recent work realized a plethora of efforts made in gas sensors calibration [114][115][116][117][118][119][120][121][122][123][124][125][126][127] presented in Figures 23-26.…”

“…In 2009, the first structured gas sensors calibration system was designed and implemented by a measurement calibration system that was developed by Casey, J.G. et al [114] presented in the Figure 23 (a). A standard AQS Calibration follows the [114] schematic as: a) An Air-sealed chamber with inflow and outflow valves; b) mass flow controllers (MFCs) for the desired concentration of gas from a cylinder or a PID controller gas flow loop; c) gas cylinders with different concentrations; d) temperature actuator (heater); e) humidity actuator (steam regulator); f) gas collectors for the environmental model; g) measurement instruments other than gas sensors for comparison interfaced with a computer.…”

“…In Figure 23 (a), an industry-standard instrumentation topology, i.e. piping and instrumentation diagram (P&ID) is presented for AQGS [114]. It consists of 14 components: 1) CGB: Calibration gas bottle; 2) MFC: Mass flow control; 3) TCE: Testing chamber electro-valve; 4) DCE: Drying chamber electro-valve; 5) SC: Saturation chamber electro-valve; 6) DCP: Drying chamber pump; 7) SCP: Saturation chamber pump; 8) DC: Drying chamber; 9) SC: Saturation chamber; 10) HC: Heater control; 11) H: Heater; 12) WT: Water tank; 13) TCV: Testing chamber ventilator and 14) SCV: Saturation chamber ventilator.…”

A Systematic Review of Low-Cost Real-time Air Quality Sensors, Systems, and Evaluation Methods

“…In 2009, the first structured gas sensors calibration system was designed and implemented by a measurement calibration system that was developed by Octavian et al [114]. A standard AQS Calibration follows the [114] schematic as: a) An Air-sealed chamber with inflow and outflow valves; b) mass flow controllers (MFCs) for the desired concentration of gas from a cylinder or a PID controller gas flow loop; c) gas cylinders with different concentrations; d) temperature actuator (heater); e) humidity actuator (steam regulator); f) gas collectors for the environmental model; g) measurement instruments other than gas sensors for comparison interfaced with a computer. The recent work realized a plethora of efforts made in gas sensors calibration [114][115][116][117][118][119][120][121][122][123][124][125][126][127] presented in Figures 23-26 In Figure 23a, an industry-standard instrumentation topology, i.e.…”

Short-Time Fourier Transform and Decision Tree-Based Pattern Recognition for Gas Identification Using Temperature Modulated Microhotplate Gas Sensors

“…The previous works have showed that temperature modulation of sensors can improve selectivity [17][18][19], while more complicated algorithms are required [20]. In the last ten years, the focus has been on analysis of dynamic sensor signals [21][22][23][24], and the Fast Fourier Transform (FFT) or the Discrete Wavelet Transform (DWT) [25,26] for feature extraction has been frequently used. Many scholars used the FFT transform to extract the harmonic components as the features (e.g., Vergara and coworkers computed the absolute values of FFT and extracted the values of six harmonics corresponding to six modulating frequencies) [27].…”