False alarm rate

False positive alarms and false negative alarms must be addressed when making decisions on appropriate detectors. 

A false positive alarm occurs when the instrument reports that the sample contains targeted chemicals when in fact it does not These alarms can be caused by various factors, depending on the specifics of techniques used. For example, phosphorus and sulfur compounds, such as pesticides, would generate a false positive 

CWA alarm when a flame photometric detector is used for detection. This is because flame photometric detectors are made specifically to respond to chemicals containing phosphorus and sulfur. Detectors are always subject to yielding false positive alarms since no detector can be made 100% selective to only targeted chemicals. Most detection devices are made to detect multiple compounds. Therefore, a different technique may be necessary to serve as a backup to confirm alarms to reduce the potential for false positive alarms. 

False negative alarms occur when instruments fail to respond to targeted chemicals that are present in a sample. These responses are viewed as more problematic than false positive alarms, because failure to produce a necessary alarm may lead to dangerous/disastrous situations. Causes of false negative alarms include changing environmental conditions humidity effects presence of interfering chemicals that mask normal detection capabilities and detector malfunction, such as improper calibration and detection algorithm deviations. 

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At PicArsn (Ref 19), the fast neutron activation approach for detection of expls in suitcases was extended to the activation of both nitrogen and oxygen using two 7-ray detector stations in sequence. After 14 MeV neutron irradiation, the baggage is first monitored for 6.1 MeV 7-rays from the l60(n,p),6N reaction (7.5 sec half-life), followed by measurement of the 10 min 13N. Because expls are also rich in oxygen and have characteristic ratios of N/O, it was felt that this approach would increase the probability of detection with a corresponding decrease in the false alarm rate... 

A staggering number of papers are published each year in the literature on various candidate chemical/biological detection systems. Researchers and manufacturers make diverse claims of detection limits, sensitivity, false-alarm rates, and robustness for these systems. The committee believes that in many cases, researchers emphasize the strengths of their particular detection systems while minimizing or ignoring their flaws. This practice makes it virtually impossible to evaluate the likely performance of a detection system in real-world air transportation environments. 

For the detection of slow-acting biological agents (which may not produce symptoms for several days), the system response time would depend on the frequency of sampling and analysis. The frequency of sampling and analysis would be determined by factors such as the cost of the assay, the frequency with which critical reagents need to be replaced, the robustness of the detector, and so on. The minimum response time would be determined by the time required to collect a sample, prepare it for analysis, conduct the assay, and report the results. In the event of an alarm from a detector with a significant false-alarm rate, additional time would be required to determine its validity and to decide on an appropriate response. 

In addition to sensitivity, an important feature for any biochemical sensor is selectivity, i.e., the ability to response to a specific chemical or agent. In additional to the obvious significance (reducing the false alarm rate, etc.), this ability is... 

To achieve early and reliable warnings of leakages, the sensitivity of detectors should be at the highest level commensurate with the level of false alarm rates. 

Using the field model described in section 1, detection probabilities are to be computed for each grid point to find the breach probability. The optimal decision rule that maximizes the detection probability subject to a maximum allowable false alarm rate a is given by the Neyman-Pearson formulation [20]. Two hypotheses that represent the presence and absence of a target are set up. The Neyman-Pearson (NP) detector computes the likelihood ratio of the respective probability density functions, and compares it against a threshold which is designed such that a specified false alarm constraint is satisfied. 

The breach probability P is quite sensitive to the false alarm rate a. As shown in Fig. 3b, as a increases, the SWSN allows more false alarms, and consequently, the NP detection probability and the detection probability pv of the targets at grid point v both increase in a. Consequently, the breach probability decreases. 

The model and results developed herein give clues that link false alarms to energy efficiency. Enforcing a low false alarm rate to avoid unnecessary response costs implies either a larger data-set (L) and hence a greater battery consumption, or a denser sensor network, which increases the deployment cost. Similar qualitative and/or quantitative inferences about the relationships between various other parameters can also be made. 

The general objective of all radar detection procedures is to get a constant false alarm rate (CFAR) due to the fact that the test cell almost always contains clutter and noise and only in a very few cases contains radar target echo signals. The statistical model and general detection procedure, in which the detector is fixed only with regard to the noise and clutter statistic and independently to the target statistic, has been developed by Neyman and Pearson. 

If CAGO-CFAR is applied in such typical clutter situations the false alarm rate is reduced at the clutter edges but the detection rate is simultaneously reduced slightly, see Figure 13. 

This method separates the window cells into a leading and a lagging part. Before the mean values of these parts are averaged, they are weighted by the factors a and (3. Optimum values for a and /3 are calculated in accordance with the level of interference of present targets, so that a constant false alarm rate and a high detection probability can be guaranteed. 

Dual-energy CT capability, providing measurement of a second, independent material quantity Zgfr of candidate threat objects, is expected to aid significantly in reducing false alarm rates and thereby to improve performance over single-energy CT systems. 

Generally, the performance characteristics of greatest interest for an explosives detection system are sensitivity, selectivity, and response time. As used here, sensitivity is the ability to detect the target analyte in extremely small concentrations, while selectivity is the ability to distinguish the target analyte from other materials that may be present. In combination, good sensitivity and selectivity mean a high probability of detection when the analyte is present and a low false alarm rate when the analyte is not present. 

In any practical application in the mineralogy field the main task is composition analyses. There is no single detection technique that can by itself provide a 100% probability of different minerals detection combined with a low false alarm rate. We suggest the system approach, which combines different laser-based technologies having orthogonal detection and identification capabilities. In such a way the strength of one technique may compensate for the weaknesses of the others, and the vulnerability of one detection device could be compensated for by another detection device. Clever combination of the detection techniques may achieve detection probabilities and false alarm rates that are more acceptable than those of systems based on one method only. 

Note that the first condition is the familiar Shewhart chart limits. Pattern tests can be used to augment Shewhart charts. This combination enables out-of-control behavior to be detected earlier, but the false-alarm rate is higher than that for Shewhart charts alone. 

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