the discriminant analysis may require several days of statistical analysis. With the BP technique, only a few hours of set up time are required by a computer literate person, and a day or two of computer time (to train the system).
As seen in FIG. 9, the performance of the ANS network at low false alarm rates (less than 1.75%) is substantially lower than for the discriminant analysis technique. This is believed to be caused by the particular activation function used in the ANS network. (The activation function is that function which defines the relationship between the output of a particular neuron and its input(s).) The sigmoid function was used, and this function is very steep in the middle (i.e., at net input values close to zero). Consequently, most of the output activation values tend to cluster around -0.5 and +0.5. Inevitably, there will be a few non-threatening objects that have similar characteristics to threatening objects. In these instances, the non-threatening objects will also have a value close to +0.5. In order to remove these objects, the threshold would need to be adjusted close to +0.5. However, doing so would cause the detection rate to drop rapidly. Hence, it is believed that a better solution to this problem (of low performance at low PFA rates) is to use a smoother activation function.
The ANS portion of the present invention may be implemented through custom software and/or hardware installed in or coupled to the computer 116 (FIG. 1) or the computer 58 (FIG. 4), hereafter the "host" computer. One implementation, for example, includes an array processor network, or "card", that is installed in or coupled to the host computer. A suitable array processor network that can perform this function is described in U.S. patent application Ser. No. 07/191,207, filed 05/06/88, (assigned to the same assignee as is this application), which patent application is incorporated herein by reference. The advantage of using an array processor of the type described in the referenced patent application is that the ANS computations can be quickly performed, with many of the computations being performed in parallel.
Another implementation involves modeling the ANS using software. A suitable modeling program that simulates an ANS network on a conventional IBM PC/AT or compatible computer, for example, is commercially available from Science Applications International Corporation (SAIC), San Diego, CA 92121 under the name of "ANSim." By using the ANSim program, an IBM PC/AT or compatible computer, and related equipment as detailed in applicants' prior application, Ser. No. 07/367,534, which application is incorporated herein by reference, it is thus possible to readily model the ANS portions of the present invention.
In conclusion, the present invention is directed to an explosive detection system using neutrons from a source to impinge on a object potentially containing explosive material and with the neutrons reacting with the nitrogen or other elements contained in the object to produce gamma rays. The gamma rays are detected by suitable detectors, such as inorganic scintillators, and in a preferred embodiment, the scintillators are arranged so as to surround the object to provide for detection of a two dimensional slice or plane of the object under observation. A neutron detector may also be used to measure neutron flux in the same vicinity as the gamma ray detectors. The object is moved continuously through the ring of detectors so that successive slices or planes provide for the build up of a three dimensional profile of the nitrogen bearing material within the object under observation. The three dimensional profile may then be used to determine the concentration and distribution of the nitrogen (or other element, or combinations of elements) bearing material and to make a determination whether such nitrogen (or other element) bearing material has a profile likely to be an explosive material, or other contraband, as manifest, e.g., by a high nitrogen, chlorine and/or hydrogen density. This determination is enhanced by