Results of the AMS experiment

The Alpha Magnetic Spectrometer (AMS) was flown in June 1998 on the space shuttle Discovery during flight STS-91 in a 51.7◦ orbit at altitudes between 320 and 390 km. The major detector elements were a permanent magnet with an analyzing power B∗L2 of 0.14 Tm2, a six-layer, double-sided silicon tracker, time-of-flight hodoscopes, a ˇCerenkov counter and anti-coincidence counters. A total of 2.86 × 106 He nuclei were observed in the rigidity range 1 to 140 GeV. No He nuclei were detected at any rigidity. The upper limit on the flux ratio of He to He is 1.1×10−6. The proton spectrum in the kinetic energy range 0.1 to 200 GeV was measured, and is parameterized by a power law above the geomagnetic cutoff. Below the geomagnetic cutoff, a substantial second spectrum was observed concentrated at equatorial latitudes with a flux of ∼70m−2s−1sr−1. The lepton spectra in the kinetic energy ranges 0.2 to 40 GeV for electrons and 0.2 to 3 GeV for positrons were measured. Two distinct spectra were observed, a higher energy spectrum and a substantial second spectrum with positrons much more abundant than electrons. Tracing leptons from the second spectra shows that most of these leptons travel for an extended period of time in the geomagnetic field and that the positrons and electrons originate from two complementary geographic regions. Long-lived secondary spectra protons (antiprotons) originate from the same regions as positrons (electrons)

Solving Multiple Objective Programming Problems Using Feed-Forward Artificial Neural Networks: The Interactive FFANN Procedure

In this paper, we propose a new interactive procedure for solving multiple objective programming problems. Based upon feed-forward artificial neural networks (FFANNs), the method is called the Interactive FFANN Procedure. In the procedure, the decision maker articulates preference information over representative samples from the nondominated set either by assigning preference "values" to the sample solutions or by making pairwise comparisons in a fashion similar to that in the Analytic Hierarchy Process. With this information, a FFANN is trained to represent the decision maker's preference structure. Then, using the FFANN, an optimization problem is solved to search for improved solutions. An example is given to illustrate the Interactive FFANN Procedure. Also, the procedure is compared computationally with the Tchebycheff Method (Steuer and Choo 1983). From the computational results, the Interactive FFANN Procedure produces good results and is robust with regard to the neural network architecture

Interactive Multiple Objective Programming Using Tchebycheff Programs and Artificial Neural Networks

A new interactive multiple objective programming procedure is developed that combines the strengths of the Interactive Weighted Tchebycheff Procedure (Steuer and Choo 1983) and the Interactive FFANN Procedure (Sun, Stam and Steuer 1993). In this new procedure, nondominated trial solutions are generated by solving Augmented Weighted Tchebycheff Programs (Steuer 1986), based on which the decision maker articulates his/her preference information by assigning "values" to these solutions or by making pairwise comparisons. The elicited preference information is used to train a feed-forward artificial neural network, which in turn is used to screen new trial solutions for presentation to decision maker in the next iteration. Computational results are reported, comparing the current procedure with the Interactive Weighted Tchebycheff Procedure and the Interactive FFANN Procedure. The results show that this new procedure yields good quality solutions