Learning Confirmatory Patterns in Exploratory Factor Analysis Using ICOMP and Genetic Algorithm

The dissertation intends to develop a new approach to the identification of the best factor pattern structure. This new approach is a multivariate regression analysis where factor scores are regressed on original variables. The dissertation shows the versatility of information model selection criteria, Bozdogan\u27s ICOMP- type criteria in particular, in two types of modeling problems: determining the number of factors in factor analysis and working as the fitness function for Genetic Algorithm

THE N-P SCATTERING CROSS SECTION FROM 90 KEV TO 1.8 MEV

There have been very few measurements of the total cross section for n-p scattering below 500 keV. In order to differentiate among NN potential models, improved cross section data between 20 and 600 keV are required. We measured the n-p and n-C total cross sections in this energy region by transmission; a collimated neutron beam was passed through CH2 and C samples and transmitted neutrons were detected by a BC-501A deuterated liquid scintillator. Cross sections were obtained by taking the ratios of normalized neutron yields with the samples in the beam and with no sample in the beam. Both better precision and larger range between 90 keV and 1.8 MeV results are presented. The parameters resulting from fitting effective range theory to the data for n-p scattering are in good agreement with parameters determined from previous fits

Design and implementation of an intelligent car obstacle avoidance system based on deep learning

Through the integration of deep learning technology, from the simplest driving method to the realizatio n of the “carnetwork road” interaction, the use of STM32F103 microprocessor control chip, and through the PWM technology to achieve the speed and steering gear regulation, at the same time, the use of deep learning self-cognition technology, so that intelligent vehicles can make selfcognitive decisions like human minds , by looking for the best route to avoid some obstacles on the road surface, and the selection of the optimal forecast route, and through the tracking controller to achieve the black line function, through the anti-collision system to achieve the vehicle detection and obstacle avoidance function

Probability hypothesis density filter with adaptive parameter estimation for tracking multiple maneuvering targets

AbstractThe probability hypothesis density (PHD) filter has been recognized as a promising technique for tracking an unknown number of targets. The performance of the PHD filter, however, is sensitive to the available knowledge on model parameters such as the measurement noise variance and those associated with the changes in the maneuvering target trajectories. If these parameters are unknown in advance, the tracking performance may degrade greatly. To address this aspect, this paper proposes to incorporate the adaptive parameter estimation (APE) method in the PHD filter so that the model parameters, which may be static and/or time-varying, can be estimated jointly with target states. The resulting APE-PHD algorithm is implemented using the particle filter (PF), which leads to the PF-APE-PHD filter. Simulations show that the newly proposed algorithm can correctly identify the unknown measurement noise variances, and it is capable of tracking multiple maneuvering targets with abrupt changing parameters in a more robust manner, compared to the multi-model approaches