Investigation of empennage buffeting

Theoretical methods of predicting aircraft buffeting are reviewed. For the buffeting due to leading-edge vortex breakdown, a method is developed to convert test data of mean square values of fluctuating normal force to buffeting vortex strength through an unsteady lifting-surface theory and unsteady suction analogy. The resulting buffeting vortex from the leading-edge extension of an F-18 configuration is used to generate a fluctuating flow field which produces unsteady pressure distribution on the vertical tails. The root mean square values of root bending moment on the vertical tails are calculated for a rigid configuration. Results from a flow visualization and hot films study in a water tunnel facility using a 1/48 scale model of an F-18 are included in an appendix. The results confirm that the LEX vortex is the dominant forcing function of fin buffet at high angles of attack

Realistic interpretation of a superposition state does not imply a mixture

Contrary to previous claims, it is shown that, for an ensemble of either single-particle systems or multi-particle systems, the realistic interpretation of a superposition state that mathematically describes the ensemble does not imply that the ensemble is a mixture. Therefore it cannot be argued that the realistic interpretation is wrong on the basis that some predictions derived from the mixture are different from the corresponding predictions derived from the superposition state

Ground states and excited states of hypernuclei in Relativistic Mean Field approach

Hypernuclei have been studied within the framework of Relativistic Mean Field theory. The force FSU Gold has been extended to include hyperons. The effective hyperon-nucleon and nucleon-nucleon interactions have been obtained by fitting experimental energies in a number of hypernuclei over a wide range of mass. Calculations successfully describe various features including hyperon separation energy and single particle spectra of single-\Lambda hypernuclei throughout the periodic table. We also extend this formalism to double-\Lambda hypernuclei.Comment: 16 pages,3 figure

Beyond Gaussian Pyramid: Multi-skip Feature Stacking for Action Recognition

Most state-of-the-art action feature extractors involve differential operators, which act as highpass filters and tend to attenuate low frequency action information. This attenuation introduces bias to the resulting features and generates ill-conditioned feature matrices. The Gaussian Pyramid has been used as a feature enhancing technique that encodes scale-invariant characteristics into the feature space in an attempt to deal with this attenuation. However, at the core of the Gaussian Pyramid is a convolutional smoothing operation, which makes it incapable of generating new features at coarse scales. In order to address this problem, we propose a novel feature enhancing technique called Multi-skIp Feature Stacking (MIFS), which stacks features extracted using a family of differential filters parameterized with multiple time skips and encodes shift-invariance into the frequency space. MIFS compensates for information lost from using differential operators by recapturing information at coarse scales. This recaptured information allows us to match actions at different speeds and ranges of motion. We prove that MIFS enhances the learnability of differential-based features exponentially. The resulting feature matrices from MIFS have much smaller conditional numbers and variances than those from conventional methods. Experimental results show significantly improved performance on challenging action recognition and event detection tasks. Specifically, our method exceeds the state-of-the-arts on Hollywood2, UCF101 and UCF50 datasets and is comparable to state-of-the-arts on HMDB51 and Olympics Sports datasets. MIFS can also be used as a speedup strategy for feature extraction with minimal or no accuracy cost

Temporal Extension of Scale Pyramid and Spatial Pyramid Matching for Action Recognition

Historically, researchers in the field have spent a great deal of effort to create image representations that have scale invariance and retain spatial location information. This paper proposes to encode equivalent temporal characteristics in video representations for action recognition. To achieve temporal scale invariance, we develop a method called temporal scale pyramid (TSP). To encode temporal information, we present and compare two methods called temporal extension descriptor (TED) and temporal division pyramid (TDP) . Our purpose is to suggest solutions for matching complex actions that have large variation in velocity and appearance, which is missing from most current action representations. The experimental results on four benchmark datasets, UCF50, HMDB51, Hollywood2 and Olympic Sports, support our approach and significantly outperform state-of-the-art methods. Most noticeably, we achieve 65.0% mean accuracy and 68.2% mean average precision on the challenging HMDB51 and Hollywood2 datasets which constitutes an absolute improvement over the state-of-the-art by 7.8% and 3.9%, respectively