A system for learning statistical motion patterns

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction

Neural-Network Vector Controller for Permanent-Magnet Synchronous Motor Drives: Simulated and Hardware-Validated Results

This paper focuses on current control in a permanentmagnet synchronous motor (PMSM). The paper has two main objectives: The first objective is to develop a neural-network (NN) vector controller to overcome the decoupling inaccuracy problem associated with conventional PI-based vector-control methods. The NN is developed using the full dynamic equation of a PMSM, and trained to implement optimal control based on approximate dynamic programming. The second objective is to evaluate the robust and adaptive performance of the NN controller against that of the conventional standard vector controller under motor parameter variation and dynamic control conditions by (a) simulating the behavior of a PMSM typically used in realistic electric vehicle applications and (b) building an experimental system for hardware validation as well as combined hardware and simulation evaluation. The results demonstrate that the NN controller outperforms conventional vector controllers in both simulation and hardware implementation

A system for learning statistical motion patterns

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction

Momentum Distribution of Near-Zero-Energy Photoelectrons in the Strong-Field Tunneling Ionization in the Long Wavelength Limit

We investigate the ionization dynamics of Argon atoms irradiated by an ultrashort intense laser of a wavelength up to 3100 nm, addressing the momentum distribution of the photoelectrons with near-zero-energy. We find a surprising accumulation in the momentum distribution corresponding to meV energy and a \textquotedblleft V"-like structure at the slightly larger transverse momenta. Semiclassical simulations indicate the crucial role of the Coulomb attraction between the escaping electron and the remaining ion at extremely large distance. Tracing back classical trajectories, we find the tunneling electrons born in a certain window of the field phase and transverse velocity are responsible for the striking accumulation. Our theoretical results are consistent with recent meV-resolved high-precision measurements.Comment: 5 pages, 4 figure

Maximal Entanglement of Two-qubit States Constructed by Linearly Independent Coherent States

In this paper, we find the necessary and sufficient condition for the maximal entanglement of the state, $|\psi>=\mu|\alpha>|\beta>+\lambda|\alpha>|\delta>+ \rho|\gamma>|\beta>+\nu|\gamma>|\delta>,$ constructed by linearly independent coherent states with \emph{real parameters} when $=$. This is a further generalization of the classified nonorthogonal states discussed in Ref. Physics Letters A {\bf{291}}, 73-76 (2001).Comment: some examples added; Int J Theor Phys 201

Nondestructive Post-fire Damage Assessment of Structural Steel Members using Leeb Harness method

Assessment of steel damage is one of the key methods in retrofitting and reconstruction of the steel structure after fire. The traditional assessment method is to cut the samples from the steel members and check the levels of damage. This method will damage the structural member and the process is time consuming. In this paper, a quick, simple and efficient nondestructive detection method to measure the strength of steel after fire is developed using so called Leeb hardness method by means of establishment the relationship between the residual strength of steel members after fire and the Leeb hardness, the post-fire steel strength can be fast determined without damage to the structural members. In this paper, in total 120 Chinese H-shaped steel sections were selected for testing the Leeb hardness after fire. The influence of the parameters such as the duration of the fire exposure, cooling mode, steel grade, stress state and location of the Leeb hardness test on the test results was investigated. The relationship between the steel Leeb hardness and the parameters were developed. In addition, regression functions between the residual strength of steel members after fire and the Leeb hardness was established based on these test results which can accurate predict the residual strength of the steel members after fire, providing the engineers a new fast assessment method for the residual strength of the steel after fire