Dynamic neighborhood genetic learning particle swarm optimization for high-power-density electric propulsion motor

To maximize the power density of the electric propulsion motor in aerospace application,this paper proposes a novel Dynamic Neighborhood Genetic Learning Particle Swarm Optimization(DNGL-PSO)for the motor design,which can deal with the insufficient population diversity and non-global optimal solution issues.The DNGL-PSO framework is composed of the dynamic neighborhood module and the particle update module.To improve the population diversity,the dynamic neighborhood strategy is first proposed,which combines the local neighborhood exemplar generation mechanism and the shuffling mechanism.The local neighborhood exemplar generation mechanism enlarges the search range of the algorithm in the solution space,thus obtaining highquality exemplars.Meanwhile,when the global optimal solution cannot update its fitness value,the shuffling mechanism module is triggered to dynamically change the local neighborhood members.The roulette wheel selection operator is introduced into the shuffling mechanism to ensure that particles with larger fitness value are selected with a higher probability and remain in the local neighborhood.Then,the global learning based particle update approach is proposed,which can achieve a good balance between the expansion of the search range in the early stage and the acceleration of local convergence in the later stage.Finally,the optimization design of the electric propulsion motor is conducted to verify the effectiveness of the proposed DNGL-PSO.The simulation results show that the proposed DNGL-PSO has excellent adaptability,optimization efficiency and global optimization capability,while the optimized electric propulsion motor has a high power density of 5.207 kW/kg with the efficiency of 96.12%.

Kinematics of solar neighborhood stars and its dependence on age and metallicity

We have constructed a catalog containing the best available astrometric, photometric, radial velocity and astrophysical data for mainly F-type and G-type stars （called the Astrometric Catalog associated with Astrophysical Data, ACAD）. This contains 27 553 records and is used for the purpose of analyzing stellar kinematics in the solar neighborhood. Using the Lindblad-Oort model and compiled ACAD, we calculated the solar motion and Oort constants in different age-metallicity bins. The evolution of kinematical parameters with stellar age and metallicity was investigated directly. The results show that the component of the solar motion in the direction of Galactic rotation （denoted S_2） linearly increases with age, which may be a conse- quence of the scattering processes, and its value for a dynamical cold disk was found to be 8.0 ± 1.2 km s^-1. S_2 also linearly increases with metallicity, which indicates that radial migration is correlated to the metallicity gradient. On the other hand, the rotational velocity of the Sun around the Galactic center has no clear correlation with ages or metallicities of stars used in the estimation.

Velocity Space of Galactic O-B Stars

Based on the Hipparcos proper motions and available radial velocity data of O-B stars, we have re-examined the local kinematical structure of the young disk population of 1500 O-B stars not including the Gould-belt stars. A systematic warping motion of the stars about the direction to the Galactic center has been reconfirmed. A negative K-term implying a systematic contraction of stars in the solar vicinity has been detected. Two different distance scales are used in order to find out their impact on the kinematical parameters, and we conclude that the adopted distance scale plays an important role in characterizing the kinematical parameters at the present level of the measurement uncertainty.

A temporal-spatial background modeling of dynamic scenes

Moving object detection in dynamic scenes is a basic task in a surveillance system for sensor data collection. In this paper, we present a powerful back- ground subtraction algorithm called Gaussian-kernel density estimator （G-KDE） that improves the accuracy and reduces the computational load. The main innovation is that we divide the changes of background into continuous and stable changes to deal with dynamic scenes and moving objects that first merge into the background, and separately model background using both KDE model and Gaussian models. To get a temporal- spatial background model, the sample selection is based on the concept of region average at the update stage. In the detection stage, neighborhood information content （NIC） is implemented which suppresses the false detection due to small and un-modeled movements in the scene. The experimental results which are generated on three separate sequences indicate that this method is well suited for precise detection of moving objects in complex scenes and it can be efficiently used in various detection systems.