Diffusive versus displacive contact plasticity of nanoscale asperities: Temperature- and velocity-dependent strongest size

We predict a strongest size for the contact strength when asperity radii of curvature decrease below ten nanometers. The reason for such strongest size is found to be correlated with the competition between the dislocation plasticity and surface diffusional plasticity. The essential role of temperature is calculated and illustrated in a comprehensive asperity size-strengthtemperature map taking into account the effect of contact velocity. Such a map should be essential for various phenomena related to nanoscale contacts such as nanowire cold welding, self-assembly of nanoparticles and adhesive nano-pillar arrays, as well as the electrical, thermal and mechanical properties of macroscopic interfaces

Lattice dynamical wavelet neural networks implemented using particle swarm optimization for spatio-temporal system identification

In this brief, by combining an efficient wavelet representation with a coupled map lattice model, a new family of adaptive wavelet neural networks, called lattice dynamical wavelet neural networks (LDWNNs), is introduced for spatio-temporal system identification. A new orthogonal projection pursuit (OPP) method, coupled with a particle swarm optimization (PSO) algorithm, is proposed for augmenting the proposed network. A novel two-stage hybrid training scheme is developed for constructing a parsimonious network model. In the first stage, by applying the OPP algorithm, significant wavelet neurons are adaptively and successively recruited into the network, where adjustable parameters of the associated wavelet neurons are optimized using a particle swarm optimizer. The resultant network model, obtained in the first stage, however, may be redundant. In the second stage, an orthogonal least squares algorithm is then applied to refine and improve the initially trained network by removing redundant wavelet neurons from the network. An example for a real spatio-temporal system identification problem is presented to demonstrate the performance of the proposed new modeling framework

Lattice dynamical wavelet neural networks implemented using particle swarm optimisation for spatio-temporal system identification

Starting from the basic concept of coupled map lattices, a new family of adaptive wavelet neural networks, called lattice dynamical wavelet neural networks (LDWNN), is introduced for spatiotemporal system identification, by combining an efficient wavelet representation with a coupled map lattice model. A new orthogonal projection pursuit (OPP) method, coupled with a particle swarm optimisation (PSO) algorithm, is proposed for augmenting the proposed network. A novel two-stage hybrid training scheme is developed for constructing a parsimonious network model. In the first stage, by applying the orthogonal projection pursuit algorithm, significant wavelet-neurons are adaptively and successively recruited into the network, where adjustable parameters of the associated waveletneurons are optimised using a particle swarm optimiser. The resultant network model, obtained in the first stage, may however be redundant. In the second stage, an orthogonal least squares (OLS) algorithm is then applied to refine and improve the initially trained network by removing redundant wavelet-neurons from the network. The proposed two-stage hybrid training procedure can generally produce a parsimonious network model, where a ranked list of wavelet-neurons, according to the capability of each neuron to represent the total variance in the system output signal is produced. Two spatio-temporal system identification examples are presented to demonstrate the performance of the proposed new modelling framework

The Implementation of Driver Model Based on the Attention Transfer Process

To describe the characteristics of driver’s attention changing with driving environment, establish the relation between driver model parameter and driver’s attention, seek for mapping relation between driver’s behavior and vehicle’s running status data, and provide individualized driver simulation model for unmanned car controller or for driver’s mental state inversion based on vehicle’s running status data, the paper established a driver model based on driver’s attention and deduced the relation between attention intensity and continuous driving time according to the process of driver’s attention change from concentration to distraction and the distribution characteristics of their durations. The relationship between driver’s mental state and manual closed-loop driving model parameters is established according to the transfer rule of attention in the driving course, and it is applied to driver model based on dynamical regulation neural network. Finally the paper researched dynamics evolution characteristics of vehicle running caused by fatigue driving in the environment of double lane change and large curvature, with test result verifying the effectiveness and accuracy of the driver model based on the attention transfer process