The motion of a neutrally buoyant particle of an elliptic shape in two dimensional shear flow: a numerical study

In this paper, we investigate the motion of a neutrally buoyant cylinder of an elliptic shape freely moving in two dimensional shear flow by direct numerical simulation. An elliptic shape cylinder in shear flow, when initially being placed at the middle between two walls, either keeps rotating or has a stationary inclination angle depending on the particle Reynolds number $Re=G_r r_a^2/\nu$, where $G_r$ is the shear rate, $r_a$ is the semi-long axis of the elliptic cylinder and $\nu$ is the kinetic viscosity of the fluid. The critical particle Reynolds number $Re_{cr}$ for the transition from a rotating motion to a stationary orientation depends on the aspect ratio $AR=r_b/r_a$ and the confined ratio $K=2r_a/H$ where $r_b$ is the semi-short axis of the elliptic cylinder and $H$ is the distance between two walls. Although the increasing of either parameters makes an increase in $Re_{cr}$, the dynamic mechanism is distinct. The $AR$ variation causes the change of geometry shape; however, the $K$ variation influences the wall effect. The stationary inclination angle of non-rotating slender elliptic cylinder with smaller confined ratio seems to depend only on the value of $Re-Re_{cr}$. An expected equilibrium position of the cylinder mass center in shear flow is the centerline between two walls, but when placing the particle away from the centerline initially, it migrates either toward an equilibrium height away from the middle between two walls or back to the middle depending on the confined ratio and particle Reynolds number.Comment: arXiv admin note: substantial text overlap with arXiv:1209.080

H-Si bonding-induced unusual electronic properties of silicene: a method to identify hydrogen concentration

Hydrogenated silicenes possess peculiar properties owing to the strong H-Si bonds, as revealed by an investigation using first principles calculations. The various charge distributions, bond lengths, energy bands, and densities of states strongly depend on different hydrogen configurations and concentrations. The competition of strong H-Si bondings and weak sp3 hybridization dominate the electronic properties. Chair configurations belong to semiconductors, while the top configurations show a nearly dispersionless energy band at the Fermi level. Both two systems display H-related partially flat bands at middle energy, and recovery of low-lying \pi bands during the reduction of concentration. Their densities of states exhibit prominent peaks at middle energy, and the top systems have a delta-funtion-like peak at E=0. The intensity of these peaks are gradually weakened as the concentration decreases, providing an effective method to identify the H-concentration in scanning tunneling spectroscopy experiments

An Intelligent Auxiliary Vacuum Brake System

The purpose of this paper focuses on designing an intelligent, compact, reliable, and robust auxiliary vacuum brake system (VBS) with Kalman filter and self-diagnosis scheme. All of the circuit elements in the designed system are integrated into one programmable system-on-chip (PSoC) with entire computational algorithms implemented by software. In this system, three main goals are achieved: (a) Kalman filter and hysteresis controller algorithms are employed within PSoC chip by software to surpass the noises and disturbances from hostile surrounding in a vehicle. (b) Self-diagnosis scheme is employed to identify any breakdown element of the auxiliary vacuum brake system. (c) Power MOSFET is utilized to implement PWM pump control and compared with relay control. More accurate vacuum pressure control has been accomplished as well as power energy saving. In the end, a prototype has been built and tested to confirm all of the performances claimed above

Energy-Efficient Non-Orthogonal Transmission under Reliability and Finite Blocklength Constraints

This paper investigates an energy-efficient non-orthogonal transmission design problem for two downlink receivers that have strict reliability and finite blocklength (latency) constraints. The Shannon capacity formula widely used in traditional designs needs the assumption of infinite blocklength and thus is no longer appropriate. We adopt the newly finite blocklength coding capacity formula for explicitly specifying the trade-off between reliability and code blocklength. However, conventional successive interference cancellation (SIC) may become infeasible due to heterogeneous blocklengths. We thus consider several scenarios with different channel conditions and with/without SIC. By carefully examining the problem structure, we present in closed-form the optimal power and code blocklength for energy-efficient transmissions. Simulation results provide interesting insights into conditions for which non-orthogonal transmission is more energy efficient than the orthogonal transmission such as TDMA.Comment: accepted by IEEE GlobeCom workshop on URLLC, 201

Compatibility Family Learning for Item Recommendation and Generation

Compatibility between items, such as clothes and shoes, is a major factor among customer's purchasing decisions. However, learning "compatibility" is challenging due to (1) broader notions of compatibility than those of similarity, (2) the asymmetric nature of compatibility, and (3) only a small set of compatible and incompatible items are observed. We propose an end-to-end trainable system to embed each item into a latent vector and project a query item into K compatible prototypes in the same space. These prototypes reflect the broad notions of compatibility. We refer to both the embedding and prototypes as "Compatibility Family". In our learned space, we introduce a novel Projected Compatibility Distance (PCD) function which is differentiable and ensures diversity by aiming for at least one prototype to be close to a compatible item, whereas none of the prototypes are close to an incompatible item. We evaluate our system on a toy dataset, two Amazon product datasets, and Polyvore outfit dataset. Our method consistently achieves state-of-the-art performance. Finally, we show that we can visualize the candidate compatible prototypes using a Metric-regularized Conditional Generative Adversarial Network (MrCGAN), where the input is a projected prototype and the output is a generated image of a compatible item. We ask human evaluators to judge the relative compatibility between our generated images and images generated by CGANs conditioned directly on query items. Our generated images are significantly preferred, with roughly twice the number of votes as others.Comment: 9 pages, accepted to AAAI 201