Smoothed Boundary Method for Solving Partial Differential Equations with General Boundary Conditions on Complex Boundaries

In this article, we describe an approach for solving partial differential equations with general boundary conditions imposed on arbitrarily shaped boundaries. A function that has a prescribed value on the domain in which a differential equation is valid and smoothly but rapidly varying values on the boundary where boundary conditions are imposed is used to modify the original differential equations. The mathematical derivations are straight forward, and generically applicable to a wide variety of partial differential equations. To demonstrate the general applicability of the approach, we provide four examples: (1) the diffusion equation with both Neumann and Dirichlet boundary conditions, (2) the diffusion equation with surface diffusion, (3) the mechanical equilibrium equation, and (4) the equation for phase transformation with additional boundaries. The solutions for a few of these cases are validated against corresponding analytical and semi-analytical solutions. The potential of the approach is demonstrated with five applications: surface-reaction diffusion kinetics with a complex geometry, Kirkendall-effect-induced deformation, thermal stress in a complex geometry, phase transformations affected by substrate surfaces, and a self-propelling droplet.Comment: A better smooth algorithm has been developed and tested, will soon replace Eq. 58 in page 16. We have also developed a level-set moving boundary SBM method, and it will replace the Navier-Stokes-Cahn-Hilliard type domain parameter tracking method in Section 5.

Revisiting the problem of audio-based hit song prediction using convolutional neural networks

Being able to predict whether a song can be a hit has impor- tant applications in the music industry. Although it is true that the popularity of a song can be greatly affected by exter- nal factors such as social and commercial influences, to which degree audio features computed from musical signals (whom we regard as internal factors) can predict song popularity is an interesting research question on its own. Motivated by the recent success of deep learning techniques, we attempt to ex- tend previous work on hit song prediction by jointly learning the audio features and prediction models using deep learning. Specifically, we experiment with a convolutional neural net- work model that takes the primitive mel-spectrogram as the input for feature learning, a more advanced JYnet model that uses an external song dataset for supervised pre-training and auto-tagging, and the combination of these two models. We also consider the inception model to characterize audio infor- mation in different scales. Our experiments suggest that deep structures are indeed more accurate than shallow structures in predicting the popularity of either Chinese or Western Pop songs in Taiwan. We also use the tags predicted by JYnet to gain insights into the result of different models.Comment: To appear in the proceedings of 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP

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

EFFECTS OF BACKPACK LOADS ON NECK-TRUNK MUSCLE ACTIVATION AMONG OFFICE WORKERS

The main purposes of this study were to investigate the effect of weight carriage on necktrunk muscle activation during standing and walking among office workers and to compare electromyography activation between healthy and symptomatic office workers. Twenty-one participants were recruited. Three load trials (0%, 10%, and 15% BW) and two conditions (standing and walking) were encountered. Repeated measure ANOVA was used to test main effect of load and condition on kinetic data. There was a significant condition*load interaction on right trapezius. Significantly increasing activation of right abdominis was found as carrying 15% BW. There was a significant decrease on activation of left erector spinae while carrying 10% BW. Considering to electromyography data, we suggest the backpack load under 10% BW was suitable for office workers