5,528 research outputs found
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
How does Organic Agriculture Contribute to Sustainable Development? Organic Agriculture in Taiwan
Sustainability issues in agrifood chains are receiving increasing attention. However, few studies have demonstrated the dynamic interrelationships between economic, environmental, and social indicators. Regarding these indicators as components of sustainable development, through sensitivity simulations, we found that (1) organic farming techniques as key to environmental and economic improvement by indirect sales and (2) direct sales channels can strengthen environmental and social benefits. The findings suggest that developing diversified production and sales channels is essential for the sustainable development of organic agriculture to maintain economic, social, and environmental sustainability
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
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