3,754 research outputs found
Laws of 4D printing
The main difference between 3D and 4D printed structures is one extra
dimension that is smart evolution over time. However, currently, there is no
general formula to model and predict this extra dimension. Here, by starting
from fundamental concepts, we derive and validate a universal bi-exponential
formula that is required to model and predict the fourth D of 4D printed
multi-material structures. 4D printing is a new manufacturing paradigm to
elaborate stimuli-responsive materials in multi-material structures for
advanced manufacturing (and construction) of advanced products (and
structures). It conserves the general attributes of 3D printing (such as the
elimination of molds, dies, and machining) and further enables the fourth
dimension of products and structures to provide intelligent behavior over time.
This intelligent behavior is encoded (usually by an inverse mathematical
problem) into stimuli-responsive multi-materials during printing and is enabled
by stimuli after printing. Here, we delve into the fourth dimension and reveal
three general laws that govern the time-dependent shape-shifting behaviors of
almost all (photochemical-, photothermal-, solvent-, pH-, moisture-,
electrochemical-, electrothermal-, ultrasound-, enzyme-, etc.-responsive)
multi-material 4D structures. We demonstrate that two different types of
time-constants govern the shape-shifting behavior of almost all the
multi-material 4D printed structures over time. Our results starting from the
most fundamental concepts and ending with governing equations can serve as
general design principles for future research in the 4D printing field, where
the time-dependent behaviors should be understood, modeled, and predicted,
correctly. Future software and hardware developments in 4D printing can also
benefit from these results.Comment: This manuscript is currently under review in a journa
Invited Article: 4D Printing as a New Paradigm for Manufacturing with Minimum Energy Consumption
4D printing is a new manufacturing paradigm that combines stimuli-responsive
materials, mathematics, and multi-material additive manufacturing to yield
encoded multi-material structures with intelligent behavior over time. This
emerging field has received growing interests from various disciplines such as
space exploration, renewable energy, bioengineering, textile industry,
infrastructures, soft robotics, and so on. Here, as a first attempt, we
consider the energy aspect of 4D printing. By a thermodynamic analysis, we
obtain the theoretical limit of energy consumption in 4D printing and prove
that 4D printing can be the most energy-efficient manufacturing process. Before
that, we clearly underpin 4D printing as a new manufacturing process and
identify its unique attributes.Comment: Under Revisio
Lamb shift calculated by simple noncovariant method
The Lamb Shift (LS) of Hydrogenlike atom is evaluated by a simple method of
quantum electrodynamics in noncovariant form, based on the relativistic
stationary Schr\"odinger equation. An induced term proportional to
in the effective Hamiltonian is emphasized. Perturbative
calculation of second order leads to the LS of state and that of
states in H atom with the high accuracy within 0.1%Comment: 9 pages, Revtex, 1 Postscript figur
Autonomous Driving System Design for Formula Student Driverless Racecar
This paper summarizes the work of building the autonomous system including
detection system and path tracking controller for a formula student autonomous
racecar. A LIDAR-vision cooperating method of detecting traffic cone which is
used as track mark is proposed. Detection algorithm of the racecar also
implements a precise and high rate localization method which combines the
GPS-INS data and LIDAR odometry. Besides, a track map including the location
and color information of the cones is built simultaneously. Finally, the system
and vehicle performance on a closed loop track is tested. This paper also
briefly introduces the Formula Student Autonomous Competition (FSAC) in 2017.Comment: The 2018 IEEE Intelligent Vehicles Symposiu
Half-CNN: A General Framework for Whole-Image Regression
The Convolutional Neural Network (CNN) has achieved great success in image
classification. The classification model can also be utilized at image or patch
level for many other applications, such as object detection and segmentation.
In this paper, we propose a whole-image CNN regression model, by removing the
full connection layer and training the network with continuous feature maps.
This is a generic regression framework that fits many applications. We
demonstrate this method through two tasks: simultaneous face detection &
segmentation, and scene saliency prediction. The result is comparable with
other models in the respective fields, using only a small scale network. Since
the regression model is trained on corresponding image / feature map pairs,
there are no requirements on uniform input size as opposed to the
classification model. Our framework avoids classifier design, a process that
may introduce too much manual intervention in model development. Yet, it is
highly correlated to the classification network and offers some in-deep review
of CNN structures
Comparison among Klein-Gordon equation, Dirac equation and Relativistic Stationary Schrodinger equation
A particle is always not pure. It always contains hiding antiparticle
ingredient which is the essence of special relativity. Accordingly, the
Klein-Gordon (KG) equation and Dirac equation are restudied and compared with
the Relativistic Stationary Schr\"odinger Equation (RSSE). When an electron is
bound in a Hydrogenlike atom with pointlike nucleus having charge number ,
the critical value of , equals to 137 in Dirac equation whereas
in RSSE with and being the total mass of
atom and the reduced mass of the electron.Comment: 10 pages, Revtex, 10 Postscript figure
Why can an electron mass vary from zero to infinity?
When a particle is in high speed or bound in the Coulomb potential of point
nucleus, the variation of its mass can be ascribed to the variation of relative
ratio of hiding antimatter to matter in the particle. At two limiting cases,
the ratio approaches to 1.Comment: 8 pages, Latex, No Figur
Effects of the non-uniform initial environment and the guide field on the plasmoid instability
Effects of non-uniform initial mass density and temperature on the plasmoid
instability are studied via 2.5-dimensional resistive magnetohydrodynamic(MHD)
simulations. Our results indicate that the development of the plasmoid
instability is apparently prevented when the initial plasma density at the
center of the current sheet is higher than that in the upstream region. As a
result, the higher the plasma density at the center and the lower the plasma
in the upstream region, the higher the critical Lundquist number needed
for triggering secondary instabilities. When , the critical
Lundquist number is higher than . For the same Lundquist number, the
magnetic reconnection rate is lower for the lower plasma case.
Oppositely, when the initial mass density is uniform and the Lundquist number
is low, the magnetic reconnection rate turns out to be higher for the lower
plasma case. For the high Lundquist number case () with uniform
initial mass density, the magnetic reconnection is not affected by the initial
plasma and the temperature distribution. Our results indicate that the
guide field has a limited impact on the plasmoid instability in resistive MHD
Klein paradox and antiparticle
The Klein paradox of Klein-Gordon (KG) equation is discussed to show that KG
equation is self-consistent even at one-particle level and the wave function
for antiparticle is uniquely determined by the reasonable explanation of Klein
paradox. No concept of ``hole'' is needed.Comment: 4 pages, no figures, revte
Numerical experiments on the detailed energy conversion and spectrum studies in a corona current sheet
In this paper, we study the energy conversion and spectra in a corona current
sheet by 2.5-dimensional MHD numerical simulations. Numerical results show that
many Petschek-like fine structures with slow-mode shocks mediated by plasmoid
instabilities develop during the magnetic reconnection process. The termination
shocks can also be formed above the primary magnetic island and at the head of
secondary islands. These shocks play important roles in generating thermal
energy in a corona current sheet. For a numerical simulation with initial
conditions close to the solar corona environment, the ratio of the generated
thermal energy to the total dissipated magnetic energy is around before
secondary islands appear. After secondary islands appear, the generated thermal
energy starts to increase sharply and this ratio can reach a value about .
In an environment with a relatively lower plasma density and plasma ,
the plasma can be heated to a much higher temperature. After secondary islands
appear, the one dimensional energy spectra along the current sheet do not
behave as a simple power law and the spectrum index increases with the wave
number. The average spectrum index for the magnetic energy spectrum along the
current sheet is about . The two dimensional spectra intuitively show that
part of the high energy is cascaded to large and space after
secondary islands appear. The plasmoid distribution function calculated from
numerical simulations behaves as a power law closer to
in the intermediate regime. By using ,
the effective magnetic diffusivity is estimated about
~m\,s.Comment: arXiv admin note: text overlap with arXiv:1011.4035 by other author
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