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Jian Cao

Publications and source records attributed to Jian Cao.

118 records · Page 7Linked to original sources
arXiv2020

Toolpath design for additive manufacturing using deep reinforcement learning

Toolpath optimization of metal-based additive manufacturing processes is currently hampered by the high-dimensionality of its design space. In this work, a reinforcement learning platform is proposed that dynamically learns toolpath strategies to build an arbitrary part. To this end, three prominent model-free reinforcement learning formulations are investigated to design additive manufacturing toolpaths and demonstrated for two cases of dense and sparse reward structures. The results indicate that this learning-based toolpath design approach achieves high scores, especially when a dense reward structure is present.

cs.AI↗
arXiv2020

Transmission electron microscopy of organic-inorganic hybrid perovskites: myths and truths

Organic-inorganic hybrid perovskites (OIHPs) have attracted extensive research interest as a promising candidate for efficient and inexpensive solar cells. Transmission electron microscopy characterizations that can benefit the fundamental understanding and the degradation mechanism are widely used for these materials. However, their sensitivity to the electron beam illumination and hence structural instabilities usually prevent us from obtaining the intrinsic information or even lead to significant artifacts. Here, we systematacially investigate the structural degradation behaviors under different experimental factors to reveal the optimized conditions for TEM characterizations of OIHPs by using low-dose electron diffraction and imaging techniques. We find that a low temperature does not slow down the beam damage but instead induces a rapid amorphization for OIHPs. Moreover, a less severe damage is observed at a higher accelerating voltage. The beam-sensitivity is found to be facet-dependent that a (100) exposed MAPbI3 surface is more stable than (001) surface. With these guidance, we successfully acquire the atomic structure of pristine MAPbI3 and identify the characterization window that is very narrow. These findings are helpful to guide future electron microscopy characterization of these beam-sensitive materials, which are also useful for finding strategies to improve the stability and the performance of the perovskite solar cells.

physics.app-ph↗
arXiv2019

A real-time iterative machine learning approach for temperature profile prediction in additive manufacturing processes

Additive Manufacturing (AM) is a manufacturing paradigm that builds three-dimensional objects from a computer-aided design model by successively adding material layer by layer. AM has become very popular in the past decade due to its utility for fast prototyping such as 3D printing as well as manufacturing functional parts with complex geometries using processes such as laser metal deposition that would be difficult to create using traditional machining. As the process for creating an intricate part for an expensive metal such as Titanium is prohibitive with respect to cost, computational models are used to simulate the behavior of AM processes before the experimental run. However, as the simulations are computationally costly and time-consuming for predicting multiscale multi-physics phenomena in AM, physics-informed data-driven machine-learning systems for predicting the behavior of AM processes are immensely beneficial. Such models accelerate not only multiscale simulation tools but also empower real-time control systems using in-situ data. In this paper, we design and develop essential components of a scientific framework for developing a data-driven model-based real-time control system. Finite element methods are employed for solving time-dependent heat equations and developing the database. The proposed framework uses extremely randomized trees - an ensemble of bagged decision trees as the regression algorithm iteratively using temperatures of prior voxels and laser information as inputs to predict temperatures of subsequent voxels. The models achieve mean absolute percentage errors below 1% for predicting temperature profiles for AM processes. The code is made available for the research community at https://github.com/paularindam/ml-iter-additive.

cs.LG↗
arXiv2019

Sympathetic cooling of $^{40}\textbf{Ca}^+$ - $^{27}\textbf{Al}^+$ ion pair crystal in a linear Paul trap

The $^{27}$Al$^+$ ion optical clock is one of the most attractive optical clocks due to its own advantages, such as low blackbody radiation shift at room temperature and insensitive to the magnetic drift. However, it cannot be laser-cooled directly in the absence of 167 nm laser to date. This problem can be solved by sympathetic cooling. In this work, a linear Paul trap is used to trap both $^{40}$Ca$^{+}$ and $^{27}$Al$^+$ ions simultaneously, and a single Doppler-cooled $^{40}$Ca$^+$ ion is employed to sympathetically cool a single $^{27}$Al$^+$ ion. Thus a "bright-dark" two-ion crystal has been successfully synthesized. The temperature of the crystal has been estimated to be about 7 mK by measuring the ratio of carrier and sideband spectral intensities. Finally, the dark ion is proved to be an $^{27}$Al$^+$ ion by precise measuring of the ion crystal`s secular motion frequency, which means that it is a great step for our $^{27}$Al$^+$ quantum logic clock.

physics.atom-ph↗
arXiv2018

O(N) Hierarchical algorithm for computing the expectations of truncated multi-variate normal distributions in N dimensions

In this paper, we study the $N$-dimensional integral $ϕ(a,b; A) = \int_{a}^{b} H(x) f(x | A) \text{d} x$ representing the expectation of a function $H(X)$ where $f(x | A)$ is the truncated multi-variate normal (TMVN) distribution with zero mean, $x$ is the vector of integration variables for the $N$-dimensional random vector $X$, $A$ is the inverse of the covariance matrix $Σ$, and $a$ and $b$ are constant vectors. We present a new hierarchical algorithm which can evaluate $ϕ(a,b; A)$ using asymptotically optimal $O(N)$ operations when $A$ has "low-rank" blocks with "low-dimensional" features and $H(x)$ is "low-rank". We demonstrate the divide-and-conquer idea when $A$ is a symmetric positive definite tridiagonal matrix, and present the necessary building blocks and rigorous potential theory based algorithm analysis when $A$ is given by the exponential covariance model. Numerical results are presented to demonstrate the algorithm accuracy and efficiency for these two cases. We also briefly discuss how the algorithm can be generalized to a wider class of covariance models and its limitations.

math.NA↗
arXiv2016

Evaluation of blackbody radiation shift with temperature associated fractional uncertainty at 10E-18 level for 40Ca+ ion optical clock

In this paper, blackbody radiation (BBR) temperature rise seen by the $^{40}$Ca$^+$ ion confined in a miniature Paul trap and its uncertainty have been evaluated via finite-element method (FEM) modelling. The FEM model was validated by comparing with thermal camera measurements, which were calibrated by PT1000 resistance thermometer, at several points on a dummy trap. The input modelling parameters were analyzed carefully in detail, and their contributions to the uncertainty of environment temperature were evaluated on the validated FEM model. The result shows that the temperature rise seen by $^{40}$Ca$^+$ ion is 1.72 K with an uncertainty of 0.46 K. It results in a contribution of 2.2 mHz to the systematic uncertainty of $^{40}$Ca$^+$ ion optical clock, corresponding to a fractional uncertainty 5.4$\times$10$^{-18}$. This is much smaller than the uncertainty caused by the BBR shift coefficient, which is evaluated to be 4.8 mHz and at 10$^{-17}$ level in fractional frequency units.

physics.atom-ph↗
arXiv2016

A transportable 40Ca+ single-ion clock with $7.7\times 10^{-17}$ systematic uncertainty

A transportable optical clock refer to the $4s^2S_{1/2}-3d^2D_{5/2}$ electric quadrupole transition at 729 nm of single $^{40}Ca^+$ trapped in mini Paul trap has been developed. The physical system of $^{40}Ca^+$ optical clock is re-engineered from a bulky and complex setup to an integration of two subsystems: a compact single ion unit including ion trapping and detection modules, and a compact laser unit including laser sources, beam distributor and frequency reference modules. Apart from the electronics, the whole equipment has been constructed within a volume of 0.54 $m^3$. The systematic fractional uncertainty has been evaluated to be $7.7\times 10^{-17}$, and the Allan deviation fits to be $2.3\times {10}^{-14}/\sqrtτ$ by clock self-comparison with a probe pulse time 20 ms.

physics.atom-ph↗
arXiv2015

Towards a Decoupled Context-Oriented Programming Language for the Internet of Things

Easily programming behaviors is one major issue of a large and reconfigurable deployment in the Internet of Things. Such kind of devices often requires to externalize part of their behavior such as the sensing, the data aggregation or the code offloading. Most existing context-oriented programming languages integrate in the same class or close layers the whole behavior. We propose to abstract and separate the context tracking from the decision process, and to use event-based handlers to interconnect them. We keep a very easy declarative and non-layered programming model. We illustrate by defining an extension to Golo-a JVM-based dynamic language.

cs.PL↗
arXiv2006

Asymptotically Optimal Multiple-access Communication via Distributed Rate Splitting

We consider the multiple-access communication problem in a distributed setting for both the additive white Gaussian noise channel and the discrete memoryless channel. We propose a scheme called Distributed Rate Splitting to achieve the optimal rates allowed by information theory in a distributed manner. In this scheme, each real user creates a number of virtual users via a power/rate splitting mechanism in the M-user Gaussian channel or via a random switching mechanism in the M-user discrete memoryless channel. At the receiver, all virtual users are successively decoded. Compared with other multiple-access techniques, Distributed Rate Splitting can be implemented with lower complexity and less coordination. Furthermore, in a symmetric setting, we show that the rate tuple achieved by this scheme converges to the maximum equal rate point allowed by the information-theoretic bound as the number of virtual users per real user tends to infinity. When the capacity regions are asymmetric, we show that a point on the dominant face can be achieved asymptotically. Finally, when there is an unequal number of virtual users per real user, we show that differential user rate requirements can be accommodated in a distributed fashion.

cs.IT↗