8,822 research outputs found
Adjoint-based predictor-corrector sequential convex programming for parametric nonlinear optimization
This paper proposes an algorithmic framework for solving parametric
optimization problems which we call adjoint-based predictor-corrector
sequential convex programming. After presenting the algorithm, we prove a
contraction estimate that guarantees the tracking performance of the algorithm.
Two variants of this algorithm are investigated. The first one can be used to
solve nonlinear programming problems while the second variant is aimed to treat
online parametric nonlinear programming problems. The local convergence of
these variants is proved. An application to a large-scale benchmark problem
that originates from nonlinear model predictive control of a hydro power plant
is implemented to examine the performance of the algorithms.Comment: This manuscript consists of 25 pages and 7 figure
Hidden Broad Line Seyfert 2 Galaxies in the CfA and 12micron Samples
We report the results of a spectropolarimetric survey of the CfA and 12micron
samples of Seyfert 2 galaxies (S2s). Polarized (hidden) broad line regions
(HBLRs) are confirmed in a number of galaxies, and several new cases
(F02581-1136, MCG -3-58-7, NGC 5995, NGC 6552, NGC 7682) are reported. The
12micron S2 sample shows a significantly higher incidence of HBLR (50%) than
its CfA counterpart (30%), suggesting that the latter may be incomplete in
hidden AGNs. Compared to the non-HBLR S2s, the HBLR S2s display distinctly
higher radio power relative to their far-infrared output and hotter dust
temperature as indicated by the f25/f60 color. However, the level of
obscuration is indistinguishable between the two types of S2. These results
strongly support the existence of two intrinsically different populations of
S2: one harboring an energetic, hidden S1 nucleus with BLR, and the other, a
``pure S2'', with weak or absent S1 nucleus and a strong, perhaps dominating
starburst component. Thus, the simple purely orientation-based unification
model is not applicable to all Seyfert galaxies.Comment: 5 pages with embedded figs, ApJ Letters, in pres
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Sequence Classification Restricted Boltzmann Machines With Gated Units
For the classification of sequential data, dynamic Bayesian networks and recurrent neural networks (RNNs) are the preferred models. While the former can explicitly model the temporal dependences between the variables, and the latter have the capability of learning representations. The recurrent temporal restricted Boltzmann machine (RTRBM) is a model that combines these two features. However, learning and inference in RTRBMs can be difficult because of the exponential nature of its gradient computations when maximizing log likelihoods. In this article, first, we address this intractability by optimizing a conditional rather than a joint probability distribution when performing sequence classification. This results in the ``sequence classification restricted Boltzmann machine'' (SCRBM). Second, we introduce gated SCRBMs (gSCRBMs), which use an information processing gate, as an integration of SCRBMs with long short-term memory (LSTM) models. In the experiments reported in this article, we evaluate the proposed models on optical character recognition, chunking, and multiresident activity recognition in smart homes. The experimental results show that gSCRBMs achieve the performance comparable to that of the state of the art in all three tasks. gSCRBMs require far fewer parameters in comparison with other recurrent networks with memory gates, in particular, LSTMs and gated recurrent units (GRUs)
A molecular perspective on the limits of life: Enzymes under pressure
From a purely operational standpoint, the existence of microbes that can grow
under extreme conditions, or "extremophiles", leads to the question of how the
molecules making up these microbes can maintain both their structure and
function. While microbes that live under extremes of temperature have been
heavily studied, those that live under extremes of pressure have been
neglected, in part due to the difficulty of collecting samples and performing
experiments under the ambient conditions of the microbe. However, thermodynamic
arguments imply that the effects of pressure might lead to different organismal
solutions than from the effects of temperature. Observationally, some of these
solutions might be in the condensed matter properties of the intracellular
milieu in addition to genetic modifications of the macromolecules or repair
mechanisms for the macromolecules. Here, the effects of pressure on enzymes,
which are proteins essential for the growth and reproduction of an organism,
and some adaptations against these effects are reviewed and amplified by the
results from molecular dynamics simulations. The aim is to provide biological
background for soft matter studies of these systems under pressure.Comment: 16 pages, 8 figure
Advanced Fabrication and Properties of Aligned Carbon Nanotube Composites: Experiments and Modeling
Aligned carbon nanotube (CNT) composites have attracted a lot of interest due to their superb mechanical and physical properties. This article presents a brief overview of the synthesis approaches of aligned CNT composites. The three major methods for fabricating aligned CNT fibers are first reviewed, including wet-spinning, dry-spinning and floating catalysts. The obtained CNT fibers, however, have limited mechanical and physical properties due to their porous structure and poor CNT alignment within the fibers. Appropriate treatments are required to densify the fibers to enhance their properties. The main approaches for the densification of CNT fibers are then discussed. To further enhance load transfer within CNT fibers, polymer infiltration is always used. Typical studies on polymer infiltration of CNT fibers are reviewed, and the properties of the obtained composites indicate the superiority of this composite fabrication method over the conventional dispersion method. Since aligned CNT composites are usually obtained in structures of long fiber or thin film, it is difficult to measure the thermal conductivity of these composites. An off-lattice Monte Carlo model is developed to accurately predict the thermal conductivity of aligned CNT composites
Fuzzy controller for better tennis ball robot
This paper aims at designing a tennis ball robot as a training facility for tennis players. The robot is built with fuzzy controller which provides proper techniques for the players to gain practical experience as well as technical skills; thus, it can effectively serve the community and train athletes in the high-performance sport. It is found that it is more economically efficient by using the sensorless fuzzy control algorithm to replace the high-resolution optical encoders traditionally used in two main servo motors. From our simulation and practical experiment, the tennis ball robot can provide accurate speed and various directions as expected
Asymptotic normality of the Parzen-Rosenblatt density estimator for strongly mixing random fields
We prove the asymptotic normality of the kernel density estimator (introduced
by Rosenblatt (1956) and Parzen (1962)) in the context of stationary strongly
mixing random fields. Our approach is based on the Lindeberg's method rather
than on Bernstein's small-block-large-block technique and coupling arguments
widely used in previous works on nonparametric estimation for spatial
processes. Our method allows us to consider only minimal conditions on the
bandwidth parameter and provides a simple criterion on the (non-uniform) strong
mixing coefficients which do not depend on the bandwith.Comment: 16 page
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