72,385 research outputs found
Full Flow: Optical Flow Estimation By Global Optimization over Regular Grids
We present a global optimization approach to optical flow estimation. The
approach optimizes a classical optical flow objective over the full space of
mappings between discrete grids. No descriptor matching is used. The highly
regular structure of the space of mappings enables optimizations that reduce
the computational complexity of the algorithm's inner loop from quadratic to
linear and support efficient matching of tens of thousands of nodes to tens of
thousands of displacements. We show that one-shot global optimization of a
classical Horn-Schunck-type objective over regular grids at a single resolution
is sufficient to initialize continuous interpolation and achieve
state-of-the-art performance on challenging modern benchmarks.Comment: To be presented at CVPR 201
An efficient multigrid strategy for large-scale molecular mechanics optimization
Static mechanical properties of materials require large-scale nonlinear optimization of the molecular mechanics model under various controls. This paper presents an efficient multigrid strategy to solve such problems. This strategy approximates solutions on grids in a quasi-atomistic and inexact manner, transfers solutions on grids following a coarse-to-fine (oneway) schedule, and finds physically relevant minimizers with linear scaling complexity. Compared to the full multigrid method which has the same complexity, the prefactor of this strategy is orders of magnitude smaller. Consequently, the required CPU time of this strategy is orders of magnitude smaller than that of the full multigrid method, and is smaller than that of the brute-force optimization for systems with more than 200,000 atoms. Considerable savings are found if the number of atoms becomes even larger due to the super-linear scaling complexity of the brute-force optimization. For systems with 1,000,000 atoms (over three million degrees of freedom), on average a more than 70% reduction of CPU time is observed regardless of the type of defects, including vacancies, dislocations, and cracks. In addition, linear scalability of the proposed strategy is tested in the presence of a dislocation pair for systems with more than 100 million atoms (over 400 million degrees of freedom)
Exact algorithms for -TV regularization of real-valued or circle-valued signals
We consider -TV regularization of univariate signals with values on the
real line or on the unit circle. While the real data space leads to a convex
optimization problem, the problem is non-convex for circle-valued data. In this
paper, we derive exact algorithms for both data spaces. A key ingredient is the
reduction of the infinite search spaces to a finite set of configurations,
which can be scanned by the Viterbi algorithm. To reduce the computational
complexity of the involved tabulations, we extend the technique of distance
transforms to non-uniform grids and to the circular data space. In total, the
proposed algorithms have complexity where is the length
of the signal and is the number of different values in the data set. In
particular, the complexity is for quantized data. It is the
first exact algorithm for TV regularization with circle-valued data, and it is
competitive with the state-of-the-art methods for scalar data, assuming that
the latter are quantized
Phase transitions in Pareto optimal complex networks
The organization of interactions in complex systems can be described by
networks connecting different units. These graphs are useful representations of
the local and global complexity of the underlying systems. The origin of their
topological structure can be diverse, resulting from different mechanisms
including multiplicative processes and optimization. In spatial networks or in
graphs where cost constraints are at work, as it occurs in a plethora of
situations from power grids to the wiring of neurons in the brain, optimization
plays an important part in shaping their organization. In this paper we study
network designs resulting from a Pareto optimization process, where different
simultaneous constraints are the targets of selection. We analyze three
variations on a problem finding phase transitions of different kinds. Distinct
phases are associated to different arrangements of the connections; but the
need of drastic topological changes does not determine the presence, nor the
nature of the phase transitions encountered. Instead, the functions under
optimization do play a determinant role. This reinforces the view that phase
transitions do not arise from intrinsic properties of a system alone, but from
the interplay of that system with its external constraints.Comment: 14 pages, 7 figure
DOA Estimation for Local Scattered CDMA Signals by Particle Swarm Optimization
This paper deals with the direction-of-arrival (DOA) estimation of local scattered code-division multiple access (CDMA) signals based on a particle swarm optimization (PSO) search. For conventional spectral searching estimators with local scattering, the searching complexity and estimating accuracy strictly depend on the number of search grids used during the search. In order to obtain high-resolution and accurate DOA estimation, a smaller grid size is needed. This is time consuming and it is unclear how to determine the required number of search grids. In this paper, a modified PSO is presented to reduce the required search grids for the conventional spectral searching estimator with the effects of local scattering. Finally, several computer simulations are provided for illustration and comparison
Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems
Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions
Semantic 3D Occupancy Mapping through Efficient High Order CRFs
Semantic 3D mapping can be used for many applications such as robot
navigation and virtual interaction. In recent years, there has been great
progress in semantic segmentation and geometric 3D mapping. However, it is
still challenging to combine these two tasks for accurate and large-scale
semantic mapping from images. In the paper, we propose an incremental and
(near) real-time semantic mapping system. A 3D scrolling occupancy grid map is
built to represent the world, which is memory and computationally efficient and
bounded for large scale environments. We utilize the CNN segmentation as prior
prediction and further optimize 3D grid labels through a novel CRF model.
Superpixels are utilized to enforce smoothness and form robust P N high order
potential. An efficient mean field inference is developed for the graph
optimization. We evaluate our system on the KITTI dataset and improve the
segmentation accuracy by 10% over existing systems.Comment: IROS 201
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