135,546 research outputs found
Parametric characteristic analysis for the output frequency response function of nonlinear volterra systems
The output frequency response function (OFRF) of nonlinear systems is a new concept, which defines an analytical relationship between the output spectrum and the parameters of nonlinear systems. In the present study, the parametric characteristics of the OFRF for nonlinear systems described by a polynomial form differential equation model are investigated based on the introduction of a novel coefficient extraction operator. Important theoretical results are established, which allow the explicit structure of the OFRF for this class of nonlinear systems to be readily determined, and reveal clearly how each of the model nonlinear parameters has its effect on the system output frequency response. Examples are provided to demonstrate how the theoretical results are used for the determination of the detailed structure of the OFRF. Simulation studies verify the effectiveness and illustrate the potential of these new results for the analysis and synthesis of nonlinear systems in the frequency domain
InLoc: Indoor Visual Localization with Dense Matching and View Synthesis
We seek to predict the 6 degree-of-freedom (6DoF) pose of a query photograph
with respect to a large indoor 3D map. The contributions of this work are
three-fold. First, we develop a new large-scale visual localization method
targeted for indoor environments. The method proceeds along three steps: (i)
efficient retrieval of candidate poses that ensures scalability to large-scale
environments, (ii) pose estimation using dense matching rather than local
features to deal with textureless indoor scenes, and (iii) pose verification by
virtual view synthesis to cope with significant changes in viewpoint, scene
layout, and occluders. Second, we collect a new dataset with reference 6DoF
poses for large-scale indoor localization. Query photographs are captured by
mobile phones at a different time than the reference 3D map, thus presenting a
realistic indoor localization scenario. Third, we demonstrate that our method
significantly outperforms current state-of-the-art indoor localization
approaches on this new challenging data
Program Synthesis using Natural Language
Interacting with computers is a ubiquitous activity for millions of people.
Repetitive or specialized tasks often require creation of small, often one-off,
programs. End-users struggle with learning and using the myriad of
domain-specific languages (DSLs) to effectively accomplish these tasks.
We present a general framework for constructing program synthesizers that
take natural language (NL) inputs and produce expressions in a target DSL. The
framework takes as input a DSL definition and training data consisting of
NL/DSL pairs. From these it constructs a synthesizer by learning optimal
weights and classifiers (using NLP features) that rank the outputs of a
keyword-programming based translation. We applied our framework to three
domains: repetitive text editing, an intelligent tutoring system, and flight
information queries. On 1200+ English descriptions, the respective synthesizers
rank the desired program as the top-1 and top-3 for 80% and 90% descriptions
respectively
Protein-based materials, toward a new level of structural control
Through billions of years of evolution nature has created and refined structural proteins for a wide variety of specific purposes. Amino acid sequences and their associated folding patterns combine to create elastic, rigid or tough materials. In many respects, nature’s intricately designed products provide challenging examples for materials scientists, but translation of natural structural concepts into bio-inspired materials requires a level of control of macromolecular architecture far higher than that afforded by conventional polymerization processes. An increasingly important approach to this problem has been to use biological systems for production of materials. Through protein engineering, artificial genes can be developed that encode protein-based materials with desired features. Structural elements found in nature, such as β-sheets and α-helices, can be combined with great flexibility, and can be outfitted with functional elements such as cell binding sites or enzymatic domains. The possibility of incorporating non-natural amino acids increases the versatility of protein engineering still further. It is expected that such methods will have large impact in the field of materials science, and especially in biomedical materials science, in the future
Constrained optimal control theory for differential linear repetitive processes
Differential repetitive processes are a distinct class of continuous-discrete two-dimensional linear systems of both systems theoretic and applications interest. These processes complete a series of sweeps termed passes through a set of dynamics defined over a finite duration known as the pass length, and once the end is reached the process is reset to its starting position before the next pass begins. Moreover the output or pass profile produced on each pass explicitly contributes to the dynamics of the next one. Applications areas include iterative learning control and iterative solution algorithms, for classes of dynamic nonlinear optimal control problems based on the maximum principle, and the modeling of numerous industrial processes such as metal rolling, long-wall cutting, etc. In this paper we develop substantial new results on optimal control of these processes in the presence of constraints where the cost function and constraints are motivated by practical application of iterative learning control to robotic manipulators and other electromechanical systems. The analysis is based on generalizing the well-known maximum and -maximum principles to the
Sparse Positional Strategies for Safety Games
We consider the problem of obtaining sparse positional strategies for safety
games. Such games are a commonly used model in many formal methods, as they
make the interaction of a system with its environment explicit. Often, a
winning strategy for one of the players is used as a certificate or as an
artefact for further processing in the application. Small such certificates,
i.e., strategies that can be written down very compactly, are typically
preferred. For safety games, we only need to consider positional strategies.
These map game positions of a player onto a move that is to be taken by the
player whenever the play enters that position. For representing positional
strategies compactly, a common goal is to minimize the number of positions for
which a winning player's move needs to be defined such that the game is still
won by the same player, without visiting a position with an undefined next
move. We call winning strategies in which the next move is defined for few of
the player's positions sparse.
Unfortunately, even roughly approximating the density of the sparsest
strategy for a safety game has been shown to be NP-hard. Thus, to obtain sparse
strategies in practice, one either has to apply some heuristics, or use some
exhaustive search technique, like ILP (integer linear programming) solving. In
this paper, we perform a comparative study of currently available methods to
obtain sparse winning strategies for the safety player in safety games. We
consider techniques from common knowledge, such as using ILP or SAT
(satisfiability) solving, and a novel technique based on iterative linear
programming. The results of this paper tell us if current techniques are
already scalable enough for practical use.Comment: In Proceedings SYNT 2012, arXiv:1207.055
- …