Search results for " processing"
showing 10 items of 7549 documents
Vine leaf roughness estimation by image processing
2013
International audience; The application of plant protection product has an important role in agricultural production processes. With current pesticides management, a huge amount of them are applied to worldwide orchards. In precision spraying, spray application efficiency depends on the pesticide application method, the phytosanitary product as well as the leaf surface properties. For environmental and economic reasons, the global trend is to reduce the pesticide application rate of the few approved active substances. Under these constraints, one of the challenges is to improve the efficiency of pesticide application. Different parameters can influence pesticide application such as nozzle t…
A Scheme for Continuous Input to the Tsetlin Machine with Applications to Forecasting Disease Outbreaks
2019
In this paper, we apply a new promising tool for pattern classification, namely, the Tsetlin Machine (TM), to the field of disease forecasting. The TM is interpretable because it is based on manipulating expressions in propositional logic, leveraging a large team of Tsetlin Automata (TA). Apart from being interpretable, this approach is attractive due to its low computational cost and its capacity to handle noise. To attack the problem of forecasting, we introduce a preprocessing method that extends the TM so that it can handle continuous input. Briefly stated, we convert continuous input into a binary representation based on thresholding. The resulting extended TM is evaluated and analyzed…
A formal proof of the e-optimality of discretized pursuit algorithms
2015
Learning Automata (LA) can be reckoned to be the founding algorithms on which the field of Reinforcement Learning has been built. Among the families of LA, Estimator Algorithms (EAs) are certainly the fastest, and of these, the family of discretized algorithms are proven to converge even faster than their continuous counterparts. However, it has recently been reported that the previous proofs for ??-optimality for all the reported algorithms for the past three decades have been flawed. We applaud the researchers who discovered this flaw, and who further proceeded to rectify the proof for the Continuous Pursuit Algorithm (CPA). The latter proof examines the monotonicity property of the proba…
Stochastic discretized learning-based weak estimation: a novel estimation method for non-stationary environments
2016
The task of designing estimators that are able to track time-varying distributions has found promising applications in many real-life problems.Existing approaches resort to sliding windows that track changes by discarding old observations. In this paper, we report a novel estimator referred to as the Stochastic Discretized Weak Estimator (SDWE), that is based on the principles of discretized Learning Automata (LA). In brief, the estimator is able to estimate the parameters of a time varying binomial distribution using finite memory. The estimator tracks changes in the distribution by operating a controlled random walk in a discretized probability space. The steps of the estimator are discre…
Distributed learning automata for solving a classification task
2016
In this paper, we propose a novel classifier in two-dimensional feature spaces based on the theory of Learning Automata (LA). The essence of our scheme is to search for a separator in the feature space by imposing a LA based random walk in a grid system. To each node in the gird we attach an LA, whose actions are the choice of the edges forming the separator. The walk is self-enclosing, i.e, a new random walk is started whenever the walker returns to starting node forming a closed classification path yielding a many edged polygon. In our approach, the different LA attached at the different nodes search for a polygon that best encircles and separates each class. Based on the obtained polygon…
Scheduling Domestic Shiftable Loads in Smart Grids: A Learning Automata-Based Scheme
2017
In this paper, we consider the problem of scheduling shiftable loads, over multiple users, in smart grids. We approach the problem, which is becoming increasingly pertinent in our present energy-thirsty society, using a novel distributed game-theoretic framework. From a modeling perspective, the distributed scheduling problem is formulated as a game, and in particular, a so-called “Potential” game. This game has at least one pure strategy Nash Equilibrium (NE), and we demonstrate that the NE point is a global optimal point. The solution that we propose, which is the pioneering solution that incorporates the theory of Learning Automata (LA), permits the total supplied loads to approach the p…
Enabling XCSF to cope with dynamic environments via an adaptive error threshold
2020
The learning classifier system XCSF is a variant of XCS employed for function approximation. Although XCSF is a promising candidate for deployment in autonomous systems, its parameter dependability imposes a significant hurdle, as a-priori parameter optimization is not feasible for complex and changing environmental conditions. One of the most important parameters is the error threshold, which can be interpreted as a target bound on the approximation error and has to be set according to the approximated function. To enable XCSF to reliably approximate functions that change during runtime, we propose the use of an error threshold, which is adapted at run-time based on the currently achieved …
Adaptive algorithms robust to impulsive noise with low computational cost using order statistic
2009
Abstract In this paper a family of adaptive algorithms robust to impulsive noise and with low computational cost are presented. Unlike other approaches, no cost functions or filtering of the gradient are considered in order to update the filter coefficients. Its initial basis is the basic LMS algorithm and its sign-error variant. The proposed algorithms can be considered as some sign-error variants of the LMS algorithm. The algorithms are successfully tested in terms of accuracy and convergence in a standard system identification simulation in which an impulsive noise is present. Simulations show that they improve the performance of LMS variants that are robust to impulsive noise.
Vertical representation of C∞-words
2015
We present a new framework for dealing with C ∞ -words, based on their left and right frontiers. This allows us to give a compact representation of them, and to describe the set of C ∞ -words through an infinite directed acyclic graph G. This graph is defined by a map acting on the frontiers of C ∞ -words. We show that this map can be defined recursively and with no explicit reference to C ∞ -words. We then show that some important conjectures on C ∞ -words follow from analogous statements on the structure of the graph G.
Use of a grating in a coherent optical-processing configuration for evaluating the refractive index of a lens: comments
2010
La methode consiste en la determination de l'indice de refraction de la lentille d'essai a partir de la separation entre 2 ordres de diffraction successifs dans la figure de diffraction de Fraunhofer d'un reseau a 1 dimension, lorsque la lentille, qui effectue la transformation de Fourier a 2 dimensions, est immergee dans differents liquides a l'interieur d'une cellule en verre