This paper describes development of the module, which analyzes medical images and its use is also possible in sectors other than medicine. A software module can base on events captured on the ECG graph store images, w...
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This paper aims to describe a new identification method for Hammerstein systems relying on the framework of basis functions approximation in order to obtain an adequate model for the nonlinear static component. The sp...
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ISBN:
(纸本)9781467345033
This paper aims to describe a new identification method for Hammerstein systems relying on the framework of basis functions approximation in order to obtain an adequate model for the nonlinear static component. The specific coefficients of the basis functions approximation and also the parameters of the linear dynamic component are estimated using a nonlinear least squares method based on a modified version of Gauss-Newton algorithm. An algorithm is introduced based on wavelet multiresolution analysis that returns a low complexity approximation of the nonlinear component built on a grid hierarchy using adaptive bases. Such bases provides a powerful means to detect local singularities and often lead to quite simple refinement strategies. Finally, we present some numerical results for our method that show its efficiency.
This paper describes the processing of photographs in the newly created module for 2D modeling, which will be integrated into the system FOTOM NG. This is a usable application in medicine to analyze images of probes a...
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When a mixture of particles with different attributes undergoes vibration, a segregation pattern is often observed. For example, in muesli cereal packs, the largest particles-the Brazil nutstend to end up at the top. ...
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ISBN:
(纸本)9781627481564
When a mixture of particles with different attributes undergoes vibration, a segregation pattern is often observed. For example, in muesli cereal packs, the largest particles-the Brazil nutstend to end up at the top. For this reason, the phenomenon is known as the Brazil nut effect. In previous research, an algorithm inspired by this effect was designed to produce segregation patterns in swarms of simulated agents that move on a horizontal plane. In this paper, we adapt this algorithm for implementation on robots with directional vision. We use the e-puck robot as a platform to test our implementation. In a swarm of epucks, different robots mimic disks of different sizes (larger than their physical dimensions). The motion of every robot is governed by a combination of three components: (i) attraction towards a point, which emulates the effect of a gravitational pull, (ii) random motion, which emulates the effect of vibration, and (iii) repulsion from nearby robots, which emulates the effect of collisions between disks. The algorithm does not require robots to discriminate between other robots; yet, it is capable of forming annular structures where the robots in each annulus represent disks of identical size. We report on a set of experiments performed with a group of 20 physical e-pucks. The results obtained in 100 trials of 20 minutes each show that the percentage of incorrectly-ordered pairs of disks from different groups decreases as the size ratio of disks in different groups is increased. In our experiments, this percentage was, on average, below 0.5% for size ratios from 3.0 to 5.0. Moreover, for these size ratios, all segregation errors observed were due to mechanical failures that caused robots to stop moving.
This paper dresses the optimal control of multiple linear agents in the presence of a set of adversary constraints. This type of constraints makes the convergence of the agents’ dynamics towards the “natural” equil...
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This paper dresses the optimal control of multiple linear agents in the presence of a set of adversary constraints. This type of constraints makes the convergence of the agents’ dynamics towards the “natural” equilibrium position, impossible to fulfill. Therefore, this default equilibrium point has to be replaced by a set of equilibrium points or even accept the existence of limit cycles. Furthermore, the constraints introduced in the transitory optimization problem are non convex. The present paper proposes a dual-mode control strategy which switches between an unconstrained optimum controller and a constrained control law whenever the adversary constraints are activated. The proposed method builds on invariance concepts and proves to be related to eigenstructure assignment problems. The technique exhibits effective performance and is validated here by an illustrative example.
Recently we developed supervisor localization, a top-down approach to distributed control of discrete-event systems in the Ramadge-Wonham supervisory control framework. Its essence is the allocation of monolithic (glo...
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ISBN:
(纸本)9783902823281
Recently we developed supervisor localization, a top-down approach to distributed control of discrete-event systems in the Ramadge-Wonham supervisory control framework. Its essence is the allocation of monolithic (global) control action among the local control strategies of individual agents. In this paper, we start by presenting several refinements of our localization theory. First, we drop the original assumption that the event sets of component agents are pairwise disjoint. Second, we show that consistent marking information can be enforced by just one agent, which can be selected arbitrarily. Third, the event sets of localized controllers are explicitly defined, in general as proper subsets of the entire event set. For these generalizations, we again prove that the collective local controlled behavior is identical to the global optimal and nonblocking controlled behavior. We go on to apply supervisor localization to solve a multi-agent formation problem. A suitable formulation of convergence to formations is first introduced;then local strategies are synthesized for a group of agents to arrive at an arbitrarily pre-specified formation, and issues of information exchange and control logic are examined.
This paper presents a single multiplicative neuron model based on a polynomial architecture. The proposed neuron model consists of a non-linear aggregation function based on the concept of generalized mean of all mult...
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This paper presents a single multiplicative neuron model based on a polynomial architecture. The proposed neuron model consists of a non-linear aggregation function based on the concept of generalized mean of all multiplicative inputs. This neuron model has the same number of parameters as the single multiplicative neuron model (SMN). The SMN model is a special case of the proposed generalized mean single multiplicative neuron (GMSMN) model. The structure of this model is simpler than higher-order neuron model. The simulation results show that the performance of the proposed neuron model is better than SMN model.
This paper presents a single multiplicative neuron model based on a polynomial architecture. The proposed neuron model consists of a non-linear aggregation function based on the concept of generalized mean of all mult...
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This paper presents a single multiplicative neuron model based on a polynomial architecture. The proposed neuron model consists of a non-linear aggregation function based on the concept of generalized mean of all multiplicative inputs. This neuron model has the same number of parameters as the single multiplicative neuron model (SMN). The SMN model is a special case of the proposed generalized mean single multiplicative neuron (GMSMN) model. The structure of this model is simpler than higher-order neuron model. The simulation results show that the performance of the proposed neuron model is better than SMN model.
The problem of H ∞ filtering for nonlinear singular Markovian jumping systems with interval time-varying delays is investigated. The delay factor is assumed to be time-varying and belongs to a given interval, which m...
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The problem of H ∞ filtering for nonlinear singular Markovian jumping systems with interval time-varying delays is investigated. The delay factor is assumed to be time-varying and belongs to a given interval, which means that the lower and upper bounds of the interval time-varying delays are available. Furthermore, the derivative of the time-varying delay function can be larger than one. With partial knowledge of the jump rates of the Markov process, a new delay-range-dependent bounded real lemma for the solvability of the jump system is obtained based on the Lyapunov–Krasovskii functional, which is in terms of strict linear matrix inequalities (LMIs). When these LMIs are feasible, an expression of a desired H ∞ filter is given. Numerical examples are given to illustrate the effectiveness of the developed techniques. [ABSTRACT FROM AUTHOR]
Animals use stochastic search strategies to find a potential nest or to localize the food source. Animals do not rely much on the environmental cues or their sensory inputs. Since their cognitive capabilities are rath...
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