Abstract
In this paper, we present a sound synthesis method that utilizes evolution as generative paradigm. Such sounds will be thereon referred to as evolutionary sounds. Upon defining a population of complex sounds, i.e. sound segments sampled from acoustical instruments and speech; we generated sounds that resulted from evolution applied to those populations. The methodology presented here is an extension to the Evolutionary Sound Synthesis Method (ESSynth) created recently. In ESSynth, a set of waveforms, the Population, is evolved towards another set, the Target, through the application of a Genetic Algorithm (GA). Fitness evaluation is a mathematical distance metric. We enhance features of the previous implementation herein and present the codification. The genetic operators and selection criterion applied are depicted together with the relevant genetic parameters involved in the process. To evaluate the results we present a sound taxonomy based on an objective and a subjective criterion. Those criteria are discussed, the experimental procedure is explained and the results are depicted and evaluated.

Abstract
This paper presents a system for induction of forest of functional trees from data streams able to detect concept drift. The Ultra Fast Forest of Trees (UFFT) is an incremental algorithm, which works online, processing each example in constant time, and performing a single scan over the training examples. It uses analytical techniques to choose the splitting criteria, and the information gain to estimate the merit of each possible splitting-test. For multi-class problems the algorithm builds a binary tree for each possible pair of classes, leading to a forest of trees. Decision nodes and leaves contain naive-Bayes classifiers playing different roles during the induction process. Naive-Bayes in leaves are used to classify test examples. Naive-Bayes in inner nodes play two different roles. They can be used as multivariate splitting-tests if chosen by the splitting criteria, and used to detect changes in the class-distribution of the examples that traverse the node. When a change in the class-distribution is detected, all the sub-tree rooted at that node will be pruned. The use of naive-Bayes classifiers at leaves to classify test examples, the use of splitting-tests based on the outcome of naive-Bayes, and the use of naive-Bayes classifiers at decision nodes to detect changes in the distribution of the examples are directly obtained from the sufficient statistics required to compute the splitting criteria, without no additional computations. This aspect is a main advantage in the context of high-speed data streams. This methodology was tested with artificial and real-world data sets. The experimental results show a very good performance in comparison to a batch decision tree learner, and high capacity to detect drift in the distribution of the examples.

Abstract

Abstract
In this paper we introduce an identification algorithm for MIMO bilinear systems subject to deterministic inputs. The new algorithm is based on an expanding dimensions concept, leading to a rectangular, dimension varying, linear system. In this framework the observability, controllability, and Markov parameters are similar to those of a time-varying system. The fact that the system is time invariant, leads to an equaivaleet linear deterministic subspace algorithm. Provided a rank condition is satisfied, the algorithm will produce unbiased parameter estimates. This rank condition can be guaranteed to hold if the ratio of the number of outputs to the number of inputs is larger than the system order. This is due to the typical exponential blow-out in the dimensions of the Hankel data matrices of bilinear systems, in particular for deterministic inputs since part of the input subspace cannot be projected out. Other algorithms in the literature, based on Walsh functions, require that the number of outputs is at least equal to the system order. For ease of notation and clarification, the algorithm is presented as an intersection based subspace algorithm. Numerical results show that the algorithm reproduces the system parameters very well, provided the rank condition is satisfied. When the rank condition is not satisfied, the algorithm will return biased parameter estimates, which is a typical bottleneck of bilinear system identification algorithms for deterministic inputs. © 2005 IEEE.

Abstract

Abstract
In this paper will be compared, by means of a next event dynamic simulation, two different stock refill policies applied to the same in-lined one-product Supply Chain. The first method is the Japanese method usually known as KANBAN(1) (similar to the "Two Bins" method, in terms of dynamics), and the second is a naive method named by the authors the "BanKan" for the reasons that will be explained later in this text. The surprising results obtained show an obvious operational advantage of the naive method, at least under the conditions of the present Supply Chain design and configuration. This method does not make use of any criteria dependent on the actual stock level at the facility, and it was also observed that the materials flow through the Supply Chain as in a "river without dams", and with extremely low local inventory, as it was defended by Taiichi Ohno, the father of just-in-time (JIT) systems, in the 1980s.