Switching Machine Improvisation Models by Latent Transfer Entropy Criteria (original) (raw)

A framework for Anticipatory Machine Improvisation and Style

2006

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Relations between Performers/parameters and Level of Automation

IFAC workshop on manufacturing modelling, management and control, 2007

Manufacturing companies struggles with the fact that costumers are becoming more and more aggressive in demanding new products and services within a short period of time. A result of this is that companies have to find the most effective manufacturing for their products. This means finding new tactics to decrease the time-and costparameters in a system i.e. cycle-time, ramp-up time, investments etc. It is a challenge to find the best solution for this when it comes to flexibility, robustness and efficiency. This paper presents some of the parameters that exist in a manufacturing system and the relationship between them. This is done to be able to identify the parameters that is the most important when it comes to simulate and visualise different Levels of Automation (LoA).

Entropy as a measure of operational flexibility

European Journal of Operational Research, 2005

As competition among firms is being based more and more on the level of service, there is a great interest in using a measure of flexibility to indicate a firm's capability to respond to customer demands. Entropy is proposed as a measure of flexibility for manufacturing operations. The properties of entropy that make it a suitable measure of flexibility are outlined. A computer simulation of a job shop was used to test the measure through a number of different scenarios using a discrete-event stochastic simulator. Results showed that entropy succeeded in measuring flexibility when the relative demand for the fabrication of products changed. Entropy was also used to monitor process flexibility as time progressed.

Advances in Markov-Switching Models

2002

The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.

Simulation and properties of randomly switched control systems

Design, Characterization, and Packaging for MEMS and Microelectronics, 1999

Power electronics has made great advances since the introduction of the thyristor in 1958. Even a casual study of consumer electronics, such as computer power supplies, reveals that switched mode power electronics have steadily replaced passive circuits. Switched mode circuits can accommodate higher power densities,' they are lighter, cheaper and easier to control. The use of microprocessors and microcontrollers can make switched mode circuits even more versatile. Unfortunately, there are some problems with switched mode circuits. The higher power densities handled by these circuits can cause catastrophic failure. Periodic switching can give rise to acoustic noise2 or undesirable electromagnetic radiation. These problems can be reduced through the use of random switching policies.3 One theoretical disadvantage of random switching policies is that the time averaged switched system is not strictly equivalent to the classical system with the same average parameters. The stability limits for the randomly switched and classical systems are different. This is a possible area for concern, given the high power densities and the possibility of catastrophic failure. In this paper we examine the stability of randomly switched control systems. We provide simulations, some analysis and derive some practical rules for stability. We show that some randomness can beneficial from the point of view of minimising the maximum power spectral density of the noise waveforms in the output current.

On Joint Determination of the Number of States and the Number of Variables in Markov-Switching Models: A Monte Carlo Study

… in Statistics: Simulation …, 2009

In this paper we examine the performance of two newly developed procedures that jointly select the number of states and variables in Markov-switching models by means of Monte Carlo simulations. They are Smith, Naik and Tsai (2006) and Psaradakis and Spagnolo (2006), respectively. The former develops Markov switching criterion (MSC) designed specifically for Markov-switching models, while the latter recommends the use of standard complexitypenalised information criteria (BIC, HQC, & AIC). The Monte Carlo evidence shows that BIC outperforms MSC while MSC and HQC are preferable over AIC.

Parameter Tuning by the Cross-Entropy Method

2000

Recently, Monte-Carlo Tree Search (MCTS) has become a popular approach for intelligent play in games. Amongst others, it is successfully used in most state-of-the-art Go programs. To improve the playing strength of these Go programs any further, many parameters dealing with MCTS should be ne-tuned. In this paper, we propose to apply the Cross-Entropy Method (CEM) for this task. The

Identifying parameters of local switching models: a geometrical approach

2004

Abstract—In this paper we look at the problem where we have two mixed sets of data on the same scatter plot. We want to calculate linear regression lines for each of the two sets, but first of all we need to decide which data belong to which set. We propose one possible solution to this problem involving data classification and parameters estimation. Keywords: scatter plots, switching regression, mixture of models, change of regime, differential geometry.