KBERG: A MatLab toolbox for nonlinear kernel-based regularization and system identification

Abstract We present KBERG, a MatLab package for nonlinear Kernel-BasEd ReGularization and system identification. The toolbox provides a complete environment for running experiments on simulated and experimental data from both static and dynamical systems. The whole identification procedure is supported: (i) data generation, (ii) excitation signals design; (iii) kernel-based estimation and (iv) evaluation of the results. One of the main differences of the proposed package with respect to existing frameworks lies in the possibility to separately define experiments, algorithms and test, then combining them as desired by the user. Once these three quantities are defined, the user can simply run all the computations with only a command, waiting for results to be analyzed. As additional noticeable feature, the toolbox fully supports the manifold regularization rationale, in addition to the standard Tikhonov one, and the possibility to compute different (but equivalent) types of solutions other than the standard one

A cellular automaton model of laser-plasma interactions

This paper deals with the realization of a CA model of the physical interactions occurring when high-power laser pulses are focused on plasma targets. The low-level and microscopic physical laws of interactions among the plasma and the photons in the pulse are described. In particular, electron–electron interaction via the Coulomb force and photon–electron interaction due to ponderomotive forces are considered. Moreover, the dependence on time and space of the index of refraction is taken into account, as a consequence of electron motion in the plasma. Ions are considered as a fixed background. Simulations of these interactions are provided in different conditions and the macroscopic dynamics of the system, in agreement with the experimental behavior, are evidenced

Identification of dynamic textures using Dynamic Mode Decomposition

Abstract Dynamic Textures (DTs) are image sequences of moving scenes that present stationary properties in time. In this paper, we apply Dynamic Mode Decomposition (DMD) and Dynamic Mode Decomposition with Control (DMDc) to identify a parametric model of dynamic textures. The identification results are compared with a benchmark method from the dynamic texture literature, both from a mathematical and from a computational complexity point of view. Extensive simulations are carried out to assess the performance of the proposed algorithms with regards to synthesis and denoising purposes, with different types of dynamic textures. Results show that DMD and DMDc present lower error, lower residual noise and lower variance compared to the benchmark approach

Mechatronics applications of condition monitoring using a statistical change detection method

Abstract In this paper, we propose the use of a change detection strategy to perform condition monitoring of mechanical components. The method looks for statistical changes in the distribution of features extracted from raw measurements, such as Root Mean Square or Crest Factor indicators. The proposed method works in a batch fashion, comparing data from one experiment to another. When these distributions differ by a specified amount, a degradation score is increased. The approach is tested on three experimental applications: (i) an ElectroMechanical Actuator (EMA) employed in flight applications, where the focus of the monitoring is on the ballscrew transmission; (ii) a CNC workbench, where the focus is on the vertical shaft bearing, (iii) an industrial EMA with focus on the ballscrew bearing. All components have undergone a severe experimental degradation process, that ultimately led to their failure. Results show how the proposed method is able to assess component degradation prior to their failure

Experimental Development of a Health Monitoring Method for Electro-Mechanical Actuators of Flight Control Primary Surfaces in More Electric Aircrafts

This paper presents a health monitoring approach for Electro-Mechanical Actuators (EMA). We define four different indicators to continuously evaluate the health state of the system. The four indicators are computed by leveraging the output from a Statistical Process Monitoring (SPM) method based on multivariate statistics, such as the Hotelling's T2 statistic and the Q statistic. SPM approaches give a dichotomous answer, i.e. the presence/absence of a fault. In this work, we propose four ways to compute a continuous indicator starting from the discrete SPM output, that is better suited for health monitoring. We test the approach using a dataset collected from a large experimental campaign on a 1:1 scale EMA for primary flight controls of small aircrafts, that led to EMA failure. Results show the effectiveness of the method

Determining the Importance of Physicochemical Properties in the Perceived Quality of Wines

Wine is a relevant part of the diet in many countries, showing significant nutritional properties, providing health benefits to consumers, and having a significant weight in economy. Also, wine plays an important role in many cultures as a part of their social relationships, feasts, or religion where some of them may become a sign of luxury and distinction. For those reasons, objective and subjective quality of wines is an important issue in their production and marketing. To improve wine excellence, some production methods try to relate its physicochemical properties to the quality as it is perceived by humans. Then, modern data prescriptive analysis can be applied to measure the importance (the influence) of each wine attribute. This paper examines and compare several metrics of the attribute importance and its application to the quality-aware design and production of wines. Moreover, for the cases where the perceived quality is characterized using a discrete value, a novel importance metric, based on the Jensen-Shannon Divergence (JSD) is introduced and compared to the existing ones. The results show that JSD clearly overperforms other metrics previously proposed in the literature. Also, it can be asserted that JSD properly reflects the importance of discrete multivalued functions. The results, using this metric in an importance performance analysis of a public wine dataset, show that the main physicochemical attributes of a red wine are citric acidity, alcohol, sulphates and fixed acidity. As for the white wine case, the main attributes are alcohol, free sulfure dioxide and pH

GLISp-r: A preference-based optimization algorithm with convergence guarantees

Preference-based optimization algorithms are iterative procedures that seek the optimal value for a decision variable based only on comparisons between couples of different samples. At each iteration, a human decision-maker is asked to express a preference between two samples, highlighting which one, if any, is better than the other. The optimization procedure must use the observed preferences to find the value of the decision variable that is most preferred by the human decision-maker, while also minimizing the number of comparisons. In this work, we propose GLISp-r, an extension of a recent preference-based optimization procedure called GLISp. The latter uses a Radial Basis Function surrogate to describe the tastes of the individual. Iteratively, GLISp proposes new samples to compare with the current best candidate by trading off exploitation of the surrogate model and exploration of the decision space. In GLISp-r, we propose a different criterion to use when looking for a new candidate sample that is inspired by MSRS, a popular procedure in the black-box optimization framework (which is closely related to the preference-based one). Compared to GLISp, GLISp-r is less likely to get stuck on local optimizers of the preference-based optimization problem. We motivate this claim theoretically, with a proof of convergence, and empirically, by comparing the performances of GLISp and GLISp-r on different benchmark optimization problems.Comment: 26 pages, 8 figures and 3 table

A Stochastic Cellular Automaton for Modelling Radiation-Matter Interaction in Semiconduc tor Lasers

The paper presents a Cellular Automaton model of a semiconductor laser. The basic physical laws of quantum theory of light-matter interaction are directly described as cellular evolution rules. The use of such a model allows a deep insight in the fundamental properties of radiationmatter interaction in modern optical devices. Simulation results are presented in very good agreement with other classical modelling techniques based on differential equations and with experimental results