Search results for "Tuning"

Fine-tuning of the confined space in microporous metal–organic frameworks for efficient mercury removal

2017

Offsetting the impact of human activities on the biogeochemical cycle of mercury has become necessary for a sustainable planet. Herein, we report the development of a water-stable and eco-friendly metal–organic framework, which has the formula {Cu4II[(S,S)-methox]2}·5H2O (1), where methox is bis[(S)-methionine]oxalyl diamide. Its features include narrow functional channels decorated with thioalkyl chains, which are able to capture HgCl2 from aqueous media in an efficient, selective, and rapid manner. The conscious design effort in terms of size, shape, and reactivity of the channels results in extremely efficient immobilization of HgCl2 guest species in a very stable conformation, similar t…

Generalization of the Den Hartog model and rule-of-thumb formulas for optimal tuned mass dampers

2022

In recent years, the need of improving safety standards for both existing and new buildings against earthquake and wind loads has created a growing interest in the use of the so-called tuned mass dampers, exploited to control, in active or passive way, the dynamic response of structures. To design and optimize tuned mass damper systems, the effective analytical procedure proposed by Den Hartog in his seminal work (Den Hartog, 1985) has been widely adopted over the years, without including damping of the main structure. However, in many cases of engineering interest, the damping of the primary system plays a key role in the overall mechanical response, with the result of an increase in compl…

Dendrites are dispensable for basic motoneuron function but essential for fine tuning of behavior.

2014

Dendrites are highly complex 3D structures that define neuronal morphology and connectivity and are the predominant sites for synaptic input. Defects in dendritic structure are highly consistent correlates of brain diseases. However, the precise consequences of dendritic structure defects for neuronal function and behavioral performance remain unknown. Here we probe dendritic function by using genetic tools to selectively abolish dendrites in identified Drosophila wing motoneurons without affecting other neuronal properties. We find that these motoneuron dendrites are unexpectedly dispensable for synaptic targeting, qualitatively normal neuronal activity patterns during behavior, and basic …

Automatic EKF tuning for UAS path following in turbulent air

2018

By using two simultaneously working Extended Kalman Filters, a procedure is implemented in order to perform in a fully autonomous way the path following in turbulent air. To guarantee the robustness of the proposed algorithm, an automatic tuning procedure is proposed to determine optimal values of Process and Measurement Noise statistics. Such a procedure is based on both the characteristics of the disturbances and the desired flight path; in particular, a specific performance index is applied to tune filters. In this way control laws are adapted to the flight condition and these lead to an optimal path-following. This research represents an upload of previous papers. It allows eliminating …

A fuzzy framework to explain musical tuning in practice

2013

A theoretical tuning system is a set of pitches that can be used to play music. It is a fact that the human ear perceives notes with very close frequencies as if they were the same note. Therefore, in our approach a musical note and its pitch sensation are modeled as L-R fuzzy numbers with a modal interval and a bounded support. We pay particular attention to the 12-tone equal temperament (12-TET) for being the most widely used tuning system and we define the fuzzy 12-TET composed of 12 fuzzy notes. A similarity relation between a fuzzy note and a theoretical note can be defined, and subsequently a similarity class associated to each one of the fuzzy notes in the fuzzy 12-TET arises. Finall…

Tuning parameter selection in LASSO regression

2016

We propose a new method to select the tuning parameter in lasso regression. Unlike the previous proposals, the method is iterative and thus it is particularly efficient when multiple tuning parameters have to be selected. The method also applies to more general regression frameworks, such as generalized linear models with non-normal responses. Simulation studies show our proposal performs well, and most of times, better when compared with the traditional Bayesian Information Criterion and Cross validation.

Penalized regression and clustering in high-dimensional data

The main goal of this Thesis is to describe numerous statistical techniques that deal with high-dimensional genomic data. The Thesis begins with a review of the literature on penalized regression models, with particular attention to least absolute shrinkage and selection operator (LASSO) or L1-penalty methods. L1 logistic/multinomial regression models are used for variable selection and discriminant analysis with a binary/categorical response variable. The Thesis discusses and compares several methods that are commonly utilized in genetics, and introduces new strategies to select markers according to their informative content and to discriminate clusters by offering reduced panels for popul…

Model-based automatic tuning of a filtration control system for submerged anaerobic membrane bioreactors (AnMBR)

2014

This paper describes a model-based method to optimise filtration in submerged AnMBRs. The method is applied to an advanced knowledge-based control system and considers three statistical methods: (1) sensitivity analysis (Morris screening method) to identify an input subset for the advanced controller; (2) Monte Carlo method (trajectory-based random sampling) to find suitable initial values for the control inputs; and (3) optimisation algorithm (performing as a supervisory controller) to re-calibrate these control inputs in order to minimise plant operating costs. The model-based supervisory controller proposed allowed filtration to be optimised with low computational demands (about 5min). E…

ℓ1-Penalized Methods in High-Dimensional Gaussian Markov Random Fields

2016

In the last 20 years, we have witnessed the dramatic development of new data acquisition technologies allowing to collect massive amount of data with relatively low cost. is new feature leads Donoho to define the twenty-first century as the century of data. A major characteristic of this modern data set is that the number of measured variables is larger than the sample size; the word high-dimensional data analysis is referred to the statistical methods developed to make inference with this new kind of data. This chapter is devoted to the study of some of the most recent ℓ1-penalized methods proposed in the literature to make sparse inference in a Gaussian Markov random field (GMRF) defined …

In situ tuning of a photonic band gap with laser pulses

2008

We report on light-induced optical tuning of colloidal photonic crystals doped with gold nanoparticles (Au-nps). By resonantly exciting the Au-np surface plasmon absorption with picosecond pulses at 0.53 micron in a standard pump-probe setup, we observed permanent changes in the stop band resonance around 1.7 micron, with blue wavelength shifts as large as 30 nm and associated to a nanoparticle reshaping. Fine tuning was achieved by controlling either the pulse energy or the irradiation time.