Search results for "Tuning"
showing 10 items of 67 documents
Viscoelastic bearings with fractional constitutive law for fractional tuned mass dampers
2015
Abstract The paper aims at studying the effects of the inherent fractional constitutive law of viscoelastic bearings used as devices for tuned mass dampers. First, the proper constitutive law of the viscoelastic supports is determined by the local constitutive law. Then, the characteristic force–displacement relationship at the top of the bearing is found. Taking advantage of the whole bearing constitutive laws, the tuning of the mass damper is proposed by defining the damped fractional frequency, which is analogous to the classical damped frequency. The effectiveness of the optimal tuning procedure is validated by a numerical application on a system subjected to a Gaussian white noise.
Sensorless low Range Speed Estimation and Parameter Identification of Induction Motor Drives Devoted to Lifts Automatic Rescue Devices
2010
In this paper a sensorless rotor speed estimation and parameter identification algorithm is presented. The algorithm is designed specifically for induction motor (IM) drives devoted to automatic rescue devices (ARD) used in lifts and hoist applications. Its peculiarity is that it is based on the sinusoidal steady state mathematical model of the IM and, therefore, can be implemented on a low cost micro-controller in a simple way, however without lacking in terms of dynamic performance. It is also capable of self tuning so that no information is required about the specific IM used in the ARD drive. Finally the algorithm allows also an appreciable energy saving of the ARD when compared to olde…
A systematic approach for fine-tuning of fuzzy controllers applied to WWTPs
2010
A systematic approach for fine-tuning fuzzy controllers has been developed and evaluated for an aeration control system implemented in a WWTP. The challenge with the application of fuzzy controllers to WWTPs is simply that they contain many parameters, which need to be adjusted for different WWTP applications. To this end, a methodology based on model simulations is used that employs three statistical methods: (i) Monte-Carlo procedure: to find proper initial conditions, (ii) Identifiability analysis: to find an identifiable parameter subset of the fuzzy controller and (iii) minimization algorithm: to fine-tune the identifiable parameter subset of the controller. Indeed, the initial locatio…
Application of the Morris method for screening the influential parameters of fuzzy controllers applied to wastewater treatment plants
2011
In this paper,we evaluate the application of a sensitivity analysis to help fine-tuning a fuzzy controller for a biological nitrogen and phosphorus removal (BNPR) plant. TheMorris Screeningmethod is proposed and evaluated as a prior step to obtain the parameter significance ranking. First, an iterative procedure has been performed in order to find out the proper repetition number of the elementary effects (r) of the method. The optimal repetition number found in this study (r = 60) is in direct contrast to previous applications of the Morris method, which usually use low repetition number, e.g. r = 10 ~ 20. Working with a non-proper repetition number (r) could lead to Type I error (identify…
SELF-TUNING FUZZY CONTROL OF A ROTARY DRYER
2002
Abstract Drying, especially rotary drying is without doubt one of the oldest and most common unit operations in industries. It is a very complex non-linear process including the movement of solids in addition to thermal drying. This means that both the modelling and control of a rotary dryer is difficult with conventional methods. The aim of this research was to improve dryer control by developing control systems based on self-tuning PID-type fuzzy logic controllers. The behaviour of the control systems has been tested with simulations based on the model of a pilot plant dryer located in the Control Engineering Laboratory at the University of Oulu. The control results have been compared ach…
DEVELOPMENT AND IMPLEMENTATION OF MACHINE LEARNING METHODS FOR THE IIF IMAGES ANALYSIS
2021
A methodology for assessing the effect of correlations among muscle synergy activations on task-discriminating information
2013
Delis, Ioannis | Berret, Bastien | Pozzo, Thierry | Panzeri, Stefano; International audience; ''Muscle synergies have been hypothesized to be the building blocks used by the central nervous system to generate movement. According to this hypothesis, the accomplishment of various motor tasks relies on the ability of the motor system to recruit a small set of synergies on a single-trial basis and combine them in a task-dependent manner. It is conceivable that this requires a fine tuning of the trial-to-trial relationships between the synergy activations. Here we develop an analytical methodology to address the nature and functional role of trial-to-trial correlations between synergy activation…
Performance of Fine-Tuning Convolutional Neural Networks for HEp-2 Image Classification
2020
The search for anti-nucleus antibodies (ANA) represents a fundamental step in the diagnosis of autoimmune diseases. The test considered the gold standard for ANA research is indirect immunofluorescence (IIF). The best substrate for ANA detection is provided by Human Epithelial type 2 (HEp-2) cells. The first phase of HEp-2 type image analysis involves the classification of fluorescence intensity in the positive/negative classes. However, the analysis of IIF images is difficult to perform and particularly dependent on the experience of the immunologist. For this reason, the interest of the scientific community in finding relevant technological solutions to the problem has been high. Deep lea…
Deep Convolutional Neural Networks for Fire Detection in Images
2017
Detecting fire in images using image processing and computer vision techniques has gained a lot of attention from researchers during the past few years. Indeed, with sufficient accuracy, such systems may outperform traditional fire detection equipment. One of the most promising techniques used in this area is Convolutional Neural Networks (CNNs). However, the previous research on fire detection with CNNs has only been evaluated on balanced datasets, which may give misleading information on real-world performance, where fire is a rare event. Actually, as demonstrated in this paper, it turns out that a traditional CNN performs relatively poorly when evaluated on the more realistically balance…