Search results for "fine-tuning"

High-Power Multicarrier Generation for RF Breakdown Testing

2017

Testing of satellite components for high RF power breakdown effects, such as multipactor and corona or passive-intermodulation, is a topic of growing interest in the aerospaceindustry. Switching fromthe classical single carrier approach to the more realisticmulticarrier scenario is very challenging from the experimental point of view. Themulticarrier signals, amplifiedby several RF power amplifiers, need to have controlled phase, amplitude, and frequency in each carrier. Fine tuning of the signal generator phases is required in order to compensate the phase drift occurring in the active elements of the test bed. This paper presents an efficient and low-cost technique to generate multicarrie…

Fine-tuning the extent and dynamics of binding cleft opening as a potential general regulatory mechanism in parvulin-type peptidyl prolyl isomerases

2017

AbstractParvulins or rotamases form a distinct group within peptidyl prolyl cis-trans isomerases. Their exact mode of action as well as the role of conserved residues in the family are still not unambiguously resolved. Using backbone S2 order parameters and NOEs as restraints, we have generated dynamic structural ensembles of three distinct parvulins, SaPrsA, TbPin1 and CsPinA. The resulting ensembles are in good agreement with the experimental data but reveal important differences between the three enzymes. The largest difference can be attributed to the extent of the opening of the substrate binding cleft, along which motional mode the three molecules occupy distinct regions. Comparison w…

Chapter 3. Fine-tuning lexical bundles

2018

Fine tuning of thermoelectric performance in phase-separated half-Heusler compounds

2015

Two successful recipes to enhance the thermoelectric performance, namely carrier concentration optimization and reduction of thermal conductivity, have been combined and applied to the p-type (Ti/Zr/Hf)CoSb1−xSnx system. An intrinsic micrometer-scale phase separation increases the phonon scattering and reduces the lattice thermal conductivity. A substitution of 15% Sb by Sn optimizes the electronic properties. Starting from this, further improvement of the thermoelectric properties has been achieved by a fine tuning of the Ti to Hf ratio. The microstructuring of the samples was studied in detail with high-resolution synchrotron powder X-ray diffraction and element mapping electron microscop…

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…

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…