Search results for "Machine"
showing 10 items of 2592 documents
Strategies to develop radiomics and machine learning models for lung cancer stage and histology prediction using small data samples
2021
Abstract Predictive models based on radiomics and machine-learning (ML) need large and annotated datasets for training, often difficult to collect. We designed an operative pipeline for model training to exploit data already available to the scientific community. The aim of this work was to explore the capability of radiomic features in predicting tumor histology and stage in patients with non-small cell lung cancer (NSCLC). We analyzed the radiotherapy planning thoracic CT scans of a proprietary sample of 47 subjects (L-RT) and integrated this dataset with a publicly available set of 130 patients from the MAASTRO NSCLC collection (Lung1). We implemented intra- and inter-sample cross-valida…
Design and Low-Cost Implementation of an Optimally Robust Reduced-Order Rotor Flux Observer for Induction Motor Control
2007
The aim of this paper is to design and analyze reduced-order observers of the rotor flux of induction motors. The design is carried out in two steps. In the first step, a boundary of the stability region of the observation error is obtained corresponding to a chosen Lyapunov function. In the second step, the boundary is translated into a performance index that is minimized with respect to stator and rotor resistance variations and differences of voltages supplying the motor and those supplying the observer in order to obtain the largest stability region. Implementation of the observer on a low-cost fixed-point digital signal processor using look-up tables is described. Experimental results …
Faults diagnosis based on proportional integral observer for TS fuzzy model with unmeasurable premise variable
2014
In this work, we focus on the synthesis of a Proportional Integral (PI) observer for the actuators and sensors faults diagnosis based on Takagi-Sugeno (TS) fuzzy model with unmeasurable premise variables. The faults estimation method is based on the assumption that these faults act as unknown inputs under polynomials form whose their kth derivatives are bounded. The convergence conditions of the observer as well as the faults reconstruction are established on the basis of the Lyapunov stability theory and the L 2 optimization technique, expressed as Linear Matrix Inequalities (LMI) constraints. In order to validate the proposed approach, a hydraulic system with two tanks is proposed.
An Automatic Sleep Scoring Toolbox : Multi-modality of Polysomnography Signals’ Processing
2019
Sleep scoring is a fundamental but time-consuming process in any sleep laboratory. To speed up the process of sleep scoring without compromising accuracy, this paper develops an automatic sleep scoring toolbox with the capability of multi-signal processing. It allows the user to choose signal types and the number of target classes. Then, an automatic process containing signal pre-processing, feature extraction, classifier training (or prediction) and result correction will be performed. Finally, the application interface displays predicted sleep structure, related sleep parameters and the sleep quality index for reference. To improve the identification accuracy of minority stages, a layer-w…
Design and Development of a Multi-Mode, Two-Wheeled, Self-Balancing Service Platform
2019
Master's thesis Mechatronics MAS500 - University of Agder 2019 This thesis covers the design and development of a multi-mode, two-wheeled, self-balancing serviceplatform. The purpose of such a platform is to assist people with transportation tasks in crowdedspaces like airports by consuming as little space as possible and minimizing its overall footprint.This is solved by constructing a robot that can transition between bicycle and differential driveconfigurations and keep its balance in both configurations as well as in the transition processbetween them. A two-wheeled, self-balancing robot is an inherently unstable system that can bebased on the theory of an inverted pendulum. The unstabl…
Tool support for MOLA
2006
AbstractThe paper describes the MOLA Tool, which supports the model transformation language MOLA. MOLA Tool consists of two parts: MOLA definition environment and MOLA execution environment. MOLA definition environment is based on the GMF (Generic Modeling Framework) and contains graphical editors for metamodels and MOLA diagrams, as well as the MOLA compiler. The main component of MOLA execution environment is a MOLA virtual machine, which performs model transformations, using an SQL database as a repository. The execution environment may be used as a plug-in for Eclipse based modeling tools (e.g., IBM Rational RSA). The current status of the tool is truly academic.
PED in 2021: a major update of the protein ensemble database for intrinsically disordered proteins
2020
Abstract The Protein Ensemble Database (PED) (https://proteinensemble.org), which holds structural ensembles of intrinsically disordered proteins (IDPs), has been significantly updated and upgraded since its last release in 2016. The new version, PED 4.0, has been completely redesigned and reimplemented with cutting-edge technology and now holds about six times more data (162 versus 24 entries and 242 versus 60 structural ensembles) and a broader representation of state of the art ensemble generation methods than the previous version. The database has a completely renewed graphical interface with an interactive feature viewer for region-based annotations, and provides a series of descriptor…
Leakage Detection via Edge Processing in LoRaWAN-based Smart Water Distribution Networks
2022
The optimization and digitalization of Water Distribution Networks (WDNs) are becoming key objectives in our modern society. Indeed, WDNs are typically old, worn and obsolete. These inadequate conditions of the infrastructures lead to significant water loss due to leakages inside pipes, junctions and nodes. It has been measured that in Europe the average value of lost water is about 26 %. Leakage control in current WDNs is typically passive, repairing leaks only when they are visible. Emerging Low Power Wide Area Network (LPWAN) technologies, and especially IoT ones, can help monitor water consumption and automatically detect leakages. In this context, LoRaWAN can be the right way to deploy…
A linear regression-based machine learning pipeline for the discovery of clinically relevant correlates of gait speed reserve from multiple physiolog…
2021
Frailty in older adults is characterized by reduced physiological reserve. Gait speed reserve (GSR: maximum minus usual gait speed) could help identify frailty and act as a proxy for physiological reserve. Utilizing data from 2397 participants aged 50+ from wave 3 of The Irish Longitudinal Study on Ageing, we developed a stepwise linear regression-based machine learning pipeline to select the most important GSR predictors from 34 manually selected features across multiple domains. Variables were selected one at a time such that they maximized the mean adjusted r-squared score from a 5-fold cross-validation. A peak score of (0.16 +/- 0.03) was achieved with 14 variables (giving adjusted-r-sq…
A Tour of Learned Static Sorted Sets Dictionaries: From Specific to Generic with an Experimental Performance Analysis
2022
In recent years, in the era of Big Data, studying new methods to improve the performance of well-known procedures, such as searching in a Sorted Set, has become crucial in many fields. A new trend emerging in this scenario combines Machine Learning models with Data Structures, generating the so-called Learned Data Structures. In this thesis, we provide an in-depth experimental study of the use of these models, starting from some evidence known to experts in the field but not experimentally investigated concerning the use of very complex models such as Neural Networks. Then, we document a time/space trade-off scenario that is very important for practitioners and designers users. Furthermore,…