The paper is devoted to developing a new fault detection scheme for an Automated Guided Vehicle (AGV) on the basis of so-called virtual sensors (VSs) which provide the information regarding the current status of a vehicle. This set contains the estimates of lateral and longitudinal forces as well as the torque. The paper proposes a novel robust VSs design scheme which yields such estimates taking into account inevitable disturbances/noise and modelling uncertainty without any knowledge about tire models used in the AGV. The obtained estimates are used to generate the residuals and to diagnose the current status of the vehicle. Finally, the paper shows the experimental results concerning the application of the developed methods to fault detection of the self-designed and constructed AGV.
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http://dx.doi.org/10.1016/j.isatra.2020.05.012 | DOI Listing |
J Cardiovasc Electrophysiol
January 2025
Cardiology department, "S. Maria dei Battuti" Hospital, Via Brigata Bisagno, Conegliano (TV), Italy.
Background: Recent advancements in ultra-high-density mapping (UHDM) featuring automated functionalities have enhanced our understanding of micro-reentrant atrial tachycardias (mAT) circuits and the precise localization of the origin.
Purpose: To evaluate the diagnostic support provided by an automated UHDM algorithm in guiding the ablation of mATs.
Methods: Consecutive patients eligible for AT ablation in 22 Italian centers were prospectively enrolled.
Med Phys
January 2025
Department of Radiation Medicine and Applied Sciences, University of California at San Diego, La Jolla, California, USA.
Background: Proton pencil beam scanning (PBS) treatment planning for head and neck (H&N) cancers is a time-consuming and experience-demanding task where a large number of potentially conflicting planning objectives are involved. Deep reinforcement learning (DRL) has recently been introduced to the planning processes of intensity-modulated radiation therapy (IMRT) and brachytherapy for prostate, lung, and cervical cancers. However, existing DRL planning models are built upon the Q-learning framework and rely on weighted linear combinations of clinical metrics for reward calculation.
View Article and Find Full Text PDFEnviron Sci Technol
January 2025
Environmental and Public Health Analytical Chemistry, Research Institute for Pesticides and Water (IUPA), Universitat Jaume I, Av. Sos Baynat S/N, Castellón de la Plana 12071, Spain.
This study explores the capabilities of GC-APCI-IMS-QTOF MS and GC-EI-QOrbitrap MS in screening applications and different strategies for wide-scope screening of organic microcontaminants using target suspect and nontarget approaches. On one side, GC-APCI-IMS-QTOF MS excels at preserving molecular information and adds ion mobility separation, facilitating screening through the list of componentized features containing accurate mass, retention time, CCS, and fragmentation data. On the other side, the extensive and robust fragmentation of GC-EI-QOrbitrap MS allows the application of different strategies for target and nontarget approaches using the NIST library spectra.
View Article and Find Full Text PDFLab Chip
January 2025
University of Novi Sad, BioSense Institute, Dr Zorana Djindjica 1, 21000 Novi Sad, Serbia.
Microfluidic technology, which involves the manipulation of fluids in microchannels, faces challenges in channel design and performance optimization due to its complex, multi-parameter nature. Traditional design and optimization approaches usually rely on time-consuming numerical simulations, or on trial-and-error methods, which entail high costs associated with experimental evaluations. Additionally, commonly used optimization methods require many numerical simulations, and to avoid excessive computation time, they approximate simulation results with faster surrogate models.
View Article and Find Full Text PDFCommun Biol
January 2025
Department of Infectious Diseases, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia.
Critical to the success of CRISPR-based diagnostic assays is the selection of a diagnostic target highly specific to the organism of interest, a process often requiring iterative cycles of manual selection, optimisation, and redesign. Here we present PathoGD, a bioinformatic pipeline for rapid and high-throughput design of RPA primers and gRNAs for CRISPR-Cas12a-based pathogen detection. PathoGD is fully automated, leverages publicly available sequences and is scalable to large datasets, allowing rapid continuous monitoring and validation of primer/gRNA sets to ensure ongoing assay relevance.
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