INDUCTION MOTOR MODEL IDENTIFICATION VIA FREQUENCY-DOMAIN FRISCH SCHEME

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Actuator Fault Reconstruction via Dynamic Neural Networks for an Autonomous Underwater Vehicle Model

This paper proposes the development of a scheme for the fault diagnosis of the actuators of a simulated model accurately representing the behaviour of an autonomous underwater vehicle. The Fossen model usually adopted to describe the dynamics of the underwater vehicle has been generalised in this paper to take into account time-varying sea currents. The proposed fault detection and isolation strategy uses a data-driven approach relying on multi-layer perceptron neural networks that include auto-regressive exogenous prototypes that provide the fault reconstruction. These tools are thus exploited to design a bank of dynamic neural networks for residual generation that are trained on the basis of the input and outputmeasurements acquired from the simulator. In this work, the residuals are designed to represent the reconstruction of the fault signals themselves. Moreover, the neural network bank is also able to perform the isolation task, in case of simultaneous and concurrent faults affecting the actuators. The paper firstly describes the steps performed for deriving the proposed fault diagnosis solution. Secondly, the effectiveness of the scheme is demonstrated by means of high-fidelity simulations of a realistic autonomous underwater vehicle, in the presence of faults and marine current

Fast saccadic eye-movements in humans suggest that numerosity perception is automatic and direct

Fast saccades are rapid automatic oculomotor responses to salient and ecologically important visual stimuli such as animals and faces. Discriminating the number of friends, foe, or prey may also have an evolutionary advantage. In this study, participants were asked to saccade rapidly towards the more numerous of two arrays. Participants could discriminate numerosities with high accuracy and great speed, as fast as 190 ms. Intermediate numerosities were more likely to elicit fast saccades than very low or very high numerosities. Reaction-times for vocal responses (collected in a separate experiment) were slower, did not depend on numerical range, and correlated only with the slow not the fast saccades, pointing to different systems. The short saccadic reaction-times we observe are surprising given that discrimination using numerosity estimation is thought to require a relatively complex neural circuit, with several relays of information through the parietal and prefrontal cortex. Our results suggest that fast numerosity-driven saccades may be generated on a single feed-forward pass of information recruiting a primitive system that cuts through the cortical hierarchy and rapidly transforms the numerosity information into a saccade command

Robust Attitude Control of an Agile Aircraft Using Improved Q-Learning

Attitude control of a novel regional truss-braced wing (TBW) aircraft with low stability characteristics is addressed in this paper using Reinforcement Learning (RL). In recent years, RL has been increasingly employed in challenging applications, particularly, autonomous flight control. However, a significant predicament confronting discrete RL algorithms is the dimension limitation of the state-action table and difficulties in defining the elements of the RL environment. To address these issues, in this paper, a detailed mathematical model of the mentioned aircraft is first developed to shape an RL environment. Subsequently, Q-learning, the most prevalent discrete RL algorithm, will be implemented in both the Markov Decision Process (MDP) and Partially Observable Markov Decision Process (POMDP) frameworks to control the longitudinal mode of the proposed aircraft. In order to eliminate residual fluctuations that are a consequence of discrete action selection, and simultaneously track variable pitch angles, a Fuzzy Action Assignment (FAA) method is proposed to generate continuous control commands using the trained optimal Q-table. Accordingly, it will be proved that by defining a comprehensive reward function based on dynamic behavior considerations, along with observing all crucial states (equivalent to satisfying the Markov Property), the air vehicle would be capable of tracking the desired attitude in the presence of different uncertain dynamics including measurement noises, atmospheric disturbances, actuator faults, and model uncertainties where the performance of the introduced control system surpasses a well-tuned Proportional–Integral–Derivative (PID) controller

Understanding and recognition of the right ventricular function and dysfunction via a numerical study

The role played by the right ventricular (RV) dysfunction has long been underestimated in clinical practice. Recent findings are progressively confirming that when the RV efficiency deteriorates both the right and the left circulation is (significantly) affected, but studies dedicated to a detailed description of RV hemodynamic role still lack. In response to such a gap in knowledge, this work proposes a numerical model that for the first time evaluates the effect of isolated RV dysfunction on the whole circulation. Lumped parameter modelling was applied to represent the physio-pathological hemodynamics. Different grades of impairment were simulated for three dysfunctions i.e., systolic, diastolic, and combined systolic and diastolic. Hemodynamic alterations (i.e., of blood pressure, flow, global hemodynamic parameters), arising from the dysfunctions, are calculated and analysed. Results well accord with clinical observations, showing that RV dysfunction significantly affects both the pulmonary and systemic hemodynamics. Successful verification against in vivo data proved the clinical potentiality of the model i.e., the capability of identifying the degree of RV impairment for given hemodynamic conditions. This study aims at contributing to the improvement of RV dysfunction recognition and treatment, and to the development of tools for the clinical management of pathologies involving the right heart

Localization and Registration of 2D Histological Mouse Brain Images in 3D Atlas Space

To accurately explore the anatomical organization of neural circuits in the brain, it is crucial to map the experimental brain data onto a standardized system of coordinates. Studying 2D histological mouse brain slices remains the standard procedure in many laboratories. Mapping these 2D brain slices is challenging; due to deformations, artifacts, and tilted angles introduced during the standard preparation and slicing process. In addition, analysis of experimental mouse brain slices can be highly dependent on the level of expertise of the human operator. Here we propose a computational tool for Accurate Mouse Brain Image Analysis (AMBIA), to map 2D mouse brain slices on the 3D brain model with minimal human intervention. AMBIA has a modular design that comprises a localization module and a registration module. The localization module is a deep learning-based pipeline that localizes a single 2D slice in the 3D Allen Brain Atlas and generates a corresponding atlas plane. The registration module is built upon the Ardent python package that performs deformable 2D registration between the brain slice to its corresponding atlas. By comparing AMBIA’s performance in localization and registration to human ratings, we demonstrate that it performs at a human expert level. AMBIA provides an intuitive and highly efficient way for accurate registration of experimental 2D mouse brain images to 3D digital mouse brain atlas. Our tool provides a graphical user interface and it is designed to be used by researchers with minimal programming knowledge

Heterogeneity of covid-19 outbreak in italy

An outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) started in December 2019 in China and was declared a pandemic on 11.03.2020 by WHO. Italy is one of the most afflicted Country by this epidemic with 136,110 confirmed cases and 16,654 deaths on 9.4.2020 (at the same date, the Ministry of Health was reporting 143,626 cases). During these few months the National Health Service have made a great effort to cope with the increasing request of intensive care beds and all the elective activities in hospital have been suspended. Data from the different Italian regions shows different patterns of positive and dead for this syndrome. Moreover, striking differences of the observed lethality of the infections among different areas were immediately evident from the epidemic reports. It will be of critical relevance to understand the expected evolution of the first lock-down phase, driving the exhaustion of the Covid-19 outbreak

Underwater Drone Architecture for Marine Digital Twin: Lessons Learned from SUSHI DROP Project

The ability to observe the world has seen significant developments in the last few decades, alongside the techniques and methodologies to derive accurate digital replicas of observed environments. Underwater ecosystems present greater challenges and remain largely unexplored, but the need for reliable and up-to-date information motivated the birth of the Interreg Italy–Croatia SUSHI DROP Project (SUstainable fiSHeries wIth DROnes data Processing). The aim of the project is to map ecosystems for sustainable fishing and to achieve this goal a prototype of an Unmanned Underwater Vehicle (UUV), named Blucy, has been designed and developed. Blucy was deployed during project missions for surveying the benthic zone in deep waters of the Adriatic Sea with noninvasive techniques compared to the use of trawl nets. This article describes the strategies followed, the instruments applied and the challenges to be overcome to obtain an accurately georeferenced underwater survey with the goal of creating a marine digital twin

A network perspective to the measurement of sense of coherence (SOC): an exploratory graph analysis approach

The measurement of sense of coherence (SOC) has received attention for more than three decades. Despite the extensive use of SOC-13, there is still a long debate regarding its dimensionality structure. Recently, there has been an increasing use of network modeling as a valid alternative to latent-variable modeling. This study proposes an exploratory approach to the structure of SOC-13 by adopting a network perspective. The network structure was estimated with a Gaussian Graphical Model, and Exploratory Graph Analysis (EGA) was used to inspect network dimensionality. We fit and compared the unidimensional, first- and second-order confirmatory factor analysis (CFA), bifactor-CFA, and structure derived from EGA. Our results showed unacceptable fit values for the CFA models, suggesting that SOC-13 is not unidimensional. Inspection of the estimated network suggested that the SOC-13 items emerged as a dynamic system of mutually interacting nodes that formed three distinct clusters of items (communities) that are not those defined in the literature. EGA identified three communities of items: the first community was characterized by comprehensibility and manageability items, the second community was characterized by comprehensibility and manageability items, and the third dimension was characterized by all meaningfulness items and one comprehensibility item. Our study presented a novel perspective in investigating the structure of SOC-13 that strengthens the assumption that SOC should be conceptualized as a complex system of cognitive (comprehensibility), behavioral (manageability), and motivational dimensions (meaningfulness) that are deeply linked and not necessarily distinct