1,721,032 research outputs found
State Observers for Systems Subject to Bounded Disturbances Using Quadratic Boundedness
Full text linkQuadratic boundedness is adopted to design state observers for linear, piecewise linear, and Lipschitz nonlinear systems subject to bounded disturbances. Upper bounds on the estimation error are derived by exploiting quadratic boundedness and a design method based on linear matrix inequalities is proposed to minimize such bounds. Simulation results are provided to show the effectiveness of the proposed approach
Distributed Model-Based Fault Diagnosis with Stochastic Uncertainties
Full text linkThis paper proposes a novel distributed fault detection and isolation approach for the monitoring of non linear large-scale systems. The proposed architecture considers stochastic characterization of the measurement noises and modeling uncertainties, computing at each step stochastic timevarying thresholds with guaranteed false alarms probability
levels. The convergence properties of the distributed estimation are demonstrated. A novel fault isolation method is proposed basing on a Generalized Observer Scheme, providing guaranteed error probabilities of the fault exclusion task. A consensus approach is used for the estimation of variables shared among more than one subsystem; a method is proposed to define the time-varying consensus weights in order to minimize at each step the variance of the uncertainty of the fault detection and isolation thresholds. Detectability and isolability conditions are provided
A decentralized fault-tolerant control scheme based on Active Fault Diagnosis
No full textThis paper deals with a decentralized fault-tolerant control methodology based on an Active Fault Diagnosis approach. The proposed technique addresses the important problem of monitoring interconnected Large-Scale Systems (LSS). The fault diagnosis approach is made of a passive set-based fault detection method and an active fault isolation technique, able to guarantee isolability subject to local input and state constraints. The proposed scheme can be implemented locally in a decentralized way. A significant feature is the decentralized design constructed on tube-based Model Predictive Control to possibly allow the disconnection of faulty subsystems or the reconfiguration of local controllers. The Active Fault Diagnosis tool is designed to support the decision-making process for the control and monitoring of the LSS
The link “Cancer and autoimmune diseases” in the light of microbiota: Evidence of a potential culprit
No full textEvidence establishes that chronic inflammation and autoimmunity are associated with cancer development and patients with a primary malignancy may develop autoimmune-like diseases. Despite immune dysregulation is a common feature of both cancer and autoimmune diseases, precise mechanisms underlying this susceptibility are not clarified and different hypotheses have been proposed, starting from genetic and environmental common features, to intrinsic properties of immune system. Moreover, as the development and use of immunomodulatory therapies for cancer and autoimmune diseases are increasing, the elucidation of this relationship must be investigated in order to offer the best and most secure therapeutic options. The microbiota could represent a potential link between autoimmune diseases and cancer. The immunomodulation role of microbiota is widely recognized and under eubiosis, it orchestrates both the innate and adaptive response of immunity, in order to discriminate and modulate the immune response itself in the most appropriate way. Therefore, a dysbiotic status can alter the immune tonus rendering the host prone to exogenous or endogenous infections, breaking the tolerance against self-components and activating the immune responses in an excessive (i.e. chronic inflammation) or deficient way, favoring the onset of neoplastic and autoimmune diseases
The Cochrane Case: An Epistemic Analysis on Decision-Making and Trust in Science in the Age of Information
Full text linkIn this study we analyze a recent controversy within the biomedical world, concerning the evaluation of safety of certain vaccines. This specific struggle took place among experts: the Danish epidemiologist Peter Gøtzsche on one side and a respected scientific institution, the Cochrane, on the other. However, given its relevance, the consequences of such a conflict invest a much larger spectrum of actors, last but not least the public itself. Our work is aimed at dissecting a specific aspect happening in this complex scenario: strategy. In other words, we want to highlight the value and the impact of strategic decisions when complex issues, as those analyzed, are at stake. In order to address this we have decided to adopt a game-theoretic approach. Our work will be structured as it follows. First, we will introduce the controversy and the two main actors: Peter Gøtzsche and the Cochrane. Second, we will explain why this controversy is important and its value beyond its academic relevance. Third, we will frame the controversy as a game and will provide several models representing different situations, also furnishing an analysis of these distinct scenarios. In the end we will argue why such game-theoretic approach can be useful in dissecting this type of issues
A Distributed Pareto-Optimal Dynamic Estimation Method
Full text linkIn this paper, a novel distributed model-based prediction method is proposed using sensor networks. Each sensor communicates with the neighboring nodes for state estimation based on a consensus protocol without centralized coordination. The proposed distributed estimator consists of a consensus-filtering scheme, which uses a weighted combination
of sensors information, and a model-based predictor. Both the consensus-filtering weights and the model-based prediction parameter for all the state components are jointly optimized to minimize the variance and bias of the prediction error in a Pareto framework. It is assumed that the weights of the consensus-filtering phase are unequal for the different state components, unlike consensus-based approaches from literature. The state, the measurements, and the noise components are assumed to be individually correlated, but no probability distribution knowledge is assumed for the noise variables. The optimal weights are derived and it is established that the consensus-filtering weights and the model-based prediction parameters cannot be designed separately in an optimal way. The asymptotic convergence of the mean of the prediction error is demonstrated. Simulation results show the performance of the proposed method, obtaining better results than distributed Kalman filtering
Partition-based Pareto-optimal state prediction method for interconnected systems using sensor networks
Full text linkIn this paper a novel partition-based state prediction method is proposed for interconnected stochastic systems using sensor networks. Each sensor locally computes a prediction of the state of the monitored subsystem based on the knowledge of the local model and the communication with neighboring nodes of the sensor network. The prediction is performed in a distributed way, not requiring a centralized coordination or the knowledge of the global model. Weights and parameters of the state prediction are locally optimized in order to minimise at each time-step bias and variance of the prediction error by means of a multi-objective Pareto optimization framework. Individual correlations between the state, the measurements, and the noise components are considered, thus assuming to have in general unequal weights and parameters for each different state component. No probability distribution knowledge is required for the noise variables. Simulation results show the effectiveness of the proposed method
Optimal Topology for Distributed Fault Detection of Large-scale Systems
Full text linkThe paper deals with the problem of defining the optimal topology for a distributed fault detection architecture for non-linear large-scale systems. A stochastic modelbased framework for diagnosis is formulated. The system structural graph is decomposed into subsystems and each subsystem is monitored by one local diagnoser. It is shown that overlapping of subsystems allows to improve the detectability properties of the monitoring architecture. Based on this theoretical result, an optimal decomposition design method is proposed, able to define the minimum number of detection units needed to guarantee the detectability of certain faults while minimizing the communication costs subject to some computation cost constraints.
An algorithmic procedure is presented to solve the proposed optimal decomposition problem.
Preliminary simulation results show the potential of the proposed approach
Why Genes Are Like Lemons
No full textIn the last few years, the lack of a unitary notion of gene across biological sciences has troubled the philosophy of biology community. However, the debate on this concept has remained largely historical or focused on particular cases presented by the scientific empirical advancements. Moreover, in the literature there are no explicit and reasonable arguments about why a philosophical clarification of the concept of gene is needed. In our paper, we claim that a philosophical clarification of the concept of gene does not contribute to biology. Unlike the question, for example, “What is a biological function?”, we argue that the question “What is a gene?” could be answered by means of empirical research, in the sense that biologists’ labour is enough to shed light on it
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