In recent years, stochastic detectors have gained prominence in networked systems for anomaly detection. These detectors have demonstrated advantages over their traditional counterparts, particularly in safe-guarding ...
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ISBN:
(数字)9798350354409
ISBN:
(纸本)9798350354416
In recent years, stochastic detectors have gained prominence in networked systems for anomaly detection. These detectors have demonstrated advantages over their traditional counterparts, particularly in safe-guarding against data integrity attacks targeting state estimation. Despite these advancements, the impact of the detector on alarm performance-such as alarm-triggering rates at normal conditions-remains under-explored, especially in scenarios where delay timers are applied to the raw alarm sequence. This study delves into the monitoring of a correlated Gaussian process variable using stochastic detectors. An explicit formula for the alarm performance is given, highlighting how it is influenced by the duration of delay timers. The efficacy of the proposed approach is validated through numerical examples and a simplified process model.
In this paper, we address the problem of robust guidance for a three-body pursuit-evasion problem involving a target, a defender, and an attacker, where the goal is to safeguard the target (an aircraft) from the attac...
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ISBN:
(数字)9798350382655
ISBN:
(纸本)9798350382662
In this paper, we address the problem of robust guidance for a three-body pursuit-evasion problem involving a target, a defender, and an attacker, where the goal is to safeguard the target (an aircraft) from the attacker, using the defender. Thus, the target and the defender may act as a team of vehicles whose common goal is to safeguard the target from the attacker. Additionally, it is desired to maintain necessary constraints on certain states. An adaptive guidance law is proposed for the target-defender team to ensure optimal performance within this set-up. The target-defender dynamics are considered for designing the guidance law with only approximate knowledge of the unknown parameters of the attacker. A robust guidance command is designed using a super-twisting algorithm with adaptive gains. The unknown disturbance bounds required for control implementation are obtained using a novel norm observer. We consider the complete nonlinear model of the system for control design, thereby enabling a wider domain of applicability of the proposed approach. The convergence is established using Lyapunov analysis, which also ensures satisfaction of the required state constraints. Relevant simulation results are presented to study the efficacy of the proposed approach.
Hand hygiene (HH) is one of the most important activities of protection against pandemic disease in the current difficult situation around the world. To achieve this, World Health Organization (WHO) recommends frequen...
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In order to mitigate periodic blade loads in wind turbines, recent research has analyzed different Individual Pitch control (IPC) approaches, which typically use the multi-blade coordinate (MBC) transformation. Some o...
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ISBN:
(数字)9783907144107
ISBN:
(纸本)9798331540920
In order to mitigate periodic blade loads in wind turbines, recent research has analyzed different Individual Pitch control (IPC) approaches, which typically use the multi-blade coordinate (MBC) transformation. Some of these studies show that the introduction of an additional tuning parameter in the MBC, namely the azimuth offset, helps to decouple the nonrotating axes in the MBC transformation and enhances the IPC performance. However, these improvements have been studied without considering the increased control effort performed by the pitch signal, which is the main negative side effect of the IPC. This work addresses this trade-off between pitch signal effort and blade fatigue reduction for IPC applied to a wind turbine operating in the full load region. Here, two IPC schemes, with and without additional azimuth offset, are designed and applied to a 15 MW monopile offshore wind turbine simulated with OpenFAST software. The optimal tuning of the IPC parameters is performed by means of a multi-objective optimization solved by genetic algorithms. The optimization procedure minimizes two objective functions related to pitch signal effort and blade fatigue load. The resulting Pareto fronts show a range of optimal solutions for each IPC scheme. The selected optimal solution for IPC with azimuth offset compared to the optimal solution for IPC without offset achieves improvements of more than 10% in blade load reduction maintaining similar pitch signal effort.
Observer-based methods are widely used to estimate the disturbances of different dynamic systems. However, a drawback of the conventional disturbance observers is that they all assume persistent excitation (PE) of the...
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In order to further understand the mechanism of material volume change in the drying process,numerical simulations(considering or neglecting shrinkage)of heat and mass transfer during convective drying of carrot slice...
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In order to further understand the mechanism of material volume change in the drying process,numerical simulations(considering or neglecting shrinkage)of heat and mass transfer during convective drying of carrot slices under constant and controlled temperature and relative humidity were carried *** results were validated with experimental *** results of the simulation show that the Quadratic model fitted well to the moisture ratio and the material temperature data trend with average relative errors of 5.9%and 8.1%,***,the results of the simulation considering shrinkage show that the moisture and temperature distributions during drying are closer to the experimental data than the results of the simulation disregarding *** material moisture content was significantly related to the shrinkage of dried *** and relative humidity significantly affected the volume shrinkage of carrot *** volume shrinkage increased with the rising of the constant temperature and the decline of relative *** model can be used to provide more information on the dynamics of heat and mass transfer during drying and can also be adapted to other products and dryers devices.
This paper proposes a time-varying matrix solution to the Brockett stabilization problem. The key matrix condition shows that if the system matrix product CB is a Hurwitz H-matrix, then there exists a time-varying dia...
This paper proposes a time-varying matrix solution to the Brockett stabilization problem. The key matrix condition shows that if the system matrix product CB is a Hurwitz H-matrix, then there exists a time-varying diagonal gain matrix K(t) such that the MIMO minimum-phase linear system with decentralized output feedback is exponentially convergent. The proposed solution involves several analysis tools such as diagonal stabilization properties of special matrices, stability conditions of diagonal-dominant linear systems, and solution bounds of linear time-varying integro-differential systems. A review of other solutions to the general Brockett stabilization problem (for a general unstructured time-varying gain matrix K(t)) and a comparison study are also provided.
There is a need for robust Reinforcement Learning (RL) algorithms that can cope with model misspecification, parameter uncertainty, disturbances, etc. Risk-sensitive methods offer an approach to developing robust RL a...
There is a need for robust Reinforcement Learning (RL) algorithms that can cope with model misspecification, parameter uncertainty, disturbances, etc. Risk-sensitive methods offer an approach to developing robust RL algorithms by hedging against undesirable outcomes in a probabilistic manner. The Probabilistic Graphical Model (PGM) framework offers systematic exploration for risk-sensitive RL. In this paper, we bridge the Markov Decision Process (MDP) and the PGM frameworks. We exploit the equivalence of optimizing a certain risk-sensitive criterion in the MDP formalism with optimizing a log-likelihood objective in the PGM formalism. By utilizing this equivalence, we offer an approach for developing risk-sensitive algorithms by leveraging the PGM framework. We explore the Expectation-Maximization (EM) algorithm under the PGM formalism. We show that risk-sensitive policy gradient methods can be obtained by applying sampling-based approaches to the EM algorithm, e.g., Monte-Carlo EM, with the log-likelihood. We show that Monte-Carlo EM leads to a risk-sensitive Monte-Carlo policy gradient algorithm. Our simulations illustrate the risk-sensitive nature of the resulting algorithm.
Preterm births have been seen to have psychological and financial implications;current surveys suggest that amongst the various methods of preterm prediction,there is yet to exist a reliable and standard means of pred...
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Preterm births have been seen to have psychological and financial implications;current surveys suggest that amongst the various methods of preterm prediction,there is yet to exist a reliable and standard means of predicting preterm *** study investigates the application of electrohysterogram and tocogram signals acquired at various points during the third pregnancy trimester,alongside information from the patients'medical health record regarding the pregnancy,towards preterm prediction and an associated delivery imminency *** addition to this,the impact of both linear and non-linear dimensional embedding methods towards the preterm prediction is *** classification exercises were carried out using a support vector machine and decision tree,both of which have a certain degree of model interpretability and have potential to be introduced into a clinical operating framework.
Iterative learning control (ILC) is capable of improving the tracking performance of repetitive controlsystems by utilizing data from past iterations. The aim of this paper is to achieve both task flexibility, which ...
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