This paper outlines the design of a fractional-order proportional–integral–derivative controller for regulating the induction phase of Propofol infusion in lean and obese patients. The obtained controller is impleme...
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This paper outlines the design of a fractional-order proportional–integral–derivative controller for regulating the induction phase of Propofol infusion in lean and obese patients. The obtained controller is implemented within the pharmacokinetic-pharmacodynamic model and the nonlinear Hill function to conduct closed-loop simulations. The latter are employed using a dataset comprising 24 patients to obtain clinical evaluation results. The design of the controller relies on a generic second-order plus dead time approximation model, which characterizes interpatient variability in response to Propofol infusion within the studied population. The fractional-order proportional–integral–derivative is tuned to achieve sufficient robustness margins ie phase margin, cutoff frequency, and the consideration of the iso-damping properties. The results show no undershoot and a smooth convergence to the desired value of the bispectral index, which indicates the depth of hypnosis.
This study employs nine distinct deep learning models to categorize 12,444 blood cell images and automatically extract from them relevant information with an accuracy that is beyond that achievable with traditional **...
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This study employs nine distinct deep learning models to categorize 12,444 blood cell images and automatically extract from them relevant information with an accuracy that is beyond that achievable with traditional *** work is intended to improve current methods for the assessment of human health through measurement of the distribution of four types of blood cells,namely,eosinophils,neutrophils,monocytes,and lymphocytes,known for their relationship with human body damage,inflammatory regions,and organ illnesses,in particular,and with the health of the immune system and other hazards,such as cardiovascular disease or infections,more in *** results of the experiments show that the deep learning models can automatically extract features from the blood cell images and properly classify them with an accuracy of 98%,97%,and 89%,respectively,with regard to the training,verification,and testing of the corresponding datasets.
The leader-following asymptotic consensus problem for general discrete-time linear multi-agent systems over jointly con-nected switching networks was solved about a decade ***,the leader-following exponential consensu...
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The leader-following asymptotic consensus problem for general discrete-time linear multi-agent systems over jointly con-nected switching networks was solved about a decade ***,the leader-following exponential consensus was further established using the so-called Krasovskii-LaSalle theorem for a class of discrete-time linear switched *** this method involves some advanced concepts such as the weak zero-state detectability of some limiting *** this paper,we offer a simpler solution to the leader-following exponential consensus problem for general discrete-time linear multi-agent systems over jointly connected switching *** converting the solvability of the problem to the establishment of the exponential stability for a class of discrete-time linear switched systems,we first show that this class of linear switched systems is uniformly completely ***,we further conclude that the uniform complete observability for this class of linear switched systems implies the exponential stability for the same class of linear switched systems,thus leading to the solution of the leader-following exponential consensus ***,our approach also gives rise to an explicit characterization of the exponential convergence rate of the leader-following consensus problem.
Genetic algorithm (GA) is an effective method for path planning problems. As a powerful variant of GA, island genetic algorithm (IGA) has considerable improvement in performance. In this paper, a new island model of G...
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The day-ahead management schedules of hybrid energy hubs are intricate and usually exposed to various uncertainties with the penetration of renewable sources and different ***,it is difficult to access to precise prob...
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The day-ahead management schedules of hybrid energy hubs are intricate and usually exposed to various uncertainties with the penetration of renewable sources and different ***,it is difficult to access to precise probability distribution functions and exact moment information of uncertain *** cope with these issues,an energy management scheme based on the distributionally robust optimization approach is developed for the energy *** makes no assumptions of certain probability distributions and can be implemented with limited empirical data and partial information of underlying *** operational strategy can provide decision makers with a preliminary and robust optimal solution in the day-ahead *** results illustrate the economical benefit of the energy model,and the effectiveness of the proposed approach in chance-constrained energy management is demonstrated by comparing with other *** Terms-Chance constraint,distributionally robust optimization,energy hub,energy management.
This paper investigates the path-guided distributed formation control of networked autonomous surface vehicles(ASVs) subject to model uncertainties and environmental disturbances. A safety-certified path-guided coordi...
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This paper investigates the path-guided distributed formation control of networked autonomous surface vehicles(ASVs) subject to model uncertainties and environmental disturbances. A safety-certified path-guided coordinated control method is proposed for multiple ASVs to achieve a distributed formation in obstacle environments. Specifically, a neural predictor with a high-order tuner is presented to approximate unknown nonlinearities with accelerated learning performance. Subsequently, control Lyapunov functions(CLFs) and control barrier functions(CBFs) are constructed for mapping stability constraints and safety constraints on states to control inputs. A quadratic optimization problem is constructed with the norm of control inputs as the objective function, CLFs and CBFs as constraints. Neurodynamic optimization is used to deal with the quadratic programming problem and generate the optimal kinetic control signals, thereby attaining the desired safe formation. Unlike the high-order CBF, a CBF backstepping method is proposed to establish safety constraints such that repeated time derivatives of system nonlinearities can be avoided. The multi-ASVs system is ensured to be input-to-state safe irrespective of high-order relative degree. Through the Lyapunov theory, the multi-ASVs system is proven to be input-to-state stable. Finally, simulation results are presented to validate the efficacy of the presented safety-certified distributed formation control for networked ASVs.
Accurate prediction of solar irradiance is crucial for the effective utilization of solar energy. However, in real-world scenarios, complex irradiance patterns and prevalent incomplete data pose challenges to precise ...
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Close and efficient cooperation between devices in the Industrial Internet of Things (IIoT) requires precise clock synchronization as a prerequisite. The uncertainty of IIoT networks and the complexity of industrial e...
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Dear editor,Solving linear matrix equations is a basic and important problem in many fields such as the computation of generalized inverses of matrices and(generalized) Sylvester equations. Also, the linear algebraic ...
Dear editor,Solving linear matrix equations is a basic and important problem in many fields such as the computation of generalized inverses of matrices and(generalized) Sylvester equations. Also, the linear algebraic equation is a fundamental problem, which is a special form of linear matrix equations.
Changes in coal seam hardness cause fluctuations in the feed resistance at the drill bit during the drilling process, leading to unstable feeding speed. This paper proposes a robust dynamic output feedback controller ...
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