Understanding driving pressure is of general interest in lung mechanics, especially in ventilated neonatal and pediatric patients. The aim of this study was to investigate the effects of endotracheal tube size (ETT) a...
Understanding driving pressure is of general interest in lung mechanics, especially in ventilated neonatal and pediatric patients. The aim of this study was to investigate the effects of endotracheal tube size (ETT) and bi-directional linear flow resistors mimicking different degrees of tube and airway obstruction on tracheal pressure (Ptr) during ventilation with decelerating flow. A mechanical lung simulator with unchanged respiratory mechanics like the mimicked compliance and resistance, three ETTs (inner diameters of 7.5 mm, 5.5 mm, and 3.5 mm), and four different linear flow resistors were alternatively connected to a commercial ventilator operating in pressure-controlled mode. Four resistors mimicking various degrees of ETT obstructions were placed consecutively between the Y-piece and the ETT. Varying sequentially ETT size, resistance, and ventilator settings resulted in 90 sets of measurements. Data acquisition, signal processing, and data analysis were performed using Python. The results clarify that decreasing ETT sizes and increasing flow resistances cause an increase in the time necessary to reach zero-flow conditions both during in- and expiration. This results in a decrease of Ptr at the end of inspiration and an increase of Ptr at the end of expiration, if inspiratory and expiratory times remain unchanged. The degree to which peak inspiratory pressure overestimates Ptr (or plateau pressure) in decelerating flow can be substantial and increases with increasing flow resistance. This highlights the importance of measuring plateau pressure or Ptr to understand pressure dynamics delivered to the lung.
The use of distributed optimization in machine learning can be motivated either by the resulting preservation of privacy or the increase in computational efficiency. On the one hand, training data might be stored acro...
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The use of distributed optimization in machine learning can be motivated either by the resulting preservation of privacy or the increase in computational efficiency. On the one hand, training data might be stored across multiple devices. Training a global model within a network where each node only has access to its confidential data requires the use of distributed algorithms. Even if the data is not confidential, sharing it might be prohibitive due to bandwidth limitations. On the other hand, the ever-increasing amount of available data leads to large-scale machine learning problems. By splitting the training process across multiple nodes its efficiency can be significantly increased. This paper aims to demonstrate how dual decomposition can be applied for distributed training of K-means clustering problems. After an overview of distributed and federated machine learning, the mixed-integer quadratically constrained programming-based formulation of the K-means clustering training problem is presented. The training can be performed in a distributed manner by splitting the data across different nodes and linking these nodes through consensus constraints. Finally, the performance of the subgradient method, the bundle trust method, and the quasi-Newton dual ascent algorithm are evaluated on a set of benchmark problems. The main benefit stemming from the formulation of the clustering problem as a mixed-integer program and from the use of dual decomposition within a federated learning framework, apart from the preservation of privacy, is the computation of a lower bound of the objective of the overall clustering problem. In this way, the worst-case distance of any found solution to the global optimum can be easily assessed. While the mixed-integer programming-based formulation of the clustering problems suffers from weak integer relaxations, the presented approach can potentially be used to enable an efficient solution in the future, both in a central and distributed sett
This paper presents a dual decomposition-based distributed optimization algorithm and applies it to distributed model predictive control (DMPC) problems. The considered DMPC problems are coupled through shared limited...
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ISBN:
(数字)9781665406734
ISBN:
(纸本)9781665406741
This paper presents a dual decomposition-based distributed optimization algorithm and applies it to distributed model predictive control (DMPC) problems. The considered DMPC problems are coupled through shared limited resources. Lagrangian duality can be used to decompose an MPC problem, so that each subsystem can compute its individual resource utilization, without sharing information, such as dynamics or constraints, with the other subsystems. The feasibility of the central problem is ensured by the coordination of the subproblems through dual variables which can be interpreted as prices on the shared limited resources. The proposed coordination algorithm makes efficient use of information collected from previous iterations by performing a quadratic approximation of the dual function of the central MPC problem. Aggressive update steps of the dual variables are prevented through a covariance-based step size constraint. The nonsmoothness encountered in dual optimization problems is addressed by the construction of cutting planes, similar to bundle methods for nonsmooth optimization. The cutting planes ensure that the updated dual variables do not lie outside the range of validity of the dual approximation. The proposed algorithm is evaluated on a two-tank system and compared to the standard subgradient method. The results show that the rate of convergence towards the centralized solution can be significantly improved while still preserving privacy between the subsystems through limited information exchange.
This paper presents a hierarchical distributed optimization algorithm based on quasi-Newton update steps. Separable convex optimization problems are decoupled through dual decomposition and solved in a distributed fas...
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ISBN:
(数字)9781665406734
ISBN:
(纸本)9781665406741
This paper presents a hierarchical distributed optimization algorithm based on quasi-Newton update steps. Separable convex optimization problems are decoupled through dual decomposition and solved in a distributed fashion by coordinating the solutions of the subproblems through dual variables. The proposed algorithm updates the dual variables by approximating the Hessian of the dual function through collected subgradient information, analogously to quasi-Newton methods. As the dual maximization problem is generally nonsmooth, a smooth approximation might show poor performance. To this end cutting planes, analogous to bundle methods, are constructed that take the nonsmoothness of the dual function into account and lead to a better convergence behavior near the optimum. The proposed algorithm is evaluated on a large set of benchmark problems and compared to the subgradient method and to the bundle trust method for nonsmooth optimization.
Operation of robotic manipulators is limited to structured environments and well-defined tasks due to an offline path planning. However, flexible production processes and human-robot collaboration necessitates a real ...
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ISBN:
(数字)9781665406734
ISBN:
(纸本)9781665406741
Operation of robotic manipulators is limited to structured environments and well-defined tasks due to an offline path planning. However, flexible production processes and human-robot collaboration necessitates a real time path planning to allow for replanning a path in changing environments. In this work, we investigate established planning algorithms for their applicability to dynamic path planning problems. We further compare these methods with our approach based on model predictive control. We consider a single manipulator with six degrees of freedom in static and dynamic environments. We investigate three experimental setups and show the advantages of the proposed MPC-ELS approach over more traditional path planning algorithms in terms of several metrics, such as path-length, execution time or trajectory smoothness. In addition, we propose a scheduling algorithm for object allocation to determine an optimal sequence for pick and place tasks with regard to minimum execution time.
To increase the volumetric energy density modern high-energy Lithium-Ion cells consist of Graphite/Silicon ($\mathbf{G r} / \mathbf{S i}$) composite negative electrodes. Due to the high volume expansion of up to 300% ...
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ISBN:
(数字)9798350350623
ISBN:
(纸本)9798350350630
To increase the volumetric energy density modern high-energy Lithium-Ion cells consist of Graphite/Silicon ($\mathbf{G r} / \mathbf{S i}$) composite negative electrodes. Due to the high volume expansion of up to 300% caused by (de)lithiation of Si, challenges arise by the usage of $\mathbf{G r} / \mathrm{Si}$-electrodes to prevent accelerated degradation. Therefore, accurate models to predict the cell expansion and pressure are of increasing interest. To investigate the mechanical behavior, half-cell Equivalent Circuit Models (ECMs) are coupled with a solid and liquid diffusion model in this article. The current density distribution among $\mathbf{S i}$ - and Gr -particles is determined by the assumption of an ideal parallel-connected of the active materials. An electrochemical dilatometer is used to measure the electrode expansion. Based on these measurements and literature values of volume changes of the crystal structure, changes of the pore volume are estimated. For the cell expansion and force development a visco-elastic model is implemented by using a spring in serial to a Kelvin-Voigt-Model. Simulations show high agreement with experimental data and literature values regarding cell voltage and expansion.
This paper proposes a model-free extremum seeking control (ESC) approach to optimize the productivity of continuous cultures of microalgae, considering the dilution rate and the light intensity as manipulated variable...
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This paper proposes a model-free extremum seeking control (ESC) approach to optimize the productivity of continuous cultures of microalgae, considering the dilution rate and the light intensity as manipulated variables, and the biomass concentration as single measurement. The resulting two-input single-output optimization problem is first solved using a recursive least-squares strategy based on the representation of the process by a Hammerstein block-oriented model. In order to face the presence of noise on the regressor variables (input and output signals), the problem is then reformulated as a maximum-likelihood estimation problem, which is solved on a moving horizon. Simulation results demonstrate the method performance.
作者：
Jens BremerJan HeilandPeter BennerKai SundmacherMax Planck Institute Magdeburg
Dpt. Process Systems Engineering Sandtorstraße 2 39106 Magdeburg Germany and Otto von Guericke University Magdeburg Chair for Process Systems Engineering Universitätsplatz 2 39106 Magdeburg (Sundmacher) Max Planck Institute Magdeburg
Dpt. Computational Methods in Systems and Control Theory and Otto von Guericke University Magdeburg Fakultät für Mathematik
The optimization of a controlled process in a simulation without access to the model itself is a common scenario and very relevant to many chemical engineering applications. A general approach is to apply a black-box ...
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The optimization of a controlled process in a simulation without access to the model itself is a common scenario and very relevant to many chemical engineering applications. A general approach is to apply a black-box optimization algorithm to a parameterized control scheme. The success then depends on the quality of the parametrization that should be low-dimensional though rich enough to express the salient features. This work proposes using solution snapshots to extract dominant modes of the temporal dynamics of a process and use them for low-dimensional parametrizations of control functions. The approach is called proper orthogonal decomposition in time (time-POD). We provide theoretical reasoning and illustrate the performance for the optimal control of a methanation reactor.
Filamentous fungal cell factories are efficient producers of platform chemicals, proteins, enzymes and natural products. Stirred-tank bioreactors up to a scale of several hundred m³ are commonly used for their cu...
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Catalytic filters including catalytic bag filters and catalytic filter candles, which couple the filters with denitrification catalysts to obtain the ability to simultaneously remove SOx, NOx and dust, have become the...
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Catalytic filters including catalytic bag filters and catalytic filter candles, which couple the filters with denitrification catalysts to obtain the ability to simultaneously remove SOx, NOx and dust, have become the promising applied technology for the integrated flue gas treatment because of their huge advantages in reducing the initial investment, floor occupancy and maintenance cost. In this review, we will summarize the recent advances in the development of catalytic filters in terms of their process principles, filter material, denitrification catalysts, structure-function relationships and industrial applications. Moreover, suggestions about the current challenges and future opportunities are also given from the viewpoints of catalysts and filter material design, catalytic filter preparation methods, and their poisoning and regeneration, etc. With the further development of theory and engineering research, the extensive industrial application of catalytic filters in the field of multiple pollutants flue gas treatment is highly anticipated in the future.
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