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Energy consumption will become one of the dominant cost factors that will govern the next generation of large HPC centers. In this paper we present the Dynamic Voltage Frequency Scaling (DVFS) Plugin to automatically tune several ...
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Energy consumption will become one of the dominant cost factors that will govern the next generation of large HPC centers. In this paper we present the Dynamic Voltage Frequency Scaling (DVFS) Plugin to automatically tune several energy related tuning objectives at a region-level of HPC applications. This plugin works with the Periscope Tuning Framework which provides an automatic tuning framework including analysis, experiment creation, and evaluation. The tuning actions are based on changes in the frequency via the DVFS. The tuning objectives include the tuning of energy consumption, total cost of ownership, energy delay product and power capping. The tuning is based on a model that relies on performance data and predicts energy consumption, time, and power consumption at different CPU frequencies. The derivation of the models for the DVFS plugin with the principal component analysis is included.
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Metasurfaces can be opportunely and specifically designed to manipulate electromagnetic wavefronts. In recent years, a large variety of metasurface-based optical devices such as planar lenses, beam deflectors, polarization convert...
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Metasurfaces can be opportunely and specifically designed to manipulate electromagnetic wavefronts. In recent years, a large variety of metasurface-based optical devices such as planar lenses, beam deflectors, polarization converters, and so on have been designed and fabricated. Of particular interest are tunable metasurfaces, which allow the modulation of the optical response of a metasurface; for instance, the variation in the focal length of a converging metalens. Response tunability can be achieved through external sources that modify the permittivity of the materials constituting the nanoatoms, the substrate, or both. The modulation sources can be classified into electromagnetic fields, thermal sources, mechanical stressors, and electrical bias. Beside this, we will consider optical modulation and multiple approach tuning strategies. A great variety of tunable materials have been used in metasurface engineering, such as transparent conductive oxides, ferroelectrics, phase change materials, liquid crystals, and semiconductors. The possibility of tuning the optical properties of these metamaterials is very important for several applications spanning from basic optics to applied optics for communications, depth sensing, holographic displays, and biochemical sensors. In this review, we summarize the recent progress on electro-optical magnetic, mechanical, and thermal tuning of metasurfaces actually fabricated and experimentally tested in recent years. At the end of the review, a short section on possible future perspectives and applications is included.
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Two multi-objective optimization based tuning methods for model predictive control are proposed. Both methods consider the minimization of the error between the closed-loop response and an output reference trajectory as tuning goa...
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Two multi-objective optimization based tuning methods for model predictive control are proposed. Both methods consider the minimization of the error between the closed-loop response and an output reference trajectory as tuning goals. The first approach is based on the ranking of the outputs according to their importance to the plant operation and it is solved by a lexicographic optimization algorithm. The second method solves a compromise optimization problem. The former is designed for systems in which the number of inputs is equal to the number of outputs, while the latter can also be applied to non-square systems. The main contribution is an automated tuning framework based on a straightforward goal definition. The proposed methods are tested on a finite horizon model predictive controller in closed-loop with a 3x3 subsystem of the Shell Heavy Oil Fractionator benchmark system. The simulation results show that the methods proposed here can be a useful tool to reduce the commissioning time of the controller. The methods are compared to an existing multi-objective optimization based tuning approach. The computational time required to run the proposed tuning algorithms is considerably reduced when compared to the existing approach and, moreover, it does not need an a posteriori decision to select a solution from a set of Pareto optimal solutions.
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In this article, the wavelength of a non-pumped light-emitting diode (LED) is shown to be practically linear with temperature, as a novel consequence of Varshni's empirical expression. The formula models the bandgap variation for ...
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In this article, the wavelength of a non-pumped light-emitting diode (LED) is shown to be practically linear with temperature, as a novel consequence of Varshni's empirical expression. The formula models the bandgap variation for most semiconductors from 0 K to their high-temperature limits, subject to fitting parameters. An external thermal mechanism is then suggested to tune the wavelength of a constant-current biased LED. The approach is first demonstrated on published aluminium nitride data on the 200 K-420 K temperature range, and shows that the fitting parameters follow trivially. Tests of the model on five commercial LEDs of different peak wavelengths show excellent agreement with the model over the 300 K-440 K temperature range. The standard error analysis shows high accuracy and linearity, with Pearson's coefficients, R-2 approximate to 0.99. The approach not only adds a novel tool to characterize semiconductors, but can form a temperature control strategy to stabilize LED wavelength in spectroscopic applications. (C) 2020 Elsevier Ltd. All rights reserved.
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Iterative Feedback Tuning constitutes an attractive control loop tuning method for processes in the absence of process insight. It is a purely data driven approach for optimization of the loop performance. The standard formulation...
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Iterative Feedback Tuning constitutes an attractive control loop tuning method for processes in the absence of process insight. It is a purely data driven approach for optimization of the loop performance. The standard formulation ensures an unbiased estimate of the loop performance cost function gradient, which is used in a search algorithm for minimizing the performance cost. A slow rate of convergence of the tuning method is often experienced when tuning for disturbance rejection. This is due to a poor signal to noise ratio in the process data. A method is proposed for increasing the data information content by introducing an optimal perturbation signal in the tuning algorithm. The theoretical analysis is supported by a simulation example where the proposed method is compared to an existing method for acceleration of the convergence by use of optimal prefilters.
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Traditionally the tuning of dynamic matrix control (DMC) type multivariable controllers is done by trial and error. The APC engineer chooses arbitrary starting values and tests the performance on a simulated controller. The engine...
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Traditionally the tuning of dynamic matrix control (DMC) type multivariable controllers is done by trial and error. The APC engineer chooses arbitrary starting values and tests the performance on a simulated controller. The engineer then either increases the values to suppress movement more, or decreases them to have the manipulated variables move faster. When the controller performs acceptably in simulation, then the tuning is improved during the commissioning of the controller on the plant. This is a time consuming and unscientific exercise and therefore often does not get the required attention. This leads to unacceptable controller behaviour during commissioning and sub-optimal control once commissioning is completed. This paper presents a new method to obtain move suppression factors for DMC type multivariable controllers by using a Nelder Mead search algorithm to find move suppressions that will provide acceptable control behaviour. Acceptable behaviour is described by characterising the dynamic move plan calculated by the controller for each of the manipulated variables.
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Performance tuning can improve the system performance and thus enable the reduction of cloud computing resources needed to support an application. Due to the ever increasing number of parameters and complexity of systems, there is...
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Performance tuning can improve the system performance and thus enable the reduction of cloud computing resources needed to support an application. Due to the ever increasing number of parameters and complexity of systems, there is a necessity to automate performance tuning for the complicated systems in the cloud. The state-of-the-art tuning methods are adopting either the experience-driven tuning approach or the data-driven one. Data-driven tuning is attracting increasing attentions, as it has wider applicability. But existing data-driven methods cannot fully address the challenges of sample scarcity and high dimensionality simultaneously. We present ClassyTune, a data-driven automatic configuration tuning tool for cloud systems. ClassyTune exploits the machine learning model of classification for auto-tuning. This exploitation enables the induction of more training samples without increasing the input dimension. Experiments on seven popular systems in the cloud show that ClassyTune can effectively tune system performance to seven times higher for high-dimensional configuration space, outperforming expert tuning and the state-of-the-art auto-tuning solutions. We also describe a use case in which performance tuning enables the reduction of 33 percent computing resources needed to run an online stateless service.
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Iterative feedback tuning is a direct tuning method using closed-loop experimental data. The method is based on numerical optimization and in each iteration an unbiased gradient estimate is used. Due to these unbiased gradient est...
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Iterative feedback tuning is a direct tuning method using closed-loop experimental data. The method is based on numerical optimization and in each iteration an unbiased gradient estimate is used. Due to these unbiased gradient estimates, the method converges to a stationary point of the control criterion provided the closed loop signals remain bounded throughout the iterations. In this contribution, it is shown how such unbiased estimates can be obtained for multivariable linear time-invariant systems. Particular attention is given to the issue of keeping the experiment time to a minimum and several efficient algorithms are presented. It is shown that, for tuning an arbitrary linear time-invariant multivariable controller with n_w inputs and n_u. outputs, l + n_u x n_w experiments are sufficient in each iteration of the algorithm. For disturbance rejection, an alternative algorithm is proposed which requires n_n + n_w experiments. As an illustration, the method is applied to a simulation model of a gas turbine engine. Copyright
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A system and method for the automated tuning of coupled resonator tunable filters is presented. The system and method are amenable to integration and are developed for on-board and on-chip automatic tuning of tunable filters witho...
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A system and method for the automated tuning of coupled resonator tunable filters is presented. The system and method are amenable to integration and are developed for on-board and on-chip automatic tuning of tunable filters without the use of a vector network analyzer and with minimal additional hardware. An analytical coupling matrix-based model of the tuning algorithm is developed to analyze and predict the performance of the tuning algorithm. The tuning model is verified with the automated tuning algorithm operating on a realized tunable filter. Finally, a low-cost hardware prototype for scalar transmission measurement for standalone implementation of the algorithm is also presented.
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