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检索条件"主题词=Optimization algorithms"

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Sizing of hybrid energy storage system in independent microgrid based on BP neural network

Sizing of hybrid energy storage system in independent microg...

引用

2nd IET Renewable Power Generation Conference (RPG 2013)

作者： Chengchen Sun Yue Yuan College of Energy and Electrical Engineering Hohai University No.8 Focheng Road (West) Nanjing Jiangsu 211100 China

ISBN: (纸本)9781629936017

As a result of the uncertainties and significant fluctuations of both power generation and consumption in a microgrid, the battery energy storage system (BESS) endures large oscillations in absorbing and releasing active power. This paper proposes a hybrid energy storage system (HESS) composed of supercapacitors and batteries. An optimal design method of HESS capacity is introduced so that HESS can meet the technical requirements, such as smoothing out the wind power fluctuations and the economic requirement in a microgrid. Based on the level of smoothing (LOS) of power curves, a neural network model was established to reflect the interrelationship between characteristic parameters of HESS and LOS of output power transmitted to the independent microgrid. Besides, taking the economic demand into consideration, a long-term mathematical model was built. Optimal algorithm was used to determine optimal size of HESS by optimizing the objective function, which was derived from the built model. Finally, an example indicates the effectiveness of the proposed method.

关键词： Independent microgrid Hybrid energy storage system Neural network battery energy storage system Neural network THERMAL ENERGY STORAGE SYSTEMS flywheel energy storage system SIZING MICRO-GRID energy storage optimization algorithms HYBRID Neural Networks (Computer) Objective function Supercapacitors

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Optimized Digital Compatible Pulse Sequences for Testing of RF Front End Modules

Optimized Digital Compatible Pulse Sequences for Testing of ...

引用

IEEE International Mixed-Signals, Sensors and Systems Test Workshop

作者： Aritra Banerjee Shyam Kumar Devarakond Vishwanath Natarajan Shreyas Sen Abhijit Chatterjee School of Electrical and Computer Engineering Georgia Institute of Technology

ISBN: (纸本)9781424477920;9781424477937

Due to the escalating costs associated with testing of precision RF components, there is a pressing need for identifying novel test methods that allow RF circuits to be tested with low cost test instrumentation. Specifically, test costs can be lowered significantly if stimulus from a digital pattern generator can be adapted to serve as test stimulus for analog/RF circuits. In this paper, we propose a novel digital pulse based test signal generation technique for testing RF circuits. An optimized pulse stream containing a combination of spectral components within a specified RF frequency band is used as the test stimulus. The optimization is performed using a genetic optimization algorithm and allows gain and nonlinearity specifications to be computed accurately from the observed (down-converted) test response. Simulation data for an RF power amplifier shows excellent correlation of the test results with the test specification values of the device under test (DUT) while allowing all the specifications to be measured from a single data acquisition.

关键词： RF circuit RF power amplifier Data Collection Methods optimization algorithms radiofrequency Stimuli Spectral composition front-ends Test Method Specifications Pulses Vital Signs Frequency bands

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Design and optimization of LNAs through the Scattering Parameters

Design and Optimization of LNAs through the Scattering Param...

引用

IEEE Mediterranean Electrotechnical Conference

作者： M. Boughariou M. Fakhfakh M. Loulou University of Sfax

ISBN: (纸本)9781424457939

This brief presents a symbolic approach for the optimal design of low noise amplifiers through the scattering parameters. An optimization algorithm, which is also proposed in this paper, generates the optimal values of all the design parameters that meet imposed specifications on the scattering parameters. ADS simulations, using 0.35u.m CMOS technology, are presented to show the good agreement between theoretical and simulation results.

关键词： scattering parameters optimization algorithms Optimal design Low-noise amplifiers

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Model-Based Sediment Characterization Using Multibeam Angular Backscatter Data

Model-Based Sediment Characterization Using Multibeam Angula...

引用

OCEANS

作者： Haiyan Ni Wenbo Wang Qunyan Ren Licheng Lu Jinrong Wu Li Ma University of Chinese Academy of Sciences Beijing China Institute of Acoustics Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781728114521

This paper discusses a model-based approach to sediment characterization using multibeam backscatter data collected offshore from Qingdao, China in 2018. In this experiment, the NORBIT wideband multibeam sonar was utilized to collect backscatter data, the angular response curves of which reflect sediment-type differences. For the synthetic analysis of experimental data, a high-frequency backscatter model based on small-slope approximation and volume-scattering theory was adapted to simulate the variation of the backscatter strength with the incident angle. Afterward, these generated model replicas were matched with experimental backscatter data using a simulated annealing optimization algorithm to estimate sediment parameters, such as sediment-water sound-speed ratio, density ratio, roughness spectral parameters, volume-scattering strength, and acoustics attenuation. The preliminary inversion results are basically consistent with the experimental data on sediment distribution and texture.

关键词： Multibeam Backscatter data High-frequency backscatter model Simulated annealing optimization algorithm backscattering Sediment distribution Deposits Angle simulated annealing algorithm Experimental data optimization algorithms

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Welding Parameter optimization Based on Gaussian Process Regression Bayesian optimization Algorithm

Welding Parameter Optimization Based on Gaussian Process Reg...

引用

IEEE International Conference on Automation Science and Engineering

作者： Dillon Sterling Tyler Sterling YuMing Zhang Heping Chen Ingram School of Engineering Texas State University Department of Electrical and Computer Engineering University of Kentucky

ISBN: (纸本)9781467381840

In welding processes, welding parameters have a significant impact on weld quality and mechanical properties of welded joints. For example, if the welding current is not tuned properly, the welding arc becomes unstable which will cause an unacceptable weld. Therefore welding parameters must be optimized in order to achieve best weld quality. However current methods have many limitations in exploring optimal welding parameters. In this paper, Gaussian Process Regression is applied to model the relationship between the welding performance indices and welding parameters. Bayesian optimization Algorithm is adopted to balance the modeling and optimization processes and optimize welding parameters. Experiments were performed for the Gas tungsten arc welding (GTAW) process and the results demonstrate the effectiveness of the proposed algorithm. Compared to the existing methods, the proposed method greatly improves the welding parameter optimization process;moreover it can be applied with fewer experiments compared with existing methods which will reduce the testing cost and effort.

关键词： Welding parameters Parameter optimization Weld quality Gas tungsten arc welding optimization Procedure Welding current performance indicators Solder Joint Device Component optimization algorithms TUNGSTEN INERT GAS WELDING Welding Welding technology Bayesian inference Gauss Unstable

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Symmetric Upwind Scheme for Discrete Weighted Total Variation

Symmetric Upwind Scheme for Discrete Weighted Total Variatio...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Sonia Tabti Julien Rabin Abderrahim Elmoata Normandie Univ UNICAEN ENSICAEN CNRS GREYC 14000 Caen France

ISBN: (纸本)9781538646595

This paper is devoted to the study of the discrete formulations of the weighted Total Variation (TV) based on upwind schemes that have been proposed for imaging problems in a local setting in [1] and in a non-local setting for graphs and point-clouds in [2]. We focus on two new symmetric formulations based on the I~2 and I~∞ norms respectively and propose a dedicated optimization algorithm to solve convex problems based on such TV penalties. We demonstrate the theoretical and practical interest of such formulations for image processing tasks.

关键词： Weighted Total Variation Upwind scheme image denoising isotropy preservation co-area formula upwind scheme image denoising Area formula CENTROSYMMETRIC image processing task optimization algorithms Television

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Compressive imaging with iterative forward models

Compressive imaging with iterative forward models

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Hsiou-Yuan Liu Ulugbek S. Kamilov Dehong Liu Hassan Mansour Petros T. Boufounos Department of Electrical Engineering & Computer Sciences University of California Berkeley 94720 USA Mitsubishi Electric Research Laboratories (MERL) Cambridge MA 02139 USA

ISBN: (纸本)9781509041183

We propose a new compressive imaging method for reconstructing 2D or 3D objects from their scattered wave-field measurements. Our method relies on a novel, nonlinear measurement model that can account for the multiple scattering phenomenon, which makes the method preferable in applications where linear measurement models are inaccurate. We construct the measurement model by expanding the scattered wave-field with an accelerated-gradient method, which is guaranteed to converge and is suitable for large-scale problems. We provide explicit formulas for computing the gradient of our measurement model with respect to the unknown image, which enables image formation with a sparsity-driven numerical optimization algorithm. We validate the method both analytically and with numerical simulations.

关键词： Computational imaging inverse scattering sparse reconstruction total variation regularization inverse scattering Scattering optimization algorithms Image formation Imaging Linear measurement Inaccurate Iterative

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ECC: Platform-Independent Energy-Constrained Deep Neural Network Compression via a Bilinear Regression Model

ECC: Platform-Independent Energy-Constrained Deep Neural Net...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Haichuan Yang Yuhao Zhu Ji Liu University of Rochester Kwai Seattle AI Lab

ISBN: (纸本)9781728132945

Many DNN-enabled vision applications constantly operate under severe energy constraints such as unmanned aerial vehicles, Augmented Reality headsets, and smart-phones. Designing DNNs that can meet a stringent energy budget is becoming increasingly important. This paper proposes ECC, a framework that compresses DNNs to meet a given energy constraint while minimizing accuracy loss. The key idea of ECC is to model the DNN energy consumption via a novel bilinear regression function. The energy estimate model allows us to formulate DNN compression as a constrained optimization that minimizes the DNN loss function over the energy constraint. The optimization problem, however, has nontrivial constraints. Therefore, existing deep learning solvers do not apply directly. We propose an optimization algorithm that combines the essence of the Alternating Direction Method of Multipliers (ADMM) framework with gradient-based learning algorithms. The algorithm decomposes the original constrained optimization into several subproblems that are solved iteratively and efficiently. ECC is also portable across different hardware platforms without requiring hardware knowledge. Experiments show that ECC achieves higher accuracy under the same or lower energy budget compared to state-of-the-art resource-constrained DNN compression techniques.

关键词： Error correction codes Elliptic curve cryptography EMERGENCY CONTROL CENTERS Hardware Platform Constrained optimization Energy budget optimization algorithms bilinearity platform-independent Energy sources Essences simple regression (model)

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Approximate Optimal Control of Nonlinear Systems with Quadratic Performance Criteria

Approximate Optimal Control of Nonlinear Systems with Quadra...

引用

American Control Conference

作者： Robert A. E. Zidek Ilya V. Kolmanovsky Department of Aerospace Engineering University of Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9781479917730

A method is presented that approximates the solution to a nonlinear optimal control problem with quadratic cost function. We assume that the nonlinear system is accurately represented by a high-fidelity (hf) model which can be of high complexity or even of "black-box" type. The hf-model is oftentimes unsuitable for solving the optimal control problem. The proposed solution method is based on an Iterative Model and Trajectory Refinement (IMTR) strategy that uses a low-fidelity (lf) model to solve the optimal control problem. The lf-model is obtained through linearization of the hf-model, where the linearization point is variable by the optimization algorithm. The method is demonstrated for two problems of orbital transfer and of underactuated spacecraft attitude control with two reaction wheels. In both examples the solutions are shown to be in good agreement with the optimal solutions obtained by solving the respective nonlinear two-point-boundary value problem.

关键词： Optimal control reaction wheels nonlinear optimal control space vehicle attitude control TRANSFER ORBITS Linearization Performance metrics Quadratic optimal solution optimization algorithms

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State-Constrained Optimal Spatial Field Control for Controlled Release in Tissue Engineering

State-Constrained Optimal Spatial Field Control for Controll...

引用

American Control Conference

作者： Masako Kishida Daniel W. Pack Richard D. Braatz University of Illinois at Urbana-Champaign Urbana IL 61801 USA

ISBN: (纸本)9781424474264

Distributed parameter control problems involving manipulation within the spatial domain arise in a variety of applications including vibration control, active noise reduction, epidemiology, tissue engineering, and cancer treatment. A state-constrained spatial field control problem motivated by a biomedical application is solved in which the manipulation occurs over a spatial field and the state field is constrained both in spatial frequency and by a partial differential equation (PDE) that effects the manipulation. An optimization algorithm combines three-dimensional Fourier series, which are truncated to satisfy the spatial frequency constraints, with exploitation of structural characteristics of the PDEs. The computational efficiency and performance of the optimization algorithm are demonstrated in a numerical example, for which the spatial tracking error is almost entirely due to the limitation on the spatial frequency of the manipulated field. The numerical results suggest that optimal control approaches have promise for controlling the release of macromolecules in tissue engineering applications.

关键词： field control Controlled Release Dosage Form spatial frequency Tissue Engineering optimization algorithms spatial domain Fourier series cancer therapy numerical example Structural properties

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