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Hawaii International Conference on System Sciences

作者： Seyyed H. Elyas Zhifang Wang Virginia Commonwealth Univ. Richmond VA USA

ISBN: (纸本)9781509019816

Our previous work proposed a random topology power grid model, called RT-nested-smallworld, formulated based on a comprehensive study of the real-world grid topologies and electrical properties. The model can be used to produce a sufficiently large number of power grid test cases with scalable network size and featuring the same kind of small-world topology and electrical characteristics found in realistic grids. However, the proposed RT-power grid model has a shortcoming that is its random assignment of bus types (i.e. Generation, Load, or Connection), which is inconsistent with that of a realistic grid. Because our recent study found that the bus type assignment in a realistic power grid is not random but a correlated one. With the help of Bus Type Entropy, a novel measure which provides a quantitative means to better represent the correlation of bus type assignments in a grid topology, we propose a multi-objective optimization algorithm for the bus type assignments in the random topology power grid modeling. The proposed search algorithm is able to locate the best set of bus type assignments for a given random "electrical" topology generated by RT-nested-smallworld, so that each bus type assignment in the target set will have a Bus Type Entropy value close to that of a realistic grid with comparable network size. In order to demonstrate the performance of the proposed multi-objective algorithm, we experiment our algorithm on three sets of realistic power systems, namely, the IEEE-300 bus system, the NYISO system and the MPC system. The obtained results confirm the validity of our proposed method.

关键词： Power grids Motor buses optimization algorithms SEARCHING ALGORITHM topology target setting Room temperature grids Grid Randomisation

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optimization of overlapping activities in the design phase of construction projects

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AUTOMATION IN CONSTRUCTION 2015年 59卷 81-95页

作者： Dehghan, Reza Hazini, Kamran Ruwanpura, Janaka Univ Calgary Ctr Project Management Excellence Schulich Sch Engn Calgary AB T2N 1N4 Canada

A well-known practice to accelerate construction projects is to overlap the design phase activities. For a typical construction project, a number of overlapping strategies exist during the design phase which all can result in timesaving. However, the cost of these strategies varies significantly depending on the total rework and complexity they generate. A favorable overlapping strategy is one that generates the required timesaving at the minimum cost. To find such a strategy, the question "Which activities have to be overlapped and to what extent to reduce the project duration at the minimum cost?" should be answered. This research aimed at answering the question through generating an overlapping optimization algorithm. The algorithm works based on the principles of genetic algorithms (GAs). The algorithm explained in the paper is unique compared to previous algorithms and frameworks available in the literature, as it can optimize multi-path networks and can handle all types of activity dependencies (i.e. finish-to-start, start-to-start, and finish-to-finish). It also takes both critical and non-critical activities into account and follows the critical path if the critical path changes or new critical paths emerge. A computer tool was also developed to run, examine and validate the overlapping optimization algorithm. This paper introduces the algorithm and the computer tool in detail and explains the results of their validation through optimizing a real-world project schedule. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Overlapping Fast-tracking optimization algorithms Time-cost trade-off Rework Construction management

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Asymptotic normality and efficiency analysis of the cyclic seesaw stochastic optimization algorithm

Asymptotic normality and efficiency analysis of the cyclic s...

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American Control Conference

作者： Karla Hernandez James C. Spall Department of Applied Mathematics and Statistics The Johns Hopkins University Baltimore MD USA Johns Hopkins University Applied Physics Laboratory and with the JHU Department of Applied Mathematics and Statistics Baltimore MD USA

ISBN: (纸本)9781467386838

In many real-world optimization problems, the loss function to minimize is unknown and only observable in the presence of noise. In other words, it is possible to obtain a noisy measurement of the loss function at a given input parameter. Here, stochastic optimization algorithms (which are often iterative in nature) can be used for optimization. Cyclic stochastic optimization is a type of stochastic optimization where only a subset of the parameter vector is updated at a time. While cyclic algorithms are commonly used in practice, most of the results on the asymptotic behavior of the iterates are restricted to the deterministic optimization setting (where the loss function is known). In this paper, we derive asymptotic normality results for the iterates produced by cyclic stochastic optimization algorithms. We also define and investigate the relative efficiency between a stochastic optimization algorithm and its cyclic formulation.

关键词： multi-agent optimization stochastic gradient alternating directions block coordinate descent simultaneous perturbation stochastic approximation SPSA optimization algorithms Stochastic LOSS FUNCTION normality Seesaws cyclic Noise input parameter

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Gradient-based optimization techniques for the design of static controllers for Markov jump linear systems with unobservable modes

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INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS 2015年第3期28卷 239-253页

作者： Vargas, Alessandro N. Bortolin, Daiane C. Costa, Eduardo F. do Val, Joao B. R. UTFPR BR-86300000 Cornelio Procopio PR Brazil Basque Ctr Appl Math E-48009 Bilbao Vizcaya Spain Univ Sao Paulo Dept Matemat Aplicada & Estat BR-13560970 Sao Carlos SP Brazil Univ Estadual Campinas UNICAMP FEEC DT BR-13083852 Campinas SP Brazil

The paper formulates the static control problem of Markov jump linear systems, assuming that the controller does not have access to the jump variable. We derive the expression of the gradient for the cost motivated by the evaluation of 10 gradient-based optimization techniques. The numerical efficiency of these techniques is verified by using the data obtained from practical experiments. The corresponding solution is used to design a scheme to control the velocity of a real-time DC motor device subject to abrupt power failures. Copyright (c) 2014 John Wiley & Sons, Ltd.

关键词： optimal control stochastic systems optimization algorithms DC motor device

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A PRACTICAL MEANS FOR THE optimization OF STRUCTURED LIGHT SYSTEM CALIBRATION PARAMETERS

A PRACTICAL MEANS FOR THE OPTIMIZATION OF STRUCTURED LIGHT S...

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IEEE International Conference on Image Processing

作者： Yuping Ye Zhan Song Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences

ISBN: (纸本)9781467399623

This paper presents a novel approach for the optimization of calibration parameters in structured light system (SLS). Different with conventional calibration algorithms, the proposed optimization algorithm is implemented in 3D space instead of 2D image space. The object used for parameter optimization can be a simple plane with some markers. A global optimal function is constructed to contain all the intrinsic and extrinsic parameters of the SLS. Using the primary calibration parameters by conventional methods as initial values, the optimal function can be solved by minimizing the 3D measurement errors like distance, angle between markers, and the planarity of the reference plane. Experimental results show that, 3D reconstruction accuracy can be greatly improved by the proposed approach in comparison with traditional SLS calibration methods.

关键词： Calibration Structured light system 3D reconstruction three-dimensional displays Calibration Optical Systems Selective laser sintering base face Parameter optimization Light optimization algorithms

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Alternating Information Bottleneck optimization for Weighted Sum Rate and Resource Allocation in the Uplink of C-RAN

Alternating Information Bottleneck Optimization for Weighted...

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WSA 2016;20th International ITG Workshop on Smart Antennas

作者： Di Chen Volker Kuehn

The quantizer design and resource allocation in the uplink of Cloud Radio Access Network (C-RAN) are studied. In C-RAN, multiple Radio Units (RUs) act as soft relays by compressing and forwarding the correlated received signals simultaneously to the Central Processor (CP) in the cloud, via the fronthaul links with finite capacities. Wyner-Ziv coding is utilized in order to exploit the correlation between the received signals at neighboring RUs. Thus, a joint optimization of the quantizers is necessary. Moreover, when the capacity resource is shared among fronthauls, the design of quantizers is closely related to the resource allocation. In this paper we aim to maximize the achievable weighted sum rate by joint optimizing all the quantizers and resource allocation. To make the problem more tractable, at first we assume that the resource allocation is predetermined, and perform a joint optimization of all quantizers, the optimization algorithm is a combination of the Alternating Information Bottleneck (AIB) method and the Alternating Bi-Section method, which is proposed in our previous work. We extend it to solve the problem in this work. Then we optimize the resource allocation based on it. The simulation results justify the correctness and effectiveness of our proposed algorithms.

关键词： Resource utilization quantizer up link management of resources received signal weight sums optimization algorithms Traffic Bottlenecks Clouds

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Simplified Design, optimization and Comparative Study of SPM, BLDC and Halbach Machines for Microsatellite Electro-Mechanical Batteries

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2015年第1期51卷 31-39页

作者： Abdi, B. Milimonfared, J. Moghani, J. S. Islamic Azad Univ Damavand Branch Tehran Iran Amirkabir Univ Technol Dept Elect Engn Tehran Iran

Electro-mechanical batteries (EMBs) have many important advantages compared with chemical batteries, especially in low Earth orbit (LEO) satellite applications. Different slotless, external rotor, permanent magnet (PM) machines are suggested and used in these systems as motor/generator. Synchronous permanent magnet (SPM), brushless dc (BLDC) and Halbach (HB) machines are three types that are most commonly used. In this paper, the best machine will be introduced using a comparative study. Proper material and structure for space applications are introduced and simplified. The analytic design methods are given for three types of machines. Then, all three types of machines are optimized in order to have a fair comparison. Particle swarm optimization (PSO) is used as the optimization algorithm and the finite element-based simulations are used to confirm the design and optimization process. Finally, the best machine is introduced based on the important parameters in a microsatellite.

关键词： BLDC Optimal design optimization Procedure Batteries electric machines Space applications machinery machine optimization algorithms Microsatellite Repeats Scanning Probe Microscopy

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Online Convex optimization with Unconstrained Domains and Losses 16

Online Convex Optimization with Unconstrained Domains and Lo...

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Annual Conference on Neural Information Processing Systems

作者： Ashok Cutkosky Kwabena Boahen Department of Computer Science Stanford University Department of Bioengineering Stanford University

ISBN: (纸本)9781510838819

We propose an online convex optimization algorithm (rescaledexp) that achieves optimal regret in the unconstrained setting without prior knowledge of any bounds on the loss functions. We prove a lower bound showing an exponential separation between the regret of existing algorithms that require a known bound on the loss functions and any algorithm that does not require such knowledge. RESCALEDEXP matches this lower bound asymptotically in the number of iterations. rescaledexp is naturally hyperparameter-free and we demonstrate empirically that it matches prior optimization algorithms that require hyperparameter optimization.

关键词： LOSS FUNCTION Regret optimization algorithms algorithms Prior Learning Lower bound asymptotic Require Hyperparameter Bound domains Phyla

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optimization Algorithm for Vehicle Braking Force Distribution of Front and Rear Axles Based on Brake Strength

Optimization Algorithm for Vehicle Braking Force Distributio...

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World Congress on Intelligent Control and Automation

作者： Jinlong Xu Xiangwen Zhang School of Electrical Engineering and Automation Guilin University of Electronic Technology Guilin 541000 China School of Electronic Engineering and Automation Guilin University of Electronic Technology Guilin 541000 China

ISBN: (纸本)9781467384155

The vehicle braking force distribution of front and rear axles affects its safety directly. In order to enhance the braking safety, the changes of brake strength are considered in this paper, and an optimization algorithm for braking force distribution is designed. The method is the variable ratio method, and it can change the braking force distribution rate of front and rear axles according to the brake strength variation, so its braking effect can be optimal. To verify the validity of the algorithm, two braking force distribution algorithm models with the fixed ratio and variable ratio were built in Matlab/Simulink respectively, and the simulation test was done based on the ASM model of dSPACE. The result show that the optimization distribution algorithm with variable ratio has better stability of braking direction, and higher adhesion coefficient utilization and shorter braking distance. Therefore, the optimization braking force distribution algorithm with the variable ratio is a promising method, and it can be used to improve the braking performance effectively, thereby to improve the vehicle safety ultimately.

关键词： Force Vehicles Wheels Adhesives Brakes Algorithm design and analysis optimization Brake variable ratio Algorithm design and analysis optimization algorithms Wheels rear axle vehicle Force adhesives Braking braking force vehicle brakes Axle simulation test Allocation Algorithm

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Stochastic optimization for Large-scale Optimal Transport 16

Stochastic Optimization for Large-scale Optimal Transport

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Annual Conference on Neural Information Processing Systems

作者： Aude Genevay Marco Cuturi Gabriel Peyre Francis Bach CEREMADE Universite Paris-Dauphine INRIA - Mokaplan project-team CREST ENSAE Universite Paris-Saclay CNRS and DMA Ecole Normale Superieure INRIA - Mokaplan project-team INRIA - Sierra project-team DI ENS

ISBN: (纸本)9781510838819

Optimal transport (OT) defines a powerful framework to compare probability distributions in a geometrically faithful way. However, the practical impact of OT is still limited because of its computational burden. We propose a new class of stochastic optimization algorithms to cope with large-scale OT problems. These methods can handle arbitrary distributions (either discrete or continuous) as long as one is able to draw samples from them, which is the typical setup in high-dimensional learning problems. This alleviates the need to discretize these densities, while giving access to provably convergent methods that output the correct distance without discretization error. These algorithms rely on two main ideas: (a) the dual OT problem can be re-cast as the maximization of an expectation; (b) the entropic regularization of the primal OT problem yields a smooth dual optimization which can be addressed with algorithms that have a provably faster convergence. We instantiate these ideas in three different setups: (i) when comparing a discrete distribution to another, we show that incremental stochastic optimization schemes can beat Sinkhorn's algorithm, the current state-of-the-art finite dimensional OT solver; (ii) when comparing a discrete distribution to a continuous density, a semi-discrete reformulation of the dual program is amenable to averaged stochastic gradient descent, leading to better performance than approximately solving the problem by discretization ; (iii) when dealing with two continuous densities, we propose a stochastic gradient descent over a reproducing kernel Hilbert space (RKHS). This is currently the only known method to solve this problem, apart from computing OT on finite samples. We backup these claims on a set of discrete, semi-discrete and continuous benchmark problems.

关键词： output transformer optimization algorithms Stochastic discrete data discretization error Benchmark testing algorithms discrete element Probability distribution

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