检索结果-内蒙古大学图书馆

25th European Signal Processing Conference (EUSIPCO)

作者： Zhou, Lin Khan, Fahd Ahmed Ratnarajah, Tharmalingam Papadias, Constantinos B. Univ Edinburgh Inst Digital Commun Edinburgh Midlothian Scotland Natl Univ Sci & Technol Sch Elect Engn & Comp Sci Islamabad Pakistan Athens Informat Technol Athens Greece

ISBN: (纸本)9780992862671

Electronically steerable parasitic array radiator (ESPAR) technology provides multi-antenna transmission with a single radio frequency (RF) unit. In order to achieve stable transmission using an ESPAR antenna (EA), two approaches have been proposed in literature. One is to increase the self-resistance of an EA, the other is to transmit signals closely approximating the actual signals that keep the EA stable. In both approaches, no constraint on the transmission power of an EA was considered. This is not the case in actual systems, as the practical power amplifier normally has limited peak power. Taking into account the limited power availability, an optimization problem is formulated with the objective to minimize the MSE between the currents corresponding to the ideal and the approximate transmission signals. The non-convex problem is solved analytically by coordination transformation and a novel algorithm is proposed. It is shown that the system employing the proposed transmission scheme gives similar performance to that of a standard multiple antenna system, especially at low SNRs. In addition, it is shown that increasing the self-resistance of an EA to achieve stability is highly power inefficient.

关键词： Reconfigurable antenna ESPAR MIMO transmission single RF chain optimization

来源：评论

学校读者我要写书评

暂无评论

Accelerated Inference in Markov Random Fields via Smooth Riemannian optimization

arXiv

引用

arXiv 2018年

作者： Hu, Siyi Carlone, Luca Laboratory for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States

Markov Random Fields (MRFs) are a popular model for several pattern recognition and reconstruction problems in robotics and computer vision. Inference in MRFs is intractable in general and related work resorts to approximation algorithms. Among those techniques, semidefinite programming (SDP) relaxations have been shown to provide accurate estimates while scaling poorly with the problem size and being typically slow for practical applications. Our first contribution is to design a dual ascent method to solve standard SDP relaxations that takes advantage of the geometric structure of the problem to speed up computation. This technique, named Dual Ascent Riemannian Staircase (DARS), is able to solve large problem instances in seconds. Our second contribution is to develop a second and faster approach. The backbone of this second approach is a novel SDP relaxation combined with a fast and scalable solver based on smooth Riemannian optimization. We show that this approach, named Fast Unconstrained SEmidefinite Solver (FUSES), can solve large problems in milliseconds. Contrarily to local MRF solvers, e.g., loopy belief propagation, our approaches do not require an initial guess. Moreover, we leverage recent results from optimization theory to provide per-instance sub-optimality guarantees. We demonstrate the proposed approaches in multi-class image segmentation problems. Extensive experimental evidence shows that (i) FUSES and DARS produce near-optimal solutions, attaining an objective within 0.1% of the optimum, (ii) FUSES and DARS are remarkably faster than general-purpose SDP solvers, and FUSES is more than two orders of magnitude faster than DARS while attaining similar solution quality, (iii) FUSES is faster than local search methods while being a global solver.65K05, 62F10, 65D19 Copyright © 2018, The Authors. All rights reserved.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

multiCRITERIA optimization OF DESIGN OF A GRID INTERFACED PV-WIND POWER SYSTEM

MULTICRITERIA OPTIMIZATION OF DESIGN OF A GRID INTERFACED PV...

引用

作者： ABUBOKAR TALUKDAR Bangladesh University Of Engineering And Technology

学位级别：硕士

Integrated renewable energy systems are considered to be the most promising sustainable power generation solution in the near future. Due to the technological developments in the last two decades, development of renewable energy system has greatly increased to face the negative environmental impact caused by conventional energy sources and continuous rising of prices of conventional fuels (coal, gas and oil etc). Among several renewable energy source alternatives, hybrid configuration of photovoltaic (PV) and wind turbine (WT) system steadily connected to the grid is a potential resource. Development of an optimal design approach for hybrid configuration of PV-WT system interfaced to a grid requires to take into consideration of different system variables such as potential solar irradiance and wind speed profiles, daily load profile, technical specifications of devices used in PV-WT system, grid electricity cost, initial investment cost, operation and maintenance cost of PV-WT system, land availability and cost of land etc. Optimal balancing among PV, WT and grid electricity requires particular attention to achieve a good engineering solution. In previous literature, different design approaches represented by several optimization algorithms such as particle swarm optimization (PSO), genetic algorithm, modified PSO, discrete harmony search algorithm etc are used to make hybrid configuration of renewable energy sources. These optimization algorithms are used to minimize single objective function, this objective function is represented mainly by the total system cost. Sometimes technical and/ or economical and/ or environmental requirements are converted into a single mathematical expression to make single objective function. These methods are not always practical since some of the system variables might not be easily converted into a single unified unit. multi- criteria optimization without the need for conversion of several design criteria to a single function is a v

关键词：

来源：评论

学校读者我要写书评

暂无评论

Algorithm design and analysis of optimal decision problem

Boletin Tecnico/Technical Bulletin

引用

Boletin Tecnico/Technical Bulletin 2017年第16期55卷 326-330页

作者： Zhang, Xin Army Military Transportation University Zhenjiang Campus Zhenjiang212003 China

optimization theory is mainly using mathematics method to study optimization approaches and solutions of various systems, providing scientific decision-making basis for policymakers solving practical problems. It has the features of optimal choice for discuss decision-making problem. multi-objective optimization is an important branch of mathematical programming, and it has broad application in engineering design, economic planning, project management and other fields. This paper introduces the multi-objective optimization problems in real life as well as two kinds of algorithms solving multi-objective optimization problem and also the advantages and disadvantages of each algorithm. Then dynamic programming algorithm solving optimization problems in segmentation process has been discussed. The optimization decision-making problems presented in various fields in recent years has been listed, finally the future development of optimization decision problem algorithm is prospected.

关键词： multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

A knee-based multi-objective evolutionary algorithm: an extension to network system optimization design problem

引用

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2016年第1期19卷 411-425页

作者： Sudeng, Sufian Wattanapongsakorn, Naruemon KMUTT Fac Engn Dept Comp Engn Bangkok Thailand

High performance computing (HPC) research is confronted with multiple competing goals such as reducing makespan and reducing cost in clouds. These competing goals must be optimized simultaneously. multi-objective optimization techniques to tackle such HPC problems have received significant research attention. Most multi-objective optimization approaches provide a large number of potential solutions. Choosing the best or most preferred solution becomes a problem. In some practical contexts, even if the decision maker does not have an explicit preference, there exist the regions of the solution space that can be viewed as implicitly preferred because of the way the problem has been formulated. Solutions located in these regions are called "knee solutions". Evolutionary approaches have become popular and effective in solving complex and large problems that require HPC. The aim of this paper is to develop a knee-based multi-objective evolutionary algorithm (MOEA) which can prune the set of optimal solutions with a controllable parameter to focus on knee regions. The proposed approach uses a concept called extended dominance to guide the solution process towards knee regions. A user-supplied density controller parameter determines the number of preferred solutions retained. We verify our approach using two and three-objective knee-based test problems. The results show that our approach is competitive with other well-known knee-based MOEAs when evaluated by a convergence metric. We then apply the approach to a network optimization design problem in order to demonstrate how it can be useful in a practical context related to HPC.

关键词： multi-objective optimization Cloud computing Knee-based MOEAs Knee solutions High performance computing Network optimization

来源：评论

学校读者我要写书评

暂无评论

multi-objective optimization of expensive electromagnetic simulation models

引用

APPLIED SOFT COMPUTING 2016年 47卷 332-342页

作者： Koziel, Slawomir Bekasiewicz, Adrian Reykjavik Univ Sch Sci & Engn IS-101 Reykjavik Iceland Gdansk Univ Technol Fac Elect Telecommun & Informat PL-80233 Gdansk Poland

Vast majority of practical engineering design problems require simultaneous handling of several criteria. For the sake of simplicity and through a priori preference articulation one can turn many design tasks into single-objective problems that can be handled using conventional numerical optimization routines. However, in some situations, acquiring comprehensive knowledge about the system at hand, in particular, about possible trade-offs between conflicting objectives may be necessary. This calls for multi objective optimization that aims at identifying a set of alternative, Pareto-optimal designs. The most popular solution approaches include population-based metaheuristics. Unfortunately, such methods are not practical for problems involving expensive computational models. This is particularly the case for microwave and antenna engineering where design reliability requires utilization of CPU-intensive electromagnetic (EM) analysis. In this work, we discuss methodologies for expedited multi-objective design optimization of expensive EM simulation models. The solution approaches that we present here rely on surrogate-based optimization (SBO) paradigm, where the design speedup is obtained by shifting the optimization burden into a cheap replacement model (the surrogate). The latter is utilized for generating the initial approximation of the Pareto front representation as well as further front refinement (to elevate it to the high-fidelity EM simulation model level). We demonstrate several application case studies, including a wideband matching transformer, a dielectric resonator antenna and an ultra-wideband monopole antenna. Dimensionality of the design spaces in the considered examples vary from six to fifteen, and the design optimization cost is about one hundred of high-fidelity EM simulations of the respective structure, which is extremely low given the problem complexity. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Computer-aided design (CAD) Computational electromagnetics Electromagnetic (EM)-simulation models Simulation-driven design multi-objective optimization Surrogate modeling Evolutionary algorithms Space mapping

来源：评论

学校读者我要写书评

暂无评论

Many objective Particle Swarm optimization

引用

INFORMATION SCIENCES 2016年 374卷 115-134页

作者： Figueiredo, E. M. N. Ludermir, T. B. Bastos-Filho, C. J. A. Univ Fed Pernambuco Ctr Informat Rua Estrela 52 BR-52060160 Recife PE Brazil Univ Pernambuco Escola Politecn Pernambuco Recife PE Brazil

Many-objective problems refer to the optimization problems containing more than three conflicting objectives. To obtain a representative set of well-distributed non-dominated solutions close to Pareto front in the objective space remains a challenging problem. Many papers have proposed different multi-objective Evolutionary Algorithms to solve the lack of the convergence and diversity in many-objective problems. One of the more promising approaches uses a set of reference points to discriminate the solutions and guide the search process. However, this approach was incorporated mainly in multi-objective Evolutionary Algorithms, and there are just some few promising adaptations of Particle Swarm optimization approaches for effectively tackling many-objective problems regarding convergence and diversity. Thus, this paper proposes a practical and efficient Many-objective Particle Swarm optimization algorithm for solving many-objective problems. Our proposal uses a set of reference points dynamically determined according to the search process, allowing the algorithm to converge to the Pareto front, but maintaining the diversity of the Pareto front. Our experimental results demonstrate superior or similar performance when compared to other state-of-art algorithms. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Many-objective problems multi-objective Evolutionary Algorithms Particle Swarm optimization Swarm intelligence

来源：评论

学校读者我要写书评

暂无评论

multi-objective optimization in sensor networks: optimization classification, applications and solution approaches

引用

COMPUTER NETWORKS 2016年 99卷 134-161页

作者： Iqbal, M. Naeem, M. Anpalagan, A. Qadri, N. N. Imran, M. COMSATS Inst Informat Technol Dept Elect Engn Wah Campus Islamabad Pakistan Ryerson Univ Dept Elect & Comp Engn Toronto ON M5B 2K3 Canada BZU Univ Coll Engn & Technol Dept Comp Engn Multan Pakistan

A number of the practical scenarios relating to sensor networks are modeled as multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to choose one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To tackle different nature of optimization problems relating to sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We analyze the existing literature to show the trend of the research community with respect to sensor network technologies being used, different engineering applications, simulation tools being used and the research emanating from different geographical areas. We also present a generic resource allocation problem in sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also tabulated to give an overview of different constraints which are considered while formulating the optimization problem in sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to sensor networks. (C) 2016 Elsevier B.V. All rights reserved.

关键词： multi-objective optimization Sensor networks Algorithms

来源：评论

学校读者我要写书评

暂无评论

“人在回路”思想在飞机气动优化设计中演变与发展

引用

空气动力学学报 2017年第4期35卷 529-543页

作者：李润泽张宇飞陈海昕清华大学航天航空学院北京100084

近年来优化设计得到了大量的研究和发展,多学科、多设计点、多目标、多约束、鲁棒优化等优化算法已经相对成熟,但这些算法在工程设计中的实际应用尚有诸多困难。从优化在工程设计中应用的发展历程及其遇到的问题入手,介绍"人在回路... 详细信息

近年来优化设计得到了大量的研究和发展,多学科、多设计点、多目标、多约束、鲁棒优化等优化算法已经相对成熟,但这些算法在工程设计中的实际应用尚有诸多困难。从优化在工程设计中应用的发展历程及其遇到的问题入手,介绍"人在回路"作为一种面向工程的优化设计思想的内涵、实施方式和历史沿革。以气动优化设计为例阐述"人在回路"思想在优化设计各个环节中的实现方式和表现。另一方面,当下人工智能的发展使得计算机在非线性映射构建、数据挖掘等方面体现出了超越人能力的性能,从而进一步探讨人工智能在优化设计中替代"人在回路"的作用,分析面向工程的优化设计几个可能的发展方向。

关键词：空气动力学优化设计人在回路人工智能

来源：评论

学校读者我要写书评

暂无评论

multi-objective approach for robot motion planning in search tasks

引用

APPLIED INTELLIGENCE 2016年第2期45卷 305-321页

作者： Jeddisaravi, Kossar Alitappeh, Reza Javanmard Pimenta, Luciano C. A. Guimaraes, Frederico G. Univ Fed Minas Gerais BR-31270901 Belo Horizonte MG Brazil

This work addresses the problem of single robot coverage and exploration in an environment with the goal of finding a specific object previously known to the robot. As limited time is a constraint of interest we cannot search from an infinite number of points. Thus, we propose a multi-objective approach for such search tasks in which we first search for a good set of positions to place the robot sensors in order to acquire information from the environment and to locate the desired object. Given the interesting properties of the Generalized Voronoi Diagram, we restrict the candidate search points along this roadmap. We redefine the problem of finding these search points as a multi-objective optimization one. NSGA-II is used as the search engine and ELECTRE I is applied as a decision making tool to decide among the trade-off alternatives. We also solve a Chinese Postman Problem to optimize the path followed by the robot in order to visit the computed search points. Simulation results show a comparison between the solution found by our method and solutions defined by other known approaches. Finally, a real robot experiment indicates the applicability of our method in practical scenarios.

关键词： Mobile robot exploration Area coverage multi-objective optimization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：