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Model-driven multi-quality auto-Tuning of robotic applications 15

Model-driven multi-quality auto-Tuning of robotic applicatio...

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Joint MORSE/VAO workshop on Model-Driven Robot Software Engineering and View-Based Software-Engineering, MORSE/VAO 2015

作者： Piechnick, Christian Götz, Sebastian Schöne, Rene Abmann, Uwe Technische Universität Dresden Nöthnitzer Str. 46 Dresden01187 Germany

ISBN: (纸本)9781450336147

For the Simultaneous Localization and Mapping (SLAM) problem, many implementations exist, which meet different requirements w.r.t. non-functional properties (e.g., performance). If those requirements change during runtime, the application should change the SLAM implementation. Implementing the selection of the optimal SLAM-algorithms for robots by hand is time consuming and leads to bad code maintainability by mixing application and adaptation logic. Moreover, the realization of the optimization in code requires the developer to re-implement parts of general purpose optimizers, which impairs reusability. An external adaptation logic selecting the optimal SLAM algorithm addresses the maintainability and programmability issues. A model-driven approach for both, application and selection problem, highly increases reusability. To reach these goals, we propose to use Multi-Quality Auto-Tuning (MQuAT), a model-driven approach to build and operate self-optimizing systems following the Models@*** paradigm. We evaluate our approach by a case study, where robots have to choose between several variants of a distributed SLAM algorithm. © 2015 ACM.

关键词： Reusability

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Energy optimization in a HW/SW tool: design of low power architecture system

Energy optimization in a HW/SW tool: design of low power arc...

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IEEE international workshop on System-on-Chip for Real-Time applications

作者： P. Guitton-Ouhamou C. Belleudy M. Auguin Lab. d'Informatique Signaux et Systemes de Sophia-Antipolis France

Minimizing power consumption in system on chip is a crucial task. So the parameter of consumption has to be introduced in HW/SW tool. This paper describes how our HW/SW codesign tool, CODEF, is extended to have power consumption and optimization ability. Some strategies of consumption optimizations are presented. First, we present how to build the library composed of consumption models of hardware and software modules (that take into account frequency and supply voltage). Then, we describe the algorithm that computes the peak power and the energy. To reduce the energy, we describe a strategy during allocation step to minimize energy. In this way, the partitioning algorithm has been modified and we present some results of architectures optimization with some important gains of 50%.

关键词： Design optimization Computer architecture Software libraries Hardware Processor scheduling Partitioning algorithms Clocks Energy consumption System-on-a-chip Frequency

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A Memetic Differential Evolution Algorithm for Continuous optimization

A Memetic Differential Evolution Algorithm for Continuous Op...

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international Conference on Intelligent Systems Design and applications (ISDA)

作者： Santiago Muelas Antonio La Torre José-María Peña CeSViMa Parque Tecnológico UPM Campus de Montegancedo Facultad de Informática Universidad Politécnica de Madrid Boadilla del Monte Madrid Spain CeSViMa Pozuelo de Alarcon Spain

Continuous optimization is one of the most active research lines in evolutionary and metaheuristic algorithms. Since CEC 2005 and CEC 2008 competitions, many different algorithms have been proposed to solve continuous problems. Despite there exist very good algorithms reporting high quality results for a given dimension, the scalability of the search methods is still an open issue. Finding an algorithm with competitive results in the range of 50 to 500 dimensions is a difficult achievement. This contribution explores the use of a hybrid memetic algorithm based on the differential evolution algorithm, named MDE-DC. The proposed algorithm combines the explorative/exploitative strength of two heuristic search methods, that separately obtain very competitive results in either low or high dimensional problems. This paper uses the benchmark problems and conditions required for the workshop on ¿evolutionary algorithms and other metaheuristics for "Continuous optimization Problems - A Scalability Test¿ chaired by Francisco Herrera and Manuel Lozano.

关键词： Evolution (biology) Search methods Large-scale systems Design optimization Scalability Evolutionary computation optimization methods Intelligent systems Benchmark testing Biology

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workshop 14: iWAPT Automatic Performance Tuning

Workshop 14: iWAPT Automatic Performance Tuning

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IEEE international Symposium on Parallel and Distributed Processing workshops and Phd Forum (IPDPSW)

作者： I-Hsin Chung Kazuhiko Komatsu IBM T. J. Watson Research Center USA Tohoku University Japan

ISBN: (数字)9781728174457

ISBN: (纸本)9781728174570

iWAPT (international workshop on Automatic Performance Tuning) is a series of workshops that focus on research and techniques related to performance sustainability issues. The series provides an opportunity for researchers and users of automatic performance tuning (AT) technologies to exchange ideas and experiences acquired when applying such technologies to improve the performance of algorithms, libraries, and applications; in particular, on cutting edge computing platforms. Topics of interest include performance modeling; adaptive algorithms; autotuned numerical algorithms; libraries and scientific applications; empirical compilation; automated code generation; frameworks and theories of AT and software optimization; autonomic computing; and context-aware computing. iWAPT 2020 is the fifteenth in a series of successful workshops devoted to AT. The series started in Tokyo in 2006.

关键词： Conferences Tuning Software algorithms Libraries Software Computer science Distributed processing

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Hardware Software Partitioning using Particle Swarm optimization Technique

Hardware Software Partitioning using Particle Swarm Optimiza...

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IEEE international workshop on System-on-Chip for Real-Time applications

作者： M. B. Abdelhalim A. E. Salama S. E.-D. Habib Faculty of Engineering Cairo University Giza Egypt

ISBN: (纸本)1424408989

In this paper the authors investigate the application of the particle swarm optimization (PSO) technique for solving the hardware/software partitioning problem. The PSO is attractive for the hardware/software partitioning problem as it offers reasonable coverage of the design space together with O(n) main loop's execution time, where n is the number of proposed solutions that will evolve to provide the final solution. The authors carried out several tests on a hypothetical, relatively-large hardware/software partitioning problem using the PSO algorithm as well as the genetic algorithm (GA), which is another evolutionary technique. The authors found that PSO outperforms GA in the cost function and the execution time. For the case of unconstrained design problem, the authors tested several hybrid combinations of PSO and GA algorithm; including PSO then GA, GA then PSO, GA followed by GA, and finally PSO followed by PSO. We found that a PSO followed by GA algorithm gives small or no improvement at all, while a GA then PSO algorithm gives the same results as the PSO alone. The PSO algorithm followed by another PSO round gave the best result as it allows another round of domain exploration. The second PSO round assign new randomized velocities to the particles, while keeping best particle positions obtained in the first round. The paper proposes to name this successive PSO algorithm as the re-excited PSO algorithm

关键词： Hardware Particle swarm optimization Partitioning algorithms Genetic algorithms Costs Clustering algorithms Application software Embedded system Software algorithms Embedded software

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Bio-inspired heuristic for optimizing protein structure alignment using distributed modified extremal optimization

Bio-inspired heuristic for optimizing protein structure alig...

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IEEE international workshop on Computational Intelligence & applications (IWCIA)

作者： Keiichi Tamura Hajime Kitakami Yoshifumi Takahashi Graduate School of Information Sciences Hiroshima City University Hiroshima Japan

Proteins with similar three-dimensional structures usually have similar biological functions and interactions in living organisms. Thus, detecting similar structures in proteins is presently one of the most attractive research topics in computational biology. Many techniques for the comparison of protein structures are based on protein structure alignment. This is similar to sequence alignment, and is one of the most effective methods for extracting similar structures. The contact map overlap (CMO) maximization problem (hereafter, the CMO problem) is formulated as a combinatorial optimization to find the optimal structure alignment. The CMO problem is known to be NP-hard; hence, a number of researchers have been developing heuristic approaches. In this paper, we propose a novel bio-inspired heuristic algorithm using distributed modified extremal optimization (DMEO) for the CMO problem. DMEO is a hybrid of population-based modified extremal optimization (PMEO) and distributed genetic algorithms inspired by the island model. In the island model, a population is divided into two or more sub-populations (called islands), and each island evolves separately. Islands can maintain different types of individuals at the end of alternation of generations. Therefore, DMEO can maintain more diversity than PMEO. The experiments compared the DMEO-based heuristic with the PMEO-based heuristic, and the results show that DMEO performs well for 25 out of 32 protein pairs.

关键词： Proteins optimization Sociology Statistics Heuristic algorithms Biological system modeling Linear programming

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Genetic algorithm for searching a Doppler resilient multilevel complementary waveform

Genetic algorithm for searching a Doppler resilient multilev...

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IEEE international Symposium on Intelligent Signal Processing

作者： E. García J. Ureña J. J. García M. C. Pérez D. Ruíz D. Gualda R. Gutiérrez F. J. Álvarez Electronics Department University of Alcalá Madrid Spain Department of Electrical Engineering Electronics and Automatics University of Extremadura Badajoz Spain

This work proposes the use of Genetic algorithms (GAs) to search a Doppler resilient multilevel complementary pair of sequences. The fitness function deals with the desired ambiguity function to be fit, while it minimizes the crest factor of the resulting sequences. The simulated results show how the waveform obtained has a better performance than that achieved with binary sequences.

关键词： Genetic algorithms Doppler shift optimization Correlation Equations Radar applications

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optimization of time domain models applied to structural health monitoring 4

Optimization of time domain models applied to structural hea...

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4th international workshop on Structural Health Monitoring: From Diagnostics and Prognostics to Structural Health Management, IWSHM 2003

作者： Allen, D.W. Inmann, D.J. Farrar, C.R. Center for Intelligent Material Systems and Structures Virginia Polytechnic Institute 310 Durham Hall MC 0261 BlacksburgVA24061 United States Engineering Sciences and Applications - Weapons Response Los Alamos National Laboratory MS T006 PO Box 1663 Los AlamosNM87545 United States

ISBN: (纸本)1932078207

Many new approaches for extracting damage sensitive features for structural health monitoring include the use of time series modeling. Time series models are being explored to avoid computationally expensive transforms of data into the frequency domain and the ability to remove environmental and operational variability from the data through normalization. The process of fitting time series models requires that an appropriate model and order of the model be chosen. The order is crucial to the accuracy and computational overhead of the model. Models with too low of an order may fail to capture the underlying mechanism of the system, while models with to high of an order will begin fitting error in the system becoming less robust. Computational efficiency is also becoming critical as structural health monitoring technology moves closer to implementing algorithms on distributed integrated systems consisting of a sensor, transmitter and a processor with limited resources. Typically, order estimation is preformed manually by observing several diagnostics;in this paper however, criteria that may be extended to an automated procedure for selecting the optimal model order are explored. These criteria are applied to optimize the order of a simple ARMA model for an eight degree-of-freedom system. Results from the criteria are compared to each other and to a manual estimation. Copyright © 2003 by DEStech Publications, Inc. All right reserved.

关键词： Structural health monitoring

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Computational optimization of a vehicle dynamic simulation model

Computational optimization of a vehicle dynamic simulation m...

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international workshop/Symposium on Distributed Simulation and Real-Time applications (DS-RT)

作者： Ruslan Shaiakhmetov Department of Computer Science and Engineering Alma Mater Studiorum—Università di Bologna Cesena Italy

ISBN: (数字)9798331527211

ISBN: (纸本)9798331527228

The importance of simulation in motor-sport racing has steadily increased. Fine-tuning these simulators is complex and traditionally manual, relying on engineers’ *** study develops a virtual driver and automates a parameter tuning pipeline for a race car simulator. The virtual driver based on the Data-enabled Predictive Control (DeePC) algorithm outperforms traditional Proportional Integral Derivative (PID) and Model Predictive Control (MPC) controllers and can potentially be extended to compute an optimal trajectory.A YAML-configured optimization pipeline with a custom Domain-Specific Language (DSL) was developed to accelerate parameter tuning. However, parameter interdependency is problematic for traditional optimization algorithms. Bayesian optimization can potentially find a global minimum despite interdependencies and converge to the optimum faster than traditional optimization methods.

关键词： Heuristic algorithms Pipelines Prediction algorithms Trajectory Bayes methods Automobiles optimization Tuning Vehicles Predictive control

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17th IEEE international workshop on Automatic Performance Tuning (iWAPT2022)

17th IEEE International Workshop on Automatic Performance Tu...

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IEEE international Symposium on Parallel and Distributed Processing workshops and Phd Forum (IPDPSW)

作者： Che-Rung Lee Satoshi Ohshima

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

The goal of the Seventeenth international workshop on Automatic Performance Tuning (iWAPT2022) is to bring together researchers who are investigating automated techniques for constructing and/or adapting algorithms and software for high-performance on modern complex machine architectures. iWAPT is a series of workshops that focus on research and techniques related to performance sustainability issues. The series provides an opportunity for researchers and users of automatic performance tuning (AT) technologies to exchange ideas and experiences acquired when applying such technologies to improve the performance of algorithms, libraries, and applications; in particular, on cutting edge computing platforms. The half-day workshops consist of presentations of research papers. Topics of interest include performance modeling; adaptive algorithms; autotuned numerical algorithms; libraries and scientific applications; empirical compilation; automated code generation; frameworks and theories of AT and software optimization; autonomic computing; and context-aware computing.

关键词： Conferences Tuning Software algorithms Software Libraries Sustainable development Scientific computing

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