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21st international IEEE workshop on Rapid System Prototyping, RSP-2010

作者： Girodias, B. Gheorghe, L. Bouchebaba, Y. Nicolescu, G. Aboulhamid, E.M. Langevin, M. Paulin, P. ÉCole Polytechnique de Montréal Canada Université de Montréal Canada STMicroelectronics France

ISBN: (纸本)9781424470747

Multiprocessor systems-on-chips (MPSoCs) are defined as one of the main drivers of the industrial semiconductors revolution. They are good candidates for systems and applications such as multimedia. Memory is becoming a key player for significant improvements in these applications (power, performance and area). With the emergence of more embedded multimedia applications in the industry, this issue becomes increasingly vital. The large amount of data manipulated by these applications requires high-capacity calculation and memory. This leads to the need of new optimization and mapping techniques. This paper presents a novel approach for combining memory optimization with mapping of data-driven applications. This approach consists of task graph transformation and its integration to existing mapping algorithms. Some significant improvements are obtained for memory gain, communication load and physical links. © 2010 IEEE.

关键词： Conformal mapping

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Arrangements in higher dimensions: Voronoi diagrams, motion planning, and other applications 4th

Arrangements in higher dimensions: Voronoi diagrams, motion ...

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4th international workshop on algorithms and Data Structures (WADS 95)

作者： Sharir, M NYU COURANT INST MATH SCINEW YORKNY 10012 TEL AVIV UNIV SCH MATH SCIIL-69978 TEL AVIVISRAEL

ISBN: (纸本)3540602208

We review recent progress in the study of arrangements of surfaces in higher dimensions. This progress involves new and nearly tight bounds on the complexity of lower envelopes, single cells, zones, and other substructures in such arrangements, and the design of efficient algorithms (near optimal in the worst case) for constructing and manipulating these structures. We then present applications of the new results to motion planning, Voronoi diagrams, visibility, and geometric optimization.

关键词： Motion planning

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A framework for optimizing OpenVX applications performance on embedded manycore accelerators 15

A framework for optimizing OpenVX applications performance o...

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18th international workshop on Software and Compilers for Embedded Systems, SCOPES 2015

作者： Tagliavini, Giuseppe Haugou, Germain Marongiu, Andrea Benini, Luca University of Bologna Italy ETH Zurich Switzerland

ISBN: (纸本)9781450335935

Nowadays Computer Vision application are ubiquitous, and their presence on embedded devices is more and more widespread. Heterogeneous embedded systems featuring a clustered manycore accelerator are a very promising target to execute embedded vision algorithms, but the code optimization for these platforms is a challenging task. Moreover, designers really need support tools that are both fast and accurate. In this work we introduce ADRENALINE, an environment for development and optimization of OpenVX applications targeting manycore accelerators. ADRENALINE consists of a custom OpenVX run-time and a virtual platform, and overall it is intended to provide support to enhance performance of embedded vision applications. © 2015 ACM.

关键词： Embedded systems

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Approximation, Randomization, and Combinatorial optimization: algorithms and Techniques - 12th international workshop, APPROX 2009 and 13th international workshop, RANDOM 2009, Proceedings

Approximation, Randomization, and Combinatorial Optimization...

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12th international workshop on Approximation algorithms for Combinatorial optimization Problems, APPROX 2009 and 13th international workshop on Randomization and Computation, RANDOM 2009

ISBN: (纸本)3642036848

The proceedings contain 52 papers. The topics discussed include: approximation algorithms and hardness results for packing element-disjoint Steiner trees in planar graphs;approximations for aligned coloring and spillage minimization in interval and chordal graphs;unsplittable flow in paths and trees and column-restricted packing integer programs;truthful mechanisms via greedy iterative packing;resource minimization job scheduling;the power of preemption on unrelated machines and applications to scheduling orders;new hardness results for Diophantine approximation;optimal sherali-adams gaps from pairwise independence;an approximation scheme for terrain guarding;strong parallel repetition theorem for free projection games;random low degree polynomials are hard to approximate;pseudorandom generators and typically- correct derandomization;and tolerant linearity testing and locally testable codes.

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Maximal falsifiability

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AI COMMUNICATIONS 2016年第2期29卷 351-370页

作者： Ignatiev, Alexey Morgado, Antonio Planes, Jordi Marques-Silva, Joao Univ Lisbon IST INESC ID P-1699 Lisbon Portugal Univ Lleida Lleida Spain UCD CASL Dublin Ireland

Similarly to Maximum Satisfiability (MaxSAT), Minimum Satisfiability (MinSAT) is an optimization extension of the Boolean Satisfiability (SAT) decision problem. In recent years, both problems have been studied in terms of exact and approximation algorithms. In addition, the MaxSAT problem has been characterized in terms of Maximal Satisfiable Subsets (MSSes) and Minimal Correction Subsets (MCSes), as well as Minimal Unsatisfiable Subsets (MUSes) and minimal hitting set dualization. However, and in contrast with MaxSAT, no such characterizations exist for MinSAT. This paper addresses this issue by casting the MinSAT problem in a more general framework. The paper studies Maximal Falsifiability, the problem of computing a subset-maximal set of clauses that can be simultaneously falsified, and shows that MinSAT corresponds to the complement of a largest subset-maximal set of simultaneously falsifiable clauses, i.e. the solution of the Maximum Falsifiability (MaxFalse) problem. Additional contributions of the paper include novel algorithms for Maximum and Maximal Falsifiability, as well as minimal hitting set dualization results for the MaxFalse problem. Moreover, the proposed algorithms are validated on practical instances.

关键词： Maximum falsifiability minimum satisfiability minimal hitting set duality Boolean optimization

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A hybrid test optimization framework using memetic algorithm with cuckoo flocking based search approach 7

A hybrid test optimization framework using memetic algorithm...

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7th international workshop on Search-Based Software Testing, SBST 2014

作者： Sabarinathan, K. Balamurugan, S. Dharmalingam, Jeya Mala Thiagarajar College of Engineering Tamil Nadu India Tata Consultancy Services Chennai Tamil Nadu India

ISBN: (纸本)9781450328524

The testing process of industrial strength applications usually takes more time to ensure that all the components are rigorously tested to have failure-free operation upon delivery. This research work proposed a hybrid optimization approach that combines the population based multi-objective optimization approach namely Memetic Algorithm with Cuckoo Search (MA-CK) to generate optimal number of test cases that achieves the specified test adequacy criteria based on mutation score and branch coverage. Further, GA, HGA and MA based heuristic algorithms are empirically evaluated and it has been shown that the proposed MA with cuckoo search based optimization algorithm provides an optimal solution. © 2014 ACM.

关键词： Heuristic algorithms

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Artificial neural network weights optimization using ICA, GA, ICA-GA and R-ICA-GA: Comparing performances

Artificial neural network weights optimization using ICA, GA...

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IEEE workshop on Hybrid Intelligent Models and applications

作者： Khorani, Vahid Forouzideh, Nafiseh Nasrabadi, Ali Motie Islamic Azad University Qazvin Branch Qazvin Iran University of Tehran Kish International Campus Kish Island Iran Shahed University Biomedical Engineering Department Tehran Iran

ISBN: (纸本)9781424499090

Artificial neural networks (ANN) and evolutionary algorithms are two relatively young research areas that were subject to a steadily growing interest during the past years. This paper examines the use of different evolutionary algorithms, imperialist competitive algorithm (ICA), genetic algorithm (GA), ICA-GA and recursive ICA-GA (R-ICA-GA) to train a classification problem on a multi layer perceptron (MLP) neural network. All of named evolutionary training algorithms are compared together in this paper. The first goal of the paper is to apply new evolutionary optimization algorithms ICA-GA and R-ICA-GA for training the ANN and the second goal of the paper is to compare different evolutionary algorithms. It is shown that the ICA-GA has the best performance, in number of epochs, compared to the other algorithms. For this purpose, learning algorithms are applied on six known datasets (WINE, PIMA, WDBC, IRIS, SONAR and GLASS) which are used for classification problems. © 2011 IEEE.

关键词： Neural networks

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Parachute decelerate trajectory optimization design based on genetic algorithm

Parachute decelerate trajectory optimization design based on...

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2009 international workshop on Intelligent Systems and applications, ISA 2009

作者： Yong, Zhu Li, Liu School of Aerospace Science and Engineering Beijing Institute of Technology Beijing China

ISBN: (纸本)9781424438945

In this paper, the optimal parachute decelerating trajectory of mini-UAVs is studied. The mini-UAV trajectory model of parachute decelerating segment is established. The influences to parachute drag are analyzed and the parachute canopy limit area formula is derived. In order to ensure the time effectiveness of mini-UAVs entering working destination, taking flight time and range as the performance function, considering end velocity and end height as optimization constraints, and choosing parachute parameters-- canopy area and parachute opening time as design variables, the optimal decelerating trajectory is solved by sequential quadratic programming method and genetic algorithm. Compared the results of these two kinds of optimized algorithms, their applicability to this decelerating trajectory is analyzed. The mechanism of timeliness during parachute decelerating trajectory is analyzed briefly. A numerical example results indicates the validity of the parachute decelerating trajectory design in this paper, which possesses practical value to guide research of parachute design. © 2009 IEEE.

关键词： Genetic algorithms

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Short-term load forecasting based on GA-BPNN-SVR optimization algorithm 8

Short-term load forecasting based on GA-BPNN-SVR optimizatio...

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8th international Symposium on Computational Intelligence and Industrial applications and 12th China-Japan international workshop on Information Technology and Control applications, ISCIIA and ITCA 2018

作者： He, Lianghua Ge, Lai Zhang, Hao Wang, Guowei School of automation China University of Geosciences Wuhan Hubei430074 China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan Hubei430074 China

In order to improve the accuracy of short-term power load forecasting, an algorithm that simultaneously optimizes SVR parameters and optimizes the selection of input features is proposed. Firstly, the input features are selected by BP neural network. In order to avoid the influence of the randomness of the initial weights and thresholds on the optimization, the GA algorithm is used to optimize the weights and thresholds. Secondly, using GA algorithm to optimize the parameters of support vector regression, a short-term load forecasting model based on GA-BPNN-SVR optimization algorithm is obtained. Finally, the GA-BPNN-SVR model is applied to practical short-term power load forecasting, and the forecasting effect is compared with that of GA-SVM and BPNN-SVR models. The results show that the average absolute percentage error obtained by this method is only 1.74%, which is 1.64% and 3.11% lower than the average absolute percentage error obtained with GA-SVM model and BPNN-SVR. This algorithm can be effectively used for short-term load forecasting of power systems. © ISCIIA and ITCA 2018 - 8th international Symposium on Computational Intelligence and Industrial applications and 12th China-Japan international workshop on Information Technology and Control Application.

关键词： Genetic algorithms

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Optimal Energy Consumption and Throughput for Workflow applications on Distributed Architectures

Optimal Energy Consumption and Throughput for Workflow Appli...

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21st IEEE international workshop on Enabling Technologies - Infrastructure for Collaborative Enterprises (WETICE)

作者： Ben Othman, Abdallah Nicod, Jean-Marc Philippe, Laurent Rehn-Sonigo, Veronika FEMTO ST Inst CNRS UFC ENSMMUTBM Besancon France

ISBN: (纸本)9780769547176

In this paper we study both the throughput and the energy optimization problem for a distributed system subject to failures that executes a workflow at different speed levels. The application is modeled as a directed acyclic graph composed of typed tasks linked by dependency constraints. A continuous flow, or a great number of application instances has to be processed optimizing the collaborative system performance which implies to increase the throughput - the number of application instances processed by time unit - or to decrease the period - the time needed to output one instance of the system. The system is designed as a collaborative platform of distributed machines. Each machine collaborates with others by performing all the instances of at least one task of the DAG. The problem we tackle is to optimize the configuration of the platform. In this article we propose two polynomial algorithms that optimize the two objectives of period (i.e., throughput) and energy minimization and we prove that the proposed algorithms give optimal results. Our optimization approach is hierarchic in the sens that we either minimize the energy consumption for an optimal period or minimize the period for the optimal energy consumption.

关键词： Scheduling workflow applications energy minimization fault tolerance throughput polynomial complexity

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