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检索条件"任意字段=International Workshop on Multilevel Optimization - Algorithms and Applications"

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Scheduling Geo-processing Workflow applications with QoS

Scheduling Geo-processing Workflow Applications with QoS

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ETT and GRS international Education Technology and Training, and international workshop on Geoscience and Remote Sensing

作者： Xianfeng Song Junzhi Liu College of Resources and Environment Graduate School of Chinese Academy of Sciences Beijing China

With the development of OGC Web service program (OWS), Web GIS has progressed towards a service-oriented paradigm that enables a new way to distributed geospatial computing. Many applications require geo-processing workflow (GPW) to take advantage of spatial databases or computing resources distributed over Web based their control or task. This paper presents a dual-layer framework to implement geo-processing workflow, in which an abstract workflow is defined in abstract layer to represent a user-specific task flowchart and a concrete workflow is orchestrated in concrete layer to bind geospatial Web services distributed world wide. Within the framework, we present a tree-coded genetic algorithm (GA) approach to address optimization problems in the process of instantiating an abstract workflow as a concrete workflow. The algorithm performance is analyzed through an example and well verified by the result from integer linear programming (ILP).

关键词： Concrete Web services Distributed computing Processor scheduling Geographic Information Systems Distributed databases Spatial databases Flowcharts Genetic algorithms Performance analysis

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Fault tolerant sensor placement optimization with minimum detection probability guaranteed

Fault tolerant sensor placement optimization with minimum de...

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international workshop on Design of Reliable Communication Networks (DRCN)

作者： Pakpoom Hoyingcharoen Wiklom Teerapabkajorndet Department of Electrical Engineering Prince of Songkla University Songkhla Thailand

Due to the potential of various applications of wireless sensor networks (WSNs) in emergency and security monitoring fields, fault tolerance has become one of the major fundamental concerns in network design. Previous studies in fault tolerant coverage have solely focused on k-coverage as a measurement for the degree of fault tolerance. Unlike the previous works, we have adopted the minimum detection probability at interest points in the entire sensing field after sensor node (SN) failures as the evaluator for fault tolerance in sensing coverage. The objective of our sensor placement optimization is to employ the minimum number of SNs to achieve the desired minimum detection probability even when some SNs have failed. Since our objective function is nondifferentiable, combinatorial and the size of the problem can get fairly large, we have selected Genetic Algorithm (GA) as our optimization tool. Results obtained from GA show that we can guarantee the required minimum detection probability in the worst cases of one and two SNs failures with reasonably low number of SNs.

关键词： Tin Sensors Fault tolerance Fault tolerant systems Genetic algorithms optimization Wireless sensor networks

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Detailed placement with net length constraints

Detailed placement with net length constraints

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

作者： B. Halpin N. Sehgal C.Y.R. Chen Intel Corporation Syracuse University Santa Clara CA USA Intel Corporation Santa Clara CA USA Syracuse University Syracuse NY USA

Increasing demands created by Systems-On-Chip (SOC) and process advances have increased the difficulty of timing driven placement. The primary issue in SOC is timing closure. This requires us to look at timing at all design levels, especially placement. Recently, several promising approaches for timing-driven placement have been presented using net length constraints for timing optimization (Alpert et al., 2001). A Net Length Constraint (NLC) is an upper limit on a net's length. These net-constrained global placement techniques give excellent timing results by meeting NLCs on timing-critical nets. These works focused only on global NLC placement. Detailed placement and legalization are important steps in the placement flow. Current algorithms, which are not NLC aware, give back the gains from global NLC placement. The contributions of this paper are a new NLC global placement rebalancing method and two detailed placement algorithms that work in conjunction with the recursive bisection net-constrained global placer (Alpert et al., 2001). The first detailed placer uses grid-based placement and transportation solving to assign instances to the grid. The second detailed placer uses simulated annealing to optimize placement for NLC. On benchmark circuits from MCNC and Intel Corporation, the grid and simulated annealing placers are able to achieve placements which exceed constraints by, on average only, 2.7% and 1.9%, respectively.

关键词： Timing Design engineering Automatic control Delay Circuit simulation Simulated annealing optimization methods Constraint optimization Transportation Very large scale integration

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Evolutionary applications to cellular automata models for volcano risk mitigation 1

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9th Italian workshop on Artificial Life and Evolutionary Computation, WIVACE 2014

作者： Filippone, Giuseppe Parise, Roberto Spataro, Davide D’ambrosio, Donato Rongo, Rocco Spataro, William Department of Mathematics and Computer Science University of Calabria Via Pietro Bucci Rende87036 Italy CNR-IRPI Sezione di Cosenza Rende Italy

ISBN: (数字)9783319127453

ISBN: (纸本)9783319127446

A GPGPU accelerated evolutionary computation-based decision support system for defining and optimizing volcanic hazard mitigation interventions is proposed. Specifically, the new Cellular Automata numerical model SCIARA-fv3 for simulating lava flows atMt Etna (Italy) and Parallel Genetic algorithms (PGA) have been applied for optimizing protective measures construction by morphological evolution. A case study is considered, where PGA are applied for the optimization of the position, orientation and extension of earth barriers built to protect a touristic facility located near the summit of Mt. Etna (Italy) volcano which was interested by the 2001 lava eruption. The methodology has produced extremely positive results and, in our opinion, can be applied within a broader risk assessment framework, having immediate and far reaching implications both in land use and civil defense planning. © Springer international Publishing Switzerland 2014.

关键词： Cellular automata

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Euclidean reconstruction independent on camera intrinsic parameters

Euclidean reconstruction independent on camera intrinsic par...

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IEEE international workshop on Intelligent Robots and Systems (IROS)

作者： E. Malis A. Bartoli I.N.R.I.A. Sophia-Antipolis France INRlA Rhone-Alpes France

Standard bundle adjustment techniques for Euclidean reconstruction consider camera intrinsic parameters as unknowns in the optimization process. Obviously, the speed of an optimization process is directly related to the number of unknowns and to the form of the cost function. The scheme proposed in this paper differs from previous standard techniques since unknown camera intrinsic parameters are not considered in the optimization process. Considering fewer unknowns in the optimization process produces a faster algorithm, which is more adapted to time-dependent applications such as robotics. Computationally expensive metric reconstruction, using for example several zooming cameras, considerably benefits from an intrinsics-free bundle adjustment.

关键词： Cameras Image reconstruction Cost function Robot vision systems Computational efficiency Reconstruction algorithms Layout Predictive models Computer applications Computer vision

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An Improved Clustering Method Based on K-Means Algorithm for MRI Brain Tumor Segmentation

An Improved Clustering Method Based on K-Means Algorithm for...

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international Conference on Image and Signal Processing and their applications (ISPA)

作者： Imane Mehidi Djamel Eddine Chouaib Belkhiat Dalel Jabri DAC HR Laboratory Ferhat Abbas University Setif Algeria

ISBN: (纸本)9781728131573

The field of medical image segmentation is incredibly broad. Several works has been conducted on the study of such a field, especially Magnetic Resonance Imaging (MRI). This article aims to perform brain tumor segmentation on MRI images using an improved clustering method based on K-Means (KM) algorithm. The proposed approach deals with some limitations of the standard KM algorithm such as, the random initialization of clusters centroids, and the noise sensitivity. The main idea consists to combine the Darwinian Particle Swarm optimization (DPSO) technique, the KM algorithm and the Morphological Reconstruction (MR) operation. The DPSO technique is applied to the MRI medical images for initialization of cluster centroids. Moreover, the MR filter is used to eliminate the noise and generate more compact and well separated clusters. The performance of the proposed method was evaluated by comparing it to some state of the art segmentation algorithms such as, standard KM clustering algorithm and DPSO-based multilevel thresholding. The results show the effectiveness of the proposed method.

关键词： K-means clustering Darwinian particle swarm optimization Tumor segmentation Morphological reconstruction Brain tumor Medical image Clustering methods Brain Neoplasms K-means algorithm Medical imaging initialization DPSO image segmentation Magnetic resonance imaging of brain Magnetic Resonance Imaging Noise sensitivity algorithms Centroid

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A Modified Grasshopper optimization Algorithm

A Modified Grasshopper Optimization Algorithm

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IEEE international workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and applications

作者： Man Gao Hui Xu Jun Su Lingyu Yan Hubei University of Technology Wuhan China

ISBN: (纸本)9781665426060

In order to solve the problems of Grasshopper optimization Algorithm (GOA) in the optimization process, such as easily falling into the local optimum and the slow convergence speed, a Modified Grasshopper optimization Algorithm (MGOA) is proposed. And the proposed MGOA improves the original fixed parameters to linear decreasing parameters in order to avoid the original algorithm from falling into the local optimum problem. At the same time, weighting calculation is introduced to improve the optimization accuracy of the MGOA algorithm. Additionally, the proposed MGOA is combined with the Simulated Annealing (SA) to improve the convergence speed of the original GOA algorithm. Both the unimodal test function and the multimodal test function are respectively used for simulation experiments, and experimental results show that, compared with the other five swarm intelligent algorithms, the proposed MGOA has significant advantages in aspects of accuracy, stability and convergence speed.

关键词： Annealing Conferences Data acquisition Simulated annealing Stability analysis optimization Convergence

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Extraction of classification rules from socio-demographics and biochemistry datasets of schizophrenia patients using multi-objective genetic algorithms

Extraction of classification rules from socio-demographics a...

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IEEE international workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and applications

作者： Buket Kaya İbrahim Türkoğlu Organized Industry Area Vocational School of Mines Firat University Elazlğ Turkey Department of Software Engineering Firat University Elazlğ Turkey

This paper presents a method for extracting automatically classification rules via multi-objective genetic algorithms. The paper also proposes a novel objective measure to quantify the similarity of the rules. The other objectives of the rules are average support value and accuracy. We experimentally evaluate our approach on socio-demographics and biochemistry datasets of schizophrenia patients and demonstrate that our algorithm encourages us to improve and apply this strategy in many real-world applications.

关键词： Genetic algorithms Data mining Accuracy Sociology Statistics optimization Databases

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A broadband cage antenna optimized by genetic algorithm

A broadband cage antenna optimized by genetic algorithm

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international workshop on Antenna Technology: Small Antennas and Novel Metamaterials, iWAT

作者： Jingya Deng Xiaojun Chen Rui Yu Xiang Wen Institute of China Electronic System Engineering Corporation Beijing P. R. China School of Physics and Optoelectronic Engineering Xidian University Xi'an P. R. China

ISBN: (纸本)9781479981137

An optimized design of cage antenna Using FEKO and genetic algorithm (GA) is presented. The geometry of the antenna is optimized by GA to get best VSWR and gain performances over the entire 100-400MHz band. The height of the optimized geometry of the cage antenna is only 0.75m, while the diameter is only 0.15m. The antenna can work over entire 100-400MHz with the VSWR lower than 3 and the gain higher than 1.55dBi.

关键词： Broadband antennas Helical antennas Dipole antennas Genetic algorithms optimization Broadband communication

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Energy harvesting techniques and circuits for maritime applications

Energy harvesting techniques and circuits for maritime appli...

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Biomedical applications, Technologies and Sensors (BATS), international workshop on

作者： Giuseppe Ferri Alfiero Leoni Vincenzo Stornelli Lorenzo Nazzicone Ante Markota Leonardo Pantoli DIIIE University of L’Aquila Italy DEI Politecnico di Bari Italy Dip.Ing.Ind.e Inf. Univ.of Pavia Italy University of Split Croatia

ISBN: (数字)9798331516390

ISBN: (纸本)9798331516406

This work presents the basic concepts of energy harvesting (EH) techniques and circuits, with reference to maritime applications. The main EH sources and related sensors are recalled as well as the DC-DC conversion circuits. Multi-input energy sources, piezoelectric and thermoelectric, have been considered. algorithms for maximum power point tracking are also cited as well as energy storage options. Matlab-Simulink models are finally described.

关键词： Maximum power point trackers Industries Circuits Voltage Sensors Energy harvesting MATLAB Integrated circuit modeling optimization Energy storage

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