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

共 1021 条记录，以下是871-880 订阅

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Antenna Structure Prediction and optimization Based on Machine Learning and Grid Search

Antenna Structure Prediction and Optimization Based on Machi...

引用

IEEE international workshop on Electromagnetics, applications and Student Innovation (iWEM)

作者： Chung-Hao Huang Han-Hsiang Tsao Chang-Chen Hsu Amir Ali Sen-Yu Liao Department of Electrical Engineering Chung Yuan Christian University Taoyuan Taiwan (R.O.C.)

ISBN: (数字)9798350366976

ISBN: (纸本)9798350366983

In the antenna design process, designers often modify antenna dimensions iteratively to meet specified performance criteria, using simulation software to guide adjustments based on predefined targets. In this paper, a rectangular patch microstrip antenna is used as a research target, and it proposes the use of hyperparameter algorithms and machine learning to predict the optimal size of the antenna, focusing on the frequency range of 2 to 5 GHz. With simulated antenna widths ranging from 20 to 24 mm, lengths ranging from 17 to 21 mm, and slot intervals ranging from 0 to 5 mm. This research utilizes Decision Trees, Random Forests, and XGBoost as three distinct machine learning algorithms to evaluate their effectiveness. The results show that the random forest algorithm using the Grid Search CV achieves the best result with an MSE of 0.108, RMSE of 0.329, and R-squared of 0.94. Based on these results, the antenna design process can be accelerated by machine learning to meet the desired performance targets.

关键词： Machine learning algorithms Patch antennas Microstrip antennas Prediction algorithms Distance measurement Microstrip Antennas

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The JAMES II Framework for Modeling and Simulation

The JAMES II Framework for Modeling and Simulation

引用

international workshop on High Performance Computational Systems Biology, HIBI

作者： Jan Himmelspach Adelinde M. Uhrmacher Institute of Computer Science University of Rostock Rostock Germany

JAMES II is a general and open framework based on the ldquoPlug'n simulaterdquo concept, which enables developers to integrate their modeling and simulation methodological ideas into, and to create their applications upon an existing *** concept together with currently more than 400 plug-ins,and an explicit representation and storage of experiments ease developing modeling and simulation methods and contribute to a systematic experimental evaluation of methods. Modelers benefit from the flexibility that the framework provides with respect to modeling, simulation, and analysis methods, supporting effective and efficient simulation studies, and the well tested methods add to the credibility of the results achieved.

关键词： Biological system modeling Computational modeling Analytical models Computer simulation Computational systems biology Application software optimization methods Partitioning algorithms Computer science Testing

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A novel multi-robot exploration approach based on Particle Swarm optimization algorithms

A novel multi-robot exploration approach based on Particle S...

引用

IEEE international workshop on Safety, Security, and Rescue Robotics (SSRR)

作者： Micael S. Couceiro Rui P. Rocha Nuno M. F. Ferreira Institute of Systems and Robotics University of Coimbra Coimbra Portugal Electrical Engineering Department Engineering Institute of Coimbra Robo Corporation Coimbra Portugal

This paper proposes two extensions of Particle Swarm optimization (PSO) and Darwinian Particle Swarm optimization (DPSO), respectively named as RPSO (Robotic PSO) and RDPSO (Robotic DPSO), so as to adapt these promising biological-inspired techniques to the domain of multi-robot systems, by taking into account obstacle avoidance. These novel algorithms are demonstrated for groups of simulated robots performing a distributed exploration task. The concepts of social exclusion and social inclusion are used in the RDPSO algorithm as a “punish-reward” mechanism enhancing the ability to escape from local optima. Experimental results obtained in a simulated environment show that biological and sociological inspiration can be useful to meet the challenges of robotic applications that can be described as optimization problems (e.g. search and rescue).

关键词： Robot sensing systems Algorithm design and analysis Particle swarm optimization Collision avoidance Search problems

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A Hybrid Rice optimization Algorithm with Ant System for Feature Selection

A Hybrid Rice Optimization Algorithm with Ant System for Fea...

引用

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

作者： Zhiwei Ye Zhe Shu Shiuin Liu Xiaoyu Xia School of Computer Science Hubei University of Technology Wuhan China

ISBN: (纸本)9781665426060

Feature selection is an important processing step of data mining and aims at selecting a subset with high quality. The feature subset is obtained by reducing the irrelevant and redundant features based on maintaining satisfactory accuracy. Many algorithms might be trapped in local optima on solving high-dimensional feature selection. Hybrid rice optimization (HRO) is a newly presented algorithm, its population is divided into three types such as the maintainer, restorer, and sterile line to simulate the breeding process of Chinese hybrid rice. Three main stages are designed to update the restorer and sterile line. However, the maintainer line is not updated in HRO, which degrades the performance of the algorithm on high-dimensional feature selection. In this paper, a hybrid model named HROAS is proposed for feature selection by combining HRO with ant system (AS). AS is embedded as an operator in HRO to update the maintainer line due to its unique updating mechanism. The performance of HROAS is evaluated on nine standard benchmark datasets and compared with several classic and novel algorithms. The experimental results demonstrate the outperformance of HROAS.

关键词： Measurement Sociology Diversity reception Data acquisition Feature extraction Classification algorithms Data mining

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Joint optimization of radio and computational resources for multicell mobile cloud computing

Joint optimization of radio and computational resources for ...

引用

IEEE workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： S. Sardellitti G. Scutari S. Barbarossa Dept. of Information Engineering Electronics and Telecommunications Sapienza University of Rome Rome Italy Dept. of Electrical Engineering State University of New York at Buffalo Buffalo USA

We consider a MIMO multicell system wherein several Mobile Users (MUs) ask for computation offloading to a common cloud server through their femto-access points. We formulate the computation offloading problem as a joint optimization of the radio resources??the transmit precoding matrices of the MUs??and the computational resources??the CPU cycles/second assigned by the cloud to each MU??in order to minimize the overall users' energy consumption while meeting the latency constraints imposed by the applications running on the MUs. The resulting optimization problem is nonconvex (in the objective function and the constraints), and there are constraints coupling all the optimization variables. To cope with the nonconvexity, we hinge on successive convex approximation techniques and propose an iterative algorithm converging to a local optimal solution of the original nonconvex problem. The algorithm is also suitable for a parallel implementation across the access point, with limited coordination/signaling with the cloud. Numerical results show that the proposed joint optimization yields significant energy savings with respect to more traditional schemes performing a separate optimization of the radio and computational resources.

关键词： Signal processing algorithms optimization Energy consumption Approximation methods Covariance matrices Joints Mobile communication

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Hierarchical critical area extraction with the EYE tool

Hierarchical critical area extraction with the EYE tool

引用

IEEE international Symposium on Defect and Fault Tolerance in VLSI Systems

作者： G.A. Allan A.J. Walton Department Electrical Engineering University of Edinburgh Edinburgh UK

A software tool to extract critical areas from commercial IC mask data is reported. The EYE (Edinburgh Yield Estimator) tool uses fast O(NlogN) critical area algorithms and is able to perform operations hierarchically making it suitable for use on large devices. The tool has applications in yield prediction, optimising the manufacturability of IC layout, and the generation of defect sensitivity visualisation aids in the form of fault probability maps.

关键词： Data mining Fabrication Control systems Yield estimation Integrated circuit yield Circuit faults Software tools Computational geometry Design optimization Integrated circuit manufacture

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Large-Spacing Scanning Phased Array with U-Slot Patch Antennas Using Genetic Algorithm optimization

Large-Spacing Scanning Phased Array with U-Slot Patch Antenn...

引用

IEEE MTT-S international Microwave workshop Series on Advanced Materials and Processes for RF and THz applications (IMWS-AMP)

作者： Zhongyang Chen Kangna Zhang Xue Ren State Key Laboratory of Radio Frequency Heterogeneous Integration Shenzhen University College of Electronics and Information Engineering Shenzhen University Shenzhen Guangdong China

ISBN: (数字)9798331520717

ISBN: (纸本)9798331520724

This paper proposes a large-spacing scanning phased array based on U-slot patch antennas. A genetic algorithm is used to optimize the beamforming for a $1 \times 5$ subarray. And taking into account the mutual coupling between elements, we used the active element pattern (AEP) to calculate the pattern of the subarray. Finally, a $1 \times 21$ linear array is formed using five subarrays, with a subarray spacing of $1.6 \lambda_{0}$ and the center frequency of 10 GHz. The array achieves a sidelobe level of -6.2 dB at a scanning angle of ± 10°.

关键词： Phased arrays Radio frequency Mutual coupling Array signal processing Patch antennas Terahertz radiation Scattering parameters Linear antenna arrays optimization Genetic algorithms

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Evolving Fuzzy Classifier System Using PSO for RoboCup Vision applications

Evolving Fuzzy Classifier System Using PSO for RoboCup Visio...

引用

3rd international workshop on Genetic and Evolving Fuzzy Systems (GEFS 2008)

作者： Narges Shamshiri Milani Alireza Kashanipour Amir Reza Kashanipour Department Electrical Engineering Sahand University of Technology Tabriz Iran Department of Electrical Engineering Azad University of Qazvin Qazvin Iran Department of Electrical Engineering Azad University Tehran Iran

In this paper we propose a color classification algorithm in which an evolutionary design optimizes a fuzzy system for Color Classification and Image Segmentation. This system works with the least number of rules and has minimum error rate by the mean of Particle Swarm optimization (PSO) method. In this approach each particle of the swarm codes a set of fuzzy rules. During evolution, each member of a population tries to maximize a fitness criterion which has designed to raise classification rate and to reduce the number of rules. Finally, the particle with the highest fitness value is selected as the best set of fuzzy rules for image segmentation. Fuzzy sets are defined on the H, S and L components of the HSL Color Space to provide a fuzzy logic model which aims to follow the human intuition of Color Classification. Color-based vision applications face the challenge of color variations by illumination. The final system designed by this method is adaptive to continuous variable lighting according to its evolving-fuzzy nature. In this method parameters setting is done only once. The experimental results in RoboCup leagues demonstrate that the presented approach can be very robust to noise and light variations.

关键词： Fuzzy systems Fuzzy sets Image segmentation Classification algorithms Algorithm design and analysis Design optimization Error analysis Particle swarm optimization Fuzzy logic Humans

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multilevel Approximate Robust Principal Component Analysis

Multilevel Approximate Robust Principal Component Analysis

引用

international Conference on Computer Vision workshops (ICCV workshops)

作者： Vahan Hovhannisyan Yannis Panagakis Stefanos Zafeiriou Panos Parpas Imperial College London UK

Robust principal component analysis (RPCA) is currently the method of choice for recovering a low-rank matrix from sparse corruptions that are of unknown value and support by decomposing the observation matrix into low-rank and sparse matrices. RPCA has many applications including background subtraction, learning of robust subspaces from visual data, etc. Nevertheless, the application of SVD in each iteration of optimisation methods renders the application of RPCA challenging in cases when data is large. In this paper, we propose the first, to the best of our knowledge, multilevel approach for solving convex and non-convex RPCA models. The basic idea is to construct lower dimensional models and perform SVD on them instead of the original high dimensional problem. We show that the proposed approach gives a good approximate solution to the original problem for both convex and non-convex formulations, while being many times faster than original RPCA methods in several real world datasets.

关键词： Sparse matrices Principal component analysis Robustness Matrix decomposition Computational modeling optimization Approximation algorithms

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Research into Self-Adaptive Hybrid Ant Colony Algorithm Based on Flow Control

Research into Self-Adaptive Hybrid Ant Colony Algorithm Base...

引用

international workshop on Intelligent Systems and applications, ISA

作者： Jingyao Li Shudong Sun Yuan Huang Ning Wang Key Laboratory of Contemporary Design and Integrated Manufacturing Technology Northwestern Polytechnical University Xi'an China School of Mechatronics Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9781424458721;9781424458745

A hybrid ant colony algorithm with self-adaptive parameters has been researched in this paper. Two schemes of adjusting parameters have been put forward according to the simulation analysis on different affect of different parameter sets of TACOSA algorithm when solving the dual resource constrained job shop scheduling problem with heterogeneous workers which based on decreasing production cost. Based on the analysis of performances of both schemes, a self-adaptive routing choice mechanism based on ant flow control has been introduced to improve the global search ability and convergence performance. According to the comparing experiments of different algorithms, the advantage of the hybrid ant colony algorithm and the optimized capability of the control mechanism based on ant flow have been validated.

关键词： Analytical models Algorithm design and analysis Scheduling algorithm Job shop scheduling Job production systems Costs Performance analysis Routing Convergence Ant colony optimization

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