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

共 1022 条记录，以下是341-350 订阅

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A stock price forecasting application using neural networks with multi-optimizer

A stock price forecasting application using neural networks ...

引用

IEEE international workshop on Computational Intelligence & applications (IWCIA)

作者： Chukiat Worasucheep Department of Mathematics King Mongkut's University of Technology Thonburi (KMUTT) Bangkok Thailand

ISBN: (纸本)9781509027767

This paper proposes an application prototype for forecasting of stock prices using feed-forward neural network with back propagation, Particle Swarm optimization and Differential Evolution. The prototype provides a convenient graphical user interface that allows choosing stocks, period of data, percentage of training set, technical indicators for model inputs and other algorithmic parameters. Multithreading is provided for efficient running and the downloaded historical data and forecasted output can be save for future use. An experiment was performed to investigate the performance of the three algorithms as well as the effects of number of hidden nodes of the neural networks.

关键词： Forecasting Prototypes Algorithm design and analysis Graphical user interfaces Training Neural networks Particle swarm optimization

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A Robust Predictive MPPT Strategy: An Enabler for Improving the Photovoltaic Conversion Source

A Robust Predictive MPPT Strategy: An Enabler for Improving ...

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IEEE international Conference on Renewable Energy Research and applications

作者： Hamed Nademi Zareh Soghomonian Lars Norum ABB AS Industrial Automation division CACI Enterprise Solutions Department of Electric Power Engineering NTNU University

ISBN: (纸本)9781538620960;9781538620953

The key drivers for Maximum Power Point Tracking (MPPT) solutions in photovoltaic (PV) power plants are independency from solar panels, fast tracking of irradiance and temperature variations, and efficiency of conversion capacity. In the most available MPPT solutions, some issues such as high implementation burden and performance degradation during abrupt changes have not been sufficiently investigated. To tackle these issues, an optimized scheme for MPPT is implemented in large-scale PV power conversion system, which is designed based on improved circuit of the Modular multilevel Converter (MMC) configuration. The developed method is featured through an optimized version of Finite-Control-Set Model Predictive Control (FCS-MPC) to be an enabler for steadying the harvesting mechanism of solar power. The estimation design provides the current sensors elimination in PV panels against commonly utilized MPPT algorithms, which is hampered by costly sensing devices used in situ. By designing such control strategy, fast tracking response for the PV system along with an acceptable control bandwidth are implemented to account for solar irradiation transients and PV panels uncertainties. The improved power circuit of conversion topology allows decreased number of solar cells and energy storage sizing by half in comparison to the basic MMC. The effectiveness of the designed MPPT method over the solar irradiance variations is compared with both Ripple-Correlation Control (RCC) and Fractional Open-Circuit Voltage (FOCV) control schemes. The performance of the discussed PV power station is also assessed with respect to different operational scenarios via simulations to validate the developed concept.

关键词： multilevel Converter Maximum Power Point Tracking Photovoltaic Source Model Predictive Control optimization problem

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Combined based on minimum distance orthogonal matching pursuit method for support recovery improvement in super-resolution compressed sensing

Combined based on minimum distance orthogonal matching pursu...

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international workshop on Compressed Sensing Theory and its applications to Radar, Sonar and Remote Sensing (CoSeRa)

作者： Vinh Nguyen Xuan Klaus Hartmann Wolfgang Weihs Otmar Loffeld Center for Sensorsystems University of Siegen Siegen Germany

ISBN: (纸本)9781509029211

This paper introduces a new scenario, named combined based on minimum distance orthogonal matching pursuit (CMD-OMP) which utilizes the strength of two available recovery methods at different ranges of minimum distance. Particularly, OMP with a global optimization and non-negative constrained least square algorithm (a MATLAB function implemented as OMP with a modification step) will be studied comparatively and then interacted together to improve signal support recovery performance in super-resolution compressed sensing.

关键词： Matching pursuit algorithms Signal resolution Sensors Spatial resolution Compressed sensing optimization

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Electrical Energy Consumption Forecast Using Support Vector Machines

Electrical Energy Consumption Forecast Using Support Vector ...

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international workshop on Database and Expert Systems applications

作者： Eugénia Vinagre Tiago Pinto Sérgio Ramos Zita Vale Juan M. Corchado Polytechnic of Porto GECAD - Knowledge Engineering and Decision Support Research Center Portugal BISITE Research Centre University of Salamanca Salamanca Spain

ISBN: (纸本)9781509036363

Smart Grid (SG) concept is defined as an electricity network operated intelligently to integrate the behavior and actions of all energy resources connected to the network to ensure efficient, sustainable, economic and secure supply of electricity. This concept emerged in recent decades not only for economic reasons but also ecological and even political. SG have been the subject of major studies and investments and continues to represent an area of enormous challenges. Some of the problems of intelligent systems connected to the managed SG are: the real-time processing optimization algorithms and demand response programs; and more accurate predictions in the management of production and consumption. This paper presents a case study for evaluating the performance and accuracy of energy consumption forecast with use of SVM (Support Vector Machines) in different frameworks.

关键词： Support vector machines Business Forecasting Training data optimization Kernel MATLAB

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Contact map overlap maximization using adaptive distributed modified extremal optimization

Contact map overlap maximization using adaptive distributed ...

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IEEE international workshop on Computational Intelligence & applications (IWCIA)

作者： Keiichi Tamura Hajime Kitakami Tatsuhiro Sakai Graduate School of Information Sciences Hiroshima City University Hiroshima Japan

ISBN: (纸本)9781509027767

The detection of similar structures in proteins has received considerable attention in the post-genome era. Protein structure alignment, which is similar to sequence alignment, can detect the structural homology between two proteins according to their three-dimensional structures. One of the simplest yet most robust techniques for finding optimal protein structure alignment is to maximize the contact map overlap (CMO). This optimization is known as the CMO problem. We have been developing bio-inspired heuristic models using distributed modified extremal optimization (DMEO) for the CMO problem. DMEO is inspired by distributed genetic algorithms, which are known as island models. DMEO is a hybrid of population-based modified extremal optimization (PMEO) and the island model. In our previous work, we proposed a novel bio-inspired heuristic model, i.e., DMEO with different evolutionary strategies (DMEODES) to maintain population diversity. DMEODES is based on the island model; however, some of the islands, called hot-spot islands, have a different evolutionary strategy. In this paper, we propose a state-of-art heuristic model to improve the DMEO's ability to prevent evolution stagnation. The new model integrates an adaptive generation alternation mechanism in DMEO called ADMEO. To evaluate ADMEO, we used actual protein structures. Experimental results show that ADMEO outperforms DMEODES.

关键词： Proteins Biological system modeling optimization Adaptation models Amino acids Linear programming Computational modeling

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Waveform optimization for large-scale multi-antenna multi-sine wireless power transfer

Waveform optimization for large-scale multi-antenna multi-si...

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

作者： Yang Huang Bruno Clerckx Department of Electrical and Electronic Engineering Imperial College London London United Kingdom School of Electrical Engineering Korea University Korea

ISBN: (纸本)9781509017508

Wireless power transfer (WPT) is expected to be a technology reshaping the landscape of low-power applications such as the Internet of Things, machine-to-machine communications and radio frequency identification networks. Although there has been some progress towards multi-antenna multi-sine WPT design, the large-scale design of WPT, reminiscent of massive multiple-input multiple-output (MIMO) in communications, remains an open problem. Considering the nonlinear rectifier model, a multiuser waveform optimization algorithm is derived based on successive convex approximation (SCA). A lower-complexity algorithm is derived based on asymptotic analysis and sequential approximation (SA). It is shown that the difference between the average output voltage achieved by the two algorithms can be negligible provided the number of antennas is large enough. The performance gain of the nonlinear model based design over the linear model based design can be large, in the presence of a large number of tones.

关键词： Mathematical model Antennas Approximation algorithms optimization Algorithm design and analysis Taylor series MIMO

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Bird body and wing-beat radar Doppler signature separation using sparse optimization

Bird body and wing-beat radar Doppler signature separation u...

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international workshop on Compressed Sensing Theory and its applications to Radar, Sonar and Remote Sensing (CoSeRa)

作者： Masoud Farshchian Ivan Selesnick Ankit Parekh Empyreal Waves LLC Virginia United States Tandon School of Engineering New York University Brooklyn NY USA

ISBN: (纸本)9781509029211

Radar bird detection and discrimination has many civilian and non-civilian applications such as collision avoidance, false alarm reduction for detection radars, stealthy target detection, classification of military unmanned aerial vehicles (UAVs) and civilian drones, and conservation ecology. In order to develop new and improve existing detection and discrimination algorithms, this paper proposes a feature extraction technique in which the wing-beat Doppler radar signature of a bird is separated from its respective body signature. More specifically, we propose non-linear morphological component analysis (MCA) using invertible short-time Fourier transform (STFT) for feature extraction. The method is applied to the Peregrine falcon data measured by Alabaster et al. (2012) resulting in successful separation of the aforementioned signatures.

关键词： Birds Radar cross-sections Doppler effect Doppler radar Radar remote sensing Transforms

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Topology-Aware Performance optimization and Modeling of Adaptive Mesh Refinement Codes for Exascale

Topology-Aware Performance Optimization and Modeling of Adap...

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international workshop on Communication optimizations in HPC (COMHPC)

作者： Cy P Chan John D Bachan Joseph P Kenny Jeremiah J Wilke Vincent E Beckner Ann S Almgren John B Bell Lawrence Berkeley National Laboratory Berkeley CA

We introduce a topology-aware performance optimization and modeling workflow for AMR simulation that includes two new modeling tools, ProgrAMR and Mota Mapper, which interface with the BoxLib AMR framework and the SSTmacro network simulator. ProgrAMR allows us to generate and model the execution of task dependency graphs from high-level specifications of AMR-based applications, which we demonstrate by analyzing two example AMR-based multigrid solvers with varying degrees of asynchrony. Mota Mapper generates multiobjective, network topology-aware box mappings, which we apply to optimize the data layout for the example multigrid solvers. While the sensitivity of these solvers to layout and execution strategy appears to be modest for balanced scenarios, the impact of better mapping algorithms can be significant when performance is highly constrained by network hop latency. Furthermore, we show that network latency in the multigrid bottom solve is the main contributing factor preventing good scaling on exascale-class machines.

关键词： Slabs Computational modeling Algorithm design and analysis Semantics optimization Programming Runtime

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Single-snapshot DoA estimation using adaptive elastic net in the complex domain

Single-snapshot DoA estimation using adaptive elastic net in...

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international workshop on Compressed Sensing Theory and its applications to Radar, Sonar and Remote Sensing (CoSeRa)

作者： Muhammad Naveed Tabassum Esa Ollila Dept. of Signal Processing and Acoustics Aalto University Aalto Finland

ISBN: (纸本)9781509029211

The elastic net (EN) is a popular regularization and variable selection method that overcomes the shortcomings of Lasso such as poor recovery in the face of high mutual coherence. In this paper, we develop an efficient algorithm to solve the weighted EN criterion for complex-valued measurements applying the cyclic coordinate descent approach. We illustrate that usage of smartly chosen adaptive (i.e., data-dependent) weights revamps the algorithm to overcome the shortcomings of naive (non-weighted) EN and enhances the exact recovery. Usefulness of the proposed algorithm is illustrated for compressed beamforming (CBF) with the single-snapshot DoA (direction-of-arrival) estimation, in a demanding multi-source scenario that contains closely spaced sources having large variation in source powers. Accurate DoA's estimation performance and error plummet by the proposed algorithm validates its application and advocates its potential usage in other signal processing problems.

关键词： Signal processing algorithms Charge coupled devices Coherence optimization Estimation Compressed sensing Direction-of-arrival estimation

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A new improved fruit fly optimization algorithm for traveling salesman problem

A new improved fruit fly optimization algorithm for travelin...

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international workshop on Advanced Computational Intelligence (IWACI)

作者： Lvjiang Yin Xinyu Li Liang Gao Chao Lu The State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan China

Traveling salesman problem (TSP) which is a classic combinational optimization problem has a wide range of applications in many areas. Many researchers focus on this problem and propose several algorithms. However, it was proved to be NP-hard, which is very difficult to be solved. No algorithm can solve any types of this problem effectively. In order to propose an effective algorithm for TSP, this paper improves the fruit fly optimization algorithm (FOA) proposed recently. As far as we know, the FOA has not yet been applied to solve TSP. Therefore, several modifications of FOA have to be made to meet the characteristics of TSP. Based on the whole search framework and the essence of FOA, some operations of particle swarm optimization (PSO) have been introduced into this method. In the smell search phase, the cluster mechanism of the fruit flies has been used to copy flies to one point and the mutation operation of genetic algorithm is used as the method of information exchanging among fruit flies for random search. In the visual search phase, the generalized PSO is applied to balance the global search and local search abilities of proposed algorithm. To evaluate the performance of proposed algorithm, some experiments and comparisons with other reported algorithms have been conducted. The results show the feasibility and effectiveness of proposed algorithm in solving TSP.

关键词： optimization Sociology Statistics Visualization Convergence Traveling salesman problems Approximation algorithms

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