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检索条件"主题词=Selection algorithm"

共 226 条记录，以下是131-140 订阅

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Auto-Tuning Online POMDPs for Multi-Object Search in Uncertain Environments

Auto-Tuning Online POMDPs for Multi-Object Search in Uncerta...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech)

作者： Wakayama, Shohei Ahmed, Nisar Univ Colorado Boulder CO 80303 USA

ISBN: (数字)9781624105951

ISBN: (纸本)9781624105951

Future exploration missions will require mobile robots to autonomously solve the challenging problem of searching for and localizing multiple objects of interest in uncertain environments. Such problems can be posed as partially observable Markov decision processes (POMDPs), which require approximations to obtain practically implementable policies for sequential decision making under uncertainty. Although online solvers such as Partially Observable Monte Carlo Planning (POMCP) have recently attracted much interest due to their fast anytime closed-loop capabilities, they require extensive hyperparameter tuning before implementation to achieve acceptable performance. However, in missions where robots cannot receive any external aid for long periods of time, they must be able to adjust and adapt hyperparameters to varying operating conditions and environmental contexts. In this paper, we cast this hyperparameter selection problem for search-tree based online solvers a Multi-Armed Bandit (MAB). Our contributions for this problem are as follows. First of all, we evaluate which existing MAB policy approximation strategies are suitable for this problem and examine the definition of utility function for MAB auto-tuning. Then, the performance of auto-tuning robot is evaluated in a simple baseline fully observable Markov Decision Process (MDP) single goal navigation problem using Monte Carlo Tree Search policy approximation. It is shown that the performance of the robot using auto-tuned MCTS planner is shown to be almost the same as the best theoretical performance produced by offline value iteration. Next, the auto-tuning robot is evaluated in a simulated fully observable multi-goal navigation problem in more complex environments, showing that the auto-tuning robot performs equally/better than an offline tuned fixed hyperparameter robot. Finally, the auto-tuning robot is evaluated in simulation for a multi-object search problem in uncertain environment where locations and nu

关键词： Mobile Robots Markov Decision Process Closed Loop selection algorithm Annealing Number of Particles Space Exploration Reinforcement Learning Value Function Probability Distribution

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A Multi-Armed Bandit Approach to Atmospherically-Aware Altitude Optimization

A Multi-Armed Bandit Approach to Atmospherically-Aware Altit...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech)

作者： Bird, John J. Langelaan, Jack W. Univ Colorado Boulder Dept Aerosp Engn Sci Boulder CO 80303 USA Penn State Univ Dept Aerosp Engn University Pk PA 16802 USA

ISBN: (数字)9781624105951

ISBN: (纸本)9781624105951

Small UAS typically fly at low altitudes where the atmospheric environment is complex and uncertain. Typical error in even the best modern weather forecasting systems introduces significant uncertainty into the optimal flight path for a UAS to complete a mission and the energy required. Existing approaches to managing this uncertainty involve creating flight plans whose cost is robust to uncertainty or assimilating aircraft observations into a weather model to eliminate forecast error. Robust approaches cannot respond to the real atmospheric environment and data assimilation requires a high bandwidth data link and significant computational resources be dedicated to the aircraft. This paper proposes a multi-armed bandit inspired approach to altitude optimization. By taking advantage of the spectral characteristics of atmospheric motion this approach enables an aircraft to seek low-cost altitudes while maintaining a model of the atmosphere using in situ sensing and computational resources. In simulation the approach demonstrates performance superior to a collocation based optimal control technique, identifying low cost regions and rejecting poor information provided to the planner by a forecast.

关键词： Atmospheric Environment Trajectory Optimization Flight Planning Weather Forecasting Energy Consumption Kalman Filter selection algorithm Transport Aircraft Solar Power Markov Decision Process

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HReplica: A Dynamic Data Replication Engine with Adaptive Compression for Multi-Tiered Storage 8

HReplica: A Dynamic Data Replication Engine with Adaptive Co...

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8th IEEE International Conference on Big Data (Big Data)

作者： Devarajan, Hariharan Kougkas, Anthony Sun, Xian-He IIT Dept Comp Sci Chicago IL 60616 USA

ISBN: (纸本)9781728162515

As the diversity of big data applications increases, their requirements diverge and often conflict with one other. Managing this diversity in any supercomputer or data center is a major challenge for system designers. Data replication is a popular approach to meet several of these requirements, such as low latency, read availability, durability, etc. This approach can be enhanced using new modern heterogeneous hardware and software techniques such as data compression. However, both these enhancements work in isolation to the detriment of both. In this work, we present HReplica: a dynamic data replication engine which harmoniously leverages data compression and hierarchical storage to increase the effectiveness of data replication. We have developed a novel dynamic selection algorithm that facilitates the optimal matching of replication schemes, compression libraries, and tiered storage. Our evaluation shows that HReplica can improve scientific and cloud application performance by 5.2x when compared to other state-of-the-art replication schemes.

关键词： data replication dynamic selection algorithm multi-tiered data compression intelligent selection dynamic programming cloud application scientific application big data

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Climatic data analysis for input to ShipIR

Climatic data analysis for input to ShipIR

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Conference on Infrared Imaging Systems - Design, Analysis, Modeling, and Testing XXIV

作者： Vaitekunas, David A. Kim, Yoonsik WR Davis Engn Ltd Ottawa ON K1B 3V3 Canada

ISBN: (纸本)9780819494979

A key input to any thermal infrared signature model is the environment, more specifically the model inputs specific to the thermal infrared background model. This paper describes a new method of analysing the climatic data for input to ShipIR. Historical hourly data from a stationary marine buoy are used to select a small number of data points (N=100) to adequately cover the range of statistics (CDF, PDF) displayed by the original data set (S=46,072). The method uses a coarse bin (1/3) to subdivide the variable space (3(5)=243 bins), and a single-point ranking system to select individual points so that uniform coverage (1/N = 0.01) is obtained for each variable. The selected data points are used in Vaitekunas and Kim (2013) to demonstrate how the new methodology is used to provide a more rigorous and comprehensive analysis of platform IR susceptibility based on the statistics of IR detection.

关键词： platform signature environments effects of climate climate statistics selection algorithm

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Efficient and exact mesh deformation using multiscale RBF interpolation 55

Efficient and exact mesh deformation using multiscale RBF in...

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55th AIAA Aerospace Sciences Meeting

作者： Kedward, L. Allen, C.B. Rendall, T.C.S. Department of Aerospace Engineering University of Bristol BristolBS8 1TR United Kingdom

ISBN: (数字)9781624104473

ISBN: (纸本)9781624104473

Mesh deformation schemes are vital in many areas of numerical simulation, and radial basis function(RBF)-based interpolation schemes are popular due to their excellent quality preservation properties. However, the system solution cost scales with moving surface points as N3surface, and so there have been numerous works investigating efficient methods of reducing the data set. However, reduced data methods require the addition of a secondary ‘correction vector field’ to ensure surface points not included in the primary deformation are moved to the correct location, and the volume mesh moved accordingly. A new method is presented here, which captures global and local motions at multiple scales using all the surface points, and so there is no need for a correction stage. The multiscale formulation developed means that although all surface points are used and a single interpolation built, the cost and conditioning issues associated with RBF methods are eliminated while still retaining exact recovery of the surface. Moreover, the sparsity introduced can be exploited, using an existing wall distance function, to further reduce the cost. The method is com- pared with a conventional greedy method on two- and three-dimensional meshes with large deformations. It is shown that mesh quality is always comparable with or better than with the greedy method, and cost and complexity is also comparable or cheaper for all stages. The most expensive stage of reduced point methods is surface mesh preprocessing, and the cost is reduced significantly here;a three or four orders of magnitude reduction in cost is demonstrated compared to greedy-type methods. © 2017 by C.B. Allen.

关键词： Computational Fluid Dynamics Fluid Structure Interaction Greedy algorithm Cost Reduction NACA 0012 Aerodynamic Shape Optimization Multivariate Interpolation Structural Element selection algorithm Mesh Free Methods

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Buffer-Aided Relay selection for Physical-Layer Security in Wireless Networks

Buffer-Aided Relay Selection for Physical-Layer Security in ...

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WSA 2015;19th International ITG Workshop on Smart Antennas

作者： Xiaotao Lu Rodrigo C. de Lamare Communications Research Group Department of Electronics University of York

In this work, we consider the use of buffer-aided relays, linear precoding techniques and multiple antennas for physical-layer security in wireless networks. We develop relay selection algorithms to improve the secrecy-rate performance of cooperative multi-user multiple-antenna wireless networks. In particular, we propose a novel finite buffer-aided relay selection algorithm that employs the maximum likelihood (ML) criterion to select sets of relays which fully exploit the flexibility offered by relay nodes equipped with buffers. Numerical results show the benefits of the proposed techniques as compared to prior art.

关键词： Physical-layer security Cooperative relaying Relay selection Physical layer relay Wireless networks selection algorithm multiple antenna

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Combined Antenna selection and Beamforming in Cross-Layer Design for Cognitive Networks

Combined Antenna Selection and Beamforming in Cross-Layer De...

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IEEE International Conference on Communications

作者： Amiotosh Ghosh Walaa Hamouda Department of Electrical and Computer Engineering Concordia University

ISBN: (纸本)9781457720529

We investigate the performance of an antenna selection algorithm applied with precoding to improve the throughput performance of cognitive multiple-input multiple-output (MIMO) radios. We assume cognitive MIMO radios coexist in the same frequency band with the primary users. In such event, for concurrent spectrum access, cognitive nodes use beamforming techniques to cancel the mutual information between cognitive and primary users. In that, cognitive nodes exploit the degrees of freedom offered by MIMO systems to beamform the transmitted signal in an attempt to cancel interference at primary users. In the cross-layer design, cognitive nodes maximize an objective function for the link layer throughput where precoding is applied at the physical layer on the transmitted spatial multiplexed signals. We present a closed form solution for the overall throughput in-terms of the physical layer parameters. Numerical results are presented to show the efficacy of the proposed scheme for different network settings.

关键词： Cross layer design Beamforming multiple input multiple output systems prediction coding selection algorithm combined antenna Physical layer Throughput Frequency bands Radio layer design Closed-form solutions Cancel Ganglia Consumers

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Insense: Incoherent Sensor selection for Sparse Signals

Insense: Incoherent Sensor Selection for Sparse Signals

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Amirali Aghazadeh Mohammad Golbabaee Andrew Lan Richard Baraniuk Stanford University University of Edinburgh Princeton University Rice University

ISBN: (纸本)9781538646595

Sensor selection refers to the problem of intelligently selecting a small subset of a collection of available sensors to reduce the sensing cost while preserving signal acquisition performance. The majority of sensor selection algorithms find the subset of sensors that best recovers an arbitrary signal from a number of linear measurements that is larger than the dimension of the signal. In this paper, we develop a new sensor selection algorithm for sparse (or near sparse) signals that finds a subset of sensors that best recovers such signals from a number of measurements that is much smaller than the dimension of the signal. Existing sensor selection algorithms cannot be applied in such situations. Our proposed Incoherent Sensor selection (Insense) algorithm minimizes a coherence-based cost function that is adapted from recent results in sparse recovery theory. Using three datasets, including a real-world dataset on microbial diagnostics, we demonstrate the superior performance of Insense for sparse-signal sensor selection.

关键词： Sensor selection coherence optimization compressive sensing Sensors Compressed sensing signals coherence Subset dimensions selection algorithm Linear measurement

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Smart PDR integration for ubiquitous pedestrian navigation service 13

Smart PDR integration for ubiquitous pedestrian navigation s...

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13th International Wireless Communications and Mobile Computing Conference (IWCMC)

作者： Hassen, Wiem Fekih Najjar, Faiza Brunie, Lionel Kosch, Harald Slimani, Yahya Carthage Univ INSAT LISI Lab Tunis 1080 Tunisia INSA Lyon LIRIS Lab F-69100 Lyon France ENSI Campus Univ Manouba Manouba 2010 Tunisia Univ Passau Passau Germany

ISBN: (纸本)9781509043729

Quite recently, considerable attention has been paid to pedestrian navigation services, since they provide people with guidance and tracking information everywhere especially in an unfamiliar environment. In the literature, indoor and outdoor navigation has been widely investigated. However, studies on the continuity of pedestrian navigation service, still lacking. This paper presents a novel positioning technology selection algorithm able to integrate the pedestrian dead reckoning (PDR) technique, for short-term, through the navigation process either to improve the calculation of the mobile unit's (MU) position and/or to maintain the continuity of the service. The integration of the PDR is inferred based on the fuzzy logic approach. In fact, the proposed algorithm defines a fuzzy variable called "margin" which identifies four different states, namely, confidence, intermediate, confusion and critic. The PDR technique is only incorporated in the last two states in two ways: either fused with the used radio frequency (RF) positioning technology or stand-alone if no RF technology is available. The algorithm performance is evaluated based on an empirical study.

关键词： Pedestrian navigation system selection algorithm positioning technology PDR fuzzy logic

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ADAPTIVE SEARCH RANGE selection IN MOTION ESTIMATION

ADAPTIVE SEARCH RANGE SELECTION IN MOTION ESTIMATION

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Chung-Cheng Lou Szu-Wei Lee C.-C. Jay Kuo Ming Hsieh Department of Electrical Engineering and Signal and Image Processing Institute University of Southern California Los Angeles CA 90089-2564 USA

ISBN: (纸本)9781424442959

An adaptive search range (SR) selection algorithm in motion estimation for video encoding is proposed in this work with an objective to achieve excellent coding performance with reduced memory access bandwidth. In modern fast motion search algorithms, a motion vector predictor (MVP) is obtained from the motion vectors (MVs) of spatially and temporally neighboring blocks, which is called the predictive MV set. By exploiting the relationship between the variance of the predictive MV set and the SR, we develop an adaptive SR selection algorithm. That is, a larger variance implies lower accuracy of the MVP and, thus, a larger SR. The superior performance of the proposed SR selection algorithm is demonstrated by experimental results.

关键词： Motion estimation motion vector predictor adaptive search range selection motion estimation motion vector SAVANNAH RIVER OPERATIONS OFFICE Strontium selection algorithm cisplatin/mitomycin/vindesine Variance

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