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Genetic algorithms for multi-agent fusion-system learning

Genetic algorithms for multi-agent fusion-system learning

引用

conference on sensor fusion - architectures, algorithms, and applications V

作者： Pigeon, L Inglada, J Solaiman, B Def Res Estab Valcartier Informat Syst Technol Sect Val Belair PQ G3J 1X5 Canada

ISBN: (纸本)0819440809

The development of efficient semi-automatic systems for heterogeneous information fusion is actually a great challenge. The efficiency can be represented as the system openness, the system evolution capabilities and the system performance. Multi-agent architecture can be designed in order to respect the first two efficiency constraints. As for third constraint, which is the performance, the key point is the interaction between each information component of the system. The context of this study is the development of a semi-automatic information fusion system for cartographic features interpretation. Combining heterogeneous sources of information such as expert rules and strategies, domain models, image processing tools, interpolation techniques, etc. completes the system development task The information modeling and fusion is performed within the evidential theory concepts. The purpose of this article is to propose a learning approach for interaction-oriented multi-agent systems. The optimization of the interaction weight is tackled with genetic algorithms technique because it provides solution for the whole set of weights at once. In this paper, the context of the multi-agent system development is presented first The need for such system and its parameters is explained. A brief review of learning techniques leads to genetic algorithms as a choice for the learning of the developed multi-agent system. After a brief introduction to genetic algorithms, these are adapted to the particularity of this study. Two approaches are designed to measure the system's fitness based on either binary or fuzzy decisions. The conclusion presents suggestions for further research in the area of multi-agent system-learning with genetic algorithms.

关键词： information fusion multi-agent genetic algorithms intelligent system learning interaction oriented-programming

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Optimising multiresolution pixel-level image fusion

Optimising multiresolution pixel-level image fusion

引用

conference on sensor fusion - architectures, algorithms, and applications V

作者： Petrovic, V Xydeas, C Denaro Telecommun Technol YU-11080 Zemun Yugoslavia

ISBN: (纸本)0819440809

A number of pixel level image fusion schemes have been proposed in the past which combine registered input sensor images into a single fused output image. The two general objectives that underpin the operations of these schemes are a) the transfer of all visually important information from input images into a fused image and b) the minimisation of undesirable distortions and artefacts which may be generated in the fused image. fusion is usually achieved by i) the "decomposition" of input images into representations of their spectral bands and ii) a "selection" process which transfers information from input bands to yield the required representation of a single fused output image. Furthermore, decomposition is often based on multiresolution pyramidal representations and the selection process operates on corresponding input image pyramidal levels using "selection templates" which focus on local spectral characteristics. The performance of such a multiresolution pixel level image fusion system depends primarily on the actual decomposition and selection algorithms used. Thus for a given decomposition/selection arrangement, fusion performance is dependent on the pyramid size (i.e. number of level) and template size. Pyramid and template sizes on the other hand greatly influence the system's computational complexity. This paper is concerned with the performance optimisation/characterisation of several multiresolution image fusion schemes, in general and with performance/complexity tradeoffs in particular. Performance is measured using a subjectively meaningful, objective fusion metric which has been proposed recently by the authors and which is based on the preservation of image edge information. Thus fusion systems based on derivatives of Gaussian low-pass pyramid and the Discrete Wavelet transform are examined and their performances versus decomposition/selection parameters are defined and compared. The performance/algorithmic complexity results presented for these mult

关键词： pixel-level image fusion multiresolution fusion fusion performance evaluation fusion complexity

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Odor source identification by grounding linguistic descriptions in an artificial nose

Odor source identification by grounding linguistic descripti...

引用

conference on sensor fusion - architectures, algorithms, and applications V

作者： Loutfi, A Coradeschi, S Duckett, T Wide, P Univ Orebro Dept Technol Ctr Appl Autonomous Sensor Syst S-70182 Orebro Sweden

ISBN: (纸本)0819440809

This paper addresses the problem of enabling autonomous agents (e.g., robots) to carry out human oriented tasks using an electronic nose. The nose consists of a combination of passive gas sensors with different selectivity, the outputs of which are fused together with an artificial neural network in order to recognize various human-determined odors. The basic idea is to ground human-provided linguistic descriptions of these odors in the actual sensory perceptions of the nose through a process of supervised learning. Analogous to the human nose, the paper explains a method by which an electronic nose can be used for substance identification. First, the receptors of the nose are exposed to a substance by means of inhalation with an electric pump. Then a chemical reaction takes place in the gas sensors over a period of time and an artificial neural network processes the resulting sensor patterns. This network was trained to recognize a basic set of "pure" substances such as "vanilla", "lavender" and "yogurt" under controlled laboratory conditions. The complete system was then validated through a series of experiments on various combinations of the basic substances. First, we showed that the nose was able to consistently recognize unseen samples of the same substances on which it had been trained. In addition, we present some first results where the nose was tested on novel combinations of substances on which it had not been trained by combining the learned descriptions - for example, it could distinguish "lavender yogurt" as a combination of "lavender" and "yogurt".

关键词： sensors Nose Principal component analysis Artificial neural networks Gas sensors Neural networks Feature extraction Toxicity Machine learning Robots

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Improved diagnostic and prognostic assessments using health management information fusion

Improved diagnostic and prognostic assessments using health ...

引用

37th Annual AUTOTESTCON conference

作者： Roemer, MJ Kacprzynski, GJ Schoeller, MH

ISBN: (纸本)0780370945

Various data, feature and knowledge fusion strategies and architectures have been developed over the last several years for improving upon the accuracy, robustness and overall effectiveness of anomaly, diagnostic and prognostic technologies. fusion of relevant sensor data, maintenance database information, and outputs from various diagnostic and prognostic technologies has proven effective in reducing false alarm rates, increasing confidence levels in early fault detection, and predicting time to failure or degraded condition requiring maintenance action. The data fusion strategies discussed in this paper are principally probabilistic or AI-based in nature and are used to aid in directly identifying confidence bounds associated with specific component fault identifications and predictions. Dempster-Shafer fusion, Bayesian inference, fuzzy-logic inference, neural network fusion and simple weighting/voting are the algorithmic approaches that are discussed in this paper. Data fusion architectures such as centralized fusion, autonomous fusion, and hybrid fusion are described in terms of their applicability to fault diagnosis and prognosis. The final goal is to find the optimal combination of measured system data, data fusion algorithms, and associated architectures for obtaining the highest overall prediction/detection confidence levels associated with a specific application. A gas turbine engine test cell sensor validation example is provided in the paper that is specifically related data fusion approaches for test cell sensor validation using Dempster-Shafer fusion.

关键词： data fusion diagnostics prognostics health management Bayesian Dempster-Shafer neural networks fuzzy logic

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Programmable multi sensor interface for industrial applications

引用

International conference on Multisensor fusion and Integration for Intelligent Systems

作者： Born, H Bunsendal, J KUKA Roboter GmbH Augsburg Germany

ISBN: (纸本)3000082603

Most complex robot applications rely on use of multiple sensors and efficient algorithms. Substantial know-how has been developed for various applications, but very little got implemented in industry. This paper looks into the some of the problems and proposes a flexible and efficient controller interface that provides a platform for the integration of new algorithms and devices into existing industrial hardware.

关键词： sensors industrial robots implementation software Architecture

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Model fusion for road extractions from multi-source satellite images

Model fusion for road extractions from multi-source satellit...

引用

conference on sensor fusion - architectures, algorithms, and applications V

作者： Pigeon, L Lecornu, L Solaiman, B Brunet, G DREV Informat Syst Technol Sect Val Belair PQ G3J 1X5 Canada

ISBN: (纸本)0819440809

In the field of pattern recognition from satellite images, the existing road extraction methods have been either too specialized or too time-consuming. The challenge then has been to develop a general and close to real-time road extraction method. This study falls in this perspective and aims at developing a close to real-time semi-automatic system able to extract linear planimetric features (including roads). The major concern of the study is to combine the most efficient tools to deal with the road primitive extraction process in order to handle multi-resolution and multi-type raw images. Hence, this study brought along a new model fusion characterized by the combination of operator input points (in 2D or 3D), fuzzy image filtering, cubic natural splines and the A* algorithm First, a cubic natural splines interpolation of the operator points is used to parameterize the A* algorithm cost function with the consequence to restrict the mining research area. Second, the heuristic function of the same algorithm is combined with the fuzzy filtering which proves to be a fast and efficient tool for the selection of the primitive most promising points. The combination of the cost function and the heuristic function leads to a limited number of hypothetical paths, hence decreasing the computation time. Moreover, the combination of the A* algorithm and the splines leads to a new way to solve the perceptual grouping problems. Results related to the problem of feature discontinuity suggest new research perspectives in relation to noisy area (urban) as well as noisy data (radar images).

关键词： fusion road extraction remote-sensing hypothetical reasoning intelligent system cubic splines interpolation fuzzy filtering A* algorithm perceptual grouping

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Neural networks sensor fusion of meteorological measurements

Neural networks sensor fusion of meteorological measurements

引用

sensor fusion: architectures, algorithms and applications V

作者： Yee, Y. Measure, E.M. Cogan, J. Bleiweiss, M. Army Research Laboratory Computational/Info. Sci. Directorate White Sands Missile Range Watertown NM 88002 United States

The collection and management of vast quantities of meteorological data, including satellite-based as well as ground-based measurements, is presenting great challenges in the optimal usage of this information. To address these issues, the Army Research Laboratory has developed neural networks for combining multisensor meteorological data for Army battlefield weather forecasting models. As a demonstration of this data fusion methodology, multi-sensor data was taken from the Meteorological Measurement Set Profiler (MMSP-POC) system and from satellites with orbits coinciding with the geographical locations of interest. The MMS Profiler-POC comprises a suite of remote sensing instrumentation and surface measuring devices. Neural network techniques were used to retrieve temperature and wind information from a combination of polar orbiter and/or geostationary satellite observations and ground-based measurements. Back-propagation neural networks were constructed which used satellite radiances, simulated microwave radiometer measurements, and other ground-based measurements as inputs and produced temperature and wind profiles as outputs. The network was trained with rawinsonde measurements used as truth-values. The final outcome will be an integrated, merged temperature/wind profile from the surface up to the upper troposphere.

关键词： sensor data fusion

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Comparison of methods for association and fusion of multi sensor data for tracking applications

Comparison of methods for association and fusion of multi se...

引用

AIAA Guidance, Navigation, and Control conference and Exhibit 2001

作者： Girija, G. Raol, J.R. System Identification Laboratory Flight Mechanics and Control Division National Aerospace Laboratories Bangalore - 560017 India

ISBN: (纸本)9781563479786

In this paper, the two commonly used algorithms for association namely the Nearest Neighbour (NN) and Probabilistic Data Association (PDA) are applied for measurement to track association, estimation/filtering and fusion. The algorithms are first validated on simulated data of a target moving with constant velocity and tracked by two sensors with different measurement noise characteristics. Each of the sensors is equipped with a Kalman Filter which is essentially used to update the track states after associating the incoming measurement with the existing track. The estimated states are then fused using hierarchal sensor architecture to generate a single fused track. A comparison of the two methods is made in terms of various statistical performance measures for the filters. For this case of simulated data of a single target, multisensor scenario the performance of the NN Filter (NNF) is superior to that of PDA Filter (PDAF) when the measurement noise levels are high. For low measurement noise levels, the performance of the two filters is similar. The algorithms are then applied to generate a fused trajectory from real data of a moving object. © 2001. The American Institute of Aeronautics and Astronautics Inc. All rights reserved.

关键词： Kalman filters

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Development of a robust algorithm for detection of mine targets in image data from electrooptic and acoustic sensors

Development of a robust algorithm for detection of mine targ...

引用

conference on Detection and Remediation Technologies for Mines and Minelike Targets VI

作者： Williams, A Meyer, R Pachowicz, P Maksymonko, G Witten, T Habersat, J Sci Applicat Int Corp Longwood FL 32750 USA

ISBN: (纸本)0819440892

Current minefield detection research indicates that operationally no single sensor technology will likely be capable of detecting mines/minefields in a real-time manner and at a performance level suitable for a forward maneuver unit. Minefield detection involves a particularly wide range of operating scenarios and environmental conditions, which requires deployment of complementary sensor suites, We have focused, therefore, on the development of a computationally efficient and robust detection algorithm that exploits robust image processing techniques centered on meaningful target feature sets applicable to a variety of imaging sensors. This paper presents the detection technique, emphasizing its robust architecture, and provides performance results for image data generated by complementary sensors. The paper also briefly discusses the application of this detector as a component of fusion architectures for processing returns from diverse imaging sensors, including multichannel image data from disparate sensors.

关键词： mine detection fusion robust algorithms ground penetrating radar imaging acoustic imaging

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Assessment of data and knowledge fusion strategies for prognostics and health management

Assessment of data and knowledge fusion strategies for progn...

引用

IEEE conference on Aerospace

作者： M.J. Roemer G.J. Kacprzynski R.F. Orsagh Impact Technologies Rochester NY USA Impact Technologies Rochester NY

ISBN: (纸本)0780365992

Various data, feature and knowledge fusion strategies and architectures have been developed over the last several years for improving upon the accuracy, robustness and overall effectiveness of anomaly, diagnostic and prognostic technologies. fusion of relevant sensor data, maintenance database information, and outputs from various diagnostic and prognostic technologies has proven effective in reducing false alarm rates, increasing confidence levels in early fault detection, and predicting time to failure or degraded condition requiring maintenance action. The data fusion strategies discussed are principally probabilistic in nature and are used to aid in directly identifying confidence bounds associated with specific component fault identifications and predictions. Dempster-Shafer fusion, Bayesian inference, fuzzy-logic inference, neural network fusion and simple weighting/voting are the algorithmic approaches that are discussed. Data fusion architectures such as centralized fusion, autonomous fusion, and hybrid fusion are described in terms of their applicability to fault diagnosis and prognosis. The final goal is to find the optimal combination of measured system data, data fusion algorithms, and associated architectures for obtaining the highest overall prediction/detection confidence levels associated with a specific application. To achieve this goal, a set of metrics has been developed for gauging the performance and effectiveness of a fusion strategy. Specifically, this paper demonstrates how various metrics are used for assessing individual and fused vibration-based diagnostic algorithms. Evaluation of the diagnostic strategies was performed using gearbox seeded-fault and accelerated failure data.

关键词： Fault diagnosis Robustness sensor fusion Spatial databases Fault detection Degradation Bayesian methods Neural networks Voting Inference algorithms

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