Cooperative Kalman Filtering With Data Fusion in Time Varying Communication Networks

2010 ◽  
Author(s):  
Davide Spinello ◽  
Daniel J. Stilwell
Keyword(s):  
Data Fusion ◽  
Communication Networks ◽  
Kalman Filtering ◽  
Network Topology ◽  
Dynamical Process ◽  
Time Varying ◽  
Process Data ◽  
Individual Estimate ◽  
Fusion Scheme ◽  
Opinion Pool

We consider a time varying sensor network comprised of a group of agents equipped with communication capabilities, and we address applications where communication between agents is highly bandwidth limited as for instance in underwater missions. We use the Bayesian formalism to derive data fusion equations in which each sensor maintains an individual estimate of the state of a dynamical process. Data sharing between agents is defined by a time-varying network topology. We show that error covariances associated to estimates obtained with the independent opinion pool fusion scheme asymptotically agree if the communication network is partially asynchronous.

In-Process Data Fusion for Process Monitoring and Control of Metal Additive Manufacturing

2021 ◽  
Author(s):  
Zhuo Yang ◽  
Yan Lu ◽  
Simin Li ◽  
Jennifer Li ◽  
Yande Ndiaye ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Process Control ◽  
Data Fusion ◽  
Real Time ◽  
Process Data ◽  
Multi Scale ◽  
And Control ◽  
Fusion Framework ◽  
Scale Data ◽  
Metal Additive

Abstract To accelerate the adoption of Metal Additive Manufacturing (MAM) for production, an understanding of MAM process-structure-property (PSP) relationships is indispensable for quality control. A multitude of physical phenomena involved in MAM necessitates the use of multi-modal and in-process sensing techniques to model, monitor and control the process. The data generated from these sensors and process actuators are fused in various ways to advance our understanding of the process and to estimate both process status and part-in-progress states. This paper presents a hierarchical in-process data fusion framework for MAM, consisting of pointwise, trackwise, layerwise and partwise data analytics. Data fusion can be performed at raw data, feature, decision or mixed levels. The multi-scale data fusion framework is illustrated in detail using a laser powder bed fusion process for anomaly detection, material defect isolation, and part quality prediction. The multi-scale data fusion can be generally applied and integrated with real-time MAM process control, near-real-time layerwise repairing and buildwise decision making. The framework can be utilized by the AM research and standards community to rapidly develop and deploy interoperable tools and standards to analyze, process and exploit two or more different types of AM data. Common engineering standards for AM data fusion systems will dramatically improve the ability to detect, identify and locate part flaws, and then derive optimal policies for process control.

A Data Fusion Scheme in Wireless Sensor Network Based on Optimizing Parameters of Neural Network

2021 ◽  
pp. 315-323
Author(s):  
Thi-Kien Dao ◽  
Trong-The Nguyen ◽  
Van-Dinh Vu ◽  
Truong-Giang Ngo
Keyword(s):  
Neural Network ◽  
Wireless Sensor Network ◽  
Data Fusion ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Fusion Scheme

scholarly journals Intelligent Control of Bulk Tobacco Curing Schedule Using LS-SVM- and ANFIS-Based Multi-Sensor Data Fusion Approaches

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1778 ◽  
Author(s):  
Juan Wu ◽  
Simon X. Yang
Keyword(s):  
Data Fusion ◽  
Sensor Data ◽  
Support Vector ◽  
Curing Process ◽  
Inference System ◽  
Tobacco Leaves ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion ◽  
Fusion Scheme ◽  
Hybrid Fusion

The bulk tobacco flue-curing process is followed by a bulk tobacco curing schedule, which is typically pre-set at the beginning and might be adjusted by the curer to accommodate the need for tobacco leaves during curing. In this study, the controlled parameters of a bulk tobacco curing schedule were presented, which is significant for the systematic modelling of an intelligent tobacco flue-curing process. To fully imitate the curer’s control of the bulk tobacco curing schedule, three types of sensors were applied, namely, a gas sensor, image sensor, and moisture sensor. Feature extraction methods were given forward to extract the odor, image, and moisture features of the tobacco leaves individually. Three multi-sensor data fusion schemes were applied, where a least squares support vector machines (LS-SVM) regression model and adaptive neuro-fuzzy inference system (ANFIS) decision model were used. Four experiments were conducted from July to September 2014, with a total of 603 measurement points, ensuring the results’ robustness and validness. The results demonstrate that a hybrid fusion scheme achieves a superior prediction performance with the coefficients of determination of the controlled parameters, reaching 0.9991, 0.9589, and 0.9479, respectively. The high prediction accuracy made the proposed hybrid fusion scheme a feasible, reliable, and effective method to intelligently control over the tobacco curing schedule.

scholarly journals A Time-Varying Information Measure for Tracking Dynamics of Neural Codes in a Neural Ensemble

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 880
Author(s):  
Mohammad R. Rezaei ◽  
Milos R. Popovic ◽  
Milad Lankarany
Keyword(s):  
Kalman Filtering ◽  
Probability Distributions ◽  
Neural Code ◽  
Time Varying ◽  
Amount Of Information ◽  
Neural Ensemble ◽  
Different Types ◽  
Types Of Information ◽  
Different Time Scales ◽  
Filtering Approach

The amount of information that differentially correlated spikes in a neural ensemble carry is not the same; the information of different types of spikes is associated with different features of the stimulus. By calculating a neural ensemble’s information in response to a mixed stimulus comprising slow and fast signals, we show that the entropy of synchronous and asynchronous spikes are different, and their probability distributions are distinctively separable. We further show that these spikes carry a different amount of information. We propose a time-varying entropy (TVE) measure to track the dynamics of a neural code in an ensemble of neurons at each time bin. By applying the TVE to a multiplexed code, we show that synchronous and asynchronous spikes carry information in different time scales. Finally, a decoder based on the Kalman filtering approach is developed to reconstruct the stimulus from the spikes. We demonstrate that slow and fast features of the stimulus can be entirely reconstructed when this decoder is applied to asynchronous and synchronous spikes, respectively. The significance of this work is that the TVE can identify different types of information (for example, corresponding to synchronous and asynchronous spikes) that might simultaneously exist in a neural code.

scholarly journals Clustering Algorithm-Based Data Fusion Scheme for Robust Cooperative Spectrum Sensing

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 5777-5786
Author(s):  
Shunchao Zhang ◽  
Yonghua Wang ◽  
Pin Wan ◽  
Jiawei Zhuang ◽  
Yongwei Zhang ◽  
...  
Keyword(s):  
Data Fusion ◽  
Spectrum Sensing ◽  
Clustering Algorithm ◽  
Cooperative Spectrum Sensing ◽  
Fusion Scheme
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