Data Fusion Structure Research Articles

An interpretation-based convolution neural network framework for geophysical data fusion and aquifer structure identification

Identification of 3D realistic aquifer structures is essential for predicting physicochemical processes in groundwater systems. However, the characterization of highly heterogeneous aquifers remains challenging because it relies on the effective fusion of multiple geophysical data sources having wide areal coverage, as well as downhole geophysical data featuring high resolution. This study establishes a novel 3D convolutional neural network model to generate aquifer structure from 3D seismic data, constrained by sparse downhole sonic and lithology logs. In the model, the data fusion procedure is designed to follow the logics of conventional manual interpretation of multiple geophysical data, and to address the 3D spatial relationships between geophysical data and lithology. The method is implemented in a typical fluvial aquifer featuring coarse paleovalley sediments (sandstone) embedded in the tight surrounding rocks (claystone), in order to identify channelized sandstone from low-permeability claystone. It is confirmed that the proposed model reliably generates 3D aquifer structures based on seismic amplitudes, downhole sonic and lithology logs. The method is compared to traditional machine learning models that focus on 1D conversion from geophysical attributes to lithology. The results show that the newly-developed model performs more robustly and accurately because the use of 3D convolution allows considering the relationships between seismic amplitude, sonic velocity and lithology in both vertical and horizontal directions. Moreover, the inclusion of sonic logs constraint in the model, following the logics of manual seismic data interpretation, significantly improves the model accuracy. The method can find broad applications for the characterization of subsurface heterogeneity even featuring non-gaussian permeability distribution like the demonstrated fluvial aquifer.

An adaptive reinforcement learning-based multimodal data fusion framework for human–robot confrontation gaming

Playing games between humans and robots have become a widespread human–robot confrontation (HRC) application. Although many approaches were proposed to enhance the tracking accuracy by combining different information, the problems of the intelligence degree of the robot and the anti-interference ability of the motion capture system still need to be solved. In this paper, we present an adaptive reinforcement learning (RL) based multimodal data fusion (AdaRL-MDF) framework teaching the robot hand to play Rock–Paper–Scissors (RPS) game with humans. It includes an adaptive learning mechanism to update the ensemble classifier, an RL model providing intellectual wisdom to the robot, and a multimodal data fusion structure offering resistance to interference. The corresponding experiments prove the mentioned functions of the AdaRL-MDF model. The comparison accuracy and computational time show the high performance of the ensemble model by combining k-nearest neighbor (k-NN) and deep convolutional neural network (DCNN). In addition, the depth vision-based k-NN classifier obtains a 100% identification accuracy so that the predicted gestures can be regarded as the real value. The demonstration illustrates the real possibility of HRC application. The theory involved in this model provides the possibility of developing HRC intelligence.

Hierarchical network security situation awareness data fusion method in cloud computing environment

In order to improve the accuracy of hierarchical network security situational awareness data fusion and shorten the fusion time, this paper proposes a hierarchical network security situational awareness data fusion method in cloud computing environment. The hierarchical model is established to obtain the hierarchical structure of data fusion. Hierarchical network security situational awareness data are collected and processed in parallel by cloud computing technology. According to the data collection results, the similarity between security events is calculated by using the clustering idea, and the similar security events are merged to achieve the purpose of removing redundant events. The hierarchical network security situational awareness data is fused by grey relational analysis. Finally, the simulation results show that the accuracy of data fusion of this method is high, up to 98%, and the fusion time is short, the longest is 13 s. Compared with the comparison method, this method has a better performance, indicating that this method is suitable for data fusion of hierarchical network security situation awareness.

Internet of Vehicles Information Processing Method with Vehicle-Mounted Cloud Grid as the Underlying Data Fusion Structure

With the development of electronic information network technology, large car networking systems can produce all kinds of data such as text, images, and videos, a large number of heterogeneous data, different features of heterogeneous data, and different data structures. In the Internet of vehicles, the beacon message generation strategy needs to be researched and designed on the premise of meeting the requirements of vehicle location accuracy and wireless communication performance. According to the Kalman filter differential prediction equation, the message generation model of Kalman filter beacon is established. In the deep learning research on the underlying data fusion algorithm, the most effective way to solve the problem of insufficient integration degree between the underlying data is to improve the data quality and ensure data sharing and reuse between multisource heterogeneous data. Therefore, the D-S evidence theory fusion model and rough set underlying model are proposed in the vehicle-mounted cloud network. Among them, the D-S evidence theory fusion model ensures the improvement of underlying data quality, forms effective rule combination, and reduces conflicts between rules through filtering evidence theory. The rough set underlying data fusion model optimizes the underlying data of each device by improving the rough set attribute reduction method of particle swarm optimization algorithm.

Construction of an Intelligent Processing Platform for Equestrian Event Information Based on Data Fusion and Data Mining

In the past two years, equestrian sports have become more and more popular with the public. Due to the comprehensive development of equestrian preparations for the 2020 Olympic Games in China, the equestrian sports industry presents an unprecedented favorable development environment in China. This article is aimed at studying the construction of an equestrian event information intelligent processing platform based on data fusion and data mining. This article introduces the relevant theoretical knowledge of data mining and data fusion, including the description of the concept of data mining, the common analysis methods and algorithms of data mining, the basic concepts of data fusion, and the functional structure of data fusion. It discusses various algorithms in cluster analysis and focuses on the analysis of distance measurement and similarity coefficient in cluster analysis. In the experimental part, in order to intelligently process and acquire information, an information intelligent processing platform is constructed based on data fusion and data mining technology. The experimental results of this paper show that the precision rate, recall rate, and F‐score of the platform under closed test are much higher than those under open test, and the precision rate is increased by about 7.26%.

Application of Intelligent Robot Based on Multi-sensor Control in Modern Home Life

In view of the accuracy and rapidity of intelligent robot information processing, this paper designs a multi-sensor hardware system of robots by using ultrasonic sensors, infrared sensors, Hall sensors, contact sensors and other sensors, and focuses on multi-sensor data. A hybrid data fusion structure and a data fusion algorithm using Bayesian inference algorithm are constructed. The fuzzy obstacle avoidance algorithm is proposed and designed to solve the problem of robot obstacle avoidance. The hardware experimental environment of the system, the experimental results verify the effectiveness of the above-mentioned robot control function implementation. After experimental monitoring, the smart home robot has the advantages of low cost, multi-function, easy maintenance and convenient upgrade, and realizes functions such as voice recognition, home entertainment, indoor cleaning, security detection and alarm.

Ship Navigation System Based on Wireless Sensor

To study the application of wireless sensor in the ship dynamic positioning system, the distributed fusion function model and structure model of wireless sensor network were set up.Using the DJC model-based SVM prediction algorithm, the quadratic optimal performance index in ship dynamic positioning MPC control was solved and the optimal control thrust was obtained.According to the classic cluster routing protocol, a data fusion structure based on residual energy and dormancy scheduling mechanism was proposed. The results showed that the proposed routing protocol based on the residual energy and sleep scheduling mechanism data fusion structure was superior to the Leach protocol. It improved the real-time performance of data transmission. Thus, the network data fusion structure achieves the goal of energy balance. The energy consumption is reduced to a certain extent and the design is reasonable.

RETRACTED ARTICLE: Multimedia and multi-feature cluster fusion model based on saliency for mobile network applications

This paper introduces the concept, advantages, structure, method and application of multisensor integration and data fusion, and lists four different integration characteristics of sensors. Data fusion technology combines data from different sensors or other information sources in order to improve the accuracy of location and feature estimation. In the process of data fusion, modeling includes signal model, noise model, converter model, data transformation model and fusion model. The data fusion model includes the fusion method and the structure. This paper introduces the integrated, distributed and hybrid fusion structures, and compares them. The visual saliency map to image processing technology depends on the quality of the obtained good results, the existing visual saliency detection method is usually only detected by visual saliency map attribute rough, seriously affected the image processing results. Therefore, a visual saliency detection method based on Bayesian theory and statistical learning is proposed to detect the visual saliency of the image. The method is based on Bayesian theory of the significance of static images. According to the bottom-up visual saliency model, the ROC curve was used for quantitative evaluation in the two standard data sets. The results show that the nonlinear combination effect is better than the linear combination.

Application of Multi-sensor Information Fusion Based on Improved Particle Swarm Optimization in Unmanned System Path Planning

Intelligent vehicle driving performance is safe and stable, which can significantly improve the efficiency of road traffic and reduce energy consumption, and intelligent vehicle is also the development direction of modern transport. Its core technology is intelligent environment perception module, by using a variety of sensors on the car in which the surrounding environment for data collection, processing module to provide effective control for the basis. In this paper, a new SINS / CNS / GPS integrated navigation observation equation is proposed, and a new federated data fusion structure is designed for the integrated navigation system. The particle filter is used to fuse the multi-source data of the federated filter subsystem, thus eliminating the limitations of the classical Kalman filter. The traditional Kalman filter structure and the federal particle filter mechanism are designed. The comparison shows that the proposed algorithm is effective in the information fusion of the integrated navigation system, and the filtering effect is superior to the traditional filtering method.

The Research of Multi-sensor Data Fusion Technology

Multi-source information from multi-sensors is very important to command information system. Processed multi-source information can offer useful decision information to commander. On the basis of explaining concept, function and advantage of multi-sensor data fusion, the common model of the data fusion was introduced. Multi-sensor data fusion technologies and system structure of multi-sensor data fusion were analyzed.

A Systematic Approach of Data Fusion Technique in RFID Sensor Network using Neuro-Fuzzy Technique

In this paper, a systematic approach is used for sale prediction in a multistoried retail business with the help of multi sensor data fusion technique using Neural Network and Fuzzy Logic. This method can better solve problems existing in traditional sale prediction which are basically depends on the personal experience. In this work a 3-layers data fusion structure is used. In this system, the sale data experiential characteristic and the sale data-fitting characteristic are fused by fuzzy inference system to get sale prediction. After using the Feed Forward Back Propagation algorithm, the system is trained for predefined target value and then the system calculate the sale statistic in runtime which is fused with the data of expert databases using fuzzy logic technique.

Developing a computational solution of mobile robot self-localisation problem

This paper presents a computational solution for the mobile robot self-localization problem using a new adaptive data fusion structure. Input sensors are employed for feeding the fusion structure, and consequently more precise perception from a highly noisy environment can be obtained. This adaptation process relies on two main steps; firstly, to choose the sensors and secondly, employing a model adaptation process. Using this process, the more incorrect data are eliminated, the more accurate localization results are obtained. Also, implementing more accurate sensor models, the values of error obtained in the final iterations will be less. By implanting the proposed process, simulation results indicate that the proposed structure is very useful and effective, especially when compared to the conventional techniques of localization problems solutions which rely mainly on the information theory.

A Dynamic Evidential Network for Fall Detection

This study is part of the development of a remote home healthcare monitoring application designed to detect distress situations through several types of sensors. The multisensor fusion can provide more accurate and reliable information compared to information provided by each sensor separately. Furthermore, data from multiple heterogeneous sensors present in the remote home healthcare monitoring systems have different degrees of imperfection and trust. Among the multisensor fusion methods, Dempster-Shafer theory (DST) is currently considered the most appropriate for representing and processing the imperfect information. Based on a graphical representation of the DST called evidential networks, a structure of heterogeneous data fusion from multiple sensors for fall detection has been proposed. The evidential networks, implemented on our remote medical monitoring platform, are also proposed in this paper to maximize the performance of automatic fall detection and thus make the system more reliable. However, the presence of noise, the variability of recorded signals by the sensors, and the failing or unreliable sensors may thwart the evidential networks performance. In addition, the sensors signals nonstationary nature may degrade the experimental conditions. To compensate the nonstationary effect, the time evolution is considered by introducing the dynamic evidential network which was evaluated by the simulated fall scenarios corresponding to various use cases.

English

Data fusion technology is widely used in automatic target recognition system. Problems in data fusion system are complex by nature and can often be characterised by not only randomness but also by fuzziness. To accommodate complex natural problems with both types of uncertainties, it is profitable to construct a data fusion structure based on fuzzy set theory and Dempster Shafer evidence theory. In this paper, after representing both, the individual attribute of target in the model database and the sensor observation or report as fuzzy membership function, a likelihood function was constructed to deal with fuzzy data collected by each sensor. The method to determine basic probability assignments of each sensor report is proposed. Sensor reports are fused through classical Dempster combination rule. A numerical example is illustrated to show the target recognition application of the fuzzy-Dempster approach. Defence Science Journal, 2010, 60(5), pp.525-530 , DOI:http://dx.doi.org/10.14429/dsj.60.576

ENVIRONMENTAL PERCEPTION OF MOBILE ROBOT BASED ON MULTI-SENSORS' DATA FUSION

We have built up a system to apperceive unknown environment based on multi-sensors' data fusion, which enabled the self-determinative mobile robot recognize timely the type of obstacles around it in the course of travel, and the mobile robot would be provided with more intelligent operations. Firstly, the paper introduced the theory of environmental perception for mobile robot and the configuration of the multi-sensors' data fusion technology. Secondly, combining with the characteristics of neural network (NN) and aiming at the shortages of the former solving projects, it was brought forward that the MLP (Multi-layer perceptron) based on the improved back propagation (BP) arithmetic and four mature items of fusion rules, which were applied to apperceive the real surroundings. Thirdly, the paper presents a kind of structure of multi-sensor information data fusion. Two sub-networks for barrier recognition are separately built and incorporated with each other to form a high-powered recognition system by parallel connection.

Polarimetric fusion for synthetic aperture radar target classification

The problem of target classification using Synthetic Aperture Radar (SAR) polarizations is considered from a Bayesian decision point of view. This problem is analogous to the multi-sensor problem. We investigate the optimum design of a data fusion structure given that each classifier makes a target classification decision for each polarimetric channel. Though the optimal structure is difficult to implement without complete statistical information, we show that significant performance gains can be made even without a perfect model. First, we analyze the problem from an optimal classification point of view using a simple classification example by outlining the relationship between classification and fusion. Then, we demonstrate the performance improvement on real SAR data by fusing the decisions from a Gram-Schmidt image classifier for each polarization. ¢ I997 Pattern Recognition Society. Published by Elsevier Science Ltd.

Optimal Data Fusion in Multiple Sensor Detection Systems

There is an increasing interest in employing multiple sensors for surveillance and communications. Some of the motivating factors are reliability, survivability, increase in the number of targets under consideration, and increase in required coverage. Tenney and Sandell have recently treated the Bayesian detection problem with distributed sensors. They did not consider the design of data fusion algorithms. We present an optimum data fusion structure given the detectors. Individual decisions are weighted according to the reliability of the detector and then a threshold comparison is performed to obtain the global decision.