Inconsistency detection and data fusion in USAR task

Purpose The historical data usually consist of overlapping reports, and these reports may contain inconsistent data, which may return incorrect results of a query search. Moreover, users who issue the query may not learn of this inconsistency even after a data cleaning process (e.g. schema matching or data screening). The inconsistency can exist in different types of data, such as temporal or spatial data. Therefore, this paper aims to introduce an information fusion method that can detect data inconsistency in the early stages of data fusion. Design/methodology/approach This paper introduces an information fusion method for multi-robot operations, for which fusion is conducted continuously. When the environment is explored by multiple robots, the robot logs can provide more information about the number and coordination of targets or victims. The information fusion method proposed in this paper generates an underdetermined linear system of overlapping spatial reports and estimates the case values. Then, the least squares method is used for the underdetermined linear system. By using these two methods, the conflicts between reports can be detected and the values of the intervals at specific times or locations can be estimated. Findings The proposed information fusion method was tested for inconsistency detection and target projection of spatial fusion in sensor networks. The proposed approach examined the values of sensor data from simulation that robots perform search tasks. This system can be expanded to data warehouses with heterogeneous data sources to achieve completeness, robustness and conciseness. Originality/value Little research has been devoted to the linear systems for information fusion of tasks of mobile robots. The proposed information fusion method minimizes the cost of time and comparison for data fusion and also minimizes the probability of errors from incorrect results.

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Multi-source data fusion method for structural safety assessment of water diversion structures

Journal of Hydroinformatics ◽

10.2166/hydro.2021.154 ◽

2021 ◽

Author(s):

Sherong Zhang ◽

Ting Liu ◽

Chao Wang

Keyword(s):

Data Fusion ◽

Safety Assessment ◽

Evidence Theory ◽

Weighted Average ◽

Water Diversion ◽

Sensor Data ◽

Structural Safety ◽

Fusion Method ◽

Bhattacharyya Distance ◽

Source Data

Abstract Building safety assessment based on single sensor data has the problems of low reliability and high uncertainty. Therefore, this paper proposes a novel multi-source sensor data fusion method based on Improved Dempster–Shafer (D-S) evidence theory and Back Propagation Neural Network (BPNN). Before data fusion, the improved self-support function is adopted to preprocess the original data. The process of data fusion is divided into three steps: Firstly, the feature of the same kind of sensor data is extracted by the adaptive weighted average method as the input source of BPNN. Then, BPNN is trained and its output is used as the basic probability assignment (BPA) of D-S evidence theory. Finally, Bhattacharyya Distance (BD) is introduced to improve D-S evidence theory from two aspects of evidence distance and conflict factors, and multi-source data fusion is realized by D-S synthesis rules. In practical application, a three-level information fusion framework of the data level, the feature level, and the decision level is proposed, and the safety status of buildings is evaluated by using multi-source sensor data. The results show that compared with the fusion result of the traditional D-S evidence theory, the algorithm improves the accuracy of the overall safety state assessment of the building and reduces the MSE from 0.18 to 0.01%.

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Spatial Data Lake for Smart Cities: From Design to Implementation

AGILE: GIScience Series ◽

10.5194/agile-giss-1-8-2020 ◽

2020 ◽

Vol 1 ◽

pp. 1-15

Author(s):

Rodrique Kafando ◽

Rémy Decoupes ◽

Lucile Sautot ◽

Maguelonne Teisseire

Keyword(s):

Remote Sensing ◽

Spatial Data ◽

Smart Cities ◽

Real Data ◽

Heterogeneous Data ◽

Geographic Information ◽

Sensor Data ◽

Remote Sensing Images ◽

Thematic Information ◽

General Data

Abstract. In this paper, we propose a methodology for designing data lake dedicated to Spatial Data and an implementation of this specific framework. Inspired from previous proposals on general data lake Design and based on the Geographic information – Metadata normalization (ISO 19115), the contribution presented in this paper integrates, with the same philosophy, the spatial and thematic dimensions of heterogeneous data (remote sensing images, textual documents and sensor data, etc). To support our proposal, the process has been implemented in a real data project in collaboration with Montpellier Métropole Méditerranée (3M), a metropolis in the South of France. This framework offers a uniform management of the spatial and thematic information embedded in the elements of the data lake.

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A Multi-sensor Data Fusion Method for Nondestructive Testing of Oil Pipelines

Instrumentation Mesure Métrologie ◽

10.18280/i2m.180304 ◽

2019 ◽

Vol 18 (3) ◽

pp. 249-255

Author(s):

Guiqing Xi ◽

Caojun Huang ◽

Shiqi Liu

Keyword(s):

Nondestructive Testing ◽

Data Fusion ◽

Sensor Data ◽

Fusion Method ◽

Sensor Data Fusion ◽

Multi Sensor Data Fusion ◽

Oil Pipelines

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An EKF-based multiple data fusion for mobile robot indoor localization

Assembly Automation ◽

10.1108/aa-12-2020-0199 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Guangbing Zhou ◽

Jing Luo ◽

Shugong Xu ◽

Shunqing Zhang ◽

Shige Meng ◽

...

Keyword(s):

Data Fusion ◽

Mobile Robot ◽

Autonomous Navigation ◽

Indoor Localization ◽

Robot Navigation ◽

Sensor Data ◽

Measurement Unit ◽

Indoor Environments ◽

Content Type ◽

Multiple Data

Purpose Indoor localization is a key tool for robot navigation in indoor environments. Traditionally, robot navigation depends on one sensor to perform autonomous localization. This paper aims to enhance the navigation performance of mobile robots, a multiple data fusion (MDF) method is proposed for indoor environments. Design/methodology/approach Here, multiple sensor data i.e. collected information of inertial measurement unit, odometer and laser radar, are used. Then, an extended Kalman filter (EKF) is used to incorporate these multiple data and the mobile robot can perform autonomous localization according to the proposed EKF-based MDF method in complex indoor environments. Findings The proposed method has experimentally been verified in the different indoor environments, i.e. office, passageway and exhibition hall. Experimental results show that the EKF-based MDF method can achieve the best localization performance and robustness in the process of navigation. Originality/value Indoor localization precision is mostly related to the collected data from multiple sensors. The proposed method can incorporate these collected data reasonably and can guide the mobile robot to perform autonomous navigation (AN) in indoor environments. Therefore, the output of this paper would be used for AN in complex and unknown indoor environments.

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Incomplete Information Management Using an Improved Belief Entropy in Dempster-Shafer Evidence Theory

Entropy ◽

10.3390/e22090993 ◽

2020 ◽

Vol 22 (9) ◽

pp. 993 ◽

Cited By ~ 1

Author(s):

Bin Yang ◽

Dingyi Gan ◽

Yongchuan Tang ◽

Yan Lei

Keyword(s):

Data Fusion ◽

Incomplete Information ◽

Information Fusion ◽

Evidence Theory ◽

Sensor Data ◽

Uncertain Information ◽

Open World ◽

Belief Entropy ◽

Open World Assumption ◽

Deng Entropy

Quantifying uncertainty is a hot topic for uncertain information processing in the framework of evidence theory, but there is limited research on belief entropy in the open world assumption. In this paper, an uncertainty measurement method that is based on Deng entropy, named Open Deng entropy (ODE), is proposed. In the open world assumption, the frame of discernment (FOD) may be incomplete, and ODE can reasonably and effectively quantify uncertain incomplete information. On the basis of Deng entropy, the ODE adopts the mass value of the empty set, the cardinality of FOD, and the natural constant e to construct a new uncertainty factor for modeling the uncertainty in the FOD. Numerical example shows that, in the closed world assumption, ODE can be degenerated to Deng entropy. An ODE-based information fusion method for sensor data fusion is proposed in uncertain environments. By applying it to the sensor data fusion experiment, the rationality and effectiveness of ODE and its application in uncertain information fusion are verified.

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Towards the development of a smart fused filament fabrication system using multi-sensor data fusion for in-process monitoring

Rapid Prototyping Journal ◽

10.1108/rpj-06-2019-0167 ◽

2020 ◽

Vol 26 (7) ◽

pp. 1249-1261 ◽

Cited By ~ 3

Author(s):

Michele Moretti ◽

Federico Bianchi ◽

Nicola Senin

Keyword(s):

Data Fusion ◽

Process Monitoring ◽

Sensor Data ◽

Fused Filament Fabrication ◽

Content Type ◽

Heterogeneous Sensors ◽

Sensor Data Fusion ◽

Part Quality ◽

Multi Sensor Data Fusion ◽

Smart Machine

Purpose This paper aims to illustrate the integration of multiple heterogeneous sensors into a fused filament fabrication (FFF) system and the implementation of multi-sensor data fusion technologies to support the development of a “smart” machine capable of monitoring the manufacturing process and part quality as it is being built. Design/methodology/approach Starting from off-the-shelf FFF components, the paper discusses the issues related to how the machine architecture and the FFF process itself must be redesigned to accommodate heterogeneous sensors and how data from such sensors can be integrated. The usefulness of the approach is discussed through illustration of detectable, example defects. Findings Through aggregation of heterogeneous in-process data, a smart FFF system developed upon the architectural choices discussed in this work has the potential to recognise a number of process-related issues leading to defective parts. Research limitations/implications Although the implementation is specific to a type of FFF hardware and type of processed material, the conclusions are of general validity for material extrusion processes of polymers. Practical implications Effective in-process sensing enables timely detection of process or part quality issues, thus allowing for early process termination or application of corrective actions, leading to significant savings for high value-added parts. Originality/value While most current literature on FFF process monitoring has focused on monitoring selected process variables, in this work a wider perspective is gained by aggregation of heterogeneous sensors, with particular focus on achieving co-localisation in space and time of the sensor data acquired within the same fabrication process. This allows for the detection of issues that no sensor alone could reliably detect.

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An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox

Sensors ◽

10.3390/s17020414 ◽

2017 ◽

Vol 17 (2) ◽

pp. 414 ◽

Cited By ~ 136

Author(s):

Luyang Jing ◽

Taiyong Wang ◽

Ming Zhao ◽

Peng Wang

Keyword(s):

Neural Networks ◽

Fault Diagnosis ◽

Data Fusion ◽

Convolutional Neural Networks ◽

Sensor Data ◽

Fusion Method ◽

Deep Convolutional Neural Networks ◽

Planetary Gearbox ◽

Sensor Data Fusion ◽

Multi Sensor Data Fusion

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Heterogeneous Data Fusion Method to Estimate Travel Time Distributions in Congested Road Networks

Sensors ◽

10.3390/s17122822 ◽

2017 ◽

Vol 17 (12) ◽

pp. 2822 ◽

Cited By ~ 3

Author(s):

Chaoyang Shi ◽

Bi Yu Chen ◽

William H. K. Lam ◽

Qingquan Li

Keyword(s):

Data Fusion ◽

Travel Time ◽

Road Networks ◽

Heterogeneous Data ◽

Fusion Method ◽

Estimate Travel Time

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Distributed Multi-Source Spatial Data Fusion Model Construction and Performance Evaluation

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.460-461.404 ◽

2011 ◽

Vol 460-461 ◽

pp. 404-408

Author(s):

Yue Shun He ◽

Jun Zhang ◽

Jie He

Keyword(s):

Performance Evaluation ◽

Data Fusion ◽

Information Fusion ◽

Spatial Data ◽

Evaluation Model ◽

Distributed Model ◽

Fusion System ◽

Fusion Model ◽

Test Environment ◽

Sensor Information

This paper mainly analyzed the principle of multi-source spatial data fusion, and expounded the multi-source spatial data fusion of the distributed model structure. The paper considers a distributed multi-sensor information fusion system factors, A performance evaluation model was established which was suitable for distributed multi-sensor information fusion system, It can estimate the system's precision, track quality, filtering quality, and the relevant between Navigation Paths and so on. Meanwhile, we had a lot of experiments by the datum which generated by the simulation test environment, experiments show that this evaluation model is valid.

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