Application of Multi-Source Data Fusion Method in Updating Topography and Estimating Sedimentation of the Reservoir

The underwater terrain of a reservoir can experience significant changes due to the effects of erosion and siltation during decades of operation. Therefore, existing topographic data no longer reflect current reservoir terrains and need to be updated. In this paper, we propose a fast and economical method for updating the topography of a reservoir. According to multi-source data fusion, we effectively integrated sonar sounding data, cartographic data, and manual measurement data to update and reconstruct the bottom topography of a reservoir in Northeast China. By comparing the updated topography with the measured elevation, the average error of the simulation results is only 0.56%, which shows that the updated topography can accurately reflect the actual topography of the reservoir. Furthermore, by using the surface volume tool in ArcGIS, we developed the original and updated the elevation and volume curves of the reservoir. Finally, the amount of silting and its distribution in the reservoir were obtained by calculating the difference between the original and updated elevation and volume curves. The results show that the total sedimentation volume in the researching reservoir is about 4.3 million m3, which is mainly concentrated in the areas with an elevation below 50 m and above 60 m.

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Modeling the Carbon Cycle of a Subtropical Chinese Fir Plantation Using a Multi-Source Data Fusion Approach

Forests ◽

10.3390/f11040369 ◽

2020 ◽

Vol 11 (4) ◽

pp. 369

Author(s):

Longwei Hu ◽

Honglin He ◽

Yan Shen ◽

Xiaoli Ren ◽

Shao-kui Yan ◽

...

Keyword(s):

Data Fusion ◽

Terrestrial Ecosystem ◽

Chinese Fir ◽

Model Parameters ◽

Ecosystem Models ◽

Flux Data ◽

Source Data ◽

Simulation Results ◽

Chinese Fir Plantation

Process-based terrestrial ecosystem models are increasingly being used to predict carbon (C) cycling in forest ecosystems. Given the complexity of ecosystems, these models inevitably have certain deficiencies, and thus the model parameters and simulations can be highly uncertain. Through long-term direct observation of ecosystems, numerous different types of data have accumulated, providing valuable opportunities to determine which sources of data can most effectively reduce the uncertainty of simulation results, and thereby improve simulation accuracy. In this study, based on a long-term series of observations (biometric and flux data) of a subtropical Chinese fir plantation ecosystem, we use a model–data fusion framework to evaluate the effects of different constrained data on the parameter estimation and uncertainty of related variables, and systematically evaluate the uncertainty of parameters. We found that plant C pool observational data contributed to significant reductions in the uncertainty of parameter estimates and simulation, as these data provide information on C pool size. However, none of the data effectively constrained the foliage C pool, indicating that this pool should be a target for future observational activities. The assimilation of soil organic C observations was found to be important for reducing the uncertainty or bias in soil C pools. The key findings of this study are that the assimilation of multiple time scales and types of data stream are critical for model constraint and that the most accurate simulation results are obtained when all available biometric and flux data are used as constraints. Accordingly, our results highlight the importance of using multi-source data when seeking to constrain process-based terrestrial ecosystem models.

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Filtering Performance of Digital Alternate Array on Target Tracking

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.644-650.4269 ◽

2014 ◽

Vol 644-650 ◽

pp. 4269-4272

Author(s):

Dong Kai Cao ◽

Jun Li ◽

Hong Ming Liu

Keyword(s):

Kalman Filter ◽

Measurement Error ◽

Data Fusion ◽

Target Tracking ◽

Measurement Data ◽

New Method ◽

Present Moment ◽

Simulation Results ◽

Novel Model ◽

Error Filtering

For target tracking of agile digital array, we propose a novel model called alternate array. The model means that total array is divided into vertical subarray at the present moment, and vertical subarray is transformed into horizontal subarray at the next moment, then the subarray comes to the first subarray divisions, and so on. Alternate array gives rise to differences of statistical characters of measurement error. Filtering performance is influenced by the feature of measurement data. In the paper, we come up with a new method based on Kalman filter (KF) to solve the difficulty. The new method merges pre-processing and data fusion thought together. Simulation results demonstrate the validity of the proposed algorithm.

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Study on Data Fusion Algorithm of TCAS/ADS-B Integrated Surveillance System Based on the Present Statistical Model

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.427-429.1311 ◽

2013 ◽

Vol 427-429 ◽

pp. 1311-1314

Author(s):

Ya Jun Xu

Keyword(s):

Data Fusion ◽

Statistical Model ◽

Collision Avoidance ◽

Surveillance System ◽

Position Error ◽

Integrated System ◽

Fusion Algorithm ◽

Simulation Results ◽

Collision Avoidance System ◽

The Difference

The difference between Traffic Alert and Collision Avoidance System and Automatic Dependent Surveillance Broadcast surveillance principle was compared. A TCAS/ ADS-B integrated surveillance system based on the present statistical model was built. Using the data fusion algorithm, the optimal local track of TCAS and ADS-B ,as well as the optimal fused track of the integrated system were estimated. The simulation results show that the maximum optimal fused estimated position error is 100m, it is certificated that the integrated system can improve track estimates accuracy, improve the surveillance precision of TCAS.

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Effective Passive Multitarget Localization Using Maximum Likelihood

Wireless Communications and Mobile Computing ◽

10.1155/2021/6567346 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Yasir Munir ◽

Muhammad Umar Aftab ◽

Danish Shehzad ◽

Ali M. Aseere ◽

Habib Shah

Keyword(s):

Computational Complexity ◽

Maximum Likelihood ◽

Data Fusion ◽

Prior Knowledge ◽

Experimental Testing ◽

Measurement Data ◽

Grid Search ◽

Multiple Targets ◽

Time Difference Of Arrival ◽

Simulation Results

Localization of multiple targets is a challenging task due to immense complexity regarding data fusion received at the sensors. In this context, we propose an algorithm to solve the problem for an unknown number of emitters without prior knowledge to address the data fusion problem. The proposed technique combines the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurement data fusion which further uses the maximum likelihood of the measurements received at each sensor of the surveillance region. The measurement grids of the sensors are used to perform data association. The simulation results show that the proposed algorithm outperforms the multipass grid search and further effectively eliminated the ghost targets created due to the fusion of measurements received at each sensor. Moreover, the proposed algorithm reduces the computational complexity compared to other existing algorithms as it does not use repeated steps for convergence or any biological evolutions. Furthermore, the experimental testing of the proposed technique was executed successfully for tracking multiple targets in different scenarios passively.

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A multi-source data fusion approach to assess spatial-temporal variability and delineate homogeneous zones: A use case in a table grape vineyard in Greece

The Science of The Total Environment ◽

10.1016/j.scitotenv.2019.05.324 ◽

2019 ◽

Vol 684 ◽

pp. 155-163 ◽

Cited By ~ 2

Author(s):

Evangelos Anastasiou ◽

Annamaria Castrignanò ◽

Konstantinos Arvanitis ◽

Spyros Fountas

Keyword(s):

Data Fusion ◽

Temporal Variability ◽

Table Grape ◽

Use Case ◽

Source Data ◽

Fusion Approach

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Modeling of German Low Voltage Cables with Ground Return Path

Energies ◽

10.3390/en14051265 ◽

2021 ◽

Vol 14 (5) ◽

pp. 1265 ◽

Cited By ~ 1

Author(s):

Johanna Geis-Schroer ◽

Sebastian Hubschneider ◽

Lukas Held ◽

Frederik Gielnik ◽

Michael Armbruster ◽

...

Keyword(s):

Low Voltage ◽

Measurement Data ◽

Analytical Calculation ◽

Laboratory Measurement ◽

Model Parameters ◽

Calculation Methods ◽

Modeling Approach ◽

Measurement Results ◽

Simulation Results ◽

Return Path

In this contribution, measurement data of phase, neutral, and ground currents from real low voltage (LV) feeders in Germany is presented and analyzed. The data obtained is used to review and evaluate common modeling approaches for LV systems. An alternative modeling approach for detailed cable and ground modeling, which allows for the consideration of typical German LV earthing conditions and asymmetrical cable design, is proposed. Further, analytical calculation methods for model parameters are described and compared to laboratory measurement results of real LV cables. The models are then evaluated in terms of parameter sensitivity and parameter relevance, focusing on the influence of conventionally performed simplifications, such as neglecting house junction cables, shunt admittances, or temperature dependencies. By comparing measurement data from a real LV feeder to simulation results, the proposed modeling approach is validated.

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A novel multi-source data fusion method based on Bayesian inference for accurate estimation of chlorophyll-a concentration over eutrophic lakes

Environmental Modelling & Software ◽

10.1016/j.envsoft.2021.105057 ◽

2021 ◽

pp. 105057

Author(s):

Cheng Chen ◽

Qiuwen Chen ◽

Gang Li ◽

Mengnan He ◽

Jianwei Dong ◽

...

Keyword(s):

Bayesian Inference ◽

Data Fusion ◽

Chlorophyll A ◽

Accurate Estimation ◽

Fusion Method ◽

Eutrophic Lakes ◽

Chlorophyll A Concentration ◽

Source Data

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Modulation instability in nonlinear chiral fiber

Journal of Optical Communications ◽

10.1515/joc-2020-0130 ◽

2020 ◽

Vol 0 (0) ◽

Author(s):

Demissie Jobir Gelmecha ◽

Ram Sewak Singh

Keyword(s):

Theoretical Model ◽

Numerical Simulations ◽

Pulse Propagation ◽

Modulation Instability ◽

Constitutive Relations ◽

Gain Spectrum ◽

Circularly Polarized ◽

Left Handed ◽

Simulation Results ◽

The Difference

AbstractIn this paper, the rigorous derivations of generalized coupled chiral nonlinear Schrödinger equations (CCNLSEs) and their modulation instability analysis have been explored theoretically and computationally. With the consideration of Maxwell’s equations and Post’s constitutive relations, a generalized CCNLSE has been derived, which describes the evolution of left-handed circularly polarized (LCP) and right-handed circularly polarized (RCP) components propagating through single-core nonlinear chiral fiber. The analysis of modulation instability in nonlinear chiral fiber has been investigated starting from CCNLSEs. Based on a theoretical model and numerical simulations, the difference on the modulation instability gain spectrum in LCP and RCP components through chiral fiber has been analyzed by considering loss and chirality into account. The obtained simulation results have shown that the loss distorts the sidebands of the modulation instability gain spectrum, while chirality modulates the gain for LCP and RCP components in a different manner. This suggests that adjusting chirality strength may control the loss, and nonlinearity simultaneously provides stable modulated pulse propagation.

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