Modified Fourth-Order Runge-Kutta Method Based on Trapezoid Approach

Nurhakim, Abdurrahman; Ismail, Nanang; Saputra, Hendri Maja; Uyun, Saepul

doi:10.1109/icwt.2018.8527811

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…The GPS readings are given the same timestamp as the temporally nearest image to be included in the sliding window because it is believed that they would only be accessible at low rates. As opposed to [ 14 ], we efficiently compute the global positional factors by closely coupling the global position measurements using the Runge–Kutta 4th-order gyroscope preintegration scheme [ 31 ]. This enables the sliding window to incorporate numerous global parameters and each keyframe with barely any additional processing load.…”

Section: Related Workmentioning

confidence: 99%

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MAV Localization in Large-Scale Environments: A Decoupled Optimization/Filtering Approach

Soliman

Hadj-Abdelkader

Bonardi

et al. 2023

Sensors

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Developing new sensor fusion algorithms has become indispensable to tackle the daunting problem of GPS-aided micro aerial vehicle (MAV) localization in large-scale landscapes. Sensor fusion should guarantee high-accuracy estimation with the least amount of system delay. Towards this goal, we propose a linear optimal state estimation approach for the MAV to avoid complicated and high-latency calculations and an immediate metric-scale recovery paradigm that uses low-rate noisy GPS measurements when available. Our proposed strategy shows how the vision sensor can quickly bootstrap a pose that has been arbitrarily scaled and recovered from various drifts that affect vision-based algorithms. We can consider the camera as a “black-box” pose estimator thanks to our proposed optimization/filtering-based methodology. This maintains the sensor fusion algorithm’s computational complexity and makes it suitable for MAV’s long-term operations in expansive areas. Due to the limited global tracking and localization data from the GPS sensors, our proposal on MAV’s localization solution considers the sensor measurement uncertainty constraints under such circumstances. Extensive quantitative and qualitative analyses utilizing real-world and large-scale MAV sequences demonstrate the higher performance of our technique in comparison to most recent state-of-the-art algorithms in terms of trajectory estimation accuracy and system latency.

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Section: Related Workmentioning

confidence: 99%

“…The operator ⊖ is the

logarithmic map as defined in [ 39 ]. The error transformation

, where

is the CT-GPS measurement increment and

is the gyroscope integrated increment

using the Runge–Kutta 4th order (RK4) integration method [ 31 ] between the keyframes i and j .…”

Section: System Architecturementioning

confidence: 99%

MAV Localization in Large-Scale Environments: A Decoupled Optimization/Filtering Approach

Soliman

Hadj-Abdelkader

Bonardi

et al. 2023

Sensors

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show abstract

“…In (11), i = 1,2,3; Ci and Mi correspond to the specific heat capacity and mass of insulating oil, iron core and winding respectively, and their units are kJ/(kg•K) and kg, respectively.…”

Section: Calculation Of Model Parametersmentioning

confidence: 99%

Calculation model of hot-spot temperature of transformer winding based on top-oil temperature

Dong,

Liu,

Chen

et al. 2023

Third International Conference on Electronics, Electrical and Information Engineering (ICEEIE 2023)

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Oil-immersed type transformer is an essential part of electric power system, and the hot-spot temperature of the winding is closely related to the oil temperature at the top of the transformer. In this paper, modeling of the thermal circuit based on the distribution of the top oil temperature of the double-wound transformer. On the basis of the consideration of nonlinear characteristics of thermal resistance of insulating oil, we also considered the influence of oil stickiness and the thermal capacitance of transformer components (iron core, insulating oil, windings) with temperature changes, and introduced the cooling ways and winding loss correction factors in the model. The Runge-Kutta method is selected to resolve the model parameters, which further simplified the calculation process. Above all, the improved model of hotspot temperature of transformer winding is proposed. Contrast with testing transformer measured data in laboratory and calculation result of traditional hot spot analogy method, the model shows good consistency in the light load (around 20% and 40%), which can well describe the transient temperature change and propose a new method to calculate the hotspot temperature of transformer winding.

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Dynamic simulation and parameter fitting method of cometary dust based on machine learning

Yue

Cao

et al. 2021

Exp Astron

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Modified Fourth-Order Runge-Kutta Method Based on Trapezoid Approach

Cited by 7 publications

References 12 publications

MAV Localization in Large-Scale Environments: A Decoupled Optimization/Filtering Approach

MAV Localization in Large-Scale Environments: A Decoupled Optimization/Filtering Approach

Calculation model of hot-spot temperature of transformer winding based on top-oil temperature

Dynamic simulation and parameter fitting method of cometary dust based on machine learning

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