This study proposes an adaptive local time stepping (LTS) method based on a two-dimensional shallow water model for simulating multi-scale storm surge inundation in coastal regions. By dynamically monitoring the variation in the proportion of dry/wet...
The engineering applications of two-dimensional (2D) hydrodynamic models are restricted by the enormous number of meshes needed and the overheads of simulation time. The aim of this study is to improve computational efficiency and optimize the balanc...
A shallow water equations-based model with an improved local time-stepping (LTS) scheme is developed for modeling coastal hydrodynamics across multiple scales, from large areas to detailed local regions. To enhance the stability of the shallow water...
Alongside the development of smart water management and digital twin construction, hydrodynamic models have become a critical scientific tool in flood forecasting, with increasing attention and research focused on model computational efficiency. At t...
In this paper, a computationally efficient shallow water model is developed for sediment transport in the Yangtze Estuary by considering mixed cohesive and non-cohesive sediment transport. It is firstly shown that the model is capable of reproducing...
This paper presents a GPU (Graphics Processing Unit)-accelerated and LTS (Local-time-Step)-based finite volume Shallow Water Model (SWM). The model performance is compared against the other five model versions (Single CPU versions with/without LTS, M...
A GPU-accelerated shallow water model with a local time-step (LTS) is employed in this work to examine how the Coriolis forces affect the tidal level difference and, consequently, the water–sediment exchange between Hangzhou Bay (HZB) and the Y...
This study discusses the localization problem based on time delay and Doppler shift for a far-field scenario. The conventional location methods employ two steps that first extract intermediate parameters from the received signals and then determine t...
This paper focuses on passive emitter localization using moving sensors. The increase in observation time is beneficial to improve the localization accuracy, but it could cause deterioration of the relative motion between the emitter and the sensors,...
Rapid detection and follow-up of electromagnetic (EM) counterparts to gravitational wave (GW) signals from binary neutron star (BNS) mergers are essential for constraining source properties and probing the physics of relativistic transients. Observat...
A method for the direct outdoor localization of multiple impulse acoustic sources by a distributed microphone array is proposed. This localization problem is of great interest for gunshot, firecracker and explosion detection localization in a civil e...
This article presents an efficient method for the numerical modeling of time fractional mixed diffusion and wave-diffusion equations with two Caputo derivatives of order
The localization of electromagnetic interference (EMI) sources is of high importance in electromagnetic compatibility applications. Recently, a novel localization technique based on the time-reversal cavity (TRC) concept was proposed using only one s...
Wireless localization and target sensing both rely on precise extraction of parameters such as signal amplitude, propagation delay, and Doppler shift from the received signals. Due to the high multi-path resolution and strong penetration, both locali...
The knowledge of the exact position and orientation of a sensor with respect to a source (distribution) is essential for the correct solution of inverse problems. Especially when measuring with magnetic field sensors, the positions and orientations o...
Accurate indoor localization estimation has important social and commercial values, such as indoor location services and pedestrian retention times. Acoustic-based methods can achieve high localization accuracies in specific scenarios with special eq...
Underwater source localization is an important task, especially for real-time operation. Recently, machine learning methods have been combined with supervised learning schemes. This opens new possibilities for underwater source localization. However,...
The performance of passive target localization is significantly influenced by the positions of unmanned aerial vehicle swarms (UAVs). In this paper, we investigate the problem of UAV path optimization to enhance the localization accuracy. Firstly, a...
Nonlinearity may cause a model deviation problem, and hence, it is a challenging problem for process monitoring. To handle this issue, local kernel principal component analysis was proposed, and it achieved a satisfactory performance in static proces...
Soil degradation is a major threat for European soils and therefore, the European Commission recommends intensifying research on soil monitoring to capture changes over time and space. Imaging spectroscopy is a promising technique to create spatially...
This paper investigates the problem of fixed-time distributed time-varying optimization of a nonlinear fractional-order multiagent system (FOMAS) over a weight-unbalanced directed graph (digraph), where the heterogeneous unknown nonlinear functions a...
In this paper, an AGV path planning method fusing multiple heuristics rapidly exploring random tree (MH-RRT) with an improved two-step Timed Elastic Band (TEB) is proposed. The modified RRT integrating multiple heuristics can search a safer, optimal...
This paper focuses on the weighted complementarity problem (WCP), which is widely used in the fields of economics, sciences and engineering. Not least because of its local superlinear convergence rate, smoothing Newton methods have widespread applica...
In substations, a localization system based on a wireless sensor network (WSN) is a challenge, because the propagation of the measured signal could be blocked by various devices. In other words, non-line-of-sight (NLOS) propagation, where the signal...
Fluctuating bottom-pressures on stepped chutes are relevant for the spillway design. An abrupt slope reduction causes a local alteration of the bottom-pressure development. Little information is available regarding the air–water flow properties...
A time-domain adaptive algorithm was developed for solving elasto-dynamics problems through a mixed meshless local Petrov-Galerkin finite volume method (MLPG5). In this time-adaptive algorithm, each time-dependent variable is interpolated by a time s...
We propose high-order schemes for nonlinear fractional initial value problems. We split the fractional integral into a history term and a local term. We take advantage of the sum of exponentials (SOE) scheme in order to approximate the history term....
The continuous Hopfield network (CHN) is a common recurrent neural network. The CHN tool can be used to solve a number of ranking and optimization problems, where the equilibrium states of the ordinary differential equation (ODE) related to the CHN g...
A sparse dictionary reconstruction algorithm based on grid selection is introduced to solve the grid mismatch when using the sparse recovery space time adaptive processing (SR-STAP) algorithm. First, the atom most closely related to clutter is select...
Based on the direct differentiation method, sensitivity analysis of transient responses with respect to local nonlinearity is developed in this paper. Solutions of nonlinear equations and time-domain integration are combined to compute the response s...
In this work, we consider a polymer flooding process in heterogeneous media. A system of equations for pressure, water saturation, and polymer concentration describes a mathematical model. For the construction of the fine grid approximation, we use a...
Aiming at effectively generating safe and reliable motion paths for quadruped robots, a hierarchical path planning approach driven by dynamic 3D point clouds is proposed in this article. The developed path planning model is essentially constituted of...
Due to the intermittency and randomness of photovoltaic (PV) power, the PV power prediction accuracy of the traditional data-driven prediction models is difficult to improve. A prediction model based on the localized emotion reconstruction emotional...
Time reversal has been demonstrated to be effective for source and novelty detection and localization. We extend here previous work in the case of a coupled structural-acoustic system, to which we refer to as vibro-acoustic. In this case, novelty mea...
Distributed acoustic sensing shows great potential for pipeline monitoring. However, internally deployed and unfixed sensing cables are highly susceptible to disturbances from water flow noise, severely challenging impact source localization. This st...
Power system transient stability analysis requires an appropriate integration time step to avoid numerical instability as well as to reduce computational demands. For fast system dynamics, which vary more rapidly than what the time step covers, a fra...
Indoor RF-based localization using fingerprint mapping requires an initial training step, which represents a time consuming process. This location methodology needs a database conformed with RSSI (Radio Signal Strength Indicator) measures from the co...
Semi- and non-parametric mixture of normal regression models are a flexible class of mixture of regression models. These models assume that the component mixing proportions, regression functions and/or variances are non-parametric functions of the co...
The hysteresis model can be used to accurately predict the magnetic hysteresis characteristics of ferromagnetic materials. Incorporating the hysteresis model into finite element calculations enables precise prediction of field distributions, voltage...
To improve the accuracy and completeness of three-dimensional sculpture reconstruction, this study proposes a global–local two-step scanning method for industrial robot-based scanning. First, a global model is generated through stepped rotary s...
Visual localization, i.e., the camera pose localization within a known three-dimensional (3D) model, is a basic component for numerous applications such as autonomous driving cars and augmented reality systems. The most widely used methods from the l...
Background: The job shop scheduling problem (JSSP) and its solution algorithms have been of enduring interest in both academia and industry for decades. In recent years, machine learning (ML) has been playing an increasingly important role in advanci...
Fuel starvation is a major cause of anode corrosion in low temperature polymer electrolyte fuel cells. The fuel cell start-up is a critical step, as hydrogen may not yet be evenly distributed in the active area, leading to local starvation. The prese...
Recently, analysis and decision-making based on spatiotemporal unmanned aerial vehicle (UAV) high-resolution imagery are gaining significant attention in smart agriculture. Constructing a spatiotemporal dataset requires multiple UAV image mosaics tak...
This paper discusses numerical analysis methods for different geometrical features that have limited interval values for typically used sensor wavelengths. Compared with existing Finite Difference Time Domain (FDTD) methods, the alternating direction...
When the near-field and far-field information of a target is uncertain, it is necessary to choose a suitable localization method. The modified polar representation (MPR) method integrates the two scenarios and achieves a unified localization with dir...
Accurate location information can offer huge commercial and social value and has become a key research topic. Acoustic-based positioning has high positioning accuracy, although some anomalies that affect the positioning performance arise. Inertia-ass...
In marine ecological environment monitoring, the acquisition of node location information is crucial, and the absence of location information can render the collected data meaningless. Compared to the rest of the distance-based localization methods,...
The evolution of the dislocation density during Czochralski growth is computed by the combination of global thermal calculations and local computation of the stress and dislocation density in the crystal. The global simulation was performed using the...
Cloud image prediction is a spatio-temporal sequence prediction task, similar to video prediction. Spatio-temporal sequence prediction involves learning from historical data and using the learned features to generate future images. In this process, t...
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