arXiv subjects

Ruo-Qian Wang

Publications and source records attributed to Ruo-Qian Wang.

10 recordsLinked to original sources

arXiv2025

Environmental Co-design: Fish-Blade Collision Model for Hydrokinetic Turbines

A major challenge in the deployment of hydrokinetic turbines in aquatic environments is the risk of fish collisions. Traditional fish collision models often oversimplify this risk by neglecting critical factors, such as the thickness of the turbine and accessory structures. Additionally, variations in fish size and species are frequently overlooked. This study addresses these gaps by developing a swimming mechanics-based fish-blade collision model. Using a Lagrangian particle tracking approach, we simulate fish movements and evaluate collision risks with a representative hydrokinetic turbine, both with and without ducts. The model is applied to the velocity field at Baton Rouge, Louisiana, allowing for the assessment of collision risks across different fish species. The results offer valuable insights for turbine siting, optimization of turbine placement, and evaluation of protective designs to reduce environmental impacts in complex flow environments.

physics.flu-dyn↗

arXiv2024

Towards Democratized Flood Risk Management: An Advanced AI Assistant Enabled by GPT-4 for Enhanced Interpretability and Public Engagement

Real-time flood forecasting is vital for effective emergency responses, but bridging the gap between complex numerical models and practical decision-making remains challenging. Decision-makers often rely on experts, while the public struggles to interpret flood risk information. To address this, we developed a customized AI Assistant powered by GPT-4. This tool enhances communication between decision-makers, the public, and forecasters, requiring no specialized knowledge. The framework leverages GPT-4's advanced natural language capabilities to search flood alerts, answer inquiries, and integrate real-time warnings with flood maps and social vulnerability data. It simplifies complex flood zone information into actionable advice. The prototype was evaluated on relevance, error resilience, and contextual understanding, with performance compared across different GPT models. This research advances flood risk management by making critical information more accessible and engaging, demonstrating the potential of AI tools like GPT-4 in addressing social and environmental challenges.

cs.AI↗

arXiv2023

Determining Undersampled Coastal Tidal Harmonics using Regularized Least Squares

Satellite altimetry, which measures water level with global coverage and high resolution, provides an unprecedented opportunity for a wide and refined understanding of the changing tides in the coastal area, but the sampling frequency is too low to satisfy the Nyquist frequency requirement and too few data points per year are available to recognize a sufficient number of tidal constituents to capture the trend of tidal changes on a yearly basis. To address these issues, a novel Regularized Least-Square approach is developed to relax the limitation to the range of satellite operating conditions. In this method, the prior information of the regional tidal amplitudes is used to support a least square analysis to obtain the amplitudes and phases of the tidal constituents for water elevation time series of different lengths and time intervals. Synthetic data experiments performed in Delaware Bay and Galveston Bay showed that the proposed method can determine the tidal amplitudes with high accuracy and the sampling interval can be extended to the application level of major altimetry satellites. The proposed algorithm was further validated using the data of the altimetry mission, Jason-3, to show its applicability to irregular and noisy data. The new method could help identify the changing tides with sea-level rise and anthropogenic activities in coastal areas, informing coastal flooding risk assessment and ecosystem health analysis.

stat.AP↗

arXiv2022

Dynamics of real-time forecasting failure and recovery due to data gaps

Real-time forecasting is important to the society. It uses continuous data streams to update forecasts for sustained accuracy. But the data source is vulnerable to attacks or accidents and the dynamics of forecasting failure and recovery due to data gaps is poorly understood. As the first systematic study, a Lorenz model-based forecasting system was disrupted with data gaps of various lengths and timing. The restart time of data assimilation is found to be the most important factor. The forecasting accuracy is found not returning to the original even long after the data assimilation recovery.

physics.data-an↗

arXiv2022

Semi-supervised Identification and Mapping of Surface Water Extent using Street-level Monitoring Videos

Urban flooding is becoming a common and devastating hazard to cause life loss and economic damage. Monitoring and understanding urban flooding in the local scale is a challenging task due to the complicated urban landscape, intricate hydraulic process, and the lack of high-quality and resolution data. The emerging smart city technology such as monitoring cameras provides an unprecedented opportunity to address the data issue. However, estimating the water accumulation on the land surface based on the monitoring footage is unreliable using the traditional segmentation technique because the boundary of the water accumulation, under the influence of varying weather, background, and illumination, is usually too fuzzy to identify, and the oblique angle and image distortion in the video monitoring data prevents georeferencing and object-based measurements. This paper presents a novel semi-supervised segmentation scheme for surface water extent recognition from the footage of an oblique monitoring camera. The semi-supervised segmentation algorithm was found suitable to determine the water boundary and the monoplotting method was successfully applied to georeference the pixels of the monitoring video for the virtual quantification of the local drainage process. The correlation and mechanism-based analysis demonstrates the value of the proposed method in advancing the understanding of local drainage hydraulics. The workflow and created methods in this study has a great potential to study other street-level and earth surface processes.

cs.CV↗

arXiv2021

A Recommender System-Inspired Cloud Data Filling Scheme for Satellite-based Coastal Observation

Filling missing data in cloud-covered areas of satellite imaging is an important task to improve data quantity and quality for enhanced earth observation. Traditional cloud filling studies focused on continuous numerical data such as temperature and cyanobacterial concentration in the open ocean. Cloud data filling issues in coastal imaging is far less studied because of the complex landscape. Inspired by the success of data imputation methods in recommender systems that are designed for online shopping, the present study explored their application to satellite cloud data filling tasks. A numerical experiment was designed and conducted for a LandSat dataset with a range of synthetic cloud covers to examine the performance of different data filling schemes. The recommender system-inspired matrix factorization algorithm called Funk-SVD showed superior performance in computational accuracy and efficiency for the task of recovering landscape types in a complex coastal area than the traditional data filling scheme of DINEOF (Data Interpolating Empirical Orthogonal Functions) and the deep learning method of Datawig. The new method achieved the best filling accuracy and reached a speed comparable to DINEOF and much faster than deep learning. A theoretical framework was created to analyze the error propagation in DINEOF and found the algorithm needs to be modified to converge to the ground truth. The present study showed that Funk-SVD has great potential to enhance cloud data filling performance and connects the fields of recommender systems and cloud filling to promote the improvement and sharing of useful algorithms.

cs.LG↗

arXiv2021

AI-supported Framework of Semi-Automatic Monoplotting for Monocular Oblique Visual Data Analysis

In the last decades, the development of smartphones, drones, aerial patrols, and digital cameras enabled high-quality photographs available to large populations and, thus, provides an opportunity to collect massive data of the nature and society with global coverage. However, the data collected with new photography tools is usually oblique - they are difficult to be georeferenced, and huge amounts of data is often obsolete. Georeferencing oblique imagery data may be solved by a technique called monoplotting, which only requires a single image and Digital Elevation Model (DEM). In traditional monoplotting, a human user has to manually choose a series of ground control point (GCP) pairs in the image and DEM and then determine the extrinsic and intrinsic parameters of the camera to establish a pixel-level correspondence between photos and the DEM to enable the mapping and georeferencing of objects in photos. This traditional method is difficult to scale due to several challenges including the labor-intensive inputs, the need of rich experience to identify well-defined GCPs, and limitations in camera pose estimation. Therefore, existing monoplotting methods are rarely used in analyzing large-scale databases or near-real-time warning systems. In this paper, we propose and demonstrate a novel semi-automatic monoplotting framework that provides pixel-level correspondence between photos and DEMs requiring minimal human interventions. A pipeline of analyses was developed including key point detection in images and DEM rasters, retrieving georeferenced 3D DEM GCPs, regularized gradient-based optimization, pose estimation, ray tracing, and the correspondence identification between image pixels and real world coordinates. Two numerical experiments show that the framework is superior in georeferencing visual data in 3-D coordinates, paving a way toward fully automatic monoplotting methodology.

cs.CV↗

arXiv2021

A Surrogate-model-based Approach for Estimating the First and Second-order Moments of Offshore Wind Power

Power curve is widely used in the wind industry to estimate power output for planning and operational purposes. Existing methods for power curve estimation have three main limitations: (i) they mostly rely on wind speed as the sole input, thus ignoring the secondary, yet possibly significant effects of other environmental factors, (ii) they largely overlook the complex marine environment in which offshore turbines operate, potentially compromising their value in offshore wind energy applications, and (ii) they solely focus on the first-order properties of wind power, with little (or null) information about the variation around the mean behavior, which is important for ensuring reliable grid integration, asset health monitoring, and energy storage, among others. This study investigates the impact of several wind- and wave-related factors on offshore wind power variability, with the ultimate goal of accurately predicting its first two moments. Our approach couples OpenFAST with Gaussian Process (GP) regression to reveal the underlying relationships governing offshore weather-to-power conversion. We first find that a multi-input power curve which captures the combined impact of wind speed, direction, and air density, can provide double-digit improvements relative to univariate methods which rely on wind speed as the sole explanatory variable (e.g. the standard method of bins). Wave-related variables are found not important for predicting the average power output, but interestingly, appear to be extremely relevant in describing the fluctuation of the offshore power around its mean. Tested on real-world data collected at the New York/New Jersey bight, our proposed multi-input models demonstrate a high explanatory power in predicting the first two moments of offshore wind generation, testifying their potential value to the offshore wind industry.

stat.AP↗

arXiv2020

Modeling the Impact of 5G Leakage on Weather Prediction

The 5G band allocated in the 26 GHz spectrum referred to as 3GPP band n258, has generated a lot of anxiety and concern in the meteorological data forecasting community including the National Oceanic and Atmospheric Administration (NOAA). Unlike traditional spectrum coexistence problems, the issue here stems from the leakage of n258 band transmissions impacting the observations of passive sensors (e.g. AMSU-A) operating at 23.8 GHz on weather satellites used to detect the amount of water vapor in the atmosphere, which in turn affects weather forecasting and predictions. In this paper, we study the impact of 5G leakage on the accuracy of data assimilation based weather prediction algorithms by using a first order propagation model to characterize the effect of the leakage signal on the brightness temperature (atmospheric radiance) and the induced noise temperature at the receiving antenna of the passive sensor (radiometer) on the weather observation satellite. We then characterize the resulting inaccuracies when using the Weather Research and Forecasting Data Assimilation model (WRFDA) to predict temperature and rainfall. For example, the impact of 5G leakage of -20dBW to -15dBW on the well-known Super Tuesday Tornado Outbreak data set, affects the meteorological forecasting up to 0.9 mm in precipitation and 1.3 °C in 2m-temperature. We outline future directions for both improved modeling of 5G leakage effects as well as mitigation using cross-layer antenna techniques coupled with resource allocation.

eess.SP↗

arXiv2016

An axisymmetric steady state vortex ring model

Based on the solution of Atanasiu et al. (2004), a theoretical model for axisymmetric vortex flows is derived in the present study by solving the vorticity transport equation for an inviscid, incompressible fluid in cylindrical coordinates. The model can describe a variety of axisymmetric flows with particular boundary conditions at a moderately high Reynolds number. This paper shows one example: a high Reynolds number laminar vortex ring. The model can represent a family of vortex rings by specifying the modulus function using a Rayleigh distribution function. The characteristics of this vortex ring family are illustrated by numerical methods. For verification, the model results compare well with the recent direct numerical simulations (DNS) in terms of the vorticity distribution and streamline patterns, cross-sectional areas of the vortex core and bubble, and radial vorticity distribution through the vortex center. Most importantly, the asymmetry and elliptical outline of the vorticity profile are well captured.

physics.flu-dyn↗