The performance of a rotor system is influenced by various design parameters that are neither precise nor constant. Uncertainties in rotor operation arise from factors such as assembly errors, material defects, and wear. To obtain more reliable analy...
Multiphase pumps are used as an important tool for natural gas hydrate extraction owing to their excellent gas–liquid mixing and transport properties. This paper proposes an adaptive response surface-based integrated optimization design method....
This paper is concerned with developing an efficient numerical algorithm for the fast implementation of the sparse grid method for computing the d-dimensional integral of a given function. The new algorithm, called the MDI-SG (multilevel dimension it...
Nonlinear state estimation plays an important role in aerospace sensing applications, where estimation accuracy must be balanced against computational efficiency. In this paper, a sparse-grid Gaussian kernel quadrature Kalman filter (SGKQKF) is propo...
To mitigate the degradation of multi-baseline InSAR elevation reconstruction caused by interferometric phase noise, a hybrid optimization framework integrating fringe-aware feature extraction, statistical estimation, and sparse-grid quadrature inform...
In the environment of unknown mutual coupling, many works on direction-of-arrival (DOA) estimation with sensor array are prone to performance degradation or even failure. Moreover, there are few literatures on off-grid direction finding using regular...
The traditional sparse recovery (SR) space-time adaptive processing (STAP) algorithms are greatly affected by grid mismatch, leading to poor performance in airborne bistatic radar clutter suppression. In order to address this issue, this paper propos...
The paper deals with the Bayesian state estimation of nonlinear stochastic dynamic systems. The stress is laid on the point-mass filter, solving the Bayesian recursive relations for the state estimate conditional density computation using the determi...
Unmanned aerial vehicles (UAVs) have become important equipment for overhead power grid inspection, yet general-purpose path planning methods are inefficient in large mountainous inspection areas and produce jagged paths that violate UAV turning limi...
Sparse-scan ptychography accelerates data acquisition but compromises the overlap-induced redundancy essential for stable blind reconstruction. In low-overlap cases, object errors are often transferred to the probe update, causing crosstalk and artif...
Navigation is a precondition for ocean space vehicles to work safely in polar regions. The traditional polar algorithms employ the grid strapdown inertial navigation system (SINS) as the backbone and Doppler velocity log (DVL) output velocity as meas...
Sparse-view computed tomography (CT) reconstruction aims to recover high-quality CT volumes from a limited number of X-ray projection images, thereby reducing radiation exposure during image acquisition. However, this problem is inherently ill-posed...
The bandwidth shortage has motivated the exploration of the millimeter wave (mmWave) frequency spectrum for future communication networks. To compensate for the severe propagation attenuation in the mmWave band, massive antenna arrays can be adopted...
Sparse antenna arrays are attractive for compact microwave and millimeter-wave front ends because they can achieve prescribed radiation performance with fewer radiating elements, thereby reducing the number of feeding channels, hardware cost, weight,...
Efficient and realistic large-scale scene modeling is an important application of low-altitude remote sensing. Although the emerging 3DGS technology offers a simple process and realistic results, its high computational resource demands hinder direct...
To address the urgent demand for high-precision positioning in power industry operations within sparse reference station areas, this paper proposes a real-time kinematic positioning method integrating BeiDou multi-antenna Precise Point Positioning&nd...
The localized method of fundamental solutions (LMFS) is a domain-type, meshless numerical method. Compared with numerical methods that have a high grid dependence, it does not require grid generation and numerical integration, so it can effectively i...
The direction-of-arrival (DOA) estimation is predominantly influenced by the antenna’s aperture size. However, space constraints on flight platforms often necessitate the use of antennas with smaller apertures and fewer array elements. This ine...
This article presents a simple, original data augmentation algorithm for Received Signal Strength Indicator (RSSI), dedicated to indoor localization systems. The aim of the research was to develop a synthetic data generation method to serve as a regu...
Regional image coverage retrieval is a critical problem in large-scale remote sensing data processing. However, traditional vector topology-based methods suffer from rapidly increasing computational costs when handling massive and highly overlapping...
Semantic segmentation of large-scale power-corridor LiDAR point clouds is essential for remote sensing-based transmission line inspection, vegetation encroachment monitoring, and intelligent grid maintenance. However, existing methods still struggle...
The rough topography, harsh climate, and sparse monitoring stations have limited hydro-climatological studies in arid regions of Pakistan. Gauge-based gridded precipitation datasets provide an opportunity to assess the climate where stations are spar...
Sparse ergodic control provides a natural framework for long-run stochastic decision-making under resource constraints. Existing formulations, however, are typically restricted to control-affine systems with control-independent diffusion. When the di...
The complexity of modern power grids, caused by integrating renewable energy sources, especially inverter-based resources, presents a significant challenge to grid operation and planning, since linear models are unable to capture the complex nonlinea...
Evaluation of the population density in many ecological and biological problems requires a satisfactory degree of accuracy. Insufficient information about the population density, obtained from sampling procedures negatively, impacts on the accuracy o...
The accuracy of numerical model outputs strongly depends on the quality of the initial wind field, yet ground observation data are typically sparse and provide incomplete spatial coverage. More importantly, many current mainstream correction models r...
Fourth-order cumulants (FOCs) vector-based direction of arrival (DOA) estimation methods of non-Gaussian sources may suffer from poor performance for limited snapshots or difficulty in setting parameters. In this paper, a novel FOCs vector-based spar...
The widespread popularity of smart meters enables the collection of an immense amount of fine-grained data, thereby realizing a two-way information flow between the grid and the customer, along with personalized interaction services, such as precise...
Power transmission line monitoring is critical for grid stability. However, existing object detection methods remain challenged by small-scale fittings in aerial transmission-line images because of feature sparsity, complex backgrounds, and computati...
Zenith Tropospheric Delay (ZTD) and its associated atmospheric water vapor information constitute essential environmental variables for Earth observation (EO)-based atmospheric monitoring and environmental variable retrieval. High-quality ZTD product...
Airborne streak tube imaging LiDAR (ASTIL) enables high-frame-rate 3D imaging but suffers from real-time processing bottlenecks due to massive data throughput and costly full decompression. We propose a compressed-domain detection framework that dire...
To address the high dimensionality, redundancy, and noise interference present in wind turbine Supervisory Control and Data Acquisition (SCADA) data, as well as the limitations of conventional K-means algorithms—including excessive reliance on...
Ground Penetrating Radar (GPR) is an important non-destructive sensing technology for subsurface detection. However, strong clutter from surface reflection and heterogeneous media often masks weak target responses in B-scan images. Existing low-rank...
Accurate six-degree-of-freedom (6-DoF) visual localization is a fundamental component for modern mapping and navigation. While recent data-centric approaches have leveraged Novel View Synthesis (NVS) to augment training datasets, these methods typica...
Most automated vehicles (AVs) are equipped with abundant sensors, which enable AVs to improve ride comfort by sensing road elevation, such as speed bumps. This paper proposes a method for estimating the road impulse features ahead of vehicles in urba...
On 7 January 2025, an Mw7.1 earthquake struck Dingri County, Tibet, causing severe damage in a high-altitude, sparsely instrumented region where traditional damage assessment methods are limited. To address this, we developed an integrated "source si...
The Neural Engineering Framework (Eliasmith & Anderson, 2003) is a long-standing method for implementing high-level algorithms constrained by low-level neurobiological details. In recent years, this method has been expanded to incorporate more bi...
High-precision wind power prediction improves grid stability and reduces curtailment losses. Existing methods face three limitations: static graphs cannot capture dynamic spatial correlations under weather changes, time series models miss multi-scale...
Energy systems are crucial to residential life and industrial production. During practical operation, these systems may experience various anomalies that disrupt the stability of system operation. Recent years have witnessed remarkable progress in po...
Driven by the rapid advancement of smart grid frameworks, the volume of visual data collected from power system diagnostic equipment has surged exponentially. A substantial portion of these images (30–40%) are redundant or highly similar, prima...
Impervious surfaces are an important land surface indicator of urbanization level and human activity intensity, playing a crucial role in urban development monitoring and ecological environment assessment. However, in complex high-altitude regions su...
Influence Maximization (IM) is a fundamental problem in social network analysis that aims to identify a set of k seed nodes that maximizes influence spread under a given propagation model. Despite its importance in applications such as viral marketin...
Traditional visual simultaneous localization and mapping (SLAM) systems typically generate sparse or semi-dense point cloud maps, which are insufficient for effective navigation and path planning. Constructing navigation maps through dense depth esti...
The growing integration of DG enhances distribution network resilience, but existing centralized fault recovery strategies face critical limitations: excessive computational burdens on DCs and the insufficient utilization of local control capabilitie...
This paper presents an optimization-based method to support distribution system operators (DSOs) in planning large-scale photovoltaic (PV) integration at the medium-voltage (MV) level. The PV hosting capacity (PV-HC) problem is formulated as a mixed-...
Integrated energy load forecasting plays a crucial role in optimizing the operation and economic dispatch of integrated energy systems. Its forecasting accuracy is not only time-dependent but also influenced by the coupling characteristics among ener...
Maximizing the power output of wind farms is critical for improving the economic viability and grid integration of renewable energy. Active wake control (AWC) strategies, such as yaw-based wake steering, offer significant potential for power generati...
Frost prediction in tropical high-mountain agricultural regions is difficult because sparse meteorological networks must represent strong terrain-driven microclimatic variability. This article presents a topoclimatic graph dataset for frost predictio...
With the large-scale integration of renewable energy resources into power grids, fault behaviors in new power systems have become more frequent and more complex. To address the difficulty of fault feature extraction under limited measurement configur...
Cyber–physical systems (CPSs) serve as the pivotal core of Internet of Things (IoT) infrastructures, such as smart grids and intelligent transportation, deploying interconnected sensing devices to monitor operating status. With increasing decen...
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