Naval University of Engineering

Advanced Variance Reduction Methods for Monte Carlo Particle Transport: A Comprehensive Review

The Monte Carlo (MC) method is the reference approach for particle transport simulation in complex three-dimensional geometries and continuous-energy problems, where it avoids the phase-space discretization required by deterministic methods; yet its 1/N convergence rate renders b...

Deep residual physics-informed neural network for underwater electric potential reconstruction

Underwater electrostatic field reconstruction is a key technology for underwater target detection, equipment electric-field diagnostics, and marine engineering measurements. Conventional numerical methods require complete boundary conditions, whereas purely data-driven neural net...

Failure Propagation and Reliability Degradation in Maritime Kill Webs with Multi-Domain Heterogeneous Function-Carrying Platforms

Maritime kill webs integrate heterogeneous platforms across air, surface, and underwater domains, with each platform potentially supporting multiple mission functions. Existing reliability models often represent attacks as complete node removals and therefore cannot distinguish o...

Decoupled operational transfer path analysis for vibration source identification in marine steam turbine generator sets

To address strong multi-source coupling, complex transmission paths, and reference-channel crosstalk in operational transfer path analysis (OTPA) of marine steam turbine generator sets, this study proposes a decoupled OTPA method based on vibration source coupling separation. Unl...

MOOS-IvP/ArduSub-Based AUV Three-Dimensional Path-Following Control System: Design and Implementation

To fulfill the requirements for rapid development of high-performance AUV motion control systems, a three-dimensional (3D) path- following control system is designed and implemented based on the MOOS-IvP/ArduSub architecture. Using Lyapunov stability theory, an adaptive line-of-s...

Efficient Parametric Analysis Framework for Rotor/Wing Hover Interference of DEP Unmanned Aerial Vehicles

During hover, DEP unmanned aerial vehicles (UAVs) suffer severe aerodynamic interference between rotor downwash and the wing, causing thrust loss. High-fidelity CFD can capture the interference physics but is too costly for iterative design. Existing reduced-order models (ROMs) a...

Mathematical Resolution of Ill-Posedness in Padé-Based Subgroup Parameter Calculation via Regularization and Pole–Residue Constraints

The Padé-based method for subgroup parameter calculation in resonance self-shielding analysis is ill-posed: the truncated overdetermined system, the Vandermonde-type coefficient matrix, and the unenforced pole–residue structure jointly produce unstable poles and subgroup paramete...

Near-infrared optical communication via deep learning-enhanced computational temporal ghost imaging in fog scattering environments

Fog scattering is one of the major factors limiting the performance of near-infrared optical communication (NIRC) systems, since it causes severe attenuation of optical signals. In this work, a NIRC system employing computational temporal ghost imaging (CTGI) is established based...

Design and comparative assessment of the mooring system for the deepwater artificial seabed in internal solitary waves

The Deepwater Artificial Seabed (DAS), a novel submerged floating structure for offshore oil production, mitigates the direct impact of harsh sea surface conditions but faces severe challenges from large-amplitude internal solitary waves (ISWs) in the South China Sea. The mooring...

Adaptive Trajectory Tracking Optimization for ROVs Based on RLS Online Identification Under Varying Water Depth Conditions

Remotely operated vehicles (ROVs) suffer from severe navigation trajectory optimization problems in variable water-depth environments, as near-wall hydrodynamic effects cause synchronous scaling drift of added mass and damping coefficients. This parameter variation leads to obvio...

Near-field acoustic holography using prolate spheroidal wave functions and Sparse Bayesian Learning

Accurate reconstruction and prediction of radiated acoustic fields from large-aspect-ratio sound sources, such as underwater vehicles and slender structures, are limited by model mismatch, dense measurement requirements, and sensitivity to measurement noise. This study develops a...

Document-Level Claim Extraction and Decontextualization Method Based on Large Language Models

Claim extraction and decontextualization are fundamental steps in automated fact-checking tasks. Claim extraction aims to identify core check-worthy claims from unverified text, while decontextualization seeks to eliminate the contextual dependencies of core claims so that they c...

Development and Proof-of-Concept Evaluation of a Compact Autonomous Multi-Parameter Instrumentation Platform for Confined Dynamic Systems

A compact autonomous recorder was developed for short-duration measurements in confined moving systems where event-time cabling is unavailable. The cylindrical platform combines pressure, high-range acceleration, and angular-velocity interfaces with channel-specific conditioning,...

Osteoclast precursor cell membrane-engineered bone-targeting calcium carbonate nanoparticles for anti-osteoporosis therapy

Osteoporosis (OP) is a metabolic bone disease characterized by reduced bone mass and deterioration of bone microarchitecture, resulting in an increased risk of fragility fractures. Current anti-osteoporotic therapies are often limited by poor targeting efficiency, suboptimal ther...

Spherical disturbance elements for compound enhancement in bionic fish-scale tubes: Wake control and thermo-hydraulic performance

Enhancing convective heat transfer in circular tubes is crucial for compact heat exchangers, but conventional passive structures often suffer from weak heat transfer in downstream recirculation zones. To address this limitation, a bionic fish-scale composite sphere enhanced heat...

Integrity-aware multi-sensor health monitoring of bridges via denoising variational autoencoder

Accurate bridge health monitoring is essential for safety, yet existing methods still suffer from false alarms and limited multi-sensor integration. This study proposes DAVE-PI (Denoising Autoencoder–Variational Embedding Probabilistic Index), a unified and reliable alarming fram...

Big Data-Driven Production Scheduling Risk Identification Using a Lightweight Adaptive Graph Attention Network

The advancement of Industry 4.0 and smart manufacturing has led to the generation of high-volume, multisource data that evolve in real-time within production scheduling systems. This poses significant challenges to traditional risk identification methods. Models that ignore topol...

Auxiliary Power Prediction for Underwater Vehicles Based on CEEMDAN–VMD–LSTM Multi-Stage Decomposition Framework

This study proposes a multi-stage decomposition framework for auxiliary power prediction of underwater vehicles to support energy management. Isolation Forest first removes outliers from the load record; Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)...

A Domain-Guided Feature-Fusion Framework for Ship Equipment Based on Multi-Type Features

Reliability assessment of ship equipment is often constrained by insufficient or unavailable failure data, particularly for highly reliable components with extremely low failure frequencies. To support the use of reference information from similar equipment in subsequent reliabil...

Extrapolation-Aware Gaussian Process Configuration for Small-Sample Complex-Equipment Cost Estimation

Complex-equipment cost estimation often relies on very small historical datasets, and new targets may fall partly outside the feature ranges represented by those data. This study examines whether two observable data-state indicators can provide a transparent basis for configuring...