arXiv subjects

James Afful

Publications and source records attributed to James Afful.

4 recordsLinked to original sources

arXiv2026

Semi-automated reconstruction of indoor geometry from 360-degree video for CFD-based airflow analysis in classrooms

Computational Fluid Dynamics (CFD) is widely used to evaluate ventilation and contaminant transport in occupied buildings, but deployment at scale is limited by three bottlenecks: acquiring room geometry without costly scanning hardware or manual CAD modeling, decomposing the scene into individually manipulable objects, and reconfiguring those objects for alternative layouts without re-capturing the room. We present a semi-automated workflow that converts a single 360-degree video of a room into individually editable, simulation-ready geometry assets. A dense point cloud is reconstructed using Neural Radiance Fields (NeRF), and 2D instance masks from text-prompted SAM 3 segmentation are lifted to 3D using multi-view consensus and depth-band filtering. Points are separated into object instances with an octree, and occlusion gaps are healed with a connectivity graph. Chair templates are fitted by Iterative Closest Point (ICP) alignment, and table geometry is generated procedurally. A browser-based editor supports quality assurance and rapid construction of alternative layout configurations. A steady Reynolds-averaged OpenFOAM solution then drives transient passive-scalar transport; the setup is verified using a mesh-sensitivity study and validated against an IEA Annex 20 benchmark. We apply the workflow to two university classrooms and a tiered lecture-hall auditorium. The capture-to-geometry pass takes two to five hours per room on a consumer workstation. In a controlled obstruction sequence in one classroom, the modeled half-clearance time varies non-monotonically as furniture is added, and a cross-room comparison indicates that clearance behavior cannot be reliably extrapolated between rooms, motivating per-room geometry acquisition. By making that acquisition low-cost, the workflow makes geometry-resolved comparative ventilation studies practical for spaces such as classrooms.

cs.CV↗

arXiv2025

ExplainBench: A Benchmark Framework for Local Model Explanations in Fairness-Critical Applications

As machine learning systems are increasingly deployed in high-stakes domains such as criminal justice, finance, and healthcare, the demand for interpretable and trustworthy models has intensified. Despite the proliferation of local explanation techniques, including SHAP, LIME, and counterfactual methods, there exists no standardized, reproducible framework for their comparative evaluation, particularly in fairness-sensitive settings. We introduce ExplainBench, an open-source benchmarking suite for systematic evaluation of local model explanations across ethically consequential datasets. ExplainBench provides unified wrappers for popular explanation algorithms, integrates end-to-end pipelines for model training and explanation generation, and supports evaluation via fidelity, sparsity, and robustness metrics. The framework includes a Streamlit-based graphical interface for interactive exploration and is packaged as a Python module for seamless integration into research workflows. We demonstrate ExplainBench on datasets commonly used in fairness research, such as COMPAS, UCI Adult Income, and LendingClub, and showcase how different explanation methods behave under a shared experimental protocol. By enabling reproducible, comparative analysis of local explanations, ExplainBench advances the methodological foundations of interpretable machine learning and facilitates accountability in real-world AI systems.

cs.LG↗

arXiv2025

A Review of HPC-Accelerated CFD in National Security and Defense

Using High-Performance Computing (HPC), Computational Fluid Dynamics (CFD) now serves as an essential component in defense-related national security applications including missile interception and hypersonic propulsion as well as naval stealth optimization and urban hazard dispersion. This review combines two decades of open-source and public-domain research on HPC-accelerated CFD in defense, addressing three key questions: Which security-sensitive simulations have utilized open-source CFD frameworks such as OpenFOAM, SU2 and ADflow? Which HPC techniques, such as MPI domain decomposition and GPU acceleration together with hybrid parallelism best enhance open-source frameworks to manage large defense CFD simulations? Which technological advancements and research voids currently drive the directional development of the field? Examining several research studies sourced from NASA, DoD HPC centers, and academic institutions, scientific contributions have been classified into air, maritime, and space domains. Modular frameworks like NavyFOAM and SU2 and ADflow's adjoint-based solvers show how custom open-source solutions support workflows with rapid completion of multi-million cell simulations. The conclusion highlights new trends that combine exascale readiness with machine learning surrogate models for real-time CFD applications and interdisciplinary HPC-driven multi-physics integration to deliver practical insights for improving CFD use in defense research and development.

cs.CE↗

arXiv2024

Evaluating Neural Radiance Fields (NeRFs) for 3D Plant Geometry Reconstruction in Field Conditions

We evaluate different Neural Radiance Fields (NeRFs) techniques for the 3D reconstruction of plants in varied environments, from indoor settings to outdoor fields. Traditional methods usually fail to capture the complex geometric details of plants, which is crucial for phenotyping and breeding studies. We evaluate the reconstruction fidelity of NeRFs in three scenarios with increasing complexity and compare the results with the point cloud obtained using LiDAR as ground truth. In the most realistic field scenario, the NeRF models achieve a 74.6% F1 score after 30 minutes of training on the GPU, highlighting the efficacy of NeRFs for 3D reconstruction in challenging environments. Additionally, we propose an early stopping technique for NeRF training that almost halves the training time while achieving only a reduction of 7.4% in the average F1 score. This optimization process significantly enhances the speed and efficiency of 3D reconstruction using NeRFs. Our findings demonstrate the potential of NeRFs in detailed and realistic 3D plant reconstruction and suggest practical approaches for enhancing the speed and efficiency of NeRFs in the 3D reconstruction process.

cs.CV↗