AI Eng., Volume 1, Issue 2 (September 2026) – 5 articles

High-dimensional engineering datasets often combine class imbalance, noisy structures, and limited ground truth, making unsupervised analysis difficult to evaluate reliably. This study quantifies how three properties—partition stability, minority class recovery, and topological interpretability—are traded off across clustering methods, using a capacity-matched 25-seed comparison on a TCGA-derived RNA expression dataset (10,095 samples, 19 cancer types, 13,634 genes). We compare K-means across cluster counts $k\in \{19,\dots ,400\}$, self-organizing maps (SOMs) across lattice sizes from 25 to 625 nodes, consensus K-means, a granularity-matched SOM-Super20 control, and four modern baselines (HDBSCAN, spectral clustering, Gaussian mixtures, and Leiden). At matched prototype budgets, K-means is both more reproducible and substantially better at recovering minority classes than SOMs: at 400 prototypes, K-means achieves pairwise NMI $0.819$ versus $0.621$ for the $20\times 20$ SOM and recovers the smallest cancers 6–$14\times$ more effectively (pancreas effective coverage $0.760$ vs. $0.054$).Crucially, the SOM does not close this gap even when given more prototypes ($0.07$ at 625 nodes), so, under matched capacity, minority recovery is better explained by representational capacity and centroid allocation freedom than by topology preservation. The recovery is not free: increasing k overfragments the partition and lowers the pairwise ARI stability ($0.643\to 0.419$ from $k=20$ to $k=400$), while the NMI remains robust ($\approx 0.82$). The hardest minority, pancreas, is recovered only by high-capacity K-means and by no other method evaluated, including SOMs at any size, consensus K-means, SOM-Super20, HDBSCAN, Gaussian mixtures, spectral clustering, and Leiden. The SOM’s distinct value is therefore not stability or recovery but the interpretable two-dimensional topological visualization that it uniquely provides, including a gradient-organized structure that is reproducible across seeds for kidney (weaker for uterus). No single method optimizes all three properties; the appropriate choice depends on whether a task prioritizes reproducibility, minority recovery, or visual interpretability. Because these conclusions follow from the shape of the data and the allocation behavior of the algorithms rather than from biological semantics, we expect them to transfer to high-dimensional imbalanced engineering data, such as those from fault clustering, condition monitoring, and anomaly detection. Full article

Precise station-keeping of construction barges during offshore operations remains a demanding control problem because the underlying dynamics are highly nonlinear and the disturbance environment is seldom known a priori. This work investigates how a learning-based controller can be embedded into the coordinated winch-control architecture of a specialized engineering vessel to deliver accurate positioning in shallow water. Vessels such as rock-dumping platforms and pipe-laying barges routinely rely on a spread of mooring lines to hold station, and the tensions on these lines are, in current industrial practice, still adjusted manually by the winch operator. The scheme proposed here replaces that manual loop with an adaptive neural feedback law synthesized through backstepping, allowing the unknown portions of the ship model and the exogenous environmental loads to be compensated online without requiring prior identification. The 3DOF control wrench produced by the feedback law is then mapped to the physical line tensions through a constrained allocation that respects the unilateral and breaking-load constraints of the spread. The closed-loop system is shown to be semi-globally uniformly ultimately bounded (SGUUB) in the Lyapunov sense, and its performance is benchmarked against a conventional PD regulator and a nominal model-based design through simulation of a full-scale rock installation barge. When the model-based baseline is given the nominal plant, it attains the cleanest tracking; the proposed neural law achieves comparable steady-state accuracy without requiring prior identification of the hydrodynamic coefficients. A model-free deep reinforcement learning (PPO) controller is additionally benchmarked under irregular (JONSWAP) seas; it attains bounded sub-metre station-keeping without any model knowledge, on par with the PD baseline but less precise than the model-based and adaptive-neural laws. Full article

Recent advances in artificial intelligence (AI) and computer vision technologies have enabled practical applications in industrial environments where safety, quality, and productivity are critical. To support both researchers and practitioners, this survey categorizes AI-based vision systems by their functional objectives rather than algorithmic classification. Specifically, representative implementations are organized across key application areas including safety monitoring, product quality inspection, assembly line support, and worker productivity enhancement. Most of the surveyed studies are in the manufacturing and construction sectors, where real-world deployments have demonstrated measurable improvements. Unlike many previous reviews, this survey focuses on image-centric applications, using visually interpretable outputs such as photographs, video frames, and real-world examples to illustrate the on-site usability of AI vision systems. It also organizes prior work by functional roles and practical deployment considerations, rather than algorithm-centric evaluations, to provide practitioners with actionable insights for industrial adoption. Full article

This pilot study presents a surrogate modeling framework for estimating carbon emissions for 35 data centers in the New York City metropolitan area. Using publicly available facility data (square footage, operator type, location), we calculated the annual CO₂e emissions based on standard industry assumptions. These calculated values, which represent modeled emissions rather than measured data, served as the target variable for surrogate model development. A Random Forest regression model was implemented. The model achieved strong performance in producing the calculated emissions with the test set with cross-validated performance (CV R² = 0.960 ± 0.022 and CV MAE = 2431 ± 739 MT CO₂e). Analysis indicated that data center size was the major predictor, accounting for 79.7% of the total feature importance, while location and operator type contributed 13.6% and 6.6%, respectively. As a localized, preliminary feasibility study, this case study demonstrates that surrogate modeling using only publicly available facility data can provide modeled carbon footprint estimates for infrastructure planning and grid decarbonization efforts. The reproducible methodology can be applied to other metropolitan regions, though generalizability requires further validation with larger datasets. Full article

Fast and accurate resistance prediction is critical in early-stage ship design. While Michell’s thin-ship theory provides rapid evaluations, its linear assumptions limit accuracy, particularly as hull forms deviate from ideal slenderness. This paper introduces a physics-preserving neural calibration method that improves Michell’s theory without replacing the underlying solver. We train a two-dimensional convolutional encoder–decoder, conditioned on Froude numbers via global FiLM modulation, to predict a bounded correction to the geometric effective-slope field. Because the solver remains unchanged, the learned correction acts as an interpretable spatial perturbation rather than a black-box resistance map. Evaluated under a strict leave-one-family-out (LOFO) protocol on a fleet of five slender hull families (DTMB, NPL-4A, Wide-Light Canoe, Wigley, and Delft 372), the neural calibration achieves a mean absolute percentage error (MAPE) of 0.0741. This represents a 24% improvement over a reproduced 2020 baseline and a 7.9% improvement over the uncorrected Michell solver. The 2020 baseline is the rigid boundary-layer and phase-deflection correction of an earlier study by the present group, re-evaluated here on the present hulls at their measured attitudes. Ablation studies show that much of this aggregate gain is captured by a bounded global slope offset, indicating that a spatially uniform displacement correction accounts for most of the improvement on slender hulls, while the spatially varying field mainly adds per-family headroom. Finally, we map the physical boundaries of this approach. Dedicated recovery campaigns on fuller forms (KCS and Series 60) show that the model regresses compared to baselines. This confirms that while the correction successfully refines the linear source distribution for slender hulls, it cannot synthesize missing physics, such as stagnation pressure, separated flow, or wave interference, for fuller or unrelated geometries. Full article