Cold spray is emerging as an additive manufacturing technique, particularly advantageous when high production rate and large build sizes are in demand. To further accelerate technology’s industrial maturity, the problem of geometric control must be i...
This study proposes a data-efficient fine-tuning strategy for multi-band antenna synthesis using a Wasserstein Auxiliary-Guided Progressive Growing GAN (WAG-PGGAN). Starting from a pretrained 512 × 512 dual-band PIFA-like generator trained on 4...
Hyperspectral band selection algorithms are crucial for processing high-dimensional data, which enables dimensionality reduction, improves data analysis, and enhances computational efficiency. Among these, attention-based algorithms have gained promi...
Target tracking is one of the broad applications of underwater wireless sensor networks (UWSNs). However, as a result of the temporal and spatial variability of acoustic channels, underwater acoustic communications suffer from an extremely limited ba...
Predicting evacuation demand, including its generation and dissipation process, for urban rail transit systems under disruptions, such as line and station closure, often requires comprehensive historical data recorded under homogeneous situations. Ho...
The static magnetic field from large seafloor exploration platforms severely interferes with weak geological signals. Accurately predicting and compensating for this interference is critical for deep-sea surveys. However, traditional inversion method...
This study addresses the challenge of accurate short-term bus travel time prediction in data-constrained urban environments. While many existing models rely on extensive external data, we propose a data-efficient spatiotemporal framework that integra...
Efficient multimodal instruction tuning requires reducing both training data volume and computational cost without substantially sacrificing downstream performance. Existing data selection methods can remove redundancy but cannot repair imperfect sup...
Data inefficiency is one of the major challenges for deploying deep reinforcement learning algorithms widely in industry control fields, especially in regard to long-horizon sparse reward tasks. Even in a simulation-based environment, it is often pro...
Early lithium-ion battery lifetime prediction is difficult because only a small fraction of a cell’s lifetime is observed when a prediction is needed. We evaluate a transparent, validation-tuned combination of a supervised early-cycle predictor...
This research evaluates the suitability of Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) for improving financial return predictions across 15 major worldwide stock indices. The proposed method uses graph modeling to represent...
Recent studies suggest that few-shot and zero-shot learning methods, drawing on meta-learning, self-supervised approaches and metric-learning ideas, can classify encrypted traffic (TLS 1.3 and QUIC) with competitive accuracy across different protocol...
Generating synthetic data is a promising solution to the challenge of limited training data for industrial deep learning applications. However, training on synthetic data and testing on real-world data creates a sim-to-real domain gap. Research has s...
Background/Objectives: Accurate detection of pulmonary nodules on chest CT is critical for lung cancer screening, yet training robust detectors remains challenging due to the high cost of reliable annotations. In this work, we present a systematic st...
In this paper, we present an approach for the multi-label classification of remote sensing images based on data-efficient transformers. During the training phase, we generated a second view for each image from the training set using data augmentation...
Automated pneumonia screening from chest X-rays (CXRs) faces two coupled difficulties. Annotated radiographs are scarce. The disease also presents in two forms at once: focal lobar consolidation and diffuse interstitial infiltration. Purely spatial e...
This study aimed to develop and validate a physics-informed neural network (PINN) framework for data-efficient and physically consistent process optimization in the laser powder bed fusion (LPBF) of Inconel 718 (IN718) superalloy. Laser powder bed fu...
Computational models for water resources often experience slow execution times, limiting their application. Metamodels, especially those based on machine learning, offer a promising alternative. Our research extends a prior Graph Neural Network (GNN)...
Deep learning has revolutionized medical image segmentation; however, the clinical deployment of state-of-the-art models is severely impeded by their quadratic computational complexity and substantial resource demands, particularly in multisensor and...
Accurate photovoltaic (PV) power prediction under limited experimental data remains a significant challenge, particularly when purely data-driven models generate predictions that violate fundamental physical constraints. This study proposes a physics...
Coal, as a vital global energy resource, directly impacts the efficiency of power generation and environmental protection. Thus, rapid and accurate coal quality analysis is essential to promote its clean and efficient utilization. However, combined n...
Satellite-derived bathymetry (SDB) enables the efficient mapping of shallow waters such as coastal zones but typically requires extensive local ground truth data to achieve high accuracy. This study evaluates the effectiveness of transfer learning in...
Non-charge-based logic devices are promising candidates for the replacement of conventional complementary metal-oxide semiconductors (CMOS) devices. These devices utilize magnetic properties to store or process information making them power efficient...
Reinforcement learning (RL) is a promising direction in automated parking systems (APSs), as integrating planning and tracking control using RL can potentially maximize the overall performance. However, commonly used model-free RL requires many inter...
With the rapid growth of real-world untrimmed videos, video moment localization (VML) has become a fundamental task in query-guided video understanding, aiming to identify the temporal moment that semantically matches a natural language query. Althou...
16 October 2026
Aircraft-door detection is a key perception component for automated passenger boarding bridges, but its performance can degrade substantially under limited annotations, illumination changes, partial occlusion, and viewpoint variation. To address this...
Physics-informed neural networks (PINNs) are an emerging technology in the scientific computing domain. Contrary to data-driven methods, PINNs have been shown to be able to approximate and generalize well a wide range of partial differential equation...
The semantic segmentation of bone structures demands pixel-level classification accuracy to create reliable bone models for diagnosis. While Convolutional Neural Networks (CNNs) are commonly used for segmentation, they often struggle with complex sha...
A data-efficient training method, namely Q-AL-GPR, is proposed for visible light positioning (VLP) systems with Gaussian process regression (GPR). The proposed method employs the methodology of active learning (AL) to progressively update the effecti...
We propose a data-efficient framework for automating sowing operations by agricultural robots in densely mixed polyculture environments. This study addresses the challenge of enabling robots to identify suitable sowing positions with minimal labeled...
Remanufacturing, a key element of the circular economy, enables products and parts to have new life cycles through a systematic process. Initially, used products (cores) are visually inspected and categorized according to their manufacturer and varia...
This study explores the impact of transfer learning on enhancing deep learning models for detecting defects in aero-engine components. We focused on metrics such as accuracy, precision, recall, and loss to compare the performance of models VGG19 and...
The intricate nature of multi-class histopathological images, combined with pronounced class imbalances, complicates automated breast cancer diagnosis and demands AI models capable of generalizing well beyond often limited training data. To address t...
High-energy-density hydrocarbon (HEDH) fuels are essential for aerospace propulsion, yet the design of such fuels is limited by the scarcity of reliable property data. In this work, we fine-tuned Uni-Mol, a pretrained three-dimensional (3D) molecular...
In the era of increasing application of artificial intelligence (AI) and machine learning (ML) tools and services, AI agents, and agentic AI within organizations on data originating from multiple sources, it is becoming necessary for organizations to...
Automatic history matching traditionally depends on a large number of time-consuming numerical simulations, which makes the overall workflow computationally expensive. Deep learning-based surrogate models provide an efficient alternative, but their p...
Large distributed sensor arrays require repeated recalibration as radiation damage, material aging, gain variation, and readout drift alter channel responses. We studied a high-granularity calorimeter as a large sensor array and addressed unsupervise...
Background/Objectives: Breast ultrasound is widely used for the diagnostic evaluation of breast lesions; however, reliable lesion characterization remains challenging due to substantial image heterogeneity and the limited size of most clinically avai...
Airborne and spaceborne hyperspectral sensors collect information which is derived from the electromagnetic spectrum of an observed area. Hyperspectral data are used in several studies and they are an important aid in different real-life applications...
European buildings are producing a massive amount of data from a wide spectrum of energy-related sources, such as smart meters’ data, sensors and other Internet of things devices, creating new research challenges. In this context, the aim of th...
Energy efficient retrofitting projects of urban areas require to analyze data from multiple sources and domains—BIM, GIS, statistics, energy data, and climate. An interoperability solution is needed to overcome the semantic and structural heterogenei...
Background/Objectives: Survival prediction in non-small cell lung cancer (NSCLC) remains challenging, particularly in limited-sample settings where end-to-end deep learning models may suffer from limited generalization. This study aimed to develop a...
Manual assembly remains critical in Industry 5.0 high-mix/low-volume manufacturing, but it introduces resilience challenges due to cognitive load, training demands and frequent product changes. While AI-based supervision can mitigate errors, deployin...
Electroencephalography (EEG) is an emerging biometric trait because it is difficult to steal or forge and requires acquisition from living subjects. However, many EEG-based person identification methods rely on large enrollment datasets, increasing a...
In this paper, a memory-efficient outlier detection (MEOD) approach for streaming data is proposed. The approach uses a local correlation integral (LOCI) algorithm for outlier detection, finding the outlier based on the density of neighboring points...
Shrinkage estimators are often used to mitigate the consequences of multicollinearity in linear regression models. Despite the ease with which these techniques can be applied to small- or moderate-size datasets, they encounter significant challenges...
The growing adoption of wearable devices creates a critical need for robust and secure Internet of Things solutions to manage biometric data streams. Current architectures often lack emphasis on seamless data capture, secure cloud storage and integra...
The massive Internet of Things (IoT) connecting various types of intelligent sensors for goods tracking in logistics, environmental monitoring and smart grid management is a crucial future ICT. High-end security and low power consumption are major re...
With the rapid advancement in artificial intelligence, autonomous driving has emerged as a prominent research frontier. Autonomous vehicles rely on high-precision high-definition map data, necessitating timely map updates by map companies to accurate...
Reversible data hiding has attracted significant attention from researchers because it can extract an embedded secret message correctly and recover a cover image without distortion. In this paper, a novel, efficient reversible data hiding scheme is p...
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