Search Results (1,929)

Reliable quantification of industrial energy savings requires accurate detection of non-routine events (NREs) that distort post-retrofit baselines. Conventional statistical and rule-based anomaly detection methods often misinterpret operational variability, leading to biased or overstated savings under the International Performance Measurement and Verification Protocol (IPMVP). This study develops a novel IPMVP-compliant hybrid deep learning framework that integrates a deterministic Deep Neural Network (DNN) for baseline modeling with stochastic architectures, namely the Factored Conditional Restricted Boltzmann Machine (FCRBM) and Generative Adversarial Network (GAN), to capture probabilistic reconstruction patterns. Their outputs are fused using a hybrid thresholding mechanism that balances detection sensitivity and specificity. Using high-resolution data from an industrial glove manufacturing facility, the hybrid DNN–FCRBM model achieved the best trade-off, demonstrating an accuracy of 94.3%, a precision of 91.1%, and a low false positive rate of 5.1%. This model validated 11.32% industrial energy savings (approximately 478,050 kWh), equivalent to 237 tonnes of CO₂ avoided. The integration of stochastic generative learning within a deterministic framework strengthens transparency, auditability, and IPMVP compliance, offering a scalable pathway for credible industrial energy savings verification. Full article

The availability of high-resolution oceanographic data is critical for evidence-based coastal environmental management and climate resilience planning, yet it remains constrained by observational gaps and the prohibitive computational cost of fine-scale hydrodynamic modeling. While downscaling techniques provide a viable pathway, current data-driven approaches often lack statistical physical associations, overlook multivariate environmental interactions, and struggle to represent complex coastal topography. To address these limitations, we present MEOFGAN—an environmentally informed downscaling framework that integrates multivariate empirical orthogonal function (MEOF) decomposition with a generative adversarial network (GAN). The model extracts physically interpretable spatial modes of coupled ocean variables, learns their cross-scale transitions through adversarial training, and systematically incorporates high-resolution bathymetry as a static environmental constraint to enhance spatial fidelity. When applied to the Bohai Sea, MEOFGAN successfully downscales sea surface temperature (SST) and sea surface height (SSH) from 1/4° to 1/12°, achieving error reductions of 30–68% compared to benchmark methods while preserving ecologically relevant structural patterns (SSIM > 0.92). The framework demonstrates strong generalization by reconstructing 500 m resolution distributions of chlorophyll-a (Chl-a), dissolved oxygen (DO), and salinity in Bohai Bay, capturing fine-scale environmental gradients during a documented algal bloom event. This work establishes a methodological framework that can be transferred as a paradigm for generating high-resolution coastal datasets. Rather than serving as a universally transferable pre-trained model, the framework requires region-specific training and application. Data generated in this manner can directly support water quality monitoring, eutrophication assessment, habitat mapping, and regionally tailored climate adaptation strategies. Full article

Background: The development of robust artificial intelligence (AI) models for diagnosing Temporomandibular Disorders (TMDs) is severely constrained by data scarcity and patient privacy regulations. Cone-beam computed tomography (CBCT), the gold standard for assessing osseous changes in the temporomandibular joint (TMJ), inherently contains sensitive biometric facial features, making de-identification difficult without losing critical anatomical information. This study aims to develop and evaluate TMJCTGenerator, a specialized latent diffusion model (LDM) framework designed to synthesize high-fidelity, diverse, and anonymous TMJ CBCT images. We hypothesize that this LDM approach can achieve superior anatomical fidelity and diversity compared to traditional generative adversarial network (GAN)- and variational autoencoder (VAE)-based methods, specifically in capturing fine osseous details within sagittal and coronal views of the mandibular condyle. Methods: A training dataset comprising 348 anonymized CBCT volumes was obtained in this retrospective comparative study to extract high-resolution sagittal and coronal regions of interest of the mandibular condyle. An independent test set of 39 anonymized CBCT volumes was further included. We developed a class-conditional LDM that integrates a pre-trained VAE for perceptual compression with a conditional U-Net for iterative denoising in the latent space. Performance was evaluated via qualitative anatomical fidelity assessment, Fréchet Inception Distance (FID), and a blinded Visual Turing test conducted by experienced clinicians to determine the distinguishability of synthetic images from real data. Results: Qualitative analysis revealed that TMJCTGenerator produced images with superior sharpness and anatomical consistency compared to baseline models, successfully reconstructing fine bone structures essential for diagnosing degenerative joint disease. TMJCTGenerator achieved lower FID scores than both VAE and GAN baselines. In the visual Turing test, clinicians were unable to reliably distinguish the generated images from real scans, and non-inferiority analysis confirmed that the synthetic data were statistically non-inferior to real data. Furthermore, TMJCTGenerator demonstrated the capability to generate diverse pathological conditions, ranging from normal anatomy to severe osteoarthritic changes. Conclusions: The proposed LDM framework effectively addresses the data scarcity and privacy bottlenecks in TMJ AI research by generating realistic, fully anonymous medical imaging data. TMJCTGenerator outperforms traditional generative methods in both visual fidelity and diversity, offering a viable solution for training downstream diagnostic algorithms. The source code and pre-trained models of TMJCTGenerator have been made open-source. Full article

Missing data are a persistent challenge in large healthcare datasets, often undermining both statistical validity and machine learning performance when handled using simplistic assumptions. In this work, we examine how entropy-based diagnostics can guide the selection of imputation strategies for high-dimensional health survey data using the National Health and Nutrition Examination Survey (NHANES) 2021–2023. Shannon entropy is used to identify variables with structurally complex missingness, and a range of classical approaches (mean imputation, k-nearest neighbors, and multiple imputation by chained equations) are evaluated alongside deep generative methods, including variational autoencoders, generative adversarial networks (GANs), Wasserstein GANs (WGANs), and diffusion-based models. All methods are compared under a controlled masked-entry evaluation using root mean square error (RMSE) and Kolmogorov–Smirnov (KS) statistics to capture both reconstruction accuracy and distributional fidelity. Results show that diffusion-based models provide the most consistent balance between numerical accuracy and distributional preservation across high-entropy dietary variables, while WGAN demonstrates competitive performance for selected distributions. Structural equation modeling further indicates that entropy is a useful diagnostic signal for identifying variables that are difficult to reconstruct. Overall, this study provides a reproducible framework for aligning imputation strategy with missingness complexity in healthcare data, with implications for improving reliability in downstream analytics. Full article

With the increasing occurrence of extreme high-temperature weather events, the traditional bundled operation of wind power (WP), photovoltaic power (PV), and pumped storage hydropower (PSH) is facing dual challenges, namely intensified renewable energy fluctuations and insufficient flexible regulation capability of PSH. Therefore, this paper proposes an optimal scheduling strategy for bundled operation based on capacity interval matching of PSH with WP and PV under extreme high-temperature weather. First, typical scenarios are generated based on a Time-series Generative Adversarial Network (TimeGAN), and an interval matching transaction model is established based on the forecast intervals of WP and PV capacity and the corrected intervals of PSH capacity. Second, considering PSH as an independent market entity, a bi-level optimization model is constructed, in which the upper-level objective is to maximize the revenue of PSH, while the lower-level objective is to minimize the total cost of the joint clearing of the energy and ancillary service markets. Finally, simulation case studies verify that under extreme high-temperature weather, the proposed optimal scheduling method increases the bundled operation capacity by 17.9% and improves the revenue of PSH in the reserve ancillary service market by 14.8%, thereby effectively enhancing the economic performance of PSH while ensuring the safe and stable operation of the system. Full article

Automated detection of defects in welds are inevitable in the assurance of structural integrity, but this faces serious challenges due to the microscopic characteristics of the discontinuities, low visual contrast and infrequent occurrence of defect samples. Conventional deep learning methods, while accurate, often lack interpretability and exhibit low recall for rare defects. This paper proposes a novel hybrid system combining a Generative Adversarial Network (GAN), a Convolutional Neural Network (CNN), and Extreme Gradient Boosting (XGBoost 2.0.0) to enhance weld defect classification performance and transparency. Firstly, a Deep Convolutional GAN (DCGAN) creates synthetic images of the minority classes; thus, the problem of class imbalance is resolved. Then, a pretrained ResNet50V2 CNN is used to extract features of the deep layers from the original images as well as from the generated ones. After that, these features are fed into an XGBoost classifier, which uses tree-based learning to optimize classification results and make the process more understandable to the user. Furthermore, interpretation is also facilitated by Grad-CAM rendering of the CNN regions of interest and SHAP analysis to measure the involvement of the features in XGBoost. Experiments using the available LoHi-WELD datasets show that the overall accuracy is significantly improved, the per-class recall of the rare defects is also enhanced, and the robustness is also improved. The proposed hybrid method not only achieves better results but also generates visual/explainable output, which is very valuable when the system is implemented in industrial welding inspection systems. This paper serves as a liaison between the latest AI technology and the practical interpretability requirements of the mechanical and welding engineering fields. Full article

Data imbalance significantly hinders the performance of deep learning models in rolling bearing fault diagnosis. While Generative Adversarial Networks (GANs) are widely used for data augmentation, traditional architectures employing pixel-level loss functions often fail to capture complex time-frequency textures, resulting in blurred spectrograms and the loss of transient fault characteristics. To address this, we propose a data augmentation framework based on a Perceptually Optimized Deep Convolutional GAN (PerDCGAN). By integrating a perceptual loss function derived from a pre-trained VGG-16 network, the generator is constrained at the feature level rather than the pixel level, explicitly enforcing the preservation of structural details and high-frequency impact patterns. Extensive experiments on the Case Western Reserve University (CWRU) and Paderborn University (PU) datasets demonstrate that the proposed method effectively mitigates spectral blurring. Ablation studies confirm the synergistic effect of the joint loss function. Furthermore, under extreme 0 dB noise conditions, the classifier augmented by PerDCGAN maintains a robust diagnostic accuracy of 89.65% on the PU dataset, significantly outperforming standard DCGAN and demonstrating strong potential for complex industrial applications. Full article

Deploying conditional Generative Adversarial Networks (cGANs) for industrial texture synthesis faces two barriers: the prohibitive cost of manual data annotation and the uncertain alignment between automated evaluation metrics and human perception. This study addresses both challenges for marble texture synthesis using 289 high-resolution industrial scans. We adapt an unsupervised segmentation pipeline combining Simple Linear Iterative Clustering (SLIC) superpixels, Gaussian Mixture Models (GMMs), and graph cut optimization to extract vein structures without manual annotation. Four cGAN architectures—baseline cGAN, Pix2Pix, BicycleGAN, and GauGAN—are benchmarked using a dual-evaluation protocol contrasting ten automated metrics with structured human-centered assessment. The results reveal a significant metric–perception discrepancy. Pix2Pix achieved the best Fréchet Inception Distance (FID = 85.3) yet received the lowest human ratings due to periodic texture artifacts. GauGAN produced textures statistically indistinguishable from real marble, achieving a Visual Turing Pass Rate (VTPR) of 0.533 and a Mean Opinion Score on Marble Authenticity (MOS-MA) of 2.89, despite an inferior FID (87.3). These findings make three contributions: an annotation-free segmentation pipeline, empirical evidence that automated metrics alone are insufficient for architecture selection, and a dual-evaluation framework that establishes human-in-the-loop assessment as essential for quality-critical industrial deployment. Full article

The increasing complexity of oncology diagnostics requires advanced Clinical Decision Support Systems (CDSS) capable of integrating multimodal data. Traditional discriminative models often struggle with missing data and cross-modal dependencies. This review provides a novel, systematic analysis of conditional generative artificial intelligence (AI), including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), diffusion models and Multimodal Large Language Models (MLLMs), specifically tailored for oncological CDSS. We examine how these architectures move beyond simple prediction to learn joint data distributions, enabling robust data imputation, virtual staining, and automated clinical reporting. A central focus of this work is the assessment of translational application, identifying the gaps between experimental proof-of-concepts and clinical deployment. We address critical hurdles such as model hallucinations, domain shift, and demographic bias, providing a roadmap for biological consistency and regulatory compliance. This review highlights the transition from task-specific generators to multimodal reasoning systems. Ultimately, we argue that the integration of generative AI into diagnostic workflows is essential for precision oncology, provided that human-in-the-loop validation and uncertainty-aware inference remain central to their implementation. Full article

Deep learning-based recommendation systems are highly vulnerable to data poisoning attacks, where adversaries manipulate user interactions to degrade model integrity. We hypothesize that combining an active robust loss with a passive GAN-based detection will significantly reduce poisoning impact in recommendation systems without sacrificing utility. We propose a robust and adaptive dual-defense framework: the active defense integrates a crafted loss function to mitigate poisoning effects while maintaining model performance. The passive defense employs a Generative Adversarial Network (GAN)-based detection model to identify and filter poisoned data, enhancing detection accuracy and system security. The framework supports classical matrix factorization (MF) model and large language model (LLM)-based pipelines and scales to large datasets. Extensive experiments across multiple real-world datasets at varying poison rates show that our method outperforms representative defenses, consistently reducing attack success without sacrificing recommendation quality. The framework also admits a federated instantiation, where robust training and GAN-based detection run on clients and only privacy-preserving summaries are aggregated. The proposed method significantly improves the robustness and adaptability of recommendation systems under data poisoning attacks. Full article

Flexible deformation sensors inevitably suffer from sensitivity degradation and severe measurement errors during long-term cyclic stretching due to structural fatigue. Traditional material-level optimizations are costly and lack dynamic adaptability. Herein, we propose an artificial intelligence (AI)-driven predict-and-compensate framework for the online calibration of flexible sensors. To overcome training sample scarcity, a generative adversarial network (GAN) performs temporal data augmentation. Subsequently, a hybrid deep learning framework integrating long short-term memory (LSTM) networks and a Sequence Attention mechanism is employed. This architecture accurately captures both local signal fluctuations and multiscale long-term decay trends, enabling precise multi-step prediction and output compensation. Experimental evaluations validate that this strategy significantly suppresses sensor response drift. Under cyclic loading, an initially substantial relative measurement error of 48.63% plummets to 7.16% post-calibration, with typical errors consistently reduced to the ~1% level. Furthermore, when deployed in a smart glove gesture recognition system, this method successfully restores the recognition accuracy from a fatigue-induced low of 75.73% (after 200 stretch cycles) back to 97.70%. This generative and attention-based deep learning paradigm offers robust, real-time error calibration, providing a highly viable solution for extending the long-term reliability and stability of flexible sensor systems. Full article

Three-dimensional seed phenotyping requires imaging systems capable of achieving micron-level resolution across a centimeter-level field of view (FOV), a goal constrained by the resolution–FOV trade-off in conventional light field architectures. This paper presents a hardware–software co-optimized framework that integrates a reconfigurable optical system with computational imaging pipelines to address this limitation. At the hardware level, we develop a tunable-focus lens module that enables flexible adjustment of the effective focal length, combined with a custom-designed microlens array (MLA). A mathematical model is established to analyze the interdependencies among FOV, lateral resolution, depth of field (DOF), and system configuration, guiding the design of individual optical components. On the computational side, we propose a hybrid aberration correction strategy: first, a co-calibration of lens and MLA aberrations based on line-feature detection; second, a conditional generative adversarial network (cGAN) with attention-guided residual learning to enhance sub-aperture images, achieving a PSNR of 34.63 dB and an SSIM of 0.9570 on seed datasets. Experimentally, the system achieves a resolution of 6.2 lp/mm at MTF50 over a 2–3 cm FOV, representing a 307% improvement over the initial configuration (1.52 lp/mm). The reconstruction pipeline combines epipolar plane image (EPI) analysis with multi-view consistency constraints to generate dense 3D point clouds at a density of approximately 1.5 × 10⁴ points/cm² while preserving spectral and textural features. Validation on bitter melon and rice seeds demonstrates accurate 3D reconstruction and accurate extraction of morphological parameters across a large area. By integrating optical and computational design, this work establishes a reconfigurable imaging framework that overcomes the resolution–FOV limitations of conventional light field systems. The proposed architecture is also applicable to robotic vision and biomedical imaging. Full article

Metal artifacts generated by dental implants significantly degrade cone-beam computed tomography (CBCT) volumes, obscuring critical anatomical structures and compromising diagnostic precision. To address this, an unsupervised deep learning framework has been proposed for Metal Artifact Reduction (MAR) utilizing a Cycle-Consistent Adversarial Network (CycleGAN) optimized for high-fidelity restoration. Unlike supervised methods that rely on unattainable voxel-aligned paired datasets, the proposed approach leverages an unpaired dataset of approximately 4000 images, curated from the public ToothFairy dataset. The architecture integrates U-Net-based generators and PatchGAN discriminators, specifically tuned to mitigate generative hallucinations and preserve morphological integrity. Quantitative benchmarking on a held-out test set demonstrates a 34.6% improvement in the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) score, a substantial reduction in Fréchet Inception Distance (FID) from 207.03 to 157.04, and a superior Structural Similarity Index Measure (SSIM) of 0.9105. The framework achieves real-time efficiency with a 3.03 ms inference time per slice, effectively suppressing artifacts while preserving anatomical detail. Expert validation confirms high fidelity; however, to ensure reliability in extreme cases, the architecture is recommended as a clinical decision-support tool under human-in-the-loop oversight. By enhancing diagnostic clarity via a scalable software pipeline, this study provides a robust solution for high-fidelity dental implant imaging. Full article

Due to complex terrain, Earth surface curvature, and limited distribution of radars, there are often serious data gaps in base radar data or in 3D radar reflectivity mosaics of a radar network. These gaps greatly limit the application of radar data in short-term severe convection forecasting and quantitative precipitation estimation for flood events. This paper develops a generative adversarial network (GAN)-based radar data gap-filling model, named RadGF-GAN, for completing gaps in 3D radar reflectivity mosaic data. The 2020–2025 high-resolution (at 1 km grid spacing) outputs of a Weather Research and Forecasting and four-dimensional data assimilation model (WRF-FDDA) in an eastern China region are used to generate the data to train and test RadGF-GAN. Observations of the geostationary satellite FY-4A 15-channel AGRI (Advanced Geostationary Radiation Imager) are simulated with the radiative transfer for TOVS (RTTOV), and the radar reflectivity data are simulated with an empirical diagnostic model. By testing on 1705 test samples for satellite-only, radar-only, and radar–satellite fused inputs, it is demonstrated that the proposed RadGF-GAN gap-filling model significantly outperforms the existing interpolation methods in restoring the spatial distribution and structural textures of the radar reflectivity in the 3D gaps. Furthermore, satellite imager measurements play a great role in reconstructing the overall rainband structures in large 3D gaps, and by jointly inputting radar and satellite data, RadGF-GAN greatly outperforms the model with either radar data or satellite data alone. Full article

Electric Vehicle Charging Systems (EVCSs) are increasingly connected with the Internet of Things (IoT) and smart grid infrastructure, yet they face growing cyber risks due to expanded attack interfaces. These systems are vulnerable to various attacks that potentially impact both charging operations and user privacy. Intrusion Detection Systems (IDSs) are essential for identifying suspicious activities and mitigating risks to protect EVCS networks, but conventional ML-based IDSs are often unable to achieve optimal performance due to imbalanced datasets, complex traffic distributions, and human design limitations. In practice, EVCS traffic is typically multi-class, imbalanced, and safety-critical, where both missed attacks and false alarms can lead to denial of charging, service interruption, unnecessary incident escalation, financial loss, and reduced user trust. Automated ML (AutoML) and Generative Artificial Intelligence (GAI) have emerged as promising solutions in cybersecurity. Existing GAI and augmentation methods are mostly class-frequency-driven, but this does not necessarily improve the error-prone regions where IDSs actually fail. In this paper, we propose a GAI and an AutoML-based IDS that incorporates a Conditional Generative Adversarial Network (cGAN) with the optimized XGBoost model to improve the effectiveness of intrusion detection in EVCS networks and IoT systems. The proposed framework involves two techniques: (1) a novel cGAN-based error-guided generative augmentation (EGGA) method that extracts misclassified samples and generates a more robust training set for IDS development, and (2) an optimized IDS model that automatically constructs an optimized XGBoost model based on Bayesian Optimization with Tree-structured Parzen Estimator (BO-TPE). The main algorithmic novelty lies in EGGA, which uses model errors to guide generative augmentation toward difficult decision regions, while the overall pipeline represents a practical system-level integration of EGGA, XGBoost, and BO-TPE. To the best of our knowledge, this is the first work that combines GAI and AutoML to specifically improve detection on hard samples, enabling more autonomous and reliable identification of diverse cyber attacks in EV charging networks and IoT systems. Experiments are conducted on two benchmark EVCS and cybersecurity datasets, CICEVSE2024 and CICIDS2017, demonstrating consistent and statistically meaningful improvements over state-of-the-art IDS models. This research highlights the importance of combining automation, generative balancing, and optimized learning to strengthen cybersecurity solutions for EV charging networks and IoT systems. Full article