Search Results (1,797)

Traditional methods of asset management in electric power systems rely upon fixed schedules and reactive measurements, leading to challenges in the transparent prioritization of maintenance under evolving operating conditions and incomplete data. In this paper, we introduce a new, fully integrated artificial intelligence (AI)-driven approach for enhancing the resilience and reliability of open-source asset management tools to support improved performance and decisions in electric power system operations. This methodology addresses and overcomes several significant challenges, including data heterogeneity, algorithmic limitations, and inflexible decision-making, through a three-module workflow. The data fidelity module provides a domain-aware pipeline for identifying structural (missing) values from explicit missingness using sophisticated imputation methods, including Multiple Imputation Chain Equations (MICE) and Generative Adversarial Network (GAN)-based hybrids. The characterization module employs seven complementary weighting strategies, including PCA, Autoencoder, GA-based optimization, SHAP, Decision-Tree Importance, and Entropy Weighting, to achieve objective feature weight assignment, thereby eliminating the need for subjective manual rules. The optimization module enhanced the action space through multi-objective optimization, balancing reliability maximization and cost minimization. A synthetic dataset of 100 power transformers was used to validate that the MICE achieved better imputation than other methods. The optimized weighting framework successfully categorizes Health Index values into five condition levels, while the multi-objective maintenance policy optimization generates decisions that align with real-world asset management practices. The proposed framework provides the Transmission and Distribution System Operators (TSOs/DSOs) with an adaptable, industry-oriented decision-support workflow system for enhancing reliability, optimizing maintenance expenses, and improving asset management policies for critical power infrastructure. Full article

Keeping the levels of soil major nutrients (total nitrogen, TN; available phosphorous, AP; and available potassium, AK) in optimum condition is important to achieve the goals of precision agriculture systems. To address the issues of slow speed and low accuracy in soil nutrient detection, this study developed a prediction model for soil major nutrients content based on an improved Extreme Learning Machine (ELM) algorithm. This model utilizes a soil major nutrients detection system integrating pyrolysis and artificial olfaction. First, the Bootstrap Aggregating (Bagging) ensemble strategy was introduced during the model integration phase to effectively reduce prediction variance through multi-submodel fusion. Second, Generative Adversarial Networks (GAN) were employed for sample augmentation, enhancing the diversity and representativeness of the dataset. Subsequently, a multi-scale convolutional and Efficient Lightweight Attention Network (ELA-Net) was embedded in the feature mapping layer to strengthen the representation capability of soil gas features. Finally, adaptive hyperparameter tuning was achieved using the Adaptive Chaotic Bald Eagle Optimization Algorithm (ACBOA) to enhance the model’s generalization capability. Results demonstrate that this model achieves varying degrees of performance improvement in predicting total nitrogen (R² = 0.894), available phosphorus (R² = 0.728), and available potassium (R² = 0.706). Overall prediction accuracy surpasses traditional models by 8–12%, with significant reductions in both RMSE and MAE. These results demonstrate that the method can rapidly, accurately, and non-destructively estimate key soil nutrients, providing theoretical guidance and practical support for field fertilization, soil fertility assessment, and on-site decision-making in precision agriculture. Full article

The growing demand for digital content protection has significantly increased the importance of image watermarking, particularly in light of the rising vulnerability of multimedia content to unauthorized modifications. In recent years, research has increasingly focused on leveraging deep learning architectures to enhance watermarking performance, addressing challenges related to transparency, robustness, and payload capacity. Numerous deep learning-based watermarking methods have demonstrated superior effectiveness compared to traditional approaches, particularly those based on Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Transformers, and diffusion models. This paper presents a comprehensive survey of recent developments in both conventional and deep learning-based image watermarking techniques. While traditional methods remain prevalent, deep learning approaches offer notable improvements in embedding and extraction efficiency, particularly when facing complex attacks, including those generated by advanced AI models. Applications in areas such as deepfake detection, cybersecurity, and Internet of Things (IoT) systems highlight the practical significance of these advancements. Despite substantial progress, challenges remain in achieving an optimal balance between invisibility, robustness, and capacity, particularly in high-resolution and real-time scenarios. This study concludes by outlining future research directions toward develop robust, scalable, and efficient deep learning-based watermarking systems capable of addressing emerging threats in digital media environments. Full article

With the rapid evolution of computing technologies and the increased proliferation of online services, secure remote user authentication methods have become essential. Among these methods, password-based authentication remains dominant due to its straightforward implementation and ease of use. Nevertheless, password-based systems are particularly prone to credential theft from keylogging attacks, making user passwords easily compromised. To address these risks, image-based authentication methods were developed, allowing users to enter passwords through mouse clicks rather than keyboard input, thereby reducing vulnerabilities associated with conventional password entry. However, subsequent studies have shown that mouse movement and click information can still be obtained using APIs such as the GetCursorPos() function or WM_INPUT message, thus undermining the intended security benefits of image-based authentication. In response, various defense strategies have sought to inject artificial or random mouse data through functions such as SetCursorPos() or by utilizing the WM_INPUT message, in an effort to disguise authentic user input. Despite these defenses, recent machine learning-based attacks have demonstrated that such naïve bogus input can be distinguished from legitimate mouse data with up to 99% classification accuracy, resulting in substantial exposure of actual user actions. To address this, a technique leveraging Generative Adversarial Networks (GAN) was introduced to produce artificial mouse data closely mimicking genuine user input, which has been shown to reduce the attack success rate by roughly 37%, offering enhanced protection for mouse-driven authentication systems. This article seeks to advance GAN-based mouse data protection by integrating multiple adversarial generative models and conducting a comprehensive evaluation of their effectiveness with respect to data processing techniques, feature selection, generation intervals, and model-specific performance differences. Our experimental findings reveal that the enhanced approach reduces attack success rates by up to 48%, marking an 11% performance gain over previous mouse data protection approaches, and providing stronger empirical support that our method offers superior protection for user authentication data compared to prior techniques. Full article

Understanding surface textures through visual cues is crucial for applications in haptic rendering and virtual reality. However, accurately translating visual information into tactile feedback remains a challenging problem. To address this challenge, this paper presents a class-conditional Generative Adversarial Network (cGAN) for cross-modal translation from texture images to vibrotactile spectrograms, using samples from the LMT-108 dataset. The generator is adapted from pix2pix and enhanced with Conditional Batch Normalization (CBN) at the bottleneck to incorporate texture class semantics. A dedicated label predictor, based on a DenseNet-201 and trained separately prior to cGAN training, provides the conditioning label. The discriminator is derived from pix2pixHD and uses a multi-scale architecture with three discriminators, each comprising three downsampling layers. A grid search over multi-scale discriminator configurations shows that this setup yields optimal perceptual similarity measured by Learned Perceptual Image Patch Similarity (LPIPS). The generator is trained using a hybrid loss that combines adversarial, $L_1$, and feature matching losses derived from intermediate discriminator features, while the discriminators are trained using standard adversarial loss. Quantitative evaluation with LPIPS and Fréchet Inception Distance (FID) confirms superior similarity to real spectrograms. GradCAM visualizations highlight the benefit of class conditioning. The proposed model outperforms pix2pix, pix2pixHD, Residue-Fusion GAN, and several ablated versions. The generated spectrograms can be converted into vibrotactile signals using the Griffin–Lim algorithm, enabling applications in haptic feedback and virtual material simulation. Full article

In the context of mineral resource exploration, spatial prediction must cope with heterogeneous, non-normal data distributions and limited sampling. While conventional geostatistics and standard machine learning provide baseline estimates, they often suffer from excessive smoothing or fail to capture continuous spatial dependencies. This study proposes a geospatially aware Wasserstein conditional Generative Adversarial Network (GSA-cGAN) to complement existing workflows for multivariate mineral interpolation. The framework augments a baseline cGAN with WGAN-GP for stable adversarial training, CoordConv to encode absolute spatial coordinates and Self-Attention to capture long-range spatial dependencies. Eight model configurations were trained on 272 samples from a mineralized zone in the Taebaek Mountains, Korea, and strictly benchmarked against Ordinary/Universal Kriging and multivariate machine learning baselines (Random Forest, XGBoost). Under the adopted experimental design, the full GSA-cGAN achieved the lowest test root mean squared error and highest coefficient of determination, demonstrating a significant performance improvement over the baselines. Furthermore, distribution analysis confirmed that the model effectively overcomes the smoothing limitations of regression-based methods, generating high-resolution 10 m × 10 m maps that preserve statistical variance, hotspot anomalies, and complex spatial patterns. The results indicate that deep generative models can serve as practical decision-support tools for identifying drilling targets and prioritizing follow-up exploration in geologically complex settings. Full article

Bearings are important elements of mechanical systems and the correct forecasting of their remaining useful life (RUL) is key to successful predictive maintenance. Nevertheless, noise interference during different operating conditions is also a significant problem in predicting their RUL. Existing denoising-based RUL prediction models often show degraded performance when exposed to heterogeneous and non-stationary noise, resulting in unstable feature extraction and reduced generalisation. To address the challenge of heterogeneous and non-stationary noise in bearing RUL prediction, this study proposes a hybrid framework that combines a noise-conditioned convolutional denoising autoencoder (NC-CDAE) and a temporal attention transformer (TAT). The NC-CDAE adaptively suppresses diverse noise types through conditional modulation, while the TAT captures long-term temporal dependencies to enhance degradation trend learning. This synergistic design improves both the noise robustness and temporal modelling capability of the system. To further validate the model under varying conditions, synthetic datasets with different noise intensities were generated using a conditional generative adversarial network (cGAN). Comprehensive experiments show that the proposed NC-CDAE + TAT framework achieves lower and more stable errors than state-of-the-art methods, reducing RMSE by up to 23.6% and MAE by 18.2% on average and maintaining consistent performance (an RMSE between 0.155 and 0.194) across diverse conditions. Full article

Hyperspectral imaging (HSI) captures rich spectral information across a wide range of wavelengths, enabling advanced applications in remote sensing, environmental monitoring, medical diagnosis, and related domains. However, the high dimensionality, spectral variability, and inherent noise of HSI data present significant challenges for efficient processing and reliable analysis. In recent years, Generative Adversarial Networks (GANs) have emerged as transformative deep learning paradigms, demonstrating strong capabilities in data generation, augmentation, feature learning, and representation modeling. Consequently, the integration of GANs into HSI analysis has gained substantial research attention, resulting in a diverse range of architectures tailored to HSI-specific tasks. Despite these advances, existing survey studies often focus on isolated problems or individual application domains, limiting a comprehensive understanding of the broader GAN–HSI landscape. To address this gap, this paper presents a comprehensive review of GAN-based hyperspectral imaging research. The review systematically examines the evolution of GAN–HSI integration, categorizes representative GAN architectures, analyzes domain-specific applications, and discusses commonly adopted hyperparameter tuning strategies. Furthermore, key research challenges and open issues are identified, and promising future research directions are outlined. This synergy addresses critical hyperspectral data analysis challenges while unlocking transformative innovations across multiple sectors. Full article

Hyperspectral image (HSI) classification is pivotal for remote sensing applications, including environmental monitoring, precision agriculture, and urban land-use analysis. However, its accuracy is often limited by scarce labeled data, class imbalance, and domain discrepancies between standard RGB and HSI imagery. Although recent deep learning approaches, such as 3D convolutional neural networks (3D-CNNs), transformers, and generative adversarial networks (GANs), show promise, they struggle with spectral fidelity, computational efficiency, and cross-domain adaptation in label-scarce scenarios. To address these challenges, we propose the Transformer–Graph Convolutional Network–Diffusion with Hybrid Domain Adaptation (TGDHTL) framework. This framework integrates domain-adaptive alignment of RGB and HSI data, efficient synthetic data generation, and multi-scale spectral–spatial modeling. Specifically, a lightweight transformer, guided by Maximum Mean Discrepancy (MMD) loss, aligns feature distributions across domains. A class-conditional diffusion model generates high-quality samples for underrepresented classes in only 15 inference steps, reducing labeled data needs by approximately 25% and computational costs by up to 80% compared to traditional 1000-step diffusion models. Additionally, a Multi-Scale Stripe Attention (MSSA) mechanism, combined with a Graph Convolutional Network (GCN), enhances pixel-level spatial coherence. Evaluated on six benchmark datasets including HJ-1A and WHU-OHS, TGDHTL consistently achieves high overall accuracy (e.g., 97.89% on University of Pavia) with just 11.9 GFLOPs, surpassing state-of-the-art methods. This framework provides a scalable, data-efficient solution for HSI classification under domain shifts and resource constraints. Full article

Text-to-Speech (TTS) methods typically employ a sequential approach with an Acoustic Model (AM) and a vocoder, using a Mel spectrogram as an intermediate representation. However, in home environments, TTS systems often struggle with issues such as inadequate robustness against environmental noise and limited adaptability to diverse speaker characteristics. The quality of the Mel spectrogram directly affects the performance of TTS systems, yet existing methods overlook the potential of enhancing Mel spectrogram quality through more comprehensive speech features. To address the complex acoustic characteristics of home environments, this paper introduces AirSpeech, a post-processing model for Mel-spectrogram synthesis. We adopt a Generative Adversarial Network (GAN) to improve the accuracy of Mel spectrogram prediction and enhance the expressiveness of synthesized speech. By incorporating additional conditioning extracted from synthesized audio using specified speech feature parameters, our method significantly enhances the expressiveness and emotional adaptability of synthesized speech in home environments. Furthermore, we propose a global normalization strategy to stabilize the GAN training process. Through extensive evaluations, we demonstrate that the proposed method significantly improves the signal quality and naturalness of synthesized speech, providing a more user-friendly speech interaction solution for smart home applications. Full article

With the widespread deployment of the Advanced Metering Infrastructure (AMI) in Power Industrial Control Systems (PICS), a significant and inherent property of network traffic data is its pronounced class imbalance. The continuous emergence of new types of cyberattacks significantly limits the detection accuracy of Intrusion Detection Systems (IDS). To overcome the limitations of traditional methods—particularly their poor adaptability in complex conditions and vulnerability to emerging threats—this paper introduces a novel hybrid intrusion detection framework. This framework synergistically combines data augmentation and a discriminative classification model for improved performance. Within this framework, a Multi-feature Constrained Conditional Generative Adversarial Network (MC-CGAN) is proposed. Its multi-feature constraint module (MC) preserves protocol-related invariant features, while the CGAN is responsible for conditionally generating the remaining continuous features based on class labels. By preserving the core semantic information of samples, this method reduces the risk of generating unrealistic data and decreases computational overhead. Furthermore, we develop ADS-Net, a lightweight Convolutional Neural Network that not only replaces traditional convolutions with depth-wise separable ones for efficiency, but also incorporates an attention mechanism to adaptively weight feature channels, thus improving discriminative focus. Extensive experiments demonstrate that, under conditions of extreme data imbalance, the proposed hybrid framework can generate industrially valid synthetic data while achieving accurate intrusion detection with an accuracy of 98.35%. Full article

With the rapid advancement of generative AI (AIGC) technology, synthetic images are increasingly approaching real pictures in terms of resolution and semantic consistency. Traditional detection methods face numerous challenges, such as insufficient cross-modal generalization capabilities and difficulty in identifying hidden generative traces. Existing solutions primarily design feature extractors for single generative models, struggling to address the complexity of multimodal forgeries. Therefore, we propose a multi-domain feature fusion Transformer network that integrates spatial, frequency, and wavelet transform features and introduce a cross-domain feature fusion module (CDAF) to detect subtle forgery traces in deepfake images. This model demonstrates superior detection performance on current forged images generated by generative adversarial networks (GANs) and diffusion models while exhibiting enhanced robustness. Full article

Educational Data Mining (EDM) increasingly depends on large, high-quality datasets to drive predictive and adaptive learning systems. However, data scarcity, privacy restrictions, and limited accessibility severely hinder research reproducibility and cross-institutional collaboration. Synthetic data generation provides an emerging solution, enabling the creation of artificial yet statistically realistic datasets that preserve analytical utility while preserving student privacy. This study benchmarks four generative approaches, namely Gaussian Copula, CopulaGAN, Conditional Tabular Generative Adversarial Networks (CTGAN), and Tabular Variational Auto Encoders (TVAE), on student data from six undergraduate courses at a European university. Using the open-source Synthetic Data Vault (SDV) framework, we evaluate the fidelity and Machine Learning utility of synthetic student records through Random Forest classifiers across five metrics, namely accuracy, F1-score, precision, recall, and Area Under Curve (AUC). The results show that synthetic data can achieve 96–98% of the predictive performance obtained when training on real data, with TVAE consistently demonstrating the highest multivariate fidelity. Our contributions are threefold: (i) we introduce a reproducible benchmarking pipeline for synthetic data evaluation in educational settings; (ii) we empirically compare statistical and deep generative synthesizers on real-world tabular student data; and (iii) we identify critical research directions related to privacy and reproducibility. The findings position synthetic data generation as a foundational technology for ethical and privacy-preserving EDM. Full article

Road marking lines can be extracted from aerial images using semantic segmentation (SS) models; however, in this work, a conditional generative adversarial network, RoadMark-cGAN, is proposed for direct extraction of these representations with image-to-image translation techniques. The generator features residual and attention blocks added in a functional bottleneck, while the discriminator features a modified PatchGAN, with an optimized encoder and an attention block added. The proposed model is improved in three versions (v2 to v4), in which dynamic dropout techniques and a novel “Morphological Boundary-Sensitive Class-Balanced” (MBSCB) loss are progressively added to better handle the high class imbalance present in the data. All models were trained on a novel “RoadMarking-binary” dataset (29,405 RGB orthoimage tiles of 256 × 256 pixels and their corresponding ground truth masks) to learn the distribution of road marking lines found on pavement. The metrical evaluation on the test set containing 2045 unseen images showed that the best proposed model achieved average improvements of 45.2% and 1.7% in the Intersection-over-Union (IoU) score for the positive, underrepresented class when compared to the best Pix2Pix and SS models, respectively, trained for the same task. Finally, a qualitative, visual comparison was conducted to assess the quality of the road marking predictions of the best models and their mapping performance. Full article

Rotating machinery plays a critical role in industrial systems, and effective anomaly detection and assessment are indispensable for ensuring operational safety and reliability. However, the performance of existing methods is often constrained by the difficulty in acquiring fault samples—such samples are typically scarce during the initial operational phase of equipment. To address this challenge, this paper proposes a novel anomaly detection and evaluation framework based on Data-Accumulation-Aware Generative Adversarial Networks (DAA-GANs) and similarity estimation. The core innovation of this framework lies in its adaptability across different data accumulation stages. During the early operational phase dominated by normal samples, only normal data is used to train the DAA-GAN to establish a baseline detector. As fault data gradually accumulates, the detection threshold undergoes adaptive adjustment through collaborative optimization of normal and abnormal samples, thereby enhancing the detector’s generalization capability. Upon amassing annotated fault samples of varying severity, the framework assesses anomaly severity by analyzing the similarity between test outputs of unknown samples and known fault samples. The framework is validated through two case studies: a fault simulation model for a torque-splitting transmission system and the publicly available Case Western Reserve University (CWRU) bearing dataset. In the simulation case, the detection accuracy reaches 100% for the gear tooth breakage levels. On the CWRU dataset, the proposed method achieves an overall average detection accuracy of 99.83% across three operating speeds (1730/1750/1772 rpm), and the similarity-based assessment provides consistent severity identification. These results demonstrate that the proposed framework can support reliable anomaly detection and severity assessments under progressive data accumulation. Full article