Search Results (226)

This study aims to develop and systematically evaluate a data-driven inverse-design framework for determining silver nanoparticle (AgNP) synthesis conditions that achieve a prescribed particle size. To this end, a forward surrogate model is first developed to learn the nonlinear mapping from synthesis parameters to particle size, and it is then coupled with target-conditioned inverse models, including conditional generative adversarial network (cGAN), conditional Wasserstein GAN (cWGAN), conditional Wasserstein GAN with gradient penalty (cWGAN-GP), Wasserstein GAN with gradient penalty (WGAN-GP), and other comparable frameworks, to generate feasible synthesis conditions. Three AgNP datasets covering microfluidic and chemical synthesis routes are used for evaluation, and the models are assessed using both experimentally observed target sizes and constructed targets spanning the attainable output range. The results show that conditional adversarial models generally outperform the non-adversarial baselines. Among them, cWGAN shows the most consistent performance across the three datasets, while cGAN remains competitive in the more difficult inverse-design cases. The proposed framework also captures the one-to-many nature of inverse design by producing multiple candidate synthesis conditions for a single target size. In addition, prediction errors increase near the lower and upper boundaries of the feasible size interval. Inverse design is therefore more challenging near these boundaries, although the main comparative conclusions remain unchanged under stricter validation. These findings support the use of forward-constrained conditional generative modeling for target-oriented AgNP synthesis design in limited-data settings. 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 increasing port automation and operational intensity, the gearboxes of gantry cranes widely used in bulk cargo terminals are prone to bearing and gear failures under prolonged heavy loads, intense vibrations, and complex operating conditions. Since fault samples often exhibit imbalanced distributions, this imposes two higher requirements on diagnostic methods—first, the ability to effectively address sample imbalance and, second, the capability to simultaneously identify multiple fault categories. To address these challenges, this paper proposes a joint diagnostic method integrating an improved Conditional Wasserstein Generative Adversarial Network with Gradient Penalty (CWGAN-GP) and Multi-Task Learning (MTL). First, the modified CWGAN-GP performs conditional augmentation for minority fault classes, evaluating synthetic sample authenticity and diversity through multiple metrics. Subsequently, a multi-channel diagnostic network is constructed, in which vibration signals are fed into two parallel sub-networks: time–frequency features are extracted from the Short-Time Fourier Transform (STFT)-based time–frequency representations via a residual-block Convolutional Neural Network (CNN), while temporal features are captured from the raw time-domain signal using a Bidirectional Long Short-Term Memory (Bi-LSTM) with an attention mechanism. An attention fusion layer then integrates these two feature types, enabling joint classification of bearings and gears within a multi-task learning framework. Experimental validation on public gearbox datasets and port gantry crane gearbox datasets demonstrates that this method achieves an average diagnostic accuracy exceeding 97%. The proposed method reduces the impact of class imbalance, thereby improving the accuracy and stability of multi-task fault identification. Full article

This study proposes ViTWGAN, a novel and effective intrusion detection model designed to enhance data privacy protection by detecting malicious traffic within network flows. By improving the discriminator’s loss function, our approach reduces blind spots in the discriminator by explicitly reinforcing the learning of hard negative samples, thereby mitigating the forgetting of negative samples in the generative adversarial network. A Vision Transformer is employed as the backbone architecture for both the generator and the discriminator, while the Wasserstein distance is introduced to prevent mode collapse, enabling the generator to produce diverse normal traffic and consequently improving the discriminator’s detection capability. Extensive experiments on the NSL-KDD and CIC-DDoS2019 datasets demonstrate the superior performance of the proposed model, achieving accuracy rates of 96.45% and 99.37%, respectively. These results highlight the effectiveness of ViTWGAN as a high-performance solution for general intrusion detection systems. Full article

Extreme class imbalance is a persistent obstacle for machine learning-driven intrusion detection, as rare but high-impact cyberattacks occur far less frequently than benign traffic in training data. In many real-world cybersecurity datasets, this imbalance becomes extreme, with certain attack types containing a handful of samples, effectively placing the problem in a few-shot learning regime. This paper presents a controlled benchmarking study of Generative Adversarial Network (GAN) objectives for synthesizing minority-class cyberattack data. Using the UWF-ZeekData22 network traffic dataset, each MITRE ATT&CK tactic is framed as a separate binary detection task, and tactic-specific GANs are trained solely on minority samples to generate synthetic attack records. Four widely used GAN variants—Vanilla GAN, Conditional GAN (cGAN), Wasserstein GAN (WGAN), and Wasserstein GAN with Gradient Penalty (WGAN-GP)—are compared under unified training steps and fixed augmentation conditions. The utility of generated data is assessed by evaluating downstream detection performance using five traditional classifiers: Logistic Regression, Support Vector Machine, k-Nearest Neighbors, Decision Tree, and Random Forest. The results indicate that GAN augmentation generally strengthens minority-class detection across tactics and models, reducing false negatives and improving recall consistency, while not systematically harming majority-class performance. However, the effectiveness of each GAN objective varies significantly with data sparsity. Specifically, simpler adversarial objectives often outperform more complex architectures by preserving discriminative feature structure, while heavily regularized models may overly smooth minority-class distributions and reduce separability. Wasserstein-based objectives provide improved training stability, but additional regularization does not consistently translate to better detection performance. Overall, the results demonstrate that in extreme-imbalance settings, GAN effectiveness is governed more by data sparsity and structure preservation than by architectural complexity. These findings establish class-specific generative augmentation as a practical strategy for intrusion detection and provide empirical guidance for selecting appropriate GAN objectives for tabular cybersecurity data under highly imbalanced conditions. Full article

Silicon carbide (SiC) power converters offer superior switching performance but generate severe broadband electromagnetic interference (EMI) that challenges regulatory compliance. Existing prediction methods face a fundamental trade-off between physical fidelity and computational efficiency, while conventional suppression strategies lack adaptability to varying operating conditions. This paper proposes a frequency-band-aware physics-informed generative adversarial network (FBA-PIGAN) that integrates electromagnetic domain knowledge into data-driven generative modeling for joint EMI prediction and adaptive suppression in SiC power converters. The framework employs a Wasserstein GAN with gradient penalty as the adversarial backbone and introduces feature-wise linear modulation (FiLM) to inject converter operating parameters into the generator through learned affine transformations. A hierarchical physics-informed loss function enforces three frequency-dependent constraints, namely, harmonic structure consistency, parasitic resonance characterization, and high-frequency envelope regularization, coordinated by a curriculum-based weight-scheduling strategy. An end-to-end differentiable suppression module maps predicted spectra to optimal passive filter parameters through an analytically embedded transfer function. Experimental validation on a 10 kW SiC inverter platform with 5120 measured spectra across 32 operating conditions demonstrates that FBA-PIGAN achieves a mean spectral error of 2.1 dB, 93.8% peak frequency accuracy, and a physical consistency score of 0.93, improving prediction accuracy by 56% over conventional conditional GANs while maintaining sub-millisecond inference latency. The integrated suppression pipeline attains 19.2 dB average attenuation with 98.5% CISPR 25 compliance, and the framework generalizes to unseen operating conditions with only 19% performance degradation, compared with 56% for data-driven baselines. Full article

High renewable energy penetration creates significant operational challenges for power systems, especially during extreme weather that disrupts supply–demand balance. This study introduces a framework that integrates extreme scenario identification, data augmentation, and power balance optimization. It defines extreme wind speed events, such as sudden drops, surges, and persistent anomalies, and uses a sliding-window algorithm to extract these events from historical meteorological data. To address the scarcity of extreme samples, a new data augmentation method combines the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) and iterative distribution shifting. This approach focuses the generated data on distribution tails while preserving diversity and temporal consistency. An optimization model, which includes various generation resources, energy storage, and load shedding, is developed to assess system flexibility under extreme conditions. Case studies on the projected 2030 Northeast China Power Grid show that the augmentation method expands extreme scenario datasets from 150 to 1000 samples, maintains extremity and temporal consistency, and reveals that wind curtailment rises sharply above 70% renewable share, with storage systems providing key flexibility in high-output scenarios. Full article

Due to high intraclass variability and subtle intercharacter differences, automatic Arabic handwriting recognition remains a challenging task, particularly for children’s handwriting. This study proposes a hybrid framework that combines class-conditional Wasserstein generative adversarial networks with gradient penalty (CWGAN-GP) for data augmentation and a convolutional neural network (CNN) enhanced with squeeze-and-excitation (SE) blocks for improved feature discrimination. Experiments were restricted to disconnected (isolated) characters from the Hijja dataset, which comprised 12,355 samples divided as follows: 80% for training (9884), 10% for validation (1236), and 10% for testing (1235). Training the CNN on real data alone yielded an accuracy of 93.47%, while incorporating CWGAN-GP-generated samples improved performance to 96.27%. Notably, the proposed SE-CNN trained with the CWGAN-GP-augmented data achieved the highest accuracy of 99.27%. This result demonstrates that the combination of advanced generative data augmentation and architectural refinement significantly enhances Arabic handwritten character recognition performance. Full article

Healthcare fraud poses significant risks to insurance systems, undermining both financial sustainability and equitable access to care. Accurate detection of fraudulent claims is therefore critical to ensuring the integrity of healthcare insurance operations. However, the increasing sophistication of fraud techniques and limited data availability have undermined the performance of traditional detection approaches. To address these challenges, this paper proposes WCGAN-GA-RF, an integrated fraud detection framework that synergistically combines Wasserstein Conditional Generative Adversarial Network with gradient penalty (WCGAN-GP) for synthetic data generation, genetic algorithm-based feature selection (GA-RF) for dimensionality reduction, and Random Forest (RF) for classification. The proposed framework was empirically validated on a real-world dataset of 16,000 healthcare insurance claims from a Chinese healthcare technology firm, characterized by a 16:1 class imbalance ratio ($5.9\%$ fraudulent samples) and 118 original features. Using a stratified $80/20$ train–test split with results averaged over five independent runs, the WCGAN-GA-RF framework achieved a precision of $96.47\pm 0.5\%$, a recall of $97.05\pm 0.4\%$, and an F1-score of $96.26\pm 0.4\%$. Notably, the GA-RF component achieved a $65\%$ feature reduction (from 80 to 28 features) while maintaining competitive detection accuracy. Comparative experiments demonstrate that the proposed approach outperforms conventional oversampling methods, including Random Oversampling (ROS), Synthetic Minority Oversampling Technique (SMOTE), and Adaptive Synthetic Sampling (ADASYN), particularly in handling high-dimensional, severely imbalanced healthcare fraud data. Full article

The risks associated with hazardous materials (HazMat) transportation exhibit typical characteristics of chain-like distribution, spatiotemporal regularity, and individual heterogeneity. A personalized trip-chain feature spectra recognition framework for HazMat vehicles is proposed to enhance the capability to assess and analyze individual risks using vehicle positioning data. The proposed framework addresses the challenges of deriving personalized risk feature maps arising from missing real-time trajectory data, complex sub-trip-chain segmentation, and the extraction of personalized risk feature representations. An improved conditional Wasserstein Generative Adversarial Network (WGAN) model is initially developed to impute trajectories with missing positional data, and it can robustly reconstruct trajectories with large-scale missing segments by integrating a multi-head self-attention mechanism and a gradient penalty. A two-layer clustering algorithm, K-Means-multiplE-THreshOlds-adaptive-DBSCAN (KMETHOD), which combines an adaptive mechanism with threshold rules, is subsequently designed to identify the dwell time and related spatial attributes of dwell points along vehicle trips. A BERT-based model is incorporated to filter Points of Interest (POIs) around dwell points, which enables the extraction of their detailed location semantics and trip characteristics and thus supports trip chain identification and segmentation. A threshold-activated multilayer trajectory feature-map method (TAFEM) is constructed to generate feature maps for each trip chain. The Liquefied Natural Gas (LNG) transportation trajectory data from Guangdong Province is selected to evaluate the effectiveness of the proposed methods. The experimental results demonstrate that the proposed framework can effectively identify trip chains and generate their corresponding feature maps. The trajectory imputation model achieved the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Dynamic Time Warping (DTW) of 2.34–3.33, 6.05–7.74, and 0.74–1.21, respectively, across different missing-rate scenarios, outperforming other benchmark models. The identification accuracy of dwell-point duration and location reaches 98.35%. The BERT-based method achieves a maximum accuracy of 92.83% in origin–destination (OD) point recognition, effectively capturing comprehensive trip-chain information. TAFEM accurately characterizes the spatiotemporal distribution and potential causal factors of personalized HazMat transportation safety risks, providing a reliable foundation for risk identification and safety management strategies. Full article

Generative adversarial networks (GANs) are increasingly applied to mitigate extreme class imbalance in intrusion detection systems, yet reported improvements often obscure role augmentation intensity and adversarial stability. This paper presents a controlled ablation study that isolates the impact of adversarial objective choice, augmentation ratio, and training duration on GAN-based minority data augmentation for highly imbalanced tabular cybersecurity data. Using the UWF-ZeekData22 dataset, nine MITRE ATT&CK tactic-versus-benign classification tasks are evaluated under augmentation ratios of 0.25 and 0.50 and training durations of 400 and 800 epochs. Four GAN variants—Vanilla GAN, Conditional GAN (cGAN), WGAN, and WGAN-GP—are assessed using stratified cross-validation and five classical classifiers representing diverse inductive biases. The results reveal consistent structural patterns. Moderate augmentation (r = 0.25) with controlled training (400 epochs) yields the most stable and reliable improvement in minority recall. Wasserstein-based objectives demonstrate superior stability under aggressive augmentation and prolonged training, while conditional GANs frequently exhibit recall collapse in ultra-sparse regimes. Increasing augmentation volume does not uniformly improve performance and may introduce distributional overlaps that degrade linear and margin-based classifiers. Tree-based classifiers remain largely invariant once sufficient minority density is achieved. These findings demonstrate that adversarial calibration is more important than architectural complexity for improving the detection of rare attacks. The study provides practical guidance for designing robust GAN-based augmentation pipelines under extreme cybersecurity class imbalance. Full article

With the rapid advancement of deep learning, data acquisition remains a persistent challenge, as model effectiveness heavily relies on the quality and quantity of training data. To address the difficulties of time-consuming and labor-intensive data collection, data augmentation techniques are commonly adopted. In this study, the proposed WACGAN-GP, a Generative Adversarial Network (GAN) architecture, serves as an effective data augmentation tool designed to augment training datasets and bolster model performance. This method integrates the advantages of the Auxiliary Classifier GAN and the Wasserstein GAN with gradient penalty to generate diverse and realistic samples. Experiments were conducted on three image datasets—MNIST, CIFAR-10, and a ship classification dataset—under limited training data conditions. By incorporating WACGAN-GP generated synthetic samples into the original training sets, classification performance was evaluated in both balanced and imbalanced scenarios. The results demonstrate that the proposed GAN-based approach significantly improves recognition accuracy and outperforms conventional augmentation methods, such as horizontal and vertical flipping. Full article

Industrial-Internet security faces a core challenge: improving detection accuracy for critical minority-class network attacks. The existing intrusion detection methods based on Conditional Generative Adversarial Nets (CGANs) aim to achieve data balance by reconstructing minority-class attack samples. However, they encounter problems such as generating deceptive samples, poor sample quality, vanishing gradients and difficulties in training. This paper proposes an intrusion detection method based on the Multi-Discriminator Conditional Classification Generative Adversarial Network (MDCCGAN), an improved variant of CGAN, which integrates multiple discriminators and an independent classifier into the traditional CGAN framework. The multiple discriminators reduce the probability of generating deceptive samples, the independent classifier decouples the classification loss to clarify the direction of gradient updates, and the introduction of the Wasserstein distance fundamentally addresses the gradient-vanishing problem. Experiments conducted on the NSL-KDD and UNSW-NB15 datasets demonstrate that the proposed method significantly improves the recall, F1-score and accuracy for minority-class attacks. Specifically, on the NSL-KDD dataset, the overall accuracy increases from 74% to 94%, and the F1-score for the extremely rare U2R attack surges from 0% to 77%. Similarly, on the UNSW-NB15 dataset, the accuracy reaches 88%, a 10% improvement over the baseline DNN, and the F1-scores for extreme minority attacks such as Analysis, Backdoor, and Worms improved to 97%, 62%, and 84%, respectively. These results confirm that our method effectively outperforms traditional generation models and common class-balancing methods. It provides reliable technical support for industrial-Internet security. Full article

To address the issues of sample imbalance, unstable generation quality, and insufficient feature extraction in spectrum anomaly signal detection under complex electromagnetic environments, this paper proposes a VAE-GANGP identification model that integrates a Variational Autoencoder (VAE) with a Gradient Penalty-based Generative Adversarial Network (GAN-GP). First, the VAE is employed to encode the original spectrum, generating structured latent features that follow a standard normal distribution. This replaces the random noise input in traditional GANs, significantly enhancing the semantic consistency of generated samples and training stability. Second, an adversarial training mechanism based on Wasserstein distance with gradient penalty (WGAN-GP) is introduced, effectively mitigating mode collapse and gradient vanishing, thereby improving the model’s capability to fit complex signal distributions. Furthermore, a multi-objective optimization function combining reconstruction error and adversarial loss is constructed, establishing an end-to-end integrated framework for feature learning, signal reconstruction, and anomaly discrimination. Experiments are conducted using a synthetic dataset comprising various modulation types and simulated environments with different signal-to-noise ratios for systematic validation. The results demonstrate that the spectrum data generated by VAE-GANGP closely matches the distribution of real signals. Under AWGN-dominated synthetic test conditions, the model achieves an anomaly detection accuracy of 98.1%. When evaluated under more realistic channel impairments (phase noise, multipath, impulsive interference), the model maintains competitive performance, outperforming existing methods and demonstrating promising potential for practical electromagnetic spectrum monitoring. Its performance significantly surpasses traditional detection methods and single deep learning models, providing a highly reliable and adaptive solution for spatial electromagnetic spectrum anomaly detection. Full article

Under the “dual carbon” goals, this study focuses on realizing energy exchange among multiple microgrids via shared energy storage to promote sustainable energy transition. Accordingly, a distributed robust optimwhichization strategy is proposed in this paper. Addressing the uncertainty of distributed renewable energy sources within microgrids, the scenario set generated by the Wasserstein generative adversarial network with gradient penalty and pruned by the K-means++ clustering algorithm serves as the initial renewable energy scenario for the distributed robust optimization set. Combining Nash theory, a cooperative game operation model is constructed. The benefit distribution model based on contribution factors ensures a fair benefit allocation scheme. The parallelizable column and constraint generation algorithm is employed to enhance computational efficiency. Case studies demonstrate that compared to scenes produced by other methods, the proposed model has the lowest alliance operating cost. It more effectively captures renewable energy uncertainty and lowers system operational costs. The respective efficiency improvement rates for each microgrid are as follows: 4.6%, 5.0%, and 4.1%, ensuring a fair profit distribution scheme. This study provides a technical reference for realizing the sustainable development of a multiple microgrid system, contributing to the global goal of low-carbon energy transition and sustainable development. Full article