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The purpose of this paper is to construct an evaluation system for AI painting software based on generative adversarial network (GAN) technology, which optimizes the performance of the related software in terms of functionality, ease of use, system performance, and safety. The results of the questionnaires are statistically analyzed. In addition, an exploratory factor analysis was conducted to extract the data of the study, which were ultimately used to calculate the weight and importance of each index using the fuzzy hierarchical analysis method. This study constructed an evaluation system for AI painting software based on GAN technology, including 16 indicators of functionality, 16 indicators of ease of use, 7 indicators of system performance, and 8 indicators of safety, respectively, whose alpha coefficients were 0.882, 0.962, 0.932, 0.932, and 0.932, respectively. In addition, the accumulated explanatory variances of their coefficients were 84.405%, 84.897%, 84.013%, 72.606%, 73.013%, and 72.606%, respectively. It is clear that the items included in each of the indicators are homogeneous, with a high degree of internal consistency. This paper suggests that the development of AI painting software focusing on functionality, ease of use, system performance, and safety can enhance the market competitiveness of the software. [ABSTRACT FROM AUTHOR]

Generative adversarial networks (GANs) have significantly advanced synthetic image generation, yet ensuring topological coherence remains a challenge. This paper introduces TopoSinGAN, a topology-aware extension of the SinGAN framework, designed to enhance the topological accuracy of generated images. TopoSinGAN incorporates a novel, differentiable topology loss function that minimizes terminal node counts along predicted segmentation boundaries, thereby addressing topological anomalies not captured by traditional losses. We evaluate TopoSinGAN using agricultural and dendrological case studies, demonstrating its capability to maintain boundary continuity and reduce undesired loop openness. A novel evaluation metric, Node Topology Clustering (NTC), is proposed to assess topological attributes independently of geometric variations. TopoSinGAN significantly improves topological accuracy, reducing NTC index values from 15.15 to 3.94 for agriculture and 14.55 to 2.44 for dendrology, compared to the baseline SinGAN. Modified FID evaluations also show improved realism, with lower FID scores: 0.1914 for agricultural fields compared to 0.2485 for SinGAN, and 0.0013 versus 0.0014 for dendrology. The topology loss enables end-to-end training with direct topological feedback. This new framework advances the generation of topologically accurate synthetic images, with applications in fields requiring precise structural representations, such as geographic information systems (GIS) and medical imaging. [ABSTRACT FROM AUTHOR]

Sewage discharge from outfalls significantly contaminates the environment. However, due to the unique characteristics of environmental policy, challenges such as data acquisition difficulties arise. This study introduces an enhanced approach by utilizing an improved Cycle GAN, the core function of which involves extrapolating a small sample to a large sample. An enhanced YOLOv5 model is used to focus on lightweight model construction and model performance enhancement. The proposed Cycle GAN incorporating Self-Attention and residual modules is suggested to tackle the problem of limited data at the outfall. Additionally, a refined YOLOv5 model (YOLOV5-BIFC) is proposed, integrating the C3Ghost module, BiFPN module, and CBAM attention mechanism to overcome low model recognition efficiency and large model size concerns. The research employs an augmented dataset for training and evaluates model performance using metrics such as mAP, F1 score, model size, and FPS. The results indicate that the YOLOV5-BIFC model has a size of 7.8 MB, representing a 44.2% reduction compared to the original YOLOv5 model. The FPS of this model is 26.7, enabling real-time discharge monitoring. The F1 score and mAP achieved by YOLOv5-BiFC are 89.8% and 87.3%. Comparative analysis with other neural networks demonstrates the superior accuracy and efficiency of the enhanced model. The YOLOv5-BiFC model facilitates precise, rapid, and intelligent discharge inspection. [ABSTRACT FROM AUTHOR]

Gravel soil faces significant engineering challenges such as leakage erosion and soil flow due to its complex composition and susceptibility to groundwater effects. This study integrates the entire machine learning process, including pre- and post-processing of images, WGAN implementation, and validation of hydraulic and morphological properties. Obtaining intact gravel soil samples is difficult and costly due to their erodible nature in the Li River, China. A μ-CT scanning series is employed to capture detailed images with three microstructural characteristics of gravel soil, forming the basis for training datasets using WGANs. This approach allows the generation of similar 3D realizations that replicate the microstructural characteristics and hydraulic behaviors of a prototype of gravel soils. Through computational fluid dynamics (CFD) simulations, the effectiveness of the realizations in hydraulic behavior within reconstructed porous structures is verified. This process indirectly validates the consistency between the realization′s microstructure and the prototype. This integrated methodology not only enhances understanding but also aids in the optimization of engineering designs and applications in geotechnical and materials science disciplines. [ABSTRACT FROM AUTHOR]

With the rapid development of intelligent transportation systems and information technology, the security of road traffic signal systems has increasingly attracted the attention of managers and researchers. This paper proposes a new method for detecting attacks on traffic signal systems based on game theory and Generative Adversarial Networks (GAN). First, a game theory model was used to analyze the strategic game between the attacker and the defender, revealing the diversity and complexity of potential attacks. A Bayesian game model was employed to calculate and analyze the attacker's choice of position. Then, leveraging the advantages of GAN, an adversarial training framework was designed. This framework can effectively generate attack samples and enhance the robustness of the detection model. Using empirical research, we simulated the mapping of real traffic data, road network data, and network attack data into a simulation environment to validate the effectiveness of this method. In a comparative experiment, we contrasted the method proposed in this paper with the traditional Support Vector Machine (SVM) algorithm, demonstrating that the model presented here can achieve efficient detection and recognition across various attack scenarios, with significantly better recall and F1 scores compared to traditional methods. Finally, this paper also discusses the application prospects of this method and its potential value in future intelligent transportation systems. [ABSTRACT FROM AUTHOR]

The monitoring of fog density is of great importance in meteorology and its applications in environment, aviation and transportation. Nowadays, vision-based fog estimation from images taken with surveillance cameras has made a great supplementary contribution to the scarcely traditional meteorological fog observation. In this paper, we propose a new Random Forest (RF) approach for image-based fog estimation. In order to reduce the impact of data imbalance on recognition, the StyleGAN2-ADA (generative adversarial network with adaptive discriminator augmentation) algorithm is used to generate virtual images to expand the data of low proportions. Key image features related to fog are extracted, and an RF method, integrated with the hierarchical and k-medoid clustering, is deployed to estimate the fog density. The experiment conducted in Sichuan in February 2024 shows that the improved RF model has achieved an average accuracy of fog density observation of 93%, 6.4% higher than the RF model without data expansion, 3–6% higher than the VGG16, the VGG19, the ResNet50, and the DenseNet169 with or without data expansion. What is more, the improved RF method exhibits a very good convergence as a cost-effective solution. [ABSTRACT FROM AUTHOR]

Data augmentation is commonly utilized to increase the size and diversity of training sets for deep learning tasks. In this study, we propose a novel application of an existing image generation approach in the domain of realistic eye images that leverages data collected from 40 subjects. This hybrid method combines the benefits of precise control over the image content provided by 3D rendering, while introducing the previously lacking photorealism and diversity into synthetic images through neural style transfer. We prove its general efficacy as a data augmentation tool for appearance-based gaze estimation when generated data are mixed with a sparse train set of real images. It improved the results for 39 out of 40 subjects, with an 11.22 % mean and a 19.75 % maximum decrease in gaze estimation error, achieving similar metrics for train and held-out subjects. We release our data repository of eye images with gaze labels used in this work for public access. [ABSTRACT FROM AUTHOR]

Voice conversion (VC) refers to the technique of modifying one speaker's voice to mimic another's while retaining the original linguistic content. This technology finds its applications in fields such as speech synthesis, accent modification, medicine, security, privacy, and entertainment. Among the various deep generative models used for voice conversion, including variational autoencoders (VAEs) and generative adversarial networks (GANs), diffusion models (DMs) have recently gained attention as promising methods due to their training stability and strong performance in data generation. Nevertheless, traditional DMs focus mainly on learning reconstruction paths like VAEs, rather than conversion paths as GANs do, thereby restricting the quality of the converted speech. To overcome this limitation and enhance voice conversion performance, we propose a cycle-consistent diffusion (CycleDiffusion) model, which comprises two DMs: one for converting the source speaker's voice to the target speaker's voice and the other for converting it back to the source speaker's voice. By employing two DMs and enforcing a cycle consistency loss, the CycleDiffusion model effectively learns both reconstruction and conversion paths, producing high-quality converted speech. The effectiveness of the proposed model in voice conversion is validated through experiments using the VCTK (Voice Cloning Toolkit) dataset. [ABSTRACT FROM AUTHOR]

Accurate pedestrian trajectory prediction is crucial in many fields. This requires the full use and learning of pedestrians' social interactions, movements, and environmental information. In view of the current research on pedestrian trajectory prediction, wherein most of the pedestrian interaction information is explored from the level of overall interaction, this paper proposes the SISGAN model, which designs a social interaction module from the perspective of the target pedestrian, and takes four kinds of interaction information as the influencing factors of pedestrian interaction, so as to describe the influence mechanism of pedestrian–pedestrian interaction. In addition, in terms of environmental information, the index density of pedestrian historical trajectory in space is taken into account in the extraction of environmental information, which increases the potential correlation between environmental information and pedestrians. Finally, we integrate social interaction information and environmental information and make the final trajectory prediction based on GAN. Experiments on ETH and UCY datasets demonstrate the effectiveness of the SISGAN model proposed in this paper. [ABSTRACT FROM AUTHOR]

The document discusses the importance of vibration monitoring in diagnosing faults in rotating machinery, emphasizing the role of signal processing and pattern analysis. Vibration monitoring involves measuring mechanical oscillations in machinery components to detect faults early. Signal processing techniques are used to extract meaningful information from raw vibration data, while pattern analysis techniques help diagnose specific faults. The integration of vibration monitoring with signal processing and pattern analysis offers benefits such as early fault detection, improved reliability, and data-driven maintenance decisions. Challenges include the complexity of machinery, variability of operating conditions, and the gap between research advancements and industrial adoption. The document also highlights the use of benchmark datasets for research and the application of machine learning in fault detection and machinery health monitoring. [Extracted from the article]

Developing solutions to reduce traffic accidents requires experimentation and much data. However, due to confidentiality issues, not all datasets used in previous research are publicly available, and those that are available may be insufficient for research. Building datasets with real data is costly. Given this reality, this paper proposes a procedure to generate synthetic data sequences of driving events using the Time series GAN (TimeGAN) and Real-world time series (RTSGAN) frameworks. First, a 15-feature driving event dataset is constructed with real data, which forms the basis for generating datasets using the two mentioned frameworks. The generated datasets are evaluated using the qualitative metrics PCA and T-SNE, as well as the discriminative and predictive score quantitative metrics defined in TimeGAN. The generated synthetic data are then used in an unsupervised algorithm to identify clusters representing vehicle crash risk levels. Next, the generated data are used in a supervised classification algorithm to predict risk level categories. Comparison results between the data generated by TimeGAN and RTSGAN show that the data generated by RTSGAN achieve better scores than the the data generated with TimeGAN. On the other hand, we demonstrate that the use of datasets trained with synthetic data to train a supervised classification model for predicting the level of accident risk can obtain accuracy comparable to that of models that use datasets with only real data in their training, proving the usefulness of the generated data. [ABSTRACT FROM AUTHOR]

When implementing outside-the-window (OTW) visuals in aviation tactical simulators, maintaining terrain image color consistency is critical for enhancing pilot immersion and focus. However, due to various environmental factors, inconsistent image colors in terrain can cause visual confusion and diminish realism. To address these issues, a color correction technique based on a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) is proposed. The proposed WGAN-GP model utilizes multi-scale feature extraction and Wasserstein distance to effectively measure and adjust the color distribution difference between the input image and the reference image. This approach can preserve the texture and structural characteristics of the image while maintaining color consistency. In particular, by converting Bands 2, 3, and 4 of the BigEarthNet-S2 dataset into RGB images as the reference image and preprocessing the reference image to serve as the input image, it is demonstrated that the proposed WGAN-GP model can handle large-scale remote sensing images containing various lighting conditions and color differences. The experimental results showed that the proposed WGAN-GP model outperformed traditional methods, such as histogram matching and color transfer, and was effective in reflecting the style of the reference image to the target image while maintaining the structural elements of the target image during the training process. Quantitative analysis demonstrated that the mid-stage model achieved a PSNR of 28.93 dB and an SSIM of 0.7116, which significantly outperforms traditional methods. Furthermore, the LPIPS score was reduced to 0.3978, indicating improved perceptual similarity. This approach can contribute to improving the visual elements of the simulator to enhance pilot immersion and has the potential to significantly reduce time and costs compared to the manual methods currently used by the Republic of Korea Air Force. [ABSTRACT FROM AUTHOR]

Featured Application: This article developed an improved InfoGAN wind–solar–load extreme scenario generation approach that can inform power system evaluation in extreme scenarios. In recent years, extreme events have frequently occurred, and the extreme uncertainty of the source-demand side of high-ratio renewable energy systems poses a great challenge to the safe operation of power systems. Accurately generating extreme scenarios related to the source-demand side under a high percentage of new power systems is vital for the safe operation of power systems and the assessment of their reliability. However, at this stage, methods for extreme scenario generation that fully consider the correlation between wind power, solar power, and load are lacking. To address these problems, this paper proposes a method for extreme scenario generation based on information-maximizing generative adversarial networks (InfoGANs) for high-proportion renewable power systems. The example analysis shows that the method for extreme scenario generation proposed in this paper can fully explore the correlation between historical wind–solar–load data, greatly improve the accuracy with which extreme scenarios are generated, and provide effective theories and methodologies for the safe operation of a new type of power system. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel approach to coordinate-based acoustic emission (AE) source localization to address the challenges of limited and imbalanced datasets from fiber-optic AE sensors used for structural health monitoring (SHM). We have developed a hybrid deep learning model combining four generative adversarial network (GAN) variants for data augmentation with an adapted inception neural network for regression-based prediction. The experimental setup features a single fiber-optic AE sensor based on a tightly coiled fiber-optic Fabry-Perot interferometer formed by two identical fiber Bragg gratings. AE signals were generated using the Hsu-Nielsen pencil lead break test on a grid-marked thin aluminum plate with 35 distinct locations, simulating real-world structural monitoring conditions in bounded isotropic plate-like structures. It is demonstrated that the single-sensor configuration can achieve precise localization, avoiding the need for a multiple sensor array. The GAN-based signal augmentation expanded the dataset from 900 to 4500 samples, with the Wasserstein distance between the original and synthetic datasets decreasing by 83% after 2000 training epochs, demonstrating the high fidelity of the synthetic data. Among the GAN variants, the standard GAN architecture proved the most effective, outperforming other variants in this specific application. The hybrid model exhibits superior performance compared to non-augmented deep learning approaches, with the median error distribution comparisons revealing a significant 50% reduction in prediction errors, accompanied by substantially improved consistency across various AE source locations. Overall, this developed hybrid approach offers a promising solution for enhancing AE-based SHM in complex infrastructures, improving damage detection accuracy and reliability for more efficient predictive maintenance strategies. [ABSTRACT FROM AUTHOR]

In response to the challenges of bearing fault diagnosis under small sample sizes and variable operating conditions, this paper proposes a novel method based on the two-dimensional analysis of vibration acceleration signals and a Multi-Task Conditional Generative Adversarial Network (MTC-GAN). This method first constructs two-dimensional images of vibration signals by leveraging the physical properties of the bearing acceleration signals and employs Local Binary Patterns (LBP) to extract subtle texture features from these images, thereby generating fault feature signatures with high discriminative power across different operating conditions. Subsequently, MTC-GAN is utilized for data augmentation, and the trained discriminator is used to perform fault classification tasks, improving classification accuracy under conditions with small sample sizes. Experimental results demonstrate that the proposed method achieves excellent fault diagnosis accuracy and robustness under both small sample sizes and varying operating conditions. Compared to traditional methods, this approach exhibits higher efficiency and reliability in handling complex operating conditions and data scarcity. [ABSTRACT FROM AUTHOR]

Ancient paintings, as a vital component of cultural heritage, encapsulate a profound depth of cultural significance. Over time, they often suffer from different degradation conditions, leading to damage. Existing ancient painting inpainting methods struggle with semantic discontinuities, blurred textures, and details in missing areas. To address these issues, this paper proposes a generative adversarial network (GAN)-based ancient painting inpainting method named RG-GAN. Firstly, to address the inconsistency between the styles of missing and non-missing areas, this paper proposes a Regional Attention-Style Transfer Module (RASTM) to achieve complex style transfer while maintaining the authenticity of the content. Meanwhile, a multi-scale fusion generator (MFG) is proposed to use the multi-scale residual downsampling module to reduce the size of the feature map and effectively extract and integrate the features of different scales. Secondly, a multi-scale fusion mechanism leverages the Multi-scale Cross-layer Perception Module (MCPM) to enhance feature representation of filled areas to solve the semantic incoherence of the missing region of the image. Finally, the Global Context Perception Discriminator (GCPD) is proposed for the deficiencies in capturing detailed information, which enhances the information interaction across dimensions and improves the discriminator's ability to identify specific spatial areas and extract critical detail information. Experiments on the ancient painting and ancient Huaniao++ datasets demonstrate that our method achieves the highest PSNR values of 34.62 and 23.46 and the lowest LPIPS values of 0.0507 and 0.0938, respectively. [ABSTRACT FROM AUTHOR]

When considering agricultural commodity transaction data, long sampling intervals or data sparsity may lead to small samples. Furthermore, training on small samples can lead to overfitting and makes it hard to capture the fine-grained fluctuations in the data. In this study, a multi-scale forecasting approach combined with a Generative Adversarial Network (GAN) and Temporal Convolutional Network (TCN) is proposed to address the problems related to small sample prediction. First, a Time-series Generative Adversarial Network (TimeGAN) is used to expand the multi-dimensional data and t-SNE is utilized to evaluate the similarity between the original and synthetic data. Second, a greedy algorithm is exploited to calculate the information gain, in order to obtain important features, based on XGBoost. Meanwhile, TCN residual blocks and dilated convolutions are used to tackle the issue of gradient disappearance. Finally, an attention mechanism is added to the TCN, which is beneficial in terms of improving the forecasting accuracy. Experiments are conducted on three products, garlic, ginger and chili. Taking garlic as an example, the RMSE of the proposed method was reduced by 1.7% and 1% when compared to the SVR and RF models, respectively. Its R 2 accuracy was also improved (by 4.3% and 3.4%, respectively). Furthermore, TCN-attention and TCN were found to require less time compared to GRU and LSTM. The accuracy of the proposed method increased by about 5% when compared to that without TimeGAN in the ablation study. Moreover, compared with TCN, the Gated Recurrent Unit (GRU), and the Long Short-term Memory (LSTM) model in the multi-scale price forecasting task, the proposed method can better utilize small samples and high-dimensional data, leading to improved performance. Additionally, the proposed model is compared to the Transformer and TimesNet models in terms of its accuracy, deployment cost, and other metrics. [ABSTRACT FROM AUTHOR]

As the fleet of high-speed rail vehicles expands, ensuring train safety is of the utmost importance, emphasizing the critical need to enhance the precision of axel temperature warning systems. Yet, the limited availability of data on the unique features of high thermal axis temperature conditions in railway systems hinders the optimal performance of intelligent algorithms in alarm detection models. To address these challenges, this study introduces a novel dynamic principal component analysis preprocessing technique for tolerance temperature data to effectively manage missing data and outliers. Furthermore, a customized generative adversarial network is devised to generate distinct data related to high thermal axis temperature, focusing on optimizing the network's objective functions and distinctions to bolster the efficiency and diversity of the generated data. Finally, an integrated model with an optimized transformer module is established to accurately classify alarm levels, provide a comprehensive solution to pressing train safety issues, and, in a timely manner, notify drivers and maintenance departments (DEPOs) of high-temperature warnings. [ABSTRACT FROM AUTHOR]

In practical working environments, rolling bearings are one of the components that are prone to failure. Their vibration signal samples are faced with challenges, mainly including the imbalance between normal and fault samples as well as an insufficient number of labeled samples. This study proposes a sample-expansion method based on generative adversarial networks (GANs) and a fault diagnosis method based on a transformer to solve the above issues. First, selective kernel networks (SKNets) and a genetic algorithm (GA) were introduced to construct a conditional variational autoencoder–evolutionary generative adversarial network with a selective kernel (CVAE-SKEGAN) to achieve a balance between the proportion of normal and faulty samples. Then, a semi-supervised learning–variational convolutional Swin transformer (SSL-VCST) network was built for the fault classification, specifically introducing variational attention and semi-supervised mechanisms to reduce the overfitting risk of the model and solve the problem of a shortage of labeled samples. Three typical operating conditions were designed for the multi-case applicability verification. The results show that the method proposed in this study had good application effects when solving both sample imbalances and labeled-sample deficiencies and improved the accuracy of fault diagnosis in the above scenarios. [ABSTRACT FROM AUTHOR]

In recent years, deep learning (DL) has garnered significant attention for its successful applications across various domains in solving complex problems. This interest has spurred the development of numerous neural network architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and the more recently introduced Transformers. The choice of architecture depends on the data characteristics and the specific task at hand. In the 1D domain, one-dimensional CNNs (1D CNNs) are widely used, particularly for tasks involving the classification and recognition of 1D signals. While there are many applications of 1D CNNs in the literature, the technical details of their training are often not thoroughly explained, posing challenges for those developing new libraries in languages other than those supported by available open-source solutions. This paper offers a comprehensive, step-by-step tutorial on deriving feedforward and backpropagation equations for 1D CNNs, applicable to both regression and classification tasks. By linking neural networks with linear algebra, statistics, and optimization, this tutorial aims to clarify concepts related to 1D CNNs, making it a valuable resource for those interested in developing new libraries beyond existing ones. [ABSTRACT FROM AUTHOR]

Deep learning techniques have flourished in recent years and have shown great potential in ground-penetrating radar (GPR) data interpretation. However, obtaining sufficient training data is a great challenge. This paper proposes an intelligent recognition method based on slicing-aided hyper inference (SAHI) for GPR images. Firstly, for the problem of insufficient samples of GPR images with structural loose distresses, data augmentation is carried out based on deep convolutional generative adversarial networks (DCGAN). Since distress features occupy fewer pixels on the original image, to allow the model to pay greater attention to the distress features, it is necessary to crop the original images centered on the distress labeling boxes first, and then input the cropped images into the model for training. Then, the YOLOv5 model is used for distress detection and the SAHI framework is used in the training and inference stages. The experimental results show that the detection accuracy is improved by 5.3% after adding the DCGAN-generated images, which verifies the effectiveness of the DCGAN-generated images. The detection accuracy is improved by 10.8% after using the SAHI framework in the training and inference stages, which indicates that SAHI is a key part of improving detection performance, as it significantly improves the ability to recognize distress. [ABSTRACT FROM AUTHOR]

Generating food images aims to convert textual food ingredients into corresponding images for the visualization of color and shape adjustments, dietary guidance, and the creation of new dishes. It has a wide range of applications, including food recommendation, recipe development, and health management. However, existing food image generation models, predominantly based on GANs (Generative Adversarial Networks), face challenges in maintaining semantic consistency between image and text, as well as achieving visual realism in the generated images. These limitations are attributed to the constrained representational capacity of sparse ingredient embedding and the lack of diversity in GAN-based food image generation models. To alleviate this problem, this paper proposes a food image generation network, named MLA-Diff, in which ingredient and image features are learned and integrated as ingredient-image pairs to generate initial images, and then image details are refined by using an attention fusion module. The main contributions are as follows: (1) The enhanced CLIP (Contrastive Language-Image Pre-Training) module is constructed by transforming sparse ingredient embedding into compact embedding and capturing multi-scale image features, providing an effective solution to alleviate semantic consistency issues. (2) The Memory module is proposed by embedding a pre-trained diffusion model to generate initial images with diversity and reality. (3) The attention fusion module is proposed by integrating features from diverse modalities to enhance the comprehension between ingredient and image features. Extensive experiments on the Mini-food dataset demonstrate the superiority of the MLA-Diff in terms of semantic consistency and visual realism, generating high-quality food images. [ABSTRACT FROM AUTHOR]

Procedural modeling offers significant advantages over traditional methods of geovisualizing 3D building models, particularly in its use of scripts or machine language for model description. This approach is highly suitable for computer processing and allows for the rapid rendering of entire building models and cities, especially when the buildings are not highly diverse, thus fully leveraging the strengths of procedural modeling. The first hypothesis is that buildings in the real world are mostly different and they should still be able to be displayed through procedural modeling procedures, and the second hypothesis is that this can be achieved in several ways. The first hypothesis suggests that real-world buildings, despite their diversity, can still be effectively represented through procedural modeling. The second hypothesis explores various methods to achieve this representation. The first approach involves recognizing the basic characteristics of a building from photographs and creating a model using machine learning. The second approach utilizes artificial intelligence (AI) to generate detailed building models based on comprehensive input data. A script is generated for each building, making reverse procedural modeling in combination with AI an intriguing field of study, which is explored in this research. To validate this method, we compare AI-generated building models with manually derived models created through traditional procedural modeling techniques. The research demonstrates that integrating AI and machine learning techniques with procedural modeling significantly improves the efficiency and accuracy of generating 3D building models. Specifically, the use of convolutional neural networks (CNNs) for image-to-geometry translation, and Generative Adversarial Networks (GANs) for texture generation, showed promising results in creating detailed and realistic 3D structures. This research is significant as it introduces a novel methodology that bridges the gap between traditional procedural modeling and modern AI-driven techniques. It offers a robust solution for automated 3D building modeling, potentially revolutionizing the fields of urban planning and architectural design by enabling more efficient and accurate digital representations of complex building geometries. [ABSTRACT FROM AUTHOR]

With the large-scale penetration of distributed generation (DG), the volatility problems of active distribution networks (ADNs) have become more prominent, which can no longer be met by traditional regulation means and need to be regulated by introducing flexible resources. Soft open points (SOP) and energy storage systems (ESS) can regulate the tidal currents on spatial and temporal scales, respectively, to improve the flexibility of ADN. To this end, in-depth consideration of DG admission is given to establish flexibility assessment indicators from the power side of ADN. The conditional deep convolution generative adversarial network (C-DCGAN) is used to generate the output scenario of DG. On this basis, the SOP and ESS two-layer planning models, which take account of the potential for improvement in the flexibility of ADN, are constructed. Among them, the upper layer is the site selection and volume determination layer, which considers the economy of the system with the optimization objective of minimizing the annual integrated cost; the lower layer is the operation optimization layer, which considers the flexibility of the system and takes the highest average daily flexibility level as the optimization objective. The planning model is solved using genetic algorithm-particle swarm optimization (GA-PSO) and second-order cone programming (SOCP). The case analysis verifies the rationality and effectiveness of the planning model. [ABSTRACT FROM AUTHOR]

The integration of advanced image analysis using artificial intelligence (AI) is pivotal for the evolution of autonomous vehicles (AVs). This article provides a thorough review of the most significant datasets and latest state-of-the-art AI solutions employed in image analysis for AVs. Datasets such as Cityscapes, NuScenes, CARLA, and Talk2Car form the benchmarks for training and evaluating different AI models, with unique characteristics catering to various aspects of autonomous driving. Key AI methodologies, including Convolutional Neural Networks (CNNs), Transformer models, Generative Adversarial Networks (GANs), and Vision Language Models (VLMs), are discussed. The article also presents a comparative analysis of various AI techniques in real-world scenarios, focusing on semantic image segmentation, 3D object detection, vehicle control in virtual environments, and vehicle interaction using natural language. Simultaneously, the roles of multisensor datasets and simulation platforms like AirSim, TORCS, and SUMMIT in enriching the training data and testing environments for AVs are highlighted. By synthesizing information on datasets, AI solutions, and comparative performance evaluations, this article serves as a crucial resource for researchers, developers, and industry stakeholders, offering a clear view of the current landscape and future directions in autonomous vehicle image analysis technologies. [ABSTRACT FROM AUTHOR]

Generative adversarial networks (GANs) create images by pitting a generator (G) against a discriminator (D) network, aiming to find a balance between the networks. However, achieving this balance is difficult because G is trained based on just one value representing D's prediction, and only D can access image features. We introduce a novel approach for training GANs using explainable artificial intelligence (XAI) to enhance the quality and diversity of generated images in histopathological datasets. We leverage XAI to extract feature information from D and incorporate it into G via the loss function, a unique strategy not previously explored in this context. We demonstrate that this approach enriches the training with relevant information and promotes improved quality and more variability in the artificial images, decreasing the FID by up to 32.7% compared to traditional methods. In the data augmentation task, these images improve the classification accuracy of Transformer models by up to 3.81% compared to models without data augmentation and up to 3.01% compared to traditional GAN data augmentation. The Saliency method provides G with the most informative feature information. Overall, our work highlights the potential of XAI for enhancing GAN training and suggests avenues for further exploration in this field. [ABSTRACT FROM AUTHOR]

The utilization of deep learning algorithms for side-scan sonar target detection is impeded by the restricted quantity and representativeness of side-scan sonar (SSS) samples. To address this issue, this paper proposes a method for image augmentation using a CC-WGAN network. First, the generator incorporates the Convolutional Block Attention Module (CBAM) to enhance the assimilation of global information and local features in the input images. This integration also improves stability and avoids mode collapse problems associated with the original Generative Adversarial Network. Subsequently, the CBAM is incorporated into the discriminator to facilitate a better understanding of the relevance and significance of input data, thereby enhancing the model's generalization ability. Finally, based on this model, existing few-sample SSS images are augmented, and we utilize the augmented images for discrimination and detection with YOLOv5. The experimental results show that following training with the SSS dataset that is augmented by this network, the accuracy of target detection increased by 7.6%, validating the feasibility of our proposed method. This method presents a novel solution to the problem of low model accuracy in underwater target detection with side-scan sonar due to limited samples. [ABSTRACT FROM AUTHOR]

Addressing the challenge of limited samples arising from the difficulty and high cost of pavement crack, image collecting and labeling, along with the inadequate ability of traditional data augmentation methods to enhance sample feature space, we propose VDCrackGAN, a generative adversarial network combining VAE and DCGAN, specifically tailored for pavement crack data augmentation. Furthermore, spectral normalization is incorporated to enhance the stability of network training, and the self-attention mechanism Swin Transformer is integrated into the network to further improve the quality of crack generation. Experimental outcomes reveal that in comparison to the baseline DCGAN, VDCrackGAN achieves notable improvements of 13.6% and 26.4% in the Inception Score (IS) and Fréchet Inception Distance (FID) metrics, respectively. [ABSTRACT FROM AUTHOR]

The goal of image-to-image translation (I2I) is to translate images from one domain to another while maintaining the content representations. A popular method for I2I translation involves the use of a reference image to guide the transformation process. However, most architectures fail to maintain the input's main characteristics and produce images that are too similar to the reference during style transfer. In order to avoid this problem, we propose a novel architecture that is able to perform source-coherent translation between multiple domains. Our goal is to preserve the input details during I2I translation by weighting the style code obtained from the reference images before applying it to the source image. Therefore, we choose to mask the reference images in an unsupervised way before extracting the style from them. By doing so, the input characteristics are better maintained while performing the style transfer. As a result, we also increase the diversity in the generated images by extracting the style from the same reference. Additionally, adaptive normalization layers, which are commonly used to inject styles into a model, are substituted with an attention mechanism for the purpose of increasing the quality of the generated images. Several experiments are performed on the CelebA-HQ and AFHQ datasets in order to prove the efficacy of the proposed system. Quantitative results measured using the LPIPS and FID metrics demonstrate the superiority of the proposed architecture compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Generative Adversarial Networks (GANs) have achieved remarkable success in various tasks, including image generation, editing, and reconstruction, as well as in unsupervised and representation learning. Despite their impressive capabilities, GANs are often plagued by challenges such as unstable training dynamics and limitations in generating complex patterns. To address these challenges, we propose a novel image style transfer method, named C3GAN, which leverages CycleGAN architecture to achieve consistent and stable transformation of image style. In this context, "image style" refers to the distinct visual characteristics or artistic elements, such as the color schemes, textures, and brushstrokes that define the overall appearance of an image. Our method incorporates cyclic consistency, ensuring that the style transformation remains coherent and visually appealing, thus enhancing the training stability and overcoming the generative limitations of traditional GAN models. Additionally, we have developed a robust and efficient image style transfer system by integrating Flask for web development and MySQL for database management. Our system demonstrates superior performance in transferring complex styles compared to existing model-based approaches. This paper presents the development of a comprehensive image style transfer system based on our advanced C3GAN model, effectively addressing the challenges of GANs and expanding application potential in domains such as artistic creation and cinematic special effects. [ABSTRACT FROM AUTHOR]

The energy consumption of buildings presents a significant concern, which has led to a demand for materials with better thermal performance. Thermal conductivity (TC), among the most relevant thermal properties, is essential to address this demand. This study introduces a methodology integrating a Multilayer Perceptron (MLP) and a Generative Adversarial Network (GAN) to predict the TC of concrete based on its mass composition and density. Three scenarios using experimental data from published papers and synthetic data are compared and reveal the model's outstanding performance across training, validation, and test datasets. Notably, the MLP trained on the GAN-augmented dataset outperforms the one with the real dataset, demonstrating remarkable consistency between the model's predictions and the actual values. Achieving an RMSE of 0.0244 and an R2 of 0.9975, these outcomes can offer precise quantitative information and advance energy-efficient materials. [ABSTRACT FROM AUTHOR]

Featured Application: The importance of generative adversarial networks (GANs) in economics is growing and is driven by successes in other fields. Many economic problems could benefit from GANs, although few studies exist and progress is needed. This paper argues for the use of GANs as a novel and effective tool in economics. An important issue is the need for large datasets, where traditional techniques fall short due to a multiplicity of problems, making the use of GANs very useful in this task. Generative adversarial networks (GANs) have become a recent and rapidly developing research topic in machine learning. Since their inception in 2014, a significant number of variants have been proposed to address various topics across many fields, and they have particularly excelled not only in image and language processing but also in the medical and data science domains. In this paper, we aim to highlight the significance of and advancements that these GAN models can introduce in the field of Business Economics, where they have yet to be fully developed. To this end, a review of the literature of GANs is presented in general together with a more specific review in the field of Business Economics, for which only a few papers can be found. Furthermore, the most relevant papers are analysed in order to provide approaches for the opportunity to research GANs in the field of Business Economics. [ABSTRACT FROM AUTHOR]

Formulating a mix design for 3D concrete printing (3DCP) is challenging, as it involves an iterative approach, wasting a lot of resources, time, and effort to optimize the mix for strength and printability. A potential solution is mix formulation through artificial intelligence (AI); however, being a new and emerging field, the open-source availability of datasets is limited. Limited datasets significantly restrict the predictive performance of machine learning (ML) models. This research explores data augmentation techniques like deep generative adversarial network (DGAN) and bootstrap resampling (BR) to increase the available data to train three ML models, namely support vector machine (SVM), artificial neural network (ANN), and extreme gradient boosting regression (XGBoost). Their performance was evaluated using R2, MSE, RMSE, and MAE metrics. Models trained on BR-augmented data showed higher accuracy than those trained on the DGAN-augmented data. The BR-trained XGBoost exhibited the highest R2 scores of 0.982, 0.970, 0.972, 0.971, and 0.980 for cast compressive strength, printed compressive strength direction 1, 2, 3, and slump flow respectively. The proposed method of predicting the slump flow (mm), cast, and anisotropic compressive strength (MPa) can effectively predict the mix design for printable concrete, unlocking its full potential for application in the construction industry. [ABSTRACT FROM AUTHOR]

Three-dimensional reconstruction based on optical satellite images has always been a research hotspot in the field of photogrammetry. In particular, the 3D reconstruction of building areas has provided great help for urban planning, change detection and emergency response. The results of 3D reconstruction of satellite images are greatly affected by the input images, and this paper proposes an improvement method for 3D reconstruction of satellite images based on the generative adversarial network (GAN) image enhancement. In this method, the perceptual loss function is used to optimize the network, so that it can output high-definition satellite images for 3D reconstruction, so as to improve the completeness and accuracy of the reconstructed 3D model. We use the public benchmark dataset of satellite images to test the feasibility and effectiveness of the proposed method. The experiments show that compared with the satellite stereo pipeline (S2P) method and the bundle adjustment (BA) method, the proposed method can automatically reconstruct high-quality 3D point clouds. [ABSTRACT FROM AUTHOR]

Computer vision has advanced much in recent years. Several tasks, such as image recognition, classification, or image restoration, are regularly solved with applications using artificial intelligence techniques. Image restoration comprises different use cases such as style transferring, improvement of quality resolution, or completing missing parts. The latter is also known as image inpainting, virtual image inpainting in this case, which consists of reconstructing missing regions or elements. This paper explores how to evaluate the performance of a deep learning method to do virtual image inpainting to reconstruct missing architectonical elements in images of ruined Greek temples to measure the performance of different activation functions. Unlike a previous study related to this work, a direct reconstruction process without segmented images was used. Then, two evaluation methods are presented: the objective one (mathematical metrics) and an expert (visual perception) evaluation to measure the performance of the different approaches. Results conclude that ReLU outperforms other activation functions, while Mish and Leaky ReLU perform poorly, and Swish's professional evaluations highlight a gap between mathematical metrics and human visual perception. [ABSTRACT FROM AUTHOR]

In medical imaging, access to data is commonly limited due to patient privacy restrictions, and it can be difficult to acquire enough data in the case of rare diseases. The purpose of this investigation was to develop a reusable open-source synthetic image-generation pipeline, the GAN Image Synthesis Tool (GIST), that is easy to use as well as easy to deploy. The pipeline helps to improve and standardize AI algorithms in the digital health space by generating high quality synthetic image data that is not linked to specific patients. Its image generation capabilities include the ability to generate imaging of pathologies or injuries with low incidence rates. This improvement of digital health AI algorithms could improve diagnostic accuracy, aid in patient care, decrease medicolegal claims, and ultimately decrease the overall cost of healthcare. The pipeline builds on existing Generative Adversarial Networks (GANs) algorithms, and preprocessing and evaluation steps were included for completeness. For this work, we focused on ensuring the pipeline supports radiography, with a focus on synthetic knee and elbow X-ray images. In designing the pipeline, we evaluated the performance of current GAN architectures, studying the performance on available X-ray data. We show that the pipeline is capable of generating high-quality and clinically relevant images based on a lay person's evaluation and the Fréchet Inception Distance (FID) metric. [ABSTRACT FROM AUTHOR]

Surface defect inspection based on deep learning has demonstrated outstanding performance in improving detection accuracy and model generalization. However, the small scale of defect datasets always limits the application of deep models in industry. Generative models can obtain realistic samples in a very cheap way, which can effectively solve this problem and thus has received widespread attention in recent years. This paper provides a comprehensive analysis and summary of the current studies of surface defect inspection methods proposed between 2022 and 2024. First, according to the use of generative models, these methods are classified into four categories: Variational Auto-Encoders (VAEs), Generative Adversarial Networks (GANs), Diffusion Models (DMs), and multi-models. Second, the research status of surface defect inspection based on generative models in recent years is discussed from four aspects: sample generation, detection objective, inspection task, and learning model. Then, the public datasets and evaluation metrics that are commonly used for surface defect inspection are discussed, and a comparative evaluation of defect inspection methods based on generative models is provided. Finally, this study discusses the existing challenges for the defect inspection methods based on generative models, providing insights for future research. [ABSTRACT FROM AUTHOR]

In this study, we introduce an advanced system for sustainable pavement management that leverages cutting-edge machine learning and computer vision techniques to detect and classify pavement damage. By utilizing models such as EfficientNetB3, ResNet18, and ResNet50, we develop robust classifiers capable of accurately identifying various types of pavement distress. To further enhance our dataset, we employ a Swin Transformer-based Generative Adversarial Network (GAN) to synthetically generate images of pavement cracks, thereby augmenting the training data. Our approach aims to improve the efficiency and accuracy of pavement damage assessment, contributing to more effective and sustainable road maintenance practices. This research aligns with the sustainable development goals by fostering innovative methods that extend the lifespan of infrastructure, reducing the need for resource-intensive repairs, and promoting the longevity and reliability of road networks. The outcomes of this study are discussed in terms of their potential impact on infrastructure safety and sustainability, with suggestions for future research directions. This study demonstrates how integrating advanced machine learning techniques into pavement management systems can enhance decision-making, optimize resource allocation, and improve the sustainability of infrastructure maintenance practices. By leveraging big data and sophisticated algorithms, stakeholders can proactively address pavement deterioration, extend asset lifespan, and optimize maintenance efforts based on real-time data-driven insights. [ABSTRACT FROM AUTHOR]

Featured Application: Image generation of steel microstructures, enhancing materials research, quality control, and education by providing efficient and accurate visual representations without extensive reliance on traditional microscopy. Characterizing the microstructures of steel subjected to heat treatments is crucial in the metallurgical industry for understanding and controlling their mechanical properties. In this study, we present a novel approach for generating images of steel microstructures that mimic those obtained with optical microscopy, using the deep learning technique of generative adversarial networks (GAN). The experiments were conducted using different hyperparameter configurations, evaluating the effect of these variations on the quality and fidelity of the generated images. The obtained results show that the images generated by artificial intelligence achieved a resolution of 512 × 512 pixels and closely resemble real microstructures observed through conventional microscopy techniques. A precise visual representation of the main microconstituents, such as pearlite and ferrite in annealed steels, was achieved. However, the performance of GANs in generating images of quenched steels with martensitic microstructures was less satisfactory, with the synthetic images not fully replicating the complex, needle-like features characteristic of martensite. This approach offers a promising tool for generating steel microstructure images, facilitating the visualization and analysis of metallurgical samples with high fidelity and efficiency. [ABSTRACT FROM AUTHOR]

The rise of comics and games has led to increased artistic processing of portrait photos. With growing commercial demand and advancements in deep learning, neural networks for rapid facial style transfer have become a key research area in computer vision. This involves converting face photos into different styles while preserving content. Face images are more complex than regular images, requiring extensive modification. However, current methods often face issues such as unnatural color transitions, loss of detail in highlighted areas, and noticeable artifacts along edges, resulting in low-quality stylized images. In this study, an enhanced generative adversarial network (GAN) is proposed, which is based on Adaptive Layer Instance Normalization (AdaLIN) + Laplacian. This approach incorporates the AdaLIN normalization method, allowing for the dynamic adjustment of Instance Normalization (IN) and Layer Normalization (LN) parameters' weights during training. By combining the strengths of both normalization techniques, the model selectively preserves and alters content information to some extent, aiming to strike a balance between style and content. This helps address problems such as unnatural color transitions and loss of details in highlights that lead to color inconsistencies. Furthermore, the introduction of a Laplacian regularization term aids in denoising the image, preventing noise features from interfering with the color transfer process. This regularization also helps reduce color artifacts along the face's edges caused by noise while maintaining the image's contour information. These enhancements significantly enhance the quality of the generated face images. To compare our method with traditional CycleGAN and recent algorithms such as XGAN and CariGAN, both subjective and objective evaluations were conducted. Subjectively, our method demonstrates more natural color transitions and superior artifact elimination, achieving higher scores in Mean Opinion Score (MOS) evaluations. Objectively, experiments using our method yielded better scores across three metrics: FID, SSIM, and MS-SSIM. The effectiveness of the proposed methods is validated through both objective and subjective evaluations. [ABSTRACT FROM AUTHOR]

In response to the challenges of high noise, high adhesion, and a low signal-to-noise ratio in microscopic cell images, as well as the difficulty of existing deep learning models such as UNet, ResUNet, and SwinUNet in segmenting images with clear boundaries and high-resolution, this study proposes a CellGAN semantic segmentation method based on a generative adversarial network with a Feature Completion Mechanism. This method incorporates a Transformer to supplement long-range semantic information. In the self-attention module of the Transformer generator, bilinear interpolation for feature completion is introduced, reducing the computational complexity of self-attention to O(n). Additionally, two-dimensional relative positional encoding is employed in the self-attention mechanism to supplement positional information and facilitate position recovery. Experimental results demonstrate that this method outperforms ResUNet and SwinUNet in segmentation performance on rice leaf cell, MuNuSeg, and Nucleus datasets, achieving up to 23.45% and 19.90% improvements in the Intersection over Union and Similarity metrics, respectively. This method provides an automated and efficient analytical tool for cell biology, enabling more accurate segmentation of cell images, and contributing to a deeper understanding of cellular structure and function. [ABSTRACT FROM AUTHOR]

Natural language-processing tasks have been improved greatly by large language models (LLMs). However, numerous parameters make their execution computationally expensive and difficult on resource-constrained devices. For this problem, as well as maintaining accuracy, some techniques such as distillation and quantization have been proposed. Unfortunately, current methods fail to integrate model pruning with downstream tasks and overlook sentence-level semantic modeling, resulting in reduced efficiency of distillation. To alleviate these limitations, we propose a novel distilled lightweight model for BERT named MicroBERT. This method can transfer the knowledge contained in the "teacher" BERT model to a "student" BERT model. The sentence-level feature alignment loss (FAL) distillation mechanism, guided by Mixture-of-Experts (MoE), captures comprehensive contextual semantic knowledge from the "teacher" model to enhance the "student" model's performance while reducing its parameters. To make the outputs of "teacher" and "student" models comparable, we introduce the idea of a generative adversarial network (GAN) to train a discriminator. Our experimental results based on four datasets show that all steps of our distillation mechanism are effective, and the MicroBERT (101.14%) model outperforms TinyBERT (99%) by 2.24% in terms of average distillation reductions in various tasks on the GLUE dataset. [ABSTRACT FROM AUTHOR]

Featured Application: This study's findings hold significant implications for enhancing data privacy and utility in healthcare analytics. By evaluating synthetic data generation methods like CTGAN, TVAE, CopulaGAN and Copula across diverse medical datasets containing sensitive patient information, such as genetic profiles and medical histories, the research aims to improve the development of predictive models without compromising patient privacy. The generation of synthetic data holds significant promise for augmenting limited datasets while avoiding privacy issues, facilitating research, and enhancing machine learning models' robustness. Generative Adversarial Networks (GANs) stand out as promising tools, employing two neural networks—generator and discriminator—to produce synthetic data that mirrors real data distributions. This study evaluates GAN variants (CTGAN, CopulaGAN), a variational autoencoder, and copulas on diverse real datasets of different complexity encompassing numerical and categorical attributes. The results highlight CTGAN's sensitivity to training parameters and TVAE's robustness across datasets. Scalability challenges persist, with GANs demanding substantial computational resources. TVAE stands out for its high utility across all datasets, even for high-dimensional data, though it incurs higher privacy risks, which is indicative of the curse of dimensionality. While no single model universally excels, understanding the trade-offs and leveraging model strengths can significantly enhance synthetic data generation (SDG). Future research should focus on adaptive learning mechanisms, scalability enhancements, and standardized evaluation metrics to advance SDG methods effectively. Addressing these challenges will foster broader adoption and application of synthetic data. [ABSTRACT FROM AUTHOR]

This paper presents a method of pitting failure detection in toothed gears based on the reconstruction of the gear case vibrational signal. The effectiveness of the proposed method was tested in an experiment on a power circulation test stand. The autoencoder deep neural network architecture, semi-supervised training, and validation, along with the latent data convex hull-based clustering, are presented. The proposed method offers high efficiency (0.99 F1-measure) in gear state prediction (100% in failure detection, 98.9% in normal state prediction) and provides more capabilities in terms of generalization in comparison with linear machine learning techniques such as principal component analysis and nonlinear like the generative adversarial network. Moreover, it is distinguished by high sensitivity while also being able to detect even slight surface damage (initial pitting). These findings will be of particular relevance to a range of scientists and practitioners working with gear drives who are willing to implement machine learning in signal processing and diagnosis. [ABSTRACT FROM AUTHOR]

Although deep learning has been proven to significantly outperform most traditional methods in the classification of large-scale balanced image datasets, collecting enough samples for defect classification is extremely time-consuming and costly. In this paper, we propose a lightweight defect classification method based on few-shot image generation and self-attention fused convolution features. We constructed a 4-class dataset using welding seam images collected from a solar cell module packaging production line. To address the issue of limited defect samples, especially for classes with less than 10 images, we implemented two strategies. Geometric enhancement techniques were first used to extend the defective images. Secondly, multi-scale feature fusion Generative Adversarial Networks (GANs) were utilized to further enhance the dataset. We then performed the feature-level fusion of convolution neural networks and self-attention networks, achieving a classification accuracy of 98.19%. Our experimental results demonstrate that the proposed model performs well in small sample defect classification tasks. And, it can be effectively applied to product quality inspection tasks in industrial production lines. [ABSTRACT FROM AUTHOR]

With the development of computer technology, landslide recognition based on machine learning methods has been widely applied in geological disaster management and research. However, in landslide identification, the problems of an insufficient number of samples and an imbalance of samples are often ignored; that is, landslide samples are much smaller than non-landslide samples. In order to solve this problem, taking the main urban area of Lanzhou City as an example, this paper proposes to construct a semi-supervised generated countermeasure network (SSGAN) model, which aims to achieve high performance with a limited number of labeled samples for precise landslide identification, and to help prevent and reduce the harm caused by disasters. In order to express the environmental characteristics of landslide development and the optical texture features of landslide occurrence, the study constructs three sets of samples to represent landslide features, including a landslide influencing factor sample set, a Sentinel-2A optical remote sensing sample set, a joint influencing factor and Sentinel-2A sample set. The three kinds of sample sets are transferred to SSGAN for training to form a comparative study. The results show that the joint sample set has excellent feature results in discriminator and generator. Through the experimental comparison, the model proposed in this paper is compared with the model without semi-supervised generated confrontation training. The experimental results show that the proposed method is better than the unsupervised adversarial learning model in terms of accuracy, F1 score, Kappa coefficient, and MIoU. A total of 160 landslides have been identified in the study area, with a total area of 10.328 km2, with an accuracy rate of 83%. Therefore, the generated results are accurate and reliable, and show that SSGAN can better distinguish landslides from non-landslides in an image, under the condition of obtaining a large number of unmarked environmental features; enhance the effect of landslide classification in complex geographical environment; and then put forward effective suggestions for the prevention and control of landslides and geological disasters in the main urban area of Lanzhou. [ABSTRACT FROM AUTHOR]

The performance of facial recognition systems significantly decreases when faced with a lack of training images. This issue is exacerbated when there is only one image per subject available. Probe images may contain variations such as illumination, expression, and disguise, which are difficult to recognize accurately. In this work, we present a model that generates six facial variations from a single neutral face image. Our model is based on a CGAN, designed to produce six highly realistic facial expressions from one neutral face image. To evaluate the accuracy of our approach comprehensively, we employed several pre-trained models (VGG-Face, ResNet-50, FaceNet, and DeepFace) along with a custom CNN model. Initially, these models achieved only about 76% accuracy on single-sample neutral images, highlighting the SSPP challenge. However, after fine-tuning on the synthetic expressions generated by our CGAN from these single images, their accuracy increased significantly to around 99%. Our method has proven highly effective in addressing SSPP issues, as evidenced by the significant improvement achieved. [ABSTRACT FROM AUTHOR]

Due to the multi-scale and spectral features of remote sensing images compared to natural images, there are significant challenges in super-resolution reconstruction (SR) tasks. Networks trained on simulated data often exhibit poor reconstruction performance on real low-resolution (LR) images. Additionally, compared to natural images, remote sensing imagery involves fewer high-frequency components in network construction. To address the above issues, we introduce a new high–low-resolution dataset GF_Sen based on GaoFen-2 and Sentinel-2 images and propose a cascaded network CSWGAN combined with spatial–frequency features. Firstly, based on the proposed self-attention GAN (SGAN) and wavelet-based GAN (WGAN) in this study, the CSWGAN combines the strengths of both networks. It not only models long-range dependencies and better utilizes global feature information, but also extracts frequency content differences between different images, enhancing the learning of high-frequency information. Experiments have shown that the networks trained based on the GF_Sen can achieve better performance than those trained on simulated data. The reconstructed images from the CSWGAN demonstrate improvements in the PSNR and SSIM by 4.375 and 4.877, respectively, compared to the relatively optimal performance of the ESRGAN. The CSWGAN can reflect the reconstruction advantages of a high-frequency scene and provides a working foundation for fine-scale applications in remote sensing. [ABSTRACT FROM AUTHOR]

The increasing range of faults encountered by mechanical systems has brought great challenges for conducting intelligent fault diagnosis based on insufficient samples, in recent years. To tackle the issue of unbalanced samples, an improved methodology based on a generative adversarial network that uses sample generation and classification is proposed. First, 1D vibration signals are transformed into 2D images considering the features of the vibrating signals. Next, the optimized generation adversarial network is constructed for adversarial training to synthesize diverse fake 2D images according to actual sample characteristics with the generative model as a generator and the discriminative model as a discriminator. Our model uses an attenuated learning rate with a cross-iteration batch normalization layer to enhance the validity of the generator. Last, the discriminative model as a classifier is used to identify the fault states. The experimental results demonstrate that the proposed strategy efficiently improves fault identification accuracy in the two cases of sample imbalance. [ABSTRACT FROM AUTHOR]

Featured Application: The neural network proposed allows for the removal of the CT simulation from the clinical workflow, paving the way for fast-track radiotherapy. Artificial Intelligence (AI) is revolutionising many aspects of radiotherapy (RT), opening scenarios that were unimaginable just a few years ago. The aim of this study is to propose a Deep Leaning (DL) approach able to quickly generate synthetic Computed Tomography (CT) images from low-dose Cone Beam CT (CBCT) acquired on a modern linear accelerator integrating AI. Methods: A total of 53 patients treated in the pelvic region were enrolled and split into training (30), validation (9), and testing (14). A Generative Adversarial Network (GAN) was trained for 200 epochs. The image accuracy was evaluated by calculating the mean and mean absolute error (ME and ME) between sCT and CT. RT treatment plans were calculated on CT and sCT images, and dose accuracy was evaluated considering Dose Volume Histogram (DVH) and gamma analysis. Results: A total of 4507 images were selected for training. The MAE and ME values in the test set were 36 ± 6 HU and 7 ± 6 HU, respectively. Mean gamma passing rates for 1%/1 mm, 2%/2 mm, and 3%/3 mm tolerance criteria were respectively 93.5 ± 3.4%, 98.0 ± 1.3%, and 99.2 ± 0.7%, with no difference between curative and palliative cases. All the DVH parameters analysed were within 1 Gy of the difference between sCT and CT. Conclusion: This study demonstrated that sCT generation using the DL approach is feasible on low-dose CBCT images. The proposed approach can represent a valid tool to speed up the online adaptive procedure and remove CT simulation from the RT workflow. [ABSTRACT FROM AUTHOR]