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Application of Artificial Intelligence Virtual Image Technology in Photography Art Creation Under Deep Learning

IEEE ACCESS 2025年 13卷 14542-14556页

作者： Yao, Qiong Mahasarakham Univ Fac Fine Appl Arts & Cultural Sci Maha Sarakham 44150 Thailand

With the continuous advancement of artificial intelligence (AI) and deep learning technologies, virtual image generation exhibits significant potential for application in photographic art creation. The primary objective of this study is to investigate the use of AI virtual image technology in photography, particularly focusing on achieving creative expression and artistic style transfer through deep learning models. Consequently, this study proposes a novel model that integrates conditional generative adversarial networks (cGANs) with variational autoencoders (VAEs). This model aims to effectively address the challenges associated with image generation and style conversion in photographic art by leveraging the realistic generation capabilities of cGANs alongside the diversity maintenance features of VAEs. In the experimental section, the proposed cGANs + VAEs model is systematically compared with traditional Deep Convolutional GANs (DCGAN) and Pix2Pix models through empirical analysis. The experimental results indicate that the cGANs + VAEs model significantly outperforms traditional models in terms of image quality, artistic expression, and user satisfaction. Expert reviews further confirm the model's superiority in artistic style imitation and creative generation. Additionally, user surveys reveal that most participants are highly satisfied with the images generated by the model, particularly regarding artistic perception and visual effects. Moreover, the cGANs + VAEs model demonstrates strong performance in Frechet Inception Distance (FID) and Inception Score (IS) across multiple datasets, yielding FID values of 13.67, 9.45, and 11.90 on the COCO, CelebA, and WikiArt datasets, respectively. In summary, the proposed cGANs + VAEs model not only achieves remarkable advancements in the technical performance of image generation but also exhibits considerable potential for practical applications in photographic art creation.

关键词： Image synthesis Deep learning Art Artificial intelligence Photography Training Diversity reception Data models Analytical models Translation conditional generative adversarial networks variational autoencoder photography artistic creation virtual image technology

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Asynchronous multimodal PINN pre-train framework based on TransVNet(MPP-TV) and its application in numerical solutions of the Cauchy problem for the Hamilton-Jacobi equation

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2025年 187卷 203-230页

作者： Chen, Tianhao Li, Zeyu Xu, Pengbo Zheng, Haibiao East China Normal Univ Sch Math Sci Key Lab MEA Shanghai Key Lab PMMPMinist Educ Shanghai 200241 Peoples R China Sanda Univ Innovat Res Ctr AI Finance Shanghai 201209 Peoples R China Shanghai Zhangjiang Inst Math Shanghai 201203 Peoples R China

The Hamilton-Jacobi(HJ) equation represents a class of highly nonlinear partial differential equations. Classical numerical techniques, such as finite element methods, face significant challenges when addressing the numerical solutions of such nonlinear HJ equations. However, recent advances in neural network-based approaches, particularly Physics-Informed Neural Networks (PINNs) and neural operator methods, have ushered in innovative paradigms for numerically solving HJ equations. In this work, we leverage the PINN approach, infused with the concept of neural operators. By encoding and extracting features from the discretized images of functions through TransVNet, which is a novel autoencoder architecture proposed in this paper, we seamlessly integrate Hamiltonian information into PINN training, thereby establishing a novel scientific computation framework. Additionally, we incorporate the vanishing viscosity method, introducing viscosity coefficients in our model, which equips our model to tackle potential singularities in nonlinear HJ equations. These attributes signify that our MPP-TV framework paves new avenues and insights for the generalized solutions of nonlinear HJ equations.

关键词： Physics-informed neural network Numerical methods for PDE Hamilton-Jacobi equation variational autoencoder

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Multi-agent reinforcement learning for cooperative search under aperiodically intermittent communication

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 280卷

作者： Fu, Longyue Wang, Jiao Luo, Hongchen Northeastern Univ Coll Informat Sci & Engn 3-11 Wenhua Rd Shenyang 110819 Liaoning Peoples R China

Aperiodically Intermittent Communication (AIC) refers to the unpredictable irregular disruptions in exchange of information among agents. These disruptions pose significant challenges to multi-agent cooperative search tasks. The challenges are information loss, increased uncertainty, and reduced collaboration efficiency, thus limiting overall search performance. The Aperiodically Intermittent Communication Reinforcement Learning (AICoRL) algorithm is proposed to achieve multi-agent cooperative search under AIC to address these challenges. Firstly, AICoRL integrates an Adaptive Predictive variational autoencoder (AP-VAE) to reconstruct lost information during communication interruptions. Predicting uncertain information through adaptive communication states and global features enables agents to maintain an approximate global perspective. Secondly, to enhance agents' adaptability in complex dynamic environments, the policy network employs multi-agent reinforcement learning with noisy parameterization. It integrates individual observations with reconstructed features and encourages a more diverse exploration of the action space. Extensive experimental results demonstrate that AICoRL significantly outperforms traditional methods in the search task under AIC. Our algorithm achieves higher search efficiency, better reward performance, and stronger robustness. This framework demonstrates its potential for enhancing the performance of multi-agent systems operating under AIC.

关键词： Aperiodically intermittent communication Multi-agent reinforcement learning Multi-agent cooperative search variational autoencoder

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VKAD: A novel fault detection and isolation model for uncertainty-aware industrial processes

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NEURAL NETWORKS 2025年 189卷 107664页

作者： Zheng, Shengbin Pi, Dechang Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China

Fault detection and isolation (FDI) are essential for effective monitoring of industrial processes. Modern industrial processes involve dynamic systems characterized by complex, high-dimensional nonlinearities, posing significant challenges for accurate modeling and analysis. Recent studies have employed deep learning methods to capture and model these complexities in dynamic systems. In contrast, Koopman operator theory offers an alternative perspective, as the Koopman operator describes the linear evolution of observables in nonlinear systems within a high-dimensional space. This linearization simplifies complex nonlinear dynamics, making them easier to analyze and interpret in higher-dimensional settings. However, the Koopman operator theory does not inherently incorporate uncertainties in dynamical systems, which can hinder its performance in process monitoring. To tackle this issue, we integrate Koopman operator theory with variational autoencoders to propose a novel fault detection and isolation model called the variational Koopman Anomaly Detector (VKAD). VKAD is capable of inferring the distribution of observables from time series data of dynamical systems. By advancing the distribution through the Koopman operator over time, VKAD can capture the uncertainty in the evolution of dynamic systems. The uncertainty estimates yielded by VKAD are applicable for both fault detection and isolation in industrial processes. The effectiveness of the proposed VKAD were illustrated using the Tennessee Eastman Process (TEP) and a real satellite on-orbit telemetry dataset (SAT). The experimental results demonstrate that the Fault Detection Rate (FDR) of VKAD achieves superior performance on both the TEP and SAT datasets compared to advanced methods, while the Fault Alarm Rate (FAR) is also highly competitive.

关键词： Fault detection Fault isolation Koopman operator theory variational autoencoder Industrial process

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Structure-to-process modeling drives experimentally validated unified dual-phase steel

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ACTA MATERIALIA 2025年 295卷

作者： Ma, Xudong Zhang, Yuqi Wang, Chenchong Wang, Ming Huang, Mingxin Xu, Wei Northeastern Univ State Key Lab Rolling & Automat Shenyang 110819 Liaoning Peoples R China Univ Hong Kong Dept Mech Engn Hong Kong Peoples R China

Unified dual-phase (UniDP) steels enable tailored performance from a single composition, revolutionizing sustainable material systems by addressing recyclability and weldability challenges. Traditional design frameworks, constrained by forward "process-structure" models and costly uncertainty quantification, falter under sparse data and complex microstructures. Here, a microstructure-centric inverse design strategy is proposed that replaces uncertainty quantification with direct "structure-to-composition/process modeling", leveraging real microstructural features to map composition and processing parameters. Specifically, our approach integrates a variational autoencoder to encode authentic microstructural features into a latent space and a multilayer perceptron to predict composition, processing routes, and properties. Combined with specific latent space sampling, the framework achieves high-efficacy design exploration. The experimental success of UniDP steels stands as a cornerstone of this work: the designed alloy consistently achieves the target properties in all three performance tiers, at a lower cost than other commercial alloys. Latent space analysis further validated the model's ability to interpolate seamlessly between microstructures and encode multi-scale property relationships, confirming its robustness for real-world applications. By experimentally demonstrating the viability of microstructure-driven inverse design, this work not only resolves longstanding barriers in complex alloy systems but also establishes a replicable, uncertainty quantification-free framework for sustainable material innovation.

关键词： Unified dual-phase steel Microstructure Alloy design variational autoencoder

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Accelerating Energy Forecasting with Data Dimensionality Reduction in a Residential Environment

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ENERGIES 2025年第7期18卷 1637-1637页

作者： Goncalves, Rafael Magalhaes, Diogo Teixeira, Rafael Antunes, Mario Gomes, Diogo Aguiar, Rui L. Inst Telecomunicacoes P-3810193 Aveiro Portugal Univ Aveiro Dept Eletron Telecomunicacoes & Informat P-3810193 Aveiro Portugal

The non-stationary nature of energy data is a serious challenge for energy forecasting methods. Frequent model updates are necessary to adapt to distribution shifts and avoid performance degradation. However, retraining regression models with lookback windows large enough to capture energy patterns is computationally expensive, as increasing the number of features leads to longer training times. To address this problem, we propose an approach that guarantees fast convergence through dimensionality reduction. Using a synthetic neighborhood dataset, we first validate three deep learning models-an artificial neural network (ANN), a 1D convolutional neural network (1D-CNN), and a long short-term memory (LSTM) network. Then, in order to mitigate the long training time, we apply principal component analysis (PCA) and a variational autoencoder (VAE) for feature reduction. As a way to ensure the suitability of the proposed models for a residential context, we also explore the trade-off between low error and training speed by considering three test scenarios: a global model, a local model for each building, and a global model that is fine-tuned for each building. Our results demonstrate that by selecting the optimal dimensionality reduction method and model architecture, it is possible to decrease the mean squared error (MSE) by up to 63% and accelerate training by up to 80%.

关键词： residential energy forecasting machine learning deep learning artificial neural network convolutional neural network long short-term memory dimensionality reduction principal component analysis variational autoencoder

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Industrial multivariate time-series data anomaly detection incorporating attention mechanisms and adversarial training

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2025年

作者： Yang, Wenjie Chu, Wenchao Wu, Xingfu Zhou, Lianlin Wang, Jiayi Yang, Hua Li, Zirui Hebei Univ Technol Sch Artificial Intelligence State Key Lab Reliabil & Intelligence Elect Equipm Tianjin Peoples R China AECC South Ind Co Ltd Blade Machining Ctr Zhuzhou Peoples R China Hunan Univ Coll Mech & Vehicle Engn Changsha Peoples R China Suzhou Shushe Technol Co Ltd Suzhou Peoples R China Hebei Univ Technol Sch Mech Engn Tianjin Peoples R China

To address the challenges faced in industrial anomaly detection, including data sample imbalance, lack of anomaly labels, and complex spatiotemporal relationships in high-dimensional data, this paper proposes a novel multi-modal time-series anomaly detection model that combines attention mechanisms and adversarial training. In this model, the first step involves utilizing graph attention mechanisms to extract sequence correlation features from multi-modal time-series data, which are then summed with the original data to form a dual-feature-based data representation. Subsequently, a self-supervised learning approach is employed to input this data representation into a variational autoencoder's encoding-decoding network for reconstruction. Anomaly detection is performed by analyzing the error between the input and reconstructed data. The model also employs spatiotemporal attention mechanisms and adversarial training during reconstruction to enhance feature extraction and model generalization. By comparing our proposed model to five commonly used baseline models, we demonstrate its effectiveness in detecting anomalies in scenarios involving high-dimensional data and imbalanced abnormal samples, demonstrating superior anomaly detection performance, as well as excellent performance on real industrial production and processing datasets.

关键词： Anomaly detection deep learning multivariate time series variational autoencoder adversarial learning Self-supervised learning

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Longitudinal Alzheimer's Disease Progression Prediction With Modality Uncertainty and Optimization of Information Flow

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2025年第1期29卷 259-272页

作者： Dao, Duy-Phuong Yang, Hyung-Jeong Kim, Jahae Ho, Ngoc-Huynh Chonnam Natl Univ Dept Artificial Intelligence Convergence Gwangju 61186 South Korea Chonnam Natl Univ Hosp Dept Nucl Med Gwangju 61469 South Korea

Alzheimer's disease (AD) is a global neurodegenerative disorder that affects millions of individuals worldwide. Actual AD imaging datasets challenge the construction of reliable longitudinal models owing to imaging modality uncertainty. In addition, they are still unable to retain or obtain important information during disease progression from previous to followup time points. For example, the output values of current gates in recurrent models should be close to a specific value that indicates the model is uncertain about retaining or forgetting information. In this study, we propose a model which can extract and constrain each modality into a common representation space to capture intermodality interactions among different modalities associated with modality uncertainty to predict AD progression. In addition, we provide an auxiliary function to enhance the ability of recurrent gate robustly and effectively in controlling the flow of information over time using longitudinal data. We conducted comparative analysis on data from the Alzheimer's Disease Neuroimaging Initiative database. Our model outperformed other methods across all evaluation metrics. Therefore, the proposed model provides a promising solution for addressing modality uncertainty challenges in multimodal longitudinal AD progression prediction.

关键词： Multimodal longitudinal data modality uncertainty variational autoencoder recurrent gate Alzheimer's disease progression

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Identifying local useful information for attribute graph anomaly detection

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NEUROCOMPUTING 2025年 617卷

作者： Xi, Penghui Cheng, Debo Lu, Guangquan Deng, Zhenyun Zhang, Guixian Zhang, Shichao Guangxi Normal Univ Guangxi Key Lab Multisource Informat Min & Secur Guilin Peoples R China Univ South Australia UniSA STEM Adelaide Australia Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Peoples R China China Univ Min & Technol Sch Comp Sci & Technol Xuzhou Jiangsu Peoples R China

Graph anomaly detection primarily relies on shallow learning methods based on feature engineering and deep learning strategies centred on autoencoder-based reconstruction. However, these methods frequently fail to harness the local attributes and structural information within graph data, making it challenging to capture the underlying distribution in scenarios with class-imbalanced graph anomalies, which can result in overfitting. To deal with the above issue, this paper proposes anew anomaly detection method called LIAD (Identifying Local Useful Information for Attribute Graph Anomaly Detection), which learns the data's underlying distribution and captures richer local information. First, LIAD employs data augmentation techniques to create masked graphs and pairs of positive and negative subgraphs. Then, LIAD leverages contrastive learning to derive rich embedding representations from diverse local structural information. Additionally, LIAD utilizes a variational autoencoder (VAE) to generate new graph data, capturing the neighbourhood distribution within the masked graph. During the training process, LIAD aligns the generated graph data with the original to deepen its comprehension of local information. Finally, anomaly scoring is achieved by comparing the discrimination and reconstruction scores of the contrastive pairs, enabling effective anomaly detection. Extensive experiments on five real-world datasets demonstrate the effectiveness of LIAD compared to state-of-the-art methods. Comprehensive ablation studies and parametric analyses further affirm the robustness and efficacy of our model.

关键词： Graph neural networks Contrastive learning Graph anomaly detection variational autoencoder

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An intelligent sequential fraud detection model based on deep learning

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JOURNAL OF SUPERCOMPUTING 2024年第10期80卷 14824-14847页

作者： Zioviris, Georgios Kolomvatsos, Kostas Stamoulis, George Univ Thessaly Dept Elect & Comp Engn Glavani 37 Volos 38221 Thessaly Greece Univ Thessaly Dept Informat & Telecommun Papasiopoulou 2-4 Lamia 35131 Thessaly Greece

Fraud detection and prevention has received a lot of attention from the research community due to its high impact on financial institutions' revenues and reputation. The increased use of the web and the provision of online services open up the pathway for exposing these systems to numerous threats and jeopardizing their effective functioning. Naturally, financial frauds are increased in number and form imposing various requirements for their efficient and immediate detection. These requirements are related to the performance of the adopted models as well as the timely response of the decision-making mechanism. Machine learning and data mining are two research domains that can provide a number of techniques/algorithms for fraud detection and setup the road for mitigation actions. However, these methods still need to be improved with respect to the detection of unknown fraud patterns and the incorporation of big data processing mechanisms. This paper presents our attempt to build a hybrid system, i.e., a sequential scheme for combining two deep learning models and efficiently detecting potential financial frauds. We elaborate on the combination of an autoencoder and a Long Short-Term Memory Recurrent Neural Network trained upon datasets which are processed through the use of an oversampling technique. Oversampling is adopted to handle heavily imbalanced datasets which is the 'natural' scenario due to the limited number of frauds compared to the humongous volumes of transactions. The proposed approach tends to capture much more fraud events in comparison with other conventional ML techniques. Our experimental evaluation exposes that our model exhibits a good performance in terms of recall and precision.

关键词： Fraud detection autoencoder variational autoencoder Dimensionality reduction Oversampling Recurrent Neural Networks Long Short-Term Memory networks

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