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IEEE Conference on Global Communications (IEEE GLOBECOM) - Intelligent Communications for Shared Prosperity

作者： Amhoud, El Mehdi Jouhari, Mohammed Maksymyuk, Taras Zerhouni, Kawtar Ibrahimi, Khalil Mohammed VI Polytech Univ Coll Comp Ben Guerir Morocco Lviv Polytech Natl Univ Dept Telecommun Lvov Ukraine Ibn Tofail Univ Lab Res Informat LaRI Fac Sci Kenitra Morocco

ISBN: (纸本)9798350310900

Wireless communication systems rely on channel estimation and equalization to ensure reliable and efficient data transmission. However, with the increasing demand for high connectivity in massive IoT networks, these processes are facing significant challenges. The complexity and intensive computation required for channel estimation and equalization results in high communication latency and power consumption, which can ultimately prevent the transceiver from restoring the originally transmitted signal. In this paper, we propose a novel approach to simplify wireless communication systems by using a conditional generative adversarial network (cGAN) model to replace both channel estimation and equalization blocks. We formulate the data recovery task as a translation from received data to the corresponding transmitted signal and introduce the concept of Rx-to-Tx translation based on a cGAN, which was initially developed for image-to-image translation. Our preliminary results demonstrate the feasibility and effectiveness of this approach, particularly for digital modulations. By carefully tuning the model's hyperparameters, we achieve the theoretical symbol error rate (SER) of QAMs in a Rayleigh propagation channel. Our proposed approach has the potential to significantly reduce the computational complexity and overhead typically associated with traditional channel estimation and equalization blocks. This can lead to more efficient and cost-effective wireless communication systems.

关键词： Channel estimation channel equalization conditional generative adversarial networks Rx-to-Tx translation

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Graph comparison efficient conditional generative adversarial networks for parameter identification of synchronous generators

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 269卷

作者： Yin, Linfei Wang, Zixuan Guangxi Key Lab Power Syst Optimizat & Energy Tech Nanning 530004 Guangxi Peoples R China

Synchronous generator parameters have nonlinear characteristics, and traditional parameter identification methods cannot effectively capture the dynamic nonlinear behavior of synchronous generators in actual operation. Therefore, the parameters recognized by traditional methods have significant deviations from the actual operating parameters. In this work, an algorithm for graph comparison efficient conditional generative adversarial networks (GCECGANs) is proposed. Efficient neural network is an network structure specialized in processing image and graph data, fully utilizing the local information and global structure of image and graph data, with high computational efficiency and low resource consumption. The graph comparison learning network improves feature extraction and representation learning by optimizing the relative distance between data elements of the graph structure, enhancing the understanding of data similarities and differences in the model. Based on conditional generative adversarial networks, the efficient neural network focuses on efficient encoding of graph data, while the graph comparison learning network focuses on optimizing the network parameters by comparing different graph samples to enhance the generalization capability. By designing loss functions and optimization strategies, GCECGANs effectively improve the ability to handle graph-structured samples and data adversarial. Finally, the effectiveness of GCECGANs in generator parameter identification is verified in three algorithms. The accuracy and practicability of GCECGANs are effectively improved compared with traditional parameter identification algorithms.

关键词： Synchronous generators Parameter identification conditional generative adversarial networks Graph comparison learning

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A Cross-Modal Tactile Reproduction Utilizing Tactile and Visual Information Generated by conditional generative adversarial networks

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IEEE ACCESS 2025年 13卷 9223-9229页

作者： Hatori, Koki Morikura, Takashi Funahashi, Akira Takemura, Kenjiro Keio Univ Sch Sci Open & Environm Syst Yokohama 2238522 Japan Keio Univ Dept Biosci & Informat Yokohama 2238522 Japan Keio Univ Dept Mech Engn Yokohama 2238522 Japan

Tactile reproduction technology represents a promising advancement within the rapidly expanding field of virtual/augmented reality, necessitating the development of innovative methods specifically tailored to correspond with tactile sensory labels. Since human tactile perception is known to be influenced by visual information, this study has developed a cross-modal tactile sensory display using conditional generative adversarial networks, CGANs, to generate both mechanical and visual information. Initially, sensory evaluation experiments were conducted with 32 participants using twelve metal plate samples to collect tactile information. Subsequently, we prepared 320 images of variety of materials and conducted sensory evaluation experiments with 30 participants per image to gather tactile information evoked by viewing the images. Utilizing the collected tactile information, used as labels, and images as a dataset, we developed four types of visual information generation models using CGAN, each trained with weighted concatenated data of images and labels, in which image elements are amplified by factors of 1, 1,000, 5,000, and 10,000, respectively. Each of these four models was then used to generate twelve images corresponding to the sensory evaluation result of twelve different metal plate samples. We performed a cross-modal tactile reproduction experiment using the previously developed tactile information generation model to input signals to a tactile display, alongside the images generated by the visual information generation model. In this experiment, 20 subjects conducted sensory evaluations where tactile sensations were displayed concurrently with the visual display of the images. The results confirmed that the concurrent display of mechanical and visual information significantly reduced the mean absolute error between the displayed tactile information and that of the metal plate samples from 2.2 to 1.6 out of a 7-digit scale in sensory evaluation. These findin

关键词： Generators Visualization Vectors Training Acoustics Solid modeling generative adversarial networks Fingers Ultrasonic transducers Tactile sensors Tactile reproduction cross-modal recognition conditional generative adversarial networks

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Use of conditional generative adversarial networks to create demographic collaborative filtering datasets

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APPLIED SOFT COMPUTING 2025年 169卷

作者： Bobadilla, Jesus Gutierrez, Abraham Univ Politecn Madrid Ctra Valencia Km 7 Madrid 28031 Spain

This paper proposes a method to create synthetic collaborative filtering datasets that can be used to test both current and new fair recommender systems models. The proposed "conditional generative adversarial Network for Recommender Systems (CGANRS)" method generalizes the existing generative adversarial network for recommender systems one, and it makes use of a conditional generative adversarial network to artificially generate synthetic profiles from a source dataset such as MovieLens. The created datasets can be parameterized to have different sizes and to include different number of users and items. Additionally, the provided parameters include the proportion of multi-categorical demographic information such as the number of male vs. female users, or the proportions of very young, young, adult, and senior users. To test the proposed method, three sets of synthetic databases have been created, containing different a) numbers of users, b) numbers of items, and c) proportions of male users versus female users. Results show an adequate behavior of the generated datasets, testing their a) profiles separability, b) main statistical distributions, and c) recommendation accuracies. Synthetic data sets created using the proposed conditional generative adversarial network for recommender systems method are particularly useful to improve research in the fairness field of the recommender systems area. To extend its use and to facilitate reproducibility, the source code is provided to generate as many demographic datasets as desired, as well as the artificially generated datasets in this research. Some promising future works are proposed, including a) the variation of the stochastic Gaussian distribution used to create the random noise vectors that feed the adversarial network generator model, and b) testing the fairness of the most relevant collaborative filtering models on different synthetic scenarios.

关键词： CGANRS conditional generative adversarial networks Fairness Collaborative Filtering Recommender Systems Synthetic Datasets

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Thermal field predictions and boiling curve reconstruction from bubble dynamics using conditional generative adversarial networks

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APPLIED THERMAL ENGINEERING 2025年 261卷

作者： Na, Ungjin Lee, Seockyong Park, Sehyeon Choi, Moonhee Jo, Hangjin Pohang Univ Sci & Technol POSTECH Dept Mech Engn Pohang 37673 South Korea Pohang Univ Sci & Technol POSTECH Div Adv Nucl Engn Pohang 37673 South Korea Seoul Natl Univ Dept Nucl Engn Seoul 08826 South Korea

Although the accurate prediction of boiling phenomena is crucial for preventing equipment damage and ensuring energy system safety, it remains challenging owing to the intertwined thermal-hydraulic interactions. We predicted the temperature field of boiling surfaces from the visualization results of bubble dynamics using conditional generative adversarial networks (cGANs). The network was trained to generate infrared images from sideview images in a flow boiling experiment, and its rationale is supported by intermediate activation maps. Additionally, the model was applied to a data-deficient scenario based on pool boiling to derive heat flux components and reconstruct the boiling curve. This approach enhances the insight into both the visible and invisible aspects of boiling phenomena, which is particularly advantageous when direct and elaborate data acquisition faces facility limitations.

关键词： Boiling conditional generative adversarial networks Infrared Thermography

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Probabilistic Brain Extraction in MR Images via conditional generative adversarial networks

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2024年第3期43卷 1071-1088页

作者： Moazami, Saeed Ray, Deep Pelletier, Daniel Oberai, Assad A. Univ Southern Calif Viterbi Sch Engn Dept Aerosp & Mech Engn Los Angeles CA 90007 USA Univ Southern Calif Keck Sch Med Dept Neurol Los Angeles CA 90033 USA

Brain extraction, or the task of segmenting the brain in MR images, forms an essential step for many neuroimaging applications. These include quantifying brain tissue volumes, monitoring neurological diseases, and estimating brain atrophy. Several algorithms have been proposed for brain extraction, including image-to-image deep learning methods that have demonstrated significant gains in accuracy. However, none of them account for the inherent uncertainty in brain extraction. Motivated by this, we propose a novel, probabilistic deep learning algorithm for brain extraction that recasts this task as a Bayesian inference problem and utilizes a conditional generative adversarial network (cGAN) to solve it. The input to the cGAN's generator is an MR image of the head, and the output is a collection of likely brain images drawn from a probability density conditioned on the input. These images are used to generate a pixel-wise mean image, serving as the estimate for the extracted brain, and a standard deviation image, which quantifies the uncertainty in the prediction. We test our algorithm on head MR images from five datasets: NFBS, CC359, LPBA, IBSR, and their combination. Our datasets are heterogeneous regarding multiple factors, including subjects (with and without symptoms), magnetic field strengths, and manufacturers. Our experiments demonstrate that the proposed approach is more accurate and robust than a widely used brain extraction tool and at least as accurate as the other deep learning methods. They also highlight the utility of quantifying uncertainty in downstream applications.

关键词： Biomedical imaging Task analysis Brain modeling Image segmentation Head Brain Uncertainty Bayesian inference brain extraction conditional generative adversarial networks medical imaging neuroimaging skull stripping uncertainty quantification

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MS-CGAN: Fusion of conditional generative adversarial networks and multi-scale spatio-temporal features for lithology identification

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JOURNAL OF APPLIED GEOPHYSICS 2024年 230卷

作者： Zhang, Pengwei Ren, Jiadong Zhao, Fengda Li, Xianshan He, Haitao Jia, Yufeng Shao, Xiaoqing Yanshan Univ Sch Informat Sci & Engn Qinhuangdao Peoples R China Xinjiang Coll Sci & Technol Sch Informat Sci & Engn Korla Peoples R China Yanshan Univ Key Lab Software Engn Hebei Prov Qinhuangdao Peoples R China

Lithology identification constitutes a crucial undertaking in formation evaluation and reservoir characterization. However, the need for improved precision arises in conventional lithology identification models due to the difficulties presented by unequal distributions of small-sample logging data. An effective combination of domain expertise and data-driven models to predict lithology is essential due to the intricate and nonlinear connection between logging parameters and lithology, combined with the distinct characteristics of the oilfield environments. In this paper, we proposed a multi-scale conditional generative adversarial network(MS-CGAN) method, which combines conditional generative adversarial networks with multi-scale spatio-temporal features to address data imbalance issues and enhance the accuracy of lithology classification. Our approach, tested on two small datasets from the Hugoton and Panoma fields, USA, and the Daqing production wells, China, stands out as the optimal choice compared to other models. Comprehensive evaluation results indicate promising practical applications and potential benefits of the new model in enhancing lithology identification using limited data.

关键词： Lithology identification Classification enhancement conditional generative adversarial networks Machine learning

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Graph-Based conditional generative adversarial networks for Major Depressive Disorder Diagnosis With Synthetic Functional Brain Network Generation

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2024年第3期28卷 1504-1515页

作者： Oh, Ji-Hye Lee, Deok-Joong Ji, Chang-Hoon Shin, Dong-Hee Han, Ji-Wung Son, Young-Han Kam, Tae-Eui Korea Univ Dept Artificial Intelligence Seoul 02841 South Korea

Major Depressive Disorder (MDD) is a pervasive disorder affecting millions of individuals, presenting a significant global health concern. Functional connectivity (FC) derived from resting-state functional Magnetic Resonance Imaging (rs-fMRI) serves as a crucial tool in revealing functional connectivity patterns associated with MDD, playing an essential role in precise diagnosis. However, the limited data availability of FC poses challenges for robust MDD diagnosis. To tackle this, some studies have employed Deep Neural networks (DNN) architectures to construct generative adversarial networks (GAN) for synthetic FC generation, but this tends to overlook the inherent topology characteristics of FC. To overcome this challenge, we propose a novel Graph Convolutional networks (GCN)-based conditional GAN with Class-Aware Discriminator (GC-GAN). GC-GAN utilizes GCN in both the generator and discriminator to capture intricate FC patterns among brain regions, and the class-aware discriminator ensures the diversity and quality of the generated synthetic FC. Additionally, we introduce a topology refinement technique to enhance MDD diagnosis performance by optimizing the topology using the augmented FC dataset. Our framework was evaluated on publicly available rs-fMRI datasets, and the results demonstrate that GC-GAN outperforms existing methods. This indicates the superior potential of GCN in capturing intricate topology characteristics and generating high-fidelity synthetic FC, thus contributing to a more robust MDD diagnosis.

关键词： conditional generative adversarial networks graph convolutional networks major depressive disorder resting-state functional Magnetic Resonance Imaging (rs-fMRI) synthetic functional connectivity

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conditional generative adversarial networks to generate pseudo low monoenergetic CT image from a single-tube voltage CT scanner

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PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS 2021年 83卷 46-51页

作者： Funama, Yoshinori Oda, Seitaro Kidoh, Masafumi Nagayama, Yasunori Goto, Makoto Sakabe, Daisuke Nakaura, Takeshi Kumamoto Univ Fac Life Sci Dept Med Radiat Sci 4-24-1 Kuhonji Kumamoto 8620976 Japan Kumamoto Univ Fac Life Sci Dept Diagnost Radiol Kumamoto Japan Kumamoto Univ Hosp Dept Radiol Kumamoto Japan

Purpose: To generate pseudo low monoenergetic CT images of the abdomen from 120-kVp CT images with cGAN. Materials and Methods: We retrospectively included 48 patients who underwent contrast-enhanced abdominal CT using dual-energy CT. We reconstructed paired data sets of 120 kVp CT images and virtual low monoenergetic (55-keV) CT images. cGAN was prepared to generate pseudo 55-keV CT images from 120-kVp CT images. The pseudo 55 keV CT images in epoch 10, 50, 100, and 500 were compared to the 55 keV images generated using peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Results: The PSNRs were 28.0, 28.5, 28.6, and 28.8 at epochs 10, 50, 100, and 500, respectively. The SSIM was approximately constant from epochs 50 to 500. Conclusion: Pseudo low monoenergetic abdominal CT images were generated from 120-kVp CT images using cGAN, and the images had good quality similar to that of monochromatic images obtained with DECT software.

关键词： Virtual monochromatic image Conventional tube-voltage image Dual-energy computed tomography conditional generative adversarial networks

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Listwise learning to rank method combining approximate NDCG ranking indicator with conditional generative adversarial networks

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PATTERN RECOGNITION LETTERS 2024年 179卷 31-37页

作者： Li, Jinzhong Zeng, Huan Xiao, Cunwei Ouyang, Chunjuan Liu, Hua Jinggangshan Univ Dept Comp Sci & Technol Coll Elect & Informat Engn Jian Peoples R China

Some previous empirical studies have shown that the performances of the listwise learning to rank approaches are in general better than the pointwise or pairwise learning to rank techniques. The listwise learning to rank methods which directly optimize information retrieval indicators are a type of essential and popular method of learning to rank. However, the existing learning to rank approaches based on generative adversarial networks (GAN) do not utilize a loss function based on information retrieval indicators to optimize the generator and/or discriminator. Thus, an approach of learning to rank that combines approximate Normalized Discounted Cumulative Gain (NDCG) ranking indicators with conditional generative adversarial networks (CGAN) is proposed in this paper, named NCGAN-LTR. The NCGAN-LTR approach constructs loss functions of the generator and discriminator based on the Plackett-Luce model and an approximate version of the NDCG ranking indicator, which is utilized to train the network parameters of CGAN. The experimental results on four benchmark datasets of learning to rank, i.e., TREC TD2004, OHSUMED, MQ2008, and MSLR-WEB10K demonstrate that our proposed NCGAN-LTR approach has superior performance across almost various ranking indicators of information retrieval compared with the IRGAN-List approach.

关键词： Learning to rank conditional generative adversarial networks Approximate Normalized Discounted Cumulative Gain Ranking model Ranking indicator

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